bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interpersonal synchronization of movement intermittency
Authors: Tomassini Alice , Laroche Julien , Emanuele Marco , Nazzaro Giovanni , Petrone Nicola , Fadiga Luciano , D’Ausilio Alessandro 
Published: June 2021

AbstractMost animal species group together and coordinate their behavior in quite sophisticated manners for mating, hunting or defense purposes. In humans, coordination at a macroscopic level (the pacing of movements) is evident both in daily life (e.g., walking) and skilled (e.g., music and dance) behaviors. By examining the fine structure of movement, we here show that interpersonal coordination is established also at a microscopic – sub-movement – level. Natural movements appear as marked by recurrent (2-3 Hz) speed breaks, i.e., submovements, that are traditionally considered the result of intermittency in (visuo)motor control. In a series of interpersonal motor coordination tasks, we demonstrate that submovements are not independent between interacting partners but produced in a tight temporal relation that reflects the directionality in the partners’ informational coupling. These findings unveil a potential core mechanism for behavioral coordination that is based on between-persons synchronization of the intrinsic dynamics of action-perception cycles.
Somatic HCN channels augment and speed up GABAergic basket cell input-output function in human neocortex
Authors: Szegedi Viktor , Bakos Emőke , Furdan Szabina , Barzo Pal , Tamas Gabor , Lamsa Karri 
Published: June 2021

ABSTRACTNeurons in the mammalian brain exhibit evolution-driven species-specific differences in their functional properties. Therefore, understanding the human brain requires unraveling the human neuron “uniqueness” and how it contributes to the operation of specific neuronal circuits. We show here that a highly abundant type of inhibitory neurons in the neocortex, GABAergic parvalbumin-expressing basket cell (pv+BC), exhibits in the human brain a specific somatic leak current mechanism, which is absent in their rodent neuronal counterparts. Human pv+BC soma shows electric leak conductance mediated by hyperpolarization-activated cyclic nucleotide-gated channels. This leak conductance has depolarizing effects on the resting membrane potential and it accelerates the rise of synaptic potentials in the cell soma. The leak facilitates the human pv+BC input-to-output fidelity and shortens the action potential generation to excitatory inputs. This mechanism constitutes an adaptation that enhances signal transmission fidelity and speed in the common inhibitory circuit in the human but not in the rodent neocortex.
Adolescent to young adult longitudinal development of subcortical volumes in two European sites with four waves
Authors: Backhausen Lea L. , Fröhner Juliane H. , Lemaître Hervé , Artiges Eric , Palillère Martinot Marie-Laure , Herting Megan M. , Sticca Fabio , Banaschewski Tobias , Barker Gareth J. , Bokde Arun L.W. , Desrivières Sylvane , Flor Herta , Grigis Antoine , Garavan Hugh , Gowland Penny , Heinz Andreas , Brühl Rüdiger , Nees Frauke , Papadopoulos-Orfanos Dimitri , Poustka Luise , Hohmann Sarah , Robinson Lauren , Walter Henrik , Winterer Jeanne , Whelan Robert , Schumann Gunter , Martinot Jean-Luc , Smolka Michael N. , Vetter Nora C. ,   the IMAGEN Consortium
Published: June 2021

AbstractAdolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age-related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls.
Bee foraging preferences, microbiota and pathogens revealed by direct shotgun metagenomics of honey
Authors: Galanis Anastasios , Vardakas Philippos , Reczko Martin , Harokopos Vaggelis , Hatzis Pantelis , Skoulakis Efthimios M. C. , Pavlopoulos Georgos A. , Patalano Solenn 
Published: June 2021

AbstractHoneybees (Apis mellifera) continue to succumb to human and environmental pressures despite their crucial role in providing essential ecosystem services. Owing to their foraging and honey production activities, honeybees form complex relationships with species across all domains, such as plants, viruses, bacteria (symbiotic and pathogenic), and other hive pests, making honey a valuable biomonitoring tool for assessing their ecological niche. Thus, the application of honey shotgun metagenomics (SM) has paved the way for a detailed description of the species honeybees interact with, in order to better assess the multiple factors governing their health. Here, we describe the implementation of optimized honey DNA extraction methodology coupled to direct shotgun metagenomics (Direct-SM) analysis, and to a computationally optimised and validated pipeline for taxonomic classification of species detected in honey. By comparing honey collected across 3 harvesting seasons in a stable apiary, we show that Direct-SM can describe the variability of sampled plant species, revealing honeybee behavioural adaptation. In addition, we reveal that Direct-SM can non-invasively capture the diversity of species comprising the core and non-core bacterial communities of the gut microbiome. Finally, we show that this methodology is applicable for the monitoring of pathogens and particularly for the biomonitoring varroa infestation. These results suggest that Direct-SM can accurately and comprehensively describe honeybee ecological niches and can be deployed to assess bee health in the field.
A Comprehensive Model of Glucose-Insulin Regulation Including Acute and Prolonged Effects of Physical Activity in Type 1 Diabetes
Authors: Deichmann Julia , Bachmann Sara , Pfister Marc , Szinnai Gabor , Kaltenbach Hans-Michael 
Published: June 2021

AbstractObjectiveFor type 1 diabetic patients, accurate adjustment of insulin treatment to physical activity (PA) is a challenging open problem. Glucose uptake by the exercising muscles increases acutely, causing increased hepatic glucose production to maintain glucose homeostasis. Meanwhile, insulin sensitivity is elevated for a prolonged period to drive glycogen repletion during recovery. These processes strongly depend on PA duration and intensity, making their combined effects difficult to predict accurately. In this work, we develop a model of glucose-insulin regulation that captures PA from low to high intensity including acute and prolonged effects on glucose metabolism.MethodsWe extended an existing minimal model of glucose-insulin regulation to capture PA-driven changes in glucose metabolism. We incorporated the insulin-independent increase in glucose uptake and production, including the effects of glycogen depletion and of high-intensity PA on production. The model also captures the prolonged increase in insulin sensitivity.ResultsThe model accurately predicts glucose dynamics of published data during short and prolonged PA of moderate to high intensity and during subsequent recovery. In-silico full-day studies elucidate the effects of timing, duration and intensity of PA and of insulin bolus reduction on glucose levels during and after the activity.ConclusionThe proposed model captures the blood glucose dynamics during all main PA regimes.SignificanceMathematical models of glucose-insulin regulation are critical components of closed-loop insulin delivery and clinical decision support systems for achieving good glycemic control. The presented model shows potential for the development and assessment of algorithms targeting treatment adjustment to PA.
Frontal cortical functional connectivity is impacted by anaesthesia in macaques
Authors: Giacometti C , Dureux A , Autran-Clavagnier D , Wilson C. R. E. , Sallet J , Dirheimer M. , Procyk E , Hadj-Bouziane F , Amiez C 
Published: June 2021

AbstractA critical aspect of neuroscience is to establish whether and how brain networks evolved across primates. To date, most comparative studies have used resting-state functional Magnetic Resonance Imaging (rs-fMRI) in anaesthetized non-human primates and in awake humans. However, anaesthesia strongly affects rs-fMRI signals. The present study investigated the impact of the awareness state (anaesthesia vs. awake) within the same group of macaque monkeys on the rs-fMRI functional connectivity (FC) organization of a well characterized network in the human brain, the cingulo-frontal lateral network. Results in awake macaques revealed a similar FC pattern to that previously uncovered in the human brain. Rostral seeds in the cingulate sulcus exhibited stronger correlation strength with rostral compared to caudal lateral frontal cortical areas while caudal seeds in the cingulate sulcus displayed stronger correlation strength with caudal compared to anterior lateral frontal cortical areas. Critically, this inverse rostro-caudal functional gradient was abolished under anaesthesia. This study demonstrates that the FC pattern of cingulo-frontal cortical networks is preserved from macaque to human but some of its properties can only be observed in the awake state, warranting significant caution when comparing FC patterns across species under different states.
Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor revealed by binding site image processing
Authors: Eguida Merveille , Rognan Didier 
Published: June 2021

ABSTRACTRationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.AUTHOR SUMMARYComputational comparison of binding sites in proteins can provide insights on potential unrelated proteins that may bind to similar ligands. However, accurate prediction of binding site similarity requires powerful methods, ideally able to detect even local similarities. We herewith applied a recently developed computer vision method to identify an unexpected binding site similarity between two totally unrelated proteins that was confirmed experimentally by in vitro biophysical binding assays. Considering more precisely local similarities can therefore efficiently guide drug discovery, notably to repurpose existing drug candidates.
Formation, collective motion, and merging of macroscopic bacterial aggregates
Authors: Courcoubetis George , Gangan Manasi , Lim Sean , Guo Xiaokan , Haas Stephan , Boedicker James Q. 
Published: June 2021

AbstractChemotactic bacteria form emergent spatial patterns of variable cell density within cultures that are initially spatially uniform. These patterns are the result of chemical gradients that are created from the directed movement and metabolic activity of billions of cells. A recent study on pattern formation in wild bacterial isolates has revealed unique collective behaviors of the bacteria Enterobacter cloacae. As in other bacteria species, Enterobacter cloacae form macroscopic aggregates. Once formed, these bacterial clusters can migrate several millimeters, sometimes resulting in the merging of two or more clusters. To better understand these phenomena, we examine the formation and dynamics of thousands of bacterial clusters that form within a 22 cm square culture dish filled with soft agar over two days. At the macroscale, the aggregates display spatial order at short length scales, and the migration of cell clusters is superdiffusive, with a merging acceleration that is correlated with aggregate size. At the microscale, aggregates are composed of immotile cells surrounded by low density regions of motile cells. The collective movement of the aggregates is the result of an asymmetric flux of bacteria at the boundary. An agent based model is developed to examine how these phenomena are the result of both chemotactic movement and a change in motility at high cell density. These results identify and characterize a new mechanism for collective bacterial motility driven by a transient, density-dependent change in motility.Author summaryBacteria growing and swimming in soft agar often aggregate to form elaborate spatial patterns. Here we examine the patterns formed by the bacteria Enterobacter cloacae. An unusual behavior of this bacteria is the movement of cell clusters, millions of bacteria forming a tiny spot and moving together in the same direction. These spots sometimes run into each other and combine. By looking at the cells within these spots under a microscope, we find that cells within each spot stop swimming. The process of switching back and forth between swimming and not swimming causes the movement and fusion of the spots. A numerical simulation shows that the migration and merging of these spots can be expected if the cells swim towards regions of space with high concentrations of attractant molecules and stop swimming in locations crowded with many cells. This work identifies a novel process through which populations of bacteria cooperate and control the movement of large groups of cells.
ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes
Authors: Iida Keita , Kondo Jumpei , Wibisana Johannes Nicolaus , Inoue Masahiro , Okada Mariko 
Published: June 2021

AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret the biological implications of computational results. Hence, a theory for efficiently annotating individual cells is necessary.ResultsWe present ASURAT, a computational pipeline for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process, and signaling pathway activity for single-cell transcriptomic data, using correlation graph-based decomposition of genes based on database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for small cell lung cancer and pancreatic ductal adenocarcinoma, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data.AvailabilityA GPLv3-licensed implementation of ASURAT is on GitHub (https://github.com/keita-iida/ASURAT).
Desmosomal connectomics of all somatic muscles in an annelid larva
Authors: Jasek Sanja , Verasztó Csaba , Brodrick Emelie , Shahidi Réza , Kazimiers Tom , Kerbl Alexandra , Jékely Gáspár 
Published: June 2021

AbstractCells form networks in animal tissues through synaptic, chemical and adhesive links. Invertebrate muscle cells often connect to other cells through desmosomes, adhesive junctions anchored by intermediate filaments. To study desmosomal networks, we skeletonised 853 muscle cells and their desmosomal partners in volume electron microscopy data covering an entire larva of the annelidPlatynereis. Muscle cells adhere to each other, to epithelial, glial, ciliated, and bristle-producing cells and to the basal lamina, forming a desmosomal connectome of over 2,000 cells. The aciculae – chitin rods that form an endoskeleton in the segmental appendages – are highly connected hubs in this network. This agrees with the many degrees of freedom of their movement, as revealed by video microscopy. Mapping motoneuron synapses to the desmosomal connectome allowed us to infer the extent of tissue influenced by motoneurons. Our work shows how cellular-level maps of synaptic and adherent force networks can elucidate body mechanics.
Characterisation of the symbionts in the Mediterranean fruitfly gut
Authors: Darrington M. , Leftwich P.T. , Holmes N.A. , Friend L.A. , Clarke N.V.E. , Worsley S.F. , Margaritopolous J.T. , Hogenhout S.A. , Hutchings M.I. , Chapman T 
Published: June 2021

AbstractSymbioses between bacteria and their insect hosts can range from very loose associations through to obligate interdependence. While fundamental evolutionary insights have been gained from the in-depth study of obligate mutualisms, there is increasing interest in the evolutionary potential of flexible symbiotic associations between hosts and their gut microbiomes. Understanding relationships between microbes and hosts also offers the potential for exploitation for insect control. Here, we investigate the gut microbiome of a global agricultural pest, the Mediterranean fruitfly (Ceratitis capitata). We used 16S rRNA profiling to compare the gut microbiomes of laboratory and wild strains raised on different diets and from flies collected from various natural plant hosts. The results showed that medfly guts harbour a fairly simple microbiome, primarily determined by the larval diet in both wild and laboratory flies. However, regardless of the laboratory diet or natural plant host on which flies were raised,Klebsiellaspp dominated the medfly microbiomes and resisted removal by antibiotic treatment. We sequenced the genome of the dominant putativeKlebsiellaspp (designated ‘Medkleb’) isolated from the gut of the Toliman wild type fruitfly strain. Genome-wide ANI analysis placed Medkleb within theK. oxytoca / michiganensisgroup. Molecular, sequence and phenotypic analyses supported its identity asK. oxytoca. Medkleb has a genome size (5 825 435 bp) which is 1.6 standard deviations smaller than the mean genome size of free-livingKlebsiellaspp, and lacks some genes involved in environmental sensing. Moreover, the Medkleb genome contains at least two recently acquired unique genomic islands as well as genes that encode pectinolytic enzymes capable of degrading plant cell walls. This may be advantageous given that the medfly diet includes unripe fruits containing high proportions of pectin. These results suggest that the medfly harbours a commensal gut bacterium that may have developed a mutualistic association with its host and provide nutritional benefits.
Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets
Authors: Kar Anuradha , Petit Manuel , Refahi Yassin , Cerutt Guillaume , Godin Christophe , Traas Jan 
Published: June 2021

AbstractSegmenting three dimensional microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state-of-the-art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3 dimensional (3D) cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted which isolates segmentation errors due to under/over segmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end to end 3D and were originally designed for cell boundary detection, show high performance, and offer clear advantages in terms of adaptability to new data.Author summaryIn recent years a number of deep learning (DL) algorithms based on computational neural networks have been developed which claim to achieve high accuracy and automatic segmentation of 3D microscopy images. Although these algorithms have received considerable attention in the literature, it is difficult to evaluate their relative performances, while it remains unclear whether they really perform better than other, more classical segmentation methods.To clarify these issues, we performed a detailed, quantitative analysis of a number of representative DL pipelines for cell instance segmentation from 3D confocal microscopy image datasets. We developed a protocol for benchmarking the performances of such DL based segmentation pipelines using common training and test datasets, evaluation metrics and visualizations. Using this protocol, we evaluated and compared four different DL pipelines to identify their strengths and limitations. A high performance non-DL method was also included in the evaluation. We show that DL pipelines may show significant differences in their performances depending on their model architecture and pipeline components but overall show excellent adaptability to unseen data. We also show that our benchmarking protocol could be extended to a variety of segmentation pipelines and datasets.