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Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. “Health hackathons” and “data marathons”, in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled.

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines—including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology—gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

This study investigated the effect of exogenous plant growth regulators (PGR), namely jasmonic acid (JA) and methyl jasmonate (MeJA), on the physicochemical properties of flesh and peels of red-fleshed dragon fruit (Hylocereus polyrhizus). The fruit was sprayed with 100 and 1,000 ppm of JA and MeJA at 15 and 22 days of anthesis and harvested after 35 days. Then, the flesh and peels were analyzed for total soluble solids (TSS), total betacyanins, betanin, total phenolics (TP), total flavonoids (TF), and color characteristics. The fruit peels contained significantly higher (p<0.05) TP and antioxidant activities compared to flesh. No significant difference was detected between the variables in the peels, except for significantly higher (p<0.05) of total betacyanins (~295.6 and ~299.9 mg/100 g) and TP (~614.1 and 566.1 mg GAE/100 g) were recorded in control and 100 ppm MeJA, respectively. In the flesh, 1,000 ppm MeJA-treated fruit possessed the highest total betacyanins (~139.2 mg/100 g), betanin (~356.0 mg/g), TP (~244.9 mg GAE/100 g), TF (~329.0 mg CE/100 g), Trolox equivalent antioxidant capacity (TEAC) (63.2 µmol TE/g) and reducing power (~21.5 µmol TE/g). Overall, 1,000 ppm MeJA was more effective in enhancing the accumulation of bioactive compounds and antioxidant activities in the flesh of red-fleshed dragon fruit compared to other PGR treatments. [ABSTRACT FROM AUTHOR]

Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geo-correction, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47 000 AVHRR images (1993-2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and [+ or -] 3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent ([+ or -] 0.5 h) data from the SEAKEYS in situ stations in the Florida Keys showed near-zero bias errors (< 0.05 [degrees]C) in the weekly anomaly for SST anomalies between -3[degrees]C and 3[degrees]C, with standard deviations < 0.5[degrees]C. The cloud filter was implemented using Interactive Data Language for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential for coral reef stress. The mean and anomaly products are updated weekly, with periodic updates of the DHW products, on a Web site. The SST data at specific geographical locations were also automatically ingested in near real time into National Oceanic and Atmospheric Administration's (NOAA) Integrated Coral Observing Network Web-based application to assist in management and decision making through a novel expert system tool (G2) implemented at NOAA. Index Terms--Advanced Very High Resolution Radiometer (AVHRR), cloud detection, coastal ocean observing system (COOS), Florida Keys, MODerate-resolution Imaging Spectroradiometer (MODIS), remote sensing, sea surface temperature (SST), water quality.

Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geo-correction, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47000 AVHRR images (1993-2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and ±3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent (±0.5 h) data from the SEAKEYS in situ stations in the Florida Keys showed near-zero bias errors (< 0.05 °C) in the weekly anomaly for SST anomalies between -3 °C and 3 °C, with standard deviations < 0.5 °C. The cloud filter was implemented using Interactive Data Language for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential for coral reef stress. The mean and anomaly products are updated weekly, with periodic updates of the DHW products, on a Web site. The SST data at specific geographical locations were also automatically ingested in near real time into National Oceanic and Atmospheric Administration's (NOAA) Integrated Coral Observing Network Web-based application to assist in management and decision making through a novel expert system tool (G2) implemented at NOAA. [ABSTRACT FROM AUTHOR]

We describe the appearance of endogenous bacterial endophthalmitis in a patient who underwent MR imaging of the orbits before and during the course of treatment. Intraocular hyperintensity on fluid-attenuated inversion recovery and diffusion-weighted images were found very useful for diagnosing endophthalmitis. After a few days of treatment, a marked relative increase in intraocular mean apparent diffusion coefficient values was observed, which appears to indicate good treatment response.