bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biochemically validated structural model of the 15-subunit IFT-B complex
Authors: Petriman Narcis A. , Loureiro-López Marta , Taschner Michael , Zacharia Nevin K. , Georgieva Magdalena M. , Boegholm Niels , Mourão André , Russell Robert B. , Andersen Jens S. , Lorentzen Esben 
Published: August 2022

AbstractCilia are ubiquitous eukaryotic organelles important to cellular motility, signalling and sensory reception. Cilium formation requires intraflagellar transport for trafficking of structural and signalling components. The large MDa IFT-B complex constitutes the backbone of polymeric IFT trains that carry ciliary cargo between the cilium and the cell body. Currently, high-resolution structures are only available for smaller IFT-B sub-complexes leaving >50% of the IFT-B complex structurally uncharacterized. We have used recent advances in protein structure prediction as implemented in Alphafold to assemble a structural model for the 15-subunit IFT-B complex. The model was validated using crosslinking/MS data on reconstituted IFT-B complexes, X-ray scattering in solution and diffraction from crystals as well as site-directed mutagenesis and protein binding assays. The IFT-B structural model reveals an elongated and highly flexible complex consistent with cryo-electron tomographic reconstructions of IFT trains. The >400Å long IFT-B complex can roughly be divided into IFT-B1 and IFT-B2 parts with binding sites for ciliary cargo and the inactive IFT dynein motor, respectively. Interestingly, our structural modelling and crosslinking/MS results are consistent with two different binding sites for IFT81/74 on IFT88/70/52/46 suggesting the possibility of two different structural architectures for the IFT-B1 complex. Our data present a structural framework to understand IFT-B complex assembly, function, and ciliopathy variants.
Single-cell profiling reveals the sustained immune infiltration, surveillance, and tumor heterogeneity of infiltrative BCC
Authors: Huang Lingjuan , Wang Xianggui , Pei Shiyao , Li Xin , Dong Liang , Bian Xiaohui , Sun Hongyin , Jin Liping , Hou Huihui , Shi Wensheng , Zhang Xiyuan , Zhang Lining , Zhao Shuang , Chen Xiang , Yin Mingzhu 
Published: August 2022

AbstractBackgroundInfiltrative basal cell carcinoma (iBCC) is a particularly aggressive subtype of BCC that tends to recur after surgery, and its progression and malignancy are closely related to its interaction with the local tumor microenvironment (TME).MethodsHere, we performed single-cell RNA sequencing (scRNA-seq) from 5 patients to determine the dynamic changes of TME between iBCC and adjacent normal skin (ANS).ResultsWe found active immune collaborations among CXCL13+ follicular helper-like T cells (Tfh-like cells), SPP1+CXCL9/10high Macro1, and plasma cells, which were enriched in iBCC. Specifically, SPP1+CXCL9/10high Macro1 could activate plasma cells by BAFF signaling, and Tfh-like cells potentially recruited B/Plasma cells through CXCL13. The proinflammatory SPP1+CXCL9/10high Macro1 and angiogenesis-related SPP1+CCL2high Macro1 were characterized, revealing their heterogeneous phenotype within the TME. We also discovered a novel iBCC-enriched ANGPT2+ lymphatic endothelial cell (LEC) subtype with strong abilities to promote leukocyte migration and activation. Interestingly, we found upregulation of MHC-I molecules in fibroblasts in iBCC compared to those in ANS. However, we found that MDK signals derived from malignant basal cells (MBCs) were markedly increased, and their expression was an independent factor in predicting the infiltration depth of iBCC, emphasizing its role in driving malignancy and remodeling the TME. Additionally, we identified differentiation-associated SOSTDC1+IGFBP5+CTSV+ MB1 and epithelial mesenchyme transition (EMT)-associated TNC+SFRP1+CHGA+ MB2. The components of the two heterogeneous subpopulations in the TME might be effective predictors of the malignancy and prognosis of iBCC.ConclusionsAltogether, our study is beneficial for understanding the cellular heterogeneity involved in the pathogenesis of iBCC and provides potential therapeutic targets for clinical research.
Changes in consumer purchasing patterns at New York City chain restaurants following adoption of the sodium warning icon rule, 2015-2017
Authors: Prasad Divya , Jasek John P. , Anekwe Amaka V. , Dominianni Christine , Mezzacca Tamar Adjoian , Sisti Julia S. , Farley Shannon M. , Kessler Kimberly 
Published: August 2022

AbstractIn 2016, New York City (NYC) began enforcing a sodium warning regulation at chain restaurants, requiring placement of an icon next to any menu item containing ≥2,300 mg sodium. As shifts in consumer purchases are a potential outcome of menu labeling, we investigated whether high-sodium purchases from NYC chains changed following policy implementation. Consumer purchases, using receipts for verification, were assessed at 2 full-service (FSR) and 2 quick-service (QSR) chain restaurants in NYC and Yonkers, NY, which did not have sodium menu labeling, in 2015 (baseline) and 2017 (follow-up). Difference-in-difference regression models, adjusted for demographic and location co-variates, tested whether the proportion of NYC respondents purchasing high-sodium item(s) (containing ≥2,300 mg sodium) or whether mean sodium content of purchases changed, in FSR or QSR. Relative to Yonkers, the proportion of NYC respondents purchasing 1 or more high-sodium items did not significantly differ from baseline to follow-up at FSR (difference-in-difference: -4.6%, p=0.364) or QSR (difference-in-difference: -8.9%, p=0.196). Among NYC FSR respondents, mean sodium purchased significantly declined compared to Yonkers (difference-in-difference: -524 mg, p=0.012); no changes in mean sodium were observed among QSR participants (difference-in-difference: 258 mg, p=0.185). While the reduction in mean sodium purchased among NYC FSR patrons after the sodium warning implementation is encouraging, there was not a corresponding reduction in the proportion of respondents purchasing high-sodium items. Further research that evaluates longer-term changes in the restaurant environment, including menu offerings and consumer purchases, following such policies is warranted.
A cross-sectional study to assess beta cell function in individuals with recently diagnosed young onset type 2 diabetes mellitus and its complications
Authors: Nagaratnam Shamharini , Rajoo Subashini , Long Bidin Mohamed Badrulnizam , Che Rahim Nur Shafini , Tharmathurai Sangeetha , Arip Masita , Ching Yee Ming , Foo Siew Hui 
Published: August 2022

AbstractThe primary objective of this study was to assess beta cell function of recently diagnosed young onset type 2 diabetes mellitus (T2DM) individuals. The secondary objective examined the association between C-peptide with metabolic factors and diabetes complications. A cross-sectional study was conducted for young onset T2DM individuals aged 18-35 years with disease duration not more than 5 years. Plasma basal and stimulated C-peptide was measured before and after intravenous glucagon injection. Demographic data, medical history and complications were obtained from medical records and clinical assessment. A total of 113 participants with young onset T2DM with mean age of 29 years and median disease duration of 24 months were included in this study. The median (interquartile range) basal and stimulated C-peptide was 619 (655) pmol/L and 1231(1024) pmol/L. Adequate beta cell function was present in 78-86% of the participants based on the basal and stimulated C-peptide levels. Obesity and hypertension were independently associated with higher basal and stimulated C-peptide while diabetic kidney disease was independently associated with higher basal C-peptide. We found most recently diagnosed young onset T2DM have adequate beta cell function. Elevated C-peptide levels associated with obesity, hypertension and diabetic kidney disease suggests insulin resistance as the key driving factor for complications.
scraps: an end-to-end pipeline for measuring alternative polyadenylation at high resolution using single-cell RNA-seq
Authors: Fu Rui , Riemondy Kent A. , Sheridan Ryan M. , Hesselberth Jay R. , Jordan Craig T. , Gillen Austin E. 
Published: August 2022

AbstractAlternative cleavage and polyadenylation (APA) contributes to the diversity of mRNA 3′ ends, affecting post-transcriptional regulation by including or excluding cis-regulatory elements in mRNAs, altering their stability and translational efficiency. While APA analysis has been applied broadly in mixed populations of cells, the heterogeneity of APA among single cells has only recently begun to be explored. We developed an approach we termed scraps (Single Cell RNAPolyA Site Discovery), implemented as a user-friendly, scalable, and reproducible end-to-end workflow, to identify polyadenylation sites at near-nucleotide resolution in single cells using 10X Genomics and other TVN-primed single-cell RNA-seq (scRNA-seq) libraries. Our approach, which performs best with long (>100bp) read 1 sequencing and paired alignment to the genome, is both unbiased relative to existing methods that utilize only read 2 and recovers more sites at higher resolution, despite the reduction in read quality observed on most modern DNA sequencers following homopolymer stretches. For libraries sequenced without long read 1, we implement a fallback approach using read 2-only alignments that performs similarly to our optimal approach, but recovers far fewer polyadenylation sites per experiment. scraps also enables assessment of internal priming capture events, which we demonstrate occur commonly but at higher frequency during apoptotic 3′ RNA decay. We also provide an R package, scrapR, that integrates the results of the scaps pipeline with the popular Seruat single-cell analysis package. Refinement and expanded application of these approaches will further clarify the role of APA in single cells, as well as the effects of internal priming on expression measurements in scRNA-seq libraries.
Interactive webtool for analyzing drug sensitivity and resistance associated with genetic signatures of cancer cell lines
Authors: Boeschen Myriam , Le Duc Diana , Stiller Mathias , von Laffert Maximilian , Schöneberg Torsten , Horn Susanne 
Published: August 2022

AbstractA wide therapeutic repertoire has become available to oncologists including radio- and chemotherapy, small molecules and monoclonal antibodies. However, drug efficacy can be limited by genetic changes that allow cancer cells to escape therapy. Here, we designed a webtool that facilitates the data analysis of the Genomics of Drug Sensitivity in Cancer (GDSC) database on 265 approved compounds in association with a plethora of genetic changes documented for 1001 cell lines in the Cancer Cell Line Encyclopedia (CCLE, cBioPortal). The webtool computes odds ratios of drug resistance for a queried set of genetic alterations. It provides results on the efficacy of single or groups of compounds assigned to cellular signaling pathways. Using this webtool we replicated known genetic drivers and identified new candidate genes, germline variants, co-mutation, and pharmacogenomic modifiers of both cancer drug resistance and drug repurposing. Webtool availability: https://tools.hornlab.org/GDSC/.Author SummaryTumors develop through uncontrolled cell growth enabled by various alterations that create tumor heterogeneity. Changes of the genome and thus cancer cells cause every patient to react differently to drugs and can lead to drug resistance in cancer therapies. To overcome drug resistance, researchers focus on developing personalized therapies. Here, we provide a straightforward tool to test public in vitro drug sensitivity data on a range of drugs for custom analyses of genetic changes. This may inform the identification of potential drug candidates and improve our understanding of signaling pathways as we can test drug response with custom sets of genetic changes according to specific research questions. The tool and underlying code can be adapted to larger drug response datasets and other data types, e.g. metabolic data, to help structure and accommodate the increasingly large biomedical knowledge base.
Acute intermittent hypoxia enhances volitional elbow strength, and modulates spatial distribution of muscle activation patterns in persons with chronic incomplete spinal cord injury
Authors: Afsharipour Babak , Pearcey Gregory EP , Rymer W Zev , Sandhu Milap S 
Published: August 2022

AbstractBackgroundAcute intermittent hypoxia (AIH) is an emerging technique for facilitating neural plasticity in individuals with major neurological deficits. In people with chronic incomplete spinal cord injury (iSCI), a single sequence of AIH enhances motor function such as hand grip strength and ankle plantarflexion torque, but the underlying mechanisms are not yet clear.ObjectiveTo examine how AIH-induced changes in magnitude and spatial distribution of electromyography (EMG) activity over the surface of the biceps and triceps brachii muscles contributes to improved strength.MethodsSeven individuals with iSCI visited the laboratory on two occasions, at least a week apart, and received either AIH or Sham AIH intervention in a randomized order. AIH consisted of 15 brief (∼60s) periods of low oxygen (fraction of inspired O2 = 0.09) alternating with 60s of normoxia, whereas Sham AIH consisted of repeated exposures to normoxic gas mixtures. Muscle activity of biceps and triceps brachii was recorded with high-density surface EMG during maximal elbow flexion and extension contractions. We used these EMG recordings to generate spatial maps which distinguished active muscle regions prior to and 60 minutes after AIH or Sham AIH.ResultsAfter an AIH sequence, elbow flexion and extension forces increased by 91.7 ± 33.5% and 51.7 ± 21.9% from baseline, respectively, whereas there was no difference after Sham AIH exposure. Changes in strength were associated with an altered spatial distribution of EMG activity and increased root mean squared EMG amplitude in both biceps and triceps brachii muscles.ConclusionsThese data suggest that altered motor unit activation profiles may underlie improved volitional strength after a single dose of AIH and warrant further investigation using single motor unit analysis techniques to further elucidate mechanisms of AIH-induced plasticity.
Phenotype-by-phenome-wide association study of treatment resistant depression
Authors: Coombes Brandon J , Sanchez Ruiz Jorge A , Fennessy Brian , Pazdernik Vanessa , Adekkanattu Prakash , Nunez Nicolas A , Lepow Lauren , Ryu Euijung , Talati Ardesheer , Jenkins Greg D , Pendegraft Richard , Wickramaratne Priya , Mann J John , Olfson Mark , Weissman Myrna M , Pathak Jyotishman , Charney Alexander W , Biernacka Joanna M 
Published: August 2022

AbstractObjectiveTreatment-resistant depression (TRD), defined as inadequate response to at least one or at least two antidepressant (AD) trials, is common in major depressive disorder (MDD). In this study, electronic health records (EHR) were used to identify clinical associations with TRD.MethodsUsing two biobanks, phenomes of patients with at least one MDD-related diagnostic code and one AD prescription (N=17,049) were generated using aggregated diagnostic codes (phecodes) from EHRs. Phenotype-by-phenome-wide association analyses were performed for two binary definitions of TRD, based on either one or more, or two or more, AD switches after at least 30 days but within 14 weeks, and a quantitative measure defined as the number of unique ADs prescribed for at least 30 days.ResultsOf the 17,049 patients with MDD, 1624 (9.5%) had at least one switch, 422 (2.5%) had at least two switches, and the number of unique antidepressant prescriptions ranged from one to twelve. After accounting for multiple testing, 142, 18, and 7 phecodes were significantly associated with the quantitative definition and the two binary definitions (≥1 AD switch or ≥2 AD switches), respectively. All three outcomes were significantly associated with known TRD risk factors including anxiety disorders, insomnia, and suicidal ideation. The quantitative measure was uniquely associated with other conditions including irritable bowel syndrome and decreased white blood cell count.ConclusionsIn addition to identifying known clinical associations, the quantitative measure of treatment resistance uncovered new factors potentially associated with TRD. This measure may also facilitate discovery of genetic correlates of TRD in future analyses.
Automated Expert-level Scleral Spur Detection and Quantitative Biometric Analysis on the ANTERION Anterior Segment OCT System
Authors: Bolo Kyle , Apolo Galo , Pardeshi Anmol A. , Chiang Michael , Burkemper Bruce , Xie Xiaobin , Huang Alex S. , Simonovsky Martin , Xu Benjamin Y. 
Published: August 2022

ABSTRACTAimTo perform an independent validation of deep learning (DL) algorithms for automated scleral spur detection and measurement of scleral spur-based biometric parameters in anterior segment optical coherence tomography (AS-OCT) images.MethodsPatients receiving routine eye care underwent AS-OCT imaging using the ANTERION OCT system (Heidelberg Engineering, Heidelberg, Germany). Scleral spur locations were marked by three human graders (Reference, Expert, and Novice) and predicted using DL algorithms developed by Heidelberg Engineering that prioritize a false positive rate <4% (FPR4) or true positive rate >95% (TPR95). Performance of human graders and DL algorithms were evaluated based on agreement of scleral spur locations and biometric measurements with the Reference Grader.Results1,308 AS-OCT images were obtained from 117 participants. Median differences in scleral spur locations from reference locations were significantly smaller (p<0.001) for the FPR4 (52.6±48.6μm) and TPR95 (55.5±50.6μm) algorithms compared to the Expert (61.1±65.7μm) and Novice (79.4±74.9μm) Graders. Inter-grader reproducibility of biometric measurements was excellent overall for all four (intraclass correlation coefficient [ICC] range 0.918-0.997). Inter-grader reproducibility of the Expert Grader [0.567-0.965] and DL algorithms [0.746-0.979] exceeded that of the Novice Grader [0.146-0.929] for images with narrow angles, defined as angle opening distance 500μm from the scleral spur (AOD500) <150μm.ConclusionsDL algorithms on the ANTERION approximate expert-level measurement of scleral spur-based biometric parameters in an independent patient population. These algorithms could enhance clinical utility of AS-OCT imaging, especially for evaluating patients with angle closure and performing intraocular lens (IOL) calculations.KEY MESSAGESWhat is already known on this topicDeep learning (DL) algorithms can detect scleral spur locations in AS-OCT images with expert-level performance; however, there is sparse information about the accuracy of AS-OCT measurements associated with these predicted scleral spur locations.What this study addsDL algorithms on the ANTERION OCT system (Heidelberg Engineering, Heidelberg, Germany) approximate expert-level detection of the scleral spur and measurement of anterior segment biometric parameters in a real-world clinical cohort. Performance of the algorithms generally exceeds that of a novice grader.How this study might affect research, practice or policyThe automation of scleral spur detection and quantitative biometric analysis overcomes the time-and expertise-dependent nature of AS-OCT imaging in the clinical setting. This technology provides clinicians with convenient access to data that could enhance care of patients with angle closure disease or patients receiving intraocular lens implantation.
Graph Neural Networks for Z-DNA prediction in Genomes
Authors: Voytetskiy Artem , Herbert Alan , Poptsova Maria 
Published: August 2022

AbstractDeep learning methods have been successfully applied to the tasks of predicting functional genomic elements such as histone marks, transcriptions factor binding sites, non-B DNA structures, and regulatory variants. Initially convolutional neural networks (CNN) and recurrent neural networks (RNN) or hybrid CNN-RNN models appeared to be the methods of choice for genomic studies. With the advance of machine learning algorithms other deep learning architectures started to outperform CNN and RNN in various applications. Thus, graph neural network (GNN) applications improved the prediction of drug effects, disease associations, protein-protein interactions, protein structures and their functions. The performance of GNN is yet to be fully explored in genomics. Earlier we developed DeepZ approach in which deep learning model is trained on information both from sequence and omics data. Initially this approach was implemented with CNN and RNN but is not limited to these classes of neural networks. In this study we implemented the DeepZ approach by substituting RNN with GNN. We tested three different GNN architectures – Graph Convolutional Network (GCN), Graph Attention Network (GAT) and inductive representation learning network GraphSAGE. The GNN models outperformed current state-of the art RNN model from initial DeepZ realization. Graph SAGE showed the best performance for the small training set of human Z-DNA ChIP-seq data while Graph Convolutional Network was superior for specific curaxin-induced mouse Z-DNA data that was recently reported. Our results show the potential of GNN applications for the task of predicting genomic functional elements based on DNA sequence and omics data.Availability and implementationThe code is freely available at https://github.com/MrARVO/GraphZ.
Mood variability during adolescent development and its relation to sleep and brain development
Authors: Toenders Yara J. , van der Cruijsen Renske , Runze Jana , van de Groep Suzanne , Wierenga Lara , Crone Eveline A. 
Published: August 2022

AbstractMood swings, or mood variability, are associated with negative mental health outcomes. Since adolescence is a time when mood disorder onset peaks, mood variability during this time is of significant interest. Understanding biological factors that might be associated with mood variability, such as sleep and structural brain development, could elucidate the mechanisms underlying mood and anxiety disorders. Data from the longitudinal Leiden Self-Concept study (N=171) over 5 yearly timepoints was used to study the association between sleep, brain structure, and mood variability in healthy adolescents aged 11-21 at baseline in this pre-registered study. Sleep was measured both objectively, using actigraphy, as well as subjectively, using a daily diary self-report. Negative mood variability was defined as day-to-day negative mood swings over a period of 5 days after an MRI scan. It was found that negative mood variability peaked in mid-adolescence in females while it linearly increased in males, and average negative mood showed a similar pattern. Sleep duration (subjective and objective) generally decreased throughout adolescence, with a larger decrease in males. Mood variability was not associated with sleep, but average negative mood was associated with lower self-reported energy. In addition, higher thickness in the dorsolateral prefrontal cortex (dlPFC) compared to same-age peers, suggesting a delayed thinning process, was associated with higher negative mood variability in early and mid-adolescence. Together, this study provides an insight into the development of mood variability and its association with brain structure.