Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

This study presents the results of a series of simulation experiments that evaluate and compare four different manifold alignment methods under the influence of noise. The data was created by simulating the dynamics of two slightly different double pendulums in three-dimensional space. The method of semi-supervised feature-level manifold alignment using global distance resulted in the most convincing visualisations. However, the semi-supervised feature-level local alignment methods resulted in smaller alignment errors. These local alignment methods were also more robust to noise and faster than the other methods.Comment: The final version will appear in ICONIP 2018. A DOI identifier to the final version will be added to the preprint, as soon as it is availabl

Electrocardiographic patch devices and contemporary wireless cardiac monitoring.

Cardiac electrophysiologic derangements often coexist with disorders of the circulatory system. Capturing and diagnosing arrhythmias and conduction system disease may lead to a change in diagnosis, clinical management and patient outcomes. Standard 12-lead electrocardiogram (ECG), Holter monitors and event recorders have served as useful diagnostic tools over the last few decades. However, their shortcomings are only recently being addressed by emerging technologies. With advances in device miniaturization and wireless technologies, and changing consumer expectations, wearable “on-body” ECG patch devices have evolved to meet contemporary needs. These devices are unobtrusive and easy to use, leading to increased device wear time and diagnostic yield. While becoming the standard for detecting arrhythmias and conduction system disorders in the outpatient setting where continuous ECG monitoring in the short to medium term (days to weeks) is indicated, these cardiac devices and related digital mobile health technologies are reshaping the clinician-patient interface with important implications for future healthcare delivery

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity.

Background:Neuroimaging studies have identified obesity-related differences in the brain's resting state activity. An imbalance between homeostatic and reward aspects of ingestive behaviour may contribute to obesity and food addiction. The interactions between early life adversity (ELA), the reward network and food addiction were investigated to identify obesity and sex-related differences, which may drive obesity and food addiction. Methods:Functional resting state magnetic resonance imaging was acquired in 186 participants (high body mass index [BMI]: ≥25: 53 women and 54 men; normal BMI: 18.50-24.99: 49 women and 30 men). Participants completed questionnaires to assess ELA (Early Traumatic Inventory) and food addiction (Yale Food Addiction Scale). A tripartite network analysis based on graph theory was used to investigate the interaction between ELA, brain connectivity and food addiction. Interactions were determined by computing Spearman rank correlations, thresholded at q < 0.05 corrected for multiple comparisons. Results:Participants with high BMI demonstrate an association between ELA and food addiction, with reward regions playing a role in this interaction. Among women with high BMI, increased ELA was associated with increased centrality of reward and emotion regulation regions. Men with high BMI showed associations between ELA and food addiction with somatosensory regions playing a role in this interaction. Conclusions:The findings suggest that ELA may alter brain networks, leading to increased vulnerability for food addiction and obesity later in life. These alterations are sex specific and involve brain regions influenced by dopaminergic or serotonergic signalling

Engaging Undergraduates in Science Research: Not Just About Faculty Willingness.

Despite the many benefits of involving undergraduates in research and the growing number of undergraduate research programs, few scholars have investigated the factors that affect faculty members' decisions to involve undergraduates in their research projects. We investigated the individual factors and institutional contexts that predict faculty members' likelihood of engaging undergraduates in their research project(s). Using data from the Higher Education Research Institute's 2007-2008 Faculty Survey, we employ hierarchical generalized linear modeling to analyze data from 4,832 science, technology, engineering, and mathematics (STEM) faculty across 194 institutions to examine how organizational citizenship behavior theory and social exchange theory relate to mentoring students in research. Key findings show that faculty who work in the life sciences and those who receive government funding for their research are more likely to involve undergraduates in their research project(s). In addition, faculty at liberal arts or historically Black colleges are significantly more likely to involve undergraduate students in research. Implications for advancing undergraduate research opportunities are discussed

Patient safety in dentistry: development of a candidate 'never event' list for primary care

Introduction The 'never event' concept is often used in secondary care and refers to an agreed list of patient safety incidents that 'should not happen if the necessary preventative measures are in place'. Such an intervention may raise awareness of patient safety issues and inform team learning and system improvements in primary care dentistry. Objective To identify and develop a candidate never event list for primary care dentistry. Methods A literature review, eight workshops with dental practitioners and a modified Delphi with 'expert' groups were used to identify and agree candidate never events. Results Two-hundred and fifty dental practitioners suggested 507 never events, reduced to 27 distinct possibilities grouped across seven themes. Most frequently occurring themes were: 'checking medical history and prescribing' (119, 23.5%) and 'infection control and decontamination' (71, 14%). 'Experts' endorsed nine candidate never event statements with one graded as 'extreme risk' (failure to check past medical history) and four as 'high risk' (for example, extracting wrong tooth). Conclusion Consensus on a preliminary list of never events was developed. This is the first known attempt to develop this approach and an important step in determining its value to patient safety. Further work is necessary to develop the utility of this method

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

Kinetic roughening of ion-sputtered Pd(001) surface: Beyond the Kuramoto-Sivashinsky model

The kinetic roughening of Ar+ ion-sputtered Pd(001) surface was investigated. The facet formation on the sputtered surface was studied by tracing the extradiffraction peaks or satellites around the diffraction peaks corresponding to the sample surface. The morphological evolution of the sputtered Pd(001) surface was also investigated by an scanning tunneling microscopy (STM). It was shown that the nanoscale adatom islands form and grow with increasing sputter time.open313

Higgs production in CP-violating supersymmetric cascade decays: probing the `open hole' at the Large Hadron Collider

A benchmark CP-violating supersymmetric scenario (known as 'CPX-scenario' in the literature) is studied in the context of the Large Hadron Collider (LHC). It is shown that the LHC, with low to moderate accumulated luminosity, will be able to probe the existing `hole' in the $m_{h_1}$-$\tan\beta$ plane, which cannot be ruled out by the LEP data. We explore the parameter space with cascade decay of third generation squarks and gluino with CP-violating decay branching fractions. We propose a multi-channel analysis to probe this parameter space some of which are background free at an integrated luminosity of 5-10 fb$^{-1}$. Specially, multi-lepton final states (3\l,\, 4\l and like sign di-lepton) are almost background free and have $5\sigma$ reach for the corresponding signals with very early data of LHC for both 14 TeV and 7 TeV center of mass energy.Comment: 24 pages, 9 figures, references added as in the journal versio

Top mass dependent alpha_s^3 corrections to B-meson mixing in the MSSM

We compute the top mass dependent NLO strong interaction matching conditions to the Delta F=2 effective Hamiltonian in the general MSSM. We study the relevance of such corrections, comparing its size with that of previously known NLO corrections in the limit mt->0, in scenarios with degeneracy, alignment, and hierarchical squarks. We find that, while these corrections are generally small, there are regions in the parameter space where the contributions to the Wilson coefficients C1 and C4 could partially overcome the expected suppression m_t/M_SUSY.Comment: 15 pages, 6 figure