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In this paper, (0.2–1 wt%) Yb was added to improve the tensile properties and high-cycle fatigue behavior of the as-cast and as-extruded Mg-10Li-5Zn-0.5Er alloys. It is found that Yb mainly affects the mechanical properties of the alloy by changing the grain size, type and morphology of the second phases. Yb mainly exists in the formation of Mg2Yb and Mg-Zn-Yb phases in the metallographic structure. With the addition of Yb, the grains are refined and these Yb-containing phases replace the large-sized MgLiZn phase to be enriched at the grain boundaries. While the addition of excess Yb reduces the number of small-sized MgLiZn phases in the grain, thus reducing the alloys’ mechanical performance. After extrusion, the small-sized MgLiZn phase is refined and the number increases, which effectively improves the tensile and fatigue strength of the alloy. The fatigue strength is mainly affected by the number and morphology of the second phase, positively correlated with the strength. Balanced in grain size and number and size of second phases, the extruded alloy with 0.2Yb added exhibits excellent mechanical properties with the yield strength, ultimate tensile strength and elongation of 292 MPa, 303 MPa and 11.7%, and an fatigue strength of 130 MPa.

The semi-solid Mg-Gd-Ca-Zr alloys slurries with fine and spherical α-Mg particles were successfully prepared. The influence of solute Gd and Zr contents, and cooling rate on the morphological evolution of α-Mg are systematically investigated. The results showed that addition of Zr will turn the coarse dendritic α-Mg particles into fine and globular particles. With the increase of Zr addition from 0 to 0.6 wt%, the particle size decreases, and the particle density and shape factor increase continuously. With the change of solute Gd content from 5 to 15 wt% in the Mg-yGd-2Ca-0.6Zr alloy, the particle size of α-Mg particles decreases continuously, and shape factor increases continuously. With cooling rate decreases from 3 to 1.5 °C/min, the α-Mg particles are slightly coarsened and the morphology are kept nearly globular, while with further decreasing into 0.5 °C/min, the α-Mg particles are severely coarsened and turned into irregular shape. The optimal Zr addition is ∼0.6 wt% and Gd addition is no less than 10 wt%, and the optimal cooling rate is 1.5–3 °C/min. The mechanisms for obtaining fine and spherical α-Mg particles through Zr and Gd additions are discussed to be different roles of heterogenous nucleation effects and growth restriction effects.

Abstract Purpose The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use. Methods A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator. Results Training and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment ( https://nicolazhang.shinyapps.io/refracture_shap/ ). Conclusions The SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.

Background: The association between playing Ma-jong, chess and cards (PMCC) and shoulder pain among elderly adults in China remains unclear. The present study aimed to examine the association based on a nationally representative large-scale survey. Methods: The study used two waves of data from China Health and Retirement Longitudinal Study (CHARLS) in 2015 and 2018. All subjects met the inclusion criteria were classified based on the results of the CHARLS wave questionnaire. Logistic regression models were conducted to analyze the cross-sectional and longitudinal association between PMCC and shoulder pain. Odds ratios (OR) were reported to indicate the strength of the association. Results: In total, 8125 participants aged over 60 years (48.3 % male; mean age 67.9 ± 6.4 years) were enrolled in a cross-sectional association study in 2015, and further 6861 elderly adults were followed up in 2018. The overall prevalence of shoulder pain in the cross-sectional population was 15.6 %. In the non-PMCC and PMCC groups, the incidence of shoulder pain was 16.6 % and 10.7 %, respectively (P

Abstract Objective Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. Methods A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. Results In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. Conclusion The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

In this work, Cu/1060Al bimetallic composite materials manufactured by high-temperature free-oxygen rolling process are reported. The results show that with the increase of rolling passes, the microstructure of Cu and 1060Al gradually extends and elongates in the rolling direction, and the interface that has few compounds is dominated by mechanical bonding. It is worth noting that layered fracture occurs during the tensile process. After annealing, the interface changes from mechanical bonding to metallurgical bonding, and the layered fracture is alleviated under the traction of the interface layer. With the increase of annealing time and temperature, the thickness of the interface compounds gradually increase, and the interface compounds from the copper layer to the aluminum layer are Cu9Al4, CuAl and CuAl2, respectively. Annealing at 350 °C for 2 h exhibits the best comprehensive performance, a tensile strength of 138 MPa and an elongation of 64 %. Tensile behavior in the rolled state and annealed state has been discussed based on failure theory, and compound growth after annealing has been analyzed on account of the theories of dynamics and thermodynamics.

Abstract Purpose This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. Methods Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. Results Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. Conclusions Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic.clinicaltrials.gov/ct2/show/NCT05867732 .

Objective: To develop and evaluate a nomogram prediction model for recurrence of acute ischemic stroke (AIS) within one year. Method: Patients with AIS treated at the second affiliated hospital of Xuzhou Medical University from August 2017 to July 2019 were enrolled. Clinical data such as demographic data, risk factors, laboratory tests, TOAST etiological types, MRI features, and treatment methods were collected. Cox regression analysis was done to determine the parameters for entering the nomogram model. The performance of the model was estimated by receiver operating characteristic curves, decision curve analysis, calibration curves, and C-index. Result: A total of 645 patients were enrolled in this study. Side of hemisphere (SOH, Bilateral, HR = 0.35, 95 % CI = 0.15–0.84, p = 0.018), homocysteine (HCY, HR = 1.38, 95 % CI = 1.29–1.47, p

The development and application of lightweight materials boasting an elevated elastic modulus have emerged as a focal point in the realm of materials research. A novel Mg-8Li-3Al-2Zn-0.5Y (wt.%, LAZ832) matrix composite reinforced with ex situ micron-sized TiB2 particles was introduced in this work. The uniformly distributed TiB2 particles were achieved through ball milling pretreatment and subsequent hot extrusion. A comprehensive analysis of the composite's microstructure and mechanical properties was conducted. Mg-Li-Al-Zn precipitation regions formed at the TiB2 interface was identified firstly. This softening region significantly enhanced the ductility of the composite by reducing the dislocation density and stress concentration around the TiB2 particles. The ductility and the elastic modulus of the as-extruded composite increased significantly with ideal ultimate tensile strength (UTS = 270 MPa). This work highlights an ultra-light Mg-Li matrix composite attaining high modulus (60 GPa) and notable elongation to fracture (19.1 %), demonstrating significant potential for future applications.

Abstract Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100β, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100β, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100β, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance.

Background The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians’ decision-making. Methods Six machine learning (ML) algorithms, including logistic regression (LR), plain Bayesian classifier (NBC), decision tree (DT), random forest (RF), gradient boosting machine (GBM) and extreme gradient boosting (XGBoost). A 10-fold cross-validation was performed for each model separately, multicenter data was used as external validation, and the best (highest AUC) model was selected to build the network calculator. Results A total of 1,385 patients met the inclusion criteria, including 82 (5.9%) patients with metastatic CS. Multivariate logistic regression analysis showed that the risk of DM was significantly higher in patients with higher pathologic grades, T-stage, N-stage, and non-left primary lesions, as well as those who did not receive surgery and chemotherapy. The AUC of the six ML algorithms for predicting DM ranged from 0.911–0.985, with the extreme gradient enhancement algorithm (XGBoost) having the highest AUC. Therefore, we used the XGB model and uploaded the results to an online risk calculator for estimating DM risk. Conclusions In this study, combined with adequate SEER case database and external validation with data from multicenter institutions in different geographic regions, we confirmed that CS, T, N, laterality, and grading of surgery and chemotherapy were independent risk factors for DM. Based on the easily available clinical risk factors, machine learning algorithms built the XGB model that predicts the best outcome for DM. An online risk calculator helps simplify the patient assessment process and provides decision guidance for precision medicine and long-term cancer surveillance, which contributes to the interpretability of the model.

In this study, the feasibility of TIG welding of as-cast Mg–8Li–3Al–2Zn-0.5Y alloys was investigated, and the quality of welded joints using different welding currents was evaluated in terms of microstructural evolution and mechanical properties. The results showed that the welding position of plates was successfully welded with good formability and no obvious welding defects at the investigated processing parameters. Significant refinement of phase size was beneficial to improve the mechanical properties of the welded joints. Under the influence of heat input, the AlLi phase in the heat-affected zone dissolved into the matrix, which improved the mechanical properties of the welded joint. The ultimate tensile strength, yield strength and elongation of the welded joint at 100 A were 224.8 MPa, 177.4 MPa and 9.9%, respectively, which is 159.5%, 159% and 43.8% of the corresponding values of the base alloy in the as-cast state. The burning loss of Li element in the welded joint was effectively controlled, which confirmed the feasibility of this welding process for welding Mg–Li alloys.

Background and purposeRecurrent stroke accounts for 25–30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS).MethodsA total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator.ResultsLogistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly.ConclusionThis study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.

ObjectiveTo explore the predictors of death in acute ischemic stroke (AIS) patients within 1 year based on machine learning (ML) algorithms.MethodsThis study retrospectively analyzed the clinical data of patients hospitalized and diagnosed with AIS in the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. The patients were randomly divided into training and validation sets at a ratio of 7:3, and the clinical characteristic variables of the patients were screened using univariate and multivariate logistics regression. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGB), random forest (RF), decision tree (DT), and naive Bayes classifier (NBC), were applied to develop models to predict death in AIS patients within 1 year. During training, a 10-fold cross-validation approach was used to validate the training set internally, and the models were interpreted using important ranking and the SHapley Additive exPlanations (SHAP) principle. The validation set was used to externally validate the models. Ultimately, the highest-performing model was selected to build a web-based calculator.ResultsMultivariate logistic regression analysis revealed that C-reactive protein (CRP), homocysteine (HCY) levels, stroke severity (SS), and the number of stroke lesions (NOS) were independent risk factors for death within 1 year in patients with AIS. The area under the curve value of the XGB model was 0.846, which was the highest among the six ML algorithms. Therefore, we built an ML network calculator (https://mlmedicine-de-stroke-de-stroke-m5pijk.streamlitapp.com/) based on XGB to predict death in AIS patients within 1 year.ConclusionsThe network calculator based on the XGB model developed in this study can help clinicians make more personalized and rational clinical decisions.

The sand castings of Mg–Y–RE–Zr series alloys are widely utilized in the large scales and complex shapes in the aerospace industry, as a result of which there are always some cast defects in the products. In this study, the feasibility of repair welding of sand-cast Mg–4Y–3RE–0.5Zr alloy by tungsten inert gas (TIG) welding was scrutinized with different welding currents from 150 to 210 A. The results indicated that defect-free repaired joints with good appearance could be acquired at 170 and 190 A. Interestingly, the grain size of the fusion zone (FZ) was refined initially and then increased with the linear increment of welding current. Because at the higher heat inputs, although the cooling rate of the molten pool was reduced, substantial constitutional supercooling for the grain refinement was attained after the Zr particles were transformed into Zr solutes. The tensile strength of the repaired joint at 170 A was 195 MPa with the maximum joint efficiency of 87.8%, and the elongation reached to 124.4% of the sand-cast base material (BM). However, serious grain coarsening and continuous eutectic structures generated in heat-affected zone (HAZ) above 190 A resulted in the weakening of the joint due to the brittle intergranular fracture.

BackgroundRenal cell carcinoma (RCC) is a highly metastatic urological cancer. RCC with liver metastasis (LM) carries a dismal prognosis. The objective of this study is to develop a machine learning (ML) model that predicts the risk of RCC with LM, which is used to assist clinical treatment.MethodsThe retrospective study data of 42,547 patients with RCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. ML includes algorithmic methods and is a fast-rising field that has been widely used in the biomedical field. Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), random forest (RF), decision tree (DT), and naive Bayesian model [Naive Bayes Classifier (NBC)] were applied to develop prediction models to predict the risk of RCC with LM. The six models were 10-fold cross-validated, and the best-performing model was selected based on the area under the curve (AUC) value. A web online calculator was constructed based on the best ML model.ResultsBone metastasis, lung metastasis, grade, T stage, N stage, and tumor size were independent risk factors for the development of RCC with LM by multivariate regression analysis. In addition, the correlation of the relative proportions of the six clinical variables was shown by a heat map. In the prediction models of RCC with LM, the mean AUC of the XGB model among the six ML algorithms was 0.947. Based on the XGB model, the web calculator (https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py) was developed to evaluate the risk of RCC with LM.ConclusionsThis XGB model has the best predictive effect on RCC with LM. The web calculator constructed based on the XGB model has great potential for clinicians to make clinical decisions and improve the prognosis of RCC patients with LM.

ObjectiveThis study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.MethodsA retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).ResultsThe malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.ConclusionsCombining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.

Simple summaryStudies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects.BackgroundLymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.MethodsData on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.ResultsThe training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.ConclusionsThe predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

In this study, the tensile and high-cycle fatigue properties of as-cast, solid solution treated and as-extruded Mg-8Li-3Al-2Zn-0.5Y alloy are investigated. The results show that the yield strength and ultimate tensile strength of as-cast alloy is 198.1 MPa and 222.5 MPa, which are improved to 274.7 MPa and 321.7 MPa, 282.4 MPa and 319.3 MPa after solid solution and extrusion treatment, respectively. The high-cycle fatigue strength (at 107 cycles, R = -1) of as-cast studied alloy is 65 MPa, which is improved to 90 MPa and 105 MPa after solid solution and extrusion treatment, respectively. The improvement of fatigue property of the solid solution treated alloy is mainly due to the lattice distortion caused by solid solution hindering the crack propagation. However, the improvement of fatigue property of the as-extruded alloy is mainly due to that the refined grains, stacked dislocations and dispersed secondary particles impede the crack propagation.

The exploitation of selective and sensitive dopamine (DA) sensors is essential to more deeply understand its biological function and diagnosis of related diseases. In this study, gold nanocluster-encapsulated hyperbranched polyethyleneimine (hPEI-Au NCs) has been explored as the specific and ratiometric DA nanoprobe through hPEI-assisted DA self-polymerization reactions. The Au NCs encapsulation not only provides a fluorescent internal reference but also enhances the DA self-polymerization by weakening the proton sponge effect of the hPEI layer. Rapid and sensitive DA detection is realized through the proposed hPEI-Au NC nanoprobe with a limit of detection of 10 nM. The favorable selectivity over other possible interferents including amino acids, sugars, and salts is due to the specific self-polymerization reaction. The DA analysis in urine samples with small relative standard deviations has been accomplished with an hPEI-Au NC nanoprobe.

Manganese (Mn) could adversely affect water quality and is a concern for drinking water safety. In contrast to Mn-contaminated groundwater systems, Mn concentrations can increase up to 2 orders of magnitude in low background level groundwater systems (Mn < 100 µg/L). It is interesting to explore related hydrogeological and hydrochemical processes and controlling mechanisms. Our data showed that Mn of groundwater from Quaternary porous aquifers in the Changping region of Beijing in China varied from 0.02 to 522 µg/L, with an average of 45 µg/L. The high Mn concentration (>100 µg/L) in groundwater occurred in the lower plain, while the low Mn concentration was distributed in the upper plain. Association was not obvious between the Mn concentration and the depth of the groundwater. The water–rock interaction (e.g., dissolution of Mn-bearing minerals) and redox reactions dominated the distribution of Mn in groundwater. Particularly, the reduction of Mn4+ was a major cause of increasing Mn2+ concentration in groundwater. This is further evidenced by declining dissolved oxygen (DO) and NO3 in groundwater from the upper plain to the lower plain part. This study contributes to further understandings of hydrochemical processes for the enrichment of Mn in groundwater and offers a reference for the management and oversight of groundwater containing an excessive Mn level.

In this paper, one of most widely utilized rolling bearings in rotating machinery is selected as the research object. Automatic rolling bearing fault identification model including support vector machine (SVM) training module, fault classification knowledge base module, and fault automatic identification module is proposed. A generalized method for automatic identification of rolling bearing faults based on refined composite multi-scale dispersion entropy (RCMDE) is developed. First, in order to solve the problem of setting the value range of the decomposition level $K$ based on empirical knowledge for variational modal decomposition (VMD), a maximum kurtosis value method is proposed to determine the preset value range of $K$ in whale optimization algorithm. Then, an improved VMD method is used to adaptively decompose the signal into a series of intrinsic mode function components. Next, the correlation coefficient method is employed to screen effective feature components of bearings in different health states for reconstruction. Through theoretical analysis, the calculated RCMDE value of reconstructed signal is screened and input as a feature value into the optimized SVM classifier for fault pattern recognition. The input of rolling bearing vibration data without preprocessing and the output of the fault identification which don’t rely on empirical knowledge of external experts is realized. Experimental and engineering case data of rolling bearings under different equipment and operating environments are tested and validated. The results indicate that the model proposed in this paper shows good fault identification, demonstrates good generalization performance, and has beneficial industrial application prospect.

BackgroundRegional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms.MethodsA total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals.ResultsOf all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals.ConclusionsT and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients.

BackgroundThis study aimed to develop and validate machine learning (ML)-based prediction models for lung metastasis (LM) in patients with Ewing sarcoma (ES), and to deploy the best model as an open access web tool.MethodsWe retrospectively analyzed data from the Surveillance Epidemiology and End Results (SEER) Database from 2010 to 2016 and from four medical institutions to develop and validate predictive models for LM in patients with ES. Patient data from the SEER database was used as the training group (n = 929). Using demographic and clinicopathologic variables six ML-based models for predicting LM were developed, and internally validated using 10-fold cross validation. All ML-based models were subsequently externally validated using multiple data from four medical institutions (the validation group, n = 51). The predictive power of the models was evaluated by the area under receiver operating characteristic curve (AUC). The best-performing model was used to produce an online tool for use by clinicians to identify ES patients at risk from lung metastasis, to improve decision making and optimize individual treatment.ResultsThe study cohort consisted of 929 patients from the SEER database and 51 patients from multiple medical centers, a total of 980 ES patients. Of these, 175 (18.8%) had lung metastasis. Multivariate logistic regression analysis was performed with survival time, T-stage, N-stage, surgery, and bone metastasis providing the independent predictive factors of LM. The AUC value of six predictive models ranged from 0.585 to 0.705. The Random Forest (RF) model (AUC = 0.705) using 4 variables was identified as the best predictive model of LM in ES patients and was employed to construct an online tool to assist clinicians in optimizing patient treatment. (https://share.streamlit.io/liuwencai123/es_lm/main/es_lm.py).ConclusionsMachine learning were found to have utility for predicting LM in patients with Ewing sarcoma, and the RF model gave the best performance. The accessibility of the predictive model as a web-based tool offers clear opportunities for improving the personalized treatment of patients with ES.

ObjectiveIn order to provide reference for clinicians and bring convenience to clinical work, we seeked to develop and validate a risk prediction model for lymph node metastasis (LNM) of Ewing’s sarcoma (ES) based on machine learning (ML) algorithms.MethodsClinicopathological data of 923 ES patients from the Surveillance, Epidemiology, and End Results (SEER) database and 51 ES patients from multi-center external validation set were retrospectively collected. We applied ML algorithms to establish a risk prediction model. Model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis in external validation set. After determining the best model, a web-based calculator was made to promote the clinical application.ResultsLNM was confirmed or unable to evaluate in 13.86% (135 out of 974) ES patients. In multivariate logistic regression, race, T stage, M stage and lung metastases were independent predictors for LNM in ES. Six prediction models were established using random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR). In 10-fold cross-validation, the average area under curve (AUC) ranked from 0.705 to 0.764. In ROC curve analysis, AUC ranged from 0.612 to 0.727. The performance of the RF model ranked best. Accordingly, a web-based calculator was developed (https://share.streamlit.io/liuwencai2/es_lnm/main/es_lnm.py).ConclusionWith the help of clinicopathological data, clinicians can better identify LNM in ES patients. Risk prediction models established in this study performed well, especially the RF model.

A unique lytic phage infecting Hafnia paralvei was isolated and identified. Hafnia phage Ca belongs to the family Autographiviridae, possessing an icosahedral head with a diameter of 55 nm and a short non-contractile tail. Unusually, the burst size of Hafnia phage Ca of 10,292 ± 1,097 plaque-forming units (PFUs)/cell is much larger than other dsDNA phages reported before. Compared to the genome of the related phage, Hafnia phage Ca genome contains extra genes including DNA mimic ocr, dGTP triphosphohydrolase inhibitor, endonuclease, endonuclease VII, and HNH homing endonuclease gene. Extraordinarily, the phage developed different sizes of plaques when a single plaque was picked out and inoculated on a double-layer Luria broth agar plate with its host. Furthermore, varied packaging tightness for the tails of Hafnia phage Ca was observed (tail length: 4.35–45.92 nm). Most of the tails appeared to be like a cone with appendages, some were dot-like, bun-like, table tennis racket handle-like, and ponytail-like. Although the complete genome of Hafnia phage Ca is 40,286 bp, an incomplete genome with a deletion of a 397-bp fragment, containing one ORF predicted as HNH homing endonuclease gene (HEG), was also found by high throughput sequencing. Most of the genome of the virus particles in large plaques is complete (>98%), while most of the genome of the virus particles in small plaques is incomplete (>98%), and the abundance of both of them in medium-sized plaques is similar (complete, 40%; incomplete, 60%). In an experiment to see if the phage could be protective to brocade carps intramuscularly injected with H. paralvei LY-23 and phage Ca, the protection rate of Hafnia phage Ca to brocade carp (Cyprinus aka Koi) against H. paralvei was 33.38% (0.01 < p < 0.05). This study highlights some new insights into the peculiar biological and genomic characteristics of phage.

The aim of this work is firstly to optimize T6 heat-treatment of low-pressure sand-cast Mg–10Gd–3Y–0.5Zr alloy, and then systematically investigate the mechanical behavior of the T6-treated alloy from room temperature to 300 °C. It turned out that the optimum T6 heat-treatments for the tested alloy are 525 °C × 12 h + 225 °C × 14 h and 525 °C × 12 h + 250 °C × 12 h which integrated age-hardening and tensile properties into account, respectively. The strength of the T6-treated alloy indicates obvious anomalous temperature dependence from room temperature to 300 °C, namely both ultimate tensile strength and yield strength of the tested alloy firstly increase with tensile temperature, and then decrease as temperature increases further. Elongation increased with temperature monotonously. The tensile fracture mode of the tested alloy changes from transgranular fracture to intergranular fracture with the increasing of test temperature. Keywords: Mg–Gd–Y–Zr, Heat treatment, High temperature tensile property, Anomalous temperature dependence

Background: Bone cement leakage is a common complication of percutaneous vertebroplasty and it could be life-threatening to some extent. The aim of this study was to develop a machine learning model for predicting the risk of cement leakage in patients with osteoporotic vertebral compression fractures undergoing percutaneous vertebroplasty. Furthermore, we developed an online calculator for clinical application.Methods: This was a retrospective study including 385 patients, who had osteoporotic vertebral compression fracture disease and underwent surgery at the Department of Spine Surgery, Liuzhou People's Hospital from June 2016 to June 2018. Combing the patient's clinical characteristics variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multilayer perceptron (MLP), which could predict the risk of bone cement leakage. We tested the results with ten-fold cross-validation, which calculated the Area Under Curve (AUC) of the six models and selected the model with the highest AUC as the excellent performing model to build the web calculator.Results: The results showed that Injection volume of bone cement, Surgery time and Multiple vertebral fracture were all independent predictors of bone cement leakage by using multivariate logistic regression analysis in the 385 observation subjects. Furthermore, Heatmap revealed the relative proportions of the 15 clinical variables. In bone cement leakage prediction, the AUC of the six ML algorithms ranged from 0.633 to 0.898, while the RF model had an AUC of 0.898 and was used as the best performing ML Web calculator (https://share.streamlit.io/liuwencai0/pvp_leakage/main/pvp_leakage) was developed to estimate the risk of bone cement leakage that each patient undergoing vertebroplasty.Conclusion: It achieved a good prediction for the occurrence of bone cement leakage with our ML model. The Web calculator concluded based on RF model can help orthopedist to make more individual and rational clinical strategies.

Chemical epigenetic regulation (CER) is an effective method to activate the silent pathway of fungal secondary metabolite synthesis. However, conventional methods for CER study are laborious and time-consuming. In the meantime, the overall profile of the secondary metabolites in the fungi treated by the CER reagent is not well characterized. In this study, suberohydroxamic acid (SBHA), a histone deacetylase inhibitor, was added to a culture of Aspergillus aculeatus DL1011 and a new strategy based on LC-MS/MS analysis integrated with various metabolomic tools (MetaboAnalyst, MS-DIAL, SIRIUS and GNPS) was developed to characterize the profile of induced metabolites. As a result, 13.6%, 29.5% and 27.2% of metabolites were identified as newly biosynthesized, increasing and decreasing in abundance by CER, respectively. The structures of the 18 newly induced secondary metabolites were further identified by the new strategy to demonstrate that 72.2% of them (1 novel compound and 12 known compounds) were first discovered in A. aculeatus upon SBHA treatment. The accuracy of the new approach was confirmed by purification and NMR data analysis of major newly biosynthesized secondary metabolites. The bioassay showed that the newly biosynthesized compounds, roseopurpurin analogues, showed selective activities against DPPH scavenging, cytotoxicity and SHP1 inhibition. Our research demonstrated that CER was beneficial for changing the secondary metabolic profile of fungi and was an effective means of increasing the diversity of active metabolites. Our work also supplied a metabolomic strategy to characterize the profile changes and determine the newly induced compounds in the secondary metabolites of fungi treated with the chemical epigenetic regulator.

Aeromonas hydrophila is a zoonotic pathogen and an important fish pathogen. A new lytic phage, Ahy-yong1, against multi-antibiotic-resistant pathogen A. hydrophila was isolated, identified, and tentatively used in therapy. Ahy-yong1 possesses a head of approximately 66 nm in diameter and a short tail of approximately 26 nm in length and 32 nm in width. Its complete dsDNA genome is 43,374 bp with a G + C content of 59.4%, containing 52 predicted opening reading frames (ORFs). Taxonomic analysis indicated Ahy-yong1 as a new species of the Ahphunavirus genus of the Autographiviridae family of the Caudoviricetes class. Ahy-yong1 was active only against its indicator host strain among the 35 strains tested. It is stable at 30–40 °C and at pH 2–12. Aeromonas phage Ahy-yong1 revealed an effective biofilm removal capacity and an obvious protective effect in brocade carp (Cyprinus aka Koi). The average cumulative mortality for the brocade carp in the blank groups intraperitoneally injected with PBS was 1.7% ± 2.4%;for the control groups treated with A. hydrophila (108 CFU/fish) via intraperitoneal injection, it was 100.00%;and for the test group I, successively treated with A. hydrophila (108 CFU/fish) and Aeromonas phage Ahy-yong1 (107 PFU/fish) via intraperitoneal injection witha time interval of 2 hours, it was only 43.4% ± 4.7%. Furthermore, the cumulative mortality of the test group II, successively treated with Aeromonas phage Ahy-yong1 (107 PFU/fish) and A. hydrophila (108 CFU/fish), was only 20.0% ± 8.2%, and that of the test group III, simultaneously treated with Aeromonas phage Ahy-yong1 (107 PFU/fish) and A. hydrophila (108 CFU/fish), was only 30.0% ± 8.2%. The results demonstrated that phage Ahy-yong1 was very effective in the therapies against A. hydrophila A18, prophylaxis was more effective than rescue, and earlier treatment was better for the reduction of mortality. This study enriches knowledge about Aeromonas phages.

Microcystis aeruginosa is a major harmful cyanobacterium causing water bloom worldwide. Cyanophage has been proposed as a promising tool for cyanobacterial bloom. In this study, M. aeruginosa FACHB-1326 was used as an indicator host to isolate cyanophage from Lake Taihu. The isolated Microcystis cyanophage Mae-Yong1326-1 has an elliptical head of about 47 nm in diameter and a slender flexible tail of about 340 nm in length. Mae-Yong1326-1 could lyse cyanobacterial strains across three orders (Chroococcales, Nostocales, and Oscillatoriales) in the host range experiments. Mae-Yong1326-1 was stable in stability tests, maintaining high titers at 0–40 °C and at a wide pH range of 3–12. Mae-Yong 1326-1 has a burst size of 329 PFU/cell, which is much larger than the reported Microcystis cyanophages so far. The complete genome of Mae-Yong1326-1 is a double-stranded DNA of 48, 822 bp, with a G + C content of 71.80% and long direct terminal repeats (DTR) of 366 bp, containing 57 predicted ORFs. No Mae-Yong1326-1 ORF was found to be associated with virulence factor or antibiotic resistance. PASC scanning illustrated that the highest nucleotide sequence similarity between Mae-Yong1326-1 and all known phages in databases was only 17.75%, less than 70% (the threshold to define a genus), which indicates that Mae-Yong1326-1 belongs to an unknown new genus. In the proteomic tree based on genome-wide sequence similarities, Mae-Yong1326-1 distantly clusters with three unclassified Microcystis cyanophages (MinS1, Mwe-Yong1112-1, and Mwes-Yong2). These four Microcystis cyanophages form a monophyletic clade, which separates at a node from the other clade formed by two independent families (Zierdtviridae and Orlajensenviridae) of Caudoviricetes class. We propose to establish a new family to harbor the Microcystis cyanophages Mae-Yong1326-1, MinS1, Mwe-Yong1112-1, and Mwes-Yong2. This study enriched the understanding of freshwater cyanophages.

A kind of fully rotational decoupled and partly translational decoupled mechanical leg (PUPR-PUPR-PRPAS) is synthesized by combining the screw theory, the practical function of the robot and the motion transformation matrix of the mechanical leg. First, the DOF of mechanical leg is obtained by analyzed the practical function of walking machining robot. Then, the number of branches and the distribution of DOF on branches can be obtained according to DOF. Next, the actuation screw is used to obtain the driving screw with the aim of movement decoupling by analyzing the kinematics screw and constraint screw of the mechanical leg. The configuration synthesis of decoupled parallel mechanical leg is carried out with the practical function of the robots. The branches are selected out in those obtained configurations according to the norm of functions and coordination to constitute the primary configuration. Finally, the compound simplification is carried out among single-degree-of-freedom joint pairs of mechanical leg, and the simplified motion transformation matrix is analyzed. The third branch is further improved to determine the final configuration of the mechanical leg.

Aiming at the requirements of high machining accuracy and high production efficiency for large workpieces such as aircraft wings in the field of aviation manufacturing. Firstly, the degree of freedom of 6-SPU parallel robot is analyzed by using screw theory, and the trajectory planning of moving platform of the 6-SPU parallel mechanism is analyzed and studied based on the inverse kinematics method and three element space vector according to the Frenet-Serret, the movement platform corresponding position and gesture in the processing process is got. Then, the homogeneous coordinate transformation matrix and the inverse solution calculation formula are used to further determine the movement rules exerted on the end actuator when it is moving on the desired trajectory. Finally, the virtual model of this mechanism is created in SolidWorks software and the planning trajectory is verified through simulation in Simulink simulation module in MATLAB modeling simulation, and the theoretical foundation for the engineering design of mobile parallel processing platform is provided.

In the application of WE43, it is found that Y2O3 inclusion formed in the process of casting seriously reduced the mechanical properties of the products. The reduction of the mechanical properties is even more distinct when it comes to the application in the thin walled complex-precision castings. In order to decrease the Y2O3 inclusions, Gd element was used to replace part of the Y element in Mg-Y-RE series alloys. The effect of Y content (Mg-xY-1Gd-2Nd-0.5Zn-0.5Zr) and Y/Gd ratio (Mg-xY-(5-x)Gd-2Nd-0.5Zn-0.5Zr) on the microstructure and mechanical properties of Mg–Y–RE alloys were investigated in this paper. With decreasing Y content, the grain size of the alloys increased, both ultimate tensile strength (UTS) and yield strength (YS) of alloys decreased monotonically. Replacing part of Y content with Gd and keeping the total rare earth content unchanged, the low Y content Mg-2Y-3Gd-2Nd-0.5Zn-0.5Zr alloy showed the same mechanical properties as the high Y content Mg-4Y-1Gd-2Nd-0.5Zn-0.5Zr alloy at both room and elevated temperatures. After solution treatment at 525 °C for 8 h and aging treatment at 225 °C for 10 h, the UTS, YS and elongation (ε) of Mg-2Y-3Gd-2Nd-0.5Zn-0.5Zr alloy reached 281.7 MPa, 198 MPa and 11.1% at room temperature, and 216.7 MPa, 171.6 MPa and 16.1% at 250 °C. The new low Y content Mg-2Y-2Nd-3Gd-0.5Zn-0.5Zr alloy is expected to replace the high Y content WE43 alloys, which can be used in the complex thin-walled parts of aviation products. Keywords: Mg–Y–RE alloy, Microstructure, Mechanical property, Y/Gd ratio, Heat treatment

Cyanobacterial blooms are a worldwide ecological issue. Cyanophages are aquatic viruses specifically infecting cyanobacteria. Little is known about freshwater cyanophages. In this study, a freshwater cyanophage, Mae-Yong924-1, was isolated by the double-layer agar plate method using Microcystis aeruginosa FACHB-924 as an indicator host. Mae-Yong924-1 has several unusual characteristics: a unique shape, cross-taxonomic order infectivity and a very unique genome sequence. Mae-Yong924-1 contains a nearly spherical head of about 100 nm in diameter. The tail or tail-like structure (approximately 40 nm in length) is like the tassel of a round Chinese lantern. It could lyse six diverse cyanobacteria strains across three orders including Chroococcales, Nostocales and Oscillatoriales. The genome of the cyanophage is 40,325 bp in length, with a G + C content of 48.32%, and 59 predicted open reading frames (ORFs), only 12 (20%) of which were functionally annotated. Both BLASTn and BLASTx scanning resulted in “No significant similarity found”, i.e., the Mae-Yong924-1 genome shared extremely low homology with sequences in NCBI databases. Mae-Yong924-1 formed a root node alone and monopolized a root branch in the proteomic tree based on genome-wide sequence similarities. The results suggest that Mae-Yong924-1 may reveal a new unknown family apparently distinct from other viruses.

In terms of the structural health inspection of storage tanks by ultrasonic guided wave technology, many scholars are currently focusing on the tanks’ floor and walls, while little research has been conducted on storage tank roofs. However, the roof of a storage tank is prone to corrosion because of its complex structure and unique working environment. For this purpose, this paper proposes a reflection/transmission signal amplitude ratio (RTAR) coefficient method for corrosion depth assessment. We studied the relationship between the RTAR coefficient, the corrosion depth, and the guided wave frequency to establish a depth assessment model. More importantly, unlike the traditional reflection coefficient method, the characteristics of guided wave signals, including the propagation and attenuation, are introduced in this model for accurate assessment. To eliminate the interference of residual vibration and improve the detection accuracy of defects, we built a corrosion detection system by using piezoelectric sensors and carried out field tests to verify the performance of the proposed method. We demonstrate that corrosion defects with a minimum depth of 0.2 mm can be quantitatively evaluated.

Vibrio mediterranei 117-T6 is extensively pathogenic to several Pyropia species, leading to the death of conchocelis. In this study, the first V. mediterranei phage (named Vibrio phage Yong-XC31, abbreviated as Yong-XC31) was isolated. Yong-XC31 is a giant phage containing an icosahedral head about 113 nm in diameter and a contractible tail about 219 nm in length. The latent period of Yong-XC31 is 30 min, and burst size is 64,227. Adsorption rate of Yong-XC31 to V. mediterranei 117-T6 can reach 93.8% in 2 min. The phage genome consisted of a linear, double-stranded 290,532 bp DNA molecule with a G + C content of 45.87%. Bioinformatic analyses predicted 318 open reading frames (ORFs), 80 of which had no similarity to protein sequences in current (26 January 2021) public databases. Yong-XC31 shared the highest pair-wise average nucleotide identity (ANI) value of 58.65% (below the ≥95% boundary to define a species) and the highest nucleotide sequence similarity of 11.71% (below the >50% boundary to define a genus) with the closest related phage. In the proteomic tree based on genome-wide sequence similarities, Yong-XC31 and three unclassified giant phages clustered in a monophyletic clade independently between the family Drexlerviridae and Herelleviridae. Results demonstrated Yong-XC31 as a new evolutionary lineage of phage. We propose a new phage family in Caudovirales order. This study provides new insights and fundamental data for the study and application of giant phages.

The microstructure, high-temperature tensile properties and fracture behavior of the Mg-10Gd-5Y-0.5Zr alloy after thermo-mechanical processing (pre-tension between solution and aging treatment) were investigated. The pre-deformed alloy shows the accelerated aging kinetics compared to the un-deformed alloy. Microstructure of pre-deformed samples showed not only the homogeneous nucleation of the precipitate but also heterogeneous nucleation of precipitates on the dislocation and twin boundaries. Tensile results show that the pre-deformation enhanced the strength of the alloy, while it deteriorates the ductility of the alloy. The ultimate tensile strength (UTS) of the T6 treated un-deformed and pre-deformed alloy at room temperature are 331 MPa and 366 MPa, respectively. Tensile strength of the T6 treated alloy in both un-deformed and deformed conditions was enhanced by raising the test temperature and then reduced by further raising the test temperature. The higher strength of the pre-deformed alloy could be related to the higher density of the precipitates, which grow on the twin boundaries and can hinder the dislocation movement and strengthen the alloy. The results shows that thermo-mechanical processing can significantly improve the room- and high-temperature mechanical properties and enhance the formation of precipitates in Mg-10Gd-5Y-0.5Zr alloy, which can lead to wider application of the alloy in industries such as aerospace or powertrains that need better room- and high-temperature mechanical properties.

We prove sharp spectral transition in the arithmetics of phase between localization and singular continuous spectrum for Diophantine almost Mathieu operators. We also determine exact exponential asymptotics of eigenfunctions and of corresponding transfer matrices throughout the localization region. This uncovers a universal structure in their behavior governed by the exponential phase resonances. The structure features a new type of hierarchy, where self-similarity holds upon alternating reflections [ABSTRACT FROM AUTHOR]