Miracle-dz/UniFAR-FTUs huggingface dataset:
Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

Objective: Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data. Methods: We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation. Results: We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71). Conclusions: EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.
A Pitch-aware Approach to Single-channel Speech Separation

Despite significant advancements of deep learning on separating speech sources mixed in a single channel, same gender speaker mix, i.e., male-male or female-female, is still more difficult to separate than the case of opposite gender mix. In this study, we propose a pitch-aware speech separation approach to improve the speech separation performance. The proposed approach performs speech separation in the following steps: 1) training a pre-separation model to separate the mixed sources; 2) training a pitch-tracking network to perform polyphonic pitch tracking; 3) incorporating the estimated pitch for the final pitch-aware speech separation. Experimental results of the new approach, tested on the WSJ0-2mix public dataset, show that the new approach improves speech separation performance for both same and opposite gender mixture. The improved performance in signal-to-distortion (SDR) of 12.0 dB is the best reported result without using any phase enhancement.
Exploring Complex Time-series Representations for Riemannian Machine Learning of Radar Data

Classification of radar observations with machine learning tools is of primary importance for the identification of non-cooperative radar targets such as drones. These observations are made of complex-valued time series which possess a strong underlying structure. These signals can be processed through a time-frequency analysis, through their self-correlation (or covariance) matrices or directly as the raw signal. All representations are linked but distinct and it is known that the input representation is critical for the success of any machine learning method. In this article, we explore these three possible input representation spaces with the help of two kinds of neural networks: a temporal fully convolutional network and a Riemannian network working direcly on the manifold of covariances matrices. We show that all the considered input representations are a particular case of a generic machine learning pipeline which goes from the raw complex data to the final classification stage through con-volutional layers and Riemannian layers. This pipeline can be learnt end-to-end and is shown experimentally to give the best classification accuracy together with the best robustness to lack of data.
High-Pressure-Mediated Survival of Clostridium botulinum and Bacillus amyloliquefaciens Endospores at High Temperature

ABSTRACT Endospores of proteolytic type B Clostridium botulinum TMW 2.357 and Bacillus amyloliquefaciens TMW 2.479 are currently described as the most high-pressure-resistant bacterial spores relevant to food intoxication and spoilage in combined pressure-temperature applications. The effects of combined pressure (0.1 to 1,400 MPa) and temperature (70 to 120°C) treatments were determined for these spores. A process employing isothermal holding times was established to distinguish pressure from temperature effects. An increase in pressure (600 to 1,400 MPa) and an increase in temperature (90 to 110°C) accelerated the inactivation of C. botulinum spores. However, incubation at 100°C, 110°C, or 120°C with ambient pressure resulted in faster spore reduction than treatment with 600 or 800 MPa at the same temperature. This pressure-mediated spore protection was also observed at 120°C and 800, 1,000, or 1,200 MPa with the more heat-tolerant B. amyloliquefaciens TMW 2.479 spores. Inactivation curves for both strains showed a pronounced pressure-dependent tailing, which indicates that a small fraction of the spore populations survives conditions of up to 120°C and 1.4 GPa in isothermal treatments. Because of this tailing and the fact that pressure-temperature combinations stabilizing bacterial endospores vary from strain to strain, food safety must be ensured in case-by-case studies demonstrating inactivation or nongrowth of C. botulinum with realistic contamination rates in the respective pressurized food and equipment.
IDENTIFIKASI JENIS SERANGGA HAMA DAN TINGKAT KERUSAKAN PADA Diospyros Celebica Bakh.

ABSTRACT Diospyros celebica Bakh is luxury wood which is endemic to Sulawesi. The Environment and Forestry Research and Development Istitute of Manado is implementing an ex situ conservation of D. celebica through planting it in The Arboretum since 2012. The D. celebica stand at the BP2LHK Arboretum in Manado was attacked by insects on the leaves, the leaf part is gnawed from the edge to the vein and midrib. The purpose of this study is to identify the types of insects which is pest to leaves of D celebica and calculate the level of damage caused. The study was conduct on the plot of D. celebica stands by census observation. Two types of insects were identified, namely leaf cutting bees (Megachile sp) and caterpillars from the order of Lepidoptera. The main insects that damage the leaves of D. celebica is leaf-cutting bee (Megachile sp) because this insect has no natural predators. The caterpillars of the order lepidóptera have a number of natural predators, such as Pycnonotus aurigaster, Centropus bengalensis, Phaenicophaeus calyorhynchus, Hypothymis azurea, and Oecophylla smaragdina. The results showed that the frequency of insect attacks on D. celebica leaves was 97.44%, there are 38 of 39 plant observed were attacked by insect. The intensity of insect attacks on stands of D. celebica is 11.54%, this result shows that pest control in stands is not yet necessary, since of the intensity of the attack is including minor damage.Keywords: insect, pests, Diospyros celebica Bakh. , Intensity of damage
Constrained type families

We present an approach to support partiality in type-level computation without compromising expressiveness or type safety. Existing frameworks for type-level computation either require totality or implicitly assume it. For example, type families in Haskell provide a powerful, modular means of defining type-level computation. However, their current design implicitly assumes that type families are total, introducing nonsensical types and significantly complicating the metatheory of type families and their extensions. We propose an alternative design, using qualified types to pair type-level computations with predicates that capture their domains. Our approach naturally captures the intuitive partiality of type families, simplifying their metatheory. As evidence, we present the first complete proof of consistency for a language with closed type families.
Histone H2B Monoubiquitination Is Involved in Regulating the Dynamics of Microtubules during the Defense Response to Verticillium dahliae Toxins in Arabidopsis1[OPEN]

Histone monoubiquitination plays an important role in regulating the dynamics of microtubules in response to Verticillium dahliae toxins. Histone H2B monoubiquitination (H2Bub) is being recognized as a regulatory mechanism that controls a range of cellular processes in plants, but the molecular mechanisms of H2Bub that are involved in responses to biotic stress are largely unknown. In this study, we used wild-type and H2Bub loss-of-function mutations of Arabidopsis (Arabidopsis thaliana) to elucidate which of its mechanisms are involved in the regulation of the plant’s defense response to Verticillium dahliae (Vd) toxins. We demonstrate that the depolymerization of the cortical microtubules (MTs) was different in the wild type and the mutants in the response to Vd toxins. The loss-of-function alleles of HISTONE MONOUBIQUITINATION1 and HISTONE MONOUBIQUITINATION2 mutations present a weaker depolymerization of the MTs, and protein tyrosine phosphorylation plays a critical role in the regulation of the dynamics of MTs. Moreover, H2Bub is a positive regulator of the gene expression of protein tyrosine phosphatases. These findings provide direct evidence for H2Bub as an important modification with regulatory roles in the defense against Vd toxins and demonstrate that H2Bub is involved in modulating the dynamics of MTs, likely through the protein tyrosine phosphatase-mediated signaling pathway.
Tracking global trends in the effectiveness of antibiotic therapy using the Drug Resistance Index

Background Evaluating trends in antibiotic resistance and communicating the results to a broad audience are important for dealing with this global threat. The Drug Resistance Index (DRI), which combines use and resistance into a single measure, was developed as an easy-to-understand measure of the effectiveness of antibiotic therapy. We demonstrate its utility in communicating differences in the effectiveness of antibiotic therapy across countries. Methods We calculated the DRI for countries with data on antibiotic use and resistance for the disease-causing organisms considered by the WHO as priority pathogens: Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium and Enterococcus faecalis. Additionally, we estimated pooled worldwide resistance rates for these pathogens. Results 41 countries had the requisite data and were included in the study. Resistance and use rates were highly variable across countries, but A. baumannii resistance rates were uniformly higher, on average, than other organisms. High-income countries, particularly Sweden, Canada, Norway, Finland and Denmark, had the lowest DRIs; the countries with the highest DRIs, and therefore the lowest effectiveness of antibiotic therapy, were all low-income and middle-income countries. Conclusions The DRI is a useful indicator of the problem of resistance. By combining data on antibiotic use with resistance, it captures a snapshot of how the antibiotics a country typically uses match their resistance profiles. This single measure of the effectiveness of antibiotic therapy provides a means of benchmarking against other countries and can, over time, indicate changes in drug effectiveness that can be easily communicated.
Impact of Rheumatoid Arthritis Functional Status on Oral and Periodontal Health in a Multi-Ethnic Population

Periodontal disease (PD) has both been associated with the pathogenesis of Rheumatoid Arthritis (RA) and is also a comorbidity of RA. The aim of this study was to investigate the impact of the functional status of RA on oral and periodontal health in a multi-ethnic population. This is a hospital-based, multi-ethnic, observational study. Duration of illness, Disease Activity Score (DAS28), seropositivity and other rheumatological parameters were obtained from the records of RA patients attending the Rheumatology Clinic. Oral health examination was conducted on the subjects. Their functional status was assessed using the Health Assessment Questionnaire (HAQ) which includes categories in ‘dressing’, ‘rising’, ‘eating’, ‘walking’, ‘hygiene’, ‘reach’, ‘grip’ and ‘usual activities’. Data were analysed using ANOVA or Kruskal-Wallis test and Pearson’s Correlation or Spearman’s Rank Correlation Test. The 63 RA patients recruited had a mean disease duration of 10.7 ± 9.85 years with a mean of 19.9 teeth (± 7.18). The severity of PD worsens the longer the patients were suffering from RA (p=0.028). A high frequency (70%) of PD with 27% moderate and 27% severe, was observed. A significant association (p=0.035) was found between the ‘eating’ category of the HAQ and the average periodontal pocket depth. This study shows that there is a higher prevalence and severity of PD among RA patients in the selected population. The impact of functional status on periodontal health is mostly in the ‘eating’ category of the HAQ where patients reporting a disability in eating had higher average periodontal pocket depth.
The profiles of dysbiotic microbial communities

Alterations in the human gut microbiota play an important role in disease pathogenesis. Although next-generation sequencing has provided observational evidence linking shifts in gut microbiota composition to alterations in the human host, underlying mechanisms remain elusive. Metabolites generated within complex microbial communities and at the crossroads with host cells may be able to explain the impact of the gut microbiome on human homeostasis. Emerging technologies including novel culturing protocols, microfluidic systems, engineered organoids, and single-cell imaging approaches are providing new perspectives from which the gut microbiome can be studied paving the way to new diagnostic markers and personalized therapeutic interventions.
Affecting off-task behaviour: how affect-aware feedback can improve student learning

This paper describes the development and evaluation of an affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a student's affective state, which is deduced both from speech and interaction. The affect prediction is used to determine which type of feedback is provided and how that feedback is presented (interruptive or non-interruptive). The system includes two Bayesian networks that were trained with data gathered in a series of ecologically-valid Wizard-of-Oz studies, where the effect of the type of feedback and the presentation of feedback on students' affective states was investigated. This paper reports results from an experiment that compared a version that provided affect-aware feedback (affect condition) with one that provided feedback based on performance only (non-affect condition). Results show that students who were in the affect condition were less bored and less off-task, with the latter being statically significant. Importantly, students in both conditions made learning gains that were statistically significant, while students in the affect condition had higher learning gains than those in the non-affect condition, although this result was not statistically significant in this study's sample. Taken all together, the results point to the potential and positive impact of affect-aware intelligent support.
Minimum probability of error image retrieval

We address the design of optimal architectures for image retrieval from large databases. Minimum probability of error (MPE) is adopted as the optimality criterion and retrieval formulated as a problem of statistical classification. The probability of retrieval error is lower- and upper-bounded by functions of the Bayes and density estimation errors, and the impact of the components of the retrieval architecture (namely, the feature transformation and density estimation) on these bounds is characterized. This characterization suggests interpreting the search for the MPE feature set as the search for the minimum of the convex hull of a collection of curves of probability of error versus feature space dimension. A new algorithm for MPE feature design, based on a dictionary of empirical feature sets and the wrapper model for feature selection, is proposed. It is shown that, unlike traditional feature selection techniques, this algorithm scales to problems containing large numbers of classes. Experimental evaluation reveals that the MPE architecture is at least as good as popular empirical solutions on the narrow domains where these perform best but significantly outperforms them outside these domains.
Minimization of OKFDDs by Genetic Algorithms

A Genetic Algorithm (GA) is applied to minimize the size of Ordered Kronecker Functional Decision Diagrams (OKFDDs). Our approach chooses a good variable ordering and decomposition type list in parallel. We apply our minimization algorithm to technology mapping for FPGAs. Experimental results are given to show the eeciency of our approach.
An archaeology of caring knowledge.

BACKGROUND There have been repeated attempts, especially during the last 20 years, to say precisely what caring in nursing is. Authors who undertake this task usually begin with the observation that the concept of caring is complex and elusive, and suggest that their contribution will help to clarify this most confused of notions. However, they are always followed by other authors, who do exactly the same thing. We seem to be no closer, now, to a clarification of caring than we have ever been. AIM The paper offers a diagnosis of this situation, and explains why the project of retrieving caring from its elusiveness is an impossible one. I will suggest that this has nothing to do with the concept of caring, as such. Rather, the impossibility of the task follows from what these authors take to be knowledge of caring. METHOD I present an analysis of some presuppositions about what knowledge is. These presuppositions pervade the literature on caring, and can be summarized as follows: knowledge of caring is an aggregate of things said about it, derived from a potentially endless series of associations, grouped into attributes on the basis of resemblances, and conceived as a holistic description of the phenomenon. Further, I suggest that this analysis is akin to the one which Foucault offers of sixteenth century knowledge. CONCLUSIONS The analysis suggests that this way of knowing is approximately 350 years out of date, and explains why the task of arriving at knowledge (in this sense) is impossible. Moreover, Foucault's claim that sixteenth century knowledge is "plethoric yet absolutely poverty-stricken" applies, with equal force, to nursing's knowledge of caring.
Hints on Parallelization of the SPEC CPU 2006

Being multiprocessors (both on-chip and/or off-chip), the contemporary computers can automatically exploit the benefits of parallel programs, but their resources remain underutilized while executing still prevailing sequential applications. An obvious solution is in parallelization of these applications. The paper first discusses the possibilities and potentials of automatic parallelization of sequential applications. Since it is widely accepted that these potentials are generally limited, it explores the issues in manual parallelization of typical sequential applications. For this purpose, the SPEC CPU 2006 suite is examined as a representative collection of typical sequential applications. First, the efforts in parallelization of these programs outside the SPEC suite are described and effects of autoparallelization and vectorization are estimated. Then, it was attempted to define typical parallelization patterns and to search for them throughout this benchmark suite. After these patterns were applied by source-tosource code modifications, the effects of parallelization were measured by profiling and running on two different parallel machines. The speedup of up to 6% is achieved in some applications.
Nonsmall cell lung cancer from HIV-infected patients expressed programmed cell death-ligand 1 with marked inflammatory infiltrates

Objective: Immunotherapies targeting the programmed cell death-1 (PD-1)/PD-ligand 1 (PD-L1) checkpoint improved prognosis in lung cancer. PD-1/PD-L1 status, however, has not been investigated in human immunodeficiency virus (HIV)-positive patients. This study assessed PD-L1 status and tumor immune-cell infiltration in nonsmall cell lung cancer (NSCLC) in HIV patients. Methods: Consecutive HIV patients treated between 1996 and 2014 were enrolled. PD-L1 tumor expression was assessed using immunohistochemistry with two antibodies (clones 5H1 and E1L3N), and tumor immune-cell infiltration with CD3, CD4, CD8, CD20, CD163, and MPO. PD-L1 expression and immune infiltration results were compared with those of 54 NSCLCs from unknown HIV status patients. Results: Thirty-four HIV-positive patients were evaluated: predominantly men (88.2%) (median age: 51.1 years) presenting stage IV (38.2%) adenocarcinomas (76.5%). The median blood CD4+ count was 480 cells/&mgr;L (86–1120) and 64% exhibited undetectable viral load. The PD-L1 score (percentage of positive cells × intensity) was higher in HIV-positive than HIV-undetermined patients with the E1L3N clone [median (range) 0 (0–150) versus 0 (0–26.7), P = 0.047], yet not with the 5H1 clone [0 (0–120) versus 0 (0–26.7) P = 0.07, respectively]. PD-L1 expression frequency did not differ between both cohorts (18.7 versus 9.3% using E1L3N and 10 versus 5.6% using 5H1 clone, respectively). There were significantly greater cytotoxic T-cell (P < 0.001), B-lymphocyte (P = 0.005), and activated macrophage (P < 0.001) infiltrations in the HIV-positive patients, but no differences for CD4+ T cells. Conclusion: Tumors in HIV-positive patients seem to express higher PD-L1 levels with increased immune infiltration, supporting their inclusion in clinical trials assessing immune checkpoint inhibitors.
Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound

Describes a new approach based on the time-frequency representation of transient nonlinear chirp signals for modeling the aortic (A2) and the pulmonary (P2) components of the second heart sound (S2). It is demonstrated that each component is a narrow-band signal with decreasing instantaneous frequency defined by its instantaneous amplitude and its instantaneous phase. Each component Is also a polynomial phase signal, the instantaneous phase of which can be accurately represented by a polynomial having an order of thirty. A dechirping approach is used to obtain the instantaneous amplitude of each component while reducing the effect of the background noise. The analysis-synthesis procedure is applied to 32 isolated A2 and 32 isolated P2 components recorded in four pigs with pulmonary hypertension. The mean /spl plusmn/ standard deviation of the normalized root-mean-squared error (NRMSE) and the correlation coefficient (/spl rho/) between the original and the synthesized signal components were: NRMSE=2.1/spl plusmn/0.3% and /spl rho/=0.97/spl plusmn/0.02 for A2 and NRMSE=2.52/spl plusmn/0.5% and /spl rho/=0.96/spl plusmn/0.02 for P2. These results confirm that each component can be modeled as mono-component nonlinear chirp signals of short duration with energy distributions concentrated along its decreasing instantaneous frequency.