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Xiaojing Liu,&ast; You Song,&ast; Limin Wan, Rong Du Department of Rheumatology and Immunology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Rong Du, Department of Rheumatology and Immunology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China, Email dudurong2003@126.comBackground: This study aims to investigate the knowledge, attitudes, and practices (KAP) among patients with systemic lupus erythematosus (SLE) toward disease management and biologic therapy.Methods: This cross-sectional study was conducted between April 20, 2023, and May 5, 2023, among patients with SLE at Union Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology. A self-designed questionnaire was developed to collect demographic information of SLE patients and assess their KAP.Results: A total of 463 SLE patients participated. The mean scores for knowledge, attitudes, and practices were 8.52 ± 2.36 (possible range: 0– 11), 39.40 ± 3.38 (possible range: 11– 55), and 27.10 ± 6.29 (possible range: 8– 40), respectively. The path analysis demonstrated a significant and positive association between knowledge and attitudes, as indicated by a path coefficient of 0.455 (p < 0.001), and a significant and positive relationship between knowledge and practices, with a path coefficient of 0.709 (p < 0.001).Conclusion: Patients with SLE exhibited insufficient knowledge, negative attitudes, and poor practices.Keywords: knowledge, attitudes, practices, systemic lupus erythematosus, biologic therapy, cross-sectional study

QC 20230922

Band selection has a great impact on the spectral recovery quality. To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit clustering or sparse regularization constraints to find most prominent bands. These methods are either very slow due to the computational cost of repeatedly training with respect to different selection frequencies or different band combinations. Many traditional methods rely on the scene prior and thus are not applicable to other scenarios. In this paper, we present a novel one-shot Neural Band Selection (NBS) framework for spectral recovery. Unlike conventional searching approaches with a discrete search space and a non-differentiable search strategy, our NBS is based on the continuous relaxation of the band selection process, thus allowing efficient band search using gradient descent. To enable the compatibility for se- lecting any number of bands in one-shot, we further exploit the band-wise correlation matrices to progressively suppress similar adjacent bands. Extensive evaluations on the NTIRE 2022 Spectral Reconstruction Challenge demonstrate that our NBS achieves consistent performance gains over competitive baselines when examined with four different spectral recov- ery methods. Our code will be publicly available., Comment: Accepted by ICASSP 2023, any questions contact xuzhenbo@mail.ustc.edu.cn

Quantitative adverse outcome pathway network (qAOPN) is gaining momentum due to the predictive nature, alignment with quantitative risk assessment and great potential as a computational new approach methodology (NAM) to reduce laboratory animal tests. The present work aimed to demonstrate two advanced modeling approaches, piecewise structural equation modeling (PSEM) and Bayesian network (BN), for de novo qAOPN model construction based on routine ecotoxicological data. A previously published AOP network comprised of four linear AOPs linking excessive reactive oxygen species production to mortality in aquatic organisms was employed as a case study. The demonstrative case study intended to answer: Which linear AOP in the network contributed the most to the AO? Can any of the upstream KEs accurately predict the AO? What are the advantages and limitations of PSEM or BN in qAOPN development? The outcomes from the two approaches showed that both PSEM and Bayesian network were suitable for constructing a complex qAOPN based on limited experimental data. Besides quantification of response-response relationships, both approaches were capable of identifying the most influencing linear AOP in a complex network and evaluating the predictive ability of the AOP, albeit some discrepancies in predictive ability were identified for the two approaches using this specific dataset. The PROs and CONs of the two approaches for qAOPN construction were discussed in detail and suggestions on optimal workflows of PSEM and BN were provided to guide future qAOPN development., Funding agencies:NIVA Computational Toxicology Program, NCTP 5 (RCN) 301397RCN-funded Centre for Environmental Radioactivity (CERAD) 160016 223268

General Circulation Models (GCMs) have for decades exhibited difficulties in modelling the diurnal cycle of precipitation (DCP). This issue can be related to inappropriate representation of the processes controlling sub-diurnal phenomena like convection. In this study, 11 single-column versions of GCMs are used to investigate the interactions between convection and environmental conditions, processes that control nocturnal convections, and the transition from shallow to deep convection on a diurnal time-scale. Long-term simulations are performed over two continental land sites: the Southern Great Plains (SGP) in the USA for 12 summer months from 2004 to 2015 and the Manacapuru site at the central Amazon (MAO) in Brazil for two full years from 2014 to 2015. The analysis is done on two regimes: afternoon convective regime and nocturnal precipitation regime. Most models produce afternoon precipitation too early, likely due to the missing transition of shallow-to-deep convection in these models. At SGP, the unified convection schemes better simulate the onset time of precipitation. At MAO, models produce the heating peak in a much lower level compared with observation, indicating too shallow convection in the models. For nocturnal precipitation, models that produce most of nocturnal precipitation all allow convection to be triggered above the boundary layer. This indicates the importance of model capability to detect elevated convection for simulating nocturnal precipitation. Sensitivity studies indicate that (a) nudging environmental variables towards observations has a minor impact on DCP, (b) unified treatment of shallow and deep convection and the capability to capture mid-level convection can help models better capture DCP, and (c) the interactions of the atmosphere with other components in the climate system (e.g. land) are also important for DCP simulations in coupled models. These results provide long-term statistical insights on which physical processes are ess

General Circulation Models (GCMs) have for decades exhibited difficulties in modelling the diurnal cycle of precipitation (DCP). This issue can be related to inappropriate representation of the processes controlling sub-diurnal phenomena like convection. In this study, 11 single-column versions of GCMs are used to investigate the interactions between convection and environmental conditions, processes that control nocturnal convections, and the transition from shallow to deep convection on a diurnal time-scale. Long-term simulations are performed over two continental land sites: the Southern Great Plains (SGP) in the USA for 12 summer months from 2004 to 2015 and the Manacapuru site at the central Amazon (MAO) in Brazil for two full years from 2014 to 2015. The analysis is done on two regimes: afternoon convective regime and nocturnal precipitation regime. Most models produce afternoon precipitation too early, likely due to the missing transition of shallow-to-deep convection in these models. At SGP, the unified convection schemes better simulate the onset time of precipitation. At MAO, models produce the heating peak in a much lower level compared with observation, indicating too shallow convection in the models. For nocturnal precipitation, models that produce most of nocturnal precipitation all allow convection to be triggered above the boundary layer. This indicates the importance of model capability to detect elevated convection for simulating nocturnal precipitation. Sensitivity studies indicate that (a) nudging environmental variables towards observations has a minor impact on DCP, (b) unified treatment of shallow and deep convection and the capability to capture mid-level convection can help models better capture DCP, and (c) the interactions of the atmosphere with other components in the climate system (e.g. land) are also important for DCP simulations in coupled models. These results provide long-term statistical insights on which physical processes are ess

Background: Reproductive effects of ionizing radiation in organisms have been observed under laboratory and field conditions. Such assessments often rely on associations between exposure and effects, and thus lacking a detailed mechanistic understanding of causality between effects occurring at different levels of biological organization. The Adverse Outcome Pathway (AOP), a conceptual knowledge framework to capture, organize, evaluate and visualize the scientific knowledge of relevant toxicological effects, has the potential to evaluate the causal relationships between molecular, cellular, individual, and population effects. This paper presents the first development of a set of consensus AOPs for reproductive effects of ionizing radiation in wildlife. This work was performed by a group of experts formed during a workshop organized jointly by the Multidisciplinary European Low Dose Initiative (MELODI) and the European Radioecology Alliance (ALLIANCE) associations to present the AOP approach and tools. The work presents a series of taxon-specific case studies that were used to identify relevant empirical evidence, identify common AOP components and propose a set of consensus AOPs that could be organized into an AOP network with broader taxonomic applicability. Conclusion: Expert consultation led to the identification of key biological events and description of causal linkages between ionizing radiation, reproductive impairment and reduction in population fitness. The study characterized the knowledge domain of taxon-specific AOPs, identified knowledge gaps pertinent to reproductive-relevant AOP development and reflected on how AOPs could assist applications in radiation (radioecological) research, environmental health assessment, and radiological protection. Future advancement and consolidation of the AOPs is planned to include structured weight of evidence considerations, formalized review and critical assessment of the empirical evidence prior to formal submission

A number of epigenetic modulating chemicals are known to affect multiple generations of a population from a single ancestral exposure, thus posing transgenerational hazards. The present study aimed to establish a high-throughput (HT) analytical workflow for cost-efficient concentration-response analysis of epigenetic and phenotypic effects, and to support the development of novel Adverse Outcome Pathway (AOP) networks for DNA methyltransferase (DNMT) inhibitor-mediated transgenerational effects on aquatic organisms. The model DNMT inhibitor 5-azacytidine (5AC) and the model freshwater crustacean Daphnia magna were used to generate new experimental data and served as prototypes to construct AOPs for aquatic organisms. Targeted HT bioassays (DNMT ELISA, MS-HRM and qPCR) in combination with multigenerational ecotoxicity tests revealed concentration-dependent transgenerational (F0-F3) effects of 5AC on total DNMT activity, DNA promoter methylation, gene body methylation, gene transcription and reproduction. Top sensitive toxicity pathways related to 5AC exposure, such as apoptosis and DNA damage responses were identified in both F0 and F3 using Gaussian Bayesian network modeling. Two novel epigenetic AOP networks on DNMT inhibitor mediated one-generational and transgenerational effects were developed for aquatic organisms and assessed for the weight of evidence. The new HT analytical workflow and AOPs can facilitate future ecological hazard assessment of epigenetic modulating chemicals.

Peer-to-peer (P2P) lending platforms have grown rapidly over the past decade as the network infrastructure has improved and the demand for personal lending has grown. Such platforms allow users to create peer-to-peer lending relationships without the help of traditional financial institutions. Assessing the borrowers' credit is crucial to reduce the default rate and benign development of P2P platforms. Building a personal credit scoring machine learning model can effectively predict whether users will repay loans on the P2P platform. And the handling of data outliers and sample imbalance problems can affect the final effect of machine learning models. There have been some studies on balanced sampling methods, but the effect of outlier detection methods and their combination with balanced sampling methods on the effectiveness of machine learning models has not been fully studied. In this paper, the influence of using different outlier detection methods and balanced sampling methods on commonly used machine learning models is investigated. Experiments on 44,487 Lending Club samples show that proper outlier detection can improve the effectiveness of the machine learning model, and the balanced sampling method only has a good effect on a few machine learning models, such as MLP.

Dataset shift is common in credit scoring scenarios, and the inconsistency between the distribution of training data and the data that actually needs to be predicted is likely to cause poor model performance. However, most of the current studies do not take this into account, and they directly mix data from different time periods when training the models. This brings about two problems. Firstly, there is a risk of data leakage, i.e., using future data to predict the past. This can result in inflated results in offline validation, but unsatisfactory results in practical applications. Secondly, the macroeconomic environment and risk control strategies are likely to be different in different time periods, and the behavior patterns of borrowers may also change. The model trained with past data may not be applicable to the recent stage. Therefore, we propose a method based on adversarial validation to alleviate the dataset shift problem in credit scoring scenarios. In this method, partial training set samples with the closest distribution to the predicted data are selected for cross-validation by adversarial validation to ensure the generalization performance of the trained model on the predicted samples. In addition, through a simple splicing method, samples in the training data that are inconsistent with the test data distribution are also involved in the training process of cross-validation, which makes full use of all the data and further improves the model performance. To verify the effectiveness of the proposed method, comparative experiments with several other data split methods are conducted with the data provided by Lending Club. The experimental results demonstrate the importance of dataset shift in the field of credit scoring and the superiority of the proposed method.

A number of epigenetic modulating chemicals are known to affect multiple generations of a population from a single ancestral exposure, thus posing transgenerational hazards. The present study aimed to establish a high-throughput (HT) analytical workflow for cost-efficient concentration-response analysis of epigenetic and phenotypic effects, and to support the development of novel Adverse Outcome Pathway (AOP) networks for DNA methyltransferase (DNMT) inhibitor-mediated transgenerational effects on aquatic organisms. The model DNMT inhibitor 5-azacytidine (5AC) and the model freshwater crustacean Daphnia magna were used to generate new experimental data and served as prototypes to construct AOPs for aquatic organisms. Targeted HT bioassays (DNMT ELISA, MS-HRM and qPCR) in combination with multigenerational ecotoxicity tests revealed concentration-dependent transgenerational (F0-F3) effects of 5AC on total DNMT activity, DNA promoter methylation, gene body methylation, gene transcription and reproduction. Top sensitive toxicity pathways related to 5AC exposure, such as apoptosis and DNA damage responses were identified in both F0 and F3 using Gaussian Bayesian network modeling. Two novel epigenetic AOP networks on DNMT inhibitor mediated one-generational and transgenerational effects were developed for aquatic organisms and assessed for the weight of evidence. The new HT analytical workflow and AOPs can facilitate future ecological hazard assessment of epigenetic modulating chemicals.

A number of epigenetic modulating chemicals are known to affect multiple generations of a population from a single ancestral exposure, thus posing transgenerational hazards. The present study aimed to establish a high-throughput (HT) analytical workflow for cost-efficient concentration-response analysis of epigenetic and phenotypic effects, and to support the development of novel Adverse Outcome Pathway (AOP) networks for DNA methyltransferase (DNMT) inhibitor-mediated transgenerational effects on aquatic organisms. The model DNMT inhibitor 5-azacytidine (5AC) and the model freshwater crustacean Daphnia magna were used to generate new experimental data and served as prototypes to construct AOPs for aquatic organisms. Targeted HT bioassays (DNMT ELISA, MS-HRM and qPCR) in combination with multigenerational ecotoxicity tests revealed concentration-dependent transgenerational (F0-F3) effects of 5AC on total DNMT activity, DNA promoter methylation, gene body methylation, gene transcription and reproduction. Top sensitive toxicity pathways related to 5AC exposure, such as apoptosis and DNA damage responses were identified in both F0 and F3 using Gaussian Bayesian network modeling. Two novel epigenetic AOP networks on DNMT inhibitor mediated one-generational and transgenerational effects were developed for aquatic organisms and assessed for the weight of evidence. The new HT analytical workflow and AOPs can facilitate future ecological hazard assessment of epigenetic modulating chemicals.

Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) of the reactants or generalize to unseen reaction templates (i.e., chemical reaction rules). In this paper, we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. To this end, we design an efficient reactant selection framework, named RetCL (retrosynthesis via contrastive learning), for enumerating all of the candidate molecules based on selection scores computed by graph neural networks. For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining. Extensive experiments demonstrate the benefits of the proposed selection-based approach. For example, when all 671k reactants in the USPTO {database} are given as candidates, our RetCL achieves top-1 exact match accuracy of $71.3\%$ for the USPTO-50k benchmark, while a recent transformer-based approach achieves $59.6\%$. We also demonstrate that RetCL generalizes well to unseen templates in various settings in contrast to template-based approaches., Comment: Accepted to IJCAI 2021. Short version was accepted to Machine Learning for Molecules Workshop at NeurIPS 2020

A number of epigenetic modulating chemicals are known to affect multiple generations of a population from a single ancestral exposure, thus posing transgenerational hazards. The present study aimed to establish a high-throughput (HT) analytical workflow for cost-efficient concentration-response analysis of epigenetic and phenotypic effects, and to support the development of novel Adverse Outcome Pathway (AOP) networks for DNA methyltransferase (DNMT) inhibitor-mediated transgenerational effects on aquatic organisms. The model DNMT inhibitor 5-azacytidine (5AC) and the model freshwater crustacean Daphnia magna were used to generate new experimental data and served as prototypes to construct AOPs for aquatic organisms. Targeted HT bioassays (DNMT ELISA, MS-HRM and qPCR) in combination with multigenerational ecotoxicity tests revealed concentration-dependent transgenerational (F0-F3) effects of 5AC on total DNMT activity, DNA promoter methylation, gene body methylation, gene transcription and reproduction. Top sensitive toxicity pathways related to 5AC exposure, such as apoptosis and DNA damage responses were identified in both F0 and F3 using Gaussian Bayesian network modeling. Two novel epigenetic AOP networks on DNMT inhibitor mediated one-generational and transgenerational effects were developed for aquatic organisms and assessed for the weight of evidence. The new HT analytical workflow and AOPs can facilitate future ecological hazard assessment of epigenetic modulating chemicals., Funding Agencies:NIVA institutional funding scheme "Strategic Institutional Initiatives programme (SIS) for Restoration Ecology" Research Council of Norway 301397NIVA Computational Toxicology Program (NCTP) FWO Science Foundation Flanders

Ionizing radiation is known to induce oxidative stress and DNA damage as well as epigenetic effects in aquatic organisms. Epigenetic changes can be part of the adaptive responses to protect organisms from radiation-induced damage, or act as drivers of toxicity pathways leading to adverse effects. To investigate the potential roles of epigenetic mechanisms in low-dose ionizing radiation-induced stress responses, an ecologically relevant crustacean, adult Daphnia magna were chronically exposed to low and medium level external 60Co gamma radiation ranging from 0.4, 1, 4, 10, and 40 mGy/h for seven days. Biological effects at the molecular (global DNA methylation, histone modification, gene expression), cellular (reactive oxygen species formation), tissue/organ (ovary, gut and epidermal histology) and organismal (fecundity) levels were investigated using a suite of effect assessment tools. The results showed an increase in global DNA methylation associated with loci-specific alterations of histone H3K9 methylation and acetylation, and downregulation of genes involved in DNA methylation, one-carbon metabolism, antioxidant defense, DNA repair, apoptosis, calcium signaling and endocrine regulation of development and reproduction. Temporal changes of reactive oxygen species (ROS) formation were also observed with an apparent transition from ROS suppression to induction from 2 to 7 days after gamma exposure. The cumulative fecundity, however, was not significantly changed by the gamma exposure. On the basis of the new experimental evidence and existing knowledge, a hypothetical model was proposed to provide in-depth mechanistic understanding of the roles of epigenetic mechanisms in low dose ionizing radiation induced stress responses in D. magna.

Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, the CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity while simultaneously capturing graph dynamics. In the proposed framework, the traditional graph convolution is generalized into two phases, feature transformation and feature aggregation, which gives the CTGCN more flexibility and enables the CTGCN to learn connective and structural information under the same framework. Experimental results on 7 real-world graphs demonstrate that the CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks, including link prediction and structural role classification. The source code of this work can be obtained from \url{https://github.com/jhljx/CTGCN}.

The water flea Daphnia magna is a keystone species in freshwater ecosystems and has been widely used as a model organism in environmental ecotoxicology. This aquatic crustacean is sensitive to environmental stressors and displays considerable plasticity in adapting to changing environmental conditions. Part of this plasticity may be due to epigenetic regulation of gene expression, including changes to DNA methylation and histone modifications. Because of the generally hypomethylated genome of this species, we hypothesized that the histone code may have an essential role in the epigenetic control and that histone modifications might be an early marker for stress. This study aims to characterize the epigenetic, transcriptional and phenotypic responses and their causal linkages in directly exposed adult (F0) Daphnia and peritoneal exposed neonates (F1) after a chronic (7-day) exposure to a sublethal concentration (10 mg/l) of 5-azacytidine, a well-studied vertebrate DNA methylation inhibitor. Exposure of the F0 generation significantly reduced the cumulative fecundity, accompanied with differential expression of genes in the one-carbon-cycle metabolic pathway. In the epigenome of the F0 generation, a decrease in global DNA methylation, but no significant changes on H3K4me3 or H3K27me3, were observed. In the F1 offspring generation, changes in gene expression, a significant reduction in global DNA methylation and changes in histone modifications were identified. The results indicate that exposure during adulthood may result in more pronounced effects on early development in the offspring generation, though interpretation of the data should be carefully done since both the exposure regime and developmental period is different in the two generations examined. The obtained results improve our understanding of crustacean epigenetics and the tools developed may promote use of epigenetic markers in hazard assessment of environmental stressors.

In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time consuming and extremely inefficient. To improve the efficiency and accuracy of system failure detection and thereby reduce the impact of system failures on financial services, we propose a novel machine learning-based framework to predict the occurrence of system exceptions and failures in a financial software system. In particular, we first extract rich information from system logs and eliminate noises in the data. Then the cleaned data is leveraged as the input of our proposed anomaly prediction framework which consists of three modules: key performance indicator(KPI) data prediction module, anomaly identification module and severity classification module. Notably, we design a hierarchical architecture of alarm classifiers and try to alleviate the influence of class-imbalance problem on the overall performance. Empirically, the experimental results demonstrate the superior performance of our proposed method on a real-world financial software system log data set.

Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any supervision in the form of ground truth disparity or depth. The estimated depth provides additional information complementary to individual semantic features, which can be helpful for other vision tasks such as tracking, recognition and detection. However, there are large appearance variations between images from different spectral bands, which is a challenge for cross-spectral stereo matching. Existing deep unsupervised stereo matching methods are sensitive to the appearance variations and do not perform well on cross-spectral data. We propose a novel unsupervised cross-spectral stereo matching framework based on image-to-image translation. First, a style adaptation network transforms images across different spectral bands by cycle consistency and adversarial learning, during which appearance variations are minimized. Then, a stereo matching network is trained with image pairs from the same spectra using view reconstruction loss. At last, the estimated disparity is utilized to supervise the spectral-translation network in an end-to-end way. Moreover, a novel style adaptation network F-cycleGAN is proposed to improve the robustness of spectral translation. Our method can tackle appearance variations and enhance the robustness of unsupervised cross-spectral stereo matching. Experimental results show that our method achieves good performance without using depth supervision or explicit semantic information., Comment: accepted by AAAI-19

Incomplete penetrance of congenital heart defects (CHDs) was observed in a mouse model. We hypothesized that the contribution of a major genetic locus modulates the manifestation of the CHDs. After genome-wide linkage mapping, fine mapping, and high-throughput targeted sequencing, a recessive frameshift mutation of the heterogeneous nuclear ribonucleoprotein A1 (Hnrnpa1) gene was confirmed (Hnrnpa1ct). Hnrnpa1 was expressed in both the first heart field (FHF) and second heart field (SHF) at the cardiac crescent stage but was only maintained in SHF progenitors after heart tube formation. Hnrnpa1ct/ct homozygous mutants displayed complete CHD penetrance, including truncated and incomplete looped heart tube at E9.5, ventricular septal defect (VSD) and persistent truncus arteriosus (PTA) at E13.5, and VSD and double outlet right ventricle at P0. Impaired development of the dorsal mesocardium and sinoatrial node progenitors was also observed. Loss of Hnrnpa1 expression leads to dysregulation of cardiac transcription networks and multiple signaling pathways, including BMP, FGF, and Notch in the SHF. Finally, two rare heterozygous mutations of HNRNPA1 were detected in human CHDs. These findings suggest a role of Hnrnpa1 in embryonic heart development in mice and humans.

Running composite insulators are prone to failure due to their harsh surrounding work environment, which directly affects the safe operation of transmission lines. This paper puts forward the method of using fiber Bragg grating (FBG) as the monitors to parameters correlated with thermal and stress of the composite insulators in transmission lines at working status. Firstly, monitoring points are found out by the mechanical test on composite insulator samples. Secondly, based on the monitoring theory, this paper introduces the feasibility design frame of the composite insulator with FBG implanted in the rod and the online monitor system. At last, it describes applications of this monitor system in the field of transmission lines.

Incomplete penetrance of congenital heart defects (CHDs) was observed in a mouse model. We hypothesized that the contribution of a major genetic locus modulates the manifestation of the CHDs. After genome-wide linkage mapping, fine mapping, and high-throughput targeted sequencing, a recessive frameshift mutation of the heterogeneous nuclear ribonucleoprotein A1 (Hnrnpa1) gene was confirmed (Hnrnpa1ct). Hnrnpa1 was expressed in both the first heart field (FHF) and second heart field (SHF) at the cardiac crescent stage but was only maintained in SHF progenitors after heart tube formation. Hnrnpa1ct/ct homozygous mutants displayed complete CHD penetrance, including truncated and incomplete looped heart tube at E9.5, ventricular septal defect (VSD) and persistent truncus arteriosus (PTA) at E13.5, and VSD and double outlet right ventricle at P0. Impaired development of the dorsal mesocardium and sinoatrial node progenitors was also observed. Loss of Hnrnpa1 expression leads to dysregulation of cardiac transcription networks and multiple signaling pathways, including BMP, FGF, and Notch in the SHF. Finally, two rare heterozygous mutations of HNRNPA1 were detected in human CHDs. These findings suggest a role of Hnrnpa1 in embryonic heart development in mice and humans.

Adolescent idiopathic scoliosis (AIS) is the most common musculoskeletal disorder of childhood development. The genetic architecture of AIS is complex, and the great majority of risk factors are undiscovered. To identify new AIS susceptibility loci, we conducted the first genome-wide meta-analysis of AIS genome-wide association studies, including 7956 cases and 88 459 controls from 3 ancestral groups. Three novel loci that surpassed genome-wide significance were uncovered in intragenic regions of the CDH13 (P-value_rs4513093 = 1.7E-15), ABO (P-value_ rs687621 = 7.3E-10) and SOX6 (P-value_rs1455114 = 2.98E-08) genes. Restricting the analysis to females improved the associations at multiple loci, most notably with variants within CDH13 despite the reduction in sample size. Genome-wide gene-functional enrichment analysis identified significant perturbation of pathways involving cartilage and connective tissue development. Expression of both SOX6 and CDH13 was detected in cartilage chondrocytes and chromatin immunoprecipitation sequencing experiments in that tissue revealed multiple HeK27ac-positive peaks overlapping associated loci. Our results further define the genetic architecture of AIS and highlight the importance of vertebral cartilage development in its pathogenesis.

With the rapid development of urbanization, the boom of vehicle numbers has resulted in serious traffic accidents, which led to casualties and huge economic losses. The ability to predict the risk of traffic accident is important in the prevention of the occurrence of accidents and to reduce the damages caused by accidents in a proactive way. However, traffic accident risk prediction with high spatiotemporal resolution is difficult, mainly due to the complex traffic environment, human behavior, and lack of real-time traffic-related data. In this study, we collected big traffic accident data. By analyzing the spatial and temporal patterns of traffic accident frequency, we presented the spatiotemporal correlation of traffic accidents. Based on the patterns we found in analysis, we proposed a high accurate deep learning model based on recurrent neural network toward the prediction of traffic accident risk. The predictive accident risk can be potential applied to the traffic accident warning system. The proposed method can be integrated into an intelligent traffic control system toward a more reasonable traffic prediction and command organization.

The recent discovery of a Weyl semimetal in TaAs offers the first Weyl fermion observed in nature and dramatically broadens the classification of topological phases. However, in TaAs it has proven challenging to study the rich transport phenomena arising from emergent Weyl fermions. The series MoxW1-xTe2 are inversion-breaking, layered, tunable semimetals already under study as a promising platform for new electronics and recently proposed to host Type II, or strongly Lorentz-violating, Weyl fermions. Here we report the discovery of a Weyl semimetal in MoxW1-xTe2 at x=25%. We use pump-probe angle-resolved photoemission spectroscopy (pump-probe ARPES) to directly observe a topological Fermi arc above the Fermi level, demonstrating a Weyl semimetal. The excellent agreement with calculation suggests that MoxW1-xTe2 is a Type II Weyl semimetal. We also find that certain Weyl points are at the Fermi level, making MoxW1-xTe2 a promising platform for transport and optics experiments on Weyl semimetals.

Diradicals, molecules with two unpaired electrons, are reactive intermediates that play an important role in many fields. Their defining feature is the energy difference between their singlet and triplet states, which provides direct information on the extent of their electron exchange interactions. Such knowledge is essential for understanding their diradical character, which is controllable internally by modification of the electronic and steric properties of the substituents. We now report that the energy gap of a diradical in the solid state can also be controlled by an external stimulus. The dication diradical of 4,4''-di(bisphenylamino)-p-terphenyl exhibits two singlet states with different exchange coupling constants at different temperatures as determined by SQUID and EPR measurements. The behavior is induced by the conformation change of the terphenyl bridge, the key structural unit of the species. The work presents an unprecedented instance of a thermally controllable singlet-triplet gap for a crystalline diradical and provides a novel diradical material relevant to the design of functional materials.

The recent discovery of a Weyl semimetal in TaAs offers the first Weyl fermion observed in nature and dramatically broadens the classification of topological phases. However, in TaAs it has proven challenging to study the rich transport phenomena arising from emergent Weyl fermions. The series MoxW1-xTe2 are inversion-breaking, layered, tunable semimetals already under study as a promising platform for new electronics and recently proposed to host Type II, or strongly Lorentz-violating, Weyl fermions. Here we report the discovery of a Weyl semimetal in MoxW1-xTe2 at x=25%. We use pump-probe angle-resolved photoemission spectroscopy (pump-probe ARPES) to directly observe a topological Fermi arc above the Fermi level, demonstrating a Weyl semimetal. The excellent agreement with calculation suggests that MoxW1-xTe2 is a Type II Weyl semimetal. We also find that certain Weyl points are at the Fermi level, making MoxW1-xTe2 a promising platform for transport and optics experiments on Weyl semimetals.

Diradicals, molecules with two unpaired electrons, are reactive intermediates that play an important role in many fields. Their defining feature is the energy difference between their singlet and triplet states, which provides direct information on the extent of their electron exchange interactions. Such knowledge is essential for understanding their diradical character, which is controllable internally by modification of the electronic and steric properties of the substituents. We now report that the energy gap of a diradical in the solid state can also be controlled by an external stimulus. The dication diradical of 4,4''-di(bisphenylamino)-p-terphenyl exhibits two singlet states with different exchange coupling constants at different temperatures as determined by SQUID and EPR measurements. The behavior is induced by the conformation change of the terphenyl bridge, the key structural unit of the species. The work presents an unprecedented instance of a thermally controllable singlet-triplet gap for a crystalline diradical and provides a novel diradical material relevant to the design of functional materials.

Diradicals, molecules with two unpaired electrons, are reactive intermediates that play an important role in many fields. Their defining feature is the energy difference between their singlet and triplet states, which provides direct information on the extent of their electron exchange interactions. Such knowledge is essential for understanding their diradical character, which is controllable internally by modification of the electronic and steric properties of the substituents. We now report that the energy gap of a diradical in the solid state can also be controlled by an external stimulus. The dication diradical of 4,4′′-di(bisphenylamino)-p-terphenyl exhibits two singlet states with different exchange coupling constants at different temperatures as determined by SQUID and EPR measurements. The behavior is induced by the conformation change of the terphenyl bridge, the key structural unit of the species. The work presents an unprecedented instance of a thermally controllable singlet-triplet gap for a crystalline diradical and provides a novel diradical material relevant to the design of functional materials.

Diradicals, molecules with two unpaired electrons, are reactive intermediates that play an important role in many fields. Their defining feature is the energy difference between their singlet and triplet states, which provides direct information on the extent of their electron exchange interactions. Such knowledge is essential for understanding their diradical character, which is controllable internally by modification of the electronic and steric properties of the substituents. We now report that the energy gap of a diradical in the solid state can also be controlled by an external stimulus. The dication diradical of 4,4′′-di(bisphenylamino)-p-terphenyl exhibits two singlet states with different exchange coupling constants at different temperatures as determined by SQUID and EPR measurements. The behavior is induced by the conformation change of the terphenyl bridge, the key structural unit of the species. The work presents an unprecedented instance of a thermally controllable singlet-triplet gap for a crystalline diradical and provides a novel diradical material relevant to the design of functional materials.

A series of bis[N,N-di-(4-methoxylphenyl)amino]arene dications 12+-32+ have been synthesized and characterized. Their electronic structures were investigated by various experiments assisted by theoretical calculations. It was found that they are singlets in the ground state and that their diradical character is dependent on the bridging moiety. 32+ has a smaller singlet-triplet energy gap and its excited triplet state is thermally readily accessible. The work provides a nitrogen analogue of Thiele's hydrocarbon with considerable diradical character.

A series of bis[N,N-di-(4-methoxylphenyl)amino]arene dications 12+-32+ have been synthesized and characterized. Their electronic structures were investigated by various experiments assisted by theoretical calculations. It was found that they are singlets in the ground state and that their diradical character is dependent on the bridging moiety. 32+ has a smaller singlet-triplet energy gap and its excited triplet state is thermally readily accessible. The work provides a nitrogen analogue of Thiele's hydrocarbon with considerable diradical character.

Three bis(triarylamine) dications were isolated by using weakly coordinating anions. Their electronic structures in the ground state were investigated by various experiments in conjunction with theoretical calculations. The ground-state electronic structures of these species were tunable by substituent effects, with two of them as closed-shell singlets and one of them as an open-shell singlet in the solid state. The excited state of the latter is thermally accessible, indicated by EPR and SQUID measurements. The work provides a new and stable diradicaloid structure motif with an excited triplet sate. Getting excited: The ground-state electronic structures of three bis(triarylamine) dications (R=H, Me, OMe), which were isolated using weakly coordinating anions and investigated experimentally and theoretically, were tunable by substituent effects. The work provides a new and stable diradicaloid structure motif with an excited triplet state. CS=closed-shell singlet, OS=open-shell singlet, T=excited triplet.

Three bis(triarylamine) dications were isolated by using weakly coordinating anions. Their electronic structures in the ground state were investigated by various experiments in conjunction with theoretical calculations. The ground-state electronic structures of these species were tunable by substituent effects, with two of them as closed-shell singlets and one of them as an open-shell singlet in the solid state. The excited state of the latter is thermally accessible, indicated by EPR and SQUID measurements. The work provides a new and stable diradicaloid structure motif with an excited triplet sate. Getting excited: The ground-state electronic structures of three bis(triarylamine) dications (R=H, Me, OMe), which were isolated using weakly coordinating anions and investigated experimentally and theoretically, were tunable by substituent effects. The work provides a new and stable diradicaloid structure motif with an excited triplet state. CS=closed-shell singlet, OS=open-shell singlet, T=excited triplet.

This paper describes the implementation of the orographic gravity wave drag (GWDO) processes induced by subgrid-scale orography in the global version of the Weather Research and Forecasting (WRF) model. The sensitivity of the model simulated climatology to the representation of shortwave radiation and the addition of the GWDO processes is investigated using the Kim-Arakawa GWDO parameterization and the Goddard, RRTMG(Rapid Radiative Transfer Model for GCMs), and Dudhia shortwave radiation schemes. This sensitivity study is a part of efforts of selecting the physics package that can be useful in applying the WRF model to global and seasonal configuration. The climatology is relatively well simulated by the global WRF; the zonal mean zonal wind and temperature structures are reasonably represented with the Kim-Arakawa GWDO scheme using the Goddard and RRTMG shortwave schemes. It is found that the impact of the shortwave radiation scheme on the modeled atmosphere is pronounced in the upper atmospheric circulations above the tropopause mainly due to the ozone heating. The scheme that excludes the ozone process suffers from a distinct cold bias in the stratosphere. Moreover, given the improper thermodynamic environment conditions by the shortwave scheme, the role of the GWDO process is found to be limited., Published in Advances in Meteorology: (An open access journal) v2010 ID: 959014.

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r(2) of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r(2) of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.

The yeast transcription factor Ste12 controls both mating and filamentation pathways. Upon pheromone induction, the mitogen-activated protein kinases, Fus3 and Kss1, activate Ste12 by relieving the repression of two functionally redundant Ste12 inhibitors, Dig1 and Dig2. Mating genes are controlled by the Ste12/Dig1/Dig2 complex through Ste12-binding sites, whereas filamentation genes are regulated by the Tec1/Ste12/Dig1 complex through Tec1-binding sites. The two Ste12 complexes are mutually exclusive. During pheromone response, Tec1 is degraded upon phosphorylation by Fus3, preventing cross-activation of the filamentation pathway. Here, we show that a stable Tec1 also impairs the induction of mating genes. A mathematical model is developed to capture the dynamic formation of the two Ste12 complexes and their interactions with pathway-specific promoters. By model simulations and experimentation, we show that excess Tec1 can impair the mating transcriptional output because of its ability to sequester Ste12, and because of a novel function of Dig2 for the transcription of mating genes. We suggest that Fus3-triggered Tec1 degradation is an important part of the transcriptional induction of mating genes during the pheromone response.

The yeast transcription factor Ste12 controls both mating and filamentation pathways. Upon pheromone induction, the mitogen-activated protein kinases, Fus3 and Kss1, activate Ste12 by relieving the repression of two functionally redundant Ste12 inhibitors, Dig1 and Dig2. Mating genes are controlled by the Ste12/Dig1/Dig2 complex through Ste12-binding sites, whereas filamentation genes are regulated by the Tec1/Ste12/Dig1 complex through Tec1-binding sites. The two Ste12 complexes are mutually exclusive. During pheromone response, Tec1 is degraded upon phosphorylation by Fus3, preventing cross-activation of the filamentation pathway. Here, we show that a stable Tec1 also impairs the induction of mating genes. A mathematical model is developed to capture the dynamic formation of the two Ste12 complexes and their interactions with pathway-specific promoters. By model simulations and experimentation, we show that excess Tec1 can impair the mating transcriptional output because of its ability to sequester Ste12, and because of a novel function of Dig2 for the transcription of mating genes. We suggest that Fus3-triggered Tec1 degradation is an important part of the transcriptional induction of mating genes during the pheromone response.

The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged-average ensemble, perturbed physics ensemble, mixed physics ensemble, and multi-model ensemble. The results show that the similarity in spread between the mixed-physics and multi-model ensembles raises the possibility that a mixed-physics ensemble approach could be considered as a proxy for a multi-model ensemble. The mixed-physics ensemble performed well in terms of equitable threat score, especially for higher precipitation amounts.

The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged-average ensemble, perturbed physics ensemble, mixed physics ensemble, and multi-model ensemble. The results show that the similarity in spread between the mixed-physics and multi-model ensembles raises the possibility that a mixed-physics ensemble approach could be considered as a proxy for a multi-model ensemble. The mixed-physics ensemble performed well in terms of equitable threat score, especially for higher precipitation amounts.