A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping

This study compares the landslide susceptibility maps from four application models, namely, (1) the bivariate model of the Dempster–Shafer based evidential belief function (EBF); (2) integration of the EBF in the knowledge-based analytical hierarchy process (AHP) as a pairwise comparison model processed by using all available causative factors; (3) integration of the EBF in the knowledge-based AHP as a pairwise comparison model by using high nominated causative factor weights only; and (4) integrated EBF in the logistic regression (LR) as a multivariate model by using nominated causative factor weights only. These models were tested in Pohang and Gyeongju Cities (South Korea) by using the geographic information system GIS platform. In the first step, a landslide inventory map consisting of 296 landslide locations were prepared from various data sources. Then, a total of 15 landslide causative factors (slope angle, slope aspect, curvature, surface roughness, altitude, distance from drainages, stream power index, topographic wetness index, wood age, wood diameter, wood type, forest density, soil thickness, soil texture, and soil drainage) were extracted from the database and then converted into a raster. Final susceptibility maps exhibit close results from the two models. Models 1 and 3 predicted 82.3% and 80% of testing data during the analysis, respectively. Thus, Models 1 and 3 show better performance than LR. These resultant maps can be used to extend the capability of bivariate statistical based model, by finding the relationship between each single conditioning factor and landslide locations, moreover, the proposed ensemble model can be used to show the inter-relationships importance between each conditioning factors, without the need to refer to the multivariate statistic. The research outcome may provide powerful tools for natural hazard assessment and land use planning

A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping

An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. CHAID as a multivariate method has an automatic classification capacity to analyze large numbers of landslide conditioning factors. Moreover, it results two or more nodes for each independent variable, where every node contains numbers of presence or absence of landslides (dependent variable). Other DT methods such as Quick, Unbiased, Efficient Statistic Tree (QUEST) and Classification and Regression Trees (CRT) are not able to produce multi branches based tree. Thus, the main objective of this paper is to use CHAID method to perform the best classification fit for each conditioning factors, then, combined it with logistic regression (LR) to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. In the first step, a landslide inventory map with 296 landslide locations were extracted from various sources over the Pohang-Kyeong Joo catchment (South Korea). Then, the inventory was randomly split into two datasets, 70 % was used for training the models, and the remaining 30 % was used for validation purpose. Thirteen landslide conditioning factors were used for the susceptibility modeling. Then, CHAID was applied and revealed that some conditioning factors such as altitude, soil drain, soil texture and TWI, as terminal nodes and reflected the best classification fit. Then, a proposed ensemble technique was applied and the interpretations of the coefficients showed that the relationship between the decision tree branch nodes distance from drain, soil drain, and TWI, respectively, leads to better consequences assessment of landslides in the current study area. The validation results showed that both success and prediction rates, 75 and 79 %, respectively. This study proved the efficiency and reliability of ensemble DT and LR model in landslide susceptibility mapping

Urinary bladder rupture during voiding cystourethrography

Voiding cystourethrography (VCUG) is a commonly performed diagnostic procedure for the evaluation of vesicoureteral reflux with urinary tract infection or congenital renal diseases in children. The procedure is relatively simple and cost-effective, and complications are very rare. The iatrogenic complication of VCUG range from discomfort, urinary tract infection to bacteremia, as well as bladder rupture. Bladder rupture is a rare complication of VCUG, and only a few cases were reported. Bladder rupture among healthy children during VCUG is an especially uncommon event. Bladder rupture associated with VCUG is usually more common in chronically unused bladders like chronic renal failure. Presented is a case of bladder rupture that occurred during a VCUG in a healthy 9-month-old infant, due to instilled action of dye by high pressure. This injury completely healed after 7 days of operation, and it was confirmed with a postoperative cystography. The patient's bladder volume, underlying disease, velocity of the contrast media instilled, catheter size, and styles of instillation are important factors to prevent bladder rupture during VCUG. Management of bladder rupture should be individualized, but the majority of infants are treated with the operation. In conclusion, bladder rupture is a rare complication, however, delicate attention is needed in order to prevent more dire situations

Fabrication of pyramidal probes with various periodic patterns and a single nanopore

The nanometer-scale patterned pyramidal probe with an electron beam-induced nanopore on the pyramid apex is an excellent candidate for an optical biosensor. The nanoapertures surrounded with various periodic groove patterns on the pyramid sides were fabricated using a focused ion beam technique, where the optical characteristics of the fabricated apertures with rectangular, circular, and elliptical groove patterns were investigated. The elliptical groove patterns on the pyramid were designed to maintain an identical distance between the grooves and the apex for the surface waves and, among the three patterns, the authors observed the highest optical transmission from the elliptically patterned pyramidal probe. A 103-fold increase of the transmitted optical intensity was observed after patterning with elliptical grooves, even without an aperture on the pyramid apex. The nanopore on the apex of the pyramid was fabricated using electron beam irradiation and was optically characterized

Generation of Nitric Oxide in the Opossum Lower Esophageal Sphincter during Physiological Experimentation

Lipopolysaccharide (LPS), given in vivo, modulates opossum esophageal motor functions by inducing the inducible nitric oxide synthase (iNOS), which increases nitric oxide (NO) production. Superoxide, a NO scavenger, is generated during this endotoxemia. Superoxide is cleared by superoxide dismutase (SOD) and catalase (CAT) to protect the physiological function of NO. This study examined whether lower esophageal sphincter (LES) motility, NO release, and iNOS and nitrotyrosine accumulation in the LES are affected by LPS in vitro. Muscle strips from the opossum LES were placed in tissue baths containing oxygenated Krebs buffer. NO release was measured with a chemiluminescence NOx analyzer, and Western blots were performed to analyze iNOS and nitrotyrosine production. The percent change in resting LES tone after a 6-hour exposure to LPS was significantly increased compared to pretreatment values. The percent LES relaxation upon electrical stimulation was significantly decreased in the control group at 6 hours, indicating that the LPS treatment had an effect. The NO concentration in the tissue bath of LPS-treated muscle without nerve stimulation was significantly less than that of LPS treatment combined with SOD/CAT or SOD/CAT alone. iNOS and nitrotyrosine were detectable and increased over time in the LES muscle of both the control and LPS-treated groups. Antioxidant enzymes may play a role in regulating NO-mediated neuromuscular functions in the LES

Exosomes from Human Adipose Tissue-Derived Mesenchymal Stem Cells Promote Epidermal Barrier Repair by Inducing de Novo Synthesis of Ceramides in Atopic Dermatitis.

Atopic dermatitis (AD) is a multifactorial, heterogeneous disease associated with epidermal barrier disruption and intense systemic inflammation. Previously, we showed that exosomes derived from human adipose tissue-derived mesenchymal stem cells (ASC-exosomes) attenuate AD-like symptoms by reducing multiple inflammatory cytokine levels. Here, we investigated ASC-exosomes' effects on skin barrier restoration by analyzing protein and lipid contents. We found that subcutaneous injection of ASC-exosomes in an oxazolone-induced dermatitis model remarkably reduced trans-epidermal water loss, while enhancing stratum corneum (SC) hydration and markedly decreasing the levels of inflammatory cytokines such as IL-4, IL-5, IL-13, TNF-α, IFN-γ, IL-17, and TSLP, all in a dose-dependent manner. Interestingly, ASC-exosomes induced the production of ceramides and dihydroceramides. Electron microscopic analysis revealed enhanced epidermal lamellar bodies and formation of lamellar layer at the interface of the SC and stratum granulosum with ASC-exosomes treatment. Deep RNA sequencing analysis of skin lesions demonstrated that ASC-exosomes restores the expression of genes involved in skin barrier, lipid metabolism, cell cycle, and inflammatory response in the diseased area. Collectively, our results suggest that ASC-exosomes effectively restore epidermal barrier functions in AD by facilitating the de novo synthesis of ceramides, resulting in a promising cell-free therapeutic option for treating AD

Cellular and Tissue Selectivity of AAV Serotypes for Gene Delivery to Chondrocytes and Cartilage

Background: Despite several studies on the effect of adeno-associated virus (AAV)-based therapeutics on osteoarthritis (OA), information on the transduction efficiency and applicable profiles of different AAV serotypes to chondrocytes in hard cartilage tissue is still limited. Moreover, the recent discovery of additional AAV serotypes makes it necessary to screen for more suitable AAV serotypes for specific tissues. Here, we compared the transduction efficiencies of 14 conventional AAV serotypes in human chondrocytes, mouse OA models, and human cartilage explants obtained from OA patients. Methods: To compare the transduction efficiency of individual AAV serotypes, green fluorescent protein (GFP) expression was detected by fluorescence microscopy or western blotting. Likewise, to compare the transduction efficiencies of individual AAV serotypes in cartilage tissues, GFP expression was determined using fluorescence microscopy or immunohistochemistry, and GFP-positive cells were counted. Results: Only AAV2, 5, 6, and 6.2 exhibited substantial transduction efficiencies in both normal and OA chondrocytes. All AAV serotypes except AAV6 and rh43 could effectively transduce human bone marrow mesenchymal stem cells. In human and mouse OA cartilage tissues, AAV2, AAV5, AAV6.2, AAV8, and AAV rh39 showed excellent tissue specificity based on transduction efficiency. These results indicate the differences in transduction efficiencies of AAV serotypes between cellular and tissue models. Conclusions: Our findings indicate that AAV2 and AAV6.2 may be the best choices for AAV-mediated gene delivery into intra-articular cartilage tissue. These AAV vectors hold the potential to be of use in clinical applications to prevent OA progression if appropriate therapeutic genes are inserted into the vector

Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer's V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area