bluuebunny/crossref_metadata_2025_split huggingface dataset:
Working memory dysfunction in fibromyalgia is associated with genotypes of the catechol- O-methyltransferase gene: An event-related potential study.
Authors: Ferrera David , Gómez-Equer Francisco , Peláez Irene , Barjola Paloma , Fernandes-Magalhaes Roberto , Carpio Alberto , De Lahoz María E. , Martín-Buro María Carmen , Mercado Francisco 
Published: April 2022

Abstract

Recent findings have associated different COMT genotypes with working memory capacity in patients with fibromyalgia. Although it is thought that the COMT gene may influence neural correlates (P2 and P3 ERP components) underlying this cognitive dysfunction it has not been still explored. 51 patients and 51 healthy control participants took part in this study. Event-related potentials and behavioural responses were recorded while participants performed a spatial n-back task (1-back and 2-back conditions). Genotypes of the COMT gene were determined through a saliva sample from all participants. Present results showed lower working memory performance (higher rate of errors and slower reaction times) in patients with fibromyalgia than in control participants. Temporal dynamics of working memory processing were analysed on P2 and P3 components. We found enhanced frontocentral and parieto-occipital P2 amplitudes for patients as compared to the control group. Interestingly, we also observed a significant influence of COMT gene that was specifically explained by the Val/Val genotype. Thus, patients with fibromyalgia exhibited higher frontocentral P2 amplitudes than control participants, being specifically pronounced for those carrying the Val/Val genotype. Current results (behavioural outcomes and P2 amplitudes) confirmed the presence of an alteration in working memory functioning in fibromyalgia. The enhancement of frontocentral P2 amplitudes might be reflecting an inefficient manner to activate executive attention processes by patients carrying Val/Val genotype of the COMT gene. To our knowledge, the present findings are the first linking neural indices of working memory dysfunctions and COMT genotypes in fibromyalgia. Applying a subgroup of patient’s strategy based on this genetic marker could be useful to establish more tailored therapeutical approaches.
Themes Surrounding COVID-19 and Its Infodemic: Qualitative Analysis of the COVID-19 Discussion on the Multidisciplinary Healthcare Information for All Health Forum
Authors: Gangireddy Rakshith , Chakraborty Stuti , Pakenham-Walsh Neil , Nagarajan Branavan , Krishan Prerna , McGuire Richard , Vaghela Gladson , Sriharan Abi 
Published: April 2022

Background
Healthcare Information for All (HIFA) is a multidisciplinary global campaign consisting of more than 20,000 members worldwide committed to improving the availability and use of health care information in low- and middle-income countries (LMICs). During the COVID-19 pandemic, online HIFA forums saw a tremendous amount of discussion regarding the lack of information about COVID-19, the spread of misinformation, and the pandemic’s impact on different communities.

Objective
This study aims to analyze the themes and perspectives shared in the COVID-19 discussion on English HIFA forums.

Methods
Over a period of 8 months, a qualitative thematic content analysis of the COVID-19 discussion on English HIFA forums was conducted. In total, 865 posts between January 24 and October 31, 2020, from 246 unique study participants were included and analyzed.

Results
In total, 6 major themes were identified: infodemic, health system, digital health literacy, economic consequences, marginalized peoples, and mental health. The geographical distribution of study participants involved in the discussion spanned across 46 different countries in every continent except Antarctica. Study participants’ professions included public health workers, health care providers, and researchers, among others. Study participants’ affiliation included nongovernment organizations (NGOs), commercial organizations, academic institutions, the United Nations (UN), the World Health Organization (WHO), and others.

Conclusions
The themes that emerged from this analysis highlight personal recounts, reflections, suggestions, and evidence around addressing COVID-19 related misinformation and might also help to understand the timeline of information evolution, focus, and needs surrounding the COVID-19 pandemic.
Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis
Authors: Niu Qian , Liu Junyu , Kato Masaya , Shinohara Yuki , Matsumura Natsuki , Aoyama Tomoki , Nagai-Tanima Momoko 
Published: April 2022

Background
COVID-19 vaccines are considered one of the most effective ways for containing the COVID-19 pandemic, but Japan lagged behind other countries in vaccination in the early stages. A deeper understanding of the slow progress of vaccination in Japan can be instructive for COVID-19 booster vaccination and vaccinations during future pandemics.

Objective
This retrospective study aims to analyze the slow progress of early-stage vaccination in Japan by exploring opinions and sentiment toward the COVID-19 vaccine in Japanese tweets before and at the beginning of vaccination.

Methods
We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords between August 1, 2020, and June 30, 2021. We visualized the trend of the tweets and sentiments and identified the critical events that may have triggered the surges. Correlations between sentiments and the daily infection, death, and vaccination cases were calculated. The latent dirichlet allocation model was applied to identify topics of negative tweets from the beginning of vaccination. We also conducted an analysis of vaccine brands (Pfizer, Moderna, AstraZeneca) approved in Japan.

Results
The daily number of tweets continued with accelerating growth after the start of large-scale vaccinations in Japan. The sentiments of around 85% of the tweets were neutral, and negative sentiment overwhelmed the positive sentiment in the other tweets. We identified 6 public-concerned topics related to the negative sentiment at the beginning of the vaccination process. Among the vaccines from the 3 manufacturers, the attitude toward Moderna was the most positive, and the attitude toward AstraZeneca was the most negative.

Conclusions
Negative sentiment toward vaccines dominated positive sentiment in Japan, and the concerns about side effects might have outweighed fears of infection at the beginning of the vaccination process. Topic modeling on negative tweets indicated that the government and policy makers should take prompt actions in building a safe and convenient vaccine reservation and rollout system, which requires both flexibility of the medical care system and the acceleration of digitalization in Japan. The public showed different attitudes toward vaccine brands. Policy makers should provide more evidence about the effectiveness and safety of vaccines and rebut fake news to build vaccine confidence.
Modeling Trust in COVID-19 Contact-Tracing Apps Using the Human-Computer Trust Scale: Online Survey Study
Authors: Sousa Sonia , Kalju Tiina 
Published: April 2022

Background
The COVID-19 pandemic has caused changes in technology use worldwide, both socially and economically. This pandemic crisis has brought additional measures such as contact-tracing apps (CTAs) to help fight against spread of the virus. Unfortunately, the low adoption rate of these apps affected their success. There could be many reasons for the low adoption, including concerns of security and privacy, along with reported issues of trust in CTAs. Some concerns are related with how CTAs could be used as surveillance tools or their potential threats to privacy as they involve health data. For example, in Estonia, the CTA named HOIA had approximately 250,000 downloads in the middle of January 2021. However, in 2021, only 4.7% of the population used HOIA as a COVID-19 CTA. The reasons for the low adoption include lack of competency, and privacy and security concerns. This lower adoption and the lack of trustworthiness persist despite efforts of the European Union in building ethics and trustworthy artificial intelligence (AI)-based apps.

Objective
The aim of this study was to understand how to measure trust in health technologies. Specifically, we assessed the usefulness of the Human-Computer Trust Scale (HCTS) to measure Estonians’ trust in the HOIA app and the causes for this lack of trust.

Methods
The main research question was: Can the HCTS be used to assess citizens’ perception of trust in health technologies? We established four hypotheses that were tested with a survey. We used a convenience sample for data collection, including sharing the questionnaire on social network sites and using the snowball method to reach all potential HOIA users in the Estonian population.

Results
Among the 78 respondents, 61 had downloaded the HOIA app with data on usage patterns. However, 20 of those who downloaded the app admitted that it was never opened despite most claiming to regularly use mobile apps. The main reasons included not understanding how it works, and privacy and security concerns. Significant correlations were found between participants’ trust in CTAs in general and their perceived trust in the HOIA app regarding three attributes: competency (P<.001), risk perception (P<.001), and reciprocity (P=.01).

Conclusions
This study shows that trust in the HOIA app among Estonian residents did affect their predisposition to use the app. Participants did not generally believe that HOIA could help to control the spread of the virus. The result of this work is limited to HOIA and health apps that use similar contact-tracing methods. However, the findings can contribute to gaining a broader understanding and awareness of the need for designing trustworthy technologies. Moreover, this work can help to provide design recommendations that ensure trustworthiness in CTAs, and the ability of AI to use highly sensitive data and serve society.
Social Robot Interventions in Mental Health Care and Their Outcomes, Barriers, and Facilitators: Scoping Review
Authors: Guemghar Imane , Pires de Oliveira Padilha Paula , Abdel-Baki Amal , Jutras-Aswad Didier , Paquette Jesseca , Pomey Marie-Pascale 
Published: April 2022

Background
The use of social robots as innovative therapeutic tools has been increasingly explored in recent years in an effort to address the growing need for alternative intervention modalities in mental health care.

Objective
The aim of this scoping review was to identify and describe social robot interventions in mental health facilities and to highlight their outcomes as well as the barriers and facilitators to their implementation.

Methods
A scoping review of the literature published since 2015 was conducted using the Arksey and O’Malley’s framework. The MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO databases were searched, and 2239 papers were retrieved. The papers included were primary empirical studies published in peer-reviewed literature. Eligible studies were set in mental health facilities and they included participants with a known mental health disorder. The methodological quality of the included papers was also assessed using the Mixed Methods Appraisal Tool.

Results
A total of 30 papers met the eligibility criteria for this review. Studies involved participants with dementia, cognitive impairment, schizophrenia, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, and an intellectual disability. The outcomes studied included engagement, social interaction, emotional state, agitation, behavior, and quality of life.

Conclusions
The methodological weaknesses of the studies conducted this far and the lack of diversity in the conditions studied limit the generalizability of the results. However, despite the presence of certain barriers to their implementation (eg, technical problems, unsuitable environment, staff resistance), social robot interventions generally show positive effects on patients with mental health disorders. Studies of stronger methodological quality are needed to further understand the benefits and the place of social robots in mental health care.
COVID-19 and Tweets About Quitting Cigarette Smoking: Topic Model Analysis of Twitter Posts 2018-2020
Authors: Westmaas J Lee , Masters Matthew , Bandi Priti , Majmundar Anuja , Asare Samuel , Diver W Ryan 
Published: April 2022

Background
The risk of infection and severity of illness by SARS-CoV-2 infection is elevated for people who smoke cigarettes and may motivate quitting. Organic public conversations on Twitter about quitting smoking could provide insight into quitting motivations or behaviors associated with the pandemic.

Objective
This study explored key topics of conversation about quitting cigarette smoking and examined their trajectory during 2018-2020.

Methods
Topic model analysis with latent Dirichlet allocation (LDA) identified themes in US tweets with the term “quit smoking.” The model was trained on posts from 2018 and was then applied to tweets posted in 2019 and 2020. Analysis of variance and follow-up pairwise tests were used to compare the daily frequency of tweets within and across years by quarter.

Results
The mean numbers of daily tweets on quitting smoking in 2018, 2019, and 2020 were 133 (SD 36.2), 145 (SD 69.4), and 127 (SD 32.6), respectively. Six topics were extracted: (1) need to quit, (2) personal experiences, (3) electronic cigarettes (e-cigarettes), (4) advice/success, (5) quitting as a component of general health behavior change, and (6) clinics/services. Overall, the pandemic was not associated with changes in posts about quitting; instead, New Year’s resolutions and the 2019 e-cigarette or vaping use–associated lung injury (EVALI) epidemic were more plausible explanations for observed changes within and across years. Fewer second-quarter posts in 2020 for the topic e-cigarettes may reflect lower pandemic-related quitting interest, whereas fourth-quarter increases in 2020 for other topics pointed to a late-year upswing.

Conclusions
Twitter posts suggest that the pandemic did not generate greater interest in quitting smoking, but possibly a decrease in motivation when the rate of infections was increasing in the second quarter of 2020. Public health authorities may wish to craft messages for specific Twitter audiences (eg, using hashtags) to motivate quitting during pandemics.
Conditional Probability Joint Extraction of Nested Biomedical Events: Design of a Unified Extraction Framework Based on Neural Networks
Authors: Wang Yan , Wang Jian , Lu Huiyi , Xu Bing , Zhang Yijia , Banbhrani Santosh Kumar , Lin Hongfei 
Published: April 2022

BackgroundEvent extraction is essential for natural language processing. In the biomedical field, the nested event phenomenon (event A as a participating role of event B) makes extracting this event more difficult than extracting a single event. Therefore, the performance of nested biomedical events is always underwhelming. In addition, previous works relied on a pipeline to build an event extraction model, which ignored the dependence between trigger recognition and event argument detection tasks and produced significant cascading errors.ObjectiveThis study aims to design a unified framework to jointly train biomedical event triggers and arguments and improve the performance of extracting nested biomedical events.MethodsWe proposed an end-to-end joint extraction model that considers the probability distribution of triggers to alleviate cascading errors. Moreover, we integrated the syntactic structure into an attention-based gate graph convolutional network to capture potential interrelations between triggers and related entities, which improved the performance of extracting nested biomedical events.ResultsThe experimental results demonstrated that our proposed method achieved the best F1 score on the multilevel event extraction biomedical event extraction corpus and achieved a favorable performance on the biomedical natural language processing shared task 2011 Genia event corpus.ConclusionsOur conditional probability joint extraction model is good at extracting nested biomedical events because of the joint extraction mechanism and the syntax graph structure. Moreover, as our model did not rely on external knowledge and specific feature engineering, it had a particular generalization performance.
Primer registro de albinismo en la guatusa (Dasyprocta punctata Gray, 1942) para Honduras
Authors: Elvir-Valle Fausto Antonio , Núñez-Figueroa Lidia Josefina , Díaz-Sánchez María Angelina 
Published: April 2022

Resumen
La hipopigmentación o albinismo es el resultado de una condición genética recesiva que se caracteriza por la falta de pigmentación en el pelaje, piel e iris de los ojos. Aquí se documenta el primer registro de albinismo en agutí o guatusa (Dasyprocta punctata) en Honduras, el sitio del hallazgo es en el Ecoparque y Zoológico Joya Grande en el municipio de Santa Cruz de Yojoa en el departamento de Cortés.
Palabras clave: Albinismo, guatusa, pigmentación, Honduras.
Burn skin treatment by nanoparticle – review article
Authors: Jaddoa Nihad Taha Mohammed , Ghafil Jenan A. , Zgair Ayaid Khadem 
Published: April 2022

Background: The nanotechnology considers as one of the most promising technologies in the treatment of diseases and the manufacture of drugs with high efficiency. This review highlighted on the designing of nanoparticles (NPs) for therapeutics. The size of nanoparticle important in reaching to the target and get out from the body by kidney. Nanostructured and nanocrystalline nanomaterials are the two basic types of nanomaterials. The safe of nanoparticle is one of the most attractive attribute of nanoparticle when it will chose as a medicine. Dendrimers are extensively employed in therapeutic implementation because of their structural attribution of this component. Moreover, the capsid protein of virus is extremely natural nanoparticles can be used in medicine. Nanogels can be used for biological sensors, and other biological applications and administration of pharmaceuticals. Beside that there are many nanoparticles that have many medical applications, i.e. nanodiamonds (NDs), metallic NPs, quantum dots (QDs), Silica-Based NPs (SBNPs), Lipid-Based Nanoparticles (Liposomes).
Conclusion: One of the most important applications of NPs, which was highlighted in this review, is the use of nanoparticles in the treatment of microbial contaminated burns. Finally, it can be concluded that the using of NPs and nanomaterials with no cytotoxic effect in medication are considered one of the most important technologies that have emerged in recent years.
A review of the treatment techniques of VOC
Authors: Gao Min , Mao Mingming , Shi Junrui , Liu Yongqi , Chen Qiang , Li Jia 
Published: April 2022

Abstract
Volatile organic compounds (VOC) are important sources of environmental pollution. Several commonly used techniques for the treatment of VOC are reviewed. Recovery techniques can be classified into absorption, adsorption, condensation and membrane separation. Destruction techniques include biodegradation, photocatalysis, low-temperature plasma and combustion. The basic principle, applicable conditions, advantages and disadvantages of the above technologies are discussed. By contrast, the rotational regenerative combustion method has a broad prospect. Suggestions on the improvement of rotary regenerative combustion technology are presented. The treatment of VOC requires comprehensive consideration of the advantages and disadvantages of various technologies.
Performance evaluation of college laboratories based on fusion of decision tree and BP neural network
Authors: Yujie Chang , Weimin Gao , Chelli Karim , Muttar Ahmed K. H. 
Published: April 2022

Abstract
Performance evaluation can promote the continuous improvement of the laboratories in a college. It is necessary to take into account the scientific evaluation method during the process of the performance evaluation. In this paper, a performance evaluation method based on the fusion of the decision tree and BP neural network is presented. In detail, the decision tree model is used to select performance evaluation indexes with high weight. The BP neural network was adopted aiming to reduce the impact of assessment prediction of classification by non-core factors. First, the data were pre-processed by trapezoidal membership function. Then, the decision tree was generated by the C4.5 algorithm to select the evaluation indexes with high weight. Then, the BP neural network was trained with as many samples as possible by evaluation indexes; it possesses experts’ experience which can be used to predict the performance evaluation results. The method overcomes the shortages of the separate model, eliminates the disturbance of human factors and improves the accuracy of the evaluation. Experiments show that the model is feasible and effective in performance evaluation of college laboratories. The outcomes of this work can provide a scientific evaluation method for people such as researchers, college administrators and laboratory managers. Also, this paper will help them to improve the management of laboratories and provide them with decision references for constructing the laboratories.
Modern server ARM processors for supercomputers: A64FX and others. Initial data of benchmarks
Authors: Кузьминский Михаил Борисович 
Published: April 2022

Дан сравнительный анализ производительности серверных ARM/̄процессоров, используемых на суперЭВМ или ориентированных в частности на высокопроизводительные вычисления (HPC). В стартовый анализ производительности были отобраны Fujitsu A64FX, Marvell ThunderX2 и Huawei Kunpeng 920. Обзор производительности для HPC сосредоточен в первую очередь на тестах и приложениях для A64FX, поддерживающего более длинные, чем у других ARM/̄процессоров, вектора и имеющего большую пиковую производительность. Производительность A64FX сопоставлена с соответствующими данными для Intel Xeon Skylake и Cascade Lake, и AMD EPYC с Zen 2 и 3 (Roma и Milan), а также с GPU Nvidia V100 и A100. Сформулирован краткий набор потенциальных плюсов и минусов микроархитектуры A64FX. Сопоставлены данные о производительности, получаемой с применением различных компиляторов для A64FX. Сформированы признаки, когда A64FX дает обычно преимущества в производительности относительно x86-64, а когда — проигрывает x86-64.Подтверждается, что применение A64FX в суперЭВМ может расти далее. Возможно, гегемония x86-64 в HPC будет уменьшаться, в том числе за счет расширения применения серверных ARM/̄процессоров. Однако проведенный анализ A64FX и ожидаемых в ближайшее время новых процессоров архитектуры AArch64 показал, что ведущим в этом процессе не обязательно окажется A64FX.