Miracle-dz/UniFAR-FTUs huggingface dataset:
Daily Collection of Self-Reporting Sleep Disturbance Data via a Smartphone App in Breast Cancer Patients Receiving Chemotherapy: A Feasibility Study

Background Improvements in mobile telecommunication technologies have enabled clinicians to collect patient-reported outcome (PRO) data more frequently, but there is as yet limited evidence regarding the frequency with which PRO data can be collected via smartphone applications (apps) in breast cancer patients receiving chemotherapy. Objective The primary objective of this study was to determine the feasibility of an app for sleep disturbance-related data collection from breast cancer patients receiving chemotherapy. A secondary objective was to identify the variables associated with better compliance in order to identify the optimal subgroups to include in future studies of smartphone-based interventions. Methods Between March 2013 and July 2013, patients who planned to receive neoadjuvant chemotherapy for breast cancer at Asan Medical Center who had access to a smartphone app were enrolled just before the start of their chemotherapy and asked to self-report their sleep patterns, anxiety severity, and mood status via a smartphone app on a daily basis during the 90-day study period. Push notifications were sent to participants daily at 9 am and 7 pm. Data regarding the patients’ demographics, interval from enrollment to first self-report, baseline Beck’s Depression Inventory (BDI) score, and health-related quality of life score (as assessed using the EuroQol Five Dimensional [EQ5D-3L] questionnaire) were collected to ascertain the factors associated with compliance with the self-reporting process. Results A total of 30 participants (mean age 45 years, SD 6; range 35-65 years) were analyzed in this study. In total, 2700 daily push notifications were sent to these 30 participants over the 90-day study period via their smartphones, resulting in the collection of 1215 self-reporting sleep-disturbance data items (overall compliance rate=45.0%, 1215/2700). The median value of individual patient-level reporting rates was 41.1% (range 6.7-95.6%). The longitudinal day-level compliance curve fell to 50.0% at day 34 and reached a nadir of 13.3% at day 90. The cumulative longitudinal compliance curve exhibited a steady decrease by about 50% at day 70 and continued to fall to 45% on day 90. Women without any form of employment exhibited the higher compliance rate. There was no association between any of the other patient characteristics (ie, demographics, and BDI and EQ5D-3L scores) and compliance. The mean individual patient-level reporting rate was higher for the subgroup with a 1-day lag time, defined as starting to self-report on the day immediately after enrollment, than for those with a lag of 2 or more days (51.6%, SD 24.0 and 29.6%, SD 25.3, respectively; P=.03). Conclusions The 90-day longitudinal collection of daily self-reporting sleep-disturbance data via a smartphone app was found to be feasible. Further research should focus on how to sustain compliance with this self-reporting for a longer time and select subpopulations with higher rates of compliance for mobile health care.
ANÁLISIS DE REDES SOCIALES EN LA EDUCACIÓN A DISTANCIA; ESTUDIO DE CASODE UNA METODOLOGÍA ALTERNATIVA PA AESTUDIAR LA INTERACCIÓN SOCIAL EN AMBIENTES DIGITALES DE APRENDIZAJE

A continuacion se presenta una investigacion del campo de la educacion: la poblacion y muestra con la que se adelanto la investigacion fue de 300 estudiantes de pregrado de la VUAD pertenecientes a cuatro cursos de nucleo comun y cuatro especificos de la Facultad de Tecnologia, utilizando el enfoque ARS, ya que se estudia el comportamiento de la comunidad academica entendida como red. Al final se presenta una corta reflexion del aporte que puede tener el analisis de redes sociales a los metodos alternativos venidos del enfoque mixto de investigacion.
Depression, Dementia and Disability in the Elderly

A structured sample of mobile elderly patients in a rural community practice was assessed on validated rating scales for depression, dementia and disability. A total of 62% of the sample was abnormal on at least one variable. The overall prevalence of depression was 13%; the overall prevalence of dementia was either 10 or 18% depending on the criterion of Mental Test Score (MTS). Depression and dementia were related, depression being more common in females. In depressed and demented patients, MTS was age-related in those over 60 years; in depression alone, MTS was not age-related. Dementia was age-related, particularly over the age of 75. Disability increased with age and was more common in females. Disability was associated with both depression and dementia.
Emotion Recognition in Speech of Parents of Depressed Adolescents

This paper investigates automatic affect classification in spontaneous speech within normal and clinical family environments. The data base used in this study comprised speech recordings of parents of depressed adolescents (19 fathers and 20 mothers) and parents of non-depressed adolescents (25 fathers and 7 mothers). The speech data were recorded during natural parent-child conversations. Five emotional classes were considered: neutral, angry, anxious, dysphoric, and happy. Four different combinations of features (set A, B, C, and D) derived from the Teager energy operator (TEO) and two different classifiers: Probabilistic Neural Network (PNN) and Gaussian Mixture model (GMM) were tested and compared. The feature extraction process was combined with an optimal feature selection algorithm based on the mutual information criteria. The GMM classifier provided consistently higher correct classification rates (49.6% to 62.0%) compared with the PNN classifier (31.6% to 42.7%). Set C/GMM was found to be the best performing feature/classifier combination. In all cases, the classification rates for parents of depressed adolescents were higher than for parents of non-depressed adolescents. Similarly, the classification rates for mothers were higher than for fathers. The results appear to suggest that parents of depressed adolescents express their emotions with higher degree of discrimination between different types of affect than parents of non-depressed adolescents. Similarly, mothers appear to express their affect with higher degree of discrimination between different types of affect than fathers.
Interleukin 2 augmentation of the defective natural killer cell activity in patients with primary Sjögren's syndrome.

Natural Killer (NK) cell activity against K562 target cells was measured in 21 female patients with primary Sjögren's syndrome (primary SS) and in 20 female normal controls matched for age. The in vitro effect of alpha-interferon (IF) and interleukin 2 (IL-2) on NK cell activity was examined and the percentage of large granular lymphocytes (LGL) in blood was measured. Median baseline NK cell activity in primary SS was 15.4% versus 24.4% in the controls (P less than 0.05). Median IF-enhanced NK cell activity in the SS group was 35.5% versus 49.6% in the controls (P less than 0.02). IL-2-enhanced NK cell activity was 35.5% versus 37.6% in the controls (n.s.) The proportion of LGL did not differ in the two groups. Median LGL/lymphocytes was 4.0% in the primary SS patients versus 4.5% in the controls (n.s.). We conclude that the defective NK cell activity in patients with primary SS is functional, as the number of LGL is normal. Further the NK cell activity off SS was restored by IL-2.
Applications of the DM-GRASP heuristic: a survey

Recent research has shown that the hybridization of metaheuristics is a powerful mechanism to develop more robust and efficient methods to solve hard optimization problems. The combination of different techniques and concepts behind metaheuristics, if well designed, has the potential to exploit their advantages while diminishing their drawbacks, which results in methods suited to a more diverse set of real problems. The DM-GRASP heuristic is one such hybrid method that has achieved promising results. It is a hybrid version of the GRASP metaheuristic that incorporates a data-mining process. In this work, we review how this hybridization was designed and survey the results of its practical applications evaluated until now.
A Comprehensive Network and Pathway Analysis of Human Deafness Genes

Objective To perform comprehensive network and pathway analyses of the genes known to cause genetic hearing loss. Study Design In silico analysis of deafness genes using ingenuity pathway analysis (IPA). Methods Genes relevant for hearing and deafness were identified through PubMed literature searches and the Hereditary Hearing Loss Homepage. The genes were assembled into 3 groups: 63 genes that cause nonsyndromic deafness, 107 genes that cause nonsyndromic or syndromic sensorineural deafness, and 112 genes associated with otic capsule development and malformations. Each group of genes was analyzed using IPA to discover the most interconnected, that is, “nodal” molecules, within the most statistically significant networks (p < 10−45). Results The number of networks that met our criterion for significance was 1 for Group 1 and 2 for Groups 2 and 3. Nodal molecules of these networks were as follows: transforming growth factor beta1 (TGFB1) for Group 1, MAPK3/MAPK1 MAP kinase (ERK 1/2) and the G protein coupled receptors (GPCR) for Group 2, and TGFB1 and hepatocyte nuclear factor 4 alpha (HNF4A) for Group 3. The nodal molecules included not only those known to be associated with deafness (GPCR), or with predisposition to otosclerosis (TGFB1), but also novel genes that have not been described in the cochlea (HNF4A) and signaling kinases (ERK 1/2). Conclusion A number of molecules that are likely to be key mediators of genetic hearing loss were identified through three different network and pathway analyses. The molecules included new candidate genes for deafness. Therapies targeting these molecules may be useful to treat deafness.
1 WEATHER ADAPTIVE TRAFFIC PREDICTION USING NEURO-WAVELET MODELS

Climate change is a prevalent issue facing the world today. Unexpected increase in rainfall intensity and events is one of the major signatures of climate change. Rainfall influences traffic conditions and in turn traffic volume in urban arterials. For improved traffic management under adverse weather conditions, it is important to develop traffic prediction algorithm considering the effect of rainfall. This inclusion is not intuitive as the effect is not immediate and the influence of rainfall on traffic volume is often unrecognizable in a direct correlation analysis between the two time-series datasets; it can only be observed at certain frequency levels. Accordingly, it is useful to employ a multiresolution prediction framework to develop a weather adaptive traffic forecasting algorithm. Discrete Wavelet Transform (DWT) is a well-known multiresolution data analysis methodology. However, DWT imparts time-variance in the transformed signal and makes it unsuitable for further time-series analysis. Therefore, the stationary form of DWT known as Stationary Wavelet Transform (SWT) has been used in this paper to develop a neuro-wavelet prediction algorithm to forecast hourly traffic flow considering the effect of rainfall. The proposed prediction algorithm has been evaluated at two urban arterial locations in Dublin, Ireland. The study shows that the rainfall data successfully augments the traffic flow data as an exogenous variable in periods of inclement weather, resulting in accurate predictions of future traffic flow at the two chosen locations. The forecasts from the neuro-wavelet model outperform the same from the standard Artificial Neural Network (ANN) model.
A Parallel Algorithm of Frequent Itemsets Mining Based on Bit Matrix

Mining frequent item sets is an important issue in association rules community. This paper proposes a parallel algorithm for mining frequent item sets based on bit matrix. The algorithm reduces the memory space and the I/O overhead, for it scans database only once and builds a compressed bit matrix. It combines both top-down approach and bottom-up approach to improve the efficiency of pruning, and uses dynamic scheduling parallel multi-threaded of OpenMP to mine frequent item sets. The experiments show that this algorithm has higher computing efficiency than Apriori algorithm.
Raised Plasma Aldosterone and Natriuretic Peptides in Atrial Fibrillation

Background and Aims: During atrial fibrillation (AF), the renin-angiotensin-aldosterone system (RAAS) may be activated.In this study, our aim was to evaluate at a long-term follow-up visit the levels of plasma aldosterone and natriuretic peptides as markers of neurohormonal remodeling in patients with earlier, documented AF in relation to present heart rhythm, clinical data, and the left ventricular ejection fraction (LVEF). We hypothesized that increased levels of aldosterone and natriuretic peptides were significantly associated with present AF as markers of RAAS activation during the arrhythmia. Methods: We studied 158 patients with earlier ECG-documented AF followed by restored sinus rhythm (SR) attending a follow-up visit 2.6 years (mean) after primary inclusion. Results: At follow-up, 93 patients had SR. Heart rhythm at follow-up visit (SR/AF), plasma aldosterone, plasma N-terminal pro Brain Natriuretic Peptide (Nt- proBNP), plasma N-terminal pro Atrial Natriuretic Peptide (Nt-proANP), LVEF, medication, and clinical characteristics were recorded. Standard linear multiple regression analysis including age, sex, weight, hypertension, congestive heart failure, ischemic heart disease, present AF at follow-up, total duration of AF disease, ongoing medication, and the LVEF as explanatory variables showed that only ongoing treatment with diuretics was significantly associated (likelihood ratio test, p = 0.0057) with a raised log-transformed plasma aldosterone, although present AF at follow-up was related to a high aldosterone level (p = 0.09). For the natriuretic peptides, present AF at follow-up (p < 0.0001), age (p < 0.0001), female gender (p = 0.0047), ischemic heart disease (p = 0.0154), and ongoing treatment with sotalol (p = 0.0003) were all independently associated with high log-transformed plasma Nt-proANP. Likewise, present AF at follow-up (p = 0.0008) as well as age (p < 0.0001) were associated with high log-transformed plasma Nt-proBNP. Conclusions: In patients with earlier AF, AF at long-term follow-up visit was independently associated with raised levels of Nt-proANP and Nt-proBNP and to some extent with plasma aldosterone indicating neurohormonal activation during arrhythmia.
Fault Tolerant Model Predictive Control applied on the Barcelona Sewer Network

Fault-tolerant model predictive control strategies of sewer networks are investigated in this article. A general linear model of sewer networks is presented and the corresponding expression into MPC formalism are discussed. From FTC and MPC, two strategies are proposed and compared: Passive Fault Tolerant MPC, that takes advantage of natural tolerance (robustness) of MPC and Active Fault Tolerant MPC, that uses active fault tolerance techniques in combination with MPC. Finally, some results are obtained applying these strategies over a portion of the Barcelona sewage network.
Dose‐dependent biphasic effects of phenobarbital on growth and differentiation of primary culture rat hepatocytes

The actions of phenobarbital, a liver tumour promoter, on growth and differentiation of primary culture normal rat hepatocytes change biphasically as a function of its concentration. At low concentrations of 0.5–2 mmol/L, phenobarbital enhances DNA synthesis of normal adult rat hepatocytes in the presence of epidermal growth factor (EGF) and/or dexamethasone. This is also true for normal suckling (1–2‐week‐old) rat hepatocytes, without added growth factor(s), in serum‐free primary culture. Contrarily, phenobarbital at high concentrations (3–4 mmol/L) suppresses DNA synthesis of suckling rat hepatocytes. Furthermore, phenobarbital inhibits DNA synthesis of transforming growth factor‐a‐stimulated primary hepatocytes from normal adult rats in a dose‐dependent manner within a concentration range of 3–6 mmol/L. When normal adult rat hepatocytes are led to undergo multiple proliferative cycles upon stimulation with hepatocyte growth factor (HGF) and EGF in the chemically defined hepatocyte growth medium (HGM), 3 mmol/L phenobarbital also remarkably suppresses DNA synthesis. Phenobarbital at 3 mmol/L effectively keeps these hepatocytes morphologically differentiated and accelerates restoration of the expression of markers characteristic of differentiated cells after the initial cellular growth phase. In addition, phenobarbital efficiently supports prolonged survival of the hepatocytes.
Benchmarking Blocking Algorithms for Web Entities

An increasing number of entities are described by interlinked data rather than documents on the Web. Entity Resolution (ER) aims to identify descriptions of the same real-world entity within one or across knowledge bases in the Web of data. To reduce the required number of pairwise comparisons among descriptions, ER methods typically perform a pre-processing step, called blocking, which places similar entity descriptions into blocks and thus only compare descriptions within the same block. We experimentally evaluate several blocking methods proposed for the Web of data using real datasets, whose characteristics significantly impact their effectiveness and efficiency. The proposed experimental evaluation framework allows us to better understand the characteristics of the missed matching entity descriptions and contrast them with ground truth obtained from different kinds of relatedness links.
GPUguard: Towards supporting a predictable execution model for heterogeneous SoC

The deployment of real-time workloads on commercial off-the-shelf (COTS) hardware is attractive, as it reduces the cost and time-to-market of new products. Most modern high-end embedded SoCs rely on a heterogeneous design, coupling a general-purpose multi-core CPU to a massively parallel accelerator, typically a programmable GPU, sharing a single global DRAM. However, because of non-predictable hardware arbiters designed to maximize average or peak performance, it is very difficult to provide timing guarantees on such systems. In this work we present our ongoing work on GPUguard, a software technique that predictably arbitrates main memory usage in heterogeneous SoCs. A prototype implementation for the NVIDIA Tegra TX1 SoC shows that GPUguard is able to reduce the adverse effects of memory sharing, while retaining a high throughput on both the CPU and the accelerator.
Eye state prediction using ensembled machine learning models

As electric signals are transmitted between the brain cell s for transferring of data within the brain, capturing of these signals can result in understanding the functionality of brain and other directly linked parts (like eyes, ears, spinal nerves etc) of our body. We can also capture epileptic seizures that are caused by a disruption in the working of brain, by the Electro Encephalogram Test. These electric signals are to be captured by small electrodes placed on human scalp using a standard 10/20 system on an Electro Encephalograph monitor. In this work, we will predict the state of eye (open or closed) by exploring 13 machine learning models on a 15 features dataset of an EEG test. The records of 14 electrodes are used for this prediction. Results are evaluated using 6 different machine learning parameters i.e. Sensitivity, Confusion matrix, Kappa value, Specificity, Accuracy and Receiver Operating Characteristics (ROC) curve. K-fold validation and ensembling of models will be done on best three predictive models pertaining to our dataset.
Obesity may influence the relationship between sex hormones and lower urinary tract symptoms.

PURPOSE The effects of serum testosterone in the lower urinary tract symptoms (LUTS) in patients with benign prostatic hyperplasia (BPH) are not well established. The objective of the study is to evaluate the association of sex hormones with LUTS and control the results by patient weight. MATERIALS AND METHODS The study comprised a cross-sectional analysis of 725 men included in a prostate cancer screening program at University of Sao Paulo Medical School. The serum concentrations of total testosterone (TT), free testosterone (FT) and sex hormone binding globulin (SHBG) were measured. Variables analyzed were age, American Urological Association (AUA) symptom score, storage symptoms, voiding symptoms, quality of life score, prostate specific antigen levels and prostate volume. Obesity was measured through the calculation of body mass index (BMI). A regression analysis model was performed. RESULTS Median patient age was 65 years (48 to 94). A higher TT level was significantly associated with a severe AUA symptom score only among patients with a BMI ≥ 25. Median TT was 371, 370 and 427ng/dL (p = 0.017) in patients with mild, moderate and severe LUTS respectively. The multivariate regression analysis in patients with BMI ≥ 25 showed that only age, TT and sex score were related to LUTS. CONCLUSIONS A higher TT is associated with a severe AUA score symptom index only in obese patients. Further analysis are necessary to evaluate the mechanisms through which testosterone may influence LUTS in these patients.
Managing Cardiovascular Risk in Patients With HIV Infection

Highly active antiretroviral therapy (HAART) improves the survival of patients with HIV infection; however, several observational studies have described associations between HIV infection, HAART, and cardiovascular disease. Important limitations of these studies included a low incidence of cardiovascular events, short duration of HAART exposure, and retrospective design. Nevertheless, the weight of evidence from observational and surrogate end point studies suggests that the dyslipidemia and other metabolic changes that are common in patients with HIV infection and those using HAART may be associated with increased cardiovascular risk. The Infectious Disease Society of America/Adults AIDS Clinical Trials Group guidelines for the evaluation and management of dyslipidemia recommend target lipid levels and treatment of dyslipidemia in patients with HIV infection. Although practitioners should consider dyslipidemia and cardiovascular risk when making plans for initiating or altering HAART therapy, maintaining viremic control should be the overriding factor, because short-term absolute rates of cardiovascular disease are significantly lower than death rates from AIDS in inadequately suppressed patients. This article reviews the cardiovascular risks in patients receiving HAART and discusses the implementation of the new guidelines.
Cáncer de mama Modalidades terapéuticas y marcadores tumorales

El cancer de mama sigue siendo actualmente un problema de salud publica que amenaza a las mujeres chilenas, sobre todo despues de los 40 anos de edad. Constituye el cancer mas frecuente de la mujer occidental, siendo la principal causa de muerte en Europa, Estados Unidos de Norteamerica, Australia y la segunda causa de muerte por cancer en la mujer en Chile, superado solo por el cancer de vesicula biliar. Una vez hecho el diagnostico, basandonos en antecedentes clinicos, examen fisico e imagenologia complementaria, debemos optar por el mejor tratamiento. Para ello contamos con terapia local, dentro de la cual podemos nombrar la cirugia, la cual sera conservadora o no conservadora dependiendo de la etapa en la cual se haya diagnosticado el cancer y la radioterapia (RDT). Ademas, existe la terapia sistemica, donde tenemos la quimioterapia (QMT) y la hormonoterapia, la cual se basa en la gran hormonodependencia de esta neoplasia. Actualmente se ha investigado el rol de ciertos marcadores tumorales, los cuales son utilizados actualmente como factores pronosticos, para determinar riesgo de recidiva, realizar seguimiento post tratamiento y evaluar respuesta al tratamiento. (Palabras claves/