bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rational positioning of 3D printed micro-bricks to realize high-fidelity, multi-functional soft-hard interfaces
Authors: Saldívar M. C. , Salehi S. , Veeger R. P. E. , Fenu M. , Cantamessa A. , Klimopoulou M. , Talò G. , Moretti M. , Lopa S. , Ruffoni D. , van Osch G.J.V.M. , Fratila-Apachitei L. E. , Doubrovski E. L. , Mirzaali M. J. , Zadpoor A. A. 
Published: January 2023

AbstractLiving organisms have developed design principles, such as functional gradients (FGs), to interface hard materials with soft ones (e.g., bone and tendon). Mimicking such design principles can address the challenges faced when developing engineered constructs with soft-hard interfaces. To date, implementing these FG design principles has been primarily performed by varying the ratio of the hard phase to that of the soft phase. Such design approaches, however, lead to inaccurate mechanical properties within the transition zone. That is due to the highly nonlinear relationship between the material distribution at the microscale and the macroscale mechanical properties. Here, we 3D print micro-bricks from either a soft or a hard phase and study the nonlinear relationship between their arrangements within the transition zone and the resulting macroscale properties. We carry out experiments at the micro- and macroscales as well as finite element simulations at both scales. Based on the obtained results, we develop a co-continuous power-law model relating the arrangement of the micro-bricks to the local mechanical properties of the micro-brick composites. We then use this model to rationally design FGs at the individual micro-brick level and create two types of biomimetic soft-hard constructs, including a specimen modeling bone-ligament junctions in the knee and another modeling the nucleus pulposus-annulus fibrosus interface in intervertebral discs. We show that the implemented FGs drastically enhance the stiffness, strength, and toughness of both types of specimens as compared to non-graded designs. Furthermore, we hypothesize that our soft-hard FGs regulate the behavior of murine preosteoblasts and primary human bone marrow-derived mesenchymal stromal cells (hBMSCc). We culture those cells to confirm the effects of soft-hard interfaces on cell morphology as well as on regulating the expression of focal adhesion kinase, subcellular localization, and YAP nuclear translocation of hBMSCs. Taken together, our results pave the way for the rational design of soft-hard interfaces at the micro-brick level and (biomedical) applications of such designs.
Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism
Authors: Acosta Nicole , Dai Xiaotian , Bautista Maria A. , Waddell Barbara J. , Lee Jangwoo , Du Kristine , McCalder Janine , Pradhan Puja , Papparis Chloe , Lu Xuewen , Chekouo Thierry , Krusina Alexander , Southern Danielle , Williamson Tyler , Clark Rhonda G. , Patterson Raymond A. , Westlund Paul , Meddings Jon , Ruecker Norma , Lammiman Christopher , Duerr Coby , Achari Gopal , Hrudey Steve E. , Lee Bonita E. , Pang Xiaoli , Frankowsk Kevin , Hubert Casey R.J. , Parkins Michael D. 
Published: January 2023

AbstractWastewater-based surveillance (WBS) is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19’s impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with local workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.3 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5% (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4,524 unrelated absences COVID-19 cases were recorded. Employee absences significantly increased as wastewater signal increased through the pandemic’s waves. Strong correlations between COVID-19-confirmed absences and wastewater SARS-CoV-2 signals (N1 gene: r=0.824, p<0.0001 and N2 gene: r=0.826, p<0.0001) were observed. Linear regression with adjusted R2-value demonstrated a robust association (adjusted R2=0.783), when adjusted by 7 days, indicating wastewater provides a one-week leading signal. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P<0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.HighlightsWBS is a useful strategy for monitoring infectious diseases in workersSARS-CoV-2 RNA in wastewater correlated with workforce absenteeismWorkplace absenteeism secondary to COVID-19 can be predicted using WBSWBS can be used by employers to anticipate and mitigate work force absenteeismGraphical abstract
Joint Temporal Patterns By Integrating Diet and Physical Activity
Authors: Guo Jiaqi , Lin Luotao , Aqeel Marah M. , Gelfand Saul B. , Eicher-Miller Heather A. , Bhadra Anindya , Hennessy Erin , Richards Elizabeth A. , Delp Edward J. 
Published: January 2023

AbstractBoth diet and physical activity are associated with obesity and chronic diseases such as diabetes and metabolic syndrome. Early efforts in connecting dietary and physical activity behaviors to generate patterns rarely considered the use of time. In this paper, we propose a distance-based cluster analysis approach to find joint temporal diet and physical activity patterns among U.S. adults ages 20-65. Dynamic Time Warping (DTW) generalized to multi-dimensions is combined with commonly used clustering methods to generate unbiased partitioning of the National Health and Nutrition Examination Survey 2003-2006 (NHANES) dataset. The clustering results are evaluated using visualization of the clusters, the Silhouette Index, and the associations between clusters and health status indicators based on multivariate regression models. Our experiments indicate that the integration of diet, physical activity, and time has the potential to discover joint temporal patterns with association to health.
Real-world effectiveness of Azvudine in hospitalized patients with COVID-19: a retrospective cohort study
Authors: Shen Minxue , Xiao Chenggen , Sun Yuming , Li Daishi , Wu Ping , Jin Liping , Wu Qingrong , Dian Yating , Meng Yu , Zeng Furong , Chen Xiang , Deng Guangtong 
Published: January 2023

ABSTRACTCurrent guidelines prioritize the use of the Azvudine in coronavirus disease 2019 (COVID-19) patients. However, the clinical effectiveness of Azvudine in real-world studies was lacking, despite the clinical trials showed shorter time of nucleic acid negative conversion. To evaluate the clinical effectiveness following Azvudine treatment in hospitalized COVID-19 patients, we identified 1505 hospitalized COVID-19 patients during the study period, with a follow-up of up to 29 days. After exclusions and propensity score matching, we included 226 Azvudine recipients and 226 matched controls. The lower crude incidence rate of composite disease progression outcome (4.21 vs. 10.39 per 1000 person-days,P=0.041) and all-cause mortality (1.57 vs. 6.00 per 1000 person-days,P=0.027) were observed among Azvudine recipients compared with matched controls. The incidence rates of initiation of invasive mechanical ventilation were also statistically different between the groups according to the log-rank tests (P=0.020). Azvudine treatment was associated with significantly lower risks of composite disease progression outcome (hazard ratio [HR]: 0.43; 95% confidence interval [CI]: 0.18 to 0.99) and all-cause death (HR: 0.26; 95% CI: 0.07 to 0.94) compared with matched controls. Subgroup analyses indicated robustness of the point estimates of HRs (ranged from 0.14 to 0.84). Notably, male Azvudine recipients had a stronger effectiveness than female recipients with respect to both composite outcome and all-cause death. These findings suggest that Azvudine treatment showed substantial clinical benefits in hospitalized COVID-19 patients, and should be considered for use in this population of patients.
DOPA residues endow collagen with radical scavenging capacity
Authors: Kurth Markus , Barayeu Uladzimir , Gharibi Hassan , Kuzhelev Andrei , Riedmiller Kai , Zilke Jennifer , Noack Kasimir , Denysenkov Vasyl , Kappl Reinhard , Prisner Thomas F. , Zubarev Roman A. , Dick Tobias P. , Gräter Frauke 
Published: January 2023

AbstractHere we uncover collagen, the main structural protein of all connective tissues, as a redox-active material. We identify dihydroxyphenylalanine (DOPA) residues, post-translational oxidation products of tyrosine residues, to be common in collagen derived from different connective tissues. We observe that these DOPA residues endow collagen with substantial radical scavenging capacity. When reducing radicals, DOPA residues work as redox relay: they convert to the quinone and generate hydrogen peroxide. In this dual function, DOPA outcompetes its amino acid precursors and ascorbic acid. Our results establish DOPA residues as redox-active side chains of collagens, probably protecting connective tissues against radicals formed under mechanical stress and/or inflammation.
Lung infection byP. aeruginosainduces neuroinflammation and blood-brain barrier dysfunction in mice
Authors: Villalba Nuria , Ma Yonggang , Gahan Sarah A. , Joly-Amado Aurelie , Spence Sam , Yang Xiaoyuan , Nash Kevin , Yuan Sarah Y. 
Published: January 2023

AbstractBackgroundSevere lung infection can lead to brain dysfunction and neurobehavioral disorders. The mechanisms that regulate the lung-brain axis of inflammatory response to respiratory infection are incompletely understood. This study examined the effects of lung infection causing systemic and neuroinflammation as a potential mechanism contributing to blood-brain barrier (BBB) leakage and behavioral impairment.MethodsPneumonia was induced in adult C57BL/6 mice by intratracheal inoculation ofPseudomonas aeruginosa(PA). Solute extravasation, histology, immunofluorescence, RT-PCR, multiphoton imaging and neurological testing were performed in this study.ResultsLung infection caused alveolar-capillary barrier injury as indicated by leakage of plasma proteins across pulmonary microvessels and histopathological characteristics of pulmonary edema (alveolar wall thickening, microvessel congestion, and neutrophil infiltration). PA also caused significant BBB dysfunction characterized by leakage of different sized molecules across cerebral microvessels and a decreased expression of cell-cell junctions (VE-cadherin, claudin-5) in the brain. BBB leakage peaked at 24 hours and lasted for 7 days post-inoculation. Additionally, mice with lung infection displayed hyperlocomotion and anxiety-like behaviors. To test whether cerebral dysfunction was caused by PA directly or indirectly, we measured bacterial load in multiple organs. While PA loads were detected in the lungs up to 7 days post-inoculation, bacteria were not detected in the brain as evidenced by negative cerebral spinal fluid (CSF) cultures and lack of distribution in different brain regions or isolated cerebral microvessels. However, mice with PA lung infection demonstrated increased mRNA expression in the brain of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), chemokines (CXCL-1, CXCL-2) and adhesion molecules (VCAM-1 and ICAM-1) along with CD11b+ cell recruitment, corresponding to their increased blood levels of white cells (polymorphonuclear cells) and cytokines. To confirm the direct effect of cytokines on endothelial permeability, we measured cell-cell adhesive barrier resistance and junction morphology in mouse brain microvascular endothelial cell monolayers, where administration of IL-1β induced a significant reduction of barrier function coupled with tight junction (TJ) diffusion and disorganization. Combined treatment with IL-1β and TNFα augmented the barrier injury.ConclusionsThese results suggest that lung bacterial infection causes cerebral microvascular leakage and neuroinflammation via a mechanism involving cytokine-induced BBB injury.
DRACC1, a major postsynaptic protein, regulates the condensation of postsynaptic proteins via liquid-liquid phase separation
Authors: Kaizuka Takeshi , Hirouchi Taisei , Saneyoshi Takeo , Hayashi Yasunori , Takumi Toru 
Published: January 2023

AbstractNumerous proteome analyses have been conducted on the postsynaptic density (PSD), a protein condensate beneath the postsynaptic membrane of excitatory synapses. Each has identified several hundred to thousands of proteins. While proteins with predictable functions have been well studied, functionally uncharacterized proteins are mostly overlooked. In this study, we perform a meta-analysis of the 35 PSD proteome datasets, including 5,869 proteins, identifying 97 uncharacterized proteins that appeared in multiple datasets. We focus on the top-ranked protein, FAM81A, renamed DRACC1. DRACC1 is expressed in forebrain neurons and enriched at the synapse. DRACC1 interacts with PSD proteins, including PSD-95, SynGAP, and NMDA receptors, and promotes liquid-liquid phase separation of those proteins. Consistently, the downregulation of DRACC1 in neurons causes a decrease in the size of PSD-95 puncta and the frequency of neuronal firing. Our results characterize DRACC1 as a novel synaptic protein facilitating the assembly of proteins within PSD. It also indicates the effectiveness of a meta-analytic approach of existing proteome datasets in identifying uncharacterized proteins.
3Din vitromodeling of the exocrine pancreatic unit using tomographic volumetric bioprinting
Authors: Sgarminato Viola , Madrid-Wolff Jorge , Boniface Antoine , Ciardelli Gianluca , Tonda-Turo Chiara , Moser Christophe 
Published: January 2023

1.AbstractPancreatic ductal adenocarcinoma (PDAC) is the most frequent type of pancreatic cancer, one of the leading causes of cancer-related deaths worldwide. The first lesions associated with PDAC occur within the functional units of exocrine pancreas. The crosstalk between PDAC cells and stromal cells plays a key role in tumor progression. Thus, in vitro, fully human models of the pancreatic cancer microenvironment are needed to foster the development of new, more effective therapies. However, it is challenging to make these models anatomically and functionally relevant. Here, we used tomographic volumetric bioprinting, a novel method to fabricate three-dimensional cell-laden constructs, to produce a portion of the complex convoluted exocrine pancreasin vitro. Human fibroblast-laden gelatin methacrylate-based pancreatic models were processed to reassemble the tubuloacinar structures of the exocrine pancreas and, then human pancreatic ductal epithelial (HPDE) cells overexpressing the KRAS oncogene (HPDE-KRAS) were seeded in the acinar lumen to reproduce the pathological exocrine pancreatic tissue. The growth and organization of HPDE cells within the structure was evaluated and the formation of a thin epithelium which covered the acini inner surfaces in a physiological way inside the 3D model was successfully demonstrated. Interestingly, immunofluorescence assays revealed a significantly higher expressions of alpha smooth muscle actin (α-SMA) vs. actin in the fibroblasts co-cultured with cancerous than with wild-type HPDE cells. Moreover, α-SMA expression increased with time, and it was found to be higher in fibroblasts that laid closer to HPDE cells than in those laying deeper into the model. Increased levels of interleukin (IL)-6 were also quantified in supernatants from co-cultures of stromal and HPDE-KRAS cells. These findings correlate with inflamed tumor-associated fibroblast behavior, thus being relevant biomarkers to monitor the early progression of the disease and to target drug efficacy.To our knowledge, this is the first demonstration of a 3D bioprinted portion of pancreas that recapitulates its true 3-dimensional microanatomy, and which shows tumor triggered inflammation.
Failed down-regulation of PI3K signaling makes autoreactive B cells receptive to bystander T cell help
Authors: Fiske Brigita E. , Getahun Andrew 
Published: January 2023

AbstractThe role of T cell help in autoantibody responses is not well understood. Since tolerance mechanisms govern both T and B cell responses, one might predict that both T cell tolerance and B cell tolerance must be defeated in autoantibody responses requiring T cell help. To define whether autoreactive B cells depend on T cells to generate autoantibody responses, we studied the role of T cells in autoantibody responses resulting from acute cell-specific deletion of regulatory phosphatases. Ars/A1 B cells are DNA-reactive and require continuous inhibitory signaling by the tyrosine phosphatase SHP-1 and the inositol phosphatases SHIP-1 and PTEN to maintain unresponsiveness. Acute B cell-restricted deletion of any of these phosphatases results in an autoantibody response. Here we show that CD40-CD40L interactions are required to support autoantibody responses of B cells whose anergy has been compromised. If the B cell-intrinsic driver of loss of tolerance is failed negative regulation of PI3K signaling, bystander T cells provide sufficient CD40-mediated signal 2 to support an autoantibody response. However, while autoantibody responses driven by acute B cell-targeted deletion of SHP-1 also require T cells, bystander T cell help does not suffice. These results demonstrate that upregulation of PI3K signaling in autoreactive B cells, recapitulating the effect of multiple autoimmunity risk alleles, promotes autoantibody responses both by increasing B cells’ cooperation with non-cognate T cell help, as well as by altering BCR signaling. Receptiveness to bystander T cell help enables autoreactive B cells to circumvent the fail-safe of T cell tolerance.SignificancePhosphatase suppression of PI3K signaling is an important mechanism by which peripheral autoreactive B cells are kept in an unresponsive/anergic state. Loss of this suppression, due to genetic alleles that confer risk of autoimmunity, often occurs in autoreactive B cells of individuals who develop autoimmune disease. Here we demonstrate that de-repression of PI3K signaling promotes autoantibody responses of a DNA-reactive B cell clone by relaxing dependence of autoantibody responses on T cell-derived helper signals. These results suggest that impaired regulation of PI3K signaling can promote autoantibody responses in two ways: by restoring antigen receptor signaling and by enabling autoreactive B cells to circumvent restrictions imposed by T cell tolerance mechanisms.
Explain-seq: an end-to-end pipeline from training to interpretation of sequence-based deep learning models
Authors: Zhao Nanxiang , Wang Shuze , Huang Qianhui , Dong Shengcheng , Boyle Alan P. 
Published: January 2023

AbstractInterpreting predictive machine learning models to derive biological knowledge is the ultimate goal of developing models in the era of genomic data exploding. Recently, sequence-based deep learning models have greatly outperformed other machine learning models such as SVM in genome-wide prediction tasks. However, deep learning models, which are black-box models, are challenging to interpret their predictions. Here we represented an end-to-end computational pipeline, Explain-seq, to automate the process of developing and interpreting deep learning models in the context of genomics. Explain-seq takes input as genomic sequences and outputs predictive motifs derived from the model trained on sequences. We demonstrated Explain-seq with a public STARR-seq dataset of the A549 human lung cancer cell line released by ENCODE. We found our deep learning model outperformed gkm-SVM model in predicting A549 enhancer activities. By interpreting our well-performed model, we identified 47 TF motifs matched with known TF PWMs, including ZEB1, SP1, YY1, and INSM1. They are associated with epithelial-mesenchymal transition and lung cancer proliferation and metagenesis. In addition, there were motifs that were not matched in the JASPAR database and may be considered asde novoenhancer motifs in the A549 cell line.Availabilityhttps://github.com/nsamzhao/Explain-seqContactapboyle@umich.eduSupplementary informationSupplementary data are available as attachment.
Detecting cultural evolution in a songbird species using community-science data and computational modeling
Authors: Pichkar Yakov , Searfoss Abigail M. , Creanza Nicole 
Published: January 2023

AbstractSong in oscine birds is learned across generations, and aspects of the song-learning process parallel genetic transmission: variation can be introduced into both cultural and genetic traits via copy-error, and both types of traits are subject to drift and selective pressure. Similarly to allele frequencies in population genetics, observing frequencies of birdsong features can improve our understanding of cultural transmission and evolution. Uniquely, community-science databases of birdsong provide rich spatiotemporal data with untapped potential to evaluate cultural evolution in songbirds. Here we use both community-science and field-study recordings of chipping sparrows to examine trends across nearly seven decades of song. We find that some syllable types tend to persist in the population for much longer than others. Persistent songs tend to contain more syllables of shorter duration than songs that were observed across fewer years. To draw inferences about the effects of learning biases on chipping sparrow syllables, we construct a spatially explicit agent-based model of song learning. By comparing our empirical analysis to simulated song distributions using three different song-learning strategies—neutral transmission, conformity bias, and directional selection—we suggest that chipping sparrows are unlikely to select tutors neutrally or with a conformity bias and that they learn their songs with a remarkably low copy-error rate.