CTEN-induced TGF-ß1 expression facilitates EMT and enhances paclitaxel resistance in bladder cancer cells.

OBJECTIVES: To investigate the role of C-terminal tensin-like (CTEN) in mediating chemotherapy resistance via epithelial-mesenchymal transition (EMT) in bladder cancer (BC) cells, through the regulation of transforming growth factor-ß1 (TGF-ß1) expression. METHODS: Lentiviral vectors were used to create CTEN overexpression and knockdown constructs, which were then introduced into paclitaxel-resistant BC cell lines. The effects of CTEN manipulation on cell proliferation and drug sensitivity was assessed using the CCK-8 assay, and apoptosis was evaluated by flow cytometry. The expression levels of CTEN, TGF-ß1, and EMT markers were quantified by RT-qPCR and Western blot analysis. The interaction between CTEN and TGF-ß1 and its effect on TGF-ß1 methylation were studied using bisulfite sequencing PCR and co-immunoprecipitation. RESULTS: Overexpression of CTEN in BC cells was associated with decreased paclitaxel efficacy, reduced apoptosis, and elevated levels of TGF-ß1 and EMT-related proteins. CTEN was found to bind TGF-ß1, inhibiting its methylation and thereby promoting TGF-ß1 upregulation. This increase in TGF-ß1 expression facilitated the EMT process and enhanced drug resistance in BC cells. CONCLUSIONS: The induction of TGF-ß1 expression by CTEN promotes EMT and increases chemotherapy resistance in BC cells. Targeting CTEN or the EMT pathway could improve chemosensitivity in treatment-resistant BC, suggesting a novel therapeutic strategy to enhance chemotherapy effectiveness.

The specificities, influencing factors, and medical implications of bone circadian rhythms.

Physiological processes within the human body are regulated in approximately 24-h cycles known as circadian rhythms, serving to adapt to environmental changes. Bone rhythms play pivotal roles in bone development, metabolism, mineralization, and remodeling processes. Bone rhythms exhibit cell specificity, and different cells in bone display various expressions of clock genes. Multiple environmental factors, including light, feeding, exercise, and temperature, affect bone diurnal rhythms through the sympathetic nervous system and various hormones. Disruptions in bone diurnal rhythms contribute to the onset of skeletal disorders such as osteoporosis, osteoarthritis and skeletal hypoplasia. Conversely, these bone diseases can be effectively treated when aimed at the circadian clock in bone cells, including the rhythmic expressions of clock genes and drug targets. In this review, we describe the unique circadian rhythms in physiological activities of various bone cells. Then we summarize the factors synchronizing the diurnal rhythms of bone with the underlying mechanisms. Based on the review, we aim to build an overall understanding of the diurnal rhythms in bone and summarize the new preventive and therapeutic strategies for bone disorders.

Susceptibility Analysis of Glacier Debris Flow Based on Remote Sensing Imagery and Deep Learning: A Case Study along the G318 Linzhi Section.

Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention.

Developmental growth plate cartilage formation suppressed by artificial light at night via inhibiting BMAL1-driven collagen hydroxylation.

Exposure to artificial light at night (LAN) can induce obesity, depressive disorder and osteoporosis, but the pernicious effects of excessive LAN exposure on tissue structure are poorly understood. Here, we demonstrated that artificial LAN can impair developmental growth plate cartilage extracellular matrix (ECM) formation and cause endoplasmic reticulum (ER) dilation, which in turn compromises bone formation. Excessive LAN exposure induces downregulation of the core circadian clock protein BMAL1, which leads to collagen accumulation in the ER. Further investigations suggest that BMAL1 is the direct transcriptional activator of prolyl 4-hydroxylase subunit alpha 1 (P4ha1) in chondrocytes, which orchestrates collagen prolyl hydroxylation and secretion. BMAL1 downregulation induced by LAN markedly inhibits proline hydroxylation and transport of collagen from ER to golgi, thereby inducing ER stress in chondrocytes. Restoration of BMAL1/P4HA1 signaling can effectively rescue the dysregulation of cartilage formation within the developmental growth plate induced by artificial LAN exposure. In summary, our investigations suggested that LAN is a significant risk factor in bone growth and development, and a proposed novel strategy targeting enhancement of BMAL1-mediated collagen hydroxylation could be a potential therapeutic approach to facilitate bone growth.

Activation of metabotropic glutamate receptor 1 regulates hippocampal CA1 region excitability in rats with status epilepticus by suppressing the HCN1 channel.

Dysregulation of hyperpolarization-activated cyclic nucleotide-gated cation (HCN) channels alters neuronal excitability. However, the role of HCN channels in status epilepticus is not fully understood. In this study, we established rat models of pentylenetetrazole-induced status epilepticus. We performed western blot assays and immunofluorescence staining. Our results showed that HCN1 channel protein expression, particularly HCN1 surface protein, was significantly decreased in the hippocampal CA1 region, whereas the expression of HCN2 channel protein was unchanged. Moreover, metabolic glutamate receptor 1 (mGluR1) protein expression was increased after status epilepticus. The mGluR1 agonist (RS)-3,5-dihydroxyphenylglycine injected intracerebroventricularly increased the sensitivity and severity of pentylenetetrazole-induced status epilepticus, whereas application of the mGluR1 antagonist (+)-2-methyl-4-carboxyphenylglycine (LY367385) alleviated the severity of pentylenetetrazole-induced status epilepticus. The results from double immunofluorescence labeling revealed that mGluR1 and HCN1 were co-localized in the CA1 region. Subsequently, a protein kinase A inhibitor (H89) administered intraperitoneally successfully reversed HCN1 channel inhibition, thereby suppressing the severity and prolonging the latency of pentylenetetrazole-induced status epilepticus. Furthermore, H89 reduced the level of mGluR1, downregulated cyclic adenosine monophosphate (cAMP)/protein kinase A expression, significantly increased tetratricopeptide repeat-containing Rab8b-interacting protein (TRIP8b) (1a-4) expression, and restored TRIP8b (1b-2) levels. TRIP8b (1a-4) and TRIP8b (1b-2) are subunits of Rab8b interacting protein that regulate HCN1 surface protein.

A Deep Learning Approach Considering Image Background for Pneumonia Identification Using Explainable AI (XAI).

Pneumonia mainly refers to lung infections caused by pathogens, such as bacteria and viruses. Currently, deep learning methods have been applied to identify pneumonia. However, the traditional deep learning methods for pneumonia identification take less account of the influence of the lung X-ray image background on the model's testing effect, which limits the improvement of the model's accuracy. In this paper, we propose a deep learning method that considers image background factors and analyzes the proposed method with explainable deep learning for explainability. The essential idea is to remove the image background, improve the pneumonia recognition accuracy, and apply the Grad-CAM method to obtain an explainable deep learning model for pneumonia identification. In the proposed approach, (1) preliminary deep learning models for pneumonia X-ray image identification without considering the background are built; (2) deep learning models for pneumonia X-ray image identification with background consideration are built to improve the accuracy of pneumonia identification; (3) Grad-CAM method is employed to analyze the explainability. The proposed approach improves the accuracy of pneumonia identification, and the highest accuracy of VGG16 reaches 95.6%. The proposed approach can be applied to real pneumonia identification for early detection and treatment.

The effect of TLR-4 on the proliferation and differentiation of bone mesenchymal stem cells and its relationship with the Wnt signal transduction pathway during bone nonunion.

Background: Bone nonunion is a special fracture complication that occurs in about 5% to 10% of cases. This type of fracture is difficult to heal, and causes great pain to patients and affects their quality of life. The mechanism of bone nonunion is not clear. In our study, we investigated the influence of Toll-like receptor (TLR)-3, TLR-4, and Wnt signaling pathways on the occurrence of bone nonunion. Methods: Firstly, we established a Sprague Dawley (SD) rat model of femoral nonunion, and detected the expression levels of TLR-3, TLR-4, ß-catenin, nemo-like kinase (NLK), c-Jun N-terminal kinase (JNK), and other proteins during model construction. For in vitro experiments, primary cultured bone mesenchymal stem cells (BMSCs) were divided into 4 groups: lipopolysaccharide (LPS, agonist of TLR-4) group, LPS + CLI095 (inhibitor of TLR-4) group, control group, and LPS + substance P (SP) group. The expression of ß-catenin, NLK, JNK, and ALP and the osteogenic differentiation ability of cells were detected during culture. Results: X-ray and hematoxylin and eosin (HE) staining results confirmed the successful modeling of bone nonunion. During the formation of the bone nonunion model, the expression of TLR-4 showed an upward trend. In vitro experiment results showed that inhibition of TLR-4 expression could enhance the proliferation and differentiation ability of BMSCs. The expression of ß-catenin, the core protein of the canonical Wnt signaling pathway, increased rapidly in the first 2 weeks of bone nonunion construction, and decreased after 2 weeks. Non-canonical Wnt signaling pathway proteins NLK and JNK had no change in the first 2 weeks, and showed an upward trend after 2 weeks. In vitro experiment results showed that the expression of ß-catenin was dominant in BMSCs with strong proliferation and differentiation ability, while the expression of NLK and JNK was dominant in BMSCs with weak proliferation and differentiation ability. These results suggest that the Wnt signaling pathway may regulate the occurrence of bone nonunion. Conclusions: TLR-4 inhibits the proliferation and differentiation of BMSCs, and the transformation of the canonical Wnt signaling pathway to the non-canonical Wnt signaling pathway may lead to bone nonunion. Our study may provide new insights into the treatment of bone nonunion.

An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors.

Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment. Analyzing contributing factors that affect injury severity facilitates injury severity prediction and further application in developing countermeasures to guarantee VRUs safety. Recently, machine learning approaches have been introduced, in which analyses tend to be one-sided and may ignore important information. To solve this problem, this paper proposes a comprehensive analytic framework that employs a deep learning model referred to as the stacked sparse autoencoder (SSAE) to predict the injury severity of traffic accidents based on contributing factors. The essential idea of the method is to integrate various analyses into an analytical framework that performs corresponding data processing and analysis by different machine learning approaches. In the proposed method, first, we utilize a machine learning approach (i.e., Catboost) to analyze the importance and dependence of the contributing factors to injury severity and remove low correlation factors; second, according to the geographical information, we classify the data into different classes by utilizing a machine learning approach (i.e., k-means clustering); third, by employing high correlation factors, we employ an SSAE-based deep learning model to perform injury severity prediction in each data class. By experiments with a real-world traffic accident dataset, we demonstrated the effectiveness and applicability of the framework. Specifically, (1) the importance and dependence of contributing factors were obtained by CatBoost and the Shapley value, and (2) the SSAE-based deep learning model achieved the best performance compared to other baseline models. The proposed analytic framework can also be utilized for other accident data for severity or other risk indicator analyses involving VRUs safety.

General Paradigm of Edge-Based Internet of Things Data Mining for Geohazard Prevention.

Geological hazards (geohazards) are geological processes or phenomena formed under external-induced factors causing losses to human life and property. Geohazards are sudden, cause great harm, and have broad ranges of influence, which bring considerable challenges to geohazard prevention. Monitoring and early warning are the most common strategies to prevent geohazards. With the development of the internet of things (IoT), IoT-based monitoring devices provide rich and fine data, making geohazard monitoring and early warning more accurate and effective. IoT-based monitoring data can be transmitted to a cloud center for processing to provide credible data references for geohazard early warning. However, the massive numbers of IoT devices occupy most resources of the cloud center, which increases the data processing delay. Moreover, limited bandwidth restricts the transmission of large amounts of geohazard monitoring data. Thus, in some cases, cloud computing is not able to meet the real-time requirements of geohazard early warning. Edge computing technology processes data closer to the data source than to the cloud center, which provides the opportunity for the rapid processing of monitoring data. This article presents the general paradigm of edge-based IoT data mining for geohazard prevention, especially monitoring and early warning. The paradigm mainly includes data acquisition, data mining and analysis, and data interpretation. Moreover, a real case is used to illustrate the details of the presented general paradigm. Finally, this article discusses several key problems for the general paradigm of edge-based IoT data mining for geohazard prevention.

RYR2 Mutations Are Associated With Benign Epilepsy of Childhood With Centrotemporal Spikes With or Without Arrhythmia.

RYR2 encodes ryanodine receptor 2 protein (RYR-2) that is mainly located on endoplasmic reticulum membrane and regulates intracellular calcium concentration. The RYR-2 protein is ubiquitously distributed and highly expressed in the heart and brain. Previous studies have identified the RYR2 mutations in the etiology of arrhythmogenic right ventricular dysplasia 2 and catecholaminergic polymorphic ventricular tachycardia. However, the relationship between RYR2 gene and epilepsy is not determined. In this study, we screened for novel genetic variants in a group of 292 cases (families) with benign epilepsy of childhood with centrotemporal spikes (BECTS) by trio-based whole-exome sequencing. RYR2 mutations were identified in five cases with BECTS, including one heterozygous frameshift mutation (c.14361dup/p.Arg4790Pro fs∗6), two heterozygous missense mutations (c.2353G > A/p.Asp785Asn and c.8574G > A/p.Met2858Ile), and two pairs of compound heterozygous mutations (c.4652A > G/p.Asn1551Ser and c.11693T > C/p.Ile3898Thr, c.7469T > C/p.Val2490Ala and c.12770G > A/p.Arg4257Gln, respectively). Asp785Asn was a de novo missense mutation. All the missense mutations were suggested to be damaging by at least three web-based prediction tools. These mutations do not present or at low minor allele frequency in gnomAD database and present statistically higher frequency in the cohort of BECTS than in the control populations of gnomAD. Asp785Asn, Asn1551Ser, and Ile3898Thr were predicted to affect hydrogen bonds with surrounding amino acids. Three affected individuals had arrhythmia (sinus arrhythmia and occasional atrial premature). The two probands with compound heterozygous missense mutations presented mild cardiac structural abnormalities. Strong evidence from ClinGen Clinical Validity Framework suggested an association between RYR2 variants and epilepsy. This study suggests that RYR2 gene is potentially a candidate pathogenic gene of BECTS. More attention should be paid to epilepsy patients with RYR2 mutations, which were associated with arrhythmia and sudden unexpected death in previous reports.

Method for generating high-quality tetrahedral meshes of geological models by utilizing CGAL.

High-quality computational meshes are crucial in the analysis of displacements and stabilities of rock and soil masses. In this paper, we present a method for generating high-quality tetrahedral meshes of geological models to be used in stability analyses of rock and soil masses. The method is implemented by utilizing the Computational Geometry Algorithms Library (CGAL). The input is a geological model consisting of triangulated surfaces, and the output is a high-quality tetrahedral mesh of the geological model. To demonstrate the effectiveness of the presented method, we apply it to generate a series of computational meshes of geological model, and we then analyse the stabilities of the rock and soil slopes on the basis of the generated tetrahedral mesh models. The applications demonstrate the effectiveness and practicability of the present method.•A method for generating high-quality tetrahedral meshes of geological models is presented.•We evaluate the quality of the tetrahedral mesh of geological model using four metrics.•Three applications demonstrate the effectiveness and practicability of the presented method.

A deep learning approach for facility patient attendance prediction based on medical booking data.

Nowadays, data-driven methodologies based on the clinical history of patients represent a promising research field in which personalized and intelligent healthcare systems can be opportunely designed and developed. In this perspective, Machine Learning (ML) algorithms can be efficiently adopted to deploy smart services to enhance the overall quality of healthcare systems. In this work, starting from an in-depth analysis of a data set composed of millions of medical booking records collected from the public healthcare organization in the region of Campania, Italy, we have developed a predictive model to extract useful knowledge on patients, medical staff, and related healthcare structures. In more detail, the main contribution is to suggest a Deep Learning (DL) methodology able to predict the access of a patient in one or more medical facilities of a fixed set in the immediate future, the subsequent 2 months. A structured Temporal Convolutional Neural Network (TCNN) is designed to extract temporal patterns from the administrative medical history of a patient. The experiment shows the goodness of the designed methodology. Finally, this work represents a novel application of a TCNN model to a multi-label classification problem not linked to text categorization or image recognition.

A parallel solution to finding nodal neighbors in generic meshes.

In this paper we specifically present a parallel solution to finding the one-ring neighboring nodes and elements for each vertex in generic meshes. The finding of nodal neighbors is computationally straightforward but expensive for large meshes. To improve the efficiency, the parallelism is adopted by utilizing the modern Graphics Processing Unit (GPU). The presented parallel solution is heavily dependent on the parallel sorting, scan, and reduction. Our parallel solution is efficient and easy to implement, but requires the allocation of large device memory.•Our parallel solution can generate the speedups of approximately 55 and 90 over the serial solution when finding the neighboring nodes and elements, respectively.•It is easy to implement due to the reason it does not need to perform the mesh-coloring before finding neighbors•There are no complex data structures, only integer arrays are needed, which makes our parallel solution very effective.

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems.

In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing Physician and Other Supplier Data CY 2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies.

Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation models.

The building of large-scale Digital Elevation Models (DEMs) using various interpolation algorithms is one of the key issues in geographic information science. Different choices of interpolation algorithms may trigger significant differences in interpolation accuracy and computational efficiency, and a proper interpolation algorithm needs to be carefully used based on the specific characteristics of the scene of interpolation. In this paper, we comparatively investigate the performance of parallel Radial Basis Function (RBF)-based, Moving Least Square (MLS)-based, and Shepard's interpolation algorithms for building DEMs by evaluating the influence of terrain type, raw data density, and distribution patterns on the interpolation accuracy and computational efficiency. The drawn conclusions may help select a suitable interpolation algorithm in a specific scene to build large-scale DEMs.

Comparative modelling of crack propagation in elastic-plastic materials using the meshfree local radial basis point interpolation method and eXtended finite-element method.

The modelling and understanding of crack propagation for elastic-plastic materials is critical in various engineering applications, such as safety analysis of concrete structures and stability analysis of rock slopes. In this paper, the local radial basis point interpolation method (LRPIM) combined with elastic-plastic theory and fracture mechanics is employed to analyse crack propagation in elastic-plastic materials. Crack propagation in elastic-plastic materials is compared using the LRPIM and eXtended finite-element method (XFEM). The comparative investigation indicates that: (i) the LRPIM results are close to the model test results, which indicates that it is feasible for analysing the crack growth of elastic-plastic materials; (ii) compared with the LRPIM, the XFEM results are closer to the experimental results, showing that the XFEM has higher accuracy and computational efficiency; and (iii) compared with the XFEM, when the LRPIM method is used to analyse crack propagation, the propagation path is not smooth enough, which can be explained as the crack tip stress and strain not being accurate enough and still needing further improvement.

Comparison of Anterior only and Combined Anterior and Posterior Approach in Treating Lumbosacral Tuberculosis.

A combined anterior and posterior (AP) surgical approach is a popular treatment modality of lumbosacral tuberculosis, but it is often traumatic and complicated. The present study aims to find whether the anterior only approach with the ARCH plate system is less invasive than the AP approach in treating lumbosacral tuberculosis. The ARCH plate system is an innovative anatomic lumbosacral anterior multi-directional locking plate system which was devised with due consideration to the anatomic features of the lumbosacral spine and irregular destruction of involved vertebral endplates. In this retrospective study, 32 patients with lumbosacral tuberculosis underwent surgeries via either the anterior only approach (ARCH group, 18 patients) using the ARCH system or the conventional combined anterior and posterior approach (AP group, 14 patients). American Spinal Injury Association (ASIA) scores, Visual Analogue Scale (VAS) scores, Oswestry Disability Index (ODI), bone union status, ESR, CRP, intervertebral foraminal height between L5 and S1, the vertical height between the anterior upper edge of L5 and S1 vertebral body, lumbosacral angle, and the physiological lordosis of between L1 and S1 from both groups were recorded and compared. All patients were followed up for at least two years. The average duration of operation, blood loss, and length of hospital admission of the ARCH group (154.6 min, 361.1 ml&18.3days) was significantly smaller and shorter(p < 0.001, p < 0.001 & p = 0.008) that those of the AP group(465.5 min, 814.3 ml & 24.6days). The ODI score(p = 0.08, 0.471, 0.06, 0.07, 0.107), the VAS score(p = 0.099, 0.249, 0.073, 0.103, 0.273), the intervertebral foraminal height between L5 and S1(p = 0.826, 0.073, 0.085), L5-S1 height(p = 0.057, 0.234, 0.094), lumbosacral angle(p = 0.052, 0.242, 0.825), and L5-S1 lordosis(p = 0.146, 0.129, 0.053) of both groups showed no significant difference in any of the time points. The anterior only approach using the ARCH system is as effective as the combined anterior and posterior approach and is less traumatic in treating lumbosacral tuberculosis.

Non-intubated anesthesia in patients undergoing video-assisted thoracoscopic surgery: A systematic review and meta-analysis.

INTRODUCTION: Non-intubated anesthesia (NIA) has been proposed for video-assisted thoracoscopic surgery (VATS), although how the benefit-to-risk of NIA compares to that of intubated general anesthesia (IGA) for certain types of patients remains unclear. Therefore, the aim of the present meta-analysis was to understand whether NIA or IGA may be more beneficial for patients undergoing VATS. METHODS: A systematic search of Cochrane Library, Pubmed and Embase databases from 1968 to April 2019 was performed using predefined criteria. Studies comparing the effects of NIA or IGA for adult VATS patients were considered. The primary outcome measure was hospital stay. Pooled data were meta-analyzed using a random-effects model to determine the standard mean difference (SMD) with 95% confidence intervals (CI). RESULTS AND DISCUSSION: Twenty-eight studies with 2929 patients were included. The median age of participants was 56.8 years (range 21.9-76.4) and 1802 (61.5%) were male. Compared to IGA, NIA was associated with shorter hospital stay (SMD -0.57 days, 95%CI -0.78 to -0.36), lower estimated cost for hospitalization (SMD -2.83 US, 95% CI -4.33 to -1.34), shorter chest tube duration (SMD -0.32 days, 95% CI -0.47 to -0.17), and shorter postoperative fasting time (SMD, -2.76 days; 95% CI -2.98 to -2.54). NIA patients showed higher levels of total lymphocytes and natural killer cells and higher T helper/T suppressor cell ratio, but lower levels of interleukin (IL)-6, IL-8 and C-reactive protein (CRP). Moreover, NIA patients showed lower levels of fibrinogen, cortisol, procalcitonin and epinephrine. CONCLUSIONS: NIA enhances the recovery from VATS through attenuation of stress and inflammatory responses and stimulation of cellular immune function.

Design and implementation of levee project information management system based on WebGIS.

The China Levee Project Information Management System (CLPIMS) is an information management platform that was established for levee project management within the seven major river basins in China. The system was developed on the basis of the VS.NET and ArcGIS Server and was combined with the database theory and key techniques of WebGIS, which has the features of real-time display, enquiry, statistics and management of spatial data under browser/server mode. Moreover, additional applications, such as real-time monitoring, safety assessment, early warning and danger forecasting and online analysis, can be further explored through reserved modules. The CLPIMS can serve not only as a scientific, systematic, visual tool for analysis and decision management in levee projects in China but also as a technical platform for flood control practice. Furthermore, the system is capable of unified management and sharing of the levee project information for the seven major river basins in China, and it is important for the improvement of office automation, E-government applications and the level of flood control operations.

A sample implementation for parallelizing Divide-and-Conquer algorithms on the GPU.

The strategy of Divide-and-Conquer (D&C) is one of the frequently used programming patterns to design efficient algorithms in computer science, which has been parallelized on shared memory systems and distributed memory systems. Tzeng and Owens specifically developed a generic paradigm for parallelizing D&C algorithms on modern Graphics Processing Units (GPUs). In this paper, by following the generic paradigm proposed by Tzeng and Owens, we provide a new and publicly available GPU implementation of the famous D&C algorithm, QuickHull, to give a sample and guide for parallelizing D&C algorithms on the GPU. The experimental results demonstrate the practicality of our sample GPU implementation. Our research objective in this paper is to present a sample GPU implementation of a classical D&C algorithm to help interested readers to develop their own efficient GPU implementations with fewer efforts.