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Spf1 is an important P-type ATPase in Candida albicans, which functions as an endoplasmic reticulum calcium pump to maintain calcium homoeostasis. The deficiency of Spf1 attenuates the virulence of C. albicans. However, its impact on immune response remains to be investigated. This study discovered that deletion of SPF1 resulted in a reduction of endoplasmic reticulum-plasma membrane contacts, an important structure mediating material and information exchange. This effect was attributed to the reduced plasma membrane localisation of the crucial endoplasmic reticulum-plasma membrane tethering proteins Ist2 and Tcb1/3. The reduction of the contacts led to a decrease in secretion of the virulence factors phospholipase, secreted aspartyl protease (SAP), candidalysin, and the cell wall-anchored protein Hwp1 during infection. Immunofluorescence staining and quantitative PCR assays further showed that the SPF1 deletion led to a remarkable decrease in the levels of pro-inflammatory cytokines, suggesting the alleviation of the fungus-induced inflammatory response. Ultimately, the regulatory role of Spf1 in immune response significantly weakened the infectivity of C. albicans, and increased the survival rate of the hosts. This finding elucidated the role of fungal calcium pump-governed endoplasmic reticulum-plasma membrane contacts in regulation of immune response. It also makes it possible to regulate the host’s immune response via control of SPF1 expression and functions, providing a theoretical basis for treating fungal infections.

Abstract Little is known about biological processes controlling inorganic phosphate (PO4) in groundwater ecosystems. Here we present analyses of groundwater samples from the Hetao Basin, China that show an increasing contribution of microbial cycling to groundwater PO4 from oxic to anoxic conditions along a flow path with phosphate-bound oxygen isotopes (δ18OPO4). Under oxic conditions, although 25–47% of the dissolved PO4 inherited the initial source signal of igneous apatite, groundwater δ18OPO4 reflected a pronounced impact of intracellular enzymatic cycling. Under anoxic conditions, dissolved PO4 carried a nearly exclusive equilibrium isotope signal, which was probably due to (i) release of PO4 with an equilibrium δ18OPO4 from Fe(III) oxides, as a result of Fe(III) reduction in the presence of Fe(III)- and sulfate-reducing bacteria; and/or (ii) cumulative microbial cycling of dissolved PO4 with increasing groundwater residence time. Our study highlights that PO4 in groundwater is tightly microbially cycled under a wide range of redox conditions and microbial cycling contributes to the release of PO4 in groundwater.

SDGSAT-1, the world's first science satellite dedicated to assisting the United Nations 2030 Sustainable Development Agenda, has been operational for over two and a half years. It provides valuable data to aid in implementing the Sustainable Development Goals internationally. Through its Open Science Program, the satellite has maintained consistent operations and delivered free data to scientific and technological users from 88 countries. This program has produced a wealth of scientific output, with 72 papers, including 28 on data processing methods and 44 on applications for monitoring progress toward SDGs related to sustainable cities, clean energy, life underwater, climate action, and clean water and sanitation. SDGSAT-1 is equipped with three key instruments: a multispectral imager, a thermal infrared spectrometer, and a glimmer imager, which have enabled ground-breaking research in a variety of domains such as water quality analysis, identification of industrial heat sources, assessment of environmental disaster impacts, and detection of forest fires. The precise measurements and ongoing monitoring made possible by this invaluable data significantly advance our understanding of various environmental phenomena. They are essential for making well-informed decisions on a local and global scale. Beyond its application to academic research, SDGSAT-1 promotes global cooperation and strengthens developing countries’ capacity to accomplish their sustainable development goals. As the satellite continues to gather and distribute data, it plays a pivotal role in developing strategies for environmental protection, disaster management and relief, and resource allocation. These initiatives highlight the satellite’s vital role in fostering international collaboration and technical innovation to advance scientific knowledge and promote a sustainable future.

To improve the activity of single-atom nanozymes (SAzymes) for applications in food analysis, a new bimetal SAzyme FeCe/NC was developed. Its oxidase-like activity is 40% higher than that of single metal SAzyme Fe/NC. Based on a series of characterization investigations, the catalytic mechanism is that it directly catalyzed O2 to generate •OH, O2•-and 1O2. It could directly catalyze oxidation 3,3′,5,5′-tetramethylbenzidine (TMB) to blue oxTMB, thereunder, a FeCe/NC SAzyme-TMB colorimetric method for the detection of tannic acid (TA) was constructed after the optimization of catalytic conditions. The method has a high R2 of 0.995, a low limit of detection (LOD) of 0.26 μmol/L, and high stability. The detection performance was validated by the real samples (tea). Therefore, the prepared bimetallic SAzyme FeCe/NC can be applied for TA detection without the addition of H2O2, and will have broad applications in the areas of food, feed, and life science.

【Objective】 Fertilizer topdressing is an agricultural practice to provide crops with additional nutrients, but how topdressing frequency affects crop growth and yield is inadequately understood. The objective of this paper is to determine the optimal nitrogen topdressing frequency for improving maize cultivation in aeolian sandy soil. 【Method】 The experiment was conducted in a maize field with nitrogen fertilizer applied in one (F1), two (F2), four (F3), seven (F4) and eleven (F5) topdressings. For each treatment, we measured the growth and grain yield of the maize. 【Result】 Topdressing once at the jointing stage or before the earing stage increased vegetative growth, stem length and diameter, leaf area index, and SPAD content. In contrast, multiple topdressing improved spike length and diameter, kernel numbers per spike, and 100-seed weights, ultimately leading to an elevated yield. The grain yield depended not only on topdressing frequency but also on when and how the fertilizer was applied in each topdressing. For example, in the multiple topdressing, applying fertilizer during earing and tasseling stage reduced the kernel numbers, 100-seed weights, yield, and fertilizer use efficiency. Aligning fertilizer application with plant requirements in the multiple topdressing was critical to improving plant growth. Among all treatments, topdressing 11 times was optimal, with the yield reaching 7.36 t/hm2, irrigation water use efficiency, water use efficiency, nitrogen use efficiency reaching 3.27 kg/m3, 1.25 kg/m3 and 24.52 kg/kg, respectively. 【Conclusion】 Increasing the frequency of fertilizer topdressing proves to be a highly effective strategy for improving maize growth and yield in aeolian sandy soil, presenting a promising approach for optimizing fertilization in this region.

With the development of smart grids, power grids have accumulated massive amounts of data in various links such as power generation, transmission, substation, distribution, power consumption, and dispatch. More and more big data applications are beginning to be applied in various professional fields of the power grid. Promote the application and value discovery of smart grid big data through data fusion inside and outside the grid. Grid data has become an important asset for enterprise development, but power grid enterprises lack effective technical means to solve the whole life cycle monitoring and relationship of power grid data assets. Aiming at the relationship between power grid data assets, this paper proposes a set of grid data asset relationship and intelligent classification framework that integrates knowledge graph and Internet of Things. First, the grid knowledge graph extraction relationship is carried out by ProjE algorithm. Then, the relationship between power grid data assets and intelligent classification framework that integrates knowledge graph and Internet is proposed. Finally, the corresponding classification application is proposed by using intelligent classification algorithm. Experimental results show that the intelligent classification accuracy rate can reach 93.12% under the relationship between the knowledge graph and the Internet data assets, which has a new idea for the future development of the relationship between power grid data assets.

This paper numerically analyzes the influence of heat release rate (HRR) and longitudinal ventilation velocity on smoke distribution characteristics in a T-shaped roadway when the fire source was located upstream of the T-junction. The back-layering length, critical ventilation velocity, smoke temperature, and CO concentration in the main and branched roadway were investigated and analyzed. The results showed that the ventilation velocity is the key factor influencing back-layering length, while the effect of HRR on back-layering length is gradually weakened as HRR increases. The critical ventilation velocity in the T-shaped roadway is higher than in a single-tube roadway, and the predicted model of dimensional critical ventilation velocity in a T-shaped bifurcated roadway is proposed. The correlation between average temperature (Z = 1.6 m) (both in the main roadway I and the branched roadway) and ventilation velocity fits the power function, and the variation in average temperature (Z = 1.6 m) according to HRR fits the linear formula. The relation between average concentration of CO (Z = 1.6 m) (both inside the main roadway I and the branched roadway) and longitudinal ventilation velocity follows the power relation, and the variation in average concentration of CO (Z = 1.6 m) according to HRR follows the linear function.

Paleo-oxbow lake sediments can provide archives to reconstruct paleo-channel evolution and flooding history of the river. Multi-proxy approaches including detailed sediment stratigraphy, sedimentology and geochemistry have been implemented in a high-resolution sedimentary section of paleo-oxbow lake of the Yellow River within the Zoige Basin on the NE Tibetan Plateau, to reconstruct regional environmental changes and extreme overbank flooding history. Our results suggest that not only traditional sedimentological proxies, but also chemical elements can be applied in defining sequences with different genetic types, especially the paleoflood deposits in the paleo-oxbow profile. Two units of late-Holocence extreme overbank flooding deposits (OFDs) are identified in terms of the significantly higher proportions of sand, high contents of SiO2, Na2O, Ba, low contents of Al2O3, Fe2O3, MgO, K2O, Ti, Rb and high values of Zr/Fe, Zr/Rb ratios. These extraordinary flood events within the Zoige Basin were dated back to 2,960 ± 240–2,870 ± 270 a and 1840 ± 200–1700 ± 160 a, in response to the mid-Holocene climatic optimum to the late Holocene and the Dark Age Cold Period (DACP). And the strong rainfall caused by the abnormal atmospheric circulation during the period of climate transition and abrupt change may led to the frequent occurrence of extreme flood events in the source region of the Yellow River. These findings are important for understanding the response of regional fluvial system to high climatic instability and provide a new perspective for us to analyze the risk of flood disasters on the Tibetan Plateau under the background of climate change.

Paleohydrological investigations were carried out in the Yellow River source area on the northeast (NE) Tibetan Plateau. During our fieldwork investigations, two units of overbank flood deposits (OFD) were found in the Maqu-Kesheng reach. These OFD units were studied using a multi-index approach, including magnetic susceptibility, hygroscopic water, grain size distribution and micro-morphological features. It can be inferred that they have recorded two episodes of overbank flooding of the Yellow River. Using the optically stimulated luminescence (OSL) and AMS 14C dating techniques, the first episode was dated to 34,680 ± 1880-29000 ± 1790 a and correlated with the late Marine Isotope Stage 3a (MIS 3a) with a warm and wet climate. The second episode occurred at 16,500 ± 1,250-14000 ± 1,280 a, which is coincided with the Bølling-Allerød warm stage during the last deglaciation, a period of coexisting global warming and rapidly shrinking ice sheets. At these two periods, the water was mainly derived from the accelerated melting of mountain glaciers surrounding the basin and/or the large-scale precipitation, which led to the overbank flooding events. At the same time, the related overbank flood deposits were interbedded within glacial outwash/flashflood deposits in the valley bottom of the Yellow River, which intensified the valley aggradation. These results are of great significance in enriching the paleoflood records in the Yellow River source area, recognizing the effect of river system on the development of valley landform, as well as understanding the hydro-climatic response of the Tibetan Plateau to global climate warming occurring presently.

With the widespread use of digital technologies such as big data, cloud computing and artificial intelligence in higher education, how to establish a scientific and systematic evaluation system to turn the traditional classroom with the one-way transmission of knowledge into an interactive space for exchanging ideas and inspiring wisdom has become an essential task for human resource management in universities, and a key to improving teaching quality. However, due to the debate between scientism and humanism in teaching evaluation, studies related to teaching performance have been isolated from human resource management, resulting in the lack of a systematic vision and framework for such studies. Relevant studies are still limited to the evaluation contents of different evaluation subjects. Evaluations also tend to focus only on the teaching process, ignoring the objectives of talent training, making it difficult for evaluations to play a goal-oriented role and hindering the further development of relevant studies. Therefore, this paper draws on human resource management methodologies and analyzes knowledge teaching evaluation system characteristics in colleges and universities in a big data context to construct a “multiple evaluations, trinity and four-step closed-loop” big data-based knowledge teaching evaluation system. “Trinity” represents evaluation from three performance dimensions: teaching effect, teaching behavior and teaching ability. “Multiple evaluations” represents the design of teaching performance indicators based on teaching data, breaking the barriers between different evaluation subjects. “Four-step closed-loop” draws on performance management theory to standardize the teaching performance management process from four aspects: planning, implementation, evaluation, and feedback. This evaluation system provides a systematic methodology for unifying the theory and practice of innovative knowledge teaching evaluation system in universities in a big data context.