Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals

Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable operation. Based on a continuous acquisition of signals with a data acquisition system, it is possible to classify the faults. This is achieved by the extraction of features from the measured data and employing data mining approaches to explore the structural information hidden in the signals acquired. In the present study, statistical features derived from the vibration data are used as the features. In order to increase the robustness of the classifier and to reduce the data processing load, dimensionality reduction is necessary. In this paper dimensionality reduction is performed using traditional dimensionality reduction techniques and nonlinear dimensionality reduction techniques. The effectiveness of each dimensionality reduction technique is also verified using visual analysis. The reduced feature set is then classified using a decision tree. The results obtained are compared with those generated by classifiers such as Naïve Bayes, Bayes Net and kNN. The effort is to bring out the better dimensionality reduction technique–classifier combination

TLFS23 Tamil language fingerspelling dataset

Tamil is one of the oldest existing languages, spoken by around 65 million people across India, Sri Lanka and South-East Asia. Countries such as Fiji and South Africa also have a significant population with Tamil ancestry. Tamil is a complex language and has 247 characters. A labelled dataset for Tamil Fingerspelling named TLFS23 has been created for research related to vision-based Fingerspelling translators for the Speech and hearing Impaired. The dataset would open up avenues to develop automated systems as translators and interpreters for effective communication between fingerspelling language users and non- users, using computer vision and deep learning algorithms. One thousand images representing each unique finger flexion motion for every Tamil character was collected overall constituting a large dataset with 248 classes with a total of 2,55,155 images. The images were contributed by 120 individuals from different age groups. The dataset is made publicly available at: https://data.mendeley.com/datasets/39kzs5pxmk/2

A Stock Trading Recommender System Based on Temporal Association Rule Mining

Recommender systems capable of discovering patterns in stock price movements and generating stock recommendations based on the patterns thus discovered can significantly supplement the decision-making process of a stock trader. Such recommender systems are of great significance to a layperson who wishes to profit by stock trading even while not possessing the skill or expertise of a seasoned trader. A genetic algorithm optimized Symbolic Aggregate approXimation (SAX)–Apriori based stock trading recommender system, which can mine temporal association rules from the stock price data set to generate stock trading recommendations, is presented in this article. The proposed system is validated on 12 different data sets. The results indicate that the proposed system significantly outperforms the passive buy-and-hold strategy, offering scope for a layperson to successfully invest in capital markets

Improving Returns from the Markowitz Model using GA- AnEmpirical Validation on the BSE

Abstract—Portfolio optimization is the task of allocating the investors capital among different assets in such a way that the returns are maximized while at the same time, the risk is minimized. The traditional model followed for portfolio optimization is the Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear constraints, can be solved using quadratic programming, however, in real-life scenario, the presence of nonlinear constraints such as limits on the number of assets in the portfolio, the constraints on budgetary allocation to each asset class, transaction costs and limits to the maximum weightage that can be assigned to each asset in the portfolio etc., this problem becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing based approaches seem best suited for solving such a problem. An attempt has been made in this study to use soft computing technique (specifically, Genetic Algorithms), to overcome this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters of the Markowitz model such that overall portfolio returns are maximized with the standard deviation of the returns being minimized at the same time. The proposed system is validated by testing its ability to generate optimal stock portfolios with high returns and low standard deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange (BSE). Results show that the proposed system is able to generate much better portfolios when compared to the traditional Markowitz model. Index Terms—Genetic Algorithm, portfolio optimization, Markowitz, BSE I

Contrasting effects of engineered carbon nanotubes on plants: a review

Rapid surge of interest for carbon nanotube (CNT) in the last decade has made it an imperative member of nanomaterial family. Because of the distinctive physicochemical properties, CNTs are widely used in a number of scientific applications including plant sciences. This review mainly describes the role of CNT in plant sciences. Contradictory effects of CNT on plants physiology are reported. CNT can act as plant growth inducer causing enhanced plant dry biomass and root/shoot lengths. At the same time, CNT can cause negative effects on plants by forming reactive oxygen species in plant tissues, consequently leading to cell death. Enhanced seed germination with CNT is related to the water uptake process. CNT can be positioned as micro-tubes inside the plant body to enhance the water uptake efficiency. Due to its ability to act as a slow-release fertilizer and plant growth promoter, CNT is transpiring as a novel nano-carbon fertilizer in the field of agricultural sciences. On the other hand, accumulation of CNT in soil can cause deleterious effects on soil microbial diversity, composition and population. It can further modify the balance between plant-toxic metals in soil, thereby enhancing the translocation of heavy metal(loids) into the plant system. The research gaps that need careful attention have been identified in this review