bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rapid evolution of post-zygotic reproductive isolation is widespread in Arctic plant lineages
Authors: Gustafsson A Lovisa S , Gussarova Galina , Borgen Liv , Ikeda Hajime , Antonelli Alexandre , Marie-Orleach Lucas , Rieseberg Loren H , Brochmann Christian 
Published: October 2021

AbstractBackground and AimsThe Arctic tundra, with its extreme temperatures and short growing season, is evolutionarily young and harbours one of the most species-poor floras on Earth. Arctic species often show little phenotypic and genetic divergence across circumpolar ranges. However, strong intraspecific post-zygotic reproductive isolation (RI) in terms of hybrid sterility has frequently evolved within selfing Arctic species of the genus Draba. Here we assess whether incipient biological species are common in the Arctic flora.MethodsWe conducted an extensive crossing experiment including six species representing four phylogenetically distant families collected across the circumpolar Arctic. We crossed conspecific parental populations representing different spatial scales, raised 740 F1 hybrids to maturity and measured fertility under laboratory conditions. We examined genetic divergence between populations for two of these species (Cardamine bellidifolia and Ranunculus pygmaeus).Key ResultsIn five of the six species, we find extensive reduction in pollen fertility and seed set in F1 hybrids; 219 (46 %) of the 477 F1 hybrids generated between parents separated by ≥427 km had &lt;20 % pollen fertility. Isolation with migration (IM) and *BEAST analyses of sequences of eight nuclear genes in C. bellidifolia suggests that reproductively isolated populations of this species diverged during, or even after, the last glaciation. Likewise, Arctic populations of R. pygmaeus were genetically very similar despite exhibiting strongly reduced fertility in crosses, suggesting that RI evolved recently also in this species.ConclusionWe show that post-zygotic RI has developed multiple times within taxonomically recognized Arctic species belonging to several distantly related lineages, and that RI may have developed over just a few millennia. Rapid and widespread evolution of incipient biological species in the Arctic flora might be associated with frequent bottlenecks due to glacial cycles, and/or selfing mating systems, which are common in the harsh Arctic environment where pollinators are scarce.
Real-Time Ultrasound Imaging of the Tear Trough: Lessons Learned From Functional Anatomy
Authors: Calomeni Mariana , Alfertshofer Michael G , Frank Konstantin , Moellhoff Nicholas , Dennison Rebekah , Bravo Bruna , Schelke Leonie , Velthuis Peter , Green Jeremy B , Cotofana Sebastian 
Published: October 2021

AbstractBackgroundThe tear trough is one of the most challenging facial regions for soft-tissue filler injections. A thorough understanding of the underlying facial, muscular, and vascular anatomy is crucial to perform safe and effective tear trough injectable treatments.ObjectivesThe authors sought to evaluate the location and function of the angular vein in the tear trough in 3 different facial expressions: repose, smiling, and max. orbicularis oculi contraction.MethodsTwenty study participants with a mean age of 48.3 years and mean BMI of 24.5 kg/m2 were investigated via functional ultrasound imaging. The diameter of the angular vein and the velocity and direction of venous blood flow were analyzed in repose, smiling, and during max. orbicularis oculi contraction.ResultsThe angular vein was identified in 100% of the cases to travel inside the orbicularis oculi muscle (intra-muscular course) within the tear trough, whereas the angular artery was not identified in this location. The distance between the angular vein and the inferior orbital rim was (lateral to medial): 4.6 mm, 4.5 mm, 3.9 mm, and 3.8 mm. The caudally directed blood flow was in repose 10.2 cm/s and was 7.3 cm/s at max. orbicularis oculi muscle contraction; however, no blood flow was detectable during smiling.ConclusionsThe diameter and the venous blood flow of the angular vein varied between the 3 tested facial expressions. Based on these anatomical findings, the deep injection approach to the tear trough is recommended due to the intramuscular course of the angular vein.
Enzymatic transformation products of phloretin as potent antiadipogenic compounds
Authors: Jeong Gyeong Han , Cho Jae-Hyeon , Park Eui Kyun , Kim Tae Hoon 
Published: October 2021

ABSTRACT
Enzymatic structure modification of the representative chalcone phloretin (1) with polyphenol oxidase from Agaricus bisporus origin produced 2 new biphenyl-type phloreoxin (2) and phloreoxinone (3), and a previously undescribed (2R)-5,7,3′,5′-tetrahydroxyflavanone (4). The structure of these new oxidized products 2-4 elucidated by interpreting the spectroscopic data (NMR and FABMS) containing the absolute stereochemistry is established by the analysis of the circular dichroism spectrum. Compared to the original phloretin, the new products (2) and (3) showed highly improved antiadipogenic potencies both toward pancreatic lipase and accumulation of 3T3-L1 cells. Also, phloreoxin (2) effectively inhibited the expression of C/EBPβ, PPARγ, and aP2 at the mRNA level in the 3T3 adipocytes. Thus, phloreoxin (2), containing a biphenyl moiety catalyzed by A. bisporus polyphenol oxidase, have the potential to influence the antiadipogenic capacity.
The role of calcium binding to the EF-hand-like motif in bacterial solute-binding protein for alginate import
Authors: Okumura Kenji , Maruyama Yukie , Takase Ryuichi , Mikami Bunzo , Murata Kousaku , Hashimoto Wataru 
Published: October 2021

ABSTRACT
Gram-negative Sphingomonas sp. A1 incorporates acidic polysaccharide alginate into the cytoplasm via a cell-surface alginate-binding protein (AlgQ2)-dependent ATP-binding cassette transporter (AlgM1M2SS). We investigated the function of calcium bound to the EF-hand-like motif in AlgQ2 by introducing mutations at the calcium-binding site. The X-ray crystallography of the AlgQ2 mutant (D179A/E180A) demonstrated the absence of calcium binding and significant disorder of the EF-hand-like motif. Distinct from the wild-type AlgQ2, the mutant was quite unstable at temperature of strain A1 growth, although unsaturated alginate oligosaccharides stabilized the mutant by formation of substrate/protein complex. In the assay of ATPase and alginate transport by AlgM1M2SS reconstructed in the liposome, the wild-type and mutant AlgQ2 induced AlgM1M2SS ATPase activity in the presence of unsaturated alginate tetrasaccharide. These results indicate that the calcium bound to EF-hand-like motif stabilizes the substrate-unbound AlgQ2 but is not required for the complexation of substrate-bound AlgQ2 and AlgM1M2SS.
Dying to cooperate: the role of environmental harshness in human collaboration
Authors: Ibbotson Paul , Jimenez-Romero Cristian , Page Karen M 
Published: October 2021

AbstractIt has been proposed that environmental stress acted as a selection pressure on the evolution of human cooperation. Through agent-based evolutionary modelling, mathematical analysis, and human experimental data we illuminate the mechanisms by which the environment influences cooperative success and decision making in a Stag Hunt game. The modelling and mathematical results show that only cooperative foraging phenotypes survive the harshest of environments but pay a penalty for miscoordination in favourable environments. When agents are allowed to coordinate their hunting intentions by communicating, cooperative phenotypes outcompete those who pursue individual strategies in almost all environmental and payoff scenarios examined. Data from human participants show flexible decision-making in face of cooperative uncertainty, favouring high-risk, high-reward strategy when environments are harsher and starvation is imminent. Converging lines of evidence from the three approaches indicate a significant role for environmental variability in human cooperative dynamics and the species-unique cognition designed to support it.
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors
Authors: Bonidia Robson P , Domingues Douglas S , Sanches Danilo S , de Carvalho André C P L F 
Published: October 2021

AbstractOne of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350–0.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.
FastqCLS: a FASTQ compressor for long-read sequencing via read reordering using a novel scoring model
Authors: Lee Dohyeon , Song Giltae 
Published: October 2021

Abstract

Motivation
Over the past decades, vast amounts of genome sequencing data have been produced, requiring an enormous level of storage capacity. The time and resources needed to store and transfer such data cause bottlenecks in genome sequencing analysis. To resolve this issue, various compression techniques have been proposed to reduce the size of original FASTQ raw sequencing data, but these remain suboptimal. Long-read sequencing has become dominant in genomics, whereas most existing compression methods focus on short-read sequencing only.

Results
We designed a compression algorithm based on read reordering using a novel scoring model for reducing FASTQ file size with no information loss. We integrated all data processing steps into a software package called FastqCLS and provided it as a Docker image for ease of installation and execution to help users easily install and run. We compared our method with existing major FASTQ compression tools using benchmark datasets. We also included new long-read sequencing data in this validation. As a result, FastqCLS outperformed in terms of compression ratios for storing long-read sequencing data.

Availability and implementation
FastqCLS can be downloaded from https://github.com/krlucete/FastqCLS.

Supplementary information
Supplementary data are available at Bioinformatics online.
scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets
Authors: Yuan Musu , Chen Liang , Deng Minghua 
Published: October 2021

Abstract

Motivation
Single-cell RNA-seq (scRNA-seq) has been widely used to resolve cellular heterogeneity. After collecting scRNA-seq data, the natural next step is to integrate the accumulated data to achieve a common ontology of cell types and states. Thus, an effective and efficient cell-type identification method is urgently needed. Meanwhile, high-quality reference data remain a necessity for precise annotation. However, such tailored reference data are always lacking in practice. To address this, we aggregated multiple datasets into a meta-dataset on which annotation is conducted. Existing supervised or semi-supervised annotation methods suffer from batch effects caused by different sequencing platforms, the effect of which increases in severity with multiple reference datasets.

Results
Herein, a robust deep learning-based single-cell Multiple Reference Annotator (scMRA) is introduced. In scMRA, a knowledge graph is constructed to represent the characteristics of cell types in different datasets, and a graphic convolutional network serves as a discriminator based on this graph. scMRA keeps intra-cell-type closeness and the relative position of cell types across datasets. scMRA is remarkably powerful at transferring knowledge from multiple reference datasets, to the unlabeled target domain, thereby gaining an advantage over other state-of-the-art annotation methods in multi-reference data experiments. Furthermore, scMRA can remove batch effects. To the best of our knowledge, this is the first attempt to use multiple insufficient reference datasets to annotate target data, and it is, comparatively, the best annotation method for multiple scRNA-seq datasets.

Availability and implementation
An implementation of scMRA is available from https://github.com/ddb-qiwang/scMRA-torch.

Supplementary information
Supplementary data are available at Bioinformatics online.
Volitional control of individual neurons in the human brain
Authors: Patel Kramay , Katz Chaim N , Kalia Suneil K , Popovic Milos R , Valiante Taufik A 
Published: October 2021

Abstract
Brain–machine interfaces allow neuroscientists to causally link specific neural activity patterns to a particular behaviour. Thus, in addition to their current clinical applications, brain–machine interfaces can also be used as a tool to investigate neural mechanisms of learning and plasticity in the brain. Decades of research using such brain–machine interfaces have shown that animals (non-human primates and rodents) can be operantly conditioned to self-regulate neural activity in various motor-related structures of the brain. Here, we ask whether the human brain, a complex interconnected structure of over 80 billion neurons, can learn to control itself at the most elemental scale—a single neuron.
We used the unique opportunity to record single units in 11 individuals with epilepsy to explore whether the firing rate of a single (direct) neuron in limbic and other memory-related brain structures can be brought under volitional control. To do this, we developed a visual neurofeedback task in which participants were trained to move a block on a screen by modulating the activity of an arbitrarily selected neuron from their brain.
Remarkably, participants were able to volitionally modulate the firing rate of the direct neuron in these previously uninvestigated structures. We found that a subset of participants (learners), were able to improve their performance within a single training session. Successful learning was characterized by (i) highly specific modulation of the direct neuron (demonstrated by significantly increased firing rates and burst frequency); (ii) a simultaneous decorrelation of the activity of the direct neuron from the neighbouring neurons; and (iii) robust phase-locking of the direct neuron to local alpha/beta-frequency oscillations, which may provide some insights in to the potential neural mechanisms that facilitate this type of learning.
Volitional control of neuronal activity in mnemonic structures may provide new ways of probing the function and plasticity of human memory without exogenous stimulation. Furthermore, self-regulation of neural activity in these brain regions may provide an avenue for the development of novel neuroprosthetics for the treatment of neurological conditions that are commonly associated with pathological activity in these brain structures, such as medically refractory epilepsy.
Brain injections of glial cytoplasmic inclusions induce a multiple system atrophy-like pathology
Authors: Teil Margaux , Dovero Sandra , Bourdenx Mathieu , Arotcarena Marie-Laure , Camus Sandrine , Porras Gregory , Thiolat Marie-Laure , Trigo-Damas Ines , Perier Celine , Estrada Cristina , Garcia-Carrillo Nuria , Morari Michele , Meissner Wassilios G , Herrero María Trinidad , Vila Miquel , Obeso Jose A , Bezard Erwan , Dehay Benjamin 
Published: October 2021

Abstract
Synucleinopathies encompass several neurodegenerative diseases, which include Parkinson's disease, dementia with Lewy bodies and multiple system atrophy. These diseases are characterized by the deposit of α-synuclein aggregates in intracellular inclusions in neurons and glial cells. Unlike Parkinson’s disease and dementia with Lewy bodies, where aggregates are predominantly neuronal, multiple system atrophy is associated with α-synuclein cytoplasmic inclusions in oligodendrocytes. Glial cytoplasmic inclusions are the pathological hallmark of multiple system atrophy and are associated with neuroinflammation, modest demyelination and, ultimately, neurodegeneration.
To evaluate the possible pathogenic role of glial cytoplasmic inclusions, we inoculated glial cytoplasmic inclusion-containing brain fractions obtained from multiple system atrophy patients into the striatum of non-human primates. After a 2-year in vivo phase, extensive histochemical and biochemical analyses were performed on the whole brain.
We found loss of both nigral dopamine neurons and striatal medium spiny neurons, as well as loss of oligodendrocytes in the same regions, which are characteristics of multiple system atrophy. Furthermore, demyelination, neuroinflammation and α-synuclein pathology were also observed. These results show that the α-synuclein species in multiple system atrophy-derived glial cytoplasmic inclusions can induce a pathological process in non-human primates, including nigrostriatal and striatofugal neurodegeneration, oligodendroglial cell loss, synucleinopathy and gliosis.
The present data pave the way for using this experimental model for MSA research and therapeutic development.
Dementia in the older population is associated with neocortex content of serum amyloid P component
Authors: Ellmerich Stephan , Taylor Graham W , Richardson Connor D , Minett Thais , Schmidt Amand Floriaan , Brayne Carol , Matthews Fiona E , Ince Paul G , Wharton Stephen B , Pepys Mark B ,   Cognitive Function and Ageing Study, Brayne Carol , Matthews Fiona E , Robinson Louise , Comas-Herrera Adelina , Woods Bob , Stephan Blossom , Parry Bronwyn , Jagger Carol , Clare Linda , Dening Tom , Barnes Linda , Forster Gill , McKeith Ian , Wittenberg Raphael , Wharton Stephen B , Pendlebury Sarah , Harrison Simon , Arthur Antony , Weller Roy , Pickering-Brown Stuart , Ince Paul G 
Published: October 2021

AbstractDespite many reported associations, the direct cause of neurodegeneration responsible for cognitive loss in Alzheimer’s disease and some other common dementias is not known. The normal human plasma protein, serum amyloid P component, a constituent of all human fibrillar amyloid deposits and present on most neurofibrillary tangles, is cytotoxic for cerebral neurones in vitro and in experimental animals in vivo. The neocortical content of serum amyloid P component was immunoassayed in 157 subjects aged 65 or more with known dementia status at death, in the large scale, population-representative, brain donor cohort of the Cognitive Function and Ageing Study, which avoids the biases inherent in studies of predefined clinico-pathological groups. The serum amyloid P component values were significantly higher in individuals with dementia, independent of serum albumin content measured as a control for plasma in the cortex samples. The odds ratio for dementia at death in the high serum amyloid P component tertile was 5.24 (95% confidence interval 1.79–15.29) and was independent of Braak tangle stages and Thal amyloid-β phases of neuropathological severity. The strong and specific association of higher brain content of serum amyloid P component with dementia, independent of neuropathology, is consistent with a pathogenetic role in dementia.
Persistent sodium currents in SCN1A developmental and degenerative epileptic dyskinetic encephalopathy
Authors: Gorman Kathleen M , Peters Colin H , Lynch Bryan , Jones Laura , Bassett Dani S , King Mary D , Ruben Peter C , Rosch Richard E 
Published: October 2021

Abstract
Pathogenic variants in the voltage-gated sodium channel gene (SCN1A) are amongst the most common genetic causes of childhood epilepsies. There is considerable heterogeneity in both the types of causative variants and associated phenotypes; a recent expansion of the phenotypic spectrum of SCN1A associated epilepsies now includes an early onset severe developmental and epileptic encephalopathy with regression and a hyperkinetic movement disorder. Herein, we report a female with a developmental and degenerative epileptic-dyskinetic encephalopathy, distinct and more severe than classic Dravet syndrome. Clinical diagnostics indicated a paternally inherited c.5053G&gt;T; p. A1685S variant of uncertain significance in SCN1A. Whole-exome sequencing detected a second de novo mosaic (18%) c.2345G&gt;A; p. T782I likely pathogenic variant in SCN1A (maternal allele). Biophysical characterization of both mutant channels in a heterologous expression system identified gain-of-function effects in both, with a milder shift in fast inactivation of the p. A1685S channels; and a more severe persistent sodium current in the p. T782I. Using computational models, we show that large persistent sodium currents induce hyper-excitability in individual cortical neurons, thus relating the severe phenotype to the empirically quantified sodium channel dysfunction. These findings further broaden the phenotypic spectrum of SCN1A associated epilepsies and highlight the importance of testing for mosaicism in epileptic encephalopathies. Detailed biophysical evaluation and computational modelling further highlight the role of gain-of-function variants in the pathophysiology of the most severe phenotypes associated with SCN1A.
Striatal BOLD and midfrontal theta power express motivation for action
Authors: Algermissen Johannes , Swart Jennifer C , Scheeringa René , Cools Roshan , den Ouden Hanneke E M 
Published: October 2021

Abstract
Action selection is biased by the valence of anticipated outcomes. To assess mechanisms by which these motivational biases are expressed and controlled, we measured simultaneous EEG-fMRI during a motivational Go/NoGo learning task (N = 36), leveraging the temporal resolution of EEG and subcortical access of fMRI. VmPFC BOLD encoded cue valence, importantly predicting trial-by-trial valence-driven response speed differences and EEG theta power around cue onset. In contrast, striatal BOLD encoded selection of active Go responses and correlated with theta power around response time. Within trials, theta power ramped in the fashion of an evidence accumulation signal for the value of making a “Go” response, capturing the faster responding to reward cues. Our findings reveal a dual nature of midfrontal theta power, with early components reflecting the vmPFC contribution to motivational biases, and late components reflecting their striatal translation into behavior, in line with influential recent “value of work” theories of striatal processing.