Knowledge of Antiretroviral Treatment and Associated Factors in HIV-Infected Patients

This study aimed to assess the knowledge of antiretroviral (ARV) treatment and the associated factors in HIV-infected patients in Vietnam. We conducted a cross-sectional descriptive study of 350 human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) patients being treated with ARV at outpatient clinics at Soc Trang, Vietnam, from June 2019 to December 2019. Using an interview questionnaire, patients who answered at least eight out of nine questions correctly, including some required questions, were considered to have a general knowledge of ARV treatment. Using multivariate logistic regression to identify factors associated with knowledge of ARV treatment, we found that 62% of HIV-infected patients had a general knowledge of ARV treatment, with a mean score of 8.2 (SD 1.4) out of 9 correct. A higher education level (p < 0.001); working away from home (p = 0.013); getting HIV transmitted by injecting drugs or from mother-to-child contact (p = 0.023); the presence of tension, anxiety, or stress (p = 0.005); self-reminding to take medication (p = 0.024); and a high self-evaluated adherence (p < 0.001) were found to be significantly associated with an adequate knowledge of ARV treatment. In conclusion, education programs for patients, as well as the quality of medical services and support, should be strengthened

Search for the direct production of charginos and neutralinos in final states with tau leptons in √s=13 TeV collisions with the ATLAS detector

A search for the direct production of charginos and neutralinos in final states with at least two hadronically decaying tau leptons is presented. The analysis uses a dataset of pp collisions corresponding to an integrated luminosity of 36.1 fb−1, recorded with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of 13TeV.Nosignificant deviation from the expected Standard Model background is observed. Limits are derived in scenarios of ˜χ+1 ˜χ−1 pair production and of ˜χ±1 ˜χ02 and ˜χ+1 ˜χ−1 production in simplified models where the neutralinos and charginos decay solely via intermediate left-handed staus and tau sneutrinos, and the mass of the ˜ τL state is set to be halfway between the masses of the ˜χ±1 and the ˜χ01. Chargino masses up to 630 GeV are excluded at 95% confidence level in the scenario of direct production of ˜χ+1 ˜χ−1 for a massless ˜χ01. Common ˜χ±1 and ˜χ02 masses up to 760 GeV are excluded in the case of production of ˜χ±1 ˜χ02 and ˜χ+1 ˜χ−1 assuming a massless ˜χ01. Exclusion limits for additional benchmark scenarios with large and small mass-splitting between the ˜χ±1 and the ˜χ01 are also studied by varying the ˜ τL mass between the masses of the ˜χ±1 and the ˜χ01

Transformer vibration and noise monitoring system using internet of things

Abstract During continuous operation, transformer problems can occur due to various reasons. In reality, the operating parameters of the transformer have been collected and monitored through the supervisory control and data acquisition (SCADA) systems. However, these systems face many challenges when applied to no‐human substations. Currently, noise signals have been used to detect transformer errors. Abnormal noise recognition and vibration monitoring can recognize the transformer's potential defects and errors. In this study, the authors built an internet of things (IoT) system that allows remote control centres to monitor the condition of transformers through noise and vibration at non‐human substations. The proposed model was equipped with a wireless sensor network node consisting of vibration sensors, audio collectors, Arduino modules, and Lora modules. The authors set up two schemes for the IoT network: one sensor node for a 220‐kV transformer and three sensor nodes for all three phases of the 500‐kV transformer. The data obtained from the sensor node were sent to LoRa Gateway and displayed on the computer through LabVIEW. The study also enabled monitoring of parameters through IoT devices such as Desktops, Laptops, Smartphones from LabVIEW NXG Web VI platform, ThingSpeak, and Amazon S3 storage cloud. In addition, a Model Predictive Control (MPC) algorithm was applied to predict the deterioration of transformer health to maintain the system stability and, hence, prolong the transformer life and operability

TDP-43 pathology disrupts nuclear pore complexes and nucleocytoplasmic transport in ALS/FTD

The cytoplasmic mislocalization and aggregation of TAR DNA-binding protein-43 (TDP-43) is a common histopathological hallmark of the amyotrophic lateral sclerosis and frontotemporal dementia disease spectrum (ALS/FTD). However, the composition of aggregates and their contribution to the disease process remain unknown. Here we used proximity-dependent biotin identification (BioID) to interrogate the interactome of detergent-insoluble TDP-43 aggregates and found them enriched for components of the nuclear pore complex and nucleocytoplasmic transport machinery. Aggregated and disease-linked mutant TDP-43 triggered the sequestration and/or mislocalization of nucleoporins and transport factors, and interfered with nuclear protein import and RNA export in mouse primary cortical neurons, human fibroblasts and induced pluripotent stem cell-derived neurons. Nuclear pore pathology is present in brain tissue in cases of sporadic ALS and those involving genetic mutations in TARDBP and C9orf72. Our data strongly implicate TDP-43-mediated nucleocytoplasmic transport defects as a common disease mechanism in ALS/FTD.ALS Association [17-IIP-353, 16-IIP-278]; Emory Medicine Catalyst Funding Program; Muscular Dystrophy Association [MDA348086]; NIH [K08-NS087121, P30-NS055077, AG025688, R01-NS091299, R35-NS097261, R01-NS085207, R01NS091749, R01-NS093362, R01-AG053960]; The Bluefield Project to Cure FTD; Alzheimers Drug Discovery Foundation; Alzheimers Association (ALZ); Alzheimers Research UK (ARUK); The Michael J. Fox Foundation for Parkinsons Research (MJFF); Weston Brain Institute Biomarkers Across Neurodegenerative Diseases Grant [11060]; UBRP; UA Provosts Office; ARCS Fellowship Roche Foundation Award6 month embargo; published online: 08 January 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]