An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

High-throughput screening of encapsulated islets using wide-field lens-free on-chip imaging

Islet microencapsulation is a promising solution to diabetes treatment, but its quality control based on manual microscopic inspection is extremely low-throughput, highly variable and laborious. This study presents a high-throughput islet-encapsulation quality screening system based on lens-free on-chip imaging with a wide field-of-view of 18.15 cm^2, which is more than 100 times larger than that of a lens-based optical microscope, enabling it to image and analyze ~8,000 microcapsules in a single frame. Custom-written image reconstruction and processing software provides the user with clinically important information, such as microcapsule count, size, intactness, and information on whether each capsule contains an islet. This high-throughput and cost-effective platform can be useful for researchers to develop better encapsulation protocols as well as perform quality control prior to transplantation

Pharmacological activation of FOXO3 suppresses triple-negative breast cancer in vitro and in vivo

Triple-negative breast cancer (TNBC) is the most lethal form of breast cancer. Lacking effective therapeutic options hinders treatment of TNBC. Here, we show that bepridil (BPD) and trifluoperazine (TFP), which are FDA-approved drugs for treatment of schizophrenia and angina respectively, inhibit Akt-pS473 phosphorylation and promote FOXO3 nuclear localization and activation in TNBC cells. BPD and TFP inhibit survival and proliferation in TNBC cells and suppress the growth of TNBC tumors, whereas silencing FOXO3 reduces the BPD- and TFP-mediated suppression of survival in TNBC cells. While BPD and TFP decrease the expression of oncogenic c-Myc, KLF5, and dopamine receptor DRD2 in TNBC cells, silencing FOXO3 diminishes BPD- and TFP-mediated repression of the expression of these proteins in TNBC cells. Since c-Myc, KLF5, and DRD2 have been suggested to increase cancer stem cell-like populations in various tumors, reducing these proteins in response to BPD and TFP suggests a novel FOXO3-dependent mechanism underlying BPD- and TFP-induced apoptosis in TNBC cells

Phase sensitivity at the Heisenberg limit in an SU(1,1) interferometer via parity detection

We theoretically investigate the phase sensitivity with parity detection on an SU(1,1) interferometer with a coherent state combined with a squeezed vacuum state. This interferometer is formed with two parametric amplifiers for beam splitting and recombination instead of beam splitters. We show that the sensitivity of estimation phase approaches Heisenberg limit and give the corresponding optimal condition. Moreover, we derive the quantum Cram\'er-Rao bound of the SU(1,1) interferometer.Comment: 9 pages, 2 figures, 3 table

Using Image-Processing Settings to Determine an Optimal Operating Point for Object Detection on Imaging Devices

This publication describes techniques and processes for using image-processing settings (e.g., Auto-Exposure (AE), Auto-Focus (AF), and/or Auto-White Balance (AWB)) to determine an optimal operating point for object detection by an object detector on an imaging device. An operating point is provided to the object detector by a manufacturer to enable the object detector to execute object detection. Through object detection, the object detector determines if an object is identified in the scene based on a confidence score. The optimal operating point has a computed image-processing setting that is closest to an ideal value of the image-processing setting. In an example, a fixed penalty function allows an optimal operating point to be determined using computed AE results for the image at different operating points compared to an ideal AE for the image. The smallest difference between the computed AEs and ideal AE corresponds to the optimal operating point for the image. The process can be repeated for many images to determine an optimal operating point across many types of images. Additionally, the process can be conducted with other image-processing settings, such as AF and AWB, to guide the selection of an optimal operating point across many settings. The determined optimal operating point can be provided to an object detector on an imaging device to provide a positive user experience with the imaging device

National Football League Skilled and Unskilled Positions Vary in Opportunity and Yield in Return to Play After an Anterior Cruciate Ligament Injury.

BACKGROUND: Anterior cruciate ligament (ACL) injuries pose a significant risk to the careers of players in the National Football League (NFL). The relationships between draft round and position on return to play (RTP) among NFL players are not well understood, and the ability to return to preinjury performance levels remains unknown for most positions. PURPOSE: To test for differences in RTP rates and changes in performance after an ACL injury by position and draft round. We hypothesized that skilled positions would return at a lower rate compared to unskilled positions. We further hypothesized that early draft-round status would relate to a greater rate of RTP and that skilled positions and a lower draft round would correlate with decreased performance for players who return to sport. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: Utilizing a previously established database of publicly available information regarding ACL tears among NFL players, athletes with ACL tears occurring between the 2010 and 2013 seasons were identified. Generalized linear models and Kaplan-Meier time-to-event models were used to test the study hypotheses. RESULTS: The overall RTP rate was 61.7%, with skilled players and unskilled players returning at rates of 64.1% and 60.4%, respectively (P = .74). Early draft-round players and unskilled late draft-round players had greater rates of RTP compared to skilled late draft-round players and both unskilled and skilled undrafted free agents (UDFAs). Skilled early draft-round players constituted the only cohort that played significantly fewer games after an injury. Unskilled UDFAs constituted the only cohort to show a significant increase in the number of games started and ratio of games started to games played, starting more games in which they played, after an injury. CONCLUSION: Early draft-round and unskilled players were more likely to return compared to their later draft-round and skilled peers. Skilled early draft-round players, who displayed relatively high rates of RTP, constituted the only cohort to show a decline in performance. Unskilled UDFAs, who exhibited relatively low rates of RTP, constituted the only cohort to show an increase in performance. The significant effect of draft round and position type on RTP may be caused by a combination of differences in talent levels and in opportunities given to returning to play

Control theory for principled heap sizing

We propose a new, principled approach to adaptive heap sizing based on control theory. We review current state-of-the-art heap sizing mechanisms, as deployed in Jikes RVM and HotSpot. We then formulate heap sizing as a control problem, apply and tune a standard controller algorithm, and evaluate its performance on a set of well-known benchmarks. We find our controller adapts the heap size more responsively than existing mechanisms. This responsiveness allows tighter virtual machine memory footprints while preserving target application throughput, which is ideal for both embedded and utility computing domains. In short, we argue that formal, systematic approaches to memory management should be replacing ad-hoc heuristics as the discipline matures. Control-theoretic heap sizing is one such systematic approach

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens