A systems biology analysis of brain microvascular endothelial cell lipotoxicity.

BackgroundNeurovascular inflammation is associated with a number of neurological diseases including vascular dementia and Alzheimer's disease, which are increasingly important causes of morbidity and mortality around the world. Lipotoxicity is a metabolic disorder that results from accumulation of lipids, particularly fatty acids, in non-adipose tissue leading to cellular dysfunction, lipid droplet formation, and cell death.ResultsOur studies indicate for the first time that the neurovascular circulation also can manifest lipotoxicity, which could have major effects on cognitive function. The penetration of integrative systems biology approaches is limited in this area of research, which reduces our capacity to gain an objective insight into the signal transduction and regulation dynamics at a systems level. To address this question, we treated human microvascular endothelial cells with triglyceride-rich lipoprotein (TGRL) lipolysis products and then we used genome-wide transcriptional profiling to obtain transcript abundances over four conditions. We then identified regulatory genes and their targets that have been differentially expressed through analysis of the datasets with various statistical methods. We created a functional gene network by exploiting co-expression observations through a guilt-by-association assumption. Concomitantly, we used various network inference algorithms to identify putative regulatory interactions and we integrated all predictions to construct a consensus gene regulatory network that is TGRL lipolysis product specific.ConclusionSystem biology analysis has led to the validation of putative lipid-related targets and the discovery of several genes that may be implicated in lipotoxic-related brain microvascular endothelial cell responses. Here, we report that activating transcription factors 3 (ATF3) is a principal regulator of TGRL lipolysis products-induced gene expression in human brain microvascular endothelial cell

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

Remote sensing of Earth terrain

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images

Multivariate morphometric analysis of Apis cerana of southern mainland Asia

Multivariate morphometric analyses were performed on a series of worker honeybees, Apis cerana, representing 557 colonies from all of southern mainland Asia extending from Afghanistan to Vietnam south of the Himalayas. Scores from the principal components analysis revealed five statistically separable but not entirely distinct morphoclusters of bees: (1) the Hindu Kush, Kashmir, N. Myanmar, N. Vietnam and S. China; (2) Himachal Pradesh region of N. India; (3) N. India, Nepal; (4) central and S. Myanmar and Vietnam, Cambodia, Thailand, S. China and peninsular Malaysia; (5) central and S. India. The major morphoclusters are distributed coherently with the different climatic zones of the region. While populations are definable, nomenclatural adjustments remain for the future

Phase I study of azacitidine and oxaliplatin in patients with advanced cancers that have relapsed or are refractory to any platinum therapy.

BackgroundDemethylation process is necessary for the expression of various factors involved in chemotherapy cytotoxicity or resistance. Platinum-resistant cells may have reduced expression of the copper/platinum transporter CTR1. We hypothesized that azacitidine and oxaliplatin combination therapy may restore platinum sensitivity. We treated patients with cancer relapsed/refractory to any platinum compounds (3 + 3 study design) with azacitidine (20 to 50 mg/m(2)/day intravenously (IV) over 15 to 30 min, D1 to 5) and oxaliplatin (15 to 30 mg/m(2)/day, IV over 2 h, D2 to 5) (maximum, six cycles). Platinum content, LINE1 methylation (surrogate of global DNA methylation), and CTR1 expression changes (pre- vs. post-treatment) were assessed. Drug pharmacokinetics were analyzed.ResultsThirty-seven patients were treated. No dose-limiting toxicity (DLT) was noted at the maximum dose. The most common adverse events were anemia and fatigue. Two (5.4%) patients had stable disease and completed six cycles of therapy. Oxaliplatin (D2) and azacitidine (D1 and 5) mean systemic exposure based on plasma AUCall showed dose-dependent interaction whereby increasing the dose of oxaliplatin reduced the mean azacitidine exposure and vice versa; however, no significant differences in other non-compartmental modeled parameters were observed. Blood samples showed universal reduction in global DNA methylation. In tumor samples, hypomethylation was only observed in four out of seven patients. No correlation between blood and tumor demethylation was seen. The mean cytoplasmic CTR1 score decreased. The pre-dose tumor oxaliplatin levels ranged from <0.25 to 5.8 μg/g tumor. The platinum concentration increased 3- to 18-fold. No correlation was found between CTR1 score and oxaliplatin level, which was found to have a trend toward correlation with progression-free survival.ConclusionsOxaliplatin and azacitidine combination therapy was safe. CTR1 expression was not correlated with methylation status or tissue platinum concentration

CrossCheck:toward passive sensing and detection of mental health changes in people with schizophrenia

Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new user