bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic evidence for T-wave area from 12-lead electrocardiograms to monitor cardiovascular diseases in patients taking diabetes medications
Authors: Qi Mengling , Zhang Haoyang , Xiu Xuehao , He Dan , Cooper David N. , Yang Yuanhao , Zhao Huiying 
Published: September 2023

AbstractAimsMany studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC).MethodsGenome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank. The genetic correlations, causal relationships, and phenotypic relationships of these ETCs with medication usage, as well as the risk of cardiovascular diseases (CVDs), were estimated by linkage disequilibrium score regression (LDSC), Mendelian randomization (MR), and regression model, respectively.ResultsThe GWAS identified 124 independent single nucleotide polymorphisms (SNPs) that were study-wise and genome-wide significantly associated with at least one ETC. Regression model and LDSC identified significant phenotypic and genetic correlations of T-wave area in lead aVR (aVR_T-area) with usage of diabetes medications (ATC code: A10 drugs, and metformin), and the risks of ischemic heart disease (IHD) and coronary atherosclerosis (CA). MR analyses support a putative causal effect of the use of diabetes medications on decreasing aVR_T-area, and on increasing risk of IHD and CA.ConclusionPatients taking diabetes medications are prone to have decreased aVR_T-area and an increased risk of IHD and CA. The aVR_T-area is therefore a potential ECG marker for pre-clinical prediction of IHD and CA in patients taking diabetes medications.
Using Artificial Intelligence To Label Free-Text Operative And Ultrasound Reports For Grading Pediatric Appendicitis
Authors: Abu-Ashour Waseem , Emil Sherif , Poenaru Dan 
Published: September 2023

AbstractPurposeData science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models (LLMs) can structure free-text EMR data. Here we compare data extraction quality between ChatGPT-4 and human data collectors.MethodsTo train AI models to grade pediatric appendicitis preoperatively, several data collectors (medical students and research assistants) extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis between 2014-2021. Collectors were trained and certified for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate the Pediatric Appendicitis Grade (PAG) from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method.ResultsWithin the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The PAG was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approx. 100x faster than manually.ConclusionAn AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the PAG score. While wider validation is required and data safety concerns must be fully addressed, these novel AI tools show significant promise in improving the accuracy and efficiency of research data collection.Highlights1. What is known about this topic?AI chatbots have several benefits and implications including healthcare uses.2. What new information is contained in this article?AI chatbot was proven to be more accurate when compared to human data extraction.
Convergent evolution of SARS-CoV-2 XBB lineages on receptor-binding domain 455-456 synergistically enhances antibody evasion and ACE2 binding
Authors: Jian Fanchong , Feng Leilei , Yang Sijie , Yu Yuanling , Wang Lei , Song Weiliang , Yisimayi Ayijiang , Chen Xiaosu , Xu Yanli , Wang Peng , Yu Lingling , Wang Jing , Liu Lu , Niu Xiao , Wang Jing , Xiao Tianhe , An Ran , Wang Yao , Gu Qingqing , Shao Fei , Jin Ronghua , Shen Zhongyang , Wang Youchun , Wang Xiangxi , Cao Yunlong 
Published: September 2023

SummarySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) XBB lineages have achieved dominance worldwide and keep on evolving. Convergent evolution of XBB lineages on the receptor-binding domain (RBD) L455F and F456L is observed, resulting in variants like EG.5, FL.1.5.1, XBB.1.5.70, and HK.3. Here, we show that neutralizing antibody (NAb) evasion drives the convergent evolution of F456L, while the epistatic shift caused by F456L enables the subsequent convergence of L455F through ACE2 binding enhancement and further immune evasion. L455F and F456L evade Class 1 NAbs, reducing the neutralization efficacy of XBB breakthrough infection (BTI) and reinfection convalescent plasma. Importantly, L455F single substitution significantly dampens receptor binding; however, the combination of L455F and F456L forms an adjacent residue flipping, which leads to enhanced NAbs resistance and ACE2 binding affinity. The perturbed receptor-binding mode leads to the exceptional ACE2 binding and NAb evasion, as revealed by structural analyses. Our results indicate the evolution flexibility contributed by epistasis cannot be underestimated, and the evolution potential of SARS-CoV-2 RBD remains high.
Exploring Disease Model Mouse Using Knowledge Graphs: Combining Gene Expression, Orthology, and Disease Datasets
Authors: Kushida Tatsuya , de Farias Tarcisio Mendes , Sima Ana C. , Dessimoz Christophe , Chiba Hirokazu , Bastian Frederic B. , Masuya Hiroshi 
Published: September 2023

AbstractBackgroundThe RIKEN BRC develops and maintains the RIKEN BioResource MetaDatabase to help users explore appropriate target bioresources for their experiments and prepare precise and high-quality data infrastructures. The Swiss Institute of Bioinformatics develops two RDF datasets across multi species for the study of gene expression and orthology: Bgee and Orthologous MAtrix (OMA, an orthology database).MethodsThis study integrates the RIKEN BioResource knowledge graph with Resource Description Framework (RDF) datasets from Bgee, a gene expression database, the OMA, the DisGeNET, a human gene-disease association, Mouse Genome Informatics (MGI), UniProt, and four disease ontologies in the RIKEN BioResource MetaDatabase. Our aim is to explore which model organisms are most appropriate for specific medical science research applications, using SPARQL queries across the integrated datasets. More precisely, we attempt to explore disease-related genes, as well as anatomical parts where these genes are overexpressed and subsequently identify appropriate bioresource candidates available for specific disease research applications.ResultsWe illustrate the above through two use cases targeting either Alzheimer’s disease or melanoma. We identified two Alzheimer’s disease-related genes that were overexpressed in the prefrontal cortex (APP and APOE) and 21 RIKEN bioresources predicted to be relevant to Alzheimer’s disease research. Furthermore, executing a transitive search for the Uberon terms by using the Property Paths function, we identified two melanoma-related genes (HRAS and PTEN), and eight anatomical parts in which these genes were overexpressed, such as the “skin of limb” as an example. Finally, we compared the performance of the federated SPARQL query via the remote Bgee SPARQL endpoint with the performance of a centralized SPARQL query using the Bgee dataset as part of the RIKEN BioResource MetaDatabase.ConclusionsAs a result, we demonstrated that the performance of the federated approach degraded. We concluded that we improved the degradation of the query performance of the federated approach from the BioResource MetaDatabase to the SIB by refining the transferred data through the subquery and enhancing the server specifications, that is optimizing the triple store query evaluation.
Sex classification from functional brain connectivity: Generalization to multiple datasets
Authors: Wiersch Lisa , Friedrich Patrick , Hamdan Sami , Komeyer Vera , Hoffstaedter Felix , Patil Kaustubh R. , Eickhoff Simon B. , Weis Susanne 
Published: September 2023

AbstractMachine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to “match” in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.
Apolipoprotein-based regulation of ganglioside metabolism upon secretase activity inhibition in iPSC-derived cerebral organoids
Authors: Jha Durga , Váňová Tereza , Bohačiaková Dáša , Spáčil Zdeněk 
Published: September 2023

AbstractBeta and gamma-secretase inhibitors have been of pharmacological interest to reduce abeta (Aβ) formation and aggregation, one of the defining characteristics of Alzheimer’s disease (AD). Recent research indicates that Apolipoprotein E (ApoE), a genetic risk factor for AD, can regulate secretase activity. Secretase inhibitor-induced elevation of neuronal membrane lipids has been documented in 2D models. Due to their enhanced ability to reproduce AD-like pathology and ease of performing the experimental intervention, we utilized a 3D cerebral organoid model derived from human pluripotent stem cells generated from an AD patient. We treated cerebral organoids carrying ApoE3 and ApoE4 variants with beta and gamma-secretase inhibitors to determine if organoids could reproduce the differences observed in the 2D model and if the alteration in secretase activity could affect the regulation of neuronal lipids synthesis in an ApoE-dependent manner. Ganglioside profiling was accomplished using liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) via selective reaction monitoring (SRM). The inhibitor administration elevated the levels of ganglioside and ceramide in ApoE4-derived organoids. Since gangliosides are known to enhance Aβ fibrillogenesis, our study implies that reduction in secretase activity affects neuronal membrane architecture that could eventually aggravate AD, particularly in patients with the ApoE4 isoform. In addition, the ability of organoids to replicate results from other experimental models demonstrates their potential to improve translatability significantly.
DeepGEEP: Data-Driven Prediction of Bacterial Biofilm Gene Expression Profiles
Authors: Arjmandi Hamidreza , Corre Christophe , Jahangir Hamidreza , Noel Adam 
Published: September 2023

AbstractPredicting the gene expression profile (GEEP) of bacterial biofilms in response to spatial, temporal, and concentration profiles of stimulus molecules holds significant potential across microbiology, biotechnology, and synthetic biology domains. However, the resource and time-intensive nature of experiments within Petri dishes presents significant challenges. Data-driven methods offer a promising avenue to replace or reduce such experiments given sufficient data. Through wellcrafted data generation techniques, the data scarcity issue can be effectively addressed. In this paper, an innovative methodology is presented for generating GEEP data over a Petri dish that results from a specific chemical stimulus release profile. A twodimensional convolutional neural network (2D-CNN) architecture is subsequently introduced to leverage the synthesized dataset to predict GEEP variations across bacterial biofilms within the Petri dish. The approach, coined DeepGEEP, is applied to data generated by a particle-based simulator (PBS) to enable a flexible evaluation of its efficacy. The proposed method attains a significant level of accuracy in comparison to established benchmark models such as Linear SVM, Radial Basis Function SVM, Decision Tree, and Random Forest.
Substrate geometry affects population dynamics in a bacterial biofilm
Authors: Postek Witold , Staskiewicz Klaudia , Lilja Elin , Wacław Bartłomiej 
Published: September 2023

AbstractBiofilms inhabit a range of environments, such as dental plaques or soil micropores, often characterized by intricate, non-even surfaces. However, the impact of surface irregularities on the population dynamics of biofilms remains elusive as most biofilm experiments are conducted on flat surfaces. Here, we show that the shape of the surface on which a biofilm grows influences genetic drift and selection within the biofilm. We cultureE. colibiofilms in micro-wells with an undulating bottom surface and observe the emergence of clonal sectors whose size corresponds to that of the undulations, despite no physical barrier separating different areas of the biofilm. The sectors are remarkably stable over time and do not invade each other; we attribute this stability to the characteristics of the velocity field within the growing biofilm, which hinders mixing and clonal expansion. A microscopically-detailed computer model fully reproduces these findings and highlights the role of mechanical (physical) interactions such as adhesion and friction in microbial evolution. The model also predicts clonal expansion to be severely limited even for clones with a significant growth advantage – a finding which we subsequently confirm experimentally using a mixture of antibiotic-sensitive and antibiotic-resistant mutants in the presence of sub-lethal concentrations of the antibiotic rifampicin. The strong suppression of selection contrasts sharply with the behavior seen in bacterial colonies on agar commonly used to study range expansion and evolution in biofilms. Our results show that biofilm population dynamics can be controlled by patterning the surface, and demonstrate how a better understanding of the physics of bacterial growth can pave the way for new strategies in steering microbial evolution.
How do antimicrobial peptides interact with the outer membrane of Gram-negative bacteria? Role of lipopolysaccharides in the peptide binding, anchoring and penetration
Authors: Stephani Justus C. , Gerhards Luca , Khairalla Bishoy , Solov’yov Ilia A. , Brand Izabella 
Published: September 2023

AbstractGram-negative bacteria possess a complex structural cell envelope that constitutes a barrier for antimicrobial peptides which neutralize the microbes by disrupting their cell membranes. Computational and experimental approaches were used to study a model outer membrane interaction with an antimicrobial peptide, melittin. The investigated membrane included di[3-deoxy-D-manno-octulosonyl]-lipid A (KLA) in the outer leaflet and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) in the inner leaflet. Molecular dynamics simulations revealed, that the positively charged helical C-terminus of melittin anchors rapidly into the hydrophilic head-group region of KLA, while the flexible N-terminus makes contacts with the phosphate groups of KLA moving melittin into the boundary between the hydrophilic and hydrophobic regions of the lipids. Electrochemical techniques confirmed binding of melittin to the model membrane. To probe the peptide conformation and orientation during interaction with the membrane, polarization modulation infrared reflection absorption spectroscopy was used. The measurements revealed conformational changes in the peptide accompanied by reorientation and translocation of the peptide at the membrane surface. The study suggests that melittin insertion into the outer membrane affects its permeability and capacitance, but does not disturb the membrane’s integrity, indicating a distinct mechanism of the peptide action on the outer membrane of Gram-negative bacteria.
Loss of the polarity protein Par3 promotes dendritic spine neoteny and enhances learning and memory
Authors: Voglewede Mikayla M. , Ozsen Elif Naz , Ivak Noah , Bernabucci Matteo , Sun Miao , Pang Zhiping P. , Zhang Huaye 
Published: September 2023

SummaryThe Par3 polarity protein is critical for subcellular compartmentalization in different developmental processes. Variants ofPARD3, which encodes PAR3, are associated with intelligence and neurodevelopmental disorders. However, the role of Par3 in glutamatergic synapse formation and cognitive functionsin vivoremains unknown. Here, we show that forebrain conditional knockout of Par3 leads to an increase in long, thin dendritic spines without significantly impacting mushroom spinesin vivo. In addition, we observed a decrease in the amplitude of miniature excitatory postsynaptic currents. Surprisingly, loss of Par3in vivoenhances hippocampal- dependent spatial learning. Phosphoproteomic analysis revealed proteins regulating cytoskeletal dynamics are significantly dysregulated downstream of Par3. Mechanistically, we found Par3 deletion causes increased activation of the Rac1 pathway. Together, our data reveal an unexpected role for Par3 as a molecular gatekeeper in regulating the pool of immature dendritic spines, a rate-limiting step of learning and memory, through modulating Rac1 activationin vivo.
Interlinked Bi-stable Switches Govern the Cell Fate Commitment of Embryonic Stem Cells
Authors: Giri Amitava , Kar Sandip 
Published: September 2023

AbstractCell fate decision-making events of embryonic stem (ES) cells are often governed by bi-stable switch-like steady state dynamics of two key transcription factors Oct4 and Nanog due to mutual positive feedback regulation. Intriguingly, for the differentiation of ES cells to either extraembryonic trophectoderm (TE) and primitive endoderm (PrE) lineages, Oct4 maintains either low (TE) or high PrE) expression levels relative to a moderate level in ES cells, while Nanog exhibits high expression in ES state and low expression in both the differentiated states. The dynamical origin for such kind of disparate steady-state level maintenance of Oct4 and Nanog remains elusive. Herein, we demonstrate that such complex steady-state dynamics can be hypothesized and explained by two different bistable switches interconnected either in a stepwise (Oct4) or in a mushroom-like (Nanog) manner. Our hypothesis qualitatively reconciles various experimental observations and elucidates how different feedback and feed-forward motifs orchestrate the extraembryonic development and stemness maintenance of ES cells. Importantly, the model predicts strategies to optimize the dynamics of self-renewal and differentiation of embryonic stem cells that may have therapeutic relevance in the future.
Arabidopsis CML13 and CML14 Have Essential And Overlapping Roles In Plant Development
Authors: Symonds Kyle , Teresinski Howard , Hau Bryan , Chiasson David , Snedden Wayne A. 
Published: September 2023

AbstractCalmodulin-like proteins (CaM-like; CML) are the largest family of calcium-binding proteins in plants, yet the functions of most CMLs are unknown. Arabidopsis CML13 and CML14 are closely related paralogs that interact with the isoleucine-glutamine (IQ) domains of myosins, IQ-domain (IQD) proteins, and CaM-activated transcription factors (CAMTAs). Here, we explored the physiological roles of CML13 and CML14 during development by using dexamethasone-inducible RNA silencing to suppress eitherCML13orCML14transcript levels. In the absence of inducible suppression,CML13- andCML14-RNA-interference lines were indistinguishable from WT plants throughout development. In contrast, induction of silencing treatment led to rapid increases in RNA-hairpin production that correlated with a targeted reduction inCML13orCML14transcript levels and a range of developmental and morphological effects. RNA suppression treatment did not impair the germination ofCML13- or 14-RNA-interference lines, but these seedlings were chlorotic, displayed high mortality, and failed to achieve seedling establishment. Induced RNA suppression of mature plants led to reduced silique length, shorter roots, and rapid leaf senescence inCML13-and14-RNA-interference plants. Plants induced for RNA suppression at 2 weeks post-germination exhibited a much stronger phenotype than treatment of 3-, 4-, or 5-week-old plants. Collectively, our data indicate that both CML13 and CML14 are essential for normal development and function across a broad range of tissues and developmental stages.HighlightCML13 and CML14 are biochemically unique among the CML family and interact with proteins that possess IQ domains. Here, we show that both paralogs are essential for normal plant growth and development.
Colour-analyzer: A new dual colour model-based imaging tool to quantify plant disease
Authors: Loranger Mackenzie Eli William , Yim Winfield , Accomazzi Vittorio , Morales Lizcano Nadia , Moeder Wolfgang , Yoshioka Keiko 
Published: September 2023

AbstractDespite major efforts over the last decades, the rising demands of the growing global population makes it of paramount importance to increase crop yields and reduce losses caused by plant pathogens. One way to tackle this is to screen novel resistant genotypes and immunity-inducing agents, which must be conducted in a high-throughput manner. Colour-analyzer is a free web-based tool that can be used to rapidly measure the formation of lesions on leaves. Pixel colour values are often used to distinguish infected from healthy tissues. Some programs employ colour models, such as RGB, HSV or L*a*b*. Colour-analyzer uses two colour models, utilizing both HSV (Hue, Saturation, Value) and L*a*b* values. We found that thea* b*values of the L*a*b* colour model provided the clearest distinction between infected and healthy tissue, while theHandSchannels were best to distinguish the leaf area from the background. By combining the a* andb*channels to determine the lesion area, while using theHandSchannels to determine the leaf area, Colour-analyzer provides highly accurate information on the size of the lesion as well as the percentage of infected tissue in a high throughput manner and can accelerate the plant immunity research field.