Biology subjects

Steinberg, D.

Publications and source records attributed to Steinberg, D..

5 recordsLinked to original sources

bioRxiv2026

AI in Practice: A Multilingual Survey of 2025 BioHackathon Participants

This dataset arises from a multilingual survey of AI use among participants and community members in the DBCLS BioHackathon 2025 in Japan. The questionnaire, offered in English, Japanese, and Thai, asked about how often respondents use AI tools, what they use them for, obstacles they encounter, institutional support, satisfaction, and concerns. Additional items captured role, institution type, work country, and other demographics, totaling 105 responses. The dataset includes both raw anonymized responses and a cleaned, standardized English-only version suitable for quantitative analysis, along with the full questionnaire, a data dictionary for cleaned dataset, and a translation lookup table. Free-text answers were screened and redacted to remove URLs, names, and other potentially identifiable information. Together, these materials provide a community-level view of AI practice in genomics, bioinformatics, software development, and related areas, and can support work on AI adoption, policy, and methods for analyzing survey data on AI use in science.

scientific communication and education↗

bioRxiv2025

HSP70 is a chaperone for IL-33 secretion and function in chronic airway disease

IL-33 is a key driver of type 2 inflammation relevant to airway epithelial biology. However, the mechanisms for IL-33 secretion and regulation in the context of chronic airway disease is poorly understood. Here we report a disease-associated isoform IL-33{Delta}34 that escapes nuclear sequestration to be tonically secreted by recruitment to unconventional protein secretory pathways by HSP70. IL-33{Delta}34 interacts with HSP70 within cells to be targeted to secretory organelles through coordinated binding to phosphatidylserine (PS). Once secreted, this HSP70/cytokine complex stabilizes IL-33{Delta}34 by inhibiting oxidation/degradation thereby enhancing IL-33{Delta}34-receptor binding and activity. We find evidence for IL-33, HSP70 and chaperonin TCP-1 are dysregulated in human chronic airway disease. This phenomenon is reflected in differential proteomics of diseased bronchial wash extracellular vesicles. This study confirms proteostasis intermediates, chiefly HSP70, as a chaperone for non-canonical IL-33{Delta}34 secretion and activity that may be amenable for therapeutic targeting in airway diseases such as asthma and COPD. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/635799v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@b01ea9org.highwire.dtl.DTLVardef@ad18c9org.highwire.dtl.DTLVardef@364c0forg.highwire.dtl.DTLVardef@bba135_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Computational analysis of morphological changes in Lactiplantibacillus plantarum under acidic stress

Cell shape and size often define characteristics of individual or communities of microorganisms in changing environments. Hence, characterizing cell morphology using computational image analysis can aid in the accurate identification of bacterial responses to these changes. Modifications in cell morphology of Lactiplantibacillus plantarum were determined in response to acidic stress, specifically during growth stage of the cells at pH 3.5 compared to pH 6.5. Consequently, we developed a computational method to sort, detect, analyze, and measure bacterial size in a single-species culture. We applied a deep learning methodology composed of object detection followed by image classification to measure the bacterial cell dimensions of the pre-identified cells. The results of our computational analysis show a significant change in cell morphology in response to alteration of environmental pH. Specifically, we found that the cell was dramatically elongated at low pH, while the width was not altered. Those changes could be attributed to modifications in membrane properties, for instance increased cell membrane fluidity in acidic pH. Integration of deep learning with microbial microscopic imaging is an advanced methodology for studying cellular structures. These trained models and scripts can be applied to other microbes and cells and are publicly available at: https://github.com/OraMoyal26/bacteria_dimensions/tree/main

systems biology↗

bioRxiv2022

Bacillus subtilis Biofilms in the Presence of Chlorhexidine - a Computational Approach to Morphology and Growth Kinetics Analysis

Bacillus subtilis biofilms are well known for their complex and highly adaptive morphology. Indeed, their phenotypical diversity and intra-biofilm heterogeneity make this gram-positive bacterium the subject of many scientific papers on the structure of biofilms. The robustness of biofilms is a term often used to describe their level of susceptibility to anti-microbial agents and various mechanical and molecular inhibition/eradication methods. In this paper, we use computational analytics to quantify Bacillus subtilis morphological response to proximity to an anti-microbial source, in the form of the antiseptic chlorhexidine. Chlorhexidine droplets, placed in proximity to Bacillus subtilis macrocolonies at different distances result in morphological changes, quantified using Python-based code, which we have made publicly available. Our results quantify peripheral and inner core deformation as well as differences in cellular viability of the two regions. The results reveal that the inner core, which is often characterized by the presence of wrinkled formations in the macrocolony, is more preserved than the periphery. Furthermore, the paper describes a crescent shaped colony morphology which occurs when the distance from the chlorhexidine source is 0.5 cm, as well as changes observed in the growth substrate of macrocolonies exposed to chlorhexidine.

bioinformatics↗

bioRxiv2017

Unifying intra- and inter-specific variation in tropical tree mortality

Tree death is a fundamental process driving population dynamics, nutrient cycling, and evolution within plant communities. While past research has identified factors influencing tree mortality across a variety of scales, these distinct drivers are yet to be integrated within a unified predictive framework. In this study, we use a cross-validated Bayesian framework coupled with classic survival analysis techniques to derive instantaneous mortality functions for 203 tropical rainforest tree species at Barro Colorado Island (BCI) Panama. Specifically, we develop mortality functions that not only integrate individual, species, and temporal effects, but also partition the contributions of growth-dependent and growth-independent effects on the overall instantaneous mortality rate. We show that functions that separate mortality rates into growth-dependent and growth-independent hazards, use stem diameter growth rather than basal-area growth, and attribute the effect of wood density to growth-independent mortality outperform alternative formulations. Moreover, we show that the effect of wood density - a prominent trait known to influence tree mortality - explains only 22% of the total variability observed among species. Lastly, our analysis show that growth-dependent processes are the predominant contributor to rates of tree mortality at BCI. Combined, this study provides a framework for predicting individual-level mortality in highly diverse tropical forests. It also highlights how little we know about the causes of species-level and temporal plot-scale effects needed to effectively predict tree mortality.

ecology↗