Analysing the outbreaks of leptospirosis after floods in Kerala, India

A growing number of studies have linked the incidence of leptospirosis with the occurrence of flood events. Nevertheless, the interaction between flood and leptospirosis has not been extensively studied to understand the influence of flood attributes in inducing new cases. This study reviews leptospirosis cases in relation to multiple flood occurrences in Kerala, India. Leptospirosis data were obtained for three years: 2017 (non-flood year) and two years with flooding—2018 (heavy flooding) and 2019 (moderate flooding). We considered the severity of flood events using the discharge, duration and extent of each flooding event and compared them with the leptospirosis cases. The distribution of cases regarding flood discharge and duration was assessed through descriptive and spatiotemporal analyses, respectively. Furthermore, cluster analyses and spatial regression were completed to ascertain the relationship between flood extent and the postflood cases. This study found that postflood cases of leptospirosis can be associated with flood events in space and time. The total cases in both 2018 and 2019 increased in the post-flood phase, with the increase in 2018 being more evident. Unlike the 2019 flood, the flood of 2018 is a significant spatial indicator for postflood cases. Our study shows that flooding leads to an increase in leptospirosis cases, and there is stronger evidence for increased leptospirosis cases after a heavy flood event than after a moderate flooding event. Flood duration may be the most important factor in determining the increase in leptospirosis infections.</p

Do Votes Speak Louder than Motives? Moral Judgments and Tolerance in the 2016 Presidential Election

When judging a voter’s decision, does that voter’s reason for casting their vote influence moral and interpersonal judgments about them? In the context of the 2016 U.S. Presidential Election, past research suggests two competing predictions. First, people regularly account for an actor’s intentions when forming judgments of the actor, indicating that judgments may vary according to a voter’s motives. However, people are unlikely to see nuance among outgroups, especially amid divisive political partisanship, suggesting that judgments would ignore information about voters’ motives. In Study 1, results supported the first prediction, showing that both Hillary Clinton and Donald Trump supporters distinguished between different voting motives when making moral and interpersonal judgments of outgroup voters. In Studies 2 and 3, when some voters’ motives became more extreme, Clinton and Trump supporters again distinguished between voting motives for outgroup and ingroup voters, respectively, albeit in a different pattern of results.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147150/1/asap12153.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147150/2/asap12153_am.pd

Exploring functional regression for dynamic modeling of shallow landslides in South Tyrol, Italy

Shallow landslides are ubiquitous hazards in mountainous regions worldwide that arise from an interplay of static predisposing factors and dynamic preparatory and triggering conditions. Modeling shallow landslides at regional scales has leveraged data-driven approaches to separately investigate purely spatial landslide susceptibility and temporally varying conditions. Yet, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging. Furthermore, dynamic factors have been typically included in data-driven landslide models as scalar predictors by employing aggregated descriptors over time (e.g., mean, maximum, or total precipitation over a defined time window), where many choices are possible for the considered time scales and aggregation operators. Therefore, incorporating the time-varying behavior of dynamic factors remains difficult.This study addresses these challenges by exploring Functional Generalized Additive Models (FGAMs) to predict the occurrence of shallow landslides in space and time within the Italian province of South Tyrol (7,400 km²). In contrast to conventional techniques, we test the benefits of using functional predictors to describe dynamic factors (e.g., precipitation and temperature) leading to landslide events. In other words, we evaluate dynamic factors as collections of measurements over time (i.e., time series). To do so, our approach uses a binomial FGAM to analyze the statistical associations between the static factors (scalar predictors), the dynamic weather conditions prior to a potential landslide occurrence (functional predictors), and the occurrence of shallow landslides in space and time.Potential outcomes of this novel approach show an overview of the added value of using functional predictors for space and time shallow landslide modeling. These research findings are positioned within the context of the PROSLIDE project, which has received financial support from the Research Südtirol/Alto Adige 2019 research program of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige

The application of numerical debris flow modelling for the generation of physical vulnerability curves

For a quantitative assessment of debris flow risk, it is essential to consider not only the hazardous process itself but also to perform an analysis of its consequences. This should include the estimation of the expected monetary losses as the product of the hazard with a given magnitude and the vulnerability of the elements exposed. A quantifiable integrated approach of both hazard and vulnerability is becoming a required practice in risk reduction management. This study aims at developing physical vulnerability curves for debris flows through the use of a dynamic run-out model. Dynamic run-out models for debris flows are able to calculate physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk could suffer an impact. These results can then be applied to consequence analyses and risk calculations. On 13 July 2008, after more than two days of intense rainfall, several debris and mud flows were released in the central part of the Valtellina Valley (Lombardy Region, Northern Italy). One of the largest debris flows events occurred in a village called Selvetta. The debris flow event was reconstructed after extensive field work and interviews with local inhabitants and civil protection teams. The Selvetta event was modelled with the FLO-2D program, an Eulerian formulation with a finite differences numerical scheme that requires the specification of an input hydrograph. The internal stresses are isotropic and the basal shear stresses are calculated using a quadratic model. The behaviour and run-out of the flow was reconstructed. The significance of calculated values of the flow depth, velocity, and pressure were investigated in terms of the resulting damage to the affected buildings. The physical damage was quantified for each affected structure within the context of physical vulnerability, which was calculated as the ratio between the monetary loss and the reconstruction value. Three different empirical vulnerability curves were obtained, which are functions of debris flow depth, impact pressure, and kinematic viscosity, respectively. A quantitative approach to estimate the vulnerability of an exposed element to a debris flow which can be independent of the temporal occurrence of the hazard event is presented

Correlation between arterial blood volume obtained by arterial spin labelling and cerebral blood volume in intracranial tumours.

OBJECTIVE: To compare measurements of the arterial blood volume (aBV), a perfusion parameter calculated from arterial spin labelling (ASL), and cerebral blood volume (CBV), calculated from dynamic susceptibility contrast (DSC) MRI. In the clinic, CBV is used for grading of intracranial tumours. MATERIALS AND METHODS: Estimates of aBV from the model-free ASL technique quantitative STAR labelling of arterial regions (QUASAR) experiment and of DSC-CBV were obtained at 3T in ten patients with eleven tumours (three grade III gliomas, four glioblastomas and four meningiomas, two in one patient). Parametric values of aBV and CBV were determined in the tumour as well as in normal grey matter (GM), and tumour-to-GM aBV and CBV ratios were calculated. RESULTS: In a 4-pixel ROI representing maximal tumour values, the coefficient of determination R (2) was 0.61 for the comparison of ASL-based aBV tumour-to-GM ratios and DSC-MRI-based CBV tumour-to-GM ratios and 0.29 for the comparison of parametric values of ASL-aBV and DSC-CBV, under the assumption of proportionality. Both aBV and CBV showed a non-significant tendency to increase when going from grade III gliomas to glioblastomas to meningiomas. CONCLUSION: These results suggest that measurement of aBV is a potential tool for non-invasive assessment of blood volume in intracranial tumours

Novel technologies for metabolomics: More for less

The human metabolome provides a direct physiological read-out of an individual's actual health state and includes biomarkers that may predict disease or response to a treatment. The discovery and validation of these metabolomic biomarkers requires large-scale cohort studies, typically involving thousands of samples. This analytical challenge drives novel technological developments to enable faster, cheaper, and more comprehensive metabolomic analysis: more for less.This review summarises recent (2012–2018) developments towards this goal in all aspects of the analytical workflow, in relation to NMR but primarily to mass spectrometry (MS). Recent trends include miniaturisation and automation of extraction techniques, online coupling to fast analysis methods including direct infusion ion mobility MS, integrated microfluidic devices, and sharing and standardizing metabolomics software and data.The technological advances in metabolomics support its widespread application, integration with other -omics fields, and ultimately disease prediction and precision medicine.Pharmacolog

Inter-rater agreement of comorbid DSM-IV personality disorders in substance abusers

<p>Abstract</p> <p>Background</p> <p>Little is known about the inter-rater agreement of personality disorders in clinical settings.</p> <p>Methods</p> <p>Clinicians rated 75 patients with substance use disorders on the DSM-IV criteria of personality disorders in random order, and on rating scales representing the severity of each.</p> <p>Results</p> <p>Convergent validity agreement was moderate (range for r = 0.55, 0.67) for cluster B disorders rated with DSM-IV criteria, and discriminant validity was moderate for eight of the ten personality disorders. Convergent validity of the rating scales was only moderate for antisocial and narcissistic personality disorder.</p> <p>Discussion</p> <p>Dimensional ratings may be used in research studies and clinical practice with some caution, and may be collected as one of several sources of information to describe the personality of a patient.</p

Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development

In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied ‘proteochemometric’ modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untested compound – mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict with high accuracy which compound will be most effective on a sequence with a previously unknown genotype. Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research. The modeling concept is likely to be applicable also to other target families with genetic variability like other viruses or bacteria, or with similar orthologs like GPCRs