Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling.

We conducted a field study using multiple wearable devices on 231 federal office workers to assess the impact of the indoor environment on individual wellbeing. Past research has established that the workplace environment is closely tied to an individual's wellbeing. Since sound is the most-reported environmental factor causing stress and discomfort, we focus on quantifying its association with physiological wellbeing.

Network analysis reveals dysregulated functional patterns in type II diabetic skin.

Skin disorders are one of the most common complications of type II diabetes (T2DM). Long-term effects of high blood glucose leave individuals with T2DM more susceptible to cutaneous diseases, but its underlying molecular mechanisms are unclear. Network-based methods consider the complex interactions between genes which can complement the analysis of single genes in previous research.

Epidural blood patch for spontaneous intracranial hypotension (SIH): a report of two cases.

One of the latest diagnoses that need to be considered when evaluating patients with persistent headache is spontaneous (postural) intracranial hypotension (SIH). The diagnosis can be clinched by magnetic resonance imaging (MRI) findings of subdural collections, meningeal enhancement and tonsillar descent. Cerebrospinal fluid leak has been postulated as the cause, and both medical and surgical treatment options have been documented.

A Novel Fracture Prediction Model Using Machine Learning in a Community-Based Cohort.

The prediction of fracture risk in osteoporotic patients has been a topic of interest for decades, and models have been developed for the accurate prediction of fracture, including the fracture risk assessment tool (FRAX). As machine-learning methodologies have recently emerged as a potential model for medical prediction tools, we aimed to develop a novel fracture prediction model using machine-learning methods in a prospective community-based cohort. In this study, 2227 participants (1257 females) with a baseline bone mineral density (BMD) and trabecular bone score were enrolled from the Ansung cohort.

Giant Cervicodorsal Schwannoma.

Schwannomas are benign slow growing tumors that arise from myelin producing Schwann cells. Schwannomas developing in cervical-dorsal region are rare benign neoplasms which are emerges leisurely remains asymptomatic some times and functionally inactive tumours. Giant Schwannomas extending over two or more vertebral levels have been documented and attempts have been made to classify these in available literature, however inadequate.

Effects of office workstation type on physical activity and stress.

Objective: Office environments have been causally linked to workplace-related illnesses and stress, yet little is known about how office workstation type is linked to objective metrics of physical activity and stress. We aimed to explore these associations among office workers in US federal office buildings.

Methods: We conducted a wearable, sensor-based, observational study of 231 workers in four office buildings.

Predicting High-Cost Patients at Point of Admission Using Network Science.

Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in the literature. This is due to the lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a subset of the state inpatient database of Arizona.

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.

Context: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner.

Objectives: Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge.

Predicting asthma-related emergency department visits using big data.

Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks.

The iPlant Collaborative: Cyberinfrastructure for Plant Biology.

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies.