ObjectivesHousehold contacts (HHCs) of pulmonary tuberculosis patients are at high risk of Mycobacterium tuberculosis infection and early disease development. Identification of individuals at risk of tuberculosis disease is a desirable goal for tuberculosis control. Interferon-gamma release assays (IGRAs) using specific M. tuberculosis antigens provide an alternative to tuberculin skin testing (TST) for infection detection. Additionally, the levels of IFNγ produced in response to these antigens may have prognostic value. We estimated the prevalence of M. tuberculosis infection by IGRA and TST in HHCs and their source population (SP), and assessed whether IFNγ levels in HHCs correlate with tuberculosis development.MethodsA cohort of 2060 HHCs was followed for 2–3 years after exposure to a tuberculosis case. Besides TST, IFNγ responses to mycobacterial antigens: CFP, CFP-10, HspX and Ag85A were assessed in 7-days whole blood cultures and compared to 766 individuals from the SP in Medellín, Colombia. Isoniazid prophylaxis was not offered to child contacts because Colombian tuberculosis regulations consider it only in children under 5 years, TST positive without BCG vaccination.ResultsUsing TST 65.9% of HHCs and 42.7% subjects from the SP were positive (OR 2.60, p<0.0001). IFNγ response to CFP-10, a biomarker of M. tuberculosis infection, tested positive in 66.3% HHCs and 24.3% from the SP (OR = 6.07, p<0.0001). Tuberculosis incidence rate was 7.0/1000 person years. Children <5 years accounted for 21.6% of incident cases. No significant difference was found between positive and negative IFNγ responders to CFP-10 (HR 1.82 95% CI 0.79–4.20 p = 0.16). However, a significant trend for tuberculosis development amongst high HHC IFNγ producers was observed (trend Log rank p = 0.007).DiscussionCFP-10-induced IFNγ production is useful to establish tuberculosis infection prevalence amongst HHC and identify those at highest risk of disease. The high tuberculosis incidence amongst children supports administration of chemoprohylaxis to child contacts regardless of BCG vaccination.

A competitive inhibition enzyme-linked immunosorbent assay (ELISA) based on a major surface protein 5 (MSP5) B-cell epitope conserved among Anaplasma species was used to detect goats infected with Anaplasma ovis. We examined strains of A. ovis isolated from goats in Kenya and demonstrated that MSP5 and the target B-cell epitope, bound by monoclonal antibody ANAF16C1, were conserved. Sera from 149 goats in four regions of Kenya and from 302 goats in six U.S. states were tested for the presence of epitope-specific antibodies with the MSP5 competitive inhibition ELISA. Evidence that the assay can be used to detect A. ovis-infected goats includes the following: (i) 53 goats raised in confinement with arthropod control were all seronegative; (ii) six goats experimentally infected with A. ovis seroconverted at the same time that they developed detectable rickettsemia; (iii) seroconverted goats remained seropositive, consistent with the persistence of A. ovis in goats and the presence of anti-MSP5 antibody in cattle persistently infected with Anaplasma marginale; and (iv) 119 of 127 known A. ovis-infected goats in Kenya were seropositive. A. ovis infection, as determined serologically and by demonstration of infected erythrocytes, in goats from the four regions in Kenya was highly prevalent. In contrast, despite the presence of A. ovis and competent arthropod vectors in the United States, the prevalence of infection appeared to be very low. The high prevalence in Kenya and the occurrence of anemia in persistently infected goats may be impediments to current efforts to increase milk yields on small farms.

Nicotinamide N-methyltransferase (NNMT, E.C. 2.1.1.1) N-methylates nicotinamide to 1-methylnicotinamide. We have previously shown that NNMT is significantly overexpressed in the brains of patients who have died of Parkinson's disease, and others have shown that NNMT is significantly overexpressed in a variety of diseases ranging from cancer to hepatic cirrhosis. In vitro overexpression has revealed many cytoprotective effects of NNMT, in particular increased complex I activity and ATP synthesis. Although this appears to be mediated by an increase in 1-methylnicotinamide production, the molecular mechanisms involved remain unclear. In the present study, we have investigated the role that sirtuins 1, 2 and 3, class III DNA deacetylase enzymes known to regulate mitochondrial energy production and cell cycle, have in mediating the effects of NNMT upon complex I activity. Expression of NNMT in SH-SY5Y human neuroblastoma cells, which have no endogenous expression of NNMT, significantly increased the expression of all three sirtuins. siRNA-mediated silencing of sirtuin 3 expression decreased complex I activity in NNMT-expressing SH-SY5Y cells to that observed in wild-type SH-SY5Y, and significantly reduced cellular ATP content also. These results demonstrate that sirtuin 3 is a key mediator of NNMT-induced complex I activity and ATP synthesis. These results further reinforce a central role for NNMT in the regulation of energy homeostasis and provide further mechanistic insight into the consequences of enhanced NNMT expression.

During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as input for many algorithms. Some features are computationally expensive, cannot be updated quickly and hence for large datasets such as the LSST cannot be applied. Previous work has been done to develop alternative unsupervised feature extraction algorithms for light curves, but the cost of doing so still remains high. In this work, we propose an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification. We study a series of deep learning architectures based on Recurrent Neural Networks and test them in automated classification scenarios. Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets. We transform each light curve into an input matrix representation whose elements are the differences in time and magnitude, and the outputs are classification probabilities. We test our method in three surveys: OGLE-III, Gaia and WISE. We obtain accuracies of about 95% in the main classes and 75% in the majority of subclasses. We compare our results with the Random Forest classifier and obtain competitive accuracies while being faster and scalable. The analysis shows that the computational complexity of our approach grows up linearly with the light curve size, while the traditional approach cost grows as N log (N).

PHARC (polyneuropathy, hearing loss, cerebellar ataxia, retinitis pigmentosa, and cataract) is a human neurological disorder caused by deleterious mutations in the ABHD12 gene, which encodes an integral membrane lyso-phosphatidylserine (lyso-PS) lipase. Pharmacological or genetic disruption of ABHD12 leads to higher levels of lyso-PS lipids in human cells and the central nervous system (CNS) of mice. ABHD12 loss also causes rapid rewiring of PS content, resulting in selective increases in the level of arachidonoyl (C20:4) PS and decreases in the levels of other PS species. The biochemical basis for ABHD12-dependent PS remodeling and its pathophysiological significance remain unknown. Here, we show that genetic deletion of the lysophospholipid acyltransferase LPCAT3 blocks accumulation of brain C20:4 PS in mice lacking ABHD12 and concurrently produces hyperincreases in the level of lyso-PS in these animals. These lipid changes correlate with exacerbated auditory dysfunction and brain microgliosis in mice lacking both ABHD12 and LPCAT3. Taken together, our findings reveal that ABHD12 and LPCAT3 coordinately regulate lyso-PS and C20:4 PS content in the CNS and point to lyso-PS lipids as the likely bioactive metabolites contributing to PHARC-related neuropathologies.

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Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.