Enabling open research practices - connecting Octopus with research repositories Dr Agustina Martínez García, John Kaye, Dr Alexandra Freeman Open practices § The research landscape is evolving rapidly, with a wide range of tools and platforms being developed to enable open research practices, and increased expectations from funders to openly share research outputs. § Repositories play a critical role to ensure discoverability, access and long-term preservation of research outputs Using open and well-adopted standards, this collaborative project aims to increase interoperability between innovative research publishing tools like Octopus and remove barriers to depositing research outputs into research repositories. The project uses the DSpace platform as the case study. Octopus overview § Octopus is a new scholarly publishing platform designed to incentivise best practices in research § Open and free to use: all publications and the source code itself are available under open access licenses § Its unit of publication is not a traditional ‘paper’ but instead one of eight constituent units supporting the research lifecycle stages § Open, post-publication peer review, with red- flagging capabilities, to assess quality and integrity What we will do § The project will produce an implementation case study using Apollo repository (DSpace) § Develop deposit mechanisms into repositories via Jisc Publications Router native API § Align with COAR guidelines by implementing a Resourcesync endpoint - Users will be able to view Octopus outputs via repositories or content aggregators Key benefits § Reassure researchers of their outputs being available and preserved for the long-term § Facilitate tracking of research impact through linking outputs together § Support institutions with funder compliance and reporting CRIS integrations via Jisc Publications Router Apollo (DSpace), Cambridge’s trustworthy repository Figure placeholder References 1) Octopus, https://www.octopus.ac/ 2) JISC Publications Router, https://pubrouter.jisc.ac.uk/ 3) DSpace Repository Platform, https://dspace.lyrasis.org/ 4) UK Reproducibility Network, https://www.ukrn.org/ Octopus Apollo Scan the QR code to download the poster Dr. Agustina Martínez García am857@cam.ac.uk John Kaye John.kaye@jisc.ac.uk Dr. Alexandra Freeman Alex.freeman@maths.cam.ac.uk


334 G077 European consumers’ purchase intentions towards plant-based meat products: the mediation effect of social media involvement. Listia Rini1, Simoun Bayudan1, Ilona Faber2, Michael Bom Frøst2, Federico J. A. Perez-Cueto3, Joachim Schouteten1, Hans De Steur1 1Ghent University, Belgium. 2University of Copenhagen, Denmark. 3Umeå University, Sweden Abstract The OECD projects that by 2031, meat supplies will elevate to almost 400 MT to meet global demands, correspondingly increasing total greenhouse gas (GHG) emissions by 9%. Meat consumption has been associated with many health adversities, such as diabetes and cardiovascular disease. The environmental and health challenges related to meat production and consumption have thus contributed to the rise of new plant-based food (PBF) products. In Europe, the market growth of PBF products is encouraging, with a total sales value of around EUR 3.6 billion in 2020. Plant-based meats (PBM) are some of the most popularly developed PBF products, with countries such as France, Germany, the Netherlands and Sweden already producing and marketing their own PBM products to European consumers. However, European consumers’ acceptance of PBM products is generally lower than conventional meat. One of the barriers to PBF consumption is related to consumers’ information needs, but social media (SM) can nowadays help alleviate these needs through information dissemination. Therefore, the objective of this study is to investigate whether consumers’ SM involvement mediates the relationship between consumers’ pro-meat-reduction attitudes and their PBM purchase intention. Data were collected from European countries (AT, DK, FR, DE, IT, NL, PL, RO, ES, UK), with a total of 4589 omnivore respondents. Linear regression with mediation analyses was conducted via SPSS PROCESS v4.2 with pro-meat reduction attitudes as the independent variable and PBM purchase intention as the dependent variable. Two SM-related variables, namely likelihood to use SM to find PBF information and trust towards PBF information from SM were utilized as the mediators. Preliminary analyses showed that SM-related variables partially mediated the pro-meat reduction attitudes towards PBM purchase intention. The result suggests SM can be a driver to satisfy consumers’ need of information regarding PBF products and increase their purchase intentions. Keywords Social media, Plant-based meat, Meat reduction, Purchase intention


Copyright 2019. The Trustees of Columbia University in the City of New York. This work is licensed under a Creative Commons Attribution 4.0 International License. Meeting the Challenges of the TRUST Principles for Digital Repositories Robert R. Downs 1 NASA Socioeconomic Data and Applications Center (SEDAC) Center for International Earth Science Information Network (CIESIN), Columbia University, 1 rdowns@ciesin.columbia.edu Prepared for Presentation to the 2020 ESIP Summer Meeting July 14-24, 2020, Virtual Event July 17, 2020 Poster Session The work reported here has been supported by the National Aeronautics and Space Administration under Contract 80GSFC18C0111 for the Socioeconomic Data and Applications Distributed Active Archive Center (DAAC). Endorsement of the TRUST Principles for Digital Repositories TRUST Principles for Digital Repositories* References Certification Programs CoreTrustSeal (2020) Nestor (2013) Audit and Certification of Trustworthy Digital Repositories. ISO 16363/CCSDS 652.0-M-1, https://public.ccsds.org/Pubs/652x0m1.pdf (2011). CoreTrustSeal Trustworthy Data Repositories Requirements. 2020. https://www.coretrustseal.org/why-certification/requirements/ ESIP Data Management Training Clearinghouse. 2020. https://dmtclearinghouse.esipfed.org/ Lehnert et al. 2019. Building Resilience by Sharing Infrastructure Among Data Facilities: An Initiative of the EarthCube Council of Data Facilities. https://agu2019fallmeeting-agu.ipostersessions.com/default.aspx?s=A6-0C-96-FA-24-07-F6-36-77-00-78-35-E6-7C-AC-51 Lin, D., et al. The TRUST Principles for digital repositories. Sci Data 7, 144 (2020). https://doi.org/10.1038/s41597-020-0486-7 Harmsen, H. et al. Explanatory notes on the Nestor seal for trustworthy digital archives. Nestor Certification Working Group, http://nbn- resolving.de/urn:nbn:de:0008-2013100901 (2013). Wilkinson, MD. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). https://doi.org/10.1038%2Fsdata.2016.18 Transparency To be transparent about specific repository services and data holdings that are verifiable by publicly accessible evidence. Responsibility To be responsible for ensuring the authenticity and integrity of data holdings and for the reliability and persistence of its service. User Focus To ensure that the data management norms and expectations of target user communities are met. Sustainability To sustain services and preserve data holdings for the long-term. Technology To provide infrastructure and capabilities to support secure, persistent, and reliable services. *Reproduced from Lin, et al., 2020. The TRUST Principles for Digital Repositories. Digital repositories, including scientific data centers, archives, and other data facilities must provide stewardship for the resources that they have been entrusted to manage and disseminate for their user communities. The tenets of transparency, responsibility, user focus, sustainability, and technology that are described in the TRUST Principles for Digital Repositories offer guidance for meeting the needs of users who can use research data, today, as well as succeeding generations of users (Lin, et al., 2020). Developed by several stakeholders from across the digital preservation and research data stewardship community, the TRUST Principles for Digital Repositories can be built upon by the broader community of data stewards to ensure that the data that are being collected now and in the future will be treated as valuable intellectual assets so that they are findable, accessible, interoperable, and reusable, as articulated in the FAIR Principles (Wilkinson, et al., 2016). Diligent digital curation and stewardship efforts will help to ensure that research data products and services can be used to continue scientific progress and achieve the societal benefits that such research can provide. Along with future stewards of research data and related resources, the digital repository professio


Continuing Large-Scale Global Trade and Illegal Trade of Highly Hazardous Chemicals Zhanyun Wang1,2, Hongyan Zou3, Tao Wang3, Zhong-Liang Wang3 1. EMPA – Swiss Federal Laboratories for Materials Science and Technology, 9014 St. Gallen, Switzerland, zhanyun.wang@empa.ch 2. Chair of Ecological Systems Design, Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Swtizerland 3. Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin, P.R. China Methods Introduction ü Chemical pollution, along with climate change and biodiversity loss, is causing in the triple planetary crisis. ü To complement national/regional frameworks on chemicals, the Rotterdam Convention on the Prior Informed Consent Procedure for Certain Hazardous Chemicals and Pesticides in International Trade has been established. ü For each listed chemical, all parties are required to make a decision as to whether they will allow future import of the chemical. Then, it is illegal to export the chemicals to a party that has decided not to further import them. However, the Convention does not have a formal mechanism to evaluate its effectiveness. ü This study aims to capture an up-to-date, accurate, comprehensive overview of the global trade of highly hazardous chemicals listed under the Convention from 2004, when it entered into force, to 2019, to shed light on the Convention’s impacts on the global trade of hazardous chemicals and provide insights on areas for improvement. Acknowledgements H.Z. acknowledges funding from the National Natural Science Foundation of China (NSFC grant no. 21906118), and Z.W. acknowledges funding from the European Union under the Horizon 2020 Research and Innovation Programme (project ZeroPM; grant agreement no. 101036756). View full article Continuing large-scale global trade of hazardous chemicals ü The cumulative global trade of the 46 listed chemicals among all countries reached 64.5 megatonnes in 2004–2019. The highly hazardous pesticides and formulations had lower trade volumes (2.4 megatonnes), dominated by pentachlorophenol and its salts and esters (see Fig. 1, left). ü It is an especially pressing problem for developing countries and countries with economies in transition (see Fig. 1, right). Main Findings ü We develop and use a novel workflow of analysing trade records from the UN Comtrade database (79,194 entries), providing a minimum scenario of the global trade and illegal trade of 46 highly hazardous chemicals listed under the Convention. The workflow included: ü an outlier analysis using clustering and statistical methods to identify erroneous trade records, and ü a subsequent mirror analysis to reconcile asymmetric bilateral transactions. Complex geographical distributions of the global trade (see Figure 2) ü Most of Asia were the major importers of pesticides chemicals, while U.S. and Middle East were the major exporters. Central and Western Europe were not only major importers but also big exporters of industrial chemicals. Prevalent illegal trade of hazardous chemicals (see Figure 3) ü Defaulting was prevalent all over the world, especially in Central, Western and Southern Europe, South and Southeast Asia, and the Middle East. ü In total, they accounted for 40% of the total trade volume (25.7 of 64.5 megatonnes), underlining insufficient effectiveness of the Convention. ü The defaulting counts are seemingly on the rise over time. Tackling the global trade of highly hazardous chemicals ü expanding the Convention’s geographical coverage ü enabling swifter listing of chemicals under the Convention ü enhancing data availability, quality and accessibility ü Understanding and addressing the “lock-in” of hazardous chemicals Fig. 1 Trade volumes by chemical (a) and net imports by geographic region (b) of highly hazardous chemicals Fig. 2 Global transfer of the listed highly hazardous chemicals Fig. 3 Defaulting counts of the trade for exporting parties (countries) and counts of being


Energy Reconstruction with Autoencoders for Dual-Phase Time Projection Chambers Ivy Li, Aarón Higuera, Shixiao Liang, Christopher Tunnell | CHEP 2023 Energy Reconstruction for XENONnT References Weight Layer ELU(𝛼=1) x Weight Layer F(x) F(x) + x x identity Residual Block (ResBlock) ELU(𝛼=1) Input Hit Pattern (494,) Reconstructed Hit Pattern (494,) Latent Space Representation (50,) 5 ResBlocks (494,494) 1 LinBlock (494,50) 1 LinBlock (50,494) 1 LinBlock (494,494) ENCODER DECODER 5 ResBlocks (494,494) Inferring Number of Electrons in the Gas Gap Example Reconstruction of Top Array Hit Pattern , , Input Hit Pattern (494,) Reconstructed Hit Pattern (494,) Inferred ne True ne Loss = (1 - 𝛽) MSE + 𝛽 MSE Loss Function with Cyclic Annealing Inferred ne True ne ENCODER: encodes input into a lower dimensional latent space↑ LATENT SPACE REPRESENTATION: constrains one value as the number of electrons in the gas gap but allows the others to evolve freely ↗ DECODER: decodes input from latent space representation ↑ LOSS FUNCTION: weighted sum of the mean squared error (MSE) losses below ↓ CYCLIC ANNEALING: 𝛽 evolves from 𝛽=0 (prioritize reconstruction) to 𝛽=1 (prioritize latent space constraint) Weight Layer Linear Block (LinBlock) x ELECTRIC FIELD DRIFTS ELECTRONS TO GAS GAP: not all of the freed electrons from the interaction point reach the gas gap SCINTILLATION SIGNAL (S1): incoming particle interacts with xenon and causes a scintillation signal with energy which is proportional to the initial signal’s number of photons IONIZATION SIGNAL (S2): electrons which reach the gas gap cause a brighter ionization signal with energy which is proportional to the number of electrons at the interaction point XENONnT has a detector target of 6 tonnes of ultra-pure liquid xenon [1] ↗ HIT PATTERNS: Photosensors above and below measure signals → Future Work TOTAL ENERGY 6163194 trainable parameters Adam optimizer, starting learning rate 5e-4 Reduce learning rate by factor of 0.1 if loss does not decrease after 5 epochs Semi-Supervised Autoencoder DATASET: simulated hit patterns from a given [0, 2000] ne INPUT SIZE: 494 photosensors total TRAIN/VALIDATION/TEST SPLIT: 447500/447500/100000 hit patterns NUMBER OF ELECTRONS IN GAS GAP Inference Difference Normalized Difference DYNAMIC RANGE: autoencoder can reconstruct hit patterns well without normalizing or log-scaling hit patterns due to skip connections [3] ENERGY RESOLUTION: precise reconstruction is critical for rare event searches such as the search for dark matter evidence [2] INTERPRETABILITY: constraining the latent space to be physically meaningful e.g. a variational autoencoder constrains latent parameters as probability distributions → meaningful parameter uncertainties COMPUTATIONAL EFFICIENCY: simulating data through Monte Carlo methods is computationally expensive and time-consuming → ongoing efforts to create more efﬁcient machine-learning algorithms for fast simulation [0] Check out our experiment! xenonexperiment.org [1] Aprile, E., et al. "Search for new physics in electronic recoil data from XENONnT." Physical Review Letters 129.16 (2022): 161805. [2] Billard, Julien, et al. "Direct detection of dark matter—APPEC committee report." Reports on Progress in Physics 85.5 (2022): 056201. [3] Dieng, Adji B., et al. "Avoiding latent variable collapse with generative skip models." The 22nd International Conference on Artiﬁcial Intelligence and Statistics. PMLR, (2019). GOAL TO IMPROVE RESOLUTION: the spread of the inferred number of electrons in the gas gap decreases as the inverse square root for increasing number of electrons ← UNDERPREDICTION: WHY? currently the autoencoder underpredicts the number of electrons in the gas gap ↑ Ground Truth Gas Gap Reconstructed True ne Inferred ne True ne (X) Inferred ne (Y) Counts


Spectral Model Components › KERRBB [4]: The thermal spectrum of the accretion disc around a Kerr (rotating) black hole. › SIMPL [5]: Converts soft disc photons to hard X-ray photons with a power-law spectrum expected for the disc corona. › RelXill [6]: The ionised disc reflection, with relativistic effects. › ISMabs [7]: Galactic absorption. Observations We use observations from March & September 2019: › 3 from XMM-Newton (blue): › rev3531, rev3533, rev3623 › 8.5 ks, 11.3 ks, 56.3 ks exposure times. › 2 from NuSTAR (orange): › Simultaneous with rev3533, rev3623 › 57.3 ks and 94.9 ks exposure times. rev3531 rev3533 rev3623 Fig 3. The Swift/BAT & XRT light curve of MAXI J1820+070. Fig 6. Mass posterior (grey) and prior (red). The Current Debate Different groups have reached very different conclusions, sometimes from the same data. These often correspond to the extreme cases: Model 1: › Zero or low spin and/or › A truncated disc (Rin ≥ 6 rg) Model 2: › High (near maximal) spin (a > 0.9) › Rin ~ 1 rg Fig 2. An overview of the two different, but extreme XRB models. Models like these are common in the literature, they all make implicit assumptions. Black hole Black hole Disc Disc Rin ≥ 6 rg Rin ~ 1 rg Where is the Inner Edge of the Accretion Disc? The location of the inner edge of the disc effects the spectrum in various ways: › The luminosity and temperature profile of the thermal emission from the disc. › The relativistic distortion on discrete features in the X-ray reflection spectrum, especially the Fe-Kline. It is theoretically possible to use the detailed shape of the spectrum to infer the location of the inner edge of the accretion disc. These spectral effects are subtle and modelling them is subject to several assumptions. A Bayesian analysis of X-ray spectra from the black hole LMXB MAXI J1820+070 using XMM-Newton and NuSTAR. Abstract We adopt a fully Bayesian approach with the best available prior information, making use of the latest spectral models and the most accurate models of ISM absorption, using XMM-Newton RGS data. Our test case is the recently confirmed black hole LMXB MAXI J1820+070. We fit data over the 0.8 – 70 keV range using XMM-Newton and NuSTAR observations. Fig 1. Structure of a low-mass X-ray Binary [1]. Sachin Dias, Simon Vaughan sdd9@leicester.ac.uk System Parameters Our modelling of MAXI J1820+070 includes proper priors on system parameters based on the best available radio through UV observations. System parameters are typically frozen or left entirely free, permitting other parameters to find unphysical values. › Distance: A gamma prior based on VLBI measurements [2]. › Inclination: A Gaussian prior from proper motion of ejecta [2] [3]. › Mass: The mass function (𝑓(𝑀)), mass ratio (𝑞) and inclination (𝑖) combine to give a shifted log-normal prior on the mass. 𝑴𝑩𝑯= 𝒇𝑴 𝟏+ 𝒒𝟐 𝒔𝒊𝒏𝟑(𝒊) › Mass Accretion Rate: A Jeffreys prior between 10−6 ṀEdd and ṀEdd, calculated based on the 10th and 90th percentile masses respectively, assuming a standard accretion disc. References: 1. Image Credit: NASA/R. Hynes 2. Atri et al., 2020, MNRAS, 493, 81 Fig 4. Distance posterior (black) and prior (red). Priors are broader due to effects of unknown errors. Additional Priors We adopt additional priors for the parameters: › Disc Colour Correction Factor (fcol): fcol is typically fixed at 1.7 [8]. Others argue the range could be broader [9] [10]. We assigned a gamma prior over the range [1, 10] with the highest density over [1.4, 2] [9]. › Reflection Emissivity Indices: A gamma prior over the range [0, 10] with a mode at 3 (near Euclidean case) and 10-6 lower density at 10. Ne K-edge Fe L2-edge Oi K-edge Fe L3-edge Fig 10. XMM-Newton RGS spectrum for rev3531 (black) and rev3533 (red). › ISM Column Densities: We fit XMM-Newton RGS spectra using ISMabs. H, O, Ne and Fe column densities were free parameters (having the largest effect in the 11 – 24 Å range) with other column densities fixed (relative to H) to the values given in


Introduction Permaculture is an agricultural system, intended to be sustainable and self- sufficient, that uses natural ecosystems as a model. Permaculture is relevant on a global stage as the world’s population is expected to grow to 10.5 billion people by 2050 (Reid 2017), creating an urgency for effective and sustainable agriculture. Beyond investigating how The Island School produces its food, this Permaculture study concentrates on food insecurity in the immediate vicinity of the Island School campus. At present, The Island School imports about 89% of the food. Permaculture practices seek to address the economic, environmental, and social issues prevalent in South Eleuthera and Small Island Developing States (SIDS). By finding possible solutions to Food insecurity and instability within The Island School’s campus, Permaculture feeds into the larger global conversation regarding the negative environmental impacts of the global food system, the widespread food insecurity and instability, and the future of food in society. Permaculture: Planting Seeds of Sustainability on The Island School Campus Susanna Cabot, Isabel Cullen, Maeve Goodrich, Kelvin Monje, Kate Pausic, Ezra Stern, Nora Verdier Research Advisors: Joseph Plant, Leigh Schmitt Citations Garnett, T. 2013. Food Sustainability: problems perspectives and solutions. Proceedings of the nutrition society. 72,29- 39. Reid, J. 2017. Carbon Narrative. Waterfield Farms. Robards, C. 2017. Survey outlines food insecurity on Eleuthera. The Nassau Guardian. Tilman, D & Balzer, C & Hill, J & Befort, B. 2011. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences. 108; 50. Viviano, F. 2017. “This Tiny Country Feeds the World.” National Geographic. http://www.nationalgeographic.com/magazine/2017/09/ho lland-agriculture-sustainable-farming.html. Methods The semester’s research is focused around campus design and planning. The Permaculture Research Cohort cultivated and planted crops, added soil amendments, conducted animal husbandry, and informed the outside community through social media and a research blog. The Permaculture Team also collaborated with the dining hall and the Aquaponics team to prepare and serve a local meal for Parent’s Weekend. Data and Results Throughout the semester, the Permaculture research cohort studied how to best communicate their scientific findings through social outreach. In addition, the group has been working on harvesting crops on the Island School farm that was then a part of the “locavore” Parent’s Weekend meal. Campus Planning: Local Meal: Social Media and Outreach: Acknowledgements: We would like to acknowledge all those who have assisted us in our research, mainly the Permaculture research advisors: Leigh Schmitt and Joseph Plant, as well as the Farm Team, the Kitchen Team, and the Communications Team. We would also like to acknowledge the Aquaponics Research Group. Figure 8: A map of the United States shows the frequency of visits from each state to the Permaculture blog. Figure 4: Permaculture students clear a plot of land for agricultural use. Figure 6: The aquaponics system on campus produces leafy greens throughout the year for The Island School dinning services. Figure 1: The Island School has a model farm on campus designed with permaculture principles. Figure 3: The orchard near the CSD will be split up into sectors for a crop rotation. Figure 5: Radishes collected from Permaculture Research farm plots harvested in preparation for the Parents Weekend local meal. Figure 7: This graph shows the daily views of the Permaculture Research blog throughout the course of a week. Figure 2: Students filet tilapia from the Aquaponics system in preparation for the local meal. Permaculture Essential Questions • How do we feed ourselves? • What are the true costs (economic, social, environmental) to successfully growing food on campus? • For the long-term, what are the best and most logi


Last Year Before Death: Observations of SN 2020fqv by TESS and Friends Kaew Samaporn Tinyanont (UCSC), Ryan Ridden-Harper (JHU), Ryan Foley (UCSC), Viktoriya Morozova (PSU), Charlie Kilpatrick (Northwestern) Georgios Dimitriadis (Trinity), Armin Rest (STScI), Qinan Wang (JHU) on behalf of the Young Supernova Experiment (YSE) Collaboration. Early observations are key to understanding what happened in the last year prior to the star’s death. SN 2020fqv was discovered within a day and we obtained the first spectrum 26 hr post-explosion. SN 2020fqv shows signs of interaction and is within an active TESS sector prompting us to trigger the first ultra-rapid HST program ever to obtain early-time UV-optical spectra with STIS and COS. The earliest STIS spectrum was obtained only 79 hr post-explosion. TESS captures the rise of this SN, allowing us to model it to constrain the explosion time and other properties of the explosion. SN 2020fqv comes from a red supergiant with a dense circumstellar environment. From the light curve fitting, pre- explosion image from HST (non-detection, F606W, shown on left), and late-time nebular spectroscopy (see different ranges of mass below), we are able to constrain that SN 2020fqv is an explosion of a red supergiant (RSG) with a zero-age main sequence mass (ZAMS) of 13.5-15 M⊙. Further, the fast rise time and the early-time excess flux of SN 2020fqv, along with narrow emission lines in the spectra, point to a dense circumstellar medium (CSM) immediately outside of the progenitor star of SN 2020fqv. Circumstellar Medium of SN 2020fqv We find 0.23 M⊙of CSM within 1450 R⊙from the star. Modelling our observations shows that the density profile of the RSG progenitor of SN 2020fqv does not just drop off like normal at about 1000 R⊙, but has a CSM component extending out to ~1450 R⊙, containing ~0.23 M⊙of material. The density and optical depth is so large that the SN shock only breaks out at the edge of this CSM, marked with a red cross in the bottom left figure. These CSM properties are not unusual among the population of SNe II (Morozova+ 2018). Our models show that the CSM is a result of an energy injection of 5x1046 erg into the envelope about 300 days before the explosion, which may be related to instability in late-stage nuclear burning (e.g., Quataert & Shiode 2012, Fuller 2017, Wu & Fuller 2021). The inner part of the CSM leaves an imprint on the SN’s early light curve. Middle right figure compares the light curve models with and without the CSM. The data points are binned TESS light curve with uncertainty. The solid line model shows the light curve expected with the CSM component; it matches the data best. Bare RSG models (dashed and dotted-dash lines) cannot explain the entire light curve. This light curve is affected most by the bulk of the CSM inward of the shock breakout point. The outer part of the CSM forms narrow emission lines. Lower right figure shows the early-time spectra of SN 2020fqv both from the ground and from HST/STIS. They show the emission features around 4600 Åfrom high-ionization metal species like N III and C III. They originate from the outer CSM getting radiatively ionized by the shock breakout. These features have been observed in a growing number of CCSNe with early-time spectra, e.g. SN 2013cu shown here (Gal-Yam+ 2014). Select References: Morozova+ 2015, 2018, 2020; Vallely+ 2021; Jerkstrand+ 2014, 2018; Gal-Yam+ 2014, Wu&Fuller 2021, Fuller 2017, Quataert&Shiode 2012, Bowen 1935. SN shock breaks out from here Inner, optically thick, CSM Outer, optically thin, CSM


“THE SPINE” “THE TORSO” “THE ARMS” “THE LEGS" THE ANATOMY BEHIND TASK DESIGN WITHIN HIGHER EDUCATION Within the fundamentals of lesson planning in Higher Education, task design is vital to support student learning. The aim of the infographic is to provide "THE SKELETON" to assist the task design process for new academics. Claire Farquharson & Sarah Sandford, Hartpury University How does the task link to the lesson plan? Communicate this to the group. What Learning Outcomes does the task map too? Which transferable skills are embedded in the task? Think about how you can develop a range of skills across different tasks! For example working with others, presentation skills, team work. Do you need to "scaffold skills" for task success? For example if they are required to present information to a full group, have previous tasks assist them in presenting to to each other in pairs, then smaller groups first Identify any pre-task activities or previous learning that will assist students in completion of the task Do you need a ice breaker to help group dynamics? What are the clears aims of the task and it’s duration? Is the task emphasising the level of study and expected skills? What’s your exit task - take home messages! What type of task do you want to do? Is it a closed, structured task to identify key concepts? If it is a larger task, can this be split into smaller tasks first Is it more open, unstructured for students to apply their understanding of the concept in a wider environment and to create discussion? Through the group or individual task: How can we reinforce the concept? How can we get students to verify their understanding of the concept? How can we consolidate the concept, with context and relevance, to the group? How can you assist the student engagement? Can you get students to think individually, discuss in pairs and then share with a wider group. THINK - PAIR- SHARE. How can you assist group dynamics? Do you allocate groups? Choose groups? How do you identify roles within the tasks? How can they work with each other for a common goal? Consider the environment you are delivering the session in. Is it online or face to face? How does the environment work for you and them? Don’t feel that you need to provide a variety of activities. Focus on the aim of the task and how it assists the lesson/module/unit more widely. What resources can you use to facilitate the task? Can this resource be collated for all the group to share and access after completion? Can you direct students for further resources and reading to support the task? “THE FINGERS & TOES" Be innovative! Try different approaches! How did it go? Reflect on; Did the task achieve what you wanted? Were students engaged? Did you achieve the session aims? Did students improve their understanding? How did the task work within the session? What would you change or do differently next time?


Improved enhancer discovery in Drosophila and other insects Hasiba Asma1, Chad M. Jaenke2, Michael L. Weinstein2, Thomas M. Williams2 and Marc S. Halfon1 Introduction Transcriptional enhancers, or more broadly, cis- regulatory modules (CRMs), are essential building blocks of gene regulatory networks. We previously developed the SCRMshaw method for computational CRM discovery[1,2,3]. SCRMshaw uses the wealth of known D. melanogaster CRMs as training data to facilitate CRM discovery in not just Drosophila but in diverse holometabolous insects including mosquitoes, beetles, and bees. Here we present three approaches for increasing SCRMshaw’s effectiveness. SCRMshaw: predicting CRMs (a) SCRMshaw takes a training set of similar CRMs and a set of similarly-sized non- CRMs as a background (BKG) set, (b) builds up kmer profiles of the sequence sets, (c) generates scores using statistical models , and (d) searches the genome for high scoring windows (predicted CRMs). 1. pCRM_eval: A comprehensive pipeline for in silico evaluation of CRM prediction approaches[4] 3. Iterative searching can serve to augment weak training sets to improve true-positive : false-positive ratios We used SCRMshaw on a small training set of 7 CRMs to identify CRMs within a gene regulatory network for Drosophila abdominal pigmentation. 1. Kantorovitz, Miriam R., et al.. Developmental cell 17.4 (2009): 568-79. 1. 2. Kazemian, M., et al. Nucleic Acids Res 39.22 (2011): 9463-72. 2. 3. Kazemian M, Halfon MS: CRM Discovery Beyond Model Insects.: Insect Genomics: Methods and Protocols; 2019: 117 3. 4. Asma H, Halfon MS:. BMC Bioinformatics 2019, 20(1):174. 4. 5. Suryamohan K, Halfon MS:. Wiley interdisciplinary reviews Developmental biology 2015, 4(2):59-84 1University at Buffalo-State University of New York, Buffalo, NY 2University of Dayton, Dayton, OH ~24000 CRMs pCRMeval compares prediction results with the existing extensive corpus of validated Drosophila CRMs to calculate recovery of true CRMs and estimate the specificity of a given method. pCRMeval can also assess the performance of a specific training set in terms of both sensitivity and specificity. 0 20 40 60 80 100 Training set sensistivity REDfly recovery Expression pattern specificity Percent Percent Percent Percent Training Set Sensitivity REDfly recovery Expression Pattern Specificity True Expectation True Expectation True Random Random Random Expectation Expectation Expectation True True A B C D 0 25 0 20 40 60 0 20 40 60 50 75 100 True Expectation Three measures of Assessment pCRM_eval demonstrates that SCRMshaw performs better than random expectations (A) Aggregate performance for 29 true training sets, 62 random training sets, and random expectation. Comparison of training set sensitivity (B), REDfly recovery (C), and expression pattern specificity (D) for true versus random expectations for each of the 29 training sets. 2. Toward individual prediction confidence scores A true CRM might be predicted in multiple related species. We are developing a “weighted comparative confidence” score to identify such conserved CRMs, even in the absence of sequence alignment. SCRMshaw Prediction SCRMshaw Prediction < 10kb SCRMshaw Prediction < 10kb Reference Species Genome Target Species 1 Genome Target Species 2 Genome [5] Assessment measures These 10 new validated CRMs were combined with the original 7 and the 2.5-fold expanded training set used for a new round of SCRMshaw prediction. Following is the summary of the results after getting predictions from updated training set. *Notably prediction results from updated training set contain all the previous true positives and only one false positive demonstrating a marked improvement in prediction specificity using the updated training data. Empirical testing of 18 SCRMshaw predictions revealed 10 true (e.g. Kr-h1) and 8 false positive (e.g. Pdp1) prediction results. Assessment measures In vivo validation Total No. of CRMs No. of CRMs predicted by original Training s


Planet gap opening across stellar masses Cat Sinclair (Cambridge), Giovanni Rosotti (Leiden), Cathie Clarke (Cambridge) Motivation and questions Many discs have rings and they may be due to planets. • How massive must a planet be to create a ring? • How does this vary with the distance from the star? • How does this vary with the host stellar mass? Theoretical expectation Depends on disc aspect ratio, i.e. temper- ature. Therefore varies with distance from the star and stellar luminosity Methods To verify the prediction, we do: • Dust and gas hydro simulations • Radiative transfer • CASA simulations to include ALMA observational effects Results Possible to use stellar mass with average luminosity, but keep in mind there is large spread in luminosities for any stellar mass. Luminosity spread in Lupus Theoretical expectations matched. Gap opening more difficult far from the star and for high stellar luminosities Conclusions • Gap opening more difficult far from the star and for high stellar lu- minosities. Quantitative expressions in the paper (QR code on the right) • Stellar luminosity crucial parameter for gap opening mass


Practice Abstracts Series BRANCHES #28 28 Monte Holiday Ecoturismo+Bosque - Regenerative tourism to prevent forest fires The company Monte Holiday Ecotourism has developed a set of coordinated actions in the tourist complex located in a privately owned holm oak forest in the mountainous area Sierra de Madrid, whose objective is to establish an adaptive forest management that allows both the prevention of forest fires and the energy recovery of the biomass obtained by means of the heating network of the campsite. In this way a double benefit is achieved, on the one hand the preservation and protection of the surrounding forest and people and on the other hand it contributes to achieve a neutral carbon footprint. The proposed forest management focuses on the selection and conservation of the trees that present best condition, including silvicultural treatments such as pruning and cutting to reduce the trees’ density and the introduction of extensive livestock. The biomass obtained is used to produce renewable thermal energy for the comfort of visitors staying at the campsite. The "MHE+Forest" project aimed at energy self-sufficiency started 10 years ago with a hybrid solar/biomass heat network. Now, the installation has been expanded with a 99 kW photovoltaic plant and a 500 kW high-efficiency ETA boiler, which enables the recovery of biomass from forest management and ensures virtually zero emissions of particulate matter. In 2023, the first silvicultural intervention was carried out in the holm oak forest, obtaining 200 tonnes of green biomass. In the coming years, the adaptive management work will continue in accordance with the provisions of the forest management plan and higher volumes will be obtained. Part of the biomass will also come from nearby woodlands identified as being at high risk of fire spread by the campsite's self-protection plan. Monte Holiday Ecotourism aims to serve as an example of adaptive forest management that contributes to increase the resilience of the forest to climate change and forest fires while enabling the development of the local and circular bioeconomy and the production of renewable energy in the tourism sector, which also leads to savings in the campsite's energy bill (in 2022 over 30,000 € for an average consumption of 500 MWh of thermal energy per year, which implies a saving of over 50%). KEY WORDS Bioenergy, sustainable forest management, biomass, ecotourism COUNTRY Spain DISCLAIMER This Practice Abstract reflects only the author’s view and the Branches project is not responsible for any use that may be made of the information it contains. LINKS www.intercambiom.org www.branchesproject.eu AUTHORS Maider Gómez Daniel García Pablo Rodero Alicia Mira


Adapted from the Design made by pikisuperstar / Freepik “ ” A gateway to the European Open Science Cloud OpenAIRE Content Provider Dashboard: enabling trust and value within the EOSC repositories Authors: André Vieira, andre.vieira@usdb.uminho.pt Pedro Príncipe, pedro.principe@usdb.uminho.pt OpenAIRE has received funding from the European Union's Horizon Europe Research and InnovaƟon programme under Grant Agreements No. 101017452, 101017536. What is PROVIDE? Is the scholarly gateway that receives content registraƟon requests and provides guidance for content providers by viewing a personalised, user-friendly dashboard that shows the necessary steps and status of the services to complete the process. Beneﬁts Extending repository metadata models to Open Science practices. Keeping the collections up-to-date: alerts, enrichments, additions. Fostering notiﬁcation-based and federated dissemination of knowledge. Improve Repository interoperability, collections and content visibility. Advance Institution memory & research assessment. Support Features Validator service and data source registration. Collection monitor to track the aggregation history. OA Broker service offers metadata enrichments. Usage Counts provides metrics (downloads and views) of data sources. hƩps://provide.openaire.eu Validate and register your data source using OpenAIRE authoritative registries Role in the EOSC onboarding Interoperability compliance via the OpenAIRE Guidelines. Every data source validated in OpenAIRE is EOSC compliant (Guidelines: v3.0, v4.0, data, CRIS) Integration of the data sources registration workﬂow within the EOSC Research Product Catalogue OpenAIRE Graph becomes a key asset for discovery and monitoring within the EOSC: https://graph.openaire.eu/ OpenAIRE Support Learn more: www.openaire.eu/guides Ask a question: www.openaire.eu/helpdesk EOSC Providers Hub: https://eosc-portal.eu/eosc-provid ers-hub Instructions to EOSC onboarding: https://eosc-portal.eu/eosc-providers- hub/how-become-eosc-provider


THE DELIVERY STATUS OF JUNO 20" PMTS AND THEIR PERFORMANCE Rong Zhao 1 (on behalf of JUNO collaboration) 1Sun Yat-Sen University zhaor25@mail2.sysu.edu.cn Abstract The Jiangmen Underground Neutrino Observatory (JUNO) [1] is a multi-purposed neutrino experiment currently under construction in South China. Due to its unprecedented requirement on the photocathode coverage rate in order to reach enough energy res- olution, totally 20k 20-inch photomultipliers (PMTs) will be de- ployed in the detector system, including 5k conventional Hama- matsu dynode PMTs(R12860) and 15k newly developed PMTs us- ing micro-channel plates (MCP-PMT) by North Night Vision Tech- nology (NNVT). The JUNO collaboration has developed a PMT mass testing system to characterize these PMTs and selected the qualified ones for the final installation. Up to now, we have re- ceived and completed the characterization of 5k Hamamatsu PMTs and 13.1k MCP-PMTs. In this poster, we will present the latest de- livery details and the performance results including the typical sin- gle photon-electron (SPE) waveform, SPE spectra of two types of PMTs, SPE resolution, photon detection efficiency(PDE) and dark count rate(DCR) etc. Introduction of JUNO Detector The JUNO central detector will be filled with liquid scintillator, about 15k 20"MCP-PMTs and 5k 20"Hamamatsu PMTs will be used to collect scintillation light and Cherenkov light; the geometry of JUNO detector and PMTs are shown in Figure 1. Figure 1:JUNO detector and geometry of Hamamatsu PMT and MCP-PMT PMT Delivery and Test Status By May 2020, 18k of the total 20k PMTs have been delivered to JUNO PMT storage site, the acceptance test of 15k PMTs have been finished and 5k MCP-PMTs have been potted, as illustrated in figure below. 13.1k delivered 10k tested 15k total 5k tested 5.3k potted HAMAMATSU DYNODE PMT NNVT MCP-PMT All of the delivered PMTs will experience a detailed visual check and electronic performance test before they are potted and finally installed into JUNO detector. PMT Testing System Two container PMT testing systems with electromagnetic shield- ing are currently used for mass acceptance tests[4]. Each of them consists of 36 individual drawers (see Figure 2). The PMT inside each drawer can be illuminated with a self-stabilized LED or a pico- second laser, both with 420 nm wavelength. Two PMT scanning stations [2][3] were built for characterization of whole PMT photo- cathode. It allows for the testing of individual PMTs in all relevant aspects by scanning the photocathode and identifying any poten- tial problems. Figure 2:The container PMT mass test system and PMT scanning station JUNO PMT Performance of tested PMTs The typical single photon electron waveform of Hamamatsu PMT and MCP-PMT is shown in Figure 3: 100 150 200 250 300 350 400 t[ns] 0 2 4 6 8 10 12 14 U[mV] 100 150 200 250 300 350 400 t[ns] 6 8 10 12 14 16 18 20 U[mV] Figure 3:typical SPE waveform and 2D waveforms of Hamamatsu and MCP-PMTs. left: wave- form of Hamamatsu PMT EA0419; right: waveform of MCP-PMT PA1707-1090 SPE charge spectrum of PMTs obtained by LED with low illumina- tion(average photon number µ ≤1 ) are shown in Figure 4: Figure 4:charge spectrum of PMTs. left: Hamatsu PMT EA0419 with gain=9.9E6 and µ = 0.16, 0.79; right: MCP-PMT PA1707-1090 with gain=1.04E7 and µ = 0.14, 0.84. Performance parameters of PMTs can be derive from the charge spectrum and other dedicated measurements. Table 1:peformance parameters of JUNO accepted PMTs PMT PDE(%) DCR(kHz) FWHM(ns) TTS(ns) resolution risetime(ns) falltime(ns) p/V Hamamatsu 28.17 15.38 11.56 1.42 0.28 6.94 10.20 3.83 MCP 27.20 48.94 8.18 6.35 0.32 4.01 17.97 4.28 MCP(HQE) 30.14 49.73 7.83 6.35 0.33 4.95 17.10 3.93 PDE and DCR Distributions The PDE and DCR(the methodology and the exact definition can be found in [4]) distributions of PMTs are shown in Figure 5, for MCP-PMT the high quantum efficiency version has higher detec- tion efficiency benefiting from the new photocathode technol


Characterising the detector response of the SuperFGD as part of the T2K near detector upgrade Tristan Doyle (on behalf of the T2K collaboration) tristan.doyle@stonybrook.edu 1. The T2K Experiment The Tokai-to-Kamioka (T2K) experiment is a long-baseline neutrino experiment based in Japan νµ (¯νµ) beam produced at J-PARC, characterised by near detectors and detected at Super-Kamiokande Measures νµ (¯νµ) disappearance and νe (¯νe) appearance 2. Near Detector (ND280) Upgrade Replace π0 detector with three new sub-detectors: →Super Fine-Grained Detector (SuperFGD): Highly segmented target material with ability to reconstruct neutrons and lower momentum protons →High-Angle Time Projection Chambers (HATPCs): measure momentum, charge and particle ID with better angular acceptance than before See posters by Matteo Feltre and Ulysse Virginet →Time-of-Flight (ToF): Precise timing information to reject backgrounds and improve reconstruction See physics capabilities in posters by Liz Kneale, Katharina Lachner and Weijun Li 3. SuperFGD Concept and Construction 2 million optically isolated 1 cm3 plastic scintillator cubes [1] 56,000 wavelength shifting (WLS) fibers →Each coupled to a multi-pixel photon counter (MPPC) →Three orthogonal fibers per cube Concept proven in charged particle [2] and neutron [3] beam tests Detector assembly at J-PARC October 2022 - April 2023: →Cube layers installed with fishing lines →Vertical alignment using metallic rods →Fishing lines replaced with WLS fibers →MPPCs and LED calibration system installed SuperFGD installed in October 2023 Find description of the electronics in poster by Viet Nguyen 4. Light Yield Measure ADC counts from electronics Can convert ADC counts to photoelectrons (p.e.) See poster on calibration by Daniel Ferlewicz For each hit there is a high gain (HG) ADC and a low gain (LG) ADC Linear relationship between HG and LG provides larger dynamic range Also measure time over threshold (ToT) for each hit Can convert ToT to HG using exponential relationship Provides even larger dynamic range than LG 5. Attenuation Length Having three fibers per cube allows construction of attenuation length plot →More reliable characterisation of response and calibration For a given distance from the MPPCs, plot observed light from hits in cosmic events Fit distribution as a function of distance with an exponential function to extract attenuation length →Measured attenuation length consistent with specification of WLS fibers 6. Time Resolution Select hits > 40 p.e. matched in all three dimensions Compare mean time of hit to mean time for event Gives ∼1.2 ns time resolution →Can be improved by electronics firmware update! 7. Neutrino Interactions in the SuperFGD Some of the first neutrino interaction candidates in the SuperFGD. Possible proton (left) and pion (right) candidates are highlighted. 7. Next Steps Continue tuning Monte Carlo simulation using measurements of light yield and attenuation length Measure dE/dx in the SuperFGD and make selection of proton candidates using Bragg peak Head towards first physics analyses with the SuperFGD! References [1] Y. Abreu et al 2017 JINST 12 P04024 [2] A. Blondel et al 2020 JINST 15 P12003 [3] A. Agarwal et al 2023 Phys. Lett. B 840 137843


MYRIAD-EU has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement ID: 101003276 Introduction MYRIAD-EU: Multi-hazard and sYstemic framework for enhancing Risk- Informed mAnagement and Decision-making in the EU Philip J. Ward, Marleen C. de Ruiter, Timothy Tiggeloven, Judith N. Claassen, Ruoying Dai While the last decade saw huge scientific advances in the understanding of natural hazard risk, most research and policy still address risk from a single-hazard, single-sector perspective not taking into account the interconnected reality of these events. MYRIAD-EU’s vision is to catalyse the paradigm shift required to move towards a multi-risk, multi-sector, systemic approach to risk assessment and management. To achieve this vision, the overall aim is that by the end of MYRIAD-EU policy-makers, decision-makers, and practitioners will be able to develop forward-looking disaster risk management pathways that assess trade-offs and synergies across sectors, hazards, and scales. The project will focus on five multi-scale Pilot regions, in which we aim to better understand indirect, interregional, and cross-sectoral risk within Europe. Through this project we aim to reduce risks together: please contact us if you are interested in collaboration. Work Packages The MYRIAD-EU project is centred around eight Work Packages (WP’s) summarized in Figure 1. Hazard Classes Geological Hydrological Climatological Biological Meteorological The following hazard classes are included in the project Pilots and Sectors The Pilots (see Fig.2) are selected to provide: a spread of different spatial scales, geographical locations, institutional settings across the EU; hazard ranges and hazard interrelation types (triggering, amplifying, consecutive, and compound). The key sectors that will be covered in MYRIAD- EU are: 1. infrastructure & transport 2. food & agriculture 3. ecosystems & forestry 4. energy 5. finance 6. tourism Partners Figure 1: Overview of MYRIAD-EU Work Packages (WPs) Figure 2: Overview of MYRIAD-EU Pilots #MYRIAD-EU Prof. Dr. Philip Ward philip.ward@vu.nl


Process Microstructural analysis after sintering at 1500°C Mechanical properties • Cold Crushing Strength determination (CCS): • Resonant Frequency Damping Analysis (RFDA): Beneficiaries Use of Metallurgical Residues as potential Raw materials for High Performance Refractory Castables Mathilda Derensy1, Thorsten Tonnesen1, Jong-Won Shin2, Jesus Gonzalez-Julian1 1. Institute of Mineral Engineering, RWTH Aachen University, Forckenbeckestraße 33, 52074 Aachen, Germany 2. Calderys Deutschland GmbH, In der Sohl 122, 56564 Neuwied, Germany CESAREF PhD 04 : Industrial and Academic supervisors: Acknowledgements: Mathilda DERENSY MSCA Doctoral candidate mail: derensy@ghi.rwth-aachen.de This project has received funding from the European Union's Horizon Europe research and innovation program under grant agreement no.101072625 Goals: Results and Discussion: Development of mineral processing route of aggregates and study of the behavior in refractory castables. Further thermo-mechanical tests with castables containing vanadium slag as the bonding phase. Different trials to extract the vanadium in a sustainable way Contact different slag suppliers for further comparison with current slag composition Ongoing work: • Strength decreases significantly when adding slag to the formulation but the reference is based on high alumina castables, rarely employed in industries • E and G modulus data do not display considerable deviation with the different amounts of slag • Vanadium reacts preferably with Mg, Al and Ca towards a stable phase after sintering and does not attack the alumina grains. Conclusion: Context: The steelmaking industry generates several by-products during the different stages of steel production. As a by-product, vanadium slag account for more than 60% of the world’s overall production. However, it is tough to find vanadium in its pure state as it occurs in combination with various minerals. Using secondary resources for refractory castables solves the issue of natural raw materials, reduces costs related to their extraction and processing, and is friendly to the environment. Figure 1. Towards a new steel production route. Figure 2. Microstructure analysis of a castable with 0% slag; 2.5 wt.% slag and 2.5 wt.% cement and 5 wt.% slag after sintering at 1500°C for 6h and corresponding EDS scans of the different points on the graph. 1 2 3 4 5 6 7 O 56.28 57.09 57.56 55.28 57.02 56.68 57.55 Na 0.06 0.15 0.30 1.84 Mg 0.59 0.20 0.82 1.48 Al 39.54 39.40 42.44 41.16 42.98 38.88 37.55 Cl 0.19 Ca 3.25 3.16 3.07 3.27 1.37 V 0.28 0.35 0.21 Figure 3. Cold crushing strength experiment (Top) and resonant frequency damping analysis (Bottom) on castables after sintering at 1500°C/6h. 2.5 wt.% slag ; 2.5 wt.% cement 1 2 3 20 µm 5 wt.% slag 5 wt.% slag 6 7 100 µm 20 µm 0% slag (5 wt.% cement) 0% slag (5 wt.% cement) 4 5 20 µm 100 µm Advanced metal extraction of vanadium 2 min Dry mixing 5 min mixing Wet chamber 24h Demolding 1500°C 6h Dryer 24h Coarse Aggregate Fine Aggregate Porosity Matrix (ultra-fine) As coarse aggregates As bonding phase Castable Microstructure Raw Materials H2O + Deflocculant Mold Use Vanadium slag in castables composition Good properties to withstand harsh conditions Reducing emissions and costs related to extraction Reducing waste by recycling


SPHERE quality control and dataflow operations The first observing period Wolfgang Hummel, ESO, Data Management and Operations Division (DMO) SPHERE calibration plan Science templates One peculiarity of the SPHERE calibration plan are the science templates that generate in advance of the science data also calibration data valid for the particular pointing only. These include an optional image of the waffle pattern to reconstruct the source center behind the coronograph (called CENTER) , an optional source offset image for the flux calibration (called FLUX) and optional sky frames. Introduction Since April 2015 ESO operates the new planet finder instrument SPHERE (Fig. 1). SPHERE is one of the most complex instruments at the VLT. It consists of the common path and infrastructure (CPI) for AO and coronography and of three arms: IRDIS, IFS and ZIMPOL. A total of eight different observing modes is supported, each one with its own independent calibration plan and data association scheme. The modes include NIR differential beam imaging, integral field spectroscopy and differential polarimetric imaging with 30msec time resolution in the visual. [1] The first observing period of an instrument is usually the most critical phase in its life time, in particular in the context of quality control (QC) which is a task shared between science operations on site and the QC group in Garching. Here we report on the data flow design challenges, the QC system and operational highlights of the first six months of SPHERE operations. Operational Highlights: IFS light leakage We found a light leakage in the IFS-arm detector. As a consequence the dark calibrations show:  The dark current is linear function of DIT (Fig. 4, Box 4).  The dark current depends on the temperature at the optical bench (Fig. 4, Box 6).  The statistical noise measured is dominated by the shot noise introduced by the light leakage (and not by the readout noise ~3 ADU)  The statistical noise scales as square root with DIT (Fig.5, Box 4) ACKNOWLEDGEMENTS/REFERENCES Narrow band - broad band filter match The time to acquire day-time calibrations scales with the number of different instrument setups the observations use in one night, but the time window for taking day-time calibrations is confined due to operational reasons. Since it is justified for IRDIS and ZIMPOL to calibrate narrow band filter science observations with broad band filter day- time calibrations (covering the narrow band), this filter association was implemented for reasons to make use of these synergies. Insert other logos (optional) Fig.2: Left: Waffle pattern with the four cross points marked to find the center of the star behind the coronographic mask. CENTER calibration. Middle: Star at offset position, FLUX calibration. Right: Star behind the coronographic mask, science frame. Fig. 4: Sphere Health Check Plot to monitor QC parameters retrieved from DARK calibrations of the IFS-arm. The upper three boxes show the dark levels in ADU for three different DIT values. The lower middle plot shows the optical bench temperature with nearly the same pattern. The lower left box shows the linear dependence of the dark level with exposure time. (Cyan circles mark last entries.) The lower right box show the temperature dependence for the three mentioned exposure times (2sec, 8sec and 30sec). CONTACT Wolfgang Hummel Astronomical Data Quality Control Scientist ESO, Karl-Schwarzschild Str. 2, D-85748 Garching whummel@eso.org Live versions of the presented health check plots can be found here: Insert other logos (optional) Fig. 5: Sphere Health Check Plot to monitor QC parameters retrieved from DARK calibrations of the IFS-arm. The upper three boxes show the readout noise in ADU for three different DIT values. The lower middle plot shows the optical bench temperature with nearly the same pattern. The lower left box shows the square root dependence of the readout noise with exposure time. (Cyan circles mark last entries.) T


Elliott C. Shuppy University of Wisconsin-Madison School of Library and Information Studies Developing an Ecology-specific Research Data Management Workshop Acknowledgments: Thanks to Erin Carrillo, Brianna Marshall, and Ryan Schryver of University of Wisconsin-Madison Research Data Services References: Borer, E.T., Seabloom, E.W., Jones, M.B., & Schildhauer, M. (2009). Some simple guidelines for effective data management. Bulletin of the Ecological Society of America. 90: 2, 205-214. Michener, W. K. (2000). Ecological data: design, management, and processing. Oxford: Blackwell Science. Key Campus Unit Stakeholders • Research Data Services (RDS): Interdisciplinary organization committed to advancing research data management practice on the UW-Madison campus. • University of Wisconsin-Madison Center for Limnology (CFL): Research center dedicated to the study of fresh-water ecosystems and ecology programs, houses graduate students from zoology, ecology, and engineering. Problem: Research Data Literacy Graduate students from the University of Wisconsin Center for Limnology were awarded a grant funding from Integrative Graduate Education and Research Traineeship (IGERT) to host a research data management education workshop for graduate colleagues. Although data are used frequently by the graduate student group, sound data management practices and a thorough understanding of the importance of data stewardship are still challenges. Implication Facilitation Organization Conception  Proposal Initial development of the workshop was proposed to RDS by graduate student leaders from CFL in late spring 2014. RDS subsequently identified four of its own members to lead and plan the workshop.  Focus on ecology Data management practices in limnology share a considerable number of similarities with ecology. The planning stage of the workshop focused on collecting information about discipline and data workflow characteristics of ecology from published resources, conversations about the importance of teaching certain topics, and the graduate students’ own personal beliefs and values associated with data management which were collected by survey prior to the proposal.  Needs One meeting with CFL graduate student leaders allowed RDS leads to gather support and input about attendee needs. Subsequent regular emails were sent to CFL leaders and audience members to gather more information about data management barriers and challenges.  Objectives RDS leads identified three objectives for the audience: 1) Increase awareness about RDS services 2) Expand data management proficiency with module content 3) Build understanding of complete data management workflow  Module topics and organization Based on models of ecological data workflows, topics selected and organized into a two-day workshop format. Day One (10am-4pm): Intro to RDS, spreadsheets, metadata, storage, file organization, and sharing Day Two (10am-12:30pm): Preservation and data management plans Hands-on work time was initially built into each module  Instructor selection The planning team chose several instructors from departments affiliated with Research Data Services who are likely to train future researchers on campus. Also chosen were instructors from ecology-associated departments who are likely to influence data management education for ecologists.  Realized module workflow Due to instructor schedules, and content overlap workshop modules were reorganized thusly: Day One (10am-2pm), November 10, 2014: Intro to RDS, spreadsheets, file organization & collaboration, and time allotted at end for collaborative work time Day Two (9am-12:30pm), November 11, 2014: Storage & preservation, data management plans, scholar presentation on ecology data management research  Activities Since hands-on activities were not practical for modules on the second day, time was allotted at the end of Day One to allow audience members to work with each other and instructors to garner feedback about data ma


Statistics of annotated validation dataset. Statistics of annotated validation dataset. The preliminary results of using Point Transformer trained on a public 3DEP point cloud classification dataset are promising. BACKGROUND: The USGS 3D Elevation Program (3DEP) is collecting lidar point data for the entire continental U.S. Minimum classification is following for current 3DEP. Accurately classified point clouds can support many down-stream applications such as in hydrologic analysis, urban planning, and forest management. This project will employ proven Deep Learning (DL) technologies in the development of a user-friendly open-source toolkit that will automate point cloud classification to refine and enrich the attributes of existing and future 3DEP data. MATERIALS and METHODS 1. 3DEP lidar data: National Map Download Client site (https://apps.nationalmap.gov/downloader/) 2. Training data: 2019 IEEE GRSS Data Fusion Contest (https://ieee-dataport.org/open- access/data-fusion-contest-2019-dfc2019) 3. DL model: Point Transformer 4. Validation RESULTS 1. Buildings and vegetation are now in different classes 2. Most of noisy points are re- classified/removed 3. Point Transformer had good performance in major classes (bare earth, vegetation, building) 4. The performance of the classes with fewer points (“water” and “bridge deck”) was poor NEXT A deep learning-based point cloud classification tool on 3DEP PRESENTER: Jung kuan Liu (jliu@usgs.gov) Automated Deep Learning-based point cloud classification on USGS 3DEP LiDAR Data Using a Transformer Jung kuan Liu1, Rongjun Qin2 1 U.S. Geological Survey, Center of Excellence for Geospatial Information Science 2 Department of Civil, Environmental, and Geodetic Engineering, The Ohio State UniversityU.S. Department of the InteriorU.S. Geological Survey Alameda County, CA IoU Accuracy Num. of Points Bare earth 0.97 0.98 24.6 M Vegetation 0.86 0.89 3.6 M Building 0.84 0.98 3.2 M Water 0.19 0.26 254.9 K Bridge deck 0.44 0.47 88.5K Average 0.69 0.73 / Class # of points (original) # of points (re-labeled) % of points (re-labeled) Unclassified 12,911,236 405,179 1.26 Bare earth 18,317,594 24,580,630 76.47 Vegetation 0 3,601,862 11.20 Building 0 3,210,429 9.98 Water 194,817 254,956 0.79 Bridge deck 12,831 88,502 0.27 Noise 32,487 0 0 Others 672,593 0 0 Statistics of annotated validation dataset. Quantitative evaluation


(1) Xi’An Jiaotong-Liverpool University ; (2) Leiden university; (3) Chinese Academy of Sciences; (4) Kavli Institute for Astronomy and Astrophysics at Peking University; (5) Zentrum für Astronomie der Universität Heidelberg Abstract Most stars in the Galaxy form in stellar groupings that either dissolve within several tens of millions of years, or evolve into open clusters. It is important to study the dynamics of planetary systems in such structures to explain the properties of observed exoplanets. Here, we numerically evolve open star clusters with planetary systems by combining the star cluster code NBODY6++GPU code with the planetary system code REBOUND. We use different sets of initial conditions, such as the virial ratio and stellar density, and evolve the system for 50 Myr. We find that stellar encounter properties, the star cluster density, and the planetary system architecture are the main factors that affect the evolution of the planetary systems. Although most planetary systems remain intact, others lose most of their outer planets, while some are stripped of all planets. Finally, we find that the presence of the planet Jupiter plays a prominent role in the survival chances of terrestrial planets. References [1] Lada 2003, A&A, 03011540 [2]Rein and Liu 2012, A&A 537, A128 [3] Wang, Spurzem et al,2015, MNRAS 450, 4070–4080 [4] Cai et al 2015, ApJ, 1506.0759 [5] hdfgroup.org [6] Cai et al 2017, 1706.03789 [7]Flammini Dotti et al 2020a [8]Flammini Dotti et al,2019 [9] Fujii et al 2019, A&A, 624, A110 [10]Flammini Dotti et al 2020b Initial Conditions Star Clusters: N= 500, 5000, 10000 stars; Q = 0.4,0.5,0.6; Rcluster = 1 pc; IMF Kroupa (2001), Plummer sphere, Standard Galactic Tidal Field Planetary Systems: ~1 MSun Host,: 6 planets (Earth, Mars and four gas giants), no protoplanetary disk, number of planetary system per star cluster, respectively, 25, 125, 200 Conclusions and Discussion We have carried out a set of N-body simulation in order to understand the influence of the star cluster on a Solar system-like planetary system. Most planetary system remain stable during the whole simulation, while most of the unstable one have ejected the outermost planets. The terrestrial planets are ejected mostly if Jupiter orbits gets perturbed, and it is completely destroyed with a impulsive encounter[8] . In the presence of a encounter, Jupiter acts both as dynamical barrier for weak encounters and as a perturber for the terrestrial planet for stronger encounters-. The final architecture of planets can be seen in the table, where the 1 and 2 planetary system are always, for all simulation and initial conditions, Jupiter for the 1 planet Solar system and Jupiter and Saturn for the 2 planet Solar system. The three planets scenario is unlikely for the empirical consequence of the Saturn presence in the 2-planets system. Black holes have also shown to impact the ejection of planets from their stellar systems[10]. Methodology We use REBOUND[2 ] as integrator for planetary systems and NBODY6++GPU [3] for the star cluster in order to model the gravitational dynamics, via N-Body codes, of both. The latter is using a C code environment, also working along with Python, optimised for few-body integrations, while the last is optimised for highly accurate and fast calculation and also supports MPI and GPU implementations, built for many-body integrations. We used 50 Myr as an evolutionary time, while 1000 years is the time-step for the star cluster output. The planetary output time-step is adaptive, and reproduces the previous 1000 years time-steps in the data storage, while it warns the user if the planets either escape or merge during this period (in short block time storage[4] ). We use the HDF5 format, which allows us to store data with high temporal resolution.[5] Finally the simulation can be resumed in 3 principal steps: star cluster and planetary system initial conditions, stars gravitational evolution with the nearby stars and plane


They have SCLC that has progressed or relapsed after at least two previous lines of therapy, including at least one platinum-based treatment They have a tumor tissue sample available They do not have unresolved side effects from previous treatments They have epNEC or LCNEC of the lung that progressed or relapsed, after at least one platinum-based treatment The cancer has spread to the brain and is growing uncontrollably They received a previous anti-cancer treatment that targets DLL3 They have interstitial lung disease, which refers to a group of diseases that causes inflammation and scarring of the lungs They have a weak immune system that struggles to fight off diseases. Also referred to as immunodeficient What is the percentage of patients whose tumors shrunk by at least 30% This is referred to as the ORR How long patients remain on treatment before the treatment stops working or the cancer progresses? This are referred to as DOR and PFS What are the side effects related to the treatment? These are referred to as TRAEs How long do patients live after the start of treatment? This is referred to as OS How often do the side effects occur? What are the effects of the treatment on the patients’ quality of life? Disclosures This study was funded by Boehringer Ingelheim. This summary is based on a poster presentation at AACR-NCI-EORTC 2023, Boston, MA, USA, October 11–15, 2023. The authors were fully responsible for all content and editorial decisions of the associated infographic Congress presentation authors Valentina Gambardella, Alastair Greystoke, Martin Reck, Meiruo Liu, Martha Mueller, Ulrich Duenzinger, Emily B Bergsland, Taofeek Owonikoko Disclaimer: BI 764532 is an investigational agent and has not been approved by any regulatory authority Reference 1. Wermke M, et al. J Clin Oncol 2023;41(suppl 16):abstr 8502 Acknowledgments The authors thank the patients and their caregivers for making the study possible. Medical writing support for the development of this infographic summary was provided by Frans Everson (PhD) of Ashfield MedComms, an Inizio Company, and funded by Boehringer Ingelheim Abbreviations CD3, cluster of differentiation 3; DLL3, delta-like 3; DOR, duration of objective response; IgG, immunoglobulin G; ORR, objective response rate; OS, overall survival; PFS, progression- free survival; TRAEs, treatment related adverse events What is the study design? Where is the trial taking place? What is BI 764532 and why is it important? How does BI 764532 work? The DareonTM-5 study will test how safe and effective BI 764532 is in patients with SCLC, epNEC, or LCNEC of the lung at two dose levels that were selected during Phase I CD3 is present on immune cells called T-cells that are key to initiating immune responses. DLL3 is present on the surface of many SCLC, epNEC, and LCNEC of the lung tumors BI 764532 acts as a bridge between tumor cells and T-cells. When it binds to T-cells they become activated. Activated T cells release toxins and cytokines - substances that attract other immune cells - leading to tumor cell death DareonTM-5: An open-label Phase II trial of BI 764532, a DLL3-targeting T-cell engager, in patients with relapsed/refractory small cell lung cancer or other neuroendocrine carcinomas Who can take part in the study? What are the key questions that the trial will answer? Inactive T-cell CD3 DLL3 Cancer cell Human IgG- like structure BI 764532 anti-DLL3 anti-CD3 Given the promising activity of BI 764532 in patients with DLL3-positive SCLC, epNEC, and LCNEC of the lung (NCT04429087), researchers have designed a Phase II clinical trial called DareonTM-5 (NCT05882058). BI 764532 is an IgG-like ‘T-cell engager’ investigational compound. It binds to immune system cells and to cancer cells that express DLL3 Many tumors can grow unchecked by evading the immune system DLL3 is a protein found on the surface of some cancer cells but not on healthy cells Cancers that express DLL3 and could be targeted with BI 764532


UNIVERSITY OF LEEDS Results III The basic Microscale Model has been extended to include moist dynamics - including the contribution from latent heat exchange Exploring a cut-cell representation of terrain in a microscale model Abstract Moves to increasingly high-resolution models give rise to more variations in the underlying orography being captured by the model grid. Consequently, high-resolution models must overcome instabilities associated with terrain-following (see Fig. 1a) approaches. This work further explores the capabilities of a cut-cell representation of orography for idealised orographically-forced and moist microscale flows. The model is based on terrain-intersecting coordinates (see Fig. 1b) and solves flow in the resulting cut-cells using a finite-volume approximation. The model has been designed for the purposes of very high-resolution simulations. Comparisons with benchmark orographic and moist test cases demonstrate very good results. Further tests show the potential for the cut-cell approach for stably resolving flows over very steep orography. Institute for Climate & Atmospheric Science SCHOOL OF EARTH AND ENVIRONMENT, UNIVERSITY OF LEEDS S. Lock, A. Coals, A. Gadian, S. Mobbs Microscale Model is a research model for very high-resolution studies - of grid-resolutions x ~ z < 100m. The project has focussed particularly on flows over steep and complex terrain. The model is 3- dimensional, nonhydrostatic and fully compressible, with prognostic variables u,v,w,’,’. Integration is computed with a time-splitting approach (based on Klemp & Wilhelmson, 1978) using 2nd-order leapfrog for the long timestep and a 1st-order forward-backward scheme for the short step. A 2nd-order centred scheme is used for spatial differencing on a staggered grid (Arakawa-C in the horizontal, Charney-Phillips in the vertical). The orographic surface is represented by a piecewise bilinear function, and a finite-volume approach is used to solve flows through the resulting cut-cells. Background Cut-cells were first explored in environmental modelling by Marshall et al. (1997) for representing ocean-bottom topography. Results showed a piecewise linear representation of the surface compared favourably with step representations, particularly for surfaces that show large variation at the grid-resolution. The cut-cell work was extended to a compressible atmospheric model by Bonaventura (2000) and results were seen to compare well with a traditional terrain-following model. Steppeler et al. (2002, 2006) further extended the work, implementing a 3D piecewise bilinear representation of orography in a full atmospheric model (COSMO-DE). Tests showed good comparison with the equivalent terrain-following model, and studies of real cases showed evidence of improved precipitation forecasts from the cut-cell approach. Model equations describe a 3D, nonhydrostatic, fully compressible system. Written in time-split form, with fast modes (acoustic and gravity) on the LHS and slow modes on the RHS, the equations take the form: Cut-cell approach is based on the method discussed in Steppeler et al. (2002, 2006). The orographic surface is represented by continuous piecewise bilinear surfaces, that intersect the model grid – Fig. 2. The orographic surface results in some cells that are cut – partially beneath the surface; partially above. Flow through the cut-cells is solved by Fig. 1: Vertical levels in (a) a terrain-following, and (b) a terrain-intersecting grid a) b) Fig. 2: Example orographic surface intersecting a grid- column, from Steppeler et al. (2006) using a finite-volume method to compute the divergence term in the pressure equation, using Gauss’s theorem over a volume V, bounded by surface area A: Applied to a cut grid-cell, the method requires the fluxes of the wind-field across the cell surfaces. For surfaces that coincide with the grid, the wind components are conveniently located (staggered grid), and there is zero flux across the orographic


The sage2 Project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 800999 Sage2 – Percipient StorAGe for Exascale Data Centric Computing Consortium: 9 partners led by Seagate (UK) Ltd. covering technology providers, computing centres and application providers and users Objective: Design an exascale ready architecture based on hierarchical storage layers and object storage (MERO), adaptable to novel storage media . Justification: I/O can represent a bottleneck in many applications . This bottleneck can be addressed by facilitating use of fast but expensive storage media in an hierarchical architecture. The use of storage-class memory additionally to spinning disks blurs the line between byte addressable and block data store Unique Features: • Containers for treating many objects the same • Distributed Transaction Management • Supports global memory abstraction • Integrated AI framework • Function shipping to move processing to the data • Data Aware Scheduling based on SLURM • QoS based Architecture Global Memory Abstraction: applications, tools and programming models access data on the SAGE system either directly by mapping objects to memory in the tier nodes, or, by accessing them as a storage system through the Clovis API. Three scenarios envisaged: • Make NVRAM-C (NVRAM within compute nodea) appear as a single addressable space • For larger objects, make use of external NVRAM (NVRAM-E), directly accessing using RDMA • Using standard MERO APIs to access data in any tier Under Development: • Differential checkpointing of objects to back them up to lower level storage • Data Reshaping to allow restart with different numbers of nodes Co-Design: SAGE architecture has been co-designed with application developers and users. Applications cover • Space weather (IPIC3D) • Satellite Data Processing (JÜRASSIC) • Finite Element Modelling (paraFEM) • CFD (BOUT++) • Neural Simulation Tool (NEST) • CAT (Savu) Application Support and Programming Models ARM have developed extensions to their Allinea Forge toolkit and are supporting integration of MERO onto ARM processors. Kitware are adapting their visualization tools to optimize user experience when analysing results on SAGE. Current and Future Status: • Prototype system installed and operational at Jülich Supercomputing Centre • MERO object store under consideration for Open Sourcing S. de Witt1, D. Samaddar1, A.Davis1, S. Narasimhamurthy2, G. Umanesan2, D. Pleiter3, M. Salem El Sayed3 1 United Kingdom Atomic Energy Authority 2 Seagate (UK) Ltd 3 Jülich Supercomputing Centre


Optofluidic Force Induction Scheme for the Characterization of Nanoparticle Ensembles Marko Šimić,a,b Gerhard Prossliner,b,c Ruth Prassl,c Christian Hill,b,c Ulrich Hohenestera a Institute of Physics, University of Graz, Austria; b Brave Analytics GmbH, Austria; c Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Austria Abstract Momentum transfer from light to matter provides the basic principle of optical tweezers. Most studies have hitherto employed this principle for trapping and manipulation of single nanoparticles. However, in a microfluidic channel one can also monitor the effect of optical forces exerted on ensembles of dielectric nanoparticles, to acquire knowledge about various nanoparticle parameters, such as size, shape or material distributions. Here, we present an optofluidic force induction scheme (OF2i) for real-time, on-line optical characterization of nanoparticles. Our experimental setup builds on precisely controlled fluidics as well as optical elements, in combination with a focused laser beam with orbital angular momentum. By monitoring the single particle light scattering and trajectories in presence of optical and fluidic forces, we obtain detailed information about the properties of the individually tracked particles. We analyse the trajectories using a detailed simulation approach based on Maxwell’s equations and Mie’s theory, in combination with laser fields and fluidic forces. We discuss the basic physical principles underlying the OF2i scheme and demonstrate its applicability. Our results prove that OF2i provides a flexible work bench for numerous applications. Introduction Here we employ optofluidic forces on ensembles of nanoparticles using a laser tuned at 532 nm with precise micro-fluidic pumps. Both, optical and fluidic components generate forces acting on dielectric nanoparticles as shown in figure and . Under certain conditions, particles are constrained to a 2D-optical trap and travel along characteristic trajectories, see figure . Single-particle trajectories shown in are processed in real-time by recording single particle light scattering via an ultramicroscope setup and a CMOS camera. References [1] Ashkin A. A., PNAS 1997 , 94 , 4853 - 4860 [2] C. Hill. (2020). EU Patent No. 3422364B1. European Patent Office. [3] A. D. Kiselev and D. O. Plutenko, Phys. Rev. A 2014, 89, 043803. Institute of Physics Brave Analytics GmbH Gottfried Schatz Research Center Author Mail: marko.simic@uni-graz.at Methods In order to simulate particle motion within our capillary, we perform a multipole expansion of the incoming fields and solve for the scattered fields employing Mie’s theory for Laguerre- Gaussian beams. The time-averaged optical forces are computed by where is Maxwell’s Stress Tensor. The integration is performed using the total fields and a Gauss- Legendre quadrature for spherical particles with and being material constants. Results The experimental data for 400 nm Standard-Latex particles is compared to simulated velocities and depicted in figure . The resulting size distributions are shown in figure for mono- and polydisperse samples. We compare our results to those of Nanoparticle Tracking Analysis (NTA). Discussion The OF2i scheme is presented with its underlying physical principles together with a theoretical description based on Mie’s theory and higher order Laguerre-Gaussian modes. Our results show very good agreement between experimental and theoretical data on the example of various standardized Latex particles. Furthermore, we prove the working principle of OF2i and demonstrate its applicability to various nanoparticles. Simulations We now combine Newton’s equation of motion with Stokes’ drag and obtain for the particle’s velocity at any position within the capillary. Integrating particle velocity, we obtain the corresponding trajectory using a Runge-Kutta scheme. Figure shows selected trajectories for 200 nm, 400 nm, 600 nm and 900 nm using above’s


Figure t Introduction Methods References Cortical synchronization controls EEG Trough-max vs EEG Peak-max Funding / Conflicts of Interest Disclosure A B C D E F 50 -100 50 -100 Single PYdr Voltage Zoomed TC→PY AMPA current Single TC Voltage Mean TC→PY AMPA current [μA/cm2] [μA/cm2] [mV] [mV] 1.5 0 1.6 1.40 1 2 Time [sec] 0 0 0 2 2 2 π -π 0 Alpha Amplitude (12 Hz) SWO Phase Bin (1.0 Hz) Un-normalized Modulation Index 30 5 G H I 50 -100 50 -100 Single PYdr Voltage Single PYso Voltage Mean PY→PY AMPA current [μA/cm2] [mV] [mV] 0.03 00 0 0 2 2 2 1 Time [sec] J π -π 0 Alpha Amplitude (12 Hz) SWO Phase Bin (1.0 Hz) PY→PY Comodulogram 0.1 0.03 0 15 Time [sec] 7.5 K Coupling Map 0.5 1 Alpha Frequency 8 10 12 SWO Frequency 1.5 2 2.5 Un-normalized Modulation Index 0.2 0.8 EEG Trough-max Spike Rastergrams EEG Peak-max Spike Rastergrams Austin E. Soplata1,2,3(austin.soplata@gmail.com), Michelle M. McCarthy2,3, Erik Roberts1,2, Emery N. Brown2,4,5,6,7, Patrick L. Purdon2,4,5, Nancy Kopell2,3 Cortical UP/DOWN state synchrony drives propofol phase-amplitude coupling in slow waves 289.18 / D4 1Graduate Program for Neuroscience, 2Cognitive Rhythms Collaborative, and 3Department of Mathematics & Statistics, Boston University, Boston, MA, 4Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, 5Department of Brain and Cognitive Sciences, 6Division of Health Sciences and Technology, and 7Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA Download this poster at: asoplata.com/asoplata-poster.pdf Or scan QR code here: • The anesthetic propofol induces beta (12-20 Hz), alpha (8-12 Hz), and Slow Wave Oscillations (SWO, 0.1-1.5 Hz) on the EEG of human patients [1] • At low propofol, near Loss of Consciousness, alpha amplitude is maximal during the trough of the SWO phase, called "Trough-max" phase- amplitude coupling (PAC) [1] • At high propofol, in deep anesthesia, alpha amplitude is maximal during the peak of the SWO phase, called "Peak-max" PAC [1] • SWOs in natural sleep often begin in the cortex [2], while simulations suggest propofol alpha is generated by the thalamus [3] • Propofol "directly" affects properties of thalamic and cortical cells and synapses such as GABA-A conductance, GABA-A decay time, and H-current conductance [1,3] • Propofol "indirectly" affects thalamic and cortical cells via decreasing cortical acetylcholine (ACh) [4], which affects K(Na)-current conductance, corticocortical, and thalamocortical synaptic strengths [5] • We hypothesized that the direct effects of propofol would produce and control both trough-max and peak-max PAC in a full, thalamocortical model, primarily by modulating thalamic behavior. However, we found that indirect effects from propofol on ACh and changes to the thalamocortical feedback loop could control trough-max vs peak-max changes. •Our simulations modeled 100 cortical dendrite compartments (PYdr), 100 cortical axo-somatic compartments (PYso), 20 cortical interneurons (IN), 20 thalamic reticular neurons (TRN), and 20 thalamocortical neurons (TC) using the biophysical Hodgkin-Huxley formalism [3,5]. Synapses are connected via a nearest-neighbor radius. •Our artificial EEG signal was modeled from the combination of AMPAergic corticocortical (PY→PY) and thalamocortical (TC→PY) synaptic currents onto cortical dendrites •Our PAC analysis was based on the standard Modulation Index coupling measure [6] EEG Trough-max Involves Synaptic Competition • We found that while direct effects were necessary for thalamic propofol alpha, indirect effects were also necessary for SWO expression • EEG signal had two components: thalamocortical and corticocortical synapses onto cortical dendrites • In the thalamocortical synapse case: • Thalamic cells exhibit a persistent alpha oscillation, while target cortical cells exhibit a SWO rhythm • The TC→PY synaptic current produces a Trough-max PAC signal since the alpha amplitude


Pragmatic service development and customisation with the CEDA OGC Web Services framework (COWS) Stephen Pascoe, Ag Stephens, and Dominic Lowe British Atmospheric Data Centre, Rutherford Appleton Laboratory, UK (Stephen.Pascoe@stfc.ac.uk) Application: UK Climate Projections '09 User Interface http://ukclimateprojections-ui.defra.gov.uk COWS Architecture The CEDA OGC Web Services framework (COWS) emphasises rapid service development by providing a lightweight layer of OGC web service logic on top of Pylons, a mature web application framework for the Python language. This approach gives developers a flexible web service development environment without compromising access to the full range of web application tools and patterns: Model-View-Controller paradigm, XML templating, Object-Relational-Mapper integration and authentication/authorization. Application: QESDI Data Portal http://phobos.badc.rl.ac.uk/qesdi/ The QUEST Earth System Data Initiative (QESDI) facilitates exchange of data within the QUEST thematic programme. It provides access to a diverse range of data products produced within QUEST and complementary external datasets. The QESDI portal's data visualisation interface is driven by COWS WMS server and client components. Data download is available through WCS. Features: ●Overlay WMS layers from internal and external WMS servers ●Browse arbitrary WMS dimensions ●Integrates with COWS Server to provide extended capabilities ●Styling Options: contouring, colourmap configuration ●Data download via WCS KVP request ●Publication quality plot generation ●Intuitive display of Climatology dimensions QESDI map view showing WMS layer selection and styling options discovered from WMS GetCapabilities extensions. The contour plotting feature of COWS server facilitates overlay of overlapping layers. Geoserver (Java) Geoserver (Java) UI (PHP) UI (PHP) COWS-WPS (Python) COWS-WPS (Python) COWS-WMS (Python) COWS-WMS (Python) UKCP Data lib UKCP Data lib geoplot geoplot chartplot chartplot Weather Generator Weather Generator Threshold Detector Threshold Detector Sun Grid Engine Sun Grid Engine cdat_lite, matplotlib, rpy, nappy cdat_lite, matplotlib, rpy, nappy User Interface Layer Application Layer Library Layer Processing Layer ddp-ps1 ddp-ps2 ddp-ps3 ddp-ui1 UI (php) geoserver (tomcat) spatialdb (postgres) ddp-app1 WMS (python) WPS (python) ddp-store1 SGE execd archive ddp-u2i UI (php) geoserver (tomcat) spatialdb (postgres) ddp-app2 WMS (python) WPS (python) ddp-store2 SGE execd archive ddp-ui3 UI (php) geoserver (tomcat) spatialdb (postgres) ddp-app3 WMS (python) WPS (python) ddp-store3 SGE execd archive acache 1 acache 2 acache 3 Master server and backup Physical servers Virtual Machines ddp-ps5 ddp-admin1 haproxy SGE master userdb (postgres) cache ddp-ps4 ddp-adminbak1 mirror state cachebak haproxy The UK Climate Projections 2009 are a set of probabilistic data products for the UK climate over the 21st century. The UKCP09 UI provides a rich, interactive user experience including map-based selections and outputs. The implementation integrates and extends Open Source GIS technologies including GeoServer, PostGIS, OpenLayers and Tilecache. Where necessary new components have been developed using the COWS framework. The UKCP-UI graphics page allows geospatial and probabilistic plots to be customised by the user. Requests for publication quality plots or the underlying data can then be sent to the WPS for processing before arriving in the user's download area. The UKCP09 UI was designed from the outset to be highly scalable and fault tolerant. The system is deployed as a cluster of XEN Virtual Machines, dividing the system into a set of functional VM types. These VMs are spread over multiple physical servers to provide both physical and virtual system failover. Load-balanced VM types are deployed in parallel allowing the system to scale up and down according to load, whereas the VMs managing state employ a separate hot-backup failover strategy


fBLS is a novel technique for detecting transiting planets, based fBLS is a novel technique for detecting transiting planets, based on the fast-folding algorithm [1], extensively used in pulsar on the fast-folding algorithm [1], extensively used in pulsar astronomy [2]. astronomy [2]. fBLS is efficient and scalable. It can substantially reduce the fBLS is efficient and scalable. It can substantially reduce the computation time required to search for transiting planets. For computation time required to search for transiting planets. For example, when applied example, when applied to the to the Kepler Kepler data, fBLS reduces the data, fBLS reduces the computation time by a factor of up to ~3000. computation time by a factor of up to ~3000. fBLS is well suited to the analysis of PLATO lightcurves. We fBLS is well suited to the analysis of PLATO lightcurves. We currently adapt fBLS and extend its operation to efficiently detect currently adapt fBLS and extend its operation to efficiently detect planets with varying transit duration and timing [4]. planets with varying transit duration and timing [4]. We used fBLS We used fBLS to detect small rocky transiting planets with to detect small rocky transiting planets with periods shorter than one day, a period range for which the periods shorter than one day, a period range for which the computation is extensive [3]. We have discovered five new planet computation is extensive [3]. We have discovered five new planet candidates (Shahaf et al., in prep.) candidates (Shahaf et al., in prep.) For a given lightcurve of measurements, fBLS simultaneously produces binned and phase-folded lightcurves for an array of ‘sufficiently different’ trial periods, with arithmetic operations. This procedure is conceptually similar to FFT. For comparison, a standard BLS implementation requires operations. For each folded lightcurve we compute the BLS statistic [5], producing a standard BLS periodogram. Demonstration: Kepler-78b Figure 1: fBLS periodogram of Kepler-78. The left panel is a grayscale diagram demonstrating the fast-folding algorithm output for a narrow frequency range centered around the periodogram peak. The fBLS periodogram appears on the Right panel, where the BLS scores were calculated for each of the fast-folded profiles. The central peak is highlighted in yellow. The required single-core computation time for the settings used in this example is ~10 sec. Ultra-short period (USP) planets are exoplanets with orbital periods shorter than one day, which tend to be terrestrial. Studies of these extreme systems can shed light on planet formation, star-planet and planet-planet interaction, and orbital evolution. A fast folding algorithm to produce BLS A fast folding algorithm to produce BLS periodograms in search for transiting planets periodograms in search for transiting planets R E L A T E D L I T E R A T U R E [1] Staelin D. H., 1969, Proceedings of the IEEE, 57, 724 [2] Morello V., et al., 2020,MNRAS, 497, 4654 [3] Winn J. N., Sanchis-Ojeda R., Rappaport S., 2018, New Astron. Rev., 83, 37 [4] Shahaf S., Mazeh T., Zucker S., Fabrycky D., 2021, MNRAS, 505, 1293 [5] Kovács G., Zucker S., Mazeh T., 2002, A&A, 391, 369 Sahar Shahaf (1, 11), Barak Zackay (1), Pascal Guterman (111), Tsevi Mazeh (11), Shay Zucker (1v) and Simchon Faigler (11). A F F I L I A T I O N S (1) Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot 7610001, Israel. (11) School of Physics and Astronomy, Tel Aviv University, Tel Aviv, 6997801, Israel. (111) Aix Marseille University, CNRS, CNES, LAM, Marseille, France. (1v) Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel. C O N T A C T : sahar.shahaf@weizmann.ac.il Figure 2: Two examples of phase folded USP planet lightcurves with the same number of bins. Top—a new USP candidate, detected by fBLS. Bottom— Kepler-78b, plotted for reference. Vertical axis range is identical. Despite the shall


Markers of Evolution in Rich Galaxy Clusters Elena Panko (panko.elena@gmail.com) and Sviatoslav Yemelianov, Anton Sirginava Department of Theoretical Physics and Astronomy, I. I. Mechnikov Odessa National University, Odessa, Ukraine Elena Panko The idea, formulation of the problem, input data, lgorithm, etc Sviatoslav Yemelianov soft, ~200 rich galaxy clusters data analysis, discussion Anton Sirginava Input data, ~200 rich galaxy clusters data analysis, discussion Abstract We detected statistically significant regular substructures in 2D distribution of galaxies in rich clusters with redshifts till to 0.15. Input data were extracted from “The Catalogue of Galaxy Clusters and Groups” (Panko & Flin, 2006). Main type of substructures is linear elongated overdence regions in the cluster field. The direction of the linear substructure points usually to the nearest neighbor. In additional we found X- or Y- type regular substructures as well as short and/or compact chains. Bright galaxies in clusters are located in the regular substructures mainly and galaxies in the chains also show significant alignment along the chain. Founded peculiarities can be considered as a result of the formation of galaxy cluster at the intersection of DM filaments or filament-wall interaction. Input data format: The real 4200"4200" field of PF 0381-1789 cluster from DSS-R (upper panel) and the CC map 4000"4000". The background stars are present on the real frame. Weak galaxies on the frame are not visible. The reasons for present study: According to CDM model computer simulations the distribution of galaxies in the clusters is the point index of the hot gas and DM distribution: Gas density tracing the cosmic web in a subvolume (12.5 Mpch-1 comoving horizontally, stacked over 25 Mpch-1 comoving along the line of sight) of the HORIZON- AGN simulation (Codis, Gavazzi, et al.,2015) Projected density map, nodes in filaments. GADGET-4 code (Wang et al., 2020) We supposed the ways of evolution in 2D distribution of galaxies are reflected as: O  I  C  cD (Struble & Rood,1982) or O  OL  IL  CL  C  cD (Panko, 2019) or O  O (X, Y, c)  I(X,Y, c)  IL  CL  C  cD (present study), were types of galaxy clusters: O  Open, I  Intermediate, C  Concentrated, cD  having cD galaxy, L  linear substructure, X,Y, c  other regular peculiarities (Panko, 2013, Panko et al., in prep.) Input Data: MRSS, the list of galaxies and PF Catalogue of galaxy clusters and groups The Muenster Red Sky Survey. It covers an area of about 5000 deg2 on the southern hemisphere. The catalogue includes 5.5 million galaxies and is complete till to rF=18m.3 (Ungruhe et al., 2003). It's a result of scanning of 217 plates of Southern Sky Atlas R (ESO) by PDS 2020GMplus and automated recognition of galaxies with careful control. White spot is the region around the SMC. PF Galaxy clusters (based in the MRSS (Panko & Flin, 2006). Our data set contains 460 rich galaxy clusters contain 100 and more members. MRSS Cluster data 0016-5711 0.1670739 -57.107367 940 2130600 88 31 48 907 617 0.32 117.4 13.02 13.72 15.05 97.4 0.41 22.8 Galaxies data 149-40043 59 2.75 5.30 3.85 0.31 24.0 16.56 16.53 0.136070 -57.054264 149-39501 21 2.15 4.26 2.51 0.49 178.0 17.98 17.95 0.139043 -57.081429 111-51504 394 6.14 12.73 7.33 0.51 16.5 14.81 14.77 0.140979 -57.240734 111-50854 111 4.09 7.88 4.37 0.54 19.1 16.35 16.32 0.143299 -57.250084 149-38602 36 3.27 7.23 2.99 0.72 34.9 17.53 17.50 0.143872 -56.993507 Cluster Cartography (CC) set allows to visualize the input data and to analyze the distribution of galaxies as well as to study the orientations of cluster members. CC allows automatically: to determine the level of the concentration to the cluster center; to detect linear substructures and other types of regular substructures, namely crosses, semicrosses. Curved chains and compact short dense stripes are need visual control. The shape of symbol corresponds to MRSS data, the size of symbols corresponds to