BMPRⅡ^(+)neural precursor cells isolated and characterized from organotypic neurospheres:an in vitro model of human fetal spinal cord development

Roof plate secretion of bone morphogenetic proteins(BMPs)directs the cellular fate of sensory neurons during spinal cord development,including the formation of the ascending sensory columns,though their biology is not well understood.Type-ⅡBMP receptor(BMPRⅡ),the cognate receptor,is expressed by neural precursor cells during embryogenesis;however,an in vitro method of enriching BMPRⅡ^(+)human neural precursor cells(hNPCs)from the fetal spinal cord is absent.Immunofluorescence was undertaken on intact second-trimester human fetal spinal cord using antibodies to BMPRⅡand leukemia inhibitory factor(LIF).Regions of highest BMPRⅡ^(+)immunofluorescence localized to sensory columns.Parenchymal and meningeal-associated BMPRⅡ^(+)vascular cells were identified in both intact fetal spinal cord and cortex by co-positivity with vascular lineage markers,CD34/CD39.LIF immunostaining identified a population of somas concentrated in dorsal and ventral horn interneurons,mirroring the expression of LIF receptor/CD118.A combination of LIF supplementation and high-density culture maintained culture growth beyond 10 passages,while synergistically increasing the proportion of neurospheres with a stratified,cytoarchitecture.These neurospheres were characterized by BMPRⅡ^(+)/MAP2ab^(+/–)/βⅢ-tubulin^(+)/nestin^(–)/vimentin^(–)/GFAP^(–)/NeuN^(–)surface hNPCs surrounding a heterogeneous core ofβⅢ-tubulin^(+)/nestin^(+)/vimentin^(+)/GFAP^(+)/MAP2ab^(–)/NeuN^(–)multipotent precursors.Dissociated cultures from tripotential neurospheres contained neuronal(βⅢ-tubulin^(+)),astrocytic(GFAP+),and oligodendrocytic(O4+)lineage cells.Fluorescence-activated cell sorting-sorted BMPRⅡ^(+)hNPCs were MAP2ab^(+/–)/βⅢ-tubulin^(+)/GFAP^(–)/O4^(–)in culture.This is the first isolation of BMPRⅡ^(+)hNPCs identified and characterized in human fetal spinal cords.Our data show that LIF combines synergistically with high-density reaggregate cultures to support the organotypic reorganization of neurospheres,characterized by surface BMPRⅡ^(+)hNPCs.Our study has provided a new methodology for an in vitro model capable of amplifying human fetal spinal cord cell numbers for>10 passages.Investigations of the role BMPRⅡplays in spinal cord development have primarily relied upon mouse and rat models,with interpolations to human development being derived through inference.Because of significant species differences between murine biology and human,including anatomical dissimilarities in central nervous system(CNS)structure,the findings made in murine models cannot be presumed to apply to human spinal cord development.For these reasons,our human in vitro model offers a novel tool to better understand neurodevelopmental pathways,including BMP signaling,as well as spinal cord injury research and testing drug therapies.

A comparison study on structure-function relationship of polysaccharides obtained from sea buckthorn berries using different methods:antioxidant and bile acid-binding capacity

In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.

Hybrid treatment of varied orthodontic appliances for a patient with skeletal class II and temporomandibular joint disorders:A case report and review of literature

BACKGROUND The relation between orthodontic treatment and temporomandibular disorders(TMDs)is under debate;the management of TMD during orthodontic treatment has always been a challenge.If TMD symptoms occur during orthodontic treatment,an immediate pause of orthodontic adjustments is recommended;the treatment can resume when the symptoms are managed and stabilized.CASE SUMMARY This case report presents a patient(26-year-old,female)with angle class I,skeletal class II and TMDs.The treatment was a hybrid of clear aligners,fixed appliances and temporary anchorage devices(TADs).After 3 mo resting and treatment on her TMD,the patient’s TMD symptom alleviated,but her anterior occlusion displayed deep overbite.Therefore,the fixed appliances with TAD were used to correct the anterior deep-bite and level maxillary and mandibular deep curves.After the levelling,the patient showed dual bite with centric relation and maximum intercuspation discrepancy on her occlusion.After careful examination of temporomandibular joints(TMJ)position,the stable bite splint and Invisible Mandibular Advancement appliance were used to reconstruct her occlusion.Eventually,the improved facial appearance and relatively stable occlusion were achieved.The 1-year follow-up records showed there was no obvious change in TMJ morphology,and her occlusion was stable.CONCLUSION TMD screening and monitoring is of great clinical importance in the TMD susceptible patients.Hybrid treatment with clear aligners and fixed appliances and TADs is an effective treatment modality for the complex cases.

Application of multi-algorithm ensemble methods in high-dimensional and small-sample data of geotechnical engineering:A case study of swelling pressure of expansive soils

Geotechnical engineering data are usually small-sample and high-dimensional,which brings a lot of challenges in predictive modeling.This paper uses a typical high-dimensional and small-sample swell pressure(P_(s))dataset to explore the possibility of using multi-algorithm hybrid ensemble and dimensionality reduction methods to mitigate the uncertainty of soil parameter prediction.Based on six machine learning(ML)algorithms,the base learner pool is constructed,and four ensemble methods,Stacking(SG),Blending(BG),Voting regression(VR),and Feature weight linear stacking(FWL),are used for the multi-algorithm ensemble.Furthermore,the importance of permutation is used for feature dimensionality reduction to mitigate the impact of weakly correlated variables on predictive modeling.The results show that the proposed methods are superior to traditional prediction models and base ML models,where FWL is more suitable for modeling with small-sample datasets,and dimensionality reduction can simplify the data structure and reduce the adverse impact of the small-sample effect,which points the way to feature selection for predictive modeling.Based on the ensemble methods,the feature importance of the five primary factors affecting P_(s) is the maximum dry density(31.145%),clay fraction(15.876%),swell percent(15.289%),plasticity index(14%),and optimum moisture content(13.69%),the influence of input parameters on P_(s) is also investigated,in line with the findings of the existing literature.

Cardiovascular computed tomography in cardiovascular disease:An overview of its applications from diagnosis to prediction

Cardiovascular computed tomography angiography(CTA)is a widely used imaging modality in the diagnosis of cardiovascular disease.Advancements in CT imaging technology have further advanced its applications from high diagnostic value to minimising radiation exposure to patients.In addition to the standard application of assessing vascular lumen changes,CTA-derived applications including 3D printed personalised models,3D visualisations such as virtual endoscopy,virtual reality,augmented reality and mixed reality,as well as CT-derived hemodynamic flow analysis and fractional flow reserve(FFRCT)greatly enhance the diagnostic performance of CTA in cardiovascular disease.The widespread application of artificial intelligence in medicine also significantly contributes to the clinical value of CTA in cardiovascular disease.Clinical value of CTA has extended from the initial diagnosis to identification of vulnerable lesions,and prediction of disease extent,hence improving patient care and management.In this review article,as an active researcher in cardiovascular imaging for more than 20 years,I will provide an overview of cardiovascular CTA in cardiovascular disease.It is expected that this review will provide readers with an update of CTA applications,from the initial lumen assessment to recent developments utilising latest novel imaging and visualisation technologies.It will serve as a useful resource for researchers and clinicians to judiciously use the cardiovascular CT in clinical practice.

In-situ measurement via the flow-through method and numerical simulations for radon exhalation during measurements of the radon exhalation rate

Small-scale measurements of the radon exhalation rate using the flow-through and closed-loop methods were conducted on the surface of a uranium tailing pond to better understand the differences between the two methods.An abnormal radon exhalation behavior was observed,leading to computational fluid dynamics(CFD)-based simulations in which dynamic radon migration in a porous medium and accumulation chamber was considered.Based on the in-situ experimental and numerical simulation results,variations in the radon exhalation rate subject to permeability,flow rate,and insertion depth were quantified and analyzed.The in-situ radon exhalation rates measured using the flow-through method were higher than those measured using the closed-loop method,which could be explained by the negative pressure difference between the inside and outside of the chamber during the measurements.The consistency of the variations in the radon exhalation rate between the experiments and simulations suggests the reliability of CFD-based techniques in obtaining the dynamic evolution of transient radon exhalation rates for diffusion and convection at the porous medium-air interface.The synergistic effects of the three factors(insertion depth,flow rate,and permeability)on the negative pressure difference and measured exhalation rate were quantified,and multivariate regression models were established,with positive correlations in most cases;the exhalation rate decreased with increasing insertion depth at a permeability of 1×10^(−11) m^(2).CFD-based simulations can provide theoretical guidance for improving the flow-through method and thus achieve accurate measurements.

Boost the Utilization of Dense FeN_(4) Sites for High-Performance Proton Exchange Membrane Fuel Cells

Iron-nitrogen-carbon(Fe-N-C)catalysts for the oxygen reduction reaction(ORR)in proton exchange membrane fuel cells(PEMFCs)have seriously been hindered by their poor ORR performance of Fe-N-C due to the low active site density(SD)and site utilization.Herein,we reported a melamine-assisted vapor deposition approach to overcome these hindrances.The melamine not only compensates for the loss of nitrogen caused by high-temperature pyrolysis but also effectively etches the carbon substrate,increasing the external surface area and mesoporous porosity of the carbon substrate.These can provide more useful area for subsequent vapor deposition on active sites.The prepared 0.20Mela-FeNC catalyst shows a fourfold higher SD value and site utilization than the FeNC without the treatment of melamine.As a result,0.20Mela-FeNC catalyst exhibits a high ORR activity with a half-wave potential(E_(1/2))of 0.861 V and 12-fold higher ORR mass activity than the FeNC in acidic media.As the cathode in a H_(2)-O_(2)PEMFCs,0.20Mela-FeNC catalyst demonstrates a high peak power density of 1.30 W cm^(-2),outstripping most of the reported Fe-N-C catalysts.The developed melamine-assisted vapor deposition approach for boosting the SD and utilization of Fe-N-C catalysts offers a new insight into high-performance ORR electrocatalysts.

Continuous-flow electrosynthesis of urea and oxalic acid by CO_(2)-nitrate reduction and glycerol oxidation

Urea and oxalic acid are critical component in various chemical manufacturing industries.However,achieving simultaneous generation of urea and oxalic acid in a continuous-flow electrolyzer is a challenge.Herein,we report a continuous-flow electrolyzer equipped with 9-square centime-ter-effective area gas diffusion electrodes(GDE)which can simultaneously catalyze the glycerol oxidation reaction in the anode region and the reduction reaction of CO_(2) and nitrate in the cathode region,producing oxalic acid and urea at both the anode and cathode,respectively.The current density at low cell voltage(0.9 V)remained above 18.7 mA cm^(-2) for 10 consecutive electrolysis cycles(120 h in total),and the Faraday efficiency of oxalic acid(67.1%) and urea(70.9%)did not decay.Experimental and theoretical studies show that in terms of the formation of C-N bond at the cathode,Pd-sites can provide protons for the hydrogenation process of CO_(2) and NO_(3)^(-),Cu-sites can promote the generation of *COOH and Bi-sites can stabilize *COOH.In addition,in terms of glycerol oxidation,the introduction of Cu and Bi into Pd metallene promotes the oxidation of hydroxyl groups and the cleavage of C-C bond in glycerol molecules,respectively.

Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

Integration of 1H-NMR-Based Metabolomics Approach with Traditional Chinese Pulse Diagnosis to Evaluate the Qi-Enhancing Activity of Herbal Tea

Human metabolism is intricately linked to an individual’s health status. Regardless of living habits, it will be reflected in the metabolic characteristics of urine. The utilization of the 1H NMR-based metabolomics method has enabled examine the metabolomic changes in urine under various physiology conditions, providing valuable insights into metabolites. In this particular study, volunteers were divided into two groups based on the strength of their spleen pulses, using the pulse diagnosis method employed in traditional Chinese medicine. Subsequently, their urine samples were analyzed, revealing notable variances in urea, creatinine, citric acid, succinic acid, trimethylamine-N-oxide (TMAO), alanine, hippuric acid, and glycine between the two groups. Interestingly, individuals with weak spleen pulses showed significant improvements after consuming herbal tea. Furthermore, we conducted LC-MS analysis on herbal tea and performed adenosine triphosphate (ATP) activity tests on the C2C12 mouse skeletal muscle cell line. The results indicated that within a reasonable concentration range, exposure to herbal tea led to an increase in the mitochondrial ATP production capacity of C2C12 cells. These findings shed light on the relationship between traditional Chinese medicine pulse diagnosis and urine metabolites, highlighting their potential as non-invasive and straightforward health assessment indicators. They can aid in the preliminary determination of necessary dietary and lifestyle changes to enhance overall bodily health.

A Critical Review of Community Resilience Building Framework: Contexts of Climate Change and Disasters in Bangladesh

The concept of community resilience in the contexts of climate change and disasters draws increasing attention and interest from practitioners and researchers in recent development discourse. This paper provides a critical review of six selected frameworks of community resilience building operationalized in Bangladesh over the span of years. In other words, this study aims to contribute to the understanding of resilience through a systematic analysis of the dimensions and indicators of community resilience frameworks. The analysis shows that comprehensive and effective community resilience frameworks should incorporate the missing components linked to fundamental elements of good governance, economic growth, environmental sustainability, social transformation, and capacity development. The paper concludes by highlighting a few other areas of grave concern that need more appropriate attention, considering the severe threats posed by climate change and natural disasters in line with sustainable development goals. Finally, this study recommends further research regarding the effectiveness of these frameworks in different climatic and disaster contexts that can lead the concept into a new dimension of community resilience and sustainability.

Association of Human Whole-blood NAD+Levels with Nabothian Cyst

Objective Little is known about the association between whole-blood nicotinamide adenine dinucleotide(NAD^(+))levels and nabothian cysts.This study aimed to assess the association between NAD^(+)levels and nabothian cysts in healthy Chinese women.Methods Multivariate logistic regression analysis was performed to analyze the association between NAD^(+)levels and nabothian cysts.Results The mean age was 43.0±11.5 years,and the mean level of NAD^(+)was 31.3±5.3μmol/L.Nabothian cysts occurred in 184(27.7%)participants,with single and multiple cysts in 100(15.0%)and84(12.6%)participants,respectively.The total nabothian cyst prevalence gradually decreased from37.4%to 21.6%from Q1 to Q4 of NAD^(+)and the prevalence of single and multiple nabothian cysts also decreased across the NAD^(+)quartiles.As compared with the highest NAD^(+)quartile(≥34.4μmol/L),the adjusted odds ratios with 95%confidence interval of the NAD^(+)Q1 was 1.89(1.14–3.14)for total nabothian cysts.The risk of total and single nabothian cysts linearly decreased with increasing NAD^(+)levels,while the risk of multiple nabothian cysts decreased more rapidly at NAD^(+)levels of 28.0 to35.0μmol/L.Conclusion:Low NAD^(+)levels were associated with an increased risk of total and multiple nabothian cysts.

Association between Low Ambient Temperature and the Severity of Acute Ischemic Stroke

Objective:To explore the relationships among ambient temperature,ischemic stroke severity,and blood pressure.Methods:Meteorological data(2005–2015)were collected from the Guangzhou Meteorological Data Service.Ischemic stroke patients from the Department of Neurology of the First Affiliated Hospital,Sun Yat-sen University were retrospectively evaluated,each winter from 2005 to 2015.Patient demographics,baseline measurements,and National Institute of Health Stroke Scale(NIHSS)score were evaluated.Results:Three hundred sixty-two patients were included.The median latency from symptom onset to admission was 2 d(IQR:1–3 d).During recruitment,the highest and lowest temperatures were 39℃and 1.3℃,respectively.Hypertension was the most common comorbidity(75.1%).NIHSS scores at admission and discharge were higher in the cold-exposed group than in the controls regardless of the average temperature at admission.In addition,systolic and diastolic blood pressure values at admission were higher in the cold-exposed group than in the controls.When stratified by hypertensive status,the average and minimum temperatures at admission were negatively associated with systolic and diastolic blood pressure values in hypertensive patients.Reductions in the average and minimum temperatures at symptom onset were associated with more severe stroke.Conclusion:Ischemic stroke patients with symptom onset in winter had higher systolic blood pressure values and more serious neurologic deficits upon admission.

Rib Osteosynthesis for Sub-Acute Management of a Flail Chest in a Tertiary Centre in a Low-Middle Income Country of Sub-Saharan Africa: Case Report at Douala Laquintinie Hospital

Background: Costal fracture surgical is still a debate, therefore we shall select between early and delay surgical management. Case Report: We are reporting two cases of post road traffic clash delay ribs fractures osteosynthesis involving a 63-year-old man with multistage fractures on the left and pulmonary pinning of one of the costal arches, complicated by a homolateral haemothorax and a 41-year-old man with a bilateral flail chest. Conclusion: The simple postoperative course and the immediate postoperative improvement in the patient’s clinical respiratory condition enabled us to discuss the time frame for management, in this case the indication for early or later surgery.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

A historical literature review of coronary microvascular obstruction and intra-myocardial hemorrhage as functional/structural phenomena

The analysis of experimental data demonstrates that platelets and neutrophils are involved in the no-reflow phenomenon,also known as microvascular obstruction(MVO).However,studies performed in the isolated perfused hearts subjected to ischemia/reperfusion(I/R)do not suggest the involvement of microembolization and microthrombi in this phenomenon.The intracoronary administration of alteplase has been found to have no effect on the occurrence of MVO in patients with acute myocardial infarction.Consequently,the major events preceding the appearance of MVO in coronary arteries are independent of microthrombi,platelets,and neutrophils.Endothelial cells appear to be the target where ischemia can disrupt the endothelium-dependent vasodilation of coronary arteries.However,reperfusion triggers more pronounced damage,possibly mediated by pyroptosis.MVO and intra-myocardial hemorrhage contribute to the adverse post-infarction myocardial remodeling.Therefore,pharmacological agents used to treat MVO should prevent endothelial injury and induce relaxation of smooth muscles.Ischemic conditioning protocols have been shown to prevent MVO,with L-type Ca2+channel blockers appearing the most effective in treating MVO.

Uncertainties of landslide susceptibility prediction considering different landslide types

Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide.Thirdly,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP considering different landslide types is superior to that taking only a single type of landslide into account.