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Despite the government's active promotion of rice production, a significant portion of the population still faces food insecurity. While existing literature often highlights the success of achieving rice surplus, few studies delve into the connections between rice surplus and food security, and critically analyze why food security is persistent. In addressing this issue, the study investigates the underlying causes of food insecurity amidst the government's efforts to increase rice production. The study entails a comprehensive review of existing literature and an examination of food security in three provinces in the Cambodian Mekong Delta. It concludes that while rice intensification has led to increased rice production available for consumption, challenges persist in terms of access to and utilization of rice for food consumption. These challenges are attributed to increased production costs with minimal profit margins, indebtedness, migration, land grabs, water conflicts, and lack of institutional integration in food systems, compounded by the effects of climate change and environmental degradation. As a result, a farming household with one hectare of farmland cannot produce enough rice for food consumption. Consequently, young people opt to sell off their land to settle debts and seek employment opportunities in urban and overseas areas. [ABSTRACT FROM AUTHOR]

The Mekong River is one of the most biodiverse, productive rivers in the world, supporting more than 1000 fish species and the livelihoods of tens of millions of people. The spatial dynamics and population status of many Mekong fish species, especially megafishes, are poorly understood. Therefore, this information is rarely incorporated into environmental risk assessments for large infrastructure projects, such as mainstream hydropower developments, which have been accelerating rapidly in the Mekong Basin. In this study, we present initial findings from the ongoing, collaborative, transnational acoustic telemetry monitoring of nearly 300 tagged fishes representing 27 species, which yield important insights into the potential impacts that proposed hydropower dams would have on populations of ecologically and economically important fish species. Included in these data are more than ten months of hydrophone records tracking the location of a 300 kg giant freshwater stingray, Urogymnus polylepis (Bleeker, 1852), currently the world’s largest known freshwater fish, used to detect its migration behavior and distribution patterns. The telemetry data, combined with fisher surveys used to gather local ecological knowledge, provide evidence that the proposed dams would fragment the existing populations of this iconic species as well as those of other fish species that support river food web balance and local food systems. Furthermore, the existence of giant freshwater stringray populations and other unique megafauna reinforces the universal natural heritage value of the stretch of the Mekong River between the Lao People’s Democratic Republic/Cambodia border and the city of Kratie. This stretch of river is located between two proposed megadams, the 900 MW Stung Treng Dam and the 2300 MW Sambor Dam. However, the Cambodian Ministry of Environment has also proposed this area for designation as a UNESCO World Heritage Site (Biosphere Reserve). The documentation of the movement of migratory fishes through this reach of the river using acoustic telemetry, the surprising discovery of the world’s largest freshwater fish, the potential threat posed by dam construction, and the management ramifications of UNESCO World Heritage Site designation underscore the importance of scientific research and community involvement in landscape-scale development decisions. The decisions made today will affect the fate of this global biodiversity hotspot, the world’s most productive inland fisheries, and the livelihoods of millions of people throughout the Lower Mekong Basin.

Cambodia faces the challenge of managing excess water during the wet season and insufficient water during the dry season. This harms human life and endangers aquatic and natural resources, agricultural practices, and food security. Water governance is crucial to ensure the well-being of both people and their food security. However, Cambodia's water governance is hindered by various obstacles, including sectoral and centralized influences, top-down and large-scale strategies, weak coordination among relevant agencies, and limited involvement of local communities. This study examines water governance across different sectors, from centralized to community-based natural resources management, and explores the opportunities that can be done to improve water governance. This study undertakes the literature and case studies of farmer water user communities (FWUCs), community fisheries (CFis), and community fish refuges (CFRs) in three Mekong Delta provinces in Cambodia. This study concludes that water governance has been challenged by FWUCs competing for water resources to intensify rice production at the expense of increased pesticides and fertilizer uses, which undermine the fishery productivity, degrade the natural resources in rivers and water bodies, and increase water conflicts among farmers and sectors in the face of climate change. To enhance water governance in Cambodia, it is critical to integrate it at the district level. This will promote sustainable water use and management across the country and pave the way for a brighter future. [ABSTRACT FROM AUTHOR]

The giant freshwater whipray is a large-bodied stingray species that is listed as endangered across its known range from India through Southeast Asia. However, little is known about the species' ecology, biology, and conservation status. We reviewed all available literature, articles, and reports on the species found through database and internet searches in order to consolidate and update information on the giant freshwater whipray. We found that remarkably little research has been done, and most existing information on the species is derived from fisher catch reports. Whiprays of 300 kg or more have been reported in most countries where it occurs. The available evidence suggests that this species is endangered across its range by numerous threats, including fishing, bycatch, bottom trawls, pollution, and habitat destruction and fragmentation. Various reports indicate declines in population size, genetic diversity, and body size. Relatively stable populations may persist in the Maeklong River, Thailand, the Mekong River in Cambodia, and insular Southeast Asia. More work is needed to understand the species' spatio-temporal distribution, reproductive behavior, migration patterns, and general ecological and biological characteristics in order to inform effective management and conservation plans. Due to difficulties in sampling this rare species and monitoring fisher catches, partnership and collaboration with all stakeholders, including local fishers, government officials, and researchers, is essential to produce conservation gains for the giant freshwater whipray. [ABSTRACT FROM AUTHOR]

Tropical lakes and their associated floodplain habitats are dynamic habitat mosaics strongly influenced by seasonal variations in hydrologic conditions. In flood-pulse systems, water level oscillations directly influence the connectivity to floodplain habitats for fish. Here, we aimed to investigate whether seasonal changes in the water level of a flood-pulse system (the Tonle Sap Lake, Cambodia) differentially affect diet breadth and dietary overlap of three common and commercially important fish species (Anabas testudineus, Boesemania microplepis and Notopterus notopterus) presenting important differences in their life-cycle (e.g. seasonal migration). For this purpose, the three fish species were sampled at four locations spread over the lake and their stomach contents extracted for analyses. Dietary differences were investigated across seasons regarding the diet composition and diet breadth of each species as well as the amount of dietary overlap between species. We found that the proportion of empty stomachs changed similarly across seasons for the three species, thus suggesting that ecological differences between species are not sufficient to outweigh the effect of seasonal variations in resource abundance. In contrast, changes in diet composition were species-specific and can be explained by ecological and behavioral differences between species. Diet breadth differed between species in all seasons, except during the wet season, and tended to be higher during the dry season when dietary overlap was the lowest. These variations likely result from changes in the diversity and amount of resources and may lead to habitat use shifts with potential implications for competitive interactions. In particular, increasing connectivity to floodplain habitats may reduce the competitive pressure during the wet season, while resource scarcity during the dry season may constrain individuals to diversify their diet to avoid competition. Overall, our results suggest a considerable plasticity in the feeding behavior of the three species as demonstrated by seasonal variation in both diet breadth and dietary overlap. Such variations can be explained by a number of factors and processes, including changes in resource availability or competitive interactions between individuals for resources, whose relative influence might vary depending on the magnitude and the timing of the flood-pulse driving the connectivity to floodplain habitats. Gaining knowledge on the seasonal evolution of fish's diet is relevant for fisheries management and conservation and our result could be used to guide aquaculture development in Cambodia.

Bifurcated tunnels have two major engineering problems of large span and small interval simultaneously, and they pose higher demands on design and construction technology. The Liantang tunnel has a maximum span of 30.1 m, a minimum interval of only 1.05 m and a single excavation face; the existing construction method cannot meet the demand. Based on these features, this paper proposes a reverse expanding excavation method and a stepwise controlled blasting excavation method. By numerical calculation and field testing, the stability and mechanical characteristics of the surrounding rock and primary support using the two methods were researched. The results show that the reverse expanding excavation method, by excavating the pilot tunnel first and then reversely excavating the remaining part, achieves stepwise excavation of the super-large-span tunnel, provides mechanised working conditions and avoids multiple conversions of the excavation method. The stepwise controlled blasting excavation method, by grouting and reinforcing the shared rock first, and then controlled blasting of the later tunnel in steps, ensures the stability of the shared rock, reduces the construction influence of the later tunnel on the existing tunnel and omits the construction of a mid-partition. This study can provide a reference for future similar projects. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things and cloud computing technologies, the Internet of Things technology based on comprehensive perception and interconnection and cloud computing based on virtualization, dynamic resources, and parallel computing have become the driving force for the innovation and development of informatization and intelligence. The cloud-based Internet of Things mobile medical is an ecosystem of health information and medical information, with the medical Internet of Things at its core and highly mobile and highly shared information. Therefore, in the context of in-depth research on mobile medical care, research on mobile postoperative thyroid monitoring and management systems based on the Internet of Things and cloud computing is a practical tool for promoting the development of mobile medical systems. Monitoring and Management. In this experiment, 48 cases of patients undergoing thyroid surgery were selected from a hospital. The experimental group was informed by the experiment that they need to be equipped with sensors. The control panel and GPS positioning module are used to obtain the exact position of the patient at the first time. The loading of the sensor is agreed by the patient and the patient's family. Afterward, the control group will not be processed. It will conduct functional tests and software performance tests on the mobile medical monitoring and management system and analyze the satisfaction of medical staff with the mobile medical monitoring and management system. Experiments have proved that the cloud computing medical monitoring and management system is used to obtain the exact location of the patient in the first time, and the response time needs to be shorter. The response time of the system increases with the increase of the number of sensors (P < 0.05), which shows that the mobile medical monitoring and management system is essential for medical care and medical care. Obtaining the exact position of the patient for the first time is of great importance for the successful rescue of the patient. [ABSTRACT FROM AUTHOR]

In urban environment, it is often unavoidable for shallow tunnels to be constructed adjacent to existing pile foundations. To obtain the ground displacements and stresses induced by shallow tunneling and existing pile foundation loads, the key procedure involves superimposing the analytical solution for shallow tunneling in green-field with the analytical solution for existing structure loads. In green-field, the complex variable method provides exact analytical solutions of ground displacements and stresses caused by shallow tunneling. However, the exact analytical solutions are not directly expressed as explicit functions of the coordinates (x, y) in the physical plane (called implicit form of exact analytical solutions), whereas the displacements and stresses induced by existing structure loads are explicit functions of the coordinates (x, y) in the physical plane, which makes it difficult to superpose the displacements and stresses induced by existing structure loads. In this paper, explicit form of exact analytical solutions of displacements and stresses induced by shallow tunneling in green-field is obtained by using the inverse conformal transformation and the Cauchy–Riemann equations. Comparison with implicit form of exact analytical solutions shows that the explicit form of exact analytical solutions is intuitional and easily used by engineers, and moreover, the calculation amount is much smaller than that for the implicit form of exact analytical solutions. Then, an application involving superimposing the explicit form of exact analytical solutions with Mindlin's solution is implemented to analyze the secondary stress field and the related potential plastic zone caused by shallow tunneling adjacent to pile foundations. Moreover, the influences of pile foundation parameters on the ranges and shapes of the potential plastic zones induced by nearby tunneling are analyzed. [ABSTRACT FROM AUTHOR]

The Tonle Sap Lake (TSL) is a flood-pulse system. It is the largest natural lake in South-East Asia and constitutes one of the largest fisheries over the world, supporting the livelihood of million peoples. Nonetheless, the Mekong River Basin is changing rapidly due to accelerating water infrastructure development (hydropower, irrigation, flood control, and water supply) and climate change, bringing considerable modifications to the annual flood-pulse of the TSL. Such modifications are expected to have strong impacts on fish biodiversity and abundance. This paper aims to characterize the spatio-temporal variations of fish taxonomic composition and to highlights the underlying determinants of these variations. For this purpose, we used data collected from a community catch monitoring program conducted at six sites during 141 weeks, covering two full hydrological cycles. For each week, we estimated beta diversity as the total variance of the site-by-species community matrix and partitioned it into Local Contribution to Beta Diversity (LCBD) and Species Contribution to Beta Diversity (SCBD). We then performed multiple linear regressions to determine whether species richness, species abundances and water level explained the temporal variation in the contribution of site and species to beta diversity. Our results indicate strong temporal variation of beta diversity due to differential contributions of sites and species to the spatial variation of fish taxonomic composition. We further found that the direction, the shape and the relative effect of species richness, abundances and water level on temporal variation in LCBD and SCBD values greatly varied among sites, thus suggesting spatial variation in the processes leading to temporal variation in community composition. Overall, our results suggest that fish taxonomic composition is not homogeneously distributed over space and time and is likely to be impacted in the future if the flood-pulse dynamic of the system is altered by human activities. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignant tumors among female. How to effectively discriminate the category of the cancers using the key features/factors is very important in the diagnosis and prediction. In this paper, Jointly Sparse Discriminant Analysis (JSDA) is proposed to explore the key factors in breast cancer and extract the key features for improving the accuracy in diagnosis and prediction. JSDA introduces the jointly sparse regular term (i.e. L 2 , 1 -norms term) to the criterion. A convergent iterative algorithm is designed to solve the optimization problem. It is shown that the proposed JSDA algorithm not only can learn the jointly sparse discriminant vectors to explore the key factors of the breast cancer in cancer pathologic diagnosis, but also can improve the diagnosis accuracy compared with the classical feature extraction and discriminant analysis algorithm. Experimental results on breast cancer datasets indicate that JSDA outperforms some well-known subspace learning algorithms in prediction accuracy, not matter they are non-sparse or sparse, particularly in the cases of small sample sizes. Data analysis shows that the key factors of the breast cancer explored by the JSDA are consistent with the practical experience. [ABSTRACT FROM AUTHOR]

Microarray techniques have been used to delineate cancer groups or to identify candidate genes for cancer prognosis. As such problems can be viewed as classification ones, various classification methods have been applied to analyze or interpret gene expression data. In this paper, we propose a novel method based on robust principal component analysis (RPCA) to classify tumor samples of gene expression data. Firstly, RPCA is utilized to highlight the characteristic genes associated with a special biological process. Then, RPCA and RPCA+LDA (robust principal component analysis and linear discriminant analysis) are used to identify the features. Finally, support vector machine (SVM) is applied to classify the tumor samples of gene expression data based on the identified features. Experiments on seven data sets demonstrate that our methods are effective and feasible for tumor classification. [ABSTRACT FROM PUBLISHER]

The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), namely, so-called oxygen electrode reactions, are two fundamental half-cell reactions in the energy storage and conversion devices, e.g., zinc–air batteries and fuel cells. However, the oxygen electrode reactions suffer from sluggish kinetics, large overpotential and complicated reaction paths, and thus require efficient and stable electrocatalysts. Transition-metal-based layered double hydroxides (LDHs) and their derivatives have displayed excellent catalytic performance, suggesting a major contribution to accelerate electrochemical reactions. The rational regulation of electronic structure, defects, and coordination environment of active sites via various functionalized strategies, including tuning the chemical composition, structural architecture, and topotactic transformation process of LDHs precursors, has a great influence on the resulting electrocatalytic behavior. In addition, an in-depth understanding of the structural performance and chemical-composition-performance relationships of LDHs-based electrocatalysts can promote further rational design and optimization of high-performance electrocatalysts. Finally, prospects for the design of efficient and stable LDHs-based materials, for mass-production and large-scale application in practice, are discussed. [ABSTRACT FROM AUTHOR]

The functional status of CD4+T cells is a critical determinant of antitumor immunity. Polyfunctional CD4+T cells possess the ability to concomitantly produce multiple Th1-type cytokines, exhibiting a functional attribute desirable for cancer immunotherapy. However, the mechanisms by which these cells are induced are neither defined nor it is clear if these cells can be used therapeutically to treat cancer. Here, we report that CD4+T cells exposed to exogenous IL-7 during antigenic stimulation can acquire a polyfunctional phenotype, characterized by their ability to simultaneously express IFNγ, IL-2, TNFα and granzyme B. This IL-7-driven polyfunctional phenotype was associated with increased histone acetylation in the promoters of the effector genes, indicative of increased chromatin accessibility. Moreover, forced expression of a constitutively active (CA) form of STAT5 recapitulated IL-7 in inducing CD4+T-cell polyfunctionality. Conversely, the expression of a dominant negative (DN) form of STAT5 abolished the ability of IL-7 to induce polyfunctional CD4+T cells. Thesein-vitro-generated polyfunctional CD4+T cells can traffic to tumor and expand intratumorally in response to immunization. Importantly, adoptive transfer of polyfunctional CD4+T cells following lymphodepletive chemotherapy was able to eradicate large established tumors. This beneficial outcome was associated with the occurrence of antigen epitope spreading, activation of the endogenous CD8+T cells and persistence of donor CD4+T cells exhibiting memory stem cell attributes. These findings indicate that IL-7 signaling can impart polyfunctionality and stemness potential to CD4+T cells, revealing a previously unknown property of IL-7 that can be exploited in adoptive T-cell immunotherapy. [ABSTRACT FROM AUTHOR]

• We propose a self-supervised FSB, which can be easily extended to any CNN architectures. • We design an GLCM loss to minimize the differences among input images. • Our FSB achieves about 6% accuracy improvement for testing datasets. With the advance of deep learning technology, convolutional neural network (CNN) has been wildly used and achieved the state-of-the-art performances in the area of medical image classification. However, most existing medical image classification methods conduct their experiments on only one public dataset. When applying a well-trained model to a different dataset selected from different sources, the model usually shows large performance degradation and needs to be fine-tuned before it can be applied to the new dataset. The goal of this work is trying to solve the cross-domain image classification problem without using data from target domain. In this work, we designed a self-supervised plug-and-play feature-standardization-block (FSB) which consisting of image normalization (INB), contrast enhancement (CEB) and boundary detection blocks (BDB), to extract cross-domain robust feature maps for deep learning framework, and applied the network for chest x-ray-based lung diseases classification. Three classic deep networks, i.e. VGG, Xception and DenseNet and four chest x-ray lung diseases datasets were employed for evaluating the performance. The experimental result showed that when employing feature-standardization-block, all three networks showed better domain adaption performance. The image normalization, contrast enhancement and boundary detection blocks achieved in average 2%, 2% and 5% accuracy improvement, respectively. By combining all three blocks, feature-standardization-block achieved in average 6% accuracy improvement. [ABSTRACT FROM AUTHOR]

Abstract As a famous linear manifold learning method, orthogonal neighborhood preserving projections (ONPP) is able to provide a set of orthogonal projections for dimensionality reduction. However, a problem of ONPP is that it takes the L 2 -norm as the basic measurement and therefore tends to be sensitive to the outliers or the variations of the data. Aiming at strengthening the robustness of the conventional method ONPP, in this paper, a robust and sparse dimensionality reduction method based on linear reconstruction, called Robust Jointly Sparse Embedding (RJSE), is proposed by introducing L 2, 1 -norm as the basic measurement and regularization term. We design a simple iterative algorithm to obtain the optimal solution of the proposed robust and sparse dimensionality reduction model. Experiments on four benchmark data sets demonstrate the competitive performance of the proposed method compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

High-dimensional small sample size data, which may lead to singularity in computation, are becoming increasingly common in the field of pattern recognition. Moreover, it is still an open problem how to extract the most suitable low-dimensional features for the support vector machine (SVM) and simultaneously avoid singularity so as to enhance the SVM's performance. To address these problems, this article designs a novel framework that integrates the discriminative feature extraction and sparse feature selection into the support vector framework to make full use of the classifiers' characteristics to find the optimal/maximal classification margin. As such, the extracted low-dimensional features from high-dimensional data are more suitable for SVM to obtain good performance. Thus, a novel algorithm, called the maximal margin SVM (MSVM), is proposed to achieve this goal. An alternatively iterative learning strategy is adopted in MSVM to learn the optimal discriminative sparse subspace and the corresponding support vectors. The mechanism and the essence of the designed MSVM are revealed. The computational complexity and convergence are also analyzed and validated. Experimental results on some well-known databases (including breastmnist, pneumoniamnist, colon-cancer, etc.) show the great potential of MSVM against classical discriminant analysis methods and SVM-related methods, and the codes can be available on https://www.scholat.com/laizhihui.

Support vector machine (SVM), as a supervised learning method, has different kinds of varieties with significant performance. In recent years, more research focused on nonparallel SVM, where twin SVM (TWSVM) is the typical one. In order to reduce the influence of outliers, more robust distance measurements are considered in these methods, but the discriminability of the models is neglected. In this article, we propose robust manifold twin bounded SVM (RMTBSVM), which considers both robustness and discriminability. Specifically, a novel norm, that is, capped L 1 -norm, is used as the distance metric for robustness, and a robust manifold regularization is added to further improve the robustness and classification performance. In addition, we also use the kernel method to extend the proposed RMTBSVM for nonlinear classification. We introduce the optimization problems of the proposed model. Subsequently, effective algorithms for both linear and nonlinear cases are proposed and proved to be convergent. Moreover, the experiments are conducted to verify the effectiveness of our model. Compared with other methods under the SVM framework, the proposed RMTBSVM shows better classification accuracy and robustness.

C-X-C chemokine receptor 7 (CXCR7) is a known promoter of tumor progression and metastasis; however, little is known about its role in colon cancer. The aim of the present study was to investigate the function of CXCR7 in human colon cancer cells. CXCR7 mRNA levels were examined in HT-29 and SW-480 human colon cancer cell lines using a quantitative polymerase chain reaction. CXCR7-knockdown was performed with small interfering RNA and lentiviral-mediated gene delivery. Immunofluorescence (IF) was conducted to examine CXCR7 expression and localization in colon cancer cells. Cell survival and migration were evaluated using MTT and migration assays, respectively. HT-29 cells expressed higher levels of CXCR7 mRNA and were therefore used in subsequent experiments. IF staining revealed that the CXCR7 protein was expressed on the cell membrane, and its expression decreased following CXCR7-short hairpin RNA lentiviral transfection. Lentiviral CXCR7-knockdown resulted in decreased cell survival and migration; however, MTT assays revealed that the lentiviral vector itself was cytotoxic. This cytotoxicity was indicated as the cell survival of the negative control group cells was significantly decreased compared with that of the blank control group cells (P<0.05). In conclusion, it is becoming increasingly evident that CXCR7 plays a role in colon cancer promotion, suggesting that CXCR7 is a promising biomarker for chemokine receptor-based drug development. Furthermore, the fact that CXCR7 is expressed on the membrane and not intracellularly makes it a prime target for drug-based intervention.