interaction detection
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The nutrition facts table is a nutrition labeling tool designed to inform consumers of food nutritional contents and enable them to make healthier choices by comparing the nutritional values of similar foods. However, its adoption level is considerably low in China. This study employed the Chi-squared Automatic Interaction Detection (CHAID) algorithm to explore the factors associated with respondents’ adoption of nutrition facts table to compare the nutritional values of similar foods. Data were gathered through a nationally representative online survey of 1500 samples. Results suggested that consumers’ comprehension of the nutrition facts table was a direct explanatory factor for its use. The usage was also indirectly explained by people’s nutrition knowledge, the usage of nutrition facts table by their relatives and friends, and their focus on a healthy diet. Therefore, to increase the use of nutrition facts table by Chinese consumers, the first consideration should be given to enhancing consumers’ comprehension of the labeling

ObjectiveIn 2017, the National Institute for Health Research (NIHR) academy produced a strategic review of training, which reported the variation in application characteristics associated with success rates. It was noted that variation in applicant characteristic was not independent of one another. Therefore, the aim of this secondary analysis was to investigate the inter-relationships in order to identify factors (or groups of factors) most associated with application numbers and success rates.DesignRetrospective data were gathered from 4388 applications to NIHR Academy between 2007 and 2016. Multinominal logistic regression models quantified the likelihood of success depending on changes in the explanatory factors; relative risk ratios with 95% CIs. A classification tree analysis was built using exhaustive χ2 automatic interaction detection to better understand the effect of interactions between explanatory variables on application success rates.Results936 (21.3%) applications were awarded. Applications from males and females were equally likely to be successful (p=0.71). There was an overall reduction in numbers of applications from females as award seniority increased from predoctoral to professorship. Applications from institutions with a medical school had a 2.6-fold increase in likelihood of success (p<0.001). Classification tree analysis revealed key predictors of application success: award level, type of programme, previous NIHR award experience and applying form a medical school.ConclusionSuccess rates did not differ according to gender, and doctors were not more likely to be successful than applications from other professions. Taken together, these findings suggest an essential fairness in how the quality of a submitted application is assessed, but they also raise questions about variation in the opportunity to submit a high-quality application. The companion qualitative study (Burkshaw et al. (2021) BMJ Open) provides valuable insight into potential candidate mechanisms and discusses how research capacity development initiatives might be targeted in the future.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the mental health of hospital health professionals has been widely described, but few studies have focused on occupational health professionals. Therefore, the objective of this study was to assess psychological distress (PD) of occupational health workers and its relationship with their work engagement (WE) and work environment characteristics. A cross-sectional survey was conducted. A sample of 499 nurses and physicians participated in the study. Variables included demographic data, work environment characteristics, work engagement Utrecht Work Engagement Scale (UWES-9) and psychological distress General Health Questionnaire (GHQ-12). The Chi-square Automatic Interaction Detection method was performed for data analysis. Data collection took place via the internet between April 23 and June 24, 2020. A total of 65.53% of the participants had PD, and the total mean score of the UWES-9 scale was 34.80 (SD = 10.69). Workload, conflicts, stressful situations, and less job satisfaction were significantly related to a higher percentage of PD (p &lt; 0.05). Participants with low engagement showed higher levels of PD (76.7%; p &lt; 0.001). The dedication was revealed as the most significant dimension. Interventions aimed at promoting resilience and coping strategies are suggested. WE should be fostered as a preventive measure against PD among occupational health workers. By protecting workers, occupational health departments have a shared responsibility with public health in containing the pandemic. Therefore, it is essential to prevent the psychological impact that this responsibility may have on occupational health workers by implementing prevention measures.

Home advantage (HA) is the tendency for sporting teams to perform better at their home ground than away from home, it is also influenced by the crowd support, and its existence has been well established in a wide range of team sports including rugby union. Among all the HA determinants, the positive contribute of the crowd support on the game outcome can be analyzed in the unique pandemic situation of COVID-19. Therefore, the aim of the present study was to analyze the HA of professional high-level rugby club competition from a complex dynamical system perspective before and during the COVID-19 pandemic. HA was analyzed in northern and southern hemisphere rugby tournaments with (2013–2019) and without (2020/21) crowd support by the means of the exhaustive chi-square automatic interaction detection (CHAID) decision trees (DT). HA was mitigated by the crowd absence especially in closed games, although differences between tournaments emerged. Both for northern and southern hemisphere, the effect of playing without the crowd support had a negative impact on the home team advantage. These findings evidenced that in ghost games, where differences in the final score were less than a converted try (7 points), HA has disappeared.

Abstract The aim of this study was to develop a predictive model for subclinical keratoconus (SKC) based on decision tree (DT) algorithms. A total of 194 eyes (including 105 normal eyes and 89 SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. DT models were generated in the training database using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms. The discriminating rules of the CART model selected variables of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in order, while the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. The CART model allowed discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.

Infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), is initiated by the interaction between a receptor protein, angiotensin-converting enzyme type 2 (ACE2) on the cell surface, and the viral spike (S) protein. This interaction is similar to the mechanism in SARS-CoV, a close relative of SARS-CoV-2, which was identified in 2003. Drugs and antibodies that inhibit the interaction between ACE2 and S proteins could be key therapeutic methods for preventing viral infection and replication in COVID-19. Here, we demonstrate the interaction between human ACE2 and a fragment of the S protein (S1 subunit) derived from SARS-CoV-2 and SARS-CoV using two-color fluorescence cross-correlation spectroscopy (FCCS), which can detect the interaction of fluorescently labeled proteins. The S1 subunit of SARS-CoV-2 interacted in solution with soluble ACE2, which lacks a transmembrane region, more strongly than that of SARS-CoV. Furthermore, one-to-one stoichiometry of the two proteins during the interaction was indicated. Thus, we propose that this FCCS-based interaction detection system can be used to analyze the interaction strengths of various mutants of the S1 subunit that have evolved during the worldwide pandemic, and also offers the opportunity to screen and evaluate the performance of drugs and antibodies that inhibit the interaction.

Abstract Cell-cell interactions are thought to drive tumor-promoting signals in the microenvironment of glioblastoma, but standard approaches for single cell analysis do not directly identify cell interactions and the mechanisms that mediate them. We recently developed a novel method to analyze cell-cell interactions—rabies barcode interaction detection followed by sequencing (RABID-seq), which combines barcoded viral tracing and single-cell RNA sequencing (scRNAseq). RABID-seq was first implemented in transgenic mice to investigate the interactions of astrocytes with other cells in the CNS enabling the study of astrocyte connectome perturbations and candidate therapeutic targets in multiple sclerosis and its pre-clinical model, experimental autoimmune encephalomyelitis (EAE). Here, we report the first use of RABID-seq in human tissues in organotypic cultures established from three IDH-wildtype glioblastoma (GBM) patients. In organotypic GBM cultures, initial infection by pseudotyped barcoded rabies virus deficient for viral glycoprotein was achieved after previous culture transduction with a lentivirus containing the avian TVA receptor and rabies glycoprotein under the human EF1a promoter. We employed this system to initially infect approximately 1,000 malignant or non malignant cells in the tumor microenvironment. After five days, infected cells were isolated from cultures and processed for single cell analysis using SMART seq. We were able to capture at least 6,000 interacting cells per tumor specimen, from which barcodes were recovered and cDNA was sent for sequencing. Here we present connectomic data from our initial cohort of three glioblastoma patients as an introduction to RABID-seq, with a focus on astrocyte-tumor interactions. Candidate mechanisms of cellular interactions will undergo functional validation in murine models of glioblastoma.