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Incomplete multi-view clustering (IMVC) aims to partition samples into different groups for datasets with missing samples. The primary goal of IMVC is to effectively address the challenge posed by missing information in clustering analysis. Most existing IMVC methods focus on balanced incomplete multi-view data, assuming a uniform missing rate across all views. However, this assumption does not accurately reflect real-life scenarios. In reality, unbalanced incomplete multi-view data, characterized by varying missing rates among different views, is more prevalent. This presents significant challenges to the clustering process, as varying missing rates can lead to information imbalance. To address these challenges, this paper introduces a novel approach called projected cross-view learning for unbalanced incomplete multi-view clustering (PCL_UIMVC). Specifically, a reconstruction term is integrated, which leverages the information from the existing samples to facilitate the completion of the unbalanced incomplete multi-view data. Next, a projection matrix is incorporated into the model to harmonize feature dimensions across views, mitigating the impact of information imbalance. Then, a graph regularization term is integrated to preserve the geometric structure of the original data. Finally, an iterative algorithm is developed to solve the proposed model. Extensive experiments on eight standard datasets, featuring various rates of missing data, validate the superior clustering performance of the proposed method.

Glacial meltwater is an important environmental variable for ecosystem dynamics along the biologically productive Western Antarctic Peninsula (WAP) shelf. This region is experiencing rapid change, including increasing glacial meltwater discharge associated with the melting of land ice. To better understand the WAP environment and aid ecosystem forecasting, additional methods are needed for monitoring and quantifying glacial meltwater for this remote, sparsely sampled location. Prior studies showed that sea surface glacial meltwater (SSGM) has unique optical characteristics which may allow remote sensing detection via ocean color data. In this study, we develop a first-generation model for quantifying SSGM that can be applied to both spaceborne (MODIS-Aqua) and airborne (PRISM) ocean color platforms. In addition, the model was prepared and verified with one of the more comprehensive in-situ stable oxygen isotope datasets compiled for the WAP region. The SSGM model appears robust and provides accurate predictions of the fractional contribution of glacial meltwater to seawater when compared with in-situ data (r = 0.82, median absolute percent difference = 6.38%, median bias = −0.04), thus offering an additional novel method for quantifying and studying glacial meltwater in the WAP region.

Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.

Glacial meltwater is an important environmental variable for ecosystem dynamics along the biologically productive Western Antarctic Peninsula (WAP) shelf. This region is experiencing rapid change, including increasing glacial meltwater discharge associated with the melting of land ice. To better understand the WAP environment and aid ecosystem forecasting, additional methods are needed for monitoring and quantifying glacial meltwater for this remote, sparsely sampled location. Prior studies showed that sea surface glacial meltwater (SSGM) has unique optical characteristics which may allow remote sensing detection via ocean color data. In this study, we develop a first-generation model for quantifying SSGM that can be applied to both spaceborne (MODIS-Aqua) and airborne (PRISM) ocean color platforms. In addition, the model was prepared and verified with one of the more comprehensive in-situ stable oxygen isotope datasets compiled for the WAP region. The SSGM model appears robust and provides accurate predictions of the fractional contribution of glacial meltwater to seawater when compared with in-situ data (r = 0.82, median absolute percent difference = 6.38%, median bias = −0.04), thus offering an additional novel method for quantifying and studying glacial meltwater in the WAP region.

The skin plays an important role in vitamin D synthesis, humoral balance, temperature regulation, and waste excretion. Due to the complexity of the skin, fluids loss, bacterial infection, and other life-threatening secondary complications caused by skin defects often lead to the damage of skin functions. 3D bioprinting technology, as a customized and precise biomanufacturing platform, can manufacture dressings and tissue engineering scaffolds that accurately simulate tissue structure, which is more conducive to wound healing. In recent years, with the development of emerging technologies, an increasing number of 3D-bioprinted wound dressings and skin tissue engineering scaffolds with multiple functions, such as antibacterial, antiinflammatory, antioxidant, hemostatic, and antitumor properties, have significantly improved wound healing and skin treatment. In this article, we review the process of wound healing and summarize the classification of 3D bioprinting technology. Following this, we shift our focus on the functional materials for wound dressing and skin tissue engineering, and also highlight the research progress and development direction of 3D-bioprinted multifunctional wound healing materials.

Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.

Understanding the evolution of river systems in southern Africa is fundamental to constrain the evolution of landscape and sediment dispersal patterns. It is widely considered that the upper Zambezi River was connected with the Limpopo River during the Cretaceous, forming what was then the largest river in Africa. Crustal flexure during the Paleogene severed the upper Zambezi drainage from the Limpopo, setting the framework of the modern Zambezi and Limpopo River systems. We present first evidence—based on heavy-mineral assemblages from cores drilled offshore of the Limpopo River mouth and samples collected in different reaches of the modern Limpopo River, integrated with magnetic susceptibility, detrital-zircon geochronology, and geomorphological analysis—suggesting that the current Limpopo River formed recently in the Plio-Quaternary. Plio-Quaternary climate change is envisaged to have controlled the recent dynamics of river drainage and consequent distribution of sediment loads, as observed in many other transcontinental rivers worldwide.

The Western Antarctic Peninsula (WAP) is rapidly changing due to climate forcing in recent decades. These changes manifest as an overall increase in ocean and air temperatures (with some regional cooling), retreating glaciers, and increases in precipitation associated with shifts in atmospheric circulation. As these changes continue and intensify, meteoric water input to the coastal ocean is expected to increase. Continued monitoring of these changes can help us better understand their impacts. The physical effects of glacial melting on sea level has been extensively studied in the past. However, the impact of glacial meltwater on phytoplankton community composition remains largely elusive. Due to the critical role of primary producers in the Antarctic food web and their significance to local biogeochemical cycle and ecosystem dynamics, the immediate ramification of meltwater input on these communities need to be better understood.In order to accomplish such task, robust datasets are required that can encompass both long-term temporal scale and broad geographic extent. Coincidentally, these datasets exist in the Western Antarctic Peninsula region. Two oceanographic expeditions (FjordEco) were conducted in an Antarctic fjord (Andvord Bay) in November to December 2015 and April to May 2016. This dataset provides insights on nearshore water properties and how they impact phytoplankton community. Over the Peninsula shelf, a multi-decadal time series was curated and collected by the Palmer Long-Term Ecological Research (LTER) program. The LTER database has extensive spatial and temporal coverage which make it suitable for understanding the impact of environmental variables on phytoplankton over the continental shelf.This dissertation contains three main chapters. In Chapter 2, I aim to understand the spatial and temporal distribution of glacial meltwater in Andvord Bay and characterize their optical features in order to develop a method for quantifying meltwater fraction

The Western Antarctic Peninsula (WAP) is rapidly changing due to climate forcing in recent decades. These changes manifest as an overall increase in ocean and air temperatures (with some regional cooling), retreating glaciers, and increases in precipitation associated with shifts in atmospheric circulation. As these changes continue and intensify, meteoric water input to the coastal ocean is expected to increase. Continued monitoring of these changes can help us better understand their impacts. The physical effects of glacial melting on sea level has been extensively studied in the past. However, the impact of glacial meltwater on phytoplankton community composition remains largely elusive. Due to the critical role of primary producers in the Antarctic food web and their significance to local biogeochemical cycle and ecosystem dynamics, the immediate ramification of meltwater input on these communities need to be better understood.In order to accomplish such task, robust datasets are required that can encompass both long-term temporal scale and broad geographic extent. Coincidentally, these datasets exist in the Western Antarctic Peninsula region. Two oceanographic expeditions (FjordEco) were conducted in an Antarctic fjord (Andvord Bay) in November to December 2015 and April to May 2016. This dataset provides insights on nearshore water properties and how they impact phytoplankton community. Over the Peninsula shelf, a multi-decadal time series was curated and collected by the Palmer Long-Term Ecological Research (LTER) program. The LTER database has extensive spatial and temporal coverage which make it suitable for understanding the impact of environmental variables on phytoplankton over the continental shelf.This dissertation contains three main chapters. In Chapter 2, I aim to understand the spatial and temporal distribution of glacial meltwater in Andvord Bay and characterize their optical features in order to develop a method for quantifying meltwater fraction

Conversion of tropical peat swamp forests to meet the demand for industrial plantations and agricultural production systems has triggered rapid and substantial carbon loss in the Asia‐Pacific region. Various management practices have been designed to reduce carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions from tropical peat soils after changes in land use. We conducted a meta‐analysis using 506 paired observations on greenhouse‐gas (GHG) emissions from peat soils under different land uses and the effects of management practices on emissions. As compared to peat swamp forest, other land uses had higher emissions of CO2 (heterotrophic respiration) and N2O from soils, whereas soil‐based CH4 emissions were also increased but not significantly so. Raising the water table decreased CO2 emissions but increased N2O emissions; reducing nitrogen fertilizer inputs led to decreased CO2 and N2O emissions in oil‐palm (Elaeis spp) plantations and cropping systems; and shading and growing cover crops decreased CO2 emissions. Practices such as these are needed for careful management of tropical peatlands, and additional measurements at appropriate spatial and temporal scales are required to guide future GHG mitigation strategies in tropical peatlands.

Dinitrogen (N2) and nitrous oxide (N2O) produced via denitrification may represent major nitrogen (N) loss in terrestrial ecosystems. A global assessment of soil denitrification rate, N2O/(N2O+N2) ratio, and their driving factors and mitigation strategies is lacking. We conducted a global synthesis using 225 studies (3367 observations) to fill this knowledge gap. We found that daily N loss through soil denitrification varied with ecosystems and averaged 0.25 kg N ha−1. The average emission factor of denitrification (EFD) was 4.8%. The average N2O/(N2O+N2) ratio from soil denitrification was 0.33. Soil denitrification rate was positively related to soil water-filled pore space (WFPS) (p < 0.01), nitrate (NO3-) content (p < 0.05) and soil temperature (p < 0.01), and decreased with higher soil oxygen content (p < 0.01). N2 emissions increased with latitude (p < 0.05), WFPS (p < 0.01) and soil mineral N (p < 0.05) but decreased with soil oxygen content (p < 0.05). The N2O/(N2O+N2) ratio increased with soil oxygen content (p < 0.01) but decreased with organic carbon (C) (p < 0.05), C/N ratio (p < 0.01), soil pH (p < 0.05) and WFPS (p < 0.01). We also found that optimizing N application rates, using ammonium-based fertilizers compared to nitrate-based fertilizers, biochar amendment, and application of nitrification inhibitors could effectively reduce soil denitrification rate and associated N2 emissions. These findings highlight that N loss via soil denitrification and N2 emissions cannot be neglected, and that mitigation strategies should be adopted to reduce N loss and improve N use efficiency. Our study presents a comprehensive data synthesis for large-scale estimations of denitrification and the refinement of relevant parameters used in the submodels of denitrification in process-based models.

Contains fulltext : 282924.pdf (Publisher’s version ) (Open Access)

Neisseria meningitidis protects itself from complement-mediated killing by binding complement factor H (FH). Previous studies associated susceptibility to meningococcal disease (MD) with variation in CFH, but the causal variants and underlying mechanism remained unknown. Here we attempted to define the association more accurately by sequencing the CFH-CFHR locus and imputing missing genotypes in previously obtained GWAS datasets of MD-affected individuals of European ancestry and matched controls. We identified a CFHR3 SNP that provides protection from MD (rs75703017, p value = 1.1 × 10-16) by decreasing the concentration of FH in the blood (p value = 1.4 × 10-11). We subsequently used dual-luciferase studies and CRISPR gene editing to establish that deletion of rs75703017 increased FH expression in hepatocyte by preventing promotor inhibition. Our data suggest that reduced concentrations of FH in the blood confer protection from MD; with reduced access to FH, N. meningitidis is less able to shield itself from complement-mediated killing.

Nitrogen losses in agricultural systems can be reduced through enhanced-efficiency fertilizers (EEFs), which control the physicochemical release from fertilizers and biological nitrogen transformations in soils. The adoption of EEFs by farmers requires evidence of consistent performance across soils, crops and climates, paired with information on the economic advantages. Here we show that the benefits of EEFs due to avoided social costs of nitrogen pollution considerably outweigh their costs—and must be incorporated in fertilizer policies. We outline new approaches to the design of EEFs using enzyme inhibitors with modifiable chemical structures and engineered, biodegradable coatings that respond to plant rhizosphere signalling molecules.

BACKGROUND: Androgen deprivation therapy (ADT) is a key component of therapy for patients with high-risk prostate carcinoma, but it may be deleterious for bone health. We sought to determine the frequency of dual energy x-ray absorptiometry (DXA) scanning in patients commencing adjuvant ADT for treatment of high-risk prostate cancer at a large integrated regional cancer centre. MATERIAL AND METHODS: The electronic medical records (EMR) of all patients with high-risk prostate carcinoma commenced on adjuvant ADT between January 1, 2016 and December 31, 2017 at the Mid-North Coast Cancer Institute, Coffs Harbour, Australia were reviewed. Patients commenced on neoadjuvant ADT and long-term suppressive ADT for metastatic disease were excluded. The following data were obtained: socio-demographic information, prostate cancer data, ADT details and DXA results. RESULTS: 188 men (mean age ± SD, 75.4 ± 7 years) were commenced on adjuvant ADT for a total duration (mean ± SD) of 23.4 ± 7 months. Most (n = 155/188, 82%) were commenced on leuprorelin acetate. While only 26/188 (14%) had a DXA scan performed prior to ADT, another 133 (71%) had a DXA scan at a median of 20 days (interquartile range 7-98), later. Of the 159 men with DXA readings, 76 (48%) were osteopaenic and 38 (24%) were osteoporotic by DXA criteria. CONCLUSION: A high level (85%) of DXA scanning in men commencing ADT for prostate cancer can be achieved at a regional centre. The high prevalence (72%) of low bone mass in our unselected cohort underscores the importance of routine DXA scanning to guide bone health management during ADT.

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach.

Despite decades of study, the factors that controlled the formation and evolution of the upper reaches of the Yellow River, including uplift of the northeastern Tibetan Plateau, Pliocene- Pleistocene climate change, and autogenetic processes are still poorly constrained. The stratigraphic record of such paleogeographic evolution is recorded in the sequence of nine terraces formed during progressive incision of the Yellow River in the last 1.7 Ma. This article investigates in detail for sediment provenance in terraces of the Lanzhou area, based on heavy-mineral and geochemical (REE) signatures. Two main provenance changes are identified, pointing each to a major paleogeographic reorganization coupled with expansion of the upper Yellow River catchment and enhanced sediment fluxes. The first change took place between the deposition of terrace T9 (formed around 1.7 Ma) and terrace T8 (formed around 1.5 Ma), when rapid fluvial incision point to tectonic control and active uplift of northeastern Tibetan Plateau. The second change took place between deposition of terrace T4 (formed around 0.86 Ma) and terrace T3 (formed around 0.14 Ma), during a period of low incision rates and notably enhanced sediment fluxes as a response to enhanced East Asian Summer Monsoon and consequently increased precipitations, pointing instead chiefly to climatic control.

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach.

As the Western Antarctic Peninsula (WAP) region responds to a warmer climate, the impacts of glacial meltwater on the Southern Ocean are expected to intensify. The Antarctic Peninsula fjord system offers an ideal system to understand meltwater's properties, providing an extreme in the meltwater's spatial gradient from the glacio-marine boundary to the WAP continental shelf. Glacial meltwater discharge in Arctic and Greenland fjords is typically characterized as relatively lower temperature, fresh and with high turbidity. During two cruises conducted in December 2015 and April 2016 in Andvord Bay, we found a water lens of low salinity and low temperature along the glacio-marine interface. Oxygen isotope ratios identified this water lens as a mixture of glacial ice and deep water in Gerlache Strait suggesting this is glacial meltwater. Conventional hydrographic measurements were combined with optical properties to effectively quantify its spatial extent. Fine suspended sediments associated with meltwater (nanoparticles of ~ 5nm) had a significant impact on the underwater light field and enabled the detection of meltwater characteristics and spatial distribution. In this study, we illustrate that glacial meltwater in Andvord Bay alters the inherent and apparent optical properties of the water column, and develop statistical models to predict the meltwater content from hydrographic and optical measurements. The predicted meltwater fraction is in good agreement with in-situ values. These models offer a potential for remote sensing and high-resolution detection of glacial meltwater in Antarctic waters. Furthermore, the possible influence of meltwater on phytoplankton abundance in the surface is highlighted; a significant correlation is found between meltwater fraction and chlorophyll concentration.

Despite decades of study, the factors that controlled the formation and evolution of the upper reaches of the Yellow River, including uplift of the northeastern Tibetan Plateau, Pliocene- Pleistocene climate change, and autogenetic processes are still poorly constrained. The stratigraphic record of such paleogeographic evolution is recorded in the sequence of nine terraces formed during progressive incision of the Yellow River in the last 1.7 Ma. This article investigates in detail for sediment provenance in terraces of the Lanzhou area, based on heavy-mineral and geochemical (REE) signatures. Two main provenance changes are identified, pointing each to a major paleogeographic reorganization coupled with expansion of the upper Yellow River catchment and enhanced sediment fluxes. The first change took place between the deposition of terrace T9 (formed around 1.7 Ma) and terrace T8 (formed around 1.5 Ma), when rapid fluvial incision point to tectonic control and active uplift of northeastern Tibetan Plateau. The second change took place between deposition of terrace T4 (formed around 0.86 Ma) and terrace T3 (formed around 0.14 Ma), during a period of low incision rates and notably enhanced sediment fluxes as a response to enhanced East Asian Summer Monsoon and consequently increased precipitations, pointing instead chiefly to climatic control.

The upper Yellow River drains the central and northeastern Tibetan Plateau. Understanding the origin of this river is essential for unraveling the interplay between fluvial incision, basement uplift, and climate change. However, the formation age of the upper Yellow River is highly debated, with estimates ranging from Eocene to late Pleistocene. In order to clarify the history of the upper Yellow River, we present a heavy-mineral dataset from drill core in the Yinchuan Basin, a depositional sink at the end of the upper Yellow River course. Our results reveal that the drainage area of the upper Yellow River (i.e., northeastern Tibetan Plateau) has been serving as a major sediment source region for the Yinchuan Basin since at least 3.3 Ma. A late Pleistocene formation of the upper Yellow River, and erosion of neighboring deserts or bounding mountains as an explanation for thick sediment accumulations in this area, is thus ruled out.

Item does not contain fulltext, The present longitudinal study examined how and why classroom-level victimization moderates the prospective association between peer victimization and depressive symptoms with 2,643 third- and fourth-graders (Mage = 10.01 years) in China. Multilevel modeling revealed that peer victimization was more strongly associated with increasing depressive symptoms in classrooms with lower classroom-level victimization. Moreover, two mechanisms were identified to explain the moderating effect of classroom-level victimization. First, low classroom-level victimization reduced victimized children's received friendship nominations from peers, thereby leading to increases in depressive affect. Second, low classroom-level victimization affected victimized children’s depressive symptoms through damage to their social self-concept. These findings provide support for the "healthy context paradox" in the Chinese culture, and highlight the mechanisms of this phenomenon.

Antibiotic contamination and the spread of antimicrobial resistant bacteria are global environmental issues. Given the growing consumption of antibiotics, it is crucial to reduce their presence in the environment. Adsorption is one of the most efficient methods for removing contaminants from water and wastewater. For this process to be effective, it is of key importance to identify adsorption mechanisms that allow an efficient and selective adsorbent to be chosen. Carbon-based materials (including activated carbon, biochar and black carbon) are typically used for the adsorptive removal of antibiotics. To enhance the efficiency of adsorption of pharmaceuticals, engineered biochars (physically, chemically and biologically modified biochar) and their composites have attracted increasing interests. Biochar-based sorbents can be produced from various feedstocks, including waste products. The use of “green”, low cost or sustainable biochar for contaminant sorption yields economic and environmental benefits. Moreover, this is in line with global trends in creating a circular economy and sustainable development. This paper collates the most recent data on the consumption of antibiotics, their related environmental contamination, and their removal using biochar-based materials. Special attention is paid to the newly emerging approaches of biochar modification and biochar composites in relation to the antibiotic removal from water. © 2020 Elsevier B.V.

Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame. As the latter is computationally more tractable, investigation to which extent this equivalence holds conducted in this paper is of immediate practical significance. To this end we formulate and compare DR, a two step approach based on the recovery of the complete volume of the photoacoustic data from the subsampled data followed by the acoustic inversion, and {p_0bf {R}}, a one step approach where the photoacoustic image (the initial pressure, p_0) is directly recovered from the subsampled data. Effective representation of the photoacoustic data requires basis defined on the range of the photoacoustic forward operator. To this end we propose a novel wedge-restriction of Curvelet transform which enables us to construct such basis. Both recovery problems are formulated in a variational framework. As the Curvelet frame is heavily overdetermined, we use reweighted ell 1 norm penalties to

Brain systems that promote maintenance of homeostasis in the face of stress have significant adaptive value. A growing body of work across species demonstrates a critical role for the amygdala in promoting homeostasis by regulating physiological and behavioral responses to stress. This review focuses on an emerging body of evidence that has begun to delineate the contribution of specific long-range amygdala circuits in mediating the effects of stress. After summarizing the major anatomical features of the amygdala and its connectivity to other limbic structures, we discuss recent findings from rodents showing how stress causes structural and functional remodeling of amygdala neuronal outputs to defined cortical and subcortical target regions. We also consider some of the environmental and genetic factors that have been found to moderate how the amygdala responds to stress and relate the emerging preclinical literature to the current understanding of the pathophysiology and treatment of stress-related neuropsychiatric disorders. Future effort to translate these findings to clinics may help to develop valuable tools for prevention, diagnosis, and treatment of these diseases.

Nitrification and immobilization compete for soil ammonium (NH4+); the relative dominance of these two processes has been suggested to reflect the potential risk of nitrogen loss from soils. Here, we compiled a database and developed a stochastic gradient boosting model to predict the global potential risk of nitrogen loss based on the ratio of nitrification to immobilization (N/I). We then conducted a meta-analysis to evaluate the effects of common management practices on the N/I ratio. The results showed that the soil N/I ratio varied with climate zones and land use. Soil total carbon, total nitrogen, pH, fertilizer nitrogen application rate, mean annual temperature, and mean annual precipitation are important factors of soil N/I ratio. Meta-analysis indicated that biochar, straw, and nitrification inhibitor application reduced the soil N/I ratio by 67, 64, and 78%, respectively. Returning plantation to forest and cropland to grassland decreased the soil N/I ratio by 88 and 45%, respectively. However, fertilizer nitrogen application increased the soil N/I ratio by 92%. Our study showed that the soil N/I ratio and its associated risk level of nitrogen loss were highly related to long-term soil and environmental properties with high spatial heterogeneity.

Nitrification is a major pathway of N2O production in aerobic soils. Measurements and model simulations of nitrification and associated N2O emission are challenging. Here we innovatively integrated data mining and machine learning to predict nitrification rate ( R nit ) and the fraction of nitrification as N2O emissions ( f N 2 O Nit ). Using our global database on R nit and f N 2 O Nit , we found that

Contains fulltext : 241716.pdf (Publisher’s version ) (Closed access), The present longitudinal study examined how and why classroom-level victimization moderates the prospective association between peer victimization and depressive symptoms with 2,643 third- and fourth-graders (Mage = 10.01 years) in China. Multilevel modeling revealed that peer victimization was more strongly associated with increasing depressive symptoms in classrooms with lower classroom-level victimization. Moreover, two mechanisms were identified to explain the moderating effect of classroom-level victimization. First, low classroom-level victimization reduced victimized children's received friendship nominations from peers, thereby leading to increases in depressive affect. Second, low classroom-level victimization affected victimized children’s depressive symptoms through damage to their social self-concept. These findings provide support for the "healthy context paradox" in the Chinese culture, and highlight the mechanisms of this phenomenon.

Contains fulltext : 241716.pdf (Publisher’s version ) (Closed access), The present longitudinal study examined how and why classroom-level victimization moderates the prospective association between peer victimization and depressive symptoms with 2,643 third- and fourth-graders (Mage = 10.01 years) in China. Multilevel modeling revealed that peer victimization was more strongly associated with increasing depressive symptoms in classrooms with lower classroom-level victimization. Moreover, two mechanisms were identified to explain the moderating effect of classroom-level victimization. First, low classroom-level victimization reduced victimized children's received friendship nominations from peers, thereby leading to increases in depressive affect. Second, low classroom-level victimization affected victimized children’s depressive symptoms through damage to their social self-concept. These findings provide support for the "healthy context paradox" in the Chinese culture, and highlight the mechanisms of this phenomenon.

Antibiotic contamination and the spread of antimicrobial resistant bacteria are global environmental issues. Given the growing consumption of antibiotics, it is crucial to reduce their presence in the environment. Adsorption is one of the most efficient methods for removing contaminants from water and wastewater. For this process to be effective, it is of key importance to identify adsorption mechanisms that allow an efficient and selective adsorbent to be chosen. Carbon-based materials (including activated carbon, biochar and black carbon) are typically used for the adsorptive removal of antibiotics. To enhance the efficiency of adsorption of pharmaceuticals, engineered biochars (physically, chemically and biologically modified biochar) and their composites have attracted increasing interests. Biochar-based sorbents can be produced from various feedstocks, including waste products. The use of “green”, low cost or sustainable biochar for contaminant sorption yields economic and environmental benefits. Moreover, this is in line with global trends in creating a circular economy and sustainable development. This paper collates the most recent data on the consumption of antibiotics, their related environmental contamination, and their removal using biochar-based materials. Special attention is paid to the newly emerging approaches of biochar modification and biochar composites in relation to the antibiotic removal from water. © 2020 Elsevier B.V.

Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame. As the latter is computationally more tractable, investigation to which extent this equivalence holds conducted in this paper is of immediate practical significance. To this end we formulate and compare DR, a two step approach based on the recovery of the complete volume of the photoacoustic data from the subsampled data followed by the acoustic inversion, and {p_0bf {R}}, a one step approach where the photoacoustic image (the initial pressure, p_0) is directly recovered from the subsampled data. Effective representation of the photoacoustic data requires basis defined on the range of the photoacoustic forward operator. To this end we propose a novel wedge-restriction of Curvelet transform which enables us to construct such basis. Both recovery problems are formulated in a variational framework. As the Curvelet frame is heavily overdetermined, we use reweighted ell 1 norm penalties to enhance the sparsity of the solution. The data reconstruction problem DR is a standard compressed sensing recovery problem, which we solve using an ADMM-type algorithm, SALSA. Subsequently, the initial pressure is recovered using time reversal as implemented in the k-Wave Toolbox. The p_0 reconstruction problem, {p_0bf {R}}, aims to recover the photoacoustic image directly via FISTA, or ADMM when in addition including a non-negativity constraint. We compare and discuss the relative merits of the two approaches and illustrate them on 2D simulated and 3D real data in a fair and rigorous manner.

This study illustrates the textural, mineralogical and geochemical signatures of sediments from the upper Yellow River. Sediments of major tributaries have also been examined to study the influence of provenance, weathering intensity, and hydraulic sorting on the spatial compositional variability of sediments. Similarly to floodplain sediments, riverbed deposits are strongly depleted in MgO and Na2O, and enriched in CaO and Si2O relative to the upper-continental-crust standard. The pattern of rare earth elements (REE) indicates LREE enrichment and negative Eu anomaly (Eu/Eu* = 0.54–0.68). Low CIA (Chemical Index of Alteration) values indicate that the sediments from the trunk-river channel, floodplain, and major tributaries have not experienced intense chemical weathering in arid to semi-arid climatic conditions. The distribution of chemical elements shows a significant increase of Zr and Hf with coarsening of grain-size, whereas Rb, Ni, and V are associated with finer sediment fractions supplied by adjacent dune fields and tributaries. The Al/Si ratio (a proxy for grain size) correlates positively with CIA, Fe2O3, MgO, and K2O, but not with Na2O, reflecting grain-size control. Clay mineral assemblages in the upper Yellow River and in tributaries draining the Loess Plateau (e.g., Qingshui and Zuli Rivers) are dominated by illite and chlorite, whereas the abundance of smectite characterizes the middle reaches of the Yellow River and the Ten tributaries draining the Ordos Plateau.

Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame. As the latter is computationally more tractable, investigation to which extent this equivalence holds conducted in this paper is of immediate practical significance. To this end we formulate and compare DR, a two step approach based on the recovery of the complete volume of the photoacoustic data from the subsampled data followed by the acoustic inversion, and {p_0bf {R}}, a one step approach where the photoacoustic image (the initial pressure, p_0) is directly recovered from the subsampled data. Effective representation of the photoacoustic data requires basis defined on the range of the photoacoustic forward operator. To this end we propose a novel wedge-restriction of Curvelet transform which enables us to construct such basis. Both recovery problems are formulated in a variational framework. As the Curvelet frame is heavily overdetermined, we use reweighted ell 1 norm penalties to

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs' precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM's skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, a

The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited, despite the relevance for various fields. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3-dimensional facial images of 10,115 Europeans identified 24 genetic loci reaching study-wide suggestive association (p < 5 × 10-8), among which 17 were previously unreported. A follow-up multi-ethnic study in additional 7917 individuals confirmed 10 loci including six unreported ones (padjusted < 2.1 × 10-3). A global map of derived polygenic face scores assembled facial features in major continental groups consistent with anthropological knowledge. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis-regulatory activities at the face-associated genetic loci. Luciferase reporter assays in neural crest progenitor cells highlighted enhancer activities of several face-associated DNA variants. These results substantially advance our understanding of the genetic basis underlying human facial variation and provide candidates for future in-vivo functional studies.

To investigate the major causes and predictive factors of death in a middle-aged and elderly Chinese population. A total of 6591 residents aged ≥ 45 years from Shanghai Changfeng community were followed up for an average of 5.4 years. The causes of death were coded according to the 10th Revision of International Classification of Diseases. The mortality rate was calculated by person-years of follow up and age-standardized according to the 2010 Chinese census data. Multivariable-adjusted Cox proportional hazards model was performed to investigate the predictors of all-cause and cause-specific mortality. During the total follow-up of 35,739 person-years, 370 deaths were documented (157 from malignant neoplasms, 70 from heart diseases, 68 from cerebrovascular diseases, 75 from other causes). The age-standardized all-cause mortality rate was 798.2 per 100,000 person-years (927.9 among men and 716.7 among women). Results from multivariable analyses showed that aging, diabetes, and osteoporosis at baseline were independent predictors of all-cause mortality, with hazard ratios (HR) of 1.11 (95% CI 1.10–1.13), 1.91 (1.51–2.42), and 1.71 (1.24–2.35), respectively. The population attributable risk percent of diabetes and osteoporosis was 19.7% and 11.7%, respectively. Cigarette smoking was associated with a higher risk of all-cause mortality in men (HR and 95%CI 1.44, 1.01–2.06). In women, diabetes and osteoporosis were related to a higher risk of cardiovascular mortality (3.27, 1.82–5.88 and 1.89, 1.04–3.46, respectively). While in men, osteoporosis was related to a higher risk of malignant neoplasms mortality (2.39, 1.07–5.33). Malignant neoplasms, heart diseases, and cerebrovascular diseases are the leading causes of death. Aging, smoking, underweight, diabetes, and osteoporosis are independent predictors of premature death among middle-aged and elderly Chinese community population. Moreover, there may have been some differences in the causes and predictors of premature de

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs' precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM's skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, a

type:Journal Article, While the layered 122 iron arsenide superconductors are highly anisotropic, unconventional, and exhibit several forms of electronic orders that coexist or compete with superconductivity in different regions of their phase diagrams, we find in the absence of iron in the structure that the superconducting characteristics of the endmember BaPd2As2 are surprisingly conventional. Here we report on complementary measurements of specific heat, magnetic susceptibility, resistivity measurements, Andreev spectroscopy, and synchrotron high pressure x-ray diffraction measurements supplemented with theoretical calculations for BaPd2As2. Its superconducting properties are completely isotropic as demonstrated by the critical fields, which do not depend on the direction of the applied field. Under the application of high pressure, Tc is linearly suppressed, which is the typical behavior of classical phononmediated superconductors with some additional effect of a pressure-induced decrease in the electronic density of states and the electron-phonon coupling parameters. Structural changes in the layeredBaPd2As2 have been studied by means of angle-dispersive diffraction in a diamond-anvil cell. At 12 GPa and 24.2 GPa we observed pressure induced lattice distortions manifesting as the discontinuity and, hence discontinuity in the Birch-Murnaghan equation of state. The bulk modulus is B0 = 40(6) GPa below 12 GPa and B0 = 142(3) GPa below 27.2 GPa.

An SDN-controlled DCN enabling QoS-driven network-slice reconfiguration and packet priority updating is experimentally assessed. Network-slice can be reconfigured within 150 ms to decrease packet loss. The measured ToR-to-ToR latency of high-priority packets is 260.55 ns., Peer Reviewed, Postprint (author's final draft)

© 2018 Elsevier B.V. Hydrocarbon recovery and reserves estimation largely depend upon the hydrocarbon-water-mineral wettability. However, wettability is a complex parameter and experimental measurements are still open to large uncertainty. We thus demonstrate here that quartz wettability correlates with the density of the non-aqueous fluid, e.g. oil, CO2, N2, etc. – which can be in a liquid, gaseous or supercritical form. This insight significantly simplifies wettability assessments, thus enhancing fundamental understanding of wettability and the related fluid dynamics in siliciclastic hydrocarbon reservoirs. Furthermore, this observed correlation may promote hydrocarbon recovery and reserves prediction in siliciclastic reservoirs.

In its upper-middle reaches the Yellow River has high sand contents after traversing through large areas of desert and the China Loess Plateau. Understanding riverbed sediment composition in the channel is critical for the interpretation of the potential provenance, aeolian sand transport and the linkage between the Loess Plateau and the Yellow River. To address these issues, we collected 52 samples from the modern riverbed, proximal deserts, and major tributaries and used analyses of grain size, grain morphology, and heavy-mineral compositions, to establish the spatial distribution and characteristics of source regions and riverbed sediments. The heavy-mineral assemblages demonstrate significant variations for the different sections of the Yellow River. The riverbed samples from the upper reach are dominated by opaque minerals (limonite and magnetite), amphibole and epidote, with minor zircon, tourmaline and rutile. Riverbed sediments from the middle reach are garnet-rich, reflecting the widespread distribution of Mesozoic sandstones. This variability closely reflects the source regions. Our data show that seasonal tributaries (the “Ten Great Gullies”) carrying detritus from the Ordos Plateau may account for the localized high garnet concentrations in the Inner Mongolia section of the upper reach. Scanning electron microscope (SEM) imaging of quartz grains show that the river sediments are characterized by composite microtextures acquired in both fluvial and eolian environments of the Hedong, Ulan Buh and Kubuq Deserts. The mineralogical composition in the upper reach (Lanzhou–Yinchuan) is similar to that of sediments in the Loess Plateau and Northeast Tibet Plateau (Western Lanzhou). However, the composition differs markedly from that in the Inner Mongolia section of the upper and middle reaches. This variation indicates that in the upper reach the Northeast Tibet Plateau contributes significant volumes of sediment to the Yellow River and Loess Plateau, but compos

We present a novel modular ROADM for ultra-high capacity SDM metro networks with aggregation/disaggregation traffic operation based on space switches, photonic integrated wavelength selective switches, and bandwidth-variable transmitters. Experimental results validate the concept by dynamically disaggregating/aggregating traffics from/to multi-core fibers

While the layered 122 iron arsenide superconductors are highly anisotropic, unconventional, and exhibit several forms of electronic orders that coexist or compete with superconductivity in different regions of their phase diagrams, we find in the absence of iron in the structure that the superconducting characteristics of the end member BaPd2As2 are surprisingly conventional. Here we report on complementary measurements of specific heat, magnetic susceptibility, resistivity measurements, Andreev spectroscopy, and synchrotron high pressure x-ray diffraction measurements supplemented with theoretical calculations for BaPd2As2. Its superconducting properties are completely isotropic as demonstrated by the critical fields, which do not depend on the direction of the applied field. Under the application of high pressure, T-c is linearly suppressed, which is the typical behavior of classical phonon-mediated superconductors with some additional effect of a pressure-induced decrease in the electronic density of states and the electron-phonon coupling parameters. Structural changes in the layered BaPd2As2 have been studied by means of angle-dispersive diffraction in a diamond-anvil cell. At 12 GPa and 24.2 GPa we observed pressure induced lattice distortions manifesting as the discontinuity and, hence discontinuity in the Birch-Murnaghan equation of state. The bulk modulus is B-0 = 40(6) GPa below 12 GPa and B-0 = 142(3) GPa below 27.2 GPa.

We present a novel modular ROADM for ultra-high capacity SDM metro networks with aggregation/disaggregation traffic operation based on space switches, photonic integrated wavelength selective switches, and bandwidth-variable transmitters. Experimental results validate the concept by dynamically disaggregating/aggregating traffics from/to multi-core fibers