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Global surface snowfall rate estimation is crucial for hydrological and meteorological applications but is still a challenging task. A novel approach is developed to comprehensively use passive microwave, infrared data and physical constraints in deep‐learning neural networks with an attention module for retrieving surface snowfall rate (PCSSR‐DNNWA). The PCSSR‐DNNWA consistently outperforms traditional approaches in predicting surface snowfall rates with a correlation coefficient of ∼0.76, mean error of ∼−0.02 mm/hr, and root mean squared error of ∼0.21 mm/hr. It is found that graupel water path is of vital importance with largest contributions in retrieving surface snowfall rate. By integrating the physical constraints, the algorithm of PCSSR‐DNNWA opens a new avenue for retrieving the surface snowfall rate from satellites since some predictors are intelligently considered, resulting in an increased accuracy, interpretability, and computational efficiency. Plain Language Summary Comprehensively monitoring surface snowfall on Earth can effectively be achieved through space‐borne instruments. However, estimating surface snowfall from space is a challenging task as the signals measured by space sensors are indirectly related to surface snowfall rate. In this study, a novel deep learning algorithm is developed based on deep neural networks, which is more accurate, interpretable and computationally efficient, compared with traditional approaches, in estimating surface snowfall rate using observations from various space‐borne sensors and physically relevant parameters. Key Points Physical constraints greatly improve the ability of surface snowfall rate retrieval Attention module in deep neural networks could intelligently adjust the weights of predictors

Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields.

Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Temperature Sounder-2 (MWTS-II) and the Microwave Humidity Sounder-2 (MWHS-II) onboard the FY-3D satellite while considering the differentiated atmosphere temperature conditions. The results show that: (1) The brightness temperature (TB) of MWTS Channel 3 is well-suited for pre-classifying atmospheric temperatures, and significant differences in TB distribution exist between the two pre-classification subsets. (2) Among six machine classifiers examined, the random forest classifier exhibits favorable classification performance on both the validation set (accuracy: 0.76, recall: 0.76, F1 score: 0.75) and test set (accuracy: 0.80, recall: 0.44, F1 score: 0.44). (3) The application of the snowfall detection algorithm showcases a reasonable spatial distribution and outperforms the IMERG and ERA5 snowfall data.

Transforming waste biomass materials into bio-oils in order to partially substitute petroleum asphalt can reduce environmental pollution and fossil energy consumption and has economic benefits. The characteristics of bio-oils and their utilization as additives of asphalts are the focus of this review. First, physicochemical properties of various bio-oils are characterized. Then, conventional, rheological, and chemical properties of bio-oil modified asphalt binders are synthetically reviewed, as well as road performance of bio-oil modified asphalt mixtures. Finally, performance optimization is discussed for bio-asphalt binders and mixtures. This review indicates that bio-oils are highly complex materials that contain various compounds. Moreover, bio-oils are source-depending materials for which its properties vary with different sources. Most bio-oils have a favorable stimulus upon the low temperature performance of asphalt binders and mixtures but exhibit a negative impact on their high-temperature performance. Moreover, a large amount of oxygen element, oxygen-comprising functional groups, and light components in plant-based bio-oils result in higher sensitivity to ageing of bio-oil modified asphalts. In order to increase the performance of bio-asphalts, most research has been limited to adding additive agents to bio-asphalts; therefore, more reasonable optimization methods need to be proposed. Furthermore, upcoming exploration is also needed to identify reasonable evaluation indicators of bio-oils, modification mechanisms of bio-asphalts, and long-term performance tracking in field applications of bio-asphalts during pavement service life.

The assembly of single-walled carbon nanotubes (CNTs) into high-density horizontal arrays is strongly desired for practical applications, but challenges remain despite myriads of research efforts. Herein, we developed a non-destructive soft-lock drawing method to achieve ultraclean single-walled CNT arrays with a very high degree of alignment (angle standard deviation of ~0.03°). These arrays contained a large portion of nanometre-sized CNT bundles, yielding a high packing density (~400 µm −1 ) and high current carrying capacity (∼1.8 × 10 8 A cm − 2 ). This alignment strategy can be generally extended to diverse substrates or sources of raw single-walled CNTs. Significantly, the assembled CNT bundles were used as nanometre electrical contacts of high-density monolayer molybdenum disulfide (MoS 2 ) transistors, exhibiting high current density (~38 µA µm −1 ), low contact resistance (~1.6 kΩ µm), excellent device-to-device uniformity and highly reduced device areas (0.06 µm 2 per device), demonstrating their potential for future electronic devices and advanced integration technologies. A non-destructive soft-lock drawing method can achieve carbon nanotube arrays with ultraclean surfaces and a very high degree of alignment. Such arrays could be used as nano-sized electrical contacts of high-density monolayer MoS 2 transistors.

Modular polyketide synthases (mPKSs) are multidomain enzymes in bacteria that synthesize a variety of pharmaceutically important compounds. mPKS genes are usually longer than 10 kb and organized in operons. To understand the transcriptional and translational characteristics of these large genes, here we split the 13-kb busA gene, encoding a 456-kDa three-module PKS for butenyl-spinosyn biosynthesis, into three smaller separately translated genes encoding one PKS module in an operon. Expression of the native and split busA genes in Streptomyces albus reveals that the majority ( >93%) of PKS mRNAs are truncated, resulting in a greater abundance of and a higher synthesis rate for the proteins encoded by genes closer to the operon promoter. Splitting the large busA gene rescues translation of truncated mRNAs into functional PKS subunits, and increases the biosynthetic efficiency of butenyl-spinosyn PKS by 13-fold. The truncated mRNA translation rescue strategy will facilitate engineering of multi-domain proteins to enhance their functions. Genes encoding bacterial modular polyketide synthases (mPKSs) are usually longer than 10 kb and their mRNAs have high risk of being truncated. Here, the authors showed that they can enhance mPKS-based natural product biosynthesis by rescuing the translation of truncated mRNAs through PKS module splitting.

Drosophila suzukii (Diptera: Drosophilidae) is a polyphagous pest native to Asia that can cause serious economic damage. In this study, the effects of low-temperature storage on growth and development of Trichopria drosophilae were studied by using the methods of cold storage pupae to evaluate the cold storage tolerance and quality assessment of T. drosophila e, providing a theoretical basis for solving the mass production, storage and concentrated release of T. drosophilae . The results showed that the emergence rate of the 4th instar larvae decreased with extension of storage time from 10 to 20 d at 8 °C, 10 °C and 12 °C. The emergence rate remained as high as 79.24% after storage for 15 d at 16 °C. At 14 °C and 16 °C, the emergence rate of the 1-day-old pupae was not significantly different from that of control (77.27%). The above results indicated that the 4th instar and 1-day-old pupae of T. drosophilae were more suitable for long-term storage at 14 °C and 16 °C. Under the above experimental conditions, T. drosophila quality after cold storage for 10, 15 and 20 d was continuously investigated. The results showed that parasitism rates of different stages after treatment were significantly affected by storage temperature and time. Long-term cold storage could reduce the emergence rate and sex ratio of F1 generation. The emergence time of the 1-day-old pupae was gradually shortened with extension of storage time at 16 °C. The starvation-resistant lifespan of T. drosophilae after cold storage was significantly longer than that of control. This study results will further clarify the effects of cold temperature storage on growth and development of T. drosophilae .

It is well known that c-Src has important roles in tumorigenesis. However, it remains unclear whether c-Src contributes to metabolic reprogramming. Here we find that c-Src can interact with and phosphorylate hexokinases HK1 and HK2, the rate-limiting enzymes in glycolysis. Tyrosine phosphorylation dramatically increases their catalytic activity and thus enhances glycolysis. Mechanistically, c-Src phosphorylation of HK1 at Tyr732 robustly decreases its K m and increases its V max by disrupting its dimer formation. Mutation in c-Src phosphorylation site of either HK1 or HK2 remarkably abrogates the stimulating effects of c-Src on glycolysis, cell proliferation, migration, invasion, tumorigenesis and metastasis. Due to its lower K m for glucose, HK1 rather than HK2 is required for tumour cell survival when glucose is scarce. Importantly, HK1-Y732 phosphorylation level remarkably correlates with the incidence and metastasis of various clinical cancers and may serve as a marker to predict metastasis risk of primary cancers. The protein tyrosine kinase c-Src is a renowned proto-oncogene with pleiotropic effects. Here, the authors show that c-Src induces the metabolic reprogramming of cancer cells by phosphorylating hexokinases HK1 and HK2, which in turns lead to increased HK catalytic activity and consequent enhancement of glycolysis.

In order to develop a new type of antioxidants with high efficiency, a series of β-ionone thiazolylhydrazone derivatives were designed and synthesized from β-ionone, and their structures were characterized by 1H-NMR, 13C-NMR, FT-IR, and HR-MS. The antioxidant test in vitro indicated that most of the target compounds had high biological activity. Among them, compound 1k exhibited very strong DPPH (1,1-diphenyl-2-picrylhydrazyl radical)-scavenging activity with a half-maximal effective concentration (IC50) of 86.525 μM. Furthermore, in the ABTS (2,2-azinobis (3-ethylbenzothiazoline-6-sulfonate) diammonium salt)-scavenging experiment, compound 1m exhibited excellent activity with an IC50 of 65.408 μM. Their biological activities were significantly better than those of the positive control Trolox. These two compounds, which have good free-radical-scavenging activity in vitro, were used as representative compounds in the anti-browning experiment of fresh-cut potatoes. The results showed that 1k and 1m had the same anti-browning ability as kojic acid, and they were effective browning inhibitors. In addition, it is well known that microbial infection is one of the reasons for food oxidation. Therefore, we investigated the antifungal activity of 25 target compounds against eight plant fungi at a concentration of 125 mg/L. The results indicated that these compounds all have some antifungal activity and may become new potential fungicides. Notably, compound 1u showed the best inhibitory effect against Poria vaporaria, with an inhibition rate as high as 77.71%; it is expected to become the dominant structure for the development of new antifungal agents.

Sialic acids are terminal monosaccharides that cap glycans on glycoconjugates. Accumulating clinical and experimental evidence shows that obesity, insulin resistance, and diabetes are accompanied by changes in sialic-acid levels. In these conditions, the sialic-acid axis is also broadly remodeled: writers (sialyltransferases), erasers (neuraminidases), and readers (Siglecs) are dysregulated across adipose tissue, liver, pancreas, endothelium, and blood, shifting insulin signaling and inflammatory tone. This review summarizes relevant studies from the perspectives of disease clinical indicators, molecular mechanisms, and interventions targeting sialic acid. Taken together, these results confirm that sialic acids and related molecules play important roles in multiple metabolic diseases; however, controversies remain due to differences in glycan structure, isoforms, and tissue specificity, particularly regarding the precise roles of neuraminidases. Future studies should build on advanced, standardized glycomic and glycoproteomic measures to define molecule- and tissue-specific roles of sialic acids in metabolic disease, enabling reliable biomarkers and guiding targeted therapy.