bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 vaccine acceptance and perceived stigma in patients with depression: a network perspective
Authors: Cai Hong , Bai Wei , Du Xiangdong , Zhang Ling , Zhang Lan , Li Yu-Chen , Liu Huan-Zhong , Tang Yi-Lang , Jackson Todd , Cheung Teris , An Feng-Rong , Xiang Yu-Tao 
Published: October 2022

AbstractThe association between coronavirus disease (COVID-19) vaccine acceptance and perceived stigma of having a mental illness is not clear. This study examined the association between COVID-19 vaccine acceptance and perceived stigma among patients with recurrent depressive disorder (depression hereafter) using network analysis. Participants were 1149 depressed patients (842 men, 307 women) who completed survey measures of perceived stigma and COVID-19 vaccine attitudes. T-tests, chi-square tests, and Kruskal–Wallis tests were used to compare differences in demographic and clinical characteristics between depressed patients who indented to accepted vaccines and those who were hesitant. Hierarchical multiple regression analyses assessed the unique association between COVID-19 vaccine acceptance and perceived stigma, independent of depression severity. Network analysis examined item-level relations between COVID-19 vaccine acceptance and perceived stigma after controlling for depressive symptoms. Altogether, 617 depressed patients (53.7%, 95 confidence intervals (CI) %: 50.82–56.58%) reported they would accept future COVID-19 vaccination. Hierarchical multiple regression analyses indicated higher perceived stigma scores predicted lower levels of COVID-19 vaccination acceptance (β = −0.125, P < 0.001), even after controlling for depression severity. In the network model of COVID-19 vaccination acceptance and perceived stigma nodes, “Feel others avoid me because of my illness”, “Feel useless”, and “Feel less competent than I did before” were the most influential symptoms. Furthermore, “COVID-19 vaccination acceptance” had the strongest connections with illness stigma items reflecting social rejection or social isolation concerns (“Employers/co-workers have discriminated”, “Treated with less respect than usual”, “Sense of being unequal in my relationships with others”). Given that a substantial proportion of depressed patients reported hesitancy with accepting COVID-19 vaccines and experiences of mental illness stigma related to social rejection and social isolation, providers working with this group should provide interventions to reduce stigma concerns toward addressing reluctance in receiving COVID-19 vaccines.
Dimensions of childhood adversity differentially affect biological aging in major depression
Authors: Rampersaud Ryan , Protsenko Ekaterina , Yang Ruoting , Reus Victor , Hammamieh Rasha , Wu Gwyneth W. Y. , Epel Elissa , Jett Marti , Gautam Aarti , Mellon Synthia H. , Wolkowitz Owen M. 
Published: October 2022

AbstractAdverse childhood experiences have been consistently linked with physical and mental health disorders in adulthood that may be mediated, in part, via the effects of such exposures on biological aging. Using recently developed “epigenetic clocks”, which provide an estimate of biological age, several studies have demonstrated a link between the cumulative exposure to childhood adversities and accelerated epigenetic aging. However, not all childhood adversities are equivalent and less is known about how distinct dimensions of childhood adversity relate to epigenetic aging metrics. Using two measures of childhood adversity exposure, we assess how the dimensions of Maltreatment and Household Dysfunction relate to epigenetic aging using two “second-generation” clocks, GrimAge and PhenoAge, in a cohort of unmedicated somatically healthy adults with moderate to severe major depression (n = 82). Our results demonstrate that the dimension of Maltreatment is associated with epigenetic age acceleration (EAA) using the PhenoAge but not the GrimAge clock. This association was observed using both the Childhood Trauma questionnaire (CTQ; β = 0.272, p = 0.013) and the Adverse Childhood Experiences (ACEs) questionnaire (β = 0.307, p = 0.005) and remained significant when adjusting for exposure to the dimension of Household Dysfunction (β = 0.322, p = 0.009). In contrast, the dimension of Household Dysfunction is associated with epigenetic age deceleration (β = −0.194, p = 0.083) which achieved significance after adjusting for exposure to the dimension of Maltreatment (β = −0.304, p = 0.022). This study is the first to investigate these effects among individuals with Major Depressive Disorder and suggests that these dimensions of adversity may be associated with disease via distinct biological mechanisms.
Endometrial stromal PRMT5 plays a crucial role in decidualization by regulating NF-κB signaling in endometriosis
Authors: Cai Xinyu , Xu Manlin , Zhang Hui , Zhang Mei , Wang Junxia , Mei Jie , Zhang Yang , Zhou Jidong , Zhen Xin , Kang Nannan , Yue Qiuling , Sun Haixiang , Jiang Ruiwei , Yan Guijun 
Published: October 2022

AbstractDecidualization is a prerequisite for successful embryo implantation, in which elongated fibroblast-like endometrial stromal cells differentiate into more rounded decidual cells. Accumulating evidence has stressed the important role of the defective eutopic endometrium in infertility in endometriosis patients. However, the role of arginine methylation in the process of physiological decidualization and pathological decidualization defects is not clear. Here, we observed that the expression level of PRMT5, the main type II PRMT, was decreased in the endometrium of endometriosis patients, predominantly in stromal cells. Compared with the undecidualized state, PRMT5 was increased in the stromal cells of normal secretory endometrium in humans and in the decidua of normal pregnant mice or mice with artificially induced decidualization. The inhibition of PRMT5 resulted in a significant decrease in uterine weight and decidualization-related regulator expression, including FOXO1, HOXA10 and WNT4, in mice and IGFBP1 and prolactin levels in human endometrial stromal cells. Transcriptome analysis showed that decreased PRMT5 activity led to NF-κB signaling activation by inducing p65 translocation to the nucleus, which was also observed in endometriosis patients. Finally, overexpression of PRMT5 rescued the defective expression of IGFBP1 and prolactin in primary endometrial stromal cells from endometriosis patients. Our results indicate that promotion of PRMT5 may provide novel therapeutic strategies for the treatment of decidualization defects in infertile women, such as those with endometriosis.
Autophagy induction promoted by m6A reader YTHDF3 through translation upregulation of FOXO3 mRNA
Authors: Hao WeiChao , Dian MeiJuan , Zhou Ying , Zhong QiuLing , Pang WenQian , Li ZiJian , Zhao YaYan , Ma JiaCheng , Lin XiaoLin , Luo RenRu , Li YongLong , Jia JunShuang , Shen HongFen , Huang ShiHao , Dai GuanQi , Wang JiaHong , Sun Yan , Xiao Dong 
Published: October 2022

AbstractAutophagy is crucial for maintaining cellular energy homeostasis and for cells to adapt to nutrient deficiency, and nutrient sensors regulating autophagy have been reported previously. However, the role of eiptranscriptomic modifications such as m6A in the regulation of starvation-induced autophagy is unclear. Here, we show that the m6A reader YTHDF3 is essential for autophagy induction. m6A modification is up-regulated to promote autophagosome formation and lysosomal degradation upon nutrient deficiency. METTL3 depletion leads to a loss of functional m6A modification and inhibits YTHDF3-mediated autophagy flux. YTHDF3 promotes autophagy by recognizing m6A modification sites around the stop codon of FOXO3 mRNA. YTHDF3 also recruits eIF3a and eIF4B to facilitate FOXO3 translation, subsequently initiating autophagy. Overall, our study demonstrates that the epitranscriptome regulator YTHDF3 functions as a nutrient responder, providing a glimpse into the post-transcriptional RNA modifications that regulate metabolic homeostasis.
Evolution of Br⋯Br contacts in enantioselective molecular recognition during chiral 2D crystallization
Authors: Yi Zhen-Yu , Yang Xue-Qing , Duan Jun-Jie , Zhou Xiong , Chen Ting , Wang Dong , Wan Li-Jun 
Published: October 2022

AbstractHalogen-mediated interactions play an important role in molecular recognition and crystallization in many chemical and biological systems, whereas their effect on homochiral versus heterochiral recognition and crystallization has rarely been explored. Here we demonstrate the evolution of Br⋯Br contacts in chiral recognition during 2D crystallization. On Ag(100), type I contacts prevail at low coverage and lead to homochiral recognition and the formation of 2D conglomerates; whereas type II contacts mediating heterochiral recognition are suppressed at medium coverage and appear in the racemates induced by structural transitions at high coverage. On Ag(111), type I contacts dominate the 2D crystallization and generate 2D conglomerates exclusively. DFT calculations suggest that the energy difference between type I and type II contacts is reversed upon adsorption due to the substrate induced mismatch energy penalty. This result provides fundamental understanding of halogen-mediated interactions in molecular recognition and crystallization on surface.
Location bias contributes to functionally selective responses of biased CXCR3 agonists
Authors: Eiger Dylan Scott , Boldizsar Noelia , Honeycutt Christopher Cole , Gardner Julia , Kirchner Stephen , Hicks Chloe , Choi Issac , Pham Uyen , Zheng Kevin , Warman Anmol , Smith Jeffrey S. , Zhang Jennifer Y. , Rajagopal Sudarshan 
Published: October 2022

AbstractSome G protein-coupled receptor (GPCR) ligands act as “biased agonists” that preferentially activate specific signaling transducers over others. Although GPCRs are primarily found at the plasma membrane, GPCRs can traffic to and signal from many subcellular compartments. Here, we determine that differential subcellular signaling contributes to the biased signaling generated by three endogenous ligands of the GPCR CXC chemokine receptor 3 (CXCR3). The signaling profile of CXCR3 changes as it traffics from the plasma membrane to endosomes in a ligand-specific manner. Endosomal signaling is critical for biased activation of G proteins, β-arrestins, and extracellular-signal-regulated kinase (ERK). In CD8 + T cells, the chemokines promote unique transcriptional responses predicted to regulate inflammatory pathways. In a mouse model of contact hypersensitivity, β-arrestin-biased CXCR3-mediated inflammation is dependent on receptor internalization. Our work demonstrates that differential subcellular signaling is critical to the overall biased response observed at CXCR3, which has important implications for drugs targeting chemokine receptors and other GPCRs.
Predictive control of selective secondary alcohol oxidation of glycerol on NiOOH
Authors: Goetz McKenna K. , Bender Michael T. , Choi Kyoung-Shin 
Published: October 2022

AbstractMany biomass intermediates are polyols and selectively oxidizing only a primary or secondary alcohol group is beneficial for the valorization of these intermediates. For example, production of 1,3-dihydroxyacetone, a highly valuable oxidation product of glycerol, requires selective secondary alcohol oxidation. However, selective secondary alcohol oxidation is challenging due to its steric disadvantage. This study demonstrates that NiOOH, which oxidizes alcohols via two dehydrogenation mechanisms, hydrogen atom transfer and hydride transfer, can convert glycerol to 1,3-dihydroxyacetone with high selectivity when the conditions are controlled to promote hydrogen atom transfer, favoring secondary alcohol oxidation. This rational production of 1,3-dihydroxyacetone achieved by selectively enabling one desired dehydrogenation pathway, without requiring alteration of catalyst composition, demonstrates how comprehensive mechanistic understanding can enable predictive control over selectivity.
Fast hydrogen purification through graphitic carbon nitride nanosheet membranes
Authors: Zhou Yisa , Wu Ying , Wu Haoyu , Xue Jian , Ding Li , Wang Rui , Wang Haihui 
Published: October 2022

AbstractTwo-dimensional graphitic carbon nitride (g-C3N4) nanosheets are ideal candidates for membranes because of their intrinsic in-plane nanopores. However, non-selective defects formed by traditional top-down preparation and the unfavorable re-stacking hinder the application of these nanosheets in gas separation. Herein, we report lamellar g-C3N4 nanosheets as gas separation membranes with a disordered layer-stacking structure based on high quality g-C3N4 nanosheets through bottom-up synthesis. Thanks to fast and highly selective transport through the high-density sieving channels and the interlayer paths, the membranes, superior to state-of-the-art ones, exhibit high H2 permeance of 1.3 × 10−6 mol m−2 s−1 Pa−1 with excellent selectivity for multiple gas mixtures. Notably, these membranes show excellent stability under harsh practice-relevant environments, such as temperature swings, wet atmosphere and long-term operation of more than 200 days. Therefore, such lamellar membranes with high quality g-C3N4 nanosheets hold great promise for gas separation applications.
Deep learning models for histologic grading of breast cancer and association with disease prognosis
Authors: Jaroensri Ronnachai , Wulczyn Ellery , Hegde Narayan , Brown Trissia , Flament-Auvigne Isabelle , Tan Fraser , Cai Yuannan , Nagpal Kunal , Rakha Emad A. , Dabbs David J. , Olson Niels , Wren James H. , Thompson Elaine E. , Seetao Erik , Robinson Carrie , Miao Melissa , Beckers Fabien , Corrado Greg S. , Peng Lily H. , Mermel Craig H. , Liu Yun , Steiner David F. , Chen Po-Hsuan Cameron 
Published: October 2022

AbstractHistologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.
Chiral superconductivity with enhanced quantized Hall responses in moiré transition metal dichalcogenides
Authors: Scherer Michael M. , Kennes Dante M. , Classen Laura 
Published: October 2022

AbstractExperimental demonstrations of tunable correlation effects in magic-angle twisted bilayer graphene have put two-dimensional moiré quantum materials at the forefront of condensed-matter research. Other twisted few-layer graphitic structures, boron-nitride, and homo- or hetero-stacks of transition metal dichalcogenides (TMDs) have further enriched the opportunities for analysis and utilization of correlations in these systems. Recent experiments within the latter material class confirmed the relevance of many-body interactions and demonstrated the importance of their extended range. Since the interaction, its range, and the filling can be tuned experimentally by twist angle, substrate engineering and gating, we here explore Fermi surface instabilities and resulting phases of matter of hetero-bilayer TMDs. Using an unbiased renormalization group approach, we establish in particular that hetero-bilayer TMDs are platforms to realize topological superconductivity with winding number$$| {{{\mathcal{N}}}}| =4$$∣N∣=4. We show that this state reflects in pronounced experimental signatures, such as distinct quantum Hall features.
Comparing models of learning and relearning in large-scale cognitive training data sets
Authors: Kumar Aakriti , Benjamin Aaron S. , Heathcote Andrew , Steyvers Mark 
Published: October 2022

AbstractPractice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals’ performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.
Large-scale audio dataset for emergency vehicle sirens and road noises
Authors: Asif Muhammad , Usaid Muhammad , Rashid Munaf , Rajab Tabarka , Hussain Samreen , Wasi Sarwar 
Published: October 2022

AbstractTraffic congestion, accidents, and pollution are becoming a challenge for researchers. It is essential to develop new ideas to solve these problems, either by improving the infrastructure or applying the latest technology to use the existing infrastructure better. This research paper presents a high-resolution dataset that will help the research community to apply AI techniques to classify any emergency vehicle from traffic and road noises. Demand for such datasets is high as they can control traffic flow and reduce traffic congestion. It also improves emergency response time, especially for fire and health events. This work collects audio data using different methods, and pre-processed them to develop a high-quality and clean dataset. The dataset is divided into two labelled classes one for emergency vehicle sirens and one for traffic noises. The developed dataset offers high quality and range of real-world traffic sounds and emergency vehicle sirens. The technical validity of the dataset is also established.
Effects of non-Darcy mixed convection over a horizontal cone with different convective boundary conditions incorporating gyrotactic microorganisms on dispersion
Authors: Ferdows M. , Alshuraiaan Bader , Nima Nayema Islam 
Published: October 2022

AbstractThis paper investigates the influence of dispersion impact on mixed convection flow over a horizontal cone within a non-Darcy porous medium. Multiple convective boundary conditions are applied to address the heat, mass and motile microorganism transfer phenomena. This paper incorporates the dispersion effect for gyrotactic microorganisms due to biological and environmental applications. By imposing appropriate similarity transformations, the nonlinear partial differential equations governing flow, temperature, concentration, and microbe fields are reduced to a system of ordinary differential equations & then solved using the MATLAB BVP4C function. The computation of grid independence test is analyzed for different flow profiles to show the precision of the points. In a few instances, our present numerical data is compared with previously published works, leading to excellent agreement. The non-Darcy effect, as well as mixed convection values from 0.1 to 0.9 and buoyancy parameters from 0.2 to 0.8, all significantly affects the velocity profile. The reduction in the microorganism profile is brought on by the increase in the bioconvection Lewis parameter and bio convection peclet number between 0.3 and 1. In the absence of dispersion, the variation of Biot numbers between 0.5 and 2, favor heat, mass, and motile microorganism transfer the most in the range of mixed convection parameter 0.5 to pure forced convection 1. Thermal, solutal and microorganism dispersion coefficients a, b, c that lie between $$\frac{1}{7}$$

1
7

and $$\frac{1}{3}$$

1
3

and higher values of modified peclet number ranges from 2 to 10 cause increased dispersion effects which lower flow transfer rates mostly in forced convection regime.
Iterative image segmentation of plant roots for high-throughput phenotyping
Authors: Seidenthal Kyle , Panjvani Karim , Chandnani Rahul , Kochian Leon , Eramian Mark 
Published: October 2022

AbstractAccurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems. Various approaches to image segmentation exist but many of them are not well suited to the thin and reticulated structures characteristic of root systems. The findings presented here describe an approach to RSA segmentation that takes advantage of the inherent structural properties of the root system, a segmentation network architecture we call ITErRoot. We have also generated a novel 2D root image dataset which utilizes an annotation tool developed for producing high quality ground truth segmentation of root systems. Our approach makes use of an iterative neural network architecture to leverage the thin and highly branched properties of root systems for accurate segmentation. Rigorous analysis of model properties was carried out to obtain a high-quality model for 2D root segmentation. Results show a significant improvement over other recent approaches to root segmentation. Validation results show that the model generalizes to plant species with fine and highly branched RSA’s, and performs particularly well in the presence of non-root objects.
An optimization model of tram timetables considering various signal priority strategies
Authors: He Jing , Xu Yuting , Li Yanhuan , Yang Jian , Long Sihui 
Published: October 2022

AbstractModern trams generally operate in a semi-independent Right Of Way that intersects with social vehicles at junctions. Typically, there are two signal priority strategies at junctions: active signal priority strategy and no-signal priority strategy. The active signal priority strategy is applied to improve the efficiency of the tram. However, it inevitably causes delays to social vehicles. The no-signal priority strategy could reduce the influence on social vehicles, but it will increase the tram travel time. Therefore, we develop a Mixed-Integer Linear Programming model to optimize the tram timetable and consider various signal priority strategies. In the model, the signal priority strategies of the tram are a set of decision variables that consider the traffic flow of social vehicles rather than fixed input parameters. The model considers minimizing the overall travel time of the tram and the negative utility of signal priority strategies. A numerical experiment is conducted to demonstrate the validity of the proposed model. The experimental results show that the proposed method can optimize the tram timetable and maximize the overall benefits of the junction. Moreover, we compare the experimental results of the proposed method with the approach of fixing the signal priority strategy for the tram at junctions. On the one hand, our proposed method can improve the operational efficiency of trams, i.e., the travel time decreases by 16.60%. On the other hand, the negative utility of signal priority for the comprehensive scheme proposed in this work reduces by 39.45%.