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Presents a world atlas that iIllustrates key indicators affecting international politics, economics, and society using maps and graphics.

Low-grade serous carcinoma of the ovary or peritoneum is characterised by MAPK pathway aberrations and its reduced sensitivity to chemotherapy relative to high-grade serous carcinoma. We compared the MEK inhibitor trametinib to physician's choice standard of care in patients with recurrent low-grade serous carcinoma. This international, randomised, open-label, multicentre, phase 2/3 trial was done at 84 hospitals in the USA and UK. Eligible patients were aged 18 years or older with recurrent low-grade serous carcinoma and measurable disease, as defined by Response Evaluation Criteria In Solid Tumors version 1.1, had received at least one platinum-based regimen, but not all five standard-of-care drugs, and had received an unlimited number of previous regimens. Patients with serous borderline tumours or tumours containing low-grade serous and high-grade serous carcinoma were excluded. Eligible patients were randomly assigned (1:1) to receive either oral trametinib 2 mg once daily (trametinib group) or one of five standard-of-care treatment options (standard-of-care group): intravenous paclitaxel 80 mg/m2 by body surface area on days 1, 8, and 15 of every 28-day cycle; intravenous pegylated liposomal doxorubicin 40–50 mg/m2 by body surface area once every 4 weeks; intravenous topotecan 4 mg/m2 by body surface area on days 1, 8, and 15 of every 28-day cycle; oral letrozole 2·5 mg once daily; or oral tamoxifen 20 mg twice daily. Randomisation was stratified by geographical region (USA or UK), number of previous regimens (1, 2, or ≥3), performance status (0 or 1), and planned standard-of-care regimen. The primary endpoint was investigator-assessed progression-free survival while receiving randomised therapy, as assessed by imaging at baseline, once every 8 weeks for 15 months, and then once every 3 months thereafter, in the intention-to-treat population. Safety was assessed in patients who received at least one dose of study therapy. This trial is registered with ClinicalTrials.gov, NCT02101788, and is active but not recruiting. Between Feb 27, 2014, and April 10, 2018, 260 patients were enrolled and randomly assigned to the trametinib group (n=130) or the standard-of-care group (n=130). At the primary analysis, there were 217 progression-free survival events (101 [78%] in the trametinib group and 116 [89%] in the standard-of-care group). Median progression-free survival in the trametinib group was 13·0 months (95% CI 9·9–15·0) compared with 7·2 months (5·6–9·9) in the standard-of-care group (hazard ratio 0·48 [95% CI 0·36–0·64]; p<0·0001). The most frequent grade 3 or 4 adverse events in the trametinib group were skin rash (17 [13%] of 128), anaemia (16 [13%]), hypertension (15 [12%]), diarrhoea (13 [10%]), nausea (12 [9%]), and fatigue (ten [8%]). The most frequent grade 3 or 4 adverse events in the standard-of-care group were abdominal pain (22 [17%]), nausea (14 [11%]), anaemia (12 [10%]), and vomiting (ten [8%]). There were no treatment-related deaths. Trametinib represents a new standard-of-care option for patients with recurrent low-grade serous carcinoma. NRG Oncology, Cancer Research UK, Target Ovarian Cancer, and Novartis.

Gaseous pollutants at the ground level seriously threaten the urban air quality environment and public health. There are few estimates of gaseous pollutants that are spatially and temporally resolved and continuous across China. This study takes advantage of big data and artificial-intelligence technologies to generate seamless daily maps of three major ambient pollutant gases, i.e., NO2, SO2, and CO, across China from 2013 to 2020 at a uniform spatial resolution of 10 km. Cross-validation between our estimates and ground observations illustrated a high data quality on a daily basis for surface NO2, SO2, and CO concentrations, with mean coefficients of determination (root-mean-square errors) of 0.84 (7.99 µg m−3), 0.84 (10.7 µg m−3), and 0.80 (0.29 mg m−3), respectively. We found that the COVID-19 lockdown had sustained impacts on gaseous pollutants, where surface CO recovered to its normal level in China on around the 34th day after the Lunar New Year, while surface SO2 and NO2 rebounded more than 2 times slower due to more CO emissions from residents' increased indoor cooking and atmospheric oxidation capacity. Surface NO2, SO2, and CO reached their peak annual concentrations of 21.3 ± 8.8 µg m−3, 23.1 ± 13.3 µg m−3, and 1.01 ± 0.29 mg m−3 in 2013, then continuously declined over time by 12 %, 55 %, and 17 %, respectively, until 2020. The declining rates were more prominent from 2013 to 2017 due to the sharper reductions in anthropogenic emissions but have slowed down in recent years. Nevertheless, people still suffer from high-frequency risk exposure to surface NO2 in eastern China, while surface SO2 and CO have almost reached the World Health Organization (WHO) recommended short-term air quality guidelines (AQG) level since 2018, benefiting from the implemented stricter “ultra-low” emission standards. This reconstructed dataset of surface gaseous pollutants will benefit future (especially short-term) air pollution and environmental health-related studies.

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research. The Human BioMolecular Atlas Program (HuBMAP) presents its production phase: the generation of spatial maps of functional tissue units across organs from diverse populations and the creation of tools and infrastructure to advance biomedical research.

Advancing crop genomics requires efficient genetic systems enabled by high-quality personalized genome assemblies. Here, we introduce RagTag, a toolset for automating assembly scaffolding and patching, and we establish chromosome-scale reference genomes for the widely used tomato genotype M82 along with Sweet-100, a new rapid-cycling genotype that we developed to accelerate functional genomics and genome editing in tomato. This work outlines strategies to rapidly expand genetic systems and genomic resources in other plant species.

Global industrialization and excessive dependence on nonrenewable energy sources have led to an increase in solid waste and climate change, calling for strategies to implement a circular economy in all sectors to reduce carbon emissions by 45% by 2030, and to achieve carbon neutrality by 2050. Here we review circular economy strategies with focus on waste management, climate change, energy, air and water quality, land use, industry, food production, life cycle assessment, and cost-effective routes. We observed that increasing the use of bio-based materials is a challenge in terms of land use and land cover. Carbon removal technologies are actually prohibitively expensive, ranging from 100 to 1200 dollars per ton of carbon dioxide. Politically, only few companies worldwide have set climate change goals. While circular economy strategies can be implemented in various sectors such as industry, waste, energy, buildings, and transportation, life cycle assessment is required to optimize new systems. Overall, we provide a theoretical foundation for a sustainable industrial, agricultural, and commercial future by constructing cost-effective routes to a circular economy.

Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers is adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a big matter of concern as it deteriorating the water quality, making it unfit for any type of use. Recently, water quality modelling using machine learning techniques has generated a lot of interest and can be very beneficial in ecological and water resources management. However, they suffer many times from high computational complexity and high prediction error. The good performance of a deep neural network like long short-term memory network (LSTM) has been exploited for the time-series data. In this paper, a deep learning–based Bi-LSTM model (DLBL-WQA) is introduced to forecast the water quality factors of Yamuna River, India. The existing schemes do not perform missing value imputation and focus only on the learning process without including a loss function pertaining to training error. The proposed model shows a novel scheme which includes missing value imputation in the first phase, the second phase generates the feature maps from the given input data, the third phase includes a Bi-LSTM architecture to improve the learning process, and finally, an optimized loss function is applied to reduce the training error. Thus, the proposed model improves forecasting accuracy. Data comprising monthly samples of different water quality factors were collected for 6 years (2013–2019) at several locations in the Delhi region. Experimental results reveal that predicted values of the model and the actual values were in a close agreement and could reveal a future trend. The performance of our model was compared with various state of the art techniques like SVR, random forest, artificial neural network, LSTM, and CNN-LSTM. To check the accuracy, metrics like root mean square errors (RMSE), the mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) have been used. Experimental analysis is carried out by measuring the COD and BOD levels. COD analysis reveals the MSE, RMSE, MAE, and MAPE values as 0.015, 0.117, 0.115, and 20.32, respectively, for the Palla region. Similarly, BOD analysis indicates the MSE, RMSE, MAE, and MAPE values as 0.107, 0.108, 0.124, and 18.22, respectively. A comparative analysis reveals that the proposed model outperforms all other models in terms of the best forecasting accuracy and lowest error rates.

The change in soil quality due to soil pollution significantly affects the ecosystem. The pollutants in the air also pass to the soil after a while, and therefore the soil is also the environment that best reflects the pollution level. Heavy metals are among soil and environmental pollutants; they are especially notorious because many of them can be toxic to living things even at low concentrations, tend to bioaccumulate in living things, and stay in nature for a long time. Therefore, monitoring heavy metal concentrations is of great importance. In this study, it is aimed to determine and map the Ni and Co pollution with the help of top soils in the city center. Top soil samples were taken from these regions and Ni and Co analyses were made, and the obtained values were evaluated statistically. Additionally, the data were transferred to geographic information systems (GIS) using ArcGIS 10.7 software, and then modeled with the kriging method, one of the interpolation methods, and pollution maps were created. As a result of the study, it was determined that the areas with the highest Ni and Co concentrations were generally located in the center of the study area. This shows that Ni and Co pollution increases in areas with high levels of human activities.

In the Taiping coal mining area in Zoucheng City, where there is a focus on agricultural production, a unique collapsed pond has been formed due to the dense population, a high phreatic water level and coal mining subsidence. A one-year field study was undertaken to investigate the concentrations of cations and anions in the pore groundwater, as well as the collapsed water and surface water, to clarify the hydrogeochemical controls, the seasonal cycle characteristics and the intended uses for the groundwater. The results, obtained from a self-organizing feature map, the K-means clustering algorithm and the Durov diagrams, revealed that the hydrochemical dataset could be classified into five clusters, corresponding to a SO4-Na type (Clusters 1 and 2), a mixed type (Cluster 3), a HCO3-Ca type (Cluster 4) and a SO4-Na∙Ca type (Cluster 5), respectively, with clear seasonal changes in the five pore groundwater samples. Based on the Gibbs, Gaillardet and chloro-alkaline index (CAI) diagrams, rock weathering, cation exchange and evaporative crystallization, especially the erosion of silicate rock, were the primary processes controlling the hydrogeochemistry. Meanwhile, the suitability of the groundwater evaluation methods of random forest (RF), genetic algorithm-support vector machine (GA-SVM) and back-propagation (BP) neural network were found to be superior to the traditional Quality Standard for Groundwater of China (SGQC), the Fisher and the F analysis methods. Among them RF has the optimal simulation accuracy and effect. As a result of quality assessment of the groundwater, the quality of the shallow groundwater was generally poor and was only fit for purpose after appropriate treatment. Moreover, it is speculated that the main factors affecting the groundwater quality were the unique mode of collapse of the pond formed as a result of the high phreatic water level, the natural conditions such as rainwater recharge and groundwater runoff, the dense population, mining and agricultural development, and chemical pollution. This innovative study describes an optimization method for assessment of groundwater suitability and highlights the importance of minimizing excessive groundwater extraction, developing continuous water quality monitoring plans, and managing and preventing potential hazards.

Groundwater quality in arid areas is an issue that requires attention, as often the only source of irrigation and drinking. In this manuscript, a research area of 4590.2 Km2, which depicts important problems of water scarcity and water-poor quality in an arid environment, was assessed. Therefore, GIS-based mapping of the region was carried out to examine the adequacy of groundwater quality. Forty-three (43) groundwater samples were gathered in the fieldwork in March 2022, and various physicochemical limits were analysed within Gibbs and Piper diagrams. Likewise, spatial distribution maps of twelve (12) main water quality parameters were performed using the Inverse Distance Weighted interpolation method (IDW) in the ArcGIS tool. The Water Quality Index (WQI) has been calculated considering Algerian drinking water quality standards and to know the suitability of water for human consumption. A Pearson’s test was carried out to explore the correlations among the different variables. Major results of the study showed that, among anions and cations, sulphates (SO4-2) and calcium (Ca) were the most common ions. The WQI showed that 30.23% of the samples have excellent water quality, 62.79% belong to the good water category, and 6.97% fall into the poor water quality category. Thus, the correlation study between many physicochemical properties also showed significant positive relationships between WQI, TDS, EC, TH, SO4-2, Ca++, and Mg++. In agreement with the Gibbs diagram, most of the samples fall into the field of rock-water interaction dominance. Hence, this research shows that using GIS and WQI as joint approaches could be useful tools for the decision-making process of local and international authorities and water resources management.