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Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, we use data augmentation to improve the model performance of YOLOv8 algorithm (the latest version of You Only Look Once) on a pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX), which is a public dataset. The experimental results show that our model has reached the state-of-the-art (SOTA) mean average precision (mAP 50). Specifically, mAP 50 of our model is 0.638, which is significantly higher than the 0.634 and 0.636 of the improved YOLOv7 and original YOLOv8 models. To enable surgeons to use our model for fracture detection on pediatric wrist trauma X-ray images, we have designed the application “Fracture Detection Using YOLOv8 App” to assist surgeons in diagnosing fractures, reducing the probability of error analysis, and providing more useful information for surgery.

Nitrogen is the main limiting nutrient after carbon, hydrogen and oxygen for photosynthetic process, phyto-hormonal, proteomic changes and growth-development of plants to complete its lifecycle. Excessive and inefficient use of N fertilizer results in enhanced crop production costs and atmospheric pollution. Atmospheric nitrogen (71%) in the molecular form is not available for the plants. For world’s sustainable food production and atmospheric benefits, there is an urgent need to up-grade nitrogen use efficiency in agricultural farming system. The nitrogen use efficiency is the product of nitrogen uptake efficiency and nitrogen utilization efficiency, it varies from 30.2 to 53.2%. Nitrogen losses are too high, due to excess amount, low plant population, poor application methods etc., which can go up to 70% of total available nitrogen. These losses can be minimized up to 15–30% by adopting improved agronomic approaches such as optimal dosage of nitrogen, application of N by using canopy sensors, maintaining plant population, drip fertigation and legume based intercropping. A few transgenic studies have shown improvement in nitrogen uptake and even increase in biomass. Nitrate reductase, nitrite reductase, glutamine synthetase, glutamine oxoglutarate aminotransferase and asparagine synthetase enzyme have a great role in nitrogen metabolism. However, further studies on carbon–nitrogen metabolism and molecular changes at omic levels are required by using “whole genome sequencing technology” to improve nitrogen use efficiency. This review focus on nitrogen use efficiency that is the major concern of modern days to save economic resources without sacrificing farm yield as well as safety of global environment, i.e. greenhouse gas emissions, ammonium volatilization and nitrate leaching.

Triggering receptor expressed on myeloid cells 2 (TREM2) is a single-pass transmembrane immune receptor that is mainly expressed on microglia in the brain and macrophages in the periphery. Recent studies have identified TREM2 as a risk factor for Alzheimer’s disease (AD). Increasing evidence has shown that TREM2 can affect lipid metabolism both in the central nervous system (CNS) and in the periphery. In the CNS, TREM2 affects the metabolism of cholesterol, myelin, and phospholipids and promotes the transition of microglia into a disease-associated phenotype. In the periphery, TREM2 influences lipid metabolism by regulating the onset and progression of obesity and its complications, such as hypercholesterolemia, atherosclerosis, and nonalcoholic fatty liver disease. All these altered lipid metabolism processes could influence the pathogenesis of AD through several means, including affecting inflammation, insulin resistance, and AD pathologies. Herein, we will discuss a potential pathway that TREM2 mediates lipid metabolism to influence the pathogenesis of AD in both the CNS and periphery. Moreover, we discuss the possibility that TREM2 may be a key factor that links central and peripheral lipid metabolism under disease conditions, including AD. This link may be due to impacts on the integrity of the blood–brain barrier, and we introduce potential pathways by which TREM2 affects the blood–brain barrier. Moreover, we discuss the role of lipids in TREM2-associated treatments for AD. We propose some potential therapies targeting TREM2 and discuss the prospect and limitations of these therapies.

The introduction of YOLOv9, the latest version of the you only look once (YOLO) series, has led to its widespread adoption across various scenarios. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer‐assisted diagnosis to help radiologists and surgeons to interpret X‐ray images. Specifically, this paper trained the model on the GRAZPEDWRI‐DX dataset and extended the training set using data augmentation techniques to improve the model performance. Experimental results demonstrate that compared to the mAP 50–95 of the current state‐of‐the‐art model, the YOLOv9 model increased the value from 42.16% to 43.73%, with an improvement of 3.7%. The implementation code is publicly available at https://github.com/RuiyangJu/YOLOv9‐Fracture‐Detection. YOLOv9 is the latest version of the you only look once (YOLO) series of object detection algorithms released in February 2024. This paper presents a framework for pediatric wrist fracture detection utilizing YOLOv9, which enhances model performance to achieve the state‐of‐the‐art level through training on the GRAZPEDWRI‐DX dataset. This is significant because misinterpretation of fracture X‐ray images may lead to surgery failure and more harm to the patients. The hope is to use artificial intelligence technology to prevent these incidents from occurring.

Between 1008 and 1016, for several times Emperor Zhenzong (968–1022, r. 997–1022) distributed Zhicao (Ganoderma Lucidum), acquired during the Fengshan 封禪 rituals. These grand-scale activities from central to local levels were completely different from the previous management of auspicious omens and calamities. Zhicao, serving as an auspicious symbol in the Confucian system of auspicious omens and calamities, underwent an elevation in status through its integration with the concept of longevity in Daoism. It began to play important roles in the political propaganda of Tang (618–907) and Song (960–1276) dynasties. On the one hand, the distribution was influenced by the political initiatives of Emperor Gaozong (628–683, r. 649–683) after his Fengshan ceremony, with the reason lying in the subtle influence of the Buddhist concept of sacred relics. By integrating the political propaganda of Three Teachings, Emperor Zhenzong reinforced the regime’s legitimacy and enhanced the personal authority of the monarch.

Sustainable food security and safety are major concerns on a global scale, especially in developed nations. Adverse agroclimatic conditions affect the largest agricultural-producing areas, which reduces the production of crops. Achieving sustainable food safety is challenging because of several factors, such as soil flooding/waterlogging, ultraviolet (UV) rays, acidic/sodic soil, hazardous ions, low and high temperatures, and nutritional imbalances. Plant growth-promoting rhizobacteria (PGPR) are widely employed in in-vitro conditions because they are widely recognized as a more environmentally and sustainably friendly approach to increasing crop yield in contaminated and fertile soil. Conversely, the use of nanoparticles (NPs) as an amendment in the soil has recently been proposed as an economical way to enhance the texture of the soil and improving agricultural yields. Nowadays, various research experiments have combined or individually applied with the PGPR and NPs for balancing soil elements and crop yield in response to control and adverse situations, with the expectation that both additives might perform well together. According to several research findings, interactive applications significantly increase sustainable crop yields more than PGPR or NPs alone. The present review summarized the functional and mechanistic basis of the interactive role of PGPR and NPs. However, this article focused on the potential of the research direction to realize the possible interaction of PGPR and NPs at a large scale in the upcoming years.

Nano-fertilizers (NFs) significantly improve soil quality and plant growth performance and enhance crop production with quality fruits/grains. The management of macro-micronutrients is a big task globally, as it relies predominantly on synthetic chemical fertilizers which may not be environmentally friendly for human beings and may be expensive for farmers. NFs may enhance nutrient uptake and plant production by regulating the availability of fertilizers in the rhizosphere; extend stress resistance by improving nutritional capacity; and increase plant defense mechanisms. They may also substitute for synthetic fertilizers for sustainable agriculture, being found more suitable for stimulation of plant development. They are associated with mitigating environmental stresses and enhancing tolerance abilities under adverse atmospheric eco-variables. Recent trends in NFs explored relevant agri-technology to fill the gaps and assure long-term beneficial agriculture strategies to safeguard food security globally. Accordingly, nanoparticles are emerging as a cutting-edge agri-technology for agri-improvement in the near future. Interestingly, they do confer stress resistance capabilities to crop plants. The effective and appropriate mechanisms are revealed in this article to update researchers widely.

Sugarcane (Saccharum officinarum L.) is an important crop for sugar and bioenergy worldwide. The increasing greenhouse gas emission and global warming during climate change result in the increased frequency and intensity of extreme weather events. Climate change is expected to have important consequences for sugarcane production in the world, especially in the developing countries because of relatively low adaptive capacity, high vulnerability to natural hazards, and poor forecasting systems and mitigating strategies. Sugarcane production may have been negatively affected and will continue to be considerably affected by increases in the frequency and intensity of extreme environmental conditions due to climate change. The degree of climate change impact on sugarcane is associated with geographic location and adaptive capacity. In this paper, we briefly reviewed sugarcane response to climate change events, sugarcane production in several different countries, and challenges for sugarcane production in climate change in order for us to better understand effects of climate change on sugarcane production and to propose strategies for mitigating the negative impacts of climate change and improving sugarcane production sustainability and profitability.

Background Nitrogen is an essential element for sugarcane growth and development and is generally applied in the form of urea often much more than at recommended rates, causing serious soil degradation, particularly soil acidification, as well as groundwater and air pollution. In spite of the importance of nitrogen for plant growth, fewer reports are available to understand the application and biological role of N 2 fixing bacteria to improve N 2 nutrition in the sugarcane plant. Results In this study, a total of 350 different bacterial strains were isolated from rhizospheric soil samples of the sugarcane plants. Out of these, 22 isolates were selected based on plant growth promotion traits, biocontrol, and nitrogenase activity. The presence and activity of the nifH gene and the ability of nitrogen-fixation proved that all 22 selected strains have the ability to fix nitrogen. These strains were used to perform 16S rRNA and rpoB genes for their identification. The resulted amplicons were sequenced and phylogenetic analysis was constructed. Among the screened strains for nitrogen fixation, CY5 ( Bacillus megaterium ) and CA1 ( Bacillus mycoides ) were the most prominent. These two strains were examined for functional diversity using Biolog phenotyping, which confirmed the consumption of diverse carbon and nitrogen sources and tolerance to low pH and osmotic stress. The inoculated bacterial strains colonized the sugarcane rhizosphere successfully and were mostly located in root and leaf. The expression of the nifH gene in both sugarcane varieties (GT11 and GXB9) inoculated with CY5 and CA1 was confirmed. The gene expression studies showed enhanced expression of genes of various enzymes such as catalase, phenylalanine-ammonia-lyase, superoxide dismutase, chitinase and glucanase in bacterial-inoculated sugarcane plants. Conclusion The results showed that a substantial number of Bacillus isolates have N-fixation and biocontrol property against two sugarcane pathogens Sporisorium scitamineum and Ceratocystis paradoxa . The increased activity of genes controlling free radical metabolism may at least in part accounts for the increased tolerance to pathogens. Nitrogen-fixation was confirmed in sugarcane inoculated with B. megaterium and B. mycoides strains using N-balance and 15 N 2 isotope dilution in different plant parts of sugarcane. This is the first report of Bacillus mycoides as a nitrogen-fixing rhizobacterium in sugarcane.

Air pollution forecasting for public health and policy-making has a critical importance, this paper employs a Long Short-Term Memory (LSTM) model to perform in-depth prediction of PM2.5 concentrations measured at the U.S. Embassy in Beijing, outperforming regular forecasting approaches. In the LSTM model, the research examines a very detailed hourly dataset and beats regular forecasting approaches. A key finding is the model’s ability to effectively generalize from historical data to predict future air quality trends, with its adeptness at handling time-dependent relationships. This research emphasizes the importance of LSTM in air pollution prediction and management in environmental science as it provides an effective means for planning and making decisions on air quality management. This research is of great importance in providing a groundwork for further enhancement of prediction modeling. By offering a more reliable and sophisticated picture of air quality variations, this study addresses the current problem about how urban air pollution control could be improved in the city.