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| 1 | Comparison of some chemical properties of Garcinia kola and hop for assessment of Garcinia brewing value 显示文摘 | Emmanuel O gu Reginald C Agu | 1995 | Bioresour Technol1995，，： | 1 |
| 2 | INPHOVIS:Interactive visual analytics for smartphone-based digital phenotyping显示文摘Digital phenotyping is the characterization of human behavior patterns based on data from digital devices such as smartphones in order to gain insights into the users’state and especially to identify ailments.To support supervised machine learning,digital phenotyping requires gathering data from study participants’smartphones as they live their lives.Periodically,participants are then asked to provide ground truth labels about their health status.Analyzing such complex data is challenging due to limited contextual information and imperfect health/wellness labels.We propose INteractive PHOne-o-typing VISualization(INPHOVIS),an interactive visual framework for exploratory analysis of smartphone health data to study phone-o-types.Prior visualization work has focused on mobile health data with clear semantics such as steps or heart rate data collected using dedicated health devices and wearables such as smartwatches.However,unlike smartphones which are owned by over 85 percent of the US population,wearable devices are less prevalent thus reducing the number of people from whom such data can be collected.In contrast,the‘‘low-level'sensor data(e.g.,accelerometer or GPS data)supported by INPHOVIS can be easily collected using smartphones.Data visualizations are designed to provide the essential contextualization of such data and thus help analysts discover complex relationships between observed sensor values and health-predictive phone-o-types.To guide the design of INPHOVIS,we performed a hierarchical task analysis of phone-o-typing requirements with health domain experts.We then designed and implemented multiple innovative visualizations integral to INPHOVIS including stacked bar charts to show diurnal behavioral patterns,calendar views to visualize day-level data along with bar charts,and correlation views to visualize important wellness predictive data.We demonstrate the usefulness of INPHOVIS with walk-throughs of use cases.We also evaluated INPHOVIS with expert feedback and received encouraging responses. | Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner Angela Incollingo Rodriguez | 2023 | Visual Informatics2023，7，2： | 0 |
| 3 | The impact of COVID-19 on the birth rate in Nigeria:a report from population-based registries显示文摘Background and objectives:Coronavirus disease 2019(COVID-19)is a pandemic that has become a major source of morbidity and mortality worldwide,affecting the physical and mental health of individuals influencing reproduction.Despite the threat,it poses to maternal health in sub-Saharan Africa and Nigeria,there is little or no data on the impact it has on fertility,conception,gestation and birth.To compare the birth rate between pre-COVID and COVID times using selected months of the year.Materials and methods:This was a secondary analysis of cross-sectional analytical study data from the birth registries of three tertiary hospitals,comparing two years[2019(Pre-COVID)]versus[2020(COVID era)]using three months of the year(October to December).The data relied upon was obtained from birth registries in three busy maternity clinics all within tertiary hospitals in South-East Nigeria and we aimed at discussing the potential impacts of COVID-19 on fertility in Nigeria.The secondary outcome measures were;mode of delivery,booking status of the participants,maternal age and occupation.Results:There was a significant decrease in tertiary-hospital based birth rate by 92 births(P=0.0009;95%CI:-16.0519 to-4.1481)among mothers in all the three hospitals in 2020 during the COVID period(post lockdown months)of October to December.There was a significant difference in the mode of delivery for mothers(P=0.0096)with a 95%confidence interval of 1.0664 to 1.5916,as more gave birth through vaginal delivery during the 2020 COVID-19 period than pre-COVID-19.Conclusion:Tertiary-hospital based birth rates were reduced during the pandemic.Our multi-centre study extrapolated on possible factors that may have played a role in this decline in their birth rate,which includes but is not limited to;decreased access to hospital care due to the total lockdowns/curfews and worsening inflation and economic recession in the country. | Charlotte Blanche Oguejiofor Kenechi Miracle Ebubechukwu George Uchenna Eleje Emmanuel Onyebuchi Ugwu Joseph Tochukwu Enebe Kingsley Emeka Ekwuazi Chukwuemeka Chukwubuikem Okoro Boniface Chukwuneme Okpala Charles Chukwunomunso Okafor Nnanyelugo Chima Ezeora Emeka Ifeanyi Iloghalu Chidebe Christian Anikwe Chigozie Geoffrey Okafor Polycarp Uchenna Agu Emeka Philip Igbodike Iffiyeosuo Dennis Ake Arinze Anthony Onwuegbuna Osita Samuel Umeononihu Onyedika Promise Anaedu Odigonma Zinobia Ikpeze David Chibuike Ikwuka Henry Ifeanyi Nwaolisa Ekene Agatha Emeka Jude Ogechukwu Okoye Ihechinyerem Kelechi Osuagwu Angela Ogechukwu Ugwu Toochukwu Benjamin Ejikeme Eziamaka Pauline Ezenkwele Chijioke Ogomegbunam Ezeigwe Malarchy Ekwunife Nwankwo Gerald Okanandu Udigwe Joseph Ifeanyichukwu Ikechebelu Grace Agbaeze Chukwuebuka Divine Nwanja Ahizechukwu Chigoziem Eke | 2023 | Infectious Diseases Research2023，4，1： | 0 |
| 4 | ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms显示文摘Human Bio-Behavioral Rhythms(HBRs)such as sleep-wake cycles(Circadian Rhythms),and the degree of regularity of sleep and physical activity have important health ramifications.Ubiquitous devices such as smartphones can sense HBRs by continuously analyzing data gathered passively by built-in sensors to discover important clues about the degree of regularity and disruptions in behavioral patterns.As human behavior is complex and smartphone data is voluminous with many channels(sensor types),it can be challenging to make meaningful observations,detect unhealthy HBR deviations and most importantly pin-point the causes of disruptions.Prior work has largely utilized computational methods such as machine and deep learning approaches,which while accurate,are often not explainable and present few actionable insights on HBR patterns or causes.To assist analysts in the discovery and understanding of HBR patterns,disruptions and causes,we propose ARGUS,an interactive visual analytics framework.As a foundation of ARGUS,we design an intuitive Rhythm Deviation Score(RDS)that analyzes users’smartphone sensor data,extracts underlying twenty-four-hour rhythms and quantifies their degree of irregularity.This score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of HBRs.ARGUS also facilitates deeper HBR insights and understanding of causes by linking multiple visualization panes that are overlaid with objective sensor information such as geo-locations and phone state(screen locked,charging),and user-provided or smartphone-inferred ground truth information.This array of visualization overlays in ARGUS enables analysts to gain a more comprehensive picture of HBRs,behavioral patterns and deviations from regularity.The design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone sensing.To demonstrate its utility and generalizability,two different datasets were explored using ARGUS and our use cases and designs were strongly validated in evaluation sessions with expert and non-expert users. | Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner | 2021 | Visual Informatics2021，5，3： | 0 |
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