LINE-1 methylation in cleft lip tissues:influence of infant MTHFR c.677C>T genotype

Objective: To investigate the influence of MTHFR c.677C>T genotype on LINE-1 methylation in lateral and medial tissues from cleft lip (CL). Methods: Forty-five consecutive non-syndromic cleft lip with or without cleft palate (nsCL/P) cases were included in the study. Genomic DNA was extracted from tissues at both sides of cleft lip, and LINE-1 methylation was detected by bisulfite conversion and pyrosequencing. MTHFR c.677C>T genotyping was carried out using the TaqMan genotyping assay. Results: LINE-1 methylation level was significantly higher on medial side of cleft lip compared with lateral side (p = 0.001). This difference was not significantly influenced by the case's sex or cleft type. However, MTHFR c.677C>T genotyping revealed that the difference in LINE-1 methylation across cleft lip was restricted to carriers of C allele of MTHFR c.677C>T and was not apparent in TT homozygous cases (p = 0.027). Conclusion: This integrated analysis supports the previous finding of differences in DNA methylation across the two sides of cleft lip and further suggests a possible role of MTHFR c.677C>T genotype in establishing this difference

Rainfall Thresholding and Susceptibility assessment of rainfall induced landslides: application to landslide management in St Thomas, Jamaica

The final publication is available at Springer via http://dx.doi.org/10.1007/s10064-009-0232-zThe parish of St Thomas has one of the highest densities of landslides in Jamaica, which impacts the residents, local economy and the built and natural environment. These landslides result from a combination of steep slopes, faulting, heavy rainfall and the presence of highly weathered volcanics, sandstones, limestones and sandstone/shale series and are particularly prevalent during the hurricane season (June–November). The paper reports a study of the rainfall thresholds and landslide susceptibility assessment to assist the prediction, mitigation and management of slope instability in landslide-prone areas of the parish

Light chain deposition disease presenting as paroxysmal atrial fibrillation: a case report

<p>Abstract</p> <p>Introduction</p> <p>Light chain deposition disease (LCDD) can involve the heart and cause severe heart failure. Cardiac involvement is usually described in the advanced stages of the disease. We report the case of a woman in whom restrictive cardiomyopathy due to LCDD presented with paroxysmal atrial fibrillation.</p> <p>Case presentation</p> <p>A 55-year-old woman was admitted to our emergency department because of palpitations. In a recent blood test, serum creatinine was 1.4 mg/dl. She was found to have high blood pressure, left ventricular hypertrophy and paroxysmal atrial fibrillation. An ACE-inhibitor was prescribed but her renal function rapidly worsened and she was admitted to our nephrology unit. On admission serum creatinine was 9.4 mg/dl, potassium 6.8 mmol/l, haemoglobin 7.7 g/dl, N-terminal pro-brain natriuretic peptide 29894 pg/ml. A central venous catheter was inserted and haemodialysis was started. She underwent a renal biopsy which showed kappa LCDD. Bone marrow aspiration and bone biopsy demonstrated kappa light chain multiple myeloma. Echocardiographic findings were consistent with restrictive cardiomyopathy. Thalidomide and dexamethasone were prescribed, and a peritoneal catheter was inserted. Peritoneal dialysis has now been performed for 15 months without complications.</p> <p>Discussion</p> <p>Despite the predominant tubular deposition of kappa light chain, in our patient the first clinical manifestation of LCDD was cardiac disease manifesting as atrial fibrillation and the correct diagnosis was delayed. The clinical management initially addressed the cardiovascular symptoms without paying sufficient attention to the pre-existing slight increase in our patient's serum creatinine. However cardiac involvement is a quite uncommon presentation of LCDD, and this unusual case suggests that the onset of acute arrhythmias associated with restrictive cardiomyopathy and impaired renal function might be related to LCDD.</p

Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam.

The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years

Constant Velocity Constraints for Self-Supervised Monocular Depth Estimation

We present a new method for self-supervised monocular depth estimation. Contemporary monocular depth estimation methods use a triplet of consecutive video frames to estimate the central depth image. We make the assumption that the ego-centric view progresses linearly in the scene, based on the kinematic and physical properties of the camera. During the training phase, we can exploit this assumption to create a depth estimation for each image in the triplet. We then apply a new geometry constraint that supports novel synthetic views, thus providing a strong supervisory signal. Our contribution is simple to implement, requires no additional trainable parameter, and produces competitive results when compared with other state-of-the-art methods on the popular KITTI corpus

Haptic Teleoperation of UAV Equipped with Gamma-Ray Spectrometer for Detection and Identification of Radio-Active Materials in Industrial Plants

Large scale factories such as steel, wood, construction, recycling plants and landfills involve the procurement of raw material which may include radiating parts, that must be monitored, because potentially dangerous for workers. Manufacturing operations are carried out in unstructured environments, where fully autonomous unmanned aerial vehicle (UAV) inspection is hardly applicable. In this work we report on the development of a haptic teleoperated UAV for localization of radiation sources in industrial plants. Radiation sources can be localized and identified thanks to a novel CZT-based custom gamma-ray detector integrated on the UAV, providing light, compact, spectroscopic, and low power operation. UAV operation with a human in the loop allows an expert operator to focus on selected candidate areas, thereby optimizing short flight mission in face of the constrained acquisition times required by nuclear inspection. To cope with the reduced situational awareness of the remote operator, force feedback is exploited as an additional sensory channel. The developed prototype has been demonstrated both in relevant and operational environments

Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning

Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning