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17447
val_tab_0500
Women aged <35 years had higher clinical pregnancy and live birth rates than women aged ≥35 years.
Refuted
Table 2: Comparison of reproductive outcomes.
table
tables_png/dev/val_tab_0500.png
Table 2 shows the clinical outcomes of FET.
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0308
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17447
val_tab_0501
Among young women, the HRT group had higher clinical pregnancy and live birth rates and lower biochemical pregnancy and miscarriage rates than the GnRHa-HRT group, although none of the differences were statistically significant.
Supported
Table 2: Comparison of reproductive outcomes.
table
tables_png/dev/val_tab_0501.png
Table 2 shows the clinical outcomes of FET. Women aged <35 years had higher clinical pregnancy and live birth rates than women aged ≥35 years.
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0309
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17447
val_tab_0502
Among young women, the HRT group had higher clinical pregnancy and live birth rates and lower biochemical pregnancy and miscarriage rates than the GnRHa-HRT group, although none of the differences were statistically significant.
Refuted
Table 2: Comparison of reproductive outcomes.
table
tables_png/dev/val_tab_0502.png
Table 2 shows the clinical outcomes of FET. Women aged <35 years had higher clinical pregnancy and live birth rates than women aged ≥35 years.
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0309
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16727
val_tab_0503
In the adjusted regression, being male (aPR = 1.05; 95% CI [1.01–1.10]; p = 0.024), recommendation by family and friends (aPR = 1.08; 95% CI [1.04–1.12]; p < 0.001), and recommendation by physicians (aPR = 1.40; 95% CI [1.27–1.55]; p < 0.001) were associated with a higher prevalence of IBV with the booster dose, while ...
Supported
Table 3: Factors associated with the intention to be vaccinated with the COVID-19 booster dose in Peru, July 2022.
table
tables_png/dev/val_tab_0503.png
In the simple regression, being related to health (PR = 1.10; 95% CI [1.04–1.16]; p < 0.001), having a good perception of its efficacy and protective effect (PR = 3.69; 95% CI [2.57–5.30]; p < 0.001), the recommendation of family and friends (PR = 1.28; 95% CI [1.22–1.34]; p < 0.001), physicians (PR = 1.52; 95% CI [1.3...
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0310
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16727
val_tab_0504
In the adjusted regression, being male (aPR = 1.05; 95% CI [1.01–1.10]; p = 0.024), recommendation by family and friends (aPR = 1.08; 95% CI [1.04–1.12]; p < 0.001), and recommendation by physicians (aPR = 1.40; 95% CI [1.27–1.55]; p < 0.001) were associated with a higher prevalence of IBV with the booster dose, while ...
Refuted
Table 3: Factors associated with the intention to be vaccinated with the COVID-19 booster dose in Peru, July 2022.
table
tables_png/dev/val_tab_0504.png
In the simple regression, being related to health (PR = 1.10; 95% CI [1.04–1.16]; p < 0.001), having a good perception of its efficacy and protective effect (PR = 3.69; 95% CI [2.57–5.30]; p < 0.001), the recommendation of family and friends (PR = 1.28; 95% CI [1.22–1.34]; p < 0.001), physicians (PR = 1.52; 95% CI [1.3...
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16901
val_tab_0505
The addition of an interfragmentary lag screw to the medially positioned plate increased the initial flexural stiffness by 153% and the shear stiffness by 352%, see Table 2 .
Supported
Table 2: Comparison of mean values of flexural stiffness, initial flexural stiffness, shear stiffness and maximum load, and their standard deviations (SDs) for different fixation methods.
table
tables_png/dev/val_tab_0505.png
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0311
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16901
val_tab_0506
The addition of an interfragmentary lag screw to the medially positioned plate increased the initial flexural stiffness by 153% and the shear stiffness by 352%, see Table 2 .
Refuted
Table 2: Comparison of mean values of flexural stiffness, initial flexural stiffness, shear stiffness and maximum load, and their standard deviations (SDs) for different fixation methods.
table
tables_png/dev/val_tab_0506.png
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16901
val_tab_0507
The greatest mean maximum load was obtained for the medially positioned plate ( Table 2 ), but the difference between this group and the dorsal LP lag screw and the medial LP lag screw groups was not significant ( p = 0.70 and p = 0.52, respectively).
Supported
Table 2: Comparison of mean values of flexural stiffness, initial flexural stiffness, shear stiffness and maximum load, and their standard deviations (SDs) for different fixation methods.
table
tables_png/dev/val_tab_0507.png
The addition of an interfragmentary lag screw to the medially positioned plate increased the initial flexural stiffness by 153% and the shear stiffness by 352%, see Table 2 . The dorsal plate with the lag screw was the stiffest construct ( Figs. 5A , 5B ). Significant differences in stiffness were observed ( p < 0.05, ...
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0312
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16935
val_fig_0208
Among SNVs, ETS2 showed the highest mutation frequency (primarily mutation type: missense) ( Figure 4B ).
Supported
Figure 4: Analysis of cellular senescence-related prognostic genes. (A) Forest plot of prognosis-related genes; (B) waterfall plot of SNVs in the seven prognosis-related genes; (C) Percentage plot of CNVs in the seven prognosis-related genes; (D) The expression of the seven prognosis-related genes expressed in tumor an...
figure
figures/dev/val_fig_0208.png
The above pathways comprised 253 genes, of which 186 were present in TCGA dataset. Subsequently, a univariate analysis of these 186 genes yielded seven prognosis-related genes, including ETS2, SERPINE1, FOS, SHISA5, IL1A, TP53AIP1, and IGFBP7 ( Figure 4A ). Mutations in these seven genes in TNBC were further examined.
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16935
val_fig_0209
Among SNVs, ETS2 showed the highest mutation frequency (primarily mutation type: missense) ( Figure 4B ).
Refuted
Figure 4: Analysis of cellular senescence-related prognostic genes. (A) Forest plot of prognosis-related genes; (B) waterfall plot of SNVs in the seven prognosis-related genes; (C) Percentage plot of CNVs in the seven prognosis-related genes; (D) The expression of the seven prognosis-related genes expressed in tumor an...
figure
figures/dev/val_fig_0209.png
The above pathways comprised 253 genes, of which 186 were present in TCGA dataset. Subsequently, a univariate analysis of these 186 genes yielded seven prognosis-related genes, including ETS2, SERPINE1, FOS, SHISA5, IL1A, TP53AIP1, and IGFBP7 ( Figure 4A ). Mutations in these seven genes in TNBC were further examined.
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0313
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16935
val_fig_0210
The prognostic prediction classification efficiency was analyzed separately for 1–5 years with obtained AUC values 0.91, 0.93, 0.74, 0.83 and 0.84, respectively , and the AUC values reached 0.7, indicating good predictive performance.
Supported
Figure 8: Construction and validation of the risk model. (A) ROC curves for the risk model constructed using four genes from TCGA dataset analysis; (B) KM curves for the risk model based on TCGA dataset; (C) ROC curves for the risk model constructed using four genes for the GSE58812 dataset; (D) KM curves for the risk ...
figure
figures/dev/val_fig_0210.png
The four genes’ expression levels were utilized to determine the risk score for each sample using the TCGA dataset as the training set. Then, using ROC analysis, the RiskScore prognostic classification was carried out ( Figure 8A ).
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17199
val_fig_0215
Clinical isolates presented the higher resistance (100%) to the three antibiotics: imipenem, meropenem, and piperacillin/tazobactam.
Supported
Figure 3: Clinical and environmental isolates susceptibility profile. The percentages of clinical isolates that were resistant, intermediate, or sensitive to each antibiotic. (A) Clinical isolates susceptibility profile, (B) river isolates susceptibility profile. Acronyms: CIP, ciprofloxacin; GEN, gentamycin; SXT, trim...
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figures/dev/val_fig_0215.png
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17199
val_fig_0216
Clinical isolates presented the higher resistance (100%) to the three antibiotics: imipenem, meropenem, and piperacillin/tazobactam.
Refuted
Figure 3: Clinical and environmental isolates susceptibility profile. The percentages of clinical isolates that were resistant, intermediate, or sensitive to each antibiotic. (A) Clinical isolates susceptibility profile, (B) river isolates susceptibility profile. Acronyms: CIP, ciprofloxacin; GEN, gentamycin; SXT, trim...
figure
figures/dev/val_fig_0216.png
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0314
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17199
val_fig_0217
Forty-eight percent were susceptible to tobramycin ( Figure 3A ).
Supported
Figure 3: Clinical and environmental isolates susceptibility profile. The percentages of clinical isolates that were resistant, intermediate, or sensitive to each antibiotic. (A) Clinical isolates susceptibility profile, (B) river isolates susceptibility profile. Acronyms: CIP, ciprofloxacin; GEN, gentamycin; SXT, trim...
figure
figures/dev/val_fig_0217.png
Clinical isolates presented the higher resistance (100%) to the three antibiotics: imipenem, meropenem, and piperacillin/tazobactam.
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17199
val_fig_0218
Forty-eight percent were susceptible to tobramycin ( Figure 3A ).
Refuted
Figure 3: Clinical and environmental isolates susceptibility profile. The percentages of clinical isolates that were resistant, intermediate, or sensitive to each antibiotic. (A) Clinical isolates susceptibility profile, (B) river isolates susceptibility profile. Acronyms: CIP, ciprofloxacin; GEN, gentamycin; SXT, trim...
figure
figures/dev/val_fig_0218.png
Clinical isolates presented the higher resistance (100%) to the three antibiotics: imipenem, meropenem, and piperacillin/tazobactam.
peerj
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Category Swap
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0315
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17199
val_fig_0219
Nevertheless, the main resistance was to piperacillin/tazobactam (100%).
Supported
Figure 3: Clinical and environmental isolates susceptibility profile. The percentages of clinical isolates that were resistant, intermediate, or sensitive to each antibiotic. (A) Clinical isolates susceptibility profile, (B) river isolates susceptibility profile. Acronyms: CIP, ciprofloxacin; GEN, gentamycin; SXT, trim...
figure
figures/dev/val_fig_0219.png
Clinical isolates presented the higher resistance (100%) to the three antibiotics: imipenem, meropenem, and piperacillin/tazobactam. Forty-eight percent were susceptible to tobramycin ( Figure 3A ). The river isolates displayed an antimicrobial profile that varied between resistant and intermediate to eight antibiotics...
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0316
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17199
val_fig_0220
Nevertheless, the main resistance was to piperacillin/tazobactam (100%).
Refuted
Figure 3: Clinical and environmental isolates susceptibility profile. The percentages of clinical isolates that were resistant, intermediate, or sensitive to each antibiotic. (A) Clinical isolates susceptibility profile, (B) river isolates susceptibility profile. Acronyms: CIP, ciprofloxacin; GEN, gentamycin; SXT, trim...
figure
figures/dev/val_fig_0220.png
Clinical isolates presented the higher resistance (100%) to the three antibiotics: imipenem, meropenem, and piperacillin/tazobactam. Forty-eight percent were susceptible to tobramycin ( Figure 3A ). The river isolates displayed an antimicrobial profile that varied between resistant and intermediate to eight antibiotics...
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0316
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18312
val_fig_0224
However, under anaerobic culture conditions, NEMM had greater alpha diversity indices of ACE and Chao1 than SHI ( Figure 5A ).
Supported
Figure 5: General bacterial community structure analysis between NEMM and SHI media during aerobic (normoxia) and anaerobic culture. (A) Alpha diversity was measured with ACE index and Chao1 index. P < 0.05 was considered statistically significant. (B) NMDS was measured with Unweighted UniFrac. Each sample is represent...
figure
figures/dev/val_fig_0224.png
To compare NEMM with SHI, which are suitable for cultivating specific bacterial types, we further analyzed 16S rRNA gene high-throughput sequencing data. Alpha diversity indices of ACE and Chao1 showed no significant difference between NEMM and SHI under the normoxia condition.
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papers/dev/peerj_18312.json
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0317
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18312
val_fig_0225
However, under anaerobic culture conditions, NEMM had greater alpha diversity indices of ACE and Chao1 than SHI ( Figure 5A ).
Refuted
Figure 5: General bacterial community structure analysis between NEMM and SHI media during aerobic (normoxia) and anaerobic culture. (A) Alpha diversity was measured with ACE index and Chao1 index. P < 0.05 was considered statistically significant. (B) NMDS was measured with Unweighted UniFrac. Each sample is represent...
figure
figures/dev/val_fig_0225.png
To compare NEMM with SHI, which are suitable for cultivating specific bacterial types, we further analyzed 16S rRNA gene high-throughput sequencing data. Alpha diversity indices of ACE and Chao1 showed no significant difference between NEMM and SHI under the normoxia condition.
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0317
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18312
val_fig_0226
Additionally, those in NEMM displayed an increased relative abundance of protein functions linked to defense metabolism, inorganic ion transport and metabolism under anaerobic conditions.
Supported
Figure 8: Function prediction by PICRUSt2. The differential analysis of COG function (A) and KEGG pathway (B) show the abundance ratio of different functions between groups, whose middle figures show the difference ratio of functional abundance within the 95% confidence interval.  
figure
figures/dev/val_fig_0226.png
To assess the effects of the two media on the predicted gene categories (COGs), we compared the predicted COGs between aNEMM and aSHI, nNEMM and nSHI ( Figure 8A ). NEMM showed more ability in carbohydrate transport and metabolism than SHI medium under both anaerobic conditions and normoxia conditions.
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17444
val_fig_0227
Cluster analysis found significant differences between the cervical cancer group and the normal group, and the data patterns of five samples in each group have high similarity, proving that some protein levels indeed differ between the two groups ( Figure 1B ).
Supported
Figure 1: Differential expression of the proteins. (A) A volcano plot is drawn using the protein expression difference multiple and P-value between the two groups of samples, showing the significant differences in data between the two groups of samples. Significantly up-regulated or down-regulated DEPs are represented in...
figure
figures/dev/val_fig_0227.png
In the analysis of significant differences in the quantitative results, we first filter the data within the sample group where at least half of the repeated experimental data are non-null values for differential comparative analysis. Proteins meeting the criteria of an expression difference multiple greater than 1.5 an...
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papers/dev/peerj_17444.json
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no pair
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17444
val_fig_0228
The results are illustrated in Figure 6D , where the top three Sub-cellular locations are cytosol (29.6%), nucleus (26.0%), and extracellular (17.6%).
Supported
Figure 6: Structural domain, transcription factor analysis, and sub-cellular localization analysis. (A) Enrichment classification statistical bubble chart for protein structure domain (top 20). The bubble chart shows that many proteins structure domains were involved in the study, such as guanine nucleotide exchange fac...
figure
figures/dev/val_fig_0228.png
Sub-cellular localization refers to the specific location within a cell where a protein or expressed product is found. The main Sub-cellular locations in eukaryotic cells include extracellular space, cytoplasm, cell nucleus, cell membrane, mitochondria, Golgi apparatus, endoplasmic reticulum, peroxisomes, cytoskeleton,...
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17446
val_fig_0229
S2 . Figure 1A displays the inhibitory effect of 23 tyrosine kinase inhibitors on tramadol metabolism, with crizotinib, sorafenib, and regorafenib exhibiting the highest inhibitory rates of 97.22%, 96.66%, and 83.65%, respectively.
Supported
Figure 1: Determine the interaction between representative tyrocine kinase inhibitors and tramadol. (A) The inhibitory effect of 23 types of tyrosine kinase inhibitors on the production of O-desmethyl tramadol in RLM compared with the control group. (B) Evaluate the half-maximal inhibitory concentration (IC50) of crizo...
figure
figures/dev/val_fig_0229.png
The Michaelis–Menten curve for tramadol in RLM and HLM, along with the corresponding K m value, is shown in Fig.
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17446
val_fig_0230
S2 . Figure 1A displays the inhibitory effect of 23 tyrosine kinase inhibitors on tramadol metabolism, with crizotinib, sorafenib, and regorafenib exhibiting the highest inhibitory rates of 97.22%, 96.66%, and 83.65%, respectively.
Refuted
Figure 1: Determine the interaction between representative tyrocine kinase inhibitors and tramadol. (A) The inhibitory effect of 23 types of tyrosine kinase inhibitors on the production of O-desmethyl tramadol in RLM compared with the control group. (B) Evaluate the half-maximal inhibitory concentration (IC50) of crizo...
figure
figures/dev/val_fig_0230.png
The Michaelis–Menten curve for tramadol in RLM and HLM, along with the corresponding K m value, is shown in Fig.
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17417
val_fig_0232
Functional enrichment analysis of the upregulated DEGs revealed that the top five biological processes were skeletal system development, sister chromatid cohesion, ossification, mitotic nuclear division, and extracellular matrix organization ( Figure 3A ).
Supported
Figure 3: GO and KEGG pathway enrichment analyses of upregulated genes. GO functional classification of DEGs. The x-axis represents the number of DEGs, with individual GO terms plotted on the y-axis. The graph displays only significantly enriched GO terms (P < 0.05). All GO terms were grouped into three categories: (A)...
figure
figures/dev/val_fig_0232.png
GO annotation and KEGG pathway enrichment analyses were performed to better understand the functional significance of the DEGs.
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16850
val_fig_0233
The ARGs signature was thus constructed via multi-variate Cox regression analysis, where 3-, 5-, and 7-year of the areas under the curve (AUCs) of the ROC curves were 0.789, 0.799 and 0.837, respectively ( Figure 3D ).
Supported
Figure 3: Identification and construction of ARGs in PCa samples. (A and B) We conducted the LASSO algorithmto identify the 10 prognostic genes in TCGA training cohort, where the optimal cutoff value was −4 and the minimum account of genes was 18. (C) The bubble plot exhibited the significantly differential expressions...
figure
figures/dev/val_fig_0233.png
To identify the essential ARGs with prognostic significance in PRAD, we selected the top 53 DEGs and performed the LASSO analysis, where 10 hub genes were found ( Figs. 3A and 3B ). Accordingly, the significant differential expression of 10 ARGs in two clusters were shown in dot plot ( Figure 3C ).
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16850
val_fig_0234
Lastly, we calculated the ARGs scores for each patient in TCGA-PRAD cohort and divided the samples into high-risk and low-risk groups, and Kaplan-Meier analysis revealed that patients in high-risk groups suffered from worse survival outcomes relative to those from the low-risk groups with log-rank test P < 0.001 ( Figu...
Supported
Figure 3: Identification and construction of ARGs in PCa samples. (A and B) We conducted the LASSO algorithmto identify the 10 prognostic genes in TCGA training cohort, where the optimal cutoff value was −4 and the minimum account of genes was 18. (C) The bubble plot exhibited the significantly differential expressions...
figure
figures/dev/val_fig_0234.png
To identify the essential ARGs with prognostic significance in PRAD, we selected the top 53 DEGs and performed the LASSO analysis, where 10 hub genes were found ( Figs. 3A and 3B ). Accordingly, the significant differential expression of 10 ARGs in two clusters were shown in dot plot ( Figure 3C ). The ARGs signature w...
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16850
val_fig_0235
Lastly, we calculated the ARGs scores for each patient in TCGA-PRAD cohort and divided the samples into high-risk and low-risk groups, and Kaplan-Meier analysis revealed that patients in high-risk groups suffered from worse survival outcomes relative to those from the low-risk groups with log-rank test P < 0.001 ( Figu...
Refuted
Figure 3: Identification and construction of ARGs in PCa samples. (A and B) We conducted the LASSO algorithmto identify the 10 prognostic genes in TCGA training cohort, where the optimal cutoff value was −4 and the minimum account of genes was 18. (C) The bubble plot exhibited the significantly differential expressions...
figure
figures/dev/val_fig_0235.png
To identify the essential ARGs with prognostic significance in PRAD, we selected the top 53 DEGs and performed the LASSO analysis, where 10 hub genes were found ( Figs. 3A and 3B ). Accordingly, the significant differential expression of 10 ARGs in two clusters were shown in dot plot ( Figure 3C ). The ARGs signature w...
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papers/dev/peerj_16850.json
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16850
val_fig_0236
Expectedly, high ARGs scores correlated positively with clinical T stages ( P = 3.414e−15), positive rates of lymph nodes ( P = 3.815e−08), as well as advanced Gleason scores ( P = 3.352e−23) ( Figs. 4A – 4C ).
Supported
Figure 4: Correlation analysis between ARGs with clinical factors and prognostic significance of ARGs in PRAD. (A–C) Kruskal-Wallis test showing that increasing ARGs-score correlated with higher T stages (P = 3.414e−15), higher positive rate of lymph nodes (P = 3.815e−08) and Gleason scores (P = 3.352e−23). (D) Univari...
figure
figures/dev/val_fig_0236.png
Since we have already established the ARGs model, we intended to figure out the underlying relationships between ARGs and other clinical variables.
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16850
val_fig_0237
Kaplan-Meier analysis implicated that patients in the cluster two group had worse PFS outcomes compared with those in the cluster 1 group, in which the log-rank test P value was 0.013 ( Figure 5K ).
Supported
Figure 5: External validation of ARGs signature and clustering analysis across PRAD samples. (A) The ROC analysis of ARGs exhibiting its predictive efficiency of 3-, 5- and 7-year in predicting PFS. (B) Kaplan-Meier curves showing the differential survival outcomes in AGRs-high and ARGs-low samples in GSE116918. (C an...
figure
figures/dev/val_fig_0237.png
Utilizing the unsupervised clustering algorithm, we conducted consensus clustering analysis to distinguish PRAD patients in the training cohort into subgroups based on the expression of ARGs. The K = 2 was identified with the optimal clustering stability ( Figs. 5G – 5I ). We further conducted the PCA analysis and coul...
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0322
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16850
val_fig_0238
Positive correlations between AR and ORC1 were confirmed in the TCGA-PRAD samples ( Figure 7D ).
Supported
Figure 7: AR activates ORC1 to sustain tumor progression and enzalutamide resistance in PRAD. (A) The anchorage-independent growth of C4-2B and 22RV-1 cells in soft agar (scale bars = 200 µm, left). Quantification of the soft agar colony formation assay results (right). (B) Sphere formation assays revealing the self-re...
figure
figures/dev/val_fig_0238.png
To elucidate the function of ORC1 in PRAD, we established stable ORC1-overexpressing PRAD cells (22RV-1 and C4-2B). The up-regulation of ORC1 significantly enhanced PRAD cell soft agar colony formation efficiency ( Figure 7A ). Besides, the self-renewal potentiality of PRAD cells was also increased when cells were tran...
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0323
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16951
val_fig_0239
Survival analysis revealed that prognosis differed significantly between the two DERMGs-related subtypes, and cluster 1 had a notable survival advantage ( Figure 2G ).
Supported
Figure 2: Identification of DERMGs-related subtypes in STAD. (A) Forest plot of univariate Cox of four DERMGs associated with overall survival (OS) in STAD patients. (B) Relative change in area under CDF area for k = 2 to 6. (C) The cumulative distribution functions. (CDF) for k = 2 to 6. (D) Tracking plot for k = 2 to...
figure
figures/dev/val_fig_0239.png
The above 14 DERMGs were then subjected into the univariate Cox analysis and four genes associated with overall survival (OS) of STAD patient ( p < 0.05) were identified, namely RBM15, DNMT1, WDR4 and ALYREF ( Figure 2A ). To probe the related subtypes in STAD, 345 STAD samples were clustered based on the expression of...
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Subsequently, a univariate analysis of these 186 genes yielded seven prognosis-related genes, including ETS2, SERPINE1, FOS, SHISA5, IL1A, TP53AIP1, and IGFBP7 ( Figure 4A ).
Supported
Figure 4: Analysis of cellular senescence-related prognostic genes. (A) Forest plot of prognosis-related genes; (B) waterfall plot of SNVs in the seven prognosis-related genes; (C) Percentage plot of CNVs in the seven prognosis-related genes; (D) The expression of the seven prognosis-related genes expressed in tumor an...
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The above pathways comprised 253 genes, of which 186 were present in TCGA dataset.
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Among CNVs, IGFBP7 had the highest frequency of acquired CNVs (CNV_gain), whereas SHISA5 had the highest frequency of deletion mutations (CNV_loss) ( Figure 4C ).
Supported
Figure 4: Analysis of cellular senescence-related prognostic genes. (A) Forest plot of prognosis-related genes; (B) waterfall plot of SNVs in the seven prognosis-related genes; (C) Percentage plot of CNVs in the seven prognosis-related genes; (D) The expression of the seven prognosis-related genes expressed in tumor an...
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The above pathways comprised 253 genes, of which 186 were present in TCGA dataset. Subsequently, a univariate analysis of these 186 genes yielded seven prognosis-related genes, including ETS2, SERPINE1, FOS, SHISA5, IL1A, TP53AIP1, and IGFBP7 ( Figure 4A ). Mutations in these seven genes in TNBC were further examined. ...
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Similarly, T cells CD4, also displayed distinct difference among three groups in TIMER and EPIC analysis ( Figs. 7C – 7D ).
Supported
Figure 7: Immune characterization of cellular senescence-related subtypes in TCGA dataset. (A) Comparison of ESTIMATE-predicted immune scores among the three subtypes (kruskal.test); (B) Comparison of MCPcounter-predicted cell scores among the three subtypes(kruskal.test); (C) Comparison of TIMER-predicted cell scores ...
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The relationship between cellular senescence-related subtypes and immunity was analyzed to investigate the immune characteristics of these subtypes. The immune scores of patients in TCGA dataset were calculated by “ESTIMATE”. Significant differences were observed among the three subtypes, with a high degree of immune i...
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Risk scores also correlated positively with “ImmuneScore”, “StromalScore”, and “ESTIMATEScore” ( Figure 10G ).
Supported
Figure 10: Immunological characteristics of RiskScore subgroups and sensitivity analysis in TCGA-TNBC cohort. (A) Differences in MCPcounter-predicted cell scores between risk groups; (B) Differences in immune and stromal scores between risk groups; (C) comparison of TIMER-predicted cell scores between risk groups; (D) ...
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A significant positive correlation between the Riskscore and T cells, cytotoxic lymphocytes, and monocytic lineage ( Figure 10F ) was observed.
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Conversely, low-frequency adaptation resulted in a 4% increase in numerosity (linear regression slope of 1.03, compared to the baseline slope of 0.99), the line shifted towards the upper side compared to the baseline (no adaptation), indicating an overestimation of perceived numerosity ( Figure 2B ).
Supported
Figure 2: Results. (A) Adaptation index under grouping and no-grouping conditions; (B) The perceived numerosity, averaged across trials and subjects, varies as a function of physical numerosity in the three adaptation conditions. The analysis includes best-fitting linear regressions (R2 >0.98 in all conditions). The re...
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The adaptation effect is measured by adaptation index. We calculated the adaptation index separately for the grouping and no-grouping conditions. The results are presented in Figure 2A , which illustrates the adaptation index under both grouping and no-grouping conditions. Notably, the grouping condition exhibits a mor...
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The sensory precision under the grouping condition was not significantly higher than that under the non-grouping condition ( Figure 3A ), the reason for this result is that the grouping effect is affected by the motor adaptation, so that no grouping advantage is found.
Supported
Figure 3: (A) CVs for different adaption level by group condition. (B) RTs for different adaption level by group condition. “G” for grouping condition, and “NG” for no-grouping condition. ***p < 0.001; **p < 0.01; *p < 0.05.  
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In Experiment 1, our findings were consistent with previous studies ( Anobile et al., 2016 ; Anobile et al., 2020a ; Anobile et al., 2020b ), demonstrating that the adaptation to high frequency hand tapping was underestimated in the subsequent numerosity estimation task, while the adaptation to low frequency hand tappi...
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Figure 1B shows that the four groups have a notable clustering of compositions of gut microbiota, with PC1 (28.61%) and PC2 (15.15%) exhibiting a total variance of 43.76%.
Supported
Figure 1: Effects of SNP upon the diversity of fecal microflora from humans. (A) ACE, Chao, Shannon, and Simpson indices; Comparison of bacterial compositions across all groups based on (B) PCoA and (C) PLS-DA at the OTU level. Data are presented as means ± SDs (n = 8).
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β diversity, also termed between-habitat diversity, is generally used for diversity comparison across ecosystems. Besides, the research also evaluated principal coordinate analysis (PCoA), which reveals diversity in the compositions of gut microbiota.
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Figure 8 shows that the T2DM group has lower contents of total SCFAs, acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid, when compared with the HC group ( P < 0.05).
Supported
Figure 8: Total SCFAs (acetic acid, butyric acid, propionic acid, isobutyric acid, isovaleric acid, and valeric acid) after 24-h in-vitro fermentation with T2DM human fecal microflora. Data were expressed as means ± SDs (n = 8).
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Figure 8 shows that the T2DM group has lower contents of total SCFAs, acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid, when compared with the HC group ( P < 0.05).
Refuted
Figure 8: Total SCFAs (acetic acid, butyric acid, propionic acid, isobutyric acid, isovaleric acid, and valeric acid) after 24-h in-vitro fermentation with T2DM human fecal microflora. Data were expressed as means ± SDs (n = 8).
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Faecalibacterium was positively associated with pentanoic acid and 4-aminobutanoic acid, while were negatively correlated with hydracrylic acid.
Supported
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
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To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Lactobacillus was positively correlated with xylitol and showed a negative correlation with 5-aminovaleric acid.
Supported
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
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To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Dorea was positively associated with phenylalanine, butyric acid, and isovaleric acid, while showed a negative correlation with hydracrylic acid, CH 4 , myo-inositol, L-threonine, glycerol, CO 2 , L-cysteine, and D-gluconic acid.
Supported
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
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figures/dev/val_fig_0256.png
To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Dorea was positively associated with phenylalanine, butyric acid, and isovaleric acid, while showed a negative correlation with hydracrylic acid, CH 4 , myo-inositol, L-threonine, glycerol, CO 2 , L-cysteine, and D-gluconic acid.
Refuted
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
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figures/dev/val_fig_0257.png
To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Phascolarctobacterium was positively associated with 1,3-propanediol, L-proline, cadaverine, and DL-phenylalanine, and was negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and myo-inositol.
Supported
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
figure
figures/dev/val_fig_0259.png
To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Phascolarctobacterium was positively associated with 1,3-propanediol, L-proline, cadaverine, and DL-phenylalanine, and was negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and myo-inositol.
Refuted
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
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figures/dev/val_fig_0260.png
To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Lachnospiraceae were positively associated with 1, 3-propanediol and L-proline, and were negatively correlated with hydracrylic acid and CH 4 .
Supported
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
figure
figures/dev/val_fig_0261.png
To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Parabacteroides had positive correlations with L-leucine, L-valine, and L-isoleucine, while negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, D-gluconic acid, and propionic acid.
Supported
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
figure
figures/dev/val_fig_0262.png
To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Parabacteroides had positive correlations with L-leucine, L-valine, and L-isoleucine, while negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, D-gluconic acid, and propionic acid.
Refuted
Figure 10: Correlation analysis of characteristic metabolites and bacteria. Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.
figure
figures/dev/val_fig_0263.png
To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2...
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Following these were premature birth ( p -value < 0.001), with 156 (82.5%) smokers and 176 (93.1%), miscarriages ( p -value = 0.048), with 149 (78.8%) smokers and 164 (86.8%) non-smokers, neck cancer ( p -value < 0.001) with 144 (76.2%) smokers and 165 (87.3%) non-smokers and lastly gangrene ( p -value < 0.001) with 11...
Supported
Figure 2: Percentage of known health effects of tobacco use among respondents (n = 378).  
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Figure 2 describes the known health effects of tobacco use among respondents. The two most recognised health effects of smoking by both groups of respondents are lung cancer ( p -value = 0.011) and mouth cancer ( p -value < 0.001), with 170 (89.9%) smokers and 184 (97.4%) non-smokers who answered ‘Yes’ for lung cancer ...
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Following these were premature birth ( p -value < 0.001), with 156 (82.5%) smokers and 176 (93.1%), miscarriages ( p -value = 0.048), with 149 (78.8%) smokers and 164 (86.8%) non-smokers, neck cancer ( p -value < 0.001) with 144 (76.2%) smokers and 165 (87.3%) non-smokers and lastly gangrene ( p -value < 0.001) with 11...
Refuted
Figure 2: Percentage of known health effects of tobacco use among respondents (n = 378).  
figure
figures/dev/val_fig_0265.png
Figure 2 describes the known health effects of tobacco use among respondents. The two most recognised health effects of smoking by both groups of respondents are lung cancer ( p -value = 0.011) and mouth cancer ( p -value < 0.001), with 170 (89.9%) smokers and 184 (97.4%) non-smokers who answered ‘Yes’ for lung cancer ...
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Most non-smokers ( n = 161, 85.2%) never had the urge to smoke upon the sight of pictorial warnings on the cigarette packaging.
Supported
Figure 3: Impact of pictorial warnings on the initiation to smoking by non-smokers (n = 189).  
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Figure 3 shows the impact of pictorial warnings on the initiation of smoking by non-smokers.
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Most non-smokers ( n = 161, 85.2%) never had the urge to smoke upon the sight of pictorial warnings on the cigarette packaging.
Refuted
Figure 3: Impact of pictorial warnings on the initiation to smoking by non-smokers (n = 189).  
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Figure 3 shows the impact of pictorial warnings on the initiation of smoking by non-smokers.
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It follows that 12 out of 189 rarely and sometimes (6.3%) had the urge to smoke, respectively.
Supported
Figure 3: Impact of pictorial warnings on the initiation to smoking by non-smokers (n = 189).  
figure
figures/dev/val_fig_0268.png
Figure 3 shows the impact of pictorial warnings on the initiation of smoking by non-smokers. Most non-smokers ( n = 161, 85.2%) never had the urge to smoke upon the sight of pictorial warnings on the cigarette packaging.
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This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]).
Supported
Figure 2: Performance of the segmentation network. (A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation ...
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A segmentation network was developed to accurately distinguish tumor regions from WSIs.
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Compared with three pathologists of varying experience (junior, medium, and senior), the model’s performance was almost equivalent to that of a medium-level pathologist ( Figure 2A ).
Supported
Figure 2: Performance of the segmentation network. (A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation ...
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figures/dev/val_fig_0270.png
A segmentation network was developed to accurately distinguish tumor regions from WSIs. This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]). The accuracy, sensitivity, and specificity were 0.888 (95% CI [0.887–0.890]), 0.859 (95% CI [0.856–0.861]), and 0.908 (95% CI [0.906–0.909]), r...
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Compared with three pathologists of varying experience (junior, medium, and senior), the model’s performance was almost equivalent to that of a medium-level pathologist ( Figure 2A ).
Refuted
Figure 2: Performance of the segmentation network. (A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation ...
figure
figures/dev/val_fig_0271.png
A segmentation network was developed to accurately distinguish tumor regions from WSIs. This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]). The accuracy, sensitivity, and specificity were 0.888 (95% CI [0.887–0.890]), 0.859 (95% CI [0.856–0.861]), and 0.908 (95% CI [0.906–0.909]), r...
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Among tumor region samples, the model correctly identified 86% as tumor, while misclassifying 14% as non-tumor.
Supported
Figure 2: Performance of the segmentation network. (A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation ...
figure
figures/dev/val_fig_0272.png
A segmentation network was developed to accurately distinguish tumor regions from WSIs. This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]). The accuracy, sensitivity, and specificity were 0.888 (95% CI [0.887–0.890]), 0.859 (95% CI [0.856–0.861]), and 0.908 (95% CI [0.906–0.909]), r...
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Among non-tumor region samples, 91% were correctly identified as non-tumor, with 9% misclassified as tumor ( Figure 2B ).
Supported
Figure 2: Performance of the segmentation network. (A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation ...
figure
figures/dev/val_fig_0273.png
A segmentation network was developed to accurately distinguish tumor regions from WSIs. This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]). The accuracy, sensitivity, and specificity were 0.888 (95% CI [0.887–0.890]), 0.859 (95% CI [0.856–0.861]), and 0.908 (95% CI [0.906–0.909]), r...
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Among non-tumor region samples, 91% were correctly identified as non-tumor, with 9% misclassified as tumor ( Figure 2B ).
Refuted
Figure 2: Performance of the segmentation network. (A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation ...
figure
figures/dev/val_fig_0274.png
A segmentation network was developed to accurately distinguish tumor regions from WSIs. This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]). The accuracy, sensitivity, and specificity were 0.888 (95% CI [0.887–0.890]), 0.859 (95% CI [0.856–0.861]), and 0.908 (95% CI [0.906–0.909]), r...
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When comparing these three alginate dental impression processing methods, there was no statistically significant difference in the impact of available chlorine spray disinfection and CA solution treatment on the dimensional accuracy of dental impressions ( P > 0.05).
Supported
Figure 3: Effect of chlorogenic acid intervention on the precision of gypsum injection of alginate impression material. The deviation between the plaster model and the standard model of different intervention methods is used as the parameter of root mean square (RMS) after fitting overlap. (BC, Blank control: The impre...
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The dimensional accuracy test results for three different oral impression methods are displayed in Figure 3 . These methods include pure distilled water mixing, disinfection with 2,000 mg/L available chlorine after distilled water mixing, and spray disinfection with 60 mg/mL CA solution after mixing with a 10mg/mL CA s...
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Interestingly, a strong bacterial growth inhibition was observed at a concentration of 2.75 µg/mL during all the incubation times ( Figure 1A ).
Supported
Figure 1: Effect of cystatin C on P. gingivalis growth and macrophage viability. (A) 5 × 106 CFU/mL of P. gingivalis were cultured in the presence of cystatin C at different concentrations, for 1, 12, 24, 48, and 96 h. Cells without stimulation were used as a control group. when compared to the control group. The signi...
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To analyze the extracellular antimicrobial activity of cystatin C against P. gingivalis , bacteria were incubated with different concentrations of the peptide (1.25, 1.5, 1.75, 2, 2.5, 2.75 µg/mL) and increasing time periods (1, 12, 24, 48, and 96 h). Cystatin C exhibited a dose-and time-dependent antimicrobial activit...
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Interestingly, a strong bacterial growth inhibition was observed at a concentration of 2.75 µg/mL during all the incubation times ( Figure 1A ).
Refuted
Figure 1: Effect of cystatin C on P. gingivalis growth and macrophage viability. (A) 5 × 106 CFU/mL of P. gingivalis were cultured in the presence of cystatin C at different concentrations, for 1, 12, 24, 48, and 96 h. Cells without stimulation were used as a control group. when compared to the control group. The signi...
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To analyze the extracellular antimicrobial activity of cystatin C against P. gingivalis , bacteria were incubated with different concentrations of the peptide (1.25, 1.5, 1.75, 2, 2.5, 2.75 µg/mL) and increasing time periods (1, 12, 24, 48, and 96 h). Cystatin C exhibited a dose-and time-dependent antimicrobial activit...
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The nucleotide sequence presented one specific change in nucleotide 280, first position of the codon (GTG) (ATG), resulting in a valine (V) to methionine (M) change in position 94 ( Figure 4 ).
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Figure 4: Alignment of OXA-143 amino acid sequences. The polymorphic sites at the amino acid sequence level. The identity of the sequences was compared to the OXA-143 variant (GenBank protein number access WP_063861042.1 or its nucleic number access NG_049441.1). Numbers in the heading row indicate the base pair positi...
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Sequencing of the bla OXA-143 PCR product obtained from A. pitti clinical isolates identified gene encoding OXA-499. Our bla OXA-143 sequence ( MF594724 ) shared 99% nucleotide and amino acid identity with bla OXA-499 of A. pittii (GenBank accession numbers NG_049775.1 and ALM96709.1 ).
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The LightGBM model demonstrated the best performance and was selected to construct the radiomics signature, with the area under curve (AUC) values of 0.896 (95% CI [0.825–0.967]) and 0.824 (95% CI [0.658–0.989]) in the training and testing datasets, respectively ( Figure 3A ).
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Figure 3: Development of radiomics model. (A) The ROC curves of the radiomics model; (B) the confusion matrix of predicting lymph node metastasis.  
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In the deep learning models, the performance of M3 was significantly higher than that of other datasets, with an AUC value of 0.826 in the training cohort and 0.674 in the test cohort ( Figure 4 ).
Supported
Figure 4: The ROC curves of M3.  
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As shown in Figure 8 , the integrated model achieved an AUC of 0.838 compared with 0.654, 0.768, and 0.893 for clinicians D1, D2, and D3, respectively, suggesting differences between the model and the expert evaluation.
Supported
Figure 8: The ROC curves of the integrated model and diagnostic results of each radiologist in the test cohort. The integrated model reached a high AUCs (AUC: 0.838), doctor1 acquired 0.654 of AUC, the AUC of doctor2 was 0.768 and doctor3 demonstrated the highest with 0.893.  
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The performance of the integrated model was compared with that of clinical experts. Three clinicians with varying levels of experience in oral diseases, i.e ., less than 5 years (D1), less than 10 years but longer than D1 (D2), and more than 15 years (D3), were asked to interpret the imaging and clinical data. The deci...
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In clinical samples, except Veillonella , Leptotrichia and Streptococcus , other bacteria occupied the most of bacterial community.
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Figure 3: Bacterial community structure analysis. (A) The distributions of the predominant bacteria at genus level. (B) The relative abundance of differential predominant bacteria among aerobic culture, anaerobic culture and clinical groups analyzed at genus level by ANOVA.  
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To display the composition of dominant bacteria, the stacked column plot showed the percentage of each high-abundance bacteria.
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The results indicated that there were more bacterial categories in clinical samples than cultures by media.
Supported
Figure 3: Bacterial community structure analysis. (A) The distributions of the predominant bacteria at genus level. (B) The relative abundance of differential predominant bacteria among aerobic culture, anaerobic culture and clinical groups analyzed at genus level by ANOVA.  
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figures/dev/val_fig_0285.png
To display the composition of dominant bacteria, the stacked column plot showed the percentage of each high-abundance bacteria. In clinical samples, except Veillonella , Leptotrichia and Streptococcus , other bacteria occupied the most of bacterial community. No matter under anaerobic and normoxia conditions, the perce...
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The results showed that there were decreases in Gram-positive bacteria, facultatively anaerobic bacteria and stress tolerance ability, but increases in Gram-negative bacteria, anaerobic respiration, biofilms forming and potential pathogenicity in clinical samples compared with cultured samples under aerobic and anaerob...
Supported
Figure 4: BugBase analysis among different groups. The three lines from the bottom up are the lower quartile, the mean and the upper quartile in BugBase analysis.  
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To assess the predicted function of the bacterial community in clinical samples and the cultured bacteria samples, microbial phenotype prediction was evaluated by BugBase followed by the Mann– Whitney U test.
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The results showed that there were decreases in Gram-positive bacteria, facultatively anaerobic bacteria and stress tolerance ability, but increases in Gram-negative bacteria, anaerobic respiration, biofilms forming and potential pathogenicity in clinical samples compared with cultured samples under aerobic and anaerob...
Refuted
Figure 4: BugBase analysis among different groups. The three lines from the bottom up are the lower quartile, the mean and the upper quartile in BugBase analysis.  
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To assess the predicted function of the bacterial community in clinical samples and the cultured bacteria samples, microbial phenotype prediction was evaluated by BugBase followed by the Mann– Whitney U test.
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Alpha diversity indices of ACE and Chao1 showed no significant difference between NEMM and SHI under the normoxia condition.
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Figure 5: General bacterial community structure analysis between NEMM and SHI media during aerobic (normoxia) and anaerobic culture. (A) Alpha diversity was measured with ACE index and Chao1 index. P < 0.05 was considered statistically significant. (B) NMDS was measured with Unweighted UniFrac. Each sample is represent...
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To compare NEMM with SHI, which are suitable for cultivating specific bacterial types, we further analyzed 16S rRNA gene high-throughput sequencing data.
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Alpha diversity indices of ACE and Chao1 showed no significant difference between NEMM and SHI under the normoxia condition.
Refuted
Figure 5: General bacterial community structure analysis between NEMM and SHI media during aerobic (normoxia) and anaerobic culture. (A) Alpha diversity was measured with ACE index and Chao1 index. P < 0.05 was considered statistically significant. (B) NMDS was measured with Unweighted UniFrac. Each sample is represent...
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To compare NEMM with SHI, which are suitable for cultivating specific bacterial types, we further analyzed 16S rRNA gene high-throughput sequencing data.
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Venn diagram showed that the OTU numbers of NEMM cultured bacteria under anaerobic and normoxia conditions were higher than that in SHI cultured bacteria under the same conditions ( Figure 5C ).
Supported
Figure 5: General bacterial community structure analysis between NEMM and SHI media during aerobic (normoxia) and anaerobic culture. (A) Alpha diversity was measured with ACE index and Chao1 index. P < 0.05 was considered statistically significant. (B) NMDS was measured with Unweighted UniFrac. Each sample is represent...
figure
figures/dev/val_fig_0290.png
To compare NEMM with SHI, which are suitable for cultivating specific bacterial types, we further analyzed 16S rRNA gene high-throughput sequencing data. Alpha diversity indices of ACE and Chao1 showed no significant difference between NEMM and SHI under the normoxia condition. However, under anaerobic culture conditio...
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Venn diagram showed that the OTU numbers of NEMM cultured bacteria under anaerobic and normoxia conditions were higher than that in SHI cultured bacteria under the same conditions ( Figure 5C ).
Refuted
Figure 5: General bacterial community structure analysis between NEMM and SHI media during aerobic (normoxia) and anaerobic culture. (A) Alpha diversity was measured with ACE index and Chao1 index. P < 0.05 was considered statistically significant. (B) NMDS was measured with Unweighted UniFrac. Each sample is represent...
figure
figures/dev/val_fig_0291.png
To compare NEMM with SHI, which are suitable for cultivating specific bacterial types, we further analyzed 16S rRNA gene high-throughput sequencing data. Alpha diversity indices of ACE and Chao1 showed no significant difference between NEMM and SHI under the normoxia condition. However, under anaerobic culture conditio...
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Of the 19 most predominant bacterial genera found in NEMM and SHI media, Ligilactobacillus , Limosilactobacillus , Lactobacillus , and Desemzia showed the highest abundance levels.
Supported
Figure 6: The relative abundance of differential predominant bacteria between NEMM and SHI analyzed at genus level by ANOVA.  
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Differential predominant bacteria between NEMM and SHI were analyzed in the relative abundance of bacteria at the genus level by ANOVA.
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The results showed that the microbiota developed in NEMM were predicted stronger anaerobic ability, whether under anaerobic conditions or normoxic conditions.
Supported
Figure 7: BugBase analysis among different groups. Note: X-axis, group name; Y-axis, Relative abundance in percentage. The three lines from bottom to top are lines indicating lower quartile, average and upper quartile.  
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Bugbase was used to predict the function of bacterial community in NEMM and SHI under anaerobic and normoxia culture conditions.
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Under normoxic culture conditions, there were no significant differences in Gram-positive bacteria among the four groups (aNEMM, aSHI, nNEMM and nSHI).
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Figure 7: BugBase analysis among different groups. Note: X-axis, group name; Y-axis, Relative abundance in percentage. The three lines from bottom to top are lines indicating lower quartile, average and upper quartile.  
figure
figures/dev/val_fig_0294.png
Bugbase was used to predict the function of bacterial community in NEMM and SHI under anaerobic and normoxia culture conditions. The results showed that the microbiota developed in NEMM were predicted stronger anaerobic ability, whether under anaerobic conditions or normoxic conditions.
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Under normoxic culture conditions, there were no significant differences in Gram-positive bacteria among the four groups (aNEMM, aSHI, nNEMM and nSHI).
Refuted
Figure 7: BugBase analysis among different groups. Note: X-axis, group name; Y-axis, Relative abundance in percentage. The three lines from bottom to top are lines indicating lower quartile, average and upper quartile.  
figure
figures/dev/val_fig_0295.png
Bugbase was used to predict the function of bacterial community in NEMM and SHI under anaerobic and normoxia culture conditions. The results showed that the microbiota developed in NEMM were predicted stronger anaerobic ability, whether under anaerobic conditions or normoxic conditions.
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Furthermore, pathways of membrane transport and cellular community-prokaryotes were associated with aNEMM group ( Figure 8B ).
Supported
Figure 8: Function prediction by PICRUSt2. The differential analysis of COG function (A) and KEGG pathway (B) show the abundance ratio of different functions between groups, whose middle figures show the difference ratio of functional abundance within the 95% confidence interval.  
figure
figures/dev/val_fig_0297.png
To assess the effects of the two media on the predicted gene categories (COGs), we compared the predicted COGs between aNEMM and aSHI, nNEMM and nSHI ( Figure 8A ). NEMM showed more ability in carbohydrate transport and metabolism than SHI medium under both anaerobic conditions and normoxia conditions. Additionally, th...
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Furthermore, pathways of membrane transport and cellular community-prokaryotes were associated with aNEMM group ( Figure 8B ).
Refuted
Figure 8: Function prediction by PICRUSt2. The differential analysis of COG function (A) and KEGG pathway (B) show the abundance ratio of different functions between groups, whose middle figures show the difference ratio of functional abundance within the 95% confidence interval.  
figure
figures/dev/val_fig_0298.png
To assess the effects of the two media on the predicted gene categories (COGs), we compared the predicted COGs between aNEMM and aSHI, nNEMM and nSHI ( Figure 8A ). NEMM showed more ability in carbohydrate transport and metabolism than SHI medium under both anaerobic conditions and normoxia conditions. Additionally, th...
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All QoL domains exhibited significant correlations among the physical, psychological, and environmental health domains ( p < 0.01).
Supported
Figure 3: Illustrates the correlations between the QoL and SWLS scores among all individuals with SCI across different domains.  
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Total protein foods consumed more only during the 6 months after intervention period ( P < 0.001).
Supported
Figure 1: Comparison of component scores of HEI-2015 measured during pre-intervention, 6 weeks after intervention and 6 months after intervention. In HEI-2015, a higher score indicates a higher diet quality. The Friedman test indicated significant differences in the component scores of HEI-2015 between pre-intervention...
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Figure 1 shows the comparison of component scores of HEI-2015 recorded during the pre-intervention, 6 weeks after intervention and 6 months after intervention. Accordingly, participants consumed significantly more total fruit ( P < 0.001) and whole fruit ( P < 0.001) after the intervention period. Although scores were ...
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After 30 min, only those receiving disinfection through mixing a CA solution with the impression material (10 mg/mL) or through spray disinfection with only a CA solution (60 mg/mL) had high colony counts for Staphylococcus aureus , Escherichia coli , Candida albicans , and Streptococcus pneumoniae .
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Figure 2: Antimicrobial effect of chlorogenic acid solution intervention on alginate impression materials. (A) Staphylococcus aureus (B) Escherichia coli (C) Candida albicans (D) Streptococcus pneumoniae. The error bar represents the standard deviation; Dashed lines indicate detection limits.  
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Figure 2 illustrates the changes in viable microorganism populations on impressions during CA intervention for up to 30 min. Values are presented as means ± SD ( n = 5). Analysis of the repeated measurements indicated that different intervention methods had varying disinfection effects on the four types of bacteria ( P...
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After 30 min, only those receiving disinfection through mixing a CA solution with the impression material (10 mg/mL) or through spray disinfection with only a CA solution (60 mg/mL) had high colony counts for Staphylococcus aureus , Escherichia coli , Candida albicans , and Streptococcus pneumoniae .
Refuted
Figure 2: Antimicrobial effect of chlorogenic acid solution intervention on alginate impression materials. (A) Staphylococcus aureus (B) Escherichia coli (C) Candida albicans (D) Streptococcus pneumoniae. The error bar represents the standard deviation; Dashed lines indicate detection limits.  
figure
figures/dev/val_fig_0303.png
Figure 2 illustrates the changes in viable microorganism populations on impressions during CA intervention for up to 30 min. Values are presented as means ± SD ( n = 5). Analysis of the repeated measurements indicated that different intervention methods had varying disinfection effects on the four types of bacteria ( P...
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NEMM showed more ability in carbohydrate transport and metabolism than SHI medium under both anaerobic conditions and normoxia conditions.
Supported
Figure 8: Function prediction by PICRUSt2. The differential analysis of COG function (A) and KEGG pathway (B) show the abundance ratio of different functions between groups, whose middle figures show the difference ratio of functional abundance within the 95% confidence interval.  
figure
figures/dev/val_fig_0304.png
To assess the effects of the two media on the predicted gene categories (COGs), we compared the predicted COGs between aNEMM and aSHI, nNEMM and nSHI ( Figure 8A ).
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We plot the proportion of conserved correct predictions for the panoramic camera rotation in Fig. 7 . First, we observe that both networks struggle to a higher extent with images taken on opposite to the position, with the light source behind the camera.
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Figure 7: Counterfactual study of all sizes of ConvNext and Swin networks for panoramic 360 ∘ camera rotation.
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2025.clpsych-1.8
val_tab_1500
As shown in Table 1 , all correlations between maximum sliding window PPL and TALD scores were statistically significant (p-value < 0.01) across all model and sliding window sizes.
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Table 1: The AVH dataset Spearman’s \rho between the maximum sliding window PPL and TALD across model size. Bold indicates the highest \rho for a model.
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tables/dev/val_tab_1500.tex
2403.17919
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It is also intriguing to observe from Figure 5 that Vanilla LLaMA-2-70B excelled in Writing, but full-parameter fine-tuning led to a decline in these areas, a phenomenon known as the “Alignment Tax” (Ouyang et al., 2022 ) .
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Figure 5: Different aspects of LLaMA-2-70B model on MT-Bench.
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Compared to participants with sleep latency of less than 10 min, those with longer sleep latency demonstrated an increased risk of suboptimal SRH.
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Table 2: Association between sleep latency and odds ratios (95% CIs) of suboptimal self-rated health among medical students.
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Table 2 presents the multivariable-adjusted ORs of suboptimal SRH associated with sleep latency.
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2023.starsem-1.9
val_tab_1502
However, the use of co-reference resolution will significantly increase the processing time, as shown in Table 5 .
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Table 5: Average processing time (in seconds) per instance in QAGS-CNN/DM. SRLScore uses ROUGE similarity. BARTScore is run with a batch size of 4.
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Results in Table 1 reveal that the co-reference system is not always improving scores, particularly on the CNN/DailyMail-derived datasets.
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19459
val_tab_1503
However, there were no significant differences in gender, residence, single child, parental education, BMI, afternoon nap, sitting time, and physical activity among these groups.
Refuted
Table 1: Characteristics of study participants.
table
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The study included 1,396 participants, with a mean age of 19.6 ±1.2 years, and 58.6% female, of whom 599 (42.9%) reported suboptimal SRH and 390 (27.9%) reported prolonged sleep latency (≥30 min). Table 1 presents the basic characteristics of the participants. Participants with longer sleep latency had a higher percent...
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papers/dev/peerj_19459.json
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0273
tables/dev/val_tab_1503.html
2211.12551
val_fig_1502
It shows that both eParam and eFlow are reasonable pruning strategy, however, as we increase the percentage of pruned parameters, eFlow has less log-likelihoods drop compared with eParam .
Refuted
(a) Comparison of heuristics eRand , eParam , and eFlow . Heuristic eFlow can prune up to 80% of the parameters without much loglikelihoods decrease.; (b) Histogram of parameters before (the same as in Figure 1 ) and after pruning. The parameter values take higher significance after pruning.; Empirical evaluation of th...
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Figure 5(a) compares the effect of pruning heuristics eParam , eFlow , as well as an uninformed strategy, prune randomly, which we denote as eRand .
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2405.17991
val_tab_1504
It lowers the memory requirement of Hydra [ 20 ] by 7% while improving the accuracy by 0.1 pp .
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Results on a subset of the VTAB-1k benchmark. All methods use a ViT-Base-224/16 model pre-trained on ImageNet-21k. The batch sizes and ranks are the same across all tasks.
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We conduct experiments evaluating the performance of VeLoRA for full-tuning and how it complements other PEFT methods. In Tab. 1 we reproduce a large set of results for LoRA [ 17 ] , SSF [ 26 ] , and Hydra [ 20 ] on a subset of the VTAB-1K benchmark, where the sub-token size for each experiment is given in the Appendix...
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17407
val_tab_1505
In addition to plastic, various other types of anthropogenic materials found in 23 out of 1,447 pellets were identified and classified as non-plastic debris.
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Table 2: Composition of plastic and non-plastic debris found in 1,447 analyzed pellets of the neotropic cormorant,N. brasilianus, on the Circuito de Playas Costa Verde (CPCV), Lima, Perú, during both the pre-pandemic and pandemic phases.
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0291
tables/dev/val_tab_1505.html
2025.clpsych-1.3
val_tab_1506
The color intensity (green shading) in Table 4 indicates better performance, visually highlighting that RACLETTE’s approaches generally outperform the benchmark models.
Refuted
Table 4: Classification results for different methods of comparing embeddings to detect risk of suicide. The higher the score (a.k.a. the greener), the better.
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The results show RACLETTE’s Combined method achieving the highest recall of 0.95, indicating superior ability to identify relevant cases, though this comes with a trade-off in precision at 0.63. Conversely, RoBERTa leads in precision at 0.72, but with lower recall at 0.84. The KL Divergence variant of RACLETTE stands o...
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tables/dev/val_tab_1506.tex
2024.acl-short.9
val_fig_1503
The first source “segment” \boldsymbol{x} (leftmost panel) has relatively high probability p(\boldsymbol{x}) , and no tradeoff is observed in this case.
Refuted
(a) Simulation with one-dimensional \boldsymbol{x} and \boldsymbol{y} . The three panels correspond to three different source “segments” \boldsymbol{x} of decreasing probability p(\boldsymbol{x}) , and the points in each panel are candidate translations \boldsymbol{y} . Brighter colors indicate translations with larger...
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We initially assume that both \boldsymbol{x} and \boldsymbol{y} are one-dimensional vectors. Figure 2(a) shows the relationship between p(\boldsymbol{y}) and p(\boldsymbol{x|y}) for 3 samples \boldsymbol{x} . Each point in each panel corresponds to a candidate translation \boldsymbol{y} , and candidates with highest p(...
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2024.acl-short.16
val_fig_1504
Compared to LoRA we yield 9.5\times parameter reduction, while remain comparable with ELoRA.
Refuted
Figure 3: A comparison of various system performance between LoRA, ELoRA, and AFLoRA.
figure
figures/dev/val_fig_1504.png
Runtime & FLOPs Comparison. Fig. 3 shows the comparison of the normalized average training runtime, normalized FLOPs, and normalized trainable parameters. For AFLoRA, we average the training time, FLOPs, and trainable parameters over six GLUE datasets (except the MNLI and QQP datasets). Note, for LoRA and ELoRA, the tr...
nlp
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18312
val_fig_1505
The results indicated that there were more bacterial categories in clinical samples than cultures by media.
Refuted
Figure 3: Bacterial community structure analysis. (A) The distributions of the predominant bacteria at genus level. (B) The relative abundance of differential predominant bacteria among aerobic culture, anaerobic culture and clinical groups analyzed at genus level by ANOVA.  
figure
figures/dev/val_fig_1505.png
To display the composition of dominant bacteria, the stacked column plot showed the percentage of each high-abundance bacteria. In clinical samples, except Veillonella , Leptotrichia and Streptococcus , other bacteria occupied the most of bacterial community. No matter under anaerobic and normoxia conditions, the perce...
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