Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Bill & Melinda Gates Foundation.

• This study quantified global soil C research trends and the proportional focus on SOC and SIC pools. • Soil inorganic C is an overlooked pool with only 4% share in global soil C research. • Approximately 40% of the soil C research was dedicated to climate change. • Soil C research is concentrated in few countries with 70% research contributed by 9 countries. • It is imperative to expand research focus on SIC to gain a comprehensive understanding of global C cycle.

Salinity and drought stress, singly or in combination, are major environmental menaces. Jatropha curcas L. is a biodiesel plant that can tolerate long periods of drought. However, the growth performance and stress tolerance based on physical, chemical, and physiological attributes of this plant have not yet been studied. To address this question, J. curcas seedlings were grown in a completely randomized design in plastic pots filled with soil to evaluate the effects of salinity and drought stresses on growth, ionic composition, and physiological attributes. The experiment consisted of six treatments: control (without salinity and drought stress), salinity alone (7.5 dS m⁻¹, 15 dS m⁻¹), drought, and a combination of salinity and drought (7.5 dS m⁻¹+ Drought, 15 dS m⁻¹+Drought). Our results revealed that, compared with the control, both plant height (PH) and stem diameter (SD) were reduced by (83%, 80%, and 77%) and (69%, 56%, and 55%) under salinity and drought combination (15 dS m⁻¹+Drought) after three, six, and nine months, respectively. There was 93% more leaf Na⁺ found in plants treated with 15 dS m⁻¹+Drought compared with the control. The highest significant average membrane stability index (MSI) and relative water content (RWC) values (81% and 85%, respectively) were found in the control. The MSI and RWC were not influenced by 7.5 dS m⁻¹ and drought treatments and mostly contributed towards stress tolerance. Our findings imply that J. curcas is moderately tolerant to salinity and drought. The Na⁺ toxicity and disturbance in K⁺: Na⁺ ratio were the main contributing factors for limited growth and physiological attributes in this plant.

The symptoms of the nutrient stress in plant canopies differ among different growth stages. It is a challenge to develop individual diagnosis models to evaluate the nutrient status for a specific growth stage in time. Therefore, this study encoded spatiotemporal information of plants in a single time-series model to evaluate the nutrient status of oilseed rape more efficiently. Specifically, we combined the convolutional neural network and long-short term memory (CNN-LSTM) to classify the oilseed rape crops according to their nutrient status. The model was validated on a large number of sequential images acquired from oilseed rape canopies at different growth stages during a two-year experiment. Different pre-trained CNNs were used to extract distinctive features from every time step of sequential images and then these features were considered as the input of LSTM to classify the oilseed rape into nine classes of nutrient statuses. We demonstrated that the LSTM outperformed the traditional machine learning method and the deep features showed better performance compared to hand-crafted features. The Inceptionv3-LSTM obtained the highest overall classification accuracy of 95% when tested on the dataset of 2017/2018 and it also provided a good generalization when using a cross-dataset validation, with the highest overall accuracy of 92%. Our proposed approach presents a pathway toward automatic nutrient status diagnosis during the whole life cycle of the plants and the LSTM technique would play an essential role in the near future for time-series analysis for precision agriculture.