bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model
Authors: Balaban Metin , Bristy Nishat Anjum , Faisal Ahnaf , Bayzid Md. Shamsuzzoha , Mirarab Siavash 
Published: November 2021

AbstractWhile aligning sequences has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods have much appeal in terms of simplifying the process of inference, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for some emerging forms of data, such as genome skims, which cannot be assembled. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is that they typically rely on simplified models of sequence evolution such as Jukes-Cantor. It is possible to compute pairwise distances under more complex models by computing frequencies of base substitutions provided that these quantities can be estimated in the alignment-free setting. A particular limitation is that for many forms of genomewide data, which arguably present the best use case for alignment-free methods, the strand of DNA sequences is unknown. Under such conditions, the so-called no-strand bias models are the most complex models that can be used. Here, we show how to calculate distances under a no-strain bias restriction of the General Time Reversible (GTR) model called TK4 without relying on alignments. The method relies on replacing letters in the input sequences, and subsequent computation of Jaccard indices between k-mer sets. For the method to work on large genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance as opposed to homology. We show in simulation that these alignment-free distances can be highly accurate when genomes evolve under the assumed models, and we examine the effectiveness of the method on real genomic data.
Molecular Evolutionary Analyses of Tooth Genes Support Sequential Loss of Enamel and Teeth in Baleen Whales (Mysticeti)
Authors: Randall Jason G. , Gatesy John , Springer Mark S. 
Published: November 2021

AbstractThe loss of teeth and evolution of baleen racks in Mysticeti was a profound transformation that permitted baleen whales to radiate and diversify into a previously underutilized ecological niche of bulk filter-feeding on zooplankton and other small prey. Ancestral state reconstructions suggest that teeth were lost in the common ancestor of crown Mysticeti. Genomic studies provide some support for this hypothesis and suggest that the genetic toolkit for enamel production was inactivated in the common ancestor of living baleen whales. However, molecular studies to date have not provided direct evidence for the complete loss of teeth, including their dentin component, on the stem mysticete branch. Given these results, several questions remain unanswered: (1) Were teeth lost in a single step or did enamel loss precede dentin loss? (2) Was enamel lost early or late on the stem mysticete branch? (3) If enamel and dentin/tooth loss were decoupled in the ancestry of baleen whales, did dentin loss occur on the stem mysticete branch or independently in different crown mysticete lineages? To address these outstanding questions, we compiled and analyzed complete protein-coding sequences for nine tooth-related genes from cetaceans with available genome data. Seven of these genes are associated with enamel formation (ACP4, AMBN, AMELX, AMTN, ENAM, KLK4, MMP20) whereas two other genes are either dentin-specific (DSPP) or tooth-specific (ODAPH) but not enamel-specific. Molecular evolutionary analyses indicate that all seven enamel-specific genes have inactivating mutations that are scattered across branches of the mysticete tree. Three of the enamel genes (ACP4, KLK4, MMP20) have inactivating mutations that are shared by all mysticetes. The two genes that are dentin-specific (DSPP) or tooth-specific (ODAPH) do not have any inactivating mutations that are shared by all mysticetes, but there are shared mutations in Balaenidae as well as in Plicogulae (Neobalaenidae + Balaenopteroidea). These shared mutations suggest that teeth were lost at most two times. Shared inactivating mutations and dN/dS analyses, in combination with cetacean divergence times, were used to estimate inactivation times of genes and by proxy enamel and tooth phenotypes. The results of these analyses are most compatible with a two-step model for the loss of teeth in the ancestry of living baleen whales: enamel was lost very early on the stem Mysticeti branch followed by the independent loss of dentin (and teeth) in the common ancestors of Balaenidae and Plicogulae, respectively. These results imply that some stem mysticetes, and even early crown mysticetes, may have had vestigial teeth comprised of dentin with no enamel. Our results also demonstrate that all odontocete species (in our study) with absent or degenerative enamel have inactivating mutations in one or more of their enamel genes.
Little heterosis found in Diploid Hybrid Potato
Authors: Adams James R. , Vries Michiel E. de , Zheng Chaozhi , van Eeuwijk Fred A. 
Published: November 2021

AbstractHybrid potato breeding has become a novel alternative to con-ventional potato breeding allowing breeders to overcome intractable barriers (e.g. tetrasomic inheritance, masked deleterious alleles, obligate clonal propagation) with the benefit of seed-based propagule, flexible population design, and the potential of hybrid vigour. Until now, however, no formal inquiry has adequately examined the relevant genetic components for complex traits in hybrid potato populations. In this present study, we use a two-step modelling approach to estimate the relevant variance components to assess the magnitude of the general and specific combining abilities (GCA and SCA, respectively) in diploid hybrid potato (DHP). SCA effects were identified for all yield components studied here warranting evidence of non-additive genetic effects in hybrid potato yield. However, the estimated GCA effects were on average two times larger than their respective SCA quantile across all yield phenotypes. Tuber number GCA’s and SCA’s were found to be highly correlated with total yield’s genetic components. Tuber volume appearing under-selected in this population. The prominence of additive effects found for all traits presents evidence that breeders can perform hybrid potato evaluation using the mid-parent value alone. Heterotic vigour stands be useful in bolstering simpler traits but this will be very dependent on the target market of a population. This study represents the first diallel analysis of its kind in diploid potato using material derived from a commercial hybrid breeding programme.
The use of principal component and factor analysis to measure fundamental cognitive processes in neuropsychological data
Authors: Sperber Christoph 
Published: November 2021

AbstractFor years, dissociation studies on neurological single cases with brain lesions were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value and even misleading. Still, principal component analysis (PCA) – an associational method for dimensionality reduction – recently became popular for the identification of fundamental functions. The current study evaluated the ability of PCA and factor analysis (FA) to overcome the association problem in behavioural data of neurological patients and to identify the fundamental variables underlying a battery of measures. Synthetic data were simulated to resemble neuropsychological data with typical dissociation patterns and, thereby, typical patterns of dependence between variables. In most experiments, PCA and FA succeeded to measure the underlying target variables with high up to almost perfect precision. However, this success was fragile and relied on the success of factor rotation, which failed its intended purpose when no test scores existed that primarily measured each underlying target variable. Further, commonly used strategies to estimate the number of meaningful factors appear to underfactor neuropsychological data, thereby consistently underestimating the dimensionality of the data. Finally, simulations suggested a high potential of PCA to denoise data, with factor rotation providing an additional filter function. This can be invaluable in neuropsychology, where measures are often inherently noisy, and PCA can be superior to common compound measures, such as the arithmetic mean, in the measurement of variables with high reliability. In summary, PCA and FA appear to be powerful tools in neuropsychology that are well capable to infer fundamental cognitive functions with high precision, but the typical structure of neuropsychological data and limited informative value of associations in neuropsychology place clear limitations and a risk of a complete failure on the methods.
Low frequency somatic copy number alterations in normal human lymphocytes revealed by large scale single-cell whole genome profiling
Authors: Liu Lu , Chen He , Sun Cheng , Zhang Jianyun , Wang Juncheng , Du Meijie , Li Jie , Di Lin , Shen Jie , Geng Shuang , Pang Yuhong , Luo Yingying , Wu Chen , Fu Yusi , Zheng Zhe , Wang Jianbin , Huang Yanyi 
Published: November 2021

AbstractGenomic-scale somatic copy number alterations in healthy humans are difficult to investigate because of low occurrence rates and the structural variations’ stochastic natures. Using a Tn5-transposase assisted single-cell whole genome sequencing method, we sequenced over 20,000 single lymphocytes from 16 individuals. Then, with the scale increased to a few thousand single cells per individual, we found that about 7.5% of the cells had large-size copy number alterations. Trisomy 21 was the most prevalent aneuploid event among all autosomal copy number alterations, while monosomy X occurred most frequently in over-30-year-old females. In the monosomy X single cells from individuals with phased genomes and identified X-inactivation ratios in bulk, the inactive X Chromosomes were lost more often than were the active ones.
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides
Authors: Yu Qinze , Dong Zhihang , Fan Xingyu , Zong Licheng , Li Yu 
Published: November 2021

AbstractIdentifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immune response and combating antibiotic resistance, and more broadly, precision medicine and public health. There have been extensive studies on the statistical and computational approaches to identify (i) whether a peptide is an antimicrobial peptide (AMP) or a non-AMP and (ii) which targets are these sequences effective to (Gram-positive, Gram-negative, etc.). Despite the existing deep learning methods on this problem, most of them are unable to handle the small AMP classes (anti-insect, anti-parasite, etc.). And more importantly, some AMPs can have multiple targets, which the previous methods fail to consider. In this study, we build a diverse and comprehensive multi-label protein sequence database by collecting and cleaning amino acids from various AMP databases. To generate efficient representations and features for the small classes dataset, we take advantage of a protein language model trained on 250 million protein sequences. Based on that, we develop an end-to-end hierarchical multi-label deep forest framework, HMD-AMP, to annotate AMP comprehensively. After identifying an AMP, it will further predict what targets the AMP can effectively kill from eleven available classes. Extensive experiments suggest that our framework outperforms state-of-the-art models in both the binary classification task and the multi-label classification task, especially on the minor classes. Compared with the previous deep learning methods, our method improves the performance on macro-AUROC by 11%. The model is robust against reduced features and small perturbations and produces promising results. We believe HMD-AMP will both contribute to the future wet-lab investigations of the innate structural properties of different antimicrobial peptides and build promising empirical underpinnings for precise medicine with antibiotics.
Resting-state functional connectivity of social brain regions predicts motivated dishonesty
Authors: Pang Luoyao , Li Huidi , Liu Quanying , Luo Yue-Jia , Mobbs Dean , Wu Haiyan 
Published: November 2021

AbstractMotivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain networks. To identify the relevant neural patterns and build an interpretable model to predict dishonesty, we scanned 8-min rsfMRI before an information-passing task. In the task, we employed monetary rewards to induce dishonesty. We applied both connectome-based predictive modeling (CPM) and region-of-interest (ROI) analysis to examine the association between FC and dishonesty. CPM indicated that the stronger FC between fronto-parietal and default mode networks can predict a higher dishonesty rate. The ROIs were set in the regions involving four cognitive processes (self-reference, cognitive control, reward valuation, and moral regulation). The ROI analyses showed that a stronger FC between these regions and the prefrontal cortex can predict a higher dishonesty rate. Our study offers an integrated model to predict dishonesty with rsfMRI, and the results suggest that the frequent motivated dishonest behavior may require a higher engagement of social brain regions.
PatchWarp: Corrections of non-uniform image distortions in two-photon calcium imaging data by patchwork affine transformations
Authors: Hattori Ryoma , Komiyama Takaki 
Published: November 2021

AbstractTwo-photon microscopy has been widely used to record the activity of populations of individual neurons at high spatial resolution in behaving animals. The ability to perform imaging for an extended period of time allows the investigation of activity changes associated with behavioral states and learning. However, imaging often accompanies shifts of the imaging field, including rapid (∼100ms) translation and slow, spatially non-uniform distortion. To combat this issue and obtain a stable time series of the target structures, motion correction algorithms are commonly applied. However, typical motion correction algorithms are limited to full field translation of images and are unable to correct non-uniform distortions. Here, we developed a novel algorithm, PatchWarp, to robustly correct slow image distortion for calcium imaging data. PatchWarp is a two-step algorithm with rigid and non-rigid image registrations. To correct non-uniform image distortions, it splits the imaging field and estimates the best affine transformation matrix for each of the subfields. The distortion-corrected subfields are stitched together like a patchwork to reconstruct the distortion-corrected imaging field. We show that PatchWarp robustly corrects image distortions of calcium imaging data collected from various cortical areas through glass window or GRIN lens with a higher accuracy than existing non-rigid algorithms. Furthermore, it provides a fully automated method of registering images from different imaging sessions for longitudinal neural activity analyses. PatchWarp improves the quality of neural activity analyses and would be useful as a general approach to correct image distortions in a wide range of disciplines.
Biocatalytic quantification of α-glucan in particulate marine organic matter
Authors: Steinke Nicola , Vidal-Melgosa Silvia , Schultz-Johansen Mikkel , Hehemann Jan-Hendrik 
Published: November 2021

AbstractMarine algae drive the marine carbon cycle, converting carbon dioxide into organic material. A major component of this produced biomass is a variety of glycans; and yet their chemical composition and individual involvement in production, sedimentation and bacterial uptake remain largely unknown due to a lack of analytical tools for glycan-specific quantification.Marine α-glucans include a range of storage glycans from red and green algae, bacteria, fungi and animals. Although these compounds are likely to account for a high amount of the carbon stored in the oceans they have not been quantified in marine samples so far.Here we present a method to extract and quantify α-glucans in particulate organic matter from algal cultures and environmental samples using a sequential physicochemical extraction and enzymes as α-glucan-specific probes. This enzymatic assay is more specific and less susceptible to side reactions than chemical hydrolysis. Using HPAEC-PAD to detect the hydrolysis products allows for a glycan quantification in particulate marine samples even at low concentration of ≈ 2-7 µg/L α-glucans.We measured α-glucans (and compared their concentration with the β-glucan laminarin) in three microalgae laboratory cultures as well as in marine particulate organic matter from the North Sea and western North Atlantic Ocean. While laminarin from diatoms and brown algae is an essential component of marine carbon turnover, our results further indicate the significant contribution of starch-like α-glucans to marine particulate organic matter.Henceforth, the combination of glycan-linkage-specific enzymes and chromatographic hydrolysis product detection can provide a powerful tool in the exploration of marine glycans and their role in the global carbon cycle.
Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials
Authors: Kim Jinmo , Lee Jungmin , Kim Eunho , Choi Joon-Ho , Rah Jong-Cheol , Choi Ji-Woong 
Published: November 2021

AbstractElectrophysiological biomarkers reflecting the pathological activities in the basal ganglia are essential to gain an etiological understanding of Parkinson’s disease (PD) and develop a method of diagnosing and treating the disease. Previous studies that explored electrophysiological biomarkers in PD have focused mainly on oscillatory or periodic activities such as beta and gamma oscillations. Emerging evidence has suggested that the nonoscillatory, aperiodic component reflects the firing rate and synaptic current changes corresponding to cognitive and pathological states. Nevertheless, it has never been thoroughly examined whether the aperiodic component can be used as a biomarker that reflects pathological activities in the basal ganglia in PD. In this study, we examined the parameters of the aperiodic component and tested its practicality as an electrophysiological biomarker of pathological activity in PD. We found that a set of aperiodic parameters, aperiodic offset and exponent, were significantly decreased by the nigrostriatal lesion. To further prove the usefulness of the parameters as biomarkers, acute levodopa treatment reverted the aperiodic offset. We then compared the aperiodic parameters with a previously established periodic biomarker of PD, beta frequency oscillation. We found a significantly low negative correlation with beta power. We showed that the performance of the machine learning-based prediction of pathological activities in the basal ganglia can be improved by using the lowly correlated parameters, beta power and aperiodic component. We suggest that the aperiodic component will provide a more sensitive measurement to early diagnosis PD and have the potential to use as the feedback parameter for the adaptive deep brain stimulation.
WormCat 2.0 defines characteristics and conservation of poorly annotated genes inCaenorhabditis elegans
Authors: Higgins Daniel P. , Weisman Caroline M. , Lui Dominique S. , D’Agostino Frank A. , Walker Amy K. 
Published: November 2021

AbstractGenome-wide measurement of mRNA or protein levels provides broad data sets for biological discovery. However, subsequent computational methods are essential for uncovering the functional implications of the data as well as intuitively visualizing the findings. Current computational tools are biased toward well-described pathways, limiting their utility for novel discovery. Recently, we developed an annotation and category enrichment tool forCaenorhabditis elegansgenomic data, WormCat, that provides an intuitive visualization output. Unlike GO, which excludes genes with no annotation information, WormCat 2.0 retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category enrichment exhibits tissue-specific expression patterns and include genes with biological functions. Poorly annotated genes have previously been considered to lack homologs in closely related species. Instead, we find that around 3% of the UNASSIGNED genes have poorly characterized human orthologs. These human orthologs are themselves have little annotation information. A recently developed method that incorporates lineage relationships (abSENSE) indicates that failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes, suggesting that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows association of UNASSIGNED genes with specific phenotypes and known pathways. Our analysis indicates that the UNASSIGNED gene category contains candidates that merit further functional study which could yield insight into understudied areas of biology.
3-dimensional arenas for the assessment of C. elegans behavior
Authors: Cardoza Steel N. , Leo Tse Lai Yu , Barton Kira , Gourgou Eleni 
Published: November 2021

AbstractC. elegans nematodes are a well-established model organism in numerous fields of experimental biology. In nature, C. elegans live in a rich 3-dimensional environment. However, their behavior has been assessed almost exclusively on the open, flat surface of NGM (Nematode Growth Medium) plates, the golden standard for C. elegans culture in the lab. We present two methods to build 3-dimensional behavioral arenas for C. elegans, by casting, and by directly 3D printing NGM hydrogel. The latter is achieved by using a highly customized fused deposition modeling (FDM) 3D-printer, modified to employ NGM hydrogel as ink. The result is the advancement of 3-dimensional complexity of behavioral assays. To demonstrate the potential of our method, we use the 3D-printed arenas to assess C. elegans physical barriers crossing. C. elegans decision to cross physical obstacles is affected by aging, physiological status (i.e., starvation), and prior experience. The 3D-printed structures can be used to spatially confine C. elegans behaviors, i.e., egg laying. We consider these findings a decisive step toward characterizing C. elegans 3-dimensional behavior, an area long overlooked due to technical constrains. We envision our method of 3D-printing NGM arenas as a powerful tool in behavioral neurogenetics, neuroethology, and invertebrate model organisms’ neurobiology.