bluuebunny/crossref_metadata_2025_split huggingface dataset:
Concise functional enrichment of ranked gene lists
Authors: Jia Xinglin , Phan An , Kadelka Claus 
Published: July 2023

AbstractGenome-wide expression data has become ubiquitous within the last two decades. Given such data, functional enrichment methods identify functional categories (e.g., biological processes) that preferentially annotate differentially expressed genes. However, many existing methods operate in a binary manner, disregarding valuable information contained in the gene ranking. The few methods that consider the ranking often return redundant or non-specific functional categories.To address these limitations, we developed a novel method called Concise Ranked Functional Enrichment (CRFE), which effectively leverages the ranking information in gene expression data to compute a non-redundant set of specific functional categories that are notably enriched for highly ranked genes. A particularly useful feature of CRFE is a tunable parameter that defines how much focus should be given to the most highly ranked genes. Using four treatment-control RNA-seq datasets, we compared the performance of CRFE with the two most widely used types of functional enrichment methods, Gene Set Enrichment Analysis and over-representation analysis. We evaluated the methods based on their ability to utilize ranking information, generate non-redundant results, and return functional categories with high information content. CRFE excelled in all evaluated criteria, outperforming the existing methods, each of which exhibits deficiencies in at least one aspect. Using lung adenocarcinoma data, we further showed that the functional categories identified by CRFE are biologically meaningful.In conclusion, CRFE computes an informative set of functional categories that summarizes genome-wide expression data. With its superior performance over existing methods, CRFE harbors great promise to become a widely used functional enrichment method.Author summaryGiven a list of differentially expressed genes as input, functional enrichment methods reveal which functional categories (e.g., biological processes) were likely activated by the cell and are responsible for the differential expression. We developed a new such method, called Concise Ranked Functional Enrichment (CRFE), which addresses the limitations of current approaches by incorporating gene ranking information to compute a concise and specific set of enriched functional categories. Using four treatment-control RNA-seq datasets, we evaluate how well CRFE and the two currently most widely used methods perform in three criteria. We find that CRFE outperforms each of the alternative methods in at least one of the evaluated criteria, demonstrating its superiority. A high-level interpretation of the functional categories identified by CRFE for lung adenocarcinoma datasets highlights its usefulness for experimentalists. Overall, CRFE harnesses the power of ranked gene lists to generate a focused and non-redundant set of enriched functional categories. Our study positions CRFE as a promising method for functional enrichment analysis, with the potential to advance research in this field.
Timed sequence task: a new paradigm to study motor learning and flexibility in mice
Authors: Urushadze Anna , Janicek Milan , Abbondanza Alice , Janickova Helena 
Published: July 2023

AbstractMotor learning and flexibility allow animals to perform routine actions efficiently while keeping them flexible. There is a number of paradigms used to test cognitive flexibility but not many of them focus specifically on learning of complex motor sequences and their flexibility. While many tests use operant or touchscreen boxes that offer high throughput and reproducibility, the motor actions themselves are mostly simple presses of a designated lever. To focus more on motor actions during the operant task and to probe the flexibility of these well-trained actions, we developed a new operant paradigm for mice, the, “timed sequence task”. The task requires mice to learn a sequence of lever presses that have to be emitted in precisely defined time limits. After training, the required pressing sequence and/or timing of individual presses is modified to test the ability of mice to alter their previously trained motor actions. We provide a code for the new protocol that can be used and adapted to common types of operant boxes. In addition, we provide a set of scripts that allow automatic extraction and analysis of numerous parameters recorded during each session. We demonstrate that the analysis of multiple performance parameters is necessary for detailed insight into animals’ behavior during the task. We validate our paradigm in an experiment using the valproate model of autism as a model of cognitive inflexibility. We show that the valproate mice show superior performance at specific stages of the task, paradoxically due to their propensity to more stereotypic behavior.Significance StatementCognitive flexibility impairment is a crucial component of many neurological disorders and it is frequently evaluated in animal models. As the commonly used tests usually do not focus on motor learning and the ability to adapt motor sequences, we designed a new paradigm to evaluate motor learning and its flexibility. The timed sequence task is automatized and easily accessible as it is based on widely available operant boxes. During the training, the task requires precise timing of each action to force stereotypic performance. Its relative complexity allows detailed analysis of multiple parameters and therefore detailed insight into animal’s behavior. The task can be used to reveal and understand subtle differences in motor and operant learning and flexibility.Graphical Abstract
An automated approach for systematic detection of Key Biodiversity Areas
Authors: Nania Dario , Ficetola Gentile Francesco , Falaschi Mattia , Pacifici Michela , Lumbierres Maria , Rondinini Carlo 
Published: July 2023

AbstractThe new Key Biodiversity Areas (KBA) standard is an important method for identifying regions of the planet hosting unique biodiversity. KBAs are identified through the implementation of threshold-based criteria that can be applied to any target species and region. Efficient methods to rapidly assess the existence of potential KBAs in different areas of the planet are still missing, although they are needed to accelerate the KBA identification process for large numbers of species globally. We developed a methodology to scan geographical regions and detect potential KBAs under multiple criteria. We tested the methodology on 59 species of reptiles and amphibians in Italy through the application of selected KBA criteria. Potential KBAs were identified for multiple species under most criteria, covering 1.4% to 12% of the study area, depending on analytical settings. Unit size used to identify KBAs played an important role in shaping the distribution of potential KBAs, also affecting the degree of overlap between areas triggered by different criteria. New potential KBAs identified in this study are only partially nested within current KBAs in Italy (previously identified for birds) and within the national protected areas.
Differential modulation of miR-122 transcription by TGFβ1/BMP6: implications for nonresolving inflammation and hepatocarcinogenesis
Authors: Paluschinski Martha , Kordes Claus , Vucur Mihael , Buettner Veronika , Roderburg Christoph , Xu Haifeng C , Shinte Prashant , Lang Philipp A , Luedde Tom , Castoldi Mirco 
Published: July 2023

AbstractChronic inflammation is widely recognized as a significant factor that promotes and worsens the development of malignancies, including hepatocellular carcinoma. This study aimed to explore the potential role of microRNAs in inflammation-associated nonresolving hepatocarcinogenesis. By conducting a comprehensive analysis of altered microRNAs in animal models with liver cancer of various etiologies, we identified miR-122 as the most significantly downregulated microRNA in the liver of animals with inflammation-associated liver cancer. Although previous research has indicated the importance of miR-122 in maintaining hepatocyte function, its specific role as either the trigger or the consequence of underlying diseases remains unclear. Through extensive analysis of animals andin vitromodels, we have successfully demonstrated thatMIR122transcription is differentially regulated by the immunoregulatory cytokines by the transforming growth factor-beta 1 (TGFβ1) and the bone morphogenetic protein-6 (BMP6). Furthermore, we presented convincing evidence directly linking reducedMIR122transcription to inflammation and in chronic liver diseases. The results of this study strongly suggest that prolonged activation of signaling pathways, leading to disruption of cytokine-mediated regulation ofMIR122, may significantly contribute to the onset and exacerbation of chronic liver disease.
P-PPI: accurate prediction of peroxisomal protein-protein interactions (P-PPI) using deep learning-based protein sequence embeddings
Authors: Anteghini Marco , Martins dos Santos Vitor AP , Saccenti Edoardo 
Published: July 2023

AbstractProtein-protein interactions (PPIs) are crucial for various biological processes, and their prediction is typically accomplished through experimental methods, which can be time-consuming and costly. Computational methods provide a faster and more cost-effective approach, leveraging protein sequences and other data sources to infer PPIs. Deep learning (DL) approaches have shown promising results in various protein-related tasks, including PPI prediction. However, DL-based embeddings are often not thoroughly compared or evaluated against state-of-the-art tools. Additionally, existing PPI predictors incorporate different types of information beyond protein sequence representation, making it important to assess the effectiveness of DL-based embeddings solely relying on protein sequences. In this work, we benchmark and compare commonly used DL-based embeddings for PPI prediction based solely on protein sequence information. We utilize high-quality training data, including experimentally validated negative interactions from the Negatome database. The best model, obtained through double cross-validation and hyperparameter optimization, is selected and evaluated to predict peroxisomal PPIs. The resulting tool, P-PPI, is further enhanced by combining AlphaFold2-Multimer predictions with the P-PPI model, leveraging DL-based embeddings and protein structure predictions for a comprehensive analysis of peroxisomal PPIs. This integrated approach holds significant potential to advance our understanding of complex protein networks and their functions.
E-PoSa: a novel and effective tool for sampling pollen directly from flowers
Authors: Pioltelli E. , Guzzetti L. , Tonietti L. , Copetta A. , Biella P. , Campone L. , Galimberti A. 
Published: July 2023

ABSTRACTPollinator insects are declining worldwide also due to the alteration of their diet with severe implications on their health status. Pollinators diet relies mainly on flower rewards (i.e., pollen and nectar) and a precise characterization of their chemical composition is crucial in defining pollinators’ nutritional ecology. In this context, the pollen represents a challenging source to investigate, especially due to operative challenges during collection operations and to the small amounts produced per flower.Here, we designed and tested a novel, easy-to-assemble tool for pollen sampling: E-PoSa (Electronic Pollen Sampler), based on the use of a portable vacuum cleaner. We compared it with some of the most used sampling methods for pollen (i.e., anthers sieving and sampling of the whole anthers) by looking at the differences in their quantitative recovery and nutritional profile. Its applicability in ecological studies was also corroborated by an assessment of its recovery rate obtained from a panel of wildflowers species in an operational environment.The data obtained showed a significantly higher pollen recovery capacity of E-PoSa compared to the conventional sieving approach and the success in retrieving enough pollen to conduct phytochemical analyses from a broad range of flower morphologies in the field. Our results also demonstrated that high purity pollen can be collected with E-PoSa and that the device does not introduce any significant variation in the nutritional analysis compared to the conventional sieving.This new sampling approach represents a cheap and easy-to-assemble tool encouraging its future use not only in the field of pollen nutrition but also in a wide variety of other contexts related to pollination ecology. Acknowledging the potential influences of the sampling techniques and moving towards shared standardized field protocols will advance the comprehension of species interactions and foraging patterns of pollinators and their nutritional needs.
SplicingLore : a web resource for studying the regulation of cassette exons by human splicing factors
Authors: Polvèche Hélène , Valat Jessica , Fontrodona Nicolas , Lapendry Audrey , Janczarski Stéphane , Mortreux Franck , Auboeuf Didier , Bourgeois Cyril F. 
Published: July 2023

ABSTRACTOne challenge faced by scientists from the alternative RNA splicing field is to decode the cooperative or antagonistic effects of splicing factors to understand and eventually predict splicing outcomes on a genome-wide scale. In this manuscript, we introduce SplicingLore, an open access database and web resource that help to fill this gap in a straightforward manner. The database contains a collection of RNA-seq-derived lists of alternative exons regulated by a total of 75 different splicing factors. All datasets were processed in a standardized manner, ensuring valid comparisons and correlation analyses. The user can easily retrieve a factor-specific set of differentially included exons from the database, or provide a list of exons and search which splicing factor(s) control(s) their inclusion. Our simple workflow is fast and easy to run, and it ensures a reliable calculation of correlation scores between the tested datasets. As a proof of concept, we predicted and experimentally validated a novel functional cooperation between the RNA helicases DDX17 and DDX5 and the HNRNPC protein. SplicingLore is available athttps://splicinglore.ens-lyon.fr/.
Bird Species diversity from the Southern West part of West Bengal, India
Authors: Dinda Sourav Ch. , Pal Arabinda , Sarkar Moulik , Das Paresh Chandra , Mondal Niladri 
Published: July 2023

AbstractBirds are essential for stabilizing the balance between various ecosystems. To estimate their diversity, we studied species distribution and made an annotated checklist along with their feeding habitats in the Southwest part of West Bengal, India. We studied the bird diversity using the point count method on various habitats, including agroforest, grassland, sub-tropical forest, and plateau forest regions. A total of 343 species were identified during our study period (2015-2019). We found high species diversity and sightings in the study sites, with 55 new records for the region. The whole study was conducted using a citizen science approach and amateur bird watchers’ community. This study will emphasize the future biodiversity conservation practice for the avifauna.
Identification of treatment-responsive genes in spatial transcriptomics data by leveraging injection site information
Authors: Masone Felicita Pia , Napolitano Francesco 
Published: July 2023

AbstractSpatial Transcriptomics assays allow to study gene expression as a function of the spatial position of cells across a tissue sample. Although several methods have been proposed to identify spatially variable genes, they do not take into account the position of the injection site in the case of treated samples. In this study, we developed a method to identify treatment-responsive genes based on the assumption that the distance of the cells from the injection site across the tissue would affect the corresponding transcriptional response. In particular, we tested our approach using a publicly available ST dataset obtained after injection of heme into the striatum nucleus of a murine brain. We observed that several biologically relevant genes were detected by our method as showing a distance-dependent expression trend. We finally compared the results against a ground-truth gene set and a state-of art pattern-based method.
Larvicidal activity of the photosensitive insecticides, methylene blue and rose bengal, inAedes aegyptiandAnopheles gambiaemosquitoes
Authors: Meier Cole J. , Hillyer Julián F. 
Published: July 2023

ABSTRACTBACKGROUNDInsecticides are critical for controlling mosquito populations and mitigating the spread of vector–borne disease, but their overuse has selected for resistant populations. A promising alternative to classical chemical insecticides is photosensitive molecules—here called photosensitive insecticides or PSIs—that when ingested and activated by light, generate broadly toxic reactive oxygen species. This mechanism of indiscriminate oxidative damage decreases the likelihood that target site modification-based resistance evolves. Here, we tested whether the PSIs, methylene blue (MB) and rose bengal (RB), are viable insecticides across the mosquito lineage.RESULTSMB and RB are phototoxic to bothAe. aegyptiandAn. gambiaeat micromolar concentrations, with greatest toxicity when larvae are incubated in the dark with the PSIs for 2 hr prior to photoactivation. MB is ten times more toxic than RB, and microscopy-based imaging suggests that this is because ingested MB escapes the larval gut and disperses throughout the hemocoel whereas RB remains confined to the gut. Adding food to the PSI-containing water has a bidirectional, concentration-dependent effect on PSI toxicity; toxicity increases at high concentrations but decreases at low concentrations. Finally, adding sand to the water increases the phototoxicity of RB toAedes aegypti.CONCLUSIONMB and RB are larvicidal via a light activated mechanism, and therefore, should be further investigated as an option for mosquito control.
Mitigating autocorrelation during spatially resolved transcriptomics data analysis
Authors: Maher Kamal , Wu Morgan , Zhou Yiming , Huang Jiahao , Zhang Qiangge , Wang Xiao 
Published: July 2023

AbstractSeveral computational methods have recently been developed for characterizing molecular tissue regions in spatially resolved transcriptomics (SRT) data. However, each method fundamentally relies on spatially smoothing transcriptomic features across neighboring cells. Here, we demonstrate that smoothing increases autocorrelation between neighboring cells, causing latent space to encode physical adjacency rather than spatial transcriptomic patterns. We find that randomly sub-sampling neighbors before smoothing mitigates autocorrelation, improving the performance of existing methods and further enabling a simpler, more efficient approach that we callspatialintegration (SPIN). SPIN leverages the conventional single-cell toolkit, yielding spatial analogies to each tool: clustering identifies molecular tissue regions; differentially expressed gene analysis calculates region marker genes; trajectory inference reveals continuous, molecularly defined ana tomical axes; and integration allows joint analysis across multiple SRT datasets, regardless of tissue morphology, spatial resolution, or experimental technology. We apply SPIN to SRT datasets from mouse and marmoset brains to calculate shared and species-specific region marker genes as well as a molecularly defined neocortical depth axis along which several genes and cell types differ across species.
Statistical prediction of microbial metabolic traits from genomes
Authors: Li Zeqian , Selim Ahmed , Kuehn Seppe 
Published: July 2023

AbstractThe metabolic activity of microbial communities is central to their role in biogeochemical cycles, human health, and biotechnology. Despite the abundance of sequencing data characterizing these consortia, it remains a serious challenge to predict microbial metabolic traits from sequencing data alone. Here we culture 96 bacterial isolates individually and assay their ability to grow on 10 distinct compounds as a sole carbon source. Using these data as well as two existing datasets, we show that statistical approaches can accurately predict bacterial carbon utilization traits from genomes. First, we show that classifiers trained on gene content can accurately predict bacterial carbon utilization phenotypes by encoding phylogenetic information. These models substantially outperform predictions made by constraint-based metabolic models automatically constructed from genomes. However, phylogeny-based predictions fail to predict traits for taxa that are phyloge-netically distant from any strains in the training set. To overcome this we train improved models on gene presence/absence to predict carbon utilization traits from gene content. We show that models that predict carbon utilization traits from gene presence/absence can generalize to taxa that are phylogenetically distant from the training set either by exploiting biochemical information for feature selection or by having sufficiently large datasets. In the latter case, we provide evidence that a statistical approach can identify putatively mechanistic genes involved in metabolic traits. Our study demonstrates the potential power for predicting microbial phenotypes from genotypes using statistical approaches.
Seasonality effects and field-estimation of colony size in desert ants
Authors: Subach Aziz , Bega Darar , Saar Maya 
Published: July 2023

AbstractThe colony level of eusocial insects is considered the reproductive unit on which natural selection operates. Therefore, seasonal demographic movements and estimations of colony size are crucial variables. Excavating colonies of ants to extract their size is daunting, unhealthy to the surrounding environment, and it may prevent long-term research, including testing seasonal effects on colony size. Previous capture-recapture methods that avoid excavating colonies have been proven inefficient when sampling mostly underground dwellers as ants. To address this issue, we offer a simple method to estimate the colony size of desert ants (Cataglyphis niger) in a field setting- based on a field experiment, a literature review, and four laboratory experiments. First, we find that between 10-15% of the colony size are outgoing foragers. Second, we find seasonal effects on colony size and foraging activity: colony size varies and is larger in winter than in summer, and in contrast - the proportion of foragers out of colony size is higher in summer than in winter. This suggests that the energetic requirements of the colonies are higher in summer than in winter. Based on uniquely large sample size, our proposed field method may be useful for other co-occurringCataglyphisspecies. Moreover, extracting ant colony sizes and evaluating ant biomass is advantageous for future studies to evaluate the carrying capacity of semi sand-dunes habitats.