bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multiplex Fragment Analysis for Flexible Detection of All SARS-CoV-2 Variants of Concern
Authors: Clark Andrew E , Wang Zhaohui , Ostman Emily , Zheng Hui , Yao Huiyu , Cantarel Brandi , Kanchwala Mohammed , Xing Chao , Chen Li , Irwin Pei , Xu Yan , Oliver Dwight , Lee Francesca M , Gagan Jeffrey R , Filkins Laura , Muthukumar Alagarraju , Park Jason Y , Sarode Ravi , SoRelle Jeffrey A 
Published: April 2022

Abstract

Background
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to emerge, and effective tracking requires rapid return of results. Surveillance of variants is typically performed by whole genome sequencing (WGS), which can be financially prohibitive and requires specialized equipment and bioinformatic expertise. Genotyping approaches are rapid methods for monitoring SARS-CoV-2 variants but require continuous adaptation. Fragment analysis may represent an approach for improved SARS-CoV-2 variant detection.

Methods
A multiplex fragment analysis approach (CoVarScan) was validated using PCR targeting variants by size and fluorescent color. Eight SARS-CoV-2 mutational hot spots in variants of concern (VOCs) were targeted. Three primer pairs (recurrently deleted region [RDR] 1, RDR2, and RDR3–4) flank RDRs in the S-gene. Three allele-specific primers target recurrent spike receptor binding domain mutants. Lastly, 2 primer pairs target recurrent deletions or insertions in ORF1A and ORF8. Fragments were resolved and analyzed by capillary electrophoresis (ABI 3730XL), and mutational signatures were compared to WGS results.

Results
We validated CoVarScan using 3544 clinical respiratory specimens. The assay exhibited 96% sensitivity and 99% specificity compared to WGS. The limit of detection for the core targets (RDR1, RDR2, and ORF1A) was 5 copies/reaction. Variants were identified in 95% of samples with cycle threshold (CT) &lt;30 and 75% of samples with a CT 34 to 35. Assay design was frozen April 2021, but all subsequent VOCs have been detected including Delta (n = 2820), Mu, (n = 6), Lambda (n = 6), and Omicron (n = 309). Genotyping results are available in as little as 4 h.

Conclusions
Multiplex fragment analysis is adaptable and rapid and has similar accuracy to WGS to classify SARS-CoV-2 variants.
Trend and factors associated with multiple arterial revascularization in coronary artery bypass grafting in the UK
Authors: Chan Jeremy , Dimagli Arnaldo , Dong Tim , Fudulu Daniel P , Sinha Shubhra , Angelini Gianni D 
Published: April 2022

Abstract

OBJECTIVES
Benefits of using multiple arterial grafting (MAG), over single arterial grafting in major adverse cardiac event rates and the need for repeat revascularization, have been widely reported. Several guidelines have recommended the use of MAG in selected patients. We report the trend of MAG in patients undergoing isolated coronary artery bypass grafting (CABG) in the UK.

METHODS
This is a retrospective analysis of a prospectively collected UK national database in patients undergoing non-emergency, isolated CABG from 1996 to 2018. Patients were divided into single arterial grafting and MAG, and trends in perioperative characteristics were analysed.

RESULTS
A total of 336 321 patients were included, of whom 284 003 (84.44%) received single arterial grafting and 52 318 (15.56%) received MAG. The use of MAG after an initial increase from 1996 to 2001, steadily decreased thereafter, particularly in the use of radial artery. MAG was likely to be performed in younger patients [66.72 (standard deviation: 9.22) vs 62.30 (standard deviation: 10.06), P &lt; 0.001] and males (85% vs 81%, P &lt; 0.001). After propensity score matching, the single arterial grafting group was more likely to undergo on-pump CABG (90% vs 69%, P &lt; 0.001), experienced a lower in-hospital mortality (1.1% vs 1.3%, P &lt; 0.001) and incidence of return to theatre for bleeding (2.5% vs 3.0%, P &lt; 0.001).

CONCLUSIONS
Our data show that the use of MAG in CABG in the UK after an initial increase from 1996 to 2001 steadily decreased thereafter until 2018. This is likely to be multifactorial and a better understanding of the main causes may contribute to establishing the best indication for MAG in everyday clinical practice.
Surgical aortic valve replacement in patients aged 50–69 years—insights from the German Aortic Valve Registry (GARY)
Authors: Vogt Ferdinand , Santarpino Giuseppe , Fujita Buntaro , Frerker Christian , Bauer Timm , Beckmann Andreas , Bekeredjian Raffi , Bleiziffer Sabine , Möllmann Helge , Walther Thomas , Beyersdorf Friedhelm , Hamm Christian , Böning Andreas , Baldus Stephan , Ensminger Stephan , Fischlein Theodor , Eckner Dennis ,   the GARY Executive Board, Möllmann Helge , Walther Thomas , Beyersdorf Friedhelm , Hamm Christian , Ensminger Stephan 
Published: April 2022

Abstract

OBJECTIVES
The aim of this study was to analyse the outcome of patients between 50 and 69 years of age undergoing biological or mechanical aortic valve replacement.

METHODS
Data were collected from the German Aortic Valve Registry. Data were analysed regarding baseline characteristics and outcome parameters such as 5-year survival, stroke and reintervention.

RESULTS
In total,

3046 patients undergoing isolated surgical aortic valve replacement between 2011 and 2012 were investigated and a propensity score matching was performed. Within this period, 2239 patients received a biological prostheses, while 807 patients received a mechanical prosthesis. Mean age in the biological group was 63.07 (±5.11) and 57.34 (±4.67) in the mechanical group (standardized mean difference 1.172). In the overall cohort, there were more female patients in the biological group (32.7% vs 28.4%) and log EuroSCORE I was higher (5.41% vs 4.26%). After propensity matching (610 pairs), there was no difference in the mortality at 5-year follow-up (12.1% biological vs 9.2% mechanical P = 0.05) nor for reoperation/reintervention (2.5% biological vs 2.0% mechanical, P = 0.546). Patients undergoing mechanical aortic valve replacement suffered from a higher stroke rate 3.3% vs 1.5% (P = 0.04) at 5-year follow-up.

CONCLUSIONS
Aortic valve replacement with biological or mechanical prostheses showed similar 5-year outcomes for survival and reoperation in a propensity-matched cohort, but significantly increased stroke rate after mechanical aortic valve replacement. This could influence the choice of a mechanical valve in younger patients.
Staging right heart failure in patients with tricuspid regurgitation undergoing tricuspid surgery
Authors: Galloo Xavier , Stassen Jan , Butcher Steele C , Meucci Maria Chiara , Dietz Marlieke F , Mertens Bart J A , Prihadi Edgard A , van der Bijl Pieter , Ajmone Marsan Nina , Braun Jerry , Bax Jeroen J , Delgado Victoria 
Published: April 2022

Abstract

OBJECTIVES
This study evaluated the prognostic value of staging right heart failure in patients with significant tricuspid regurgitation (TR) undergoing tricuspid valve (TV) surgery.

METHODS
Patients with significant TR who underwent TV surgery were divided into 4 right heart failure stages according to the presence of right ventricular (RV) dysfunction and clinical signs of right heart failure: stage 1 was defined as no RV dysfunction and no signs of right heart failure; stage 2 indicated RV dysfunction without signs of right heart failure; stage 3 included RV dysfunction and signs of right heart failure; and stage 4 was defined as RV dysfunction and refractory signs of right heart failure at rest.

RESULTS
A total of 278 patients [mean age 64 (12), 49% males] were included, of whom 34 (12%) patients were classified as stages 1 and 2, 141 (51%) as stage 3 and 103 (37%) as stage 4 right heart failure. The majority of patients (91%) had TV surgery concomitant to left-sided valve surgery or coronary artery bypass grafting and 95% underwent TV annuloplasty. Cumulative survival rates were 89%, 78% and 61% at 1 month, 1 year and 5 years, respectively. Stages 1 and 2 and stage 3 were independently associated with better survival compared to stage 4 (hazard ratio: 0.391 [95% confidence interval: 0.186–0.823] and 0.548 [95% confidence interval: 0.369–0.813], respectively).

CONCLUSIONS
Patients with significant TR undergoing TV surgery and diagnosed without advanced right heart failure have better survival as compared to patients with right heart failure.
Safety profile of intravenous administration of livePichia pastoriscells in mice
Authors: Becerril-García Miguel Á , Flores-Maldonado Orlando E , González Gloria M , García-González Gerardo , Hernández-Bello Romel , Palma-Nicolás José P 
Published: April 2022

AbstractPichia pastoris has been widely used to produce antigenic proteins aimed to integrate subunit vaccines. Moreover, increasing interest in large-scale vaccine production at the lowest cost is rapidly focusing in the development of yeast surface display (YSD) systems for delivery of antigens. In this scenario, the safety of live yeast administration must be warranted, however, such information is very scarce. Here, we assess the intravenous administration (i.v.) of live P. pastoris cells in order to trace dissemination in BALB/c mice and to evaluate the immune response raised against the yeast compared to the well-defined pathogen Candida albicans. Our results demonstrate dissemination of P. pastoris to the heart, kidney, and spleen, but it is quickly eliminated during the first 48 h postinfection (hpi), with persistence in the liver along with mild mononuclear (MN) and polymorphonuclear (PMN) infiltrate, which was resolved at 144 hpi. In vivo delayed-type hypersensitivity test (DTH) or in vitro antigenic stimulation of mice splenocytes demonstrate that transient infection of P. pastoris did not induce a cell-mediated immune response nor increase the level of circulating IgG or IgM. These results demonstrate the innocuous profile of P. pastoris and support its use as a safe delivery system for vaccine development.
An empirical evaluation of genotype imputation of ancient DNA
Authors: Ausmees Kristiina , Sanchez-Quinto Federico , Jakobsson Mattias , Nettelblad Carl 
Published: April 2022

Abstract
With capabilities of sequencing ancient DNA to high coverage often limited by sample quality or cost, imputation of missing genotypes presents a possibility to increase the power of inference as well as cost-effectiveness for the analysis of ancient data. However, the high degree of uncertainty often associated with ancient DNA poses several methodological challenges, and performance of imputation methods in this context has not been fully explored. To gain further insights, we performed a systematic evaluation of imputation of ancient data using Beagle v4.0 and reference data from phase 3 of the 1000 Genomes project, investigating the effects of coverage, phased reference, and study sample size. Making use of five ancient individuals with high-coverage data available, we evaluated imputed data for accuracy, reference bias, and genetic affinities as captured by principal component analysis. We obtained genotype concordance levels of over 99% for data with 1× coverage, and similar levels of accuracy and reference bias at levels as low as 0.75×. Our findings suggest that using imputed data can be a realistic option for various population genetic analyses even for data in coverage ranges below 1×. We also show that a large and varied phased reference panel as well as the inclusion of low- to moderate-coverage ancient individuals in the study sample can increase imputation performance, particularly for rare alleles. In-depth analysis of imputed data with respect to genetic variants and allele frequencies gave further insight into the nature of errors arising during imputation, and can provide practical guidelines for postprocessing and validation prior to downstream analysis.
The L-shaped selection algorithm for multitrait genomic selection
Authors: Amini Fatemeh , Hu Guiping , Wang Lizhi , Wu Ruoyu 
Published: April 2022

Abstract
Selecting for multiple traits as opposed to a single trait has become increasingly important in genomic selection. As one of the most popular approaches to multitrait genomic selection, index selection uses a weighted average of all traits as a single breeding objective. Although intuitive and effective, index selection is not only numerically sensitive but also structurally incapable of finding certain optimal breeding parents. This paper proposes a new selection method for multitrait genomic selection, the L-shaped selection, which addresses the limitations of index selection by normalizing the trait values and using an L-shaped objective function to find optimal breeding parents. This algorithm has been proven to be able to find any Pareto optimal solution with appropriate weights. Two performance metrics have also been defined to quantify multitrait genomic selection algorithms with respect to their ability to accelerate genetic gain and preserve genetic diversity. Computational experiments were conducted to demonstrate the improved performance of L-shaped selection over-index selection.
Lessons learned about the biology and genomics of Diaphorina citri infection with “Candidatus Liberibacter asiaticus” by integrating new and archived organ-specific transcriptome data
Authors: Mann Marina , Saha Surya , Cicero Joseph M , Pitino Marco , Moulton Kathy , Hunter Wayne B , Cano Liliana M , Mueller Lukas A , Heck Michelle 
Published: April 2022

Abstract

Background
Huanglongbing, a devastating disease of citrus, is caused by the obligate, intracellular bacterium “Candidatus Liberibacter asiaticus” (CLas). CLas is transmitted by Diaphorina citri, the Asian citrus psyllid. Development of transmission-blocking strategies to manage huanglongbing relies on knowledge of CLas and D. citri interactions at the molecular level. Prior transcriptome analyses of D. citri point to changes in psyllid biology due to CLas infection but have been hampered by incomplete versions of the D. citri genome, proper host plant controls, and/or a lack of a uniform data analysis approach. In this work, we present lessons learned from a quantitative transcriptome analysis of excised heads, salivary glands, midguts, and bacteriomes from CLas-positive and CLas-negative D. citri using the chromosomal length D. citri genome assembly.

Results
Each organ had a unique transcriptome profile and response to CLas infection. Though most psyllids were infected with the bacterium, CLas-derived transcripts were not detected in all organs. By analyzing the midgut dataset using both the Diaci_v1.1 and v3.0 D. citri genomes, we showed that improved genome assembly led to significant and quantifiable differences in RNA-sequencing data interpretation.

Conclusions
Our results support the hypothesis that future transcriptome studies on circulative, vector-borne pathogens should be conducted at the tissue-specific level using complete, chromosomal-length genome assemblies for the most accurate understanding of pathogen-induced changes in vector gene expression.
Investigating prospective gains from mergers in the agricultural sector through Inverse DEA
Authors: Oukil Amar 
Published: April 2022

AbstractThis paper presents a new application of Inverse data envelopment analysis (InvDEA) for strategic decision making: mergers & acquisitions (M&A) in the agricultural sector. Given a set of agricultural farms, the decision maker is interested in estimating the potential gains that are likely to result from the merger of two or more farms, as well as the redistribution of inputs among the merging farms, for an efficiency target set a priori for the post-merger farm. Using a sample of greenhouse (GH) farms from the Batinah region (Oman), an InvDEA approach is applied to investigate pairwise consolidations among GH farms and determine the level of inputs required for a merger to achieve full efficiency. Moreover, a DEA-based approach is introduced for selecting the best partners of a merger based on pertaining potential gains. The results highlight the importance of mergers as a strategic option for an efficient management of resources in the agricultural sector, especially scarce resources, like water and electricity.
Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach
Authors: Xiong Hao , Phan Hoai Nam , Yin Kathleen , Berkovsky Shlomo , Jung Joshua , Lau Annie Y S 
Published: April 2022

Abstract

Objective
People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such tasks) rely on self-reports, which are subject to recall and other biases. Few studies use wearable cameras and deep learning to capture and classify patient work activities automatically.

Materials and Methods
We propose a deep learning approach to classify activities of patient work collected from wearable cameras, thereby studying self-management routines more effectively. Twenty-six people with type 2 diabetes and comorbidities wore a wearable camera for a day, generating more than 400 h of video across 12 daily activities. To classify these video images, a weighted ensemble network that combines Linear Discriminant Analysis, Deep Convolutional Neural Networks, and Object Detection algorithms is developed. Performance of our model is assessed using Top-1 and Top-5 metrics, compared against manual classification conducted by 2 independent researchers.

Results
Across 12 daily activities, our model achieved on average the best Top-1 and Top-5 scores of 81.9 and 86.8, respectively. Our model also outperformed other non-ensemble techniques in terms of Top-1 and Top-5 scores for most activity classes, demonstrating the superiority of leveraging weighted ensemble techniques.

Conclusions
Deep learning can be used to automatically classify daily activities of patient work collected from wearable cameras with high levels of accuracy. Using wearable cameras and a deep learning approach can offer an alternative approach to investigate patient work, one not subjected to biases commonly associated with self-report methods.
Predicting and Estimating Burn Outcomes: An Institutional Analysis of 4622 Cases
Authors: Zhang Jacques X , Ahmed S Nafeel , Pangli Harpreet , Papp Anthony 
Published: April 2022

Abstract
Advances in burn care have improved patient outcomes, and independently validated indices and predictors of burn outcomes warrant re-evaluation. The purpose of this study is to consolidate predictors of burn outcomes and determine the factors that significantly contribute to length-of-stay (LOS) and mortality. A retrospective review was conducted of all burn patients (n = 5778) admitted to a quaternary provincial burn unit from 1973 to 2017. Our inclusion criteria yielded 4622 independent cases. Multivariate linear and logistic regression models were generated, and area-under-receiver-operator-curve (AUROC) analysis was performed. Burn predictors included %TBSA, Baux (classic and revised) index, Abbreviated Burn Severity Index (ABSI), and Ryan score. Primary outcomes were mortality and LOS. Multivariate logistic regression for mortality showed the Baux index to be the best predictor for mortality (OR = 1.11, P &lt; 0.001). The AUROC for Baux index was 0.95. With regard to LOS, ABSI was the best predictor for LOS (P &lt; 0.001). ICU stay, ventilator use, alcoholism, and age are significantly associated with increased LOS. Interestingly, hypertension had a protective effect for LOS (P &lt; 0.01) and trended towards a protective effect in mortality. Lethal score 50% (LS50) improved over the study period. The regressions show that burn mortality and LOS are best predicted with the Baux index and ABSI, respectively. Hypertension may have a protective effect on burn outcomes and may be attributed to increased perfusion to the periphery. These predictive scores are useful in determining institutional outcomes in burn surgery. Objective benchmarking of improvement in burn care outcomes can be established using LS50 trends.
Breast Imaging and Social Media: A Model Specialty for Increasing Interest and Awareness of Radiology in Pre-Medical Students
Authors: Chan Tiffany L , De Rohit , Sayre James , Reno Elaine 
Published: April 2022

Abstract

Objective
To assess how a virtual shadowing (VS) session on breast imaging (BI), shared through social media, can increase interest and awareness of radiology in pre-medical students.

Methods
This study was IRB exempt. A VS slide presentation on BI mentorship, didactic lecture, and deidentified image-rich, case-based learning was created. This session was advertised and presented live through an Instagram page (Pre-Health Virtual Shadowing, @virtualshadowing) geared toward pre-health students. Students were asked to complete a two-question self-assessment using a 5-point Likert scale on which 1 was “least” and 5 was “most.” Question 1 read: “Please rate your overall knowledge about radiology and what a radiologist does,” and question 2 read: “Please rate your interest in possibly looking into radiology as a potential career.”

Results
A total of 420 participants logged into the VS session; 82% (345/420) answered the pre-session survey and 48% (202/420) answered the post-session survey. Of post-survey respondents, 84% (170/202) were female and 70% (143/202) were 22 years old or younger. There was a significant difference in mean pre-session survey responses to question 1 when compared to mean post-session survey responses (2.67 vs 3.55, P &lt; 0.001). There was also a significant difference in mean pre-session survey responses to question 2 when compared to mean post-session survey responses (3.16 vs 3.58, P &lt; 0.001). Our VS session remains on YouTube and has 6157 views to date.

Conclusion
When shared through VS on social media, breast imaging can increase interest and awareness of radiology in pre-medical students.
Integrating climate, ecophysiology, and forest cover to estimate the vulnerability of sloths to climate change
Authors: Tourinho Luara , Sinervo Barry , Caetano Gabriel Henrique de Oliveira , Giné Gastón Andrés Fernandez , dos Santos Cinthya Chiva , Cruz-Neto Ariovaldo Pereira , Vale Mariana M 
Published: April 2022

AbstractGlobal change imposes multiple challenges on species and, thus, a reliable prediction of current and future vulnerability of species must consider multiple stressors and intrinsic traits of species. Climate, physiology, and forest cover, for example, are required to evaluate threat to thermolabile forest-dependent species, such as sloths (Bradypus spp.; Mammalia: Xenarthra). Here, we estimated future changes in the distribution of three sloth species using a metabolic-hybrid model focused on climate (climatic only, i.e., CO approach) and adding forest cover constraints to distribution of species (climate plus land cover, i.e., CL approach). We used an innovative method to generate estimates of physiological parameters for endotherms, validated with field data. The CF approach predicted a future net expansion of distribution of B. torquatus and B. variegatus, and a future net contraction of distribution of B. tridactylus. The inclusion of forest cover constraints, however, reversed the predictions for B. torquatus, with a predicted net distribution contraction. It also reduced expansion of B. variegatus, although still showing a large net expansion. Thus, B. variegatus is not predicted to be threatened in the future; B. tridactylus emerges as the species most vulnerable to climate change, but with no considerable forest losses, while B. torquatus shows the opposite pattern. Our study highlights the importance of incorporating multiple stressors in predictive models in general. To increase resilience of species to climate change, it is key to control deforestation in the Amazon for B. tridactylus, and to promote reforestation in the Atlantic Forest for B. torquatus.