Transcriptome-Wide Root Causal Inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone.

Mitigating pathogenesis for target discovery and disease subtyping.

Treatments ideally mitigate pathogenesis, or the detrimental effects of the root causes of disease. However, existing definitions of treatment effect fail to account for pathogenic mechanism. We therefore introduce the Treated Root causal Effects (TRE) metric which measures the ability of a treatment to modify root causal effects.

Discovering Root Causal Genes with High Throughput Perturbations.

Root causal gene expression levels - or for short - correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches.

Counterfactual formulation of patient-specific root causes of disease.

Objective: Root causes of disease intuitively correspond to root vertices of a causal model that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the development of computer algorithms designed to automatically detect root causes from data. We seek a definition of patient-specific root causes of disease that models the intuitive procedure routinely utilized by physicians to uncover root causes in the clinic.

Increasing countries' financial resilience through global catastrophe risk pooling.

Extreme weather events can severely impact national economies, leading the recovery of low- to middle-income countries to become reliant on foreign financial aid. Foreign aid is, however, slow and uncertain. Therefore, the Sendai Framework and the Paris Agreement advocate for more resilient financial instruments like sovereign catastrophe risk pools.

Cross-country risk quantification of extreme wildfires in Mediterranean Europe.

We estimate the country-level risk of extreme wildfires defined by burned area (BA) for Mediterranean Europe and carry out a cross-country comparison. To this end, we avail of the European Forest Fire Information System (EFFIS) geospatial data from 2006 to 2019 to perform an extreme value analysis. More specifically, we apply a point process characterization of wildfire extremes using maximum likelihood estimation.

Factors Associated With Being Overweight or Obese in Suriname.

To identify the socio-demographic risk factors that are associated with adult Body Mass Index. We apply probit and ordinal probit models to a sample of 3,803 adults aged 20 and above from the 2016/17 round of the Suriname Survey of Living Conditions. Women, the elderly, and couples who are either married and/or living together are more likely to be obese or overweight.

The role of weather conditions in COVID-19 transmission: A study of a global panel of 1236 regions.

It is believed that weather conditions such as temperature and humidity have effects on COVID-19 transmission. However, these effects are not clear due to the limited observations and difficulties in separating impact of social distancing. COVID-19 data and social-economic features of 1236 regions in the world (1112 regions at the provincial level and 124 countries with the small land area) were collected.

Can we mitigate the effect of natural disasters on child health? Evidence from the Indian Ocean tsunami in Indonesia.

The 2004 Indian Ocean tsunami was an international natural disaster unlike any seen before, killing 166,561 people in Aceh province, Indonesia. It prompted an unprecedented humanitarian response and was a catalyst in ending almost 30 years of civil conflict in Aceh. Since the tsunami was followed by a multitude of events, we first conduct a systematic review to identify those events in Indonesia.

Air Pollution Exposure and Covid-19 in Dutch Municipalities.

In light of the existing preliminary evidence of a link between Covid-19 and poor air quality, which is largely based upon correlations, we estimate the relationship between long term air pollution exposure and Covid-19 in 355 municipalities in the Netherlands. Using detailed data we find compelling evidence of a positive relationship between air pollution, and particularly concentrations, and Covid-19 cases, hospital admissions and deaths. This relationship persists even after controlling for a wide range of explanatory variables.

The impact of decriminalization on marijuana and alcohol consumption in Jamaica.

We examine whether marijuana decriminalization in Jamaica, a country that historically has had relatively widespread use of the drug, has led to an increase in its use, the frequency of use and the money spent on it. To this end, we use a national drug survey dataset with extensive information on people's use of, attitudes towards, access to marijuana. Our econometric analysis shows that awareness of the legislation has a positive correlation with the use of the substance.

Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion.

Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with missing values, before applying causal discovery algorithms. List-wise deletion is a sound and general strategy when paired with algorithms such as FCI and RFCI, but the deletion procedure also eliminates otherwise good samples that contain only a few missing values.

Markov Boundary Discovery with Ridge Regularized Linear Models.

Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response. However, many investigators are reluctant to draw causal interpretations of the selected variables due to the incomplete knowledge of the capabilities of RRLMs in causal inference. Under reasonable assumptions, we show that a modified form of RRLMs can get "very close" to identifying a subset of the Markov boundary by providing a worst-case bound on the space of possible solutions.

Impaired Recognition and Regulation of Disgust Is Associated with Distinct but Partially Overlapping Patterns of Decreased Gray Matter Volume in the Ventroanterior Insula.

Background: The ventroanterior insula is implicated in the experience, expression, and recognition of disgust; however, whether this brain region is required for recognizing disgust or regulating disgusting behaviors remains unknown.

Methods: We examined the brain correlates of the presence of disgusting behavior and impaired recognition of disgust using voxel-based morphometry in a sample of 305 patients with heterogeneous patterns of neurodegeneration. Permutation-based analyses were used to determine regions of decreased gray matter volume at a significance level p <= .

Predicting the risk of psychosis onset: advances and prospects.

Aim: To conduct a systematic review of the methods and performance characteristics of models developed for predicting the onset of psychosis.

Methods: We performed a comprehensive literature search restricted to English articles and identified using PubMed, Medline and PsychINFO, as well as the reference lists of published studies and reviews. Inclusion criteria included the selection of more than one variable to predict psychosis or schizophrenia onset, and selection of individuals at familial risk or clinical high risk.

BDNF serum concentrations show no relationship with diagnostic group or medication status in neurodegenerative disease.

Brain-derived neurotrophic factor (BDNF) is a growth factor implicated in neuronal survival. Studies have reported altered BDNF serum concentrations in patients with Alzheimer's disease (AD). However, these studies have been inconsistent.

Association between skin surface pH, temperature and Staphylococcus pseudintermedius in dogs with immunomodulatory-responsive lymphocytic-plasmacytic pododermatitis.

Secondary bacterial infection is a frequent complication in lesional skin of dogs with immunomodulatory-responsive lymphocytic-plasmacytic pododermatitis (ImR-LPP). However, the influence of skin pH and temperature in determining the composition of the cutaneous microflora at lesional sites has not been investigated. The association between ImR-LPP and pedal skin temperature, pH and Staphylococcus pseudintermedius isolates was thus evaluated.

Cutaneous infiltrates and peripheral blood immune responses in dogs with immunomodulatory-responsive lymphocytic-plasmacytic pododermatitis.

This study characterizes T- and B-lymphocyte responses in the peripheral blood and lesional skin of dogs with immunomodulatory-responsive lymphocytic-plasmacytic pododermatitis (ImR-LPP), a term previously proposed to denote a subpopulation of dogs with idiopathic pododermatitis. T-cell (CD3+, CD4+ and CD8+ ) and B-cell (CD21+) counts were significantly increased in both the epidermis and dermis of lesional ImR-LPP skin compared with that in pedal skin from healthy controls. CD3+ , CD4+, CD8+ and CD21+ cells were commonly observed in perivascular sites in the superficial dermis, periadnexally, beneath the dermal-epidermal (DE) junction and in the epidermis of lesional ImR-LPP skin.