bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the population density limit to variable renewable energy potential
Authors: Barnea Gil , Barnea Nir 
Published: February 2021

Abstract
Renewable energies are expanding rapidly around the globe. In several countries, however, their expansion is being challenged by NIMBY groups and land footprint. In this article, we suggest that indeed a major challenge for renewable energy expansion appears to be tied to land use questions, especially in regions with high population density. Our research finds a strong relationship between population density and renewable energy expansion. The power density of Variable Renewable Energy (VRE) sources, such as wind turbines or photovoltaic panels, is much lower than that of fossil fuels or nuclear power plants, to the extent that land availability seems to be the limiting factor for large scale VRE production. During the last decade, a substantial theoretical effort was dedicated to study the actual power density of VRE, and the available space in order to estimate the limits that land footprint sets on VRE production. On top of the technological, and geographical issues associated with such studies there are somewhat more complicated and less well defined sociological issues related to the willingness of population to live in near proximity to large scale VRE farms. To explore the overall issue of VRE penetration and limitations, the installed VRE capacity data from different countries is examined. It is found that in Germany, with VRE power density of 0.27 W/m2 (2018 data), there is a strong negative correlation between the population density and VRE capacity, dominated by solar power production. We interpret this correlation as an indication that Germany has reached the point where land usage is becoming the limiting factor for installation of new VRE power plants. As Germany is a worldwide leader in VRE production per capita with more than 1 kW/person, we speculate that this sets a universal barrier of 2%-3% on the fractional land area available for VRE production. Crossing this barrier the expansion of the installed VRE capacity is expected to stall. This does not, however, mean that renewable energy expansion will be doomed in countries with high population density. Rather it suggests the need for new approaches — a stronger focus on battery storage technologies, inter-sectoral coupling (renewable energy storage in automobile batteries) and continued investment in efficiency improvements of renewable energy technologies. It may also be a question of how renewable energy develops — enhancing solar on existing built infrastructure (rooftops, siding, and even roads) could help address this issue.
The Protection motivation theory for predict intention of COVID-19 vaccination in Iran: A structural equation modeling approach
Authors: Ansari-Moghaddam Alireza , Seraji Maryam , Sharafi Zahra , Mohammadi Mahdi , Okati-Aliabad Hassan 
Published: February 2021

Abstract
BackgroundMany efforts are being made around the world to discover the vaccine against COVID-19. After discovering the vaccine, its acceptance by individuals is a fundamental issue for disease control. This study aimed to examine COVID-19 vaccination intention determinants based on the protection motivation theory (PMT). Methods We conducted a cross-sectional study in the Iranian adult population and surveyed study participants from the first to the 30th of June 2020 with a web-based self-administered questionnaire. We used Structural Equation Modeling (SEM) to investigate the interrelationship between COVID-19 vaccination intention and perceived susceptibility, perceived severity, perceived self-efficacy, and perceived response efficacy. Results SEM showed that perceived severity to COVID-19 (β=.17, p < .001), perceived self-efficacy about receiving the COVID-19 vaccine (β=.26, p < .001), and the perceived response efficacy of the COVID-19 vaccine (β=.70, p < .001) were significant predictors of vaccination intention. PMT accounted for 61.5% of the variance in intention to COVID-19 vaccination, and response efficacy was the strongest predictor of COVID-19 vaccination intention. ConclusionsThis study found the PMT constructs are useful in predicting COVID-19 vaccination intention. Programs designed to increase the vaccination rate after discovering the COVID-19 vaccine can include interventions on the severity of the COVID-19, the self-efficacy of individuals receiving the vaccine, and the effectiveness of the vaccine in preventing infection.
Modeling Mortality by Causes of Death in South Africa Using Log-linear Analysis
Authors: Chireshe Exaverio 
Published: February 2021

Abstract
This study aimed at identifying underlying patterns in mortality due to causes of death in South Africa using mortality statistics from 2005 to 2015 obtained from Statistics South Africa. Log-linear analysis was used in this study on mortality by causes of death dataset having three variables, cause of death (C), province (P) and year (Y). Log-linear analysis was preferred because of its capability to tease out relationships among variables. Results revealed that there are variations in mortality due to causes of death. Mortality was found to differ widely across the country, among provinces. It is recommended that prevention and management policies for HIV and TB be intensified since they still remain South Africa's major causes of death. A replication of the study could be done in another developing country using latest data to see if it will yield the same results. A multi-population mortality modeling could also be carried out using the same approach.
Relationships of gut microbiota, short-chain fatty acids, inflammation, and the gut barrier in Parkinson's disease
Authors: Aho Velma T , Houser Madelyn Crawford , Pereira Pedro AB , Chang Jianjun , Rudi Knut , Paulin Lars , Hertzberg Vicki , Auvinen Petri , Tansey Malu Gamez , Scheperjans Filip 
Published: February 2021

Abstract
Background: Previous studies have reported that gut microbiota, permeability, short-chain fatty acids (SCFAs), and inflammation are altered in Parkinson’s disease (PD), but how these factors are linked and contribute to disease processes and symptoms remains uncertain. This study sought to compare and identify associations among these factors in PD patients and controls to elucidate their interrelations and links to clinical manifestations of PD.Methods: Stool and plasma samples and clinical data were collected from 55 PD patients and 56 controls. Levels of stool SCFAs and stool and plasma inflammatory and permeability markers were compared between patients and controls and related to one another and to the gut microbiota.Results: Calprotectin was increased and SCFAs decreased in stool in PD in a sex-dependent manner. Inflammatory markers in plasma and stool were neither intercorrelated nor strongly associated with SCFA levels. Age at PD onset was positively correlated with SCFAs and negatively correlated with CXCL8 and IL-1β in stool. Fecal zonulin correlated positively with fecal NGAL and negatively with PD motor and non-motor symptoms. Microbiota diversity and composition were linked to levels of stool SCFAs, inflammation, and zonulin. Certain relationships differed between patients and controls and by sex.Conclusions: Intestinal inflammatory responses and reductions in fecal SCFAs occur in PD, are related to the microbiota and to disease onset, and are not reflected in plasma inflammatory profiles. Some of these relationships are PD- and sex-dependent. Alterations in microbiota-host interactions and links between intestinal inflammation and reduced SCFA levels and earlier PD onset warrant further investigation.
In-Silico Validation and Fabrication of Matrix Diffusion-Based Polymeric Transdermal Patches for Repurposing Gabapentin Hydrochloride in Neuropathic Pain
Authors: Singh Manisha , Agarwal Shriya , Pancham Pranav , Agarwal Vinayak , Kaur Harleen , Kaur Ramneek 
Published: February 2021

Background:
Gabapentin (GBP) is an FDA-approved drug for the treatment of partial
and secondary generalized seizures, apart from being used for diabetic neuropathic pain. GBP displays
a highly intricate mechanism of action and its inhibitory response in elevated antagonism of
NMDA (N-methyl-D-aspartate receptor) receptor and thus, can be repurposed for controlling neuropathic
pain.

Objective:
Therefore, in the present study, we have selected hBCATc (humanPyridoxal 5’-phosphate-
dependent branched-chain aminotransferase cytosolic) gene that is highly expressed in silico
validation through neuropathic stressed conditions. Thereafter, have analysed the GBP as its competitive
inhibitor by in silico validation through homology modelling, molecular docking, also predicting
its structural alerts and pharmacokinetic suitability through ADMET. However, and GBP
was found to be a potential drug in controlling neuropathic pain, still, it has certain critical and
pharmacokinetic limitations; therefore, the need for its targeted delivery was required, and the
same was attained by designing a GBP loaded transdermal patch (GBP-TDP).

Methods:
A suitable and equally efficacious GBP - TDP was developed by a solvent evaporation
method using PVP and HPMC in the ratio of 2:1 as a polymer base for reservoir type of TDP. Also,
PEG 400 was used as a plasticizer, and PVA (4%) was taken for backing membrane preparation,
and then the optimized GBP-TDP was subjected for physical characterization, optimization
and ex vivo release kinetics.

Results:
The results showed desired specifications with uneven and flaky surface appearance giving
an avenue for controlled release of the drugs with 92.34 ± 1.43% of drug release in 10 hours,
further suggesting that GBP-TDP can be used as an effective tool against diabetic neuropathy pain.

Conclusion:
In this study, we have repurposed Gabapentin to treat diabetic neuropathy and validated
the same by conducting a detailed in silico evaluation starting from homology Modelling of the
target protein hBCATc, cross verified by the Ramachandran plot analysis with the most favoured
region of 92.1% (encompassing 303 residues out of 386).
Association between Health Literacy, Patient Activation, and Functional Capacity in Individuals with Rheumatoid Arthritis
Authors: Oliveira Isabela V. , do Nascimento Mariana M. G. , Kakehasi Adriana M. , de Morais Neves Carina , Silva Hagabo M. , Gonçalves Thuane S. , de Moura Aredes Raquel , de Oliveira Djenane R. 
Published: February 2021

Objectives:
To describe health literacy, patient activation, and functional capacity in individuals with rheumatoid arthritis and to evaluate the associations of health literacy and activation level with functional capacity.

Methods:
A cross-sectional study involving patients with rheumatoid arthritis was carried out at the rheumatology service of a teaching hospital. Health literacy was assessed by applying the short version of the Test of Functional Health Literacy in Adults. Patient Activation was measured by using the summarized version of the Patient Activation Measure. The Health Assessment Questionnaire was applied to evaluate functional capacity. Sociodemographic and clinical variables were also collected to carried out univariate and multivariate analyses by using logistic regression.

Results:
The sample was composed of 179 patients, of whom 90.5% (n = 162) were women. The participants’ average age was 58.3 ± 11.4 years. The prevalence of patients with inadequate or marginal health literacy was high (67%). In contrast, 74% of the patients showed high activation levels. The average score on the Health Assessment Questionnaire was 1.08 ± 0.7. Adequate health literacy was negatively associated with higher Health Assessment Questionnaire scores (OR = 0.42; 95% CI 0.20 – 0.86; p = 0.018), and high activation levels were negatively associated with moderate to severe functional limitation (level 3/moderate activation level – OR = 0.24; 95% CI 0.10 – 0.56; p = 0.001; level 4/high activation level – OR = 0.22; 95% CI 0.08 – 0.50; p = 0.000).

Conclusion:
Health professionals must be aware of health literacy and activation levels of patients with rheumatoid arthritis, given that interventions to improve them represent an opportunity to increase functional capacity.
Using a Personal Health Library–Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data
Authors: Ammar Nariman , Bailey James E , Davis Robert L , Shaban-Nejad Arash 
Published: February 2021

Background
Traditionally, digital health data management has been based on electronic health record (EHR) systems and has been handled primarily by centralized health providers. New mechanisms are needed to give patients more control over their digital health data. Personal health libraries (PHLs) provide a single point of secure access to patients' digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients’ health by understanding medical events in the context of their lives.

Objective
This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients’ PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults.

Methods
We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations.

Results
To showcase the main features of the mobile health app and the PHL, we mapped those features onto a framework comprising the user requirements identified in a use case scenario that features a preventive intervention from the diabetes self-management domain. Ongoing development of the app requires a formative evaluation study and a clinical trial to assess the impact of the digital intervention on patient-users. We provide synopses of both study protocols.

Conclusions
The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge. By exposing the PHL functionality as an open service, we foster the development of third-party applications or services and provide motivational technological support in several projects crossing different domains of interest.
Understanding Side Effects of Antidepressants: Large-scale Longitudinal Study on Social Media Data
Authors: Saha Koustuv , Torous John , Kiciman Emre , De Choudhury Munmun 
Published: February 2021

BackgroundAntidepressants are known to show heterogeneous effects across individuals and conditions, posing challenges to understanding their efficacy in mental health treatment. Social media platforms enable individuals to share their day-to-day concerns with others and thereby can function as unobtrusive, large-scale, and naturalistic data sources to study the longitudinal behavior of individuals taking antidepressants.ObjectiveWe aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media.MethodsOn a large-scale Twitter data set of individuals who self-reported using antidepressants, a quasi-experimental study using unsupervised language analysis was conducted to extract keywords that distinguish individuals who improved and who did not improve following the use of antidepressants. The net data set consists of over 8 million Twitter posts made by over 300,000 users in a 4-year period between January 1, 2014, and February 15, 2018.ResultsFive major side effects of antidepressants were studied: sleep, weight, eating, pain, and sexual issues. Social media language revealed keywords related to these side effects. In particular, antidepressants were found to show a spectrum of effects from decrease to increase in each of these side effects.ConclusionsThis work enhances the understanding of the side effects of antidepressants by identifying distinct linguistic markers in the longitudinal social media data of individuals showing the most and least improvement following the self-reported intake of antidepressants. One implication of this work concerns the potential of social media data as an effective means to support digital pharmacovigilance and digital therapeutics. These results can inform clinicians in tailoring their discussion and assessment of side effects and inform patients about what to potentially expect and what may or may not be within the realm of normal aftereffects of antidepressants.
Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation
Authors: Peterson Kelly S , Lewis Julia , Patterson Olga V , Chapman Alec B , Denhalter Daniel W , Lye Patricia A , Stevens Vanessa W , Gamage Shantini D , Roselle Gary A , Wallace Katherine S , Jones Makoto 
Published: February 2021

BackgroundPatient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.ObjectiveThis study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.MethodsClinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.ResultsAmong 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.ConclusionsAutomated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.
Knowledge, Attitude, Practice and Associated Factors of Adult Population towards Blood Donation in Jigjiga Town, Somali Region, Ethiopia
Authors: Jibril Abdireshid Dahir , Mohammed Ahmed , Hashi Abdiwahab , Omer Muktar , Shegere Mekonin Meskelu , Joseph Nisha Mary , Palani S. 
Published: February 2021

Background: Secure supply of safe blood components, based on voluntary, non-remunerated blood donation, is an important national goal to prevent blood shortages. Nowadays, in many low- and middle-income countries, blood supply is critically inadequate. Sub-Saharan Africa, which has the highest maternal mortality rate in the world of 510 deaths per 100000 live births, also has the lowest blood donation rates. The donated blood plays a big role during surgery, accident, delivery cases and in many bleeding cases.
The aim of this work is to assess knowledge, attitude, and practice and associated factors of adult population towards blood donation in Jigjiga town, Eastern Ethiopia, 2019. A total of20 kebeles, six was selected by simple random sampling. The sample size was calculated by using EPI info version 7.2by considering power of 80%, confidence level of 95%, by taking the comprehensive knowledge of the study Participants towards voluntary blood donation of 43.5% from study conducted in Harartown, giving totalsample size of416.
Result: About 249(61.2%), 318 (78.1%), and 77 (18.9%) study participants had adequate knowledge, good attitude and experience of blood donation. Those who were in college and higher education were 2.034 higher odds of knowledge towards blood donation relative to those in secondary and primary education. Study participants who were government employees and daily workers were 2.259(1.103, 4.629) times more knowledgable to housewife, students. Study particpants age range between 18-30 have less knowledge towards blood donation 0.408 times. Participants who attended college and higher education were more likely to have good attitude towards blood donation. Those respondents who are college and higher education and secondary were 2.450 times more likely, 2.359to have good attitude towards blood donation compared to primary school 2.161 respectively. Participant’s sex, education, and occupational status were found to be significantly associated with practice of blood donation. Being NGO worker is more likely to donate blood 8.167 higher than others. Male participants was more vulnarable to donate blood 5.245 times higher than female for blood do nation. In addition to this, those respondents who are graduated college and higher education were more likely to donate blood than others 3.247.