bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive Cerebral Aneurysm Rupture Prediction: From Clustering to Deep Learning
Authors: Zakeri Mostafa , Atef Amirhossein , Aziznia Mohammad , Jafari Azadeh 
Published: November 2024

AbstractCerebral aneurysm is a silent yet prevalent condition that affects a substantial portion of the global population. Aneurysms can develop due to various factors and present differently, necessitating diverse treatment approaches. Choosing the appropriate treatment upon diagnosis is paramount, as the severity of the disease dictates the course of action. The vulnerability of an aneurysm, particularly in the circle of Willis, is a critical concern; rupture can lead to irreversible consequences, including death. The primary objective of this study is to predict the rupture status of cerebral aneurysms using a comprehensive dataset that includes clinical, morphological, and hemodynamic data extracted from blood flow simulations of patients with actual vessels. Our goal is to provide valuable insights that can aid in treatment decision-making and potentially save the lives of future patients. Diagnosing and predicting the rupture status of aneurysms based solely on brain scans poses a significant challenge, often with limited accuracy, even for experienced physicians. However, harnessing statistical and machine learning (ML) techniques can enhance rupture prediction and treatment strategy selection. We employed a diverse set of supervised and unsupervised algorithms, training them on a database comprising over 700 cerebral aneurysms, which included 55 different parameters: 3 clinical, 35 morphological, and 17 hemodynamic features. Two of our models—stochastic gradient descent (SGD) and multi-layer perceptron (MLP) — predicted cerebral aneurysm rupture, achieving a maximum area under the curve (AUC) of 0.86, a precision rate of 0.86, and a recall rate of 0.90. Given the sensitivity of the data and the critical nature of the condition, recall is a more vital parameter than accuracy and precision; our study achieved an acceptable recall score. Key features for rupture prediction included ellipticity index, low shear area ratio, and irregularity. Additionally, a one-dimensional CNN model predicted rupture status along a continuous spectrum, achieving 0.78 accuracy on the testing dataset, providing nuanced insights into rupture propensity.
Meta-Analytical Approaches to Ordinal Outcome Data in Clinical Interventional Studies: A Scoping Review with Reproducible Research
Authors: Mulhem Ali 
Published: November 2024

AbstractBackgroundConducting meta-analyses on ordinal outcome data is more complex than on binary or continuous data. This study aims to summarise the current biomedical literature on meta-analytical methods for ordinal outcomes and attempts to reproduce the results of previous studies.MethodsA systematic search was conducted in three databases, MEDLINE, EMBASE, and PsycINFO, from inception to 05/05/2024. Forward and backward citation searches were also performed. The screening was conducted in two phases using Covidence software. Studies were included if they reported or compared methods for meta-analysis of ordinal outcomes in clinical interventional studies. Relevant studies were summarised and discussed. If sufficient data for methods comparison were available, either in the retrieved reports or after contacting the authors, an attempt at reproducible research was made.Results333 records were screened, yielding four methodological studies that met the inclusion criteria. These studies addressed meta-analytical methods for ordinal scales ranging from 5 to 20 ordered categories. The three primary approaches identified were (1) ordinal models (proportional odds and generalised odds), (2) binary models (dichotomisation of ordinal scales), and (3) continuous models (treating ordinal scales as continuous variables). None of the included studies provided a comprehensive comparison of all three approaches. Two studies compared different proportional odds models; one compared binary with proportional odds, but the full results were not published, and one compared continuous and generalised odds models using simulated data with one scenario. The latter study allowed for reproducibility, but our analysis produced different results, and attempts to clarify the discrepancy with the authors were unsuccessful.ConclusionsA significant knowledge gap exists regarding the optimal meta-analytical method for ordinal outcomes in clinical interventional studies. Further methodological research is required to establish a robust evidence base for choosing the most appropriate approach.
UNCONVENTIONAL SECRETION OF THE AUTOPHAGY-RELATED PROTEIN VMP1 VIA EXTRACELLULAR VESICLES AS A POTENTIAL BIOMARKER FOR PANCREATIC CELL INJURY
Authors: Tadic Mariana S. , Renna Felipe J. , López Malena Herrera , Orquera Tamara , Mingorance Fabiana López , Ropolo Alejandro , Vaccaro Maria I. 
Published: November 2024

AbstractCellular stress activates various mechanisms, including autophagy and vesicular trafficking, to maintain homeostasis and cope with pathological conditions such as acute pancreatitis. One of these mechanisms involves the unconventional secretion of extracellular vesicles (EVs), lipid bilayer-delimited particles released by almost all cell types into the extracellular medium. Recently, an autophagy-related secretory pathway that releases EVs has been discovered. Our study focuses on Vacuole Membrane Protein 1 (VMP1), an autophagy-related protein implicated in pancreatitis, diabetes, pancreatic cancer, and cellular stress management. VMP1 expression triggers autophagy in mammalian cells through its ubiquitination and its direct binding to BECN1. Here, we demonstrate that VMP1 is secreted to the extracellular medium integrated into the membrane of EVs. Using cell lines expressing different VMP1-tag expression plasmids, we successfully isolated and purified VMP1-containing EVs (VMP1-EVs) from the extracellular medium through ultracentrifugation and immune isolation techniques. The secretion of VMP1-EVs was reduced by mTOR inhibition and in the absence of proteins essential for autophagosome formation, such as ATG5. The secretion of endogenous VMP1 by pancreatic acinar cells was highly induced in experimental models of cell stress, including blocked autophagic flux with Bafilomycin and exposure to supramaximal doses of caerulein. Moreover, immuno-isolation of VMP1-EVs from cell models revealed that VMP1 is a component of EV membranes. Transmission electron microscopy (TEM) and dynamic light scattering (DLS) analysis of immuno-isolated VMP1-EVs indicated a diameter of around 150 nm. Western blot assay demonstrated that the autophagy related protein LC3-II and p62 are co-secreted with VMP1 in cellular stress models. Furthermore, VMP1-EVs are taken up by different host cells, suggesting that VMP1-EVs may be able to mediate remote communication between cells. In conclusion, we have demonstrated for the first time that the pancreatitis-associated transmembrane protein VMP1 is unconventionally secreted as a component of extracellular vesicles via a secretory autophagy pathway.
Exploring phenotype-related single-cells through attention-enhanced representation learning
Authors: Wu Qinhua , Ding Junxiang , He Ruikun , Hui Lijian , Liu Junwei , Li Yixue 
Published: November 2024

AbstractThe scope of atlas-level single-cell investigations reveals the pathogenesis and progression of various diseases. Accurate interpretation of phenotype-related single-cell data necessitates the pre-definition of single-cell subtypes and the identification of their abundance variations for downstream analysis. In this context, biases from batch correlation and the selection of clustering resolutions can significantly impact single-cell data analysis and result interpretation. To strengthen the associations across single cells in each sample and their clinical phenotype, and to enhance single-cell exploration by integrating cell and gene-level information. This study proposes a method to learn phenotype-related sample representations from single cells via the attention-based multiple instance learning (AMIL) mechanism. This approach incorporates gene expression profiles from each single cell for sample-level clinical phenotype prediction. By integrating deep learning interpretation methods and phenotype-specific single-cell attention weights across sample groups, this method highlights critical gene programs and cell subtypes that mostly contribute to the sample-level clinical phenotype, and facilitate mechanistic exploration. Using single-cell atlases from COVID-19 infected patients and age-related healthy human blood, we demonstrate that this method can accurately predict disease severity and age-related phenotypes. Additionally, variations in cellular attention reflect the underlying biological mechanisms associated with these phenotypes. This method proposes a supervised framework for single-cell data interpretation and can be further adapted for other atlas-level clinical phenotype analyses.
Bacterial cell-free DNA profiling reveals co-elevation of multiple bacteria in newborn foals with suspected sepsis
Authors: Chen Li-Ting , Wesdorp Emmy , Jager Myrthe , Siegers Esther W. , Theelen Mathijs J.P. , Besselink Nicolle , Vermeulen Carlo , Zomer Aldert L. , Broens Els M. , Wagenaar Jaap A. , de Ridder Jeroen 
Published: November 2024

AbstractBackgroundSepsis is the leading cause of death in newborn foals. This study investigates whether cell-free DNA (cfDNA) sequencing can enhance bacterial pathogen detection in foals with suspected sepsis and addresses existing knowledge gaps and diagnostic challenges.MethodsWe developed afoalcfDNA sequencing forbacteriaidentification (cfFBI) workflow, integrating wetlab and computational protocols to detect increased bacterial cfDNA abundance in blood. Specifically, cfFBI focusses on enriching bacterial cfDNA molecules and preventing false positive bacterial identifications. cfFBI was applied to blood samples of 25 hospitalized foals categorized according to the neonatal Systemic Inflammatory Response Syndrome (nSIRS) criteria and 7 healthy foals.ResultscfDNA levels of potential sepsis-causing bacterial genera were elevated in all 11 nSIRS-positive foals compared to healthy foals (n=7), and in 8/11 (72.7%) when compared to both nSIRS-negative (n=4; nSIRS=0) and healthy foals, with multiple genera elevated in 5/11 (45.5%). The total cfDNA concentration, bacterial cfDNA fraction and bacterial diversity were not different between the foal groups. However, nSIRS-positive foals showed significantly different end-motifs in host chromosomal cfDNA, and a decrease in host mitochondrial cfDNA fraction.ConclusionsThis study is the first to demonstrate that cfDNA sequencing in blood samples from newborn foals enables detection of pathogenic bacteria and can help identify novel host-related sepsis biomarkers. The elevated presence of multiple sepsis-causing genera in nSIRS-positive foals and the difference in end-motif, suggests that multibacterial elevation may be more common than previously thought. These findings indicate that cfDNA sequencing holds promise as a future diagnostic tool for identifying sepsis in newborn foals.
WhaleLM: Finding Structure and Information in Sperm Whale Vocalizations and Behavior with Machine Learning
Authors: Sharma Pratyusha , Gero Shane , Rus Daniela , Torralba Antonio , Andreas Jacob 
Published: November 2024

AbstractLanguage models (LMs), which are neural sequence predictors trained to model distributions over natural language texts, have come to play a central role in human language technologies like machine translation and information retrieval. They have also contributed to the scientific study of human language itself, enabling progress on long-standing questions about the learnability, optimality, and universality of key features of human languages. Many analogous questions exist in the study of communication between non-human animals—for which, in many cases, we have only a preliminary understanding of signals’ structure and use. Can neural sequence models help us understand these animal communication systems as well? We use these models to characterize the structure and information content of sperm whale vocalizations. Sperm whales (Physeter macrocephalus) engage in complex, coordinated behaviours like foraging and navigation in the darkness of the ocean while exchanging sequences of rhythmic clicks known as codas. However, little is known about whether there are any systematic patterns governing coda production, or how codas influence group decision-making and behaviour. To begin to answer these questions, we first train a neural sequence model (a ‘sperm whale language model’) to predict whales’ future vocalizations from their conversational history. By systematically manipulating the information available to this model, and measuring the change in predictive accuracy, we show that sperm whale vocalizations exhibit order dependence, long-range dependencies on up to the past eight codas in an exchange, and predictable turn-taking. Second, we train the sequence model to predict whales’ behaviour from their vocal exchanges, and find that both current behavioural context and future actions are predictable, with accuracies of 72% and 86% respectively, from coda sequences. Our study provides the first evidence that sperm whale vocalizations contain information that could be used to coordinate behaviour. More generally, it offers a framework for using modern machine learning tools for hypothesis generation and to assist in investigating the structure and function of unknown communication systems.
DJ-1 mediates regulation of metabolism and immune response in Parkinson’s disease astrocytes and Glioblastoma cells
Authors: Mencke Pauline , Ohnmacht Jochen , Jeannelle Félicia , Ries Adrien J , de la Maza Mónica Miranda , Ullrich Mathilde , Massart François , Mulica Patrycja , Badanjak Katja , Hanss Zoé , Rybicki Arkadiusz , Antony Paul , Delcambre Sylvie , Arena Giuseppe , Cruciani Gérald , Jarazo Javier , Gavotto Floriane , Jäger Christian , Ewen Anouk , Pacheco Maria Pires , Brenner Dirk , Schwamborn Jens , Sauter Thomas , Sinkkonen Lasse , Dittmar Gunnar , Taipa Ricardo , Bouvier David , Meiser Johannes , Grünewald Anne , Bonifati Vincenzo , Platten Michael , Krüger Rejko , Boussaad Ibrahim 
Published: November 2024

AbstractAn inverse correlation for the expression of Parkinson’s disease (PD)- and cancer-associated genes has been previously reported. Genes that are upregulated in cancer are frequently downregulated in PD andvice versa. PARK7, encoding DJ-1, was initially identified as an oncogene, but loss of DJ-1 causes early-onset PD. However, it remains elusive how differential DJ-1 levels contribute to opposite cell fates in cancer and PD. Here, we demonstrate specific effects of differential DJ-1 protein levels on the energy metabolism and cell growth in patient-derived cellular models of PD and glioblastoma (GBM) cell lines. Impaired energy metabolism was associated with an increased immune response upon IL-1β stimulation and increased apoptosis and decreased cell growth in models of PD, whereas in GBM cells increased metabolic activity translated into a reduced immune response and increased cell growth. Furthermore, we found decreased glutathione (GSH) synthesis and therefore increased levels of reactive oxygen species (ROS) and oxidized glutathione (GSSG) in models of DJ-1 deficiency and decreased ROS levels in GBM cell lines. Thus, the mechanism by which DJ-1 modulates these phenotypes is the same in both diseases.Graphical AbstractDJ-1 levels modulate GSSG/GSH ratio and ROS levels, which results in divergent effects on cell growth and immune response in DJ-1-dependent glial pathologies in glioblastoma and PD. In models of PD, DJ-1 level dependent phenotypes can be rescued by antioxidant treatment that reduces the GSSG/GSH ratio and ROS levels.
The N-terminal extension region of BamE coordinates interaction between the β-barrel domain of BamA and BamD for efficient assembly
Authors: Maruno Yuki , Germany Edward Marshall , Nakajima Yukari , Ohyama Rei , Masumura Yukihiko , Shiota Takuya 
Published: November 2024

AbstractIn the Gram-negative bacteria, the assembly of β-barrel proteins into the outer membrane is mediated by the β-barrel Assembly Machinery (BAM) complex. BamA is a central component of the BAM complex. The molecular mechanisms of BamA when the substrate protein is assembled has been well elucidated and further the inhibitors targeting key mechanisms of BamA have been isolated. Although the BAM complex is a multi-subunit machine and its coordination is necessary for full function, the molecular mechanism remains unclear. Here we report on a fine-tuning system with a subunit called BamE, which is required for the cooperation of two essential subunits BamA and BamD. BamE is a lipoprotein with an N-terminal extension region between the lipid-modifying cysteine on the N-terminus and the periplasmic folding domain. Our interaction mapping revealed that the N-terminal extension region of BamE interacts with the β-barrel domain of BamA, which contains a catalytic site for assembly termed lateral gate. The mutational analysis demonstrated that the N-terminal extension region of BamE fine-tunes the assembly stage dependent BamA-BamD interaction, responsible for the crucial role for assembly and integrity of the outer membrane. We suggest that this fine-tuning system plays an important role in the substrate relay system from the receptor BamD to the insertase BamA. Furthermore, structure prediction-based analysis suspects that the C-terminal region of BamD acts as the replacement of BamE in BamE-less bacteria.
Developmental and transcriptomic responses of Hawaiian bobtail squid early stages to ocean warming and acidification
Authors: Otjacques E. , Paula J.R. , Ruby E.G. , Xavier J.C. , McFall-Ngai M.J. , Rosa R. , Schunter C. 
Published: November 2024

1AbstractCephalopods play a central ecological role across all oceans and realms. However, under the current climate crisis, their physiology and behaviour are impacted, and we are beginning to comprehend the effects of environmental stressors at a molecular level. Here, we study the Hawaiian bobtail squid (Euprymna scolopes), known for its specific binary symbiosis with the bioluminescent bacteriumVibrio fischeriacquired post-hatching. We aim to understand the response (i.e., developmental and molecular) ofE. scolopesafter the embryogenetic exposure to different conditions: i) standard conditions (control), ii) increased CO2(ΔpH 0.4 units), iii) warming (+3ºC), or iv) a combination of the two treatments. We observed a decrease in hatching success across all treatments relative to the control. Using transcriptomics, we identified a potential trade-off in favour of metabolism and energy production, at the expense of development under increased CO2. In contrast, elevated temperature shortened the developmental time and, at a molecular level, showed signs of alternative splicing and the potential for RNA editing. The data also suggest that the initiation of the symbiosis may be negatively affected by these environmental drivers of change in the biosphere, although coping mechanisms by the animal may occur.
Ensuring Robustness in Scientific Research, Split-root assays as an example case
Authors: Salvatore L , Pierik R , Kajala K , ten Tusscher K.H 
Published: November 2024

AbstractScientific progress relies on reproducibility, replicability, and robustness of research outcomes. After briefly discussing these terms and their significance for reliable scientific discovery, we argue for the importance of investigating robustness of outcomes to experimental protocol variations. We highlight challenges in achieving robust, replicable results in multi-step plant science experiments, using split-root assays in Arabidopsis thaliana as a case study. These experiments are important for unraveling the contributions of local, systemic and long-distance signalling in plant responses and play a central role in nutrient foraging research. The complexity of these experiments allows for extensive variation in protocols. We investigate what variations do or do not result in similar results and provide concrete recommendations for enhancing replicability and robustness of these and other complex experiments through extending the level of detail in research protocols.
AnnotIEM: novel tool for microbiome species-level annotation of 16S gene based microbial sequencing
Authors: Bhattacharyya Madhumita , Rauer Luise , Reiger Matthias , Afghani Jamie , Huelpuesch Claudia , Traidl-Hoffmann Claudia , Neumann Avidan U. 
Published: November 2024

AbstractIdentification of microbial species is essential for the generation of meaningful results from microbiome analysis. However, correct annotation of 16S microbial sequences remains a challenge, especially down to the species-level, because most available databases of 16S sequences are not well curated. AnnotIEM is a new tool for species-level annotation of sequences derived from 16S rRNA gene sequencing. AnnotIEM’s novel hit-selection algorithm combines top-hit and majority-hit alignment results from multiple databases. Benchmarking using in-vitro and in-silico mock community control datasets showed that AnnotIEM’s annotation precision on species-level (median=0.83) is significantly (p<0.0001) better than any currently available annotation tool. Benchmarking using a number of real-world environmental microbiome studies demonstrated that AnnotIEM gives higher fraction (58-68%) of species-level annotated sequences, as compared to any other currently available tool. In conclusion, AnnotIEM significantly improves bacterial annotation, which can greatly enhance the results of any microbiome study, and could be potentially used also for other organisms.
Four ZIPs contribute to Zn, Fe, Cu and Mn acquisition at the outer root domain
Authors: Robe Kevin , Lefebvre-Legendre Linnka , Cleard Fabienne , Barberon Marie 
Published: November 2024

AbstractZinc (Zn), an essential micronutrient, plays a crucial role in plant development. However, the specific transporters involved in Zn uptake from the soil remain unclear in dicotyledonous. Using promoter-reporter lines inArabidopsis thaliana, we identified severalZIP(Zn-regulated transporter, Iron-regulated transporter (IRT)-like Protein) family members that are expressed in the epidermis and potentially involved in Zn acquisition from the outer root domain. ZIP2, ZIP3, ZIP5 and ZIP8 predominantly localize to the plasma membrane of epidermal and cortical cells, supporting their potential roles in metal uptake from the soil. Through physiology studies, ionomic profiling and genetic analysis, we determined that ZIP3 and ZIP5 are key contributors to Zn acquisition, while ZIP2 and ZIP8 are primarily involved in copper (Cu) and iron (Fe) acquisition respectively. Notably, ZIP3 and ZIP8 exhibit outer polarity in root epidermal cells, similar to IRT1, underscoring the significance of transporter polarity in mineral acquisition. These findings provide new insights into the mechanisms of metal uptake in plant roots and offer potential strategies for biofortification to enhance metal content in plants.