Heart Rate Variability based Classification of Normal and Hypertension Cases by Linear-nonlinear Method

The aim of this study is to analyse and compare the heart rate variability (HRV) of normal and hypertension cases using time domain, frequency domain, and nonlinear methods. For short term HRV analysis, a five-minute electrocardiogram (ECG) of 57 normal and 56 hypertension subjects were recorded with prior verification of their clinical status by a cardiologist. Most time domain features of hypertension cases have clearly reduced values over normal subjects, frequency domain features, like power in different spectral bands, also have the distinguishable decreased values, whereas sympathovagal balance has clear edge over hypertension cases than normal cases. Nonlinear parameters of Poincare plot, approximate entropy and sample entropy, have higher values in normal cases when compared with hypertension cases. Support vector machine-based binary system classifies these two classes with 100 per cent accuracy and 100 per cent sensitivity when all time domain, frequency domain, and nonlinear features were used. It may work as a better predictor for in patients with hypertension.Science Journal, Vol. 64, No. 6, November 2014, pp.542-548, DOI:http://dx.doi.org/10.14429/dsj.64.786

Applying Machine Learning Techniques for Type2 Diabetes Readmission Prediction Based on Retrospective Data

Roughly 9.3% of US population suffer from diabetes and the 30 day readmission rate for diabetes patients range between 14.4 to 22.7%. Hence identifying the risk of readmission is a crucial information for the service providers to not only reduce the healthcare cost but also improve the quality of patient care. This paper models machine learning algorithms to compute probability of 30-day hospital readmission for type2 diabetes patients. Along with novel pre-processing techniques to identify the challenges of noisy and non-homogenized medical data, we used methods to downsize the feature vector size without sacrificing prediction accuracy. Our method has been implemented on a publicly available dataset from University of California Irvine at https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008 which summarizes data from 130 US hospitals within span of 10 years with roughly 100,000 patients

Phase Ib study of the combination of pexidartinib (PLX3397), a CSF-1R inhibitor, and paclitaxel in patients with advanced solid tumors.

Purpose:To evaluate the safety, recommended phase II dose (RP2D) and efficacy of pexidartinib, a colony stimulating factor receptor 1 (CSF-1R) inhibitor, in combination with weekly paclitaxel in patients with advanced solid tumors. Patients and Methods:In part 1 of this phase Ib study, 24 patients with advanced solid tumors received escalating doses of pexidartinib with weekly paclitaxel (80 mg/m2). Pexidartinib was administered at 600 mg/day in cohort 1. For subsequent cohorts, the dose was increased by ⩽50% using a standard 3+3 design. In part 2, 30 patients with metastatic solid tumors were enrolled to examine safety, tolerability and efficacy of the RP2D. Pharmacokinetics and biomarkers were also assessed. Results:A total of 51 patients reported ≥1 adverse event(s) (AEs) that were at least possibly related to either study drug. Grade 3-4 AEs, including anemia (26%), neutropenia (22%), lymphopenia (19%), fatigue (15%), and hypertension (11%), were recorded in 38 patients (70%). In part 1, no maximum tolerated dose was achieved and 1600 mg/day was determined to be the RP2D. Of 38 patients evaluable for efficacy, 1 (3%) had complete response, 5 (13%) partial response, 13 (34%) stable disease, and 17 (45%) progressive disease. No drug-drug interactions were found. Plasma CSF-1 levels increased 1.6- to 53-fold, and CD14dim/CD16+ monocyte levels decreased by 57-100%. Conclusions:The combination of pexidartinib and paclitaxel was generally well tolerated. RP2D for pexidartinib was 1600 mg/day. Pexidartinib blocked CSF-1R signaling, indicating potential for mitigating macrophage tumor infiltration

Toward Silicon-Matched Singlet Fission: Energy-Level Modifications Through Steric Twisting of Organic Semiconductors

Singlet fission (SF) is a potential avenue for augmenting the performance of silicon photovoltaics, but the scarcity of SF materials energy-matched to silicon represents a barrier to the commercial realization of this technology. In this work, a molecular engineering approach is described to increase the energy of the S1 and T1 energy levels of diketopyrrolopyrrole derivatives such that the energy-level requirements for exothermic SF and energy-transfer to silicon are met. Time-resolved photoluminescence studies show that the silicon-matched materials are SF active in the solid state, forming a correlated triplet pair 1(TT) – a crucial intermediate in the SF process – as observed through Herzberg-Teller emission from 1(TT) at both 77 K and room temperature. Transient electron paramagnetic resonance studies show that the correlated triplet pair does not readily separate into the unbound triplets, which is a requirement for energy harvesting by silicon. The fact that the triplet pair do not separate into free triplets is attributed to the intermolecular crystal packing within the thin films. Nevertheless, these results demonstrate a promising route for energy-tuning silicon-matched SF materials

Testing Multi-Theory Model (MTM) in Predicting Initiation and Sustenance of Physical Activity Behavior Among College Students

Background: Most college students do not adequately participate in enough physical activity (PA) to attain health benefits. A theory-based approach is critical in developing effective interventions to promote PA. The purpose of this study was to examine the utility of the newly proposed multi-theory model (MTM) of health behavior change in predicting initiation and sustenance of PA among college students. Methods: Using a cross-sectional design, a valid and reliable survey was administered in October 2015 electronically to students enrolled at a large Southern US University. The internal consistency Cronbach alphas of the subscales were acceptable (0.65-0.92). Only those who did not engage in more than 150 minutes of moderate to vigorous intensity aerobic PA during the past week were included in this study. Results: Of the 495 respondents, 190 met the inclusion criteria of which 141 completed the survey. The majority of participants were females (72.3%) and Caucasians (70.9%). Findings of the confirmatory factor analysis (CFA) confirmed construct validity of sub-scales (initiation model: χ2 = 253.92 [df = 143], P \u3c 0.001, CFI = 0.91, RMSEA = 0.07, SRMR = 0.07; sustenance model: χ2= 19.40 [df = 22], P \u3c 0.001, CFI = 1.00, RMSEA = 0.00, SRMR = 0.03). Multivariate regression analysis showed that 26% of the variance in the PA initiation was explained by advantages outweighing disadvantages, behavioral confidence, work status, and changes in physical environment. Additionally, 29.7% of the variance in PA sustenance was explained by emotional transformation, practice for change, and changes in social environment. Conclusion: Based on this study\u27s findings, MTM appears to be a robust theoretical framework for predicting PA behavior change. Future research directions and development of suitable intervention strategies are discussed

trans-Dibromidobis(triphenyl­phosphane)platinum(II) chloro­form monosolvate

Both the platininum complex and the solvent mol­ecule of the title compound, [PtBr2(C18H15P)2]·CHCl3, are located on a twofold rotation axis. The CH unit and the Cl atoms of the CHCl3 mol­ecule are disordered over two equally occupied positions. The complex shows a trans square-planar geometry about the Pt atom

Determinants and implications of the growing scale of livestock farms in four fast-growing developing countries:

"The rapid growth in consumer demand for livestock offers an opportunity to reduce poverty among smallholder livestock farmers in the developing world. These farmers' opportunity may be threatened, however, by competition from larger-scale farms. This report assesses the potential threat, examining various forms of livestock production in Brazil, India, the Philippines, and Thailand. Findings show that the competitiveness of smallholder farms depends on the opportunity cost of family labor and farmers' ability to overcome barriers to the acquisition of production- and market-related information and assets. Pro-poor livestock development depends, therefore, on the strengthening of institutions that will help smallholders overcome the disproportionately high transaction costs in securing quality inputs and obtaining market recognition for quality outputs. These and other findings make this report a useful guide for researchers and others concerned with the opportunities and risks of smallholder livestock farming." from Authors' SummaryDeveloping countries, Economic aspects, Industrialization, Profit efficiency, Environmental externalities, Smallholder competitiveness, Livestock productivity, Livestock Industrialization, Scaling up,

Procjena prizemnog neto Sunčevog zračenja iz podataka s tornja za mjerenje turbuletnih tokova iznad tropske šume mangrova u Sundarbanu, Zapadni Bengal

In this study, net surface radiation (Rn) was estimated using artificial neural network (ANN) and Linear Model (LM). Then, estimated Rn with both the models (ANN and LM) were compared with measured Rn from eddy covariance (EC) flux tower. The routinely measured meteorological variables namely air temperature, relative humidity and wind velocity were used as input to the ANN and global solar radiation as input to the LM. All the input data are from the EC flux tower. Sensitivity analysis of ANN with all the meteorological variables is carried out by excluding one by one meteorological variable. The validation results demonstrated that, ANN and LM estimated Rn values were in good agreement with the measured values, with root mean square error (RMSE) varying between 21.63 W/m2 and 34.94 W/m2, mean absolute error (MAE) between 17.93 W/m2 and 22.28 W/m2 and coefficient of residual mass (CRM) between –0.007 and –0.04 respectively. Further we have computed modelling efficiency (0.97 for ANN and 0.99 for LM) and coefficient of determination (R2 = 0.97 for ANN and 0.99 for LM) for both the models. Even though both the models could predict Rn successfully, ANN was better in terms of minimum number of routinely measured meteorological variables as input. The results of the ANN sensitivity analysis indicated that air temperature is the more important parameter followed by relative humidity, wind speed and wind direction.U ovom je istraživanju pomoću umjetnih neuronskih mreža (ANN) i linearnog modela (LM) procijenjeno prizemno neto Sunčevo zračenje (Rn). Potom su tako procjenjeni Rn iz oba modela (ANN i LM) uspoređeni s onima izmjerenim na tornju za mjerenje kovarijance turbuluentnih tokova (EC). Kao ulazni podaci u ANN korišteni su rutinski mjerene meteorološke varijable (temperatura zraka, relativna vlaga i brzina vjetra), a za LM globalno Sunčevo zračenje, koji su dobiveni na meteorološkom tornju za mjerenje turbulentnih tokova. Uslijedila je analiza osjetljivosti ANN s uključenim svim meteorološkim varijablama te su testirani ANN iz kojih su isključeni jedna po jedna meteorološka varijabla. Rezultati validacije pokazuju da se Rn procjenjeni pomoću ANN i LM dobro slažu s izmjerenim vrijednostima, pri čemu korijen srednje kvadratne pogreške (RMSE) varira između 21,63 W/m2 i 34,94 W/m2, srednja apsolutna pogreška (MAE) između 17,93 W/m2 i 22,28 W/m2, a koeficijent preostale mase (CRM) između –0,007 i –0,04 respektivno. Nadalje smo izračunali učinkovitost modeliranja (0,97 za ANN i 0,99 za LM) i koeficijente korelacije (R2 = 0,97 za ANN i 0,99 za LM). Iako su oba modela mogla uspješno predvidjeti Rn, ANN je bio bolji u smislu korištenja minimalnog broja rutinski izmjerenih meteoroloških varijabli kao ulaza. Rezultati analize osjetljivosti ANN pokazali su da je temperatura zraka najvažniji ulazni parametar, koju slijede relativna vlažnost te brzina i smjer vjetra