bluuebunny/crossref_metadata_2025_split huggingface dataset:
A contamination focused approach for optimizing the single-cell RNA-seq experiment
Authors: Arceneaux Deronisha , Chen Zhengyi , Simmons Alan J. , Heiser Cody N. , Southard-Smith Austin N. , Brenan Michael J. , Yang Yilin , Chen Bob , Xu Yanwen , Choi Eunyoung , Campbell Joshua D. , Liu Qi , Lau Ken S. 
Published: October 2022

AbstractAchieving high data quality in single-cell RNA-seq (scRNA-seq) experiments has always been a significant challenge stemming from minute signal that can be detected in individual cells. Droplet-based scRNA-seq additionally suffers from ambient contamination, comprising nucleic acid materials released by dead cells into the loading buffer and co-encapsulated with real cells, which further washes out real biological signals. Here, we developed quantitative, ambient contamination-based metrics and an associated software package that can both evaluate current datasets and guide new experimental optimizations. We performed a series of experimental optimizations using the inDrops platform to address the mechanical and microfluidic cell encapsulation aspect of an scRNA-seq experiment, with a focus on minimizing ambient contamination. We report improvements that can be achieved via cell fixation, microfluidic loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide insights into previously obscured factors that can affect scRNA-seq data quality and suggest mitigation strategies that can guide future experiments.
Metabolic modeling ofHermetia illucenslarvae resource allocation for high-value fatty acid production
Authors: Grausa Kristina , Siddiqui Shahida A , Lameyer Norbert , Wiesotzki Karin , Smetana Sergiy , Pentjuss Agris 
Published: October 2022

AbstractAll plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation and growth functions. While biochemical network details are well known, the understanding of intense regulation principles is still limited.We chose to investigateHermetia illucensfly at the larval stage as it is crucial for successful resource accumulation and allocation for the consequential organism’s developmental stages. We combined the iterative wet lab experiments and innovative metabolic modeling design approaches, to simulate and explain theH. illucenslarval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments in larvae and Gainesville diet composition. To predict diet-based alterations on fatty acid allocation potential, we built and validated the firstH. illucensmedium-size stoichiometric metabolic model.Using optimization methods like Flux balance and Flux variability analysis on the novel insect metabolic model, it predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework to research the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable and directed high-value chemicals.Significance StatementMetabolic modeling serves as a platform for researchers to investigate and study in depth the possible states of the system based on the existing knowledgebase (e.g. metabolic reactions, substrates, products and their stoichiometry). These models can be applied for different industrial applications, to simulate resource allocation potential and growth conditions. Moreover, these models predict the required diet for living organisms and insects to improve survival and growth rates and accumulate higher-value products, like fatty acids.
Transcription factors use a unique combination of cofactors to potentiate different promoter-dependent steps in transcription
Authors: Bell Charles C , Talarmain Laure , Scolamiero Laura , Lam Enid YN , Ang Ching-Seng , Gilan Omer , Dawson Mark A 
Published: October 2022

AbstractTranscription factors use DNA binding domains to recognise specific sequences and transactivation domains to recruit the cofactor proteins necessary for transcription. However, how specific cofactors contribute to transactivation at different genes remains unclear. Here, we couple Gal4-transactivation assays with comparative CRISPR-Cas9 screens to identify the cofactors required by nine different transcription factors and nine different core promoters in human cells. We classify cofactors as ubiquitous or specific, discover novel transcriptional co-dependencies and demonstrate that submodules within large co-activator complexes, such as the tail 2 and kinase modules of Mediator, facilitate transcriptional elongation. Rather than displaying discrete mechanisms of action, we discover that each TF requires a unique combination of cofactors, which influence its ability to potentiate distinct steps in the transcriptional process. Our findings help reconcile models of cofactor-promoter compatibility by demonstrating that transcription at different classes of promoters is constrained by either initiation or pause release. These differences dictate cofactor compatibility and the dynamic range of gene expression. Overall, our screens provide insight into TF-cofactor relationships and their ability to potentiate different steps in transcription at different classes of promoters.
Gradual cerebral hypoperfusion in a knock-in mouse model of Alzheimer’s disease triggers cortical network dysfunctions
Authors: Singh Surjeet , Lacoursiere Sean G. , Mehla Jogender , Nazari Mojtaba , Sutherland Robert J. , McDonald Robert J. , Mohajerani Majid H. 
Published: October 2022

AbstractAlzheimer’s disease (AD) is characterized neuropathologically by amyloid-β (Aβ) plaques and neurofibrillary tangles. Vascular pathology caused by chronic cerebral hypoperfusion (HP) is hypothesised to exacerbate AD pathology and has emerged as an increasing cause of age-related cognitive impairment. In this study we examined the effects of gradual cerebral HP on cognitive dysfunction, Aβ pathology, microgliosis, and cortical network dynamics in C57BL/6J mice and a single App knock-in mouse model of AD (AppNL-G-F). We performed unilateral common carotid artery gradual occlusion (UCAgO) in two-month-old mice using an ameroid constrictor. At 4 months of age, animals were tested in a behavioral battery consisting of tests of spatial learning and memory (Morris water task), recognition memory (novel object recognition task), and motor coordination (balance beam). Following behavioural testing,in vivomesoscale wide-field voltage imaging was done to assess cortical functional connectivity and sensory-evoked cortical activity, and brains were harvested for pathology characterization using immunohistochemistry. We found that UCAgO reduced cerebral blood flow (CBF) in the occluded hemisphere (OH), however, subtle behavioural deficits were observed due to HP. A dissociative effect of HP was observed in resting-state functional connectivity analysis, where HP led to hyper-connectivity in C57 mice and hypo-connectivity in App mice. Interestingly, sensory stimulation of limbs contralateral to OH revealed hyper-cortical activations in the non-occluded hemisphere of C57 HP mice, however, hypo-cortical activations were observed in App HP mice. Furthermore, we found that the UCAgO increased cortical and hippocampal microgliosis in both hemispheres of C57 and App mice, a bilateral increase in Aβ deposition was only observed in App mice. These results suggest that gradual cerebral HP leads to cortical network alterations in AD, which is partly mediated via activation of microglia.
A Robust Bayesian Approach to Bulk Gene Expression Deconvolution with Noisy Reference Signatures
Authors: Ghaffari Saba , Bouchonville Kelly J. , Saleh Ehsan , Schmidt Remington E. , Offer Steven M. , Sinha Saurabh 
Published: October 2022

AbstractBackgroundDifferential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportion of cell types within the sample. Expression deconvolution methods can help differentiate these scenarios and enable more accurate inference of gene regulation by estimating the contributions of individual cell types to bulk transcriptomic profiles. However, the accuracy of these methods is sensitive to technical and biological differences between bulk profiles and the cell type-signatures required by them as references.ResultsWe present BEDwARS, a Bayesian deconvolution method specifically designed to address differences between reference signatures and the unknown true signatures underlying bulk transcriptomic profiles. Through extensive benchmarking utilizing eight different datasets derived from pancreas and brain, we demonstrate that BEDwARS outperforms leading in-class methods for estimating cell type proportions and signatures. Furthermore, we systematically show that BEDwARS is more robust to noisy reference signatures than all compared methods. Finally, we apply BEDwARS to newly generated RNA-seq and scRNA-seq data on over 100 induced pluripotent stem cell-derived neural organoids to study mechanisms underlying a rare pediatric condition (DihydropyridineDehydrogenase deficiency), identifying the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.ConclusionWe propose a new approach to bulk gene expression deconvolution which estimates the cell type proportions and cell type signatures simultaneously and is robust to commonly seen mismatches between reference and true cell type signatures. Application of our method lead to novel findings about mechanisms of a rare pediatric condition.
Development of Highly Efficient CRISPR-Mediated Gene Editing in the RotiferBrachionus manjavacas
Authors: Feng Haiyang , Bavister Gemma , Gribble Kristin E. , Welch David B. Mark 
Published: October 2022

AbstractRotifers have been studied in the laboratory and field for over 100 years and are an emerging modern model system for investigation of the molecular mechanisms of genome evolution, development, DNA repair, aging, life history strategy, and desiccation tolerance, and have a long been used in studies of microevolution, ecological dynamics, and ecotoxicology. However, a lack of gene editing tools and transgenic strains has limited the ability to link genotype to phenotype and dissect molecular mechanisms. To facilitate genetic manipulation and the creation of reporter lines, we developed a protocol for highly efficient, transgenerational, CRISPR-mediated gene editing in the monogonont rotiferBrachionus manjavacasby microinjection of Cas9 protein and synthetic single guide RNA into the vitellaria of young amictic (asexual) females. To demonstrate the efficacy of the method, we created knockout mutants of the developmental genevasaand the DNA mismatch repair genemlh3. More than half of mothers survived injection and produced offspring. Genotyping these offspring and successive generations revealed that most carried at least one CRISPR-induced mutation, with many apparently mutated at both alleles or mosaic. In addition, we achieved precise CRISPR-mediated knockin of a stop codon cassette in themlh3locus, with half of injected mothers producing 33% or more F2 offspring with an insertion of the cassette. These results demonstrate the efficacy of the CRISPR/Cas9 system in rotifers to provide insight into the function of specific genes and further advance rotifers as a model system for biological discovery.
Directed functional brain connectivity is altered in sub-threshold amyloid-β accumulators
Authors: Mijalkov Mite , Veréb Dániel , Canal-Garcia Anna , Volpe Giovanni , Pereira Joana B. ,   Alzheimer’s Disease Neuroimaging Initiative
Published: October 2022

AbstractSeveral studies have shown that amyloid-β (Aβ) deposition below the clinically relevant cut-off levels is associated with subtle changes in cognitive function and increases the risk for developing future Alzheimer’s disease (AD). Although functional MRI is sensitive to early alterations occurring during AD, sub-threshold changes in Aβ levels have not been linked to functional connectivity measures. This study aimed to apply directed functional connectivity to identify early changes in network function in cognitively unimpaired participants who, at baseline, exhibit Aβ accumulation below the clinically relevant threshold. To this end, we analyzed baseline functional MRI data from 113 cognitively unimpaired participants of the Alzheimer’s Disease Neuroimaging Initiative cohort who underwent at least one18F-florbetapir-PET after the baseline scan. Using the longitudinal PET data, we classified these participants as Aβ negative (Aβ−) non-accumulators (n=46) and Aβ-accumulators (n=31). We also included 36 individuals who were amyloid-positive (Aβ+) at baseline and continued to accumulate Aβ (Aβ+ accumulators). For each participant, we calculated whole-brain directed functional connectivity networks using our own anti-symmetric correlation method and evaluated their global and nodal properties using measures of network segregation (clustering coefficient) and integration (global efficiency). When compared to Aβ-non-accumulators, the Aβ-accumulators showed lower global clustering coefficient. Moreover, the Aβ+ accumulator group exhibited reduced global efficiency and clustering coefficient, which at the nodal level mainly affected the superior frontal gyrus, anterior cingulate cortex and caudate nucleus. In Aβ-accumulators, global measures were associated with lower baseline regional PET uptake values, as well as higher scores on the Modified Preclinical Alzheimer Cognitive Composite. Our findings indicate that directed connectivity network properties are sensitive to subtle changes occurring in individuals who have not yet reached the threshold for Aβ positivity, which makes them a potentially viable marker to detect negative downstream effects of very early Aβ pathology.Significance StatementDirected functional connectivity measures are sensitive to very early changes in amyloid-β deposition.Amyloid-β burden is associated with decreases in global and nodal directed measures of network integration and segregation.Measures of directed network integration and segregation are associated with subclinical cognitive changes in Aβ-negative accumulators.
Generalizability and Clinical Implications of Electrocardiogram Denoising with Cardio-NAFNet
Authors: Lim Chanho , Chung Yunsung , Hamm Jihun , Ding Zhengming , Mekhael Mario , Noujaim Charbel , Assaf Ala , Younes Hadi , Chouman Nour , Makan Noor , Donnellan Eoin , Marrouche Nassir 
Published: October 2022

AbstractThe rise of mobile electrocardiogram (ECG) devices came with the rise of frequent large magnitudes of noise in their recordings. Several artificial intelligence (AI) models have had great success in denoising, but the model’s generalizability and the enhancement in clinical interpretability are still questionable. We propose Cardio-NAFNet, a novel AI-based approach to ECG denoising by employing a modified version of Non-Linear Activation Free Network (NAFNET). We conducted three experiments for quantitative and qualitative evaluation of denoising, clinical implications and generalizability. In the first experiment, Cardio-NAFNet achieved 53.74dB average signal to noise ratio across varying magnitude of noise in beat-to-beat denoising, which is a significant improvement over the current state of the art model in ECG denoising. In the second experiment, we tested the enhancement in clinical interpretation of the ECG signals by utilizing a pretrained ECG classifier using 8 second long noise-free ECG signals. When the classifier was tested using noisy ECG signals and their denoised counterparts, Cardio-NAFNet’s denoised signals provided 26% boost in classification results. Lastly, we provide an external validation dataset composed of single-lead mobile ECG signals along with signal quality evaluation from physician experts. Our paper suggests a settling method to capture and reconstruct critical features of ECG signals not only in terms of quantitative evaluation, but also through generalizable qualitative evaluation.
Development and Validation of Sex-Specific Hip Fracture Prediction Models using Electronic Health Records
Authors: Li Gloria Hoi-Yee , Cheung Ching-Lung , Tan Kathryn Choon-Beng , Kung Annie Wai-Chee , Kwok Timothy Chi-Yui , Lau Wallis Cheuk-Yin , Wong Janus Siu-Him , Hsu Warrington W.Q. , Fang Christian , Wong Ian Chi-Kei 
Published: October 2022

SummaryBackgroundHip fracture is associated with immobility, morbidity, mortality, and high medical cost. Due to limited availability of dual-energy X-ray absorptiometry (DXA), hip fracture prediction models without using bone mineral density (BMD) data are essential. We aimed to develop and validate 10-year sex-specific hip fracture prediction models using electronic health records (EHR) without BMD.MethodsIn this population-based study, the derivation cohort comprised 161,051 public healthcare service users (91,926 female; 69,125 male) in Hong Kong aged≥60. Sex-stratified derivation cohort was randomly split to 80% training and 20% internal testing datasets. An external validation cohort comprised 3,046 community-dwelling participants. With 395 potential predictors (age, diagnosis and drug prescription records from EHR), 10-year sex-specific hip fracture prediction models were developed using stepwise selection by logistic regression (LR) and four machine learning (ML) algorithms (gradient boosting machine, random forest, eXtreme gradient boosting, and single-layer neural networks) in the training cohort. Model performance was evaluated in both internal and external validation cohorts.FindingsIn female, the LR model had the highest AUC (0.815) and adequate calibration in internal validation. Reclassification metrics showed ML algorithms could not further improve the performance of the LR model. Similar performance was attained by the LR model in external validation, with high AUC (0.841) comparable to other ML algorithms. In internal validation for male, LR model had high AUC (0.818) and it outperformed all ML models as indicated by reclassification metrics, with adequate calibration. In external validation, the LR model had high AUC (0.898) comparable to ML algorithms. Reclassification metrics demonstrated that LR model had the best discrimination performance.InterpretationEven without using BMD data, the 10-year hip fracture prediction models developed by conventional LR had better discrimination performance than the models developed by ML algorithms. Upon further validation in independent cohorts, the LR models could be integrated into the routine clinical workflow, aiding the identification of people at high risk for DXA scan.FundingThis study was funded by the Health and Medical Research Fund, Food and Health Bureau, Hong Kong SAR Government (reference: 17181381).
One-Step PCR Amplicon Library Construction (OSPALC, version 1)
Authors: Ni Jiahao , Pan Jiao , Wang Yaohai , Chen Tianhao , Feng Xinshi , Li Yichen , Lin Tongtong , Lynch Michael , Long Hongan , Li Weiyi 
Published: October 2022

AbstractHigh-throughput sequencing of amplicons has been widely used to precisely and efficiently identify species compositions and analyze community structures, greatly promoting biological studies involving large amounts of complex samples, especially those involving environmental and pathogen-monitoring ones. However, commercial library preparation kits for amplicon sequencing, which generally require multiple steps, including adapter ligation and indexing, are expensive and time-consuming, especially for applications at a large scale. Here, to overcome this technical hurdle, we present a one-step PCR amplicon library construction (OSPALC) protocol for amplicon library preparations in the lab along with a QIIME2-based amplicon analysis pipeline. High-quality reads have been generated by this approach to reliably identify species compositions of mock bacterial communities and environmental samples. With this protocol, the amplicon library construction is completed through one regular PCR with long primers, and the total cost per DNA/cDNA sample decreases to just 1/15 of the typical cost via service providers. Empirically tested primers to construct OSPALC libraries for 16S rDNA V4 regions are demonstrated as a case study. Criteria to design primers targeting at any regions of prokaryotic and eukaryotic genomes are also suggested. In principle, OSPALC can be conveniently used in amplicon library constructions of any target gene of both prokaryotes and eukaryotes at the DNA or RNA levels, and will facilitate research in numerous fields.
A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis
Authors: White Brian S , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B , Wang Shidan , Evrard Yvonne A , Landua John David , Mashl R Jay , Davies Sherri R , Fang Bingliang , Raso Maria Gabriela , Evans Kurt W , Bailey Matthew H , Chen Yeqing , Xiao Min , Rubinstein Jill , pour Ali Foroughi , Dobrolecki Lacey Elizabeth , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C , Mudd Jacqueline L , Xu Xiaowei , Hollingshead Melinda G , Jiwani Shahanawaz ,   PDXNet consortium, Davis-Dusenbery Brandi , Wallace Tiffany A , Moscow Jeffrey A , Doroshow James H , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Chen Moon S , Carvajal-Carmona Luis G , Welm Alana L , Welm Bryan E , Govindan Ramaswamy , Li Shunqiang , Davies Michael A , Roth Jack A , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T , Bult Carol J , Dean Dennis A , Chuang Jeffrey H 
Published: October 2022

AbstractPatient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of largehumanH&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000PDXand paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.