bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adherence to risk-assessment protocols to guide computed tomography pulmonary angiography in patients with suspected pulmonary embolism
Authors: Kauppi Juha Matias , Airaksinen K E Juhani , Saha Juuso , Bondfolk Anton , Pouru Jussi-Pekka , Purola Petra , Jaakkola Samuli , Lehtonen Jarmo , Vasankari Tuija , Juonala Markus , Kiviniemi Tuomas 
Published: March 2021

Abstract

Aims
The use of computed tomography pulmonary angiography (CTPA) in the detection of pulmonary embolism (PE) has considerably increased due developing technology and better availability of imaging. The underuse of pre-test probability scores and overuse of CTPA has been previously reported. We sought to investigate the indications for CTPA at a University Hospital emergency clinic and seek for factors eliciting the potential overuse of CTPA.

Methods and results
Altogether 1001 patients were retrospectively collected and analysed from the medical records using a structured case report form. PE was diagnosed in 222/1001 (22.2%) of patients. Patients with PE had more often prior PE/deep vein thrombosis, bleeding/thrombotic diathesis and less often asthma, chronic obstructive pulmonary disease, coronary artery disease, or decompensated heart failure. Patients were divided into three groups based on Wells PE risk-stratification score and two groups based on the revised Geneva score. A total of 9/382 (2.4%), 166/527 (31.5%), and 47/92 (52.2%) patients had PE in the CTPA in the low, intermediate, and high pre-test likelihood groups according to Wells score, and 200/955 (20.9%) and 22/46 (47.8%) patients had PE in the CTPA in the low-intermediate and the high pre-test likelihood groups according to the revised Geneva score, respectively. D-dimer was only measured from 568/909 (62.5%) and 597/955 (62.5%) patients who were either in the low or the intermediate-risk group according to Wells score and the revised Geneva score. Noteworthy, 105/1001 (10.5%) and 107/1001 (10.7%) of the CTPAs were inappropriately ordered according to the Wells score and the revised Geneva score. Altogether 168/1001 (16.8%) could theoretically be avoided.

Conclusions
This study highlights scant utilization of guideline-recommended risk-stratification tools in CTPA use at the emergency department.
Treatment target achievement after myocardial infarction and ischaemic stroke: cardiovascular risk factors, medication use, and lifestyle: the Tromsø Study 2015–16
Authors: Hopstock Laila A , Morseth Bente , Cook Sarah , Eggen Anne Elise , Grimsgaard Sameline , Lundblad Marie W , Løchen Maja-Lisa , Mathiesen Ellisiv , Nilsen Amalie , Njølstad Inger 
Published: March 2021

Abstract

Aims
To investigate European guideline treatment target achievement in cardiovascular risk factors, medication use, and lifestyle, after myocardial infarction (MI) or ischaemic stroke, in women and men living in Norway.

Methods and results
In the population-based Tromsø Study 2015–16 (attendance 65%), 904 participants had previous validated MI and/or stroke. Cross-sectionally, we investigated target achievement for blood pressure (&lt;140/90 mmHg, &lt;130/80 mmHg if diabetes), LDL cholesterol (&lt;1.8 mmol/L), HbA1c (&lt;7.0% if diabetes), overweight (body mass index (BMI) &lt;25 kg/m2, waist circumference women &lt;80 cm, men &lt;94 cm), smoking (non-smoking), physical activity (self-reported &gt;sedentary, accelerometer-measured moderate-to-vigorous ≥150 min/week), diet (intake of fruits ≥200 g/day, vegetables ≥200 g/day, fish ≥200 g/week, saturated fat &lt;10E%, fibre ≥30 g/day, alcohol women ≤10 g/day, men ≤20 g/day), and medication use (antihypertensives, lipid-lowering drugs, antithrombotics, and antidiabetics), using regression models. Proportion of target achievement was for blood pressure 55.2%, LDL cholesterol 9.0%, HbA1c 42.5%, BMI 21.1%, waist circumference 15.7%, non-smoking 86.7%, self-reported physical activity 79%, objectively measured physical activity 11.8%, intake of fruit 64.4%, vegetables 40.7%, fish 96.7%, saturated fat 24.3%, fibre 29.9%, and alcohol 78.5%, use of antidiabetics 83.6%, lipid-lowering drugs 81.0%, antihypertensives 75.9%, and antithrombotics 74.6%. Only 0.7% achieved all cardiovascular risk factor targets combined. Largely, there was little difference between the sexes, and in characteristics, medication use, and lifestyle among target achievers compared to non-achievers.

Conclusion
Secondary prevention of cardiovascular disease was suboptimal. A negligible proportion achieved the treatment target for all risk factors. Improvement in follow-up care and treatment after MI and stroke is needed.
Photoperiod-dependent developmental reprogramming of the transcriptional response to seawater entry in Atlantic salmon (Salmo salar)
Authors: Iversen Marianne , Mulugeta Teshome , West Alexander C , Jørgensen Even H , Martin Samuel A M , Sandve Simen Rød , Hazlerigg David 
Published: March 2021

Abstract
The developmental transition of juvenile salmon from a freshwater resident morph (parr) to a seawater (SW) migratory morph (smolt), known as smoltification, entails a reorganization of gill function to cope with the altered water environment. Recently, we used RNAseq to characterize the breadth of transcriptional change which takes place in the gill in the FW phase of smoltification. This highlighted the importance of extended exposure to short, winter-like photoperiods (SP) followed by a subsequent increase in photoperiod for completion of transcriptional reprogramming in FW and efficient growth following transfer to SW. Here, we extend this analysis to examine the consequences of this photoperiodic history-dependent reprogramming for subsequent gill responses upon exposure to SW. We use RNAseq to analyze gill samples taken from fish raised on the photoperiod regimes we used previously and then challenged by SW exposure for 24 hours. While fish held on constant light (LL) throughout were able to hypo-osmoregulate during a 24 hours SW challenge, the associated gill transcriptional response was highly distinctive from that in fish which had experienced a 7-week period of exposure to SP followed by a return to LL (SPLL) and had consequently acquired the characteristics of fully developed smolts. Fish transferred from LL to SP, and then held on SP for the remainder of the study was unable to hypo-osmoregulate, and the associated gill transcriptional response to SW exposure featured many transcripts apparently regulated by the glucocorticoid stress axis and by the osmo-sensing transcription factor NFAT5. The importance of these pathways for the gill transcriptional response to SW exposure appears to diminish as a consequence of photoperiod mediated induction of the smolt phenotype, presumably reflecting preparatory developmental changes taking place during this process.
Some theoretical aspects of reprogramming the standard genetic code
Authors: Nowak Kuba , Błażej Paweł , Wnetrzak Małgorzata , Mackiewicz Dorota , Mackiewicz Paweł 
Published: March 2021

Abstract
Reprogramming of the standard genetic code to include non-canonical amino acids (ncAAs) opens new prospects for medicine, industry, and biotechnology. There are several methods of code engineering, which allow us for storing new genetic information in DNA sequences and producing proteins with new properties. Here, we provided a theoretical background for the optimal genetic code expansion, which may find application in the experimental design of the genetic code. We assumed that the expanded genetic code includes both canonical and non-canonical information stored in 64 classical codons. What is more, the new coding system is robust to point mutations and minimizes the possibility of reversion from the new to old information. In order to find such codes, we applied graph theory to analyze the properties of optimal codon sets. We presented the formal procedure in finding the optimal codes with various number of vacant codons that could be assigned to new amino acids. Finally, we discussed the optimal number of the newly incorporated ncAAs and also the optimal size of codon groups that can be assigned to ncAAs.
Impairment in Activities of Daily Living and Unmet Need for Care Among Older Adults: A Population-Based Study From Burkina Faso
Authors: Brinkmann Ben , Davies Justine I , Witham Miles D , Harling Guy , Bärnighausen Till , Bountogo Mamadou , Siedner Mark J , Ouermi Lucienne , Junghanns Jana , Coulibaly Boubacar , Sié Ali , Payne Collin F , Kohler Iliana V 
Published: March 2021

Abstract

Objectives
The importance of impairment in performing activities of daily living (ADL) is likely to increase in sub-Saharan Africa because few care options for affected people exist. This study investigated the prevalence of ADL impairment, the extent to which care need was met, and described characteristics of people with ADL impairment and unmet need in Burkina Faso.

Methods
This study used data from the Centre de Recherche en Santé de Nouna Heidelberg Aging Study, a population-based study among 3,026 adults aged older than 40 years conducted in rural Burkina Faso. Information on 6 basic ADL items was sought, with a follow-up question asking whether care need was not met, partially met, or met. Bivariable correlations and multivariable logistic regression were used to determine sociodemographic and health characteristics associated with ADL impairment and unmet need.

Results
ADL impairment of any kind was reported by 1,202 (39.7%) respondents and was associated with older age (adjusted odds ratio: 1.05 [95% CI: 1.04–1.06]), being a woman (1.33 [1.06–1.60]), and reporting depressive symptoms (1.90 [1.65–2.18]). Among those with ADL impairment, 67.8% had at least one unmet need. Severe ADL impairment was found in 202 (6.7%) respondents, who reported a lower prevalence of unmet need (43.1%). Severe ADL impairment was associated with depressive symptoms (2.55 [2.11–3.07]) to a stronger degree than any ADL impairment.

Discussion
Prevalence of ADL impairment and unmet need was high in this setting. Variation in impairment across the population highlighted key groups for future interventions. Unmet need for care was highest in middle-aged adults, indicating a gap in care provision.
Are Older Adults More Optimistic? Evidence From China, Israel, and the United States
Authors: Lin Hongmei , Chang Yuanqing , Chen Chao , Ho Yuen Wan , Xi Wanyu , Zhang Xin , Fung Helene H , Ayalon Liat 
Published: March 2021

Abstract

Objectives
Optimistic bias refers to the phenomenon that individuals believe bad things are less likely to happen to themselves than to others. However, whether optimistic bias could vary across age and culture is unknown. The present study aims to investigate (a) whether individuals exhibit optimistic bias in the context of coronavirus disease 2019 (COVID-19) pandemic, and (b) whether age and culture would moderate such bias.

Method
1,051 participants recruited from China, Israel, and the United States took the online survey. Risk perceptions consist of 3 questions: estimating the infected probability of different social distance groups (i.e., self, close others, and nonclose others), the days that it would take for the number of new infections to decrease to zero and the trend of infections in regions of different geographical distances (i.e., local place, other places inside participants’ country, and other countries). Participants in China and the United States also reported their personal communal values measured by Schwartz’s Value Survey.

Results
Results from Hierarchical Linear Modeling generally confirmed that (a) all participants exhibited optimistic bias to some extent, and (b) with age, Chinese participants had a higher level of optimistic bias than the Israeli and U.S. participants. Compared to their younger counterparts, older Chinese are more likely to believe that local communities are at lower risk of COVID-19 than other countries.

Discussion
These findings support the hypothesis that age differences in risk perceptions might be influenced by cultural context. Further analysis indicated that such cultural and age variations in optimistic bias were likely to be driven by age-related increase in internalized cultural values.
Comparative analysis of 7 short-read sequencing platforms using the Korean Reference Genome: MGI and Illumina sequencing benchmark for whole-genome sequencing
Authors: Kim Hak-Min , Jeon Sungwon , Chung Oksung , Jun Je Hoon , Kim Hui-Su , Blazyte Asta , Lee Hwang-Yeol , Yu Youngseok , Cho Yun Sung , Bolser Dan M , Bhak Jong 
Published: March 2021

Abstract

Background
DNBSEQ-T7 is a new whole-genome sequencer developed by Complete Genomics and MGI using DNA nanoball and combinatorial probe anchor synthesis technologies to generate short reads at a very large scale—up to 60 human genomes per day. However, it has not been objectively and systematically compared against Illumina short-read sequencers.

Findings
By using the same KOREF sample, the Korean Reference Genome, we have compared 7 sequencing platforms including BGISEQ-500, DNBSEQ-T7, HiSeq2000, HiSeq2500, HiSeq4000, HiSeqX10, and NovaSeq6000. We measured sequencing quality by comparing sequencing statistics (base quality, duplication rate, and random error rate), mapping statistics (mapping rate, depth distribution, and percent GC coverage), and variant statistics (transition/transversion ratio, dbSNP annotation rate, and concordance rate with single-nucleotide polymorphism [SNP] genotyping chip) across the 7 sequencing platforms. We found that MGI platforms showed a higher concordance rate for SNP genotyping than HiSeq2000 and HiSeq4000. The similarity matrix of variant calls confirmed that the 2 MGI platforms have the most similar characteristics to the HiSeq2500 platform.

Conclusions
Overall, MGI and Illumina sequencing platforms showed comparable levels of sequencing quality, uniformity of coverage, percent GC coverage, and variant accuracy; thus we conclude that the MGI platforms can be used for a wide range of genomics research fields at a lower cost than the Illumina platforms.
Direct and Ambient Light Pollution Alters Recruitment for a Diurnal Plant–Pollinator System
Authors: Wilson Ashley A , Seymoure Brett M , Jaeger Sierra , Milstead Briana , Payne Helen , Peria Lindsay , Vosbigian Ryan A , Francis Clinton D 
Published: March 2021

Synopsis
Artificial light at night (ALAN) functions as a novel environmental stimulus that has the potential to disrupt interactions among species. Despite recent efforts to explain nocturnal pollinators’ responses to this stimulus, the likelihood and associated mechanisms of attraction toward artificial light and potential consequences on fitness for diurnal pollinators are still largely unclear. Here, we took advantage of the obligate mutualism between yucca moths (Tegeticula maculata maculata) and yucca plants (Hesperoyucca whipplei) to understand how direct light exposure and skyglow can influence a pairwise plant–pollinator interaction. To surmise whether adult moths exhibit positive phototaxis, we deployed a set of field-placed light towers during the peak of yucca flowering and compared the number of moths caught in traps between dark-controlled and light-treated trials. Adult moth abundance was much higher when light was present, which suggests that ALAN may alter this diurnal moth’s activity patterns to expand their temporal niche into the night. To evaluate ALAN effects on yucca fruit set and moth larva recruitment, we measured skyglow exposure above yucca plants and direct light intensity from a second set of light towers. Both larva and fruit recruitment increased with skyglow, and fruit set also increased with direct lighting, but the relationship was weaker. Contrarily, larva recruitment did not change when exposed to a gradient of direct light, which may instead reflect effects of ALAN on moth physiology, such as disrupted female oviposition, or misdirecting behaviors essential to oviposition activity. Our results suggest that ALAN can positively influence the fitness of both plants and moths in this tightly co-evolved mutualism, but the benefits to each species may depend on whether night lighting is direct or indirect. Whether such effects and mechanisms could relate to susceptibility to the presence of ALAN on this or other plant–pollinator relationships will remain an important focus of future research.
Approximate controllability of semilinear retarded stochastic differential system with non-instantaneous impulses: Fredholm theory approach
Authors: Anukiruthika K , Durga N , Muthukumar P 
Published: March 2021

Abstract
This article deals with the approximate controllability of semilinear retarded integrodifferential equations with non-instantaneous impulses governed by Poisson jumps in Hilbert space. The existence of a mild solution is established by using stochastic calculus and a suitable fixed point technique. The approximate controllability of the proposed non-linear stochastic differential system is obtained by employing the theory of interpolation spaces and Fredholm theory. Finally, applications to the stochastic heat equation and retarded type stochastic Benjamin–Bona–Mahony equation are provided to illustrate the developed theoretical results.
Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Authors: Knyshov Alexander , Hoang Samantha , Weirauch Christiane 
Published: March 2021

Abstract
Automated insect identification systems have been explored for more than two decades but have only recently started to take advantage of powerful and versatile convolutional neural networks (CNNs). While typical CNN applications still require large training image datasets with hundreds of images per taxon, pretrained CNNs recently have been shown to be highly accurate, while being trained on much smaller datasets. We here evaluate the performance of CNN-based machine learning approaches in identifying three curated species-level dorsal habitus datasets for Miridae, the plant bugs. Miridae are of economic importance, but species-level identifications are challenging and typically rely on information other than dorsal habitus (e.g., host plants, locality, genitalic structures). Each dataset contained 2–6 species and 126–246 images in total, with a mean of only 32 images per species for the most difficult dataset. We find that closely related species of plant bugs can be identified with 80–90% accuracy based on their dorsal habitus alone. The pretrained CNN performed 10–20% better than a taxon expert who had access to the same dorsal habitus images. We find that feature extraction protocols (selection and combination of blocks of CNN layers) impact identification accuracy much more than the classifying mechanism (support vector machine and deep neural network classifiers). While our network has much lower accuracy on photographs of live insects (62%), overall results confirm that a pretrained CNN can be straightforwardly adapted to collection-based images for a new taxonomic group and successfully extract relevant features to classify insect species.
SARS-CoV-2 Antibodies and Associated Factors at Different Hospitalization Time Points in 192 COVID-19 Cases
Authors: Ou Jingyi , Tan Mingkai , He Haolan , Tan Haiyan , Mai Jiewen , Long Yaoxiang , Jiang Xiaowen , He Qing , Huang Ying , Li Yan , Chen Renshen , Li Liya , Shi Yaling , Li Fang 
Published: March 2021

Abstract

Background
We launched a retrospective analysis of SARS-CoV-2 antibodies in 192 patients with COVID-19, aiming to depict the kinetic profile of SARS-CoV-2 antibodies and explore the factors related to SARS-CoV-2 antibody expression.

Methods
Data on 192 confirmed patients with COVID-19 between January and February 2020 was collected from the designated hospital that received patients with COVID-19 in Guangzhou, China. Moreover, a cohort of 130 suspected patients with COVID-19 and 209 healthy people were also enrolled in this study. IgM and IgG antibodies to SARS-CoV-2 were detected by the chemiluminescence immunoassay kits in different groups.

Results
A total of 192 COVID-19 cases were analyzed, of which had 81.8% anti-SARS-CoV-2 IgM detected and 93.2% anti-SARS-CoV-2 IgG detected, respectively, at the time of sampling. The kinetics of anti-SARS-CoV-2 IgM and IgG showed that, the confirmed cases had anti-SARS-CoV-2 IgM seroconversion occurred 5–10 days after the onset of the symptoms, and then IgM rose rapidly to reach a peak within around 2–3 weeks, maintaining at its peak for 1 week before its decline. While they had anti-SARS-CoV-2 IgG seroconversion simultaneously or sequentially with IgM, reaching its peak within around 3 to 4 weeks and began to decline after the fifth week. Besides, correlation analysis showed that in patients with COVID-19 the level of IgM was related to gender and disease severity (P &lt; 0.01), and the level of IgG was related to age and disease severity (P &lt; 0.001). The univariate analysis of relevant factors indicated that the level of IgG had a weak correlation with age (r = 0.374, P &lt; 0.01). The level of IgM in male patients was higher than that in female patients (P &lt; 0.001). The expression level of anti-SARS-CoV-2 IgM and IgG were positively correlated with the severity of COVID-19 and the duration of the virus in the patients.

Conclusion
The findings of this study show that anti-SARS-CoV-2 IgM and IgG can be important assisting COVID-19 diagnosis, especially in the early phase of infection. Furthermore, antibody expression in patients with COVID-19 is also correlated with disease severity, age, gender, and virus clearance or continuous replication.
Machine learning–based prediction of health outcomes in pediatric organ transplantation recipients
Authors: Killian Michael O , Payrovnaziri Seyedeh Neelufar , Gupta Dipankar , Desai Dev , He Zhe 
Published: March 2021

Abstract

Objectives
Prediction of post-transplant health outcomes and identification of key factors remain important issues for pediatric transplant teams and researchers. Outcomes research has generally relied on general linear modeling or similar techniques offering limited predictive validity. Thus far, data-driven modeling and machine learning (ML) approaches have had limited application and success in pediatric transplant outcomes research. The purpose of the current study was to examine ML models predicting post-transplant hospitalization in a sample of pediatric kidney, liver, and heart transplant recipients from a large solid organ transplant program.

Materials and Methods
Various logistic regression, naive Bayes, support vector machine, and deep learning (DL) methods were used to predict 1-, 3-, and 5-year post-transplant hospitalization using patient and administrative data from a large pediatric organ transplant center.

Results
DL models did not outperform traditional ML models across organ types and prediction windows with area under the receiver operating characteristic curve values ranging from 0.50 to 0.593. Shapley additive explanations (SHAP) were used to increase the interpretability of DL model results. Various medical, patient, and social variables were identified as salient predictors across organ types.

Discussion
Results showed that deep learning models did not yield superior performance in comparison to models using traditional machine learning methods. However, the potential utility of deep learning modeling for health outcome prediction with pediatric patients in the presence of large number of samples warrants further examination.

Conclusion
Results point to DL models as potentially useful tools in decision-support systems assisting physicians and transplant teams in identifying patients at a greater risk for poor post-transplant outcomes.