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Abstract Milk protein content is an important index to evaluate the quality and nutrition of milk. Accumulating evidence suggests that microRNAs (miRNAs) play important roles in bovine lactation, but little is known regarding the cross-kingdom regulatory roles of plant-derived exogenous miRNAs (xeno-miRNAs) in milk protein synthesis, particularly the underlying molecular mechanisms. The purpose of this study was to explore the regulatory mechanism of alfalfa-derived xeno-miRNAs on proliferation and milk protein synthesis in bovine mammary epithelial cells (BMECs). Our previous study showed that alfalfa miR159a (mtr-miR159a, xeno-miR159a) was highly expressed in alfalfa, and the abundance of mtr-miR159a was significantly lower in serum and whey from high-protein-milk dairy cows compared with low-protein-milk dairy cows. In this study, mRNA expression was detected by real-time quantitative PCR (qRT-PCR), and casein content was evaluated by enzyme-linked immunosorbent assay (ELISA). Cell proliferation and apoptosis were detected using the cell counting kit 8 (CCK-8) assay, 5-ethynyl-2′-deoxyuridine (EdU) staining, western blot, and flow cytometry. A dual-luciferase reporter assay was used to determine the regulation of Protein Tyrosine Phosphatase Receptor Type F (PTPRF) by xeno-miR159a. We found that xeno-miR159a overexpression inhibited proliferation of BMEC and promoted cell apoptosis. Besides, xeno-miR159a overexpression decreased β-casein abundance, and increased α-casein and κ-casein abundance in BMECs. Dual-luciferase reporter assay result confirmed that PTPRF is a target gene of xeno-miR159a. These results provide new insights into the mechanism by which alfalfa-derived miRNAs regulate BMECs proliferation and milk protein synthesis.

Promoting axonal regeneration is an effective strategy for recovery from traumatic spinal cord injury (SCI). Spastin, a microtubule-severing protein, modulates axonal outgrowth and branch formation by regulating microtubule dynamics. However, the exact role of spastin during recovery from SCI remains unknown. Therefore, we utilized a hemisection injury model of the mouse spinal cord and explored the effect of spastin using a spastin inhibitor, spastazoline. Results showed that spastazoline significantly suppressed the microtubule-severing activity of spastin in COS-7 cells and inhibited the promoting effect of spastin on neurite outgrowth in primarily cultured hippocampal neurons. The protein expression level of spastin was significantly upregulated in the injured spinal cord. Injured mice showed impaired motor functions, which included increased toe-off angle and foot fault steps and decreased stride length and Basso mouse scale score. Notably, these motor function impairments were aggravated by the application of spastazoline. Inhibition of spastin exacerbated neurogenesis impairment, as demonstrated by neuronal nuclei antigen staining, the inflammatory response, as shown by Iba-1 and GFAP staining, and axonal regeneration impairment, as shown by 5-hydroxytryptamine staining. Furthermore, mass spectrometry analysis revealed that the inhibition of spastin resulted in numerous dysregulated differentially expressed proteins that were closely associated with vesicle organization and transport. Taken together, our data suggest that spastin is critical for recovery from SCI and may be a potential target for the treatment of SCI.

Abstract Background Sepsis still threatens the lives of more than 300 million patients annually and elderly patients with sepsis usually have a more complicated condition and a worse prognosis. Existing studies have shown that both Hematocrit (HCT) and albumin (ALB) can be used as potential predictors of sepsis, and their difference HCT-ALB has a significant capacity to diagnose infectious diseases. Currently, there is no relevant research on the relationship between HCT-ALB and the prognosis of elderly sepsis patients. Therefore, this study aims to explore the association between HCT-ALB and mortality in elderly patients with sepsis. Methods This study was a multi-center retrospective study based on the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) in elderly patients with sepsis. The optimal HCT-ALB cut-off point for ICU mortality was calculated by the Youden Index based on the eICU-CRD dataset, and multivariate logistic regressions were conducted to explore the association between HCT-ALB and ICU/hospital mortality in the two databases. Subgroup analyses were performed for different parameters and comorbidity status. Results The number of 16,127 and 3043 elderly sepsis patients were selected from two large intensive care databases (eICU-CRD and MIMIC-IV, respectively) in this study. Depending on the optimal cut-off point, patients in both eICU-CRD and MIMIC-IV were independently divided into low HCT-ALB (

Abstract Background Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model’s predictive value for these patients. Methods Clinical information of elderly patients with sepsis in MIMIC IV database was collected retrospectively. Machine learning (RSF) was used to select the top 30 variables in the training cohort to build the final RSF model. The model was compared with the traditional scoring systems SOFA, SAPSII, and APSIII. The performance of the model was evaluated by C index and calibration curve. Results A total of 6,503 patients were enrolled in the study. The top 30 important variables screened by RSF were used to construct the final RSF model. The new model provided a better C-index (0.731 in the validation cohort). The calibration curve described the agreement between the predicted probability of RSF model and the observed 30-day survival. Conclusions We constructed a prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems. The risk factors affecting the patients were also ranked. In addition to the common risk factors of vasopressors, ventilator use, and urine output. Newly added factors such as RDW, type of ICU unit, malignant cancer, and metastatic solid tumor also significantly influence prognosis.

Abstract Background Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI. Methods Patients with sepsis were studied retrospectively based on the Medical Information Mart for Intensive Care IV. Latent growth mixture modeling was used to classify the trajectory of urine output changes within 24 h of sepsis diagnosis. The outcome of this study is AKI that occurs 24 h after sepsis. Cox proportional hazard model, Fine–Gray subdistribution proportional hazard model, and doubly robust estimation method were used to explore the risk of AKI in patients with different trajectory classes. Results A total of 9869 sepsis patients were included in this study, and their 24-h urine output trajectories were divided into five classes. The Cox proportional hazard model showed that compared with class 1, the HR (95% CI) values for classes 3, 4, and 5 were 1.460 (1.137–1.875), 1.532 (1.197–1.961), and 2.232 (1.795–2.774), respectively. Competing risk model and doubly robust estimation methods reached similar results. Conclusions The trajectory of urine output within 24 h of sepsis patients has a certain impact on the occurrence of AKI. Therefore, in the early treatment of sepsis, close attention should be paid to changes in the patient's urine output to prevent the occurrence of S-AKI.

Abstract Background Early and timely fluid treatment or resuscitation are the basic measures for the active treatment of sepsis. Our aim is to further explore the relationship between fluid balance and prognosis in patients with sepsis on a daily basis for 5 days. Methods Sepsis patients in eICU Collaborative Research Database were divided into the negative balance group (NB/−) and the positive balance group (PB/+) according to daily fluid balance. The primary outcome was in-hospital mortality. Survival differences between the groups were analyzed by using Cox regression. Then dose-response relationship between fluid balance and in-hospital mortality was studied using restricted cubic splines (RCSs). Furthermore, patients with fluid balance data for the previous three consecutive days were selected and divided into eight groups (“+/+/+”, “+/+/−”, “+/ −/−”, “+/ −/+”, “−/ −/−”, “−/ −/+”, “−/+/+”, and “−/+/−”). Kaplan–Meier curves and Cox regression were used to show the survival difference between groups. Results Our study, which included 19,557 patients in a multicenter database, showed that positive fluid balances on days 1, 2, and 3 after sepsis diagnosis were associated with poor prognosis with the HRs of 1.29 (1.20,1.40), 1.13 (1.01,1.27), and 1.25 (1.08,1.44), respectively, while the fluid balance on days 4 and 5 had no effect on the primary outcome. Then RCSs showed an overall trend that the risk of in-hospital mortality on days 1, 2, and 3 increased with increasing fluid balance. For three consecutive days of fluid balance, we studied 9205 patients and Kaplan–Meier curves revealed survival differences among patients in the eight groups. The cox model demonstrated that compared with the “+/+/+” group, the “+/ −/−”, “−/ −/−”, “−/ −/+”, “−/+/+”, and “−/+/−” groups had a lower risk of in-hospital mortality, with HRs of 0.65 (0.45,0.93), 0.72 (0.60,0.86), 0.63 (0.43,0.93), 0.69 (0.48,0.98), and 0.63 (0.42,0.96), respectively. Conclusions In patients with sepsis, positive fluid balance on days 1, 2, and 3 was associated with adverse outcomes. For patients with fluid balance for three consecutive days, the “+/−/−”, “−/ −/−”, “−/−/+”, “−/+/+”, and “−/+/−” groups were less likely to die in hospital than the “+/+/+” group.

Summary: Sepsis-associated acute kidney injury (S-AKI) is very common and early prediction is beneficial. This study aiming to develop an accurate ensemble model to predict the risk of S-AKI based on easily available clinical information. Patients with sepsis from the United States (US) database Medical Information Mart for Intensive Care-IV were used as a modeling cohort to predict the occurrence of AKI by combining Support Vector Machine, Random Forest, Neural Network, and Extreme Gradient Boost as four first-level learners via stacking algorithm. The external validation databases were the eICU Collaborative Research Database from US and Critical Care Database comprising infection patients at Zigong Fourth People’s Hospital from China, whose AUROC values for the ensemble model 48–12 h before the onset of AKI were 0.774–0.788 and 0.756–0.813, respectively. In this study, an ensemble model for early prediction of S-AKI onset was developed and it demonstrated good performance in multicenter external datasets.

BackgroundIntracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.MethodsICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).ResultsA total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.ConclusionThis study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

BackgroundType 2 diabetes leads to an increase in the prevalence of lipid abnormalities, which increases the risk of cardiovascular disease. Therefore, current guidelines generally recommend the use of moderate or high-intensity statins in patients with type 2 diabetes. There are still few studies on the overall risk benefit balance of statins for acute myocardial infarction (AMI) patients with diabetes. Compared with other types of lipid-lowering drugs, the advantage of statins for the prognosis of patients with AMI has not yet been determined. We investigated the effects of statins and non-statins on intensive care unit (ICU) and inpatient mortality in patients with AMI and diabetes.MethodsThis study retrospectively collected all patients with AMI and diabetes in the Medical Information Mart Intensive Care-IV database. We assessed ICU and in-hospital mortality rates during hospitalization in both groups. The clinical end point was in-hospital mortality and ICU mortality. Kaplan-Meier and Cox proportional-hazards regression models were applied to analyze the correlation between the two groups and the outcomes.ResultsData on 1,315 patients with AMI and diabetes were collected, among which 1,211 used statins during hospitalization. The overall in-hospital mortality of patients with AMI and diabetes was 17.2%, and the total ICU mortality was 12.6%. The in-hospital mortality was lower for the statin group than for the non-statin group (13.9% and 55.8%, respectively). Kaplan-Meier survival curves demonstrated that survival probability was higher in the statin group than in the non-statin group. In the cohort without hyperlipidemia, the statin group had lower risks of ICU death (HR = 0.12, 95% CI = 0.04–0.40) and in-hospital death (HR = 0.36, 95% CI = 0.16–0.84) compared with the non-statin group.ConclusionsStatins can significantly reduce ICU and in-hospital mortality rates in patients with AMI and diabetes. Even in the population without hyperlipidemia, statins can still reduce the mortality in patients with AMI and diabetes.

Background: In intensive care units (ICUs), the morbidity and mortality of ventilator-associated pneumonia (VAP) are relatively high, and this condition also increases medical expenses for mechanically ventilated patients, which will seriously affect the prognoses of critically ill patients. The purpose of this study was to determine the impact of bronchoscopy on the prognosis of patients with VAP undergoing invasive mechanical ventilation (IMV).Methods: This was a retrospective study based on patients with VAP from the Medical Information Mart for Intensive Care IV database. The outcomes were ICU and in-hospital mortality. Patients were divided based on whether or not they had undergone bronchoscopy during IMV. Kaplan-Meier (KM) survival curves and Cox proportional-hazards regression models were used to analyze the association between groups and outcomes. Propensity score matching (PSM) and propensity score based inverse probability of treatment weighting (IPTW) were used to further verify the stability of the results. The effect of bronchoscopy on prognosis was further analyzed by causal mediation analysis (CMA).Results: This study enrolled 1,560 patients with VAP: 1,355 in the no-bronchoscopy group and 205 in the bronchoscopy group. The KM survival curve indicated a significant difference in survival probability between the two groups. The survival probabilities in both the ICU and hospital were significantly higher in the bronchoscopy group than in the no bronchoscopy group. After adjusting all covariates as confounding factors in the Cox model, the HRs (95% CI) for ICU and in-hospital mortality in the bronchoscopy group were 0.33 (0.20–0.55) and 0.40 (0.26–0.60), respectively, indicating that the risks of ICU and in-hospital mortality were 0.67 and 0.60 lower than in the no-bronchoscopy group. The same trend was obtained after using PSM and IPTW. CMA showed that delta-red blood cell distribution width (RDW) mediated 8 and 7% of the beneficial effects of bronchoscopy in ICU mortality and in-hospital mortality.Conclusion: Bronchoscopy during IMV was associated with reducing the risk of ICU and in-hospital mortality in patients with VAP in ICUs, and this beneficial effect was partially mediated by changes in RDW levels.

Background: Ventilator-associated pneumonia (VAP) is a common infection complication in intensive care units (ICU). It not only prolongs mechanical ventilation and ICU and hospital stays, but also increases medical costs and increases the mortality risk of patients. Although many studies have found that thiamine supplementation in critically ill patients may improve prognoses, there is still no research or evidence that thiamine supplementation is beneficial for patients with VAP. The purpose of this study was to determine the association between thiamine and the prognoses of patients with VAP.Methods: This study retrospectively collected all patients with VAP in the ICU from the Medical Information Mart for Intensive Care-IV database. The outcomes were ICU and in-hospital mortality. Patients were divided into the no-thiamine and thiamine groups depending upon whether or not they had received supplementation. Associations between thiamine and the outcomes were tested using Kaplan-Meier (KM) survival curves and Cox proportional-hazards regression models. The statistical methods of propensity-score matching (PSM) and inverse probability weighting (IPW) based on the XGBoost model were also applied to ensure the robustness of our findings.Results: The study finally included 1,654 patients with VAP, comprising 1,151 and 503 in the no-thiamine and thiamine groups, respectively. The KM survival curves indicated that the survival probability differed significantly between the two groups. After multivariate COX regression adjusted for confounding factors, the hazard ratio (95% confidence interval) values for ICU and in-hospital mortality in the thiamine group were 0.57 (0.37, 0.88) and 0.64 (0.45, 0.92), respectively. Moreover, the results of the PSM and IPW analyses were consistent with the original population.Conclusion: Thiamine supplementation may reduce ICU and in-hospital mortality in patients with VAP in the ICU. Thiamine is an inexpensive and safe drug, and so further clinical trials should be conducted to provide more-solid evidence on whether it improves the prognosis of patients with VAP.

BackgroundVancomycin can effectively inhibit Gram-positive cocci and is widely used in critically ill patients. This study utilized a large public database to explore the effect of patients' first vancomycin trough concentration (FVTC) on the occurrence of acute kidney injury (AKI) and mortality after receiving vancomycin treatment in intensive care unit (ICU).MethodsCritically ill patients who used vancomycin in the Medical Information Mart for Intensive Care (MIMIC) IV have been retrospectively studied. The outcomes included the occurrence of AKI during the use of vancomycin or within 72 h of withdrawal, ICU mortality and hospital mortality. Restricted cubic splines (RCS) were used to analyze the linear relationship between FVTC and the outcomes. Multivariate logistic/Cox regression analysis was used to analyze the association between patient's FVTC and the occurrence of AKI, ICU mortality, and in-hospital mortality.ResultsThe study ultimately included 3,917 patients from the MIMIC-IV database who had been treated with vancomycin for more than 48 h. First of all, the RCS proved the linear relationship between FVTC and the outcomes. After controlling for all covariates as confounders in logistic/Cox regression, FVTC was a risk factor with the occurrence of AKI (OR: 1.02; 95% CI: 1.01–1.04), ICU mortality (HR: 1.02; 95% CI: 1.01–1.03), and in-hospital mortality (HR: 1.02; 95% CI: 1.01–1.03). Moreover, patients were divided into four groups in the light of the FVTC value: group1 ≤ 10 mg/L, 10 20 mg/L. Categorical variables indicated that group 3 and group 4 had a significant relationship on the occurrence of AKI [group 3: (OR: 1.36; 95% CI: 1.02–1.81); group 4: (OR: 1.76; 95% CI: 1.32–2.35)] and ICU mortality [group 3: (HR: 1.47; 95% CI: 1.03–2.09); group 4: (HR: 1.87; 95% CI: 1.33–2.62)], compared to group 1, while group 4 had a significant effect on in-hospital mortality (HR: 1.48; 95% CI: 1.15–1.91).ConclusionsFVTC is associated with the occurrence of AKI and increased ICU and in-hospital mortality in critically ill patients. Therefore, in clinical practice, patients in intensive care settings receiving vancomycin should be closely monitored for FVTC to prevent drug-related nephrotoxicity and reduce patient mortality.

The aim of the present study was to investigate the effects of dietary supplementation with different doses of rosmarinic acid (RA) on the growth performance, serum biochemical indices, liver antioxidant activity, and muscle nutritional component of broiler chickens during 1–20 days of age. A total of 1000 1-day-old Cobb 500 white-feathered broilers were divided into five groups. Each group included four replicates and 50 birds per replicate. The control group was the basic fodder group fed with a basal diet. The experimental groups included four levels of RA (100, 200, 300, and 400 mg/kg RA added to the basal diet, respectively). The results showed that a quadratic increase in final body weight (BW) and average daily gain (ADG) and a quadratic decrease in the feed/gain (F/G) ratio were observed with increasing dietary RA levels. At 200 mg/kg RA supplementation, growth performance was significantly improved. Moreover, under this RA treatment, the highest levels of total protein and globulin were detected in the serum, the activities of total superoxide dismutase and catalase in the liver of broilers were significantly increased, and the malondialdehyde content was significantly decreased, indicating a higher antioxidant capacity of the liver when 200 mg/kg RA was added to the basal diet. The flavor of the muscle meat was improved by dietary supplementation with 200 mg/kg RA. Therefore, adding 200 mg/kg of RA to the diet could significantly improve the growth performance of broilers, improve liver antioxidant capacity, and improve muscle quality, etc. The addition of RA to broiler feed as a natural antioxidant has great prospects.

Objective To construct a nomogram based on the Sequential Organ Failure Assessment (SOFA) that is more accurate in predicting 30-, 60-, and 90-day mortality risk in patients with sepsis. Methods Data from patients with sepsis were retrospectively collected from the Medical Information Mart for Intensive Care (MIMIC) database. Included patients were randomly divided into training and validation cohorts. Variables were selected using a backward stepwise selection method with Cox regression, then used to construct a prognostic nomogram. The nomogram was compared with the SOFA model using the concordance index (C-index), area under the time-dependent receiver operating characteristics curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA). Results A total of 5240 patients were included in the study. Patient’s age, SOFA score, metastatic cancer, SpO 2 , lactate, body temperature, albumin, and red blood cell distribution width were included in the nomogram. The C-index, AUC, NRI, IDI, and DCA of the nomogram showed that it performs better than the SOFA alone. Conclusion A nomogram was established that performed better than the SOFA in predicting 30-, 60-, and 90-day mortality risk in patients with sepsis.

Background: Diabetes mellitus (DM) has a high morbidity and mortality worldwide, and it is a risk factor for cardiovascular diseases. Non-diabetic stress hyperglycemia is common in severely ill patients, and it could affect prognosis. This study aimed to analyze the influence of different blood glucose levels on prognosis from the perspective of stress hyperglycemia by comparing them with normal blood glucose levels and those of patients with DM.Methods: A retrospective study of 1,401 patients in coronary care unit (CCU) from the critical care database called Medical Information Mart for Intensive Care IV was performed. Patients were assigned to the following groups 1–4 based on their history of DM, random blood glucose, and HbA1c levels: normal blood glucose group, moderate stress hyperglycemia group, severe stress hyperglycemia group and DM group. The main outcome of this study was 30- and 90-day mortality rates. The associations between groups and outcomes were analyzed using Kaplan–Meier survival analysis, Cox proportional hazard regression model and competing risk regression model.Results: A total of 1,401 patients in CCU were enrolled in this study. The Kaplan–Meier survival curve showed that group 1 had a higher survival probability than groups 3 and 4 in terms of 30- and 90-day mortalities. After controlling the potential confounders in Cox regression, groups 3 and 4 had a statistically significant higher risk of both mortalities than group 1, while no difference in mortality risk was found between groups 2 and 1. The hazard ratios [95% confidence interval (CI)] of 30- and 90-day mortality rates for group 3 were 2.77(1.39,5.54) and 2.59(1.31,5.12), respectively, while those for group 4 were 1.92(1.08,3.40) and 1.94(1.11,3.37), respectively.Conclusions: Severe stress hyperglycemia (≥200 mg/dL) in patients without DM in CCU may increase the risk of short-term death, which is greater than the prognostic effect in patients with diabetes. Patients with normal blood glucose levels and moderate stress hyperglycemia (140 mg/dL ≤ RBG

Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems.

Underwater object detection is highly complex and requires a high speed and accuracy. In this paper, an underwater target detection model based on YOLOv8 (SPSM-YOLOv8) is proposed. It solves the problems of high computational complexities, slow detection speeds and low accuracies. Firstly, the SPDConv module is utilized in the backbone network to replace the standard convolutional module for feature extraction. This enhances computational efficiency and reduces redundant computations. Secondly, the PSA (Polarized Self-Attention) mechanism is added to filter and enhance the polarization of features in the channel and spatial dimensions to improve the accuracy of pixel-level prediction. The SCDown (spatial–channel decoupled downsampling) downsampling mechanism is then introduced to reduce the computational cost by decoupling the space and channel operations while retaining the information in the downsampling process. Finally, MPDIoU (Minimum Point Distance-based IoU) is used to replace the CIoU (Complete-IOU) loss function to accelerate the convergence speed of the bounding box and improve the bounding box regression accuracy. The experimental results show that compared with the YOLOv8n baseline model, the SPSM-YOLOv8 (SPDConv-PSA-SCDown-MPDIoU-YOLOv8) detection accuracy reaches 87.3% on the ROUD dataset and 76.4% on the UPRC2020 dataset, and the number of parameters and amount of computation decrease by 4.3% and 4.9%, respectively. The detection frame rate reaches 189 frames per second on the ROUD dataset, thus meeting the high accuracy requirements for underwater object detection algorithms and facilitating lightweight and fast edge deployment.

We quantify the importance of collateral versus taxes for firms' capital structures. We estimate a dynamic model in which a taxable firm seeks financing for investment, and a dynamic contracting environment motivates endogenous collateral constraints. Optimal leverage stays a safe distance from the constraint, balancing the tax benefit of debt with the cost of lost financial flexibility. We estimate this flexibility cost to be 7.2% of firm assets, a percentage that is comparable to the tax benefit. Models with different tax rates fit the data equally well, and leverage responds to the tax rate only when taxes are low. [ABSTRACT FROM AUTHOR]

In this study, an optimization method for the motion trajectory of attitude actuators was investigated in order to improve assembly efficiency in the automatic docking process of large components. The self-developed dual-attitude adjustment mechanism (2-PPPR) is used as the research object, and the structure is symmetrical. Based on the modified Denavit–Hartenberg (MDH) parameter description method, a kinematic model of the attitude mechanism is established, and its end trajectory is parametrically expressed using a five-order B-spline curve. Based on the constraints of the dynamics and kinematics of the dual-posture mechanism, the total posturing time, the degree of urgency of each joint, and the degree of difficulty of the mechanism’s posturing are selected as the optimization objectives. The Lévy flight and Cauchy variation algorithms are introduced into the salp swarm algorithm (SSA) to solve the parameters of the multi-objective trajectory optimization model. By combining the evaluation method of the multi-objective average optimal solution, the optimal trajectory of the dual-tuning mechanism and the motion trajectory of each joint are obtained. The simulation and experiment results show that the trajectory planning method proposed in this paper is effective and feasible and can ensure that the large-part dual-posture mechanism can complete the automatic docking task smoothly and efficiently.

Autocollimators are widely used optical axis-measuring tools, but their measurement errors increase significantly when measuring under non-leveled conditions and they have a limited measurement range due to the limitations of the measurement principle. To realize axis measurement under non-leveled conditions, this paper proposes an autocollimator axis measurement method based on the strapdown inertial navigation system (SINS). First, the measurement model of the system was established. This model applies the SINS to measure the change in attitude of the autocollimator. The autocollimator was then applied to measure the angular relationship between the measured axis and its own axis, based on which the angular relationship of the axis was measured via computation through signal processing and data fusion in a multi-sensor system. After analyzing the measurement errors of the system model, the Monte Carlo method was applied to carry out a simulation analysis. This showed that the majority of the measurement errors were within ±0.002° and the overall measurement accuracy was within ±0.006°. Tests using equipment with the same parameters as those used in the simulation analysis showed that the majority of the measurement errors were within ±0.004° and the overall error was within ±0.006°, which is consistent with the simulation results. This analysis proves that this method solves the problem of the autocollimator being unable to measure the axis under non-leveled conditions and meets the needs of axis measurement with the application of autocollimators under a moving base.