Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.</p

Simulation Analysis of the Structure of an Integrated Modular House by Flat Pack Based on the Elastic–Plastic Contact Theory and Experimental Study of Its Corner Fitting Joint

Based on the elastic–plastic contact theory, elastic–plastic contact finite element models for the integrated modular house by flat pack are established. The structural stress and displacement distributions are obtained. An experimental test is conducted to study the performance of the corner fitting joint of the house. The finite element analysis and experimental test comprehensively demonstrated the safety and reliability of the house structure. The results show that the most unfavorable stress point of the house is located at the corner fitting joint. When the end plate connection holes of the corner column seal are designed with through holes and internal screw holes, respectively, the fastening effect of bolting between the corner fitting and corner column using the internal screw hole is better. The reliability of the repeated disassembly and assembly of the house is verified via multiple loading–unloading simulation cycle experiments of the corner fitting joint. The results of the study provide technical support and a reference basis for the optimal design and ensuring the service performance of the integrated modular house by flat pack

Synergistic Effect of Co-Delivering Ciprofloxacin and Tetracycline Hydrochloride for Promoted Wound Healing by Utilizing Coaxial PCL/Gelatin Nanofiber Membrane

Combining multiple drugs or biologically active substances for wound healing could not only resist the formation of multidrug resistant pathogens, but also achieve better therapeutic effects. Herein, the hydrophobic fluoroquinolone antibiotic ciprofloxacin (CIP) and the hydrophilic broad-spectrum antibiotic tetracycline hydrochloride (TH) were introduced into the coaxial polycaprolactone/gelatin (PCL/GEL) nanofiber mat with CIP loaded into the PCL (core layer) and TH loaded into the GEL (shell layer), developing antibacterial wound dressing with the co-delivering of the two antibiotics (PCL-CIP/GEL-TH). The nanostructure, physical properties, drug release, antibacterial property, and in vitro cytotoxicity were investigated accordingly. The results revealed that the CIP shows a long-lasting release of five days, reaching the releasing rate of 80.71%, while the cumulative drug release of TH reached 83.51% with a rapid release behavior of 12 h. The in vitro antibacterial activity demonstrated that the coaxial nanofiber mesh possesses strong antibacterial activity against E. coli and S. aureus. In addition, the coaxial mats showed superior biocompatibility toward human skin fibroblast cells (hSFCs). This study indicates that the developed PCL-CIP/GEL-TH nanofiber membranes hold enormous potential as wound dressing materials

Dual-Functional Nanofibrous Patches for Accelerating Wound Healing

Bacterial infections and inflammation are two main factors for delayed wound healing. Coaxial electrospinning nanofibrous patches, by co-loading and sequential co-delivering of anti-bacterial and anti-inflammation agents, are promising wound dressing for accelerating wound healing. Herein, curcumin (Cur) was loaded into the polycaprolactone (PCL) core, and broad-spectrum antibacterial tetracycline hydrochloride (TH) was loaded into gelatin (GEL) shell to prepare PCL-Cur/GEL-TH core-shell nanofiber membranes. The fibers showed a clear co-axial structure and good water absorption capacity, hydrophilicity and mechanical properties. In vitro drug release results showed sequential release of Cur and TH, in which the coaxial mat showed good antioxidant activity by DPPH test and excellent antibacterial activity was demonstrated by a disk diffusion method. The coaxial mats showed superior biocompatibility toward human immortalized keratinocytes. This study indicates a coaxial nanofiber membrane combining anti-bacterial and anti-inflammation agents has great potential as a wound dressing for promoting wound repair

Absence seizures in lesion-related epilepsy

Abstract Background In the new International League Against Epilepsy (ILAE) classification of seizure types, generalized seizures such as absence seizures (ASs) may originate from a focal point and rapidly spread to the bilaterally distributed brain network. Increasing evidence from animal and clinical studies has indicated that focal changes may occur prior to ASs; however, the relationship of ASs with epileptogenic lesions remains unclear. Methods We retrospectively collected and analyzed the clinical, imaging, and electrophysiological data of 16 patients who had ASs and structural lesions with seizure-free outcomes after lesion resection. Results In semiology analysis, nine patients displayed focal onset; only two patients showed simple ASs, and seizure types other than ASs were observed in the remaining patients. On ictal electroencephalography (EEG), four patients showed bilateral synchronous symmetric 3 Hz generalized spike-wave discharges (GSWDs), and the remaining patients showed bilateral 1.5–2.5 Hz GSWDs. Moreover, most patients (13/16, 81.3%) exhibited focal features in addition to ASs, while interictal EEG was the same in 12 patients. Furthermore, on stereoelectroencephalogram (SEEG), 2/5 patients showed focal discharges before bilateral burst GSWDs. Additionally, all patients had structural lesions on imaging. In four typical AS patients, the lesions were located in deep brain regions. Notably, in 9 patients (9/16, 56%), the lesions were located in the posterior cortex. All patients underwent lesion resection and had seizure-free outcomes during follow-up, and intelligence quotient (IQ) also improved by 10.71 ± 3.90 one year after surgery. Conclusions Patients with lesion-related epilepsy may present with ASs that have a focal onset and are associated with good surgical outcomes

Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study

Abstract Background More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images. Methods Forty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis. Results PET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70–0.83) than the LDA (AUC = 0.7506; 95% CI 0.68–0.82) and ANN models (AUC = 0.7425; 95% CI 0.67–0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71–0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71–0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74–0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75–0.87). Conclusions The 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients

Triptolide downregulates Rac1 and the JAK/STAT3 pathway and inhibits colitis-related colon cancer progression

Triptolide, a diterpenoid triepoxide from the traditional Chinese medicinal herb Tripterygium wilfordii Hook. f., is a potential treatment for autoimmune diseases as well a possible anti-tumor agent. It inhibits proliferation of coloretal cancer cells in vitro and in vivo. In this study, its ability to block progress of colitis to colon cancer, and its molecular mechanism of action are investigated. A mouse model for colitis-induced colorectal cancer was used to test the effect of triptolide on cancer progression. Treatment of mice with triptolide decreased the incidence of colon cancer formation, and increased survival rate. Moreover, triptolide decreased the incidence of tumors in nude mice inoculated with cultured colon cancer cells dose-dependently. In vitro, triptolide inhibited the proliferation, migration and colony formation of colon cancer cells. Secretion of IL6 and levels of JAK1, IL6R and phosphorylated STAT3 were all reduced by triptolide treatment. Triptolide prohibited Rac1 activity and blocked cyclin D1 and CDK4 expression, leading to G1 arrest. Triptolide interrupted the IL6R-JAK/STAT pathway that is crucial for cell proliferation, survival, and inflammation. This suggests that triptolide might be a candidate for prevention of colitis induced colon cancer because it reduces inflammation and prevents tumor formation and development