bluuebunny/crossref_metadata_2025_split huggingface dataset:
Astrogliosis mapping in individual brains using multidimensional MRI
Authors: Benjamini Dan , Priemer David S , Perl Daniel P , Brody David L , Basser Peter J 
Published: January 2022

AbstractThere are currently no noninvasive imaging methods available for astrogliosis mapping in the central nervous system despite its essential role in the response to injury, disease, and infection. We have developed a machine learning-based multidimensional MRI framework that provides a signature of astrogliosis, distinguishing it from normative brain at the individual level. We investigatedex vivocortical tissue specimen derived from subjects who sustained blast induced injuries, which resulted in scar-border forming astrogliosis without being accompanied by other types of neuropathology. By performing a combined postmortem radiology and histopathology correlation study we found that astrogliosis induces microstructural changes that are robustly detected using our framework, resulting in MRI neuropathology maps that are significantly and strongly correlated with co-registered histological images of increased glial fibrillary a cidic protein deposition. The demonstrated high spatial sensitivity in detecting reactive astrocytes at the individual level has great potential to significantly impact neuroimaging studies in diseases, injury, repair, and aging.
Learning in reverse: Dopamine errors drive excitatory and inhibitory components of backward conditioning in an outcome-specific manner
Authors: Seitz Benjamin M. , Hoang Ivy B. , Blaisdell Aaron P. , Sharpe Melissa J. 
Published: January 2022

SummaryFor over two decades, midbrain dopamine was considered synonymous with the prediction error in temporal-difference reinforcement learning. Central to this proposal is the notion that reward-predictive stimuli become endowed with the scalar value of predicted rewards. When these cues are subsequently encountered, their predictive value is compared to the value of the actual reward received allowing for the calculation of prediction errors. Phasic firing of dopamine neurons was proposed to reflect this computation, facilitating the backpropagation of value from the predicted reward to the reward-predictive stimulus, thus reducing future prediction errors. There are two critical assumptions of this proposal: 1) that dopamine errors can only facilitate learning about scalar value and not more complex features of predicted rewards, and 2) that the dopamine signal can only be involved in anticipatory learning in which cues or actions precede rewards. Recent work has challenged the first assumption, demonstrating that phasic dopamine signals across species are involved in learning about more complex features of the predicted outcomes, in a manner that transcends this value computation. Here, we tested the validity of the second assumption. Specifically, we examined whether phasic midbrain dopamine activity would be necessary for backward conditioning—when a neutral cue reliably follows a rewarding outcome. Using a specific Pavlovian-to-Instrumental Transfer (PIT) procedure, we show rats learn both excitatory and inhibitory components of a backward association, and that this association entails knowledge of the specific identity of the reward and cue. We demonstrate that brief optogenetic inhibition of VTADA neurons timed to the transition between the reward and cue, reduces both of these components of backward conditioning. These findings suggest VTADA neurons are capable of facilitating associations between contiguously occurring events, regardless of the content of those events. We conclude that these data are in line with suggestions that the VTADA error acts as a universal teaching signal. This may provide insight into why dopamine function has been implicated in a myriad of psychological disorders that are characterized by very distinct reinforcement-learning deficits.Graphical Abstract
cdonandbocaffect trunk neural crest cell migration non-cell autonomously through a reduction of hedgehog signaling in zebrafish slow-twitch muscle
Authors: Lencer Ezra , Prekeris Rytis , Artinger Kristin 
Published: January 2022

AbstractThe transmembrane proteinscdonandbocare implicated in regulating hedgehog signaling during vertebrate development. Recent work showing roles for these genes in axon guidance and neural crest cell migration suggest thatcdon/bocmay play additional functions in regulating directed cell movements. We use novel and existing mutants to investigate a role forcdonandbocin zebrafish neural crest cell migration. We find that single mutant embryos exhibit normal neural crest phenotypes, but that neural crest migration is strikingly disrupted in doublecdon;bocmutant embryos. We further show that this migration phenotype is associated with defects to the differentiation of slow-twitch muscle cells, and the loss of a Col1a1a containing extracellular matrix, suggesting that neural crest defects are a secondary consequence to defects in mesoderm development. Combined, our data add to a growing literature showing thatcdonandbocact synergistically to promote hedgehog signaling during vertebrate development, and provide a foundation for using zebrafish to study the function of hedgehog receptor paralogs.
GoPeaks: Histone Modification Peak Calling for CUT&Tag
Authors: Yashar William M , Kong Garth , VanCampen Jake , Smith Brittany M , Coleman Daniel J , Carbone Lucia , Yardimci Galip Gürkan , Maxson Julia E , Braun Theodore P 
Published: January 2022

AbstractGenome-wide mapping of the histone modification landscape is critical to understanding tran-scriptional regulation. Cleavage Under Targets and Tagmentation (CUT&Tag) is a new method for profiling the localization of covalent histone modifications, offering improved sensitivity and decreased cost compared with Chromatin Immunoprecipitation Sequencing (ChIP-seq). Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. GoPeaks implements a Binomial distribution and stringent read count cut-off to nominate candidate genomic regions. We compared the performance of GoPeaks against commonly used peak calling algorithms to detect H3K4me3, H3K4me1, and H3K27Ac peaks from CUT&Tag data. These histone modifications display a range of peak profiles and are frequently used in epigenetic studies. We found GoPeaks robustly detects genome-wide histone modifications and, notably, identifies H3K27Ac with improved sensitivity compared to other standard peak calling algorithms.
Magnetic Temporal Interference for Noninvasive Focal Brain Stimulation
Authors: Khalifa Adam , Abrishami Seyed Mahdi , Zaeimbashi Mohsen , Tang Alexander D , Coughlin Brian , Rodger Jennifer , Sun Nian X. , Cash Sydney S. 
Published: January 2022

AbstractNon-invasive stimulation of deep brain regions has been a major goal for neuroscience and neuromodulation in the past three decades. Transcranial magnetic stimulation (TMS), for instance, cannot target deep regions in the brain without activating the overlying tissues and has a poor spatial resolution. In this manuscript, we propose a new concept that relies on the temporal interference of two high-frequency magnetic fields generated by two electromagnetic solenoids. To illustrate the concept, custom solenoids were fabricated and optimized to generate temporal interfering electric fields for rodent brain stimulation. C-Fos expression was used to track neuronal activation. C-Fos expression was not present in regions impacted by only one high-frequency magnetic field indicating ineffective recruitment of neural activity in non-target regions. In contrast, regions impacted by two fields that interfere to create a low-frequency envelope display a strong increase in c-Fos expression. Therefore, this magnetic temporal interference solenoid-based system provides a framework to perform further stimulation studies that would investigate the advantages it could bring over conventional TMS systems.
Maternal immune activation induces methylation changes in schizophrenia genes
Authors: Johnson Thomas , Saatci Defne , Handunnetthi Lahiru 
Published: January 2022

ABSTRACTSusceptibility to schizophrenia is mediated by genetic and environmental risk factors. Infection driven maternal immune activation (MIA) during pregnancy is a key environmental risk factor. However, little is known about how MIA during pregnancy could contribute to adult-onset schizophrenia. In this study, we investigated if maternal immune activation induces changes in methylation of genes linked to schizophrenia. We found that differentially expressed genes in schizophrenia brain were significantly enriched among MIA induced differentially methylated genes in the foetal brain in a cell-type-specific manner. Upregulated genes in layer V pyramidal neurons were enriched among hypomethylated genes at gestational day 9 (fold change = 1.57, FDR = 0.049) and gestational day 17 (fold change = 1.97, FDR = 0.0006). We also found that downregulated genes in GABAergic Rosehip interneurons were enriched among hypermethylated genes at gestational day 17 (fold change = 1.62, FDR= 0.03). Collectively, our results highlight a connection between MIA driven methylation changes during gestation and schizophrenia gene expression signatures in the adult brain. These findings carry important implications for early preventative strategies in schizophrenia.
Rapid and Accurate Identification of SARS-CoV-2 Omicron Variants Using Droplet Digital PCR (RT-ddPCR)
Authors: Mills Margaret G. , Hajian Pooneh , Bakhash Shah Mohamed , Xie Hong , Mantzke Derrek , Zhu Haiying , Perchetti Garrett A. , Huang Meei-Li , Pepper Gregory , Jerome Keith R. , Roychoudhury Pavitra , Greninger Alexander L. 
Published: January 2022

ABSTRACTBackgroundMutations in the receptor binding domain of the SARS-CoV-2 Spike protein are associated with increased transmission or substantial reductions in vaccine efficacy, including in the recently described Omicron variant. The changing frequencies of these mutations combined with their differing susceptibility to available therapies have posed significant problems for clinicians and public health professionals.ObjectiveTo develop an assay capable of rapidly and accurately identifying variants including Omicron in clinical specimens to enable case tracking and/or selection of appropriate clinical treatment.Study DesignUsing three duplex RT-ddPCR reactions targeting four amino acids, we tested 419 positive clinical specimens from February to December 2021 during a period of rapidly shifting variant prevalences and compared genotyping results to genome sequences for each sample, determining the sensitivity and specificity of the assay for each variant.ResultsMutation determinations for 99.7% of detected samples agree with NGS data for those samples, and are accurate despite wide variation in RNA concentration and potential confounding factors like transport medium, presence of additional respiratory viruses, and additional mutations in primer and probe sequences. The assay accurately identified the first 15 Omicron variants in our laboratory including the first Omicron in Washington State and discriminated against an S-gene dropout Delta specimen.ConclusionWe describe an accurate, precise, and specific RT-ddPCR assay for variant detection that remains robust despite being designed prior to the emergence of Delta and Omicron variants. The assay can quickly identify mutations in current and past SARS-CoV-2 variants, and can be adapted to future mutations.
Identifying and correcting repeat-calling errors in nanopore sequencing of telomeres
Authors: Tan Kar-Tong , Slevin Michael K. , Meyerson Matthew , Li Heng 
Published: January 2022

AbstractNanopore long-read genome sequencing is emerging as a potential approach for the study of genomes including long repetitive elements like telomeres. Here, we report extensive basecalling induced errors at telomere repeats across nanopore datasets, sequencing platforms, basecallers, and basecalling models. We found that telomeres which are represented by (TTAGGG)nand (CCCTAA)nrepeats in many organisms were frequently miscalled (~40-50% of reads) as (TTAAAA)n, or as (CTTCTT)nand (CCCTGG)nrepeats respectively in a strand-specific manner during nanopore sequencing. We showed that this miscalling is likely caused by the high similarity of current profiles between telomeric repeats and these repeat artefacts, leading to mis-assignment of electrical current profiles during basecalling. We further demonstrated that tuning of nanopore basecalling models, and selective application of the tuned models to telomeric reads led to improved recovery and analysis of telomeric regions, with little detected negative impact on basecalling of other genomic regions. Our study thus highlights the importance of verifying nanopore basecalls in long, repetitive, and poorly defined regions of the genome, and showcases how such artefacts in regions like telomeres can potentially be resolved by improvements in nanopore basecalling models.
Foveal vision predictively sensitizes to defining features of eye movement targets
Authors: Kroell Lisa M. , Rolfs Martin 
Published: January 2022

AbstractDespite the fovea’s singular importance for active human vision, the impact of large eye movements on foveal processing remains elusive. Building on findings from passive fixation tasks, we hypothesized that during the preparation of rapid eye movements (saccades), foveal processing anticipates soon-to-be fixated visual features. Using a dynamic large-field noise paradigm, we indeed demonstrate that sensitivity for defining features of a saccade target is enhanced in the pre-saccadic center of gaze. Enhancement manifested in higher Hit Rates for foveal probes with target-congruent orientation, and a sensitization to incidental, target-like orientation information in foveally presented noise. Enhancement was spatially confined to the center of gaze and its immediate vicinity. We suggest a crucial contribution of foveal processing to trans-saccadic visual continuity which has previously been overlooked: Foveal processing of saccade targets commences before the movement is executed and thereby enables a seamless transition once the center of gaze reaches the target.
In vivoimaging of calcium dynamics in zebrafish hepatocytes
Authors: Pozo-Morales Macarena , Garteizgogeascoa Inés , Perazzolo Camille , Singh Sumeet Pal 
Published: January 2022

ABSTRACTHepatocytes were the first cell-type for which oscillations of cytoplasmic calcium levels in response to hormones were described. Since then, investigation of calcium dynamics in liver explants and culture has greatly increased our understanding of calcium signaling. A bottleneck, however, exists in observing calcium dynamics in a non-invasive manner due to the optical inaccessibility of the mammalian liver. Here we take advantage of the transparency of the zebrafish larvae to develop a setup that allowsin vivoimaging of calcium flux in zebrafish hepatocytes at cellular resolution. Using this, we provide quantitative assessment of intracellular calcium dynamics during multiple contexts, including growth, feeding, ethanol-induced stress and cell ablation. Specifically, we show that synchronized calcium oscillations are presentin vivo, which are lost upon starvation. Feeding recommences calcium waves in the liver, but in a spatially restricted manner. Further, ethanol treatment as well as cell ablation induces calcium flux, but with different dynamics. The former causes asynchronous calcium oscillations, while the latter leads to a single calcium spike. Overall, we demonstrate the presence of oscillations, waves and spikesin vivo. Thus, our study introduces a platform for observing diverse calcium dynamics while maintaining the native environment of the liver, which will help investigations into the dissection of molecular mechanisms supporting the intra- and intercellular calcium signaling in the liver.
Collaborative workflow between pathologists and deep learning for evaluation of tumor cellularity in lung adenocarcinoma
Authors: Sakamoto Taro , Furukawa Tomoi , Pham Hoa H.N. , Kuroda Kishio , Tabata Kazuhiro , Kashima Yukio , Okoshi Ethan N. , Morimoto Shimpei , Bychkov Andrey , Fukuoka Junya 
Published: January 2022

AbstractOwing to the high demand for molecular testing, the reporting of tumor cellularity in cancer samples has become a mandatory task for pathologists. However, the pathological estimation of tumor cellularity is often inaccurate.We developed a collaborative workflow between pathologists and artificial intelligence (AI) models to evaluate tumor cellularity in lung cancer samples and prospectively applied it to routine practice. We also developed a quantitative model that we validated and tested on retrospectively analyzed cases and ran the model prospectively in a collaborative workflow where pathologists could access the AI results and apply adjustments (Adjusted-Score). The Adjusted-Scores were validated by comparing them with the ground truth established by manual annotation of hematoxylin-eosin slides with reference to immunostains with thyroid transcription factor-1 and napsin A. For training, validation, retrospective testing, and prospective application of the model, we used 40, 10, 50, and 151 whole slide images, respectively.The sensitivity and specificity of tumor segmentation were 97% and 87%, and the accuracy of nuclei recognition was 99%. Pathologists altered the initial scores in 87% of the cases after referring to the AI results and found that the scores became more precise after collaborating with AI. For validation of Adjusted-Score, we found the Adjusted-Score was significantly closer to the ground truth than non-AI-aided estimates (p<0.05). Thus, an AI-based model was successfully implemented into the routine practice of pathological investigations. The proposed model for tumor cell counting efficiently supported the pathologists to improve the prediction of tumor cellularity for genetic tests.
Machine Learning-Based Enzyme Engineering of PETase for Improved Efficiency in Degrading Non-Biodegradable Plastic
Authors: Gupta Arjun , Agrawal Sangeeta 
Published: January 2022

SummaryGlobally, nearly a million plastic bottles are produced every minute (1). These non-biodegradable plastic products are composed of Polyethylene terephthalate (PET). In 2016, researchers discovered PETase, an enzyme from the bacteriaIdeonella sakaiensiswhich breaks down PET and nonbiodegradable plastic. However, PETase has low efficiency at high temperatures. In this project, we optimized the rate of PET degradation by PETase by designing new mutant enzymes which could break down PET much faster than PETase, which is currently the gold standard. We used machine learning (ML) guided directed evolution to modify the PETase enzyme to have a higher optimal temperature (Topt), which would allow the enzyme to degrade PET more efficiently.First, we trained three machine learning models to predict Topt with high performance, including Logistic Regression, Linear Regression and Random Forest. We then used Random Forest to perform ML-guided directed evolution. Our algorithm generated hundreds of mutants of PETase and screened them using Random Forest to select mutants with the highest Topt, and then used the top mutants as the enzyme being mutated.After 1000 iterations, we produced a new mutant of PETase with Topt of 71.38°C. We also produced a new mutant enzyme after 29 iterations with Topt of 61.3°C. To ensure these mutant enzymes would remain stable, we predicted their melting temperatures using an external predictor and found the 29-iteration mutant had improved thermostability over PETase.Our research is significant because using our approach and algorithm, scientists can optimize additional enzymes for improved efficiency.
Consideration of genetic variation and evolutionary history in future conservation of Indian one-horned rhinoceros (Rhinoceros unicornis)
Authors: Ghosh Tista , Kumar Shrewshree , Sharma Kirtika , Kakati Parikshit , Sharma Amit , Mondol Samrat 
Published: January 2022

AbstractThe extant members of the Eurasian rhino species have experienced severe population and range declines through a combination of natural and anthropogenic factors since Pleistocene. The one-horned rhino is the only Asian species recovered from such strong population decline but most of their fragmented populations in India and Nepal are reaching carrying capacity. Implementation of any future reintroduction-based conservation efforts would greatly benefit from currently unavailable detailed genetic assessments and evolutionary history of these populations. We sequenced wild one-horned rhino mitogenome from all the extant populations (n=16 individuals) for the first time, identified the polymorphic sites and assessed genetic variation (2531bp mtDNA, n=111 individuals) across India. Results showed 30 unique rhino haplotypes distributed as three distinct genetic clades (Fst value 0.68-1) corresponding to the states of Assam (n=28 haplotypes), West Bengal and Uttar Pradesh (both monomorphic). Phylogenetic analyses suggest earlier coalescence of Assam (∼0.5 Mya) followed by parallel divergence of West Bengal and Uttar Pradesh/Nepal (∼0.06-0.05Mya), supported by the paleobiogeographic history of the Indian subcontinent. Combined together, we propose recognising three ‘Evolutionary Significant Units (ESUs)’ of Indian rhino. As recent assessments suggest further genetic isolations of Indian rhinos at local scales, future management efforts should focus on identifying genetically variable founder animals and consider periodic supplementation events while planning future rhino reintroduction programs in India. Such well-informed, multidisciplinary approach is the only way to ensure evolutionary, ecological and demographic stability of the species across its range.
EagleImp: Fast and Accurate Genome-wide Phasing and Imputation in a Single Tool
Authors: Wienbrandt Lars , Ellinghaus David 
Published: January 2022

AbstractBackgroundReference-based phasing and genotype imputation algorithms have been developed with sublinear theoretical runtime behaviour, but runtimes are still high in practice when large genome-wide reference datasets are used.MethodsWe developed EagleImp, a software with algorithmic and technical improvements and new features for accurate and accelerated phasing and imputation in a single tool.ResultsWe compared accuracy and runtime of EagleImp with Eagle2, PBWT and prominent imputation servers using whole-genome sequencing data from the 1000 Genomes Project, the Haplotype Reference Consortium and simulated data with more than 1 million reference genomes. EagleImp is 2 to 10 times faster (depending on the single or multiprocessor configuration selected) than Eagle2/PBWT, with the same or better phasing and imputation quality in all tested scenarios. For common variants investigated in typical GWAS studies, EagleImp provides same or higher imputation accuracy than the Sanger Imputation Service, Michigan Imputation Server and the newly developed TOPMed Imputation Server, despite larger (not publicly available) reference panels. It has many new features, including automated chromosome splitting and memory management at runtime to avoid job aborts, fast reading and writing of large files, and various user-configurable algorithm and output options.ConclusionsDue to the technical optimisations, EagleImp can perform fast and accurate reference-based phasing and imputation for future very large reference panels with more than 1 million genomes. EagleImp is freely available for download from https://github.com/ikmb/eagleimp.