bluuebunny/crossref_metadata_2025_split huggingface dataset:
Recall of exposure in UK farmers and pesticide applicators: trends with follow-up time
Authors: Mueller William , Jones Kate , Mohamed Hani , Bennett Neil , Harding Anne-Helen , Frost Gillian , Povey Andrew , Basinas Ioannis , Kromhout Hans , van Tongeren Martie , Fuhrimann Samuel , Galea Karen S 
Published: January 2022

Abstract

Background
Occupational epidemiological studies on pesticide use commonly rely on self-reported questionnaire or interview data to assess exposure. Insight into recall accuracy is important, as misclassification of exposures due to imperfect recall can bias risk estimates.

Methods
We assessed the ability of workers in three UK cohorts (Prospective Investigation of Pesticide Applicators’ Health [PIPAH], Pesticide Users’ Health Study [PUHS], and Study of Health in Agricultural Work [SHAW]) to remember their working history related to pesticide exposure over time periods ranging from 3 to 14 years prior. During 2019–2020, cohort participants were re-surveyed using a similar questionnaire to that used previously. We compared recall of responses at follow-up to those reported at baseline related to crops/areas of work, use of personal protective equipment (PPE) items, hygiene habits, frequency of pesticide use, and application method. To assess the extent of recall, we used sensitivity, specificity, the percentage of overall agreement, and area under the curve (AUC) values. We also examined the presence of over or underestimation of recalled years, and days and hours per year, of working with pesticides using geometric mean ratios (GMR) and regression analysis to investigate any trends based on demographic characteristics.

Results
There were 643 individuals who completed both the baseline and follow-up surveys in the three cohorts with response rates ranging from 17 to 46%. There was a strong correlation (rho = 0.77) between the baseline and recalled years working with pesticides, though higher values were reported at follow-up (GMR = 1.18 [95% confidence interval: 1.07–1.30]) with no consistent differences by demographic characteristics. There was stronger agreement in the recalled days compared to hours per year in two of the cohorts. Recall for a number of exposure determinants across short and longer periods entailed overall agreement of &gt;70%, though with some differences: for example, sensitivity for long-term recall of crops was poor (&lt;43% in PUHS), whereas short-term recall of hygiene practices was good (AUC range = 0.65–1.00 in PIPAH).

Conclusion
Results indicate that recall ability may deteriorate over a longer period. Although low-response rates may require these findings to be interpreted with caution, recall for a number of exposure determinants appeared reliable, such as crops and hygiene practices within 3 years, as well as days per year working with pesticides.
Ability’s Two Dimensions of Robustness
Authors: Kikkert Sophie 
Published: January 2022

Abstract
The actions of able agents are often reliably successful. I argue that their success may be modally robust along two dimensions. The first dimension helps distinguish the exercise of abilities, which requires local control, from lucky success. The second concerns the global availability of acts: agents with the ability to φ can φ across a variety of circumstances. I introduce a framework that captures the two dimensions and their interaction, and show how it bears on a disagreement about the modal force the robustness of ability requires: while local control involves a kind of local necessity, global availability does not.
SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants
Authors: Yang Yaning , Wang Xiaoqi , Zhou Deshan , Wei Dong-Qing , Peng Shaoliang 
Published: January 2022

AbstractAlthough there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to predict the pathogenicity of deletions, insertions and duplications structural variations that occur in exons. We constructed three types of annotation features for each structural variation event in the ClinVar database. First, we treated complex structural variations as multiple consecutive single nucleotide polymorphisms events, and annotated them with correlation scores based on single nucleic acid substitutions, such as the impact on protein function. Second, we determined which genes the variation occurred in, and constructed gene-based annotation features for each structural variation. Third, we also calculated related features based on the transcriptome, such as histone signal, the overlap ratio of variation and genomic element definitions, etc. Finally, we employed a gradient boosting decision tree machine learning method, and used the deletions, insertions and duplications in the ClinVar database to train a structural variation pathogenicity prediction model SVPath. These structural variations are clearly indicated as pathogenic or benign. Experimental results show that our SVPath has achieved excellent predictive performance and outperforms existing state-of-the-art tools. SVPath is very promising in evaluating the clinical pathogenicity of structural variants. SVPath can be used in clinical research to predict the clinical significance of unknown pathogenicity and new structural variation, so as to explore the relationship between diseases and structural variations in a computational way.
Compositional Data Analysis using Kernels in mass cytometry data
Authors: Rudra Pratyaydipta , Baxter Ryan , Hsieh Elena W Y , Ghosh Debashis 
Published: January 2022

AbstractMotivationCell-type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small.ResultsWe proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n &lt; 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects.Availability and implementationCODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/.Contactprudra@okstate.eduSupplementary informationSupplementary data are available at Bioinformatics Advances online.
From viral evolution to spatial contagion: a biologically modulated Hawkes model
Authors: Holbrook Andrew J , Ji Xiang , Suchard Marc A 
Published: January 2022

AbstractSummaryMutations sometimes increase contagiousness for evolving pathogens. During an epidemic, scientists use viral genome data to infer a shared evolutionary history and connect this history to geographic spread. We propose a model that directly relates a pathogen’s evolution to its spatial contagion dynamics—effectively combining the two epidemiological paradigms of phylogenetic inference and self-exciting process modeling—and apply this phylogenetic Hawkes process to a Bayesian analysis of 23 421 viral cases from the 2014 to 2016 Ebola outbreak in West Africa. The proposed model is able to detect individual viruses with significantly elevated rates of spatiotemporal propagation for a subset of 1610 samples that provide genome data. Finally, to facilitate model application in big data settings, we develop massively parallel implementations for the gradient and Hessian of the log-likelihood and apply our high-performance computing framework within an adaptively pre-conditioned Hamiltonian Monte Carlo routine.Supplementary informationSupplementary data are available at Bioinformatics online.
Being ‘good enough’: Perfectionism and well-being in social workers
Authors: Kinman Gail , Grant Louise 
Published: January 2022

Abstract
Perfectionism refers to a tendency to set unrealistically high standards for oneself and others. Although often seen positively, perfectionism can threaten health, relationships and performance. This study examined the effects of three types of maladaptive perfectionism on burnout in 294 UK social workers: self-oriented (having excessively high standards for oneself), other-oriented (having excessively high expectations of others) and socially prescribed (perceiving external pressure to excel). In line with previous research, we predicted that socially prescribed perfectionism would have particularly powerful effects on well-being, but significant relationships with self and other-oriented perfectionism were also expected. We also examined whether maladaptive perfectionism intensified the negative impact of work-related emotional demands on burnout. Significant positive relationships were found between socially prescribed and other-oriented perfectionism and burnout. A higher level of socially prescribed perfectionism was found than self and other-oriented and its relationship with burnout was particularly strong. We found no evidence, however, that perfectionism was an additional risk factor for burnout when emotional demands were high. Early career social workers were found to be at greater risk of dysfunctional perfectionism and burnout. The implications of the findings for the well-being of social workers are considered and potential interventions outlined to reduce maladaptive perfectionism.
Seroprevalence and Risk Factors for Severe Acute Respiratory Syndrome Coronavirus 2 Among Incarcerated Adult Men in Quebec, Canada, 2021
Authors: Kronfli Nadine , Dussault Camille , Maheu-Giroux Mathieu , Halavrezos Alexandros , Chalifoux Sylvie , Sherman Jessica , Park Hyejin , Del Balso Lina , Cheng Matthew P , Poulin Sébastien , Cox Joseph 
Published: January 2022

Abstract

Background
People in prison are at increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We examined the seroprevalence of SARS-CoV-2 and associated carceral risk factors among incarcerated adult men in Quebec, Canada.

Methods
We conducted a cross-sectional seroprevalence study in 2021 across 3 provincial prisons, representing 45% of Quebec’s incarcerated male provincial population. The primary outcome was SARS-CoV-2 antibody seropositivity (Roche Elecsys serology test). Participants completed self-administered questionnaires on sociodemographic, clinical, and carceral characteristics. The association of carceral variables with SARS-CoV-2 seropositivity was examined using Poisson regression models with robust standard errors. Crude and adjusted prevalence ratios (aPR) with 95% confidence intervals (95% CIs) were calculated.

Results
Between 19 January 2021 and 15 September 2021, 246 of 1100 (22%) recruited individuals tested positive across 3 prisons (range, 15%–27%). Seropositivity increased with time spent in prison since March 2020 (aPR, 2.17; 95% CI, 1.53–3.07 for “all” vs “little time”), employment during incarceration (aPR, 1.64; 95% CI, 1.28–2.11 vs not), shared meal consumption during incarceration (“with cellmates”: aPR, 1.46; 95% CI, 1.08–1.97 vs “alone”; “with sector”: aPR, 1.34; 95% CI, 1.03–1.74 vs “alone”), and incarceration post-prison outbreak (aPR, 2.32; 95% CI, 1.69–3.18 vs “pre-outbreak”).

Conclusions
The seroprevalence of SARS-CoV-2 among incarcerated individuals was high and varied among prisons. Several carceral factors were associated with seropositivity, underscoring the importance of decarceration and occupational safety measures, individual meal consumption, and enhanced infection prevention and control measures including vaccination during incarceration.
Effusive–constrictive pericarditis after the second dose of BNT162b2 vaccine (Comirnaty): a case report
Authors: Viani Giacomo Maria , Pedrotti Patrizia , Seregni Romano , Antonio Brucato 
Published: January 2022

Abstract

Background
Whereas effusive–constrictive pericarditis (ECP) can rarely occur in coronavirus disease 2019 (COVID-19), to date no cases of ECP related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) vaccine have been documented.

Case summary
A 59-year-old Caucasian man presented to our emergency department with ECP. Symptoms occurred shortly after the second dose of BNT162b2 (Comirnaty) vaccine. No other aetiological causes were identified. Guidelines-directed therapy for acute pericarditis was implemented, with clinical benefit.

Discussion
Systemic inflammatory response to COVID-19 can rarely trigger pericarditis. In our case, a strong temporal relationship between the second dose of BNT162b2 vaccine and symptoms occurrence was documented, indicating a possible rare adverse reaction to the vaccine, similarly to natural infection. Further research is needed to confirm a causal relationship.
Marked recovery of cardiac function by chemotherapy and autologous stem cell transplantation of a patient with heart failure with preserved ejection fraction due to primary amyloid light-chain amyloidosis: a case report
Authors: Tanaka Hidekazu , Kitao Akihito , Minami Hironobu , Hirata Ken-Ichi 
Published: January 2022

Abstract

Background
Cardiac involvement of amyloid light-chain (AL) amyloidosis is strongly associated with poor outcome, but the early detection of cardiac involvement of AL amyloidosis can be challenging.

Case summary
We present a case of 49-year-old-female with heart failure with preserved ejection fraction. Echocardiography revealed normal left ventricular (LV) ejection fraction of 63% and an enlarged left atrium with a left atrial volume index (LAVI) of 54 mL/m2. Mild LV hypertrophy with an interventricular septum of 12.3 mm and posterior wall thickness of 11.0 mm was observed, and Doppler-derived LV diastolic filling showed a restrictive filling pattern. The conventional echocardiographic findings did not unequivocally indicate typical cardiac amyloidosis, but global longitudinal strain (GLS) was as low as 14.2%, and an apical sparing pattern was observed with relative apical longitudinal strain of 1.11. Finally, the patient was diagnosed as primary AL amyloidosis including histological examination of the endomyocardial specimen. After treatment with a regime of bortezomib and dexamethasone followed by high-dose melphalan followed by autologous peripheral blood stem cell transplantation (auto-PBSCT), Doppler-derived LV diastolic filling improved to normal filling pattern, and left atrial size had also decreased with an LAVI of 31 mL/m2. Moreover, GLS improved to 19.8%, and the apical sparing pattern had disappeared with relative apical longitudinal strain of 0.62. The patient has been asymptomatic during 18-month follow-up after auto-PBSCT, and recovered LV function has been maintained.

Discussion
An earlier diagnosis of cardiac amyloidosis by using apical sparing may therefore allow for earlier treatment intervention for AL amyloidosis.
The autophagy-resistant Mycobacterium tuberculosis Beijing strain upregulates KatG to evade starvation-induced autophagic restriction
Authors: Siregar Tegar Adriansyah Putra , Prombutara Pinidphon , Kanjanasirirat Phongthon , Kunkaew Nawapol , Tubsuwan Alisa , Boonmee Atsadang , Palaga Tanapat , Khumpanied Tanawadee , Borwornpinyo Suparerk , Chaiprasert Angkana , Utaisincharoen Pongsak , Ponpuak Marisa 
Published: January 2022

Abstract
Mycobacterium tuberculosis utilizes several mechanisms to block phagosome–lysosome fusion to evade host cell restriction. However, induction of host cell autophagy by starvation was shown to overcome this block, resulting in enhanced lysosomal delivery to mycobacterial phagosomes and the killing of the M. tuberculosis reference strain H37Rv. Nevertheless, our previous studies found that strains belonging to the M. tuberculosis Beijing genotype can resist starvation-induced autophagic elimination, though the mycobacterial factors involved remain unclear. In this study, we showed that KatG expression is upregulated in the autophagy-resistant M. tuberculosis Beijing strain (BJN) during autophagy induction by the starvation of host macrophages, while such increase was not observed in the H37Rv. KatG depletion using the CRISPR-dCas9 interference system in the BJN resulted in increased lysosomal delivery to its phagosome and decreased its survival upon autophagy induction by starvation. As KatG functions by catabolizing ROS, we determined the source of ROS contributing to the starvation-induced autophagic elimination of mycobacteria. Using siRNA-mediated knockdown, we found that Superoxide dismutase 2, which generates mitochondrial ROS but not NADPH oxidase 2, is important for the starvation-induced lysosomal delivery to mycobacterial phagosomes. Taken together, these findings showed that KatG is vital for the BJN to evade starvation-induced autophagic restriction.
Microelectrode glucose biosensor based on nanoporous platinum/graphene oxide nanostructure for rapid glucose detection of tomato and cucumber fruits
Authors: Wu Binfang , Xu Haitao , Shi Yufeng , Yao Zhijie , Yu Jiayu , Zhou Hua , Li Yunpeng , Chen Qiulan , Long Yongbing 
Published: January 2022

Abstract
A microelectrode glucose biosensor based on a three-dimensional hybrid nanoporous platinum/graphene oxide nanostructure was developed for rapid glucose detection of tomato and cucumber fruits. The nanostructure was fabricated by a two-step modification method on a microelectrode for loading a larger amount of glucose oxidase. The nanoporous structure was prepared on the surface of the platinum microelectrode by electrochemical etching, and then graphene oxide was deposited on the prepared nanoporous electrode by electrochemical deposition. The nanoporous platinum/graphene oxide nanostructure had the advantage of improving the effective surface area of the electrode and the loading quantity of glucose oxidase. As a result, the biosensor achieved a wide range of 0.1–20.0 mmol/L in glucose detection, which had the ability to accurately detect the glucose content. It was found that the three-dimensional hybrid nanostructure on the electrode surface realized the rapid direct electrochemistry of glucose oxidase. Therefore, the biosensor achieved high glucose detection sensitivity (11.64 μA·L/(mmol·cm2), low detection limit (13 μmol/L) and rapid response time (reaching 95% steady-state response within 3 s), when calibrating in glucose standard solution. In agricultural application, the as-prepared biosensor was employed to detect the glucose concentration of tomato and cucumber samples. The results showed that the relative deviation of this method was less than 5% when compared with that of high-performance liquid chromatography, implying high accuracy of the presented biosensor in glucose detection in plants.
Comparative study of the characterisation and extraction techniques of polyphenolic compounds from Acacia seyal gum
Authors: Elnour Ahmed A M , Mirghani Mohamed E S , Kabbashi Nassereldeen A , Hamid Musa Khalid , Shahabipour Fahimeh , Ashammakhi Nureddin , Hamid Abdurahman Nour 
Published: January 2022

Abstract

Background
Acacia seyal gum (ASG) is an abundant source of natural polyphenolic compounds (NPPCs) and antioxidant activity with numerous benefits and is often used in cancer treatment. The type of extraction technique can significantly impact the yield and isolation of NPPCs from ASG. The traditional use of maceration extraction reportedly yields fewer NPPCs.

Objectives
This study investigated five extraction techniques for NPPCs and ASG antioxidant activity, namely: homogenisation, shaking, ultrasonication, magnetic stirring, and maceration.

Materials and methods
The evaluation of the antioxidant activity (AoA) of the extracted NPPCs from ASG used five assays, namely: total flavonoids content, Folin–Ciocalteu index, 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity, ferric reducing antioxidant power, and cupric reducing antioxidant capacity.

Results
To minimise the data set dimensionality requires principal component analysis. The ultrasonic and maceration techniques were the best techniques to extract NPPCs and examine the AoA of ASG, with a high correlation between the NPPCs and AoA. However, the maceration process was slow (12 h) compared to ultrasonication (1 h). Slow extraction can result in a decline of the NPPCs due to polyphenol oxidase–enzyme and impact productivity.

Conclusions
These findings provide an essential guide for the choice of extraction techniques for the effective extraction of NPPCs from ASG and other plant materials.
Multidimensional Social Network Types and Their Correlates in Older Americans
Authors: Ali Talha , Elliott Michael R , Antonucci Toni C , Needham Belinda L , Zelner Jon , Mendes de Leon Carlos F 
Published: January 2022

Abstract

Background and Objectives
Social support networks of older adults have been linked to their health and well-being; however, findings regarding the effects of specific network characteristics have been mixed. Additionally, due to demographic shifts increasing numbers of older adults live outside of traditional family structures. Previous studies have not systematically examined the resulting complexity and heterogeneity of older adults’ social networks. Our objectives were to examine this complexity and heterogeneity by developing a multidimensional typology of social networks that simultaneously considers multiple structural and functional network characteristics, and to examine differences in network type membership by sociodemographic characteristics, health characteristics, and birth cohort.

Research Design and Methods
Participants included 5,192 adults aged 57–85 years in the National Social Life, Health, and Aging Project at rounds 1 (2005–2006) and 3 (2015–2016). Data were collected on social relationships including network size, diversity, frequency of contact, and perceived support and strain in relationships. We used latent class analysis to derive the network typology and multinomial logistic regression to examine differences in network type membership by sociodemographic characteristics, health characteristics, and birth cohort.

Results
Older adults were classified into 5 distinct social network types: (i) large, with strain; (ii) large, without strain; (iii) small, diverse, low contact; (iv) small, restricted, high contact; and (v) medium size and support. Membership in these network types varied by age, gender, marital status, race/ethnicity, education, mental health, and birth cohort.

Discussion and Implications
Network typologies can elucidate the varied interpersonal environments of older adults and identify individuals who lack social connectedness on multiple network dimensions and are therefore at a higher risk of social isolation.