bluuebunny/crossref_metadata_2025_split huggingface dataset:
On Pre-trained Language Models for Antibody
Authors: Wang Danqing , Ye Fei , Hao Zhou 
Published: February 2023

AbstractAntibodies are vital proteins offering robust protection for the human body from pathogens. The development of general protein and antibody-specific pre-trained language models both facilitate antibody prediction tasks. However, few studies comprehensively explore the representation capability of distinct pre-trained language models on different antibody problems. Here, to investigate the problem, we aim to answer the following key questions: (1) How do pre-trained language models perform in antibody tasks with different specificity? (2) How many benefits will the model gain if we introduce the specific biological mechanism to the pre-training process? (3) Do the learned antibody pre-trained representations make sense in real-world antibody problems, like drug discovery and immune process understanding? Previously, no benchmark available largely hindered the study to answer these questions. To facilitate the investigation, we provide anAnTibodyUnderstandingEvaluation (ATUE) benchmark. We comprehensively evaluate the performance of protein pre-trained language models by empirical study along with conclusions and new insights. OurATUEand code is released athttps://github.com/dqwang122/EATLM.
Single-cell RNA sequencing reveals dysregulated fibroblast subclusters in prurigo nodularis
Authors: Patel Jay R. , Joel Marina Z. , Lee Kevin K. , Kambala Anusha , Cornman Hannah , Oladipo Olusola , Taylor Matthew , Deng June , Parthasarathy Varsha , Cravero Karen , Marani Melika , Zhao Ryan , Sankararam Sreenidhi , Li Ruixiang , Pritchard Thomas , Rebecca Vito , Kwatra Madan M. , Jin Ho Won , Dong Xinzhong , Kang Sewon , Kwatra Shawn G. 
Published: February 2023

AbstractPrurigo nodularis (PN) is an intensely pruritic, chronic inflammatory skin disease that disproportionately affects black patients. However, the pathogenesis of PN is poorly understood. We performed single-cell transcriptomic profiling, ligand receptor analysis and cell trajectory analysis of 28,695 lesional and non-lesional PN skin cells to uncover disease-identifying cell compositions and genetic characteristics. We uncovered a dysregulated role for fibroblasts (FBs) and myofibroblasts as a key pathogenic element in PN, which were significantly increased in PN lesional skin. We defined seven unique subclusters of FBs in PN skin and observed a shift of PN lesional FBs towards a cancer-associated fibroblast (CAF)-like phenotype, with WNT5A+ CAFs increased in the skin of PN patients and similarly so in squamous cell carcinoma (SCC). A multi-center PN cohort study subsequently revealed an increased risk of SCC as well as additional CAF-associated malignancies in PN patients, including breast and colorectal cancers. Systemic fibroproliferative diseases were also upregulated in PN patients, including renal sclerosis and idiopathic pulmonary fibrosis. Ligand receptor analyses demonstrated increased FB1-derived WNT5A and periostin interactions with neuronal receptors MCAM and ITGAV, suggesting a fibroblast-neuronal axis in PN. Type I IFN responses in immune cells and increased angiogenesis/permeability in endothelial cells were also observed. As compared to atopic dermatitis (AD) and psoriasis (PSO) patients, increased mesenchymal dysregulation is unique to PN with an intermediate Th2/Th17 phenotype between atopic dermatitis and psoriasis. These findings identify a pathogenic role for CAFs in PN, including a novel targetable WNT5A+ fibroblast subpopulation and CAF-associated malignancies in PN patients.
A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells
Authors: Li Li , Bowling Sarah , Yu Qi , McGeary Sean E. , Alcedo Karel , Lemke Bianca , Ferreira Mark , Klein Allon M. , Wang Shou-Wen , Camargo Fernando D. 
Published: February 2023

SummaryCellular lineage histories along with their molecular states encode fundamental principles of tissue development and homeostasis. Current lineage-recording mouse models have limited barcode diversity and poor single-cell lineage coverage, thus precluding their use in tissues composed of millions of cells. Here, we developed DARLIN, an improved Cas9 barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) to enhance insertion events over 30 CRISPR target sites, stably integrated into 3 distinct genomic loci. DARLIN is inducible, has an estimated ~1018lineage barcodes across tissues, and enables detection of usable barcodes in ~60% of profiled single cells. Using DARLIN, we examined fate priming within developing hematopoietic stem cells (HSCs) and revealed unique features of HSC migration. Additionally, we adapted a method to jointly profile DNA methylation, chromatin accessibility, gene expression, and lineage information in single cells. DARLIN will enable widespread high-resolution study of lineage relationships and their molecular signatures in diverse tissues and physiological contexts.
Accelerating High-Performance Classification of Bacterial Proteins Secreted via Non-Classical Pathways: no needing for deepness
Authors: Oliveira Luiz Gustavo de Sousa , Lanes Gabriel Chagas , Santos Anderson Rodrigues dos 
Published: February 2023

AbstractUnderstanding protein secretion pathways is paramount in studying diseases caused by bacteria and their respective treatments. Most such paths must signal ways to identify secretion. However, some proteins, known as non-classical secreted proteins, do not have signaling ways. This study aims to classify such proteins from predictive machine-learning techniques. We collected a set of physical-chemical characteristics of amino acids from the AA index site, bolding known protein motifs, like hydrophobicity. We developed a six-step method (Alignment, Preliminary classification, mean outliers, two Clustering algorithms, and Random choice) to filter data from raw genomes and compose a negative dataset in contrast to a positive dataset of 141 proteins from the literature. Using a conventional Random Forest machine-learning algorithm, we obtained an accuracy of 91% on classifying non-classical secreted proteins in a validation dataset with 14 positive and 92 negative proteins - sensitivity and specificity of 91 and 86%, respectively, performance compared to state of the art for non-classical secretion classification. However, this work’s novelty resides in the fastness of executing non-CSP classification: instead of dozens of seconds to just one second considering a few dozen protein samples or only ten seconds to classify one hundred thousand proteins. Such fastness is more suitable for pan-genomic analyses than current methods without losing accuracy. Therefore, this research has shown that selecting an appropriate descriptors’ set and an expressive training dataset compensates for not using an advanced machine learning algorithm for the secretion by non-classical pathways purpose. Available athttps://github.com/santosardr/non-CSPs.
Rapid withdrawal from a threatening animal is movement-specific and mediated by reflex-like neural processing
Authors: Railo Henry , Kraufvelin Nelli , Santalahti Jussi , Laine Teemu 
Published: February 2023

AbstractResponses to potentially dangerous stimuli are among the most basic animal behaviors. While research has shown that threats automatically capture the attention of human participants, research has failed to demonstrate automatic behavioral responses to threats in humans. Using a novel naturalistic paradigm, we show that two species of animals humans often report fearing trigger rapid withdrawal responses: participants withdrew their arm from photos of snakes and spiders faster, and with higher acceleration when compared to bird and butterfly stimuli. The behavior was specific to withdrawal as approach movements or button-press/release tasks failed to detect a similar difference. Moreover, between-participant differences in how aversive they found the stimuli predicted the participant’s withdrawal speed, indicating that the paradigm was also sensitive to trait-level differences between individuals. Using electroencephalography (EEG), we show that the fast withdrawal was mediated by two attentional processes. First, fast withdrawal responses were associated with early amplification of sensory signals (40-110 ms after stimulus). Second, a later correlate of feature-based attention (early posterior negativity, EPN, 200-240 ms after stimulus) revealed the opposite pattern: Stronger EPN was associated with slower behavioral responses, suggesting that the deployment of attention towards the threatening stimulus features, or failure to “disengage” attention from the stimulus, was detrimental for withdrawal speed. Altogether, the results suggest that rapid behavioral withdrawal from a threatening animal is mediated by reflex-like attentional processing, and later, conscious attention to stimulus features may hinder escaping the treat.
A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis
Authors: Ellis Charles A. , Miller Robyn L. , Calhoun Vince D. 
Published: February 2023

AbstractResting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) analysis has illuminated brain network interactions across many neuropsychiatric disorders. A common analysis approach involves using hard clustering methods to identify transitory states of brain activity, and in response to this, other methods have been developed to quantify the importance of specific dFNC interactions to identified states. Some of these methods involve perturbing individual features and examining the number of samples that switch states. However, only a minority of samples switch states. As such, these methods actually identify the importance of dFNC features to the clustering of a subset of samples rather than the overall clustering. In this study, we present a novel approach that more capably identifies the importance of each feature to the overall clustering. Our approach uses fuzzy clustering to output probabilities of each sample belonging to states and then measures their Kullback-Leibler divergence after perturbation. We show the viability of our approach in the context of schizophrenia (SZ) default mode network analysis, identifying significant differences in state dynamics between individuals with SZ and healthy controls. We further compare our approach with an existing approach, showing that it captures the effects of perturbation upon most samples. We also find that interactions between the posterior cingulate cortex (PCC) and the anterior cingulate cortex and the PCC and precuneus are important across methods. We expect that our novel explainable clustering approach will enable further progress in rs-fMRI analysis and to other clustering applications.
Instant polarized light microscopy pi (IPOLπ) for quantitative imaging of collagen architecture and dynamics in ocular tissues
Authors: Lee Po-Yi , Schilpp Hannah , Naylor Nathan , Watkins Simon C. , Yang Bin , Sigal Ian A 
Published: February 2023

AbstractCollagen architecture determines the biomechanical environment in the eye, and thus characterizing collagen fiber organization and biomechanics is essential to fully understand eye physiology and pathology. We recently introduced instant polarized light microscopy (IPOL) that encodes optically information about fiber orientation and retardance through a color snapshot. Although IPOL allows imaging collagen at the full acquisition speed of the camera, with excellent spatial and angular resolutions, a limitation is that the orientation-encoding color is cyclic every 90 degrees (π/2 radians). In consequence, two orthogonal fibers have the same color and therefore the same orientation when quantified by color-angle mapping. In this study, we demonstrate IPOLπ, a new variation of IPOL, in which the orientation-encoding color is cyclic every 180 degrees (π radians). Herein we present the fundamentals of IPOLπ, including a framework based on a Mueller-matrix formalism to characterize how fiber orientation and retardance determine the color. The improved quantitative capability of IPOLπ enables further study of essential biomechanical properties of collagen in ocular tissues, such as fiber anisotropy and crimp. We present a series of experimental calibrations and quantitative procedures to visualize and quantify ocular collagen orientation and microstructure in the optic nerve head, a region in the back of the eye. There are four important strengths of IPOLπ compared to IPOL. First, IPOLπ can distinguish the orientations of orthogonal collagen fibers via colors, whereas IPOL cannot. Second, IPOLπ requires a lower exposure time than IPOL, thus allowing faster imaging speed. Third, IPOLπ allows visualizing non-birefringent tissues and backgrounds from tissue absorption, whereas both appear dark in IPOL images. Fourth, IPOLπ is cheaper and less sensitive to imperfectly collimated light than IPOL. Altogether, the high spatial, angular, and temporal resolutions of IPOLπ enable a deeper insight into ocular biomechanics and eye physiology and pathology.HighlightsWe introduce IPOLπ, addressing IPOL limitations for characterizing eye collagen.IPOLπ orientation-encoded color cycle is 180° (π radians) instead of 90° in IPOL.IPOLπ requires a lower exposure time than IPOL, allowing faster imaging speed.IPOLπ visualizes non-birefringent tissues and backgrounds from brightness.IPOLπ is cheaper and less sensitive to imperfectly collimated light than IPOL.
In vivo Modulation of Intraocular and Intracranial Pressures Causes Nonlinear and Non-monotonic Deformations of the Lamina Cribrosa and Scleral Canal
Authors: Zhu Ziyi , Waxman Susannah , Wang Bo , Wallace Jacob , Schmitt Samantha E. , Tyler-Kabara Elizabeth , Ishikawa Hiroshi , Schuman Joel S. , Smith Matthew A. , Wollstein Gadi , Sigal Ian A. 
Published: February 2023

AbstractPurposeTo evaluate changes in monkey optic nerve head (ONH) morphology under acutely controlled intraocular pressure (IOP) and intracranial pressure (ICP).MethodsSeven ONHs from six monkeys were imaged via optical coherence tomography while IOP and ICP were maintained at one of 16 conditions. These conditions were defined by 4 levels for each pressure: low, baseline, high and very high. Images were processed to determine scleral canal area, aspect ratio, and planarity and anterior lamina cribrosa (ALC) shape index and curvature. Linear mixed effect models were utilized to investigate the effects of IOP, ICP and their interactions on ONH morphological features. The IOP-ICP interaction model was compared with one based on translaminar pressure difference (TLPD).ResultsWe observed complex, eye-specific, non-linear patterns of ONH morphological changes with changes in IOP and ICP. For all ONH morphological features, linear mixed effects models demonstrated significant interactions between IOP and ICP that were unaccounted for by TLPD. Interactions indicate that the effects of IOP and ICP depend on the other pressure. The IOP-ICP interaction model was a higher quality predictor of ONH features than a TLPD model.ConclusionsIn vivo modulation of IOP and ICP causes nonlinear and non-monotonic changes in monkey ONH morphology that depend on both pressures and is not accounted for by a simplistic TLPD. These results support and extend prior findings.Translational Relevance: A better understanding of ICP’s influence on the effects of IOP can help inform the highly variable presentations of glaucoma and effective treatment strategies.
Optimizing the design of spatial genomic studies
Authors: Jones Andrew , Cai Diana , Li Didong , Engelhardt Barbara E. 
Published: February 2023

AbstractSpatially-resolved genomic technologies have shown promise for studying the relationship between the structural arrangement of cells and their functional behavior. While numerous sequencing and imaging platforms exist for performing spatial transcriptomics and spatial proteomics profiling, these experiments remain expensive and labor-intensive. Thus, when performing spatial genomics experiments using multiple tissue slices, there is a need to select the tissue cross sections that will be maximally informative for the purposes of the experiment. In this work, we formalize the problem of experimental design for spatial genomics experiments, which we generalize into a problem class that we callstructured batch experimental design. We propose approaches for optimizing these designs in two types of spatial genomics studies: one in which the goal is to construct a spatially-resolved genomic atlas of a tissue and another in which the goal is to localize a region of interest in a tissue, such as a tumor. We demonstrate the utility of these optimal designs, where each slice is a two-dimensional plane, on several spatial genomics datasets.
In Silico Evaluation of Biomarker Genes for Melanoma Carcinoma
Authors: Kwatra Bharat , Khan Mohammed Moin 
Published: February 2023

ABSTRACTSMelanoma skin cancer is a primary cutaneous malignancy. Melanoma cancer of the skin cell is one of the major skin cancer worldwide, ranking first in frequency. Melanoma skin cancer has been strongly associated with psoriasis. Here, we use computational methods in an effort to identify possible biomarkers for psoriasis related Melanoma skin cancer. To do this, we downloaded gene expression microarray data from the GEO (Gene Expression Omnibus) database in the GSE series: GSE14905 and pre-processed it in the Bioconductor repository for R. The data was screened for DEGs using a rigorous methodology, which included the use of statistical testing methodologies and tools (Differentially Expressed Genes). Psoriasis has 6749 up-regulated genes and 7142 down-regulated genes. Psoriasis DEGs were combined with the NCG dataset resulting 874 up-regulated genes and 74 down-regulated genes for an in depth analysis of how differential expression can lead to malignancy. we used stringDB diseases dataset in Cytoscape to generate network of melanoma proteins, which were further mapped to DEGs and igraph to construct a GRN. In addition, module level analysis was carried out because of the benefits it provides in terms of stability and comprehension of intricate GRNs, resulting 17 biomarkers (17 up-regulated). There is an emphasis on the network’s topology as well. The findings suggest that the network has a hierarchical structure. Additionally, survival analysis of 8 biomarkers results obtained from intersection mapping of skin cancer stringDB and biomarkers, was carried out. Significant enrichment of KEGG pathways was found. They also illuminate the interplay between biomarkers whose upregulation may lead to melanoma skin cancer. These findings may inform future investigations and the identification of potential therapeutic targets for this condition as well as shows the potential link between psoriasis and melanoma.
Organoid generation from mouse mammary tumors captures the genetic heterogeneity of clinically relevant copy number alterations
Authors: Lake Katherine E. , Colonetta Megan M. , Smith Clayton A. , Martinez-Algarin Kenneth , Saunders Kaitlyn , Mohta Sakshi , Pena Jacob , McArthur Heather L. , Reddy Sangeetha M. , Roussos-Torres Evanthia T. , Chen Elizabeth H. , Chan Isaac S. 
Published: February 2023

AbstractBreast cancer metastases exhibit many different genetic alterations, including copy number amplifications. Using publicly available datasets, we identify copy number amplifications in metastatic breast tumor samples and using our organoid-based metastasis assays, and we validate FGFR1 is amplified in collectively migrating organoids. Because the heterogeneity of breast tumors is increasingly becoming relevant to clinical practice, we demonstrate our organoid method captures genetic heterogeneity of individual tumors.
PerturbSci-Kinetics: Dissecting key regulators of transcriptome kinetics through scalable single-cell RNA profiling of pooled CRISPR screens
Authors: Xu Zihan , Sziraki Andras , Lee Jasper , Zhou Wei , Cao Junyue 
Published: February 2023

AbstractHere we describedPerturbSci-Kinetics, a novel combinatorial indexing method for capturing three-layer single-cell readout (i.e.,whole transcriptomes, nascent transcriptomes, sgRNA identities) across hundreds of genetic perturbations. ThroughPerturbSci-Kineticsprofiling of pooled CRISPR screens targeting a variety of biological processes, we were able to decipher the complexity of RNA regulations at multiple levels (e.g.,synthesis, processing, degradation), and revealed key regulators involved in miRNA and mitochondrial RNA processing pathways. Our technique opens the possibility of systematically decoding the genome-wide regulatory network underlying RNA temporal dynamics at scale and cost-effectively.
The viral packaging motor potentiates late gene expression in Kaposi’s sarcoma-associated herpesvirus
Authors: McCollum Chloe O. , Didychuk Allison L. , Liu Dawei , Murray-Nerger Laura A. , Cristea Ileana M. , Glaunsinger Britt A. 
Published: February 2023

Abstractβ- and γ-herpesviruses transcribe their late genes in a manner distinct from host transcription. This process is directed by a complex of viral transcriptional activator proteins that hijack cellular RNA polymerase II and an unknown set of additional factors. We employed proximity labeling coupled with mass spectrometry, followed by CRISPR and siRNA screening to identify proteins functionally associated with the Kaposi’s sarcoma-associated herpesvirus (KSHV) late gene transcriptional complex. These data revealed that the catalytic subunit of the viral DNA packaging motor, ORF29, is both dynamically associated with the viral transcriptional activator complex and potentiates late gene expression. Through genetic mutation and deletion of ORF29, we establish that its catalytic activity potentiates viral transcription and is required for robust accumulation of essential late proteins during infection. Thus, we propose an expanded role for ORF29 that encompasses its established function in viral packaging and its newly discovered contributions to viral transcription and late gene expression in KSHV.Author summaryβ- and γ-herpesviruses express a class of genes essential for completion of the viral life cycle late during infection. A specialized complex of viral transcriptional activator proteins drives expression of these late genes in a manner dependent on viral genome replication, although the mechanisms and regulation of this process are largely unknown. Here, we identified factors that physically and functionally associate with the late gene transcription complex and unexpectedly found that the viral DNA packaging motor in Kaposi’s sarcoma-associated herpesvirus (KSHV) contributes to late gene expression. We show that the catalytic activity of this protein is not only required for genomic packaging but also for the robust expression of late genes to ensure the successful production of progeny virions. Thus, late gene transcription is mechanistically linked to the conserved processes of viral genome replication and packaging.