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Association between Dietary Collagen Consumption and Telomere Length: National Health and Nutrition Examination Survey

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Health 2023年第10期15卷 1096-1115页

作者： Huifeng Jin Rolando L. Maddela Robert A. Sinnott Research and Development USANA Health Sciences Inc. Salt Lake City USA

Background: Bioactive peptides derived from hydrolyzed collagen have broad physiological functions and beneficial effects on human health, ranging from reducing skin aging to modifying lipid metabolism. Telomere length shortening is an established biomarker of cellular aging. It is not known if collagen consumption is associated with telomere length protection. Our purpose was to investigate the relationship between dietary collagen consumption and telomere length in a nationally representative US adult population. Methods: We analyzed the data of 6173 adults aged 20 - 84 from the National Health and Nutrition Examination Survey (NHANES) 1999-2002. Multivariable linear regression and a generalized additive model with smoothing plot were used to assess the association between the total collagen consumption and log-transformed leukocyte telomere length (LTL). Results: Compared with the lowest quartile of total collagen (Q1), we found that the second quartile of collagen (Q2) consumption (1.36 - 3.40 g/1000kcal) was positively associated with telomere length (β: 0.017;95% CI: 0.006, 0.028;P = 0.022) in the females while no association in the males (β: −0.003;95% CI: −0.019, 0.012;P = 0.678) and overall population (β: 0.008;95% CI: −0.002, 0.018;P = 0.141). The association in the females is nonlinear with an inflection point of 2.5 g/1000kcal (P for non-linearity: Conclusion: In conclusion, moderate dietary collagen has a positive and nonlinear association with telomere length in US females, while no significant associations were found in the males and the overall population.

关键词： Telomere Length Dietary Collagen NHANES linear regression

Rank-Ordering of Topographic Variables Correlated with Temperature

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Atmospheric and Climate Sciences 2012年第2期2卷 139-147页

作者： Daniel Joly Benjamin Bois Klemen Zaksek Universite de Franche-Comte BesanconFrance Universite de Bourgogne DijonFrance University of Hamburg Institute of GeophysicsHamburgGermany Centre of excellence Space-SI LjubljanaSlovenia

Spatial variations in temperature may be ascribed to many variables. Among these, variables pertaining to topography are prominent. Thus various topographic variables were calculated from 50 m-resolution digital terrain models (DTMs) for three study areas in France and for Slovenia. The “classic” geomatic variables (altitude, aspect, gradient, etc.) are supplemented by the description of landforms (amplitude of humps and hollows). Special care is taken in managing collinearity among variables and building windows with different dimensions. Statistical processing involves linear regressions of daily temperatures taken as the response variables and six topographic variables (explanatory variables). Altitude accounts significantly for the spatial variation in temperatures in 90% of cases, except in the Gironde, a lowlying area (50%). The scale of landforms also appears to be highly correlated to the measured temperature. Variations in the frequency with which topographic descriptors account for temperatures are examined from several standpoints. Altitude is less frequently taken as an explanatory variable for spatial variation of temperatures in winter (75%) than in spring (80%) and late summer (85%). Minimum temperatures are influenced on average much more by the amplitude of humps and hollows (56%) than maximum temperatures (38%) are. The frequency with which these two landforms account for the spatial variation of temperature is reversed between the minima and maxima.

关键词： Explanatory Variables Temperature Topography Collinearity linear regression

An Explanatory Study Approach,Using Machine Learning to Forecast Solar Energy Outcome

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Journal of Energy and Power Engineering 2022年第2期16卷 81-89页

作者： Agada Ihuoma Nkechi Nagata Yasunori Department of Electrical and Electronics Engineering University of the RyukyusOkinawa 903-0213Japan

AI(artificial intelligence)techniques play a crucially important role in predicting the expected energy outcome and its performance,analysis,modeling and control of renewable *** energy usage has grown exponentially over the *** the face of global energy consumption and increased depletion of most fossil fuel,the world is faced with the challenges of meeting the ever-increasing energy demands,also utility companies who provide solar energy have a challenge of unstable input of solar energy to the grid due to its intermittent nature,unlike other sources,hence the difference between expected generation and actual generation,demand and supply can lead to an unbalanced ***,incorporating accurately machine learning technology to predict the expected outcome of solar energy from the intermittent solar radiation will be crucial to keep a balance grid operation between supply and demand,production planning and energy management especially during installations of a photovoltaic power ***,one of the major problems of forecasting is the algorithms used to control,model,and predict performances of the energy systems which are complicated and involve large computer power,differential equations,and time *** having unreliable data(poor quality)for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to *** overcome these problems,we employ the Anaconda Navigator(Jupyter Notebook)for machine learning which can combine large amounts of data with fast,iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turn enables the balance between supply and demand on loads,efficient operation of the utility company as well as enhances power production planning and management.

关键词： AI backward elimination data mining machine learning linear regression solar energy.

Reinforcing of Citizen’s Trust in E-Government: The Cameroon’s Case

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Journal of Computer and Communications 2024年第1期12卷 77-109页

作者： Patrick Dany Bavoua Kenfack Check Njei Department of Electrical and Telecommunications Engineering National Advanced School of Engineering of Yaounde University of Yaounde I Yaounde Cameroon

The embracing of ICTs and related technologies has enhanced different approaches for governments worldwide to deliver services to their citizens in a smart way. However, the usage of e-government services by common citizens is recognized as one of the major setbacks of e-government development in both developed and developing countries. Moreover, government agencies in these countries are facing great challenges in keeping the citizens motivated enough to continue to use e-government services. This research aims to investigate the factors that influence citizens’ trust towards continue use of e-government services in Cameroon. The proposed research model consisted of three main constructs including technological, governmental, risk factors as well as six demographic characteristics (age, gender, educational level, income, internet experience and cultural perception). A five-point Likert scale questionnaire was designed to collect data physically and electronically, 352 valid questionnaires were retrieved. Simple and Multiple regression analysis methods were applied to build an adequate model based on the verification of hypotheses proposed. Based on results obtained, four demographic characteristics (age, education, occupation and income) have influence on citizens’ trust in e-government meanwhile gender and cultural affiliation have no influence. Furthermore, technological factors and governmental factors positively influence trust level in e-government, whereas risk factors have a negative influence on trust level. Deducing from the results, a list of recommendations is proposed to the government of Cameroon in order to reinforce citizens’ trust in e-government services.

关键词： E-Government Risk Factors Technological Factors Governmental Factors Trust linear regression

Characteristics of Ordinary Portland Cement Paste Containing Rice Husk Ash and Conplast[SP 430]

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Journal of Building Material Science 2022年第1期4卷 1-10页

作者： Elinwa Augustine Uchechukwu Olakunle Johnson Olomi Duna Samson Civil Engineering Department Abubakar Tafawa Balewa UniversityBauchiNigeria Works Department University of Jos Teaching Hospital JUTHJosNigeria

An experimental investigation on rice husk ash(RHA)and Conplast SP 430(CP-admixture)was carried out to evaluate their effects on the paste characteristics(soundness,consistency,initial and final setting times).The cement content used was 300g and the brand was‘Ashaka’Portland cement conforming to BS EN *** combinations of the mixtures OPC-RHA-CP-admixture were used to establish performance characteristics of the *** characteristics,and linear regression models(no-transformation,λ=0.5,and Box-Cox transformation)were developed on the experimental data for decisions on their performances。

关键词： Rice husk ash Conplast SP 430 Paste characteristics Statistics linear regression

Establishment of Statistical Model for Precipitation Prediction in the Flood Season in China

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Meteorological and Environmental Research 2011年第3期2卷 1-3页

作者： CHENG Zhi1,2 1.College of Atmospheric Sciences,Lanzhou University,Lanzhou 730000,China 2.Key Laboratory for Climate-Environment in East Asian Region,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China

[Objective] The research aimed to establish the regression model which was used to predict the precipitation in the flood season in China.[Method] Based on statistical model,North Atlantic oscillation index and the sea surface temperature index in development and declining stages of ENSO were used to predict East Asian summer monsoon *** the stations were divided into 16 zones,the same factors were used to establish the regression model predicting the station precipitation in the flood season in ***,it was compared with the model that predicted firstly the monsoon index and estimated the precipitation.[Result] The prediction results of summer precipitation during 2005-2009 by every model were *** was found that the model that the factor predicted indirectly the regional precipitation was better than that predicted indirectly the station ***,the model that the factor predicted directly the regional precipitation was better than that predicted indirectly the regional *** prediction score P of optimum model that three factors predicted directly the regional precipitation reached averagely 74.2,and the anomaly correlation coefficient ACC was averagely *** from the comparison situation of positive and negative zone distribution of precipitation anomaly percentage between the predicted and observed values in 5 years,the prediction effects in the south and east of Northeast China,some areas in the south of Yangtze River,the coast of South China and most areas of Xinjiang were *** predicted positive/negative distribution of precipitation anomaly percentage tallied with that of observation.[Conclusion] The model could predict well summer precipitation in China.

关键词： East Asian monsoon index linear regression Summer precipitation ENSO North Atlantic oscillation index China

Discrepancy of blood pressure between the brachial artery and radial artery

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World Journal of Emergency Medicine 2013年第4期4卷 294-297页

作者： Wen-yuan Li Xiao-hai Wang Li-chong Lu Hao Li Department of Anesthesiology Second Affiliated Hospital of Nanjing Medical University Department of Anesthesiology Affiliated Drum-Tower Hospital of Medical College of Nanjing University Department of Cardio-Thoracic Surgery Affiliated Drum-Tower Hospital of Medical College of Nanjing University Department of Anesthesiology First People's Hospital of Yancheng

BACKGROUND:In this study,we attempted to find the relations between blood pressure(BP)measured on the brachial artery(bBP) and BP assessed on the radial artery(rBP) in the right ***:Three hundred and fifteen patients were enrolled in this *** who had peripheral vascular disease,wounds of arm skin or subcutaneous tissue infection were *** a 15-minute equilibration and stabilization period after inducation of anesthesia,three bBP and rBP records were obtained sequentially using an oscillometric device with an adult cuff and infant cuff,*** for each BP was ***:The bBP was significantly lower than the rBP(P<0.05).The difference between the two values varied from 13 to 18 mmHg in systolic BP(SBP),diastolic BP(DBP) and mean blood pressure(MAP) *** the rBP was positively correlated with the bBP(r=0.872,0.754,0.765;P<0.001,<0.001,<0.001;SBP,DBP,MAP,respectively).CONCLUSION:The bBP value can be evaluated by the noninvasive measurements of rBP using an appropriate cuff in clinical practice.

关键词： Blood pressure Brachial artery Radial artery Correlation linear regression

Research on sunspot prediction based on CNN-LSTM

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Research on sunspot prediction based on CNN-LSTM

Application of the information dynamic function in prediction of protein folding rate

第43届中国控制会议

作者： Ziyue Han Lanmei Cong Ningning Hu Jie Liu Hanchao Zhao Linyi University School of Automation and Electrical Engineering

Sunspots are closely linked to the solar cycle and the behavior of the solar magnetic field. Therefore, it is significant for predicting solar activity. With the exponential growth in historical data, accurating sunspot prediction has become an urgent requirement. this study proposes a CNN-LSTM prediction model that utilizes the peak number of sunspots as a basis for forecasting the start and duration of the current and next solar cycles. Then, relevant sunspot data is collected and subjected to meticulous data preparation and pre-processing. This uses sample interpolation methods addressing missing values of historical sunspot data, also utilizes performing data exclusion and outlier treatment using the Grubbs' test(3σ rule). In study, elucidate the architecture and training process of the CNN-LSTM model and demonstrate its efficacy in predicting the start and duration of the solar cycle. Additionally, the model is employed to forecast the time and intensity of the solar peak period. By quantifying data based on individual solar cycles, the total number and total area of sunspots are determined. Subsequently, establish both CNN-LSTM model and BP neural network model, with the number and area of sunspots as output variables, and the solar cycle duration, solar peak period circumference, and duration as input variables. Through comparative analysis of the two models, this research finds that the CNN-LSTM model outperforms the BP neural network model, and the CNN-LSTM model shows superior performance is demonstrated in predicting the number and area of sunspots.

关键词： CNN-LSTM model Grubbs' test(3σ rule) linear regression BP neural network model data prediction

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Application of the information dynamic function in predictio...