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Data for Oral Presentation: Validating an automated docking pipeline for antibody cross-reactivity across viral families. Slides, figures, abstract, and references from the oral presentation given at …

Data for Oral Presentation: Validating an automated docking pipeline for antibody cross-reactivity across viral families. Slides, figures, abstract, and references from the oral presentation given at SNCURCS 2026, the State of North Carolina Undergraduate Research and Creativity Symposium, held on the campus of the University of North Carolina at Charlotte on 7 November 2026. Authors Srikritha Kosuri. Undergraduate researcher. Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA. ORCID: 0009-0007-4257-3797. Corresponding author, skosuri2@charlotte.edu. Anastasiia Duchenko. Mentor. Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA. ORCID: 0009-0000-4070-7666. Denis Jacob Machado. Advisor. Department of Bioinformatics and Genomics and CIPHER Center, University of North Carolina at Charlotte, Charlotte, North Carolina, USA. ORCID: 0000-0001-9858-4515. What this deposit contains Kosuri_SNCURCS2026_slides.pdf is the presentation as delivered, 13 slides plus 4 appendix slides. Kosuri_SNCURCS2026_abstract.pdf is the abstract as submitted, with the full conference details. references.bib lists every work cited in the talk, in BibTeX format. The five figures are provided as vector PDF files. avoidrune_workflow.pdf is the AVOIDRUNE pipeline, from input structures through docking to the summary TSV. research_approach.pdf is the study design, from the two input datasets through the cluster to the two readouts. zika-ab-complex.pdf is the docked Zika virus envelope glycoprotein and antibody Fab complex, PDB 5JHL, with the predicted binding site and a close view of one hydrogen bond. heatmap_subset.pdf is the predicted binding score for every antibody against every receptor, for six viral targets and the prion control. boxplot_by_receptor_bw_subset.pdf is the predicted binding affinity by receptor target, with pairwise significance tests. Summary of the work AVOIDRUNE is a containerized pipeline for protein-protein docking at scale. It cleans and renumbers input structures, predicts active sites on both partners, docks with HADDOCK3, and estimates binding affinity with PRODIGY. This project tested whether AVOIDRUNE holds up under load and whether its scores mean anything biologically. The benchmark dataset covered 12 viruses across Coronaviridae, Flaviviridae, and Orthomyxoviridae, four per family, with protozoan, bacterial, and prion structures as negative controls. A mock dataset repeated the run with missing files, malformed CSV, mismatched chain IDs, corrupt structures, case-mismatched filenames, empty files, and unusual character encodings. Eight stress tests ran on the University of North Carolina at Charlotte HPC cluster through SLURM, using the Nebula and Tucana partitions on 48 AMD processors, and finished in 21 hours and 43 minutes using 8.0 GB of memory in total. Seven of 12 viral targets bound their matched antibody as expected, three strongly and four moderately. Negative controls bound weakly, and the prion protein was the most consistently weak binder. The pipeline stopped on missing key files and named what was absent, and it skipped only the broken files when the input was partly malformed. A note on one figure. The color bar on heatmap_subset.pdf was relabelled to read the quantity alone. It originally read "PredGE (kcal/mol); <= -14 = strong", and that single cut point contradicts the three-tier scale used throughout the talk, where binding below -16 kcal/mol is strong, -16 to -14 is moderate, and -14 to -12 is weak. No data values were changed. A note on the abstract. It is reproduced as submitted, so it is written in the future tense. The slides report what the completed runs showed. Conference SNCURCS 2026, the State of North Carolina Undergraduate Research and Creativity Symposium, in its 22nd year. Hosted by the University of North Carolina at Charlotte and co-sponsored with Central Piedmont Community College. Saturday, 7 November 2026, Popp-Martin Student Union, Mebane Hall, and the CHHS Building, 8845 Craver Road, Charlotte, North Carolina 28223, USA. Oral presentation of 10 to 12 minutes. Program at our.charlotte.edu/sncurcs. How to cite Kosuri S, Duchenko A, Jacob Machado D. 2026. Which antibodies bind best to viral proteins? Validating an automated docking pipeline for antibody cross-reactivity across viral families. Presented at SNCURCS 2026, Charlotte, North Carolina, 7 November 2026. Zenodo. DOI: 10.5281/zenodo.24088220. License Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Share and adapt this material for non-commercial purposes, credit the authors, and release anything you build from it under the same terms. Contact Srikritha Kosuri, skosuri2@charlotte.edu Denis Jacob Machado, dmachado@charlotte.edu, https://phyloinformatics.com Updates are linked at https://phyloinformatics.com/outreach/

🧮 suanPan is a finite element method (FEM) simulation platform for applications in fields such as solid mechanics and civil/structural/seismic engineering. suanPan is written in modern high quality C+…

🧮 suanPan is a finite element method (FEM) simulation platform for applications in fields such as solid mechanics and civil/structural/seismic engineering. suanPan is written in modern high quality C++ code and is targeted to provide an efficient, concise, flexible and reliable FEM simulation platform. Please refer to the repository for more information. https://github.com/TLCFEM/suanPan

This article analyzes the theoretical foundations, mechanisms, opportunities, and risks of implementing artificial intelligence (AI)-based personalized learning in preschool education. It also discuss…

This article analyzes the theoretical foundations, mechanisms, opportunities, and risks of implementing artificial intelligence (AI)-based personalized learning in preschool education. It also discusses age-appropriate restrictions and offers practical recommendations for implementation in Uzbekistan.

Forecasting elementary-link success can inform quantum-network allocation, but telemetry collection consumes service capacity in the charged-probe setting studied here. We study temporal encoding and …

Forecasting elementary-link success can inform quantum-network allocation, but telemetry collection consumes service capacity in the charged-probe setting studied here. We study temporal encoding and optimizer choice in variational quantum circuits (VQCs), and evaluate their effects on multi-request service. A Brier-risk decomposition quantifies history-compression loss as the expected conditional variance of the full-history Bayes forecast; a hidden-Markov example illustrates why event order matters. Across 20 independent seeds, temporal re-uploading reduces Brier score relative to a parameter-matched repeated-PCA VQC by 0.0026, 0.0041, and 0.0066 on stable, bursty, and withheld-shift traces, respectively, while histogram gradient-boosted trees remain more accurate. A separate 16-seed study favors validation-selected Adam over the tested momentum-SGD grid under shift. We also derive finite-sample certification and a service-risk bound under explicit joint-law coverage and robust-risk assumptions. Separately, charged-probe scheduling on chain and diamond networks does not resolve an aggregate service improvement from noncommuting over same-axis encoding. The synthetic, exact-statevector experiments establish neither hardware feasibility nor quantum advantage.

This repository contains the dataset and R scripts supporting the study “Habitat loss and reproductive success of a Mediterranean annual herb: from pollination to seedling recruitment” (mi…

This repository contains the dataset and R scripts supporting the study “Habitat loss and reproductive success of a Mediterranean annual herb: from pollination to seedling recruitment” (minor revision at Plant Biology, 2026). The study investigates how variation in surrounding natural habitat influences floral communities, pollinator visitation, reproductive performance, seed germination, and seedling recruitment in Centaurium bianoris, an annual plant endemic to the Balearic Islands (western Mediterranean). The dataset (Data_Centaurium.xlsx) includes field observations and experimental measurements of co-flowering plant communities, floral visitors, plant biometrics, fruit set, seed production, and germination under controlled and field conditions. Analyses examine relationships with landscape characteristics, particularly the proportion of natural habitat within a 1-km radius, and assess the effects of experimental pollination treatments. The accompanying R scripts reproduce the statistical analyses and generate the associated graphical outputs. Separate README files provide descriptions of the dataset, variable definitions, and analytical scripts.

In this study, a new distribution was created, which is the Maxwell-Rayleigh distribution with two parameters (𝜃, 𝜆), using the (TX Family method), and some different characteristics of the Maxwell-Ra…

In this study, a new distribution was created, which is the Maxwell-Rayleigh distribution with two parameters (𝜃, 𝜆), using the (TX Family method), and some different characteristics of the Maxwell-Rayleigh distribution were studied, and the shape of the distribution was skewed towards the left, and its parameters were estimated using The weighted least squares method, and for the purpose of comparing the estimation methods for parameters, the Monte-Carlo simulation method was employed using the Wolfram Mathematica 12.2 program to conduct several experiments with different sample sizes (small "25,50", medium "100"). and large "150" and through the use of the statistical scale Mean Squares Error (MSE), and the results showed the preference of the weighted least squares method in estimating parameters, at medium and small sample sizes, and the preference of the method of greatest possibility at large sample sizes.

Presentation on adaptive soft-sensing for the quality indices of clinker production. Development of an adaptive soft-sensing framwork based on PLS which is integrated into two different hyperparameter…

Presentation on adaptive soft-sensing for the quality indices of clinker production. Development of an adaptive soft-sensing framwork based on PLS which is integrated into two different hyperparameter selection paradigms: ensemble learning and optimisation, namely Bayesian Optimisation.

Annotatsiya. Ushbu maqolada Hisorak suv ombori baland tosh-tuproqli to'g'onining ekspluatatsiya davridagi filtratsion xavfsizligi masalalari chuqur tahlil qilingan. Inshoot tanasi va poydevor gruntlar…

Annotatsiya. Ushbu maqolada Hisorak suv ombori baland tosh-tuproqli to'g'onining ekspluatatsiya davridagi filtratsion xavfsizligi masalalari chuqur tahlil qilingan. Inshoot tanasi va poydevor gruntlarida yuzaga keladigan filtratsiya jarayonlari nazorat-o'lchov asboblari (NOA), xususan, pyezometrlar tizimi ma'lumotlari asosida o'rganilgan. Filtratsion oqimning diagnostik ko'rsatkichlarini baholash uchun K1 (ogohlantiruvchi) va K2 (kritik) xavfsizlik mezonlari matematik-statistik usullar yordamida aniqlashtirilgan. To'g'on yadro qismidagi filtratsiya oqimining sarfi va pyezometrik bosimlar dinamikasi o'rtasidagi bog'liqliklar hisoblanib, inshoot ishonchliligini oshirish bo'yicha amaliy tavsiyalar ilgari surilgan.

This repository contains the processed dataset and analysis code associated with the study on smartphone accelerometry of post-exercise enhanced physiological tremor. The dataset includes participant …

This repository contains the processed dataset and analysis code associated with the study on smartphone accelerometry of post-exercise enhanced physiological tremor. The dataset includes participant characteristics and extracted tremor features across the study phases, including peak frequency, tremor spectral power, and tremor index. Participants are identified by coded IDs. Supplementary tables and a methods document are also provided. Variable definitions and dataset documentation are available in the workbook’s README and Dictionary sheets.

SO2+ : Sulfur dioxide cation