Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Identification and Predictive Tools to Help Identify High-risk Heart Failure Patients

OBJECTIVE Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients. METHODS Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients. RESULTS The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility. CONCLUSIONS Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care.
Learning Approximate Stochastic Transition Models

We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions. We show that currently popular generative adversarial networks struggle to learn these stochastic transition models but a modification to their loss functions results in a powerful learning algorithm for this class of problems.
Telemedicine in the Upper Amazon: Interplay with Local Health Care Practices

This article is based on the introduction of a telemedicine system in the jungles of northeastern Peru. The system was designed by a European consortium led by a Spanish polytechnic in cooperation with two universities in Lima and the Peruvian Ministry of Health. The purpose of the system was to improve health conditions by extending science-based medicine into a region with well-established traditional healing practices. The central analytical focus of this article is on the interplay between the public health care system, which used the telemedicine system, and local health care practices. The manner in which scientific medicine was delivered through information technology and public health care services is analyzed in terms of the health personnel's activity, the local population's conceptions of health, and the trajectories followed by patients seeking recovery. The author participated in the design of the second evaluation of the telemedicine system and acted as a participant observer in the regional hospital and peripheral clinics. In addition to interviewing health care staff from the study area, the author also met with traditional healers, and patients in the districts whether or not they were involved in the telemedicine project. New institutional theory provided the analytical framework for the interpretation of the observed behavior of the public health care staff, traditional healers, and potential patients. Empirically, this study describes the informal aspects of the functioning of the telemedicine system, and its partial mismatch with the definitions of health and illness employed by local communities and healers. An argument is made that people's construction of their health, which is embedded in their normal patterns of action, should be identified, and then considered in the design, implementation, and evaluation of future telemedicine projects. This article problematizes an approach to telemedicine-based health development that is weakly accountable to local social contexts and their diversity.
Attentional bias toward personally relevant health-threat words

Conflicting findings have emerged regarding the presence of attentional biases (ABs) in health anxiety, probably due to methodological limitations in the stimuli used in cognitive tasks and the assessment of health anxiety-relevant factors. The current study sought to examine ABs toward health-related threats using idiographically chosen health-threat words in a non-clinical sample. A modified dot-probe task using idiographically selected health-threat words was administered to an undergraduate sample. Self-report measures were administered to assess somatic, cognitive, and behavioral aspects of health anxiety, in addition to assessing negative affect, anxiety sensitivity, and experience of actual medical conditions. Results showed that behavioral and somatic aspects of health anxiety were significantly associated with AB toward personally relevant threat words, even after controlling for negative affect, anxiety sensitivity, and experience of actual medical conditions. Additional analyses revealed that these biases reflected difficulty disengaging attention from threat rather than a facilitated detection of threat. In contrast, illness-related cognitions were found to be unrelated to ABs. These findings suggest an association between threat-related ABs and excessive health-care seeking efforts.
Second W3c Web and Tv Workshop Community Viewing Meets Network Coding: New Strategies for Distribution, Consumption and Protection of Tv Content

Our Interest in the Workshop Network coding (NC) considers data as algebraic entities not just sets of bits. As such it assumes that digital traffic can be combined by addition and multiplication by other entities. While NC has been the topic of research in the last 10 years it is now receiving a lot of attention in the technology world as one enabler of the Internet of Information Nodes. In particular, since network codes do not need to be end to end, NC allows tailoring coding strategies to the dynamics and topology of the network and to the features of the receiver ecosystem. This is very beneficial when transporting video traffic over heterogeneous networks to a variety of end devices. NC provides strategies for our team to better deliver video on the Internet. We address end-­‐to-­‐end aspects from video codecs to rendering and social viewing. As a result, our work has recently been highlighted in MIT News (network coding) and the MIT Technology Review TR10 (the ten technologies that will shape the world, for Social Television). Of particular interest for the W3C workshop are the aspects of our research that address new delivery mechanisms beyond traditional client-­‐ server architectures such as peer to peer and distributed storage, quality of experience (QoE) with device augmentation, network combining and layered content protection that socializes TV viewing. This creates the underlying network infrastructure that will deliver the next generation of video protocols like HTML5. Hence we believe that sharing our research with the workshop community and getting the feedback of the participants will be beneficial to all.
The bionic hand movement using myo sensor and neural networks

One part of human body in which very useful in this life is a hand. This life will be disrupted if one or both of these hands do not work, due to disability or amputation. One of the contributions that help this condition is the bionic hand. This study have developed a bionic hand that consist of six servo motors to drive the fingers movement. Myo sensor is used in this bionic hand, it is located in the forearm. It has been used to record the muscle signals. Feature extraction of the muscle signal was formulated by using root mean square (rms). Kind of the bionic hand movement was found by identifying the movement signal pattern using back propagation through the artificial neural network. The output data obtained from back propagation was processed by microcontroller and transmitted to the servo motors for getting a movement of the bionic hand. Based on experimental data, five kinds of movement were tested in this system, average of recognition for each movement: handful accuracy movement 65%; pointing-fingers 75%; wave in 100%; wave out 75%; and spread 100%. Testing for the whole movements (five kinds) were performed with the average error 17%.
Averaging spectral shapes

The methods of obtaining the average spectral shape in a low statistics regime are presented. Different approaches to averaging are extensively tested with simulated spectra, based on the ASCA responses. The issue of binning up the spectrum before fitting is discussed together with the choice of statistic used to model the spectral shape. The best results are obtained with methods in which input data are represented by probability density functions. Application of weights, representing the coverage between the input and output bins, slightly improves the resolution of averaging.
Early infiltration of p40IL12+CCR7+CD11b+ cells is critical for fibrosis development

Macrophages are heterogeneous and thus can be correlated with distinct tissue outcomes after injury. Conflicting data have indicated that the M2‐related phenotype directly triggers fibrosis. Conversely, we hypothesize here that the inflammatory milieu provided by early infiltration of pro‐inflammatory macrophages dictates tissue scarring after injury.
Using a pitch-synchronous residual codebook for hybrid HMM/frame selection speech synthesis

This paper proposes a method to improve the quality delivered by statistical parametric speech synthesizers. For this, we use a codebook of pitch-synchronous residual frames, so as to construct a more realistic source signal. First a limited codebook of typical excitations is built from some training database. During the synthesis part, HMMs are used to generate filter and source coefficients. The latter coefficients contain both the pitch and a compact representation of target residual frames. The source signal is obtained by concatenating excitation frames picked up from the codebook, based on a selection criterion and taking target residual coefficients as input. Subjective results show a relevant improvement compared to the basic technique.
Dicer-Derived MicroRNAs Are Utilized by the Fragile X Mental Retardation Protein for Assembly on Target RNAs

In mammalian cells, fragile X mental retardation protein (FMRP) has been reported to be part of a microRNA (miRNA)-containing effector ribonucleoprotien (RNP) complex believed to mediate translational control of specific mRNAs. Here, using recombinant proteins, we demonstrate that human FMRP can act as a miRNA acceptor protein for the ribonuclease Dicer and facilitate the assembly of miRNAs on specific target RNA sequences. The miRNA assembler property of FMRP was abrogated upon deletion of its single-stranded (ss) RNA binding K-homology domains. The requirement of FMRP for efficient RNA interference (RNAi) in vivo was unveiled by reporter gene silencing assays using various small RNA inducers, which also supports its involvement in an ss small interfering RNA (siRNA)-containing RNP (siRNP) effector complex in mammalian cells. Our results define a possible role for FMRP in RNA silencing and may provide further insight into the molecular defects in patients with the fragile X syndrome.
Ensemble of Convolutional Neural Networks for Medicine Intake Recognition in Twitter

We present the results from our participation in the 2nd Social Media Mining for Health Applications Shared Task – Task 2. The goal of this task is to develop systems capable of recognizing mentions of medication intake in Twitter. Our best performing classification system is an ensemble of neural networks with features generated by wordand character-level convolutional neural network channels and a condensed weighted bag-of-words representation. A relatively strong performance is achieved, with an F-score of 66.3 according to the official evaluation, resulting in the 5th place in the shared task with performance close to the best systems created by other participating teams.
Recognizing Hand-Printed Digits with a Distance Quasi-Metric

A distance quasi-metric for pattern recognition is presented. The “quasi” modifier distinguishes the metric from “true” distance metrics which obey a set of standard constraints. By relaxing one of the constraints and coupling it with a fast multidimensional search technique, the metric demonstrates improved accuracy and efficiency compared to other metrics in recognizing hand-written digit samples. A high-level design of a fast optical comparator for computing the distance in O(n) is also presented.
Multidimensional detective

The display of multivariate datasets in parallel coordinates, transforms the search for relations among the variables into a 2-D pattern recognition problem. This is the basis for the application to visual data mining. The knowledge discovery process together with some general guidelines are illustrated on a dataset from the production of a VLSI chip. The special strength of parallel coordinates is in modeling relations. As an example, a simplified economic model is constructed with data from various economic sectors of a real country. The visual model shows the interelationship and dependencies between the sectors, circumstances where there is competition for the same resource, and feasible economic policies. Interactively, the model can be used to do trade-off analyses, discover sensitivities, do approximate optimization, monitor (as in a process) and provide decision support.
Vision-Based Fuzzy 2D Motion Control of a Model Helicopter

In this paper the 2D motion of a model helicopter is studied. The position control of a 2D model helicopter falls into complex nonlinear problems domain which makes it rather hard. In this paper a fuzzy controller is proposed to stabilize the helicopter on a designated target. The 3D model of the helicopter is simplified to derive a 2D model and all the states are assumed to be measurable. Based on the model, it was possible to decouple the position and orientation control of the helicopter. However the two parts are related through a fuzzy rule base. To verify the proposed method a simulation program is written in C++ which takes advantages of OpenGL to enable having 3D features. The response graphs are then presented
Bcl-2, Bax, Bcl-xL and Bcl-xS Expression in Neoplastic and Normal Endometrium

The two anti-apoptotic proteins Bcl-2 and Bcl-xL, and the two pro-apoptotic proteins Bax and Bcl-xS were measured by Western blotting in 51 neoplastic and 8 normal endometrial samples. The corresponding mRNA levels were analyzed by semiquantitative reverse transcriptase-polymerase chain reaction in a subgroup of 19 endometrial carcinomas. Neoplastic tissues had higher amounts of Bcl-2 protein than normal tissues (p < 0.051). Bcl-xL followed the same trend since its levels were higher in tumor than in normal samples (p < 0.048). Interestingly, Bcl-2 and Bcl-xL protein content showed a trend towards an inverse correlation (r = 0.27, p < 0.052). mRNA and protein levels directly correlated only with Bcl-2 (r = 0.63, p < 0.0032). Despite the fact that the amounts of Bcl-2, Bax and Bcl-xL proteins in the neoplastic population were not significantly differently distributed according to the clinicopathological features of the patients, the differences observed between normal and neoplastic samples suggest that these proteins may play a role in endometrial carcinoma: long-term follow-up studies will be required to confirm this hypothesis.
Review of Fisher

To The Editor: Thank you for inviting me to review FISHER. At the 1985 Genetic Analysis Workshop in Snowbird, Ken Lange announced that he and coworkers were shortly to offer a suite of programs for pedigree analysis. Included in this package was FISHER, an implementation of the multivariate normal (mvn) model for the analysis of classical biometric quantitative traits [Lange et al., 19761, and SEARCH, an optimization algorithm. Having spent the past 5 years using and developing the mvn model [Hopper and Mathews, 19821, I obtained the package, believing it to mark an important point in the development of the discipline. Now, after 2 years’ experience, I can report on our experiences with FISHER and, to a lesser degree, with SEARCH. FISHER is a FORTRAN 77 program for modeling, under multivariate normality, the mean and covariance matrix of one or more quantitative traits assumed to be measured on independent groups of variable size and structure. It is written principally for analyzing pedigree data, and so calculates from parental information the kinship coefficient and Jacquard’s coefficient of identity, A7 [following Lange et al., 19761. It allows for user-defined environmental covariances between individuals in a pedigree and for conditioning on probands, provides detailed analysis of outlier individuals and outlier pedigrees, and permits testing of overall statistical validity of the model. FISHER can also be used for path analysis by the mvn model [Rao et al., 1984; Hopper, 19861. The mvn model is extremely flexible, and FISHER maintains this through the user defining the mean value for each individual, and the covariance between any two individuals in the same pedigree, in terms of any function of any measured variables
Ordered and Ushered; the Assembly and Translocation of the Adhesive Type I and P Pili

Type I and P pili are chaperone-usher pili of uropathogenic Escherichia coli, which allow bacteria to adhere to host cell receptors. Pilus formation and secretion are orchestrated by two accessory proteins, a chaperone, which catalyses pilus subunit folding and maintains them in a polymerization-competent state, and an outer membrane-spanning nanomachine, the usher, which choreographs their assembly into a pilus and drives their secretion through the membrane. In this review, recent structures and kinetic studies are combined to examine the mechanism of type I and P pili assembly, as it is currently known. We also investigate how the knowledge of pilus biogenesis mechanisms has been exploited to design selective inhibitors of the process.
Nonlinear Inverse Scale Space Methods for Total Variation Blind Deconvolution

In this paper we propose a blind deconvolution algorithm based on the total variation regularization formulated as a nonlinear inverse scale space method that allows an efficient recovery of edges and textures of blurry and noisy images. The proposed explicit scheme gives the restored image solution by evolving in time the zero signal and an estimated kernel until a stopping criterion is satisfied. Numerical results indicate that our scheme is robust and converges quickly to the solution of the model for images convolved with either a Gaussian-like experimental point spread function or Gaussian blur and contaminated with Gaussian white noise.
The Anaphase Promoting Complex Contributes to the Degradation of the S. cerevisiae Telomerase Recruitment Subunit Est1p

Telomerase is a multi-subunit enzyme that reverse transcribes telomere repeats onto the ends of linear eukaryotic chromosomes and is therefore critical for genome stability. S. cerevisiae telomerase activity is cell-cycle regulated; telomeres are not elongated during G1 phase. Previous work has shown that Est1 protein levels are low during G1 phase, preventing telomerase complex assembly. However, the pathway targeting Est1p for degradation remained uncharacterized. Here, we show that Est1p stability through the cell cycle mirrors that of Clb2p, a known target of the Anaphase Promoting Complex (APC). Indeed, Est1p is stabilized by mutations in both essential and non-essential components of the APC. Mutations of putative Destruction boxes (D-boxes), regions shown to be important for recognition of known APC substrates, stabilize Est1p, suggesting that Est1p is likely to be targeted for degradation directly by the APC. However, we do not detect degradation or ubiquitination of recombinant Est1p by the APC in vitro, suggesting either that the recombinant protein lacks necessary post-translational modification and/or conformation, or that the APC affects Est1p degradation by an indirect mechanism. Together, these studies shed light on the regulation of yeast telomerase assembly and demonstrate a new connection between telomere maintenance and cell cycle regulation pathways.
Parylene-coated metal tracks for neural electrode arrays - Fabrication approaches and improvements utilizing different laser systems

In the past we developed a method for the fabrication of neural electrodes based on laser-structuring metal foil to form tracks and electrode sites within a silicone rubber substrate. Here, this process was refined by an additional coating of the laser-patterned metal tracks to improve their mechanical properties. Parylene C has been found to be the coating material of choice due to excellent electrical and mechanical characteristics and its well known biocompatibility. An almost ten times increased tensile strength compared to uncoated tracks could be achieved. Investigating the electrical properties of parylene C and silicone rubber attested both materials excellent insulating capabilities by withstanding voltages of more than 400 VDC for layer thicknesses as intended to be used in electrode array fabrication (some 10 μm). This paper outlines the feasibility of the manufacturing process using a 1064 nm Nd:YAG laser in the nanosecond pulse regime. However, an improvement of the whole processing was demonstrated when a 355 nm Nd:YVO4 laser in the picosecond regime is used. Benefits of this short pulse duration range from ablating materials independent of their optical properties to increased manufacturing speed and superior processing quality.
Fuzzy Rule - Based Model Reference Adaptive Control for PMSM Drives

The objective of the model reference adaptive fuzzy control (MRAFC) is to change the rules definition in the direct fuzzy logic controller (FLC) and rule base table according to the comparison between the reference model output signal and system output. The MRAFC is composed by the fuzzy inverse model and a knowledge base modifier. Because of its improved algorithm, the MRAFC has fast learning features and good tracking characteristics even under severe variations of system parameters. The learning mechanism observes the plant outputs and adjusts the rules in a direct fuzzy controller, so that the overall system behaves like a reference model, which characterizes the desired behavior. In the proposed scheme, the error and error change measured between the motor speed and output of the reference model are applied to the MRAFC. The latter will force the system to behave like the signal reference by modifying the knowledge base of the FLC or by adding an adaptation signal to the fuzzy controller output. In this paper, the MRAFC is applied to a permanent magnet synchronous motor drive (PMSM). High performances and robustness have been achieved by using the MRAFC. This will be illustrated by simulation results and comparisons with other controllers such as PI classical and adaptive fuzzy controller based on gradient method controllers.