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Measurements of dissolved organic carbon (DOC), nitrogen (DON), and phosphorus (DOP) concentrations are used to characterize the dissolved organic matter (DOM) pool and are important components of biogeochemical cycling in the coastal ocean. Here, we present the first edition of a global database (CoastDOM v1; available at https://doi.org/10.1594/PANGAEA.964012, Lønborg et al., 2023) compiling previously published and unpublished measurements of DOC, DON, and DOP in coastal waters. These data are complemented by hydrographic data such as temperature and salinity and, to the extent possible, other biogeochemical variables (e.g. chlorophyll a, inorganic nutrients) and the inorganic carbon system (e.g. dissolved inorganic carbon and total alkalinity). Overall, CoastDOM v1 includes observations of concentrations from all continents. However, most data were collected in the Northern Hemisphere, with a clear gap in DOM measurements from the Southern Hemisphere. The data included were collected from 1978 to 2022 and consist of 62 338 data points for DOC, 20 356 for DON, and 13 533 for DOP. The number of measurements decreases progressively in the sequence DOC > DON > DOP, reflecting both differences in the maturity of the analytical methods and the greater focus on carbon cycling by the aquatic science community. The global database shows that the average DOC concentration in coastal waters (average ± standard deviation (SD): 182±314 µmol C L−1; median: 103 µmol C L−1) is 13-fold higher than the average coastal DON concentration (13.6±30.4 µmol N L−1; median: 8.0 µmol N L−1), which is itself 39-fold higher than the average coastal DOP concentration (0.34±1.11 µmol P L−1; median: 0.18 µmol P L−1). This dataset will be useful for identifying global spatial and temporal patterns in DOM and will help facilitate the reuse of DOC, DON, and DOP data in studies aimed at better characterizing local biogeochemic

Seasonal field surveys (April 2018 to February 2019) were conducted in a subterranean estuary (STE) seepage face in Sanggou Bay (China) aiming to explore the transport and reactivity of phosphorus (P) and biogeochemical linkages with the cycling of nitrogen (N) prior to discharge. Porewater dissolved inorganic phosphorus (DIP) and dissolved organic phosphorus (DOP) together with different fractions of sedimentary P were analyzed in the upper, middle and lower intertidal covering the top 20 cm of sediment (1–4 cm, 5–8 cm, 9–12 cm, 13–16 cm and 17–20 cm depth). The accumulation of sedimentary organic P stimulated the growth of phosphate-solubilizing microorganisms and led to porewater DOP enrichment during spring. During summer, total P (TP), porewater DIP and DOP concentrations decreased, potentially due to enhanced mineralization driven by high ambient temperature. From autumn to winter, pelagic organic matter into the STE lowered, triggering a drop of TP standing stocks. Compared with the significant seasonality, sedimentary P storage was statistically identical along the intertidal. Such spatial homogeneity likely results from the rebalance driven by P adsorption dynamics and pelagic organic matter delivered by tide and wave setup. The vertical distribution of DIP, DOP, and sedimentary TP were linked to nitrate transformations. In the sediment layer with active mineralization and nitrification, concentrations of DOP, sedimentary redox and clay P increased. In the layer with active nitrate removal (2–5 cm depth), both DIP and DOP concentrations decreased. The sedimentary loosely-bound and organic P were also lower there. Notably, a substantial quantity of soluble P seeped out, acting as an important contributor to the dissolved P pool of the receiving waters. The spatial and temporal overlap of high concentrations of N and P in STEs adds variabilities and uncertainties in P out-drainage fluxes and nutrient stoichiometry balances, which should draw attention from co

Coastal wetlands, such as salt marshes, mangrove forests, seagrass meadows, and mud flats, are highly productive and valuable for sustainable development. Commonly referred to as “blue carbon” ecosystems because of their relevance in the global carbon (C) cycle, they provide climate mitigation benefits and a wide range of ecological services, such as erosion control, biodiversity support, water quality protection, and C sequestration (Lovelock and Duarte, 2019 and Macreadie et al., 2021). Despite covering a relatively small area, coastal wetlands are estimated to sequester nearly 54 Tg C yr−1, thereby serving as an efficient and natural “C sink” (Wang et al., 2021)

Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural Network (DNN) models in proximity to the sources of data. To enable the training of large models on resource-constraint edge devices and protect data privacy, parallel split learning is becoming a practical and popular approach. However, current parallel split learning neglects the resource heterogeneity of edge devices, which may lead to the straggler issue. In this paper, we propose EdgeSplit, a novel parallel split learning framework to better accelerate distributed model training on heterogeneous and resource-constraint edge devices. EdgeSplit enhances the efficiency of model training on less powerful edge devices by adaptively segmenting the model into varying depths. Our approach focuses on reducing total training time by formulating and solving a task scheduling problem, which determines the most efficient model partition points and bandwidth allocation for each device. We employ a straightforward yet effective alternating algorithm for this purpose. Comprehensive tests conducted with a range of DNN models and datasets demonstrate that EdgeSplit not only facilitates the training of large models on resource-restricted edge devices but also surpasses existing baselines in performance., Comment: Accepted by International Conference on Computing, Networking and Communications (ICNC 2024)

Classification-regression prediction networks have realized impressive success in several modern deep trackers. However, there is an inherent difference between classification and regression tasks, so they have diverse even opposite demands for feature matching. Existed models always ignore the key issue and only employ a unified matching block in two task branches, decaying the decision quality. Besides, these models also struggle with decision misalignment situation. In this paper, we propose a multi-attention associate prediction network (MAPNet) to tackle the above problems. Concretely, two novel matchers, i.e., category-aware matcher and spatial-aware matcher, are first designed for feature comparison by integrating self, cross, channel or spatial attentions organically. They are capable of fully capturing the category-related semantics for classification and the local spatial contexts for regression, respectively. Then, we present a dual alignment module to enhance the correspondences between two branches, which is useful to find the optimal tracking solution. Finally, we describe a Siamese tracker built upon the proposed prediction network, which achieves the leading performance on five tracking benchmarks, consisting of LaSOT, TrackingNet, GOT-10k, TNL2k and UAV123, and surpasses other state-of-the-art approaches.

The clustering method based on graph models has garnered increased attention for its widespread applicability across various knowledge domains. Its adaptability to integrate seamlessly with other relevant applications endows the graph model-based clustering analysis with the ability to robustly extract "natural associations" or "graph structures" within datasets, facilitating the modelling of relationships between data points. Despite its efficacy, the current clustering method utilizing the graph-based model overlooks the uncertainty associated with random walk access between nodes and the embedded structural information in the data. To address this gap, we present a novel Clustering method for Maximizing Decoding Information within graph-based models, named CMDI. CMDI innovatively incorporates two-dimensional structural information theory into the clustering process, consisting of two phases: graph structure extraction and graph vertex partitioning. Within CMDI, graph partitioning is reformulated as an abstract clustering problem, leveraging maximum decoding information to minimize uncertainty associated with random visits to vertices. Empirical evaluations on three real-world datasets demonstrate that CMDI outperforms classical baseline methods, exhibiting a superior decoding information ratio (DI-R). Furthermore, CMDI showcases heightened efficiency, particularly when considering prior knowledge (PK). These findings underscore the effectiveness of CMDI in enhancing decoding information quality and computational efficiency, positioning it as a valuable tool in graph-based clustering analyses., Comment: 9 pages, 9 figures, IJCNN 2024

Multipanel images, commonly seen as web screenshots, posters, etc., pervade our daily lives. These images, characterized by their composition of multiple subfigures in distinct layouts, effectively convey information to people. Toward building advanced multimodal AI applications, such as agents that understand complex scenes and navigate through webpages, the skill of multipanel visual reasoning is essential, and a comprehensive evaluation of models in this regard is important. Therefore, we introduce Multipanel Visual Question Answering (MultipanelVQA), a novel benchmark comprising 6,600 triplets of questions, answers, and multipanel images that specifically challenge models in comprehending multipanel images. Our evaluation shows that questions in the MultipanelVQA benchmark pose significant challenges to the state-of-the-art Large Vision Language Models (LVLMs) tested, even though humans can attain approximately 99\% accuracy on these questions. Distinctively, the MultipanelVQA benchmark features synthetically generated multipanel images specifically crafted to isolate and assess the impact of various factors, such as the layout, on LVLMs' multipanel image comprehension abilities. As a result, in addition to benchmarking the capabilities of LVLMs in understanding multipanel images, we analyze the potential causes for LVLMs' performance and offer insights for enhancement with the synthetic data. Code and data are released at https://sites.google.com/view/multipanelvqa/home.

Tropical peat swamps are essential ecosystems, which provide numerous services, and also serve as a rich source of dissolved organic carbon (DOC), hydrogen ions and trace elements to peat draining rivers. However, not much is known about trace element export from tropical peat swamps. We investigated trace element dynamics in rivers and estuaries draining tropical peat swamps on Borneo, and examined the influence of estuarine processes as well as dissolved organic carbon (DOC) on the distribution and concentration of trace elements. Our results indicate acidic conditions (pH = 3.3) and high DOC concentration (3500 µmol L −1 ) at salinities<1. We observed an initial release of trace elements at low salinity (0.05

Shan Jiang,1,2 Zhaolei Cui,3 Jianfeng Zheng,1 Qiaoling Wu,1 Haijuan Yu,1 Yiqing You,3 Chaoqiang Zheng,3 Yang Sun1 1Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People’s Republic of China; 2College of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, People’s Republic of China; 3Laboratory of Biochemistry and Molecular Biology Research, Department of Clinical Laboratory, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People’s Republic of ChinaCorrespondence: Yang Sun, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Fuzhou, Fujian, 350014, People’s Republic of China, Email sunyang@fjzlhospital.comBackground: Immunogenic cell death (ICD) can reshape the immune microenvironment of tumors. Driven by stressful pressure, by directly destroying tumor cells and activating the body’s adaptive immunity, ICD acts as a modulator of cell death, enabling the body to generate an anti-tumor immune response that produces a more effective therapeutic effect, while tumor cells are driven to kill. Hence, this research aimed to find and evaluate ICD-related genetic signatures as cervical cancer (CC) prognostic factors.Methods: Data of CC patients from the Tumor Genome Atlas (TCGA) were used as the basis to obtain immunogenic cell-death-related prognostic genes (IPGs) in patients with CC, using the least absolute shrinkage and selection operator and Cox regression screening, and the IPGs scoring system was constructed to classify patients into high- and low-risk groups, with the Gene Expression Omnibus (GEO) dataset as the validation group. Finally, the difference analysis of single-sample gene set enrichment analysis, tumor microenvironment (TME), immune cells, tumor mutational burden, and chemotherapeutic drug sensitivity between the high-ris

Niche signals maintain stem cells in a prolonged quiescence or transiently activate them for proper regeneration1. Altering balanced niche signalling can lead to regenerative disorders. Melanocytic skin nevi in human often display excessive hair growth, suggesting hair stem cell hyperactivity. Here, using genetic mouse models of nevi2,3, we show that dermal clusters of senescent melanocytes drive epithelial hair stem cells to exit quiescence and change their transcriptome and composition, potently enhancing hair renewal. Nevus melanocytes activate a distinct secretome, enriched for signalling factors. Osteopontin, the leading nevus signalling factor, is both necessary and sufficient to induce hair growth. Injection of osteopontin or its genetic overexpression is sufficient to induce robust hair growth in mice, whereas germline and conditional deletions of either osteopontin or CD44, its cognate receptor on epithelial hair cells, rescue enhanced hair growth induced by dermal nevus melanocytes. Osteopontin is overexpressed in human hairy nevi, and it stimulates new growth of human hair follicles. Although broad accumulation of senescent cells, such as upon ageing or genotoxic stress, is detrimental for the regenerative capacity of tissue4, we show that signalling by senescent cell clusters can potently enhance the activity of adjacent intact stem cells and stimulate tissue renewal. This finding identifies senescent cells and their secretome as an attractive therapeutic target in regenerative disorders.

Background: Woolly rhinoceros (Coelodonta antiquitatis) is a typical indicator of cold-stage climate that was widely distributed in Northern Hemisphere during the Middle-Late Pleistocene. Although a plethora of fossils have been excavated from Northern China, their phylogenetic status, intraspecific diversity and phylogeographical structure are still vague. Results: In the present study, we generated four mitogenomes from Late Pleistocene woolly rhinoceros in Northern China and compared them with published data. Bayesian and network analyses indicate that the analyzed individuals contain at least four maternal haplogroups, and Chinese samples fall in three of them. One of our samples belongs to a previously unidentified early diverging clade (haplogroup D), which separated from other woolly rhinoceros around 0.57 Ma (95% CI: 0.76–0.41 Ma). The timing of this clade’s origin coincides with the first occurrence of woolly rhinoceros, which are thought to have evolved in Europe. Our other three samples cluster in haplogroup C, previously only identified from one specimen from Wrangel Island (ND030) and initially considered to be an isolated clade. Herein, our findings suggest that ND030 is likely descended from a northward dispersal of the individuals carrying haplogroup C from Northern China. Additionally, Chinese woolly rhinoceros specimens exhibit higher nucleotide diversity than those from Siberia. Conclusion: Our findings highlight Northern China as a possible refugium and a key evolution center of the Pleistocene woolly rhinoceros.

The Grand Canal is located in the north-eastern and central-eastern plains of China, running from Beijing in the north to Zhejiang province in the south, and is the longest canal in the world (1800 km) and a UNESCO World Heritage Site. From a tourism perspective, the Grand Canal can be considered as a linear heritage attraction linking many individual heritage sites. This research was conducted with a mixed qualitative–quantitative method in the 2021–2022 period. First, in-depth interviews with an unstructured methodology of image measurements were conducted to determine attributes of the perceived destination image (PDI) of the Grand Canal as a whole. Next, a measurement scale was developed using these attributes to examine the PDI of two canal sites near Beijing. A total of 274 valid questionnaires were used for data analysis. The results identify differences between the two sites’ PDI and the overall image of the Grand Canal. The t-test results shows that, in terms of the CDI, Shichahai Park has much better tourism activities, while Tongzhou Canal Park has better water quality. Shichahai Park’s ADI provides a stronger image of pride/impressive heritage of the Tongzhou Canal Park. Based on these results, a marketing strategy is proposed and discussed. This is the first study to develop a method and measurement scale to examine cohesion in the PDI of linear heritage attractions. The method may be used by other linear heritage management practitioners to link their attractions. © 2023 by the authors.

The stability and safety of metro deep foundation pit in the soil-rock composite stratum in the seasonally frozen area are the key issues in the design and construction of the foundation pit. In order to ensure the soil-rock composite stratum in the seasonally frozen area, the deformation of the supporting structure is within the safe range when the deep foundation pit is excavated. At the same time, it will reduce the impact of the construction of metro deep foundation pit on the surrounding buildings. During the construction of foundation pit, close monitoring shall be carried out and the actual monitoring data shall be studied and analyzed. During the excavation of each layer of the foundation pit, monitor the displacement change of the foundation pit support structure and the settlement change of the surrounding ground surface, study the deformation law of the subway deep foundation pit in the soil-rock composite layer, and discuss the spatial effect of the foundation pit excavation in the seasonally frozen area. This paper mainly takes the deep foundation pit project of Anxin Road Station of Changchun Metro Line 5 as the research object, and analyzes the actual monitoring results. The results show that: (1) With the long side of the foundation pit, the deformation of the retaining structure of the foundation pit increases with the distance from the pit corner, and the middle of the long side of the foundation pit is less constrained by the retaining structure. (2) During the excavation of soil-rock composite foundation pit in seasonally frozen soil area, the pile displacement, axial force of support and uplift of support structure caused by rock excavation are relatively small, while the lateral displacement of foundation pit below the rock surface and thawing settlement deformation of soil are basically unchanged. The maximum lateral displacement of the foundation pit at the upper part of the rock surface moves upward. During the construction of deep foundatio

Online video streaming has fundamental limitations on the transmission bandwidth and computational capacity and super-resolution is a promising potential solution. However, applying existing video super-resolution methods to online streaming is non-trivial. Existing video codecs and streaming protocols (\eg, WebRTC) dynamically change the video quality both spatially and temporally, which leads to diverse and dynamic degradations. Furthermore, online streaming has a strict requirement for latency that most existing methods are less applicable. As a result, this paper focuses on the rarely exploited problem setting of online streaming video super resolution. To facilitate the research on this problem, a new benchmark dataset named LDV-WebRTC is constructed based on a real-world online streaming system. Leveraging the new benchmark dataset, we proposed a novel method specifically for online video streaming, which contains a convolution and Look-Up Table (LUT) hybrid model to achieve better performance-latency trade-off. To tackle the changing degradations, we propose a mixture-of-expert-LUT module, where a set of LUT specialized in different degradations are built and adaptively combined to handle different degradations. Experiments show our method achieves 720P video SR around 100 FPS, while significantly outperforms existing LUT-based methods and offers competitive performance compared to efficient CNN-based methods.

Although there is a significant development in 3D Multi-view Multi-person Tracking (3D MM-Tracking), current 3D MM-Tracking frameworks are designed separately for footprint and pose tracking. Specifically, frameworks designed for footprint tracking cannot be utilized in 3D pose tracking, because they directly obtain 3D positions on the ground plane with a homography projection, which is inapplicable to 3D poses above the ground. In contrast, frameworks designed for pose tracking generally isolate multi-view and multi-frame associations and may not be robust to footprint tracking, since footprint tracking utilizes fewer key points than pose tracking, which weakens multi-view association cues in a single frame. This study presents a Unified Multi-view Multi-person Tracking framework to bridge the gap between footprint tracking and pose tracking. Without additional modifications, the framework can adopt monocular 2D bounding boxes and 2D poses as the input to produce robust 3D trajectories for multiple persons. Importantly, multi-frame and multi-view information are jointly employed to improve the performance of association and triangulation. The effectiveness of our framework is verified by accomplishing state-of-the-art performance on the Campus and Shelf datasets for 3D pose tracking, and by comparable results on the WILDTRACK and MMPTRACK datasets for 3D footprint tracking., Comment: Accepted to Computational Visual Media

Subterranean estuaries, i.e., the mixing zone between terrestrial groundwater and recirculated seawater, host a wide range of microbiota. Here, field campaigns were conducted at the mouth of the subterranean estuary at the Sanggou Bay (Shandong Province, China) over four consecutive seasons at a seepage face (0–20 cm depth). The diversity of benthic microbiome was characterized via 16S rRNA gene sequencing and metagenomics, combined with physic-chemical parameters, e.g., organic carbon, total nitrogen and sulfate contents in sediments. During spring, the dominant species were assigned to the phylum Proteobacteria. Important opportunistic species was assigned to Acidobacteria, Actinobacteria and Bacteroidetes. The key components were identified to be species of the genera Pseudoalteromonas, Colwellia and Sphingobium, indicating the involvement of sediment microbiota in the degradation of sedimentary organic carbon, particularly that of pelagic origin, e.g., phytoplankton detritus and bivalve pseudo-feces. During spring, the microbial community was statistically similar along the depth profiles and among the three sampled stations. Similar spatial distributions were obtained in the remaining seasons. By contrast, the dominant species assemblages varied significantly among seasons, with key genera being Thioprofundum and Nitrosopumilus during summer and autumn and Thioprofundum and Ilumatobacter during winter. Network analysis revealed a seasonal shift in benthic nitrogen and sulfur metabolism associated with these variations in microbial community composition. Overall, our findings suggested that macro elements derived from pelagic inputs, particularly detrital phytoplankton, shaped the microbial community compositions at the seepage face, resulting in significant seasonal variations, while the influence of terrestrial materials transported by groundwater on the sediment microbiota at the seepage face found to be minor

Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various image corruptions, making it difficult for deep neural network (DNN) models to achieve stable diagnostic results for clinical use. In order to assess and further enhance the robustness of the models, we analyze the physical causes of the full-stack corruptions throughout the pathological life-cycle and propose an Omni-Corruption Emulation (OmniCE) method to reproduce 21 types of corruptions quantified with 5-level severity. We then construct three OmniCE-corrupted benchmark datasets at both patch level and slide level and assess the robustness of popular DNNs in classification and segmentation tasks. Further, we explore to use the OmniCE-corrupted datasets as augmentation data for training and experiments to verify that the generalization ability of the models has been significantly enhanced.

Renal medullary carcinoma (RMC) is an aggressive kidney cancer that almost exclusively develops in individuals with sickle cell trait (SCT) and is always characterized by loss of the tumor suppressor SMARCB1. Because renal ischemia induced by red blood cell sickling exacerbates chronic renal medullary hypoxia in vivo, we investigated whether the loss of SMARCB1 confers a survival advantage under the setting of SCT. Hypoxic stress, which naturally occurs within the renal medulla, is elevated under the setting of SCT. Our findings showed that hypoxia-induced SMARCB1 degradation protected renal cells from hypoxic stress. SMARCB1 wild-type renal tumors exhibited lower levels of SMARCB1 and more aggressive growth in mice harboring the SCT mutation in human hemoglobin A (HbA) than in control mice harboring wild-type human HbA. Consistent with established clinical observations, SMARCB1-null renal tumors were refractory to hypoxia-inducing therapeutic inhibition of angiogenesis. Further, reconstitution of SMARCB1 restored renal tumor sensitivity to hypoxic stress in vitro and in vivo. Together, our results demonstrate a physiological role for SMARCB1 degradation in response to hypoxic stress, connect the renal medullary hypoxia induced by SCT with an increased risk of SMARCB1-negative RMC, and shed light into the mechanisms mediating the resistance of SMARCB1-null renal tumors against angiogenesis inhibition therapies.

Copper electrodes are especially effective in catalysis of C-2 and further multi-carbon products in the CO2 reduction reaction (CO2RR) and therefore of major technological interest. The reasons for the unparalleled Cu performance in CO2RR are insufficiently understood. Here, the electrode-electrolyte interface was highlighted as a dynamic physical-chemical system and determinant of catalytic events. Exploiting the intrinsic surface-enhanced Raman effect of previously characterized Cu foam electrodes, operando Raman experiments were used to interrogate structures and molecular interactions at the electrode-electrolyte interface at subcatalytic and catalytic potentials. Formation of a copper carbonate hydroxide (CuCarHyd) was detected, which resembles the mineral malachite. Its carbonate ions could be directly converted to CO at low overpotential. These and further experiments suggested a basic mode of CO2/carbonate reduction at Cu electrodes interfaces that contrasted previous mechanistic models: the starting point in carbon reduction was not CO2 but carbonate ions bound to the metallic Cu electrode in form of CuCarHyd structures. It was hypothesized that Cu oxides residues could enhance CO2RR indirectly by supporting formation of CuCarHyd motifs. The presence of CuCarHyd patches at catalytic potentials might result from alkalization in conjunction with local electrical potential gradients, enabling the formation of metastable CuCarHyd motifs over a large range of potentials.

Background: To provide tacrolimus is first-line treatment after liver and kidney transplantation. However, hypertension and nephrotoxicity are common tacrolimus side effects that limit its use. Although tacrolimus-related hypertension is well known, the underlying mechanisms are not. Here, we test whether tacrolimus-induced hypertension involves the RhoA (Ras homolog family member A)/ROCK (Rho-associated protein kinase) pathway in male C57Bl/6 mice. methods: Intra-arterial blood pressure was measured under anesthesia. The reactivity of renal afferent arterioles and mesenteric arteries were assessed in vitro using microperfusion and wire myography, respectively. Results: Tacrolimus induced a transient rise in systolic arterial pressure that was blocked by the RhoA/ROCK inhibitor Fasudil (12.0 +/- 0.9 versus 3.2 +/- 0.7; P<0.001). Moreover, tacrolimus reduced the glomerular filtration rate, which was also prevented by Fasudil (187 +/- 20 versus 281 +/- 8.5; P<0.001). Interestingly, tacrolimus enhanced the sensitivity of afferent arterioles and mesenteric arteries to Ang II (angiotensin II), likely due to increased intracellular Ca2+ mobilization and sensitization. Fasudil prevented increased Ang II-sensitivity and blocked Ca2+ mobilization and sensitization. Preincubation of mouse aortic vascular smooth muscle cells with tacrolimus activated the RhoA/ROCK/MYPT-1 (myosin phosphatase targeting subunit 1) pathway. Further, tacrolimus increased cytoplasmic reactive oxygen species generation in afferent arterioles (107 +/- 5.9 versus 163 +/- 6.4; P<0.001) and in cultured mouse aortic vascular smooth muscle cells (100 +/- 7.5 versus 160 +/- 23.2; P<0.01). Finally, the reactive oxygen species scavenger Tempol inhibited tacrolimus-induced Ang II hypersensitivity in afferent arterioles and mesenteric arteries. Conclusions: The RhoA/ROCK pathway may play an important role in tacrolimus-induced hypertension by enhancing Ang II-specific vasoconstriction, and reactive

Knowledge-guided improvement of electrocatalytic materials is facilitated by insight in determinants of activity, selectivity, and stability. Catalyst re-construction, surface adsorbates and reaction intermediates, and the mechanistic role of the electrolyte can be addressed by operando (= in situ) Raman spectroscopy, where Raman scattering is detected during electrochemical operation. After outlining the basic experimental technology and addressing the light-damage problem, selected applications in electrocatalysis research are reviewed: (1) Redox-state changes of the catalyst material induced by electrode potentials are traced by operando Raman spectroscopy. (2) Surface-bound (adsorbed) educt, products and reaction intermediates are detected using surface-enhanced Raman spectroscopy (SERS). (3) Local-pH values are determined at the catalyst-electrolyte interface, with spatial resolution at the visible-light diffraction limit.

Astrocytes extend endfeet that enwrap the vasculature, and disruptions to this association which may occur in disease coincide with breaches in blood-brain barrier (BBB) integrity. Here we investigate if focal ablation of astrocytes is sufficient to disrupt the BBB in mice. Targeted two-photon chemical apoptotic ablation of astrocytes induced a plasticity response whereby surrounding astrocytes extended processes to cover vascular vacancies. In young animals, replacement processes occur in advance of endfoot retraction, but this is delayed in aged animals. Stimulation of replacement astrocytes results in constriction of pre-capillary arterioles, suggesting that replacement astrocytes are functional. Pharmacological inhibition of pSTAT3, as well as astrocyte specific deletion of pSTAT3, reduces astrocyte replacement post-ablation, without perturbations to BBB integrity. Similar endfoot replacement occurs following astrocyte cell death due to reperfusion in a stroke model. Together, these studies uncover the ability of astrocytes to maintain cerebrovascular coverage via substitution from nearby cells.

A scarcity of functionally validated enhancers in the human genome presents a significant hurdle to understanding how these cis-regulatory elements contribute to human diseases. We carry out highly multiplexed CRISPR-based perturbation and sequencing to identify enhancers required for cell proliferation and fitness in 10 human cancer cell lines. Our results suggest that the cell fitness enhancers, unlike their target genes, display high cell-type specificity of chromatin features. They typically adopt a modular structure, comprised of activating elements enriched for motifs of oncogenic transcription factors, surrounded by repressive elements enriched for motifs recognized by transcription factors with tumor suppressor functions. We further identify cell fitness enhancers that are selectively accessible in clinical tumor samples, and the levels of chromatin accessibility are associated with patient survival. These results reveal functional enhancers across multiple cancer cell lines, characterize their context-dependent chromatin organization, and yield insights into altered transcription programs in cancer cells.

Shan Jiang,1,* Yong-Qiang Wang,2,* Yifei Tang,1 Xi Lu,1 Dan Guo1 1Department of Rehabilitation Medicine, the China-Japan Friendship Hospital, Beijing, 100029, People’s Republic of China; 2Department of Ophthalmology, the Sunshine Union Hospital, Weifang, Shandong, 261071, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shan Jiang, Department of Rehabilitation Medicine, the China-Japan Friendship Hospital, No. 2 Ying Hua Yuan East Street, Beijing, 100029, People’s Republic of China, Tel +86 10 84205288, Fax +86 10 64217749, Email landjiang@126.comBackground: As a severe complication of sepsis, sepsis-associated encephalopathy (SAE) usually manifests as impaired learning and memory ability in survivors. Previous studies have reported that environmental enrichment (EE) can increase the learning and memory ability in different brain injury models. However, there has been no research on the possible positive effect of EE on SAE.Aim: The present study aimed to test the effect of EE on SAE-induced impairment of learning and memory and its related mechanisms.Methods: A Morris water maze test (MWM) was used to evaluate the learning and memory ability of SAE rats that received EE housing or not. The expression of vasopressin (VP) was assessed using immunofluorescence microscopy and enzyme-linked immunosorbent assays (ELISAs). The synthesis of VP in the supraoptic nucleus (SON) was determined using quantitative real-time reverse transcription-PCR analysis. Moreover, inflammatory markers and brain-derived neurotrophic factor (BDNF) were detected using ELISA.Results: The results showed that SAE induced a decreased learning and memory ability, while EE reversed this impairment. EE also enhanced the synthesis and secretion of VP in the SON. Blocking the action of VP in the hippocampus interrupted the EE-induced amelioration of learning and memory impairment. Moreover, EE induced changes to the levels o

We propose a Cascaded Buffered IoU (C-BIoU) tracker to track multiple objects that have irregular motions and indistinguishable appearances. When appearance features are unreliable and geometric features are confused by irregular motions, applying conventional Multiple Object Tracking (MOT) methods may generate unsatisfactory results. To address this issue, our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimation bias in the matching space. In addition, to reduce the risk of overexpansion of the matching space, cascaded matching is employed: first matching alive tracks and detections with a small buffer, and then matching unmatched tracks and detections with a large buffer. Despite its simplicity, our C-BIoU tracker works surprisingly well and achieves state-of-the-art results on MOT datasets that focus on irregular motions and indistinguishable appearances. Moreover, the C-BIoU tracker is the dominant component for our 2-nd place solution in the CVPR'22 SoccerNet MOT and ECCV'22 MOTComplex DanceTrack challenges. Finally, we analyze the limitation of our C-BIoU tracker in ablation studies and discuss its application scope., Comment: Accepted to WACV 2023. arXiv admin note: text overlap with arXiv:2211.13509

This is our 2nd-place solution for the ECCV 2022 Multiple People Tracking in Group Dance Challenge. Our method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering. Our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimation bias in the matching space. In addition, to reduce the risk of overexpansion of the matching space, cascaded matching is employed: first matching alive tracks and detections with a small buffer, and then matching unmatched tracks and detections with a large buffer. After using our C-BIoU for online tracking, we applied the offline refinement introduced by ReMOTS.

This is our second-place solution for CVPR 2022 SoccerNet Tracking Challenge. Our method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering. At each step, online tracking yielded HOTA scores near 90, and offline tracking further improved HOTA scores to around 93.2.

Big Data Sharing (BDS) refers to the act of the data owners to share data so that users can find, access and use data according to the agreement. In recent years, BDS has been an emerging topic due to its wide applications, such as big data trading and cross-domain data analytics. However, as the multiple parties are involved in a BDS platform, the issue of security and privacy violation arises. There have been a number of solutions for enhancing security and preserving privacy at different big data operations (e.g., data operation, data searching, data sharing and data outsourcing). To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these security and privacy solutions. In this study, we conduct a comprehensive survey of the state-of-the-art solutions introduced to tackle security and privacy issues in BDS. For a better understanding, we first introduce a general model for BDS and identify the security and privacy requirements. We discuss and classify the state-of-the-art security and privacy solutions for BDS according to the identified requirements. Finally, based on the insights gained, we present and discuss new promising research directions., Comment: 33 pages, 8 figures

Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity, while the efficiency issue is understudied. In practice, as the numerous product records accumulate, the time- and storage- efficiencies will decrease remarkably. To the best of our knowledge, this paper is the first work studying the efficiency issue in blockchain-based supply chain traceability. Compared to the traditional method, which searches the records stored in a single chunk sequentially, we replicate the records in multiple chunks and employ parallel search to boost the time efficiency. However, allocating the record searching primitives to the chunks with maximized parallelization ratio is challenging. To this end, we model the records and chunks as a bipartite graph and solve the allocation problem using a maximum matching algorithm. The experimental results indicate that the time overhead can be reduced by up to 85.1% with affordable storage overhead., Comment: IEEE Transactions on Intelligent Transportation Systems

Collaborative edge computing (CEC) is an emerging paradigm enabling sharing of the coupled data, computation, and networking resources among heterogeneous geo-distributed edge nodes. Recently, there has been a trend to orchestrate and schedule containerized application workloads in CEC, while Kubernetes has become the de-facto standard broadly adopted by the industry and academia. However, Kubernetes is not preferable for CEC because its design is not dedicated to edge computing and neglects the unique features of edge nativeness. More specifically, Kubernetes primarily ensures resource provision of workloads while neglecting the performance requirements of edge-native applications, such as throughput and latency. Furthermore, Kubernetes neglects the inner dependencies of edge-native applications and fails to consider data locality and networking resources, leading to inferior performance. In this work, we design and develop ENTS, the first edge-native task scheduling system, to manage the distributed edge resources and facilitate efficient task scheduling to optimize the performance of edge-native applications. ENTS extends Kubernetes with the unique ability to collaboratively schedule computation and networking resources by comprehensively considering job profile and resource status. We showcase the superior efficacy of ENTS with a case study on data streaming applications. We mathematically formulate a joint task allocation and flow scheduling problem that maximizes the job throughput. We design two novel online scheduling algorithms to optimally decide the task allocation, bandwidth allocation, and flow routing policies. The extensive experiments on a real-world edge video analytics application show that ENTS achieves 43\%-220\% higher average job throughput compared with the state-of-the-art., Comment: The Seventh ACM/IEEE Symposium on Edge Computing (SEC), 2022

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet., Comment: Accepted at ACM MMSports 2022

With the emerging connected-vehicle technologies and smart roads, the need for intelligent adaptive traffic signal controls is more than ever before. This paper proposes a novel Economic-driven Adaptive Traffic Signal Control (eATSC) model with a hyper control variable - interest rate defined in economics for traffic signal control at signalized intersections. The eATSC uses a continuous compounding function that captures both the total number of vehicles and the accumulated waiting time of each vehicle to compute penalties for different directions. The computed penalties grow with waiting time and is used for signal control decisions. Each intersection is assigned two intelligent agents adjusting interest rate and signal length for different directions according to the traffic patterns, respectively. The problem is formulated as a Markov Decision Process (MDP) problem to reduce congestions, and a two-agent Double Dueling Deep Q Network (DDDQN) is utilized to solve the problem. Under the optimal policy, the agents can select the optimal interest rates and signal time to minimize the likelihood of traffic congestion. To evaluate the superiority of our method, a VISSIM simulation model with classic four-leg signalized intersections is developed. The results indicate that the proposed model is adequately able to maintain healthy traffic flow at the intersection., Comment: 18 pages, 12 figures, presented at the Transportation Research Board (TRB) 100th Annual Meeting, 2021

Edge computing has become a popular paradigm where services and applications are deployed at the network edge closer to the data sources. It provides applications with outstanding benefits, including reduced response latency and enhanced privacy protection. For emerging advanced applications, such as autonomous vehicles, industrial IoT, and metaverse, further research is needed. This is because such applications demand ultra-low latency, hyper-connectivity, and dynamic and reliable service provision, while existing approaches are inadequate to address the new challenges. Hence, we envision that the future edge computing is moving towards distributed intelligence, where heterogeneous edge nodes collaborate to provide services in large-scale and geo-distributed edge infrastructure. We thereby propose Edge-as-a-Service (EaaS) to enable distributed intelligence. EaaS jointly manages large-scale cross-node edge resources and facilitates edge autonomy, edge-to-edge collaboration, and resource elasticity. These features enable flexible deployment of services and ubiquitous computation and intelligence. We first give an overview of existing edge computing studies and discuss their limitations to articulate the motivation for proposing EaaS. Then, we describe the details of EaaS, including the physical architecture, proposed software framework, and benefits of EaaS. Various application scenarios, such as real-time video surveillance, smart building, and metaverse, are presented to illustrate the significance and potential of EaaS. Finally, we discuss several challenging issues of EaaS to inspire more research towards this new edge computing framework., Comment: IEEE International Congress on Intelligent and Service-Oriented Systems Engineering, 2022

Subterranean estuaries (STEs) modulate the chemical composition of continental groundwater before it reaches the coast, but their microbial community is poorly known. Here, we explored the microbial ecology of two neighbouring, yet contrasting STEs (Panxón and Ladeira STEs; Ría de Vigo, NW Iberian Peninsula). We investigated microbial composition (16S rRNA gene sequencing), abundance, heterotrophic production and their geochemical drivers. A total of 10,150 OTUs and 59 phyla were retrieved from porewater sampled during four surveys covering each STE seepage face. In both STEs, we find a very diverse microbial community composed by abundant cosmopolitans and locally restricted rare taxa. Porewater oxygen and dissolved organic matter are the main environmental predictors of microbial community composition. More importantly, the high variety of benthic microbiota links to biogeochemical processes of different elements in STEs. The oxygen-rich Panxón beach showed strong associations of the ammonium oxidizing archaea Nitrosopumilales with the heterotrophic community, thus acting as a net source of nitrogen to the coast. On the other hand, the prevailing anoxic conditions of Ladeira beach promoted the dominance of anaerobic heterotrophs related to the degradation of complex and aromatic compounds, such as Dehalococcoidia and Desulfatiglans, and the co-occurrence of methane oxidizers and methanogens

In this paper, microwave photonic signal generation based on the period-one dynamic of optically injected discrete mode (DM) semiconductor lasers has been experimentally demonstrated and numerically simulated. The results show that the frequency of the generated microwave increases linearly with the frequency detuning or optical injection ratio. In addition, a single optical feedback loop is sufficient to reduce the microwave linewidth without significantly deteriorating side mode suppression. The simulation results using a model considering the nonlinear dependencies of the carrier recombination agree well with the experimental results, which indicates that the nonlinear carrier recombination effect is important in determining the nonlinear dynamics of optically injected DM lasers.

Causal links between subterranean estuary (STE) dynamics, their climatological drivers, and the ecology of coastal ecosystems have remained elusive. Yet, establishing these connections is essential for fully integrated management of coastal ecosystems. We test, in a semi-arid climate, whether the composition of submarine groundwater discharge (SGD) to a lagoon can be regulated by the annual oscillation of the local STE, itself driven by groundwater recharge variability. We study STE outflow samples gathered monthly for a year in the Ria Formosa lagoon, examining the temporal dynamics of salinity, EH, pH (Total scale), dissolved oxygen and nutrient (PO43−, NO2−, NO3−, NH4+, and Si(OH)4) concentrations under the local hydrological regime. The objectives were threefold: (1) to determine the annual variability of nutrient content and N:P:Si stoichiometry in SGD into the lagoon; (2) to identify the main drivers of variability in SGD composition and stoichiometry and their interactive effects; (3) to discuss links to, and implications for, ecosystem function that could help define expectations of cause-effect relationships and be useful for environmental management of the lagoon and similar systems elsewhere. We find that the terrestrial groundwater recharge cycle drives the expansion and contraction of the subterranean estuary on annual timescales, causing the pH of SGD to fluctuate in opposition to continental groundwater level. The annual dynamics of the STE and the resulting pH oscillation determine the annual variability of nutrient composition ratio in SGD and shape benthic primary production dynamics. When saltwater intrusion occurs, the pH within SGD increases, enhancing nitrification and desorption of exchangeable phosphorus, while silicate fluxes increase with seawater retreat. The result is that nutrient resource ratio availability for coastal primary production depends on the fresh groundwater level. This implies that ecosystem function in such systems is more

Coastal zones are active reactors of continental material including that transported by rivers via a series of microbiota-mediated reactions. Nevertheless, current knowledge of the ecology and functioning of the microbiota in coastal areas affected by large riverine inputs remains insufficient on a global scale. Here, an investigation on sediment microbial composition, including taxonomy and metabolic network, as well as their relationship with major benthic reaction substrates, namely carbon, nitrogen, sulphur and phosphorus, was conducted in the continental shelf affected by the spread of the Changjiang River plume. Surface sediment samples (48 samples) were collected during March 2018, obtaining a mean Operational Taxonomic Units (OTUs) number of 3 341. Proteobacteria, Acidobacteria and Actinobacteria were abundant phyla in the studied sediments. Bray-Curtis distance analysis classified the 48 samples into 4 clusters (MG1 to MG4) at the phylum-level. MG1 and MG2 are found near the river mouth, receiving substantial land-derived particles from the Changjiang River runoff. Particle-attached microbes may be settled in these regions and influenced the observed sediment microbial diversity and biomass, e.g., increased Crenarchaeota relative abundance. The relative enrichment of these two groups in heterotrophic microbes further suggests a reliance of benthic microbiota on substrates with terrestrial origin, particularly specialized on processing sulphur-rich substrates. Regions MG3 and MG4 are located in the outer margin of the area affected by the Changjiang River plume, mainly fed by settling pelagic particles from phytoplankton. Compared to MG1 and MG2, a significant increase in the abundance of Thaumarcheota (phylumlevel) and Nitrosopumilus (genus-level) was found in MG3, suggesting nitrogen-related transformations as the key reactions to sustain microbial metabolism in this region. Coupled with the identified variations in the taxonomic composition, significant d

© 2017 American Chemical Society. Microelectrodes provide a direct pathway to investigate brain activities electrically from the external world, which has advanced our fundamental understanding of brain functions and has been utilized for rehabilitative applications as brain-machine interfaces. However, minimizing the tissue response and prolonging the functional durations of these devices remain challenging. Therefore, the development of next-generation microelectrodes as neural interfaces is actively progressing from traditional inorganic materials toward biocompatible and functional organic materials with a miniature footprint, good flexibility, and reasonable robustness. In this study, we developed a miniaturized all polymer-based neural probe with carbon nanofiber (CNF) composites as recording electrodes via the scalable thermal drawing process. We demonstrated that in situ CNF unidirectional alignment can be achieved during the thermal drawing, which contributes to a drastic improvement of electrical conductivity by 2 orders of magnitude compared to a conventional polymer electrode, while still maintaining the mechanical compliance with brain tissues. The resulting neural probe has a miniature footprint, including a recording site with a reduced size comparable to a single neuron and maintained impedance that was able to capture neural activities. Its stable functionality as a chronic implant has been demonstrated with the long-term reliable electrophysiological recording with single-spike resolution and the minimal tissue response over the extended period of implantation in wild-type mice. Technology developed here can be applied to basic chronic electrophysiological studies as well as clinical implementation for neuro-rehabilitative applications.

Yuanyuan Jiang,1 Shan Jiang,1,2 Qing Long,1 Jundi Yang,1,3 Jyu-Lin Chen,4 Jia Guo1 1Xiangya School of Nursing, Central South University, Changsha, Hunan Province, People’s Republic of China; 2Zhongda Hospital Affiliated to Southeast University, Nanjing, Jiangsu, People’s Republic of China; 3School of Nursing, The University of Hong Kong, Hong Kong, People’s Republic of China; 4School of Nursing, University of California San Francisco, San Francisco, CA, USACorrespondence: Jia Guo, Tel +86 13875947418, Email guojia621@163.comBackground: Women in rural areas of China face the unique risk of developing diabetes, but data on the glycemic status among women without prior diabetes diagnosis in rural areas are lacking which may limit the decision-making for diabetes screening or prevention. This study was aimed to describe the glycemic status of women determined by hemoglobin A1c (HbA1c) and explore its associating factors.Methods: A cross-sectional survey was conducted among women without prior diabetes diagnosis from two counties of rural areas in Central South China from July to October 2018. On the basis of the life course framework, data on socio-demographic, environmental health, genetic, biological (blood pressure, weight and height, lipids), psychosocial, and healthy behavioral factors were collected on site either through questionnaires or field measurements. The questionnaires included social-demographic sheet, Perceived Stress Scale, Self-efficacy scales for health-related diet and exercise behaviors, and Chinese Diabetes Risk Questionnaire. Generalized linear model analysis was performed to determine the associating factors of glycemic status, which was measured by HbA1c level.Results: A total of 647 women were included in the study. The median age of women was 51.00 years (range 35– 65 years). The median HbA1c level was 5.1% (interquartile range 4.6%-5.5%, range 4– 13.6%), and 8.7% (n=56) of women were identified as elevated glycemic status (HbA1c&gt

Many real-world applications can be formulated as multi-agent cooperation problems, such as network packet routing and coordination of autonomous vehicles. The emergence of deep reinforcement learning (DRL) provides a promising approach for multi-agent cooperation through the interaction of the agents and environments. However, traditional DRL solutions suffer from the high dimensions of multiple agents with continuous action space during policy search. Besides, the dynamicity of agents' policies makes the training non-stationary. To tackle the issues, we propose a hierarchical reinforcement learning approach with high-level decision-making and low-level individual control for efficient policy search. In particular, the cooperation of multiple agents can be learned in high-level discrete action space efficiently. At the same time, the low-level individual control can be reduced to single-agent reinforcement learning. In addition to hierarchical reinforcement learning, we propose an opponent modeling network to model other agents' policies during the learning process. In contrast to end-to-end DRL approaches, our approach reduces the learning complexity by decomposing the overall task into sub-tasks in a hierarchical way. To evaluate the efficiency of our approach, we conduct a real-world case study in the cooperative lane change scenario. Both simulation and real-world experiments show the superiority of our approach in the collision rate and convergence speed., Comment: 11 pages, 13 figures, 2 tables

Multi-agent reinforcement learning (MARL) has been gaining extensive attention from academia and industries in the past few decades. One of the fundamental problems in MARL is how to evaluate different approaches comprehensively. Most existing MARL methods are evaluated in either video games or simplistic simulated scenarios. It remains unknown how these methods perform in real-world scenarios, especially multi-robot systems. This paper introduces a scalable emulation platform for multi-robot reinforcement learning (MRRL) called SMART to meet this need. Precisely, SMART consists of two components: 1) a simulation environment that provides a variety of complex interaction scenarios for training and 2) a real-world multi-robot system for realistic performance evaluation. Besides, SMART offers agent-environment APIs that are plug-and-play for algorithm implementation. To illustrate the practicality of our platform, we conduct a case study on the cooperative driving lane change scenario. Building off the case study, we summarize several unique challenges of MRRL, which are rarely considered previously. Finally, we open-source the simulation environments, associated benchmark tasks, and state-of-the-art baselines to encourage and empower MRRL research., Comment: 13 pages

A singularly perturbed convection-diffusion problem posed on the unit square in $\mathbb{R}^2$, whose solution has exponential boundary layers, is solved numerically using the local discontinuous Galerkin (LDG) method with piecewise polynomials of degree at most $k>0$ on three families of layer-adapted meshes: Shishkin-type, Bakhvalov-Shishkin-type and Bakhvalov-type.On Shishkin-type meshes this method is known to be no greater than $O(N^{-(k+1/2)})$ accurate in the energy norm induced by the bilinear form of the weak formulation, where $N$ mesh intervals are used in each coordinate direction. (Note: all bounds in this abstract are uniform in the singular perturbation parameter and neglect logarithmic factors that will appear in our detailed analysis.) A delicate argument is used in this paper to establish $O(N^{-(k+1)})$ energy-norm superconvergence on all three types of mesh for the difference between the LDG solution and a local Gauss-Radau projection of the exact solution into the finite element space. This supercloseness property implies a new $N^{-(k+1)}$ bound for the $L^2$ error between the LDG solution on each type of mesh and the exact solution of the problem; this bound is optimal (up to logarithmic factors). Numerical experiments confirm our theoretical results., Comment: 26 pages, 10 figures, 22 references

The anthropogenic introduction of significant amounts of reactive nitrogen in the coastal zone particularly since the discovery and application of the Haber-Bosch process has profound consequences over organic carbon storage and transformations at both regional and global scales. Here, we review our current knowledge on cause-effect chains for nitrogen, especially dissolved inorganic nitrogen, on organic carbon cycling in coastal tropical systems. We focus on the feedback mechanisms for turnover of different organic carbon species to nitrogen excess and links to current environmental and climate changes. We pay special attention to organic carbon dynamics in tropical coasts due to their high primary productivity, rapid sedimentation, and significant needs of nitrogen for agriculture and industry usages. Together with land-use changes and economy development, we highlight the vulnerability of carbon storage in tropical coasts triggered by nitrogen overloading and outline possible industrial strategies with low carbon cycling disturbance to benefit the development of tropical countries

FSHD is characterized by the misexpression of DUX4 in skeletal muscle. Although DUX4 upregulation is thought to be the pathogenic cause of FSHD, DUX4 is lowly expressed in patient samples, and analysis of the consequences of DUX4 expression has largely relied on artificial overexpression. To better understand the native expression profile of DUX4 and its targets, we performed bulk RNA-seq on a 6-day differentiation time-course in primary FSHD2 patient myoblasts. We identify a set of 54 genes upregulated in FSHD2 cells, termed FSHD-induced genes. Using single-cell and single-nucleus RNA-seq on myoblasts and differentiated myotubes, respectively, we captured, for the first time, DUX4 expressed at the single-nucleus level in a native state. We identified two populations of FSHD myotube nuclei based on low or high enrichment of DUX4 and FSHD-induced genes ("FSHD-Lo" and "FSHD Hi", respectively). FSHD-Hi myotube nuclei coexpress multiple DUX4 target genes including DUXA, LEUTX and ZSCAN4, and also upregulate cell cycle-related genes with significant enrichment of E2F target genes and p53 signaling activation. We found more FSHD-Hi nuclei than DUX4-positive nuclei, and confirmed with in situ RNA/protein detection that DUX4 transcribed in only one or two nuclei is sufficient for DUX4 protein to activate target genes across multiple nuclei within the same myotube. DUXA (the DUX4 paralog) is more widely expressed than DUX4, and depletion of DUXA suppressed the expression of LEUTX and ZSCAN4 in late, but not early, differentiation. The results suggest that the DUXA can take over the role of DUX4 to maintain target gene expression. These results provide a possible explanation as to why it is easier to detect DUX4 target genes than DUX4 itself in patient cells and raise the possibility of a self-sustaining network of gene dysregulation triggered by the limited DUX4 expression.

Schizophrenia (SCZ) is a severe psychiatric disorder with a strong genetic component. High heritability of SCZ suggests a major role for transmitted genetic variants. Furthermore, SCZ is also associated with a marked reduction in fecundity, leading to the hypothesis that alleles with large effects on risk might often occur de novo. In this study, we conducted whole-genome sequencing for 23 families from two cohorts with unaffected siblings and parents. Two nonsense de novo mutations (DNMs) in GJC1 and HIST1H2AD were identified in SCZ patients. Ten genes (DPYSL2, NBPF1, SDK1, ZNF595, ZNF718, GCNT2, SNX9, AACS, KCNQ1, and MSI2) were found to carry more DNMs in SCZ patients than their unaffected siblings by burden test. Expression analyses indicated that these DNM implicated genes showed significantly higher expression in prefrontal cortex in prenatal stage. The DNM in the GJC1 gene is highly likely a loss function mutation (pLI = 0.94), leading to the dysregulation of ion channel in the glutamatergic excitatory neurons. Analysis of rare variants in independent exome sequencing dataset indicates that GJC1 has significantly more rare variants in SCZ patients than in unaffected controls. Data from genome-wide association studies suggested that common variants in the GJC1 gene may be associated with SCZ and SCZ-related traits. Genes co-expressed with GJC1 are involved in SCZ, SCZ-associated pathways, and drug targets. These evidences suggest that GJC1 may be a risk gene for SCZ and its function may be involved in prenatal and early neurodevelopment, a vulnerable period for developmental disorders such as SCZ.