System Design for an Online Social Networking App with a Notification and Recommender System to Build Social Capital in a University Setting

Social capital in higher education commuter institutions may be declining because fewer students remain on campus. Social capital is the network of relationships in a group. Higher social capital is derived from broader and more complex networks. Social networks can grow because members who belong to a particular group possess a sense of community. If students spend less time on campus, their sense of community may decrease, because they would be less likely to participate in the community. This puts Higher Ed commuter institutions at a disadvantage in terms of generating and maintaining social capital. We investigate the possibility to counter this disadvantage by actively promoting participating in an Online Social Network (OSN); specifically, with the use of a Notification and Recommender System (NARS) in an OSN via a mobile platform. Our results suggest that introducing a purposefully designed OSN has the potential to facilitate creation of structural and relational social capital, but that it might not have an effect on cognitive social capital

Enhanced Mediawiki For Collaborative Writing In The Web 2.0 Era

The primary goal of this case study research is to investigate users’ perceptions of the efficiency of MediaWiki used in the collaborative writing process for students in graduate classes. MediaWiki version 1.15.1 was used in this study. Two case studies were used to explore situations that were occurring as students used the MediaWiki instance. The results show that MediaWiki needs some additional features, such as chat, advanced text editor, and discussion to facilitate the collaborative writing process

Characterization of early disease status in treatment-naive male paediatric patients with Fabry disease enrolled in a randomized clinical trial.

Trial designThis analysis characterizes the degree of early organ involvement in a cohort of oligo-symptomatic untreated young patients with Fabry disease enrolled in an ongoing randomized, open-label, parallel-group, phase 3B clinical trial.MethodsMales aged 5-18 years with complete α-galactosidase A deficiency, without symptoms of major organ damage, were enrolled in a phase 3B trial evaluating two doses of agalsidase beta. Baseline disease characteristics of 31 eligible patients (median age 12 years) were studied, including cellular globotriaosylceramide (GL-3) accumulation in skin (n = 31) and kidney biopsy (n = 6; median age 15 years; range 13-17 years), renal function, and glycolipid levels (plasma, urine).ResultsPlasma and urinary GL-3 levels were abnormal in 25 of 30 and 31 of 31 patients, respectively. Plasma lyso-GL-3 was elevated in all patients. GL-3 accumulation was documented in superficial skin capillary endothelial cells (23/31 patients) and deep vessel endothelial cells (23/29 patients). The mean glomerular filtration rate (GFR), measured by plasma disappearance of iohexol, was 118.1 mL/min/1.73 m(2) (range 90.4-161.0 mL/min/1.73 m(2)) and the median urinary albumin/creatinine ratio was 10 mg/g (range 4.0-27.0 mg/g). On electron microscopy, renal biopsy revealed GL-3 accumulation in all glomerular cell types (podocytes and parietal, endothelial, and mesangial cells), as well as in peritubular capillary and non-capillary endothelial, interstitial, vascular smooth muscle, and distal tubules/collecting duct cells. Lesions indicative of early Fabry arteriopathy and segmental effacement of podocyte foot processes were found in all 6 patients.ConclusionsThese data reveal that in this small cohort of children with Fabry disease, histological evidence of GL-3 accumulation, and cellular and vascular injury are present in renal tissues at very early stages of the disease, and are noted before onset of microalbuminuria and development of clinically significant renal events (e.g. reduced GFR). These data give additional support to the consideration of early initiation of enzyme replacement therapy, potentially improving long-term outcome.Trial registrationClinicalTrials.gov NCT00701415

Software systems for operation, control, and monitoring of the EBEX instrument

We present the hardware and software systems implementing autonomous operation, distributed real-time monitoring, and control for the EBEX instrument. EBEX is a NASA-funded balloon-borne microwave polarimeter designed for a 14 day Antarctic flight that circumnavigates the pole. To meet its science goals the EBEX instrument autonomously executes several tasks in parallel: it collects attitude data and maintains pointing control in order to adhere to an observing schedule; tunes and operates up to 1920 TES bolometers and 120 SQUID amplifiers controlled by as many as 30 embedded computers; coordinates and dispatches jobs across an onboard computer network to manage this detector readout system; logs over 3~GiB/hour of science and housekeeping data to an onboard disk storage array; responds to a variety of commands and exogenous events; and downlinks multiple heterogeneous data streams representing a selected subset of the total logged data. Most of the systems implementing these functions have been tested during a recent engineering flight of the payload, and have proven to meet the target requirements. The EBEX ground segment couples uplink and downlink hardware to a client-server software stack, enabling real-time monitoring and command responsibility to be distributed across the public internet or other standard computer networks. Using the emerging dirfile standard as a uniform intermediate data format, a variety of front end programs provide access to different components and views of the downlinked data products. This distributed architecture was demonstrated operating across multiple widely dispersed sites prior to and during the EBEX engineering flight.Comment: 11 pages, to appear in Proceedings of SPIE Astronomical Telescopes and Instrumentation 2010; adjusted metadata for arXiv submissio

EBEX: A balloon-borne CMB polarization experiment

EBEX is a NASA-funded balloon-borne experiment designed to measure the polarization of the cosmic microwave background (CMB). Observations will be made using 1432 transition edge sensor (TES) bolometric detectors read out with frequency multiplexed SQuIDs. EBEX will observe in three frequency bands centered at 150, 250, and 410 GHz, with 768, 384, and 280 detectors in each band, respectively. This broad frequency coverage is designed to provide valuable information about polarized foreground signals from dust. The polarized sky signals will be modulated with an achromatic half wave plate (AHWP) rotating on a superconducting magnetic bearing (SMB) and analyzed with a fixed wire grid polarizer. EBEX will observe a patch covering ~1% of the sky with 8' resolution, allowing for observation of the angular power spectrum from \ell = 20 to 1000. This will allow EBEX to search for both the primordial B-mode signal predicted by inflation and the anticipated lensing B-mode signal. Calculations to predict EBEX constraints on r using expected noise levels show that, for a likelihood centered around zero and with negligible foregrounds, 99% of the area falls below r = 0.035. This value increases by a factor of 1.6 after a process of foreground subtraction. This estimate does not include systematic uncertainties. An engineering flight was launched in June, 2009, from Ft. Sumner, NM, and the long duration science flight in Antarctica is planned for 2011. These proceedings describe the EBEX instrument and the North American engineering flight.Comment: 12 pages, 9 figures, Conference proceedings for SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy V (2010

T-Lymphoblastic Lymphoma Cells Express High Levels of BCL2, S1P1, and ICAM1, Leading to a Blockade of Tumor Cell Intravasation

The molecular events underlying the progression of T-lymphoblastic lymphoma (T-LBL) to acute T-lymphoblastic leukemia (T-ALL) remain elusive. In our zebrafish model, concomitant overexpression of bcl-2 with Myc accelerated T-LBL onset while inhibiting progression to T-ALL. The T-LBL cells failed to invade the vasculature and showed evidence of increased homotypic cell-cell adhesion and autophagy. Further analysis using clinical biopsy specimens revealed autophagy and increased levels of BCL2, S1P1, and ICAM1 in human T-LBL compared with T-ALL. Inhibition of S1P1 signaling in T-LBL cells led to decreased homotypic adhesion in vitro and increased tumor cell intravasation in vivo. Thus, blockade of intravasation and hematologic dissemination in T-LBL is due to elevated S1P1 signaling, increased expression of ICAM1, and augmented homotypic cell-cell adhesion.Stem Cell and Regenerative Biolog

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions

Downregulation of exosomal miR-204-5p and miR-632 as a biomarker for FTD: a GENFI study.

OBJECTIVE: To determine whether exosomal microRNAs (miRNAs) in cerebrospinal fluid (CSF) of patients with frontotemporal dementia (FTD) can serve as diagnostic biomarkers, we assessed miRNA expression in the Genetic Frontotemporal Dementia Initiative (GENFI) cohort and in sporadic FTD. METHODS: GENFI participants were either carriers of a pathogenic mutation in progranulin, chromosome 9 open reading frame 72 or microtubule-associated protein tau or were at risk of carrying a mutation because a first-degree relative was a known symptomatic mutation carrier. Exosomes were isolated from CSF of 23 presymptomatic and 15 symptomatic mutation carriers and 11 healthy non-mutation carriers. Expression of 752 miRNAs was measured using quantitative PCR (qPCR) arrays and validated by qPCR using individual primers. MiRNAs found differentially expressed in symptomatic compared with presymptomatic mutation carriers were further evaluated in a cohort of 17 patients with sporadic FTD, 13 patients with sporadic Alzheimer's disease (AD) and 10 healthy controls (HCs) of similar age. RESULTS: In the GENFI cohort, miR-204-5p and miR-632 were significantly decreased in symptomatic compared with presymptomatic mutation carriers. Decrease of miR-204-5p and miR-632 revealed receiver operator characteristics with an area of 0.89 (90% CI 0.79 to 0.98) and 0.81 (90% CI 0.68 to 0.93), respectively, and when combined an area of 0.93 (90% CI 0.87 to 0.99). In sporadic FTD, only miR-632 was significantly decreased compared with AD and HCs. Decrease of miR-632 revealed an area of 0.90 (90% CI 0.81 to 0.98). CONCLUSIONS: Exosomal miR-204-5p and miR-632 have potential as diagnostic biomarkers for genetic FTD and miR-632 also for sporadic FTD

Deep learning of chest X‑rays can predict mechanical ventilation outcome in ICU‑admitted COVID‑19 patients

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model