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Abstract Most cancer cells reprogram their glucose metabolic pathway from oxidative phosphorylation to aerobic glycolysis for energy production. By reducing enzyme activity of pyruvate kinase M2 (PKM2), cancer cells attain a greater fraction of glycolytic metabolites for macromolecule synthesis needed for rapid proliferation. Here we demonstrate that hydrogen sulfide (H2S) destabilizes the PKM2 tetramer into monomer/dimer through sulfhydration at cysteines, notably at C326, leading to reduced PKM2 enzyme activity and increased PKM2-mediated transcriptional activation. Blocking PKM2 sulfhydration at C326 through amino acid mutation stabilizes the PKM2 tetramer and crystal structure further revealing the tetramer organization of PKM2-C326S. The PKM2-C326S mutant in cancer cells rewires glucose metabolism to mitochondrial respiration, significantly inhibiting tumor growth. In this work, we demonstrate that PKM2 sulfhydration by H2S inactivates PKM2 activity to promote tumorigenesis and inhibiting this process could be a potential therapeutic approach for targeting cancer metabolism.

Currently, steel is mainly alloyed by adding ferrochrome alloys in the production of chromium-containing steels. However, the production of ferrochrome consumes a lot of electricity and produces a large number of additional products that are extremely harmful to the environment. For the consideration of the above problems, the experiment studied the reduction behavior of Cr2O3 using carbon powder as the reductant under different ambient pressures, different reductant dosages, and different initial oxygen contents in the molten steel. The results show that CO bubbles can be formed by decreasing the air pressure and increasing the amount of reductant, thus the reduction rate and reduction efficiency of Cr2O3 can be increased. It was found that the ambient air pressure mainly affected the reduction rate of Cr2O3, and the fastest reduction rate of Cr2O3 was found in vacuum environment. The reductant dosage has great influence on the reduction rate, reduction efficiency, density and size of inclusions of Cr2O3. Addition of carbon powder with a carbon-to-oxygen molar ratio of 2 will significantly improve the reduction rate of Cr2O3. The yield of [Cr] in steel can reach more than 95 %. In addition, the density of inclusions in steel is stabilized below 0.2/mm−2. The addition of carbon powder with nc:no ≤ 1.0 significantly reduces the reduction rate of Cr2O3 due to the decrease of carbon concentration in the steel. The density of inclusions in steel also increased significantly up to 210.4/mm−2. The initial oxygen content in the molten steel had little effect on the reduction of Cr2O3. However, the reduction rate of Cr2O3 is lower in steel with high oxygen concentration than in steel with low oxygen concentration. Under the optimum reduction conditions-nc:no = 2.0, initial oxygen content of 3 × 10−4, and ambient air pressure of 50 Pa. The yield of [Cr] in steel can reach 96.85 %, the total oxygen content can be reduced to 1.6 × 10−5, the density of inclusions is 0.04/mm−2, and the size of inclusions is below 1.8 μm.

This study investigated the drought trends, frequency, and duration in three drought-prone cities in Northwest China under various Shared Socioeconomic Pathways (SSPs) climate scenarios from 2015 to 2099. Utilizing a two-step downscaling method, we generated high-resolution (1 km) temperature and precipitation projections for the cities using multiple global climate model (GCM) data. The Standardized Precipitation Evapotranspiration Index (SPEI) was then calculated to assess drought conditions. Our results revealed that under the influence of global climate change, the 12-month SPEI exhibited a significant increasing trend for all three cities, indicating a shift towards a more humid climate. The influence of climate change on drought trends was more pronounced at higher latitudes. Additionally, the occurrence and duration of severe droughts (3-month SPEI

Abstract The fish immune system is a topic or subject that offers a unique understanding of defensive system evolution in vertebrate heredity. While gut microbiota plays several roles in fish: well-being, promoting health and growth, resistance to bacterial invasion, regulation of energy absorption, and lipid metabolism. However, studies on fish gut microbiota face practical challenges due to the large number of fish varieties, fluctuating environmental conditions, and differences in feeding habits. This study was carried out to evaluate the impacts of supplemented three autochthonous strains, Bacillus sp. RCS1, Pantoea agglomerans RCS2, and Bacillus cereus RCS3 mixture diet on cobia fish (Rachycentron canadum). Also, chromatography, mass spectrometry and high throughput sequencing were combined to explore composition and metabolite profile of gut microbiota in juvenile cobia fed with supplemented diet. In the trial group, juvenile cobia received diets supplemented with 1 × 1012 CFU mL−1 autochthonous strains for ten weeks and a control diet without supplementation. Juvenile cobia receiving diets supplementation exhibited significantly improved growth than those without additives (control). Haematological indices, such as red blood cells, white blood cells, corpuscular haemoglobin concentration, mean corpuscular volume, haemoglobin, and mean corpuscular haemoglobin, were higher in the supplemented group. Similarly, digestive enzymes (trypsin, lipase, amylase, pepsin and cellulose, activities) activities were higher in supplemented diet with an indigenous isolates mixture. Serum biochemical parameters albumin, globulin, and total protein were significantly higher, while triglyceride, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and cholesterol showed no significant difference. On the other hand, glucose was significantly (P

Bearing steel is subjected to complex alternating stress conditions for a long time which requires excellent service properties such as high hardness, high wear resistance, high elastic limit, and high contact fatigue strength. Therefore, during bearing steel production, it is necessary to strictly control the process and improve the purity of steel to ensure high precision, long service life, and high reliability of bearings. China has made considerable progress in the production technology of high-quality bearing steel, and some enterprises can produce world-class bearing steel. However, the stability of bearing steel still requires improvement. Currently, the aluminum deoxidation process is mainly used to produce bearing steel at home and abroad. Through aluminum deoxidation and the production of high-alkalinity slag, the oxygen content in liquid steel can be rapidly reduced. The total oxygen mass fraction in high-quality bearing steel can be controlled below 5×10−6. However, fatigue failure caused by occasional Ds-type inclusions still occurs. Concurrently, other problems such as blockage of small billet continuous casting nozzle and difficulty in stable control of ultralow total oxygen and titanium content also occur. To circumvent the aforementioned problems, this study proposed a nonaluminum deoxidation process by adding silicon–manganese alloy for pre-deoxidation during converter tapping, adding silicon deoxidizer to the ladle furnace (LF) slag surface for diffusion deoxidation, and Ruhrstahl‒Heraeus (RH) vacuum deep deoxidation to ensure that the total oxygen mass fraction of the molten steel was approximately 8×10−6, to produce bearing steel. While ensuring the low aluminum and low titanium contents of liquid steel, low-alkalinity slag is used to change the type of inclusions and control the plasticity of inclusions to effectively solve the problem of liquid steel fluidity. The fatigue life of bearing steels by two kinds of processes was evaluated using the ultrasonic fatigue testing machine, the effects of different types of inclusions on fatigue performance were verified, the fatigue fracture mechanism of bearing steels by different processes was analyzed, and the critical size of inclusions causing fatigue cracks was predicted. The application of the aforementioned key technologies plays a guiding role in the large-scale production of nonaluminum deoxidized high-quality bearing steel. However, its quality still lags behind the most advanced production level of bearing steel worldwide, including the poor desulfurization effect caused by the use of low-basicity slag in the refining process, which needs to be further investigated.

The steel industry is an important embodiment of national productivity and contributes to the development of the national economy and defense construction as a material foundation. Recently, China’s crude steel production ranked first in the world and in 2020, it exceeded 1 billion tons for the first time, reaching 1.065 billion tons. However, the steel industry is also a major energy consumer and polluter. In the current national coordination to do a good job of “carbon peak” and “carbon-neutral” background, the traditional steelmaking process urgently needs to be transformed into intelligent and green. Recently, as an important branch of machine learning, with artificial neural networks as the basic architecture, deep learning, a nonlinear modeling algorithm that can extract features from data and realize knowledge learning, has been applied in various industrial fields. The steelmaking process is an extremely complex industrial scenario with many influencing factors and high-security requirements. It is also an area where deep learning has not been applied on a large scale yet. Accordingly, in this study, the principles and types of deep learning were compared, and the development history and research status of deep learning in the steelmaking process with domestic and foreign application examples were summarized. The application of deep learning to the steelmaking process mainly has the advantages of simple feature extraction, strong generalization ability, and high model plasticity, but it also faces the challenges of high data dependency, difficult preprocessing, and verification of production safety. In the future, with the application of high-precision sensors, popularization of the Internet of Things, iteration of computing hardware, and innovation of algorithms, deep learning models can be effectively applied to more scenarios in steelmaking, which will promote the intelligent development of the metallurgical industry.

Mycelia products enhance edible mushrooms in alignment with future sustainability trends. To meet forthcoming market demands, the morphology of mycelial pellets was optimized for direct consumption. Among ten commercial edible mushrooms in Taiwan, Pleurotus sp. was selected for its rapid growth and was identified via an internal transcribed spacer sequence. A combination of Plackett-Burman design and Taguchi’s L9(34) orthogonal table revealed the optimal formula as potato dextrose broth (2.4%), olive oil (2%), calcium carbonate (0.5%), yeast extract (0.75%), and soy flour (0.5%). This led to a biomass increase to 19.9 ± 1.1 g/L, resulting in a 2.17-fold yield increase. To refine morphology, image processing by ImageJ quantified spherical characteristics. The addition of 0.2 to 1.0% Tween 80 enhanced pellet compaction by over 50%. Dilution of the medium improved uniformity (0.85) and conversion rate (42%), yielding mycelial pellets with 2.10 ± 0.52 mm diameters and a yield of 15.1 ± 0.6 g/L. These findings provide an alternative evaluation and application of edible mycelial pellets as future food.

The steel industry is a major energy consumer in China. As an effective measure for energy saving, cost and emission reduction, and higher efficiency among enterprises, ferroalloy reduction has attracted increased attention in our work to reduce carbon dioxide emissions and realize carbon neutrality. In the steelmaking process, the chemical composition of molten steel is required to meet the target ratio to maintain certain metallurgical and mechanical properties. The chemical composition of molten steel is mainly adjusted using ferroalloys. With the development of ferroalloy smelting technology, ferroalloys of various types are developed. These ferroalloys show major gaps in cost performance and composition. Before ferroalloy addition, it is essential to determine an appropriate and cost-effective type and its amount for cost-saving purposes. However, the traditional method of offering a manually determined amount cannot meet the above requirement. Therefore, it is necessary to explore an intelligent ferroalloy addition method without human intervention. Based on the K-means clustering algorithm, this paper studied ferroalloy loss in the basic oxygen furnace (BOF) steelmaking process. The key factors affecting the alloy loss were analyzed and divided into three clusters to obtain a process model of the lowest loss amount in the BOF steelmaking process. Using this model, an intelligent control system for alloy reduction was developed. The system is based on the principal component analysis and backpropagation neural network and mixed-integer linear programming. This system was implemented in a steelmaking plant, in which the accuracy and practicability of this model were verified by running it online. This model helped improve the accuracy of alloyed steel composition and reduce the unnecessary cost and extra composition, which are frequently seen in traditional calculations with a manual experience. The ferroalloy dosing scheme is also optimized, and the alloying cost of steelmaking is reduced. The total cost of adding ferroalloys of various types is reduced by 5.95% to 14.74%, with an average reduction of 11.72%.

Evapotranspiration (ET) is a key component of the water and carbon cycles. Because it cannot be observed directly on large regional scales at present, many satellite-based ET datasets have been widely used for different purposes. However, their use has been limited at regional and field scales because of their coarse spatial and temporal resolution. In this study, the Bayesian model averaging (BMA) method was used to simulate daily ET values with 500 m spatial resolution in the Haihe River Basin (HRB) from 2000 to 2019. Validation performed with the in-situ observations showed that the BMA ET values had higher accuracy (R2 = 0.69, RMSE = 1.15 mm/day) than those found with individual models. The BMA ET values also had higher accuracy and more credibility based on a water balance equation validation in the HRB. We used interpolated meteorological datasets, reanalysis net radiation products, and remote-sensing datasets to drive the BMA ET model. The mean annual BMA ET in the HRB from 2000 to 2019 was about 601.8 mm/year. The monthly change characteristics of this BMA ET product reflected water consumption characteristics and irrigation regularity of the winter wheat and summer maize rotation system. The BMA ET in the HRB showed a significant increasing trend from 2000 to 2019 of 3.39 mm/y2. The BMA ET values showed a strong positive trend at 80 % of HRB areas. The increasing trend of the BMA ET values was mainly attributed to an increase in the leaf area index. The high spatiotemporal resolution product showed variations in ET trends more accurately than the coarse resolution products. This high spatiotemporal product has great potential for applications in studies of regional microclimate change, interactions between human activities and climate change, drought disaster monitoring, agricultural policy making, and water security.

Given the accelerating depletion of iron ore resources, there is growing concern within the steel industry regarding the availability of high-phosphorus iron ore. However, it is important to note that the utilization of high-phosphorus iron ore may result in elevated phosphorus content and notable fluctuations in molten iron, thereby imposing additional challenges on the dephosphorization process in steelmaking. The most urgent issue in the process of converter steelmaking is how to achieve efficient dephosphorization. In this study, the influence of various factors on the logarithm of the phosphorus balance distribution ratio (lgLp), the logarithm of the P2O5 activity coefficient (lgγP2O5), and the logarithm of the phosphorus capacity (lgCp) were examined through thermodynamic calculations. The impact of each factor on dephosphorization was analyzed, and the optimal conditions for the dephosphorization stage of the converter were determined. Furthermore, the influence of basicity and FetO content on the form of phosphorus in the slag was analyzed using FactSage 7.2 software, and the precipitation rules of the slag phases were explored. The thermodynamic calculation results indicated that increasing the basicity of the dephosphorization slag was beneficial for dephosphorization, but it should be maintained below 3. The best dephosphorization effect was achieved when the FetO content was around 20%. The reaction temperature during the dephosphorization stage should be kept low, as the dephosphorization efficiency decreased sharply with the increasing temperature. In dephosphorization slag, Ca3(PO4)2 usually formed a solid solution with Ca2SiO4, so the form of phosphorus in the slag was mainly determined by the precipitation form and content of Ca2SiO4. The phases in the dephosphorization slag mainly consisted of a phosphorus-rich phase, an iron-rich phase, and a matrix phase. The results of scanning electron microscopy and X-ray diffraction analyses were consistent with the thermodynamic calculation results.