Electric cars purring along the highway differ from nearby internal combustion vehicles not just in their source of power. For parts such as wires and inverters, electric cars require more than twice as much copper as their conventional counterparts
As the largest steel-producing country, China’s steel industry has experienced rapid development in terms of production level and quality. Owing to the high consumption of coal in the iron and steel industry, air pollutants and carbon dioxide (CO2) show similar emission properties in flue gas. In view of the collaborative reduction of pollution and carbon emissions, the emission standards for pollutants and carbon were first analyzed, suggesting that carbon emission standards for the iron and steel industry should be accelerated. A collaborative technology system for the reduction of pollution and carbon emissions from the iron and steel industry in China is demonstrated, consisting of ① optimization of present ultra-low emission technology, ② low-carbon innovation for present production processes, ③ steel production process reengineering, and ④ carbon capture, utilization, and storage (CCUS). Finally, the technical prospect for collaborative reduction of pollution and carbon emissions from the iron and steel industry in China is suggested to support high-quality green development in this industry.
Ecological restoration policies and their implementation are influenced by ecological and socioeconomic drivers. Top-down approach-based spatial planning, emphasizing hierarchical control within government structures, and without a comprehensive consideration of social-ecological interactions may result in implementation failure and low efficiency. Although many researchers have indicated the necessity to engage social-ecological interactions between stakeholders in effective planning processes, socioeconomic drivers of ecological restoration on a large scale are difficult to quantify because of data scarcity and knowledge limitations. Here, we established a new ecological restoration planning approach linking a social-ecological system framework to large-scale ecological restoration planning. The new spatial planning approach integrates bottom-up approaches targeting stakeholder interests and provides social considerations for stakeholder behavior analysis. Based on this approach, a meta-analysis is introduced to recognize key socioeconomic and social-ecological factors influencing large-scale ecological restoration implementation, and a stochastic model is constructed to analyze the impact of socioeconomic drivers on the behavior of authorities and participants on a large scale. We used the Yangtze River Basin-based Conversion of Cropland to Forest Program (CCFP), one of the largest payments for ecosystem service programs worldwide, to quantify the socioeconomic impacts of large-scale ecological restoration programs. Current CCFP planning without socioeconomic considerations failed to achieve large-scale program goals and showed low investment efficiency, with 19.71% of the implemented area reconverting to cropland after contract expiry. In contrast, spatial matching between planned and actual restoration increased from 61.55% to 81.86% when socioeconomic drivers were included. In addition, compared to that with the current CCFP implementation, the cost effectiveness of spatial planning with social considerations improved by 46.94%. Thus, spatial optimization planning that integrates both top-down and bottom-up approaches can result in more practical and effective ecological restoration than top-down approaches alone. Our new approach incorporates socioeconomic factors into large-scale ecological restoration planning with high practicality and efficiency.
As a widespread emerging contaminant, chloramphenicol (CAP) adversely impacts ecological communities in the water environment. Biological treatment is widely used for aquatic pollutant removal, and the performance of functional microbes determines its outcome. Herein, a consortium with a powerful CAP-degrading capacity was domesticated from activated sludge. As the common degradation products of CAP, 4-nitrobenzoic acid (PNB) and 2,2-dichloroacetic acid (DCA) were also used as the sole substrates for long-term domestication. The successional pattern of the microbial community and critical functional genes through the 2.5-year domestication was revealed by metagenomic analysis. Sphingomonas, Caballeronia, and Cupriavidus became the most dominant populations in the CAP-, PNB-, and DCA-degrading consortia, respectively, and they were crucial degraders of PNB and DCA. Their collaboration contributed to the high mineralization rate of CAP. PNB was transformed into protocatechuic acid (PCA) and then mineralized through meta-cleavage and ortho-cleavage pathways. Crucial functional genes involved in CAP, PNB, and DCA metabolism, including CAP acetyltransferase, CAP oxidoreductase, haloacid dehalogenases, and protocatechuate dioxygenases, were significantly enriched in consortia. pH and carbon source had significant impacts on CAP biodegradation efficiency. The domesticated consortia and isolated strains are necessary microbial resources to enhance the bioremediation of CAP-, PNB-, or DCA-polluted environments.
Vertical farming systems, such as sky farms, are a potential type of agricultural system for stable and effective food production. Here, we highlight the potential of the sky farm, denoted as the “skyscraper crop factory” (SCF), for cereal crop production and discuss some nascent technologies that would be applied in this production system. SCFs are ideal crop-production systems for increasing the effective arable area for crops and ensuring food security in times of crises that cause a shock in global trade. They can also provide food in urban areas to meet producers’ and consumers’ demands for the increased nutrition, taste, and safe production of cereal crops. Moreover, as their use can reduce greenhouse gas emissions, SCFs could be a sustainable addition to conventional agricultural crop production.
Adhesives have attracted a great deal of attention as an advanced modality in biomedical engineering because of their unique wound management behavior. However, it is a grand challenge for current adhesive systems to achieve robust adhesion due to their tenuous interfacial bonding strength. Moreover, the absence of dynamic adaptability in conventional chemical adhesives restricts neoblasts around the wound from migrating to the site, resulting in an inferior tissue-regeneration effect. Herein, an extracellular matrix-derived biocomposite adhesive with robust adhesion and a real-time skin healing effect is well-engineered. Liquid-liquid phase separation is well-harnessed to drive the assembly of the biocomposite adhesive, with the active involvement of supramolecular interactions between chimeric protein and natural DNA, leading to a robustly reinforced adhesion performance. The bioadhesive exhibits outstanding adhesion and sealing behaviors, with a sheared adhesion strength of approximately 18 MPa, outperforming its reported counterparts. Moreover, the engineered bioderived components endow this adhesive material with biocompatibility and exceptional biological functions including the promotion of cell proliferation and migration, such that the use of this material eventually yields real-time in situ skin regeneration. This work opens up novel avenues for functionalized bioadhesive engineering and biomedical translations.
The widespread use of artemisinin (ART) and its derivatives has significantly reduced the global burden of malaria; however, malaria still poses a serious threat to global health. Although significant progress has been achieved in elucidating the antimalarial mechanisms of ART, the most crucial target proteins and pathways of ART remain unknown. Knowledge on the exact antimalarial mechanisms of ART is urgently needed, as signs of emerging ART resistance have been observed in some regions of the world. Here, we used a combined strategy involving mass spectrometry-coupled cellular thermal shift assay (MS-CETSA) and transcriptomics profiling to identify a group of putative antimalarial targets of ART. We then conducted a series of validation experiments on five prospective protein targets, demonstrating that ART may function against malaria parasites by interfering with redox homeostasis, lipid metabolism, and protein synthesis processes. Taken together, this study provides fresh perspectives on the antimalarial mechanisms of ART and identifies several crucial proteins involved in parasite survival that can be targeted to combat malaria.
Post-transplant diabetes mellitus (PTDM) increases the risk of graft failure and death in liver transplant (LT) recipients. Experimental studies have indicated that enteric dysbiosis mediated by immunosuppressive tacrolimus (TAC) could contribute to glucose disorders, but no data on human recipients with PTDM have been reported. Here, by combining high-throughput shotgun metagenomics sequencing and metabolomics profiling, we characterized the intestinal microbiome (IM) in LT recipient cohort with or without PTDM and deciphered the potential relationship among IM, TAC dosage, and diabetes. By comparing with both non-PTDM and classical type 2 diabetes mellitus (T2DM), we identified microbial signatures of PTDM, which was characterized by the enriched Proteobacteria and decreased Bacteroidetes. Additionally, the altered microbes, as well as the microbial metabolomics, correlated with the dosage of TAC. Specifically, the levels of beneficial microbes associated with PTDM were lowered in recipients with the high TAC trough concentrations (> 5 ng∙mL-1) than those with low ones (< 5 ng∙mL-1), which was accompanied by reduced faecal metabolites involved in the biosynthesis of α-linolenic acid and arachidonic acid-lowering factors of developing T2DM. Moreover, these microbial signatures linked with the extent of glucose disorders in LT recipients. In summary, the faecal microbiome and metabolome differed between PTDM and non-PTDM patients, which were linked with TAC dosage. This study was the first to explore taxonomic alterations and bacterial gene functions to better understand the contribution of the IM to PTDM.
Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)-susceptible-exposed-infected-removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%-82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (Rt) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical resource demand.
People could potentially mitigate heat discomfort when outdoors by combining passive radiative cooling (PRC) strategies with personal thermal management techniques. However, most current PRC materials lack wearing comfort and durability. In this study, a microarray technique is applied to fabricate the tailoring photonic-engineered textiles with intriguing PRC capability and appealing wearability. The developed radiative cooling textiles (RCTs) demonstrate appropriate air-moisture permeability, structural stability, and extended spectroscopic response with high sunlight reflectivity (91.7%) and robust heat emissivity (95.8%) through the atmospheric transparent spectral window (ATSW). In a hot outdoor cooling test, a skin simulator covered by the RCTs displays a temperature drop of approximately 4.4 °C at noon compared with cotton textiles. The evolution of our mimetic structures may provide new insights into the generation of wearable, thermal-wet comfortable, and robust textiles for exploring PRC techniques in personal thermal management applications.
The rapid development of artificial intelligence has pushed the Internet of Things (IoT) into a new stage. Facing with the explosive growth of data and the higher quality of service required by users, edge computing and caching are regarded as promising solutions. However, the resources in edge nodes (ENs) are not inexhaustible. In this paper, we propose an incentive-aware blockchain-assisted intelligent edge caching and computation offloading scheme for IoT, which is dedicated to providing a secure and intelligent solution for collaborative ENs in resource optimization and controls. Specifically, we jointly optimize offloading and caching decisions as well as computing and communication resources allocation to minimize the total cost for tasks completion in the EN. Furthermore, a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithm is designed to solve this optimization problem. In this algorithm, we construct an incentive-aware blockchain-assisted collaboration mechanism which operates during local training, with the aim to strengthen the willingness of ENs to participate in collaboration with security guarantee. Meanwhile, a contribution-based federated aggregation method is developed, in which the aggregation weights of EN gradients are based on their contributions, thereby improving the training effect. Finally, compared with other baseline schemes, the numerical results prove that our scheme has an efficient optimization utility of resources with significant advantages in total cost reduction and caching performance.
