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A coaxially integrated macro-micro composite actuator (MMCA) with large stroke and high accuracy is proposed by combining a voice coil motor (VCM) with the giant magnetostrictive actuator (GMA). The magnetic circuit model of the macro-motion part is established based on the driving principle of VCM, and the multi-field coupling model of the micro-motion part is established based on the Jiles-Atherton model. The finite element method was used to analyze the relationship between displacement, output force, velocity, acceleration, and time of the macro-motion part under different currents, the magnetic flux density, output force, and displacement curves of the micro-motion part, and the mutual influence between the macro and micro motion parts were analyzed. The prototype of the MMCA was developed, and an experimental test platform was built. The results show that the MMCA macro-motion displacement curve can fit the simulation curve well during open-loop positioning, and when the current size of the input macro-motion coil is 4 A, the experimental curve of the MMCA is the most consistent with the simulation curve. When closed-loop control, the motion curve of the drive can well follow the set displacement curve, in which the maximum stroke of the prototype developed is 50 mm, the positioning error of the macro-motion part is less than 20 μm, the maximum stroke of the micro-motion part is 40 μm, and the overall positioning accuracy of the MMCA is 0.14 μm. The research results provide a new idea and theoretical basis for further optimization and development of precision positioning platform with high precision and large stroke. [ABSTRACT FROM AUTHOR]

Background: The short-term prognosis of stroke patients is mainly influenced by the severity of the primary disease at admission and the trend of disease development during the acute phase (1–7 days after admission). Objective: The aim of this study is to explore the relationship between the bioelectrical impedance analysis (BIA) parameter trajectories during the acute phase of stroke patients and their short-term prognosis, and to investigate the predictive value of the prediction model constructed using BIA parameter trajectories and clinical indicators at admission for short-term prognosis in stroke patients. Methods: A total of 162 stroke patients were prospectively enrolled, and their clinical indicators at admission and BIA parameters during the first 1–7 days of admission were collected. A Group-Based Trajectory Model (GBTM) was employed to identify different subgroups of longitudinal trajectories of BIA parameters during the first 1–7 days of admission in stroke patients. The random forest algorithm was applied to screen BIA parameter trajectories and clinical indicators with predictive value, construct prediction models, and perform model comparisons. The outcome measure was the Modified Rankin Scale (mRS) score at discharge. Results: PA in BIA parameters can be divided into four separate trajectory groups. The incidence of poor prognosis (mRS: 4–6) at discharge was significantly higher in the "Low PA Rapid Decline Group" (85.0%) than in the "High PA Stable Group " (33.3%) and in the "Medium PA Slow Decline Group "(29.5%) (all P < 0.05). In-hospital mortality was the highest in the "Low PA Rapid Decline Group" (60%) compared with the remaining trajectory groups (P < 0.05). Compared with the prediction model with only clinical indicators (Model 1), the prediction model with PA trajectories (Model 2) demonstrated higher predictive accuracy and efficacy. The area under the receiver operating characteristic curve (AUC) of Model 2 was 0.909 [95% CI 0.863, 0.956], integrated discrimination improvement index (IDI), 0.035 (P < 0.001), and net reclassification improvement (NRI), 0.175 (P = 0.031). Conclusion: PA trajectories during the first 1–7 days of admission are associated with the short-term prognosis of stroke patients. PA trajectories have additional value in predicting the short-term prognosis of stroke patients. [ABSTRACT FROM AUTHOR]

The ecologically fragile agro-pastoral ecotone in northern China is characterized by relatively poor arable land quality. Yunzhou District in Datong City, which is situated within this transitional zone, boasts over 600 years of Hemerocallis citrina Baroni cultivation. Exploring the effects of soil physicochemical properties on daylily yield and related agronomic traits is essential for enhancing the ecological and economic value of dominant crops in ecologically fragile areas. Therefore, in this study, we focused on the daylily, a characteristic cash crop that is grown in the agro-pastoral ecotone in Yunzhou District. Physicochemical property measurement and yield estimation were performed using soil samples collected from 37 sites, with Spearman's correlation analysis, one-way analysis of variance with multiple comparisons, path analysis, and stepwise regression analysis used to analyze the generated data. The results showed the following: (1) The pathway analysis of daylily yield with each agronomic trait showed that the BN and PH directly affected the yield of daylily with direct pathway coefficients of 0.844 and 0.7, respectively, whereas the SN indirectly affected the yield of daylily through the BD and PH, with indirect pathway coefficients of 0.827 and 0.566, respectively. (2) A total of four principal components were extracted for the soil factors, of which SMC, ST and BD had large loadings on PC1; OM, TN and pH had large loadings on PC2; AK had large loadings on PC3; and AP had large loadings on PC4. (3) From the principal component regression and stepwise regression, it can be seen that SMC is the most critical factor affecting the yield of daylily, as well as the related agronomic traits, and the results also show that yield prediction was affected by OM, ST, and AK, while BN was influenced by OM and ST, and SN and PH were influenced by AP. Comparing the goodness of fit and significance of the two models, it can be concluded that the stepwise regression model is the optimal model for this study. [ABSTRACT FROM AUTHOR]

Objective: Rece nt studies ha ve revealed that hemoglobin beta (HBB) plays an important role not only in blood disorders but also in malignancies. The aim of this study is to investigate the clinical significance, diagnostic value, and biological function of HBB in lung cancer. Methods: HBB expression was examined in lung cancer tissues and plasma samples using quantitative real-time polymerase chain reaction, and its relationship with clinical pathological characteristics was analyzed. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic value of HBB in lung cancer. The proliferation of A549 and SPCA1 cells was analyzed using a cell counting kit-8 assay and protein expressions were detected by western blot. Results: The expressions of HBB were found to be down-regulated in both lung cancer tissues and plasma samples. Notably, plasma HBB levels were significantly elevated in postoperative samples when compared to their preoperative counterparts. Across 66 cases of lung cancer tissues, a correlation was observed between HBB levels and both gender and tumor, node, metastasis staging. ROC curve analysis further confirmed the high diagnostic potential of HBB expression in lung cancer. Moreover, the combination of HBB and carcinoembryonic antigen (CEA) had greater significance than HBB or CEA alone in the diagnosis of lung cancer. Knocking out or overexpressing HBB could affect lung cancer cell proliferation through the ERK1/2 signaling pathway. Conclusion: HBB can serve as a novel biomarker for the diagnosis and monitoring of lung cancer, regulating cell proliferation via the ERK1/2 pathway and playing a pivotal role in the oncogenesis and progression of the disease. [ABSTRACT FROM AUTHOR]

Background: The final step in the anaerobic decomposition of biopolymers is methanogenesis. Rice field soils are a major anthropogenic source of methane, with straw commonly used as a fertilizer in rice farming. Here, we aimed to decipher the structural and functional responses of the methanogenic community to rice straw addition during an extended anoxic incubation (120 days) of Philippine paddy soil. The research combined process measurements, quantitative real-time PCR and RT-PCR of particular biomarkers (16S rRNA, mcrA), and meta-omics (environmental genomics and transcriptomics). Results: The analysis methods collectively revealed two major bacterial and methanogenic activity phases: early (days 7 to 21) and late (days 28 to 60) community responses, separated by a significant transient decline in microbial gene and transcript abundances and CH4 production rate. The two methanogenic activity phases corresponded to the greatest rRNA and mRNA abundances of the Methanosarcinaceae but differed in the methanogenic pathways expressed. While three genetically distinct Methanosarcina populations contributed to acetoclastic methanogenesis during the early activity phase, the late activity phase was defined by methylotrophic methanogenesis performed by a single Methanosarcina genomospecies. Closely related to Methanosarcina sp. MSH10X1, mapping of environmental transcripts onto metagenome-assembled genomes (MAGs) and population-specific reference genomes revealed this genomospecies as the key player in acetoclastic and methylotrophic methanogenesis. The anaerobic food web was driven by a complex bacterial community, with Geobacteraceae and Peptococcaceae being putative candidates for a functional interplay with Methanosarcina. Members of the Methanocellaceae were the key players in hydrogenotrophic methanogenesis, while the acetoclastic activity of Methanotrichaceae members was detectable only during the very late community response. Conclusions: The predominant but time-shifted expression of acetoclastic and methylotrophic methanogenesis by a single Methanosarcina genomospecies represents a novel finding that expands our hitherto knowledge of the methanogenic pathways being highly expressed in paddy soils. 7k5euw-hvBknz_jds4sXXf Video Abstract [ABSTRACT FROM AUTHOR]