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Potassium bitartrate (KHT) crystallization, as the dominant factor compromising wine colloidal stability, necessitates advanced control strategies beyond conventional thermodynamic approaches. The formation of tartrate crystals is influenced by various factors, including temperature, pH, and the concentration of tartrate salts. Traditional methods of tartrate stabilization, such as cold stabilization and ion-exchange resins, while effective, are associated with high energy consumption and significant environmental impact. In recent years, with the growing emphasis on green and sustainable development, researchers have begun exploring more environmentally friendly innovative technologies. This review examines the factors affecting tartrate crystallization and their implications for wine quality, detailing traditional stabilization techniques as well as newer methods involving protective colloids and stabilizers. Special attention is given to recent advancements in green technologies, such as plasma surface modification, the use of zeolites as wine processing aids, and the synergistic application of algal polysaccharides. Finally, the paper outlines future directions for tartrate stabilization technology, underscoring the importance of green and sustainable practices in the wine industry.

Stroke has become an important cause of death and disability worldwide, which highlights the need for early detection and intervention. Machine learning technology can analyze patients’ historical health data and biometrics to identify high-risk individuals in a timely manner, thereby effectively predicting stroke.This paper evaluates the predictive performance Random Forest and Support Vector Machine (SVM). Data preprocessing encompasses managing missing data, processing categorical variables, and tackling issues related to class imbalance. Analysis of the quantitative results indicates that the Random Forest model reaches an accuracy of 95% and a precision of 93%, providing a slight edge over the SVM, which records an accuracy of 92% and a precision of 90%.. However, both models exhibit high false-negative rates, with Random Forest showing a false-negative rate of 12% and SVM at 15%, which significantly impacts their clinical utility. To improve performance, further model optimization, such as adjusting class weights or employing ensemble methods, is necessary to reduce these false-negative rates and enhance diagnostic accuracy. This study highlights the potential and limitations of machine learning in stroke prediction, showing that people need further optimization to enhance diagnostic performance.

Complex pest and disease features appearing during the growth of wheat crops are difficult to capture and can seriously affect the normal growth of wheat crops. The existing methods ignore the full pre-interaction of deep and shallow features, which largely affects the accuracy of identification. To address the above problems and needs, we rethink the feature representation and attention mechanism in intelligent recognition of wheat leaf diseases and pests, and propose a representation and recognition network (RReNet) based on the feature attention mechanism. RReNet captures key information more efficiently by focusing on complex pest and disease characteristics and fusing multi-semantic feature information. In addition, RReNet further enhances the perception of complex disease and pest features by using four layers of detection units and fast IoU loss function, which significantly improves the accuracy and robustness of wheat leaf disease and pest recognition. Tests on a challenging wheat leaf pest and disease dataset with twelve pest and disease types show that RReNet achieves precision, recall and mAP as high as 94.1%, 95.7% and 98.3% respectively. Also, ablation experiments proved the effectiveness of all parts of the proposed method.

Mucin glycans are an important component of the mucus barrier and a vital defence against physical and chemical damage as well as pathogens. There are 20 mucins in the human body, which can be classified into secreted mucins and transmembrane mucins according to their distributions. The major difference between them is that secreted mucins do not have transmembrane structural domains, and the expression of each mucin is organ and cell-specific. Under physiological conditions, mucin glycans are involved in the composition of the mucus barrier and thus protect the body from infection and injury. However, abnormal expression of mucin glycans can lead to the occurrence of diseases, especially cancer, through various mechanisms. Therefore, targeting mucin glycans for the diagnosis and treatment of cancer has always been a promising research direction. Here, we first summarize the main types of glycosylation (O-GalNAc glycosylation and N-glycosylation) on mucins and the mechanisms by which abnormal mucin glycans occur. Next, how abnormal mucin glycans contribute to cancer development is described. Finally, we summarize MUC1-based antibodies, vaccines, radio-pharmaceuticals, and CAR-T therapies using the best characterized MUC1 as an example. In this section, we specifically elaborate on the recent new cancer therapy CAR-M, which may bring new hope to cancer patients.

Utilization of urban green vegetation (UGV) has been recognized as a promising option to mitigate urban heat island (UHI) effect. While we still lack understanding of the contributions of local background climate to the cooling effect of UGV. Here we proposed and employed a cooling effect framework and selected eight typical cities located in Temperate Monsoon Climate (TMC) and Mediterranean Climate (MC) demonstrate that local climate condition largely affects the cooling effect of UGV. Specifically, we found increasing (artificial) rainfall and irrigation contribute to improving the cooling intensity of grassland in both climates, particularly in the hot-dry environment. The cities with high relative humidity would restrict the cooling effect of UGV. Increasing wind speed would significantly enhance the tree-covered while weakening the grass-covered UGVs’ cooling effect in MC cities. We also identified that, in order to achieve the most effective cooling with the smallest sized tree-covered UGV, the area of trees in both climate zones’ cities should generally be planned around 0.5 ha. The method and results enhance understanding of the cooling effect of UGVs on larger (climate) scales and provide important insights for UGV planning and management.

The sustainable development strategy shifts water treatment from pollution removal to resource recovery. Here, an electrochemical resource-recovery anaerobic membrane bioreactor (eRAnMBR) that employed a magnesium plate and conductive membrane as dual anodes is presented and shows excellent performance in carbon, nitrogen, and phosphorus recovery, as well as 95% membrane anti-fouling. The Mg 2+ released alters the physicochemical properties of sludge, unblocking the cake layer, and recovers ammonium and phosphate, yielding 60.64% purity and 0.08 g d −1 struvite deposited onto cathode to be separated from sludge. The enhanced direct interspecies electron transfer, along with hydrogen evolution and alkalinity increase due to the electrochemical reactions, significantly increase methane yield and purity (93.97%) of the eRAnMBR. This increased internal energy can cover the additional electricity and electrode consumption. This integrated eRAnMBR reactor boasts the benefits of short process, low maintenance, and low carbon footprint, introducing a concept for the next generation of wastewater treatment. Recovering resources from wastewater sources is an important sustainable development strategy. Here, authors build an electrochemical resource-recovery anaerobic membrane bioreactor for the simultaneous full recovery of carbon, nitrogen, and phosphorus, while demonstrating membrane fouling mitigation.

Environmental pollution of heavy metals in the typical coal industrial city should be paid more attentions nowadays. The spatial distribution and source apportionment of 8 heavy metals (i.e., Cd, Cr, Co, Zn, Ni, Cu, Pb and Mn) from topsoil samples (158) of Shizuishan city in Ningxia Hui Autonomous Region of China were investigated using principal component analysis/absolute principal component scores (PCA/APCS) receptor model and geographic information system (GIS). These results showed that the mean concentrations of Cd, Cr, Co, Zn, Ni, Cu and Pb were higher than their soil background values in Ningxia. 99.36% of soil samples were heavily polluted according to analysis of integrated Nemerow pollution index (PN), whereas 81.65% of soil samples exhibited the highly strong potential ecological risk by ERI (the comprehensive of potential ecological risk index) values. The source apportionment results showed that eight heavy metals in soil were mainly from natural (32.39%), industrial (26.56%), traffic emission/coal consumption (20.18%) and atmospheric deposition source (12.73%). Typically, Zn, Mn and Ni were derived from natural source, whereas Cr and Co were mainly derived from industrial sources. Cu was from the multiple sources, whereas Pb and Cd were weighted primarily from traffic emission/coal consumption source and atmospheric deposition source, respectively. These findings were crucial for the prevention and control of heavy metals pollution in Shizuishan city.

Complex displays that comprise multiple behavioral elements play an essential role in the communication of group-living animals. One of them is a greeting display. Greeting is performed during the reunion after a separation, and is known for maintaining social bonds in mammals and pair bonds in monogamous fish. Greeting displays have been documented in birds, but lack functional studies. Java sparrows ( Lonchura oryzivora ) are gregarious and long-term monogamous songbird species, exhibiting a complex greeting display consisting of a sequence of four repetitive behavioral elements. We hypothesized that Java sparrow greetings function as between-pair communication in social contexts. In particular, we expected that pair-bonded partners would greet more after experiencing longer separation. In addition, we also predicted that they greet more when other conspecific individuals are nearby; as it is more important for them to confirm and advertise their commitment relationships. To test these ideas, we conducted separation-reunion tests using pair-bonded Java sparrows with different separation times (long vs. short) and different social conditions (with vs. without the presence of conspecifics). We calculated and compared the sequential complexity of the greeting displays. We showed that subject pairs performed a greater number of greeting display bouts after longer separation times. In the presence of conspecifics, greeting displays were more frequent, longer, and more complex. Our finding supports the idea that greeting displays in birds are crucial to pair-bond maintenance, contributing to understanding the evolution of complex communications in birds.

With the progress of urbanization, urban management is facing a series of challenges in the new situation. The scale of the city is growing, urban management problems are increasingly prominent, the urban population is showing a rapid growth trend, and various elements of urban infrastructure management, such as rapid growth and urban expansion, have increased the load of urban infrastructure. To make overall planning for urban transportation, municipal administration, economic industry, and public service, intelligent urban planning and construction came into being. Big data technology provides important support for the construction and development of smart city, which is not only an effective means to improve the design of smart city, but also the premise for the development of smart city. Therefore, this paper first introduces the characteristics of smart city, analyses the application of big data technology in smart city design, and finally evaluates the impact of smart city planning and construction based on big data on economic and social benefits.

Air pollution may increase risk of Alzheimer’s disease and related dementias (ADRD) in the U.S., but the extent of this relationship is unclear. Here, we constructed two national U.S. population-based cohorts of those aged ≥65 from the Medicare Chronic Conditions Warehouse (2000–2018), combined with high-resolution air pollution datasets, to investigate the association of long-term exposure to ambient fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) with dementia and AD incidence, respectively. We identified ~2.0 million incident dementia cases ( N  = 12,233,371; dementia cohort) and ~0.8 million incident AD cases ( N  = 12,456,447; AD cohort). Per interquartile range (IQR) increase in the 5-year average PM 2.5 (3.2 µg/m 3 ), NO 2 (11.6 ppb), and warm-season O 3 (5.3 ppb) over the past 5 years prior to diagnosis, the hazard ratios (HRs) were 1.060 (95% confidence interval [CI]: 1.054, 1.066), 1.019 (95% CI: 1.012, 1.026), and 0.990 (95% CI: 0.987, 0.993) for incident dementias, and 1.078 (95% CI: 1.070, 1.086), 1.031 (95% CI: 1.023, 1.039), and 0.982 (95%CI: 0.977, 0.986) for incident AD, respectively, for the three pollutants. For both outcomes, concentration-response relationships for PM 2.5 and NO 2 were approximately linear. Our study suggests that exposures to PM 2.5 and NO 2 are associated with incidence of dementia and AD. Air pollution has been linked to neurodegenerative disease. Here the authors carried out a population-based cohort study to investigate the association between long-term exposure to PM 2.5 , NO 2 , and warm-season O 3 on dementia and Alzheimer’s disease incidence in the United States.