bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation
Authors: Pencovich Niv , Smith Byron H. , Attia Zachi I. , Jimenez Francisco Lopez , Bentall Andrew J. , Schinstock Carrie A. , Khamash Hasan A. , Jadlowiec Caroline C. , Jarmi Tambi , Mao Shennen A. , Park Walter D. , Diwan Tayyab S. , Friedman Paul A. , Stegall Mark D. 
Published: April 2024

Background.
Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT.

Methods.
We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms’ outputs based on a single preoperative ECG were correlated with patient mortality data.

Results.
Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00–9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751).

Conclusions.
The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.
Rehabilitation exercises for kidney transplant recipients in an organ transplant ward: a best practice implementation project
Authors: Liu Huan , Liu Jia , Chen Qirong , Zeng Le , Guo Jia , Zhu Xiao , Zhang Pengpeng , Chen Jiarui , Sun Mei , Huang Xiaoting , Ding Jinfeng , Liu Lifang 
Published: April 2024

ABSTRACT

Introduction and objectives:
Kidney transplantation is an effective treatment for end-stage kidney disease. Kidney transplant recipients (KTRs) are prone to experiencing reduced physical function, depression, fatigue, and lack of exercise motivation due to their sedentary lifestyle before surgery. Exercise is an effective intervention for KTRs, but it has not been properly implemented in many practice settings. This project aimed to promote evidence-based exercises as part of KTRs’ rehabilitation to improve their health outcomes.

Methods:
This project was informed by the JBI Evidence Implementation Framework. The project was conducted in the organ transplant ward of a tertiary comprehensive hospital in Changsha, China. Based on a summary of best evidence, 12 audit criteria were developed for the baseline and follow-up audits involving 30 patients and 20 nursing staff. The JBI Practical Application of Clinical Evidence System (PACES) and Getting Research into Practice (GRiP) tool were used to identify barriers and facilitators and develop targeted strategies to improve issues.

Results:
Compared with the baseline audit, significant improvements were achieved in most of the criteria in the follow-up audit, with 9 of the 12 criteria reaching 100% compliance. Notably, the 6-minute walk distance test results were significantly higher, while the Self-Rating Depression Scale and Self-Rating Anxiety Scale scores were significantly lower (p < 0.05).

Conclusions:
This project demonstrates that evidence-based practice can improve the clinical practice of rehabilitation exercises for KTRs. The GRiP strategies proved to be extremely useful, notably, the formulation of a standardized rehabilitation exercise protocol, training, and enhancement of the exercising environment. Head nurses’ leadership and decision-making also played an important role in the success of this project.

Spanish abstract:

http://links.lww.com/IJEBH/A180
Phototactic preference and its genetic basis in the planulae of the colonial Hydrozoan Hydractinia symbiolongicarpus
Authors: Birch Sydney , McGee Lindy , Provencher Curtis , DeMio Christine , Plachetzki David 
Published: April 2024

AbstractBackgroundMarine organisms with sessile adults commonly possess motile larval stages that make settlement decisions based on integrating environmental sensory cues. Phototaxis, the movement toward or away from light, is a common behavioral characteristic of aquatic and marine metazoan larvae, and of algae, protists, and fungi. In cnidarians, behavioral genomic investigations of motile planulae larvae have been conducted in anthozoans (corals and sea anemones) and scyphozoans (true jellyfish), but such studies are presently lacking in hydrozoans. Here, we examined the behavioral genomics of phototaxis in planulae of the hydrozoanHydractinia symbiolongicarpus.ResultsA behavioral phototaxis study of day 3 planulae indicated preferential phototaxis to green (523 nm) and blue (470 nm) wavelengths of light, but not red (625 nm) wavelengths. A developmental transcriptome study where planula larvae were collected from four developmental time points for RNA-seq revealed that many genes critical to the physiology and development of ciliary photosensory systems are dynamically expressed in planula development and correspond to the expression of phototactic behavior. Microscopical investigations using immunohistochemistry andin situhybridization demonstrated that several transcripts with predicted function in photoreceptors, including cnidops class opsin, CNG ion channel, and CRX-like transcription factor, localize to ciliated bipolar sensory neurons of the aboral sensory neural plexus, which is associated with the direction of phototaxis and the site of settlement.ConclusionsThe phototactic preference displayed by planulae is consistent with the shallow sandy marine habitats they experience in nature. Our genomic investigations add further evidence of similarities between cnidops-mediated photoreceptors of hydrozoans and other cnidarians and ciliary photoreceptors as found in the eyes of humans and other bilaterians, suggesting aspects of their shared evolutionary history.
Mu Suppression During Action Observation Only in the Lower, not in the Higher, Frequency Subband
Authors: Badakul Ayşe Nur , Soyman Efe 
Published: April 2024

AbstractMu suppression – desynchronization of neural oscillations in central EEG electrodes during action execution and observation – has been widely accepted as a marker for neural mirroring. It has been conventionally and predominantly quantified in the 8-13 Hz range, corresponding to the alpha frequency band, although few studies reported differences in lower and higher subbands that together constitute the mu frequency band. In the present study, we adopted a data-driven approach to examine the spectral and temporal dynamics of mu suppression when participants watched videos depicting hand and face actions and artificial pattern movements. Our analyses in central EEG electrodes revealed that neural oscillations were significantly suppressed during action observation only in the lower (8-10.5 Hz), not in the higher (10.5-13 Hz), subband. No such subband differentiation was observed for the alpha oscillations in the occipital electrodes. In addition, in the lower subband, significantly stronger suppressions were selective for hand actions in the central EEG electrodes placed over the hand region of the sensorimotor cortices and for facial actions in the frontotemporal electrodes placed over the face region of the sensorimotor cortices. In the higher subband, such stimulus selectivity was only observed for facial actions in the frontotemporal electrodes. Furthermore, the neural oscillations in the lower, but not the higher, subband followed the precise temporal patterning of biological motion in the videos. These results indicate that neural oscillations in the lower subband show the characteristics of neural mirroring processes, whereas those in the higher subband might reflect other mechanisms.
triangulaR:an R package for identifying AIMs and building triangle plots using SNP data from hybrid zones
Authors: Wiens Ben J. , Colella Jocelyn P. 
Published: April 2024

AbstractHybridization provides a window into the speciation process and reshuffles parental alleles to produce novel recombinant genotypes. The presence or absence of specific hybrid classes across a hybrid zone can provide support for various modes of reproductive isolation. Early generation hybrid classes can be distinguished by their combination of hybrid index and interclass heterozygosity, which can be estimated with molecular data. Hybrid index and interclass heterozygosity are routinely calculated for studies of hybrid zones, but available resources for next-generation sequencing datasets are computationally demanding and tools for visualizing those metrics as a triangle plot are lacking. Here, we provide a resource for identifying ancestry- informative markers (AIMs) from SNP datasets, calculating hybrid index and interclass heterozygosity, and visualizing the relationship as a triangle plot. Our methods are implemented in the R packagetriangulaR. We validate our methods by simulating genetic data for a hybrid zone between parental groups at low, medium, and high levels of divergence. We find that accurate and precise estimates of hybrid index and interclass heterozygosity can be obtained with sample sizes as low as five individuals per parental group. We explore various allele frequency difference thresholds for AIM identification, and how this threshold influences the accuracy and precision of hybrid index and interclass heterozygosity estimates. We contextualize interpretation of triangle plots by describing the theoretical expectations for covariance of hybrid index and interclass heterozygosity under Hardy-Weinberg Equilibrium and provide recommendations for best practices for identifying AIMs and building triangle plots.
The critical role of isomiRs in accurate differential expression analysis of miRNA-seq data
Authors: Schmauch Eloi , Attia Yassine , Laitinen Pia , Turunen Tiia A. , Bartos Piia , Vaananen Mari-Anna , Malm Tarja , Tavi Pasi , Kellis Manolis , Kaikkonen Minna U , Linna-Kuosmanen Suvi 
Published: April 2024

AbstractMicroRNAs (miRNAs) are crucial for the regulation of gene expression and are promising biomarkers and therapeutic targets. miRNA isoforms (isomiRs) differ in their start/end offsets, which can impact the target gene selection and non-canonical function of the miRNA species. In addition, isomiRs frequently differ in their expression patterns from their parent miRNAs, yet their roles and tissue-specific responses are currently understudied, leading to their typical omission in miRNA research. Here, we evaluate the expression differences of isomiRs across conditions and their impact on standard miRNA-seq quantification results. We analyze 28 public miRNA-seq datasets, showing significant expression pattern differences between the isomiRs and their corresponding reference miRNAs, leading to misinterpretation of differential expression signals for both. As a case study, we generate a new dataset assessing isomiR abundance under hypoxia in human endothelial cells between the nuclear and cytosolic compartments. The results suggest that isomiRs are dramatically altered in their nuclear localization in response to hypoxia, indicating a potential non-canonical effect of the species, which would be missed without isomiR-aware analysis. Our results call for a comprehensive re-evaluation of the miRNA-seq analysis practices.
Target Distance from the Visual Field and Increased Age Affect Visual Search Efficiency: Behavioral and Modeling Evidence
Authors: Akbari Fatemeh , Asivandzadehchaharmahali Samaneh , Tanha Alireza , Narmashiri Abdolvahed 
Published: April 2024

AbstractPrevious research has demonstrated that visual search is influenced by environmental factors, but the effects of specific variables, such as target distance from the visual field center and age, are not well understood. To address this issue, we aim to investigate their impact on visual search task. Participants engaged in target-present and target-absent trials, revealing distinct patterns in search times. Behavioral data and drift-diffusion modeling (DDM) showed that increasing the target’s distance from the center of the visual field significantly reduces search efficiency. Additionally, age negatively impacts search performance, with older individuals exhibiting reduced efficiency. This comprehensive examination contributes to understanding cognitive mechanisms in visual processing. These findings highlight the importance of considering spatial and age-related factors in visual search tasks.
Balancing the risks of mating: biogeographic evidence of cleistogamy as a bet hedging strategy
Authors: Weissman Maya , Zhang Dafeng , Kartzinel Rebecca , Weinreich Daniel 
Published: April 2024

AbstractCleistogamy is a mating system in which plants produce some proportion of closed, autonomously self-pollinating flowers. Cleistogamous flowers differ from chasmogamous flowers, which are open flowers capable of outcrossing. Both dimorphic cleistogamy (cleistogamous and chasmogamous flowers produced on the same plant) and complete cleistogamy occur. Cleistogamy has been hypothesized to be a bet hedging strategy for reducing risk in the face of unpredictable pollinator availability. However, conflicting results across species and challenges connecting theory to data have prevented researchers from proving that cleistogamy is bet hedging. To test the bet hedging hypothesis, we investigated the distribution of over 400 cleistogamous species through biogeographical analyses. We find that cleistogamy is more prevalent in cooler, more variable environments. Additionally, we find that among cleistogamous species, complete cleistogamy is more likely to occur in warmer, more stable, tropical and subtropical environments. We hypothesize that the difference in distribution between complete and dimorphic cleistogamy may be driven by the opposing forces of selection to increase cleistogamy proportion and extinction risk, which we test using a heuristic Markov transition model. We conclude that the distribution of cleistogamy suggests that the strategy has evolved in variable environments, consistent with expectations for bet hedging.
Integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits
Authors: Okamoto Jeffrey , Yin Xianyong , Ryan Brady , Chiou Joshua , Luca Francesca , Pique-Regi Roger , Im Hae Kyung , Morrison Jean , Burant Charles , Fauman Eric B. , Laakso Markku , Boehnke Michael , Wen Xiaoquan 
Published: April 2024

AbstractWe present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple gene products (e.g. encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products. In simulations, Multi-INTACT achieves higher power than existing methods, maintains calibrated false discovery rates, and detects the true causal gene product(s). We apply Multi-INTACT to GWAS on 1,408 metabolites, integrating the GTEx expression and UK Biobank protein QTL datasets. Multi-INTACT infers 52% to 109% more metabolite causal genes than protein-alone or expression-alone analyses and indicates both gene products are relevant for most gene nominations.
TemBERTure: Advancing protein thermostability prediction with Deep Learning and attention mechanisms
Authors: Rodella Chiara , Lazaridi Symela , Lemmin Thomas 
Published: April 2024

AbstractUnderstanding protein thermostability is essential for various biotechnological and biological applications. However, traditional experimental methods for assessing this property are time-consuming, expensive, and error-prone. Recently, the application of Deep Learning techniques from Natural Language Processing (NLP) was extended to the field of biology, with an emphasis on protein modeling. From a linguistic perspective, the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar.This study explores the potential of Deep Learning models trained on protein sequences to predict protein thermostability which provide improvements with respect to current approaches. We implemented TemBERTure, a Deep Learning framework to classify the thermal class (non-thermophilic or thermophilic) and predict and melting temperature of a protein, based on its primary sequence. Our findings highlight the critical role that data diversity plays on training robust models. Models trained on datasets with a wider range of sequences from various organisms exhibited superior performance compared to those with limited diversity. This emphasizes the need for a comprehensive data curation strategy that ensures a balanced representation of diverse species in the training data, to avoid the risk that the model focuses on recognizing the evolutionary lineage of the sequence rather than the intrinsic thermostability features. In order to gain more nuanced insights into protein thermostability, we propose leveraging attention scores within Deep Learning models to gain more nuanced insights into protein thermostability. We show that analyzing these scores alongside the 3D protein structure could offer a better understanding of the complex interplay between amino acid properties, their positioning, and the surrounding microenvironment, all crucial factors influencing protein thermostability.This work sheds light on the limitations of current protein thermostability prediction methods and introduces new avenues for exploration. By emphasizing data diversity and utilizing refined attention scores, future research can pave the way for more accurate and informative methods for predicting protein thermostability.Availability and ImplementationTemBERTure model and the data are available athttps://github.com/ibmm-unibe-ch/TemBERTure
Decoding Arc Transcription: A Live-Cell Study of Stimulation Patterns and Transcriptional Output
Authors: Kim Dong Wook , Moon Hyungseok C. , Lee Byung Hun , Park Hye Yoon 
Published: April 2024

AbstractActivity-regulated cytoskeleton-associated protein (Arc) plays a crucial role in synaptic plasticity, a process integral to learning and memory. Arc transcription is induced within a few minutes of stimulation, making it a useful marker for neuronal activity. However, the specifics of the neuronal activity that triggers Arc transcription remain unknown because it has not been possible to observe mRNA transcription in live cells in real time. Using a genetically encoded RNA indicator (GERI) mouse model that expresses endogenous Arc mRNA tagged with multiple GFPs, we investigated Arc transcriptional activity in response to various electrical stimulation patterns. In dissociated hippocampal neurons, we found that the pattern of stimulation significantly affects Arc transcription. Specifically, a 10 Hz burst stimulation induced the highest rate of Arc transcription. Concurrently, the amplitudes of nuclear calcium transients also reached their peak with 10 Hz stimulation, indicating a correlation between calcium concentration and transcription. However, our dual-color single-cell imaging revealed that there were no significant differences in calcium amplitudes between Arc-positive and Arc-negative neurons upon 10 Hz burst stimulation, suggesting the involvement of other factors in the induction of Arc transcription. Our live-cell RNA imaging provides a deeper insight into the complex regulation of transcription by activity patterns and calcium signaling pathways.
Comparing cell penetration of biotherapeutics across human cell lines
Authors: Batistatou Nefeli , Kritzer Joshua A. 
Published: April 2024

AbstractA major obstacle in biotherapeutics development is maximizing cell penetration. Ideally, assays would allow for optimization of cell penetration in the cell type of interest early in the drug development process. However, few assays exist to compare cell penetration across different cell types, independent of drug function. In this work, we applied the chloroalkane penetration assay (CAPA) in seven mammalian cell lines as well as primary cells. Careful controls were used to ensure data could be compared across cell lines. We compared the penetration of several peptides and drug-like oligonucleotides and saw significant differences among the cell lines. To help explain these differences, we quantified the relative activities of endocytosis pathways in these cell lines and correlated them with the penetration data. Based on these results, we knocked down clathrin in a cell line with an efficient permeability profile and observed reduced penetration of peptides, but not oligonucleotides. Finally, we used small-molecule endosomal escape enhancers and observed enhancement of cell penetration of some oligonucleotides, but only in some of the cell lines tested. CAPA data provide valuable points of comparison among different cell lines, including primary cells, for evaluating the cell penetration of various classes of peptides and oligonucleotides.TOC Graphic
moPepGen: Rapid and Comprehensive Identification of Non-canonical Peptides
Authors: Zhu Chenghao , Liu Lydia Y. , Ha Annie , Yamaguchi Takafumi N. , Zhu Helen , Hugh-White Rupert , Livingstone Julie , Patel Yash , Kislinger Thomas , Boutros Paul C. 
Published: April 2024

AbstractGene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteomic diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient detection and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at:https://github.com/uclahs-cds/package-moPepGen.