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Secondary aluminum (Al) dross (SAD) is a hazardous waste discharged from Al production, processing and recycling. Over 6.7 million tons of SAD was discharged on the planet in 2023. SAD is consisted of 40–60 wt% of alumina (Al2O3), 10–30 wt% of AlN, 5–15 wt% of salts and 3–10 wt% of heavy metal oxides. Currently, recycling of SAD to make Al2O3 by hydrometallurgy is a promising method for disposal of SAD. Hydrometallurgy method is mainly divided into acid leaching and alkali leaching. In acid leaching, Al, AlN and Al2O3 react with acid to form aluminum sulfate and aluminum chloride. In alkali leaching, Al, AlN and Al2O3 react with alkali to form sodium aluminate. High-purity Al2O3 is obtained after precipitation, washing, drying and calcination from the leachate. Resource consumption and emission was calculated to evaluate the economic and environmental benefits. About 147.9 and 172.6 dollars was earned after making Al2O3 from a ton of SAD by alkali and acid leaching process, respectively. And carbon emissions of a ton of Al2O3 was risen about 596.5 and 2216.0 kg CO2, respectively, compared with the Bayer process with bauxite. We proposed a calcination pre-treatment with quicklime on SAD to reduce the carbon emission. The Al and AlN are oxidized into Al2O3 after calcination, and the Al2O3 reacts with CaO to form CaO·Al2O3. The Al in CaO·Al2O3 can be leached out easily with a low concentration of alkali. This review provides a guidance for the recycling of SAD by hydrometallurgy, and proposes a novel idea for the energy and consumption reduction in alumina (Al₂O₃) production.

Tungsten carbide (WC) nanopowder is crucial for preparing high-performance WC-Co cemented carbides, but the synthesis of WC nanopowder still remains huge challenges. In this study, we report a novel method for synthesizing high-purity WC nanopowder by carbothermal reduction-carbonization. The effects of the reaction atmosphere, temperature, and time on the morphology and size of WC powder were studied. It was found that vacuum atmosphere was more conducive to prepare WC nanopowder, which could reduce the onset temperature of carbothermal reduction reaction and effectively improve the reaction efficiency. The final products in vacuum were more homogeneous and smaller compared with argon atmosphere. Furthermore, the mechanism of effect of atmosphere on prepared WC nanopowder was analyzed in detail. The particle size of WC showed an increasing trend with the increase of temperature and holding time. Following calcination at 1100 °C for 5 h, the as-prepared WC nanopowder attained an average particle size of 82 nm.

ABSTRACT: The bovine hemoplasmas include Mycoplasma wenyonii and Candidatus Mycoplasma haemobos, which are increasingly recognized as infecting cattle throughout the world. Infection with hemotropic mycoplasma has been reported to be widespread in mature dairy cows, but little is known about its prevalence in calves and heifers. The objective of this study was to investigate the prevalence and dynamics of infection with M. wenyonii and C. M. haemobos in calves and replacement heifers on Michigan dairy farms and assess the potential associations between infection status and hematological values. The study was designed as a prospective cross-sectional study with a longitudinal component. A convenience sample of 11 farms agreed to participate and were visited twice between March and September 2022. During the first farm visit, researchers collected blood samples from up to 94 animals per farm distributed among newborn and preweaning calves (n ≤ 31), weaned calves (n = 21), pre-breeding heifers (n = 21), and pregnant heifers (n = 21). During the first visit, blood samples (n = 174) were also collected from a convenience sample of mature cows to confirm the herd infection status. The same calves and heifers were sampled again ∼95 d (±3.0) later. During the first visit, blood samples were collected from 797 calves and replacement heifers, whereas 675 samples were collected during the second visit due to the inability to locate some animals. Detection of M. wenyonii and C. M. haemobos was based on results of real-time PCR. The hematocrit was determined using microcentrifugation, and the concentration of leukocytes using an automated cell counter. In all herds, most mature cows that were sampled tested positive for infection. The within-herd apparent prevalence of hemoplasma in calves and replacement heifers was 100% for both M. wenyonii and C. M. haemobos. The apparent prevalence of hemoplasma in youngstock was associated with age. In calves that were 1 to 6 mo old, the prevalence of infection was 6% to 8% but sharply increased to 31% by 8 mo of age. In older animals, the prevalence remained high, and was almost 100% in animals greater than 17 mo of age. Based on calves and heifers sampled twice, the cumulative incidence varied widely among herds, ranging from 3.7% to 96.0%, and increased with the age of the animals. We found no difference in hematocrit or number of lymphocytes, monocytes, neutrophils, or total leukocytes based on infection status. The number of eosinophils was greater in infected animals. This is the first study to report the prevalence of hemoplasmas in calves and replacement heifers in the United States. It indicates that young calves can be infected with hemoplasmas, but the rate of infection is low. The likelihood of infection increases as animals age, with a notable rise in the proportion of infected heifers occurring by 8 mo old, and the prevalence eventually reaching nearly 100% in older animals. Once infected, heifers appear to remain chronic carriers. Hemoplasma infection alone does not usually lead to the development of clinical signs, and most of the animals remain apparently healthy.

Mangroves, primarily found along tropical and subtropical coastlines, are crucial ecosystems that offer significant ecological services, including coastal protection, carbon sequestration, and biodiversity support. However, these ecosystems are under severe threat from human activities such as aquaculture, agriculture, and urban development, leading to deforestation and degradation. This study aims to develop a comprehensive framework for assessing the health of mangrove ecosystems using the Pressure-State-Response (PSR) model with annual temporal resolution from 2019 to 2023. It consists of three dimensions: Pressure (human activities and natural factors affecting the ecosystem), State (current condition of the ecosystem), and Response (efforts to mitigate adverse effects and restore health). This study incorporates innovative physical and structural parameters alongside traditional indicators to improve the accuracy of the assessment. The method offers advantages such as ease of measurement, low cost, early warning capabilities for ecosystem damage, and high sensitivity to ecological changes. It provides valuable insights for decision-makers and enhances the ability to detect trends in mangrove health over time through a large-scale, multi-temporal analysis. Using remote sensing techniques and the Analytic Hierarchy Process (AHP), we identified 12 key indicators. The AHP method was used to determine the weight of each indicator, achieving a consistency ratio (CR) of less than 0.1, which ensures the reliability of the assessment. The evaluation included calculating and analysing the Comprehensive Health Index (CHI), Pressure (PHI), State (SHI), and Response (RHI) health indices. Results indicated that the mangrove ecosystems in the Beibu Gulf region were “healthy” state by 2019 through 2022. However, in 2023, the health status declined to “relatively healthy,” indicating a downward trend. In conclusion, this study presents a robust framework for assessing mangrove ecosystem health using the PSR model. The findings underscore the urgent need for conservation efforts to reverse the declining health trend and ensure the long-term sustainability of mangrove ecosystems in the Beibu Gulf.

Infection by multidrug-resistant avian pathogenic Escherichia coli (APEC) in chickens always leads to the uselessness of antibiotics, highlighting the need for alternative antibacterial agents. Sophora flavescens and Coptis chinensis have been a classical combination used together in Traditional Chinese Medicine (TCM) formulas to treat diseases with similar symptoms to colibacillosis for an extended period, but the effect of their active ingredients' combination on APEC infection remains unstudied. The objective of this study was to explore the synergistic effect of matrine and berberine hydrochloride on colibacillosis caused by an isolated multidrug-resistant APEC. In this study, a highly pathogenic E. coli was isolated from the liver of a diseased chicken in a farm suspected of colibacillosis, and it was resistant to multiple antibiotics. The LD50 of the strain was approximately 3.759×108 CFU/mL. The strain harbored several antibiotic resistance genes and virulence genes. Matrine and berberine hydrochloride have synergistic antibacterial effect against the isolated strain in vitro. The combined use of matrine and berberine hydrochloride exhibited synergistic effects in the treatment of APEC infection by regulating the organ indices, improving the pathological situation, decreasing the bacterial load, and regulating the inflammatory factors to enhance the survival rate of chickens in vivo. These results provided a foundation for revealing the effective effects and possible mechanisms of matrine and berberine hydrochloride as potential antimicrobial agents on diseases caused by multidrug-resistant APEC in chickens.

The Himawari-8 satellite has the capability to rapidly retrieve sea surface temperature (SST) data at a high frequency of 10 min, demonstrating significant potential for various scientific applications. However, the presence of cloud often results in missing SST data at cloud locations, or can reduce the accuracy of retrieved SST. In contrast to the method of reconstructing missing SST data, this study focuses on exploring the potential of inverting SST under all-sky conditions. This study proposes a three-layer stacked machine learning model (TLSM), specifically designed for SST under all-sky conditions. The model integrates cloud properties into its input features to effectively account for the influence of cloud cover. Validation using 30 % match-up pairs generated an overall root mean square error (RMSE) of 0.71 °C, a Bias of −0.01 °C, and an R2 of 0.91 based on 6383 samples. For clear-sky conditions, TLSM demonstrates a noteworthy enhancement in SST inversion accuracy (R2 = 0.98, RMSE=0.35 °C) compared to the official SST product (R2 = 0.86, RMSE=0.88 °C). In cases of optically thin clouds and clouds with low cloud top pressure, TLSM exhibits commendable proficiency in the inversion of SST. The Bias and RMSE for these cloud types indicate better performance compared to the official clear-sky SST data. While including cloud samples may reduce the overall accuracy of the TLSM, it substantially enhances the spatial coverage of the inverted SST. Considering the performance for each cloud type, TLSM may serve as an alternative approach for SST retrieve under thin clouds.

ABSTRACT: The chicken comb is an essential secondary sexual characteristic to measure sexual maturity and is closely related to reproductive performance. Pendulous comb (PC) and upright comb (UC) are 2 common comb phenotypes in hens, which have been highly associated with egg production performance. However, the reasons for the formation of PC remain undetermined. In this study, we first characterized the PC and UC chicken at start (at 175 d age), peak (at 217 d age), and postlaying (at 300 d age) and found that PC and UC could transform for each other. Furthermore, we suggested that PC chicken demonstrated better egg production performance than UC chicken, especially characterizing comb type in the start-laying period. Moreover, we performed histological evaluation of PC and UC tissue, which suggested that the low density of collagen fibers and acid mucopolysaccharides might lead to the formation of PC. To further explore the possible reasons for PC formation, we performed an untargeted metabolomic analysis of serum between PC and UC chicken in the start, peak, and postlaying periods. The enrichment analysis of period-unique differentially expressed metabolites (DEMs) between PC and UC showed that the different metabolic pathways and nutritional levels might contribute to the formation of PC in the different laying periods. Our research provided critical insights into the phenotypic diversity of chicken comb, establishing a foundation for early selection of chicken egg production performance.

Mild thermal stimulation plays an active role in bone tissue repair and regeneration. In this work, a bioactive polydopamine/Ti3C2/poly(vinylidene fluoride trifluoroethylene) (PDA/Ti3C2/P(VDF-TrFE)) nanocomposite coating with excellent near-infrared light (NIR)-triggered photothermal effect was designed to improve the osteogenic ability of implants. By incorporating dopamine (DA)-modified Ti3C2 nanosheets into the P(VDF-TrFE) matrix and combining them with alkali initiated in situ polymerization, the resulting PDA/Ti3C2/P(VDF-TrFE) nanocomposite coating gained high adhesion strength on Ti substrate, excellent tribological and corrosion resistance properties, which was quite important for clinical application of implant coatings. Cell biology experiments showed that NIR-triggered mild thermal stimulation on the coating surface promoted cell spreading and growth of BMSCs, and also greatly upregulated the osteogenic markers, including Runt-Related Transcription Factor 2 (RUNX2), alkaline phosphatase (ALP), osteopontin (OPN), osteocalcin (OCN). Simultaneously, the synthesis of heat shock protein 47 (HSP47) was significantly promoted by the mild thermal stimulation, which strengthened the specific interaction between HSP47 and collagen Ⅰ (COL-Ⅰ), thereby activating the integrin-mediated MEK/ERK osteogenic differentiation signaling pathway. In addition, the results also showed that the mild thermal stimulation induced the polarization of macrophages towards M2 phenotype, which can attenuate the inflammatory response of injured bone tissue. Antibacterial results indicated that the coating exhibited an outstanding antibacterial ability against S. aureus and E. coli. Conceivably, the versatile implant bioactive coatings developed in this work will show great application potential for implant osseointegration.

Mangroves are one of the most important marine ecosystems globally, their spatial distribution is crucial for promoting mangrove ecosystems conservation, restoration, and sustainable managements. This study proposed a novel Unet-Multi-Scale High-Resolution Vision Transformer (UHRViT) model for classifying mangrove species using unmanned aerial vehicle (UAV-RGB), UAV-LiDAR, and Gaofen-3 Synthetic Aperture Radar (GF-3 SAR) images. The UHRViT utilized a multi-scale high-resolution visual Transformer as its backbone network and was designed to a multi-branch U-shaped network structure to extract features of different scales layer by layer, and to facilitate the interaction of high and low-level semantic information. We further verified the classification performance superiority of UHRViT model by comparing to HRViT and HRNetV2 algorithms. We also systematically investigated the effects of active–passive image combination ratios on mangrove communities mapping. The results revealed that: UAV-RGB images exhibited the better classification accuracy (mean F1-score＞95 %) for mangrove species than UAV-LiDAR and GF-3 SAR images; The classification performances and stability of UHRViT algorithm in the fifteen datasets outperformed the HRViT and HRNetV2 algorithms; Combining UAV-RGB with GF-3 SAR or UAV-LiDAR images respectively, both achieved better classifications than the single data source. Based on the UHRViT algorithm, the combination of UAV-RGB and UAV-LiDAR achieved the highest classification accuracy (Iou = 0.944, MIou = 50.2 %) for Avicennia corniculatum (AC). When the combination ratio of UAV-RGB with GF-3 SAR or UAV-LiDAR was 3:1, Avicennia marina and AC both obtained the optimal classification accuracy with average F1-scores of 98.19 % and 97.3 %, respectively. Our works revealed that the changes in the classification accuracies of mangrove communities under multi-sensor image combination ratios, and demonstrated that our model could effectively improve the classification accuracy of mangrove communities.

Mangroves face decline and degradation due to human activities and natural forces, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices that rely on multispectral image spectral characteristics suffer from limitations in terms of recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring.

Mangroves play a pivotal role in the attainment of United Nations Sustainable Development Goals. Leaf equivalent water thickness (EWT) and leaf mass per area (LMA) are vital functional trait parameters for monitoring heath status of mangroves. However, their inherent spectral response characteristics remain unclear, which presents a challenge for high-precision estimating EWT and LMA using remote sensing images. In this study, we attempted to synergistic retrieval of mangrove EWT and LMA using field hyperspectral and satellite data. Specifically, we proposed a novel Fractional-order assisted Spectral angle Analysis (FSA) approach to accurately identify sensitive spectral domains of EWT and LMA for three mangrove species based on in situ full-spectrum hyperspectral (350–2500 nm) data. We constructed a new spectral matching index (SMI) to screen the satellite-based sensitive spectral bands based on the field-based spectral domains, and further verified their feasibility of estimating EWT and LMA using three optical satellite sensors (Sentinel-2A, SDGSAT-1, and OHS-3D). Finally, we explored the effectiveness of combining SAR and thermal infrared (TIR) images with sensitive spectral bands on improving retrieval performance in estimating EWT and LMA, respectively. Results indicated that FSA approach could capture the sensitive spectral domains of EWT (1125–1868 nm) and LMA (1419–1999 nm) for three mangrove species. The SMI could effectively realize the spectral matching between field-based sensitive domains and satellite-based bands, and perform the synergistic retrieval of mangrove EWT (R2 = 0.65) and LMA (R2 = 0.70). Intriguing finding is that the additional TIR bands significantly enhanced the EWT and LMA inversion accuracy (R2 increased by 9.8 %–68.3 %) when compared to SAR images. We confirmed that EWT displayed an inverse trend of LMA with the flooding frequency increasing. Our works provide a scientific basis for protection and sustainable management of mangroves.

3D concrete printing technology has enabled the construction of full-scale bridges. However, structural carbon emissions due to higher cement content and limitations of embedded reinforcement have limited its widespread adoption. This paper presents a non-reinforced 3D printed concrete box arch bridge and describes its design, construction, dismantlement, and reconstruction, as well as evaluation of the carrying capacity of reconstructed primary arch ring. The bridge adheres to current technical principles and bridge engineering specifications. By taking into account the mechanical anisotropy and primary stress characteristics of the arch, the design negates the need for reinforcement. The study showcases the reusability and potential carbon emission reduction through block printing, on-site assembly, block removal, and secondary usage. The safety of the reconstructed arch bridge was confirmed through an in-situ load test.

This study aimed to depict the mineral deposition on the cellulose extracted from mineral-rich Lycium barbarum L. leaves by the basic base/acid treatment. Alkali treatment made the cellulose fluffy, and the leaf phytoliths changed to liquid silicon, depositing onto the fluffy cellulose in crystalline forms, with the mineral content (mainly Si, Ca, and Al) reaching 9.41 % in cellulose after 24 h alkali treatment. Acid treatment decreased the deposited minerals affecting the cellulose crystallinity. Sulfuric acid (SA) mainly eroded the cellulosic amorphous zone and the attached minerals, and crystallized minerals (mainly quartz-SiO2) remained in the crystalline zone, increasing the cellulose crystallinity (66.8 %). Hydrofluoric acid (HF) indiscriminately removed nearly all minerals regardless of crystalline and amorphous zones, thus lowering the crystallinity. Further application research showed that the ability of the SA-refining cellulose to stabilize emulsion (89.8 %) was much stronger than that of the HF counterpart (62.6 %), and the mineral-relevant cellulose crystallinity determined the emulsion stability. These findings might help in understanding the impact of the basic extraction on cellulosic mineralization and quality and shed light on the development of novel functional cellulose.

ABSTRACT: Coccidiosis, a protozoan disease that substantially impacts poultry production, is characterized by an intracellular parasite. The study utilized 48 one-day-old Horro chickens, randomly divided into the infected (I) and control (C) groups. The challenge group of chickens were administered Eimeria maxima oocysts via oral gavage at 21-days-old, and each chicken received 2 mL containing 7×104 sporulated oocysts. The total RNAs of chicken jejunum and cecum tissues were isolated from three samples, each from I and C groups. Our study aimed to understand the host immune-parasite interactions and compare immune response mRNA profiles in chicken jejunum and cecum tissues at 4 and 7 days postinfection with Eimeria maxima. The results showed that 823 up- and 737 down-regulated differentially expressed mRNAs (DEmRNAs) in jejunum at 4 d infection and control (J4I vs. J4C), and 710 up- and 368 down-regulated DEmRNAs in jejunum at 7 days infection and control (J7I vs. J7C) were identified. In addition, DEmRNAs in cecum tissue, 1424 up- and 1930 down-regulated genes in cecum at 4 days infection and control (C4I vs. C4C), and 77 up- and 191 down-regulated genes in cecum at 7 days infection and control (C7I vs. C7C) were detected. The crucial DEmRNAs, including SLC7A5, IL1R2, GLDC, ITGB6, ADAMTS4, IL1RAP, TNFRSF11B, IMPG2, WNT9A, and FOXF1, played pivotal roles in the immune response during Eimeria maxima infection of chicken jejunum. In addition, the potential detection of FSTL3, RBP7, CCL20, DPP4, PRKG2, TFPI2, and CDKN1A in the cecum during the host immune response against Eimeria maxima infection is particularly noteworthy. Furthermore, our functional enrichment analysis revealed the primary involvement of DEmRNAs in small molecule metabolic process, immune response function, inflammatory response, and toll-like receptor 10 signaling pathway in the jejunum at 4 and 7 days postinfection. Similarly, in the cecum, DEmRNAs at 4 and 7 days postinfection were enriched in processes related to oxidative stress response and immune responses. Our findings provide new insights and contribute significantly to the field of poultry production and parasitology.

Summary: In categorical data visualization, appropriate color arrangements can avoid perceptual ambiguity and help perceive underlying data patterns. We introduce a protocol to assign contrastive colors to neighboring categories using both Python and R packages. We describe steps for calculating the interlacement between clusters and generating a proper color palette and calculating color contrast. We then detail procedures for aligning cluster interlacement and color contrast to get an optimized cluster-color assignment, achieving clear categorical visualization.For complete details on the use and execution of this protocol, please refer to Jing et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China’s Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future.

ABSTRACT: Qingyuan partridge chicken (QYM) is a highly regarded native breed in China, highly esteemed for its exceptional breeding characteristics. However, the investigation into the selection signatures and its strains remains largely unexplored. In this study, blood sampling, DNA extracting, and high-depth resequencing were performed in 27 QYMs. Integrating the genomic data of 14 chicken (70 individuals) breeds from other researches, to analyze the genetic structure, selection signatures, and effects of selective breeding within QYM and its 3 strains (QYMA, QYMB, and QYMC). Population structure analysis revealed an independent QYM cluster, which exhibited distinct from other breeds, with each of its 3 strains displaying distinct clustering patterns. Linkage disequilibrium analysis highlighted QYMB's notably slower decay rate, potentially influenced by selection pressure from various production indicators. Examination of selection signatures uncovered genes and genetic mechanisms associated with genomic changes resulting from extensive selective breeding within the QYM and its strains. Intriguingly, diacylglycerol kinase beta (DGKB) and catenin alpha 2 (CTNNA2) were identified as commonly selected genes across the 3 QYM strains, linked to energy metabolism, muscle development, and fat metabolism. Our research validates the substantial impact of selective breeding on QYM and its strains, concurrently identifying genomic regions and signaling pathways associated with their distinctive characters. This research also establishes a fundamental framework for advancing yellow-feathered broiler breeding strategies.

ABSTRACT: This study aimed to investigate the symbiosis between Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047. In addition, the effect of their different inoculum ratios was determined, and comparison experiments of fermentation characteristics and storage stability of milk fermented by their monocultures and cocultures at optimal inoculum ratio were performed. We found the time to obtain pH 4.6 and ΔpH during storage varied among 6 inoculum ratios (1:1, 2:1, 10:1, 19:1, 50:1, 100:1). By the statistical model to evaluate the optimal ratio, the ratio of 19:1 was selected, which exhibited high acidification rate and low postacidification with pH values remaining between 4.2 and 4.4 after a 50-d storage. Among the 3 groups included in our analyses (i.e., the monocultures of S. thermophilus CICC 6038 [St] and Lb. bulgaricus CICC 6047 [Lb] and their cocultures [St+Lb] at 19:1), the coculture group showed higher acidification activity, improved rheological properties, richer typical volatile compounds, more desirable sensor quality after the fermentation process than the other 2 groups. However, the continuous accumulation of acetic acid during storage showed that acetic acid was more highly correlated with postacidification than d-lactic acid for the Lb group and St+Lb group. Our study emphasized the importance of selecting an appropriate bacterial consortium at the optimal inoculum ratio to achieve favorable fermentation performance and enhanced postacidification stability during storage.

The optimization of magnetic and mechanical properties by microstructure modification is tremendously challenging for high-grade non-oriented silicon steel. In the current study, the microstructure of cold-rolled high-grade non-oriented silicon steel was modified by recrystallization annealing to achieve high strength and good magnetic properties. The results indicate that recrystallization annealing significantly improved the magnetic properties, while negatively impacting its mechanical properties. At the early stage of annealing (2 min), the recrystallization grains grew within the fully recrystallized matrix. There was a slight decrease in both dislocation density and grain boundary density, leading to a slight decrease in iron loss and yield strength. The magnetic induction intensity of the fully recrystallized annealed sheets was primarily influenced by the recrystallization texture. Thus, the critical annealing time was 2 min to meet the demands of both magnetic and mechanical properties for the studied high-grade non-oriented silicon steel.

Dissolved organic matter (DOM) is a crucial component of natural sediments that alters Cd sequestration. Nevertheless, how different types of DOM fuel Cd mobilization in Mn-rich sediments has not been elucidated. In the present study, four typical DOM, fluvic acid (FA), bovine serum albumin (BSA), sodium alginate (SA), and sodium dodecyl benzene sulfonate (SDBS), were used to amend Cd-contaminated sediment to study their effects on Cd/Mn biotransformation and microbial community response. The results demonstrated that different DOM drive microbial community shifts and enhance microbially mediated Mn oxide (MnO) reduction and Cd release. The amendment of terrestrial- and anthropogenic-derived DOM (FA and SDBS) mainly contributed to enriching Mn-reducing bacteria phylum Proteobacteria, and its abundance increased by 38.16–74.47 % and 56.41–73.98 %, respectively. Meanwhile, microbial-derived DOM (BSA and SA) mainly stimulated the abundances of metal(loid)-resistant bacteria phylum Firmicutes. Accompanied by microbial community structure, diversity, and co-occurrence network shifts, the DOM concentration and oxidation-reduction potential changed, resulting in enhanced Cd mobilization. Importantly, FA stimulated Cd release most remarkably, probably because of the decreased cooperative interactions between bacterial populations, stronger reduction of MnOs, and higher aromaticity and hydrophobicity of the sediment DOM after amendment. This study linked DOM types to functional microbial communities, and explored the potential roles of different DOM types in Cd biotransformation in lake sediments.

Background: Hematopoietic cell signal transducer (HCST) and tyrosine kinase-binding protein (TYROBP) are triggering receptors expressed on myeloid cells 2 (TREM2), which are pivotal in the immune response to disease. Despite growing evidence underscoring the significance of TREM2, HCST, and TYROBP in certain forms of tumorigenesis, a comprehensive pan-cancer analysis of these proteins is lacking. Methods: Multiple databases were synthesized to investigate the relationship between TREM2, HCST, TYROBP, and various cancer types. These include prognosis, methylation, regulation by long non-coding RNAs and transcription factors, immune signatures, pathway activity, microsatellite instability (MSI), tumor mutational burden (TMB), single-cell transcriptome profiling, and drug sensitivity. Results: TREM2, HCST, and TYROBP displayed extensive somatic changes across numerous tumors, and their mRNA expression and methylation levels influenced patient outcomes across multiple cancer types. long non-coding RNA (lncRNA) -messenger RNA (mRNA) and TF-mRNA regulatory networks involving TREM2, HCST, and TYROBP were identified, with lncRNA MEG3 and the transcription factor SIP1 emerging as potential key regulators. Further immune analyses indicated that TREM2, HCST, and TYROBP play critical roles in immune-related pathways and macrophage differentiation, and may be significantly associated with TGF-β and SMAD9. Furthermore, the expression of TREM2, HCST, and TYROBP correlated with the immunotherapy markers TMB and MSI, and influenced sensitivity to immune-targeted drugs, thereby indicating their potential as predictors of immunotherapy outcomes. Conclusion: This study offers valuable insights into the roles of TREM2, HCST, and TYROBP in tumor immunotherapy, suggesting their potential as prognostic markers and therapeutic targets for various cancers.

ABSTRACT: Single nucleotide polymorphisms (SNPs) are valuable genetic markers that can provide insights into the genetic diversity and variation within chicken populations. In poultry breeding, SNP analysis is widely utilized to accelerate the selection of desirable traits, improving the efficiency and effectiveness of chicken breeding programs. In our previous research, we identified an association between LncEDCH1 and muscle development. To further investigate its specific mechanism, we conducted SNP detection and performed genotyping, linkage disequilibrium, and haplotype analysis. Our research findings indicate that 16 SNPs in the LncEDCH1. Among these SNPs, g.1703497 C>T and g.1704262 C>T were significantly associated with breast muscle weight percentage, g.1703497 C>T and g.1703613 T>C were significantly associated with leg weight percentage, and g.1703497 C>T, g.1703589 T>C, g.1703613 T>C, g.1703636 C>A, g.1703768 T>C, g.1704079 C>T, g.1704250 T>C, g.1704253 G>A were significantly associated with skin yellowness. Two haplotype blocks composed of 6 SNPs that were significantly associated with wing skin yellowness, breast skin yellowness, full-bore weight, and carcass weight percentage. Furthermore, through dual-luciferase reporter assays, biotin-coupled miRNA pull-down assays, 5-ethynyl-2′-deoxyuridine (EDU) assays, immunofluorescence, and quantitative real-time polymerase chain reaction (qPCR), it has been confirmed that miR-196-2-3p inhibits the expression of LncEDCH1 directly by binding to LncEDCH1 g.1703613T>C, thereby achieving indirect regulation of muscle development. These findings provide valuable molecular markers for chicken molecular breeding and broaden our understanding of the regulatory mechanisms.

In recent years, the application of Laser powder bed fusion (LPBF) technology in the manufacturing industry has become increasingly widespread. Compared to traditional casting processes, LPBF technology allows for the production of complex-shaped and structurally unique components made of 2507 duplex stainless steel (2507DSS), thus improving production efficiency and product quality. This article focuses on determining the LPBF parameters required for printing 2507DSS, successfully fabricating samples with a density of 98.71% and subjecting them to solution treatment followed by tensile testing. The results show that the LPBF-prepared samples exhibit a strength of 1173 MPa and an elongation of 18%. The presence of island-like structures enables excellent mechanical performance in the horizontal direction. The samples subjected to solution treatment at 1100 °C exhibit a strength of 860 MPa and an elongation of 45%. It is observed that when 2507DSS undergoes solution treatment from a single-phase state, the formation of large-angle grain boundaries and residual stresses within the austenite phase occurs, and a reasonable explanation is provided. Furthermore, the article conducts electrochemical tests on the samples. The results indicate that the LPBF-prepared samples possess the same electrochemical properties as the samples treated at 1100 °C, and both outperform the cast samples. Thus, this study reveals that the proportion of phases is not the sole determining factor for the corrosion resistance of DSS, providing valuable insights for the future development of more corrosion-resistant 2507DSS materials.

Summary: Background: Platinum-doublet chemotherapy plus immunotherapy has been the standard of care for the first-line treatment of advanced non-small cell lung cancer lacking actional driver mutations. However, optimization of drug combinations is still needed to find a better balance between therapeutic efficacy and safety in the immunotherapy era. We aimed to investigate the efficacy and safety of platinum-free albumin bound paclitaxel (nab-paclitaxel) combined with camrelizumab and apatinib as first-line treatment for patients with advanced lung adenocarcinoma. Methods: In this multicenter open-label, single-arm phase II trial, patients with systemic treatment-naïve advanced lung adenocarcinoma without epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations received a rational-based combination of camrelizumab (200 mg intravenously, day one), apatinib (250 mg, q.d., five continuous days per week), and nab-paclitaxel (135 mg/m2 intravenously, days one and eight) every three weeks for four to six cycles in China. Patients with controlled disease were maintained with camrelizumab and apatinib. The primary end point was progression-free survival (PFS). This trial is registered with ClinicalTrials.gov (No. NCT04459078). Findings: Between August 26, 2020 and May 20, 2022, 64 patients were enrolled. The median PFS was 14.3 (95% CI: 9.9, not reached) months. The confirmed objective response rate was 64.1% (95% CI: 51.1, 75.7). The grade 3–4 hematologic treatment-related adverse events (TRAEs) were decreased neutrophil count (14.1%), decreased white blood cell count (7.8%), and anemia (3.1%). The most common non-hematologic TRAEs of grade 3–4 were increased alanine transaminase (18.8%) and aspartate transaminase (15.6%). No treatment-related death occurred. The quality of life was on average not clinically meaningful worse through treatment cycle 14. Interpretation: Nab-paclitaxel plus camrelizumab and apatinib showed clinically meaningful anti-tumor activity and manageable safety, with few hematologic toxicities, and might be a potential treatment option in patients with advanced lung adenocarcinoma lacking EGFR/ALK mutations. Funding: Heath Research Foundation of Chinese Society of Clinical Oncology, Hunan Provincial Natural Science Foundation of China, Hunan Cancer Hospital Climb Plan, Sister Institution Network Fund of The University of Texas MD Anderson Cancer Center, The Science and Technology Innovation Program of Hunan Province, and Suzhou Sheng Diya Biomedical Co., Ltd, a subsidiary of Jiangsu Hengrui Pharmaceuticals Co., Ltd. (Shanghai, China).

Objective: The aim of these studies was to ascertain if Camelina sativa oil is harmful in both the acute and subchronic states. Methods: Wistar rats of both sexes were used in an acute toxicity test, and the fatal dosage (LD50) of oral Camelina sativa oil was greater than 27.6 g/kg bw. Rats were gavaged with Camelina sativa oil at dosages of 0.00, 0.92, 1.84, and 3.68 g/kg bw per day for 90 days. In addition, satellite groups were established in the control and high-dose groups for a 28-day recovery period. The following factors were assessed: mortality, clinical anomalies, body weight, food intake, hematological, serum biochemistry, urine, gross necropsy, and histology. Results: There were no observable toxicity-related changes in any of the three dosage groups. There is no toxicological relevance to the change in the high-dose hematological indicator PLT at the conclusion of the recovery period because it was within the usual range for this strain in our laboratory. The test material did not result in any pathological alterations, according to a pathological examination. Conclusion: Since the results of the current study, the no-observed-adverse-effect-level (NOAEL) for Camelina sativa oil in rats has been determined to be greater than 3.68 g/kg bw.

Karst wetlands are an important type of wetland among the global karst areas that suffer from serious ecosystem degradation. Chlorophyll content, an important parameter of vegetation functional traits, can indicate the photosynthetic activity, mutation and stress level, and nutritional state of vegetation. However, quantitative estimation of canopy chlorophyll content (CCC) of calcium-loving and alkali-resistant vegetation in karst wetlands using remote sensing technology for clarifying the relationship between canopy spectral reflectance and the CCC of vegetation communities remains a challenge. In this study, we propose a retrieval approach for estimating the CCC of seven typical vegetation communities in the largest karst wetland in China, using a combination of parameter/non– parameter estimation model, and unmanned aerial vehicle (UAV) multispectral images. We further evaluated the sensitivity of various spectral indices to the CCC of the vegetation communities and explored the influence of image feature distribution on CCC estimation accuracy. We revealed the variation patterns of leaf chlorophyll content (LCC) and CCC under different inundation frequencies in the karst wetland. We confirmed that the UAV-based inversion method proposed in this study achieved the CCC estimation of seven vegetation communities (R2 = 0.57 ∼ 0.93, RMSE = 8.80 SPAD ∼ 60.68 SPAD), and obtained high estimation accuracy (R2 = 0.77, RMSE = 22.04 SPAD) of Cladium chinense Nees (CCN, endemic vegetation in karst wetland). We found that the optimal reflectance ranges for estimating the CCC of the seven vegetation communities were 714 nm ∼ 746 nm and 814 nm ∼ 866 nm, and that the ChIrededge and MSR indices negatively influenced the CCC estimation of CCN and Carex baccans Nees (CBN), respectively. We demonstrated that vegetation communities with well-defined canopy structure (Triadica sebifera (Linnaeus) Small) achieved higher estimation accuracy with moderate uncertainty (ε = 10.80 % ∼ 15.49 %) than the herbaceous vegetation communities (CCN, CBN, and Cynodon dactylon (L.) Pers.). The bias of the CCC estimation based on linear regression and partial least squares regression models in different vegetation communities ranged from 1.61 % to 49.41 %. We confirmed that the magnitude of variations in LCC and CCC for these vegetation communities presented a decreasing pattern from that in high-frequency inundation communities to that in the low-frequency ones.

Collaboration between Light Detection and Ranging (LiDAR) point clouds and deep learning has been proven to be an effective approach for vegetation mapping. Current studies have predominantly focused on 2D vegetation mapping, whereas 3D mapping, which directly classifies point clouds at point level, offers a more comprehensive understanding of the stratified structural information of vegetation. However, there is a lack of research on 3D vegetation species mapping, and the disparities between 2D and 3D mapping in natural scenarios remain unclear. To resolve these issues, we compared the deep learning performance of 2D and 3D vegetation species mapping across three distinct natural scenes: karst wetland, mangrove forest, and hill forest. In addition, the 2D and 3D mapping in natural scenes are adversely affected by the elevated channel count of LiDAR-derived features and the extreme category imbalance in point cloud. To mitigate these challenges, we propose a novel Multi-resolution Feature Selection Network (MrFSNet) to select optimal feature combinations at different scales for better 2D mapping performance. Additionally, we introduce a novel Dynamic Weighted Sampling (DWS) strategy, which is combined with KPConv to address the extreme category imbalance present in 3D mapping. Results indicate that: (1) 3D vegetation species mapping exhibited the highest performance, achieving an mF1 of 89.78% for karst wetland, 92.25% for mangrove forest, and 92.05% for hill forest. (2) 3D mapping outperformed 2D mapping, improving mF1 by 3.43% to 27.08%. (3) MrFSNet adaptively extracted optimal features at various scales and performed well with the limited training data in 2D vegetation mapping, resulting in a 1.66%–18.46% higher mF1 than that of Swin Transformer. (4) DWS effectively resolved the extreme category imbalance problem and produced 1.28%–2.80% higher mF1 than the non-DWS version in 3D vegetation mapping.

Wetland vegetation is the basis for wetland ecosystems to regulate climate change, carbon sequestration and maintain biodiversity. Therefore, high-precision mapping and dynamic monitoring of wetland vegetation are essential for the effective management, restoration and sustainable development of wetland ecosystems. This study addressed to explore classification performance of backscattering coefficients and polarimetric decomposition parameters of multi-frequency SAR images, including single-polarimetric X-band TerraSAR (TS), full-polarimetric C-band Radarsat-2 (RS) and L-band ALOS PALSAR-2 (PS) for marsh vegetation mapping in Honghe National Nature Reserve, Northeast China. We proposed two transfer-learning strategies, and examined the feasibility of transfer-learning vegetation classifications between optical and SAR sensors, different frequencies SAR (X-, C- and L-band) and its derivative images, respectively. This paper further compared transfer-learning classification performance of marsh vegetation from polarimetric decomposition parameter images to backscattering coefficient images under the same SAR sensors. The results indicated that: (1) The three SAR images performed good classification ability in identifying marsh vegetation with the overall accuracies (OA) ranging from 0.74 to 0.88. For the same frequency SAR images, polarimetric decomposition parameters outperformed backscattering coefficients with an OA improvement ranging from 0.24 % to 2.41 %; (2) The longer wavelength SAR images produced better classification results, and L-band PS images realized the highest classification accuracy (OA = 0.871); (3) Full-polarimetric SAR images obtained the better transfer-learning classifications (OA > 0.8), and the transfer-learning classification ability of the same frequency SAR images outperformed the different frequencies; (4) The transfer learning classification results between SAR sensors outperformed that of between optical and SAR images. The results of this study provide a scientific basis for wetland change monitoring conservation and sustainable development.

Summary: By sorting receptor tyrosine kinases into endolysosomes, the endosomal sorting complexes required for transport (ESCRTs) are thought to attenuate oncogenic signaling in tumor cells. Paradoxically, ESCRT members are upregulated in tumors. Here, we show that disruption of hepatocyte growth factor-regulated tyrosine kinase substrate (HRS), a pivotal ESCRT component, inhibited tumor growth by promoting CD8+ T cell infiltration in melanoma and colon cancer mouse models. HRS ablation led to misfolded protein accumulation and triggered endoplasmic reticulum (ER) stress, resulting in the activation of the type I interferon pathway in an inositol-requiring enzyme-1α (IRE1α)/X-box binding protein 1 (XBP1)-dependent manner. HRS was upregulated in tumor cells with high tumor mutational burden (TMB). HRS expression associates with the response to PD-L1/PD-1 blockade therapy in melanoma patients with high TMB tumors. HRS ablation sensitized anti-PD-1 treatment in mouse melanoma models. Our study shows a mechanism by which tumor cells with high TMB evade immune surveillance and suggests HRS as a promising target to improve immunotherapy.

Cracks will inevitably occur in concrete structural during the normal use phase due to load action, fatigue effect, and environmental corrosion. Concrete resistance can reflect some internal concrete damage. In this work, three-point bending tests were performed on concrete beams. We simultaneously measured the concrete beam resistance before and after cracking to investigate the feasibility of resistance measurement for achieving self-monitoring of concrete cracking and crack development. This work provides a methodology for the monitoring of engineering structures. It establishes a quantitative reference for the relationship between the number of steel fibers used and their conductive and mechanically sensitive properties.

Mangrove is the highest productive ecosystems in the global coastal zone, which has high blue carbon sink function and carbon neutrality potential. Fine species classification is essential for mangrove conservation and sustainable development, and has attracted much attention in recent years using ensemble learning and multi-dimensional data. However, the current mangrove species classification based on traditional stacking ensemble learning still faces challenges due to the correlation between base classifiers, differences in meta-classifier capabilities, the subjectivity of parameter tuning, and data redundancy. To address these issues, this paper utilized unmanned aerial vehicle (UAV) multispectral images of three mangrove nature reserves in Beibu Gulf, south China, to examine the classification and generalization ability of our proposed Adaptive Stacking Ensemble Learning (ASEL) algorithm for different mangrove species. We also aim to verify the feasibility of the Multi-Path Vision Transformer for Dense Prediction (MPViT) algorithm for mangrove species mapping, and compare its performance with the ASEL algorithm for mangrove species classification. Finally, we used the SHapley Additive Explanations (SHAP) method to measure the contribution of feature variables to the model, exploring the sensitivity of different image features to mangrove species mapping. This study highlights that: (1) The two ASEL algorithms achieved high accuracy classification of mangrove species with the overall classification accuracy ranging from 79.8% to 96.2%. The ACE-Stacking and AOM-Stacking algorithms performed better classification ability than the traditional stacking algorithm, with the mean overall classification accuracy increasing from 0.9% to 3.3%. The McNemar test further indicated that the differences in classification results derived from three algorithms were significant at the 95% confidence interval, demonstrating the better classification and generalization ability of the ASEL algorithm. (2) The MPViT algorithm achieved better classification accuracy in the three reserves, with an overall accuracy of 95.5%-97.3%, which was 0.6%-5.4% higher than the two ASEL algorithms. The average accuracy of identifying mangrove species was over 95.3% in all reserves, demonstrating the desirable mangrove classification performance of MPViT algorithm. (3) The identification accuracies (F1 scores) of mangrove species in different reserves were ranged from 0.848 to 0.984. Cyperus malaccensi had the highest identification accuracy. (4) The SHAP method interpreted the great contribution of digital surface model and variable atmospherically resistant index features on mangrove species classification. The red band and ratio vegetation index was sensitive to Aegiceras corniculatum and to Cyperus malaccensi.

Karst wetlands are widely distributed throughout the southwest China, and play an important role in enhancing carbon sequestration and improving water quality in karst areas. The internationally important karst wetland of Huixian is the largest karst wetland in China, but its water quality has continued to deteriorate as a result of human influences in recent years. Remote sensing technology has become an important approach to estimate water quality parameters (WQPs). However, the feasibility of combining multi-sensor remote sensing images with deep learning to estimate different WQPs in karst wetlands has not been demonstrated yet. To resolve this issue, this study constructed multiple retrieval models of WQPs (Chlorophyll-a (Chla), Phycocyanin (PC), Turbidity (Turb), Dissolved Oxygen (DO)) in karst wetlands using deep learning (Transformer and Mixture Density Network (MDN)) and optimized shallow machine learning (Random Forest (RF), XGBoost (XGB) and Gradient Boosting (GB)) based on multi-sensor images from satellite and UAV platforms. The performance of deep learning in the inversion of WQPs demonstrated to compare with shallow machine learning using multispectral and hyperspectral images. We further quantitatively evaluated the retrieval performance of UAV and satellite, multispectral and hyperspectral images, and presented predictive mapping of the gradient distribution of WQPs. Finally, this study adopted the SHapley Additive exPlanations (SHAP) to tackle the local and global interpretability of the input features contribution to the output of retrieval models. The results showed that (1) Transformer model presented a good prediction of PC and DO (R2 = 0.649 ∼ 0.844), XGB and GB models achieved the highest accuracy estimation of Chla and Turb (R2 = 0.75). (2) The estimation results of WQPs based on UAV platform (R2 = 0.419 ∼ 0.695) was higher than that of satellite-based images. The estimation accuracy of multispectral images (R2 = 0.338 ∼ 0.718) was slightly higher than that of Zhuhai-1 Orbita hyperspectral (OHS) images. The average accuracy of Turb estimated by UAV images (R2 = 0.565 ∼ 0.752) was higher than that of satellite-based images. OHS hyperspectral images had the best DO estimation (R2 = 0.314 ∼ 0.649). (3) This study found 32.66% and 23.01% of water area with the Chla and Turb concentrations exceeding 60 μg/L and 60 NTU, respectively, which revealed that the Huixian karst wetland has suffered serious water pollution. (4) The SHAP analysis reveals that near infra-red and red band are sensitive to predict Chla and DO, red and red-edge bands are sensitive to predict PC and Turb in the karst wetland.

The travel burden for medical or dental care is a well-documented barrier to healthcare access, particularly in rural areas. There is limited research providing national estimates of the travel trends for medical/dental care, particularly among racial/ethnic groups, and among rural and urban populations. We analyzed data from the 2001, 2009, and 2017 National Household Travel Surveys. Main outcomes were the average travel distance (in miles), average travel time (in minutes), and travel burden, characterized as the percentage of trips lasting ≥ 30 miles or minutes for medical/dental care. We used ordinary least squares and multivariable logistic regressions to examine trends in the travel time/distance and travel burden, controlling for socio-demographic and travel dynamics. Among rural residents, the average travel distance for medical/dental care increased by 17.8% between 2001 and 2017, while no increase was observed among urban residents. Thirty-six percent of trips among rural residents lasted ≥ 30 minutes in 2001 but increased to 47.4% in 2017. Logistic regression estimates show that though Blacks experienced higher odds of a travel time burden compared to Whites, the burden lessened over time. In 2017, urban Blacks (OR = 0.41, 95% C.I. = 0.26,0.66), and rural Blacks (OR = 0.16, 95% C.I. = 0.05,0.55) were less likely to spend ≥ 30 minutes traveling for medical/dental care compared to Whites, using the year 2001 as the baseline. The travel distance and time for medical/dental care have increased in rural areas. However, the travel burden among rural and urban Black residents has decreased. Continuing to alleviate excess burdens of transportation may be beneficial.

A bottleneck in the development of high-strength non-oriented silicon steels for drive motors of new energy vehicles is the balance of strength and magnetic properties. This paper successfully tackled this problem through the optimization of thickness reduction for hot and cold rolling and the formation of coherent precipitates using a Cu-alloyed non-oriented silicon steel. More specifically, a thickness reduction of 96% in the course of hot rolling promoted the homogenization of microstructure during the following normalization treatment. Subsequently, a deformation degree of 80% during cold rolling ensured the inheritance of the uniform microstructure and inhibited the abnormal growth of recrystallized grains during annealing, thereby suppressing the iron loss to a value as low as 15.6 W/kg. At the meantime, this combined process promoted the inheritance of λ-fibers and the formation of {114}〈481〉 texture in the annealed sheet, improving the magnetic induction intensity B50 to 1.606 T. In addition, under the joint effect of a variety of strengthening mechanisms, the yield strength of this annealed sheet reached 668 MPa. A pronounced increase in strength, about 207 MPa, was derived from precipitation hardening by the great difference in the modulus between coherent Cu-rich clusters and the α-Fe matrix.

Amorphous metal alloy catalysts have attracted even-increasing attention owing to their high activity and selectivity. However, they are mainly prepared in ultrafine nanoparticles, which are easily gathered and challenging to separate. In this work, mesoporous Ru(Co, Ni)B bimetallic amorphous alloy catalysts with magnetic properties that can be easily recovered were synthesized via surfactant self-assembly. The as-prepared Ru1%CoB catalyst exhibited 578 μmol/g/h production rate and 99% selectivity to HCOOH during liquid phase CO2 hydrogenation owing to the unique amorphous alloy structure corresponding to the well-distributed, coordination-unsaturated, electron-enriched and synergistic active sites, which promoted the adsorption, activation, dissociation, and surface reaction of CO2 and H2. Moreover, the mesoporous channels also promoted the diffusion and adsorption of CO2 and H2. Besides, the Ru1%CoB catalyst also displayed good durability in CO2 hydrogenation owing to the robust stability against the crystallization of amorphous alloy, the collapse of mesopores, and the leaching of active sites.

Mangroves play a significant role in carbon sequestration and storage. Mapping mangrove species and monitoring their conditions have been a crucial issue for achieving sustainable development goals. Currently combing multidimensional optical and SAR images with machine learning have become an important approach for mangrove species classification, but there are still some challenges in feature selection and hyperparameter optimizations. In this study, we proposed a novel classification framework by combing multi-scale variable selection algorithm (MUVR) with state-of-the-art machine learning hyperparameter optimization method (Optuna) for mapping mangrove species in the Beilun Estuary and Maowei Sea nature reserves using optical and dual-polarization SAR images, and further quantified the scattering characteristics of mangrove species using SAR image time series. We found that: (1) The MUVR algorithm could determine the optimal scale features for different scenarios and mangrove species, and improve the classification performance of machine learning with an overall accuracy (OA) improvement of 12.85%; (2) The Optuna-based optimal CatBoost outperforms LightGBM and NGBoost algorithms in mapping mangrove species, which achieved the highest OA (93.18%). This study demonstrated that LightGBM was suitable for identifying Aegiceras corniculatum, while the CatBoost algorithm was suitable for discriminating Avicennia marina, Bruguiera gymnorrhiza, Cyperus malaccensis, Kandelia candel and Sonneratia apetala; (3) SAR images and its derivatives improved identification ability of mangrove species, and collaboration of multispectral images and SAR-derived features produced the better classification; (4) From 2018 to 2020, the backscattering coefficients of mangrove species in VV and VH polarization focused on 0.053–0.327 and 0.015–0.062, respectively. The coherence coefficients of mangroves displayed a seasonal change trend with the large variations in summer and small variations in winter. The range of Entropy and Alpha of mangrove species was from 0.65 to 0.88 and 17–33, which indicated that the main scattering mechanism of mangroves was moderate random surface scattering.

Heart failure (HF) is one of the diseases that lead to high mortality of patients. Finding natural compounds with cardioprotective properties and identifying their action mechanisms is of great importance. In this research, the effect of propolis and triblock copolymer nano-vesicles loaded with propolis were studied in Wistar rats HF model. After preparing propolis and Polylactide-block-poly (ethylene glycol)-block-polylactide (PLA-PEG-PLA) loaded with propolis using thin film hydration method, nanoparticles were identified using transmission electron microcopy (TEM) and Zetasizer techniques. Induction of HF in animals was done with 85 mg/kg isoproterenol for 2 consecutive days and 200 mg/kg/day of both propolis and PLA-PEG-PLA nano-vesicle loaded with propolis were administered to rats by oral gavage one week before HF induction and one week after that. On the last day, echocardiography parameters were measured and then, with blood sampling and plasma preparation, the levels of biochemical parameters such as B-type natriuretic peptide (BNP), Galectin-3, interleukin 1β (IL-1β), interleukin 6 (IL-6), interleukin 10 (IL-10), interleukin (IL-17), reduced AMP-activated protein kinase (AMPK) and silent information regulator (SIRT1) were measured using Enzyme-linked immunosorbent assay (ELISA) technique. Both propolis and PLA-PEG-PLA nano-vesicle loaded with propolis led to improvement of echocardiographic parameters in HF rats. Also, the administration of these two compounds decreased the expression of inflammatory cytokines IL-1, IL-6 and IL-17 and increased the expression of IL-10 in HF rats. The levels of SIRT1 and AMPK in HF rats receiving propolis and PEG-PLA loaded with propolis were upregulated compared to HF control ones. In conclusion, it can be indicated that propolis and PEG-PLA loaded with propolis have cardio protection effects through decreasing inflammation and activation of SIRT1-AMPK axis.

There has been evidence of rising HIV incidence attributable to opioid misuse within some areas of the U.S. The purpose of our study was to explore national trends in co-occurring HIV and opioid-related hospitalizations and to identify their risk factors. We used the 2009–2017 National Inpatient Sample to indicate hospitalizations with co-occurring HIV and opioid misuse diagnoses. We estimated the frequency of such hospitalizations per year. We fitted a linear regression to the annual HIV-opioid co-occurrences with year as a predictor. The resulting regression did not reveal any significant temporal changes. We used multivariable logistic regression to determine the adjusted odds (AOR) of hospitalization for co-occurring HIV and opioid-related diagnoses. The odds of hospitalization were lower for rural residents (AOR = 0.28; CI = 0.24–0.32) than urban. Females (AOR = 0.95, CI = 0.89–0.99) had lower odds of hospitalization than males. Patients identifying as White (AOR = 1.23, CI = 1.00–1.50) and Black (AOR = 1.27, CI = 1.02–1.57) had higher odds of hospitalization than other races. When compared to co-occuring hospitalizations in the Midwest, the odds were higher in the Northeast. (AOR = 2.56, CI = 2.07–3.17) Future research should explore the extent to which similar findings occur in the context of mortality and targeted interventions should intesify for subpopulations at highest risk of co-occuring HIV and opioid misuse diagnoses.

Creep aging forming (CAF) is a potential process to manufacture large integral components of Al–Li alloys, which exhibits poor formability and susceptibility to damage and cracking via traditional plastic forming processes. Al–Li alloys also have disadvantages, such as low creep and comprehensive properties.To address this problem, this study adopted pre-aging as a treatment process before creep aging (CA). The effects of pre-aging at different temperatures (65 °C, 90 °C, and 115 °C) on the CA of Al–Li alloys were evaluated at 155 °C for 18 h. Creep strain, the evolution of mechanical properties, and the microstructural characteristics were investigated as well. The results showed that pre-aging could effectively influence the subsequent CA behavior. As the pre-aging temperature increased, creep strain accumulation during CA decreased. Meanwhile, pre-aging significantly increased the elongation (EL) of the specimens after CA; Notably, the increase in EL did not compromise strength. Changes in the microstructure of the specimens after pre-aging and CA tests were observed by transmission electron microscopy. The effect of pre-aging on the subsequent CA properties mainly revealed that the strength was not lower than that of the specimen without pre-aging after the same CA test through adjusting the percentage and size of the T1 and θ′ phases. In addition, pre-aging could promote the transformation of the fracture mechanism from intergranular fracture to mixed fracture in the specimen after CA.

The anisotropy in creep aging behavior and microstructural evolution of AA2195-T34 A l–Li alloy plates under different stress levels at 180 °C were systematically investigated by constant-stress creep aging, mechanical property testing and microstructure characterization. The results indicated that the creep strain initially increases and then decreases with the angle relative to the rolling direction by cause of the divergences in the dislocation density and the number of movable sliding systems. The creep strain in 45° direction is 1.15 times and 1.41 times larger than that in 0° and 90° directions at a stress of 160 MPa, respectively. The creep strain in-plane anisotropy (IPA) value first increases rapidly, followed by a slight decrease with time, and declines with stress at the primary creep stage. The strength first increases and then tends to fluctuate around a constant value with the aging time because of the evolution of T1 precipitates. As shown by the gradual reduction in the difference among the volume fractions of the brass texture of the alloy in the three directions, the strength IPA rapidly declined at the primary creep stage. The IPA values of the yield strength during the steady creep stage are 4.8%, at a stress of 160 MPa. Since the Schmidt factor of the material in the 45° direction is the largest, the yield strength in this direction is the lowest. A constitutive model considering the anisotropy of creep deformation and aging strengthening was established, of which the predicted results showed excellent consistency with the experimental values. The relative errors between the predicted and experimental values of the yield strength/steady creep strain in different directions do not exceed 1.7% and 4.3%, respectively.

Rural Healthy People is a companion piece to the federal Healthy People initiative released once a decade to identify the most important Healthy People priorities for rural America, as identified by rural stakeholders, for the current decade. This study reports on the findings of Rural Healthy People 2030. The study relied on a survey of rural health stakeholders collected from July 12, 2021, to February 14, 2022, and: 1) identified the 20 Healthy People priorities most frequently selected as priorities for rural America, 2) studied the priorities that were most frequently selected as a “top 3” priority within each Healthy People 2030 category, and 3) investigated Healthy People 2030 priorities in terms of ranked importance for rural Americans. The analysis finds that for the first time across 3 decades of Rural Healthy People, a greater proportion of respondents selected “Mental Health and Mental Disorders” and “Addiction” as Healthy People priorities for rural America, than did “Health Care Access and Quality”. Even still, respondents ranked “Health Care Access and Quality” as the single-most important rural priority. “Economic Stability,” a new priority within the Social Determinant of Health category, debuted within the 10 most frequently selected priorities for rural America for the coming decade. As public health practitioners, researchers, and policymakers work toward closing the urban–rural divide, the most important rural priorities to address in the coming decade are mental health and substance use disorders, access to high quality health care services, and social determinants of health, such as economic stability.

In sediment environments, manganese (Mn) minerals have high dissolved organic matter (DOM) affinities, and could regulate the changes of DOM constituents and reactivity by fractionation. However, the effects of DOM fractionation by Mn minerals on the contaminant behaviors remain unclear. Herein, the transformations of mineral phases, DOM properties, and Cd(II) binding characteristics to sediment DOM before and after adsorption by four Mn oxides (δ-MnO2, β-MnO2, γ-MnOOH, and Mn3O4) were investigated using multi-spectroscopic tools. Results showed a subtle structural variation of Mn oxides in response to DOM reduction, and no phase transformations were observed. Two-dimensional correlation spectroscopy based on synchronous fluorescence spectra and Fourier transform infrared spectroscopy indicated that tryptophan-like substances and the amide (II) N–H groups could preferentially interact with Cd(II) for the original DOM. Nevertheless, preferential bonding of Cd(II) to tyrosine-like substances and phenolic OH groups was exhibited after fractionations by Mn oxides. Furthermore, the binding stability and capacity of each DOM fraction to Cd(II) were decreased after fractionation based on the modified Stern-Volmer equation. These differences may be attributed to DOM molecules with high aromaticity, hydrophobicity, molecular weight, and amounts of O/N-containing group were preferentially removed by Mn oxides. Overall, the environmental hazard of Cd will be more severe after DOM fractionation on Mn minerals. This study facilitates a better understanding of the Cd geochemical cycle in lake sediments under the DOM–mineral interactions, and recommends being careful with outbreaks of aquatic Cd pollution when sediments are rich in dissolved protein-like components and Mn minerals.

The creep deformation behavior, aging hardening response and microstructure evolution of 2A14 aluminum alloy (Al-4.81wt%Cu-0.66wt%Mg-0.97wt%Si) at different temperatures have been systematically investigated by creep test, tensile test, transmission electron microscopy and differential scanning calorimetry. The results show that the total creep strain and steady-state creep rate significantly increase with increasing temperature in the range of 408 K–458 K, while the creep behaviors at the optimum aging temperature of 433 K have shortest primary creep stage and lowest stress sensitivity. Pinning of dislocations by the dense and fine Q′ and θ′ phases precipitated in the alloy creep-aged at 433 K, which contributes to obtaining the best mechanical properties. Also, the primary creep deformation mechanism at different aging temperatures has been discussed. A reference temperature describing the degree of the interaction between aging precipitates and dislocations is introduced into the hyperbolic-sine creep model. The creep strain predicted through the temperature-dependent creep model is in good agreement with the experimental data. The research can provide theoretical support for the temperature-varying creep age forming application in the 2A14 aluminum alloy components.

The thawing of permafrost on the Qinghai-Tibet Plateau (QTP) leads to more frequent occurrences of thaw slump (TS), which have significant impacts on local ecosystems, carbon cycles, and infrastructure development. Accurate recognition of TS would help in understanding its occurrence and evolution. Machine learning capabilities for TS recognition are still not fully exploited. We systematically evaluate the performance of machine learning models for TS recognition from unmanned aerial vehicle (UAV) and propose an ensemble learning object-based model for TS recognition (EOTSR). The EOTSR has the following advantages: 1) pioneering the introduction of spatial information to assist in recognition; 2) the misclassification of recognition models is improved by object-based technology; and 3) attempting to integrate the strengths of different machine learning models to obtain a recognition accuracy no less than that of commonly used deep learning models. The results show that object-based technology is more suitable for TS recognition than pixel-based technology. Recursive feature elimination (RFE)-based feature selection proves that texture and geometry are effective complements to TS recognition. Among the improved object-based machine learning models, support vector machine (SVM) has the highest recognition accuracy, with an overall accuracy of 93.06 %. McNemar’s test proves that EOTSR significantly improves TS recognition compared to a single model and achieves an overall accuracy of 97.32 %. The EOTSR model provides an effective recognition method for the increasingly frequent TS events in the permafrost regions of the QTP, and can produce label data for deep learning models based on satellite imagery.

Cancer cells predominantly adapt the frequent but less efficient glycolytic process to produce ATPs rather than the highly efficient oxidative phosphorylation pathway. Such a regulated metabolic pattern in cancer cells offers promising therapeutic opportunities to kill tumors by glucose depletion or glycolysis blockade. In addition, to guarantee tumor-specific therapeutic targets, effective tumor-homing, accumulation, and retention strategies toward tumor regions should be elaborately designed. In the present work, genetically engineered tumor-targeting microbes (transgenic microorganism EcM-GDH (Escherichia coli MG1655) expressing exogenous glucose dehydrogenase (GDH) have been constructed to competitively deprive tumors of glucose nutrition for metabolic intervention and starvation therapy. Our results show that the engineered EcM-GDH can effectively deplete glucose and trigger pro-death autophagy and p53-initiated apoptosis in colorectal tumor cells/tissues both in vitro and in vivo. The present design illuminates the promising prospects for genetically engineered microbes in metabolic intervention therapeutics against malignant tumors based on catalytically nutrient deprivation, establishing an attractive probiotic therapeutic strategy with high effectiveness and biocompatibility.

Skeletal muscle is a regulator of the body's energy expenditure and metabolism. Abnormal regulation of skeletal muscle-specific genes leads to various muscle diseases. Long non-coding RNAs (lncRNAs) have been demonstrated to play important roles in muscle growth and muscle atrophy. To explore the potential function of muscle-associated lncRNA, we analyzed our previous RNA-sequencing data and selected the lncRNA (LncEDCH1) as the research object. In this study, we report that LncEDCH1 is specifically enriched in skeletal muscle, and its transcriptional activity is positively regulated by transcription factor SP1. LncEDCH1 regulates myoblast proliferation and differentiation in vitro. In vivo, LncEDCH1 reduces intramuscular fat deposition, activates slow-twitch muscle phenotype, and inhibits muscle atrophy. Mechanistically, LncEDCH1 binds to sarcoplasmic/ER calcium ATPase 2 (SERCA2) protein to enhance SERCA2 protein stability and increase SERCA2 activity. Meanwhile, LncEDCH1 improves mitochondrial efficiency possibly through a SERCA2-mediated activation of the AMPK pathway. Our findings provide a strategy for using LncEDCH1 as an effective regulator for the treatment of muscle atrophy and energy metabolism.

This study combined gas chromatography-mass spectrometry (GC-MS) and odor activity value (OAV) assessments to analyze the volatile ingredient changes in formula powder during 11 months of storage at room temperature. Orthogonal partial least squares-discriminant analysis (OPLS-DA) and variable importance in projection (VIP) were also used to assess the potential indicators of aroma changes during storage. The aim was to expose the possible changes in the aroma profile and the substances that alter infant formula (IF) aroma during storage. The results showed that the aldehyde and ketone content in the milk formula increased as the storage time was extended, while the lactone and terpenoid levels decreased significantly. The OAV indicated the presence of various major aromatic substances at different storage times. Considering consumer concern regarding product flavor, this study ascertained that monitoring substance changes during storage showed that 2-heptanone was a good indicator of milk flavor, dimethyl disulfide was a suitable indicator of protein degradation, and 3-methylbutanal, heptanal, hexanal, pentanal, and octanal were all good indicators of fat oxidation. The results of previous related studies were used to supplement the data in this work regarding the changes in IF during product shelf life and to provide support for controlling the flavor substances and quality during new product development.

ABSTRACT: Carcass traits play important roles in the broiler industry and single nucleotide polymorphism (SNP) can be efficient molecular markers for marker-assisted breeding of chicken carcass traits. Based on our previous RNA-seq data (accession number GSE58755), cysteine rich with epidermal growth factor like domains 1 (CRELD1) and DnaJ heat shock protein family member C30 (DNAJC30) are differentially expressed in breast muscle between white recessive rock chicken (WRR) and Xinghua chicken (XH). In this study, we further characterize the potential function and SNP mutation of CRELD1 and DNAJC30 in chicken for the first time. According to protein interaction network and enrichment analysis, CRELD1 and DNAJC30 may play some roles in chicken muscle development and fat deposition. In WRR and XH, the results of the relative tissue expression pattern demonstrated that CRELD1 and DNAJC30 are not only differentially expressed in breast muscle but also leg muscle and abdominal fat. Therefore, we identified 5 SNP sites of CRELD1 and 7 SNP sites of DNAJC30 and genotyped them in an F2 chicken population. There are 4 sites of CRELD1 and 3 sites of DNAJC30 are associated with chicken carcass traits like breast muscle weight, body weight, dressed weight, leg weight percentage, eviscerated weight with giblet percentage, intermuscular adipose width, shank length, and girth. These results suggest that the SNP sites of CRELD1 and DNAJC30 can be potential molecular markers to improve the chicken carcass traits and lay the foundation for marker-assisted selection.

Lakes potentially provide sensitive indicators of global climate change, but water pollution seriously affects the lake ecosystem. Therefore, mapping and monitoring water quality of lake has attracted extensive attention from scholars. However, estimating optically active (OAPs) and non-optically water quality parameters (NOAPs) and clarifying their response relationship with hydro-meteorology still face great challenges. To resolve these issues, this paper presents an approach for assessing OAPs (Chlorophyll a (Chla), Total suspended matter (TSM)) and NOAPs (total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH4-N), five-day biochemical oxygen demand (BOD5)) in Poyang Lake, the largest freshwater lake in China, by using ensemble learning (SEL), combing leave-one-out validation and machine learning (LOOCV-ML) algorithms with multi-source remote sensing images. We further quantitatively evaluated retrieval accuracy differences between OAPs and NOAPs using multispectral and hyperspectral images. Meanwhile, a novel automatic extraction method was proposed in this paper for mapping water bodies. We systematically explored the response relationship between water quality (OAP and NOAP) and hydro-meteorology. We found that NOAPs achieved higher estimation accuracy than OAPs. The LOOCV-XGBoost and LOOCV-GradientBoost provided higher inversion accuracy of OAP and NOAP than SEL, demonstrating the great potential of combining OHS-2A hyperspectral images with LOOCV-ML to estimate the water quality in inland lakes. We revealed that the estimation accuracy (MRE) of TN and TP was 1.09 %-18.03 % higher than NH4-N and BOD5. Finally, we clarified that water temperature had a significantly negative effect on NH4-N and BOD5 (Projected importance of variables (VIP) > 1, Coefficient 1, Coefficient > 1), further revealing that water temperature, water body area, wind speed, rainfall, mean relative humidity and sunshine hours were important divers of OAPs and NOAPs changes. These results provide a scientific basis for restoration and protection of water quality in lake ecosystems.