The impact of environmental change on landslides, fatal landslides, and their triggers in Pakistan (2003-2019).

The actual impact of landslides in Pakistan is highly underestimated and has not been addressed to its full extent. This study focuses on the impact which landslides had in the last 17 years, with focus on mortality, gender of deceased, main triggers (landslides and fatal landslides), and regional identification of the hotspots in Pakistan. Our study identified 1089 landslides (including rockfalls, rockslides, mudslides, mudflows, debris flows) out of which 180 landslides were fatal and claimed lives of 1072 people. We found that rain (rainfall and heavy rainfall)-related landslides were the deadliest over the entire study period. The main trigger of landslides in Pakistan is heavy rainfall which comprises over 50% of the triggers for the landslide, and combined with normal rainfall, this rate climbs to over 63%. The second main reason for landslide occurrence is spontaneous (due to rock instability, erosion, climate change, and other geological elements) with landslides accounting for 22.3% of all the landslides. Landslides caused by rain-related events amounted to 41.67% of the fatalities, whereas spontaneous landslides caused 29.44% of the deaths and the human induced events accounted for 25.5% of the fatalities. The fatal landslides accounted for 19.53% deaths of the children. Our study also found that more than 48% of the deadly landslides occurred between the months of January to April, whereas the least fatal landslides occurred in the month of June which accounted for only 3% of all the fatal landslides in Pakistan.

From a Point Cloud to a Simulation Model-Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D Modelling.

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.

Measuring the Uncertainty of Predictions in Deep Neural Networks with Variational Inference.

We present a novel approach for training deep neural networks in a Bayesian way. Compared to other Bayesian deep learning formulations, our approach allows for quantifying the uncertainty in model parameters while only adding very few additional parameters to be optimized. The proposed approach uses variational inference to approximate the intractable a posteriori distribution on basis of a normal prior. By representing the a posteriori uncertainty of the network parameters per network layer and depending on the estimated parameter expectation values, only very few additional parameters need to be optimized compared to a non-Bayesian network. We compare our approach to classical deep learning, Bernoulli dropout and Bayes by Backprop using the MNIST dataset. Compared to classical deep learning, the test error is reduced by 15%. We also show that the uncertainty information obtained can be used to calculate credible intervals for the network prediction and to optimize network architecture for the dataset at hand. To illustrate that our approach also scales to large networks and input vector sizes, we apply it to the GoogLeNet architecture on a custom dataset, achieving an average accuracy of 0.92. Using 95% credible intervals, all but one wrong classification result can be detected.

Spatial interpolation methods to predict airborne pesticide drift deposits on soils using knapsack sprayers.

Spatial predictions of drift deposits on soil surface were conducted using eight different spatial interpolation methods i.e. classical approaches like the Thiessen method and kriging, and some advanced methods like spatial vine copulas, Karhunen-Loève expansion and INLA. In order to investigate the impact of the number of locations on the prediction, all spatial predictions were conducted using a set of 39 and 47 locations respectively. The analysis revealed that taking more locations into account increases the accuracy of the prediction and the extreme behavior of the data is better modeled. Leave-one-out cross-validation was used to assess the accuracy of the prediction. The Thiessen method has the highest prediction errors among all tested methods. Linear interpolation methods were able to better reproduce the extreme behavior at the first meters from the sprayed border and exhibited lower prediction errors as the number of data points increased. Especially the spatial copula method exhibited an obvious increase in prediction accuracy. The Karhunen-Loève expansion provided similar results as universal kriging and IDW, although showing a stronger change in the prediction as the number of locations increased. INLA predicted the pesticide dispersion to be smooth over the whole study area. Using Delaunay triangulation of the study area, the total pesticide concentration was estimated to be between 2.06% and 2.97% of the total Uranine applied. This work is a first attempt to completely understand and model the uncertainties of the mass balance, therefore providing a basis for future studies.

The human cost of global warming: Deadly landslides and their triggers (1995-2014).

Worldwide, landslides incur catastrophic and significant economic and human losses. Previous studies have characterized the patterns in landslides' fatalities, from all kinds of triggering causes, at a continental or global scale, but they were based on data from periods of <10 years. The research herein presented hypothesizes that climate change associated with extreme rainfall and population distribution is contributing to a higher number of deadly landslides worldwide. This study maps and identified deadly landslides in 128 countries and it encompasses their role, for a 20 years' period from January/1995 to December/2014, considered representative for establishing a relationship between landslides and their meteorological triggers. A database of georeferenced landslides, their date, and casualties' information, duly validated, was implemented. A hot spot analysis for the daily record of landslide locations was performed, as well as a percentile-based approach to evaluate the trend of extreme rainfall events for each occurrence. The relationship between casualty, population distribution, and rainfall was also evaluated. For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally. They occurred most frequently between June and December in the Northern Hemisphere, and between December and February in the Southern Hemisphere. A significant global rise in the number of deadly landslides and hotspots across the studied period was observed. Analysis of daily rainfall confirmed that more than half of the events were in areas exposed to the risk of extreme rainfall. The relationships established between extreme rainfall, population distribution, seasonality, and landslides provide a useful basis for efforts to model the adverse impacts of extreme rainfall due to climate change and human activities and thus contribute towards a more resilient society.

Oxidative stress in drug-naïve first episode patients with schizophrenia and major depression: effects of disease acuity and potential confounders.

Oxidative stress and immune dysregulation have been linked to schizophrenia and depression. However, it is unknown whether these factors are related to the pathophysiology or whether they are an epiphenomenon. Inconsistent oxidative stress-related findings in previous studies may have resulted from the use of different biomarkers which show disparate aspects of oxidative stress. Additionally, disease severity, medication, smoking, endocrine stress axis activation and obesity are potential confounders. In order to address some of these shortcomings, we have analyzed a broader set of oxidative stress biomarkers in our exploratory study, including urinary 8-iso-prostaglandin F2α (8-iso-PGF2α), 8-OH-2-deoyxguanosine (8-OH-2-dG), and blood levels of malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione S-transferase (GST) in acutely ill drug-naïve first episode patients with schizophrenia (n = 22), major depression (n = 18), and controls (n = 43). Possible confounding factors were considered, and patients were followed-up after 6 weeks of treatment. No differences were observed regarding 8-OH-2-dG, MDA and GST. At baseline, 8-iso-PGF2α levels were higher in patients with schizophrenia (p = 0.004) and major depression (p = 0.037), with a trend toward higher SOD concentrations in schizophrenia (p = 0.053). After treatment, schizophrenia patients showed a further increase in 8-iso-PGF2α (p = 0.016). These results were not related to age, sex, disease severity, medication or adipose tissue mass. However, 8-iso-PGF2α was associated with smoking, endocrine stress axis activation, C-reactive protein levels and low plasma concentrations of brain-derived neurotrophic factor. This study suggests a role of lipid peroxidation particularly in drug-naïve acutely ill schizophrenia patients and highlights the importance of taking into account other confounding factors in biomarker studies.

Bayesian Network Model with Application to Smart Power Semiconductor Lifetime Data.

In this article, Bayesian networks are used to model semiconductor lifetime data obtained from a cyclic stress test system. The data of interest are a mixture of log-normal distributions, representing two dominant physical failure mechanisms. Moreover, the data can be censored due to limited test resources. For a better understanding of the complex lifetime behavior, interactions between test settings, geometric designs, material properties, and physical parameters of the semiconductor device are modeled by a Bayesian network. Statistical toolboxes in MATLAB® have been extended and applied to find the best structure of the Bayesian network and to perform parameter learning. Due to censored observations Markov chain Monte Carlo (MCMC) simulations are employed to determine the posterior distributions. For model selection the automatic relevance determination (ARD) algorithm and goodness-of-fit criteria such as marginal likelihoods, Bayes factors, posterior predictive density distributions, and sum of squared errors of prediction (SSEP) are applied and evaluated. The results indicate that the application of Bayesian networks to semiconductor reliability provides useful information about the interactions between the significant covariates and serves as a reliable alternative to currently applied methods.

Geo-additive modelling of malaria in Burundi.

BACKGROUND: Malaria is a major public health issue in Burundi in terms of both morbidity and mortality, with around 2.5 million clinical cases and more than 15,000 deaths each year. It is still the single main cause of mortality in pregnant women and children below five years of age. Because of the severe health and economic burden of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies/researches have been done on the subject yielding different results as which factors are most responsible for the increase in malaria transmission. This paper considers the modelling of the dependence of malaria cases on spatial determinants and climatic covariates including rainfall, temperature and humidity in Burundi. METHODS: The analysis carried out in this work exploits real monthly data collected in the area of Burundi over 12 years (1996-2007). Semi-parametric regression models are used. The spatial analysis is based on a geo-additive model using provinces as the geographic units of study. The spatial effect is split into structured (correlated) and unstructured (uncorrelated) components. Inference is fully Bayesian and uses Markov chain Monte Carlo techniques. The effects of the continuous covariates are modelled by cubic p-splines with 20 equidistant knots and second order random walk penalty. For the spatially correlated effect, Markov random field prior is chosen. The spatially uncorrelated effects are assumed to be i.i.d. Gaussian. The effects of climatic covariates and the effects of other spatial determinants are estimated simultaneously in a unified regression framework. RESULTS: The results obtained from the proposed model suggest that although malaria incidence in a given month is strongly positively associated with the minimum temperature of the previous months, regional patterns of malaria that are related to factors other than climatic variables have been identified, without being able to explain them. CONCLUSIONS: In this paper, semiparametric models are used to model the effects of both climatic covariates and spatial effects on malaria distribution in Burundi. The results obtained from the proposed models suggest a strong positive association between malaria incidence in a given month and the minimum temperature of the previous month. From the spatial effects, important spatial patterns of malaria that are related to factors other than climatic variables are identified. Potential explanations (factors) could be related to socio-economic conditions, food shortage, limited access to health care service, precarious housing, promiscuity, poor hygienic conditions, limited access to drinking water, land use (rice paddies for example), displacement of the population (due to armed conflicts).

Bayesian modelling of the effect of climate on malaria in Burundi.

BACKGROUND: In Burundi, malaria is a major public health issue in terms of both morbidity and mortality with around 2.5 million clinical cases and more than 15,000 deaths each year. It is the single main cause of mortality in pregnant women and children below five years of age. Due to the severe health and economic cost of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies have been done on the subject yielding different results as which factors are most responsible for the increase in malaria. The purpose of this study has been to undertake a spatial/longitudinal statistical analysis to identify important climatic variables that influence malaria incidences in Burundi. METHODS: This paper investigates the effects of climate on malaria in Burundi. For the period 1996-2007, real monthly data on both malaria epidemiology and climate in the area of Burundi are described and analysed. From this analysis, a mathematical model is derived and proposed to assess which variables significantly influence malaria incidences in Burundi. The proposed modelling is based on both generalized linear models (GLM) and generalized additive mixed models (GAMM). The modelling is fully Bayesian and inference is carried out by Markov Chain Monte Carlo (MCMC) techniques. RESULTS: The results obtained from the proposed models are discussed and it is found that malaria incidence in a given month in Burundi is strongly positively associated with the minimum temperature of the previous month. In contrast, it is found that rainfall and maximum temperature in a given month have a possible negative effect on malaria incidence of the same month. CONCLUSIONS: This study has exploited available real monthly data on malaria and climate over 12 years in Burundi to derive and propose a regression modelling to assess climatic factors that are associated with monthly malaria incidence. The results obtained from the proposed models suggest a strong positive association between malaria incidence in a given month and the minimum temperature (night temperature) of the previous month. An open question is, therefore, how to cope with high temperatures at night.

Effectiveness of respiratory-sinus-arrhythmia biofeedback on state-anxiety in patients undergoing coronary angiography.

AIM: This study is a report of a study conducted to evaluate the value of psychological assistance including respiratory-sinus-arrhythmia biofeedback training in its ability to reduce the level of anxiety in patients undergoing coronary angiography. BACKGROUND: Coronary angiography has been reported to cause anxiety and emotional stress. METHODS: Between March 2004 and January 2005, 212 patients undergoing routine elective coronary angiography for the evaluation of stable coronary artery disease were randomized into two groups. In the psychological support group (n = 106) a structured psychological conversation and respiratory-sinus-arrhythmia biofeedback training were offered prior to coronary angiography. In the control group (n = 106) standard care and information was provided without psychological support. State-anxiety was measured (scale 20-80) 1 day prior to and after coronary angiography, along with blood pressure and heart rate. FINDINGS: Prior to coronary angiography, state-anxiety was 54.8 +/- 11.5 (mean +/- SD) in the control group and 54.8 +/- 12.6 in the psychological support group. After coronary angiography, state-anxiety was 47.9 +/- 18.5 in the control group but 28.3 +/- 12.5 in the psychological support group (Wilcoxon rank sum test W = 7272, P < 0.001). Blood pressure was statistically significantly lower in the psychological support group prior to the intervention and the day after coronary angiography. CONCLUSION: Psychological support including respiratory-sinus-arrhythmia biofeedback is an effective and simple tool that could be used by nurses to reduce state-anxiety and emotional stress in patients undergoing coronary angiography.

Early administration of tiapride to young rats without long-lasting changes in the development of the dopaminergic system.

BACKGROUND: The benzamide tiapride, a selective dopamine D2/D3-receptor antagonist, can be used effectively in children to treat tic disorders and stuttering. Tiapride is a clinically safe substance (even during long-term treatment and when given to young children). Unfortunately, its probable effects on general brain development and the maturation of the dopaminergic system have not been investigated. Thus, important information for drug treatment in children is missing. Therefore, this study in rats describes tiapride's effects on several parameters of dopaminergic activity (dopamine transporter, D2 receptor, dopamine, DOPAC, and homovanillic acid in the striatum) seen after tiapride administration (30 mg/kg/day) to prepubertal (from day 25-39) and postpubertal (from day 50-64) rats. METHODS: Three groups of rats (n = 6) received tiapride within their drinking water for 14 days. Two groups were treated before puberty; one of those was killed at day 50, the other at day 90. The group treated after puberty was measured at day 90. A fourth group (n = 6) was treated from day 50 to day 53 and measured under tiapride at day 53. Changes were measured by ligand-binding assays (KD and Bmax values of dopamine transporter by [3H]-GBR binding and D2 receptor by [3H]- spiperone binding) and by HPLC (concentrations of dopamine, DOPAC, and homovanillic acid). RESULTS: The density of dopamine transporters and D2 receptors remained unaffected after early (day 25) and late (day 50) tiapride administration. Only during the treatment period could a significant reduction of D2-receptor binding (displacement of spiperone) and of dopamine and DOPAC levels be stated. CONCLUSIONS: These data suggest that tiapride treatment during postnatal brain development causes no long-lasting changes in the development of the central dopaminergic system and is in line with clinical experience in children.

Nocturnal urinary dopamine excretion is reduced in otherwise healthy subjects with periodic leg movements in sleep.

The pathophysiology of periodic leg movements (PLMs) in sleep remains to be elucidated. Among other hypotheses an alteration of dopaminergic function has been suggested. Nocturnal urinary dopamine and 4-hydroxy-3-methoxyphenylacetic acid excretion in otherwise healthy subjects with PLMs was significantly reduced (P < 0.001 and P < 0.05, respectively) compared to subjects without PLMs. This finding, for the first time, demonstrates a correlate of a functionally relevant hypoactivity of the dopaminergic system in subjects with PLMs.