bluuebunny/crossref_metadata_2025_split huggingface dataset:
Allopolyploidy expanded gene content but not pangenomic variation in the hexaploid oilseedCamelina sativa
Authors: Bird Kevin A. , Brock Jordan R. , Grabowski Paul P. , Harder Avril M. , Shu Shengqiang , Barry Kerrie , Boston LoriBeth , Daum Christopher , Guo Jie , Lipzen Anna , Walstead Rachel , Grimwood Jane , Schmutz Jeremy , Lu Chaofu , Comai Luca , McKay John K. , Pires J. Chris , Edger Patrick P. , Lovell John T. , Kliebenstein Daniel J. 
Published: August 2024

ABSTRACTAncient whole-genome duplications (WGDs) are believed to facilitate novelty and adaptation by providing the raw fuel for new genes. However, it is unclear how recent WGDs may contribute to evolvability within recent polyploids. Hybridization accompanying some WGDs may combine divergent gene content among diploid species. Some theory and evidence suggest that polyploids have a greater accumulation and tolerance of gene presence-absence and genomic structural variation, but it is unclear to what extent either is true. To test how recent polyploidy may influence pangenomic variation, we sequenced, assembled, and annotated twelve complete, chromosome-scale genomes ofCamelina sativa, an allohexaploid biofuel crop with three distinct subgenomes. Using pangenomic comparative analyses, we characterized gene presence-absence and genomic structural variation both within and between the subgenomes. We found over 75% of ortholog gene clusters are core inCamelina sativaand <10% of sequence space was affected by genomic structural rearrangements. In contrast, 19% of gene clusters were unique to one subgenome, and the majority of these were Camelina-specific (no ortholog in Arabidopsis). We identified an inversion that may contribute to vernalization requirements in winter-type Camelina, and an enrichment of Camelina-specific genes with enzymatic processes related to seed oil quality and Camelina’s unique glucosinolate profile. Genes related to these traits exhibited little presence-absence variation. Our results reveal minimal pangenomic variation in this species, and instead show how hybridization accompanied by WGD may benefit polyploids by merging diverged gene content of different species.
Single-cell DNA methylation analysis tool Amethyst reveals distinct noncanonical methylation patterns in human glial cells
Authors: Rylaarsdam Lauren E. , Nichols Ruth V. , O’Connell Brendan L. , Coleman Stephen , Yardımcı Galip Gürkan , Adey Andrew C. 
Published: August 2024

AbstractSingle-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties in highly heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and expedites batch integration, doublet detection, dimensionality reduction, clustering, cell type annotation, differentially methylated region calling, and interpretation of results, facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell (PBMC) and human cortex data. We further leverage Amethyst on an atlas-scale brain dataset to describe a noncanonical methylation pattern in human astrocytes and oligodendrocytes, challenging the notion that this form of methylation is principally relevant to neurons in the brain. Tools such as Amethyst will increase accessibility to single-cell methylation data analysis, catalyzing research progress across diverse contexts.
When monkeys meet an ANYmal robot in the wild
Authors: Canteloup Charlotte , Lee Joonho , Zimmermann Samuel , Alvino Morgane , Montenegro Markus , Hutter Marco , van de Waal Erica 
Published: August 2024

AbstractAnimal-robot interaction studies have been of increasing interest in research, but most of these studies have involved robots interacting with insects, birds, and frogs in laboratory settings. To date, only two studies used non-human primates and no behavioral study has tested the social integration of a robot in a group of wild primates. To fill this gap, we studied the interactions between the quadruped ANYmal robot and a group of 37 wild vervet monkeys in South Africa. The ANYmal robot is a remote-controlled sheep-sized robot with an open box of food on its back. We gradually introduced the robot to the monkeys following five different steps over 6 days for a total exposition time of about 10h. The monkeys habituated to the robot very quickly with six individuals eating the food in the robot’s box from the second day. A few individuals, mostly juveniles, emitted alarm calls towards the robot. In total, seven individuals, high rankers, spent time near the robot and 21 monkeys approached the robot from a greater distance. High rankers displayed significantly more vigilant and self-centered behaviors and they, with females and juveniles, ate more food in the robot’s box compared to low rankers, males, and adults conversely. This study offers exciting perspectives on the phenomena of social acceptance of machines in mammalian societies and the automation of field data collection.
In Silicostudies provide new structural insights intotrans-dimerization of β1and β2subunits of the Na+,K+-ATPase
Authors: Ramírez-Salinas Gema , Rosas-Trigueros Jorge L. , Huerta Christian Sosa , Shoshani Liora , Martínez-Archundia Marlet 
Published: August 2024

ABSTRACTThe Na+,K+-ATPase is an electrogenic transmembrane pump located in the plasma membrane of all animal cells. It is a dimeric protein composed of α and β subunits and has a third regulatory subunit (γ) belonging to the FXYD family . This pump plays a key role in maintaining low concentration of sodium and high concentration of potassium intracellularly. The α subunit is the catalytic one while the β subunit is important for the occlusion of the K+ions and plays an essential role in trafficking of the functional αβ complex of Na+,K+-ATPase to the plasma membrane. Interestingly, the β1and β2(AMOG) isoforms of the β subunit, function as cell adhesion molecules in epithelial cells and astrocytes, respectively. Early experiments suggested a heterotypic adhesion for the β2. Recently, we reported a homotypic trans-interaction between β2-subunits expressed in CHO cells. In this work we useIn Silicomethods to analyze the physicochemical properties of the putative homophilic trans-dimer of β2subunits and provide insights about thetrans-dimerization interface stability. Our structural analysis predicts a molecular recognition mechanism of atrans-dimeric β2-β2subunit and permits designing experiments that will shed light upon possible homophilic interactions of β2subunits in the nervous system.Author summaryThe adhesion molecule on glia (AMOG) is the β2isoform of the β-subunit of the Na+-pump that is localized in the nervous system, specifically in astrocytes. It was shown that it mediates Neuron-Astrocyte interaction, promoting neurite outgrowth and migration during brain development. In recent years we have shown that the ubiquitous β1isoform is a homophilic adhesion molecule in epithelia and therefore we hypothesized that β2could also interact as a homophilic adhesion protein. In a previous work we show that fibroblasts (CHO) transfected with the human β2subunit of the Na+-pump become adhesive. Moreover, protein-protein interaction assay in a co-culture of cells transfected with β2tagged with two different markers (His6and YFP) reveal a positive interaction between the β2-subunits. In the present work, we apply bioinformatics methods to analyze and discuss the formation of atrans-dimer of β2-subunits. OurIn Silicostudy predicts a relatively stable dimer with an interface that involves the participation of four out of the seven N-glycosylation sites. Nevertheless, interacting interface and the dynamics of the β2-β2trans-dimer is different from that of the β1-β1dimer; it involves different surfaces and therefore it explains why β-subunits can not form mixed (β1-β2)trans-dimers.
Longitudinal metabolomics data analysis informed by mechanistic models
Authors: Li Lu , Hoefsloot Huub , Bakker Barbara M. , Horner David , Rasmussen Morten A. , Smilde Age K. , Acar Evrim 
Published: August 2024

AbstractMotivationMetabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. In this paper, we introduce a novel data analysis approach where data-driven methods are guided by prior information through joint analysis of simulated data generated using a human metabolic model and real metabolomics measurements.ResultsWe arrange time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC2000cohort as a third-order tensor:subjectsbymetabolitesbytime samples. Simulated challenge test data generated using a human whole-body metabolic model is also arranged as a third-order tensor:virtual subjectsbymetabolitesbytime samples. Real and simulated data sets are coupled in themetabolitesmode and jointly analyzed using coupled tensor factorizations to reveal the underlying patterns. Our experiments demonstrate that joint analysis of simulated and real data has a better performance in terms of pattern discovery achieving higher correlations with a BMI (body mass index)-related phenotype compared to the analysis of only real data in males while in females, the performance is comparable. We also demonstrate the advantages of such a joint analysis approach in the presence of incomplete measurements and its limitations in the presence of wrong prior information.AvailabilityThe code for joint analysis of real and simulated metabolomics data sets is released as a GitHub repository. Simulated data can also be accessed using the GitHub repo. Real measurements of plasma samples are not publicly available. Data may be shared by COPSAC through a collaboration agreement. Data access requests should be directed to Morten A. Rasmussen (morten.arendt@dbac.dk).
Differences in maternal diet fiber content influence patterns of gene expression and chromatin accessibility in fetuses and piglets
Authors: Chalabi Smahane , Loonen Linda , Boekhorst Jos , Li Houcheng , Fang Lingzhao , Harrison Peter W. , Lakhal Wassim , Lluch Jerome , Sokolov Alexey , Djebali Sarah , Rau Andrea , Giuffra Elisabetta , Wells Jerry 
Published: August 2024

AbstractThis study investigates the impact of maternal gestation diets with varying fiber contents on gene expression and chromatin accessibility in fetuses and piglets fed a low fiber diet post weaning. High-fiber maternal diets, enriched with sugar beet pulp or pea internal fiber, were compared to a low-fiber maternal diet to evaluate their effects on liver and muscle tissues. The findings demonstrate that maternal high-fiber diets significantly alter the chromatin accessibility, predicted transcription factor activity and transcriptional landscape in both fetuses and piglets. A gene set enrichment analysis revealed over-expression of gene ontology terms related to metabolic processes and under-expression of those linked to immune responses in piglets from sows given the high-fiber diets during gestation. This suggests better metabolic health and immune tolerance of the fetus and offspring, in line with the documented epigenetic effects of short chain fatty acids on immune and metabolic pathways. A deconvolution analysis of the bulk RNA-seq data was performed using cell-type specific markers from a single cell transcriptome atlas of adult pigs. These results confirmed that the transcriptomic and chromatin accessibility data do not reflect different cell type compositions between maternal diet groups but rather phenotypic changes triggered by the critical role of maternal nutrition in shaping the epigenetic and transcriptional environment of fetus and offspring. Our findings have implications for improving animal health and productivity as well as broader implications for human health, suggesting that optimizing maternal diet with high-fiber content could enhance metabolic health and immune function in the formative years after birth and potentially to adulthood.
Proteochemometric modeling strengthens the role of Q299 for GABA transporter subtype selectivity
Authors: Kickinger Stefanie , Seiler Anna , Digles Daniela , Ecker Gerhard F. 
Published: August 2024

AbstractProteochemometric modeling (PCM) combines ligand information as well as target information in order to predict an output variable of interest (e.g. activity of a compound). The big advantage of PCM compared to conventional Quantitative Structure-Activity Relationship (QSAR) modeling is, that by creating a single model one can not only predict the affinity of a diverse set of compounds to a diverse set of targets, but also extrapolate the specific ligand-protein interactions that might be relevant for activity. In this study, we compiled a dataset of 323 compounds and their bioactivity data regarding the inhibition of the four GABA-transporter (GAT1/BGT1/GAT2/GAT3) subtypes, which are potential new drug targets for treating epilepsy. Proteochemometric modeling using partial least squares and random forest provided models which performed equally well than conventional QSAR models for each individual transporter. However, by analyzing the importance of the protein descriptors used in the PCM models, we identified the amino acid Leu300/Q299/L294/L314/ in GAT1/BGT1/GAT2/GAT3 to be relevant for binding and subtype selectivity.
Weed Instance Segmentation from UAV ortho-mosaic Images based on Deep Learning
Authors: Lu Chenghao , Yu Kang 
Published: August 2024

AbstractWeeds significantly impact agricultural production, and traditional weed control methods often harm soil health and the environment. This study aims to develop deep learning-based segmentation models in identifying weeds in potato fields captured by Unmanned Aerial Vehicle (UAV)orthophotos and to explore the effects of weeds on potato yield. UAVs were used to collect RGB data from potato fields, flying at an altitude of 10m, with Real-ESRGAN Super-Resolution (SR) enhancing image resolution. We applied the Segment Anything Model (SAM) to do semi-automatic annotation, followed by training the YOLOv8 and MASK-RCNN models for segmentation. Also we used ANOVA and linear regression to analyze the effects of weeds and nitrogen fertilizer on yield. Results showed that the detection accuracy mAP50 scores for YOLOv8 and Mask R-CNN were 0.902 and 0.920, respectively, with the Real-ESRGAN-enhanced model achieving 0.909. When multiple weed types were present, accuracy decreased to 0.86. The linear regression model, incorporating plant and weed coverage areas, explained 41.2% of yield variation (R2= 0.412, p-value = 0.01). Both YOLOv8 and Mask R-CNN achieved high accuracy, with YOLOv8 converging faster. Real-ESRGAN reconstruction slightly improved accuracy. Different nitrogen fertilizer treatments had no significant effect on yield, while weed control treatments significantly impacted yield, showing the importance of precise weed mapping.. This study provides insights into weed segmentation and contributes to environmentally friendly precision weed control.Graphic Abstract
Shared and Unique Neural Codes for Biological Motion Perception in Humans and Macaque Monkeys
Authors: Cheng Yuhui , Xin Yumeng , Lu Xiqian , Yang Tianshu , Ma Xiaohan , Yuan Xiangyong , Liu Ning , Jiang Yi 
Published: August 2024

AbstractThroughout evolution, living organisms have honed the ability to swiftly recognize biological motion (BM) across species. However, how the brain processes within- and cross-species BM, and the evolutionary progression of these processes, remain unclear. To investigate these questions, we examined brain activity in the lateral temporal areas of humans and monkeys as they passively observed upright and inverted human and macaque BM stimuli. In humans, the middle temporal area (hMT+) responded to both human and macaque BM stimuli, while the right posterior superior temporal sulcus (pSTS) exhibited selective responses to human BM stimuli. This selectivity was evidenced by an increased feedforward connection from hMT+ to pSTS during the processing of human BM stimuli. In monkeys, the MT region processed BM stimuli from both species, but no subregion in the STS anterior to MT was specific to conspecific BM stimuli. A comparison of these findings suggests that upstream brain regions (i.e., MT) may retain homologous functions across species, while downstream brain regions (i.e., STS) may have undergone differentiation and specialization throughout evolution. These results provide insights into the commonalities and differences in the specialized visual pathway engaged in processing within- and cross-species BMs, as well as their functional divergence during evolution.
Exploring tropical freshwater soundscapes in Southeast Asia: insights into auditory sensory adaptation of wild Siamese fighting fishBetta splendens
Authors: RAMOS Andreia , GONÇALVES David , VASCONCELOS Raquel O. 
Published: August 2024

AbstractWhile soundscapes shape the structure and function of auditory systems over evolutionary timescales, there is limited information regarding the adaptation of wild fish populations to their natural acoustic environments. This is particularly relevant for freshwater ecosystems, which are extremely diverse and face escalating pressures from human activities and associated noise pollution. The Siamese fighting fishBetta splendensis one of the most important cultured species in the global ornamental fish market and is increasingly recognized as a model organism for genetics and behavioural studies. This air-breathing species (Anabantoidei), characterized by the presence of a suprabranchial labyrinth organ, is native to Southeast Asia and inhabits low flow freshwater ecosystems that are increasingly threatened due to habitat destruction and pollution.We characterized the underwater soundscape, along with various ecological parameters, across five marshland habitats ofB. splendens, from lentic waterbodies to small canals near a lake in Chiang Rai province (Thailand). All habitats exhibited common traits of low dissolved oxygen and dense herbaceous vegetation. Soundscapes were relatively quiet with Sound Pressure Level (SPL) around 102-105 dB re 1 µPa and most spectral energy below 1000 Hz. Sound recordings captured diverse biological sounds, including potential fish vocalizations, but primarily insect sounds. Based on Auditory Evoked Potential (AEP) recordings, we identified sex-specific differences and a best hearing range within 100-400 Hz. This low-frequency tuning highlights the potential susceptibility ofB. splendensto anthropogenic noise activities.This study provides first characterization of the auditory sensitivity and natural soundscape ofB. splendens,establishing an important ground for future hearing research in this species. The information provided on the auditory sensory adaptation ofB. splendensemphasizes the importance of preserving quiet soundscapes from lentic freshwater ecosystems.
KnowVolution of an efficient polyamidase through molecular dynamics simulations of incrementally docked oligomeric substrates
Authors: Puetz Hendrik , Illig Alexander-Maurice , Vorobii Mariia , Janknecht Christoph , Contreras Francisca , Flemig Fabian , Schwaneberg Ulrich 
Published: August 2024

AbstractManagement of synthetic polymer waste is one of the most pressing challenges for society today. Enzymatic recycling of polycondensates like polyamides (PA), however, remains limited due to a lack of efficient polyamidases. This study reports the directed evolution of the polyamidase NylCp2-TS. Key positions involved in enzyme-substrate interactions and PA 6 hydrolysis are identified through random mutagenesis and molecular dynamics (MD) simulations. The final variant, NylC-HP (NylCp2-TSF134W/D304M/R330A), exhibits a 6.9-fold increased specific activity (520 ± 19 µmol6-AHAeq. h−1mgenzyme−1) and enhanced thermal stability (Tm = 90 °C, ΔTm = 4.2 °C), making NylC-HP the fastest polyamidase for PA 6 and PA 6,6 hydrolysis. Despite the improved reaction rate, the degree of depolymerization remains below 1 %. To understand the molecular basis of achieved improvements and factors limiting the degree of depolymerization, intra- and intermolecular interactions of various enzyme-substrate complexes are analyzed by incremental docking of PA 6 tetramers and MD simulations. After optimizing the activity and stability of NylC-HP, the findings suggest that widening the substrate binding pocket is likely necessary to improve substrate accessibility to target more buried attack sites on the polymer surface, and thereby enhancing the degree of depolymerization.
Integrating sequence composition information into microbial diversity analyses with k-mer frequency counting
Authors: Bokulich Nicholas A. 
Published: August 2024

AbstractK-mer frequency information in biological sequences is used for a wide range of applications, including taxonomy classification, sequence similarity estimation, and supervised learning. However, in spite of its widespread utility, k-mer counting has been largely neglected for diversity estimation. This work examines the application of k-mer counting for alpha and beta diversity as well as supervised classification from microbiome marker-gene sequencing datasets (16S rRNA gene and full-length fungal ITS sequences). Results demonstrate a close correspondence with phylogenetically aware diversity metrics, and advantages for using k-mer-based metrics for measuring microbial biodiversity in microbiome sequencing surveys. K-mer counting appears to be a suitable and efficient strategy for feature processing prior to diversity estimation as well as supervised learning in microbiome surveys. This allows incorporation of subsequence-level information into diversity estimation without the computational cost of pairwise sequence alignment. K-mer counting is proposed as a complementary approach for feature processing prior to diversity estimation and supervised learning analyses, enabling large-scale reference-free profiling of microbiomes in biogeography, ecology, and biomedical data. A method for k-mer counting from marker-gene sequence data is implemented in the QIIME 2 plugin q2-kmerizer (https://github.com/bokulich-lab/q2-kmerizer).ImportanceK-mers are all of the subsequences of lengthkthat comprise a sequence. Comparing the frequency of k-mers in DNA sequences yields valuable information about the composition of these sequences and their similarity. This work demonstrates that k-mer frequencies from marker-gene sequence surveys can be used to inform diversity estimates and machine learning predictions that incorporate sequence composition information. Alpha and beta diversity estimates based on k-mer frequencies closely correspond to phylogenetically aware diversity metrics, suggesting that k-mer-based diversity estimates are useful proxy measurements especially when reliable phylogenies are not available, as is often the case for some DNA sequence targets such as for internal transcribed spacer sequences.