Text
stringlengths 1
5.86k
| belief_strength
stringclasses 4
values |
|---|---|
Neovascularization and angiogenesis in the brain are important physiological processes for normal brain development and repair/regeneration following insults. Integrins are cell surface adhesion receptors mediating important function of cells such as survival, growth and development during tissue organization, differentiation and organogenesis. In this study, we used an integrin-binding array platform to identify the important types of integrins and their binding peptides that facilitate adhesion, growth, development, and vascular-like network formation of rat primary brain microvascular endothelial cells. Brain microvascular endothelial cells were isolated from rat brain on post-natal day 7. Cells were cultured in a custom-designed integrin array system containing short synthetic peptides binding to 16 types of integrins commonly expressed on cells in vertebrates. After 7 days of culture, the brain microvascular endothelial cells were processed for immunostaining with markers for endothelial cells including von Willibrand factor and platelet endothelial cell adhesion molecule. 5-Bromo-2'-dexoyuridine was added to the culture at 48 hours prior to fixation to assess cell proliferation. Among 16 integrins tested, we found that α5β1, αvβ5 and αvβ8 greatly promoted proliferation of endothelial cells in culture. To investigate the effect of integrin-binding peptides in promoting neovascularization and angiogenesis, the binding peptides to the above three types of integrins were immobilized to our custom-designed hydrogel in three-dimensional (3D) culture of brain microvascular endothelial cells with the addition of vascular endothelial growth factor. Following a 7-day 3D culture, the culture was fixed and processed for double labeling of phalloidin with von Willibrand factor or platelet endothelial cell adhesion molecule and assessed under confocal microscopy. In the 3D culture in hydrogels conjugated with the integrin-binding peptide, brain microvascular endothelial cells formed interconnected vascular-like network with clearly discernable lumens, which is reminiscent of brain microvascular network in vivo. With the novel integrin-binding array system, we identified the specific types of integrins on brain microvascular endothelial cells that mediate cell adhesion and growth followed by functionalizing a 3D hydrogel culture system using the binding peptides that specifically bind to the identified integrins, leading to robust growth and lumenized microvascular-like network formation of brain microvascular endothelial cells in 3D culture. This technology can be used for in vitro and in vivo vascularization of transplants or brain lesions to promote brain tissue regeneration following neurological insults.
|
No Beliefs Expressed
|
Sensory driven activity during early life is critical for setting up the proper connectivity of the sensory cortices. We ask here whether social play behavior, a particular form of social interaction that is highly abundant during postweaning development, is equally important for setting up connections in the developing prefrontal cortex (PFC). Young male rats were deprived from social play with peers during the period in life when social play behavior normally peaks [postnatal day 21-42] (SPD rats), followed by resocialization until adulthood. We recorded synaptic currents in layer 5 cells in slices from medial PFC of adult SPD and control rats and observed that inhibitory synaptic currents were reduced in SPD slices, while excitatory synaptic currents were unaffected. This was associated with a decrease in perisomatic inhibitory synapses from parvalbumin-positive GABAergic cells. In parallel experiments, adult SPD rats achieved more reversals in a probabilistic reversal learning (PRL) task, which depends on the integrity of the PFC, by using a more simplified cognitive strategy than controls. Interestingly, we observed that one daily hour of play during SPD partially rescued the behavioral performance in the PRL, but did not prevent the decrease in PFC inhibitory synaptic inputs. Our data demonstrate the importance of unrestricted social play for the development of inhibitory synapses in the PFC and cognitive skills in adulthood and show that specific synaptic alterations in the PFC can result in a complex behavioral outcome.SIGNIFICANCE STATEMENT This study addressed the question whether social play behavior in juvenile rats contributes to functional development of the prefrontal cortex (PFC). We found that rats that had been deprived from juvenile social play (social play deprivation - SPD) showed a reduction in inhibitory synapses in the PFC and a simplified strategy to solve a complex behavioral task in adulthood. Providing one daily hour of play during SPD partially rescued the cognitive skills in these rats, but did not prevent the reduction in PFC inhibitory synapses. Our results demonstrate a key role for unrestricted juvenile social play in PFC development and emphasize the complex relation between PFC circuit connectivity and cognitive function.
|
No Beliefs Expressed
|
Probiotics are considered effective microbial dietary supplements that provide beneficial effects to consumers, usually by restoring or improving gut microflora. Goat milk is one of the rich sources of probiotics as well as nutrients. Therefore, the primary aim of this research was to isolate and evaluate the potential of novel indigenous probiotic strains present in goat milk. Six different raw goat milk samples were collected from different areas of Multan, Pakistan. For bacterial characterization, samples were cultured and isolated on MRS agar plates for different morphological and biochemical tests. The probiotic potential of the six isolates, all of which were gram positive (G1, G2, G3, G4, G5, and G6) and five of which were catalase negative (all except G1), were assessed via a milk coagulation assay and antimicrobial activity, pH tolerance, phenol tolerance, and sodium chloride (NaCl) tolerance tests, which revealed that all the isolates coagulated in milk and showed protease and lipase activity, except G3. All six isolates showed tolerance against 0.2% phenol and 2-4% NaCl and were able to survive in both alkaline and acidic conditions. Only five isolates showed antimicrobial activity against indicator strain Aspergillus niger strain STA9, validating their probiotic nature. The most potent bile-tolerant and bacteriocin-producing isolate, G1, also showed γ-hemolytic activity and resistance to penicillin but showed susceptibility to other antibiotics. The lactic acid-producing (0.60% titratable acidity) G1 isolate was identified as a novel strain of Mammaliicoccus sciuri based on 16S rDNA sequencing. The above findings suggest that the potent M. sciuri GMN01 strain can serve as a potential probiotic strain. A potent probiotic strain isolated from raw goat milk could be utilized as a dietary supplement, and goat milk could become an alternative to other sources of milk, particularly cow milk. However, safety aspects of this strain require further investigation because the present safety tests are insufficient to conclude that the GMN01 isolate is safe.
|
No Beliefs Expressed
|
Substantial cortical gray matter tissue damage, which correlates with clinical disease severity, has been revealed in multiple sclerosis (MS) using advanced magnetic resonance imaging (MRI) methods at 3 T and the use of ultra-high field, as well as in histopathology studies. While clinical assessment mainly focuses on lesions using T 1 - and T 2 -weighted MRI, quantitative MRI (qMRI) methods are capable of uncovering subtle microstructural changes. The aim of this ultra-high field study is to extract possible future MR biomarkers for the quantitative evaluation of regional cortical pathology. Because of their sensitivity to iron, myelin, and in part specifically to cortical demyelination, T 1 , T 2 , R 2 * , and susceptibility mapping were performed including two novel susceptibility markers; in addition, cortical thickness as well as the volumes of 34 cortical regions were computed. Data were acquired in 20 patients and 16 age- and sex-matched healthy controls. In 18 cortical regions, large to very large effect sizes (Cohen's d ≥ 1) and statistically significant differences in qMRI values between patients and controls were revealed compared with only four regions when using more standard MR measures, namely, volume and cortical thickness. Moreover, a decrease in all susceptibility contrasts ( χ , χ + , χ - ) and R 2 * values indicates that the role of cortical demyelination might outweigh inflammatory processes in the form of iron accumulation in cortical MS pathology, and might also indicate iron loss. A significant association between susceptibility contrasts as well as R 2 * of the caudal middle frontal gyrus and disease duration was found (adjusted R2 : 0.602, p = 0.0011). Quantitative MRI parameters might be more sensitive towards regional cortical pathology compared with the use of conventional markers only and therefore may play a role in early detection of tissue damage in MS in the future.
|
No Beliefs Expressed
|
Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,(1) we propose a robust interest point based tracker in l(1) minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l(1) minimization problem. In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos. (C) 2014 Elsevier B.V. All rights reserved.
|
No Beliefs Expressed
|
In this paper, we present a novel methodology based on machine learning for identifying the most appropriate from a set of available state-of-the-art object detectors for a given application. Our particular interest is to develop a road map for identifying verifiably optimal selections, especially for challenging applications such as detecting small objects in a mixed-size object dataset. State-of-the-art object detection systems often find the localisation of small-size objects challenging since most are usually trained on large-size objects. These contain abundant information as they occupy a large number of pixels relative to the total image size. This fact is normally exploited by the model during training and inference processes. To dissect and understand this process, our approach systematically examines detectors' performances using two very distinct deep convolutional networks. The first is the single-stage YOLO V3 and the second is the double-stage Faster R-CNN. Specifically, our proposed method explores and visually illustrates the impact of feature extraction layers, number of anchor boxes, data augmentation, etc., utilising ideas from the field of explainable Artificial Intelligence (XAI). Our results, for example, show that multi-head YOLO V3 detectors trained using augmented data produce better performance even with a fewer number of anchor boxes. Moreover, robustness regarding the detector's ability to explain how a specific decision was reached is investigated using different explanation techniques. Finally, two new visualisation techniques are proposed, WS-Grad and Concat-Grad, for identifying explanation cues of different detectors. These are applied to specific object detection tasks to illustrate their reliability and transparency with respect to the decision process. It is shown that the proposed techniques can result in high resolution and comprehensive heatmaps of the image areas, significantly affecting detector decisions as compared to the state-of-the-art techniques tested.
|
No Beliefs Expressed
|
Effective livestock management is critical for cattle farms in today's competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not feasible in today's farming systems. Fortunately, automatic tracking and identification systems have greatly improved in recent years. Moreover, correctly identifying individual cows is an integral part of predicting behavior during estrus. By doing so, we can monitor a cow's behavior, and pinpoint the right time for artificial insemination. However, most previous techniques have relied on direct observation, increasing the human workload. To overcome this problem, this paper proposes the use of state-of-the-art deep learning-based Multi-Object Tracking (MOT) algorithms for a complete system that can automatically and continuously detect and track cattle using an RGB camera. This study compares state-of-the-art MOTs, such as Deep-SORT, Strong-SORT, and customized light-weight tracking algorithms. To improve the tracking accuracy of these deep learning methods, this paper presents an enhanced re-identification approach for a black cattle dataset in Strong-SORT. For evaluating MOT by detection, the system used the YOLO v5 and v7, as a comparison with the instance segmentation model Detectron-2, to detect and classify the cattle. The high cattle-tracking accuracy with a Multi-Object Tracking Accuracy (MOTA) was 96.88%. Using these methods, the findings demonstrate a highly accurate and robust cattle tracking system, which can be applied to innovative monitoring systems for agricultural applications. The effectiveness and efficiency of the proposed system were demonstrated by analyzing a sample of video footage. The proposed method was developed to balance the trade-off between costs and management, thereby improving the productivity and profitability of dairy farms; however, this method can be adapted to other domestic species.
|
No Beliefs Expressed
|
Objective: To conduct a pilot randomized control trial to assess the feasibility and acceptability of full-body interaction cognitive training (FBI-CT) inspired by instrumental activities of daily living in chronic psychiatric inpatients and to explore its preliminary impact on cognitive and noncognitive outcomes. Materials and Methods: Twenty psychiatric inpatients met the inclusion criteria and were randomly allocated to the FBI-CT group (n = 10) or the tablet-based CT group (T-CT) (n = 10). Neuropsychological assessments were performed at baseline, postintervention, and 3-month follow-up. Results: Both groups presented high completion rates at postintervention and follow-up. Participants reported high satisfaction following the interventions, with the FBI-CT group exhibiting slightly higher satisfaction. A within-group analysis showed significant improvements in the FBI-CT group for processing speed and sustained attention for short periods (P = 0.012), verbal memory (P = 0.008), semantic fluency (P = 0.027), depressive symptoms (P = 0.008), and quality of life (P = 0.008) at postintervention. At 3-month follow-up, this group maintained verbal memory improvements (P = 0.047) and depressive symptoms amelioration (P = 0.026). The T-CT group revealed significant improvements in sustained attention for long periods (P = 0.020), verbal memory (P = 0.014), and executive functions (P = 0.047) postintervention. A between-group analysis demonstrated that the FBI-CT group exhibited greater improvements in depressive symptoms (P = 0.042). Conclusions: Overall, we found support for the feasibility and acceptability of both training approaches. Our findings show promise regarding the preliminary impact of the FBI-CT intervention, but due to study limitations such as the small sample size, we cannot conclude that FBI-CT is a more effective approach than T-CT for enhancing cognitive and noncognitive outcomes of chronic psychiatric inpatients. Clinical trials (number: NCT05100849).
|
No Beliefs Expressed
|
Antimicrobial stewardship is essential to reducing antimicrobial resistance, reducing costs, and, crucially, ensuring good patient care. Community-acquired pneumonia (CAP) is a common medical condition, the symptoms of which show a significant overlap with those of COVID-19. Following the COVID-19 outbreak in Ireland, patients presenting to our hospital with features of a respiratory infection were more commonly reviewed within 24 hours (24h) of admission by an infectious disease (ID) or respiratory specialist. We aimed to assess how the change in service provision, involving frequent specialist reviews of patients admitted with features of CAP during the first wave of the COVID-19 pandemic, affected antimicrobial stewardship and prescribing practices. Patients admitted under general medical teams treated for CAP from March-April 2020 were included. Retrospective data including demographics, CURB-65 score, and antimicrobial therapy were collected, as well as information on whether the patient had undergone specialist review by an ID or respiratory physician. Data were compared to a similar cohort treated for CAP between November 2019 and January 2020, though in this cohort, before the era of COVID-19, none of the patients had undergone specialist review. Seventy-six patients were included from the March-April 2020 cohort, with 77 from November 2019-January 2020 for comparison. An ID or respiratory specialist reviewed 35 patients from the March-April cohort within 24 h of admission. There was a higher rate of appropriate escalation, de-escalation, and continuation of antibiotics among those reviewed. Less than 20% of patients were started on antibiotics in accordance with CAP guidelines on admission, though the antibiotics initiated were frequently deemed appropriate in the clinical setting. Specialist review increases rates of appropriate antimicrobial prescribing and adherence with hospital guidelines in patients with CAP.
|
No Beliefs Expressed
|
Brain region-of-interest (ROI) segmentation based on structural magnetic resonance imaging (MRI) scans is an essential step for many computer-aid medical image analysis applications. Due to low intensity contrast around ROI boundary and large inter-subject variance, it has been remaining a challenging task to effectively segment brain ROIs from structural MR images. Even though several deep learning methods for brain MR image segmentation have been developed, most of them do not incorporate shape priors to take advantage of the regularity of brain structures, thus leading to sub-optimal performance. To address this issue, we propose an anatomical attention guided deep learning framework for brain ROI segmentation of structural MR images, containing two subnetworks. The first one is a segmentation subnetwork, used to simultaneously extract discriminative image representation and segment ROIs for each input MR image. The second one is an anatomical attention subnetwork, designed to capture the anatomical structure information of the brain from a set of labeled atlases. To utilize the anatomical attention knowledge learned from atlases, we develop an anatomical gate architecture to fuse feature maps derived from a set of atlas label maps and those from the to-be-segmented image for brain ROI segmentation. In this way, the anatomical prior learned from atlases can be explicitly employed to guide the segmentation process for performance improvement. Within this framework, we develop two anatomical attention guided segmentation models, denoted as anatomical gated fully convolutional network (AG-FCN) and anatomical gated U-Net (AG-UNet), respectively. Experimental results on both ADNI and LONI-LPBA40 datasets suggest that the proposed AG-FCN and AG-UNet methods achieve superior performance in ROI segmentation of brain MR images, compared with several state-of-the-art methods.
|
No Beliefs Expressed
|
Acute pleuropneumonia in swine, caused by Actinobacillus pleuropneumoniae, is characterized by a high and sustained fever. Fever creates an adverse environment for many bacteria, leading to reduced bacterial proliferation; however, most pathogenic bacteria can tolerate higher temperatures. CpxAR is a two-component regulation system, ubiquitous among Gram-negative bacteria, which senses and responds to envelope alterations that are mostly associated with protein misfolding in the periplasm. Our previous study showed that CpxAR is necessary for the optimal growth of Actinobacillus pleuropneumoniae under heat stress. Here, we showed that mutation of the type IV pilin gene apfA rescued the growth defect of the cpxAR deletion strain under heat stress. RNA sequencing (RNA-seq) analyses revealed that 265 genes were differentially expressed in the ΔcpxAR strains grown at 42°C, including genes involved in type IV pilus biosynthesis. We also demonstrated direct binding of the CpxR protein to the promoter of the apf operon by an electrophoretic mobility shift assay and identified the binding site by a DNase I footprinting assay. In conclusion, our results revealed the important role of CpxAR in A. pleuropneumoniae resistance to heat stress by directly suppressing the expression of ApfA. IMPORTANCE Heat acts as a danger signal for pathogens, especially those infecting mammalian hosts in whom fever indicates infection. However, some bacteria have evolved exquisite mechanisms to survive under heat stress. Studying the mechanism of resistance to heat stress is crucial to understanding the pathogenesis of A. pleuropneumoniae during the acute stage of infection. Our study revealed that CpxAR plays an important role in A. pleuropneumoniae resistance to heat stress by directly suppressing expression of the type IV pilin protein ApfA.
|
No Beliefs Expressed
|
Estimating species trees from multiple genes is complicated and challenging due to gene tree-species tree discordance. One of the basic approaches to understanding differences between gene trees and species trees is gene duplication and loss events. Minimize Gene Duplication and Loss (MGDL) is a popular technique for inferring species trees from gene trees when the gene trees are discordant due to gene duplications and losses. Previously, exact algorithms for estimating species trees from rooted, binary trees under MGDL were proposed. However, gene trees are usually estimated using time-reversible mutation models, which result in unrooted trees. In this article, we propose a dynamic programming (DP) algorithm that can be used for an exact but exponential time solution for the case when gene trees are not rooted. We also show that a constrained version of this problem can be solved by this DP algorithm in time that is polynomial in the number of gene trees and taxa. We have proved important structural properties that allow us to extend the algorithms for rooted gene trees to unrooted gene trees. We propose a linear time algorithm for finding the optimal rooted version of an unrooted gene tree given a rooted species tree so that the duplication and loss cost is minimized. Moreover, we prove that the optimal rooting under MGDL is also optimal under the MDC (minimize deep coalescence) criterion. The proposed methods can be applied to both orthologous genes and gene families that by definition include both paralogs and orthologs. Therefore, we hope that these techniques will be useful for estimating species trees from genes sampled throughout the whole genome.
|
No Beliefs Expressed
|
We sequenced and analyzed the complete chloroplast genomes of Lilium amoenum, Lilium souliei, and Nomocharis forrestii in detail, including the first sequence and structural comparison of Nomocharis forrestii. We found that the lengths and nucleotide composition of the three chloroplast genes showed little variation. The chloroplast genomes of the three Lilium species contain 87 protein coding genes (PCGs), 38 tRNAs, and 8 rRNA genes. The only difference is that Nomocharis forrestii had an additional infA pseudogene. In the sequence analysis of the Lilium chloroplast genomes, 216 SSRs, 143 pairs of long repeats, 571 SNPs, and 202 indels were detected. In addition, we identified seven hypervariable regions that can be used as potential molecular markers and DNA barcodes of Lilium through complete sequence alignment. The phylogenetic tree was constructed from the three chloroplast genome sequences of Lilium obtained here and 40 chloroplast genome sequences from the NCBI database (including 35 Lilium species, 4 Fritillaria species, and one species of Smilax). The analysis showed that the species clustering of the genus Lilium essentially conformed to the classical morphological classification system of Comber, but differences in the classification of individual species remained. In our report, we support the reclassification of Lilium henryi and Lilium rosthorniiy in the genus Lilium. In general, this study not only provides genome data for three Lilium species, but also provides a comparative analysis of the Lilium chloroplast genomes. These advances will help to identify Lilium species, clarify the phylogenetic analysis of the Lilium genus, and help to solve and improve the disputes and deficiencies in the traditional morphological classification.
|
No Beliefs Expressed
|
Soft-tissue sarcomas (STS) represent a group of rare and heterogeneous tumors associated with several challenges, including incorrect or late diagnosis, the lack of clinical expertise, and limited therapeutic options. Digital pathology and radiomics represent transformative technologies that appear promising for improving the accuracy of cancer diagnosis, characterization and monitoring. Herein, we review the potential role of the application of digital pathology and radiomics in managing patients with STS. We have particularly described the main results and the limits of the studies using radiomics to refine diagnosis or predict the outcome of patients with soft-tissue sarcomas. We also discussed the current limitation of implementing radiomics in routine settings. Standard management approaches for STS have not improved since the early 1970s. Immunotherapy has revolutionized cancer treatment; nonetheless, immuno-oncology agents have not yet been approved for patients with STS. However, several lines of evidence indicate that immunotherapy may represent an efficient therapeutic strategy for this group of diseases. Thus, we emphasized the remarkable potential of immunotherapy in sarcoma treatment by focusing on recent data regarding the immune landscape of these tumors. We have particularly emphasized the fact that the development of immunotherapy for sarcomas is not an aspect of histology (except for alveolar soft-part sarcoma) but rather that of the tumor microenvironment. Future studies investigating immunotherapy strategies in sarcomas should incorporate at least the presence of tertiary lymphoid structures as a stratification factor in their design, besides including a strong translational program that will allow for a better understanding of the determinants involved in sensitivity and treatment resistance to immune-oncology agents.
|
No Beliefs Expressed
|
Modern neuromodulation systems typically provide a large number of recording and stimulation channels, which reduces the available power and area budget per channel. To maintain the necessary input-referred noise performance despite growingly rigorous area constraints, chopped neural front-ends are often the modality of choice, as chopper-stabilization allows to simultaneously improve (1/f) noise and area consumption. The resulting issue of a drastically reduced input impedance has been addressed in prior art by impedance boosters based on voltage buffers at the input. These buffers precharge the large input capacitors, reduce the charge drawn from the electrodes and effectively boost the input impedance. Offset on these buffers directly translates into charge-transfer to the electrodes, which can accelerate electrode aging. To tackle this issue, a voltage buffer with ultra-low time-averaged offset is proposed, which cancels offset by periodic reconfiguration, thereby minimizing unintended charge transfer. This article explains the background and circuit design in detail and presents measurement results of a prototype implemented in a 180 nm HV CMOS process. The measurements confirm that signal-independent, buffer offset induced charge transfer occurs and can be mitigated by the presented buffer reconfiguration without adversely affecting the operation of the input impedance booster. The presented neural recorder front-end achieves state of the art performance with an area consumption of 0.036 mm (2), an input referred noise of 1.32 mu V-rms (1 to 200 Hz) and 3.36 mu V-rms (0.2 to 10 kHz), power consumption of 13.7 mu W from 1.8 V supply, as well as CMRR and PSRR >= 83 dB at 50 Hz.
|
No Beliefs Expressed
|
The arousal of environmental concerns due to spike in environmental degradation has necessitated proper waste management and disposal. Arsenic, a potentially toxic element in cassava wastewater, requires treatment prior to the wastewater disposal to minimize environmental pollution and associated health implications. The present study thus addressed the treatment of As5+ heavy metal in cassava wastewater using an efficient biosorbent from chemically pretreated unshelled Moringa oleifera seeds. The effect of various factors influencing the biosorption process for arsenate removal was studied including pH, contact time, biosorbent dosage, and biosorbent pretreatment concentration. The results of Fourier transform infrared spectroscopy clearly suggested that additional functional groups attributed to esters were formed in the pretreated biosorbent, which is responsible for improvement in biosorption. It was found that contact time, biosorbent dosage, and biosorbent pretreatment concentration had statistically significant effect (p values < 0.05) on arsenate removal. A maximum percentage removal of 99.9% was achieved in the synthetic solution at pH 4.0, contact time of 30 min, and dosage of 2 g for biosorbent pretreated with 1 M of chemical solution. Furthermore, through isotherm and kinetics studies, it was discovered that the biosorption process for untreated biosorbent is by ion exchange, while that for treated biosorbents indicated a multifarious adsorption mechanism. Moreover, the biosorption process was exothermic and spontaneous. Also, it is noted that the sorption capability of the biosorbent increases with pretreatment concentration. A statistical model has been developed with prediction R2 of 0.898, which incorporates the effect of treatment concentration on the percentage removal of As5+ from cassava wastewater.
|
No Beliefs Expressed
|
The novel biological nitrogen removal process has been extensively studied for its high nitrogen removal efficiency, energy efficiency, and greenness. A successful novel biological nitrogen removal process has a stable microecological equilibrium and benign interactions between the various functional bacteria. However, changes in the external environment can easily disrupt the dynamic balance of the microecology and affect the activity of functional bacteria in the novel biological nitrogen removal process. Therefore, this review focuses on the microecology in existing the novel biological nitrogen removal process, including the growth characteristics of functional microorganisms and their interactions, together with the effects of different influencing factors on the evolution of microbial communities. This provides ideas for achieving a stable dynamic balance of the microecology in a novel biological nitrogen removal process. Furthermore, to investigate deeply the mechanisms of microbial interactions in novel biological nitrogen removal process, this review also focuses on the influence of quorum sensing (QS) systems on nitrogen removal microbes, regulated by which bacteria secrete acyl homoserine lactones (AHLs) as signaling molecules to regulate microbial ecology in the novel biological nitrogen removal process. However, the mechanisms of action of AHLs on the regulation of functional bacteria have not been fully determined and the composition of QS system circuits requires further investigation. Meanwhile, it is necessary to further apply molecular analysis techniques and the theory of systems ecology in the future to enhance the exploration of microbial species and ecological niches, providing a deeper scientific basis for the development of a novel biological nitrogen removal process.
|
No Beliefs Expressed
|
Automatic handwriting recognition systems are of interest for academic research fields and for commercial applications. Recent advances in deep learning techniques have shown dramatic improvement in relation to classic computer vision problems, especially in Handwritten Text Recognition (HTR). However, several approaches try to solve the problem of deep learning applied to Handwritten Digit String Recognition (HDSR), where it has to deal with the low number of trainable data, while learning to ignore any writing symbol around the digits (noise). In this context, we present a new optical model architecture (Gated-CNN-BGRU), based on HTR workflow, applied to HDSR. The International Conference on Frontiers of Handwriting Recognition (ICFHR) 2014 competition on HDSR were used as baselines to evaluate the effectiveness of our proposal, whose metrics, datasets and recognition methods were adopted for fair comparison. Furthermore, we also use a private dataset (Brazilian Bank Check - Courtesy Amount Recognition), and 11 different approaches from the state-of-the-art in HDSR, as well as 2 optical models from the state-of-the-art in HTR. Finally, the proposed optical model demonstrated robustness even with low data volume (126 trainable data, for example), surpassing the results of existing methods with an average precision of 96.50%, which is equivalent to an average percentage of improvement of 3.74 points compared to the state-of-the-art in HDSR. In addition, the result stands out in the competition's CVL HDS set, where the proposed optical model achieved a precision of 93.54%, while the best result so far had been from Beijing group (from the competition itself), with 85.29%.
|
No Beliefs Expressed
|
Tongue squamous cell carcinoma (TSCC) is considered the most common malignant tumor among the oral squamous cell carcinomas with a poor prognosis. Understanding the underlying molecular mechanisms that underpin TSCC and its treatments is the focus of the research. Deregulated expression of microRNAs (miRNAs) has recently been implicated in various biological processes linked to cancer. Therefore, in this study, we attempted to investigate miRNAs and their targets expressed in TSCC, which could be involved in its oncogenesis. We performed next-generation sequencing of small RNAs and transcriptomes in H357 TSCC cell line and human oral keratinocytes as a control to find miRNAs and mRNAs that are differentially expressed (DE), which were then supplemented with additional expression datasets from databases, yielding 269 DE miRNAs and 2094 DE genes. The target prediction followed by pathway and disease function analysis revealed that the DE targets were significantly associated with the key processes and pathways, such as apoptosis, epithelial-mesenchymal transition, endocytosis and vascular endothelial growth factor signaling pathways. Furthermore, the top 12 DE targets were chosen based on their involvement in more than one cancer-related pathway, of which 6 genes are targeted by miR-128-3p. Real-time quantitative PCR validation of this miRNA and its targets in H357 and SCC9 TSCC cells confirmed their possible targeting from their reciprocal expression, with MAP2K7 being a critical target that might be involved in oncogenesis and progression of TSCC by acting as a tumor suppressor. Further research is underway to understand how miR-128-3p regulates oncogenesis in TSCC via MAP2K7 and associated pathways.
|
No Beliefs Expressed
|
Self-motion through an environment induces various sensory signals, i.e., visual, vestibular, auditory, or tactile. Numerous studies have investigated the role of visual and vestibular stimulation for the perception of self-motion direction (heading). Here, we investigated the rarely considered interaction of visual and tactile stimuli in heading perception. Participants were presented optic flow simulating forward self-motion across a horizontal ground plane (visual), airflow toward the participants' forehead (tactile), or both. In separate blocks of trials, participants indicated perceived heading from unimodal visual or tactile or bimodal sensory signals. In bimodal trials, presented headings were either spatially congruent or incongruent with a maximum offset between visual and tactile heading of 30°. To investigate the reference frame in which visuo-tactile heading is encoded, we varied head and eye orientation during presentation of the stimuli. Visual and tactile stimuli were designed to achieve comparable precision of heading reports between modalities. Nevertheless, in bimodal trials heading perception was dominated by the visual stimulus. A change of head orientation had no significant effect on perceived heading, whereas, surprisingly, a change in eye orientation affected tactile heading perception. Overall, we conclude that tactile flow is more important to heading perception than previously thought.NEW & NOTEWORTHY We investigated heading perception from visual-only (optic flow), tactile-only (tactile flow), or bimodal self-motion stimuli in different conditions varying in head and eye position. Overall, heading perception was body or world centered and non-Bayes optimal and revealed a centripetal bias. Although being visually dominated, tactile flow revealed a significant influence during bimodal heading perception.
|
No Beliefs Expressed
|
The shift towards high-throughput technologies and automation in research and development in industrial biotechnology is highlighting the need for increased automation competence and specialized software solutions. Within bioprocess development, the trends towards miniaturization and parallelization of bioreactor systems rely on full automation and digital process control. Thus, mL-scale, parallel bioreactor systems require integration into liquid handling stations to perform a range of tasks stretching from substrate addition to automated sampling and sample analysis. To orchestrate these tasks, the authors propose a scheduling software to fully leverage the advantages of a state-of-the-art liquid handling station (LHS) and to enable improved process control and resource allocation. Fixed sequential order execution, the norm in LHS software, results in imperfect timing of essential operations like feeding or Ph control and execution intervals thereof, that are unknown a priori. However, the duration and control of, e.g., the feeding task and their frequency are of great importance for bioprocess control and the design of experiments. Hence, a software solution is presented that allows the orchestration of the respective operations through dynamic scheduling by external LHS control. With the proposed scheduling software, it is possible to define a dynamic process control strategy based on data-driven real-time prioritization and transparent, user-defined constraints. Drivers for a commercial 48 parallel bioreactor system and the related sensor equipment were developed using the SiLA 2 standard greatly simplifying the integration effort. Furthermore, this paper describes the experimental hardware and software setup required for the application use case presented in the second part.
|
No Beliefs Expressed
|
Colonoscopy is tool of choice for preventing Colorectal Cancer, by detecting and removing polyps before they become cancerous. However, colonoscopy is hampered by the fact that endoscopists routinely miss 22-28% of polyps. While some of these missed polyps appear in the endoscopist's field of view, others are missed simply because of substandard coverage of the procedure, i.e. not all of the colon is seen. This paper attempts to rectify the problem of substandard coverage in colonoscopy through the introduction of the C2D2 (Colonoscopy Coverage Deficiency via Depth) algorithm which detects deficient coverage, and can thereby alert the endoscopist to revisit a given area. More specifically, C2D2 consists of two separate algorithms: the first performs depth estimation of the colon given an ordinary RGB video stream; while the second computes coverage given these depth estimates. Rather than compute coverage for the entire colon, our algorithm computes coverage locally, on a segment-by-segment basis; C2D2 can then indicate in real-time whether a particular area of the colon has suffered from deficient coverage, and if so the endoscopist can return to that area. Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies. The C2D2 algorithm achieves state of the art results in the detection of deficient coverage. On synthetic sequences with ground truth, it is 2.4 times more accurate than human experts; while on real sequences, C2D2 achieves a 93.0% agreement with experts.
|
No Beliefs Expressed
|
The demand for nutrients and new technologies has increased with population growth. The agro-technological revolution with metal oxide engineered nanoparticles (MeOx ENPs) has the potential to reform the resilient agricultural system while maintaining the security of food. When utilized extensively, MeOx ENPs may have unintended toxicological effects on both target and non-targeted species. Since limited information about nanopesticides' pernicious effects is available, in silico modeling can be done to explore these issues. Hence, in the present work, we have applied computational modeling to explore the influence of metal oxide nanoparticles on the toxicity of bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells to bridge the data gap relating to the toxicity of MeOx NPs. Initially, partial least squares (PLS) regression models were developed applying the Small Dataset Modeler software (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) using four datasets having effective concentration (EC50%) as the endpoints and employing only periodic table descriptors. To further explore the predictions, we applied a read-across approach using the descriptors selected in the QSAR models. Also, the inter-endpoint cytotoxicity relationship modeling (quantitative toxicity-toxicity relationship or QTTR) was conducted. It was found that the result obtained by nano-read-across provided a similar level of accuracy as provided by QSAR. The information derived from the PLS models of both the cell lines suggested that metal cation formation, and bond-forming capacity influence the toxicity whereas the presence of metal has an influential impact on the ecotoxicological effects. Thus, it is feasible to design safe nanopesticides that could be more effective than conventional analogs.
|
No Beliefs Expressed
|
Clinical studies on the relationship between pesticide exposure at home and infertility in the general population are scarce. Whether the antioxidant nutrients or other health-related factors affect the pesticide-infertility relationship remains unknown. This nationwide study screened 29,400 participants of the National Health and Nutrition Examination Surveys conducted between 2013 and 2018. The participants were subdivided according to dietary zinc intake based on the recommended dietary allowances as the low-zinc and high-zinc groups (< 8 and ≥ 8 mg/day, respectively), and according to body mass index (BMI; cut-off 28 kg/m2) as the low-BMI and high-BMI groups. Participants who were exposed to pesticides at home had an increased risk of infertility (odds ratio [OR] = 1.56, 95% confidence intervals [CI]: 1.06-2.29). The incidence of infertility differed in low-zinc and high-zinc groups (OR, 95% CI: 2.38, 1.40-4.06 vs. 0.98, 0.53-1.79, respectively), indicating an interaction between pesticide exposure and zinc intake in households (P = 0.047), which suggests that a zinc-rich diet may reduce the risk of pesticide-induced infertility. Similarly, the relationship between pesticide exposure and infertility risk differed in the low-BMI and high-BMI groups (OR, 95% CI: 0.90, 0.42-1.93 vs. 2.23, 1.39-3.58, respectively; P = 0.045), suggesting that high BMI may intensify the infertility risk caused by pesticide exposure. These new findings reveal the antagonistic and synergistic effect of zinc and obesity, respectively, in pesticide-induced infertility risk and suggest that individuals who are obese and on a low-zinc diet may be more susceptible to infertility induced by household pesticide exposure.
|
No Beliefs Expressed
|
Following the continuous development characterized by large-scale constructions, Chinese urban development has shifted to the promotion of refined urban space quality. Urban sculpture, an important part of public arts, has been receiving increased attention in China as an important carrier for highlighting urban characteristics, culture, and history within cultural policies. As a type of cultural capital, it offers innovative methods to address the issues of economic, social, and environmental sustainability, in particular cultural sustainability. Interdisciplinary theories of urban planning are creatively applied to guide, coordinate, and improve the sustainable production of urban sculptures in China. This research was initiated to: (1) Illustrate how urban sculptures are produced through an urban planning system in the context of China; (2) explain what kind of influencing factors in relation to sustainability exist, mainly within the framework of planning strategies and cultural policies; and (3) put forward sustainable planning strategies to produce urban sculptures. To answer the above inquiries, we reviewed more than 100 articles, plans, and government documents, and we conducted several semi-structured interviews. The article argues that urban planning strategies and policies have been conceived as strategic instruments by the Chinese municipal governments to realize sustainable development of urban sculptures. Our findings would enrich knowledge on geographic studies of public art planning through the contextualized analysis of a Chinese urban sculpture planning system. It also fills the gap in the literature on the sustainability of urban sculptures by approaching the perspectives of planning strategies and cultural policies.
|
No Beliefs Expressed
|
An accurate description of muscular activity plays an important role in the clinical diagnosis and rehabilitation research. The electromyography (EMG) is the most used technique to make accurate descriptions of muscular activity. The EMG is associated with the electrical changes generated by the activity of the motor neurons. Typically, to decode the muscular activation during different movements, a large number of individual motor neurons are monitored simultaneously, producing large amounts of data to be transferred and processed by the computing devices. In this paper, we follow an alternative approach that can be deployed locally on the sensor side. We propose a neuromorphic implementation of a spiking neural network (SNN) to extract spatio-temporal information of EMG signals locally and classify hand gestures with very low power consumption. We present experimental results on the input data stream using a mixed-signal analog/digital neuromorphic processor. We performed a thorough investigation on the performance of the SNN implemented on the chip, by: first, calculating PCA on the activity of the silicon neurons at the input and the hidden layers to show how the network helps in separating the samples of different classes; second, performing classification of the data using state-of-the-art SVM and logistic regression methods and a hardware-friendly spike-based read-out. The traditional algorithm achieved a classification rate of $\text{84}\%$ and $\text{81}\%$, respectively, and the spiking learning method achieved $\text{74}\%$. The power consumption of the SNN is $\text{0.05 mW}$, showing the potential of this approach for ultra-low power processing.
|
No Beliefs Expressed
|
Unsupervised domain adaptation (UDA), aiming to adapt the model to an unseen domain without annotations, has drawn sustained attention in surgical instrument segmentation. Existing UDA methods neglect the domain-common knowledge of two datasets, thus failing to grasp the inter-category relationship in the target domain and leading to poor performance. To address these issues, we propose a graph-based unsupervised domain adaptation framework, named Interactive Graph Network (IGNet), to effectively adapt a model to an unlabeled new domain in surgical instrument segmentation tasks. In detail, the Domain-common Prototype Constructor (DPC) is first advanced to adaptively aggregate the feature map into domain-common prototypes using the probability mixture model, and construct a prototypical graph to interact the information among prototypes from the global perspective. In this way, DPC can grasp the co-occurrent and long-range relationship for both domains. To further narrow down the domain gap, we design a Domain-common Knowledge Incorporator (DKI) to guide the evolution of feature maps towards domain-common direction via a common-knowledge guidance graph and category-attentive graph reasoning. At last, the Cross-category Mismatch Estimator (CME) is developed to evaluate the category-level alignment from a graph perspective and assign each pixel with different adversarial weights, so as to refine the feature distribution alignment. The extensive experiments on three types of tasks demonstrate the feasibility and superiority of IGNet compared with other state-of-the-art methods. Furthermore, ablation studies verify the effectiveness of each component of IGNet. The source code is available at https://github.com/CityU-AIM-Group/Prototypical-Graph-DA.
|
No Beliefs Expressed
|
The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l(1)-SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions.
|
No Beliefs Expressed
|
Prostaglandin E1 is crucial for keeping the patent ductus arteriosus in critical congenital heart disease for the survival and palliation of particularly prematurely born babies until a cardiosurgical intervention is available. In this study, the side effects of prostaglandin E1 in newborns with critical congenital heart disease and clinical outcomes were evaluated. Thirty-five newborns diagnosed with critical congenital heart disease were treated with prostaglandin E1 between January 2012 and September 2014 at our hospital. Patient charts were examined for prostaglandin E1 side effects (metabolic, gastric outlet obstruction, apnea), clinical status, and prognosis. Acquired data were analyzed in the SPSS 20.0 program. Patients with birth weight under 2500 g needed more days of prostaglandin E1 infusion than ones with birthweight over 2500 g (P = 0.016). The ratio of patients with birth weight under 2500 g who received prostaglandin E1 longer than 7 days was higher than the patients with birth weight over 2500 g (P = 0.02). Eighteen side effects were encountered in 11 of 35 patients (31%). Of these side effects, 1 patient had 4, 4 patients had 2, and 6 patients had only 1 side effect. Discontinuation of the therapy was never needed. Prostaglandin E1 is an accepted therapy modality for survival and outcome in critical congenital heart disease in particularly low-birth-weight babies until a surgical intervention is available. Side effects are not less encountered but are almost always manageable, and discontinuation is not needed.
|
No Beliefs Expressed
|
This paper presents a real-time capable graphics processing unit (GPU)-based ultrasound simulator suitable for medical education. The main focus of the simulator is to synthesize realistic looking ultrasound images in real-time including artifacts, which are essential for the interpretation of this data. The simulation is based on a convolution-enhanced ray-tracing approach and uses a deformable mesh model. Deformations of the mesh model are calculated using the PhysX engine. Our method advances the state of the art for real-time capable ultrasound simulators by following the path of the ultrasound pulse, which enables better simulation of ultrasound-specific artifacts. An evaluation of our proposed method in comparison with recent generative slicing-based strategies as well as real ultrasound images is performed. Hereby, a gelatin ultrasound phantom containing syringes filled with different media is scanned with a real transducer. The obtained images are then compared to images which are simulated using a slicing-based technique and our proposed method. The particular benefit of our method is the accurate simulation of ultrasound-specific artifacts, like range distortion, refraction and acoustic shadowing. Several test scenarios are evaluated regarding simulation time, to show the performance and the bottleneck of our method. While being computationally more intensive than slicing techniques, our simulator is able to produce high-quality images in real-time, tracing over 5000 rays through mesh models with more than 2 000 000 triangles of which up to 200 000 may be deformed each frame.
|
No Beliefs Expressed
|
Rapid weight gain (RWG) during infancy is a known risk factor for later childhood obesity. It can be measured using a range of definitions across various time periods in the first 2 years of life. In recent years, some early childhood obesity prevention trials have included a focus on preventing RWG during infancy, with modest success. Overall, RWG during infancy remains common, yet little work has examined whether infants with this growth pattern should receive additional care when it is identified in health-care settings. In this viewpoint, we contend that RWG during infancy should be routinely screened for in health-care settings, and when identified, viewed as an opportunity for health-care professionals to instigate non-stigmatising discussions with families about RWG and general healthy practices for their infants. If families wish to engage, we suggest that six topics from early life obesity prevention studies (breastfeeding, formula feeding, complementary feeding, sleep, responsive parenting, and education around growth charts and monitoring) could form the foundations of conversations to help them establish and maintain healthy habits to support their infant's health and well-being and potentially lower the risk of later obesity. However, further work is needed to develop definitive guidelines in this area, and to address other gaps in the literature, such as the current lack of a standardised definition for RWG during infancy and a clear understanding of the time points over which it should be measured.
|
No Beliefs Expressed
|
Of the six possible orderings of the three main constituents of language (subject, verb, and object), two-SOV and SVO-are predominant cross-linguistically. Previous research using the silent gesture paradigm in which hearing participants produce or respond to gestures without speech has shown that different factors such as reversibility, salience, and animacy can affect the preferences for different orders. Here, we test whether participants' preferences for orders that are conditioned on the semantics of the event change depending on (i) the iconicity of individual gestural elements and (ii) the prior knowledge of a conventional lexicon. Our findings demonstrate the same preference for semantically conditioned word order found in previous studies, specifically that SOV and SVO are preferred differentially for different types of events. We do not find that iconicity of individual gestures affects participants' ordering preferences; however, we do find that learning a lexicon leads to a stronger preference for SVO-like orders overall. Finally, we compare our findings from English speakers, using an SVO-dominant language, with data from speakers of an SOV-dominant language, Turkish. We find that, while learning a lexicon leads to an increase in SVO preference for both sets of participants, this effect is mediated by language background and event type, suggesting that an interplay of factors together determines preferences for different ordering patterns. Taken together, our results support a view of word order as a gradient phenomenon responding to multiple biases.
|
No Beliefs Expressed
|
In the last decade, research in biometrics has been focused on augmenting the algorithmic performance to address a growing range of applications, not limited to person authentication/recognition. The concept of context awareness emerged as a possible key-factor for both performance optimization and operational adaptation of the capture, extraction, matching and decision stages. This may be particularly effective for multi-biometrics systems. The knowledge of the context in which a task is being performed, may provide useful information to the system in several manners. For example, it may allow to adapt to a specific environmental condition, such as shadow or light exposure. On the other hand, it may be possible to select the best available algorithm, among a given set to address the task at hand, which best performs within the given context. This paper aims to provide an overall vision of the main contributions available so far in the field of context-aware biometric systems and methods. The survey is not confined to a particular biometric modality or processing stage, but rather spans the state of the art of several biometric modalities and approaches. A taxonomy of context-aware biometric systems and methods is also proposed, along with a comparison of their features, aims and performances. The analysis will be complemented with a critical discussion about the state of the art also suggesting some future application scenarios. (C) 2018 Elsevier B.V. All rights reserved.
|
No Beliefs Expressed
|
Major source of carbon-containing air born particular matter that significantly pollutes environment and provokes development of neuropathology is forest fires and wood combustion. Here, water-suspended smoke particulate matter preparations (SPs) were synthesized from birch, pine, poplar wood, and also birch bark and pine needles. Taking into account importance of the gut-brain communication system, SP properties were compared regarding their capability to modulate functioning of nerve terminals and gut cells/preparations. In cortex nerve terminals, poplar wood SP was more effective in decreasing uptake and increasing the extracellular levels of excitatory and inhibitory neurotransmitters L-[14C]glutamate and [3H]GABA, respectively. Spontaneous and H2O2-stimulated ROS generation in nerve terminals decreased by SPs, the most efficient one was from poplar wood. SPs from birch, pine and poplar wood caused membrane depolarization, poplar wood SP effect was 5-fold higher vs. birch and pine wood ones. Functional characteristics of gut cells/preparations, which tightly related to nerve terminal experiments, were assessed. SPs increased paracellular permeability of proximal colon mucosal-submucosal preparations monitored in Ussing chamber system (FITC-dextran, 4 kDa), where the most prominent effect had poplar wood SP. The latter demonstrated more considerable influence on COLO 205 cell causing 30 % loss of cell viability. PM emitted to the environment during combustion of various wood caused similar unidirectional harmful effects on brain and gut cell functioning, thereby triggering development of pathologies in gut and brain and gut-brain communication system.
|
No Beliefs Expressed
|
In this study, Ag-WO3/bentonite nanocomposites were synthesized through a sol-gel process, a microwave irradiation technique, and a sol-immobilization process to examine their impact on the photocatalytic activity in the degradation of humic acids. The optical and structural properties of the synthesized materials were characterized using X-ray diffraction (XRD), Fourier-transform-infrared spectra (FTIR), field emission scanning electron microscope (FE-SEM) with energy dispersive X-ray (EDX), UV-Vis diffused reflectance spectra (UV-Vis DRS), Brunauer-Emmett-Teller (BET) method, and transmission electron microscope (TEM). The presence of Ag and WO3 peaks in the XRD and EDX spectra confirmed the synthesis of Ag-WO3 nanoparticles in the composite. The monoclinic structure of the produced WO3 samples are shown by powder X-ray diffraction patterns. The WO3-based nanocomposites' photocatalytic activity was improved by the composition of Ag and bentonite, which reduced the optical bandgap energy of WO3. The binary (Ag-WO3) nanocomposite showed improved photocatalytic activity towards the degradation of humic acid (HA) from 58% (pristine WO3) to 82% (Ag-WO3) when compared with the pristine WO3 sample under the visible light irradiation. Notably, the ternary (Ag-WO3/bent) nanocomposite demonstrated an outstanding photocatalytic efficiency of HA degradation (91.0%) under normal conditions (pH = 7.0 and 25 °C). Humic acid degradation in Ag-WO3/bent was expressed by the pseudo-first-order kinetic. To summarize, integrating Ag, WO3, bentonite, and visible light radiation to activate HA efficiently can be offered as a successful and promising technique for wastewater treatment.
|
No Beliefs Expressed
|
We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891 +/- 0.016 (mean +/- std) and an average symmetric surface distance (ASD) of 0.994 +/- 0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 +/- 0.031 DC and 1.556 +/- 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.
|
No Beliefs Expressed
|
Repetitive online searches for health information increase anxieties and result in Internet addiction. Internet addiction, cyberchondria, anxiety sensitivity, and hypochondria have been studied separately, but how these concepts are reciprocally linked has not been investigated. This study aimed to determine the levels, correlations, and predictors of Internet addiction, cyberchondria, anxiety sensitivity, and hypochondria among students based on the sample's characteristics. A sample of 143 university students participated in this cross-sectional online survey. A self-reported questionnaire was employed to collect data from students. The studied concepts had moderate to high correlations with each other and with the students' characteristics. Not getting infected with the coronavirus was among the demographic factors inserted into the regression model that only predicted cyberchondria. The model of cyberchondria was significant and explained 11.5% of the variance in the score of concepts. The results of the standard regression analysis indicated that the model predicting Internet addiction accounted for 41.2% of the variability. Our unique findings indicate that cyberchondria can contribute to developing Internet addiction compared to earlier studies. The findings suggest the importance of empowering students to overcome their anxieties by managing cyberchondria and Internet addiction. Mental health professionals, namely psychiatric nurses, are at the forefront of taking preventive mental health measures on campus, such as screening and referring students who exhibit these problems to psychological support and counseling to cope with their anxieties.
|
No Beliefs Expressed
|
Introduced species often benefit from escaping their enemies when they are transported to a new range, an idea commonly expressed as the enemy release hypothesis. However, species might shed mutualists as well as enemies when they colonize a new range. Loss of mutualists might reduce the success of introduced populations, or even cause failure to establish. We provide the first quantitative synthesis testing this natural but often overlooked parallel of the enemy release hypothesis, which is known as the missed mutualist hypothesis. Meta-analysis showed that plants interact with 1.9 times more mutualist species, and have 2.3 times more interactions with mutualists per unit time in their native range than in their introduced range. Species may mitigate the negative effects of missed mutualists. For instance, selection arising from missed mutualists could cause introduced species to evolve either to facilitate interactions with a new suite of species or to exist without mutualisms. Just as enemy release can allow introduced populations to redirect energy from defence to growth, potentially evolving increased competitive ability, species that shift to strategies without mutualists may be able to reallocate energy from mutualism toward increased competitive ability or seed production. The missed mutualist hypothesis advances understanding of the selective forces and filters that act on plant species in the early stages of introduction and establishment and thus could inform the management of introduced species.
|
No Beliefs Expressed
|
Due to the movement expressiveness and privacy assurance of human skeleton data, 3D skeleton-based action inference is becoming popular in healthcare applications. These scenarios call for more advanced performance in application-specific algorithms and efficient hardware support. Warnings on health emergencies sensitive to response speed require low latency output and action early detection capabilities. Medical monitoring that works in an always-on edge platform needs the system processor to have extreme energy efficiency. Therefore, in this paper, we propose the MC-LSTM, a functional and versatile 3D skeleton-based action detection system, for the above demands. Our system achieves state-of-the-art accuracy on trimmed and untrimmed cases of general-purpose and medical-specific datasets with early-detection features. Further, the MC-LSTM accelerator supports parallel inference on up to 64 input channels. The implementation on Xilinx ZCU104 reaches a throughput of 18 658 Frames-Per-Second (FPS) and an inference latency of 3.5 ms with the batch size of 64. Accordingly, the power consumption is 3.6 W for the whole FPGA+ARM system, which is 37.8x and 10.4x more energy-efficient than the high-end Titan X GPU and i7-9700 CPU, respectively. Meanwhile, our accelerator also keeps a 4 similar to 5x energy efficiency advantage against the low-power high-performance Firefly-RK3399 board carrying an ARM Cortex-A72+A53 CPU. We further synthesize an 8-bit quantized version on the same hardware, providing a 48.8% increase in energy efficiency under the same throughput.
|
No Beliefs Expressed
|
The Hippo signaling pathway controls cell proliferation and tissue regeneration via its transcriptional effectors yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ). The canonical pathway topology is characterized by sequential phosphorylation of kinases in the cytoplasm that defines the subcellular localization of YAP and TAZ. However, the molecular mechanisms controlling the nuclear/cytoplasmic shuttling dynamics of both factors under physiological and tissue-damaging conditions are poorly understood. By implementing experimental in vitro data, partial differential equation modeling, as well as automated image analysis, we demonstrate that nuclear phosphorylation contributes to differences between YAP and TAZ localization in the nucleus and cytoplasm. Treatment of hepatocyte-derived cells with hepatotoxic acetaminophen (APAP) induces a biphasic protein phosphorylation eventually leading to nuclear protein enrichment of YAP but not TAZ. APAP-dependent regulation of nuclear/cytoplasmic YAP shuttling is not an unspecific cellular response but relies on the sequential induction of reactive oxygen species (ROS), RAC-alpha serine/threonine-protein kinase (AKT, synonym: protein kinase B), as well as elevated nuclear interaction between YAP and AKT. Mouse experiments confirm this sequence of events illustrated by the expression of ROS-, AKT-, and YAP-specific gene signatures upon APAP administration. In summary, our data illustrate the importance of nuclear processes in the regulation of Hippo pathway activity. YAP and TAZ exhibit different shuttling dynamics, which explains distinct cellular responses of both factors under physiological and tissue-damaging conditions.
|
No Beliefs Expressed
|
Hypoxia contributes to the exaggerated yet ineffective airway inflammation that fails to oppose infections in cystic fibrosis (CF). However, the potential for impairment of essential immune functions by HIF-1α (hypoxia-inducible factor 1α) inhibition demands a better comprehension of downstream hypoxia-dependent pathways that are amenable for manipulation. We assessed here whether hypoxia may interfere with the activity of AhR (aryl hydrocarbon receptor), a versatile environmental sensor highly expressed in the lungs, where it plays a homeostatic role. We used murine models of Aspergillus fumigatus infection in vivo and human cells in vitro to define the functional role of AhR in CF, evaluate the impact of hypoxia on AhR expression and activity, and assess whether AhR agonism may antagonize hypoxia-driven inflammation. We demonstrated that there is an important interferential cross-talk between the AhR and HIF-1α signaling pathways in murine and human CF, in that HIF-1α induction squelched the normal AhR response through an impaired formation of the AhR:ARNT (aryl hydrocarbon receptor nuclear translocator)/HIF-1β heterodimer. However, functional studies and analysis of the AhR genetic variability in patients with CF proved that AhR agonism could prevent hypoxia-driven inflammation, restore immune homeostasis, and improve lung function. This study emphasizes the contribution of environmental factors, such as infections, in CF disease progression and suggests the exploitation of hypoxia:xenobiotic receptor cross-talk for antiinflammatory therapy in CF.
|
No Beliefs Expressed
|
In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-of-the-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50% (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86 % - 7.98 km/h to 1.10 km/h) and manual calibration (error reduction by 19 % - 1.35 km/h to 1.10 km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day). (C) 2017 Elsevier Inc. All rights reserved.
|
No Beliefs Expressed
|
Treatments that target fundamental processes of aging are expected to delay several aging-related conditions simultaneously. Testing the efficacy of these treatments for potential anti-aging benefits will require clinical trials with endpoints that reflect the potential benefits of slowing processes of aging. There are several potential types of endpoints to capture the benefits of slowing a process of aging, and a consensus is needed to standardize and compare the results of these trials and to guide the analysis of observational data to support trial planning. Using biomarkers instead of clinical outcomes would substantially reduce the size and the duration of clinical trials. This requires validation of surrogate markers showing that treatment induced change in the marker reliably predicts the magnitude of change in the clinical outcome. The surrogate marker must also reflect the biological mechanism for the effect of treatment on the clinical outcome. "Biological age" is a superficially attractive marker for such trials. However, it is essential to establish that treatment induced change in biological age reliably predict the magnitude of benefits in the clinical outcome. Reaching consensus on clinical outcomes for geroscience trials and then validating potential surrogate biomarkers requires time, effort, and coordination that will be worthwhile to develop surrogate outcomes that can be trusted to efficiently test the value of many anti-aging treatments under development.
|
No Beliefs Expressed
|
Accurate cost and time estimation of a query is one of the major success indicators for database management systems. SQL allows the expression of flexible queries on text-formatted data. The LIKE operator is used to search for a specified pattern (e.g., LIKE "luck%") in a string database. It is vital to estimate the selectivity of such flexible predicates for the query optimizer to choose an efficient execution plan. In this paper, we study the problem of estimating the selectivity of a LIKE query predicate over a bag of strings. We propose a new type of pattern-based histogram structure to summarize the data distribution in a particular column. More specifically, we first mine sequential patterns over a given string database and then construct a special histogram out of the mined patterns. During query optimization time, pattern-based histograms are exploited to estimate the selectivity of a LIKE predicate. The experimental results on a real dataset from DBLP show that the proposed technique outperforms the state of the art for generic LIKE queries like %s(1)%s(2)%...%s(n) % where s(i) represents one or more characters. What is more, the proposed histogram structure requires more than two orders of magnitude smaller memory space, and the estimation time is almost an order of magnitude less in comparison to the state of the art.
|
No Beliefs Expressed
|
Fast nearest neighbor search is becoming more and more crucial given the advent of large-scale data in many computer vision applications. Hashing approaches provide both fast search mechanisms and compact index structures to address this critical need. In image retrieval problems where labeled training data is available, supervised hashing methods prevail over unsupervised methods. Most state-of-the-art supervised hashing approaches employ batch-learners. Unfortunately, batch-learning strategies may be inefficient when confronted with large datasets. Moreover, with batch-learners, it is unclear how to adapt the hash functions as the dataset continues to grow and new variations appear over time. To handle these issues, we propose OSH: an Online Supervised Hashing technique that is based on Error Correcting Output Codes. We consider a stochastic setting where the data arrives sequentially and our method learns and adapts its hashing functions in a discriminative manner. Our method makes no assumption about the number of possible class labels, and accommodates new classes as they are presented in the incoming data stream. In experiments with three image retrieval benchmarks, our method yields state-of-the-art retrieval performance as measured in Mean Average Precision, while also being orders-of-magnitude faster than competing batch methods for supervised hashing. Also, our method significantly outperforms recently introduced online hashing solutions. (C) 2016 Elsevier Inc. All rights reserved.
|
No Beliefs Expressed
|
Magnetic particle imaging (MPI) is a novel tomographic imaging technique, which visualizes the distribution of a magnetic nanoparticle-based tracer material. However, reconstructed MPI images often suffer from an insufficiently compensated image background caused by rapid non-deterministic changes in the background signal of the imaging device. In particular, the signal-to-background ratio (SBR) of the images is reduced for lower tracer concentrations or longer acquisitions. The state-of-the-art procedure in MPI is to frequently measure the background signal during the sample measurement. Unfortunately, this requires a removal of the entire object from the scanner's field of view (FOV), which introduces dead time and repositioning artifacts. To overcome these considerable restrictions, we propose a novel method that uses two consecutive image acquisitions as input parameters for a simultaneous reconstruction of the tracer distribution, as well as the background signal. The two acquisitions differ by just a small spatial shift, while keeping the object always within the focus of a slightly reduced FOV. A linearly interpolated background between the initial and final background measurement is used to seed the iterative reconstruction. The method has been tested with simulations and phantom measurements. Overall, a substantial reduction of the image background was observed, and the image SBR is increased by a factor of 2(7) for the measurement (simulation) data.
|
No Beliefs Expressed
|
Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect terminations for tree-like structures based on a multiscale ray-shooting model and a termination visual prior. The multiscale ray-shooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate. The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the gray-weighted distance transform. The termination visual prior is based on a key observation-when observing a 3D termination from three orthogonal directions without occlusion, we can recognize it in at least two views. Using this prior with the multiscale ray-shooting model, we can detect 3D terminations with high accuracies. Experiments on 3D neuron image stacks, 2D neuron images, 3D bronchus image stacks, and 2D retinal blood vessel images exhibit average precision and recall rates of 87.50% and 90.54%. The experimental results confirm that the proposed method outperforms other the state-of-the-art termination detection methods.
|
No Beliefs Expressed
|
Climate change mitigation has been one of the world's most salient issues for the past three decades. However, global policy attention has been partially diverted to address the COVID-19 pandemic for the past 2 y. Here, we explore the impact of the pandemic on the frequency and content of climate change discussions on Twitter for the period of 2019 to 2021. Consistent with the "finite pool of worry" hypothesis both at the annual level and on a daily basis, a larger number of COVID-19 cases and deaths is associated with a smaller number of "climate change" tweets. Climate change discussion on Twitter decreased, despite 1) a larger Twitter daily active usage in 2020 and 2021, 2) greater coverage of climate change in the traditional media in 2021, 3) a larger number of North Atlantic Ocean hurricanes, and 4) a larger wildland fires area in the United States in 2020 and 2021. Further evidence supporting the finite pool of worry is the significant relationship between daily COVID-19 cases/deaths on the one hand and the public sentiment and emotional content of climate change tweets on the other. In particular, increasing COVID-19 numbers decrease negative sentiment in climate change tweets and the emotions related to worry and anxiety, such as fear and anger.
|
No Beliefs Expressed
|
Intellectual property is considered to provide the infrastructure of innovation, and companies could proactively generate their intellectual assets and strengthen the business opportunities by focusing on discovery phases. This paper examines whether the invention process can be managed and finds that patents appear not only as a result of inventive activity but as the purpose as well. By building on recent design theories such as the concept-knowledge design theory, this research introduces a general framework that enables controlling for 'patentability' criteria, describes a patent in a unique way using actions, effects, and associated knowledge, and defines a patentable subject matter based on the notion of the person skilled in the art. Using the introduced model, several patent design methods are compared and their performances are characterized. The model was tested within the European semiconductor manufacturer, STMicroelectronics. The results indicate that the quality of patent proposals depends on the capacity to extend existing knowledge combinations, to overcome the initial design reasoning of the person skilled in the art, and to ensure novelty and sufficient inventive step. Finally, the proposed model in this research, the 'design-for-patentability' model, demonstrates that there is an unexplored property of the concept-knowledge design theory-non-substitution-showing that the order within design is irreversible and influences the quality of results.
|
No Beliefs Expressed
|
18 F-fluoro-deoxyglucose position emission tomography (18 F-FDG-PET) has been proven as a sensitive and reliable tool for diagnosis of autoimmune encephalitis (AE). More attention was paid to this kind of imaging because of the shortage of MRI, EEG, and CSF findings. FDG-PET has been assessed in a few small studies and case reports showing apparent abnormalities in cases where MRI does not. Here, we summarized the patterns (specific or not) in AE with different antibodies detected and the clinical outlook for the wide application of FDG-PET considering some limitations. Specific patterns based on antibody subtypes and clinical symptoms were critical for identifying suspicious AE, the most common of which was the anteroposterior gradient in anti- N -methyl- d -aspartate receptor (NMDAR) encephalitis and the medial temporal lobe hypermetabolism in limbic encephalitis. And the dynamic changes of metabolic presentations in different phases provided us the potential to inspect the evolution of AE and predict the functional outcomes. Except for the visual assessment, quantitative analysis was recently reported in some voxel-based studies of regions of interest, which suggested some clues of the future evaluation of metabolic abnormalities. Large prospective studies need to be conducted controlling the time from symptom onset to examination with the same standard of FDG-PET scanning.
|
No Beliefs Expressed
|
This paper investigates the meanings of urban public space, both as a didactic platform and as a way to spread awareness of climate change through art. What are the roles of public space? How do artworks intervene in urban public space? How can public art contribute to "sustainability" issues? I have argued that the intervention of art in urban public space offers effective ways of developing climate change art, which is understood to be an educator. Public space can be categorized into three different types: everyday, social, and symbolic spaces. These can be used as a platform for opening discussion and learning about the increased issues of the global crisis in contemporary society. I have drawn upon the representative case studies about climate change to explore how they intervene in urban public space and how they engage viewers to spread awareness, which is one of the fundamental aspects of this paper. It also stimulates viewers' perceptions and awareness of a more sustainable future through phenomenological and emotional experiences. Thus, this paper contributes to the understanding and knowledge of the relationship between art and public space with respect to raising awareness about climate change and considering how art intervenes in urban public space to create an eco-didactic platform.
|
No Beliefs Expressed
|
The blastocyst is a conserved stage and distinct milestone in the development of the mammalian embryo. Blastocyst stage embryos comprise three cell lineages which arise through two sequential binary cell fate specification steps. In the first, extra-embryonic trophectoderm (TE) cells segregate from inner cell mass (ICM) cells. Subsequently, ICM cells acquire a pluripotent epiblast (Epi) or extra-embryonic primitive endoderm (PrE, also referred to as hypoblast) identity. In the mouse, nascent Epi and PrE cells emerge in a salt-and-pepper distribution in the early blastocyst and are subsequently sorted into adjacent tissue layers by the late blastocyst stage. Epi cells cluster at the interior of the ICM, while PrE cells are positioned on its surface interfacing the blastocyst cavity, where they display apicobasal polarity. As the embryo implants into the maternal uterus, cells at the periphery of the PrE epithelium, at the intersection with the TE, break away and migrate along the TE as they mature into parietal endoderm (ParE). PrE cells remaining in association with the Epi mature into visceral endoderm. In this review, we discuss our current understanding of the PrE from its specification to its maturation. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.
|
No Beliefs Expressed
|
The complex of Kamyana Mohyla is the westernmost rock art location of the Eurasian Steppe and the largest accumulation of cave art sites in the Eastern Europe. So far it has been believed that the complex contains the Upper Paleolithic cave art images as well as portable art collection that resemble the instances of Upper Paleolithic worldview. Though this belief lacked the support of archaeological context and chronological attribution it remained neither proved nor disputed. However, the application of digital photogrammetric tools allowed to perform the sub-millimeter surface modeling of the rock art objects and to re-examine and reconsider the engravings that were previously attributed to Pleistocene. The modeling results presented in this article revealed the complete absence of figurative images for the collection of portable art specimens and the dubious character of those for the cave art one. Therefore, the whole collection should be reconsidered, studied and attributed according to the state of the art and contemporary archaeological record in the region. This contribution attempts to think over the possible Upper Paleolithic origin of the motifs from Kamyana Mohyla in the light of new data and proposes three hypotheses towards the understanding of the rock art assemblage from one of the caves in the complex.
|
No Beliefs Expressed
|
During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, involving differentiation of neuronal types, dendritic arborization, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b-values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine-to coarse-scale diffusion processes, characterizing restricted and hindered diffusion stemming respectively from intra-and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy (mu FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.
|
No Beliefs Expressed
|
Human action recognition from still image has recently drawn increasing attention in human behavior analysis and also poses great challenges due to the huge inter ambiguity and intra variability. Vector of locally aggregated descriptors (VLAD) has achieved state-of-the-art performance in many image classification tasks based on local features. The great success of VLAD is largely due to its high descriptive ability and computational efficiency. In this paper, towards optimal VLAD representations for human action recognition from still images, we improve VLAD by tackling three important issues including empty cavity, ambiguity and pooling strategies. The empty cavity limits the performance of VLAD and has long been overlooked. We investigate the empty cavity and provide an effective solution to deal with it, which improves the performance of VLAD; we enhance the codewords with middle level of assignments which are more reliable and can provide more useful information for realistic activity; we propose incorporating the generalized max pooling to replace sum pooling in VLAD, which is more reliable for the final representation. We have conducted extensive experiments on four widely-used benchmarks to validate the proposed method for human action recognition from still images. Our method produces competitive performance with state-of-the-art algorithms. (C) 2016 Elsevier B.V. All rights reserved.
|
No Beliefs Expressed
|
The consequences of extremely intense long-term exercise for brain health remain unknown. We studied the effects of strenuous exercise on brain structure and function, its dose-response relationship, and mechanisms in a rat model of endurance training. Five-week-old male Wistar rats were assigned to moderate (MOD) or intense (INT) exercise or a sedentary (SED) group for 16 weeks. MOD rats showed the highest motivation and learning capacity in operant conditioning experiments; SED and INT presented similar results. In vivo MRI demonstrated enhanced global and regional connectivity efficiency and clustering as well as a higher cerebral blood flow (CBF) in MOD but not INT rats compared with SED. In the cortex, downregulation of oxidative phosphorylation complex IV and AMPK activation denoted mitochondrial dysfunction in INT rats. An imbalance in cortical antioxidant capacity was found between MOD and INT rats. The MOD group showed the lowest hippocampal brain-derived neurotrophic factor levels. The mRNA and protein levels of inflammatory markers were similar in all groups. In conclusion, strenuous long-term exercise yields a lesser improvement in learning ability than moderate exercise. Blunting of MOD-induced improvements in CBF and connectivity efficiency, accompanied by impaired mitochondrial energetics and, possibly, transient local oxidative stress, may underlie the findings in intensively trained rats.
|
No Beliefs Expressed
|
Glutamine synthetase (GS) catalyzes de novo synthesis of glutamine that facilitates cancer cell growth. In the liver, GS functions next to the urea cycle to remove ammonia waste. As a dysregulated urea cycle is implicated in cancer development, the impact of GS's ammonia clearance function has not been explored in cancer. Here, we show that oncogenic activation of β-catenin (encoded by CTNNB1) led to a decreased urea cycle and elevated ammonia waste burden. While β-catenin induced the expression of GS, which is thought to be cancer promoting, surprisingly, genetic ablation of hepatic GS accelerated the onset of liver tumors in several mouse models that involved β-catenin activation. Mechanistically, GS ablation exacerbated hyperammonemia and facilitated the production of glutamate-derived nonessential amino acids, which subsequently stimulated mechanistic target of rapamycin complex 1 (mTORC1). Pharmacological and genetic inhibition of mTORC1 and glutamic transaminases suppressed tumorigenesis facilitated by GS ablation. While patients with hepatocellular carcinoma, especially those with CTNNB1 mutations, have an overall defective urea cycle and increased expression of GS, there exists a subset of patients with low GS expression that is associated with mTORC1 hyperactivation. Therefore, GS-mediated ammonia clearance serves as a tumor-suppressing mechanism in livers that harbor β-catenin activation mutations and a compromised urea cycle.
|
No Beliefs Expressed
|
Appendiceal tumours encompass a wide spectrum of differential diagnoses and frequently present with clinical features of appendicitis. We report the case of a 43-year-old woman who presented with epigastric pain, dyspepsia and bloating. An atypical right para-iliac mass was detected on abdominal ultrasound, and computed tomography (CT) identified an appendiceal tumour. The tumour subtype remained indeterminate following Gallium-68 Dotatate positron emission tomography (PET); however, an appendiceal neuroendocrine tumour was suspected. Surgical resection with laparoscopic en bloc appendicectomy and limited caecectomy was performed, and histopathological assessment confirmed an appendiceal schwannoma. The report is followed by a review of the literature. To our knowledge, there have been fourteen reported cases of appendiceal schwannoma. The preoperative diagnosis can be challenging and appendiceal schwannoma had not been suspected in any of the reported cases, while a suspected diagnosis of neuroendocrine tumour or gastrointestinal stromal tumour was common. Definitive diagnosis requires immunohistochemical assessment and S100 is the hallmark. No personal or family history of underlying neurofibromatosis (NF) type 1 or type 2 has been reported to date. As for other gastrointestinal schwannomas, complete surgical resection is the recommended treatment for appendiceal schwannoma. Following this, despite lack of long-term follow-up, no cases of recurrence have been reported thus far.
|
No Beliefs Expressed
|
Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 +/- 0.41 mm. The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5 degrees precision).
|
No Beliefs Expressed
|
Currently, copper nanoparticles are used in various sectors of industry, agriculture, and medicine. To understand the effects induced by these nanoparticles, it is necessary to assess the environmental risk and safely expand their use. In this study, we evaluated the toxicity of copper oxide (nCuO) nanoparticles in Danio rerio adults, their distribution/concentration, and chemical form after exposure. This last assessment had never been performed on copper-exposed zebrafish. Such evaluation was done through the characterization of nCuO, acute exposure tests and analysis of distribution and concentration by microstructure X-ray fluorescence spectroscopy (µ-XRF) and atomic absorption spectroscopy (GF-AAS). Synchrotron X-ray absorption spectroscopy (XAS) was performed to find out the chemical form of copper in hotspots. The results show that the toxicity values of fish exposed to nCuO were 2.4 mg L-1 (25 nm), 12.36 mg L-1 (40 nm), 149.03 mg L-1 (80 nm) and 0.62 mg L-1 (CuSO4, used as a positive control). The total copper found in the fish was in the order of mg kg-1 and it was not directly proportional to the exposure concentration; most of the copper was concentrated in the gastric system. However, despite the existence of copper hotspots, chemical transformation of CuO into other compounds was not detected.
|
No Beliefs Expressed
|
3D shape reconstruction from multiple hand-drawn sketches is an intriguing way to 3D shape modeling. Currently, state-of-the-art methods employ neural networks to learn a mapping from multiple sketches from arbitrary view angles to a 3D voxel grid. Because of the cubic complexity of 3D voxel grids, however, neural networks are hard to train and limited to low resolution reconstructions, which leads to a lack of geometric detail and low accuracy. To resolve this issue, we propose to reconstruct 3D shapes from multiple sketches using direct shape optimization (DSO), which does not involve deep learning models for direct voxel-based 3D shape generation. Specifically, we first leverage a conditional generative adversarial network (CGAN) to translate each sketch into an attenuance image that captures the predicted geometry from a given viewpoint. Then, DSO minimizes a project-and-compare loss to reconstruct the 3D shape such that it matches the predicted attenuance images from the view angles of all input sketches. Based on this, we further propose a progressive update approach to handle inconsistencies among a few hand-drawn sketches for the same 3D shape. Our experimental results show that our method significantly outperforms the state-of-the-art methods under widely used benchmarks and produces intuitive results in an interactive application.
|
No Beliefs Expressed
|
The replication of DNA is a complex biological process that is essential for life. Bacterial DNA replication is initiated at genomic loci referred to as replication origins (oriCs). Integrating the Z-curve method, DnaA box distribution, and comparative genomic analysis, we developed a web server to predict bacterial oriCs in 2008 called Ori-Finder, which contributes to clarify the characteristics of bacterial oriCs. The oriCs of hundreds of sequenced bacterial genomes have been annotated in the genome reports using Ori-Finder and the predicted results have been deposited in DoriC, a manually curated database of oriCs. This has facilitated large-scale data mining of functional elements in oriCs and strand-biased analysis. Here, we describe Ori-Finder 2022 with updated prediction framework, interactive visualization module, new analysis module, and user-friendly interface. More species-specific indicator genes and functional elements of oriCs are integrated into the updated framework, which has also been redesigned to predict oriCs in draft genomes. The interactive visualization module displays more genomic information related to oriCs and their functional elements. The analysis module includes regulatory protein annotation, repeat sequence discovery, homologous oriC search, and strand-biased analyses. The redesigned interface provides additional customization options for oriC prediction. Ori-Finder 2022 is freely available at http://tubic.tju.edu.cn/Ori-Finder/ and https://tubic.org/Ori-Finder/.
|
No Beliefs Expressed
|
Synthetic biology has developed rapidly in the 21st century. It covers a range of scientific disciplines that incorporate principles from engineering to take advantage of and improve biological systems, often applied to specific problems. Methods important in this subject area include the systematic design and testing of biological systems and, here, we describe how synthetic biology projects frequently develop microbiology skills and education. Synthetic biology research has huge potential in biotechnology and medicine, which brings important ethical and moral issues to address, offering learning opportunities about the wider impact of microbiological research. Synthetic biology projects have developed into wide-ranging training and educational experiences through iGEM, the International Genetically Engineered Machines competition. Elements of the competition are judged against specific criteria and teams can win medals and prizes across several categories. Collaboration is an important element of iGEM, and all DNA constructs synthesized by iGEM teams are made available to all researchers through the Registry for Standard Biological Parts. An overview of microbiological developments in the iGEM competition is provided. This review is targeted at educators that focus on microbiology and synthetic biology, but will also be of value to undergraduate and postgraduate students with an interest in this exciting subject area.
|
No Beliefs Expressed
|
Efflux by resistance nodulation cell division transporters, such as AcrAB-TolC in Escherichia coli, substantially contributes to the development of Gram-negative multidrug resistance. Therefore, the finding of compounds that counteract efflux is an urgent goal in the fight against infectious diseases. Previously, an efflux inhibitory activity of the antimalarials mefloquine and artesunate was reported. In this study, we have investigated further antimalarials regarding efflux by AcrB, the pumping part of AcrAB-TolC, and their drug-enhancing potency in E. coli. We show that 10 of the 24 drugs tested are substrates of the multidrug efflux pump AcrB. Among them, tafenoquine and proguanil, when used at subinhibitory concentrations, caused an at least 4- and up to 24-fold enhancement in susceptibility to 6 and 14 antimicrobial agents, respectively. Both antimalarials are able to increase the intracellular accumulation of Hoechst 33342, with proguanil showing similar effectiveness as the efflux inhibitor 1-(1-naphthylmethyl)piperazine. In the case of proguanil, AcrB-dependent efflux inhibition could also be demonstrated in a real-time efflux assay. In addition to presenting new AcrB substrates, our study reveals two previously unknown efflux inhibitors among antimalarials. Particularly proguanil appears as a promising candidate and its chemical scaffold might be further optimized for repurposing as antimicrobial drug enhancer.
|
No Beliefs Expressed
|
From rocks to clay, a shared interest in natural materials and their physical transformation provided the initial common ground for an interdisciplinary art-geoscience collaborative project that also opened up a novel and engaging public communication channel. Scientific data collected for a location-based geomorphology mapping project was collaboratively re-interpreted and re-presented as a craft installation by using digital technologies and hand-crafted processes. The project explored how creative practice can uncover and broaden narratives, layering interpretations whilst respecting and embracing the need for accurate visual representation of scientific data. As the practice-based element of a broader digital craft PhD research programme, the project effectively demonstrated an enlarged field of practice for digital craft. The collaboration resulted in a large-scale, porcelain panelled, wall-mounted installation for public exhibition and has led to subsequent significant unforeseen developments in the scope and outlook of research work undertaken by the collaborators. This paper reflects on the synergies between disciplines that were uncovered and how project challenges were met. We conclude that the project work acted as a 'boundary object' for the two collaborating parties, able to represent different values and fulfil different objectives for each party at the same time, while also moving forward practice for both.
|
No Beliefs Expressed
|
To estimate the effects of skeletal class II malocclusion treatment using fixed mandibular repositioning appliances on the position and morphology of the temporomandibular joint (TMJ). Two independent reviewers performed comprehensive electronic searches of MEDLINE, EMBASE, EBM reviews and Scopus (until May 5, 2015). The references of the identified articles were also manually searched. All studies investigating morphological changes of the TMJ articular disc, condyle and glenoid fossa with 3D imaging following non-surgical fixed mandibular repositioning appliances in growing individuals with class II malocclusions were included in the analysis. Of the 269 articles initially reviewed, only 12 articles used magnetic resonance imaging and two articles used computed tomography (CT) or cone-beam CT images. Treatment effect on condyle and glenoid fossa was discussed in eight articles. Treatment effect on TMJ articular disc position and morphology was discussed in seven articles. All articles showed a high risk of bias due to deficient methodology: inadequate consideration of confounding variables, blinding of image assessment, selection or absence of control group and outcome measurement. Reported changes in osseous remodelling, condylar and disc position were contradictory. The selected articles failed to establish conclusive evidence of the exact nature of TMJ tissue response to fixed mandibular repositioning appliances.
|
No Beliefs Expressed
|
Automatic lymph node (LN) segmentation and detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Despite its low contrast and variety in nodal size and form, LN segmentation remains a challenging task. Deep convolutional neural networks frequently segment items in medical photographs. Most state-of-the-art techniques destroy image's resolution through pooling and convolution. As a result, the models provide unsatisfactory results. Keeping the issues in mind, a well-established deep learning technique UNet++ was modified using bilinear interpolation and total generalized variation (TGV) based upsampling strategy to segment and detect mediastinal lymph nodes. The modified UNet++ maintains texture discontinuities, selects noisy areas, searches appropriate balance points through backpropagation, and recreates image resolution. Collecting CT image data from TCIA, 5-patients, and ELCAP public dataset, a dataset was prepared with the help of experienced medical experts. The UNet++ was trained using those datasets, and three different data combinations were utilized for testing. Utilizing the proposed approach, the model achieved 94.8% accuracy, 91.9% Jaccard, 94.1% recall, and 93.1% precision on COMBO_3. The performance was measured on different datasets and compared with state-of-the-art approaches. The UNet++ model with hybridized strategy performed better than others.
|
No Beliefs Expressed
|
With the prevalence of smart mobile devices and surveillance cameras, the traffic load within the Internet of Things (IoT) has shifted away from nonmultimedia data to multimedia traffics, particularly, the video content. However, the explosive demand for real-time video communication over wireless networks in IoT is constantly challenging both video coding and communication research communities. The state-of-the-art answer to this challenge is sliding-window-based delay-aware fountain (DAF) codes, which combine the channel-adaptive feature in rateless coding and the delay-aware feature in video coding. However, the high computational cost and large delay make it impractical for real-time streaming. To address this issue, we integrate the model predictive control (MPC) technique into DAF codes, so the complexity is lowered to an affordable level so that real-time video encoding is supported. Two schemes are developed in this paper: 1) DAF-S, the smallhorizon DAF codes and 2) DAF-O, the MPC-based DAF using video bit rate prediction. The advantages of both designs are validated through theoretical analysis and comprehensive experiments. The results of simulation experiments show that the decoding ratio of DAF-S is close to the global optimum in DAF codes, and higher than the other existing schemes; DAF-O outperforms the state-of-the-art real-time video communication algorithms.
|
No Beliefs Expressed
|
Recently, sparse representation-based classification (SRC) has been widely studied and has produced state-of-the-art results in various classification tasks. Learning useful and computationally convenient representations from complex redundant and highly variable visual data is crucial for the success of SRC. However, how to find the best feature representation to work with SRC remains an open question. In this paper, we present a novel discriminative projection learning approach with the objective of seeking a projection matrix such that the learned low-dimensional representation can fit SRC well and that it has well discriminant ability. More specifically, we formulate the learning algorithm as a bilevel optimization problem, where the optimization includes an l(1)-norm minimization problem in its constraints. Through the bilevel optimization model, the relationship between sparse representation and the desired feature projection can be explicitly exploited during the learning process. Therefore, SRC can achieve a better performance in the transformed subspace. The optimization model can be solved by using a stochastic gradient ascent algorithm, and the desired gradient is computed using implicit differentiation. Furthermore, our method can be easily extended to learn a dictionary. The extensive experimental results on a series of benchmark databases show that our method outperforms many state-of-the-art algorithms.
|
No Beliefs Expressed
|
Several studies have empirically explored the association between practices in sustainable tourism and their impact on tourism marketing. However, bibliometric studies that organize the production in this field are still scarce. The objective of this study is thus to provide a bibliometric analysis of research on sustainable practices in tourism related to marketing, identifying the state of the art, trends and other indicators, by monitoring the articles published on the Web of Science (WoS) platform. A sample of 694 materials was obtained. The data were processed and the results graphically illustrated using the VOSviewer software. The study analyzed the simultaneous occurrence of publications by year, keyword trends, cocitations, bibliographic coupling and analysis of coauthorship, countries and institutions, and indicates that the literature on tourism sustainability issues in the field of tourism marketing is growing at a quick pace; merely five papers accounted for more than 2193 citations, but there are several prolific authors. Of the 694 sources included in the review, the most important ones published 40.34% of the papers; Spain is the leading country in this topic. This research provides insight about the state of the art and identifies gaps and research opportunities in sustainability and tourism marketing.
|
No Beliefs Expressed
|
Physics is considered a tough academic subject by learners. To leverage engagement in the learning of this STEM area, teachers try to come up with creative ideas about the design of their classroom lessons. Sports-related activities can foster intuitive knowledge about physics (gravity, speed, acceleration, etc.). In this context, martial arts also provide a novel way of visualizing these ideas when performing the predefined motions needed to master the associated techniques. The recent availability of cheap monitoring hardware (accelerometers, cameras, etc.) allows an easy tracking of the aforementioned movements, which in the case of aikido, usually involve genuine circular motions. In this paper, we begin by reporting a user study among high-school students showing that the physics concept of moment of inertia can be understood by watching live exhibitions of specific aikido techniques. Based on these findings, we later present Phy + Aik, a tool for educators that enables the production of innovative visual educational material consisting of high-quality videos (and live demonstrations) synchronized/tagged with the inertial data collected by sensors and visual tracking devices. We think that a similar approach, where sensors are automatically registered within an intelligent framework, can be explored to teach other difficult-to-learn STEM concepts.
|
No Beliefs Expressed
|
This paper presents an automated image registration approach that is robust to perspective distortions. State-of-the-art method affine-SIFT uses affine transform to simulate various viewpoints to increase the robustness of registration. However, affine transformation does not follow the process by which real-world images are formed. To solve this problem, we propose a perspective scale invariant feature transform (PSIFT) that uses homographic transformation to simulate perspective distortion. As for ASIFT, PSIFT is based on the scale invariant feature transform (SIFT) and has a two-resolution scheme, namely a low-resolution phase and a high-resolution phase. The low-resolution phase of PSIFT simulates several image views following a perspective transformation by varying two camera axis orientation parameters. Given those simulated images, SIFT is then used to extract features and find matches among them. In the high-resolution phase, the perspective transformations which lead the largest number of matches in the low-resolution stage are selected to generate SIFT features on the original images. Experimental results obtained on three categories of low-altitude remote sensing images and Morel-Yu's dataset show that PSIFT outperforms significantly the state-of-the-art ASIFT, SIFT, Random Ferns, Harris-Affine, MSER and Hessian Affine, especially when images suffer severe perspective distortion. (C) 2013 Elsevier B.V. All rights reserved.
|
No Beliefs Expressed
|
To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method.
|
No Beliefs Expressed
|
The maximum satisfiability (MAX-SAT) problem, especially the weighted version, has extensive applications. Weighted MAX-SAT instances encoded from real-world applications may be very large, which calls for efficient approximate methods, mainly stochastic local search (SLS) ones. However, few works exist on SLS algorithms for weighted MAX-SAT. In this paper, we propose a new heuristic called CCM for weighted MAX-SAT. The CCM heuristic prefers to select a CCMP variable. By combining CCM with random walk, we design a simple SLS algorithm dubbed CCLS for weighted MAX-SAT. The CCLS algorithm is evaluated against a state-of-the-art SLS solver IRoTS and two state-of-the-art complete solvers namely akmaxsat_ls and New WPM2, on a broad range of weighted MAX-SAT instances. Experimental results illustrate that the quality of solution found by CCLS is much better than that found by IRoTS, akmaxsat_ls and New WPM2 on most industrial, crafted and random instances, indicating the efficiency and the robustness of the CCLS algorithm. Furthermore, CCLS is evaluated in the weighted and unweighted MAX-SAT tracks of incomplete solvers in the Eighth Max-SAT Evaluation (Max-SAT 2013), and wins four tracks in this evaluation, illustrating that the performance of CCLS exceeds the current state-of-the-art performance of SLS algorithms on solving MAX-SAT instances.
|
No Beliefs Expressed
|
Video captioning is an important problem involved in many applications. It aims to generate some descriptions of the content of a video. Most of existing methods for video captioning are based on the deep encoder-decoder models, particularly, the attention-based models (say Transformer). However, the existing transformer-based models may not fully exploit the semantic context, that is, only using the left-to-right style of context but ignoring the right-to-left counterpart. In this paper, we introduce a bidirectional (forward-backward) decoder to exploit both the left-to-right and right-to-left styles of context for the Transformer-based video captioning model. Thus, our model is called bidirectional Transformer (dubbed BiTransformer). Specifically, in the bridge of the encoder and forward decoder (aiming to capture the left-to-right context) used in the existing Transformer-based models, we plug in a backward decoder to capture the right-to-left context. Equipped with such bidirectional decoder, the semantic context of videos will be more fully exploited, resulting in better video captions. The effectiveness of our model is demonstrated over two benchmark datasets, i.e., MSVD and MSR-VTT,via comparing to the state-of-the-art methods. Particularly, in terms of the important evaluation metric CIDEr, the proposed model outperforms the state-of-the-art models with improvements of 1.2% in both datasets.
|
No Beliefs Expressed
|
C-arm computed tomography (CT) is an innovative technique that enables a C-arm system to generate 3-D images from a set of 2-D X-ray projections. This technique can reduce treatment-related complications and may improve interventional efficacy and safety. However, state-of-the-art C-arm systems rely on a circular short scan for data acquisition, which limits coverage in the axial direction. This limitation was reported as a problem in hepatic vascular interventions. To solve this problem, as well as to further extend the value of C-arm CT, axially extended-volume C-arm CT is needed. For example, such an extension would enable imaging the full aorta, the peripheral arteries or the spine in the interventional room, which is currently not feasible. In this paper, we demonstrate that performing long object imaging using a reverse helix is feasible in the interventional room. This demonstration involved developing a novel calibration method, assessing geometric repeatability, implementing a reconstruction method that applies to real reverse helical data, and quantitatively evaluating image quality. Our results show that: 1) the reverse helical trajectory can be implemented and reliably repeated on a multiaxis C-arm system; and 2) a long volume can be reconstructed with satisfactory image quality using reverse helical data.
|
No Beliefs Expressed
|
The aim of the present study was to analyze the consequences of partial free latissimus dorsi muscle flap with nerve splitting technique (Partial LD transfer) for facial reanimation and compare outcomes according to innervation method (singer versus dual innervation). Patients with complete unilateral facial paralysis underwent either the single (ipsilateral masseteric nerve only) or dual (ipsilateral masseteric nerve plus contralateral buccal branch of the facial nerve) nerve innervation method for facial reanimation. An assessment was carried out to compare the outcomes between the single and dual innervation. Total of 21 patients were involved in this study. In the single innervation group, 7 out of 8 patients developed a voluntary smile. However, none were able to achieve a spontaneous smile. On the other hand, 9 out of 13 patients developed a voluntary smile and 3 out of 13 patients achieved a spontaneous smile. The mean increases of smile excursion assessed by Emotrics software and Terzis grades showed no significant differences between two groups. Within the limitations of the study it seems that partial LD transfer approach utilizing the dual innervation method has a positive effect on achieving a spontaneous smile and could be a valuable option for facial reanimation.
|
No Beliefs Expressed
|
Entity recognition and relation extraction have become an important part of knowledge acquisition, and which have been widely applied in various fields, such as Bioinformatics. However, prior state-of-the-art extraction models heavily rely on the external features obtained from hand-craft or natural language processing (NLP) tools. As a result, the performance of models depends directly on the accuracy of the obtained features. Moreover, current joint extraction approaches cannot effectively tackle the multi-head problem (i.e. an entity is related to multiple entities). In this paper, we firstly present a novel tagging scheme and then propose a joint approach based deep neural network for producing unique tagging sequences. Our approach can not only simultaneously perform entity resolution and relation extraction without any external features, but also effectively solve the multi-head problem. Besides, since arbitrary tokens may provide important cues for two components, we exploit self-attention to explicitly capture long-range dependencies among them and character embeddings to learn the features of lexical morphology, which make our method less susceptible to cascading errors. The results demonstrate that the joint method proposed outperforms the other state-of-the-art joint models. Our work is beneficial for biomedical text mining, and the construction of the biomedical knowledge base.
|
No Beliefs Expressed
|
The mammalian brain relies on significant oxygen metabolic consumption to fulfill energy supply, brain function, and neural activity. In this study, in vivo electrochemistry is combined with physiological and ethological analyses to explore oxygen metabolic consumption in an area of the mouse brain that includes parts of the primary somatosensory cortex, primary motor cortex, hippocampus, and striatum. The oxygen levels at different locations of this boundary section are spatially resolved by measuring the electrical current in vivo using ingeniously designed anti-biofouling carbon fiber microelectrodes. The characteristics of the current signals are further interpreted by simultaneously recording the physiological responses of the mice. Additionally, ethological tests are performed to validate the correlation between oxygen levels and mouse behavior. It is found that high-dose caffeine injection can evoke spatial variability in oxygen metabolic consumption between the four neighboring brain regions. It is proposed that the oxygen metabolic consumption in different brain regions is not independent of each other but is subject to spatial regulation control following the rules of "rank of brain region" and "relative distance." Furthermore, as revealed by in vivo wireless electrochemistry and ethological analysis, mice are at risk of neuronal damage from long-term intake of high-dose caffeine.
|
No Beliefs Expressed
|
Frontal view gait recognition for people identification has been carried out using single RGB, stereo RGB, Kinect 1.0, and Doppler radar. However, existing methods based on these camera technologies suffer from several problems. Therefore, we propose a four-part method for frontal view gait recognition based on the fusion of multiple features acquired from a Time-of-Flight (ToF) camera. We have developed a gait data set captured by a ToF camera. The data set includes two sessions recorded seven months apart, with 46 and 33 subjects, respectively, each with six walks with five covariates. The four-part method includes: a new human silhouette extraction algorithm that reduces the multiple reflection problem experienced by ToF cameras; a frame selection method based on a new gait cycle detection algorithm; four new gait image representations; and a novel fusion classifier. Rigorous experiments are carried out to compare the proposed method with state-of-the-art methods. The results show distinct improvements over recognition rates for all covariates. The proposed method outperforms all major existing approaches for all covariates and results in 66.1% and 81.0% Rank 1 and Rank 5 recognition rates, respectively, in overall covariates, compared with a best state-of-the-art method performance of 35.7% and 57.7%.
|
No Beliefs Expressed
|
Long-term survival in patients with acute myeloid leukemia (AML) remains low, and current treatment modalities are inadequate. Milademetan (DS-3032, RAIN-32), a small-molecule specific murine double minute 2 inhibitor, has shown a p53 status-dependent antitumor effect in vitro studies. This is the first phase I study report of milademetan monotherapy in relapsed/refractory (R/R) AML patients evaluating the safety, tolerability, pharmacokinetics, and preliminary tumor response for further clinical development. Fourteen patients received 90 (starting dose, n = 4), 120 (n = 6), or 160 mg (n = 4) of oral milademetan once daily in a 14/28 treatment cycle. The median total treatment duration was 1.5 cycles. Dose-limiting toxicity did not occur, and the maximum tolerated dose was not reached. Thus, the recommended dose was defined as 160 mg. The most common adverse events (AEs) were decreased appetite (64.3%), febrile neutropenia (50%), nausea (42.9%), and anemia (35.7%). No deaths or AEs leading to treatment discontinuation occurred. Five serious treatment-emergent AEs occurred in 4 patients. Plasma concentration increased linearly with milademetan dose. However, trends in the safety and efficacy of oral milademetan in patients with R/R AML warrant further clinical investigation. This study can inform future milademetan studies in hematologic malignancies.
|
No Beliefs Expressed
|
Crowdsourcing is a hotspot research field which can facilitate machine learning by collecting labels to train models. Consequently, the state-of-the-art research efforts in crowdsourcing focus on truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, the integrated labels will be used to fine-tune machine learning models. Particularly, in this paper, we change the target of truth inference in crowdsourcing from discrete labels to multiple comments given by online participants, that is, the integration of the crowdsourced comments. For such a goal, we propose a Self-play and Sentiment-Emphasized Comment Integration Framework (SSECIF), based on deep Q-learning, with three unique features. First, our framework SSECIF can generate the comment integration in a totally self-play way, without relying on the ground truth generated by human effort. Second, the integrated comment generated by SSECIF can include salient content with low redundancy. Third, the proposed framework SSECIF has emphasized, with a higher intensity, the sentiment in the integrated comment, in order to reflect the attitude or opinion more obviously. Extensive evaluation on real-world datasets demonstrates that SSECIF has achieved the best overall performance in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.
|
No Beliefs Expressed
|
Regulating the structure and composition of the lithium-ion (Li+) solvation shell is crucial to the performance of lithium metal batteries. The introduction of fluorine anions (F-) into the electrolyte significantly enhances the cycle efficiency and the interfacial stability of lithium metal anodes. However, the effect of dissolved F- on the solvation shell is rarely touched in the literature. Herein, we investigate the evolution processing of the fluorine-containing solvation structure to explore the underlying mechanisms via first-principles calculations. The additive F- is found to invade the first solvation shell and strongly coordinate with Li+, liberating the bis(trifluoromethanesulfonyl) imide anion (TFSI-) from the Li+ local environment, which enhances the Li+ diffusivity by altering the transport mode. Moreover, the fluorine-containing Li+ solvation shell exhibits a higher lowest unoccupied molecular orbital energy level than that of the solvation sheath without F- additives, suggesting the reduction stability of the electrolyte. Furthermore, the Gibbs free energy calculations for Li+ desolvation reveal that the energy barrier of the Li+ desolvation process will be reduced because of the presence of F-. Our work provides new insights into the mechanisms of electrolyte fluorinated strategies and leads to the rational design of high-performance lithium metal batteries.
|
No Beliefs Expressed
|
Cystic fibrosis is a common genetically inherited, multisystem disorder caused by loss of function of the cystic fibrosis transmembrane conductance regulator (CFTR) protein, an apically situated anion channel. In the lung, lack of CFTR leads to airway surface dehydration, mucociliary clearance failure and an acidic pH in which innate defence molecules are rendered ineffective. Infection occurs early in life, with P. aeruginosa dominating by adolescence. The characteristic features of the CF airway highlighted above encourage persistence of infection, but P. aeruginosa also possess an array of mechanisms with which they attack host defences and render themselves protected from antimicrobials. Early eradication is usually successful, but this is usually transient. Chronic infection is manifest by biofilm formation which is resistant to treatment. Outcomes for people with CF have improved greatly in the last few decades, but particularly so with the recent advent of small molecule CFTR modulators. However, despite impressive efficacy on lung function and exacerbation frequency, most people with chronic infection remain with their pathogens. There is an active pipeline of new treatments including anti-biofilm and anti-quorum sensing molecules and non-drug approaches such as bacteriophage. Studies are reviewed and challenges for future drug development considered.
|
No Beliefs Expressed
|
Deficits in task-related attentional engagement in attention-deficit/hyperactivity disorder (ADHD) have been hypothesised to be due to altered interrelationships between attention, default mode and salience networks. We examined the intrinsic connectivity during rest within and between these networks. Six-minute resting-state scans were obtained. Using a network-based approach, connectivity within and between the dorsal and ventral attention, the default mode and the salience networks was compared between the ADHD and control group. The ADHD group displayed hyperconnectivity between the two attention networks and within the default mode and ventral attention network. The salience network was hypoconnected to the dorsal attention network. There were trends towards hyperconnectivity within the dorsal attention network and between the salience and ventral attention network in ADHD. Connectivity within and between other networks was unrelated to ADHD. Our findings highlight the altered connectivity within and between attention networks, and between them and the salience network in ADHD. One hypothesis to be tested in future studies is that individuals with ADHD are affected by an imbalance between ventral and dorsal attention systems with the former playing a dominant role during task engagement, making individuals with ADHD highly susceptible to distraction by salient task-irrelevant stimuli.
|
No Beliefs Expressed
|
Hypohidrotic ectodermal dysplasia is a rare condition characterized by hypohidrosis, hypodontia, and hypotrichosis. The disease can show X-linked recessive, autosomal dominant or autosomal recessive inheritance trait. Of these, the autosomal forms are caused by mutations in either EDAR or EDARADD. To date, the underlying pathomechanisms or genotype-phenotype correlations for autosomal forms have not completely been disclosed. In this study, we performed a series of in vitro studies for four missense mutations in the death domain of EDAR protein: p.R358Q, p.G382S, p.I388T, and p.T403M. The results revealed that p.R358Q- and p.T403M-mutant EDAR showed different expression patterns from wild-type EDAR in both western blots and immunostainings. NF-κB reporter assays demonstrated that all the mutant EDAR showed reduced activation of NF-κB, but the reduction by p.G382S- and p.I388T-mutant EDAR was moderate. Co-immunoprecipitation assays showed that p.R358Q- and p.T403M-mutant EDAR did not bind with EDARADD at all, whereas p.G382S- and p.I388T-mutant EDAR maintained the affinity to some extent. Furthermore, we demonstrated that all the mutant EDAR proteins analyzed aberrantly bound with TRAF6. Sum of the data suggest that the degree of loss-of-function is different among the mutant EDAR proteins, which may be associated with the severity of the disease.
|
No Beliefs Expressed
|
A wide variety of cultural practices have a 'tacit' dimension, whose principles are neither obvious to an observer, nor known explicitly by experts. This poses a problem for cultural evolution: if beginners cannot spot the principles to imitate, and experts cannot say what they are doing, how can tacit knowledge pass from generation to generation? We present a domain-general model of 'tacit teaching', drawn from statistical physics, that shows how high-accuracy transmission of tacit knowledge is possible. It applies when the practice's underlying features are subject to interacting and competing constraints. Our model makes predictions for key features of the teaching process. It predicts a tell-tale distribution of teaching outcomes, with some students near-perfect performers while others receiving the same instruction are disastrously bad. This differs from standard cultural evolution models that rely on direct, high-fidelity copying, which lead to a much narrower distribution of mostly mediocre outcomes. The model also predicts generic features of the cultural evolution of tacit knowledge. The evolution of tacit knowledge is expected to be bursty, with long periods of stability interspersed with brief periods of dramatic change, and where tacit knowledge, once lost, becomes essentially impossible to recover.
|
No Beliefs Expressed
|
In state-of-the-art deep single-label classification models, the top-k (k = 2,3,4,....) accuracy is usually significantly higher than the top-1 accuracy. This is more evident in fine-grained datasets, where differences between classes are quite subtle. Exploiting the information provided in the top k predicted classes boosts the final prediction of a model. We propose Guided Zoom, a novel way in which explainabitity could be used to improve model performance. We do so by making sure the model has "the right reasons" fora prediction. The reason/evidence upon which a deep neural network makes a prediction is defined to be the grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable the evidence used to make each of the top-k predictions is. Test time evidence is deemed reasonable if it is coherent with evidence used to make similar correct decisions at training time. This leads to better informed predictions. We explore a variety of grounding techniques and study their complementarity for computing evidence. We show that Guided Zoom results in an improvement of a model's classification accuracy and achieves state-of-the-art classification performance on four fine-grained classification datasets.
|
No Beliefs Expressed
|
Text-based person re-identification aims to retrieve images of the corresponding person from a large visual database according to a natural language description. When it comes to visual local information extraction, most of the state-of-the-art methods adopt either a strict uniform strategy which can be too rough to catch local details properly, or pre-processing with external cues which may suffer from the deviations of the pre-trained model and the large computation consumption. In this paper, we proposed an Adversarial Self -aligned Part Detecting Network (ASPD-Net) model which extracts and combines multi-granular visual and textual features. A novel Self-aligned Part Mask Module was presented to autonomously learn the information of human body parts, and obtain visual local features in a soft-attention manner by using K Self-aligned Part Mask Detectors. Regarding the main model branches as a generator, a discriminator is employed to determine whether the representation vector comes from the visual modality or the textual modality. With Adversarial Loss training, ASPD-Net can learn more robust representations, as long as it successfully tricks the discriminator. Experimental results demonstrate that the proposed ASPD-Net outperforms the previous methods and achieves the state-of-the-art performance on the CUHK-PEDES and RSTPReid datasets.
|
No Beliefs Expressed
|
Deep learning-based image processing is capable of creating highly appealing results. However, it is still widely considered as a "blackbox" transformation. In medical imaging, this lack of comprehensibility of the results is a sensitive issue. The integration of known operators into the deep learning environment has proven to be advantageous for the comprehensibility and reliability of the computations. Consequently, we propose the use of the locally linear guided filter in combination with a learned guidance map for general purpose medical image processing. The output images are only processed by the guided filter while the guidance map can be trained to be task-optimal in an end-to-end fashion. We investigate the performance based on two popular tasks: image super resolution and denoising. The evaluation is conducted based on pairs of multi-modal magnetic resonance imaging and cross-modal computed tomography and magnetic resonance imaging datasets. For both tasks, the proposed approach is on par with state-of-the-art approaches. Additionally, we can show that the input image's content is almost unchanged after the processing which is not the case for conventional deep learning approaches. On top, the proposed pipeline offers increased robustness against degraded input as well as adversarial attacks.
|
No Beliefs Expressed
|
Defective DNA damage repair is a key mechanism affecting tumor susceptibility, treatment response, and survival outcome of endometrial cancer (EC). Fanconi anemia complementation group D2 (FANCD2) is the core component of the Fanconi anemia repair pathway. To explore the function of FANCD2 in EC, we examined the expression of FANCD2 in human specimens and databases, and discussed the possible mechanism of carcinogenesis by in vitro assays. Immunohistochemistry results showed overexpression of FANCD2 was detected in EC tissues compared to normal and atypical hyperplasia endometrium. Higher FANCD2 expression was correlated with deeper myometrial invasion (MI) and proficient mismatch repair status. The Cancer Genome Atlas (TCGA) database analysis showed FANCD2 was upregulated in EC compared with normal tissue. The high expression of FANCD2 was associated with poor overall survival in EC. Knockdown of FANCD2 expression in EC cell lines inhibited malignant proliferation and migration ability. We demonstrated that decreased FANCD2 expression results in increased DNA damage and decreased S-phase cells, leading to a decrease in proliferative capacity in EC cells. Down-regulated FANCD2 confers sensitivity of EC cells to interstrand crosslinking agents. This study provides evidence for the malignant progression and prognostic value of FANCD2 in EC.
|
No Beliefs Expressed
|
Since neural networks renaissance, convolutional neural networks (ConvNets) have demonstrated a state-of-the-art performance in several emerging artificial intelligence tasks. The deployment of ConvNets in real-life applications requires power-efficient designs that meet the application-level performance needs. In this context, field-programmable gate arrays (FPGAs) can provide a potential platform that can be tailored to application-specific requirements. However, with the complexity of ConvNet models increasing rapidly, the ConvNet-to-FPGA design space becomes prohibitively large. This paper presents fpgaConvNet, an end-to-end framework for the optimized mapping of ConvNets on FPGAs. The proposed framework comprises an automated design methodology based on the synchronous dataflow (SDF) paradigm and defines a set of SDF transformations in order to efficiently navigate the architectural design space. By proposing a systematic multiobjective optimization formulation, the presented framework is able to generate hardware designs that are cooptimized for the ConvNet workload, the target device, and the application's performance metric of interest. Quantitative evaluation shows that the proposed methodology yields hardware designs that improve the performance by up to 6.65x over highly optimized graphics processing unit designs for the same power constraints and achieve up to 2.94x higher performance density compared with the state-of-the-art FPGA-based ConvNet architectures.
|
No Beliefs Expressed
|
Segmentation of brain MRI images becomes a challenging task due to spatially distributed noise and uncertainty present between boundaries of soft tissues. In this work, we have presented intuitionistic fuzzy set theory based probabilistic intuitionistic fuzzy c-means with spatial neighborhood information method for MRI image segmentation. We have investigated two well known negation functions namely, Sugeno's negation function and Yager's negation function for representing the image in terms of intuitionistic fuzzy sets. The proposed approach takes leverage of intuitionistic fuzzy set theory to address vagueness and uncertainty present in the data. The spatial neighborhood information term in the segmentation process is included to dampen the effect of noise. The segmentation performance of the proposed method is evaluated in terms of average segmentation accuracy and Dice score. Further, the comparison of the proposed method with other similar state-of-art methods is carried out on two publicly available brain MRI dataset which shows the significant improvements in segmentation performance in terms of average segmentation accuracy and Dice score. The proposed approach achieves on average 91% average segmentation accuracy in the presence of noise and intensity inhomogeneity on BrainWeb simulated dataset, which outperformed the state-of-art methods.
|
No Beliefs Expressed
|
Chronic wounds can remain open for several months and have high risks of amputation due to infection. Dressing materials to treat chronic wounds should be conformable for irregular wound geometries, maintain a moist wound bed, and reduce infection risks. To that end, we developed cytocompatible shape memory polyurethane-based poly(ethylene glycol) (PEG) hydrogels that allow facile delivery to the wound site. Plant-based phenolic acids were physically incorporated onto the hydrogel scaffolds to provide antimicrobial properties. These materials were tested to confirm their shape memory properties, cytocompatibility, and antibacterial properties. The incorporation of phenolic acids provides a new mechanism for tuning intermolecular bonding in the hydrogels and corollary mechanical and shape memory properties. Phenolic acid-containing hydrogels demonstrated an increased shape recovery ratio (1.35× higher than the control formulation), and materials with cytocompatibility >90% were identified. Antimicrobial properties were retained over 20 days in hydrogels with higher phenolic acid content. Phenolic acid retention and antimicrobial efficacy were dependent upon phenolic acid structures and interactions with the polymer backbone. This novel hydrogel system provides a platform for future development as a chronic wound dressing material that is easy to implant and reduces infection risks.
|
No Beliefs Expressed
|
The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask has become an adequate protection solution many governments adopt. Manual real-time monitoring of face mask wearing for many people is becoming a difficult task. This paper applies three heterogeneous deep transfer learning models, viz., ResNet50, Inception-v3, and VGG-16, to prepare an ensemble classification model for detecting whether a person is wearing a mask. The ensemble classification model is underlined by the concept of the weighted average technique. The proposed framework is based on two phases. An off-line phase that aims to prepare a classification model by following training-testing steps to detect and locate facemasks. Then in the second online phase, it is deployed to detect real-time faces from live videos, which are captured by a web-camera. The prepared model is compared with several state-of-the-art models. The proposed model has achieved the highest classification accuracy of 99.97%, precision of 0.997, recall of 0.997, F1-score of 0.997 and kappa coefficient 0.994. The superiority of the model over state-of-the-art compared methods is well evident from the experimental results.
|
No Beliefs Expressed
|
Convolutional neural network (CNN) has demonstrated great success in pattern recognition scenarios at the cost of nearly billions of parameters and consequent convolution operations. Various dedicated hardware designs are proposed to accelerate the CNN computation in more energy-efficient manners. Especially, the bit-serial accelerator (BSA) is one of the most effective approaches on resource-limited platforms by eliminating zero-bit computations. However, the irregular distribution and varying number of effectual (nonzero) bits in weights significantly cause hardware underutilization, impeding further performance improvement of state-of-the-art BSAs. To address this issue, BitCluster, a hardware-friendly quantization method, is proposed to make each weight with the identical number of effectual bits for load-balanced computation. Considering distinct sensitivities to weight precision in different neural layers, layer-level BitCluster is proposed to design further for fine-grained weight quantization. It systematically determines the layerwise quantization configurations, which significantly improve the overall performance with <1% accuracy loss. BitCluster is comprehensively evaluated on a BitCluster-compatible BSA design by taking six mainstream CNN models as benchmarks. The experimental results show that the BitCluster-based BSA achieves 1.6x higher hardware utilization and 3.4x speedup on average than state-of-the-art BSAs, with 5x better energy efficiency on average.
|
No Beliefs Expressed
|
Shadow detection and removal is a challenging problem for several computer vision applications because shadow always makes object misclassified. A number of shadow detection and removal algorithms have been reported, and some of these algorithms require manual calibration in terms of some hypothesis and predefined specific parameters whereas others do not require manual intervention, but fail to give accurate result in various lighting and environmental conditions. This paper introduces a novel method for shadow detection and removal with Daubechies complex wavelet domain. Daubechies complex wavelet transform has been used in the proposed algorithm due to its strong edge detection, approximate shift-invariance as well as approximate rotation invariance properties. For shadow detection, we have proposed a new threshold in the form of coefficient of variation of wavelet coefficients. This threshold is automatically determined and does not require any manual calibration and training. Results of shadow detection and removal from moving objects after applying the proposed method are compared with the those of other state-of-the-art methods in terms of visual performance and number of quantitative performance evaluation parameters. The proposed method is found to perform better than other state-of-the-art methods.
|
No Beliefs Expressed
|
Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.
|
No Beliefs Expressed
|
In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods.
|
No Beliefs Expressed
|
Triple X syndrome is a sex chromosomal aneuploidy characterized by the presence of a supernumerary X chromosome, resulting in a karyotype of 47,XXX in affected females. It has been associated with a variable cognitive, behavioral, and psychiatric phenotype, but little is known about its effects on brain function. We therefore conducted 7 T resting-state functional magnetic resonance imaging and compared data of 19 adult individuals with 47,XXX and 21 age-matched healthy control women using independent component analysis and dual regression. Additionally, we examined potential relationships between social cognition and social functioning scores, and IQ, and mean functional connectivity values. The 47,XXX group showed significantly increased functional connectivity of the fronto-parietal resting-state network with the right postcentral gyrus. Resting-state functional connectivity (rsFC) variability was not associated with IQ and social cognition and social functioning deficits in the participants with 47,XXX. We thus observed an effect of a supernumerary X chromosome in adult women on fronto-parietal rsFC. These findings provide additional insight into the role of the X chromosome on functional connectivity of the brain. Further research is needed to understand the clinical implications of altered rsFC in 47,XXX.
|
No Beliefs Expressed
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.