bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrative analysis of CAM photosynthesis reveals its impact on primary metabolism inYucca
Authors: Wickell David , Field Richard , Weitz Karl , Chu Rosalie , Trejo Jesse , Tolic Nikola , Munoz Nathalie Munoz , Hundley Hope , Savage Emily , Lipzen Anna , Leebens-Mack Jim , Heyduk Karolina
Published: November 2024

Crassulacean Acid Metabolism (CAM) is an adaptation that temporally separates carbon uptake at night from photosynthesis during the day. CAM has evolved repeatedly across vascular plants, perhaps, in part, because it does not involve any novel gene functions and instead relies on regulatory modification of existing C3pathways. The co-option of established processes and corresponding alteration of metabolic flux is likely to impact other primary metabolic pathways such as nitrogen metabolism and photorespiration. However, the majority of CAM research to date has focused on a handful of genes in the core CAM pathway. Understanding the complex downstream effects associated with the emergence of CAM is essential to explaining its convergent evolution as well as efforts to engineer carbon concentrating mechanisms in crops. In this study, we integrate genomic, transcriptomic, proteomic, and metabolomic data to compare primary metabolism between the CAM speciesYucca aloifoliaand closely related C3species,Y. filamentosa. Despite substantial differences in metabolite abundance across multiple pathways, including citrate and nitrogen metabolism, we observe relatively few gene expression differences outside of the core CAM pathway. Additionally, we observe minimal correlation between protein abundance and mRNA expression, suggesting significant post-transcriptional regulation in CAM species. Our findings provide insights into the metabolic shifts associated with CAM evolution and highlight the complexity of its regulation at multiple biological levels.
Characterizing a Low-Density Neutrophil gene signature in acute and chronic infections and its impact on disease severity
Authors: de Toledo Matheus Aparecido , de Lima João Victor Souza , Salomão Reinaldo , Leite Giuseppe G. F.
Published: November 2024

Low-density neutrophils (LDNs) or polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC) are involved in the pathogenesis of cancer, autoimmune and infectious diseases. They are crucial in the host-response to invading pathogens, especially during acute illness, and are associated with poor prognosis in many infectious diseases. However, their gene expression profile and contribution to disease outcomes are not well described. We conducted a meta-analysis of gene expression datasets from peripheral blood mononuclear cells (PBMCs), focusing on patients with viral and bacterial infections. We identified a consensus set of 2,798 differentially expressed genes. Among these, 49 genes were commonly found in both the neutrophil degranulation pathway and the granule lumen-specific community. To validate this signature, we evaluated its expression in RNA-seq datasets, finding consistent upregulation of 24 genes in severe infections, 17 of them overlapped with genes overexpressed in CD16intcells. We also investigated the abundance of LDN-related proteins in a PBMC proteomics dataset from a cohort of sepsis and septic shock patients, identifying 13 proteins with significantly higher levels in sepsis and septic shock patients compared to healthy controls. In conclusion, our study identified a pattern of 17 upregulated LDN genes, common to PBMC-transcriptome and RNA-seq, and up-regulated in CD16int, associated with acute infections and severe clinical outcomes, marking the first time these genes have been collectively presented as a potential signature of LDNs in relation to disease severity. Further research with prospective cohorts is needed to validate this LDN signature and explore its clinical implications.

Summary Sentence

Meta-analysis revealed a 17-gene LDN signature associated with severity in acute infections, providing potential biomarkers for clinical outcomes in infectious diseases.
Structural Basis of GPCR-G Protein Pre-coupling and Activation: Insights from CCR1-Gi Complex
Authors: Shao Zhehua , Dong Yingjun , Jia Ruixin , Shen Qingya , Yao Bingpeng , He Xinheng , Yuan Qingning , Shen Dandan , Mao Chunyou , Zhang Chao , Chen Zhihua , Eric Xu H. , Ying Songmin , Zhang Yan , Li Wen
Published: November 2024

GPCR-mediated G protein activation cycle contains obligated step of the receptor-G protein assembly before G protein activation (pre-coupling state). However, transient nature of this pre-coupling state has prevented structural and mechanistic understanding of this essential step of G protein activation cycle. In this study, we discover that CCR1 has high level of pre-coupling state, which allows its rapid response to chemokines. Taking advantage of this observation, we uncover molecular mechanism of the pre-coupling state by solving the cryo-electron microscopy structure of the chemokine receptor CCR1 in complex with its cognate G protein (Gi) in a pre-coupled, inactivated state. This structure reveals that CCR1 adopts a conformation distinct from both its fully active and inactive states, with the G protein’s α5 helix partially inserted into the receptor’s intracellular cavity. Notably, the C-terminal four residues of the Gα subunit are disordered in this pre-coupled state, contrasting with their well-defined α-helical structure in the fully active complex. Functional assays demonstrate that while deletion of these four C-terminal Gα residues does not affect pre-coupling, it abolishes G protein activation upon agonist binding. This finding highlights the critical role of these residues in GPCR-mediated G protein activation, but not in initial recruitment. Furthermore, our studies indicate that the ability to form pre-coupled complexes is conserved among chemokine receptors, suggesting a common mechanism for rapid signal transduction in this GPCR subfamily. These results provide the first structural evidence for GPCR-G protein pre-coupling and offer molecular insights into the transition from inactive to active states. Our findings fill the long-missing gap in understanding GPCR-mediated G protein activated cycle.
Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology
Authors: Park Sunho , Pettigrew Morgan F. , Cha Yoon Jin , Kim In-Ho , Kim Minji , Banerjee Imon , Barnfather Isabel , Clemenceau Jean R. , Jang Inyeop , Kim Hyunki , Kim Younghoon , Pai Rish K , Park Jeong Hwan , Samadder Jewel J. , Song Kyo Young , Sung Ji-Youn , Cheong Jae-Ho , Kang Jeonghyun , Lee Sung Hak , Wang Sam C. , Hwang Tae Hyun
Published: November 2024

Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune check-point inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction, which was by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEER’s tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.
A generic pipeline for CADD Score generation: chickenCADD and turkeyCADD
Authors: Lensing K. , van Schipstal JGC. , de Ridder D. , Groenen MAM. , Derks MFL.
Published: November 2024

Combined Annotation Dependent Depletion (CADD) is a machine learning approach used to predict the deleteriousness of genetic variants across a genome. By integrating diverse genomic features, CADD assigns a PHRED-like rank score to each potential variant. Unlike other methods, CADD does not rely on limited datasets of known pathogenic or benign variants but uses larger and less biased training sets. The rapid increase in high-quality genomes and functional annotations across species highlights the need for an automated, non-species-specific pipeline to generate CADD scores. Here, we introduce such a pipeline, facilitating the generation of CADD scores for various species using only a high-quality genome with gene annotation and a multi-species alignment. Additionally, we present updated chickenCADD scores and newly generated turkeyCADD scores, both generated with the pipeline.
Using a citizen science approach for research-informed teaching of evolution and palaeontology
Authors: Lautenschlager Stephan
Published: November 2024

Over the past few decades, citizen science has proven to be a valuable tool for research projects by involving the public in large-scale data collection. This collaboration between researchers and volunteers has been shown to be highly beneficial, allowing for efficient data collection in shorter time frames than possible for individual researchers. This study introduces a citizen science-inspired approach to teaching and curriculum design, involving undergraduate students directly in active research. Using a case study on dinosaur eye size, integrated into a first-year undergraduate course in Geology and Palaeontology at a UK university, the study presents the advantages of this approach for both teachers and learners. As part of the study, 22 undergraduate students were involved in data collection, analysis, and the subsequent publication processes, emphasizing active student participation in research. A subsequent survey demonstrated high student engagement and perceived relevance of the citizen science-style teaching approach. Results indicate that students found the approach appealing, engaging, and beneficial for understanding scientific concepts and methods. The case study shows that a citizen science approach to research-informed teaching can enhance student engagement and learning by providing meaningful, hands-on research experiences. This approach allows students to apply theoretical knowledge in a realistic context, fostering independence in quantitative research skills and collaborative learning. Despite potential challenges related to data accuracy and student motivation, the benefits of integrating such approaches into higher education curricula are substantial, offering a valuable model for teaching in Earth Sciences and beyond.
Limelight - An open, web-based tool for visualizing, sharing, and analyzing mass spectrometry data from DDA pipelines
Authors: Riffle Michael , Zelter Alex , Jaschob Daniel , Hoopmann Michael R. , Faivre Danielle A. , Moritz Robert L. , Davis Trisha N. , MacCoss Michael J. , Isoherranen Nina
Published: November 2024

Liquid chromatography-tandem mass spectrometry employing data-dependent acquisition (DDA) is a mature, widely used proteomics technique routinely applied to proteome profiling, protein-protein interaction studies, biomarker discovery, and protein modification analysis. Numerous tools exist for searching DDA data and myriad file formats are output as results. While some search and post processing tools include data visualization features to aid biological interpretation, they are often limited or tied to specific software pipelines. This restricts the accessibility, sharing and interpretation of data, and hinders comparison of results between different software pipelines. We developed Limelight, an easy-to-use, open-source, freely available tool that provides data sharing, analysis and visualization and is not tied to any specific software pipeline. Limelight is a data visualization tool specifically designed to provide access to the whole “data stack”, from raw and annotated scan data to peptide-spectrum matches, quality control, peptides, proteins, and modifications. Limelight is designed from the ground up for sharing and collaboration and to support data from any DDA workflow. We provide tools to import data from many widely used open-mass and closed-mass search software workflows. Limelight helps maximize the utility of data by providing an easy-to-use interface for finding and interpreting data, all using the native scores from respective workflows.
Multimodal LLMs for Retinal Disease Diagnosis via OCT: Few-Shot vs Single-Shot Learning
Authors: Agbareia Reem , Omar Mahmud , Zloto Ofira , Glicksberg Benjamin S , Nadkarni Girish N , Klang Eyal
Published: November 2024

Background and Aim

Multimodal large language models (LLMs) have shown potential in processing both text and image data for clinical applications. This study evaluated their diagnostic performance in identifying retinal diseases from optical coherence tomography (OCT) images.

Methods

We assessed the diagnostic accuracy of GPT-4o and Claude Sonnet 3.5 using two public OCT datasets (OCTID, OCTDL) containing expert-labeled images of four pathological conditions and normal retinas. Both models were tested using single-shot and few-shot prompts, with an overall of 3088 models’ API calls. Statistical analyses were performed to evaluate differences in overall and condition-specific performance.

Results

GPT-4o’s accuracy improved from 56.29% with single-shot prompts to 73.08% with few-shot prompts (p < 0.001). Similarly, Claude Sonnet 3.5 increased from 40.03% to 70.98% using the same approach (p < 0.001). Condition-specific analyses revealed similar trends, with absolute improvements ranging from 2% to 64%. These findings were consistent across the validation dataset.

Conclusion

Few-shot prompted multimodal LLMs show promise for clinical integration, particularly in identifying normal retinas, which could help streamline referral processes in primary care. While these models fall short of the diagnostic accuracy reported in established deep learning literature, they offer simple, effective tools for assisting in routine retinal disease diagnosis. Future research should focus on further validation and integrating clinical text data with imaging.
Effect of Lymphocyte miRNA Expression on Influenza Vaccine-Induced Immunity
Authors: Haralambieva Iana H. , Ratishvili Tamar , Goergen Krista M. , Grill Diane E. , Simon Whitney L. , Chen Jun , Ovsyannikova Inna G. , Poland Gregory A. , Kennedy Richard B.
Published: November 2024

Alterations of gene expression by miRNAs contribute substantially to genetic regulation and cellular functions.

We conducted a comprehensive study in 53 individuals before and after the administration of the 2010-2011 seasonal inactivated influenza vaccine to characterize lymphocyte-specific miRNA expression (in purified B cells, CD4+ T cells, CD8+ T cells and NK cells) and its effect on influenza vaccine-induced immune outcomes (hemagglutination inhibition antibody titers/HAI, viral neutralizing antibody titers /VNA and memory B cell ELISPOT).

Overall, we observed relatively stable miRNA expression before/after influenza vaccination. Our statistical analysis uncovered three baseline miRNAs (miR-150-3p, miR-629-5p and miR-4443) that were significantly correlated with influenza vaccine-induced immune outcomes in different cell types. Predictive modeling of influenza vaccine-induced HAI/VNA titers identified a set of specific baseline miRNAs in CD4+T cells as factors predictive of antibody responses. A pathway enrichment analysis on the putative target genes revealed several regulated signaling pathways and functions: TGF-β signaling, PI3K-Akt signaling, p53 signaling, MAPK signaling, TNF signaling and C-type lectin receptor signaling, as well as cell adhesion and adherens junctions, and antiviral host response.

In conclusion, our study offers evidence for the role of epigenetic modification (miRNAs) on influenza vaccine-induced immunity. After validation, identified miRNAs may serve as potential biomarkers of immune response after influenza vaccination.

Highlights

Host miRNA expression is relatively stable before and after influenza vaccination

miR-150-3p, miR-629-5p and miR-4443 were correlated with immunity across cell types

Specific CD4+T cell miRNAs are predictive of antibody responses

Identified miRNAs may serve as biomarkers of immune response after influenza vaccine
Sample-specific network analysis identifies gene coexpression patterns of immunotherapy response in advanced kidney cancer
Authors: Yin Liangwei , Traversa Pietro , Elati Mohamed , Moreno Yamir , Marek-Trzonkowska Natalia , Battail Christophe
Published: November 2024

Immunotherapies have recently emerged as a standard of care for advanced cancers, offering remarkable improvements in patient prognosis. However, only a small proportion of patients respond to the treatment, and no definitive molecular hallmark has been identified for clinical use in predicting treatment outcomes. Here, we propose a sample-specific weighted gene network approach to investigate the heterogeneity of patient clinical outcomes by leveraging multiscale network features derived from gene expression data. Our results show that patients exhibiting similar clinical benefits share comparable gene coexpression patterns. Increased gene connectivity and stronger negative gene-gene associations are also pivotal factors in patients with poor prognoses. Moreover, integrating pathway genes with topological measures enables the identification of the perturbed regulation of biological pathways associated with treatment responses. Additionally, sample-level network features enhance the prediction performance of gene expression values-based machine learning models. Collectively, our approach provides valuable guidance on the use of gene network information to stratify cancer patients and to optimize treatment strategies.
Dynamic Differences in Clinically relevant Pen β-Lactamases from Burkholderia spp.
Authors: Gu Jing , Agarwal Pratul K , Bonomo Robert A , Haider Shozeb
Published: November 2024

Antimicrobial resistance (AMR) is a global threat, with Burkholderia species contributing significantly to difficult-to-treat infections. Ambler Class A Pen β-lactamases are produced by all Burkholderia spp., and their mutation or overproduction leads to the resistance of β-lactam antibiotics. In this study we investigate the dynamic differences between four clinically relevant Pen β-lactamases (PenA, PenI, PenL and PenP) using machine learning driven enhanced sampling molecular dynamics simulations and the BindSiteS-CNN deep learning model. Although sharing the same structural fold, these enzymes exhibit distinct dynamic features due low sequence identity, resulting in different substrate profiles and catalytic turnover. The BindSiteS-CNN deep learning model further reveals local active site dynamics, offering insights into the Pen β-lactamase evolutionary adaptation. The findings reported here identify critical mutations and proposes new hotspots affecting Pen β-lactamase flexibility and function, which can be used to fight emerging resistance in these enzymes.
Requirement for Fc effector function is overcome by binding potency for broadly reactive anti-alphavirus antibodies
Authors: Callahan Victoria , Sutton Matthew S. , Gardner Christina L. , Kenchegowda Doreswamy , Dunagan Megan M. , Gosavi Mrunal , Green Courtney , Chen Tammy Y. , Prado-Smith Jessica , Long Daniel , Vogel Jodi L. , Kristie Thomas M. , Clancy Chad S. , Burke Crystal W. , Roederer Mario , Fox Julie M.
Published: November 2024

Alphaviruses are emerging public health threats. Broadly reactive anti-alphavirus monoclonal antibodies (mAbs) have been shown to be protective in mouse models of infection. However, the mechanism of Fc-dependent or Fc-independent heterologous protection remains ill-definedin vivo. Here, we used two vaccine-elicited, broadly reactive, anti-alphavirus mAbs, SKT05 and SKT20, to establish correlates of mAb-mediated protection during Venezuelan equine encephalitis virus (VEEV) challenge. SKT20 required Fc effector functions to prevent lethality. In contrast, SKT05-mediated survival was independent of Fc effector functions, which is likely linked to early viral control through potent egress inhibition. However, control of virus replication and spread with SKT05 was Fc-dependent; these findings extended to additionalin vivomodels with alternative VEEV subtypes and chikungunya virus. During therapeutic delivery of SKT05, Fc effector functions were only required at 3 days post-infection. The necessity of Fc effector functions for SKT20 was related to mAb binding avidity rather than epitope and could be overcome by increasing the dose of SKT20 relative to the functional avidity of SKT05. Collectively, this study identified antibody avidity as a correlate forin vivoefficacy and associated Fc-dependent mechanisms that can be leveraged for therapeutic development of monoclonal antibodies against alphaviruses.

One sentence summary

Functional avidity of broadly reactive anti-alphavirus antibodies dictates requirement for Fc-mediated protection.
Mapping of the viral shunt across widespread coccolithophore blooms using metabolic biomarkers
Authors: Kuhlisch Constanze , Schleyer Guy , Flores J. Michel , Vincent Flora , Vallet Marine , Schatz Daniella , Vardi Assaf
Published: November 2024

The viral shunt is a fundamental ecosystem process which diverts the flux of organic carbon from grazers to heterotrophic microorganisms. Through the extracellular release of metabolites, lytic viral infections supply 2-10% of photosynthetically fixed carbon in the ocean for bacterial respiration. Despite its significance for the carbon cycle, we lack tools to detect the viral shunt in the natural environment and assess its ecological impact. Here, we study the use of exometabolites as biomarkers for the viral shunt by applying molecular, metabolomics, and oceanographic tools in blooms of the cosmopolitan microalgaGephyrocapsa huxleyiacross the Atlantic Ocean, spanning four biogeochemical provinces between Iceland and Patagonia. We mapped the distinct metabolic footprint of its viral infections using exo- and endometabolomics approaches and detected nineteen organohalogen metabolites across the blooms, showing their global formation. Time-resolved comparison of particulate and dissolved metabolite pools during an induced mesocosm bloom indicated virocells – actively infected host cells – as the source of the halogenated metabolites. Three trichloro-iodo metabolites were present during demise of all virus-infected blooms, highlighting them as suitable metabolic biomarkers. The environmental stability of these halometabolites in the DOM pool over a few days can recapitulate viral infections at earlier stages of phytoplankton bloom succession.
Emergence of alternative offspring care patterns from the evolution of parental negotiation strategies
Authors: Zheng Jia , Weissing Franz J. , Baldan Davide
Published: November 2024

The evolution of parental cooperation is challenging to explain because caring for offspring is costly, and parents face an evolutionary conflict of interests over how much care each should contribute. Evolutionary game theory suggests that this conflict may be resolved through parental negotiation, where the parents make their care level dependent on the care provided by their partner. However, mathematical negotiation models typically predict a low level of parental cooperation. As these models are not dynamically explicit and tend to neglect stochasticity, we here investigate the evolution of parental negotiation strategies by means of individual-based simulations. Our results differ markedly from earlier analytical predictions. Parental negotiation strategies readily evolve, but replicate simulations result in four alternative care patterns: uniparental care, sex-biased care and egalitarian biparental care with constant or oscillatory care levels. Hence, pronounced sex differences in parental care can evolve even in the absence of sexual selection and uncertainty of paternity. Effective parental cooperation is the most frequent outcome, but in contrast to the earlier models, it is not based on parental compensation but on a tit-for-tat negotiation strategy. Actually, compensation destabilises cooperation and leads to less effective outcomes where one parental sex exploits the other one.
A Transformer-Based Approach to Survival Outcome Prediction
Authors: Mellors Ted , Schneider Matt , Spitmann Mehran
Published: November 2024

Accurate prediction of patient survival outcomes is a critical challenge in cancer research, with the potential to inform personalized treatment strategies and improve patient care. We leveraged Geneformer, a state-of-the-art transformer model pre-trained on a massive single-cell RNA-seq dataset, to develop a model for the prediction of overall survival (OS). We adapted Geneformer for bulk tumor data analysis by appending a task-specific transformer layer and fine-tuning the model on RNA-seq data from The Cancer Genome Atlas (TCGA). Additionally, we employed a rank-value encoding scheme to prioritize informative genes and reduce noise. Our model demonstrated a robust correlation between predicted and true OS, with Pearson correlation coefficient of 0.72 (p<0.00001). Survival analysis revealed significant differences in survival between patient subgroups stratified based on the model’s predictions. The Geneformer-based model outperformed traditional machine learning approaches (Random Forest and Neural Network) in patient stratification tasks. Further analysis demonstrated the consistency of the model’s performance across different tumor stages and patient subgroups. Our study highlights the potential of leveraging pre-trained transformer models, originally developed for single-cell data analysis, to predict clinically relevant outcomes from bulk tumor gene expression data. The superior performance of our Geneformer-based model underscores its potential to enhance prognostication and treatment decision-making in cancer research. Future work will focus on refining the model architecture, incorporating multi-omics data, and validating its performance on external datasets to further advance its clinical utility.

Short Abstract

Accurate prediction of patient survival has important implications for cancer research as it enables the development of personalized treatment plans, guides clinical decision-making, and can be leveraged for clinical trial optimization. We utilized Geneformer, a transformer model pre-trained on single-cell RNA-seq data, to predict overall survival (OS) from bulk tumor gene expression. Adapting Geneformer for bulk tumor analysis and using rank-value encoding, we achieved strong correlations between predicted and true OS (r=0.72, p<0.00001). Our model outperformed traditional machine learning approaches in patient stratification, demonstrating consistent performance across tumor stages and subgroups. This study highlights the potential of pre-trained transformer models for prognostication in cancer, paving the way for refined, personalized treatment strategies.
Centromeric repeat diversity underlies non-Mendelian segregation pattern in hop (Humulus lupulus)
Authors: Horáková Lucie , Čegan Radim , Jedlička Pavel , Navrátilová Pavla , Tanaka Hiroyuki , Toyoda Atsushi , Itoh Takehiko , Akagi Takashi , Ono Eiichiro , Hudzieczek Vojtěch , Patzak Josef , Šafář Jan , Hobza Roman , Bačovský Václav
Published: November 2024

Aberrant meiosis in plants often leads to aneuploidy, genetic instability, and sterility. This can occur due to several factors, including chromosome misalignment, defective synapsis or environmental factors that may result in unusual genetic combinations in the offsprings. Unusual chromosome behavior during male meiosis inHumulus lupulusis linked to irregular chromosome segregation and genome instability. However, the origin of meiotic instability remains unclear.

We analyzed the centromeric landscape ofH.lupulus to determine its role in aberrant chromosomal segregation during cell division. Using a combination of bioinformatic, molecular and cytogenetic approaches, we identified new centromeric repeats and revealed two types of centromeric organizations. Cytogenetic localization on metaphase chromosomes confirmed the genomic distribution of major repeat arrays and revealed unique features that contribute to aberrant segregation.

Two centromeric types are composed of the major repeats SaazCEN and SaazCRM1 which are further accompanied by chromosome-specific centromeric satellites, Saaz40, Saaz293, Saaz85, and HuluTR120. Chromosome 2 displays unbalanced segregation during the cell division, implicating an important role for its centromere structure in segregation patterns. Moreover, Saaz293 is a new marker for studying aneuploidy in hop.

Our findings provide new insights on chromosome segregation in hop and highlight the diversity and complexity of the centromere organization inH.lupulus.
Local ionic conditions modulate the aggregation propensity and influence the structural polymorphism of alpha-synuclein
Authors: Zacharopoulou Maria , Seetaloo Neeleema , Ross James , Stephens Amberley D. , Fusco Giuliana , McCoy Thomas M. , Dai Wenyue , Mela Ioanna , Fernandez Villegas Ana , Martel Anne , Routh Alexander F. , De Simone Alfonso , Phillips Jonathan J. , Schierle Gabriele S. Kaminski
Published: November 2024

Parkinson’s Disease (PD) is characterized by the aggregation of alpha-synuclein (aSyn), a presynaptic protein that transitions from a disordered monomer into beta-sheet rich amyloid fibrils. The precise triggers and mechanisms underlying aSyn misfolding and aggregation remain unclear, hindering the development of effective therapeutics. Monomeric aSyn is an intrinsically disordered protein (IDP) with high conformational flexibility. Local environmental factors, such as ion concentrations, can influence the conformational ensemble of aSyn, impacting its aggregation propensity and resulting in fibril polymorphism. In this study, we explore the impact of physiologically relevant ions, mainly Ca2+and Na+, on the aggregation kinetics, monomer structural dynamics, and fibril polymorphism of aSyn. Using ThT fluorescence assays, we demonstrate that all ions accelerate aSyn aggregation, with Ca2+having the most significant effect. Using Heteronuclear Single Quantum Correlation Nuclear Magnetic Resonance (1H-15NHSQC NMR) spectroscopy, we validate the specific binding of Ca2+ions at the C-terminus, whereas Na+ions display non-specific interactions along the sequence of aSyn. Small-angle neutron scattering (SANS) and hydrogen-deuterium exchange mass spectrometry (HDX-MS) further reveal that Na+and Ca2+induce distinct conformational changes in the aSyn monomer, with Na+leading to more extended structures and Ca2+promoting a moderate extension of the protein. Molecular dynamics simulations (MD) corroborate these findings, showing that Na+ions increase the protein’s extension, particularly between the non-amyloid beta component (NAC) region and the C-terminus, whereas Ca2+ions bias the ensemble towards a more moderately elongated structure. Using MD, we further investigate the local environment and in particular the solvent effect and show the water persistence times in the hydration shell are also increased in the presence of Ca2+ions, indicating that the aggregation propensity of the monomer is due to a combination of conformational bias of the monomer and solvent mobility. Atomic force microscopy (AFM) of aSyn fibrils formed under these different ionic conditions reveal distinct fibril polymorphs, suggesting that ion-induced conformational biases in the monomer contribute to the diversity of fibril structures. Collectively, these findings underscore the pivotal influence of the local ionic milieu in shaping the structure and aggregation propensity of aSyn, thus offering valuable insights into the molecular underpinnings of PD and potential therapeutic avenues aimed at manipulating aSyn conformational dynamics.
qByte: Open-source isothermal fluorimeter for democratizing analysis of nucleic acids, proteins and cells
Authors: Quero Francisco J. , Aidelberg Guy , Vielfaure Hortense , de Kermadec Yann Huon , Cazaux Severine , Pandi Amir , Pascual-Garrigos Ana , Arce Anibal , Sakyi Samuel , Gaudenz Urs , Federici Fernan , Molloy Jennifer C. , Lindner Ariel B.
Published: November 2024

Access to affordable and reliable scientific instrumentation remains a significant barrier to the democratization of healthcare and scientific research. In the field of biotechnology, in particular, the complexity, cost, and infrastructure requirements of many instruments continue to limit their accessibility, especially in resource-limited environments. Despite the recent increase in the development of open-source tools, driven by advances in digital fabrication and electronic prototyping, few of these projects have reached large-scale implementation or validation in real-world settings. Here, we present qByte, an open-source, 8-tube isothermal fluorimeter designed to overcome these barriers by offering a cost-effective ($60) yet production-ready solution. qByte leverages standard digital manufacturing and Printed Circuit Board (PCB) assembly techniques and is designed to be portable, making it ideal for both laboratory and field use. The device has been benchmarked against commercial real-time thermocyclers and spectrophotometers, showing comparable results across four key applications: nucleic acid amplification and detection, analysis of protein activity and stability, genetic construct characterization, and bacterial viability tests. Further testing including the on-site diagnosis of human parasites in Kumasi, Ghana, and field work in Patagonia, Chile, validated qByte’s reliability in real-world conditions. Taken together, our results proved qByte as flexible and reliable equipment for a variety of biological tests and applications, while its affordability and open-source design simplify further development and allow adaptation to the needs of future users.
Cilia dysfunction in the lateral ventricles after neonatal intraventricular hemorrhage does not lead to functional changes in cilia-based CSF flow networks
Authors: Pan Shelei , Ramagiri Sruthi , Yang Lihua , Giles David A. , Xu Isabella , Bonilla Maria Garcia , DeFreitas Dakota , Siderowf Lillian W. , Halupnik Grace L. , Koneru Shriya , Koller Gretchen M. , Polavarapu Srinandan , Gupta Deepesh K. , Krishnamoorthy Praveen , Miller Mark J. , Esakky Prabagaran , Limbrick David D. , Bayly Phillip V. , Horani Amjad , Brody Steven L. , Mahjoub Moe R. , Strahle Jennifer M.
Published: November 2024

Intraventricular hemorrhage (IVH) has long been thought to lead to motile cilia dysfunction whereby intraventricular blood breakdown products damage and slough cilia from the ependymal wall. However, specifically how IVH may affect cilia development, structure, and transcriptional activation is not well-understood. Moreover, the impact of blood breakdown product-mediated cilia damage on the functional organization of cilia-based CSF flow networks is unknown. Here, we show hemoglobin exposure affects the number of ciliated ependymal cells in the lateral ventricle (LV) but does not impactin vitrobeat frequency of the remaining cilia. Ultrastructurally, IVH decreases the total number of ciliary tufts without impacting axoneme structure. IVH does not result in changes in the expression of cilia-related genes and instead leads to downregulation of neurogenesis markers in parallel with innate immune upregulation. Functionally, we identify three previously uncharacterized cilia-mediated CSF flow domains in the LV lateral wall and show that IVH does not result in widespread disruption of their functional organization. These data de-emphasize cilia as a major contributor to global CSF dysfunction after IVH, and instead call attention to preserving the neurodevelopmental environment and preventing runaway innate immune system activation, as considerations to developing treatment strategies to prevent posthemorrhagic hydrocephalus and other neurodevelopmental sequelae.
Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy
Authors: Gravel Alice , Laliberté Etienne
Published: November 2024

Foliar functional traits are key drivers of ecological processes in forests. Despite progress in forest foliar trait mapping from imaging spectroscopy, there is a need to build environment-specific, spectra-trait models trained from tree-level measurements to improve the accuracy of local trait maps.

We mapped 12 foliar functional traits in a mixed temperate forest using airborne imaging spectroscopy. Top-of-canopy foliar samples from tree crowns (n = 166), representing a total of 16 species, were collected using a drone platform to measure foliar traits for individual trees, from which tree-level crown spectra were also determined. Partial least squares regression (PLSR) models were used to predict foliar traits from tree-level reflectance spectra (400-2400 nm).

These models predicted leaf mass per area (LMA), specific leaf area (SLA) and equivalent water thickness (EWT) with high accuracy (R2> 0.8, %RMSE < 15). Models for pigment, nitrogen and cellulose concentrations showed a moderate performance (R2= 0.53–0.68, %RMSE = 17.24–21.31). Poorest performance was observed for lignin, carbon, leaf dry mass content (LDMC) and hemicellulose (R2= 0.24–0.44, %RMSE = 20.67–26.13).

High-resolution (1.25 m pixel-1) foliar trait maps were produced for the entire 16-km2study area. Our study adds to the extensive research aiming to use remote sensing to monitor forest functional trait biodiversity at larger scales and provides models that capture intraspecific variation across many tree species from a mixed temperate forest in eastern Canada.
Utilizing Pretrained Vision Transformers and Large Language Models for Epileptic Seizure Prediction
Authors: Parani Paras , Mohammad Umair , Saeed Fahad
Published: November 2024

Repeated unprovoked seizures is a major source of concern for patients suffering from epilepsy. Predicting seizures before they occur is of interest to both machine-learning scientists as well as clinicians, and is an active area of research. The variability of EEG sensors, type of seizures, and specialized knowledge required for annotating the data complicates the large-scale annotation process essential for supervised predictive models. To address these challenges, we propose the use of Vision Transformers (ViTs) and Large Language Models (LLMs) that were originally trained on publicly available image or text data. Our work leverages these pre-trained models by refining the input, embedding, and classification layers in a minimalistic fashion to predict seizures. Our results demonstrate that LLM's outperforms the ViT's in patient-independent seizure prediction achieving a sensitivity of 79.02% which is 8% higher compared to ViT's and about 12% higher compared to a custom-designed ResNet-based model. Our work demonstrates the successful feasibility of pre-trained models for seizure prediction with its potential for improving the quality of life of people with epilepsy. Our code and related materials are available open-source at: https://github.com/pcdslab/UtilLLM_EPS
Personalized cancer treatment strategies incorporating irreversible and reversible drug resistance mechanisms
Authors: He Wei , McCoy Matthew D. , Riggins Rebecca B. , Beckman Robert A. , Yeang Chen-Hsiang
Published: November 2024

Despite advances in targeted cancer therapy, the promise of precision medicine has been limited by resistance to these treatments. In this study, we propose a mathematical modelling framework incorporating cellular heterogeneity, genetic evolutionary dynamics, and non-genetic plasticity, accounting for both irreversible and reversible drug resistance. Previously we proposed Dynamic Precision Medicine (DPM), a personalized treatment strategy that designed individualized treatment sequences by simulations of irreversible genetic evolutionary dynamics in a heterogeneous tumor. Here we apply DPM to the joint model of reversible and irreversible drug resistance mechanisms, analyze the simulation results and compare the efficacy of various treatment strategies. The results indicate that this enhanced version of DPM results in superior patient outcomes compared with current personalized medicine treatment approaches. Our results provide insights into cancer treatment strategies for heterogeneous tumors with genetic evolutionary dynamics and non-genetic cellular plasticity, potentially leading to improvements in survival time for cancer patients.
Visually-guided compensation of deafening-induced song deterioration
Authors: Rolland M , Zai A , Hahnloser RHR , Del Negro C , Giret N
Published: November 2024

Human language learning and maintenance depend primarily on auditory feedback but are also shaped by other sensory modalities. Individuals who become deaf after learning to speak (post-lingual deafness) experience a gradual decline in their language abilities. A similar process occurs in songbirds, where deafness leads to progressive song deterioration. However, songbirds can modify their songs using non-auditory cues, challenging the prevailing assumption that auditory feedback is essential for vocal control. In this study, we investigated whether deafened birds could use visual cues to prevent or limit song deterioration. We developed a new metric for assessing syllable deterioration called the spectrogram divergence score. We then trained deafened birds in a behavioral task where the spectrogram divergence score of a target syllable was computed in real-time, triggering a contingent visual stimulus based on the score. Birds exposed to the contingent visual stimulus—a brief light extinction—showed more stable song syllables than birds that received either no light extinction or randomly triggered light extinction. Notably, this effect was specific to the targeted syllable and did not influence other syllables. This study demonstrates that deafness-induced song deterioration in birds can be partially mitigated with visual cues.
Defining Stable Phases of Open Quantum Systems
Authors: Rakovszky Tibor , Gopalakrishnan Sarang , von Keyserlingk Curt
Published: November 2024

The steady states of dynamical processes can exhibit stable nontrivial phases, which can also serve as fault-tolerant classical or quantum memories. For Markovian quantum (classical) dynamics, these steady states are extremal eigenvectors of the non-Hermitian operators that generate the dynamics, i.e., quantum channels (Markov chains). However, since these operators are non-Hermitian, their spectra are an unreliable guide to dynamical relaxation timescales or to stability against perturbations. We propose an alternative dynamical criterion for a steady state to be in a stable phase, which we name uniformity: Informally, our criterion amounts to requiring that, under sufficiently small local perturbations of the dynamics, the unperturbed and perturbed steady states are related to one another by a finite-time dissipative evolution. We show that this criterion implies many of the properties one would want from any reasonable definition of a phase. We prove that uniformity is satisfied in a canonical classical cellular automaton, and we provide numerical evidence that the gap determines the relaxation rate between nearby steady states in the same phase, a situation we conjecture holds generically whenever uniformity is satisfied. We further conjecture some sufficient conditions for a channel to exhibit uniformity and therefore stability.

Published by the American Physical Society
2024
Strategic nature of corporate social responsibility, competitive performance, innovation capability and environmental uncertainty
Authors: Mohammad Haruna Isa , Marcel Daniel
Published: November 2024

Purpose

The goal of this work is to evaluate how corporate social responsibility (CSR) affects competitive performance in Nigeria's banking industry, with innovation capability acting as a mediator and environmental uncertainty as a moderating factor.

Design/methodology/approach

The banking industry in Nigeria served as the site for the empirical investigation. Employees at deposit money institutions received a questionnaire. Direct and mediating effects and the moderating role were thus examined utilizing a final sample of 267 cases using consistent partial least squares structural equation modeling with ADANCO 2.2.1.

Findings

The data shows that CSR has both a significant strategic impact on innovation capability and a competitive innovation capability. In contrast, the outcome shows a strong effect of CSR's strategic character on performance in the marketplace. Furthermore, evidence for mediating and moderating effects was provided.

Research limitations/implications

The study was restricted to Nigerian banking institutions. Additionally, data on competitive performance were acquired from employees' perspectives, while considering the competitive performance of their rivals.

Originality/value

The primary contribution of this paper is the empirical investigation of the mediating impact of innovation capability and the moderating function of environmental uncertainty in banking organizations that use a CSR strategy to attain competitive performance.
Qualitative research in the Academy of Human Resource Development-sponsored journals
Authors: Johnson Karen R. , Nery-Kjerfve Tania , Yeager Katherine , McLean Gary N.
Published: November 2024

Purpose

Qualitative paradigmatic perspectives are increasingly recognized as providing in-depth, rich and nuanced interpretations and critiques of complex phenomena. Nonetheless, positivist and post-positivist epistemologies still dominate social science disciplines. The authors were unable to identify published information on the state of qualitative research in human resource development (HRD) despite the numerous theoretical foundations, epistemological stances and global approaches that shape HRD. To fill this gap, this study determined the prevalence of qualitative designs, data collection methods and data analysis techniques that have been used in Academy of Human Resource Development (AHRD)-sponsored journals. This study identified gaps in the use of qualitative research to be explored by HRD scholars to advance research and practice.

Design/methodology/approach

A descriptive case study approach was used to determine the state of qualitative research in three AHRD-sponsored journals.

Findings

Qualitative articles have increased in number but still account for a lower percentage of articles when compared with other peer-reviewed articles, including quantitative methodologies and conceptual papers. Most articles lacked grounding in stated ontology and epistemology needed to guide researchers’ investigations, potentially leading to weakened methodological choices, interpretations and authenticity of conclusions.

Research limitations/implications

To provide in-depth understanding of HRD-related phenomena, all important elements of qualitative articles should be addressed to strengthen the credibility and authenticity of the research process. Qualitative theorists and researchers can build on and embrace the soundness of qualitative research and theories in HRD. Expanding on the credibility and authenticity of rigorously conducted qualitative research will help to broaden the perspectives of researchers who may be hesitant to explore this methodology of inquiry. This will strengthen methodological sophistication and expand the knowledge base of qualitative research.

Practical implications

HRD professors/instructors will find this study to be a useful guide for graduate students who are exploring and using qualitative inquiry for their research and for understanding others’ research. Practitioners will also find it useful to understand HRD research to determine possible interventions in their workplaces.

Originality/value

This study identifies trends and patterns of epistemologies and methods used in HRD qualitative articles and provides a foundation on which to build future studies and establish unexplored, unconventional qualitative methodologies and methods.