Clinical Outcomes of an Intensity Program for Children With Movement Challenges.

Purpose: Physical therapists at an outpatient pediatric facility developed and implemented an Intensity Program for children with movement challenges. The program was initiated on the basis of best evidence, parent advocacy, and clinician expertise. The purpose of this investigation is to analyze outcome data gathered from the program since 2012 to determine the effect of the program along with any specific child characteristics that were more likely to lead to positive outcomes.

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis.

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

Exploring Occupational Health Professionals Understanding and Needs in Regard to Total Worker Health (TWH).

Background: Occupational health professionals (OHPs) are in a unique position to impact the health and well-being of employees at work and outside of work. One way of achieving this holistic health goal is to integrate the concept of Total Worker Health® (TWH) into the organization's culture. It is critical for OHPs to develop the ability to incorporate TWH into their practices, yet there are gaps in our understanding of OHP's attitudes toward change and toward TWH, their level of TWH knowledge, and the number of OHPs who have adopted TWH.

An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration.

How do analysts think about grouping and spatial operations? This overarching research question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship between grouping and spatial structures, the decisions analysts make when exploring individual observations, and the role of external information. This work contributes the design and results of such a study, in which a group of participants are asked to organize the data contained within an unfamiliar quantitative dataset. We identify several overarching approaches taken by participants to design their organizational space, discuss the interactions performed by the participants, and propose design recommendations to improve the usability of future high-dimensional data exploration tools that make use of grouping (clustering) and spatial (dimension reduction) operations.

Putting the "I" in Interaction: Interactive Interfaces Personalized to Individuals.

Interactive data exploration and analysis is an inherently personal process. One's background, experience, interests, cognitive style, personality, and other sociotechnical factors often shape such a process, as well as the provenance of exploring, analyzing, and interpreting data. This Viewpoint posits both what personal information and how such personal information could be taken into account to design more effective visual analytic systems, a valuable and under-explored direction.

A Provenance Task Abstraction Framework.

Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance.

SIRIUS: Dual, Symmetric, Interactive Dimension Reductions.

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.

Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics.

Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems.

MOSBIE: a tool for comparison and analysis of rule-based biochemical models.

Background: Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics.

Results: We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family.

FixingTIM: interactive exploration of sequence and structural data to identify functional mutations in protein families.

Background: Knowledge of the 3D structure and functionality of proteins can lead to insight into the associated cellular processes, speed up the creation of pharmaceutical products, and develop drugs that are more effective in combating disease.

Methods: We present the design and implementation of a visual mining and analysis tool to help identify protein mutations across a family of structural models and to help discover the effect of these mutations on protein function. We integrate 3D structure and sequence information in a common visual interface; multiple linked views and a computational backbone allow comparison at the molecular and atomic levels, while a novel trend-image visual abstraction allows for the sorting and mining of large collections of sequences and of their residues.

Tdap: the need to educate and immunize.

The resurgence of pertussis is puzzling, especially given the requirement that children complete recommended immunizations prior to school entry. Are adult carriers unknowingly infecting children? What do adult caregivers know about pertussis and the tetanus, diphtheria, and acellular pertussis (Tdap) booster vaccine? How can the incidence of pertussis in children younger than 2 years, a group most at risk for complications of the disease, be reduced? This article examines the incidence of pertussis and strategies to reduce pertussis incidence in the United States. If the need for Tdap vaccine is identified and favorably received by adults, immunization programs can be arranged at worksites to better protect infants who are most at risk for pertussis.

Promoting health and safety virtually: key recommendations for occupational health nurses.

Nurses' use of the Internet and social media has surfaced as a critical concern requiring further exploration and consideration by all health care organizations and nursing associations. In an attempt to support this need, the American Nurses Association (2011) published six principles of social networking that offered guidance and direction for nurses. In addition, the National Council of State Boards of Nursing (2011) published a nurse's guide to using social media.