No abstract

ObjectivesSleep disorders are common in patients with HIV/AIDS, and can lead to poor quality of life. Although many studies have investigated the aetiology of these disorders, it is still unclear whether impaired sleep quality is associated with HIV itself, social problems, or side effects of antiretroviral therapy (ART). Moreover, despite its known neurological associations, little is known about the role of the trans-activator of transcription (Tat) protein in sleep disorders in patients with HIV/AIDS. The purpose of this study was to test the hypothesis that the sleep quality of patients with HIV/AIDS affected by an altered circadian rhythm correlates with cerebrospinal HIV Tat protein concentration.MethodsNinety-six patients with HIV/AIDS between 20 and 69 years old completed the Pittsburgh Sleep Quality Index. Their circadian rhythm parameters of blood pressure, Tat concentration in cerebrospinal fluid, melatonin concentration, CD4 cell count and HIV RNA viral load in serum were measured.ResultsThe circadian amplitude of systolic blood pressure and the score for sleep quality (Pittsburgh Sleep Quality Index) were negatively correlated with HIV Tat protein concentration, while the melatonin value was positively correlated with Tat protein concentration.ConclusionsThe HIV Tat protein affects circadian rhythmicity by interfering with the circadian system in patients with HIV/AIDS and further increases the melatonin excretion value. A Tat protein-related high melatonin value may counteract HIV-related poor sleep quality during the progression of HIV infection. This study provides the first clinical evidence offering an explanation for why sleep quality did not show an association with progression of HIV infection in previous studies.

No abstract

According to the surface reflection characteristics of carbon fiber reinforced plastics and the geometric characteristics of aircraft panel parts, a robot automatic measurement system based on line laser scanning is designed. The selection criteria of measurement pose and the pre-calibration method of measurement pose based on a laser tracker are given. A 3D reconstruction method of global stitching of large-scale parts based on pre-calibration measurement pose is proposed, Finally, the correctness and effectiveness of the proposed method are verified by measuring typical samples with a prototype system.

Motivation:Rapid advances in single cell RNA sequencing have produced more granular subtypes of cells in multiple tissues from different species. There exists a need to develop rigorous methods that can i) model multiple datasets with ambiguous labels across species and studies and ii) remove systematic biases across datasets and species. Results: We developed a species-and dataset-independent transfer learning framework (LAmbDA) to train models on multiple datasets and applied our framework on scRNA-seq experiments. These models mapped corresponding cell types between datasets with inconsistent labels while simultaneously reducing batch effects. We achieved high accuracy in labeling cellular subtypes (weighted accuracy pancreas: 91%, brain: 78%) using LAmbDA Random Forest. LAmbDA Feedforward 1 Layer Neural Network achieved higher weighted accuracy in labeling cellular subtypes than CaSTLe or MetaNeighbor in brain (48%, 32%, 20% respectively). Furthermore, LAmbDA Feedforward 1 Layer Neural Network was the only method to correctly predict ambiguous cellular subtype labels in both pancreas and brain compared to CaSTLe and MetaNeighbor. LAmbDA is model-and dataset-independent and generalizable to diverse data types representing an advance in biocomputing. Availability: github.com/tsteelejohnson91/LAmbDA