bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sugar transporter Slc37a2 regulates bone metabolism in mice via a tubular lysosomal network in osteoclasts
Authors: Ng Pei Ying , Ribet Amy B. P. , Guo Qiang , Mullin Benjamin H. , Tan Jamie W. Y. , Landao-Bassonga Euphemie , Stephens Sébastien , Chen Kai , Yuan Jinbo , Abudulai Laila , Bollen Maike , Nguyen Edward T. T. T. , Kular Jasreen , Papadimitriou John M. , Søe Kent , Teasdale Rohan D. , Xu Jiake , Parton Robert G. , Takayanagi Hiroshi , Pavlos Nathan J. 
Published: February 2023

AbstractOsteoclasts are giant bone-digesting cells that harbor specialized lysosome-related organelles termed secretory lysosomes (SLs). SLs store cathepsin K and serve as a membrane precursor to the ruffled border, the osteoclast’s ‘resorptive apparatus’. Yet, the molecular composition and spatiotemporal organization of SLs remains incompletely understood. Here, using organelle-resolution proteomics, we identify member a2 of the solute carrier 37 family (Slc37a2) as a SL sugar transporter. We demonstrate in mice that Slc37a2 localizes to the SL limiting membrane and that these organelles adopt a hitherto unnoticed but dynamic tubular network in living osteoclasts that is required for bone digestion. Accordingly, mice lacking Slc37a2 accrue high bone mass owing to uncoupled bone metabolism and disturbances in SL export of monosaccharide sugars, a prerequisite for SL delivery to the bone-lining osteoclast plasma membrane. Thus, Slc37a2 is a physiological component of the osteoclast’s unique secretory organelle and a potential therapeutic target for metabolic bone diseases.
SVEP1 is an endogenous ligand for the orphan receptor PEAR1
Authors: Elenbaas Jared S. , Pudupakkam Upasana , Ashworth Katrina J. , Kang Chul Joo , Patel Ved , Santana Katherine , Jung In-Hyuk , Lee Paul C. , Burks Kendall H. , Amrute Junedh M. , Mecham Robert P. , Halabi Carmen M. , Alisio Arturo , Di Paola Jorge , Stitziel Nathan O. 
Published: February 2023

AbstractSushi, von Willebrand factor type A, EGF and pentraxin domain containing 1 (SVEP1) is an extracellular matrix protein that causally promotes vascular disease and associates with platelet reactivity in humans. Here, using a human genomic and proteomic approach, we identify a high affinity, disease-relevant, and potentially targetable interaction between SVEP1 and the orphan receptor Platelet and Endothelial Aggregation Receptor 1 (PEAR1). This interaction promotes PEAR1 phosphorylation and disease associated AKT/mTOR signaling in vascular cells and platelets. Mice lacking SVEP1 have reduced platelet activation, and exogenous SVEP1 induces PEAR1-dependent activation of platelets. SVEP1 and PEAR1 causally and concordantly relate to platelet phenotypes and cardiovascular disease in humans, as determined by Mendelian Randomization. Targeting this receptor-ligand interaction may be a viable therapeutic strategy to treat or prevent cardiovascular and thrombotic disease.
CvkR is a MerR-type transcriptional repressor of class 2 type V-K CRISPR-associated transposase systems
Authors: Ziemann Marcus , Reimann Viktoria , Liang Yajing , Shi Yue , Ma Honglei , Xie Yuman , Li Hui , Zhu Tao , Lu Xuefeng , Hess Wolfgang R. 
Published: February 2023

AbstractCertain CRISPR-Cas elements integrate into Tn7-like transposons, forming CRISPR-associated transposon (CAST) systems. How the activity of these systems is controlled in situ has remained largely unknown. Here we characterize the MerR-type transcriptional regulator Alr3614 that is encoded by one of the CAST (AnCAST) system genes in the genome of cyanobacteriumAnabaenasp. PCC 7120. We identify a number of Alr3614 homologs across cyanobacteria and suggest naming these regulators CvkR for Cas V-K repressors. Alr3614/CvkR is translated from leaderless mRNA and represses the AnCAST core modulescas12kandtnsB directly, and indirectly the abundance of the tracr-CRISPR RNA. We identify a widely conserved CvkR binding motif 5’-AnnACATnATGTnnT-3’. Crystal structure of CvkR at 1.6 Å resolution reveals that it comprises distinct dimerization and potential effector-binding domains and that it assembles into a homodimer, representing a discrete structural subfamily of MerR regulators. CvkR repressors are at the core of a widely conserved regulatory mechanism that controls type V-K CAST systems.
Phosphoproteomics reveals rewiring of the insulin signaling network and multi-nodal defects in insulin resistance
Authors: Fazakerley Daniel J. , van Gerwen Julian , Cooke Kristen C. , Duan Xiaowen , Needham Elise J. , Díaz-Vegas Alexis , Madsen Søren , Norris Dougall M. , Shun-Shion Amber S. , Krycer James R. , Burchfield James G. , Yang Pengyi , Wade Mark R. , Brozinick Joseph T. , James David E. , Humphrey Sean J. 
Published: February 2023

AbstractThe failure of metabolic tissues to appropriately respond to insulin (“insulin resistance”) is an early marker in the pathogenesis of type 2 diabetes. Protein phosphorylation is central to the adipocyte insulin response, but how adipocyte signaling networks are dysregulated upon insulin resistance is unknown. Here we employ phosphoproteomics to delineate insulin signal transduction in adipocyte cells and adipose tissue. Across a range of insults causing insulin resistance, we observe a marked rewiring of the insulin signaling network. This includes both attenuated insulin-responsive phosphorylation, and the emergence of phosphorylation uniquely insulin-regulated in insulin resistance. Identifying dysregulated phosphosites common to multiple insults reveals subnetworks containing non-canonical regulators of insulin action, such as MARK2/3, and causal drivers of insulin resistance. The presence of several bona fide GSK3 substrates among these phosphosites led us to establish a pipeline for identifying context-specific kinase substrates, revealing widespread dysregulation of GSK3 signaling. Pharmacological inhibition of GSK3 partially reverses insulin resistance in cells and tissue explants. These data highlight that insulin resistance is a multi-nodal signaling defect that includes dysregulated MARK2/3 and GSK3 activity.
Reciprocal modulation of ammonia and melanin production has implications for cryptococcal virulence
Authors: Baker Rosanna P. , Casadevall Arturo 
Published: February 2023

AbstractThe fungus Cryptococcus neoformans is the causative agent of cryptococcosis, a disease that is uniformly lethal unless treated with antifungal drugs, yet current regimens are hindered by host toxicity and pathogen resistance. An attractive alternative approach to combat this deadly disease is the direct targeting of pathogen-derived virulence mechanisms. C. neoformans expresses multiple virulence factors that have been studied previously as isolated entities. Among these, are urease, which increases phagosomal pH and promotes brain invasion, and melanization, which protects against immune cells and antifungal treatments. Here we report a reciprocal interdependency between these two virulence factors. Cells hydrolyzing urea release ammonia gas which acts at a distance to raise pH and increase melanization rates for nearby cells, which in turn reduces secretion of urease-carrying extracellular vesicles. This reciprocal relationship manifests as an emergent property that may explain why targeting isolated virulence mechanisms for drug development has been difficult and argues for a more holistic approach that considers the virulence composite.
Single-cell biological network inference using a heterogeneous graph transformer
Authors: Ma Anjun , Wang Xiaoying , Li Jingxian , Wang Cankun , Xiao Tong , Liu Yuntao , Cheng Hao , Wang Juexin , Li Yang , Chang Yuzhou , Li Jinpu , Wang Duolin , Jiang Yuexu , Su Li , Xin Gang , Gu Shaopeng , Li Zihai , Liu Bingqiang , Xu Dong , Ma Qin 
Published: February 2023

AbstractSingle-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.
Batch alignment of single-cell transcriptomics data using deep metric learning
Authors: Yu Xiaokang , Xu Xinyi , Zhang Jingxiao , Li Xiangjie 
Published: February 2023

AbstractscRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.
Recent decrease of the impact of tropical temperature on the carbon cycle linked to increased precipitation
Authors: Zhang Wenmin , Schurgers Guy , Peñuelas Josep , Fensholt Rasmus , Yang Hui , Tang Jing , Tong Xiaowei , Ciais Philippe , Brandt Martin 
Published: February 2023

AbstractThe atmospheric CO2 growth rate (CGR) variability is largely controlled by tropical temperature fluctuations. The sensitivity of CGR to tropical temperature $$\left({{{{\rm{\gamma }}}}}_{{{{\rm{CGR}}}}}^{{{{\rm{T}}}}}\right)$$

γ

CGR

T

has strongly increased since 1960, but here we show that this trend has ceased. Here, we use the long-term CO2 records from Mauna Loa and the South Pole to compute CGR, and show that $${{{{\rm{\gamma }}}}}_{{{{\rm{CGR}}}}}^{{{{\rm{T}}}}}$$

γ

CGR

T

increased by 200% from 1960–1979 to 1979–2000 but then decreased by 117% from 1980–2001 to 2001–2020, almost returning back to the level of the 1960s. Variations in $${{{{\rm{\gamma }}}}}_{{{{\rm{CGR}}}}}^{{{{\rm{T}}}}}$$

γ

CGR

T

are significantly correlated with changes in precipitation at a bi-decadal scale. These findings are further corroborated by results from a dynamic vegetation model, collectively suggesting that increases in precipitation control the decreased $${{{{\rm{\gamma }}}}}_{{{{\rm{CGR}}}}}^{{{{\rm{T}}}}}$$

γ

CGR

T

during recent decades. Our results indicate that wetter conditions have led to a decoupling of the impact of the tropical temperature variation on the carbon cycle.
Effect of microgravity on mechanical loadings in lumbar spine at various postures: a numerical study
Authors: Wu Biao , Gao Xin , Qin Bing , Baldoni Michele , Zhou Lu , Qian Zhiyu , Zhu Qiaoqiao 
Published: February 2023

AbstractThe aim of this study was to quantitatively analyze the mechanical change of spinal segments (disc, muscle, and ligament) at various postures under microgravity using a full-body musculoskeletal modeling approach. Specifically, in the lumbar spine, the vertebra were modeled as rigid bodies, the intervertebral discs were modeled as 6-degree-of-freedom joints with linear force-deformation relationships, the disc swelling pressure was deformation dependent, the ligaments were modeled as piecewise linear elastic materials, the muscle strength was dependent on its functional cross-sectional area. The neutral posture and the “fetal tuck” posture in microgravity (short as “Neutral 0G” and “Fetal Tuck 0G”, in our simulation, the G constant was set to 0 for simulating microgravity), and for comparison, the relaxed standing posture in 1G and 0G gravity (short as “Neutral 1G” and “Standing 0G”) were simulated. Compared to values at Neutral 1G, the mechanical response in the lower spine changed significantly at Neutral 0G. For example, the compressive forces on lumbar discs decreased 62–70%, the muscle forces decreased 55.7–92.9%, while disc water content increased 7.0–10.2%, disc height increased 2.1–3.0%, disc volume increased 6.4–9.3%, and ligament forces increased 59.5–271.3% at Neutral 0G. The fetal tuck 0G reversed these changes at Neutral 0G back toward values at Neutral 1G, with magnitudes much larger than those at Neutral 1G. Our results suggest that microgravity has significant influences on spinal biomechanics, alteration of which may increase the risks of disc herniation and degeneration, muscle atrophy, and/or ligament failure.
Immunization with matrix-, nucleoprotein and neuraminidase protects against H3N2 influenza challenge in pH1N1 pre-exposed pigs
Authors: Vatzia Eleni , Feest Katherine , McNee Adam , Manjegowda Tanuja , Carr B. Veronica , Paudyal Basudev , Chrun Tiphany , Maze Emmanuel A. , Mccarron Amy , Morris Susan , Everett Helen E. , MacLoughlin Ronan , Salguero Francisco J. , Lambe Teresa , Gilbert Sarah C. , Tchilian Elma 
Published: February 2023

AbstractThere is an urgent need for influenza vaccines providing broader protection that may decrease the need for annual immunization of the human population. We investigated the efficacy of heterologous prime boost immunization with chimpanzee adenovirus (ChAdOx2) and modified vaccinia Ankara (MVA) vectored vaccines, expressing conserved influenza virus nucleoprotein (NP), matrix protein 1 (M1) and neuraminidase (NA) in H1N1pdm09 pre-exposed pigs. We compared the efficacy of intra-nasal, aerosol and intra-muscular vaccine delivery against H3N2 influenza challenge. Aerosol prime boost immunization induced strong local lung T cell and antibody responses and abrogated viral shedding and lung pathology following H3N2 challenge. In contrast, intramuscular immunization induced powerful systemic responses and weak local lung responses but also abolished lung pathology and reduced viral shedding. These results provide valuable insights into the development of a broadly protective influenza vaccine in a highly relevant large animal model and will inform future vaccine and clinical trial design.
City-scale synthetic individual-level vehicle trip data
Authors: Li Guilong , Chen Yixian , Wang Yimin , Nie Peilin , Yu Zhi , He Zhaocheng 
Published: February 2023

AbstractTrip data that records each vehicle’s trip activity on the road network describes the operation of urban traffic from the individual perspective, and it is extremely valuable for transportation research. However, restricted by data privacy, the trip data of individual-level cannot be opened for all researchers, while the need for it is very urgent. In this paper, we produce a city-scale synthetic individual-level vehicle trip dataset by generating for each individual based on the historical trip data, where the availability and trip data privacy protection are balanced. Privacy protection inevitably affects the availability of data. Therefore, we have conducted numerous experiments to demonstrate the performance and reliability of the synthetic data in different dimensions and at different granularities to help users properly judge the tasks it can perform. The result shows that the synthetic data is consistent with the real data (i.e., historical data) on the aggregated level and reasonable from the individual perspective.
Analysis of economic forecasting in the post-epidemic era: evidence from China
Authors: Li Xin 
Published: February 2023

AbstractThis paper presents a predictive analysis of the Chinese economy in the post-epidemic era. Five major public health emergencies historically similar to the COVID-19 epidemic are used as the control group, and a fuzzy mathematical model is applied to forecast and analyze China’s economy after the COVID-19 epidemic. The forecast results show that China’s overall economy will have recovered to the pre-epidemic level in about 1 year, with the fastest recovery in individual economic indicators, followed by government final consumption and imports, then CPI, fiscal revenue, exports and money supply, and the slowest recovery in employment. Finally, a combination of all the parties makes policies and recommendations for China’s economic and social development in the post-epidemic era.
Four-year follow-up on fatigue and sleep quality of a three-armed partly randomized controlled study in breast cancer survivors with cancer-related fatigue
Authors: Kröz M. , Quittel F. , Reif M. , Zerm R. , Pranga D. , Bartsch C. , Brinkhaus B. , Büssing A. , Gutenbrunner C. ,   the CRF study group, Brink Fadime ten , Berger Bettina , Weninger Annette , Girke Matthias , Müller-Glinz Augustina , Heckmann Christian 
Published: February 2023

AbstractCancer-related fatigue (CRF) is a frequent long-term symptom in non-metastasized breast cancer patients (BC). This 4-year follow-up intended to compare the long-term effects of a 10-week multimodal therapy (MT: sleep education, psychoeducation, eurythmy- and painting therapy) and combination therapy [CT: MT plus aerobic training (AT)] to AT-control. BC-patients were randomized or allocated by preference to three arms in a comprehensive cohort study. Primary outcome was a composite score including Pittsburgh Sleep Quality Index (PSQI) and Cancer Fatigue Scale (CFS-D), captured at baseline, after 10 weeks of intervention (T1), 6 months later (T2), and after 4 years (T3). We exploratively tested for superiority of MT and CT versus AT after 4 years (T3) based on the statistical model of the main analysis. Of 126 (65 randomized) BC-patients included, 105 started treatments and 79 were re-assessed for long-term effects (T3). MT and CT were superior over AT after 4 years regarding PSQI/CFS-D and PSQI sum-score, respectively (all p < 0.05), but not for CFS-D. The multimodal and combination treatment for breast cancer patients with CRF indicates sustainable long-term superiority over aerobic training for the outcomes sleep quality and combined sleep quality/fatigue. A confirmative randomized controlled trial is warranted.
Effect of fissure angle on energy evolution and failure characteristics of fractured rock under uniaxial cyclic loading
Authors: Zhao Yongqiang , Li Quanshen , Zhang Kai , Yang Yingming , Gu Xuebin 
Published: February 2023

AbstractTo study the influence of fissure angle on the rock damage process and energy evolution characteristics, uniaxial cyclic loading and unloading tests were conducted on fractured rock specimens with different prefabricated fissure angles. The stress–strain curves, mechanical properties, and failure characteristics were analyzed. Subsequently, the energy evolution characteristics and failure mechanisms were investigated. The results showed that the stress–strain curves of fractured specimens fluctuated in the pre-peak phase and rapidly declined in post-peak phase. The peak stresses and strains of fractured specimens initially decreased and then increased with an increase in the fissure angle, whereas the elastic modulus first increased and then decreased. With an increase in the fissure angle, specimen failure changed from shear damage to tensile damage. The input, elastic, and dissipation energies of fractured specimens non-linearly increased with an increase in cyclic loading and unloading. As the number of cycles increased, the energy density decreased in segments with an increase in the fissure angle, and there was a rapid increase in the dissipation energy density before failure occurred. The results can provide a reference for the study of fractured rock failures and their prevention and control design in the field.
Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn's disease based on three machine learning methods
Authors: Chen Zi-An , Ma Hui-hui , Wang Yan , Tian Hui , Mi Jian-wei , Yao Dong-Mei , Yang Chuan-Jie 
Published: February 2023

AbstractCrohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis.