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Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve m

Zhe Xiong,1,2 Ying Fang,1,2 Shuangshuang Lu,1 Qiuyue Sun,1,3 Yuhui Sun,1,3 Pengcheng Yang,4,* Jin Huang1,* 1Department of Gastroenterology, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, People’s Republic of China; 2Graduate School of Dalian Medical University, Dalian, Liaoning Province, China; 3Graduate School of Nanjing Medical University, Nanjing, Jiangsu Province, People’s Republic of China; 4Department of Gastroenterology, Hengshanqiao People’s Hospital, Changzhou, Jiangsu Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Pengcheng Yang; Jin Huang, Email 496808936@qq.com; hj042153@hotmail.comBackground: Ulcerative colitis (UC) is a nonspecific inflammatory disease confined to the intestinal mucosa and submucosa, and its prevalence significantly increases each year. Disulfidptosis is a recently discovered new form of cell death that has been suggested to be involved in multiple diseases. The aim of this study was to explore the relevance of disulfidptosis in UC.Methods: First, the UC datasets were downloaded from the Gene Expression Omnibus (GEO) database, and UC samples were typed based on upregulated disulfidptosis-related genes (DRGs). Then, weighted gene co-expression network analysis (WGCNA) was performed on the datasets and molecular subtypes of UC, respectively, to obtain candidate signature genes. After validation of the validation set and qRT-PCR, we constructed a nomogram model by signature genes to predict the risk of UC. Finally, single-cell sequencing analysis was used to study the heterogeneity of UC and to demonstrate the expression of DRGs and signature genes at the single-cell level.Results: A total of 7 DRGs were significantly upregulated in the expression profiles of UC, and 180 UC samples were divided into two subtypes based on these DRGs. Five candidate signature genes were obtained by intersecting t

Jingying Wang,1,2 Ying Fang,1,2 Tong Chen,1,2 Zhimin Xin,1,2 Yumei Wu,3,4,* Xiaokui Yang1,2,* 1Department of Human Reproductive Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Human Reproductive Medicine, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China; 3Department of Gynecological Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Department of Gynecological Oncology, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaokui Yang, Department of Human Reproductive Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, 251 Yaojiayuan Road, Beijing, 100026, People’s Republic of China, Tel +86 522 766 15, Email yangxiaokui@ccmu.edu.cn Yumei Wu, Department of Gynecological Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, 251 Yaojiayuan Road, Beijing, 100026, People’s Republic of China, Tel +86 522 766 53, Email wym597118@ccmu.edu.cnAbstract: Preserving fertility is a vital concern for young women diagnosed with endometrial carcinoma. The clinical management of such patients is often disappointing. It is rare to have two consecutive successful pregnancies. We present a child-bearing-age woman who underwent fertility preservation therapy due to endometrial carcinoma. Following fertility preservation therapy, she underwent in vitro fertilization and embryo transfer. After receiving her first fresh embryo transfer, she successfully conceived and gave birth to a healthy child. Two years after the first embryo transfer and regular follow-up, she had another frozen embryo transfer of two cleavage embryos and successfully gave birth to another healthy baby. After the delivery of her second child, she

Spiking neural networks (SNNs) have garnered interest due to their energy efficiency and superior effectiveness on neuromorphic chips compared with traditional artificial neural networks (ANNs). One of the mainstream approaches to implementing deep SNNs is the ANN-SNN conversion, which integrates the efficient training strategy of ANNs with the energy-saving potential and fast inference capability of SNNs. However, under extreme low-latency conditions, the existing conversion theory suggests that the problem of misrepresentation of residual membrane potentials in SNNs, i.e., the inability of IF neurons with a reset-by-subtraction mechanism to respond to residual membrane potentials beyond the range from resting potential to threshold, leads to a performance gap in the converted SNNs compared to the original ANNs. This severely limits the possibility of practical application of SNNs on delay-sensitive edge devices. Existing conversion methods addressing this problem usually involve modifying the state of the conversion spiking neurons. However, these methods do not consider their adaptability and compatibility with neuromorphic chips. We propose a new approach based on explicit modeling of residual errors as additive noise. The noise is incorporated into the activation function of the source ANN, which effectively reduces the residual error. Our experiments on the CIFAR10/100 dataset verify that our approach exceeds the prevailing ANN-SNN conversion methods and directly trained SNNs concerning accuracy and the required time steps. Overall, our method provides new ideas for improving SNN performance under ultra-low-latency conditions and is expected to promote practical neuromorphic hardware applications for further development.

Trace gases have demonstrated their strength for oceanographic studies, with applications ranging from the tracking of glacial meltwater plumes to estimates of the abyssal overturning duration. Yet measurements of such passive tracers in the ice-covered Arctic Ocean are sparse. We here present a unique data set of trace gases collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, during which R/V Polarstern drifted along with the Arctic sea ice from the Laptev Sea to Fram Strait, from October 2019 to September 2020. During the expedition, trace gases from anthropogenic origin (chlorofluorocarbon 12 (CFC-12), sulfur hexafluoride (SF6), and tritium) along with noble gases (helium and neon) and their isotopes were collected at a weekly or higher temporal resolution throughout the entire water column (and occasionally in the snow) from the ship and from the ice. We describe the sampling procedures along with their challenges, the analysis methods, and the data sets, and we present case studies in the central Arctic Ocean and Fram Strait to illustrate possible usage for the data along with their robustness. Combined with simultaneous hydrographic measurements, these trace gas data sets can be used for process studies and water mass tracing throughout the Arctic in subsequent analyses. The two data sets can be downloaded via PANGAEA: https://doi.org/10.1594/PANGAEA.961729 (Huhn et al., 2023a) and https://doi.org/10.1594/PANGAEA.961738 (Huhn et al., 2023b).

Zhe Xiong,1,2 Ying Fang,1,2 Shuangshuang Lu,1 Qiuyue Sun,1,3 Jin Huang1 1Department of Gastroenterology, the Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, People’s Republic of China; 2Graduate School of Dalian Medical University, Dalian, Liaoning Province, People’s Republic of China; 3Graduate School of Nanjing Medical University, Nanjing, Jiangsu Province, People’s Republic of ChinaCorrespondence: Jin Huang, Email hj042153@hotmail.comBackground: Inflammatory bowel disease (IBD) and periodontitis (PD) are correlated, although the pathogenic mechanism behind their correlation has not been clarified. This study aims to explore the common signature genes and potential therapeutic targets of IBD and PD using transcriptomic analysis.Methods: The GEO database was used to download datasets of IBD and PD, and differential expression analysis was used to identify DEGs. We then conducted GO and KEGG enrichment analyses of the shared genes. Next, we applied 4 machine learning (ML) algorithms (GLM, RF, GBM, and SVM) to select the best prediction model for diagnosing the disease and obtained the hub genes of IBD and PD. The diagnostic value of the signature genes was verified by a validation set and qRT‒PCR experiments. Subsequently, immune cell infiltration in IBD samples and PD samples was analyzed by ssGSEA. Finally, we investigated and validated the response of hub genes to infliximab therapy.Results: We identified 43 upregulated genes as shared genes by intersecting the DEGs of IBD and PD. Functional enrichment analysis suggested that the shared genes were closely associated with immunity and inflammation. The ML algorithm and qRT‒PCR results indicated that IGKC and COL4A1 were the hub genes with the most diagnostic value for IBD and PD. Subsequently, through immune infiltration analysis, CD4 T cells, NK cells and neutrophils were identified to play crucial roles in the pathogenesis of IBD and PD. Finally

Cancer stem cells (CSCs) have emerged as key contributors to tumor initiation, growth, and metastasis. In addition, CSCs play a significant role in inducing immune evasion, thereby compromising the effectiveness of cancer treatments. The reciprocal communication between CSCs and the tumor microenvironment (TME) is observed, with the TME providing a supportive niche for CSC survival and self-renewal, while CSCs, in turn, influence the polarization and persistence of the TME, promoting an immunosuppressive state. Consequently, these interactions hinder the efficacy of current cancer therapies, necessitating the exploration of novel therapeutic approaches to modulate the TME and target CSCs. In this review, we highlight the intricate strategies employed by CSCs to evade immune surveillance and develop resistance to therapies. Furthermore, we examine the dynamic interplay between CSCs and the TME, shedding light on how this interaction impacts cancer progression. Moreover, we provide an overview of advanced therapeutic strategies that specifically target CSCs and the TME, which hold promise for future clinical and translational studies in cancer treatment.

Ovarian cancer (OC) is highly lethal due to late detection and frequent recurrence. Initial treatments, comprising surgery and chemotherapy, lead to disease remission but are invariably associated with subsequent relapse. The identification of novel therapies and an improved understanding of the molecular and cellular characteristics of OC are urgently needed. Here, we conducted a comprehensive analysis of primary tumor cells and their microenvironment from 16 chemonaive and 10 recurrent OC patient samples. Profiling OC tumor biomarkers allowed for the identification of potential molecular targets for developing immunotherapies, while profiling the microenvironment yielded insights into its cellular composition and property changes between chemonaive and recurrent samples. Notably, we identified CD1d as a biomarker of the OC microenvironment and demonstrated its targeting by invariant natural killer T (iNKT) cells. Overall, our study presents a comprehensive immuno-profiling of OC tumor and microenvironment during disease progression, guiding the development of immunotherapies for OC treatment, especially for recurrent disease.

Ying Ni Lin,1,2,* Li Na Zhou,1,2,* Zhuo Ran Liu,3,* Yi Wang,1,2 Shi Qi Li,1,2 Fang Ying Lu,1,2 Liu Zhang,1,2 Qing Yun Li1,2 1Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 20025, People’s Republic of China; 2Institute of Respiratory Disease, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of China; 3Department of Thyroid and Vascular Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qing Yun Li, Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of China, Tel +81-21-64370045, Email liqingyun68@hotmail.comPurpose: Sleep disturbance has been implicated in poor prognosis of coronavirus disease 2019 (COVID-19), but less is known about the influence of short sleep duration on COVID-19 outcomes. We aim to investigate whether short sleep duration is associated with prolonged virus shedding duration in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron-infected patients.Patients and Methods: A total of 270 patients with a laboratory confirmed COVID-19 diagnosis during SARS-CoV-2 Omicron-predominant period were recruited. Self-reported sleep duration of the patients was collected. The two-way analysis of variance (ANOVA) was used to determine the interactions between sleep duration and variables, and multivariate logistic regression analysis was used to analyze the effect of independent variables on longer virus shedding duration.Results: The two-way ANOVA revealed a significant sleep duration × snoring interaction effect for virus shedding duration, and a sleep duration × sex interaction effect for virus shedding duration. Multivariate logistic regression mod

This paper works on non-autoregressive automatic speech recognition. A unimodal aggregation (UMA) is proposed to segment and integrate the feature frames that belong to the same text token, and thus to learn better feature representations for text tokens. The frame-wise features and weights are both derived from an encoder. Then, the feature frames with unimodal weights are integrated and further processed by a decoder. Connectionist temporal classification (CTC) loss is applied for training. Compared to the regular CTC, the proposed method learns better feature representations and shortens the sequence length, resulting in lower recognition error and computational complexity. Experiments on three Mandarin datasets show that UMA demonstrates superior or comparable performance to other advanced non-autoregressive methods, such as self-conditioned CTC. Moreover, by integrating self-conditioned CTC into the proposed framework, the performance can be further noticeably improved., Comment: Accepted by ICASSP 2024

Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present alongdrift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling cap

The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rabe, B., Heuze, C., Regnery, J., Aksenov, Y., Allerholt, J., Athanase, M., Bai, Y., Basque, C., Bauch, D., Baumann, T. M., Chen, D., Cole, S. T., Craw, L., Davies, A., Damm, E., Dethloff, K., Divine, D., Doglioni, F., Ebert, F., Fang, Y-C., Fer, I., Fong, A. A., Gradinger, R., Granskog, M. A., Graupner, R., Haas, C., He, H., He, Y., Hoppmann, M., Janout, M., Kadko, D., Kanzow, T., Karam, S., Kawaguchi, Y., Koenig, Z., Kong, B., Krishfield, R. A., Krumpen, T., Kuhlmey, D., Kuznetsov, I., Lan, M., Laukert, G., Lei, R., Li, T., Torres-Valdés, S., Lin, L,. Lin, L., Liu, H., Liu, N., Loose, B., Ma, X., MacKay, R., Mallet, M., Mallett, R. D. C., Maslowski, W., Mertens, C., Mohrholz, V., Muilwijk, M., Nicolaus, M., O’Brien, J. K., Perovich, D., Ren, J., Rex, M., Ribeiro, N., Rinke, A., Schaffer, J., Schuffenhauer, I., Schulz, K., Shupe, M. D., Shaw, W., Sokolov, V., Sommerfeld, A., Spreen, G., Stanton, T., Stephens, M., Su, J., Sukhikh, N., Sundfjord, A., Thomisch, K., Tippenhauer, S., Toole, J. M., Vredenborg, M., Walter, M., Wang, H., Wang, L., Wang, Y., Wendisch, M., Zhao, J., Zhou, M., & Zhu, J. Overview of the MOSAiC expedition: physical oceanography. Elementa: Science of the Anthropocene, 10(1), (2022): 1, https://doi.org/10.1525/elementa.2021.00062., Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling ca, The following projects and funding agencies contributed to this work: Why is the deep Arctic Ocean Warming is funded by the Swedish Research Council, project number 2018-03859, and berth fees for this project were covered by the Swedish Polar Research Secretariat; The Changing Arctic Ocean (CAO) program, jointly funded by the United Kingdom Research and Innovation (UKRI) Natural Environment Research Council (NERC) and the Bundesministerium für Bildung und Forschung (BMBF), in particular, the CAO projects Advective Pathways of nutrients and key Ecological substances in the ARctic (APEAR) grants NE/R012865/1, NE/R012865/2, and #03V01461, and the project Primary productivity driven by Escalating Arctic NUTrient fluxeS grant #03F0804A; The Research Council of Norway (AROMA, grant no 294396; HAVOC, grant no 280292; and CAATEX, grant no 280531); Collaborative Research: Thermodynamics and Dynamic Drivers of the Arctic Sea Ice Mass Budget at Multidisciplinary drifting Observatory for the Study of the Arctic Climate; National Science Foundation (NSF) projects 1723400, Stanton; OPP-1724551, Shupe; The Helmholtz society strategic investment Frontiers in Arctic Marine monitoring (FRAM); Deutsche Forschungsgemeinschaft (German Research Foundation) through the Transregional Collaborative Research Centre TRR 172 “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3” (grant 268020496); The Japan Society for the Promotion of Science (grant numbers JP18H03745, JP18KK0292, and JP17KK0083) and the COLE grant of U. Tokyo; National Key Research and Development Plan Sub-Project of Ministry of Science and Technology of China (2016YFA0601804), “Simulation, Prediction and Regional Climate Response of Global Warming Hiatus”, 2016/07-2021/06; National Science Foundation grant number OPP-1756100 which funded two of the Ice-Tethered Profilers and all the Ice-Tethered Profiler deployments; Chinese Polar Environmental Comprehensive Investigation a

The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rabe, B., Heuze, C., Regnery, J., Aksenov, Y., Allerholt, J., Athanase, M., Bai, Y., Basque, C., Bauch, D., Baumann, T. M., Chen, D., Cole, S. T., Craw, L., Davies, A., Damm, E., Dethloff, K., Divine, D., Doglioni, F., Ebert, F., Fang, Y-C., Fer, I., Fong, A. A., Gradinger, R., Granskog, M. A., Graupner, R., Haas, C., He, H., He, Y., Hoppmann, M., Janout, M., Kadko, D., Kanzow, T., Karam, S., Kawaguchi, Y., Koenig, Z., Kong, B., Krishfield, R. A., Krumpen, T., Kuhlmey, D., Kuznetsov, I., Lan, M., Laukert, G., Lei, R., Li, T., Torres-Valdés, S., Lin, L,. Lin, L., Liu, H., Liu, N., Loose, B., Ma, X., MacKay, R., Mallet, M., Mallett, R. D. C., Maslowski, W., Mertens, C., Mohrholz, V., Muilwijk, M., Nicolaus, M., O’Brien, J. K., Perovich, D., Ren, J., Rex, M., Ribeiro, N., Rinke, A., Schaffer, J., Schuffenhauer, I., Schulz, K., Shupe, M. D., Shaw, W., Sokolov, V., Sommerfeld, A., Spreen, G., Stanton, T., Stephens, M., Su, J., Sukhikh, N., Sundfjord, A., Thomisch, K., Tippenhauer, S., Toole, J. M., Vredenborg, M., Walter, M., Wang, H., Wang, L., Wang, Y., Wendisch, M., Zhao, J., Zhou, M., & Zhu, J. Overview of the MOSAiC expedition: physical oceanography. Elementa: Science of the Anthropocene, 10(1), (2022): 1, https://doi.org/10.1525/elementa.2021.00062., Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling ca, The following projects and funding agencies contributed to this work: Why is the deep Arctic Ocean Warming is funded by the Swedish Research Council, project number 2018-03859, and berth fees for this project were covered by the Swedish Polar Research Secretariat; The Changing Arctic Ocean (CAO) program, jointly funded by the United Kingdom Research and Innovation (UKRI) Natural Environment Research Council (NERC) and the Bundesministerium für Bildung und Forschung (BMBF), in particular, the CAO projects Advective Pathways of nutrients and key Ecological substances in the ARctic (APEAR) grants NE/R012865/1, NE/R012865/2, and #03V01461, and the project Primary productivity driven by Escalating Arctic NUTrient fluxeS grant #03F0804A; The Research Council of Norway (AROMA, grant no 294396; HAVOC, grant no 280292; and CAATEX, grant no 280531); Collaborative Research: Thermodynamics and Dynamic Drivers of the Arctic Sea Ice Mass Budget at Multidisciplinary drifting Observatory for the Study of the Arctic Climate; National Science Foundation (NSF) projects 1723400, Stanton; OPP-1724551, Shupe; The Helmholtz society strategic investment Frontiers in Arctic Marine monitoring (FRAM); Deutsche Forschungsgemeinschaft (German Research Foundation) through the Transregional Collaborative Research Centre TRR 172 “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3” (grant 268020496); The Japan Society for the Promotion of Science (grant numbers JP18H03745, JP18KK0292, and JP17KK0083) and the COLE grant of U. Tokyo; National Key Research and Development Plan Sub-Project of Ministry of Science and Technology of China (2016YFA0601804), “Simulation, Prediction and Regional Climate Response of Global Warming Hiatus”, 2016/07-2021/06; National Science Foundation grant number OPP-1756100 which funded two of the Ice-Tethered Profilers and all the Ice-Tethered Profiler deployments; Chinese Polar Environmental Comprehensive Investigation a

The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rabe, B., Heuze, C., Regnery, J., Aksenov, Y., Allerholt, J., Athanase, M., Bai, Y., Basque, C., Bauch, D., Baumann, T. M., Chen, D., Cole, S. T., Craw, L., Davies, A., Damm, E., Dethloff, K., Divine, D., Doglioni, F., Ebert, F., Fang, Y-C., Fer, I., Fong, A. A., Gradinger, R., Granskog, M. A., Graupner, R., Haas, C., He, H., He, Y., Hoppmann, M., Janout, M., Kadko, D., Kanzow, T., Karam, S., Kawaguchi, Y., Koenig, Z., Kong, B., Krishfield, R. A., Krumpen, T., Kuhlmey, D., Kuznetsov, I., Lan, M., Laukert, G., Lei, R., Li, T., Torres-Valdés, S., Lin, L,. Lin, L., Liu, H., Liu, N., Loose, B., Ma, X., MacKay, R., Mallet, M., Mallett, R. D. C., Maslowski, W., Mertens, C., Mohrholz, V., Muilwijk, M., Nicolaus, M., O’Brien, J. K., Perovich, D., Ren, J., Rex, M., Ribeiro, N., Rinke, A., Schaffer, J., Schuffenhauer, I., Schulz, K., Shupe, M. D., Shaw, W., Sokolov, V., Sommerfeld, A., Spreen, G., Stanton, T., Stephens, M., Su, J., Sukhikh, N., Sundfjord, A., Thomisch, K., Tippenhauer, S., Toole, J. M., Vredenborg, M., Walter, M., Wang, H., Wang, L., Wang, Y., Wendisch, M., Zhao, J., Zhou, M., & Zhu, J. Overview of the MOSAiC expedition: physical oceanography. Elementa: Science of the Anthropocene, 10(1), (2022): 1, https://doi.org/10.1525/elementa.2021.00062., Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling ca, The following projects and funding agencies contributed to this work: Why is the deep Arctic Ocean Warming is funded by the Swedish Research Council, project number 2018-03859, and berth fees for this project were covered by the Swedish Polar Research Secretariat; The Changing Arctic Ocean (CAO) program, jointly funded by the United Kingdom Research and Innovation (UKRI) Natural Environment Research Council (NERC) and the Bundesministerium für Bildung und Forschung (BMBF), in particular, the CAO projects Advective Pathways of nutrients and key Ecological substances in the ARctic (APEAR) grants NE/R012865/1, NE/R012865/2, and #03V01461, and the project Primary productivity driven by Escalating Arctic NUTrient fluxeS grant #03F0804A; The Research Council of Norway (AROMA, grant no 294396; HAVOC, grant no 280292; and CAATEX, grant no 280531); Collaborative Research: Thermodynamics and Dynamic Drivers of the Arctic Sea Ice Mass Budget at Multidisciplinary drifting Observatory for the Study of the Arctic Climate; National Science Foundation (NSF) projects 1723400, Stanton; OPP-1724551, Shupe; The Helmholtz society strategic investment Frontiers in Arctic Marine monitoring (FRAM); Deutsche Forschungsgemeinschaft (German Research Foundation) through the Transregional Collaborative Research Centre TRR 172 “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3” (grant 268020496); The Japan Society for the Promotion of Science (grant numbers JP18H03745, JP18KK0292, and JP17KK0083) and the COLE grant of U. Tokyo; National Key Research and Development Plan Sub-Project of Ministry of Science and Technology of China (2016YFA0601804), “Simulation, Prediction and Regional Climate Response of Global Warming Hiatus”, 2016/07-2021/06; National Science Foundation grant number OPP-1756100 which funded two of the Ice-Tethered Profilers and all the Ice-Tethered Profiler deployments; Chinese Polar Environmental Comprehensive Investigation a

Peripheral nerve regeneration with large defects needs innovative design of nerve guidance conduits (NGCs) which possess anisotropic guidance, electrical induction and right mechanical properties in one. Herein, we present, for the first time, facile fabrication and efficient neural differentiation guidance of anisotropic, conductive, self-snapping, hydrogel-based NGCs. The hydrogels were fabricated via crosslinking of graphitic carbon nitride (g-C3N4) upon exposure with blue light, incorporated with graphene oxide (GO). Incorporation of GO and in situ reduction greatly enhanced surface charges, while decayed light penetration endowed the hydrogel with an intriguing self-snapping feature by the virtue of a crosslinking gradient. The hydrogels were in the optimal mechanical stiffness range for peripheral nerve regeneration and supported normal viability and proliferation of neural cells. The PC12 cells differentiated on the electroactive g-C3N4 H/rGO3 (3 mg/mL GO loading) hydrogel presented 47% longer neurite length than that of the pristine g-C3N4 H hydrogel. Furthermore, the NGC with aligned microchannels was successfully fabricated using sacrificial melt electrowriting (MEW) moulding, the anisotropic microchannels of the 10 mu m width showed optimal neurite guidance. Such anisotropic, electroactive, self-snapping NGCs may possess great potential for repairing peripheral nerve injuries., QC 20221102

One stream of reinforcement learning research is exploring biologically plausible models and algorithms to simulate biological intelligence and fit neuromorphic hardware. Among them, reward-modulated spike-timing-dependent plasticity (R-STDP) is a recent branch with good potential in energy efficiency. However, current R-STDP methods rely on heuristic designs of local learning rules, thus requiring task-specific expert knowledge. In this paper, we consider a spiking recurrent winner-take-all network, and propose a new R-STDP method, spiking variational policy gradient (SVPG), whose local learning rules are derived from the global policy gradient and thus eliminate the need for heuristic designs. In experiments of MNIST classification and Gym InvertedPendulum, our SVPG achieves good training performance, and also presents better robustness to various kinds of noises than conventional methods., Comment: Accepted to BMVC 2022

Hadoop Distributed File System (HDFS) is widely used in massive data storage. Because of the disadvantage of the multi-copy strategy, the hardware expansion of HDFS cannot keep up with the continuous volume of big data. Now, the traditional data replication strategy has been gradually replaced by Erasure Code due to its smaller redundancy rate and storage overhead. However, compared with replicas, Erasure Code needs to read a certain amount of data blocks during the process of data recovery, resulting in a large amount of overhead for I/O and network. Based on the Reed-Solomon (RS) algorithm, we propose a novel Completely Local Repairable Code (CLRC) algorithm. By grouping RS coded blocks and generating local check blocks, CLRC algorithm can optimize the locality of the RS algorithm, which can reduce the cost of data recovery. Evaluations show that the CLRC algorithm can reduce the bandwidth and I/O consumption during the process of data recovery when a single block is damaged. What's more, the cost of decoding time is only 59% of the RS algorithm.

The booming haptic data significantly improves the users'immersion during multimedia interaction. As a result, the study of Haptic,Audio-Visual Environment(HAVE)has attracted attentions of multimedia community. To realize such a system, a challenging tack is the synchronization of multiple sensorial signals that is critical to user experience. Despite of audio-visual synchronization efforts, there is still a lack of haptic-aware multimedia synchronization model. In this work, we propose a timestamp-independent synchronization for haptic-visual signal transmission. First, we exploit the sequential correlations during delivery and playback of a haptic-visual communication system. Second, we develop a key sample extraction of haptic signals based on the force feedback characteristics, and a key frame extraction of visual signals based on deep object detection. Third, we combine the key samples and frames to synchronize the corresponding haptic-visual signals. Without timestamps in signal flow, the proposed method is still effective and more robust to complicated network conditions. Subjective evaluation also shows a significant improvement of user experience with the proposed method.

Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present alongdrift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling cap

Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling ca

The dynamics of the boundary layer of the ocean significantly affect the interaction between ocean and atmosphere and, as a result, global climate. The sub-ice boundary layer of the ocean and its dynamics have not been thoroughly studied because of the extremely difficult conditions for observation, in particular during winter. Current understanding of spatial-temporal variability of (sub)mesoscales of the upper Arctic Ocean is extremely limited. At the same time, one of the most important features of the upper ocean layers are the small-scale processes that influence and possibly determine the vertical and horizontal transport of heat, salt, and biologically relevant substances. As a consequence, mathematical models, in particular climate models, experience serious difficulties in parameterization of processes not resolved by the models because of the lack sufficient knowledge to detail the spatial variability at the (sub-)mesoscale. To a better characterization and understanding of (sub)mesoscale dynamics and its role in vertical transport of energy and mass we apply a 3D regional ocean model FESOM-C. The observed vertical hydrological structure and a corresponding reconstructed horizontal temperature and salinity fields were imposed as a part of the forcing for the numerical model. These fields and information about the vertical hydrological structure were utilized by the model as initial conditions and for constraining (nudging) during the spin-up period. After the initial spin-up period, once the model had adjusted to our initial conditions, we performed several free runs. We expect that our 3D numerical studies of eddy properties will contribute to a better characterisation and understanding of (sub)mesoscale dynamics in the Arctic Ocean and its role in the vertical transport of energy and mass.

Standard Model $ZZ$ production is an important background for many searches at the LHC, especially in final states with missing transverse momentum. In this article, boson substitution is applied to estimate $ZZ \rightarrow \ell^+ \ell^- \nu \bar{\nu}$ yields from $Z\gamma \rightarrow \ell^+ \ell^- \gamma$ events. The cross-section ratio of the two processes and its uncertainties are evaluated at NNLO with the MATRIX generator as a function of the transverse momentum of the substituted boson. Uncertainties due to higher-order QCD corrections, parton distribution functions, photon isolation criteria, and electroweak corrections are evaluated. They depend strongly on the applied event selections and the considered transverse momentum range. For minimal selections, their size is 3$-$4%, dominated by QCD-related uncertainties for transverse momenta below 500 GeV and by uncertainties due to the factorization of QCD and electroweak corrections at higher transverse momenta., Comment: 32 pages, 18 figures

The dynamics of the boundary layer of the ocean significantly affect the interaction between ocean and atmosphere and, as a result, global climate. The sub-ice boundary layer of the ocean and its dynamics have not been thoroughly studied because of the extremely difficult conditions for observation, in particular during winter. Current understanding of spatial-temporal variability of (sub)mesoscales of the upper Arctic Ocean is extremely limited. At the same time, one of the most important features of the upper ocean layers are the small-scale processes that influence and possibly determine the vertical and horizontal transport of heat, salt, and biologically relevant substances. As a consequence, mathematical models, in particular climate models, experience serious difficulties in parameterization of processes not resolved by the models because of the lack sufficient knowledge to detail the spatial variability at the (sub-)mesoscale. To a better characterization and understanding of (sub)mesoscale dynamics and its role in vertical transport of energy and mass we apply a 3D regional ocean model FESOM-C. The observed vertical hydrological structure and a corresponding reconstructed horizontal temperature and salinity fields were imposed as a part of the forcing for the numerical model. These fields and information about the vertical hydrological structure were utilized by the model as initial conditions and for constraining (nudging) during the spin-up period. After the initial spin-up period, once the model had adjusted to our initial conditions, we performed several free runs. We expect that our 3D numerical studies of eddy properties will contribute to a better characterisation and understanding of (sub)mesoscale dynamics in the Arctic Ocean and its role in the vertical transport of energy and mass.

Although increasing hidden layers can improve the ability of a neural network in modeling complex nonlinear relationships, deep layers may result in degradation of accuracy due to the problem of vanishing gradient. Accuracy degradation limits the applications of deep neural networks to predict continuous variables with a small sample size and/or weak or little invariance to translations. Inspired by residual convolutional neural network in computer vision, we developed an encoder-decoder full residual deep network to robustly regress and predict complex spatiotemporal variables. We embedded full shortcuts from each encoding layer to its corresponding decoding layer in a systematic encoder-decoder architecture for efficient residual mapping and error signal propagation. We demonstrated, theoretically and experimentally, that the proposed network structure with full residual connections can successfully boost the backpropagation of signals and improve learning outcomes. This novel method has been extensively evaluated and compared with four commonly used methods (i.e., plain neural network, cascaded residual autoencoder, generalized additive model, and XGBoost) across different testing cases for continuous variable predictions. For model evaluation, we focused on spatiotemporal imputation of satellite aerosol optical depth with massive nonrandomness missingness and spatiotemporal estimation of atmospheric fine particulate matter [Formula: see text] (PM2.5). Compared with the other approaches, our method achieved the state-of-the-art accuracy, had less bias in predicting extreme values, and generated more realistic spatial surfaces. This encoder-decoder full residual deep network can be an efficient and powerful tool in a variety of applications that involve complex nonlinear relationships of continuous variables, varying sample sizes, and spatiotemporal data with weak or little invariance to translation.

Jia-Xin Shen,1 Ze-Hao Zhuang,1 Qiao-Xian Zhang,2 Jiao-Feng Huang,3 Gong-Ping Chen,4 Ying-Ying Fang,5 Ai-Guo Cheng6 1Endoscopy Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, People’s Republic of China; 2Nursing Department, First Affiliated Hospital of Fujian Medical University, Fuzhou, People’s Republic of China; 3Department of Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, People’s Republic of China; 4Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital of Fujian Medical University, Fuzhou, People’s Republic of China; 5Department of Tuberculosis, Third People’s Hospital of Yichang, Yichang, People’s Republic of China; 6Department of Critical Care, Third People’s Hospital of Yichang, Yichang, People’s Republic of ChinaCorrespondence: Ze-Hao ZhuangEndoscopy Center, First Affiliated Hospital of Fujian Medical University, 20, Chazhong Road, Taijiang District, Fuzhou, 350005, Fujian Province, People’s Republic of ChinaTel +86-131-7803-2412Fax +89-59187981370Email zhuang203@yeah.netPurpose: COVID-19 is a new infectious disease with global spread. The aim of the present study was to explore possible risk factors and evaluate prognosis in COVID-19 with liver injury.Methods: A retrospective study was conducted on 356 COVID-19 patients in the Third People’s Hospital of Yichang, Hubei, China. Clinical characteristics and laboratory tests between patients with and without liver injury were compared, while risk factors of COVID-19–related liver injury were analyzed. Univariate and multivariate Cox regression analyses were conducted to identify risk factors of in-hospital death.Results: Of the patients with liver injury, severe and critical types of COVID-19 comprised 12.43% and 14.69%, respectively, higher than in patients without liver injury (both P< 0.05). CRP and male sex

Standard Model $ZZ$ production is an important background for many searches at the LHC, especially in final states with missing transverse momentum. In this article, boson substitution is applied to estimate $ZZ \rightarrow \ell^+ \ell^- \nu \bar{\nu}$ yields from $Z\gamma \rightarrow \ell^+ \ell^- \gamma$ events. The cross-section ratio of the two processes and its uncertainties are evaluated at NNLO with the MATRIX generator as a function of the transverse momentum of the substituted boson. Uncertainties due to higher-order QCD corrections, parton distribution functions, photon isolation criteria, and electroweak corrections are evaluated. They depend strongly on the applied event selections and the considered transverse momentum range. For minimal selections, their size is 3$-$4%, dominated by QCD-related uncertainties for transverse momenta below 500 GeV and by uncertainties due to the factorization of QCD and electroweak corrections at higher transverse momenta., Comment: 32 pages, 18 figures

Fang-Ying Lu,1,2,* Rong Chen,1,2,* Ning Li,1,2 Xian-Wen Sun,1,2 Min Zhou,1,2 Qing-Yun Li,1,2 Yi Guo1,2 1Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi GuoDepartment of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Rui Jin 2nd Road, Shanghai, 200025, People’s Republic of ChinaFax +86 21 6467 4301Email guoyi621@qq.comQing-Yun LiDepartment of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Rui Jin 2nd Road, Shanghai, 200025, People’s Republic of ChinaFax +86 21 6467 4301Email liqingyun68@hotmail.comPurpose: Frequent exacerbators are a specific phenotype of chronic obstructive pulmonary disease (COPD), whose clinical characteristics and prognostic biomarkers during severe acute exacerbation (AECOPD) have not yet been fully elucidated. The aim of this study was to investigate the clinical features of severe AECOPD in frequent exacerbators and explore the predictive value of the neutrophil-to-lymphocyte ratio (NLR) for outcome in this phenotype during severe exacerbation.Patients and Methods: A total of 604 patients with severe AECOPD were retrospectively included in the study. Subjects were defined as frequent exacerbators if they experienced two or more exacerbations in the past year. Clinical characteristics and worse outcome (ICU admission, or invasive ventilation, or in-hospital mortality) during severe AECOPD were compared between frequent exacerbators and non-frequent ones. Furthermore, the relationship between NLR and worse outcome in frequent exacerbators was analyzed using logistic regression

St. Louis encephalitis virus (SLEV) is a flavivirus that circulates in an enzootic cycle between birds and mosquitoes and can also infect humans to cause febrile disease and sometimes encephalitis. Although SLEV is endemic to the United States, no activity was detected in California during the years 2004 through 2014, despite continuous surveillance in mosquitoes and sentinel chickens. In 2015, SLEV-positive mosquito pools were detected in Maricopa County, Arizona, concurrent with an outbreak of human SLEV disease. SLEV-positive mosquito pools were also detected in southeastern California and Nevada in summer 2015. From 2016 to 2018, SLEV was detected in mosquito pools throughout southern and central California, Oregon, Idaho, and Texas. To understand genetic relatedness and geographic dispersal of SLEV in the western United States since 2015, we sequenced four historical genomes (3 from California and 1 from Louisiana) and 26 contemporary SLEV genomes from mosquito pools from locations across the western US. Bayesian phylogeographic approaches were then applied to map the recent spread of SLEV. Three routes of SLEV dispersal in the western United States were identified: Arizona to southern California, Arizona to Central California, and Arizona to all locations east of the Sierra Nevada mountains. Given the topography of the Western United States, these routes may have been limited by mountain ranges that influence the movement of avian reservoirs and mosquito vectors, which probably represents the primary mechanism of SLEV dispersal. Our analysis detected repeated SLEV introductions from Arizona into southern California and limited evidence of year-to-year persistence of genomes of the same ancestry. By contrast, genetic tracing suggests that all SLEV activity since 2015 in central California is the result of a single persistent SLEV introduction. The identification of natural barriers that influence SLEV dispersal enhances our understanding of arbovirus ecology in

The California Arbovirus Surveillance Program was initiated over 50 years ago to track endemic encephalitides and was enhanced in 2000 to include West Nile virus (WNV) infections in humans, mosquitoes, sentinel chickens, dead birds and horses. This comprehensive statewide program is a function of strong partnerships among the California Department of Public Health (CDPH), the University of California, and local vector control and public health agencies. This manuscript summarizes WNV surveillance data in California since WNV was first detected in 2003 in southern California. From 2003 through 2018, 6,909 human cases of WNV disease, inclusive of 326 deaths, were reported to CDPH, as well as 730 asymptomatic WNV infections identified during screening of blood and organ donors. Of these, 4,073 (59.0%) were reported as West Nile neuroinvasive disease. California's WNV disease burden comprised 15% of all cases that were reported to the U.S. Centers for Disease Control and Prevention during this time, more than any other state. Additionally, 1,299 equine WNV cases were identified, along with detections of WNV in 23,322 dead birds, 31,695 mosquito pools, and 7,340 sentinel chickens. Annual enzootic detection of WNV typically preceded detection in humans and prompted enhanced intervention to reduce the risk of WNV transmission. Peak WNV activity occurred from July through October in the Central Valley and southern California. Less than five percent of WNV activity occurred in other regions of the state or outside of this time. WNV continues to be a major threat to public and wild avian health in California, particularly in southern California and the Central Valley during summer and early fall months. Local and state public health partners must continue statewide human and mosquito surveillance and facilitate effective mosquito control and bite prevention measures.

Platelet ice is a unique type of sea ice; its occurrence has numerous implications for physical and ecological systems. Mostly, platelet ice has been reported from the Antarctic where ice crystals grow in supercooled ice shelf water and accumulate below sea ice to form sub-ice platelet layers. In the Arctic however, platelet ice formation has only been sparsely documented so far. The associated formation processes and morphology differ significantly from the Antarctic, but currently remain poorly understood. Here, we present the first comprehensive, repeat in-situ observations of a decimeter thick sub-ice platelet layer under drifting pack ice of the Central Arctic in winter. Observations carried out with a remotely operated underwater vehicle (ROV) during the midwinter leg of the MOSAiC drift expedition provided clear evidence of the growth of platelet layers from supercooled water present in the ocean mixed layer. This process was observed under all ice types present during the surveys. Oceanographic data from autonomous observing platforms leads us to the conclusion that platelet ice formation is a widespread yet overlooked feature of Arctic winter sea ice growth.

The formation of platelet ice is well known to occur under Antarctic sea ice, where subice platelet layers form from supercooled ice shelf water. In the Arctic, however, platelet ice formation has not been extensively observed, and its formation and morphology currently remain enigmatic. Here, we present the first comprehensive, long‐term in situ observations of a decimeter thick subice platelet layer under free‐drifting pack ice of the Central Arctic in winter. Observations carried out with a remotely operated underwater vehicle (ROV) during the midwinter leg of the MOSAiC drift expedition provide clear evidence of the growth of platelet ice layers from supercooled water present in the ocean mixed layer. This platelet formation takes place under all ice types present during the surveys. Oceanographic data from autonomous observing platforms lead us to the conclusion that platelet ice formation is a widespread but yet overlooked feature of Arctic winter sea ice growth.

St. Louis encephalitis virus (SLEV) is a flavivirus that circulates in an enzootic cycle between birds and mosquitoes and can also infect humans to cause febrile disease and sometimes encephalitis. Although SLEV is endemic to the United States, no activity was detected in California during the years 2004 through 2014, despite continuous surveillance in mosquitoes and sentinel chickens. In 2015, SLEV-positive mosquito pools were detected in Maricopa County, Arizona, concurrent with an outbreak of human SLEV disease. SLEV-positive mosquito pools were also detected in southeastern California and Nevada in summer 2015. From 2016 to 2018, SLEV was detected in mosquito pools throughout southern and central California, Oregon, Idaho, and Texas. To understand genetic relatedness and geographic dispersal of SLEV in the western United States since 2015, we sequenced four historical genomes (3 from California and 1 from Louisiana) and 26 contemporary SLEV genomes from mosquito pools from locations across the western US. Bayesian phylogeographic approaches were then applied to map the recent spread of SLEV. Three routes of SLEV dispersal in the western United States were identified: Arizona to southern California, Arizona to Central California, and Arizona to all locations east of the Sierra Nevada mountains. Given the topography of the Western United States, these routes may have been limited by mountain ranges that influence the movement of avian reservoirs and mosquito vectors, which probably represents the primary mechanism of SLEV dispersal. Our analysis detected repeated SLEV introductions from Arizona into southern California and limited evidence of year-to-year persistence of genomes of the same ancestry. By contrast, genetic tracing suggests that all SLEV activity since 2015 in central California is the result of a single persistent SLEV introduction. The identification of natural barriers that influence SLEV dispersal enhances our understanding of arbovirus ecology in

The California Arbovirus Surveillance Program was initiated over 50 years ago to track endemic encephalitides and was enhanced in 2000 to include West Nile virus (WNV) infections in humans, mosquitoes, sentinel chickens, dead birds and horses. This comprehensive statewide program is a function of strong partnerships among the California Department of Public Health (CDPH), the University of California, and local vector control and public health agencies. This manuscript summarizes WNV surveillance data in California since WNV was first detected in 2003 in southern California. From 2003 through 2018, 6,909 human cases of WNV disease, inclusive of 326 deaths, were reported to CDPH, as well as 730 asymptomatic WNV infections identified during screening of blood and organ donors. Of these, 4,073 (59.0%) were reported as West Nile neuroinvasive disease. California's WNV disease burden comprised 15% of all cases that were reported to the U.S. Centers for Disease Control and Prevention during this time, more than any other state. Additionally, 1,299 equine WNV cases were identified, along with detections of WNV in 23,322 dead birds, 31,695 mosquito pools, and 7,340 sentinel chickens. Annual enzootic detection of WNV typically preceded detection in humans and prompted enhanced intervention to reduce the risk of WNV transmission. Peak WNV activity occurred from July through October in the Central Valley and southern California. Less than five percent of WNV activity occurred in other regions of the state or outside of this time. WNV continues to be a major threat to public and wild avian health in California, particularly in southern California and the Central Valley during summer and early fall months. Local and state public health partners must continue statewide human and mosquito surveillance and facilitate effective mosquito control and bite prevention measures.

The formation of platelet ice is well known to occur under Antarctic sea ice, where subice platelet layers form from supercooled ice shelf water. In the Arctic, however, platelet ice formation has not been extensively observed, and its formation and morphology currently remain enigmatic. Here, we present the first comprehensive, long‐term in situ observations of a decimeter thick subice platelet layer under free‐drifting pack ice of the Central Arctic in winter. Observations carried out with a remotely operated underwater vehicle (ROV) during the midwinter leg of the MOSAiC drift expedition provide clear evidence of the growth of platelet ice layers from supercooled water present in the ocean mixed layer. This platelet formation takes place under all ice types present during the surveys. Oceanographic data from autonomous observing platforms lead us to the conclusion that platelet ice formation is a widespread but yet overlooked feature of Arctic winter sea ice growth.

Platelet ice is a unique type of sea ice; its occurrence has numerous implications for physical and ecological systems. Mostly, platelet ice has been reported from the Antarctic where ice crystals grow in supercooled ice shelf water and accumulate below sea ice to form sub-ice platelet layers. In the Arctic however, platelet ice formation has only been sparsely documented so far. The associated formation processes and morphology differ significantly from the Antarctic, but currently remain poorly understood. Here, we present the first comprehensive, repeat in-situ observations of a decimeter thick sub-ice platelet layer under drifting pack ice of the Central Arctic in winter. Observations carried out with a remotely operated underwater vehicle (ROV) during the midwinter leg of the MOSAiC drift expedition provided clear evidence of the growth of platelet layers from supercooled water present in the ocean mixed layer. This process was observed under all ice types present during the surveys. Oceanographic data from autonomous observing platforms leads us to the conclusion that platelet ice formation is a widespread yet overlooked feature of Arctic winter sea ice growth.

We analyzed velocity and hydrographic data from 23 moorings in the northeast Chukchi Sea from 2011 to 2014. In most years the eastern side of Hanna Shoal was strongly stratified year-round, while weakly stratified regions prevailed on the shelf south and west of the Shoal. Stratification differences cause differential vertical mixing rates, which in conjunction with advection of different bottom water properties resulted in seasonally-varying along-isobath density gradients. In agreement with numerical models, we find that bottom waters flow anticyclonically around the Shoal. Whereas most of the shelf responded barotropically to wind-forcing, there was a strong baroclinic component to the flow field northeast of Hanna Shoal, resulting in no net vertically-integrated transport on average. In contrast there is a net eastward transport from west of the Shoal, which implies convergence north of the Shoal. Convergence and along-isobath density gradients may foster cross-shelf exchange north of Hanna Shoal. Modal analyses indicate that the shelf south of the Shoal and Barrow Canyon responded coherently to local and remote winds, whereas the wind-current response around Hanna Shoal was less coherent. Barotropic topographic waves, of ~3-day period, were generated episodically northeast of the Shoal and propagate clockwise around Hanna Shoal, but are blocked from entering Barrow Canyon and are possibly scattered by the horizontally sheared flow and converging isobaths on the western side of the Shoal. Analysis of water properties on the western side of Hanna Shoal suggests that these include contributions from the western and southern portions of the Chukchi Sea.

Urban canyon classification plays an important role in analyzing the impact of urban canyon geometry on urban morphology and microclimates. Existing classification methods using aspect ratios require a large number of field surveys, which are often expensive and laborious. Moreover, it is difficult for these methods to handle the complex geometry of street canyons, which is often required by specific applications. To overcome these difficulties, we develop a street canyon classification approach using publicly available Google Street View (GSV) images. Our method is inspired by the latest advances in deep multitask learning based on densely connected convolutional networks (DenseNets) and tailored for multiple street canyon classification, i.e., H/W-based (Level 1), symmetry-based (Level 2), and complex-geometry-based (Level 3) classifications. We conducted a series of experiments to verify the proposed method. First, taking the Hong Kong area as an example, the method achieved an accuracy of 89.3%, 86.6%, and 86.1%, respectively for the three levels. Even using the field survey data as the ground truth, it gained approximately 80% for different levels. Then, we tested our pretrained model in five other cities and compared the results with traditional methods. The transferability and effectiveness of the scheme were demonstrated. Finally, to enrich the representation of more complicated street geometry, the approach can separately generate thematic maps of street canyons at multiple levels to better facilitate microclimatic studies in high-density built environments. The developed techniques for the classification and mapping of street canyons provide a cost-effective tool for studying the impact of complex and evolving urban canyon geometry on microclimate changes. © 2019 Elsevier Ltd

Ying-Ji Fang,1,* Ping Jiang,2,* Hui Zhai,1 Jing-Sen Dong1 1Department of Gynecology, Jinan Maternal and Child Care Hospital, Jinan, Shandong, People’s Republic of China; 2Department of Obstetrics, Yantai Mountain Hospital, Yantai, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ping Jiang Department of Obstetrics, Yantai Mountain Hospital, Yantai, Shandong, People’s Republic of ChinaEmail jiangcong7587@126.comBackground: Early detection and diagnosis of ovarian cancer (OC) is complicated due to the concealment of the ovarian anatomical position and the lack of clinical manifestations and specific indicators of early OC. Therefore, it is urgent to study the pathogenesis of OC, especially the molecular mechanism.Results: LncRNA GAS8-AS1 was decreased in OC tissues and cell lines, and high expression of GAS8-AS1 indicated a higher 5-year survival rate of OC patients. Overexpression of GAS8-AS1 suppressed growth of OC cells, while deletion of GAS8-AS1 promoted the progression of OC cells. Further data indicated GAS8-AS1 activated autophagy in OC cells. Functional experiments showed that 3-MA removed the inhibitory effect of GAS8-AS1 in OC cells. On the contrary, Rapamycin reversed the promoting effect of GAS8-AS1 in OC cells. Furthermore, GAS8-AS1 bound with Beclin1 and promoted its expression, and silencing of Beclin1 reversed the inhibitory role of GAS8-AS1 in OC progression. In vivo tumorigenesis assay showed GAS8-AS1 suppressed OC progression and activated Beclin1 mediated autophagy.Conclusion: Our study suggested GAS8-AS1 inhibited OC progression by activating autophagy via binding with Beclin1, and GAS8-AS1 might be a potential therapeutic target for OC clinical treatment.Keywords: ovarian cancer, GAS8-AS1, autophagy, Beclin1, tumor progression

Ying Fang,1,* Hao Wang,2,* Hong-Jing Dou,2,* Xing Fan,1,* Xiao-Chun Fei,3 Lei Wang,2 Shu Cheng,1 Anne Janin,4,5 Li Wang,1,4 Wei-Li Zhao1,4 1State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Shanghai Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; 2State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China; 3Department of Pathology, Shanghai Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; 4Sino-French Research Center of Life Science and Genomics, Laboratory of Molecular Pathology, Shanghai, China; 5Joint Research Unit 1165, Inserm, University Paris VII, Saint-Louis Hospital, Paris, France *These authors contributed equally to this work Purpose: Cardiac side effects of doxorubicin (Dox) have limited its clinical application. The aim of this study was to explore new Dox-loaded dextran-based nano-carriers (NCs) in efficiently targeting tumor growth with less cardiac toxicity. Methods: Inspired by recent reports that polymeric NCs could function as sustained, controlled and targeted drug delivery systems, we developed Dox-loaded NCs which displayed a 2-fold release ratio of Dox in the mimic tumor site condition (pH 5.0 with 10 mM glutathione, GSH)as much as that in systemic circulation condition (pH 7.4). Results: Lymphoma cells treated with Dox-NCs had significantly higher intracellular Dox concentrations and more apoptotic induction, with lower P-gp expression, when compared with those treated with Dox alone. The identified mechanism of action, apoptosis, was triggered through survivin reduction and caspase-3 activation. Even in the Dox-resistant cells, Dox-NCs could significantly inhibit cell growth and induce apoptosis. In murine lymphoma xenograft models, Dox-NCs also remarkably significantly retarded tumor growth, assessed by murine weight, and demonstrated less cytotoxicity. Noticeably

Ying Fang,1,* Hao Wang,2,* Hong-Jing Dou,2,* Xing Fan,1,* Xiao-Chun Fei,3 Lei Wang,2 Shu Cheng,1 Anne Janin,4,5 Li Wang,1,4 Wei-Li Zhao1,4 1State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Shanghai Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; 2State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China; 3Department of Pathology, Shanghai Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; 4Sino-French Research Center of Life Science and Genomics, Laboratory of Molecular Pathology, Shanghai, China; 5Joint Research Unit 1165, Inserm, University Paris VII, Saint-Louis Hospital, Paris, France *These authors contributed equally to this work Purpose: Cardiac side effects of doxorubicin (Dox) have limited its clinical application. The aim of this study was to explore new Dox-loaded dextran-based nano-carriers (NCs) in efficiently targeting tumor growth with less cardiac toxicity. Methods: Inspired by recent reports that polymeric NCs could function as sustained, controlled and targeted drug delivery systems, we developed Dox-loaded NCs which displayed a 2-fold release ratio of Dox in the mimic tumor site condition (pH 5.0 with 10 mM glutathione, GSH)as much as that in systemic circulation condition (pH 7.4). Results: Lymphoma cells treated with Dox-NCs had significantly higher intracellular Dox concentrations and more apoptotic induction, with lower P-gp expression, when compared with those treated with Dox alone. The identified mechanism of action, apoptosis, was triggered through survivin reduction and caspase-3 activation. Even in the Dox-resistant cells, Dox-NCs could significantly inhibit cell growth and induce apoptosis. In murine lymphoma xenograft models, Dox-NCs also remarkably significantly retarded tumor growth, assessed by murine weight, and demonstrated less cytotoxicity. Noticeably

View factors for sky, trees, and buildings are three important parameters of the urban outdoor environment that describe the geometrical relationship between different surfaces from the perspective of radiative energy transfer. This study develops an approach for accurately estimating sky view factor (SVF), tree view factor (TVF), and building view factor (BVF) of street canyons in the high-density urban environment of Hong Kong using publicly available Google Street View (GSV) images and a deep-learning algorithm for extraction of street features (sky, trees, and buildings). As a result, SVF, TVF, and BVF maps of street canyons are generated. Verification using reference data of hemispheric photography from field surveys in compact high-rise and low-rise areas shows that the GSV-based VF estimates have a satisfying agreement with the reference data (all with R2 > 0.95), suggesting the effectiveness and high accuracy of the developed method. This is the first reported use of hemispheric photography for direct verification in a GSV-based streetscape study. Furthermore, a comparison between GSV-based and 3D-GIS-based SVFs shows that the two SVF estimates are significantly correlated (R2 = 0.40, p < 0.01) and show better agreement in high-density areas. However, the latter overestimates SVF by 0.11 on average, and the differences between them are significantly correlated with street trees (R2 = 0.53): the more street trees, the larger the difference. This suggests that a lack of street trees in a 3D-GIS model of street environments is the dominant factor contributing to the large discrepancies between the two datasets. © 2018 Elsevier Ltd

To have a superior generalization, a deep learning neural network often involves a large size of training sample. With increase of hidden layers in order to increase learning ability, neural network has potential degradation in accuracy. Both could seriously limit applicability of deep learning in some domains particularly involving predictions of continuous variables with a small size of samples. Inspired by residual convolutional neural network in computer vision and recent findings of crucial shortcuts in the brains in neuroscience, we propose an autoencoder-based residual deep network for robust prediction. In a nested way, we leverage shortcut connections to implement residual mapping with a balanced structure for efficient propagation of error signals. The novel method is demonstrated by multiple datasets, imputation of high spatiotemporal resolution non-randomness missing values of aerosol optical depth, and spatiotemporal estimation of fine particulate matter <2.5 \mu m, achieving the cutting edge of accuracy and efficiency. Our approach is also a general-purpose regression learner to be applicable in diverse domains., Comment: including supplementary materials

Oprozomib is an oral proteasome inhibitor currently under investigation in patients with hematologic malignancies or solid tumors. Oprozomib elicits potent pharmacological actions by forming a covalent bond with the active site N-terminal threonine of the 20S proteasome. Oprozomib has a short half-life across preclinical species and in patients due to systemic clearance via metabolism. Potential for drug-drug interactions (DDIs) could alter the exposure of this potent therapeutic; therefore, a thorough investigation of pathways responsible for metabolism is required. In the present study, the major drug-metabolizing enzyme responsible for oprozomib metabolism was identified in vitro. A diol of oprozomib was found to be the predominant metabolite in human hepatocytes, which formed via direct epoxide hydrolysis. Using recombinant epoxide hydrolases (EHs) and selective EH inhibitors in liver microsomes, microsomal EH (mEH) but not soluble EH (sEH) was found to be responsible for oprozomib diol formation. Coincubation with 2-nonylsulfanyl-propionamide, a selective mEH inhibitor, resulted in a significant decrease in oprozomib disappearance (>80%) with concurrent complete blockage of diol formation in human hepatocytes. On the contrary, a selective sEH inhibitor did not affect oprozomib metabolism. Pretreatment of hepatocytes with the pan-cytochrome P450 (P450) inhibitor 1-aminobenzotriazole resulted in a modest reduction (∼20%) of oprozomib metabolism. These findings indicated that mEH plays a predominant role in oprozomib metabolism. Further studies may be warranted to determine whether drugs that are mEH inhibitors cause clinically significant DDIs with oprozomib. On the other hand, pharmacokinetics of oprozomib is unlikely to be affected by coadministered P450 and sEH inhibitors and/or inducers.

Cavity flow-acoustic resonances may occur when a fluid stream flows past a recessed cavity in a wall. These resonances may lead to high unsteady pressure levels. The resonance involves a coupling between the instability wave which propagates downstream on the shear-layer that spans the open face of the cavity, and acoustic waves that propagate back upstream inside and outside the cavity. These waves are coupled by the scattering processes at the ends of the cavity. Previous theoretical research considered cavities in a wall that bounds an infinite stream. In many of the experiments on cavity resonances, however, the cavity is placed in a side wall of a wind tunnel. When the surrounding wind tunnel walls are not acoustically treated, the resonances can be very strong. My research is a theoretical investigation of the case of a cavity in a side wall of a wind tunnel. Recently, a mode trapping phenomenon has been proposed as an explanation for the very strong cavity resonances in the wind tunnel case. The mode trapping occurs when the critical frequency of a mode in the tunnel-cavity region is slightly lower than the critical frequency of the corresponding mode in the tunnel region. The region between these two critical frequencies is defined as a frequency window. Experiments show that very high pressure levels are observed in these frequency windows. The goal of my research is to develop a global theory of cavity resonances in the wind tunnel geometry. The global theory couples solutions for the instability wave and the acoustic waves through scattering analyses at the ends of the cavity. Resonance frequencies, spatial mode shapes and linear growth rates are predicted. The theoretical predictions are consistent with experimental measurements and demonstrate that the mode trapping phenomenon explains the experimentally observed behavior.

St. Louis encephalitis virus infection was detected in summer 2015 in southern California after an 11-year absence, concomitant with an Arizona outbreak. Sequence comparisons showed close identity of California and Arizona isolates with 2005 Argentine isolates, suggesting introduction from South America and underscoring the value of continued arbovirus surveillance.

Insects impact human health through vector-borne diseases and cause major economic losses by damaging crops and stored agricultural products. Insect-specific growth regulators represent attractive control agents because of their safety to the environment and humans. We identified plant compounds that serve as juvenile hormone antagonists (PJHANs). Using the yeast two-hybrid system transformed with the mosquito JH receptor as a reporter system, we demonstrate that PJHANs affect the JH receptor, methoprene-tolerant (Met), by disrupting its complex with CYCLE or FISC, formation of which is required for mediating JH action. We isolated five diterpene secondary metabolites with JH antagonist activity from two plants: Lindera erythrocarpa and Solidago serotina. They are effective in causing mortality of mosquito larvae at relatively low LD50 values. Topical application of two diterpenes caused reduction in the expression of Met target genes and retardation of follicle development in mosquito ovaries. Hence, the newly discovered PJHANs may lead to development of a new class of safe and effective pesticides.