bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of a quantitative framework to improve the accuracy of a bacterial infection model
Authors: Lewin Gina R. , Kapur Ananya , Cornforth Daniel M. , Duncan Rebecca P. , Diggle Frances L. , Moustafa Dina A. , Harrison Simone A. , Skaar Eric P. , Chazin Walter J. , Goldberg Joanna B. , Bomberger Jennifer M. , Whiteley Marvin 
Published: May 2023

Laboratory models are critical to basic and translational microbiology research. Models serve multiple purposes, from providing tractable systems to study cell biology to allowing the investigation of inaccessible clinical and environmental ecosystems. Although there is a recognized need for improved model systems, there is a gap in rational approaches to accomplish this goal. We recently developed a framework for assessing the accuracy of microbial models by quantifying how closely each gene is expressed in the natural environment and in various models. The accuracy of the model is defined as the percentage of genes that are similarly expressed in the natural environment and the model. Here, we leverage this framework to develop and validate two generalizable approaches for improving model accuracy, and as proof of concept, we apply these approaches to improve models of
Pseudomonas aeruginosa
infecting the cystic fibrosis (CF) lung. First, we identify two models, an in vitro synthetic CF sputum medium model (SCFM2) and an epithelial cell model, that accurately recapitulate different gene sets. By combining these models, we developed the epithelial cell-SCFM2 model which improves the accuracy of over 500 genes. Second, to improve the accuracy of specific genes, we mined publicly available transcriptome data, which identified zinc limitation as a cue present in the CF lung and absent in SCFM2. Induction of zinc limitation in SCFM2 resulted in accurate expression of 90% of
P. aeruginosa
genes. These approaches provide generalizable, quantitative frameworks for microbiological model improvement that can be applied to any system of interest.
Gas-phase microactuation using kinetically controlled surface states of ultrathin catalytic sheets
Authors: Bao Nanqi , Liu Qingkun , Reynolds Michael F. , Figueras Marc , Smith Evangelos , Wang Wei , Cao Michael C. , Muller David A. , Mavrikakis Manos , Cohen Itai , McEuen Paul L. , Abbott Nicholas L. 
Published: May 2023

Biological systems convert chemical energy into mechanical work by using protein catalysts that assume kinetically controlled conformational states. Synthetic chemomechanical systems using chemical catalysis have been reported, but they are slow, require high temperatures to operate, or indirectly perform work by harnessing reaction products in liquids (e.g., heat or protons). Here, we introduce a bioinspired chemical strategy for gas-phase chemomechanical transduction that sequences the elementary steps of catalytic reactions on ultrathin (<10 nm) platinum sheets to generate surface stresses that directly drive microactuation (bending radii of 700 nm) at ambient conditions (T = 20 °C; P
total
= 1 atm). When fueled by hydrogen gas and either oxygen or ozone gas, we show how kinetically controlled surface states of the catalyst can be exploited to achieve fast actuation (600 ms/cycle) at 20 °C. We also show that the approach can integrate photochemically controlled reactions and can be used to drive the reconfiguration of microhinges and complex origami- and kirigami-based microstructures.
Referential signaling in a communally breeding bird
Authors: LaPergola Joshua B. , Savagian Amanda G. , Smith Maria G. , Bennett Breanna L. , Strong Meghan J. , Riehl Christina 
Published: May 2023

Referential signaling, a complex form of communication in which specific signals are associated with external referents, was once thought to be limited to primates. Recent research has documented referential signaling in several other cooperative taxa, predominantly in kin-based societies. Here, we show that greater anis, communally nesting birds that breed in nonkin groups, give one type of alarm call in response to aerial threats (flying raptors) and another to more general threats (nonaerial predators). Observational data show that anis give these calls in response to different classes of threats, and playback experiments in the field confirmed that the alarm calls alone are sufficient to elicit appropriate behavioral responses even in the absence of an actual threat. Genetic data on a subset of groups confirmed that breeding groups are composed of nonkin, suggesting that referential alarm calls are often given in situations when no genetic relatives are present. These results suggest that complex referential communication can occur in social groups composed of nonrelatives, despite the absence of kin-selected fitness benefits.
Electrochemically active porous carbon nanospheres prepared by inhibition of pyrolytic condensation of polymers
Authors: Kim Jaehyun , Lee Dayoung , Kim Cheolho , Lee Haeli , Baek Seungjun , Moon Jun Hyuk 
Published: May 2023

Porous carbon is a pivotal material for electrochemical applications. The manufacture of porous carbon has relied on chemical treatments (etching or template) that require processing in all areas of the carbon/carbon precursor. We present a unique approach to preparing porous carbon nanospheres by inhibiting the pyrolytic condensation of polymers. Specifically, the porous carbon nanospheres are obtained by coating a thin film of ZnO on polystyrene spheres. The porosity of the porous carbon nanospheres is controlled by the thickness of the ZnO shell, achieving a BET-specific area of 1,124 m
2
/g with a specific volume of 1.09 cm
3
/g. We confirm that under the support force by the ZnO shell, a hierarchical pore structure in which small mesopores are connected by large mesopores is formed and that the pore-associated sp
3
defects are enriched. These features allow full utilization of the surface area of the carbon pores. The electrochemical capacitive performance of porous carbon nanospheres was evaluated, achieving a high capacitance of 389 F/g at 1 A/g, capacitance retention of 71% at a 20-fold increase in current density, and stability up to 30,000 cycles. In particular, we achieve a specific area-normalized capacitance of 34.6 μF/cm
2
, which overcomes the limitations of conventional carbon materials.
Utility-value intervention promotes persistence and diversity in STEM
Authors: Asher Michael W. , Harackiewicz Judith M. , Beymer Patrick N. , Hecht Cameron A. , Lamont Liana B. , Else-Quest Nicole M. , Priniski Stacy J. , Thoman Dustin B. , Hyde Janet S. , Smith Jessi L. 
Published: May 2023

We tested the long-term effects of a utility-value intervention administered in a gateway chemistry course, with the goal of promoting persistence and diversity in STEM. In a randomized controlled trial (N = 2,505), students wrote three essays about course content and its personal relevance or three control essays. The intervention significantly improved STEM persistence overall (74% vs. 70% were STEM majors 2.5 y later). Effects were larger for students from marginalized and underrepresented racial/ethnic groups, who were 14 percentage points more likely to persist in STEM fields in the intervention condition (69% vs. 55%). Mediation analysis suggests that the intervention promoted persistence for these students by bolstering their motivation to attain a STEM degree and by promoting engagement with course assignments. This theory-informed curricular intervention is a promising tool for educators committed to retaining students in STEM.
Matching single cells across modalities with contrastive learning and optimal transport
Authors: Gossi Federico , Pati Pushpak , Chouvardas Panagiotis , Martinelli Adriano Luca , Kruithof-de Julio Marianna , Rapsomaniki Maria Anna 
Published: May 2023

Abstract
Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.
STMHCpan, an accurate Star-Transformer-based extensible framework for predicting MHC I allele binding peptides
Authors: Ye Zheng , Li Shaohao , Mi Xue , Shao Baoyi , Dai Zhu , Ding Bo , Feng Songwei , Sun Bo , Shen Yang , Xiao Zhongdang 
Published: May 2023

Abstract
Peptide-major histocompatibility complex I (MHC I) binding affinity prediction is crucial for vaccine development, but existing methods face limitations such as small datasets, model overfitting due to excessive parameters and suboptimal performance. Here, we present STMHCPan (STAR-MHCPan), an open-source package based on the Star-Transformer model, for MHC I binding peptide prediction. Our approach introduces an attention mechanism to improve the deep learning network architecture and performance in antigen prediction. Compared with classical deep learning algorithms, STMHCPan exhibits improved performance with fewer parameters in receptor affinity training. Furthermore, STMHCPan outperforms existing ligand benchmark datasets identified by mass spectrometry. It can also handle peptides of arbitrary length and is highly scalable for predicting T-cell responses. Our software is freely available for use, training and extension through Github (https://github.com/Luckysoutheast/STMHCPan.git).
Sulbactam-durlobactam: A Step Forward in Treating Carbapenem-Resistant Acinetobacter baumannii (CRAB) Infections
Authors: Watkins Richard R , Bonomo Robert A 
Published: May 2023

Abstract
Antimicrobial resistance in gram-negative pathogens, such as Acinetobacter baumannii, is a serious threat to human health. Sulbactam-durlobactam, a unique β-lactam and a β-lactamase inhibitor combination, is a novel agent targeted against carbapenem-resistant A. baumannii. This supplement provides a summary of the development of SUL-DUR, discussing its unique features and role in treating infections caused by CRAB pathogens.
The Pharmacokinetics/Pharmacodynamic Relationship of Durlobactam in Combination With Sulbactam in In Vitro and In Vivo Infection Model Systems Versus Acinetobacter baumannii-calcoaceticus Complex
Authors: O’Donnell John P , Bhavnani Sujata M 
Published: May 2023

Abstract
Sulbactam-durlobactam is a β-lactam/β-lactamase inhibitor combination currently in development for the treatment of infections caused by Acinetobacter, including multidrug-resistant (MDR) isolates. Although sulbactam is a β-lactamase inhibitor of a subset of Ambler class A enzymes, it also demonstrates intrinsic antibacterial activity against a limited number of bacterial species, including Acinetobacter, and has been used effectively in the treatment of susceptible Acinetobacter-associated infections. Increasing prevalence of β-lactamase–mediated resistance, however, has eroded the effectiveness of sulbactam in the treatment of this pathogen. Durlobactam is a rationally designed β-lactamase inhibitor within the diazabicyclooctane (DBO) class. The compound demonstrates a broad spectrum of inhibition of serine β-lactamase activity with particularly potent activity against class D enzymes, an attribute which differentiates it from other DBO inhibitors. When combined with sulbactam, durlobactam effectively restores the susceptibility of resistant isolates through β-lactamase inhibition. The present review describes the pharmacokinetic/pharmacodynamic (PK/PD) relationship associated with the activity of sulbactam and durlobactam established in nonclinical infection models with MDR Acinetobacter baumannii isolates. This information aids in the determination of PK/PD targets for efficacy, which can be used to forecast efficacious dose regimens of the combination in humans.
Pathogen-Targeted Clinical Development to Address Unmet Medical Need: Design, Safety, and Efficacy of the ATTACK Trial
Authors: Watkins Richard R , Du Bin , Isaacs Robin , Altarac David 
Published: May 2023

Abstract
There is a crucial need for novel antibiotics to stem the tide of antimicrobial resistance, particularly against difficult to treat gram-negative pathogens like Acinetobacter baumannii-calcoaceticus complex (ABC). An innovative approach to addressing antimicrobial resistance may be pathogen-targeted development programs. Sulbactam-durlobactam (SUL-DUR) is a β-lactam/β-lactamase inhibitor combination antibiotic that is being developed to specifically target drug-resistant ABC. The development of SUL-DUR culminated with the Acinetobacter Treatment Trial Against Colistin (ATTACK) trial, a global, randomized, active-controlled phase 3 clinical trial that compared SUL-DUR with colistin for treating serious infections due to carbapenem-resistant ABC. SUL-DUR met the primary noninferiority endpoint of 28-day all-cause mortality. Furthermore, SUL-DUR had a favorable safety profile with a statistically significant lower incidence of nephrotoxicity compared with colistin. If approved, SUL-DUR could be an important treatment option for infections caused by ABC, including carbapenem-resistant and multidrug-resistant strains. The development program and the ATTACK trial highlight the potential for pathogen-targeted development programs to address the challenge of antimicrobial resistance.
A Systematic Review and Meta-Analysis of Tuberculous Preventative Therapy Adverse Events
Authors: Melnychuk Luca , Perlman-Arrow Sara , Lisboa Bastos Mayara , Menzies Dick 
Published: May 2023

Abstract

Background
Tuberculosis preventative therapy (TPT) is a key part of the World Health Organization’s (WHO) end tuberculosis (TB) strategy. However, the occurrence of potentially serious adverse events (AE) is a limitation of TPT regimens. We conducted a systemic review and meta-analysis to estimate the incidence of AE and hepatotoxicity with various TPT regimens to help inform clinical decision making.

Methods
We searched MEDLINE, Cochrane, Health Star, and EMBASE from 1952 to April 2021 for studies reporting AE associated with TPT. Included studies reported AE stratified by regimen and provided the number of participants receiving each regimen. We used a random-effect model to meta-analyze the cumulative incidence of AE.

Results
We included 175 publications describing TPT-related AE in 277 cohorts. Among adults, the incidence of any AE, and hepatotoxicity leading to drug discontinuation was 3.7% and 1.1%, respectively, compared to 0.4% and 0.02%, respectively, in children. The highest incidence of any AE, and AE leading to drug discontinuation was with 3 months isoniazid and rifapentine (3HP), and the lowest was with 4 months rifampin (4R). 4R also had the lowest incidence of hepato-toxic AE and drug discontinuation due to hepato-toxic AE. 3HP also had a low incidence of hepato-toxic AE.

Conclusions
Although our study was limited by variability in methods and quality of AE reporting in the studies reviewed, pediatric populations had a very low incidence of AE with all TPT regimens reviewed. In adults, compared to mono-H regimens all rifamycin-based regimens were safer, although 4R had the lowest incidence of TPT-related AE of all types and of hepatotoxicity.
The Impact of COVID-19 on Insurgency and Rebel Governance: Lessons from India's Northeast
Authors: Newman Edward , Saikia Jaideep , Waterman Alex 
Published: May 2023

Abstract
Emerging research has suggested that the COVID-19 pandemic has generally favored rebel organizations—rather than states—in situations of intrastate conflict. This article challenges this perspective by analyzing the pandemic's impact on three dimensions of rebel activity—armed activity, popular support and recruitment, and rebel governance. It does so by using illustrative evidence from long-running insurgencies in Northeast India, characterized by long-term rebel weakness and minimal, if any, territorial control. The article finds that during the early, acute phase of the pandemic in 2020, state-imposed lockdowns, rebels’ own restrictions, and disruptions to supply chains constrained most dimensions of rebel activity. The easing of restrictions in 2021 revealed complex and multidimensional impacts on different armed groups, which often hinged on pre-existing positions such as armed group strength, strategy, relations with the state, and operational circumstances. These experiences of low-level insurgencies lacking territorial control add important qualifiers to the notion that rebels are inherently best placed to capitalize on stochastic shocks such as pandemics. Beyond the case of Northeast India, these findings make a number of contributions to the analysis of counterinsurgency and rebel governance.
Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity
Authors: Sun Yuliangzi , Shim Woo Jun , Shen Sophie , Sinniah Enakshi , Pham Duy , Su Zezhuo , Mizikovsky Dalia , White Melanie D , Ho Joshua W K , Nguyen Quan , Bodén Mikael , Palpant Nathan J 
Published: May 2023

Abstract
Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.
RNA binding protein PRRC2B mediates translation of specific mRNAs and regulates cell cycle progression
Authors: Jiang Feng , Hedaya Omar M , Khor EngSoon , Wu Jiangbin , Auguste Matthew , Yao Peng 
Published: May 2023

AbstractAccumulating evidence suggests that posttranscriptional control of gene expression, including RNA splicing, transport, modification, translation and degradation, primarily relies on RNA binding proteins (RBPs). However, the functions of many RBPs remain understudied. Here, we characterized the function of a novel RBP, Proline-Rich Coiled-coil 2B (PRRC2B). Through photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation and sequencing (PAR-CLIP-seq), we identified transcriptome-wide CU- or GA-rich PRRC2B binding sites near the translation initiation codon on a specific cohort of mRNAs in HEK293T cells. These mRNAs, including oncogenes and cell cycle regulators such as CCND2 (cyclin D2), exhibited decreased translation upon PRRC2B knockdown as revealed by polysome-associated RNA-seq, resulting in reduced G1/S phase transition and cell proliferation. Antisense oligonucleotides blocking PRRC2B interactions with CCND2 mRNA decreased its translation, thus inhibiting G1/S transition and cell proliferation. Mechanistically, PRRC2B interactome analysis revealed RNA-independent interactions with eukaryotic translation initiation factors 3 (eIF3) and 4G2 (eIF4G2). The interaction with translation initiation factors is essential for PRRC2B function since the eIF3/eIF4G2-interacting defective mutant, unlike wild-type PRRC2B, failed to rescue the translation deficiency or cell proliferation inhibition caused by PRRC2B knockdown. Altogether, our findings reveal that PRRC2B is essential for efficiently translating specific proteins required for cell cycle progression and cell proliferation.
dbCAN3: automated carbohydrate-active enzyme and substrate annotation
Authors: Zheng Jinfang , Ge Qiwei , Yan Yuchen , Zhang Xinpeng , Huang Le , Yin Yanbin 
Published: May 2023

Abstract
Carbohydrate active enzymes (CAZymes) are made by various organisms for complex carbohydrate metabolism. Genome mining of CAZymes has become a routine data analysis in (meta-)genome projects, owing to the importance of CAZymes in bioenergy, microbiome, nutrition, agriculture, and global carbon recycling. In 2012, dbCAN was provided as an online web server for automated CAZyme annotation. dbCAN2 (https://bcb.unl.edu/dbCAN2) was further developed in 2018 as a meta server to combine multiple tools for improved CAZyme annotation. dbCAN2 also included CGC-Finder, a tool for identifying CAZyme gene clusters (CGCs) in (meta-)genomes. We have updated the meta server to dbCAN3 with the following new functions and components: (i) dbCAN-sub as a profile Hidden Markov Model database (HMMdb) for substrate prediction at the CAZyme subfamily level; (ii) searching against experimentally characterized polysaccharide utilization loci (PULs) with known glycan substates of the dbCAN-PUL database for substrate prediction at the CGC level; (iii) a majority voting method to consider all CAZymes with substrate predicted from dbCAN-sub for substrate prediction at the CGC level; (iv) improved data browsing and visualization of substrate prediction results on the website. In summary, dbCAN3 not only inherits all the functions of dbCAN2, but also integrates three new methods for glycan substrate prediction.
Screening for functional regulatory variants in open chromatin using GenIE-ATAC
Authors: Cooper Sarah , Schwartzentruber Jeremy , Coomber Eve L , Wu Qianxin , Bassett Andrew 
Published: May 2023

Abstract
Understanding the effects of genetic variation in gene regulatory elements is crucial to interpreting genome function. This is particularly pertinent for the hundreds of thousands of disease-associated variants identified by GWAS, which frequently sit within gene regulatory elements but whose functional effects are often unknown. Current methods are limited in their scalability and ability to assay regulatory variants in their endogenous context, independently of other tightly linked variants. Here, we present a new medium-throughput screening system: genome engineering based interrogation of enhancers assay for transposase accessible chromatin (GenIE-ATAC), that measures the effect of individual variants on chromatin accessibility in their endogenous genomic and chromatin context. We employ this assay to screen for the effects of regulatory variants in human induced pluripotent stem cells, validating a subset of causal variants, and extend our software package (rgenie) to analyse these new data. We demonstrate that this methodology can be used to understand the impact of defined deletions and point mutations within transcription factor binding sites. We thus establish GenIE-ATAC as a method to screen for the effect of gene regulatory element variation, allowing identification and prioritisation of causal variants from GWAS for functional follow-up and understanding the mechanisms of regulatory element function.