scholarly journals Cluster Analysis for Identifying Obesity Subrouops in Health and Nutritional Status Survey Data

2021 ◽  
Vol 10 (02) ◽  
pp. 146-169
Author(s):  
Usman Khalil ◽  
Owais Ahmed Malik ◽  
Daphne Teck Ching Lai ◽  
Ong Sok King
Keyword(s):  
Physical Activity ◽  
Survey Data ◽  
Hierarchical Clustering ◽  
Economic Status ◽  
Dietary Behavior ◽  
The Other ◽  
Lifestyle Factor ◽  
Affecting Factors ◽  
Data Set ◽  
Clustering Techniques

This study presents the discovery of meaningful patterns (groups) from the obese samples of health and nutritional survey data by applying various clustering techniques. Due to the mixed nature of the data (qualitative and quantitative variables) in the data set, the best-suited clustering techniques with appropriate dissimilarity metrics were chosen to interpret the meaningful results. The relationships between obesity and the lifestyle affecting factors like demography, socio-economic status, physical activity, and dietary behavior were assessed using four cluster techniques namely Two-Step clustering, Partition Around Medoids (PAM), Agglomerative Hierarchical clustering and, Kohonen Self Organizing Maps (SOMs). The solutions generated by these techniques were analyzed and validated by the help of cluster validity (CV) indices and later on their associations were determined with the obesity classes to discover the pattern from the obese sample. Two-Step clustering and hierarchical clustering outperformed the other applied techniques in identifying the subgroups based on the underlying hidden patterns in the data. Based on the CV indices values and the association analysis (obesity factor with the cluster solutions), two subgroups were generated and profiles of these groups have been reported. The first group belonged to the middle-aged individuals who seem to take care of their lifestyle while the other group belonged to young-aged individuals who in contrast to the first group presented a careless lifestyle factor (i.e., physical activity and dietary behavior). The salient features of these subgroups have been reported and can be proposed for the betterment in the health care industry. The research helped in identifying the interesting subsets/groups within survey data demonstrating similar characteristics and health status (i.e., prevalence of obesity with respect to lifestyle factors like physical activity, dietary behavior etc.) which will help to suggest appropriate measures/steps to be taken by the concerned departments to counter them and prevent in the population.

Clustering Methods in Game Data Science

2021 ◽  
pp. 133-178
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Hierarchical Clustering ◽  
Data Science ◽  
The Other ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Model Based Clustering ◽  
Fuzzy C Means ◽  
Model Based

This chapter discusses different clustering methods and their application to game data. In particular, the chapter details K-means, Fuzzy C-Means, Hierarchical Clustering, Archetypical Analysis, and Model-based clustering techniques. It discusses the disadvantages and advantages of the different methods and discusses when you may use one method vs. the other. It also identifies and shows you ways to visualize the results to make sense of the resulting clusters. It also includes details on how one would evaluate such clusters or go about applying the algorithms to a game dataset. The chapter includes labs to delve deeper into the application of these algorithms on real game data.

Haematocrit: Relationships with Blood Lipids, Blood Pressure and other Cardiovascular Risk Factors

1994 ◽  
Vol 72 (01) ◽  
pp. 058-064 ◽  
Author(s):  
Goya Wannamethee ◽  
A Gerald Shaper
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Blood Pressure ◽  
Body Mass Index ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Alcohol Intake ◽  
Blood Lipids ◽  
The Other ◽  
The Relationship

SummaryThe relationship between haematocrit and cardiovascular risk factors, particularly blood pressure and blood lipids, has been examined in detail in a large prospective study of 7735 middle-aged men drawn from general practices in 24 British towns. The analyses are restricted to the 5494 men free of any evidence of ischaemic heart disease at screening.Smoking, body mass index, physical activity, alcohol intake and lung function (FEV1) were factors strongly associated with haematocrit levels independent of each other. Age showed a significant but small independent association with haematocrit. Non-manual workers had slightly higher haematocrit levels than manual workers; this difference increased considerably and became significant after adjustment for the other risk factors. Diabetics showed significantly lower levels of haematocrit than non-diabetics. In the univariate analysis, haematocrit was significantly associated with total serum protein (r = 0*18), cholesterol (r = 0.16), triglyceride (r = 0.15), diastolic blood pressure (r = 0.17) and heart rate (r = 0.14); all at p <0.0001. A weaker but significant association was seen with systolic blood pressure (r = 0.09, p <0.001). These relationships remained significant even after adjustment for age, smoking, body mass index, physical activity, alcohol intake, lung function, presence of diabetes, social class and for each of the other biological variables; the relationship with systolic blood pressure was considerably weakened. No association was seen with blood glucose and HDL-cholesterol. This study has shown significant associations between several lifestyle characteristics and the haematocrit and supports the findings of a significant relationship between the haematocrit and blood lipids and blood pressure. It emphasises the role of the haematocrit in assessing the risk of ischaemic heart disease and stroke in individuals, and the need to take haematocrit levels into account in determining the importance of other cardiovascular risk factors.

The Determinants of Capital Adequacy Ratio: The Case of the Vietnamese Banking System in the Period 2011-2015

2017 ◽  
Vol 33 (2) ◽  
Author(s):  
Ngoc Anh Nguyen
Keyword(s):  
Banking System ◽  
Precious Metals ◽  
Capital Adequacy ◽  
The Other ◽  
Total Asset ◽  
Data Set ◽  
Hand Size ◽  
Net Interest Margin ◽  
Capital Adequacy Ratio ◽  
Positive Effect

The analysis of a data set of observation for Vietnamese banks in period from 2011 - 2015 shows how Capital Adequacy Ratio (CAR) is influenced by selected factors: asset of the bank SIZE, loans in total asset LOA, leverage LEV, net interest margin NIM, loans lost reserve LLR, Cash and Precious Metals in total asset LIQ. Results indicate based on data that NIM, LIQ have significant effect on CAR. On the other hand, SIZE and LEV do not appear to have significant effect on CAR. Variables NIM, LIQ have positive effect on CAR, while variables LLR and LOA are negatively related with CAR.

Pre-eminence of Moderate to Robust Physical Activity in Battling COVID-19: A Narrative Review

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 934-937
Author(s):  
Tasneem M. Lakkadsha ◽  
Kiran Kumar ◽  
Waqar M. Naqvi ◽  
Pratik Phansopkar
Keyword(s):  
Physical Activity ◽  
Mass Media ◽  
Physical Inactivity ◽  
Narrative Review ◽  
The Other ◽  
Physically Active ◽  
Corona Virus ◽  
Media Platform ◽  
Being There ◽  
History Books

In January 2020, we met with COVID-19 (aka SARS-Co-V-2 and/or Corona virus) on our news channels all the way from china. Little did we know that it would shake up our lives in such a manner that we had heard only in a movie or read in history books. Currently we are all in some sort of lockdown, be it in hospital/home or in our minds. Being there, most of us are facing certain kind of misery, be it emotional, mental, physical or social. To be expansive the most common stresses that have been addressed by people on mass media platform are feeling of depression and isolation caused by being away from family and friends, some are complaining of losing their enthusiasm, some of gaining weight, some of losing it and many more. Going through a pandemic is also helping people in some or the other way, one of which is being concerned about their health and habits to keep themselves fit and away from serious comorbidities which can stem out from physical inactivity and heightened stress levels. There are many ways to stay fit at home without any complex gym equipment, but far less is known about it. Thus, an understanding of methods through which one can become physically active with least complexity, easy availability, and appropriate utilization is need of the hour.

HCVS: Pinpointing Chromatin States Through Hierarchical Clustering and Visualization Scheme

2019 ◽  
Vol 14 (2) ◽  
pp. 148-156
Author(s):  
Nighat Noureen ◽  
Sahar Fazal ◽  
Muhammad Abdul Qadir ◽  
Muhammad Tanvir Afzal
Keyword(s):  
Hierarchical Clustering ◽  
Real Data ◽  
Cell Types ◽  
Computational Scheme ◽  
Data Set ◽  
Chromatin States ◽  
Functional Regions ◽  
Visualization Strategy ◽  
Hidden States ◽  
Next Generation Sequencing Ngs

Background: Specific combinations of Histone Modifications (HMs) contributing towards histone code hypothesis lead to various biological functions. HMs combinations have been utilized by various studies to divide the genome into different regions. These study regions have been classified as chromatin states. Mostly Hidden Markov Model (HMM) based techniques have been utilized for this purpose. In case of chromatin studies, data from Next Generation Sequencing (NGS) platforms is being used. Chromatin states based on histone modification combinatorics are annotated by mapping them to functional regions of the genome. The number of states being predicted so far by the HMM tools have been justified biologically till now. Objective: The present study aimed at providing a computational scheme to identify the underlying hidden states in the data under consideration. </P><P> Methods: We proposed a computational scheme HCVS based on hierarchical clustering and visualization strategy in order to achieve the objective of study. Results: We tested our proposed scheme on a real data set of nine cell types comprising of nine chromatin marks. The approach successfully identified the state numbers for various possibilities. The results have been compared with one of the existing models as well which showed quite good correlation. Conclusion: The HCVS model not only helps in deciding the optimal state numbers for a particular data but it also justifies the results biologically thereby correlating the computational and biological aspects.

Designing for Dissemination in Chronic Disease Prevention and Management

2017 ◽  
Author(s):  
Neville Owen ◽  
Ana Goode ◽  
Takemi Sugiyama ◽  
Mohammad Javad Koohsari ◽  
Genevieve Healy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Chronic Disease ◽  
Cost Effectiveness ◽  
Health Behavior Change ◽  
The Other ◽  
Research Quality ◽  
Policy Initiatives ◽  
Rigorous Study ◽  
Behavior Change Program

This chapter emphasizes the need for research that is designed and implemented explicitly with dissemination in mind. This is illustrated in relation to environmental and policy initiatives to influence physical activity through active transport, and through the example of initiatives to reduce workplace sitting. The other element of this chapter, the broad-reach intervention-dissemination case study of a health behavior-change program, highlights the need to maintain key elements of research quality in designing for dissemination, to the extent that is practically possible: a rigorous study design; the systematic tracking of implementation and related costs; and, the conduct of dose-response, maintenance and cost-effectiveness analyses. These examples of designing for dissemination illustrate not only the exciting opportunities for real-world dissemination research, but also the resourcefulness and commitment required for success.

scholarly journals Measurement of the D → K−π+π+π− and D → K−π+π0 coherence factors and average strong-phase differences in quantum-correlated $$ D\overline{D} $$ decays

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
M. Ablikim ◽  
◽  
M. N. Achasov ◽  
P. Adlarson ◽  
S. Ahmed ◽  
...  
Keyword(s):  
The Other ◽  
Unitarity Triangle ◽  
Quantum Superposition ◽  
Final State ◽  
Data Set ◽  
Strong Phase ◽  
Signal Decay ◽  
Belle Ii ◽  
Coherence Factors ◽  
Phase Differences

Abstract The decays D → K−π+π+π− and D → K−π+π0 are studied in a sample of quantum-correlated $$ D\overline{D} $$ D D ¯ pairs produced through the process e+e− → ψ(3770) → $$ D\overline{D} $$ D D ¯ , exploiting a data set collected by the BESIII experiment that corresponds to an integrated luminosity of 2.93 fb−1. Here D indicates a quantum superposition of a D0 and a $$ {\overline{D}}^0 $$ D ¯ 0 meson. By reconstructing one neutral charm meson in a signal decay, and the other in the same or a different final state, observables are measured that contain information on the coherence factors and average strong-phase differences of each of the signal modes. These parameters are critical inputs in the measurement of the angle γ of the Unitarity Triangle in B− → DK− decays at the LHCb and Belle II experiments. The coherence factors are determined to be RK3π = $$ {0.52}_{-0.10}^{+0.12} $$ 0.52 − 0.10 + 0.12 and $$ {R}_{K{\pi \pi}^0} $$ R K ππ 0 = 0.78 ± 0.04, with values for the average strong-phase differences that are $$ {\delta}_D^{K3\pi }=\left({167}_{-19}^{+31}\right){}^{\circ} $$ δ D K 3 π = 167 − 19 + 31 ° and $$ {\delta}_D^{K{\pi \pi}^0}=\left({196}_{-15}^{+14}\right){}^{\circ} $$ δ D K ππ 0 = 196 − 15 + 14 ° , where the uncertainties include both statistical and systematic contributions. The analysis is re-performed in four bins of the phase-space of the D → K−π+π+π− to yield results that will allow for a more sensitive measurement of γ with this mode, to which the BESIII inputs will contribute an uncertainty of around 6°.

scholarly journals Influence of Physical Activity and Socio-Economic Status on Depression and Anxiety Symptoms in Patients after Stroke

2021 ◽  
Vol 18 (15) ◽  
pp. 8058
Author(s):  
Małgorzata Paprocka-Borowicz ◽  
Mona Wiatr ◽  
Maria Ciałowicz ◽  
Wojciech Borowicz ◽  
Agnieszka Kaczmarek ◽  
...  
Keyword(s):  
Physical Activity ◽  
Physical Therapy ◽  
Economic Status ◽  
Depression Scale ◽  
Post Stroke ◽  
Positive Effects ◽  
Socio Economic Status ◽  
Treatment Of Depression ◽  
Post Stroke Depression ◽  
The Impact

Stroke is a high-risk factor for depression. Neurological rehabilitation is greatly difficult and often does not include treatment of depression. The post-stroke depression plays an important role in the progress of treatment, health, and the life of the patient. The appropriate treatment of depression could improve the quality of life of the patient and their family. The study aimed to evaluate the impact of physical activity and socio-economic status of the patient on the effectiveness of recovery from depression and the severity of the symptoms of depression. The study was conducted with 40 patients after stroke aged 42–82 years, and included 10 women and 30 men who were hospitalized for two weeks. The severity of depression/anxiety (D/A) symptoms were evaluated two times; at admission and after two weeks of physical therapy. The hospital anxiety and depression scale (HADS) questionnaire was used for this purpose. Socio-economic status was evaluated by several simple questions. It was revealed that physical therapy has a positive influence on mental state. The severity of D/A symptoms after stroke is related to the financial status of the patients (2 = 11.198, p = 0.024). The state of health (2 = 20.57, p = 0.022) and physical fitness (2 = 12.95, p = 0.044) changed the severity of symptoms of anxiety and depressive disorders. The kinesiotherapy in the group of patients with post-stroke depression had positive effects; however, economic and health conditions may influence the prognosis of the disease.

scholarly journals Evaluating Presence/Absence of Target Microbes in Microbiological Tests

2000 ◽  
Vol 83 (6) ◽  
pp. 1429-1434
Author(s):  
Robert J Blodgett ◽  
Anthony D Hitchins
Keyword(s):  
Performance Studies ◽  
Collaborative Study ◽  
False Negative ◽  
The Other ◽  
Test Results ◽  
Logistic Curve ◽  
Microbiological Method ◽  
Test Portion ◽  
Method Performance ◽  
Data Set

Abstract A typical qualitative microbiological method performance (collaborative) study gathers a data set of responses about a test for the presence or absence of a target microbe. We developed 2 models that estimate false-positive and false-negative rates. One model assumes a constant probability that the tests will indicate the target microbe is present for any positive concentration in the test portion. The other model assumes that this probability follows a logistic curve. Test results from several method performance studies illustrate these estimates.

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