Protecting Whiteness: Counter-Terrorism, and British Identity in the BBC’s Bodyguard

This article uses Bodyguard to trace the ways that whiteness is represented in counter-terrorism TV and so draw the links between whiteness, counter-terrorism and culture. It argues that Bodyguard offers a redemptive narrative for British whiteness that recuperates and rearticulates a British white identity after/through the War on Terror. As such it belongs to a later genre of counter-terrorism TV shows that move on from, but nonetheless still propagate, the discursive foundations of the ongoing War on Terror. This reading of Bodyguard is itself important, as popular culture is a site where much of the British population made and continues to make sense of their relationship to the UK during the War on Terror, forging often unspoken ideas about whiteness. It affords the opportunity to draw out the connections between whiteness and counter-terrorism, connections that need further scholarly attention to fully understand the complex relationships between security and race.

Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

The acceleration of industrialization and urbanization has recently brought about serious air pollution problems, which threaten human health and lives, the environmental safety, and sustainable social development. Air quality prediction is an effective approach for providing early warning of air pollution and supporting cleaner industrial production. However, existing approaches have suffered from a weak ability to capture long-term dependencies and complex relationships from time series PM2.5 data. To address this problem, this paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then, based on the constructed graph, we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. To verify the effectiveness of the proposed method, we conduct air quality prediction experiments on two real-world datasets in China, such as Beijing PM2.5 dataset ranging from 01/01/2010 to 12/31/2014 and Taizhou PM2.5 dataset ranging from 01/01/2017 to 12/31/2019. Compared with other air quality forecasting methods, such as autoregressive moving average (ARMA), support vector regression (SVR), convolutional neural network (CNN), long short-term memory (LSTM), the original Transformer, our experiment results indicate that the proposed method achieves more accurate results on both short-term (1 hour) and long-term (6, 12, 24, 48 hours) air quality prediction tasks.

‘Broken English’, ‘dialect’ or ‘Bahamianese’?

The study investigates language attitudes in The Bahamas, addressing the current status of the local creole in society as well as attitudinal indicators of endonormative reorientation and stabilization. At the heart of the study is a verbal guise test which investigates covert language attitudes among educated Bahamians, mostly current and former university students; this was supplemented by a selection of acceptance rating scales and other direct question formats. The research instrument was specifically designed to look into the complex relationships between Bahamian Creole and local as well as non-local accents of standard English and to test associated solidarity and status effects in informal settings. The results show that the situation in The Bahamas mirrors what is found for other creole-speaking Caribbean countries in that the local vernacular continues to be ‘the language of solidarity, national identity, emotion and humour, and Standard the language of education, religion, and officialdom’ (Youssef 2004: 44). Notably, the study also finds that standard Bahamian English outranks the other metropolitan standards with regard to status traits, suggesting an increase in endonormativity.

Predicting symptoms of downy mildew, powdery mildew, and gray mold diseases of grapevine through machine learning

Downy mildew, powdery mildew, and gray mold are major diseases of grapevine with a strong negative impact on fruit yield and fruit quality. These diseases are controlled by the application of chemicals, which may cause undesirable effects on the environment and on human health. Thus, monitoring and forecasting crop disease is essential to support integrated pest management (IPM) measures. In this study, two tree-based machine learning (ML) algorithms, random forest and C5.0, were compared to test their capability to predict the appearance of symptoms of grapevine diseases, considering meteorological conditions, spatial indices, the number of crop protection treatments and the frequency of monitoring days in which symptoms were recorded in the previous year. Data collected in Tuscany region (Italy), on the presence of symptoms on grapevine, from 2006 to 2017 were divided with an 80/20 proportion in training and test set, data collected in 2018 and 2019 were tested as independent years for downy mildew and powdery mildew. The frequency of symptoms in the previous year and the cumulative precipitation from April to seven days before the monitoring day were the most important variables among those considered in the analysis for predicting the occurrence of disease symptoms. The best performance in predicting the presence of symptoms of the three diseases was obtained with the algorithm C5.0 by applying (i) a technique to deal with imbalanced dataset (i.e., symptoms were detected in the minority of observations) and (ii) an optimized cut-off for predictions. The balanced accuracy achieved in the test set was 0.8 for downy mildew, 0.7 for powdery mildew and 0.9 for gray mold. The application of the models for downy mildew and powdery mildew in the two independent years (2018 and 2019) achieved a lower balanced accuracy, around 0.7 for both the diseases. Machine learning models were able to select the best predictors and to unravel the complex relationships among geographic indices, bioclimatic indices, protection treatments and the frequency of symptoms in the previous year.

From Model Organisms to Humans, the Opportunity for More Rigor in Methodologic and Statistical Analysis, Design, and Interpretation of Aging and Senescence Research

This review identifies frequent design and analysis errors in aging and senescence research and discusses best practices in study design, statistical methods, analyses, and interpretation. Recommendations are offered for how to avoid these problems. The following issues are addressed: 1) errors in randomization, 2) errors related to testing within-group instead of between-group differences, 3) failing to account for clustering, 4) failing to consider interference effects, 5) standardizing metrics of effect size, 6) maximum lifespan testing, 7) testing for effects beyond the mean, 8) tests for power and sample size, 9) compression of morbidity versus survival curve-squaring, and 10) other hot topics, including modeling high-dimensional data and complex relationships and assessing model assumptions and biases. We hope that bringing increased awareness of these topics to the scientific community will emphasize the importance of employing sound statistical practices in all aspects of aging and senescence research.

Adjustment for SNPs instead of demographic and clinical covariates in GWAS

Genome-wide association study (GWAS) is fundamentally designed to detect disease-causing genes. To reduce spurious associations or improve statistical power, about 80% of GWASs arbitrarily adjusted for demographic and clinical covariates. However, adjustment strategies in GWASs have not achieved consistent conclusions. Given the initial aim of GWAS that is to identify the causal association between a specific causal single-nucleotide polymorphism (SNP) and disease trait, we summarized all complex relationships of the target SNP, covariate and disease trait into 15 causal diagrams according to various roles of the covariate. Following each causal diagram, we conducted a series of theoretical justifications and statistical simulations. Our results demonstrate that it is unadvisable to adjust for any demographic or clinical covariates. We illustrate our point by applying GWASs for body mass index (BMI) and breast cancer, including adjusting and non-adjusting for age and smoking status. Genetic effects and P values might vary across different strategies. Instead, adjustments for SNPs (G') should be strongly recommended when G' are in linkage disequilibrium with the target SNP, and correlated with disease trait conditional on the target SNP. Specifically, adjustment for such G' can block all the confounding paths between the target SNP and disease trait, and avoid over-adjusting for colliders or intermediaries.

A functional view reveals substantial predictability of pollinator-mediated selection

A predictive understanding of adaptation to changing environments hinges on a mechanistic understanding of the extent and causes of variation in natural selection. Estimating variation in selection is difficult due to the complex relationships between phenotypic traits and fitness, and the uncertainty associated with individual selection estimates. Plant-pollinator interactions provide ideal systems for understanding variation in selection and its predictability, because both the selective agents (pollinators) and the process linking phenotypes to fitness (pollination) are generally known. Through examples from the pollination literature, I discuss how explicit consideration of the functional mechanisms underlying trait-performance relationships can clarify the relationship between traits and fitness, and how variation in the ecological context that generates selection can help disentangle biologically important variation in selection from sampling variation. I then evaluate the predictability of variation in pollinator-mediated selection through a survey, reanalysis, and synthesis of results from the literature. The synthesis demonstrates that pollinator-mediated selection often varies substantially among trait functional groups, as well as in time and space. Covariance between patterns of selection and ecological variables provides additional support for the biological importance of observed selection, but the detection of such covariance depends on careful choice of relevant predictor variables as well as consideration of quantitative measurements and their meaning, an aspect often neglected in selection studies.

From street dance to hip hop

This text historicizes the concept of street dance (dança de rua) by showing distances and approaches in relation to hip hop. For this purpose, the analysis starts from the cultural history of street dance in the city of Uberlândia, Minas Gerais (Brazil), to understand the complex relationships that gave meaning and form to the practice of street dance between the 1980s and 1990s. In a first step, I investigate the various perspectives that permeate the bond between the popular dance and dance festivals, well as between the city neighbourhoods and dance clubs. In a second step, the analysis shifts to the cultural performance that allowed street dancers to migrate to the so-called hip hop dance. Analysing street dance and hip hop considering their ruptures and continuities, the text intends to contribute to studies dedicated to the presence of dance in the construction of urban identities.

Clinical Interpretation of Working Volume and Weight Support in Upper Limb Robotic Neurorehabilitation after Stroke

In the past two decades, many studies reported the efficacy of upper limb robotic rehabilitation in patients after stroke, also in its chronic phase. Among the possible advantages of robotic therapy over conventional therapy are the objective measurements of kinematic and kinetic parameters during therapy, such as the spatial volume covered by the patient’s upper limb and the weight support provided by the robot. However, the clinical meaning and the usability of this information is still questioned. Forty patients with chronic stroke were enrolled in this study and assessed at the beginning of upper limb robotic therapy (Armeo® Power) and after two weeks (ten sessions) of therapy by recording the working volume and weight support provided by the robot and by administering six clinical scales to assess upper limb mobility, strength, spasticity, pain, neurological deficits, and independency. At baseline, the working volume significantly correlated with spasticity, whereas weight support significantly correlated with upper limb strength, pain, spasticity, and neurological deficits. After two weeks of robotic rehabilitation, all the clinical scores as well as the two parameters improved. However, the percentage changes in the working volume and weight support did not significantly correlate with any of the changes in clinical scores. These results suggest caution in using the robotic parameters as outcome measures because they could follow the general improvement of the patient, but complex relationships with clinical features are possible. Robotic parameters should be analyzed in combination with the clinical scores or other objective measures because they may be informative about therapy progression, and there is a need to combine their clinical, neuroscientific, and biomechanical results to avoid misleading interpretations.

The Role of Host Glycobiology and Gut Microbiota in Rotavirus and Norovirus Infection, an Update

Rotavirus (RV) and norovirus (NoV) are the leading causes of acute gastroenteritis (AGE) worldwide. Several studies have demonstrated that histo-blood group antigens (HBGAs) have a role in NoV and RV infections since their presence on the gut epithelial surfaces is essential for the susceptibility to many NoV and RV genotypes. Polymorphisms in genes that code for enzymes required for HBGAs synthesis lead to secretor or non-secretor and Lewis positive or Lewis negative individuals. While secretor individuals appear to be more susceptible to RV infections, regarding NoVs infections, there are too many discrepancies that prevent the ability to draw conclusions. A second factor that influences enteric viral infections is the gut microbiota of the host. In vitro and animal studies have determined that the gut microbiota limits, but in some cases enhances enteric viral infection. The ways that microbiota can enhance NoV or RV infection include virion stabilization and promotion of virus attachment to host cells, whereas experiments with microbiota-depleted and germ-free animals point to immunoregulation as the mechanism by which the microbiota restrict infection. Human trials with live, attenuated RV vaccines and analysis of the microbiota in responder and non-responder individuals also allowed the identification of bacterial taxa linked to vaccine efficacy. As more information is gained on the complex relationships that are established between the host (glycobiology and immune system), the gut microbiota and intestinal viruses, new avenues will open for the development of novel anti-NoV and anti-RV therapies.