Virtual Medication Reconciliation Simulation with Senior Nursing Students

Abstract not availabl

Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification. However, most of the current dFC studies are based on detecting discrete major brain status via spatial clustering, which ignores rich spatiotemporal dynamics contained in such chronnectome. We propose Deep Chronnectome Learning for exhaustively mining the comprehensive information, especially the hidden higher-level features, i.e., the dFC time series that may add critical diagnostic power for MCI classification. To this end, we devise a new Fully-connected Bidirectional Long Short-Term Memory Network (Full-BiLSTM) to effectively learn the periodic brain status changes using both past and future information for each brief time segment and then fuse them to form the final output. We have applied our method to a rigorously built large-scale multi-site database (i.e., with 164 data from NCs and 330 from MCIs, which can be further augmented by 25 folds). Our method outperforms other state-of-the-art approaches with an accuracy of 73.6% under solid cross-validations. We also made extensive comparisons among multiple variants of LSTM models. The results suggest high feasibility of our method with promising value also for other brain disorder diagnoses.Comment: The paper has been accepted by MICCAI201

Improving Performance of Iterative Methods by Lossy Checkponting

Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale iterative methods are running with a large number of ranks in parallel, they have to checkpoint the dynamic variables periodically in case of unavoidable fail-stop errors, requiring fast I/O systems and large storage space. To this end, significantly reducing the checkpointing overhead is critical to improving the overall performance of iterative methods. Our contribution is fourfold. (1) We propose a novel lossy checkpointing scheme that can significantly improve the checkpointing performance of iterative methods by leveraging lossy compressors. (2) We formulate a lossy checkpointing performance model and derive theoretically an upper bound for the extra number of iterations caused by the distortion of data in lossy checkpoints, in order to guarantee the performance improvement under the lossy checkpointing scheme. (3) We analyze the impact of lossy checkpointing (i.e., extra number of iterations caused by lossy checkpointing files) for multiple types of iterative methods. (4)We evaluate the lossy checkpointing scheme with optimal checkpointing intervals on a high-performance computing environment with 2,048 cores, using a well-known scientific computation package PETSc and a state-of-the-art checkpoint/restart toolkit. Experiments show that our optimized lossy checkpointing scheme can significantly reduce the fault tolerance overhead for iterative methods by 23%~70% compared with traditional checkpointing and 20%~58% compared with lossless-compressed checkpointing, in the presence of system failures.Comment: 14 pages, 10 figures, HPDC'1

Targeted Social Skills Instruction For Secondary Students With Emotional/Behavior Disorders

98 leavesThe purpose of this pre-experimental study was to determine the impact of targeted social skills instruction for 30 secondary students with Emotional/Behavior Disorders. Students participated in six weeks of social skills instruction, four days per week for 40-45 minutes per session. The Social Skills Improvement System rating scale was used pre and posttest to determine student outcomes in the Social Skills subdomain areas of Communication, Cooperation, Assertion, Responsibility, Empathy, and Self-Control. In addition the study also looked at the Problem Behavior subdomain areas of Externalizing, Bullying, Hyperactivity/Inattention, and Internalizing. Results across the group did not show significant levels of improvement in any of the subdomain areas. However, there were significant results when the groups were broken down into various smaller subgroups. Limitations, implications for practice, and implications for future research are also offered

Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State

Neuroimaging studies have shown that functional brain networks composed from select regions of interest have a modular community structure. However, the organization of functional network connectivity (FNC), comprising a purely data-driven network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain networks in this mental illness

Yeast Vaccine Vector Including Immunostimulatory And Antigenic Polypeptides And Methods Of Using The Same

Vaccine compositions including a yeast comprising an immunostimulatory polypeptide and optionally an antigenic polypeptide are provided herein. The immunostimulatory polypeptide and the antigenic polypeptide are expressed or displayed on the surface of the yeast vaccine composition. Methods of using the vaccine composition to vaccinate subjects are also provided

The Jurisdiction of the D.C. Circuit

The U.S. Court of Appeals for the D.C. Circuit is unique among federal courts, well known for an unusual caseload that is disproportionally weighted toward administrative law. What explains that unusual caseload? This Article explores that question. We identify several factors that “push” some types of cases away from the Circuit and several factors that “pull” other cases to it. We give particular focus to the jurisdictional provisions of federal statutes, which reveal congressional intent about the types of actions over which the D.C. Circuit should have special jurisdiction. Through a comprehensive examination of the U.S. Code, we identify several trends. First, the Congress is more likely to give the D.C. Circuit exclusive jurisdiction over the review of administrative rulemaking than over the review of agency decisions imposing a penalty. Second, the Congress is more likely to give the D.C. Circuit exclusive jurisdiction over the review of independent agency actions than over the review of executive agency actions. Finally, the Congress tends to grant the D.C. Circuit exclusive jurisdiction over matters that are likely to have a national effect. In sum, we explore what makes this court unique, from its history to its modern docket and jurisdiction

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures