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Abstract Background Anemia frequently occurs in patients with hip fractures and represents a risk factor that can potentially be altered. To evaluate the association between admission anemia and complications in older hip fracture patients while exploring the potential impact of anemia on complications from the perspective of overall, operation and non-operation. Methods This retrospective study enrolled in-patients over 60 years old with hip fractures from January 2020 to November 2023. At admission, anemic patients were identified as having a hemoglobin level below 12 g/dL in females and 13 g/dL in males. Anemia was further classified as mild, moderate, or severe. Data encompassing demographics, comorbidities, medications, information on fracture and surgery, and complications were collected. Results A total of 462/679 patients had anemia, including 348, 105, and 9 with mild, moderate, and severe anemia, respectively. A total of 281 individuals experienced complications, including 212 and 69 with and without operation, respectively. Multivariate regression analysis identified anemia as a greater risk for acute heart failure (OR = 2.056, p = 0.037, 95% CI 1.043–4.052) than non-anemia. Moderate to severe anemia was a significant risk factor for any complication (OR = 1.584, p = 0.028, 95% CI 1.050–2.390), ≥ 2 (OR = 2.364, p = 0.001, 95% CI 1.443–3.872) or 3 (OR = 2.311, p = 0.022, 95% CI 1.131–4.720) complications, delirium (OR = 2.301, p = 0.018, 95% CI 1.156–4.579), venous thromboembolism (OR = 2.031, p = 0.042, 95% CI 1.025–4.025), and acute heart failure (OR = 2.095, p = 0.016, 95% CI 1.145–3.834), compared with mild to non-anemia. Similar results were observed in operated patients, while anemia and its severity were not associated with complications in non-operated patients. Conclusion Moderate to severe anemia caused complications in elderly hip fracture patients, but it was not observed in non-operated individuals. These findings would support orthopedic physicians’ hierarchical management of anemic patients.

Abstract After thermal treatment, quail eggs pruduce an attractive flavor that is favored by consumers. In this study, the key aroma volatiles and their lipid precursors during thermal treatment were investigated by the electronic nose, GC-MS, GC-O-MS and UPLC-Q-Exactive HF-X. The results exhibited that the raw and fresh flavor of raw egg yolks came from 1-Octen-3-ol, 2-Ethyl-1-hexanol, 1-Decene, and 1-Undecene. 2-Methyl-3-octanone, Toluene, and some aromatic compounds worked synergistically to contribute to the aroma profile of boiled eggs. (+)-2-Bornanone, Octanal, 2-Methyl-butanal, Nonanal, (.+Dihydro-3-hydroxy-4,4-dimethyl-2(3 H)-furanone, 6,7-Dihydro-2,5-dimethyl-5 H-cyclopentapyrazine, (E)-2-methyl-6-(1-propenyl)-pyrazine, 2-Ethyl-3,5-dimethyl-pyrazine, 3,5-Diethyl-2-methyl-pyrazine, 2,5-Dimethyl-pyrazine were the key flavor compounds in the fried egg and gave it the popcorn and roasted flavors. The statistical analysis of the lipid profile revealed that brief, high-temperature heating (100 or 200 °C for 10 min) in typical boiled and fried quail eggs had minor effects on the lipid nutritional value. PE-related lipids, particularly those containing 18-carbon fatty acids, contributed to the aroma formation of fried quail eggs. Graphical Abstract

In recent years, various long non-coding RNAs (lncRNAs) involved in DNA damage response (DDR) have been identified and studied to deepen our understanding. However, there are rare reports on the association between lncRNAs and base excision repair (BER). Our designed DNA microarray identified dozens of functionally unknown lncRNAs, and their transcription levels significantly increased upon exposure to DNA damage inducers. One of them, named LIP (Long noncoding RNA Interacts with PARP-1), exhibited a significant alteration in transcription in response to methyl methanesulfonate (MMS) and temozolomide (TMZ) treatments. LIP knockdown or knockout cell lines are sensitive to MMS and TMZ, indicating that LIP plays a crucial role in DDR. The loss or insufficiency of LIP significantly influences the efficiency of BER in human cells, and it suggests that LIP participates in the BER pathway. The interaction between LIP and a key factor in BER, poly (ADP-ribose) polymerase 1 (PARP-1), has been confirmed. We identified and characterized LIP, a lncRNA, which is involved in DDR, significantly influences BER efficiency, and interacts with the BER key factor PARP-1. This advances our understanding of the connection between lncRNAs and BER, presenting the potential for the discovery of new drug targets.

Abstract All-perovskite tandem solar cells have shown great promise in breaking the Shockley–Queisser limit of single-junction solar cells. However, the efficiency improvement of all-perovskite tandem solar cells is largely hindered by the surface defects induced non-radiative recombination loss in Sn–Pb mixed narrow bandgap perovskite films. Here, we report a surface reconstruction strategy utilizing a surface polishing agent, 1,4-butanediamine, together with a surface passivator, ethylenediammonium diiodide, to eliminate Sn-related defects and passivate organic cation and halide vacancy defects on the surface of Sn–Pb mixed perovskite films. Our strategy not only delivers high-quality Sn–Pb mixed perovskite films with a close-to-ideal stoichiometric ratio surface but also minimizes the non-radiative energy loss at the perovskite/electron transport layer interface. As a result, our Sn–Pb mixed perovskite solar cells with bandgaps of 1.32 and 1.25 eV realize power conversion efficiencies of 22.65% and 23.32%, respectively. Additionally, we further obtain a certified power conversion efficiency of 28.49% of two-junction all-perovskite tandem solar cells.

This is an article in the field of mining processing engineering. This article takes Dechang Daluchao rare earth mineral processing tailings as the research object, conducts a beneficiation test study on fluorspar recovery, and determines the beneficiation process flow and reagent of "flotation desulphurization - mixed flotation - flotation separation of fluorite, strontium and barium" for fluorite recovery. In the flotation test, the new MQY collector and DC-2 high-efficiency inhibitor were used to efficiently capture fluorite while achieving precise separation of fluorite and gangue minerals. In the final full-process test, a fluorspar concentrate with a CaF2 grade of 94.39% and a recovery rate of 51.41% was obtained. The test indicators were relatively ideal. This article tested and verified the feasibility of recovering fluorspar from the Dalunchao rare earth tailings. The final beneficiation process is reasonable and has good test indicators. It can be used as a technical basis for the utilization and recovery of fluorspar resources in the Dalunchao rare earth tailings.

Abstract Background The increased resistance rate of Salmonella to third-generation cephalosporins represented by ceftriaxone (CRO) may result in the failure of the empirical use of third-generation cephalosporins for the treatment of Salmonella infection in children. The present study was conducted to evaluate a novel method for the rapid detection of CRO-resistant Salmonella (CRS). Methods We introduced the concept of the ratio of optical density (ROD) with and without CRO and combined it with matrix-assisted laser desorption–ionization time-of-flight mass spectrometry (MALDI-TOF MS) to establish a new protocol for the rapid detection of CRS. Results The optimal incubation time and CRO concentration determined by the model strain test were 2 h and 8 µg/ml, respectively. We then conducted confirmatory tests on 120 clinical strains. According to the receiver operating characteristic curve analysis, the ROD cutoff value for distinguishing CRS and non-CRS strains was 0.818 [area under the curve: 1.000; 95% confidence interval: 0.970–1.000; sensitivity: 100.00%; specificity: 100%; P

Abstract Decellularized extracellular matrix hydrogel (ECM hydrogel), a natural material derived from normal tissue with unique biocompatibility properties, is widely used for tissue repair. However, there are still problems such as poor biological activity and insufficient antimicrobial property. To overcome these drawbacks, fibroblast growth factor 2 (FGF 2) containing exosome (exoFGF 2) was prepared to increase the biological activity. Furthermore, the antimicrobial capacity of ECM hydrogel was optimised by using copper ions as a ligand-bonded cross-linking agent. The decellularized extracellular matrix hydrogel, intricately cross-linked with copper ions through ligand bonds and loaded with FGF 2 containing exosome (exoFGF 2@ECM/Cu2+ hydrogel), has demonstrated exceptional biocompatibility and antimicrobial properties. In vitro, exoFGF 2@ECM/Cu2+ hydrogel effectively promoted cell proliferation, migration, antioxidant and inhibited bacterial growth. In vivo, the wound area of rat treated with exoFGF 2@ECM/Cu2+ hydrogels were significantly smaller than that of other groups at Day 5 (45.24% ± 3.15%), Day 10 (92.20% ± 2.31%) and Day 15 (95.22% ± 1.28%). Histological examination showed that exoFGF 2@ECM/Cu2+ hydrogels promoted angiogenesis and collagen deposition. Overall, this hydrogel has the potential to inhibit bacterial growth and effectively promote wound healing in a variety of clinical applications.

Abstract Hyperuricemia (HUA), a metabolic disease caused by excessive production or decreased excretion of uric acid (UA), has been reported to be closely associated with a variety of UA transporters. Clerodendranthus spicatus (C. spicatus) is an herbal widely used in China for the treatment of HUA. However, the mechanism has not been clarified. Here, the rat model of HUA was induced via 10% fructose. The levels of biochemical indicators, including UA, xanthine oxidase (XOD), adenosine deaminase (ADA), blood urea nitrogen (BUN), and creatinine (Cre), were measured. Western blotting was applied to explore its effect on renal UA transporters, such as urate transporter1 (URAT1), glucose transporter 9 (GLUT9), and ATP-binding cassette super-family G member 2 (ABCG2). Furthermore, the effect of C. spicatus on plasma metabolites was identified by metabolomics. Our results showed that C. spicatus could significantly reduce the serum levels of UA, XOD, ADA and Cre, and improve the renal pathological changes in HUA rats. Meanwhile, C. spicatus significantly inhibited the expression of URAT1 and GLUT9, while increased the expression of ABCG2 in a dose-dependent manner. Metabolomics showed that 13 components, including 1-Palmitoyl-2-Arachidonoyl-sn-glycero-3-PE, Tyr-Leu and N-cis-15-Tetracosenoyl-C18-sphingosine, were identified as potential biomarkers for the UA-lowering effect of C. spicatus. In addition, pathway enrichment analysis revealed that arginine biosynthesis, biosynthesis of amino acids, pyrimidine metabolism and other metabolic pathways might be involved in the protection of C. spicatus against HUA. This study is the first to explore the mechanism of anti-HUA of C. spicatus through molecular biology and metabolomics analysis, which provides new ideas for the treatment of HUA.

Abstract Objective Previous studies have shown a clear link between insulin resistance (IR) and an elevated risk of atrial fibrillation (AF). However, the relationship between the estimated glucose disposal rate (eGDR), which serves as a marker for IR, and the risk of AF recurrence after radiofrequency catheter ablation (RFCA) remains uncertain. Therefore, this study aimed to examine the potential association between the eGDR and the risk of AF recurrence following RFCA. Methods This retrospective study was conducted at Nanchang University Affiliated Second Hospital. The study enrolled 899 patients with AF who underwent RFCA between January 2015 and January 2022. The formula used to calculate the eGDR was as follows: 19.02 − (0.22 * body mass index) − (3.26 * hypertension) − (0.61 * HbA1c). Cox proportional hazard regression models and exposure–effect curves were used to explore the correlation between the baseline eGDR and AF recurrence. The ability of the eGDR to predict AF recurrence was evaluated using the area under the receiver operating characteristic curve (AUROC). Results The study observed a median follow-up period of 11.63 months, during which 296 patients experienced AF recurrence. K‒M analyses revealed that the cumulative incidence AF recurrence rate was significantly greater in the group with the lowest eGDR (log-rank p

Abstract Generally, the recognition performance of lightweight models is often lower than that of large models. Knowledge distillation, by teaching a student model using a teacher model, can further enhance the recognition accuracy of lightweight models. In this paper, we approach knowledge distillation from the perspective of intermediate feature-level knowledge distillation. We combine a cross-stage feature fusion symmetric framework, an attention mechanism to enhance the fused features, and a contrastive loss function for teacher and student models at the same stage to comprehensively implement a multistage feature fusion knowledge distillation method. This approach addresses the problem of significant differences in the intermediate feature distributions between teacher and student models, making it difficult to effectively learn implicit knowledge and thus improving the recognition accuracy of the student model. Compared to existing knowledge distillation methods, our method performs at a superior level. On the CIFAR100 dataset, it boosts the recognition accuracy of ResNet20 from 69.06% to 71.34%, and on the TinyImagenet dataset, it increases the recognition accuracy of ResNet18 from 66.54% to 68.03%, demonstrating the effectiveness and generalizability of our approach. Furthermore, there is room for further optimization of the overall distillation structure and feature extraction methods in this approach, which requires further research and exploration.

Ultra-high dose rate radiotherapy (FLASH radiation) can naturally render normal tissues around the tumor tissue resistant to radiotherapy. In contrast, the tumor tissue remains sensitive to radiation under the same conditions. However, the effects of different fractions and dose rates on FLASH radiation remain unclear. This study aimed to determine the optimal dose rate and fraction of FLASH radiation for thoracic radiotherapy. Female Balb/c mice aged 6–8 weeks were irradiated with different dose rates (100 Gy/s or 250 Gy/s) and fractions (1, 2, or 4). Survival was observed in mice receiving 30Gy, with lung tissue examined for acute radiation damage 48 h post-radiation. Late radiation pneumonia and survival rates were monitored in mice irradiated with 20 Gy. The median overall survival (OS) was not reached on the 95th day for mice irradiated with 250 Gy/s FLASH radiation, while it was 89.5 days for those irradiated with 100 Gy/s (P = 0.0436). Mice irradiated with 30 Gy/2 Fr and 250 Gy/s FLASH had shorter median OS than those with 30 Gy/1F (P = 0.0132). However, there was no significant difference in OS between mice irradiated with 30 Gy/2 F and 30 Gy/4 F. Survival curves for mice receiving 20 Gy showed no significant difference in toxicity between different dose rates and fractions. FLASH radiation at 250 Gy/s reduced the incidence of acute radiation pneumonitis in mice compared to 100 Gy/s. Different fractions of irradiation influenced survival in mice, but they were only observed in acute radiation reactions and not chronic radiation reactions. Among the tested fraction methods, fraction 2 had the worst impact on the survival of mice, while fractions 1 and 4 showed similar effects and improved survival compared to fraction 2.

Soil biodiversity and the structure of soil animal communities are important foundations for forest ecosystem functions. Forest gap formation is an important forest management practice used to transform monocultures into mixed forests. However, whether and how gap size and age affect soil biodiversity and modify nematode communities remains limited. We manipulated gap size (100, 200, and 400 m2) in Pinus massoniana plantations and studied the communities of soil nematodes, bacteria, fungi, and understory plants two and four years after gap formation. Compared to the no-gap treatment, soil nematode abundance across forest gaps increased by a factor of 1.40, which was largely attributed to the increase in herbivorous nematodes as the abundance and diversity of understory plants increased. The increased abundance of soil nematodes in forest gaps was also associated with increased soil pH presumably related to reduced input of pine needles. Furthermore, the abundance (−5.3 %) and diversity (−25.1 %) of soil nematodes decreased with gap age, presumably because of increased soil temperature and decreased soil moisture in the four- compared to the two-year-old gaps. In contrast to nematodes, the abundance and diversity of soil bacteria (21.8 % and 7.1 %) and fungi (10.5 % and 10.0 %) increased significantly with gap age. Overall, forest gaps increased the diversity of understory plants and soil biota, and changed the community and functional group structure of soil nematodes. These results provide guidelines for fostering soil biodiversity and maintaining soil functioning when transforming coniferous forests into mixed forests.

ObjectiveThis study aims to evaluate whether radiomic features of pericoronary adipose tissue (PCAT) derived from coronary computed tomography angiography (CCTA) can better predict major adverse cardiovascular events (MACE) in patients with angina pectoris.MethodsA single-center retrospective study included 239 patients with angina pectoris who underwent coronary CT examinations. Participants were divided into MACE (n = 46) and non-MACE (n = 193) groups based on the occurrence of MACE during follow-up, and further allocated into a training cohort (n = 167) and a validation cohort (n = 72) at a 7:3 ratio. Automatic segmentation of PCAT surrounding the proximal segments of the left anterior descending artery (LAD), left circumflex coronary artery (LCX), and right coronary artery (RCA) was performed for all patients. Radiomic features of the coronary arteries were extracted, screened, and integrated while quantifying the fat attenuation index (FAI) for the three vessels. Univariate and multivariate logistic regression analyses were utilized to select clinical predictors of adverse cardiovascular events. Subsequently, machine learning techniques were employed to construct models based on FAI, clinical features, and radiomic characteristics. The predictive performance of each model was assessed and compared using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis for clinical utility.ResultsThe radiomics model demonstrated superior performance in predicting MACE in patients with angina pectoris within both the training and validation cohorts, yielding areas under the curve (AUC) of 0.83 and 0.71, respectively, which significantly outperformed the FAI model (AUC = 0.71, 0.54) and the clinical model (AUC = 0.81, 0.67), with statistically significant differences in AUC (p 0.05). Decision curve analysis indicated that the radiomics model provided higher clinical benefit than the traditional clinical and FAI models.ConclusionThe CCTA-based PCAT radiomics model is an effective tool for predicting MACE in patients with angina pectoris, assisting clinicians in optimizing risk stratification for individual patients. The CCTA-based radiomics model significantly surpasses traditional FAI and clinical models in predicting major adverse cardiovascular events in patients with angina pectoris.

The self-organizing biological characteristics of vegetation and human activities lead to the disruption of the continuous spatial attributes of natural watersheds, which are significant factors affecting river wetland ecosystems. To clarify the landscape ecological flow characteristics of vegetation patch distribution and fragmentation, this study used the three-dimensional Reynolds stress turbulence model in ANSYS Fluent software. The model considered three vegetation patch coverages under two different submersion states and four fragmentation types of vegetation patches under the same coverage conditions within specific vegetation areas. The flow characteristics of longitudinally discontinuous rigid vegetation patches, occupying half of the width of the river channel, were numerically simulated. The model’s applicability was verified by indoor open-channel flume experiments. The results indicated that: (1) The streamwise velocity in vegetated areas is significantly lower than in non-vegetated areas, and the difference in flow capacity between vegetated and non-vegetated areas increases with patch coverage and fragmentation degree. (2) In the non-submerged state, the maximum Reynolds stress in the vegetated area is located at the bottom of the vegetation and is negatively correlated with patch coverage but positively correlated with fragmentation degree. In the submerged state, the maximum Reynolds stress is located near the top of the canopy and is positively correlated with both patch coverage and fragmentation degree. (3) The longitudinal turbulent kinetic energy in the vegetated area is significantly higher than in the non-vegetated area. In the non-submerged state, the turbulent kinetic energy in the vegetated area is negatively correlated with patch coverage but positively correlated with fragmentation degree. In the submerged state, the turbulent kinetic energy of the longitudinal distribution in the free layer of the vegetated area is positively correlated with patch coverage, negatively correlated with fragmentation degree, and is only reflected in the upstream vegetation area.

Although China has successfully addressed absolute poverty, poor areas still face the challenge of reducing relative poverty while achieving sustainable ecological and economic development. A comprehensive sustainability assessment of poverty alleviation from multiple perspectives is crucial. However, existing studies have paid little attention to specific poverty alleviation measures and their ecological impacts, and lack a uniform unit of measurement. In this study, a comprehensive system for sustainability assessment of poverty alleviation is established via quantitative emergy analysis methods. Emergy accounting was conducted for two business development-based poverty alleviation programs in the Tibetan area of the Northwest Sichuan Plateau, China, from three perspectives: production processes, multidimensional poverty reduction, and ecosystem services. Emergy indices were calculated to evaluate sustainability. The results indicate that the emergy of ecosystem services and poverty reduction has significantly increased in both programs, demonstrating their substantial impact on poverty alleviation and ecological enhancement. According to the emergy indices, the emergy sustainability index increased by 0.48 and 2.04, respectively, and the ecological poverty reduction index reached 8.83 and 4.64, respectively, indicating that both programs have become more sustainable. This study highlights the comprehensive benefits of poverty alleviation in Tibetan areas and provides insights into sustainability assessments that incorporate ecosystem services.

Contemporary research on the walking environment focuses closely on the construction logic and internal correlation. Walkability is one of the vital characteristics of the old town street space. To understand how to improve the old town street space effectively, the investigation of the correlation mechanism of street walkability is essential. This study utilizes structural equation model (SEM) to construct a street walkability measurement model composed of four unobserved factors. Then, take Old Southern Area in Nanjing as an example, integrate Depthmap, ArcGIS and Python to obtain multi-source data, and establish a database of observed factors on street space. Finally, the matrix of the observed factors is set by SEM to calculate the correlation of the unobserved factors. This paper provides a novel technical approach for the correlation study of spatial construction logic as well as a reference for strengthening the spatial quality of the contemporary built environment.

We investigated the value of plasma cytokine levels as markers of pathogenesis and treatment response in patients with non-tuberculous mycobacteria (NTM) pulmonary disease. Plasma cytokine levels were measured and compared among patients with NTM pulmonary disease (n=111), tuberculosis (TB) patients (n=50), and healthy individuals (n=40). Changes during treatment were monitored at 3 and 6 months after treatment. According to the treatment response, NTM patients were classified as ‘resistance' or ‘sensitivity' responders. The results revealed that five out of twelve cytokines exhibited significantly higher levels in NTM patients compared to controls. Among these, interleukin (IL)-6 demonstrated the strongest discriminating capacity for NTM. Furthermore, when combined with IL-1β, they efficiently distinguished between NTM drug-resistant and drug-sensitive patients, as well as between NTM and TB groups. Additionally, IL-6 levels initially rose and then decreased in the NTM drug-resistant group during the six months of treatment, similar to the behavior of IL-1β in the NTM drug-sensitive group. Subgroup analyses of the sensitive group with differential treatment responses revealed an increase in IL-10 levels in the six-month treatment responders. A high IL-6/IL-10 ratio was associated with increased disease severity of NTM and TB. Collectively, combinations of various plasma cytokines, specifically IL-1β, IL-6, and IL-10, effectively distinguished NTM patients with varying mycobacterial burdens, with IL-6 and IL-10 emerging as potential biomarkers for early treatment response. The combination of IL-6 and IL-1β demonstrated the highest discriminatory value for distinguishing between NTM-resistant and NTM-sensitive groups as well as between NTM and TB groups.

Summary: The histone lysine methyltransferase SUV420H1 preferentially targets the H2A.Z-containing nucleosome core particle (H2A.Z-NCP) and catalyzes the H4K20me2 modification at replication origins. Here, we present a protocol for preparing SUV420H1 in complex with the nucleosome containing H2A.Z and H4K20Ecx for structure determination. We describe steps for the installation of S-ethyl-cysteine (Ecx), nucleosome and complex preparation, and performing the cryoelectron microscopy (cryo-EM) sample check. This protocol substitutes lysine 20 in histone H4 with S-ethyl-cysteine (H4K20Ecx), which enhances the stability of the interaction between SUV420H1 and nucleosomes.For complete details on the use and execution of this protocol, please refer to Huang et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Introduction Frailty has become a worldwide health burden that has a large influence on public health and clinical practice. The incidence of frailty is anticipated to increase as the ageing population increases. Myocardial injury after noncardiac surgery (MINS) is associated with short-term and long-term mortality. However, the incidence of MINS in frail geriatric patients is unknown. Methods and analysis This prospective, multicentre, real-world observational cohort study will be conducted at 18 designated centres in China from January 2023 to December 2024, with an anticipated sample size of 856 patients aged 65 years and older who are scheduled to undergo noncardiac surgery. The primary outcome will be the incidence of MINS. MINS is defined as a fourth-generation plasma cardiac troponin T (cTnT) concentration ≥ 0.03 ng/mL exhibited at least once within 30 days after surgery, with or without symptoms of myocardial ischaemia. All data will be collected via electronic data acquisition. Discussion This study will explore the incidence of MINS in frail patients. The characteristics, predictive factors and 30-day outcomes of MINS in frail patients will be further investigated to lay the foundation for identifying clinical interventions. Clinical trial registration https://beta.clinicaltrials.gov/study/NCT05635877 , NCT05635877.

Staphylococcus argenteus is a novel species within the Staphylococcus aureus complex and can cause serious bloodstream infections (BSIs) in humans, which have been mainly reported in adults, especially the elderly. In this study, we analyzed the molecular characterization of a strain of S. argenteus (22WJ8192) isolated from the peripheral vein blood sample of a seven-month-old female infant in Eastern China. The 22WJ8192 belonged to sequence type (ST)2250 and harbored six antibiotic-resistance genes and 53 virulence genes and was resistant to penicillin. Additionally, we conducted a comparative analysis of the molecular characteristics of S. argenteus sourced from various origins within the dataset, predominantly from the National Center for Biotechnology Information Collection (NCBI) genome database. Antibiotic-resistance genes blaR1, blaI_of_Z, blaZ, fosB-Saur, tet(L), aph(3”)-IIIa, mecA, and dfrG were more prevalent among the strains of human origin. Virulence genes lukF-PV, sak, sdrE, scn, sdrC, and sdrD were more prevalent among strains of human origin. The presence of antibiotic-resistance genes blaR1, blaI_of_Z, blaZ, fosB-Saur, and aph(3”)-IIIa in strain 22WJ8192 was also more common among strains of human origin in the dataset. Conversely, the antibiotic-resistance genes tet(L), mecA, and dfrG, typically found in strains of human origin, were not detected in 22WJ8192. Additionally, virulence genes lukF-PV, sak, sdrE, scn, sdrC, and sdrD present in 22WJ8192 exhibited a higher prevalence among strains of human origin in the dataset. In conclusion, this study emphasizes the potential of S. argenteus ST2250 to induce severe bloodstream infections in infants, shedding light on the molecular characteristics of this strain.

The chemokine co-receptors CXCR4 and CCR5 mediate HIV entry and signal transduction necessary for viral infection. However, to date only the CCR5 antagonist maraviroc is approved for treating HIV-1 infection. Given that approximately 50% of late-stage HIV patients also develop CXCR4-tropic virus, clinical anti-HIV CXCR4 antagonists are needed. Here, we describe a novel allosteric CXCR4 antagonist TIQ-15 which inhibits CXCR4-tropic HIV-1 infection of primary and transformed CD4 T cells. TIQ-15 blocks HIV entry with an IC50 of 13 nM. TIQ-15 also inhibits SDF-1α/CXCR4-mediated cAMP production, cofilin activation, and chemotactic signaling. In addition, TIQ-15 induces CXCR4 receptor internalization without affecting the levels of the CD4 receptor, suggesting that TIQ-15 may act through a novel allosteric site on CXCR4 for blocking HIV entry. Furthermore, TIQ-15 did not inhibit VSV-G pseudotyped HIV-1 infection, demonstrating its specificity in blocking CXCR4-tropic virus entry, but not CXCR4-independent endocytosis or post-entry steps. When tested against a panel of clinical isolates, TIQ-15 showed potent inhibition against CXCR4-tropic and dual-tropic viruses, and moderate inhibition against CCR5-tropic isolates. This observation was followed by a co-dosing study with maraviroc, and TIQ-15 demonstrated synergistic activity. In summary, here we describe a novel HIV-1 entry inhibitor, TIQ-15, which potently inhibits CXCR4-tropic viruses while possessing low-level synergistic activities against CCR5-tropic viruses. TIQ-15 could potentially be co-dosed with the CCR5 inhibitor maraviroc to block viruses of mixed tropisms.

We synthesized a core-shell resin structure with abundant architecture and excellent thermal stability through polymerization. Imidazole ionic liquid with varying carbon chain lengths was immobilized on the surface, resulting in the preparation of metal-free, halogen-free core-shell catalysts with different carbon chain lengths via HCO3- exchange. Characterization using FT-IR, XPS, and various techniques revealed the exceptional performance of our synthetic catalyst in terms of its internal structure. After extensive experimentation, we discovered that the synthesized catalyst exhibits dual functionality for epoxidation and CO2 cycloaddition reactions without requiring solvents or co-catalysts. The epoxidation system demonstrated remarkable conversion rates and selectivity while also exhibiting strong recyclability in heterogeneous reactions, according to kinetic parameters, the reaction order of styrene, TBHP and catalyst during epoxidation is approximately 1, the reference factor for this reaction was calculated to be 6.7×109 (L2·mol−2·min−1), with an activation energy of 48.9 kJ/mol obtained from analyzing reaction rates at different temperatures. In the CO2 cycloaddition reaction, our catalyst exhibited an advantage in catalyzing ring-opening reactions, achieving a conversion rate of 95 % for styrene oxide within six hours along with over 99 % selectivity towards cyclic carbonate formation. It is suggested that the epoxide reaction is carried out in steps, and it is inferred that the catalyst PS-ImC4HCO3 and TBHP are produced into peroxy intermediate active species TBA and HCO4, and the reaction between HCO4- and styrene is the determination step of the total reaction. The reaction rate constant k=0.009 of the absolute step is calculated based on the global optimization algorithm

Objective: To explore the effective targets of Celecoxib in the treatment of heterotopic ossification using network pharmacology methods. Methods: Potential molecules related to heterotopic ossification were obtained by retrieving the GEO and CTD databases and intersecting them. Potential binding targets of Celecoxib were acquired from the STITCH database. A protein-protein interaction network was constructed between potential binding targets of Celecoxib and potential related molecules of heterotopic ossification using the STRING database. Molecules in the protein-protein interaction network were further analyzed using GO and KEGG enrichment analysis in R software, followed by enrichment analysis of active molecules in the Celecoxib-heterotopic ossification target dataset. Hub genes were selected based on the “degree” value and enrichment within the protein-protein interaction network. The binding affinity of hub genes to Celecoxib was observed using molecular docking techniques. Finally, in vitro experiments were conducted to validate the effectiveness of hub genes and explore their regulatory role in the progression of heterotopic ossification. Additionally, the therapeutic effect of Celecoxib, which modulates the expression of the hub genes, was investigated in the treatment of heterotopic ossification. Results: 568 potential molecules related to heterotopic ossification and 76 potential binding targets of Celecoxib were identified. After intersection, 13 potential functional molecules in Celecoxib's treatment of heterotopic ossification were obtained. KEGG analysis suggested pathways such as Rheumatoid arthritis, NF-kappa B signaling pathway, Pathways in cancer, Antifolate resistance, MicroRNAs in cancer play a role in the treatment of heterotopic ossification by Celecoxib. Further enrichment analysis of the 13 potential functional molecules identified 5 hub genes: IL6, CCND1, PTGS2, IGFBP3, CDH1. Molecular docking results indicated that Celecoxib displayed excellent binding affinity with CCND1 among the 5 hub genes. Experimental validation found that CCND1 is highly expressed in the progression of heterotopic ossification, promoting heterotopic ossification in the early stages and inhibiting it in the later stages, with Celecoxib's treatment of heterotopic ossification depending on CCND1. Conclusion: In the process of treating heterotopic ossification with Celecoxib, immune and inflammatory signaling pathways play a significant role. The therapeutic effect of Celecoxib on heterotopic ossification depends on the hub gene CCND1, which plays different roles at different stages of the progression of heterotopic ossification, ultimately inhibiting the occurrence of heterotopic ossification.

Osteoarthritis (OA) is a prevalent chronic disease, characterized by chronic inflammation and cartilage degradation. This study aims to deepen the understanding of OA's pathophysiology and to develop novel therapeutic strategies. Our study underscores the pivotal role of Epiphycan (EPYC) and the IL-17 signaling pathway in OA. EPYC, an essential extracellular matrix constituent, has been found to exhibit a positive correlation with the severity of OA. We have discovered that EPYC modulates the activation of the IL-17 signaling pathway within chondrocytes by regulating the interaction between IL-17A and its receptor, IL-17RA. This regulatory mechanism underscores the intricate interplay between the extracellular matrix and immune signaling in the pathogenesis of OA Another finding of our study is the therapeutic effectiveness of protocatechualdehyde (PAH) in OA. PAH significantly reduces chondrocyte hypertrophy and supports cartilage tissue recovery.by targets EPYC. To reduce the side effects of orally administered PAH and maintain its effective drug concentration, we have developed a decellularized matrix hydrogel loaded with PAH for intra-articular injection. This novel drug delivery system is advantageous in minimizing drug-related side effects and ensuring sustained release PAH within the joint cavity.

The dual active bridge (DAB) converter is a power electronic device commonly used for DC voltage regulation and stabilization. However, during its control process, external disturbances, load variations, input voltage variations, switching tube voltage drops, dead time, etc. lead to errors in the control output, thus reducing the control accuracy of the system. Therefore, this article propose a robust control scheme for the output voltage based on uncertainty and disturbance estimator. In this article, an average small-signal model of the dual active bridge converter was established in terms of the basic principles and operation mechanisms, simplifying the controller’s design. Then, the basic principles of the uncertainty and disturbance estimator (UDE) are introduced. The small-signal model of the dual active bridge (DAB) converter is applied to the UDE to minimize output voltage error by enabling the controller to directly regulate the shift ratio. Finally, this article discusses the application and effectiveness of the uncertainty and disturbance estimator (UDE) in the simulation and control of dual active bridge (DAB) converters. A series of experimental comparative studies are conducted, demonstrating that this scheme offers significant advantages in suppressing system uncertainties and disturbances.

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to scarcity of surface water and over-pumping of groundwater. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed an enhanced methodology for estimating Groundwater Storage change (GWSC). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment (and Follow-on)/Global Land Data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparisons based on well water levels and residual statistics of equivalent water thickness (EWT) validate the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSC, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSC significantly enhances the equal lon-lat tile of 1/4-degree (∼27 km) from CSR GRACE/GRACE-FO Mascon solution (CSR-M) to within 1 km in aquifers with known prior hydrogeological information. Furthermore, the geographic patterns analysis of GWSC in HNP using standard deviational ellipse (SDE) reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction.

Maintaining a balance between growth needs and available food resources is critical to the development of any organism. Ecological stoichiometry provides a theoretical basis for studying stoichiometric mismatches between organisms and their food resources. Recent studies have shown that detritivore taxa occupying different multidimensional stoichiometric niches (MSNs) exhibit different stoichiometric mismatch patterns. However, changes in stoichiometric mismatch patterns of different functional groups within the same taxon in response to changes in food resources are poorly understood. Here, we investigated changes in the stoichiometric mismatch between litter as major food resource and five functional groups of soil detritivores (millipedes; Diplopoda) for eleven elements along an altitudinal gradient from 800 to 1850 m on Changbai Mountain, Northeast China. We quantified MSNs and multidimensional stoichiometric mismatch patterns, and investigated changes with altitude and relationships with body size and population density. The concentration of chemical elements of litter varied significantly with altitude and this also was true for the trophic stoichiometric mismatch in millipedes. Furthermore, millipede stoichiometry homeostasis resulted in changing stoichiometric mismatches with changing litter element ratios. Element concentrations in litter, specifically N, Ca, P, Na and Cu, impacted the body size and population density of millipedes, with different elements correlating with individual functional groups. The results indicate that consumer-resource stoichiometric mismatches significantly affect detritivore populations, highlighting the predictive power of the multidimensional stoichiometry framework for detritivore community composition in context of environmental change.

IntroductionDiabetic nephropathy (DN) is the leading cause of end-stage renal disease. Due to its complex pathogenesis, new therapeutic agents are urgently needed. Orthosiphon aristatus (Blume) Miq., commonly known as kidney tea, is widely used in DN treatment in China. However, the mechanisms have not been fully elucidated.MethodsWe used db/db mice as the DN model and evaluated the efficacy of kidney tea in DN treatment by measuring fasting blood glucose (FBG), serum inflammatory cytokines, renal injury indicators and histopathological changes. Furthermore, 16S rDNA gene sequencing, untargeted serum metabolomics, electron microscope, ELISA, qRT-PCR, and Western blotting were performed to explore the mechanisms by which kidney tea exerted therapeutic effects.ResultsTwelve polyphenols were identified from kidney tea, and its extract ameliorated FBG, inflammation and renal injury in DN mice. Moreover, kidney tea reshaped the gut microbiota, reduced the abundance of Muribaculaceae, Lachnoclostridium, Prevotellaceae_UCG-001, Corynebacterium and Akkermansia, and enriched the abundance of Alloprevotella, Blautia and Lachnospiraceae_NK4A136_group. Kidney tea altered the levels of serum metabolites in pathways such as ferroptosis, arginine biosynthesis and mTOR signaling pathway. Importantly, kidney tea improved mitochondrial damage, increased SOD activity, and decreased the levels of MDA and 4-HNE in the renal tissues of DN mice. Meanwhile, this functional tea upregulated GPX4 and FTH1 expression and downregulated ACSL4 and NCOA4 expression, indicating that it could inhibit ferroptosis in the kidneys.ConclusionOur findings imply that kidney tea can attenuate DN development by modulating gut microbiota and ferroptosis, which presents a novel scientific rationale for the clinical application of kidney tea.

Abstract Background Stress hyperglycemia ratio (SHR) has recently been recognized as a novel biomarker that accurately reflects acute hyperglycemia status and is associated with poor prognosis of heart failure. We evaluated the relationship between SHR and clinical outcomes in patients with severe aortic stenosis receiving transcatheter aortic valve replacement (TAVR). Methods There were 582 patients with severe native aortic stenosis who underwent TAVR consecutively enrolled in the study. The formula used to determine SHR was as follows: admission blood glucose (mmol/L)/(1.59×HbA1c[%]–2.59). The primary endpoint was defined as all-cause mortality, while secondary endpoints included a composite of cardiovascular mortality or readmission for heart failure, and major adverse cardiovascular events (MACE) including cardiovascular mortality, non-fatal myocardial infarction, and non-fatal stroke. Multivariable Cox regression and restricted cubic spline analysis were employed to assess the relationship between SHR and endpoints, with hazard ratios (HRs) and 95% confidence intervals (CIs). Results During a median follow-up of 3.9 years, a total of 130 cases (22.3%) of all-cause mortality were recorded. Results from the restricted cubic spline analysis indicated a linear association between SHR and all endpoints (p for non-linearity > 0.05), even after adjustment for other confounding factors. Per 0.1 unit increase in SHR was associated with a 12% (adjusted HR: 1.12, 95% CI: 1.04–1.21) higher incidence of the primary endpoint, a 12% (adjusted HR: 1.12, 95% CI: 1.02–1.22) higher incidence of cardiovascular mortality or readmission for heart failure, and a 12% (adjusted HR: 1.12, 95% CI: 1.01–1.23) higher incidence of MACE. Subgroup analysis revealed that SHR had a significant interaction with diabetes mellitus with regard to the risk of all-cause mortality (p for interaction: 0.042). Kaplan-Meier survival analysis showed that there were significant differences in the incidence of all endpoints between the two groups with 0.944 as the optimal binary cutoff point of SHR (all log-rank test: p

Abstract The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network to boost the energy efficiency and speed of the image compression/retrieval and improve the storage density. We adopt the 4-bit memristor arrays to experimentally demonstrate the functions of the system. We propose a step-by-step quantization aware training scheme and an equivalent transformation for transpose convolution to improve the system performance. The system exhibits a high (>33 dB) peak signal-to-noise ratio in the compression and decompression of the ImageNet and Kodak24 datasets. Benchmark comparison results show that the 4-bit memristor-based storage system could reduce the latency and energy consumption by over 20×/5.6× and 180×/91×, respectively, compared with the server-grade central processing unit-based/the graphics processing unit-based processing system, and improve the storage density by more than 3 times.

With the advent of Industry 4.0 era, smart manufacturing is rapidly developing, which puts higher requirements on data acquisition (DAQ). Currently, enterprises face problems in data acquisition such as a variety of equipment types, equipment heterogeneity, customized development, high costs, and so on. To solve this problem, this paper proposes an innovative approach to address the challenges in data acquisition by combining You Only Look Once version 8 (YOLOv8) and Optical Character Recognition (OCR) technology. First, the data area to be collected is labeled, and then the YOLOv8 model is trained. The model with the shortest recognition time and highest efficiency is selected with comparable detection effects to fully guarantee real-time data collection. Second, to ensure the accuracy of data collection, the OCR model is trained a second time using the dataset, improving performance compared to the most effective PaddleOCR by 8 percentage points. Additionally, considering the aging and weak computational capacity of individual devices, this study adopts a client/server architecture, deploying the computational load required for recognition to the server to achieve generalization, stability, and reliable operation of the system. Lastly, the scheme is tested on 16 types of 30 devices.

Abstract To explore the relationship between miR-373 and the occurrence and development of colorectal cancer. Additionally, it aims to predict the potential cellular signaling pathways and regulatory mechanisms in which miR-373 may be involved and provides a theoretical basis and experimental evidence for the clinical application of miR-373 as a potential biomarker, molecular target, and prognostic indicator in colorectal cancer. Real-time quantitative PCR is used to analyze the expression of miR-373 in human colorectal cancer cell lines and normal human colonic epithelial cells. Further validation of the differential expression of miR-373 in colorectal cancer cell lines is being performed. Biological functions such as cell proliferation, invasion and apoptosis are being detected by MTT, CCK-8, transwell, cell cycle analysis, and flow cytometry experiments to verify the changes in the biological behavior of colon cancer cells after overexpression and interference of miR-373 in SW-480 cells and to explore the effects of miR-373 on cell proliferation, invasion, and apoptosis in colon cancer cells. Proteomic analysis is being conducted on proteins extracted from miR-373 overexpressing SW480 cells, and mass spectrometry is used for protein identification. GO, KEGG, and enrichment analysis are being employed to analyze the significantly differentially expressed proteins. The expression levels of pathway-related proteins are being verified using Western blot. Overexpression of miR-373 increased the invasive and metastatic ability of SW-480 cells; knockdown of miR-373 decreased the invasive and metastatic ability of SW-480 cells. However, there was no statistically significant effect on cell proliferation and apoptosis in SW-480 cells. Proteomic analysis identified 78 differentially expressed proteins based on fold change (FC) > 1.2 and P

Abstract Background Dairy cows are at high risk of fatty liver disease in early lactation, but current preventative measures are not always effective. Cows with fatty liver have lower circulating branched-chain amino acid (BCAA) concentrations whereas cows with high circulating BCAA levels have low liver triglyceride (TG). Our objective was to determine the impact of BCAA and their corresponding ketoacids (branched-chain ketoacids, BCKA) on production performance and liver TG accumulation in Holstein cows in the first 3 weeks postpartum. Methods Thirty-six multiparous Holstein cows were used in a randomized block design experiment. Cows were abomasally infused for the first 21 d postpartum with solutions of 1) saline (CON, n = 12); 2) BCA (67 g valine, 50 g leucine, and 34 g isoleucine, n = 12); and 3) BCK (77 g 2-ketovaline calcium salt, 57 g 2-ketoleucine calcium salt, and 39 g 2-ketoisoleucine calcium salt, n = 12). All cows received the same diet. Treatment effects were determined using PROC GLIMMIX in SAS. Results No differences were detected for body weight, body condition score, or dry matter intake averaged over the first 21 d postpartum. Cows receiving BCK had significantly lower liver TG concentrations compared to CON (6.60% vs. 4.77%, standard error of the mean (SEM) 0.49) during the first 3 weeks of lactation. Infusion of BCA increased milk yield (39.5 vs. 35.3 kg/d, SEM 1.8), milk fat yield (2.10 vs. 1.69 kg/d, SEM 0.08), and lactose yield (2.11 vs. 1.67 kg/d, SEM 0.07) compared with CON. Compared to CON, cows receiving BCA had lower plasma glucose (55.0 vs. 59.2 mg/dL, SEM 0.86) but higher β-hydroxybutyrate (9.17 vs. 6.00 mg/dL, SEM 0.80). Conclusions Overall, BCAA supplementation in this study improved milk production, whereas BCKA supplementation reduced TG accumulation in the liver of fresh cows. Graphical Abstract

Abstract Background Engrailed homeobox 1 (EN1) is a candidate oncogene that is epigenetically modified in salivary adenoid cystic carcinoma (SACC). We investigated the expression of EN1 in SACC tissues and cells, EN1 promoter methylation, and the role of EN1 in tumour progression in SACC. Methods Thirty-five SACC samples were screened for key transcription factors that affect tumour progression. In vitro and in vivo assays were performed to determine the viability, tumorigenicity, and metastatic ability of SACC cells with modulated EN1 expression. Quantitative methylation-specific polymerase chain reaction analysis was performed on SACC samples. Results EN1 was identified as a transcription factor that was highly overexpressed in SACC tissues, regardless of clinical stage and histology subtype, and its level of expression correlated with distant metastasis. EN1 promoted cell invasion and migration through epithelial-mesenchymal transition in vitro and enhanced SACC metastasis to the lung in vivo. RNA-seq combined with in vitro assays indicated that EN1 might play an oncogenic role in SACC through the PI3K-AKT pathway. EN1 mRNA levels were negatively correlated with promoter hypermethylation, and inhibition of DNA methylation by 5-aza-dC increased EN1 expression. Conclusions The transcription factor EN1 is overexpressed in SACC under methylation regulation and plays a pivotal role in SACC progression through the PI3K-AKT pathway. These results suggest that EN1 may be a diagnostic biomarker and a potential therapeutic target for SACC.

While many studies have compared global precipitation datasets at national, continental, and global scales, few have evaluated these data at river basin scales. This study explored differences in precipitation estimates and trends of 12 widely applied precipitation datasets, including gauge-, satellite-, and reanalysis-based products, for the world's 6,292 river basins. Results showed that disparities between 12 precipitation datasets were considerable. A total of 3,125 river basins, with a land area of 5,989.1×104 km2, had differences in estimated annual average precipitation exceeding 500 mm yr−1, and these basins were mainly distributed in Greenland, Africa, Oceania, and West Asia. Disparities between the precipitation datasets were particularly large during the dry season when the percentage difference between the highest and lowest precipitation estimates exceeded 500% in 1,390 river basins (4,839.7×104 km2) expected due to numerical reasons. Differences in rainfall trends also varied markedly between data sources. The data products do not agree on precipitation trends for all river basins. These findings illustrate the importance of accurate precipitation data to ensure effective policy and planning in term of hydropower generation, domestic water supply, flood protection, and drought relief at river basin scales and highlight the uncertainty that exists in current global precipitation data. HIGHLIGHTS A comprehensive evaluation of global precipitation datasets was compared at the river basin scale.; Seasonal and annual differences in these precipitation datasets were compared.; Gives a sufficient explanation of which product is suitable for which river basin.;

In this paper, an age of information (AoI) oriented data collection scheme is proposed for wireless sensor networks with energy-constrained unmanned aerial vehicles (UAVs). Different from traditional approaches that focus on one single UAV, we advocate the activation of multiple candidate UAVs to improve the AoI performances. However, coordinating multiple UAVs complicates the data collection strategies of each UAV, rendering AoI minimization a formidable challenge. To address the intractable formulated optimization problem, we propose a heuristic two-step algorithm based on greedy search for multiple UAVs. Specifically, we address the coordination of multiple UAVs through a novel algorithm grounded in graph theory and kernel K-means methods. Concurrently, we devise an energy-constrained trajectory planning algorithm to optimize data collection for each activated UAV. The numerical results validate the accuracy of the proposed algorithm, and demonstrate that both average AoI and energy consumption per UAV can be reduced when more energy-constrained UAVs are involved in the data collection process for wireless sensor networks.

ABSTRACT Gestational diabetes mellitus (GDM) is a pathological condition during pregnancy characterized by impaired glucose tolerance, and the failure of pancreatic beta‐cells to respond appropriately to an increased insulin demand. However, while the majority of women with GDM will return to normoglycemia after delivery, they have up to a seven times higher risk of developing type 2 diabetes during midlife, compared with those with no history of GDM. Gestational diabetes mellitus also increases the risk of multiple metabolic disorders, including non‐alcoholic fatty liver disease, obesity, and cardiovascular diseases. Lipid metabolism undergoes significant changes throughout the gestational period, and lipid dysregulation is strongly associated with GDM and the progression to future type 2 diabetes. In addition to common lipid variables, discovery‐based omics techniques, such as metabolomics and lipidomics, have identified lipid biomarkers that correlate with GDM. These lipid species also show considerable potential in predicting the onset of GDM and subsequent type 2 diabetes post‐delivery. This review aims to update the current knowledge of the role that lipids play in the onset of GDM, with a focus on potential lipid biomarkers or metabolic pathways. These biomarkers may be useful in establishing predictive models to accurately predict the future onset of GDM and type 2 diabetes, and early intervention may help to reduce the complications associated with GDM.

Abstract The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM10, in the latter sets does not improve overall performance, potentially due to multicollinearity between PM10 and PM2.5 variables. The proposed methodology provides valuable insights into soft sensor modeling for air ozone prediction.Among the 72 sensors, sensor NNR[Y]C outperforms all other evaluated sensors, demonstrating exceptional predictive performance with an impressive R2 of 0.8902, low RMSE of 24.91, and remarkable MAE of 19.16. With a prediction accuracy of 81.44%, sensor NNR[Y]C is reliable and suitable for various technological applications.

The normalized difference vegetation index (NDVI) is the most widely used vegetation index for monitoring vegetation vigor and cover. As NDVI time series are usually derived at coarse or medium spatial resolutions, pixel size often represents a mixture of vegetated and non-vegetated surfaces. In heterogeneous urban areas, mixed pixels impede the accurate estimation of gross primary productivity (GPP). To address the mixed pixel effect on NDVI time series and GPP estimation, we proposed a framework to extract subpixel vegetation NDVI (NDVIvege) from Landsat OLI images in urban areas, using endmember fractions, mixed NDVI (NDVImix), and NDVI of non-vegetation endmembers. Results demonstrated that the NDVIvege extracted by this framework agreed well with the true NDVIvege cross seasons and vegetation fractions, with R2 ranging from 0.74 to 0.82 and RMSE ranging from 0.03 to 0.04. The NDVIvege time series was applied to evaluate vegetation GPP in Wuhan, China. The total annual GPP estimated with NDVIvege was 28-35% higher than the total annual GPP estimated with NDVImix, implying uncertainty in the GPP estimations caused by mixed pixels. This study showed the potential of the proposed framework to resolve NDVIvege for characterizing vegetation dynamics in heterogeneous areas.

The development of automation and intelligence in geological core drilling is not yet mature. The selection and improvement of drilling parameters rely mainly on experience, and adjustments are often made after drilling by evaluating the core, which introduces a lag and reduces the drilling efficiency. Therefore, this study first establishes a geological core drilling experiment platform to collect drilling data. Through the constructed geological core drilling experimental platform, the practical data at the drill bit can be directly obtained, solving the problem of the data from the surface equipment in the practical drilling differing from the practical data. Second, the back propagation (BP) algorithm is used to perform the ROP prediction, with weight on bit (WOB), torque (TOR), flow rate (Q), and rotation speed (RPM) as input parameters, and rate of penetration (ROP) as the output. Subsequently, correlation analysis is used to perform the feature parameter optimization, and the effects of bit wear and bit cutting depth on the experiment are considered. Finally, comparison with algorithms such as ridge regression, SVM and KNN shows that the ROP prediction model using the BP neural network has the highest prediction accuracy of 94.1%. The results provide a reference for ROP prediction and the automation of geological core drilling rigs.

Contemporary experiments often involve special factors known as branching factors. The levels of such factors determine the presence of some certain factors, referred to as nested factors. The design criteria for investigating the goodness of such designs are rarely developed. Furthermore, the existing criteria for such designs pay less attention to the space-filling property of low-dimensional projections of the design. The efficiencies of designs yielded by such criteria can markedly decrease when only a few factors are significant. To address this issue, this paper proposes a novel space-filling criterion based on the maximum projection criterion to evaluate the performance of the designs with branching and nested factors. A framework to construct optimal designs under the proposed criterion is also provided. Compared with the existing works, the resulting designs have better space-filling properties in all possible low-dimensional projections. Moreover, our strategy imposes no constraints on run size, level, and type of any factor, demonstrating its broad applicability.

Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively. However, CNNs struggle in modeling long-term dependencies, and transformers may miss subtle spatial-spectral features. To address these challenges, this paper proposes an innovative hybrid HSI classification method aggregating hierarchical spatial-spectral features from a CNN and long pixel dependencies from a transformer. The proposed aggregation multi-hierarchical feature network (AMHFN) is designed to capture various hierarchical features and long dependencies from HSI, improving classification accuracy and efficiency. The proposed AMHFN consists of three key modules: (a) a Local-Pixel Embedding module (LPEM) for capturing prominent spatial-spectral features; (b) a Multi-Scale Convolutional Extraction (MSCE) module to capture multi-scale local spatial-spectral features and aggregate hierarchical local features; (c) a Multi-Scale Global Extraction (MSGE) module to explore multi-scale global dependencies and integrate multi-scale hierarchical global dependencies. Rigorous experiments on three public hyperspectral image (HSI) datasets demonstrated the superior performance of the proposed AMHFN method.

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in the fields of heat reservoir exploitation and geological exploration owing to its advantages of high efficiency and low pollution. However, the vibration near the bit is up to 40 g while DTH hammer drilling, which significantly affects the performance and longevity of the near-bit measurement while drilling (MWD). To enhance the environmental adaptability of the near-bit MWD in pneumatic DTH operations, a design method for a vibration-damping system based on the parameter optimization of a non-dominated sorting genetic algorithm II (NSGA-II) is proposed in this study. First, the whole structure of the near-bit MWD is designed, including the MWD sub-shell, sensors, measurement circuits, batteries, and connecting structures (the circuit unit). Secondly, this study analyzes the vibration characteristics of the pneumatic DTH hammer near the bit. According to the damping structure, the vibration response model for the circuit unit and the damping model are established. Thirdly, NSGA-II is employed to optimize the parameters of the damping model in terms of the low-frequency, high-intensity vibration characteristics near the bit in pneumatic DTH operations, thereby devising a damping scheme tailored to the unique conditions of DTH hammer drilling. Finally, vibration experiments were conducted to verify the effectiveness of the vibration-damping device. The experimental results indicate that within the vibration frequency range of 5–20 Hz and vibration level of 10–40 g, the peak attenuation rate of the circuit unit is more than 86.446%, and the improvement rate of the vibration stability of the system is more than 75.214%; the anti-vibration performance of the near-bit MWD system in DTH hammer drilling is improved remarkably. This study provides strong technical support for the stability of MWD equipment under such special working conditions. It has broad engineering application prospects.

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models and analyze the key geometric information in the G-code generated during CNC machining, and it uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bézier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. The effectiveness of the proposed method is validated through the optimization of process files for gear models, pentagram bosses, and maple leaf models. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths and reduce machining defects, offering substantial application value.

FERMT2 has been identified as a participant in integrin-linked kinase signaling pathways, influencing epithelial-mesenchymal transition and thereby affecting tumor initiation, progression, and invasion. While the character of FERMT2 in the tumor microenvironment (TME) as well as its implications for immunotherapy remain unclear. Thus, we conducted a comprehensive analysis to assess the prognostic significance of FERMT2 using Kaplan-Meier analysis. In addition, we employed enrichment analysis to uncover potential underlying molecular mechanisms. Using ''Immunedeconv'' package, we evaluated the immune characteristics of FERMT2 within TME. Furthermore, we determined the expression levels of FERMT2 in various cell types within TME, based on single-cell sequencing data. To confirm the co-expression of FERMT2 and markers of cancer-associated fibroblasts (CAFs), we performed multiplex immunofluorescence staining on tissue paraffin sections across various cancer types. Our analysis disclosed a significant correlation between elevated FERMT2 expression and unfavorable prognosis in specific cancer types. Furthermore, we identified a strong correlation between FERMT2 expression and diverse immune-related factors, including immune checkpoint molecules, immune cell infiltration, microsatellite instability (MSI), and tumor mutational burden (TMB). Additionally, there was a significant correlation between FERMT2 expression and immune-related pathways, particularly those associated with activating, migrating, and promoting the growth of fibroblasts in diverse cancer types. Interestingly, we observed consistent co-expression of FERMT2 in both malignant tumor cells and stromal cells, particularly within CAFs. Notably, our findings also indicated that FERMT2, in particular, exhibited elevated expression levels within tumor tissues and co-expressed with α-SMA in CAFs based on the multiplex immunofluorescence staining results.

Abstract Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood–brain barrier (BBB) permeability is predicted and the anti‐inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean‐DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂‐induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti‐neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.

As the high carbon emission entities in the city, laying photovoltaic (PV) panels for public buildings is an effective way to reduce building carbon emissions. Meanwhile, public buildings play an important role as charging station access for the explosive growth of electric vehicles. However, the disorderly charging behavior of single-phase charging piles exacerbates the existing three-phase unbalance inside the buildings, which in turn affects operating costs and PV consumption. Energy storage system (ESS) configuration is considered an effective solution. Thus, An ESS configuration strategy is proposed for public buildings aiming at PV local consumption and three-phase unbalance management. To quantify the correlation between multiple loads and PV output, an improved affinity-propagation clustering algorithm based on the spatial weighted matrix distance is developed to obtain operational typical power supply-demand modes. Based on the construction of the three-phase power supply system with both single-phase and three-phase ESSs, a bi-level planning model is formulated for the configuration and operation optimization of ESSs inside the public building. The upper-level problem aims to minimize the life cycle cost of ESS allocation. The lower-level model deals with the coordinated economic scheduling of single-phase ESS and three-phase ESS under the obtained typical operational modes. Numerical results show the effectiveness and rationality of the proposed clustering algorithm and ESS configuration strategy.