bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design space calculation and continuous improvement considering a noise parameter: A case study of ethanol precipitation process optimization for the Carthami Flos extract
Authors: Tai Yanni , Qu Haibin , Gong Xingchu 
Published: March 2021

Abstract

Background
The optimization of process parameters in the pharmaceutical industry is often carried out according to the Quality by Design (QbD) concept. QbD also emphasizes that continuous improvement should be performed in life cycle management. Process parameters that are difficult to control in actual production could be regarded as noise parameters. In this study, a noise parameter was considered, an example of continuous improvement in the design space was provided.
Methods
The ethanol precipitation process of Carthami Flos (Honghua) extract was optimized based on the QbD concept. The critical process parameters (CPPs) were identified using a definitive screening design. Considering that the refrigeration temperature of industrial ethanol precipitation is often difficult to control with seasonal changes, the refrigeration temperature was treated as a noise parameter. The design space was then calculated using an exhaustive search-Monte Carlo method. The mathematical models were reestablished when more data were obtained and then the calculated probabilities of reaching the process standards were updated.
Results
The calculation procedure of design space based on an exhaustive search-Monte Carlo method was proposed. The density of the concentrated extract, ethanol concentration, the volume ratio of ethanol to concentrated extract, stirring time after ethanol addition, and refrigeration temperature were selected as CPPs. The mathematical models of CPPs and evaluation indicators were established, and the coefficient of determination of each model was greater than 0.81. The predictive performance of the models was good. After continuous improvement, the recalculated probability values were more reliable, the design space became larger.
Conclusions
The calculation of design space and the continuous improvement strategy considering a noise parameter was developed. In industrial production, it is also recommended to adopt this similar idea, that is, continuing to collect industrial data and regularly updating the mathematical models, which can further update the design space and make it more stable and reliable.
Analyzing Potato Market Participation, Market Surplus and Market Outlet Choice on Small Farm Household Level: the Case of Lemoworeda, Hadiya Zone, Southern Ethiopia.
Authors: Daniso Chaimiso Demisse , Sebeta Mebratu Alemu , Shapa Afework Berihanu , Abile Mesfin Melese 
Published: March 2021

Abstract
This study was aimed to analysis potato market participation, market surplus and market outlet choice in Lemo woreda of Hadiya zone southern Ethiopia. In this study, primary and secondary data source were employed. Data were obtained from a sample of 202 farm household. To take 202 sample farm household simple random sampling techniques were used. For data analysis, Heckman second stage and multivariate probit models were employed to identify factors that determine potato market participation decision and potato sale volume of the farm households, and potato farmer market outlet choice respectively. The Heckman's first stages (probit) indicates that education level, sex, membership in the cooperative, distance to the nearest market, remittent from family members, own transportation facilities, seed types, frequency of extension contact, lagged market price and amount of credit that farm household used for potato production determine potato market participation. Heckman the second stage (OLS) result shows that education level, cooperative membership, family size, total cropland, oxen owned, types of seed, and lagged market price were significantly determine potato marketed surplus. On the other side; the multivariate probit results showed that the probability of choosing wholesalers, retailers, collectors, and consumers market outlet choice significantly affected by age, educational level, sex, farm experiences, distance to near market, family size, remittance, land size, product supplied to the market, own transport facility and extension service. Based on the findings of the study, we recommend that the government and concerned stakeholders should focus on promoting improved potato seed, encouraging education, promoting farmers’ cooperatives, empowering females, strengthening rural-urban infrastructure, and protecting informal labor trading.
Comparative Analysis of Gut Microbiome between Captive and Wild Red Deer 1 (Cervus elaphus) in Inner Mongolia&nbsp;
Authors: Sun Yue , Yu Yanze , Guo Jinhao , Zhong Linqiang , Zhang Minghai 
Published: March 2021

Abstract
The digestive tract of ruminants is the home of the gut microbiome ecosystem, which plays a huge role in the diagnosis of various health conditions and the analysis of physiological conditions in wild animals. Red deer is a second-class protected animal in China. In this study, we used microsatellite and high-throughput sequencing of the 16S rRNA gene in fecal samples of red deer to investigate differences in the gut bacterial microbiota were analyzed between wild and captive in winter. Our results revealed that proportions of bacterial taxa, alpha-and beta-diversities, and relative abundances of amplicon sequence variants in the gut bacterial microbiota of the two groups differed. Firmicutes (79.46%), Bacteroidetes (16%) and Tenericutes (1.25%) were the most predominant phyla in wild red deer. While in captive red deer, Firmicutes (62.5%) was the dominant phylum, followed by Bacteroidetes (29.1%) and Tenericutes.( 3.21%). The specific function and mechanism of Tenericutes in red deer need further study. The wild red deer had higher fecal bacterial diversity than the captive in farm. These differences were attributed to the enrichment of bacterial taxa involved in the digestion of the supplementary food and to different natural diets consumed in the forest. Also the dominant and differential microflora of intestinal microflora in various populations were mined and their related metabolic pathways. In terms of functional data, most of the genes annotated are related to metabolism. The second most commented gene is related to genetic information processing. The comparative study of the intestinal flora of the two populations can not only assess the health status of the two populations, but also provide important suggestions for the breeding of captive red deer and the protection of wild populations.
Prevalence and Assessment of Potentially Inappropriate Medications among Lebanese Elderly inpatients
Authors: Hendaus Mohamed , Chirazi Alissar , Hassan Maya , Mourad Nisreen , Younes Samar , Moussa Mahmoud , Rahal Mohamad 
Published: March 2021

Abstract
Background: Potentially inappropriate medications (PIMs) prescribing in older increases the risk of hospitalization, drug-related problems, and other adverse health outcomes by two to three folds (Lu WH, 2015) (WHO, 2018). Hence several regulations have been implemented in geriatric clinical practice to improve drug appropriateness. This study aimed to assess the prevalence of PIMs identified by the Beers 2019 criteria among Lebanese elderly inpatients, and to identify the factors that may influence such inappropriateness.Methods: This observational study was conducted among elderly patients of 3 hospitals in Lebanon. Data were collected from patients using a questionnaire based on the 2019 Beers criteria. Descriptive statistics and multivariable logistic regression were used to describe and identify the predictors of PIMs use. Statistical analysis was carried out using the SPSS software version 22.0.Results: A total of 110 in-patients aging 65 years and older were analyzed. 80% of patients were taking drugs that are considered PIMs to be avoided; The most commonly prescribed PIM was proton pump inhibitors (PPIs) (60%). PIMs use was significantly associated with polypharmacy and other comorbid conditions (p-value <0.05). The prevalence of PIMs to be used with caution was 78.2%, those having drug-disease interactions were 22.7% and those having clinically important drug-drug interactions were 15.5%.Conclusion: A high prevalence of PIMs use in Lebanese hospitalized patients was observed, and was significantly associated with polypharmacy and other CNS conditions. This study supports the need for the implementation of clinical guidelines and staff to improve drug prescribing in older adults. Trial registration: retrospectively registered.
Sialic acid-Dependent Binding and Viral Entry of SARS-CoV-2
Authors: Nguyen Linh , McCord Kelli , Bui Duong , Bouwman Kim , Kitova Elena , Kumawat Dhanraj , Daskhan Gour , Tomris Ilhan , Han Ling , Chopra Pradeep , Yang Tzu-Jing , Willows Steven , Lowary Todd , West Lori , Hsu Shang-Te Danny , Tompkins Stephen , Boons Geert-Jan , Mason Andrew , de Vries Robert , Macauley Matthew , Klassen John 
Published: March 2021

Abstract
Emerging evidence suggests that host glycans influence infection by SARS-CoV-2. Here, we reveal that the receptor-binding domain (RBD) of the spike (S)-protein on SARS-CoV-2 recognizes oligosaccharides containing sialic acid (SA), with preference for the oligosaccharide of monosialylated gangliosides. Gangliosides embedded within an artificial membrane also bind the RBD. The monomeric affinities (Kd = 100-200 μM) of gangliosides for the RBD are similar to heparan sulfate, another negatively charged glycan ligand of the RBD proposed as a viral co-receptor. RBD binding and infection of SARS-CoV-2 pseudotyped lentivirus to ACE2-expressing cells is decreased upon depleting cell surface SA level using three approaches: sialyltransferase inhibition, genetic knock-out of SA biosynthesis, or neuraminidase treatment. These effects on RBD binding and pseudotyped viral entry are recapitulated with pharmacological or genetic disruption of glycolipid biosynthesis. Together, these results suggest that sialylated glycans, specifically glycolipids, facilitate viral entry of SARS-CoV-2.
EU NÃO VOU MORRER
Authors: Nascimento Letícia Carolina Pereira 
Published: March 2021

O presente texto traz relatos registrados em diários e publicações em redes sociais as minhas experiências de uma travesti negra e gorda durante o processo de distanciamento social imposto pela pandemia de COVID-19 nos meses de março, abril, maio e junho de 2020. Tendo por objetivo: compreender como as dimensões da solidão, autocuidado e resistência perpassam as corporalidades e subjetividades travestigêneres e/ou negras e/ou gordas para além da pandemia de COVID-19. A cartografia é o rizoma metodológico escolhido para esse texto, pois, permite alinhavar experiências diversas na construção de um mapa afetivo apresentado nesse texto a partir de gatilhos. A partir dos gatilhos apresentados percebe-se que apesar do isolamento o contato com o mundo social por meio das mídias digitais traz notícias dolorosas, especialmente pelo fato das assimetrias socias impostas pelas estruturas cis/hetero/branco/magro/normativas produzem uma necropolítica de corporalidades dissidentes. Paradoxalmente, também será as redes sociais um modo de articular potências que criam vida durante a pandemia por meio da criação de redes de apoio entre travestis e/ou negras e/ou gordas. O autocuidado numa dimensão pessoal e coletivo é compreendida como uma ferramenta capaz de pensar as interseccionalidades a partir de diferenças fazendo emergir novas formas de organização social. De modo afirmativo das experiências de uma travesti negra e gorda, conclui-se com o decreto: eu não vou morrer.
Palavras-chave: Travesti negra e gorda. Pandemia. Solidão. Resistência.
Predicting Outcomes in Patients Undergoing Pancreatectomy Using Wearable Technology and Machine Learning: Prospective Cohort Study
Authors: Cos Heidy , Li Dingwen , Williams Gregory , Chininis Jeffrey , Dai Ruixuan , Zhang Jingwen , Srivastava Rohit , Raper Lacey , Sanford Dominic , Hawkins William , Lu Chenyang , Hammill Chet W 
Published: March 2021

Background
Pancreatic cancer is the third leading cause of cancer-related deaths, and although pancreatectomy is currently the only curative treatment, it is associated with significant morbidity.

Objective
The objective of this study was to evaluate the utility of wearable telemonitoring technologies to predict treatment outcomes using patient activity metrics and machine learning.

Methods
In this prospective, single-center, single-cohort study, patients scheduled for pancreatectomy were provided with a wearable telemonitoring device to be worn prior to surgery. Patient clinical data were collected and all patients were evaluated using the American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC). Machine learning models were developed to predict whether patients would have a textbook outcome and compared with the ACS-NSQIP SRC using area under the receiver operating characteristic (AUROC) curves.

Results
Between February 2019 and February 2020, 48 patients completed the study. Patient activity metrics were collected over an average of 27.8 days before surgery. Patients took an average of 4162.1 (SD 4052.6) steps per day and had an average heart rate of 75.6 (SD 14.8) beats per minute. Twenty-eight (58%) patients had a textbook outcome after pancreatectomy. The group of 20 (42%) patients who did not have a textbook outcome included 14 patients with severe complications and 11 patients requiring readmission. The ACS-NSQIP SRC had an AUROC curve of 0.6333 to predict failure to achieve a textbook outcome, while our model combining patient clinical characteristics and patient activity data achieved the highest performance with an AUROC curve of 0.7875.

Conclusions
Machine learning models outperformed ACS-NSQIP SRC estimates in predicting textbook outcomes after pancreatectomy. The highest performance was observed when machine learning models incorporated patient clinical characteristics and activity metrics.
Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning
Authors: Wang Haishuai , Avillach Paul 
Published: March 2021

Background
In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children.

Objective
Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening.

Methods
After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants.

Results
The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic individuals from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic individuals from nonautistic individuals. Our classifier demonstrated a considerable improvement of ~13% in terms of classification accuracy compared to standard autism screening tools.

Conclusions
Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.
Decline of Psychological Health Following the Designation of COVID-19 as a Pandemic: Descriptive Study
Authors: Patel Darpan I , Gamez Yazmin , Shah Lajja , Patel Jaini 
Published: March 2021

Background
COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, and as of this writing, Texas, United States, has reported >675,000 cases with over 14,000 deaths. Many of the preventive measures implemented during the pandemic can increase sedentary lifestyles, which can lead to the development of chronic diseases, including obesity, among the general population and cause serious threats to people’s physical health and overall quality of life. Individuals with pre-existing comorbidities are at an increased risk of COVID-19 and may hence have higher levels of stress.

Objective
This study aimed to investigate the relationship between physical activity levels and mental health status on an individual level and to compare them between those with and those without comorbidities in a cohort of Texas residents, before and after COVID-19 was declared a pandemic.

Methods
An electronic survey was disseminated throughout various regions of Texas. In total, 160 individuals were asked questions about their demographic characteristics, time spent on daily physical activities, and daily mental health status before and after COVID-19 was declared a pandemic. Frequency distributions and descriptive statistics were analyzed.

Results
Overall, 94 (58%) participants reported having ≥1 medical condition, and 31 (13.1%) had >3 medical conditions. Physical activity levels among participants with ≥1 pre-existing comorbidity drastically—but not significantly—decreased, as evident from a 10% increase in sedentary lifestyles after COVID-19 was declared a pandemic. On the contrary, we observed a 9% increase in the number of individuals without a pre-existing comorbidity who reported 30-60 min of physical activity per week. There was a 2-fold increase in the number of participants reporting more frequent feelings of nervousness, too much worry, trouble relaxing, and the fear of something awful happening after the pandemic. More specifically, individuals with pre-existing medical conditions reported, on average, a 10% higher incidence of feelings of stress, anxiety, and sadness compared to their healthy counterparts after COVID-19 was declared a pandemic.

Conclusions
Stressful life conditions and chronic comorbidities are risk factors that can affect mental health and reduce the ability to perform activities of daily life. Therefore, when implementing pandemic protocols, municipalities should consider providing mental health support to their citizens to protect them from this rather inconspicuous adverse effect.
A Multiple Health Behavior Change, Self-Monitoring Mobile App for Adolescents: Development and Usability Study of the Health4Life App
Authors: Thornton Louise , Gardner Lauren Anne , Osman Bridie , Green Olivia , Champion Katrina Elizabeth , Bryant Zachary , Teesson Maree , Kay-Lambkin Frances , Chapman Cath ,   The Health4Life Team
Published: March 2021

Background
The link between chronic diseases and the Big 6 lifestyle risk behaviors (ie, poor diet, physical inactivity, smoking, alcohol use, sedentary recreational screen time, and poor sleep) is well established. It is critical to target these lifestyle risk behaviors, as they often co-occur and emerge in adolescence. Smartphones have become an integral part of everyday life, and many adolescents already use mobile apps to monitor their lifestyle behaviors and improve their health. Smartphones may be a valuable platform for engaging adolescents with interventions to prevent key chronic disease risk behaviors.

Objective
The aim of this paper is to describe the development, usability, and acceptability of the Health4Life app, a self-monitoring smartphone app for adolescents that concurrently targets the Big 6 lifestyle behaviors.

Methods
The development of the Health4Life app was an iterative process conducted in collaboration with adolescents and experts. The development process consisted of three stages: scoping the literature; end user consultations, which included a web-based survey (N=815; mean age 13.89, SD 0.89 years) and a focus group (N=12) among adolescents; and app development and beta testing. Following this development work, 232 adolescents were asked to rate the usability and acceptability of the app.

Results
The process resulted in a self-monitoring smartphone app that allows adolescent users to track and set goals for the Big 6 health behaviors, using in-app rewards and notifications to enhance engagement. The overall adolescent feedback was positive in terms of user-friendly design, content, relevance, and helpfulness. Commonly identified areas for improvement were to increase interactive features and display recorded health behaviors differently to improve interpretability.

Conclusions
The Health4Life app is a co-designed, self-monitoring smartphone app for adolescents that concurrently targets the Big 6 lifestyle behaviors. Adolescents rated the app as highly acceptable and usable. The app has the potential to efficiently and effectively modify important risk factors for chronic disease among young people and is currently being evaluated in a world-first trial of 6640 secondary school students in 71 schools across Australia.