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Abstract The controllable fabrication of patterned p-type and n-type channels with precise doping control presents a significant challenge, impeding the realization of complementary metal-oxide-semiconductor (CMOS) logic using a single van der Waals material. However, such an achievement could offer substantial benefits by enabling continued transistor scaling and unprecedented interlayer interconnect technologies. In this study, we devise a precise method for two-dimensional (2D) semiconductor substitutional doping, which allows for the production of wafer-scale 2H-MoTe2 thin films with specific p-type or n-type doping. Notably, we extend this approach to the synthesis of spatially selective doped 2H-MoTe2 thin films via a one-step growth method, facilitating the monolithic integration of p-type and n-type semiconductor channels. Leveraging this advancement, we successfully fabricate a chip-sized 2D CMOS inverter array that demonstrates excellent device performance and yield. Collectively, these findings represent a significant stride towards the practical incorporation of 2D semiconductors in very large-scale integration technology.

The phenomenon of arc compression in TIG welding with high-frequency longitudinal magnetic field (HF-LMF) has been observed. However, its mechanism remains unclear, and there is a lack of numerical investigation. This work presents a two-dimension axisymmetrical model of arc plasma in high-frequency longitudinal magnetic field based on the magnetohydrodynamics (MHD). The distributions of temperature, velocity and pressure of arc plasma in HF-LMF are investigated at different applied LMF frequencies ranging from 0 to 5000 Hz. In order to make the simulation results more convincing, the induced current and the electric force were considered. The results show that the HF-LMF could generate a circumferential electric field, causing particles rotating. However, since HF-LMF is variable, the direction of the electric field also changes, which causes the arc to rotate back and forth. When the frequency of HF-LMF reaches 2000 Hz, the negative pressure zone above the center of the anode disappeared and the arc was compressed. Thus, as the frequency of LMF increases, the distribution of current density, anodic heat flux, and arc pressure changes from a bimodal distribution to a unimodal distribution, and the peak value increases. The theoretical predictions were in good agreement with the experimental results.

Lianhua Qingke tablets, a patented traditional Chinese medicine that has validated clinical efficacy for treating cough caused by severe acute respiratory syndrome coronavirus 2 infection, lack rigorous evidence-based research evaluating their effect on long coronavirus disease (COVID) cough. A randomized, double-blind, placebo-controlled, multicenter clinical study was conducted among patients with long COVID cough from 19 hospitals and 23 community health centers in China. Patients were randomized 1:1 to receive either Lianhua Qingke tablets or placebo orally for 14 days (four tablets, 1.84 g, three times a day). The primary endpoint indicator was the disappearance of cough, with the remission of cough also considered. Among 482 randomized patients, 480 (full analysis set 480; per-protocol set 470; safety set 480) were included in the primary analysis. According to the full analysis, the time until cough disappearance was significantly shorter in the trial group than in the control group, with a significant increase in the 14-day cough disappearance rate. Accordingly, the time to cough remission was significantly shorter in the trial group than in the control group. The change in the total symptom score was significantly greater in the trial group than in the control group on days 7 and 14, consistent with the results indicated by the visual analog scale (VAS) and cough evaluation test (CET) scores. No serious adverse events were recorded during the study. Lianhua Qingke tablets significantly improved the clinical symptoms of patients with long COVID cough.

Abstract Background This paper attempted to clarify the role and mechanism of vacuolar protein sorting-associated protein 72 homolog (VPS72) in the progression of prostate cancer (PCa). Methods Clinical information and gene expression profiles of patients with prostate cancer were obtained from The Cancer Genome Atlas (TCGA). VPS72 expression in PCa and the potential mechanism by which VPS72 affects PCa progression was investigated. Next, we performed COX regression analysis to identify the independent prognostic factors of PCa, and constructed a nomogram. The sensitivity of chemotherapeutic medications was anticipated using “pRRophetic”. Subsequently, in vitro assays to validate the effect of VPS72 on PCa cell proliferation, migration and susceptibility to anti-androgen therapy. Results The expression of VPS72 was considerably higher in PCa tissues compared to normal tissues. Significant correlations were found between high VPS72 expression and a poor prognosis and adverse clinicopathological factors. The nomogram model constructed based on VPS72 expression has good predictive performance. According to GSEA, VPS72-related genes were enriched in the NF-kB pathways, cytokine-cytokine receptor interaction and chemokine signaling pathway in PCa. Although PCa with low VPS72 expression was more adaptable to chemotherapeutic medications, our in vitro experiment showed that VPS72 knockdown significantly decreased the PCa cell migration, proliferation, and resistance to anti-androgen therapy. Conclusions In summary our findings suggests that VPS72 could play a crucial role in the malignant progression of PCa, and its expression level can be employed as a possible biomarker of PCa prognosis.

Plastics, pervasive in humans and nature, often contain Di (2-ethylhexyl) phthalate (DEHP) that enhance plastic’s elasticity. However, DEHP is an environmental endocrine disruptor, affecting organisms upon exposure. Understanding mammary gland development in lactating females is crucial for offspring nourishment and dairy production. Employing multi-omics technology, this study aimed to uncover DEHP’s impact on the microbial–gut–mammary axis. Forty mice were exposed to varying DEHP doses for 18 d. We performed 16S sequencing, metabolomics, mammary tissue observation, and gene expression profiling. Results revealed DEHP’s influence on microbial diversity, with increased Lactobacillus abundance and reduced Proteobacteria, alongside colonic inflammation. Elevated GMP and adenosine 5′-monophosphate levels in the bloodstream were noted, while ascorbic acid, glycitein, and others decreased. MEHP, a DEHP metabolite, damaged mammary tissues, inhibiting ERK1/2 phosphorylation, triggering apoptosis and ferroptosis. These findings unveil potential therapeutic targets for DEHP-induced chronic toxicity in humans and animals, aiding dairy livestock health and human well-being. This study underscores the importance of understanding the adverse effects of DEHP exposure on mammalian systems.

Abstract With the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much attention. Efficient and accurate traffic flow prediction is one of the core tasks in the research of intelligent transportation system. Most of the existing spatiotemporal traffic flow prediction models do not make full use of various periodic spatiotemporal dynamic characteristics of traffic flow, and it is difficult to effectively capture the complex spatiotemporal changes of traffic flow. To accurately predict the traffic flow of complex urban network, this paper proposes a traffic prediction model (STBGRN) based on spatiotemporal bidirectional gated cycle unit (ST-BiGRU) and graph convolution residual network (GCN-ResNet). The spatiotemporal information in traffic data is learned using the spatiotemporal bidirectional GRU coupled with the forward and backward information of the current time step, and the topology structure of the traffic network is captured by the graph convolution residual network, which is combined to complete the prediction task of the urban traffic network. In this paper, STBGRN was tested on two real data sets, SZ-taxi and Los-loop, and compared with other baseline methods, the RMSE index reached 3.857 and 5.461, and the MAE index reached 2.702 and 3.781, respectively. Accuracy index is 72.66% and 91.35%, $${R}^{2}$$ R 2 index is 0.858 and 0.845, var index is 0.858 and 0.849. The experimental results show that STBGRN model can obtain spatiotemporal dependence from complex traffic data, and can be used in spatiotemporal traffic data processing.

Abstract Inter-satellite link (ISL) plays an essential role in current and future Global Navigation Satellite System (GNSS). In this study, we investigate the impact of ISL observations on precise orbit determination for BeiDou-3 Navigation Satellite System (BDS-3) Medium Earth Orbit (MEO) satellites based on different Extended CODE Orbit Models (ECOM). Thanks to the better observation geometry of the Ka-band ISL data compared to the L-band data for BDS-3 MEO satellites, the ISL solution substantially reduces Orbit Boundary Discontinuity (OBD) errors, except for C30, which suffers from unstable Ka-band hardware delay. From the external quality analysis, ISL significantly enhances the reliability of the orbit of MEO satellites manufactured by the China Academy of Space Technology (CAST). The standard deviation (STD) of the satellite laser ranging (SLR) residuals is approximately 2.5 cm, and the root mean square (RMS) is reduced by 10–23% compared to L-band solutions. Besides, the Sun-elongation angle dependent systematic error in SLR residuals nearly vanishes based on the reduced 5-parameter ECOM (ECOM1) or extended 7-parameter ECOM (ECOM2) with ISL data. This is because the ISL reduces the correlation between state parameters and solar radiation pressure (SRP) parameters as well as those among SRP parameters, leading to a more accurate estimation of both orbit and SRP perturbations, particularly those along B direction. This confirms that the deficiency of the SRP models for BDS-3 CAST satellites can be compensated by using better observation geometry from ISL data. On the other hand, for the satellite manufactured by Shanghai Engineering Center for Microsatellites (SECM), the ISL allows for a more accurate estimation of the Bc 1 parameter in the ECOM1 model. This only reduces linear systematic error, possibly because the impact generated by the satellite bus cannot be entirely absorbed by the B-direction parameters.

As autonomous vehicles and the other supporting infrastructures (e.g., smart cities and intelligent transportation systems) become more commonplace, the Internet of Vehicles (IoV) is getting increasingly prevalent. There have been attempts to utilize Digital Twins (DTs) to facilitate the design, evaluation, and deployment of IoV-based systems, for example by supporting high-fidelity modeling, real-time monitoring, and advanced predictive capabilities. However, the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied topic. In addition, this paper explains how DTs can benefit IoV system designers and implementers, as well as describes several challenges and opportunities for future researchers.

Abstract Traditional delivery route planning faces challenges in reducing logistics costs and improving customer satisfaction with growing customer demand and complex road traffic, especially in uncertain supply chain environment. To address these challenges, we introduce an innovative two-phase delivery route planning method integrating edge intelligence technology. The novelty of our approach lies in utilizing edge computing devices to monitor real-time changes in road conditions and dynamically adjust delivery routes, thereby providing an effective solution for efficient and flexible logistics. Initially, we construct a mixed-integer programming model that minimizes the total cost under constraints such as customer destinations and time windows. Subsequently, in the cloud-edge collaborative mode, edge computing devices are utilized to collect real-time road conditions and transmit it to the cloud server. The cloud server comprehensively considers customer demand and road condition changes and employs adaptive genetic algorithms and A-star algorithms to adjust the delivery routes dynamically. Finally, comprehensive experiments are conducted to validate the effectiveness of our method. The results demonstrate that our approach can promptly respond to changes in customer demands and road conditions and flexibly plan the optimal delivery routes, thereby significantly reducing overall costs and enhancing customer satisfaction.

Abstract Background Adult head and neck rhabdomyosarcoma (HNRMS) is an exceptionally rare malignancy, and there is a paucity of data and research dedicated to understanding its characteristics and management in adult populations. This study aimed to assess the outcomes and identify survival predictors in adult HNRMS. Methods We retrospectively evaluated 42 adult patients (> 16 years) with HNRMS who received radiotherapy (RT)-based treatment at our institute between 2008 and 2022. We analysed the clinical characteristics and prognosis of these patients, including the locoregional recurrence-free survival (LRFS), progression-free survival (PFS), and overall survival (OS), using the Kaplan–Meier method. The chi-square and Fisher’s exact tests were used to analyse differences between groups for dichotomous and categorical variables, respectively. Survival rates were calculated using the Kaplan–Meier method. Prognostic variables were assessed through univariate Cox analyses. Results The median patient age was 28 years (range, 16–82 years). Alveolar RMS was the most common histological type, observed in 21 patients (50.0%), followed by embryonal in 16 patients (38.1%). The anatomic sites of origin were orbital in one (2.4%), parameningeal in 26 (61.9%), and non-orbital/non-parameningeal in 15 (35.7%) patients. Nineteen patients (45.2%) had regional lymph node metastasis, and five patients (11.9%) presented with distant metastatic disease. Distant metastasis (n = 17) was the primary cause of treatment failure. At a median follow-up of 47.0 months, the 5-year LRFS, PFS, and OS rates were 69.0%, 39.7%, and 41.0%, respectively. Univariate analysis revealed that tumour size, lymph node involvement, and the local treatment pattern (surgery and RT vs. RT alone) were significant predictors of survival. Conclusions The main failure pattern in patients with HNRMS receiving RT-based treatment was distant metastasis. Tumour size > 5 cm and lymph node involvement were predictors of worse LRFS. Multimodality local treatment, combining surgery and RT, is effective and provides survival benefits.

ABSTRACTWith the development of advertising technology, especially the emergence of generative artificial intelligence, the generation and editing of advertising content has become more convenient and efficient. However, this also brings new security risks to digital advertising. For example, in order to generate personalized advertising content, AI can collect user behavior data without the user’s knowledge and automatically generate ads that are beneficial to advertisers. In addition, the main issue regarding outdoor advertising is whether the outdoor advertising platform can deliver the advertising content required by the advertiser at the designated place and time according to the actual requirements of the customer. Combining blockchain and edge computing technology, this paper builds a security-enhanced outdoor advertising platform. The decentralized, non-tamperable and traceable features of blockchain technology provide the advertising platform with credibility between users and advertisers, as well as credibility between advertisers and the advertising platform. At the same time, the terminal controller with edge processing capabilities improves the efficiency of advertising and enhances the task balance between the server and the playback side. Experimental results show that the platform can effectively manage user identities and maintain advertising plans. It not only improves data security and credibility, but also has good performance.

Abstract Bacteria can be utilized for cancer therapy owing to their preferential colonization at tumor sites. However, unmodified non‐pathogenic bacteria carry potential risks due to their non‐specific targeting effects, and their anti‐tumor activity is limited when used as monotherapy. In this study, a biohybrid‐engineered bacterial system comprising non‐pathogenic MG1655 bacteria modified with CDH17 nanobodies on their surface and conjugated with photosensitizer croconium (CR) molecules is developed. The resultant biohybrid bacteria can efficiently home to CDH17‐positive tumors, including gastric, pancreatic, and colorectal cancers, and significantly suppress tumor growth upon irradiation. More importantly, biohybrid bacteria‐mediated photothermal therapy (PTT) induced abundant macrophage infiltration in a syngeneic murine colorectal model. Further, that the STING pathway is activated in tumor macrophages by the released bacterial nucleic acid after PTT is revealed, leading to the production of type I interferons. The addition of CD47 nanobody but not PD‐1 antibody to the PTT regimen can eradicate the tumors and extend survival. This results indicate that bacteria endowed with tumor‐specific selectivity and coupled with photothermal payloads can serve as an innovative strategy for low‐immunogenicity cancers. This strategy can potentially reprogram the tumor microenvironment by inducing macrophage infiltration and enhancing the efficacy of immunotherapy targeting macrophages.

PurposeThis meta-analysis aims to identify whether patients with sepsis who have persistent tachycardia despite initial resuscitation can benefit from ultrashort-acting β-blockers.Materials and methodsRelevant studies from MEDLINE, the Cochrane Library, and Embase were searched by two independent investigators. RevMan version 5.3 (Cochrane Collaboration) was used for statistical analysis.ResultsA total of 10 studies were identified and incorporated into the meta-analysis. The results showed that the administration of ultrashort-acting β-blockers (esmolol/landiolol) in patients with sepsis with persistent tachycardia despite initial resuscitation was significantly associated with a lower 28-day mortality rate (risk ratio [RR], 0.73; 95% confidence interval [CI], 0.57–0.93; and p˂0.01). Subgroup analysis showed that the administration of esmolol in patients with sepsis was significantly associated with a lower 28-day mortality rate (RR, 0.68; 95% CI, 0.55–0.84; and p˂0.001), while there was no significant difference between the landiolol and control groups (RR, 0.98; 95% CI, 0.41–2.34; and p = 0.96). No significant differences between the two groups were found in 90-day mortality, mean arterial pressure (MAP), lactate (Lac) level, cardiac index (CI), and troponin I (TnI) at 24 h after enrollment.ConclusionThe meta-analysis indicated that the use of esmolol in patients with persistent tachycardia, despite initial resuscitation, was linked to a notable reduction in 28-day mortality rates. Therefore, this study advocates for the consideration of esmolol in the treatment of sepsis in cases where tachycardia persists despite initial resuscitation.

Focused on the problem that the stream computing platform was suffering from the high energy consumption and low efficiency due to the lack of consideration for energy efficiency in designing process, an energy-efficient optimization strategy based on elastic data migration in big data streaming platform (EEDM-BDSP) was proposed.Firstly, models of the load prediction and the resource judgment were set up, and the load prediction algorithm was designed, which predicted the load tendency and determine node resource occupancy, so as to find nodes of resource overload and redundancy.Secondly, models of the resource constraint and the optimal data migration were set up, and the optimal data migration algorithm was proposed, which data migration for the purpose of improving node resource utilization.Finally, model of the energy consumption was set up to calculate the energy consumption saved by the cluster after data migration.The experimental results show that the EEDM-BDSP changes node resources in the cluster can responded on time, the resource utilization and the energy-efficient are improved.

Optimal Power Flow (OPF) plays a crucial role in the optimization and operation of the bipolar DC distribution network (Bi-DCDN). However, existing OPF models encounter difficulties in power optimization of Bi-DCDNs due to the optimal power is expressed as a product form, i.e., the product of voltage and current. Hence, this brief formulates the OPF problem of Bi-DCDNs using the branch flow model (BFM). The BFM employs power, instead of current, to account for the unique structure of Bi-DCDNs. Convex relaxation and linear approximation are sequentially applied to reformulate the BFM-based OPF, presenting it as a second-order cone programming (SOCP) problem. Further, the effectiveness of the proposed OPF model is verified in case studies. The numerical results demonstrate that the BFM-based OPF is a feasible and promising approach for Bi-DCDNs.

With the development of agricultural modernization, strengthening the supervision of agricultural production activities plays a crucial role in social security management and economic development. However, the current monitoring of greenhouse usage and planning in agricultural production lacks effective regulation. Existing technological approaches involve the analysis and monitoring of agricultural activities using machine learning and simple neural networks, but their detection accuracy is limited. In this article, we present a novel deep learning model called FADNet, which includes the fusion attention mechanism and variable convolution techniques. FADNet utilizes remotely sensed images obtained from satellite sensors as input for training. It employs variable convolution and feature pyramid networks to achieve accurate segmentation of small-scale greenhouse targets. Spatial attention mechanism is employed to address the interference caused by similar features in urban areas for greenhouse identification. In addition, data augmentation techniques are utilized to address the scarcity of greenhouse datasets and the disparity in the distribution of positive and negative samples, thereby enhancing the reliability of the dataset. FADNet achieves greenhouse segmentation by performing pixel-level classification on the images. Extensive experiments have demonstrated that FADNet performs exceptionally well in greenhouse segmentation tasks.

Employing molten glass purification and cyclic overheating technologies, a Cu60Ni40 alloy was modified by introducing trace amounts of Co element. Through this process, we aimed to understand how the addition of Co element influences the microstructure morphology of the alloy. By combining the BCT model and metallographic analysis, the intrinsic factors of microstructure transformation in the alloy were studied. In the case of small undercooling, solute diffusion dominates dendritic growth, but the undercooled melt is limited to a narrow range of growth, resulting in the formation of coarse dendritic morphology in Cu–Ni alloy. As the undercooling increases, the growth of dendrites is gradually controlled by both solute diffusion and thermal diffusion, and the remelting effect of dendrites is also gradually enhanced. During rapid solidification with lower undercooling, the dendrites formed by the undercooled melt can be remelted to form numerous crystal seeds. Within a moderate undercooling range, dendrites grow directionally due to thermal diffusion and exhibit specific characteristics. At high undercooling, the dendrites' directional growth, primarily driven by thermal diffusion, undergoes stress fragmentation.

Abstract Argonaute (Ago) proteins are ubiquitous across all kingdoms of life. Eukaryotic Agos (eAgos) use small RNAs to recognize transcripts for RNA silencing in eukaryotes. In contrast, the functions of prokaryotic counterparts (pAgo) are less well known. Recently, short pAgos in conjunction with the associated TIR or Sir2 (SPARTA or SPARSA) were found to serve as antiviral systems to combat phage infections. Herein, we present the cryo-EM structures of nicotinamide adenine dinucleotide (NAD+)-bound SPARSA with and without nucleic acids at resolutions of 3.1 Å and 3.6 Å, respectively. Our results reveal that the APAZ (Analogue of PAZ) domain and the short pAgo form a featured architecture similar to the long pAgo to accommodate nucleic acids. We further identified the key residues for NAD+ binding and elucidated the structural basis for guide RNA and target DNA recognition. Using structural comparisons, molecular dynamics simulations, and biochemical experiments, we proposed a putative mechanism for NAD+ hydrolysis in which an H186 loop mediates nucleophilic attack by catalytic water molecules. Overall, our study provides mechanistic insight into the antiphage role of the SPARSA system.

Applying molten glass purification and cyclic superheat methods, deep undercooling treatment was performed on a Ni88Cu6Co6 alloy system. The alloy was undercooled to an undercooling as high as 300 K and the corresponding microstructure characteristics were analyzed systematically. Compared with the Ni88Cu12 system, the addition of a minor amount of Co had a pronounced impact on the grain refinement mechanism of Ni88Cu6Co6 system. Grain refinement observed at low undercooling was attributed to the dendritic melting mechanism. On the other hand, in the high undercooling region, the recrystallization process played a dominant role in refining the microstructure. Through the present analysis, the characteristic undercoolings of microstructure evolution had been determined. In addition, BCT model was applied to study the free growth of an undercooled Ni88Cu12 alloy, and by comparing Ni88Cu12 and Ni88Cu6Co6 alloys, we aimed to find the effect of Co on the microstructure refinement mechanisms of deeply undercooled Ni–Cu alloys.

The study of the mechanism of exchange-bias phenomena and the achievement of its efficient control are of great importance, as it promotes the revelation of unique exchange interactions and the development of exotic applications. However, it is challenging due to the elusive interface between magnetic phases. In this study, we report an unprecedented exchange-bias phenomenon observed in ultrathin, uncompensated, antiferromagnetic MnBi_{2}Te_{4}. The magnitude and direction of the exchange field can be intentionally controlled by designing a magnetic-field sweep protocol without a field cooling process. The combined experimental and theoretical simulation results indicate that the spin-flip process assisted by the ubiquitous defect-induced pinning domain sites with varying inner exchange interactions might give rise to the emergence and robustness of this peculiar exchange bias. The temperature and thickness dependence of the exchange-bias phenomena are systematically investigated for further study and exploitation of its unique properties. This mechanism allows for highly tunable exchange bias in prevalent magnetic systems by engineering the properties of domain structures and also offers promising avenues for the design of spintronic devices combing its topology based on MnBi_{2}Te_{4}.

Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results. To address the limitations of the above deep learning methods in the field of vehicle behavior prediction, this paper proposes a front vehicle behavior prediction method that combines deep learning techniques with ontology reasoning. Specifically, YOLOv5s is first selected as the base model for recognizing the brake light status of vehicles. In order to further enhance the performance of the model in complex scenes and small target recognition, the Convolutional Block Attention Module (CBAM) is introduced. In addition, so as to balance the feature information of different scales more efficiently, a weighted bi-directional feature pyramid network (BIFPN) is introduced to replace the original PANet structure in YOLOv5s. Next, using a four-lane intersection as an application scenario, multiple factors affecting vehicle behavior are analyzed. Based on these factors, an ontology model for predicting front vehicle behavior is constructed. Finally, for the purpose of validating the effectiveness of the proposed method, we make our own brake light detection dataset. The accuracy and mAP@0.5 of the improved model on the self-made dataset are 3.9% and 2.5% higher than that of the original model, respectively. Afterwards, representative validation scenarios were selected for inference experiments. The ontology model created in this paper accurately reasoned out the behavior that the target vehicle would slow down until stopping and turning left. The reasonableness and practicality of the front vehicle behavior prediction method constructed in this paper are verified.

Sheep body size can directly reflect the growth rates and fattening rates of sheep and is also an important index for measuring the growth performance of meat sheep. In this study, high-resolution resequencing data from four sheep breeds (Dorper sheep, Suffolk sheep, Ouessant sheep, and Shetland sheep) were analyzed. The nonsynonymous single nucleotide polymorphisms of three candidate genes (KIAA1217, SNTA1, and LTBP1) were also genotyped in 642 healthy Ujumqin sheep using MALDI-TOFMS and the genotyping results were associated with growth traits. The results showed that different genotypes of the KIAA1217 g.24429511T>C locus had significant effects on the chest circumferences of Ujumqin sheep. The SNTA1 g.62222626C>A locus had different effects on the chest depths, shoulder widths and rump widths of Ujumqin sheep. This study showed that these two sites can be used for marker-assisted selection, which will be beneficial for future precision molecular breeding.

China is now confronting the intertwined challenges of air pollution and climate change. Given the high synergies between air pollution abatement and climate change mitigation, the Chinese government is actively promoting synergetic control of these two issues. The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators. The Synergetic Roadmap 2022 report is the first annual update, featuring 20 indicators across five aspects: synergetic governance system and practices, progress in structural transition, air pollution and associated weather-climate interactions, sources, sinks, and mitigation pathway of atmospheric composition, and health impacts and benefits of coordinated control. Compared to the comprehensive review presented in the 2021 report, the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones. These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time, a decline in the production of crude steel and cement after years of growth, and the surging penetration of electric vehicles. Additionally, in 2022, China issued the first national policy that synergizes abatements of pollution and carbon emissions, marking a new era for China's pollution-carbon co-control. These changes highlight China's efforts to reshape its energy, economic, and transportation structures to meet the demand for synergetic control and sustainable development. Consequently, the country has witnessed a slowdown in carbon emission growth, improved air quality, and increased health benefits in recent years.

ABSTRACTThe Clp protease system is important for maintaining proteostasis in bacteria. It consists of ClpP serine proteases and an AAA+ Clp-ATPase such as ClpC1. The hexameric ATPase ClpC1 utilizes the energy of ATP binding and hydrolysis to engage, unfold, and translocate substrates into the proteolytic chamber of homo- or hetero-tetradecameric ClpP for degradation. The assembly between the hetero-tetradecameric ClpP1P2 chamber and the Clp-ATPases containing tandem ATPase domains from the same species has not been studied in depth. Here, we present cryo-EM structures of the substrate-bound ClpC1:shClpP1P2 from Streptomyces hawaiiensis, and shClpP1P2 in complex with ADEP1, a natural compound produced by S. hawaiiensis and known to cause over-activation and dysregulation of the ClpP proteolytic core chamber. Our structures provide detailed information on the shClpP1-shClpP2, shClpP2-ClpC1, and ADEP1-shClpP1/P2 interactions, reveal conformational transition of ClpC1 during the substrate translocation, and capture a rotational ATP hydrolysis mechanism likely dominated by the D1 ATPase activity of chaperones.IMPORTANCEThe Clp-dependent proteolysis plays an important role in bacterial homeostasis and pathogenesis. The ClpP protease system is an effective drug target for antibacterial therapy. Streptomyces hawaiiensis can produce a class of potent acyldepsipeptide antibiotics such as ADEP1, which could affect the ClpP protease activity. Although S. hawaiiensis hosts one of the most intricate ClpP systems in nature, very little was known about its Clp protease mechanism and the impact of ADEP molecules on ClpP. The significance of our research is in dissecting the functional mechanism of the assembled Clp degradation machinery, as well as the interaction between ADEP1 and the ClpP proteolytic chamber, by solving high-resolution structures of the substrate-bound Clp system in S. hawaiiensis. The findings shed light on our understanding of the Clp-dependent proteolysis in bacteria, which will enhance the development of antimicrobial drugs targeting the Clp protease system, and help fighting against bacterial multidrug resistance.

Digital online application is a commonly used method for professional title evaluation in colleges and universities. With the rapid development of the mobile Internet of Things, the fastest way for users to access the evaluation system is through mobile devices. However, this also poses security challenges to the traditional professional title evaluation system. Therefore, this paper proposes a decentralized and security-enhanced digital title evaluation system in mobile IoT. Since the system uses blockchain to implement a decentralized review process, the transparency and fairness of the review process are guaranteed. At the same time, the review data and reviewer information in the system are encrypted and stored in the distributed network to ensure the security and non-tamperability of the data. All review records are recorded on the blockchain, and anyone can view and verify the legitimacy of the review results. Additionally, the system incorporates security enhancement mechanisms such as identity verification, smart contracts, and auditing functions to improve the credibility of the evaluation process and prevent cheating under mobile IoT. Experiments have shown that this system has important application value in the field of professional title review in universities, which can improve review efficiency, reduce human intervention, and provide strong support for the credibility of review results.

Abstract Imaging-guided photothermal therapy (PTT) for cancers recently gathered increasing focus thanks to its precise diagnosis and potent therapeutic effectiveness. Croconaine (CR) dyes demonstrate potential in expanding utility for near infrared (NIR) dyes in bio-imaging/theranostics. However, reports on CR dyes for PTT are scarce most likely due to the short of the efficacious delivery strategies to achieve specific accumulation in diseased tissues to induce PTT. Extracellular vesicles (EVs) are multifunctional nanoparticle systems that function as safe platform for disease theragnostics, which provide potential benefits in extensive biomedical applications. Here, we developed a novel delivery system for photothermal molecules based on a CR dye that exerts photothermal activity through CDH17 nanobody-engineered EVs. The formed CR@E8-EVs showed strong NIR absorption, excellent photothermal performance, good biological compatibility and superb active tumor-targeting capability. The CR@E8-EVs can not only visualize and feature the tumors through CR intrinsic property as a photoacoustic imaging (PAI) agent, but also effectively retard the tumor growth under laser irradiation to perform PTT. It is expected that the engineered EVs will become a novel delivery vehicle of small organic photothermal agents (SOPTAs) in future clinical PTT applications.

Deep undercooling rapid solidification experiments of Cu–Ni–Co alloys were carried out using the molten glass purification cycle superheat method. 130 K, 177 K and 277 K undercooling degrees were obtained and analysed for Cu55Ni43Co2 alloy. The crystallographic information obtained by EBSD and the dynamics of the solidification front taken by high speed photography were analysed and it was concluded that both the formation of relatively rounded equiaxed grains during the first refinement and the subsequent formation of finer subcrystals were due to the release of a large amount of solidification energy during the solidification process. The re-glow stage releases a large amount of latent heat of solidification resulting in the remelting of the dendrites. TEM tests were carried out on Cu55Ni43Co2 alloy with a maximum undercooling of 277 K. It was found that part of the substructure showed a high density dislocation network. By analysing the effect of increasing undercooling on the microstructure evolution, combined with the EBSD and TEM characterisation data, it was found that the stress-induced recrystallization mechanism was the dominant factor in the grain refinement mechanism at large undercooling.