bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modelling, Analysis, and Optimization of Three-Dimensional Restricted Visual Field Metric-Free Swarms
Authors: Li Qing , Zhang Lingwei , Jia Yongnan , Lu Tianzhao 
Published: May 2021

AbstractModels of collective behaviour have been proved helpful in revealing what mechanism may underlie characteristics of a flock of birds, a school of fish, and a swarm of herds. Recently, the metric-free model gradually occupies a dominant position in the research field of collective intelligence. Most of these models endow every single individual with the ability of a global visual field, which can offer each particle sufficient external information. In this paper, we mainly focus on whether the global visual field is necessary to form a consistent and cohesive group or not. Inspired by the biological characteristic of starlings, we develop a three-dimensional restricted visual field metric-free(RVFMF) model based on Pearce and Turner’s previous work. We further investigate several vital factors governing the convergent consistency of the RVFMF model with the assistance of extensive numerical simulations. According to the simulation results, we conclude that the best view angle of each particle in a swarm increases with the expansion of the population size. Besides, the best view angle gradually becomes stable around 155 degrees when the population size is larger than 1000. We also offer quantitative analysis data to prove that a flock of birds could obtain better consistency under optimal restricted visual field than under global visual field.
Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis
Authors: Wieder Cecilia , Frainay Clément , Poupin Nathalie , Rodríguez-Mier Pablo , Vinson Florence , Cooke Juliette , Lai Rachel PJ , Bundy Jacob G , Jourdan Fabien , Ebbels Timothy 
Published: May 2021

AbstractOver-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention in the field. We developed in-silico simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases: KEGG, Reactome, and BioCyc, led to vastly different results in both the number and function of significantly enriched pathways. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.Author summaryMetabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biological status (such as health or disease) on cellular biochemistry, and has wide-ranging applications, from biomarker discovery and personalised medicine in healthcare to crop protection and food security in agriculture. Pathway analysis helps to understand which biological pathways, representing collections of molecules performing a particular function, are involved in response to a disease phenotype, or drug treatment, for example. Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community. However, ORA can give drastically different results depending on the input data and parameters used. In this work, we have established the effects of these factors on ORA results using computational simulations applied to five real-world datasets. Based on our results, we offer the research community a set of best-practice recommendations applicable not only to ORA but also to other pathway analysis methods to help ensure the reliability and reproducibility of results.
Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules
Authors: Howard Grant , Jost Tyler A. , Yankeelov Thomas E. , Brock Amy 
Published: May 2021

AbstractWhile acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present work characterizing dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600-800 hours, and this data is used to calibrate a family of cancer growth models derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-106%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.
RNA secondary structure prediction with Convolutional Neural Networks
Authors: Booy Mehdi Saman , Ilin Alexander , Orponen Pekka 
Published: May 2021

AbstractPredicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology. First-principle algorithmic approaches to this task are challenging because existing models of the folding process are inaccurate, and even if a perfect model existed, finding an optimal solution would be in general NP-complete. In this paper, we propose a simple, yet extremely effective data-driven approach. We represent RNA sequences in the form of three-dimensional tensors in which we encode possible relations between all pairs of bases in a given sequence. We then use a convolutional neural network to predict a two-dimensional map which represents the correct pairings between the bases. Our model achieves significant accuracy improvements over existing methods on two standard datasets. Our experiments show excellent performance of the model across a wide range of sequence lengths and RNA families. We also observe considerable improvements in predicting complex pseudoknotted RNA structures, as compared to previous approaches.Author summaryStructure prediction for RNA sequences is a computationally difficult task that is of increasing importance in applications such as medical diagnostics and drug design; this is because the structure of a folded RNA strand to a large extent defines its function. An open RNA strand can fold to many different structures of varying thermal stability, and the goal of structure prediction is to determine a most stable one among these. There are two main difficulties to this task. Firstly, a given RNA sequence can fold into an enormous number of alternative structures, and a computational search for a most stable one in this huge space can be very demanding. The search can however be facilitated by using heuristics that take into account some underlying principles of the folding process. Here is where machine learning methods come into play: they are suitable for discovering patterns in data, and can thus predict features of the desired structure based on previously learned patterns. Secondly, there do not yet exist fully satisfactory coarse-grained models for the most popular metric for stability, the free energy of the folded structure. Although in principle a minimum free energy (MFE) structure should be a good candidate for a most stable one, MFE structures determined according to current energy models do not match experimental data on native RNA conformations very well. We show how to use an artificial neural network design to predict the structure for a given RNA sequence with high accuracy only by learning from samples whose native structures have been experimentally characterized, independent of any stability metric or energy model.
A panel of single nucleotide polymorphism (SNP) markers identifies potential duplicates in cassava (Manihot esculentaCrantz) varieties from Côte d’Ivoire
Authors: Yéo Edwige F. , Amoakon William J-L. , Pita Justin S. , Mutuku J. Musembi , N’zué Boni , Kouassi Modeste K. , Yao Nasser , Otron Daniel H. , Shah Trushar , Vanié-Leabo Linda P. L. , Kpahé Kanh M. H. , Sié Raoul , Sorho Fatogoma , Koné Daouda , N’guetta Simon-Pierre A. , Kouassi Nazaire K. , Ferguson Morag E. 
Published: May 2021

AbstractAccurate identification of varieties is paramount to optimizing efficiencies in the management and conservation of genetic resources. A relatively inexpensive, rapid methodology is required to identify putative duplicates from any collection, when morphological traits give insufficient discrimination. Here we select a panel of 36 SNPs, visualized using the Kompetitive Allele-specific PCR (KASP) system. We used a panel of 95 cassava genotypes from Côte d’Ivoire to identify varieties that are not duplicates and few potential duplicates which could be put forward for further verification. The genetic variability and population structure of the germplasm is also described. 36 SNPs were polymorphic across the panel of 95 varieties with polymorphic information contents ranging from 0.23 to 0.37. Using these SNPs, we were able to identify 66 unique genotypes from the panel of 95 genotypes, discriminate three sets of known duplicates and identify 11 sets of unknown putative duplicates which can be subjected to further verification using higher density genotyping. As expected in an outcrossing species, both expected heterozygosity (0.46) and observed heterozygosity (0.48) were high with an analysis of molecular variance (AMOVA) indicating that the majority of variation was within individuals. Three statistical approaches i.e., hierarchical ascending clustering, Bayesian analysis and discriminant analysis of principal components were used and all revealed low genetic differentiation between sub-populations, a conclusion that was supported by the low value of the fixation index (0.05). This panel of SNPs can be used to enhance cost-effectiveness and efficiency of germplasm conservation and enhance quality control at various stages in the breeding process through varietal tracking.
The Impact Of Long-term Grazing Intensity On Functional Groups Richness, Biomass, And Species Diversity In an Inner Mongolian Steppe Grassland
Authors: Zainelabdeen Yousif Mohamed , Ahmed Ahmed Ibrahim , Yan Ruirui , Xin Xiaoping , Juan Cao , Olaide Jimoh Saheed 
Published: May 2021

AbstractLivestock grazing is one of the major land uses, causing changes in the plant community's structure and grasslands composition. We assessed the effect of grazing intensity on aboveground biomass, species richness, and plant functional group (PFG) diversity in a temperature meadow steppe in Hulunbuir in northern China, involving 78 plant species from eight functional groups. Four grazing intensity classes were characterized, including light, moderate, heavy, and no grazing, based on stocking rates of 0.23, 0.46, 0.92, and 0.00 animal units per hectare. Our results show that the richness of short species, including perennial short grass, perennial short grass, and legume increased under light to moderate grazing, while no effect of grazing was observed on the richness of shrubs. With increasing grazing intensity, the aboveground biomass of perennial tall grasses and perennial tall forbs decreased significantly, while that of annual/biennial plant functional groups increased. The community diversity and evenness of annual/biennial plants increased significantly with grazing intensity. We concluded that heavy grazing has negative impacts on plant functional group richness and aboveground biomass.
Intra-dorsal striatal acetylcholine M1 but not dopaminergic D1 or glutamatergic NMDA receptor antagonists inhibit the consolidation of duration memory in interval timing
Authors: Nishioka Masahiko , Kamada Taisuke , Nakata Atsushi , Shiokawa Naoko , Kinoshita Aoi , Hata Toshimichi 
Published: May 2021

ABSTRACTThe striatal beat frequency (SBF) model assumes that striatal medium spiny neurons encode duration via synaptic plasticity. Muscarinic 1 (M1) cholinergic receptors, as well as dopamine and glutamate receptors, are important for neural plasticity in the dorsal striatum. Therefore, we investigated the effect of inhibiting these receptors on the formation of duration memory. After sufficient training in a Peak interval (PI)-20 s procedure, rats were given a single or mixed infusion of a selective antagonist for the dopamine D1 receptor (SCH23390, 0.5 μg per side), the NMDA-type glutamate receptor (D-AP5, 3 μg), or the M1 receptor (pirenzepine, 10 μg) bilaterally in the dorsal striatum, immediately before starting a PI 40 s session (shift session). On the next day, the rats were tested for new duration memory (40 s) in a session in which no lever presses were reinforced (probe session). In the shift session, performance was tie, irrespective of the drug injected. However, in the probe session, the mean peak time (an index of duration memory) of the M1 + NMDA co-blockade group, but not of the D1 + NMDA co-blockade group, was lower than that of the control group (Exp. 1 and 2). In Exp. 3, the effect of the co-blockade of M1 and NMDA receptors was replicated. Moreover, sole blockade of M1 receptors induced the same effect as M1 and NMDA blockade. These results suggest that in the dorsal striatum, the M1 receptor, but not the D1 or NMDA receptors, are involved in the consolidation of duration memory.
Overexpression of Reticulon 3 enhances CNS axon regeneration and functional recovery after injury
Authors: Alhajlah Sharif , Thompson Adam M. , Ahmed Zubair 
Published: May 2021

AbstractCNS neurons are generally incapable of regenerating their axons after injury due to several intrinsic and extrinsic factors, including the presence of axon growth inhibitory molecules. One such potent inhibitor of CNS axon regeneration is Reticulon (RTN) 4 or Nogo-66. Here, we focused on RTN3 as its contribution in CNS axon regeneration is currently unknown. We found that RTN3 expression correlated with an axon regenerative phenotype in dorsal root ganglion neurons (DRGN) after injury to the dorsal columns, a model of spinal cord injury. Overexpression of RTN3 promoted disinhibited DRGN neurite outgrowth in vitro and dorsal column axon regeneration/sprouting and electrophysiological, sensory and locomotor functional recovery after injury in vivo. Knockdown of protrudin however, ablated RTN3-enhanced neurite outgrowth/axon regeneration in vitro and in vivo. Moreover, overexpression of RTN3 in a second model of CNS injury, the optic nerve crush injury model, enhanced retinal ganglion cell (RGC) survival, disinhibited neurite outgrowth in vitro and survival and axon regeneration in vivo, an effect that was also dependent on protrudin. These results demonstrate that RTN3 enhances neurite outgrowth/axon regeneration in a protrudin-dependent manner after both spinal cord and optic nerve injury.
Isolation of SARS-CoV-2 B.1.1.28.2 P2 variant and pathogenicity comparison with D614G variant in hamster model
Authors: Yadav Pragya , Mohandas Sreelekshmy , Sarkale Prasad , Nyayanit Dimpal , Shete Anita , Sahay Rima , Potdar Varsha , Baradkar Shreekant , Gupta Nivedita , Sapkal Gajanan , Abraham Priya , Panda Samiran , Bhargava Balram 
Published: May 2021

AbstractBackgroundConsidering the potential threat from emerging SARS-CoV-2 variants and the rising COVID-19 cases, SARS-CoV-2 genomic surveillance is ongoing in India. We report herewith the isolation of the P.2 variant (B.1.1.28.2) from international travelers and further its pathogenicity evaluation and comparison with D614G variant (B.1) in hamster model.MethodsVirus isolation was performed in Vero CCL81 cells and genomic characterization by next generation sequencing. The pathogenicity of the isolate was assessed in Syrian hamster model and compared with B.1 variant.ResultsB.1.1.28.2 variant was isolated from nasal/throat swabs of international travelers returned to India from United Kingdom and Brazil. The B.1.1.28.2 variant induced body weight loss, viral replication in the respiratory tract, lung lesions and caused severe lung pathology in infected Syrian hamster model in comparison, with B.1 variant infected hamsters. The sera from B.1.1.28.2 infected hamsters efficiently neutralized the D614G variant virus whereas 6-fold reduction in the neutralization was seen in case of D614G variant infected hamsters’ sera with the B.1.1.28.2 variant.ConclusionsB.1.1.28.2 lineage variant could be successfully isolated and characterization could be performed. Pathogenicity of the isolate was demonstrated in Syrian hamster model and in comparison, with B.1 variant was found more pathogenic. The findings of increased disease severity and neutralization reduction is of great concern and point towards the need for screening the vaccines for efficacy.
Relationships between endogenous circadian period, physiological and cognitive parameters and sex in aged non-human primates
Authors: Hozer Clara , Pifferi Fabien 
Published: May 2021

AbstractThe biological clock exhibits circadian rhythms, with an endogenous period tau close to 24h. The circadian resonance theory proposes that lifespan is reduced when endogenous period goes far from 24h. It has been suggested that daily resetting of the circadian clock to the 24h external photoperiod might induce marginal costs that would accumulate over time and forward accelerate aging and affect fitness. In this study, we aimed to evaluate the link between the endogenous period and biomarkers of aging in order to investigate the mechanisms of the circadian resonance theory. We studied 39 middle-aged and aged Microcebus murinus, a nocturnal non-human primate whose endogenous period is about 23.1h, measuring the endogenous period of locomotor activity, as well as several physiological and behavioral parameters (rhythm fragmentation and amplitude, energetic expenditure, oxidative stress, insulin-like growth factor-1 (IGF-1) concentrations and cognitive performances) in both males and females. We found that aged males with tau far from 24h displayed increased oxidative stress. We also demonstrated a positive correlation between tau and IGF-1 concentrations, as well as learning performances, in males and females. Together these results suggest that a great deviation of tau from 24h leads to increased biomarkers of age-related impairments.Summary StatementThis manuscript describes how the misalignment between the endogenous circadian period and the 24h of the environmental periodicity impacts aging biomarkers and accelerates cognitive decline.
Comprehensive Analysis of Spatial Architecture in Primary Liver Cancer
Authors: Wu Rui , Guo Wenbo , Qiu Xinyao , Wang Shicheng , Sui Chengjun , Lian Qiuyu , Wu Jianmin , Shan Yiran , Yang Zhao , Yang Shuai , Wu Tong , Wang Kaiting , Zhu Yanjing , Wang Shan , Liu Changyi , Zhang Yangqianwen , Zheng Bo , Li Zhixuan , Zhang Yani , Shen Siyun , Zhao Yan , Wang Wenwen , Bao Jinxia , Hu Ji , Wu Xuan , Jiang Xiaoqing , Wang Hongyang , Gu Jin , Chen Lei 
Published: May 2021

ABSTRACTHeterogeneity is the major challenge for cancer prevention and therapy. Here, we firstly constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from 7 patients. The sequential comparison of spatial tumor microenvironment (TME) characteristics from non-tumor to leading-edge to tumor regions revealed that the tumor capsule potentially affects intratumor spatial cluster continuity, transcriptome diversity and immune cell infiltration. Locally, we found that the bidirectional ligand-receptor interactions at the 100 μm wide cluster-cluster boundary contribute to maintaining intratumor architecture. Our study provides novel insights for diverse tumor ecosystem of PLCs and has potential benefits for cancer intervention.
The Molecular Logic Organizing the Functional Compartmentalization of Reciprocal Synapses
Authors: Wang Cosmos Yuqi , Trotter Justin H. , Liakath-Ali Kif , Lee Sung-Jin , Liu Xinran , Südhof Thomas C. 
Published: May 2021

SUMMARYReciprocal synapses are formed by neighboring dendritic processes that create the smallest possible neural circuit. Reciprocal synapses are widespread in brain and essential for information processing, but constitute a conceptual conundrum: How are adjacent pre- and post-synaptic specializations maintained as separate functional units? Here, we reveal an organizational principle for reciprocal synapses, using dendrodendritic synapses between mitral and granule cells in the mouse olfactory bulb as a paradigm. We show that mitral cells secrete cerebellin-1 to block thecis-interaction of mitral cell neurexins with neuroligins, thereby enabling their separatetrans-interactions. Ablating either cerebellin-1 or neuroligins in mitral cells severely impaired granule cell→mitral cell synapses, as did overexpression of postsynaptic neurexins that formcis-complexes with neuroligins, but not of mutant neurexins unable to bind to neuroligins. Our data uncover acis/trans-protein interaction network as a general design principle that organizes reciprocal dendro-dendritic synapses by compartmentalizing neurexin-basedtrans-synaptic protein complexes.