Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time

A procedure for the change point problem in parametric models based on phi-divergence test-statistics

This paper studies the change point problem for a general parametric, univariate or multivariate family of distributions. An information theoretic procedure is developed which is based on general divergence measures for testing the hypothesis of the existence of a change. For comparing the accuracy of the new test-statistic a simulation study is performed for the special case of a univariate discrete model. Finally, the procedure proposed in this paper is illustrated through a classical change-point example

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Early formation of carbon monoxide in the Centaurus A supernova SN 2016adj

We present near-infrared spectroscopy of the NGC 5128 supernova SN 2016adj in the first 2 months following discovery. We report the detection of first overtone carbon monoxide emission at ∼58.2 d after discovery, one of the earliest detections of CO in an erupting supernova. We model the CO emission to derive the CO mass, temperature and velocity, assuming both pure 12CO and a composition that includes 13CO; the case for the latter is the isotopic analyses of meteoritic grains, which suggest that core collapse supernovae can synthesise significant amounts of 13C. Our models show that, while the CO data are adequately explained by pure 12CO, they do not preclude the presence of 13CO, to a limit of 12C/13C>3, the first constraint on the 12C/13C ratio determined from near-infrared observations. We estimate the reddening to the object, and the effective temperature from the energy distribution at outburst. We discuss whether the ejecta of SN 2016adj may be carbon-rich, what the infrared data tell us about the classification of this supernova, and what implications the early formation of CO in supernovae may have for CO formation in supernovae in general

The use of medicinal plants in health care practices by Rohingya refugees in a degraded forest and conservation area of Bangladesh

People in developing countries traditionally rely on plants for their primary healthcare. This dependence is relatively higher in forests in remote areas due to the lack of access to modern health facilities and easy availability of the plant products.We carried out an ethno-medicinal survey in Teknaf Game Reserve (TGR), a heavily degraded forest and conservation area in southern Bangladesh, to explore the diversity of plants used by Rohingya refugees for treating various ailments. The study also documented the traditional utilization, collection and perceptions of medicinal plants by the Rohingyas residing on the edges of this conservation area. We collected primary information through direct observation and by interviewing older respondents using a semi-structured questionnaire. A total of 34 plant species in 28 families were frequently used by the Rohingyas to treat 45 ailments, ranging from simple headaches to highly complex eye and heart diseases. For medicinal preparations and treating various ailments, aboveground plant parts were used more than belowground parts. The collection of medicinal plants was mostly from the TGR. © 2009 Taylor & Francis

“Knowledge About Tuberculosis And Its Treatment Among Nursing Students At Selected Colleges, Kanpur U.P.”

Nurses play an important role in health sector. Tuberculosis is an infectious disease and spread from person to person via droplets, in order to prevent and control Tuberculosis, community participation is needed on priority basis. Health workers create awareness among people and motivate them to participate in national health programs run by government for prevention and control of various communicable, non-communicable diseases, welfare programs etc. TB control strategies will be successful when health workers will be well known about this disease, Direct Observation Therapy and administration of anti-tuberculosis medications, its side effects etc. The purpose of this study was to assess knowledge of nursing students about Tuberculosis and its treatment at selected colleges, UP. It was a descriptive cross-sectional study 160 nursing students were involved in this study. Data were collected through questionnaires, which include demographics variables and knowledge based questionnaires with yes–no options related to TB and its treatmen

Do brain networks evolve by maximizing their information flow capacity?

We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization

The conserved C-terminus of the PcrA/UvrD helicase interacts directly with RNA polymerase

Copyright: © 2013 Gwynn et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by a Wellcome Trust project grant to MD (Reference: 077368), an ERC starting grant to MD (Acronym: SM-DNA-REPAIR) and a BBSRC project grant to PM, NS and MD (Reference: BB/I003142/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

"Understanding the Role of Hypertension in Atherosclerosis and Myocardial Infarction: Implications for Prevention and Management"

Introduction: Myocardial Infarction (MI), or heart attack, is a global health concern and a leading cause of cardiovascular mortality. MI occurs due to the sudden interruption of blood supply to a portion of the heart muscle, primarily caused by the occlusion of coronary arteries. This critical condition is influenced by various risk factors, including hypertension, hyperlipidemia, smoking, and diabetes. Among these factors, hypertension plays a central role in the development and progression of atherosclerosis, a key precursor to MI. This manuscript provides a comprehensive understanding of how hypertension interacts with atherosclerosis and MI and explores the implications for prevention and management. Methods: The manuscript begins with an introduction highlighting the significance of MI, its high mortality rate, sudden onset, complications, chronic consequences, risk factors, global prevalence, and the importance of awareness and prevention. It also underscores the need for a detailed examination of the pathophysiological journey from atherosclerosis to MI to enhance our knowledge and improve strategies for diagnosis, treatment, and prevention. Results: The manuscript then delves into a comprehensive analysis of risk factors, with a focus on hypertension. It explains how hypertension leads to endothelial damage, dysfunction, inflammation, and oxidative stress, promoting the initiation and progression of atherosclerosis. The synergistic effects of risk factors and their interrelationships are highlighted, emphasizing the importance of addressing multiple risk factors in atherosclerosis prevention. Discussion: Preventive measures and management strategies, including public health campaigns, smoking cessation programs, dietary interventions, physical activity promotion, and regular health checkups, are discussed in the context of reducing cardiovascular risk. The manuscript emphasizes the role of patient education and awareness in recognizing risk factors and symptoms, seeking prompt medical attention, and adhering to heart-healthy behaviors. Conclusion: In conclusion, this manuscript underscores the complex relationship between hypertension, atherosclerosis, and MI and highlights the significance of comprehensive preventive strategies. Educating individuals about the risk factors and symptoms of heart disease is essential for reducing the overall burden of cardiovascular diseases and promoting cardiovascular health. This multi-pronged approach, involving healthcare providers, public health agencies, policymakers, and informed individuals, holds the key to improving cardiovascular outcomes and reducing the incidence of MI