The past 50 yr of advances in weed recognition technologies have poised site-specific weed control (SSWC) on the cusp of requisite performance for large-scale production systems. The technology offers improved management of diverse weed morphology over highly variable background environments. SSWC enables the use of nonselective weed control options, such as lasers and electrical weeding, as feasible in-crop selective alternatives to herbicides by targeting individual weeds. This review looks at the progress made over this half-century of research and its implications for future weed recognition and control efforts; summarizing advances in computer vision techniques and the most recent deep convolutional neural network (CNN) approaches to weed recognition. The first use of CNNs for plant identification in 2015 began an era of rapid improvement in algorithm performance on larger and more diverse datasets. These performance gains and subsequent research have shown that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition. The benefits of deep learning and improved accessibility to open-source software and hardware tools has been evident in the adoption of these tools by weed researchers and the increased popularity of CNN-based weed recognition research. The field of machine learning holds substantial promise for weed control, especially the implementation of truly integrated weed management strategies. Whereas previous approaches sought to reduce environmental variability or manage it with advanced algorithms, research in deep learning architectures suggests that large-scale, multi-modal approaches are the future for weed recognition.

Excess weight is caused by multiple factors and has increased sharply in Switzerland since the 1990s. Its consequences represent a major challenge for Switzerland, both in terms of health and the economy. Until now, there has been no cross-dataset overview study on excess weight in adults in Switzerland. Therefore, our aim was to conduct the first synthesis on excess weight in Switzerland. We included all existing nationwide Swiss studies (eight total), which included information on body mass index (BMI). Mixed multinomial logistic regression analyses were performed to assess the associations between different socio-demographic, lifestyle cofactors and the World Health Organization (WHO) categories for BMI. Along with lifestyle factors, socio-demographic factors were among the strongest determinants of BMI. In addition, self-rated health status was significantly lower for underweight, pre-obese and obese men and women than for normal weight persons. The present study is the first to synthesise all nationwide evidence on the importance of several socio-demographic and lifestyle factors as risk factors for excess weight. In particular, the highlighted importance of lifestyle factors for excess weight opens up the opportunity for further public health interventions.

We investigated whether countries with higher coverage of childhood live vaccines [BCG or measles-containing-vaccine (MCV)] have reduced risk of coronavirus disease 2019 (COVID-19)-related mortality, while accounting for known systems differences between countries. In this ecological study of 140 countries using publicly available national-level data, higher vaccine coverage, representing estimated proportion of people vaccinated during the last 14 years, was associated with lower COVID-19 deaths. The associations attenuated for both vaccine variables, and MCV coverage became no longer significant once adjusted for published estimates of the Healthcare access and quality index (HAQI), a validated summary score of healthcare quality indicators. The magnitude of association between BCG coverage and COVID-19 death rate varied according to HAQI, and MCV coverage had little effect on the association between BCG and COVID-19 deaths. While there are associations between live vaccine coverage and COVID-19 outcomes, the vaccine coverage variables themselves were strongly correlated with COVID-19 testing rate, HAQI and life expectancy. This suggests that the population-level associations may be further confounded by differences in structural health systems and policies. Cluster randomised studies of booster vaccines would be ideal to evaluate the efficacy of trained immunity in preventing COVID-19 infections and mortality in vaccinated populations and on community transmission.

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.