bluuebunny/crossref_metadata_2025_split huggingface dataset:
Natural statistics support a rational account of confidence biases
Authors: Webb Taylor W. , Miyoshi Kiyofumi , So Tsz Yan , Rajananda Sivananda , Lau Hakwan 
Published: October 2021

AbstractPrevious work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional models, necessitating strong assumptions about the representations over which confidence is computed. To address this, we used deep neural networks to develop a model of decision confidence that operates directly over high-dimensional, naturalistic stimuli. The model accounts for a number of puzzling dissociations between decisions and confidence, reveals a rational explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.
Nanometer-Scale Imaging of Compartment-Specific Localization and Dynamics of Voltage-Gated Sodium Channels
Authors: Liu Hui , Wang Hong-Gang , Pitt Geoffrey S. , Liu Zhe J. 
Published: October 2021

ABSTRACTMembrane excitability and cell-to-cell communication in the brain are tightly regulated by diverse ion channels and receptor proteins localized to distinct membrane compartments. Currently, a major technical barrier in cellular neuroscience is lack of reliable methods to label these membrane proteins and image their sub-cellular localization and dynamics. To overcome this challenge, we devised optical imaging strategies that enable systematic characterization of subcellular composition, relative abundances and trafficking dynamics of membrane proteins at nanometer scales in cultured neurons as well as in the brain. Using these methods, we revealed exquisite developmental regulation of subcellular distributions of voltage-gated sodium channel (VGSC) Nav1.2 and Nav1.6, settling a decade long debate regarding the molecular identity of sodium channels in dendrites. In addition, we discovered a previously uncharacterized trafficking pathway that targets Nav1.2 to unmyelinated fragments in the distal axon. Myelination counteracts this pathway, facilitating the installment of Nav1.6 as the dominant VGSC in the axon. Together, these imaging approaches unveiled compartment-specific trafficking mechanisms underpinning differential membrane distributions of VGSCs and open avenues to decipher how membrane protein localization and dynamics contribute to neural computation in the brain.
The extended Price equation: migration and sex
Authors: Brown Jake , Field Jared M. 
Published: October 2021

AbstractThe Price equation provides a general partition of evolutionary change into two components. The first is usually thought to represent natural selection and the second, transmission bias. Here, we provide a new derivation of the generalised equation, which contains a largely ignored third term. Unlike the original Price equation, this extension can account for migration and mixed asexual and sexual reproduction. The notation used here expresses the generalised equation explicitly in terms of fitness, rendering this otherwise difficult third term more open to biological interpretation and use. This re-derivation also permits fundamental results, derived from the Price equation, to be more easily generalised. We take Hamilton’s rule as a case study, and provide an exact, total expression that allows for population structures like haplodiploidy. Our analysis, more generally, makes clear the previously hidden assumptions in similar fundamental results, highlighting the caution that must be taken when interpreting them.
Delineation and birth of a layered intestinal stem cell niche
Authors: McCarthy Neil , Tie Guodong , Madha Shariq , Kraiczy Judith , Maglieri Adrianna , Shivdasani Ramesh A. 
Published: October 2021

SummaryWnt and Rspondin (RSPO) signaling drives proliferation, and bone morphogenetic protein inhibitors (BMPi) impede differentiation, of intestinal stem cells (ISCs). Here we identify the adult mouse ISC niche as a complex, multi-layered structure that encompasses distinct mesenchymal and smooth muscle cell populations. Diverse sub-cryptal cells provide redundant supportive factors, with certain BMPi and the most potent Wnt co-factor, RSPO2, restricted to single cell types. Niche functions refine during a critical period of postnatal crypt morphogenesis, in part to oppose dense aggregation of BMP-expressing sub-epithelial myofibroblasts that promote epithelial differentiation. A specialized muscle layer, the muscularis mucosae, first appears during this period and supplements neighboring RSPO and BMPi sources. In vivo ablation of smooth muscle raises BMP activity and potently limits a pre-weaning burst of crypt fission. Thus, distinct and progressively specialized mesenchymal components together create the milieu required to propagate crypts during rapid organ growth and to sustain adult ISCs.
Robust W1282X-CFTR rescue by a small molecule GSPT1 degrader
Authors: Lee Rhianna E. , Lewis Catherine A. , He Lihua , Gallant Samuel C. , Mascenik Teresa M. , Dang Hong , Morton Lisa C. , Minges John T. , Theile Jonathan W. , Castle Neil A. , Knowles Michael R. , Kimple Adam J. , Randell Scott H. 
Published: October 2021

AbstractWith the approval of elexacaftor/tezacaftor/ivacaftor (trade name Trikafta), the vast majority of people with cystic fibrosis (CF) are eligible for CF transmembrane conductance regulator (CFTR) modulator therapy. Remaining individuals have premature termination codons or rareCFTRvariants with limited treatment options. Although clinical modulator response can be reliably predicted using primary airway epithelial cells, primary cells carrying rareCFTRvariants are scarce. To overcome this obstacle, these cells can be expanded by overexpression of mouseBmi-1and humanTERT(hTERT). We therefore used this approach to develop two non-CF and three CF (F508del/F508del, F508del/S492F, W1282X/W1282X) nasal cell lines and two W1282X/W1282X bronchial cell lines. Bmi-1/hTERT cell lines recapitulated primary cell morphology and ion transport function. The F508del/F508del and F508del/S492F cell lines robustly responded to Trikafta, which was mirrored in the parent primary cells and the cell donors’ clinical response. CC-90009, a novel cereblon E3 ligase modulator targeting the GSPT1 protein, rescued ~20% of wildtype CFTR function in our panel of W1282X/W1282X cell lines and primary cells. Intriguingly, CC-90009 also diminished epithelial sodium channel function. These studies demonstrate that Bmi-1/hTERT cell lines faithfully mirror primary cell responses to CFTR modulators and illustrate novel therapeutic approaches for the W1282X CFTR variant.
Extracellular Electron Transfer Enables Cellular Control of Cu(I)-catalyzed Alkyne-Azide Cycloaddition
Authors: Partipilo Gina , Graham Austin J. , Belardi Brian , Keitz Benjamin K. 
Published: October 2021

AbstractExtracellular electron transfer (EET) is an anaerobic respiration process that couples carbon oxidation to the reduction of metal species. In the presence of a suitable metal catalyst, EET allows for cellular metabolism to control a variety of synthetic transformations. Here, we report the use of EET from the model electroactive bacterium Shewanella oneidensis for metabolic and genetic control over Cu(I)-catalyzed Alkyne-Azide Cycloaddition (CuAAC). CuAAC conversion under anaerobic and aerobic conditions was dependent on live, actively respiring S. oneidensis cells. In addition, reaction progress and kinetics could be further manipulated by tailoring the central carbon metabolism of S. oneidensis. Similarly, CuAAC activity was dependent on specific EET pathways and could be manipulated using inducible genetic circuits controlling the expression of EET-relevant proteins including MtrC, MtrA, and CymA. EET-driven CuAAC also exhibited modularity and robustness in ligand tolerance and substrate scope. Furthermore, the living nature of this system could be exploited to perform multiple reaction cycles without requiring regeneration, something inaccessible to traditional chemical reductants. Finally, S. oneidensis enabled bioorthogonal CuAAC membrane labelling on live mammalian cells without affecting cell viability, suggesting that S. oneidensis can act as a dynamically tunable biocatalyst in complex environments. In summary, our results demonstrate how EET can expand the reaction scope available to living systems by enabling cellular control of CuAAC.
TACI: an ImageJ plugin for 3D calcium imaging analysis
Authors: Omelchenko Alisa A. , Bai Hua , Hussain Sibtain , Tyrrell Jordan J. , Ni Lina 
Published: October 2021

AbstractResearch in the field of neuroscience has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging is a widely used technique that requires sophisticated software to obtain reproducible results, but many laboratories struggle to adopt computational methods when updating protocols to meet modern standards. Difficulties arise due to the lack of computational knowledge and paywalls for software. In addition, most calcium imaging analysis approaches ignore motion on the z-axis. Here, we described a workflow to use ImageJ to analyze 3D calcium imaging. We applied TrackMate, an open-source ImageJ plugin, to track neurons in the lateral (x/y) direction, detect regions of interest (ROIs), and extract fluorescence intensities. To track motion on the z-axis, we developed a new ImageJ plugin, TrackMate Analysis of Calcium Imaging (TACI). For neurons appearing on multiple z-positions, maximum fluorescence values were identified to represent neurons’ intensities of corresponding z-stacks. This workflow does not require coding ability, avoids human bias, and increases reproducibility. We validated this workflow using fly larval thermosensitive neurons that displayed movements in all directions during temperature fluctuation and a 3D calcium imaging dataset acquired from the fly brain.
Social framing effects in decision making
Authors: Piray Payam , Cools Roshan , Toni Ivan 
Published: October 2021

AbstractHuman decisions are known to be strongly influenced by the manner in which options are presented, the “framing effect”. Here, we ask whether decision-makers are also influenced by how advice from other knowledgeable agents are framed, a “social framing effect”. For example, do students learn better from a teacher who often frames advice by emphasizing appetitive outcomes, or do they learn better from another teacher who usually emphasizes avoiding options that can be harmful to their progress? We study the computational and neural mechanisms by which framing of advice affect decision-making, social learning, and trust. We found that human participants are more likely to trust and follow an adviser who often uses an appetitive frame for advice compared with another one who often uses an aversive frame. This social framing effect is implemented through a modulation of the integrative abilities of the ventromedial prefrontal cortex. At the time of choice, this region combines information learned via personal experiences of reward with social information, but the combination differs depending on the social framing of advice. Personally-acquired information is weighted more strongly when dealing with an adviser who uses an aversive frame. The findings suggest that social advice is systematically incorporated into our decisions, while being affected by biases similar to those influencing individual value-based learning.
Evaluation of Vicinity-based Hidden Markov Models for Genotype Imputation
Authors: Wang Su , Kim Miran , Jiang Xiaoqian , Harmanci Arif 
Published: October 2021

AbstractThe decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype-phenotype relationships, the cost of performing whole-genome sequencing on large samples is still prohibitive. In-silico genotype imputation coupled with genotyping-by-arrays is a cost-effective and accurate alternative for genotyping of common and uncommon variants. Imputation methods compare the genotypes of the typed variants with the large population-specific reference panels and estimate the genotypes of untyped variants by making use of the linkage disequilibrium patterns. Most accurate imputation methods are based on the Li-Stephens hidden Markov model, HMM, that treats the sequence of each chromosome as a mosaic of the haplotypes from the reference panel. Here we assess the accuracy of local-HMMs, where each untyped variant is imputed using the typed variants in a small window around itself (as small as 1 centimorgan). Locality-based imputation is used recently by machine learning-based genotype imputation approaches. We assess how the parameters of the local-HMMs impact the imputation accuracy in a comprehensive set of benchmarks and show that local-HMMs can accurately impute common and uncommon variants and can be relaxed to impute rare variants as well. The source code for the local HMM implementations is publicly available at https://github.com/harmancilab/LoHaMMer.
A genetic bottleneck of mitochondrial DNA during human lymphocyte development
Authors: Tang Zhongjie , Lu Zhaolian , Chen Baizhen , Zhang Weixing , Chang Howard Y. , Hu Zheng , Xu Jin 
Published: October 2021

ABSTRACTMitochondria are essential organelles in eukaryotic cells that provide critical support for energetic and metabolic homeostasis. Mutations that accumulate in mitochondrial DNA (mtDNA) in somatic cells have been implicated in cancer, degenerative diseases, and the aging process. However, the mechanisms used by somatic cells to maintain proper functions despite their mtDNA mutation load are poorly understood. Here, we analyzed somatic mtDNA mutations in more than 30,000 human single peripheral and bone marrow mononuclear cells and observed a significant overrepresentation of homoplastic mtDNA mutations in B, T and NK lymphocytes despite their lower mutational burden than other hematopoietic cells. The characteristic mutational landscape of mtDNA in lymphocytes were validated with data from multiple platforms and individuals. Single-cell RNA-seq and computational modeling demonstrated a stringent mitochondrial bottleneck during lymphocyte development likely caused by lagging mtDNA replication relative to cell proliferation. These results illuminate a potential mechanism used by highly metabolically active immune cells for quality control of their mitochondrial genomes.
A Pre-Processing Pipeline to Quantify, Visualize and Reduce Technical Variation in Protein Microarray Studies
Authors: Bérubé Sophie , Kobayashi Tamaki , Wesolowski Amy , Norris Douglas E. , Ruczinski Ingo , Moss William J. , Louis Thomas A. 
Published: October 2021

AbstractTechnical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies, and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.1Impact StatementProtein microarrays are in wide use in cancer research, infectious disease diagnostics and biomarker identification. To inform research and practice in these and other fields, technical variation must be corrected using normalization and pre-processing. Current protein microarray studies use a variety of normalization methods, many of which were developed for DNA microarrays, and therefore are based on assumptions and data that are not ideal for protein microarrays. To address this issue, we develop, evaluate, and implement a pre-processing pipeline that corrects for technical variation in protein microarrays. We show that pre-processing and normalization directly impact the validity of downstream analysis, and protein-specific approaches are essential.
Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue
Authors: O’Keeffe Rory , Shirazi Seyed Yahya , Yang Jinghui , Mehrdad Sarmad , Rao Smita , Atashzar S. Farokh 
Published: October 2021

AbstractThe possibility of muscle fatigue detection using surface electromyography has been explored and multiple biomarkers, such as median frequency, have been suggested. However, there are contradictory reports in the literature which results in an inconsistent understanding of the biomarkers of fatigue. Thus, there is an unmet need for a statistically robust sEMG-based biomarker for fatigue detection. This paper, for the first time, demonstrates the superior capability of a non-parametric muscle network to reliably detect fatigue-related changes. Seven healthy volunteers completed a lower limb exercise protocol, which consisted of 30s of a sit-to-stand exercise before and after the completion of fatiguing leg press sets. A non-parametric muscle network was constructed, using Spearman’s power correlation and showed a very reliable decrease in network metrics associated with fatigue (degree, weighted clustering coefficient (WCC)). The network metrics displayed a significant decrease at the group level (degree, WCC: p < 0.001), individual subject level (degree: p < 0.035 WCC: p < 0.004) and particular muscle level (degree: p < 0.017). Regarding the decrease in mean degree connectivity at particular muscles, all seven subjects followed the group trend. In contrast to the robust results achieved by the proposed non-parametric muscle network, classical spectrotemporal measurements showed heterogeneous trends at the particular muscle and individual subject levels. Thus, this paper for the first time shows that non-parametric muscle network is a reliable biomarker of fatigue and could be used in a broad range of applications.
Large-scale multi-omics analysis suggests specific roles for intragenic cohesin in transcriptional regulation
Authors: Wang Jiankang , Bando Masashige , Shirahige Katsuhiko , Nakato Ryuichiro 
Published: October 2021

AbstractCohesin, an essential protein complex for chromosome segregation, regulates transcription through a variety of mechanisms. It is not a trivial task to genome-widely assign the diverse cohesin functions. Moreover, the context-specific roles of cohesin-mediated interactions, especially on intragenic regions, have not been thoroughly investigated. Here we performed a comprehensive characterization of cohesin binding sites in several human cell types. We integrated epigenomic, transcriptomic and chromatin interaction data with and without transcriptional stimulation, to explore context-specific functions of intragenic cohesin related to gene activation. We identified a new subset of cohesin binding sites, decreased intragenic cohesin sites (DICs), which have a different function from previously known ones. The intron-enriched DICs were negatively correlated with transcriptional regulation: a subgroup of DICs were related to enhancer markers and paused RNA polymerase II, whereas others contributed to chromatin architecture. We implemented machine learning and successfully isolated DICs with distinct genomic features. We observed DICs in various cell types, including cells from cohesinopathy patients. These results suggest a previously unidentified function of cohesin at intragenic regions for transcription regulation.
Reduced motivation for social contact in Disrupted-in-schizophrenia transgenic rats
Authors: Seidisarouei Mohammad , van Wingerden Marijn , Schäble Sandra , Trossbach Svenja V. , Korth Carsten , Kalenscher Tobias 
Published: October 2021

AbstractThe Disrupted-in-schizophrenia 1 (DISC1) signaling pathway is considered to play a key role in schizophrenia, depression, autism and other psychiatric disorders. DISC1 is involved in regulating the dopaminergic neurotransmission in, among others, the mesolimbic reward system. A transgenic rat line tgDISC1 has been introduced as a model system to study behavioral phenotypes associated with abnormal DISC1 pathways. Here, we evaluated the impact of impaired DISC1 signaling on social (social interaction) and non-social (sucrose) reward preferences in the tgDISC1 animal model. In a plus-maze setting, rats chose between the opportunity for social interaction with an unfamiliar juvenile conspecific (social reward) or drinking sweet solutions with variable sucrose concentrations (non-social reward). tgDISC1 rats differed from wild-type rats in their social, but not in their non-social reward preferences. Specifically, DISC1 rats showed a lower interest in interaction with the juvenile conspecific, but did not differ from wild-type rats in their preference for higher sucrose concentrations. These results suggest that disruptions of the DISC1 pathway that is associated with altered dopamine transmission in the brain result in selective deficits in social motivation seen in neuropsychiatric illness.
Convergent mosaic brain evolution is associated with the evolution of novel electrosensory systems in teleost fishes
Authors: Schumacher Erika L. , Carlson Bruce A. 
Published: October 2021

AbstractBrain region size generally scales allometrically with total brain size, but mosaic shifts in brain region size independent of brain size have been found in several lineages and may be related to the evolution of behavioral novelty. African weakly electric fishes (Mormyroidea) evolved a mosaically enlarged cerebellum and hindbrain, yet the relationship to their behaviorally novel electrosensory system remains unclear. We addressed this by studying South American weakly electric fishes (Gymnotiformes) and weakly electric catfishes (Synodontis spp.), which evolved varying aspects of electrosensory systems, independent of mormyroids. If the mormyroid mosaic increases are related to evolving an electrosensory system, we should find similar mosaic shifts in gymnotiforms and Synodontis. Using micro-computed tomography scans, we quantified brain region scaling for multiple electrogenic, electroreceptive, and non-electrosensing species. We found mosaic increases in cerebellum in all three electrogenic lineages relative to non-electric lineages and mosaic increases in torus semicircularis and hindbrain associated with the evolution of electrogenesis and electroreceptor type. These results show that evolving novel electrosensory systems is repeatedly and independently associated with changes in the sizes of individual brain regions independent of brain size, which suggests that selection can impact structural brain composition to favor specific regions involved in novel behaviors.