Gender Recognition by Voice using an Improved Self-Labeled Algorithm

Speech recognition has various applications including human to machine interaction, sorting of telephone calls by gender categorization, video categorization with tagging and so on. Currently, machine learning is a popular trend which has been widely utilized in various fields and applications, exploiting the recent development in digital technologies and the advantage of storage capabilities from electronic media. Recently, research focuses on the combination of ensemble learning techniques with the semi-supervised learning framework aiming to build more accurate classifiers. In this paper, we focus on gender recognition by voice utilizing a new ensemble semi-supervised self-labeled algorithm. Our preliminary numerical experiments demonstrate the classification efficiency of the proposed algorithm in terms of accuracy, leading to the development of stable and robust predictive models.

Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries

Collecting, integrating, storing and analyzing data in a database system is nothing new in itself. To introduce a current research information system (CRIS) means that scientific institutions must provide the required information on their research activities and research results at a high quality. A one-time cleanup is not sufficient; data must be continuously curated and maintained. Some data errors (such as missing values, spelling errors, inaccurate data, incorrect formatting, inconsistencies, etc.) can be traced across different data sources and are difficult to find. Small mistakes can make data unusable, and corrupted data can have serious consequences. The sooner quality issues are identified and remedied, the better. For this reason, new techniques and methods of data cleansing and data monitoring are required to ensure data quality and its measurability in the long term. This paper examines data quality issues in current research information systems and introduces new techniques and methods of data cleansing and data monitoring with which organizations can guarantee the quality of their data.

Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer

Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals..

DVID: Distributed Versioned Image-Oriented Dataservice

Open-source software development has skyrocketed in part due to community tools like github.com, which allows publication of code as well as the ability to create branches and push accepted modifications back to the original repository. As the number and size of EM-based datasets increases, the connectomics community faces similar issues when we publish snapshot data corresponding to a publication. Ideally, there would be a mechanism where remote collaborators could modify branches of the data and then flexibly reintegrate results via moderated acceptance of changes. The DVID system provides a web-based connectomics API and the first steps toward such a distributed versioning approach to EM-based connectomics datasets. Through its use as the central data resource for Janelia's FlyEM team, we have integrated the concepts of distributed versioning into reconstruction workflows, allowing support for proofreader training and segmentation experiments through branched, versioned data. DVID also supports persistence to a variety of storage systems from high-speed local SSDs to cloud-based object stores, which allows its deployment on laptops as well as large servers. The tailoring of the backend storage to each type of connectomics data leads to efficient storage and fast queries. DVID is freely available as open-source software with an increasing number of supported storage options.

Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: a case site within the Niger Delta region of Nigeria

Terrestrial oil pollution is one of the major causes of ecological damage within the Niger Delta region of Nigeria and has caused a considerable loss of mangroves and arable croplands since the discovery of crude oil in 1956. The exact extent of landcover loss due to oil pollution remains uncertain due to the variability in factors such as volume and size of the oil spills, the age of oil, and its effects on the different vegetation types. Here, the feasibility of identifying oil-impacted land in the Niger Delta region of Nigeria with a machine learning random forest classifier using Landsat 8 (OLI spectral bands) and Vegetation Health Indices is explored. Oil spill incident data for the years 2015 and 2016 were obtained from published records of the National Oil Spill Detection and Response Agency and Shell Petroleum Development Corporation. Various health indices and spectral wavelengths from visible, near-infrared, and shortwave infrared bands were fused and classified using the machine learning random forest classifier to distinguish between oil-free and oil spill-impacted landcover. This provided the basis for the identification of the best variables for discriminating oil polluted from unpolluted land. Results showed that better results for discriminating oil-free and oil polluted landcovers were obtained when individual landcover types were classified separately as opposed to when the full study area image including all landcover types was classified at once. Similarly, the results also showed that biomass density plays a significant role in the characterization and classification of oil contaminated and oil-free pixels as tree cover areas showed higher classification accuracy compared to cropland and grassland.

A crosswalk between the Omaha System and guiding undergraduate public health nursing education documents

The Omaha System is the hallmark evidence-based clinical information management system used in nursing education, research, and practice. Multiple education documents guide public health workforce preparation. This qualitative study identified similarities and gaps between the Omaha System and seven guiding documents commonly used by nurse educators. A crosswalk design was employed. The setting was virtually based using online technology. Recommendations are for public health nurse educators to update their teaching practices using evidence-based approaches.

Towards a Smart and Sustainable City with the Involvement of Public Participation—The Case of Wroclaw

The purpose of this article is to identify the directions and scope of inclusion of the residents’ participation into the concept of city’s sustainable development and the smart city concept, taking into account national and international conditions, on the basis of Wroclaw’s practices in 1998–2018. Many researchers have emphasized the necessity of including residents’ participation in both the smart city concept and the sustainable city development concept, but they do not focus on a coherent linking of these activities during evolution toward a sustainable smart city (SSC). The in-depth case study analysis considered, i.e., three subsequent Wroclaw development strategies (1998–2018) and implementation of the smart city concept in Wroclaw (2015–2018) with particular emphasis on the issue of public participation and sustainable development of the city. The results of study show that in the case of the developments in the activities of smart city and sustainable city development carried out by Wroclaw, it is possible to identify two different approaches to residents’ participation in city activities. In Wroclaw, ‘residents’ participation’ in the framework of the sustainable city development activities currently covers all theoretical levels of participation, while within the smart city activities it focuses mainly on the participatory budget and the limited use of ICT. The conducted research indicates that for the implementation of the SSC concept it would be important to integrate these approaches in order to ensure the full range of residents’ participation in accordance with theoretical postulates. The conducted analysis therefore covers mostly unexplored area of research, which is important from the point of view of a city’s evolution toward becoming a sustainable smart city. The conclusions from the research are also an empirical contribution to the analysis of the changes of cities towards SSC and indicate the need for further, extended research on the undertaken problem.

The Internet as a Source of Health Information and Services

The Internet is increasingly used for health-related purposes and evolves with the ever-changing needs of patients. The aim of this study was to assess the level of reliance on the Internet as a health information source, to examine which online communication activities are the most common for health purposes, and to determine the attitudes and needs of patients in this area and the factors affecting its use. A total of 1000 adults were selected from the Polish population by random sampling. The survey was administered by the Computer-Assisted Telephone Interview (CATI). The study concluded that 76.9% of the participants used the Internet for health purposes, among whom 72.6% of active and 27.4% of passive users were distinguished. The role of the Internet as a source of health information has increased, which corresponds to a growing interest in online health services. The majority of individuals searching for health information in the Internet lived in urban areas, had a high level of education, and was professionally active. We conclude that the increased interest in the use of the Internet related to health determines the direction in which e-health should be developed in the future.

Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology

In this work, a new approach for training artificial neural networks is presented which utilises techniques for solving the constraint optimisation problem. More specifically, this study converts the training of a neural network into a constraint optimisation problem. Furthermore, we propose a new neural network training algorithm based on the L-BFGS-B method. Our numerical experiments illustrate the classification efficiency of the proposed algorithm and of our proposed methodology, leading to more efficient, stable and robust predictive models.

Multi-Beam Scanning Electron Microscopy for High-Throughput Imaging in Connectomics Research

Major progress has been achieved in recent years in three-dimensional microscopy techniques. This applies to the life sciences in general, but specifically the neuroscientific field has been a main driver for developments regarding volume imaging. In particular, scanning electron microscopy offers new insights into the organization of cells and tissues by volume imaging methods, such as serial section array tomography, serial block-face imaging or focused ion beam tomography. However, most of these techniques are restricted to relatively small tissue volumes due to the limited acquisition throughput of most standard imaging techniques. Recently, a novel multi-beam scanning electron microscope technology optimized to the imaging of large sample areas has been developed. Complemented by the commercialization of automated sample preparation robots, the mapping of larger, cubic millimeter range tissue volumes at high-resolution is now within reach. This Mini Review will provide a brief overview of the various approaches to electron microscopic volume imaging, with an emphasis on serial section array tomography and multi-beam scanning electron microscopic imaging.

An Edge Computing Based Smart Healthcare Framework for Resource Management

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.

Natural Computing Applied to the Underground System: A Synergistic Approach for Smart Cities

The management and proper use of the Urban Public Transport Systems (UPTS) constitutes a critical field that has not been investigated in accordance to its relevance and urgent idiosyncrasy within the Smart Cities realm. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable, and self-organized behavior to deal with dynamic and fast changing systems. The intelligence of cities can be modelled as a swarm of digital telecommunication networks (the nerves), ubiquitously embedded intelligence, sensors and tags, and software. In this paper, a new approach based on the use of the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation and Fireworks algorithms to build a system that makes the complete control of the UPTS a tangible reality.

A Scoping Review on Digital English and Education 4.0 for Industry 4.0

Industry 4.0 is a current trend of automation and digitalization of industries. The impacts and importance of Industry 4.0 are reflected in all aspects of our lives. The purpose of this article is to analyze the literatures based on a scoping review method. A lack of digital culture, training, knowledge, and language are also challenges faced by Industry 4.0 while implementing its operations. Digital English and Education 4.0 are also employee competencies of Industry 4.0. The authors have reviewed the literature related to Digital English, Education 4.0, and Industry 4.0 from various resources. Astonishingly, the results show that the studies conducted in these areas are so specific focusing only one of the above-mentioned areas; no research article was identified that detailed the interconnections among these areas. From the scoping review, the study has identified the gaps in the literature. Thus, the study concludes that filling up the gaps and conducting research in these areas are useful to sort out a few of the challenges of Industry 4.0 and it recommends that in future, researchers conduct studies based on the interconnections of Digital English and Education 4.0 for Industry 4.0.

Evaluation of User Performance in Interactive and Static 3D Maps

Interactive 3D visualizations of geospatial data are currently available and popular through various applications such as Google EarthTM and others. Several studies have focused on user performance with 3D maps, but static 3D maps were mostly used as stimuli. The main objective of this paper was to identify differences between interactive and static 3D maps. We also explored the role of different tasks and inter-individual differences of map users. In the experimental study, we analyzed effectiveness, efficiency, and subjective preferences, when working with static and interactive 3D maps. The study included 76 participants and used a within-subjects design. Experimental testing was performed using our own testing tool 3DmoveR 2.0, which was based on a user logging method and open web technologies. We demonstrated statistically significant differences between interactive and static 3D maps in effectiveness, efficiency, and subjective preferences. Interactivity influenced the results mainly in ‘spatial understanding’ and ‘combined’ tasks. From the identified differences, we concluded that the results of the user studies with static 3D maps as stimuli could not be transferred to interactive 3D visualizations or virtual reality.

Combination of exome sequencing and immune testing confirms Aicardi-Goutières syndrome type 5 in a challenging pediatric neurology case

Exome sequencing is increasingly being used to help diagnose pediatric neurology cases when clinical presentations are not specific. However, interpretation of equivocal results that include variants of uncertain significance remains a challenge. In those cases, follow-up testing and clinical correlation can help clarify the clinical relevance of the molecular findings. In this report, we describe the diagnostic odyssey of a 4-year-old girl who presented with global developmental delay and seizures, with leukodystrophy seen on MRI. Clinical evaluation, MRI, and comprehensive metabolic testing were performed, followed by whole-exome sequencing (WES), parental testing, follow-up testing, and retrospective detailed clinical evaluation. WES identified two candidate causative pathogenic variants in SAMHD1, a gene associated with the recessive condition Aicardi-Goutières syndrome (AGS) type 5 (OMIM 612952): a previously reported pathogenic variant NM_015474 c.602T>A (p.I201N), maternally inherited, and a rare missense variant of uncertain significance, c.1293A>T(p.L431F). Analysis of type I interferon-related biomarkers demonstrated that the patient has an interferon signature characteristic of AGS. Retrospective detailed clinical evaluation showed that the girl has a phenotype consistent with AGS5, a rare neurological condition. These results further define the phenotypic spectrum associated with specific SAMHD1 variants, including heterozygous variants in AGS carriers, and support the idea that autoinflammatory dysregulation is part of the disease pathophysiology. More broadly, this work highlights the issues and methodology involved in ascribing clinical relevance to interpretation of variants detected by WES.

Communities of Practice Approach for Knowledge Management Systems

In this digital world, organisations are facing global competition as well as manpower pressures leading towards the knowledge economy, which heavily impacts on their local and international businesses. The trend is to foster collaboration and knowledge sharing to cope with these problems. With the advancement of technologies and social engineering that can connect people in the virtual world across time and distance, several organisations are embarking on knowledge management (KM) systems, implementing a community of practice (CoP) approach. However, virtual communities are relatively new paradigms, and there are several challenges to their successful implementation from an organisation’s point of interest. There is lack of CoP implementation framework that can cater to today’s dynamic business and sustainability requirements. To fill the gap in literature, this paper develops a practical framework for a CoP implementation with a view to align KM strategy with business strategy of an organization. It explores the different steps of building, sharing, and using tacit and explicit knowledge in CoPs by applying the Wiig KM cycle. It proposes a practical CoP implementation framework that adopts the Benefits, Tools, Organisation, People and Process (BTOPP) model in addressing the key questions surrounding each of the BTOPP elements with a structured approach. Finally, it identifies key challenges such as organizational culture and performance measurements, and provides practical recommendations to overcome them for a successful CoP implementation.

Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device

This study condenses huge amount of raw data measured from a MEMS accelerometer-based, wrist-worn device on different levels of physical activities (PAs) for subjects wearing the device 24 h a day continuously. In this study, we have employed the device to build up assessment models for quantifying activities, to develop an algorithm for sleep duration detection and to assess the regularity of activity of daily living (ADL) quantitatively. A new parameter, the activity index (AI), has been proposed to represent the quantity of activities and can be used to categorize different PAs into 5 levels, namely, rest/sleep, sedentary, light, moderate, and vigorous activity states. Another new parameter, the regularity index (RI), was calculated to represent the degree of regularity for ADL. The methods proposed in this study have been used to monitor a subject’s daily PA status and to access sleep quality, along with the quantitative assessment of the regularity of activity of daily living (ADL) with the 24-h continuously recorded data over several months to develop activity-based evaluation models for different medical-care applications. This work provides simple models for activity monitoring based on the accelerometer-based, wrist-worn device without trying to identify the details of types of activity and that are suitable for further applications combined with cloud computing services.

FiberClay: Sculpting Three Dimensional Trajectories to Reveal Structural Insights

Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.

A Robust Distributed Big Data Clustering-based on Adaptive Density Partitioning using Apache Spark

Unsupervised machine learning and knowledge discovery from large-scale datasets have recently attracted a lot of research interest. The present paper proposes a distributed big data clustering approach-based on adaptive density estimation. The proposed method is developed-based on Apache Spark framework and tested on some of the prevalent datasets. In the first step of this algorithm, the input data is divided into partitions using a Bayesian type of Locality Sensitive Hashing (LSH). Partitioning makes the processing fully parallel and much simpler by avoiding unneeded calculations. Each of the proposed algorithm steps is completely independent of the others and no serial bottleneck exists all over the clustering procedure. Locality preservation also filters out the outliers and enhances the robustness of the proposed approach. Density is defined on the basis of Ordered Weighted Averaging (OWA) distance which makes clusters more homogenous. According to the density of each node, the local density peaks will be detected adaptively. By merging the local peaks, final cluster centers will be obtained and other data points will be a member of the cluster with the nearest center. The proposed method has been implemented and compared with similar recently published researches. Cluster validity indexes achieved from the proposed method shows its superiorities in precision and noise robustness in comparison with recent researches. Comparison with similar approaches also shows superiorities of the proposed method in scalability, high performance, and low computation cost. The proposed method is a general clustering approach and it has been used in gene expression clustering as a sample of its application.

An Outlook on Physical and Virtual Sensors for a Socially Interactive Internet

The Internet keeps changing at a rapid pace, driven mainly by the emerging concepts and applications that make it aware of the physical world and responsive to user context. The Internet of Things (IoT) concept is quickly giving way to more advanced and highly interactive environments that go well beyond the mere sensing of the physical world. Today, in addition to traditional electronic devices, IoT sensing/actuating includes both software and human-based entities. This paper provides an outlook on the future of sensing/actuating approaches on the Internet at large, which we see increasingly related to all kinds of socially interactive technologies. With these objectives in mind, we propose a taxonomy to deal with the heterogeneity of sensing/actuating approaches in IoT. We also analyse the state-of-the-art of Social Sensing. Finally, we identify open issues and associated research opportunities, the main ones being the integration of all sensing approaches, the combination of social sciences, engineering, and computing as enablers of context-aware, cognitive applications and, last but not least, the unified management of large sets of very heterogeneous sensors/actuators.

A retrospective analysis of boarding times for adolescents in psychiatric crisis

The boarding of children and adolescents with identified psychiatric conditions at medical facilities has numerous negative effects on the patients and the systems that treat them. Efforts to minimize boarding times serves to increase patients' access to appropriate levels of care, redirect medical resources to patients who need them most, and reduce safety risks to people and property. This study explores the role Clinical Social Workers can play in facilitation of care and highlights the advantages of a coordinated data collection process facilitated by the effective use of the Electronic Medical Record. A retrospective chart analysis of 100 patients admitted to the Emergency department at a pediatric hospital in Central Florida was conducted for patients seen between 1 January 2015 and 30 June 2016. The data suggest key correlates that may impact the boarding times of pediatric patients presenting in a psychiatric crisis and the average duration of boarding time in hours (M = 5.11, SD = 2.07) was found to be significantly lower than prior published studies in the adult and pediatric literature. Discussion of these data implications on behavioral health practice is discussed.

Collaborative Working Architecture for IoT-Based Applications

The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.

Medicaid Becomes the First Third-Party Payer to Cover Passive Remote Monitoring for Home Care: Policy Analysis

BACKGROUND

Recent years have seen an influx of location-tracking, activity-monitoring sensors, and Web-cameras to remotely monitor the safety of older adults in their homes and to reduce reliance on in-person assistance. The state of research on these monitoring technologies leaves open crucial financial, social, and ethical cost-benefit questions, which have prevented widespread use. Medicaid is now the first large third-party payer in the United States to pay for these technologies, and their use is likely to increase as states transition to managed long-term services and supports (MLTSS).

OBJECTIVES

This is the first study to examine how state Medicaid programs are treating passive remote monitoring technologies. This study identifies (1) which states allow location tracking, sensor systems, and cameras; (2) what policies are in place to track their use; (3) what implementation processes and program monitoring mechanisms are in place; and (4) what related insights Medicaid program stakeholders would like to learn from researchers.

METHODS

Interviews were conducted with 43 state, federal, and managed care organization (MCO) Medicaid program stakeholders about how these technologies are used in state waivers serving community-dwelling older adults in 15 states, and what policies are in place to regulate them. The interviews were analyzed by the research team using the framework analysis method for applied policy research.

RESULTS

Two-thirds of the states cover location tracking and activity-monitoring sensors and one-third cover cameras, but only 3 states have specific service categories that allow them to track when they are paying for any of these technologies, impeding regulation and understanding of their use at the state and federal level. Consideration of ethical and social risks is limited, and states struggle to understand which circumstances warrant use. They are further challenged by extreme resource restrictions and transitions to MLTSS by MCOs inexperienced in serving this growing "high-need, high-cost" population.

CONCLUSIONS

Decisions about Medicaid reimbursement of technologies that have the potential to dramatically alter the way older adults receive supportive services are being made without research on their use, social and ethical implications, or outcomes. At a minimum, new service categories are needed to enable oversight. Participants prioritized 3 research aims to inform practice: (1) determine cost-effectiveness; (2) identify what type of information beneficiaries want to be generated and whom they want it to be shared with; and (3) understand how to support ethical decision making for beneficiaries with cognitive impairment. These findings provide direction for future research and reveal that greater interaction between policy makers and researchers in this field is needed.

Diagnostic utility of exome sequencing in the evaluation of neuromuscular disorders

Objective

To evaluate the diagnostic yield and workflow of genome-scale sequencing in patients with neuromuscular disorders (NMDs).

Methods

We performed exome sequencing in 93 undiagnosed patients with various NMDs for whom a molecular diagnosis was not yet established. Variants on both targeted and broad diagnostic gene lists were identified. Prior diagnostic tests were extracted from the patient's medical record to evaluate the use of exome sequencing in the context of their prior diagnostic workup.

Results

The overall diagnostic yield of exome sequencing in our cohort was 12.9%, with one or more pathogenic or likely pathogenic variants identified in a causative gene associated with the patient's disorder. Targeted gene lists had the same diagnostic yield as a broad NMD gene list in patients with clear neuropathy or myopathy phenotypes, but evaluation of a broader set of disease genes was needed for patients with complex NMD phenotypes. Most patients with NMD had undergone prior testing, but only 10/16 (63%) of these procedures, such as muscle biopsy, were informative in pointing to a final molecular diagnosis.

Conclusions

Genome-scale sequencing or analysis of a panel of relevant genes used early in the evaluation of patients with NMDs can provide or clarify a diagnosis and minimize invasive testing in many cases.

Development of an AVF Stenosis Assessment Tool for Hemodialysis Patients Using Robotic Ultrasound System

With the aging population and lifestyle changes, the number of hemodialysis (HD) patients increases year by year. The arteriovenous fistula (AVF) is the gold standard vascular access used to access the blood for HD treatment. Since the status of stenosis affects HD efficiency, current clinical practices usually use a Doppler ultrasound imaging system to assess the parameters of the stenosis, such as the degree of stenosis (DOS). Unfortunately, this is a very time-consuming task. Furthermore, it is difficult to stably maintain the ultrasound probe for a prolonged period to give doctors clearer or reproducible images. In this study, a robotic ultrasound system (RUS) with ultrasound sequential imaging analysis was designed to evaluate the DOS of the AVF. The sequential imaging analysis was capable of image smoothing and vessel boundary detection. It enabled clinicians to mark the thickness of the plaque for further processing. Finally, the system was used to reconstruct 3D models of fistulas and calculated the DOS for clinical assessment. We also designed a pressure sensing module attached to the ultrasound probe to prevent the probe from coming loose, vibrating, and exerting abnormal pressure on the skin. In the phantom test, the results showed that the error of the DOS that was calculated by RUS was less than 3%. The results of clinical trials obtained from ten patients show that the error between the RUS and clinicians' measurement was about 10% and had a highly linear correlation (R Square > 0.95). In addition, the reproducibility error was about 3% and could effectively save about 46% of the time during clinical examinations.

Whole Exome Sequencing Identifies Truncating Variants in Nuclear Envelope Genes in Patients With Cardiovascular Disease

BACKGROUND

The genetic variation underlying many heritable forms of cardiovascular disease is incompletely understood, even in patients with strong family history or early age at onset.

METHODS AND RESULTS

We used whole exome sequencing to detect pathogenic variants in 55 patients with suspected monogenic forms of cardiovascular disease. Diagnostic analysis of established disease genes identified pathogenic variants in 21.8% of cases and variants of uncertain significance in 34.5% of cases. Three patients harbored heterozygous nonsense or splice-site variants in the nucleoporin genes NUP37, NUP43, and NUP188, which have not been implicated previously in cardiac disease. We also identified a heterozygous splice site variant in the nuclear envelope gene SYNE1 in a child with severe dilated cardiomyopathy that underwent transplant, as well as in his affected father. To confirm a cardiovascular role for these candidate genes in vivo, we used morpholinos to reduce SYNE1, NUP37, and NUP43 gene expression in zebrafish. Morphant embryos displayed cardiac abnormalities, including pericardial edema and heart failure. Furthermore, lymphoblasts from the patient carrying a SYNE1 splice-site variant displayed changes in nuclear morphology and protein localization that are consistent with disruption of the nuclear envelope.

CONCLUSIONS

These data expand the repertoire of pathogenic variants associated with cardiovascular disease and validate the diagnostic and research use of whole exome sequencing. We identify NUP37, NUP43, and NUP188 as novel candidate genes for cardiovascular disease, and suggest that dysfunction of the nuclear envelope may be an under-recognized component of inherited cardiac disease in some cases.

A Low Power Consumption Algorithm for Efficient Energy Consumption in ZigBee Motes

Wireless Sensor Networks (WSNs) are becoming increasingly popular since they can gather information from different locations without wires. This advantage is exploited in applications such as robotic systems, telecare, domotic or smart cities, among others. To gain independence from the electricity grid, WSNs devices are equipped with batteries, therefore their operational time is determined by the time that the batteries can power on the device. As a consequence, engineers must consider low energy consumption as a critical objective to design WSNs. Several approaches can be taken to make efficient use of energy in WSNs, for instance low-duty-cycling sensor networks (LDC-WSN). Based on the LDC-WSNs, we present LOKA, a LOw power Konsumption Algorithm to minimize WSNs energy consumption using different power modes in a sensor mote. The contribution of the work is a novel algorithm called LOKA that implements two duty-cycling mechanisms using the end-device of the ZigBee protocol (of the Application Support Sublayer) and an external microcontroller (Cortex M0+) in order to minimize the energy consumption of a delay tolerant networking. Experiments show that using LOKA, the energy required by the sensor device is reduced to half with respect to the same sensor device without using LOKA.

Sense-making Strategies for the Interpretation of Visualizations—Bridging the Gap between Theory and Empirical Research

Making sense of visualizations is often an open and explorative process. This process is still not very well understood. On the one hand, it is an open question which theoretical models are appropriate for the explanation of these activities. Heuristics and theories of everyday thinking probably describe this process better than more formal models. On the other hand, there are only few detailed investigations of interaction processes with information visualizations. We will try to relate approaches describing the usage of heuristics and everyday thinking with existing empirical studies describing sense-making of visualizations.

Superframe Duration Allocation Schemes to Improve the Throughput of Cluster-Tree Wireless Sensor Networks

The use of Wireless Sensor Network (WSN) technologies is an attractive option to support wide-scale monitoring applications, such as the ones that can be found in precision agriculture, environmental monitoring and industrial automation. The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable topology to build wide-scale WSNs. Despite some of its known advantages, including timing synchronisation and duty-cycle operation, cluster-tree networks may suffer from severe network congestion problems due to the convergecast pattern of its communication traffic. Therefore, the careful adjustment of transmission opportunities (superframe durations) allocated to the cluster-heads is an important research issue. This paper proposes a set of proportional Superframe Duration Allocation (SDA) schemes, based on well-defined protocol and timing models, and on the message load imposed by child nodes (Load-SDA scheme), or by number of descendant nodes (Nodes-SDA scheme) of each cluster-head. The underlying reasoning is to adequately allocate transmission opportunities (superframe durations) and parametrize buffer sizes, in order to improve the network throughput and avoid typical problems, such as: network congestion, high end-to-end communication delays and discarded messages due to buffer overflows. Simulation assessments show how proposed allocation schemes may clearly improve the operation of wide-scale cluster-tree networks.

Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve

The goal is to create an assistant for ultrasound- guided femoral nerve block. By segmenting and visualizing the important structures such as the femoral artery, we hope to improve the success of these procedures. This article is the first step towards this goal and presents novel real-time methods for identifying and reconstructing the femoral artery, and registering a model of the surrounding anatomy to the ultrasound images. The femoral artery is modelled as an ellipse. The artery is first detected by a novel algorithm which initializes the artery tracking. This algorithm is completely automatic and requires no user interaction. Artery tracking is achieved with a Kalman filter. The 3D artery is reconstructed in real-time with a novel algorithm and a tracked ultrasound probe. A mesh model of the surrounding anatomy was created from a CT dataset. Registration of this model is achieved by landmark registration using the centerpoints from the artery tracking and the femoral artery centerline of the model. The artery detection method was able to automatically detect the femoral artery and initialize the tracking in all 48 ultrasound sequences. The tracking algorithm achieved an average dice similarity coefficient of 0.91, absolute distance of 0.33 mm, and Hausdorff distance 1.05 mm. The mean registration error was 2.7 mm, while the average maximum error was 12.4 mm. The average runtime was measured to be 38, 8, 46 and 0.2 milliseconds for the artery detection, tracking, reconstruction and registration methods respectively.

Machine learning for biomedical literature triage

This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.