bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coronal Diffusion-Weighted Imaging Does Not Improve Detection of Posterior Fossa Infarcts Compared With Conventional Axial Diffusion-Weighted Imaging
Authors: Mehan Jr William A. , Shin Donghoon , Buch Karen 
Published: April 2024

Purpose
Recent publications have suggested incorporating coronal diffusion-weighted imaging (DWI) sequences and axial DWI sequences to enhance the detection of posterior fossa infarcts (PFIs). This study evaluated the utility of coronal DWIs compared with axial DWIs for assessing PFIs in the emergency department (ED).

Methods
A retrospective, institutional review board-approved study was conducted at a level I stroke center, including 118 patients who presented to the ED between 2016 and 2023 with suspected PFI. Inclusion criteria involved patients who underwent emergent 1.5 T magnetic resonance imaging (MRI) and had axial and coronal DWI sequences. Two neuroradiologists independently evaluated the DWI sequences for PFI detection in 2 rounds, with a 4-week interval between rounds. The neuroradiologists assessed the quality of axial and coronal DWIs using a 5-point Likert scale. Descriptive statistics, interrater reliability, and marginal homogeneity tests were performed.

Results
Among the 118 MRI scans, 23 (19%) showed PFI on axial and coronal DWI sequences. All 23 cases were identified on axial DWI, whereas 8 cases of PFI (35%) were not detected on coronal DWI (P value = 0.013). No PFIs were observed on coronal DWI that was not identified on axial DWI. The quality scores for both raters were significantly higher for axial DWIs than coronal DWIs (P value <0.00001).

Conclusion
Despite recent recommendations advocating for the inclusion of coronal DWI in PFI detection, this study's findings indicate no improvement in PFI detection or image quality using coronal DWI. Further research is necessary to validate these results and explore the potential benefits of incorporating coronal DWI in assessing posterior fossa strokes.
Factors Associated With Distress Related to Perceived Dignity in Patients With Rheumatic Diseases
Authors: Pascual-Ramos Virginia , Contreras-Yáñez Irazú , Cuevas-Montoya Maximiliano , Guaracha-Basañez Guillermo Arturo , García-Alanis Mario , Rodríguez-Mayoral Oscar , Chochinov Harvey Max 
Published: April 2024

Background
The loss of perceived dignity is an existential source of human suffering, described in patients with cancer and chronic diseases and hospitalized patients but rarely explored among patients with rheumatic diseases (RMDs). We recently observed that distress related to perceived dignity (DPD) was present in 26.9% of Mexican patients with different RMDs. The study aimed to investigate the factors associated with DPD.

Methods
This cross-sectional study was performed between February and September 2022. Consecutive patients with RMDs completed patient-reported outcomes (to assess mental health, disease activity/severity, disability, fatigue, quality of life [QoL], satisfaction with medical care, and family function) and had a rheumatic evaluation to assess disease activity status and comorbidity. Sociodemographic variables and disease-related and treatment-related variables were retrieved with standardized formats. DPD was defined based on the Patient Dignity Inventory score. Multivariate regression analysis was used.

Results
Four hundred patients were included and were representative of outpatients with RMDs, while 7.5% each were inpatients and patients from the emergency care unit. There were 107 patients (26.8%) with DPD. Past mental health–related comorbidity (Odds Ratio [OR]: 4.680 [95% Confidence Interval [CI]: 1.906–11.491]), the number of immunosuppressive drugs/patient (OR: 1.683 [95% CI: 1.015–2.791]), the physical health dimension score of the World Health Organization Quality of Life—Brief questionnaire (WHOQOL-BREF) (OR: 0.937 [95% CI: 0.907–0.967]), and the emotional health dimension score of the WHOQOL-BREF (OR: 0.895 [95% CI: 0.863–0.928]) were associated with DPD.

Conclusions
DPD was present in a substantial proportion of patients with RMDs and was associated with mental health–related comorbidity, disease activity/severity-related variables, and the patient QoL.
Placental Nanoparticle-mediated IGF1 Gene Therapy Corrects Fetal Growth Restriction in a Guinea Pig Model
Authors: Davenport Baylea N , Wilson Rebecca L , Williams Alyssa A , Jones Helen N 
Published: April 2024

ABSTRACTFetal growth restriction (FGR) caused by placental insufficiency is a major contributor to neonatal morbidity and mortality. There is currently no in utero treatment for placental insufficiency or FGR. The placenta serves as the vital communication, supply, exchange, and defense organ for the developing fetus and offers an excellent opportunity for therapeutic interventions. Here we show efficacy of repeated treatments of trophoblast-specific humaninsulin-like 1 growth factor(IGF1) gene therapy delivered in a non-viral, polymer nanoparticle to the placenta for the treatment of FGR. Using a guinea pig maternal nutrient restriction model (70% food intake) of FGR, nanoparticle-mediatedIGF1treatment was delivered to the placenta via ultrasound guidance across the second half of pregnancy, after establishment of FGR. This treatment resulted in correction of fetal weight in MNR +IGF1animals compared to sham treated controls on an ad libitum diet, increased fetal blood glucose and decreased fetal blood cortisol levels compared to sham treated MNR, and showed no negative maternal side-effects. Overall, we show a therapy capable of positively impacting the entire pregnancy environment: maternal, placental, and fetal. This combined with our previous studies using this therapy at mid pregnancy in the guinea pig and in two different mouse model and three different human in vitro/ex vivo models, demonstrate the plausibility of this therapy for future human translation. Our overall goal is to improve health outcomes of neonates and decrease numerous morbidities associated with the developmental origins of disease.
Zero-shot animal behavior classification with vision-language foundation models
Authors: Dussert Gaspard , Miele Vincent , Van Reeth Colin , Delestrade Anne , Dray Stéphane , Chamaillé-Jammes Simon 
Published: April 2024

Abstract1. Understanding the behavior of animals in their natural habitats is critical to ecology and conservation. Camera traps are a powerful tool to collect such data with minimal disturbance. They however produce very a large quantity of images, which can make human-based annotation cumbersome or even impossible. While automated species identification with artificial intelligence has made impressive progress, automatic classification of animal behaviors in camera trap images remains a developing field.2. Here, we explore the potential of foundation models, specifically Vision Language Models (VLMs), to perform this task without the need to first train a model, which would require some level of human-based annotation. Using an original dataset of alpine fauna with behaviors annotated by participatory science, we investigate the zero-shot capabilities of different kind of recent VLMs to predict behaviors and estimate behavior-specific diel activity patterns in three ungulate species.3. Our results show that using these methods, it is possible to achieve accuracies over 91% in behavior classification and produce activity patterns that closely align with those derived from participatory science data (overlap indexes between 84% and 90%).4. These findings demonstrate the potential of foundation models and vision-language models in ecological research. Ecologists are encouraged to adopt these new methods and leverage their full capabilities to facilitate ecological studies.
The Fat/Hippo pathway drives photoperiod-induced wing length polyphenism
Authors: Gudmunds Erik , Palahí Aleix , Armisén David , Scofield Douglas G. , Eriksen Freya Darling , Khila Abderrahman , Husby Arild 
Published: April 2024

AbstractIdentifying the genetic mechanisms that translate information from the environment into developmental programs to control size, shape and color are important for gaining insights into adaptation to changing environments. Insect polyphenisms provide good models to study such mechanisms because environmental factors are the main source of trait variation. Here we studied the genetic mechanism that controls photoperiod-induced wing length polyphenism in the water striderGerris buenoi. By sequencing RNA sampled from wing buds across developmental stages under different photoperiodic conditions known to trigger alternative wing developmental trajectories, we found that differences in transcriptional activity arose primarily in the late 5thinstar stage. Among the differentially expressed genes, the Fat/Hippo and ecdysone signaling pathways, both putative growth regulatory mechanisms showed significant enrichment. We used RNA interference against the differentially expressed genes Fat, Dachsous and Yorkie to assess whether they play a causative role in photoperiod induced wing length variation inGerris buenoi. Our results show that the conserved Fat/Hippo pathway is a key regulatory network involved in the control of wing polyphenism in this species. This study provides an important basis for future comparative studies on the evolution of wing polyphenism and significantly deepens our understanding of the genetic regulation of insect polyphenisms.
BiGCN: Leveraging Cell and Gene Similarities for Single-cell Transcriptome Imputation with Bi-Graph Convolutional Networks
Authors: Inoue Yoshitaka , Kulman Ethan , Kuang Rui 
Published: April 2024

AbstractMotivationRNA profiling at the single-cell level is essential for characterizing the molecular activities and functions of individual cells. The current technical limitations of single-cell RNA sequencing (scRNA-seq) technologies can lead to a phenomenon known as “dropout”, where a significant portion of gene expression is not captured. Dropout is particularly prominent in genes with low or sparse expression, greatly impacting the reliability and interpretability of scRNA-seq data. Consequently, various techniques have been developed to estimate missing gene expression using imputation, often by either modeling similarities in gene expression among cells or using gene co-expression, but rarely both.ResultsIn this study, we introduce a Bi-Graph Convolutional Network (BiGCN), a deep learning method that leverages both cell similarities and gene co-expression to capture cell-type-specific gene co-expression patterns for imputing scRNA-seq data. BiGCN constructs both a cell similarity graph and a gene co-expression graph, and employs them for convolutional smoothing in a dual two-layer Graph Convolutional Networks (GCNs). The embeddings from the two GCNs can subsequently be combined to facilitate the final imputation. BiGCN demonstrates superior performance compared to state-of-the-art imputation methods on both real and simulated scRNA-seq data. Additionally, BiGCN outperforms existing methods when tasked with clustering cells into cell types. We also perform a novel validation using a PBMC scRNA-seq dataset, and this experiment supports that BiGCN’s imputations are more realistic than competing imputation methods. In both the imputation and the cluster tasks, BiGCN consistently outperformed two variants of BiGCN that solely relied on either the gene co-expression graph or cell similarity graph. This indicates that the two graphs offer complimentary information for imputation and cell clustering, underscoring the importance of incorporating both types of information.Code Availabilityhttps://github.com/inoue0426/scBiGCN.Contactkuang@umn.edu
Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data
Authors: Xiang Zijin , Liu Zhihan , Dinh Khanh N. 
Published: April 2024

AbstractAneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.
Predation by a ciliate community mediates temperature and nutrient effects on a peatland prey prokaryotic community
Authors: DeWitt Katrina , Carrell Alyssa A. , Rocca Jennifer D. , Votzke Samantha , Yammine Andrea , Peralta Ariane L. , Weston David J. , Pelletier Dale A. , Gibert Jean P. 
Published: April 2024

AbstractTemperature significantly impacts microbial communities’ composition and function, which subsequently plays a vital role in the global carbon cycle that ultimately fuels climate change. Interactions between different microorganisms might be critical in shaping how these communities react to both temperature changes. Additionally, rising temperatures are occurring in the context of increasingly nutrient-rich ecosystems due to human activity. Nonetheless, we lack a comprehensive understanding of how predation influences microbial communities in future climate scenarios and an increasingly nutrient-rich world. Here, we assess whether predation by key bacterial consumers—ciliates—influences a microbial community’s freshwater temperature and nutrient response regarding biomass, diversity, structure, and function. In a three-week microcosm experiment, we exposed mostly prokaryotic microbial communities to a community of ciliate predators at two different temperature scenarios (ambient and +3°C, i.e., a conservative projection of climate change by 2050) and nutrient levels (low and elevated). Nutrients, temperature, and ciliate presence influenced microbial biomass and function separately, but their interaction had the largest explanatory power over the observed changes in microbial community biomass, structure, and function. Our study supports previous findings that temperature and nutrients are essential drivers of microbial community structure and function but also demonstrates that the presence of predators can mediate these effects, indicating that the biotic context is as important as the abiotic context to understand microbial responses to novel climates.ImportanceWhile the importance of the abiotic environment in microbial communities has long been studied, how prevalent ecological interactions, like predation and the broader abiotic context, may influence these responses is largely unknown. Our study disentangles the complex interplay between temperature, nutrients, and predation and their joint effects on microbial community diversity and function. The findings suggest that while temperature and nutrients are fundamental drivers of microbial community dynamics, the presence of predators significantly mediates these responses. Our study underscores the profound impact of abiotic factors on microbial communities, but how to properly understand, let alone predict, these responses, we need to account for the biotic context in which these are occurring.
Ultrasensitive amplification-free quantification of a methyl CpG-rich cancer biomarker by single-molecule kinetic fingerprinting
Authors: Dai Liuhan , Johnson-Buck Alexander , Laird Peter W. , Tewari Muneesh , Walter Nils G. 
Published: April 2024

AbstractThe most well-studied epigenetic marker in humans is the 5-methyl modification of cytosine in DNA, which has great potential as a disease biomarker in liquid biopsies of cell-free DNA. Currently, quantification of DNA methylation relies heavily on bisulfite conversion followed by PCR amplification and NGS or microarray analysis. PCR is subject to potential bias in differential amplification of bisulfite-converted methylatedversusunmethylated sequences. Here, we combine bisulfite conversion with single-molecule kinetic fingerprinting to develop an amplification-free assay for DNA methylation at the branched-chain amino acid transaminase 1 (BCAT1) promoter. Our assay selectively responds to methylated sequences with a limit of detection below 1 fM and a specificity of 99.9999%. Evaluating complex genomic DNA matrices, we reliably distinguish 2-5% DNA methylation at the BCAT1 promoter in whole blood DNA from completely unmethylated whole-genome amplified DNA. Taken together, these results demonstrate the feasibility and sensitivity of our amplification-free, single-molecule quantification approach to improve the early detection of methylated cancer DNA biomarkers.
ARTEMIS - a method for topology-independent superposition of RNA 3D structures and structure-based sequence alignment
Authors: Bohdan Davyd R. , Bujnicki Janusz M. , Baulin Eugene F. 
Published: April 2024

ABSTRACTNon-coding RNAs play a major role in diverse processes in living cells with their sequence and spatial structure serving as the principal determinants of their function. Superposition of RNA 3D structures is the most accurate method for comparative analysis of RNA molecules and for inferring sequence alignments. Topology-independent superposition is particularly relevant, as evidenced by structurally similar RNAs with sequence permutations such as tRNA and Y RNA. To date, state-of-the-art methods for RNA 3D structure superposition rely on intricate heuristics, and the potential for topology-independent superposition has not been exhausted. Recently, we introduced the ARTEM method for unrestrained pairwise superposition of RNA 3D modules and now we developed it further to solve the global RNA 3D structure alignment problem. Our new tool ARTEMIS significantly outperforms state-of-the-art tools in both sequentially-ordered and topology-independent RNA 3D structure superposition. Using ARTEMIS we discovered a helical packing motif to be preserved within different backbone topology contexts across various non-coding RNAs, including multiple ribozymes and riboswitches. We anticipate that ARTEMIS will be essential for elucidating the landscape of RNA 3D folds and motifs featuring sequence permutations that thus far remained unexplored due to limitations in previous computational approaches.
GCI: a continuity inspector for complete genome assembly
Authors: Chen Quanyu , Yang Chentao , Zhang Guojie , Wu Dongya 
Published: April 2024

AbstractMotivationRecent advances in long-read sequencing technologies have significantly facilitated the production of high-quality genome assembly. The telomere-to-telomere (T2T) gapless assembly has become the new golden standard of genome assembly efforts. Several recent efforts have claimed to produce T2T level reference genomes. However, a universal standard is still missing to qualify a genome assembly to be at T2T standard. Traditional genome assembly assessment metrics (N50 and its derivatives) have no capacity in differentiate between nearly T2T assembly and the truly T2T assembly in continuity either globally and locally. Also these metrics are independent of raw reads, which make them inflated easily by artificial operations. Therefore a gaplessness evaluation tool at single nucleotide resolution to reflect true completeness is urgently needed in the era of complete genomes.ResultsHere, we present a tool called Genome Continuity Inspector (GCI) to assess genome assembly continuity at the single base resolution, that can evaluate how close a genome assembly is close to T2T level. GCI utilized multiple aligners to map long reads from multiple platforms back to the assembly. By incorporating curated mapping coverage of high-confidence read alignments, GCI identifies potential assembly issues. Meanwhile, it also reports GCI scores to quantify the assembly overall continuity in the whole genome or chromosome scale.Availability and implementationThe open-source GCI code is freely available on Github (https://github.com/yeeus/GCI) under the MIT license.
Adverse outcomes and associations of dengue in pregnancy – a retrospective cohort study
Authors: Nallaperuma Oshadhi Lathmini , Senanayake Hematha Malinath , Godevithana Janaka Chandana 
Published: April 2024

AbstractObjectiveDengue infection is a significant health issue in tropical areas over the globe. Physiological changes of pregnancy can be confounding the clinical presentation of dengue in pregnancy. This study aimed to investigate the adverse outcomes of antenatal dengue infection and factors associated with maternal, fetal, and neonatal complications.MethodsA total of 115 pregnant women with laboratory-confirmed dengue infection admitted to two tertiary referral hospitals in Sri Lanka were followed from admission until delivery and discharge. Data on clinical and laboratory outcome variables were collected and analyzed using descriptive and inferential statistics.ResultsDengue fever accounted for the majority of cases (63.5%), followed by dengue hemorrhagic fever (33.9%) and, dengue expanded syndrome (2.6%). Maternal adverse outcomes were observed in 27.8% of cases with 19.1% of ICU admissions. Dengue category, trimester of infection, platelet count, and AST levels showed a significant association with adverse maternal outcomes. Fetal adverse outcomes affected 8.1% of cases, while neonatal adverse outcomes occurred in 28.7% of live births, with transplacental transmission observed in two cases.ConclusionDengue infection during pregnancy can lead to significant maternal, fetal, and neonatal morbidity and mortality. Identifying risk factors may help predict the severity of these outcomes. Further research is needed to develop robust prediction models and improve management strategies.
The proliferation and differentiation programs are only partially coupled duringC. elegansintestine development
Authors: Dieng Joris , Michaux Grégoire , Pacquelet Anne 
Published: April 2024

AbstractCell proliferation and differentiation are essential processes underlying multicellular organism development. Cell proliferation arrest usually precedes terminal differentiation, suggesting that these two processes may be coordinated. Here we took advantage of the very stereotyped development of theC. elegansintestine to address whether the control of the proliferation and differentiation programs are systematically coupled. We show that retarding cell cycle arrest leads to a delay in the recruitment of some, but not all, brush border components. Reciprocally, we find that cell cycle arrest relies on the differentiation factors ELT-2 and ELT-7 only in posterior intestinal cells. The occurrence of supernumerary divisions in the absence of ELT-2 and ELT-7 is associated with changes in the expression pattern of the cell cycle regulators CKI-1 and cyclin B1. Our work thus demonstrates the existence of reciprocal interactions between cell proliferation and cell differentiation. It nevertheless also shows that these two processes are only partially coupled, suggesting the existence of additional mechanisms ensuring their temporal control.