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Background and aims Cancer will soon become the leading cause of death in every country in the twenty-first century. This study aimed to analyze the mortality and morbidity of 29 types of cancer in 204 countries or regions from 1990 to 2019 to guide global cancer prevention and control. Methods Detailed information for 29 cancer groups was collected from the Global Burden of Disease Study in 2019. The age-standardized incidence rate (ASIR) and age-standardized death rate (ASDR) of the 29 cancer groups were calculated based on sex, age, region, and country. In addition, separate analyses were performed for major cancer types. Results In 2019, more than 10 million people died from cancer, which was approximately twice the number in 1990. Tracheal, bronchus, and lung (TBL) cancers collectively showed the highest death rate, and the ASDR of pancreatic cancer increased by 24%, which was cancer with the highest case fatality rate (CFR). The global cancer ASIR showed an increasing trend, with testicular cancer, thyroid cancer, and malignant skin melanoma showing a significant increase. The ASDR and ASIR of cancer in males were about 1.5 times higher than that in females. Individuals over 50 years had the highest risk of developing cancer, with incidences and deaths in this age group accounting for more than 85% of cancers in all age groups. Asia has the heaviest cancer burden due to its high population density, with esophageal cancer in this region accounting for 53% of the total fatalities related to this type of cancer in the world. In addition, the mortality and morbidity of most cancers increased with the increase in the development or socio-demographic index (SDI) in the SDI regions based on the World Bank's Human Development Index (HDI), with cancer characteristics varying in the different countries globally. Conclusions The global cancer burden continues to increase, with substantial mortality and morbidity differences among the different regions, ages, countries, gender, and cancer types. Effective and locally tailored cancer prevention and control measures are essential in reducing the global cancer burden in the future.

The growth of maritime transportation has been accompanied by a gradual increase in accident rates, drawing greater attention to the critical issue of man-overboard incidents and drowning. Traditional maritime search-and-rescue (SAR) methods are often constrained by limited efficiency and high operational costs. Over the past few years, drones have demonstrated significant promise in improving the effectiveness of search-and-rescue operations. This is largely due to their exceptional ability to move freely and their capacity for wide-area monitoring. This study proposes an enhanced SR-DETR algorithm aimed at improving the detection of individuals who have fallen overboard. Specifically, the conventional multi-head self-attention (MHSA) mechanism is replaced with Efficient Additive Attention (EAA), which facilitates more efficient feature interaction while substantially reducing computational complexity. Moreover, we introduce a new feature aggregation module called the Cross-Stage Partial Parallel Atrous Feature Pyramid Network (CPAFPN). By refining spatial attention mechanisms, the module significantly boosts cross-scale target recognition capabilities in the model, especially offering advantages for detecting smaller objects. To improve localization precision, we develop a novel loss function for bounding box regression, named Focaler-GIoU, which performs particularly well when handling densely packed and small-scale objects. The proposed approach is validated through experiments and achieves an mAP of 86.5%, which surpasses the baseline RT-DETR model’s performance of 83.2%. These outcomes highlight the practicality and reliability of our method in detecting individuals overboard, contributing to more precise and resource-efficient solutions for real-time maritime rescue efforts.

Background and aims Cancer has become the second most serious disease threatening human health, followed by cardiovascular diseases. This study aimed to quantitatively estimate the mortality, morbidity, and analyze the trends of 29 cancer groups in 195 countries/regions between 1990 and 2017. Methods Detailed information of 29 cancer groups were collected from the Global Burden of Disease (GBD) study in 2017 and age-standardized incidence rates (ASIR) and age-standardized death rates (ASDR) of 29 cancer groups were calculated based on gender, age, region, and country. Trend analyses were conducted for major cancer types. Results In 2017, the global death population caused by cancer reached 9 million, which was nearly twice the number in 1990. The ASDR and ASIR of cancer in males were about 1.5 times those of females. Breast cancer showed the highest mortality rate in females in 2017. Individuals aged over 50 are at high risk of developing cancer and the number of cases and deaths in this age group accounted for more than 80% of all cancers in all age groups. Asia has the heaviest cancer burden due to its large population density. Different cancers in varied countries globally have their own characteristics. The ASDR and ASIR of some major cancers demonstrated changes from 1990 to 2017. Conclusions Analyses of these data provided basis for future investigations to the common etiological factors, leading to the occurrence of different cancers, the development of prevention strategies based on local characteristics, socioeconomic and other conditions, and the formulation of more targeted interventions.

Pest detection is important for crop cultivation. Crop leaf is the main place of pest invasion. Current technologies to detect crop pests have constraints, such as low efficiency, storage demands and limited precision. Image segmentation is a fast and efficient computer-aided detection technology. High resolution image capture solidly supports the crucial processes in discerning pests from images. Study of analytical methods help parse information in the images. In this paper, a regional convolutional neural network (R-CNN) architecture is designed in combination with the radial bisymmetric divergence (RBD) method for enhancing the efficiency of image segmentation. As a special application of RBD, the hierarchical mask (HM) is produced to endorse detection and classification of the leaf-dwelling pests, offering enhanced efficiency and reduced storage requirements. Moreover, to deal with some mislabeled data, a threshold variable is introduced to adjust a fault-tolerant mechanism into HM, to generate a novel threshold-based hierarchical mask (TbHM). Consequently, the hierarchical mask R-CNN (HM-R-CNN) model and the threshold-based hierarchical mask R-CNN (TbHM-R-CNN) model are established to detect various types of healthy and pest-invasive crop leaves to select the regional image features that are rich in pest information. Then simple linear iterative clustering (SLIC) method is incorporation to finish the image segmentation for the classification of pest invasion. The models are tuned and optimized, then validated. The most optimized modeling results are from the TbHM-R-CNN model, with the classification accuracy of 96.2%, the recall of 97.5% and the F1 score of 0.982. Additionally, the HM-R-CNN model observed appreciable results second only to the best model. These results indicate that the proposed methodologies are well-suited for training and testing a dataset of plant diseases, offering heightened accuracy in pest classification. This study revealed that the proposed methods significantly outperform the existing techniques, marking a substantial improvement over current methods.

Surface charge polarity and density influence the immune clearance and cellular uptake of intravenously administered lipid nanoparticles (LNPs), thus determining the efficiency of their delivery to the target. Here, we modified the surface charge with ascorbyl palmitate (AsP) used as a negatively charged lipid. AsP-PC-LNPs were prepared by dispersion and ultrasonication of AsP and phosphatidylcholine (PC) composite films at various ratios. AsP inserted into the PC film with its polar head outward. The pKa for AsP was 4.34, and its ion form conferred the LNPs with negative surface charge. Zeta potentials were correlated with the amount and distribution of AsP on the LNPs surface. DSC, Raman and FTIR spectra, and molecular dynamics simulations disclosed that AsP distributed homogeneously in PC at 1–8% (w/w), and there were strong hydrogen bonds between the polar heads of AsP and PC (PO2−), which favored LNPs’ stability. But at AsP:PC > 8% (w/w), the excessive AsP changed the interaction modes between AsP and PC. The AsP–PC composite films became inhomogeneous, and their phase transition behaviors and Raman and FTIR spectra were altered. Our results clarified the mechanism of surface charge modification by AsP and provided a rational use of AsP as a charged lipid to modify LNP surface properties in targeted drug delivery systems. Furthermore, AsP–PC composites were used as phospholipid-based biological membranes to prepare paclitaxel-loaded LNPs, which had stable surface negative charge, better tumor targeting and tumor inhibitory effects.

When subject to vegetational shading, shade-avoiding plants detect neighbors by perceiving reduced light quantity and altered light quality. The former includes decreases in the ratio of red to far-red wavelengths (low R:FR) and low blue light ratio (LBL) predominantly detected by phytochromes and cryptochromes, respectively. By integrating multiple signals, plants generate a suite of responses, such as elongation of a variety of organs, accelerated flowering, and reduced branching, which are collectively termed the shade-avoidance syndrome (SAS). To trigger the SAS, interactions between photoreceptors and phytochrome-interacting factors are the general switch for activation of downstream signaling pathways. A number of transcription factor families and phytohormones, especially auxin, gibberellins, ethylene, and brassinosteroids, are involved in the SAS processes. In this review, shade signals, the major photoreceptors involved, and the phenotypic characteristics of the shade-intolerant plant are described in detail. In addition, integration of the signaling mechanisms that link photoreceptors with multiple hormone signaling pathways is presented and future research directions are discussed.

Due to global warming and the disturbance of the interannual variability of precipitation, the frequency of extreme drought events has increased. The impact of global climate change on water resources is becoming increasingly apparent, then it is particularly necessary to explore the carrying capacity of water ecological environment under extreme drought conditions, which can guarantee the ecological water security in river basins. This study takes the Guanzhong area of the Wei River Basin as an example, calculating the water environment carrying capacity of 40 areas in the Weihe Guanzhong area in different levels of years under extreme drought conditions by comprehensive evaluation model of carrying capacity and using geographic information system GIS to display the spatial distribution of water environment carrying capacity in 40 regions. According to the results of the spatial distribution of water environmental bearing capacity, four different schemes are designed to improve the bearing capacity. The first plan reduces the industrial water consumption and irrigation quota by 5%, the second plan increases the industrial water and sewage treatment rate on this basis. the third plan further improves the development and utilization rate of surface and groundwater, and the fourth plan, on the basis of the first three plans, supplies 600 million cubic meters of industrial and agricultural water to Guanzhong region. Through comparative analysis, without taking any measures, under the extreme drought conditions, the water environment carrying capacity of the 40 areas in Guanzhong is all in an unbearable state. Overall, plan 4 has the most significant improvement in the water environment-carrying capacity, especially the Dong zhuang Reservoir of the Jing River which has played a very important role in enhancing the water ecological environment carrying capacity of the downstream water of the Wei River.

So many biological functional disorders and diseases, such as atherosclerosis, hypertension, diabetes, Alzheimer’s disease, as well as cell malignancy are closely related with the intracellular viscosity. A safe and effective intracellular viscosity detecting method is desired by the biomedical community. Recently, a novel near-infrared fluorescent probe NI-VIS with a twisting intramolecular charge transfer mechanism was developed. The capability of this probe to visualize the viscosity variation in cirrhotic liver tissues and map the micro viscosity in vivo were testified using an experiment. In this work, the twisting intramolecular charge transfer mechanism and fluorescent properties of the probe NI-VIS were studied in detail under quantum mechanical method. The low energy barrier among the different conformations of the probe indicated the occurrence of twisting intramolecular charge transfer due to the rotation of the aryl group in the probe molecule while within the low viscosity environment. The electronic structure analysis on different probe conformations revealed the electron transfer process of the probe under optical excitation. All these theoretical results could provide insights into understand in greater depth the principles and build highly effective fluorescent probe to monitor the viscosity in biological samples.

Library data contains many students’ reading records that reflect their general knowledge acquisition. The purpose of this study is to deeply mine the library book-borrowing data, with concerns on different book catalogues and properties to predict the students’ extracurricular interests. An intelligent computing framework is proposed by the fusion of a neural network architecture and a partial differential equations (PDE) function module. In model designs, the architecture is constructed as an adaptive learning backpropagation neural network (BPNN), with automatic tuning of its hyperparameters. The PDE module is embedded into the network structure to enhance the loss functions of each neural perceptron. For model evaluation, a novel comprehensive index is designed using the calculus of information entropy. Empirical experiments are conducted on a diverse and multimodal time-series dataset of library book borrowing records to demonstrate the effectiveness of the proposed methodology. Results validate that the proposed framework is capable of revealing the students’ extracurricular reading interests by processing related book borrowing records, and expected to be applied to “big data” analysis for a wide range of various libraries.

Background: Liver cancer is one of the leading causes of cancer-related deaths worldwide. The primary causes of liver cancer include hepatitis B virus (HBV), hepatitis C virus (HCV), alcohol consumption, nonalcoholic fatty liver disease, and other factors. Aims: The objective of this study was to evaluate the global and sex-, age-, region-, country-, and etiology-related liver cancer burden, as well as the trends in liver cancer caused by different etiologies. Methods: The causes of liver cancer from 1990 to 2017, including global, regional, and national liver cancer incidence, mortality, and etiology, were collected from the Global Burden of Disease study 2017, and the time-dependent change in the trends of liver cancer burden was evaluated by annual percentage change. Results: The global liver cancer incidence and mortality have been increasing. There were 950,000 newly-diagnosed liver cancer cases and over 800,000 deaths in 2017, which is more than twice the numbers recorded in 1990. HBV and HCV are the major causes of liver cancer. HBV is the major risk factor of liver cancer in Asia, while HCV and alcohol abuse are the major risk factors in the high sociodemographic index and high human development index regions. The mean onset age and incidence of liver cancer with different etiologies have gradually increased in the past 30 years. Conclusions: The global incidence is still rising and the causes have national, regional, or population specificities. More targeted prevention strategies must be developed for the different etiologic types in order to reduce liver cancer burden.