Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Back to the drawing board:Overview of the next generation of combination therapy for inflammatory bowel disease

Inflammatory bowel disease(IBD)is entering a potentially new era of combined therapeutics.Triantafillidis et al provide an insightful review of the current state of combination therapy,with a focus on the use of a combined biologic and immunomodulator,as well as emerging data on the future potential of dual-biologic therapy(DBT).While current evidence for DBT is limited,encouraging safety profiles and ongoing trials suggest a brighter future for this approach.The importance of controlled trials should be stressed in establishing new treatment paradigms.Ongoing prospective randomized trials of DBT and perhaps future combinations of biologics and small molecule therapies will hopefully guide the next generation of IBD care.

Combination treatment of inflammatory bowel disease:Present status and future perspectives

The treatment of patients with inflammatory bowel disease(IBD),especially those with severe or refractory disease,represents an important challenge for the clinical gastroenterologist.It seems to be no exaggeration to say that in these patients,not only the scientific background of the gastroenterologist is tested,but also the abundance of“gifts”that he should possess(insight,intuition,determ-ination,ability to take initiative,etc.)for the successful outcome of the treatment.In daily clinical practice,depending on the severity of the attack,IBD is treated with one or a combination of two or more pharmaceutical agents.These combin-ations include not only the first-line drugs(e.g.,mesalazine,corticosteroids,antibiotics,etc)but also second-and third-line drugs(immunosuppressants and biologic agents).It is a fact that despite the significant therapeutic advances there is still a significant percentage of patients who do not satisfactorily respond to the treatment applied.Therefore,a part of these patients are going to surgery.In recent years,several small-size clinical studies,reviews,and case reports have been published combining not only biological agents with other drugs(e.g.,immunosuppressants or corticosteroids)but also the combination of two biologi-cal agents simultaneously,especially in severe cases.In our opinion,it is at least a strange(and largely unexplained)fact that we often use combinations of drugs in a given patient although studies comparing the simultaneous administration of two or more drugs with monotherapy are very few.As mentioned above,there is a timid tendency in the literature to combine two biological agents in severe cases unresponsive to the applied treatment or patients with severe extraintestinal manifestations.The appropriate dosage,the duration of the administration,the suitable timing for checking the clinical and laboratory outcome,as well as the treatment side-effects,should be the subject of intense clinical research shortly.In this editorial,we attempt to summarize the existing data regarding the already applied combination therapies and to humbly formulate thoughts and suggestions for the future application of the combination treatment of biological agents in a well-defined category of patients.We suggest that the application of biomarkers and artificial intelligence could help in establishing new forms of treatment using the available modern drugs in patients with IBD resistant to treatment.

Erlotinib combination with a mitochondria-targeted ubiquinone effectively suppresses pancreatic cancer cell survival

BACKGROUND Pancreatic cancer is a leading cause of cancer-related deaths.Increased activity of the epidermal growth factor receptor(EGFR)is often observed in pancreatic cancer,and the small molecule EGFR inhibitor erlotinib has been approved for pancreatic cancer therapy by the food and drug administration.Nevertheless,erlotinib alone is ineffective and should be combined with other drugs to improve therapeutic outcomes.We previously showed that certain receptor tyrosine kinase inhibitors can increase mitochondrial membrane potential(Δψm),facilitate tumor cell uptake ofΔψm-sensitive agents,disrupt mitochondrial homeostasis,and subsequently trigger tumor cell death.Erlotinib has not been tested for this effect.AIM To determine whether erlotinib can elevateΔψm and increase tumor cell uptake ofΔψm-sensitive agents,subsequently triggering tumor cell death.METHODSΔψm-sensitive fluorescent dye was used to determine how erlotinib affectsΔψm in pancreatic adenocarcinoma(PDAC)cell lines.The viability of conventional and patient-derived primary PDAC cell lines in 2D-and 3D cultures was measured after treating cells sequentially with erlotinib and mitochondria-targeted ubiquinone(MitoQ),aΔψm-sensitive MitoQ.The synergy between erlotinib and MitoQ was then analyzed using SynergyFinder 2.0.The preclinical efficacy of the twodrug combination was determined using immune-compromised nude mice bearing PDAC cell line xenografts.RESULTS Erlotinib elevatedΔψm in PDAC cells,facilitating tumor cell uptake and mitochondrial enrichment ofΔψm-sensitive agents.MitoQ triggered caspase-dependent apoptosis in PDAC cells in culture if used at high doses,while erlotinib pretreatment potentiated low doses of MitoQ.SynergyFinder suggested that these drugs synergistically induced tumor cell lethality.Consistent with in vitro data,erlotinib and MitoQ combination suppressed human PDAC cell line xenografts in mice more effectively than single treatments of each agent.CONCLUSION Our findings suggest that a combination of erlotinib and MitoQ has the potential to suppress pancreatic tumor cell viability effectively.

Advances in extracellular vesicle-based combination therapies for spinal cord injury

Spinal cord injury is a severe insult to the central nervous system that causes persisting neurological deficits.The currently available treatments involve surgical,medical,and rehabilitative strategies.However,none of these techniques can markedly reverse neurological deficits.Recently,extracellular vesicles from various cell sources have been applied to different models of spinal cord injury,thereby generating new cell-free therapies for the treatment of spinal cord injury.However,the use of extracellular vesicles alone is still associated with some notable shortcomings,such as their uncertainty in targeting damaged spinal cord tissues and inability to provide structural support to damaged axons.Therefore,this paper reviews the latest combined strategies for the use of extracellular vesicle-based technology for spinal cord injury,including the combination of extracellular vesicles with nanoparticles,exogenous drugs and/or biological scaffold materials,which facilitate the targeting ability of extracellular vesicles and the combinatorial effects with extracellular vesicles.We also highlight issues relating to the clinical transformation of these extracellular vesicle-based combination strategies for the treatment of spinal cord injury.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Recent advances in spatio-temporally controllable systems for management of glioma

Malignant glioma remains one of the most aggressive intracranial tumors with devastating clinical outcomes despite the great advances in conventional treatment approaches,including surgery and chemotherapy.Spatio-temporally controllable approaches to glioma are now being actively investigated due to the preponderance,including spatio-temporal adjustability,minimally invasive,repetitive properties,etc.External stimuli can be readily controlled by adjusting the site and density of stimuli to exert the cytotoxic on glioma tissue and avoid undesired injury to normal tissues.It is worth noting that the removability of external stimuli allows for on-demand treatment,which effectively reduces the occurrence of side effects.In this review,we highlight recent advancements in drug delivery systems for spatio-temporally controllable treatments of glioma,focusing on the mechanisms and design principles of sensitizers utilized in these controllable therapies.Moreover,the potential challenges regarding spatio-temporally controllable therapy for glioma are also described,aiming to provide insights into future advancements in this field and their potential clinical applications.

Epidemic Characteristics and Spatio-Temporal Patterns of HFRS in Qingdao City,China,2010-2022

Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.

Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

Combination Activity of Standard Antituberculosis Drugs and Extracts of Medicinal Plants Commonly Used in Traditional Treatment of Tuberculosis in Uganda

Introduction: Resistance to antituberculosis drugs and adverse drug reactions remain the leading causes of tuberculosis therapeutic failure globally. Despite the increasing acceptance of medicinal plant use in combination with conventional antituberculosis drugs in treatment of tuberculosis (TB) in Uganda, there is paucity of knowledge on their combination effect. Aim: This research aimed to determine combination activity of standard antituberculosis drugs with extracts of Zanthoxylum leprieurii Guill. & Perr. and Rubia cordifolia L., the two common antituberculosis medicinal plants in Uganda, against pansensitive (H37Rv) and multi-drug resistant (MDR) Mycobacterium tuberculosis strains. Materials and Methods: Two reference MTB strains (H37Rv and MDR strain) were inoculated on Middlebrook 7H11 medium containing a combination of standard antituberculosis drugs and methanol extracts of Z. leprieurii and R. cordifolia at varying concentrations. The number of colonies on the plates was observed and counted weekly for up to 8 weeks. In vitro combination activity was determined using proportion method. Mean percentage inhibition was calculated for the reduction of number of colonies on drug-extract combination medium in relation to drug-extract-free control medium. Results: Drug-extract combinations showed good combination activity against Mycobacterium tuberculosis strains when compared with individual standard anti-TB drugs. This was more exhibited against MDR strain. There was however a reduction in percentage inhibition when extracts were combined with ethambutol and streptomycin against H37Rv strain. Conclusions: Zanthoxylum leprieurii and Rubia cordifolia in combination with standard anti-TB drugs exhibited increased in vitro activity against Mycobacterium tuberculosis, especially MDR-TB strain. This justifies the local use of these plants in traditional treatment of tuberculosis especially in resistant cases in Uganda.

A Bioequivalence Study of Empagliflozin/Metformin Fixed-Dose Combination in Healthy Subjects under Fasting Conditions

Background: This study evaluated the bioequivalence of empagliflozin 12.5 mg/metformin 1000 mg tablets compared to Synjardy® (Empagliflozin 12.5 mg/metformin 1000 mg) tablets in healthy male subjects under fasting conditions. Methods: This was a phase I, randomized, single-dose, two-period, two-sequence, crossover study to evaluate the bioequivalence (BE) profiles of two fixed-dose combinations (FDCs) of empagliflozin/metformin. Cmax, AUC0-t and AUC0-∞ from test and reference formulations were evaluated to access BE. The plasma concentrations were measured using a validated liquid chromatography-mass spectrometry (LC-MS/MS) method. Of the 24 subjects enrolled, 23 completed both periods of the study. The two formulations test and reference were considered bioequivalent if 90% confidence interval (CI) fell within 80.00% - 125.00% for Cmax, AUC0-t and AUC0-∞. Tolerability and safety were assessed throughout the study. Results: The pharmacokinetic (PK) parameters were similar between the test product (T) and reference product (R) Synjardy®. The 90% CI of the test/reference ratios of log-transformed PK parameters point estimates was Cmax: 89.87% (85.68% - 94.27%), AUC0-t: 87.91% (83.65% - 92.39%) and AUC0-∞: 87.16% (82.80% - 91.75%) to empagliflozin and Cmax: 92.19% (87.95% - 96.65%), AUC0-t: 91.38% (84.42% - 98.91%) and AUC0-∞: 93.78% (83.82% - 104.93%) to metformin respectively (90% CI for all PK parameters fell within 80.00% - 125.00%). Conclusion: Our results demonstrated BE between the test and reference formulations of oral tablets of empagliflozin 12.5 mg/metformin 1000 mg (FDC) in healthy male subjects under fasting conditions.

Enhancing metformin-induced tumor metabolism destruction by glucose oxidase for triple-combination therapy

Despite decades of laboratory and clinical trials,breast cancer remains the main cause of cancer-related disease burden in women.Considering the metabolism destruction effect of metformin(Met)and cancer cell starvation induced by glucose oxidase(GOx),after their efficient delivery to tumor sites,GOx and Met may consume a large amount of glucose and produce sufficient hydrogen peroxide in situ.Herein,a pH-responsive epigallocatechin gallate(EGCG)-conjugated low-molecular-weight chitosan(LC-EGCG,LE)nanoparticle(Met–GOx/Fe@LE NPs)was constructed.The coordination between iron ions(Fe3+)and EGCG in this nanoplatform can enhance the efficacy of chemodynamic therapy via the Fenton reaction.Met–GOx/Fe@LE NPs allow GOx to retain its enzymatic activity while simultaneously improving its stability.Moreover,this pH-responsive nanoplatform presents controllable drug release behavior.An in vivo biodistribution study showed that the intracranial accumulation of GOx delivered by this nanoplatform was 3.6-fold higher than that of the free drug.The in vivo anticancer results indicated that this metabolism destruction/starvation/chemodynamic triple-combination therapy could induce increased apoptosis/death of tumor cells and reduce their proliferation.This triple-combination therapy approach is promising for efficient and targeted cancer treatment.

Adaptive H_(∞)Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.

Combinational therapy with Myc decoy oligodeoxynucleotides encapsulated in nanocarrier and X-irradiation on breast cancer cells

The Myc gene is the essential oncogene in triple-negative breast cancer(TNBC).This study investigates the synergistic effects of combining Myc decoy oligodeoxynucleotides-encapsulated niosomes-selenium hybrid nanocarriers with X-irradiation exposure on the MDA-MB-468 cell line.Decoy and scramble ODNs for Myc transcription factor were designed and synthesized based on promoter sequences of the Bcl2 gene.The nanocarriers were synthesized by loading Myc ODNs and selenium into chitosan(Chi-Se-DEC),which was then encapsulated in niosome-nanocarriers(NISM@Chi-Se-DEC).FT-IR,DLS,FESEM,and hemolysis tests were applied to confirm its characterization and physicochemical properties.Moreover,cellular uptake,cellular toxicity,apoptosis,cell cycle,and scratch repair assays were performed to evaluate its anticancer effects on cancer cells.All anticancer assessments were repeated under X-ray irradiation conditions(fractionated 2Gy).Physicochemical characteristics of niosomes containing SeNPs and ODNs showed that it is synthesized appropriately.It revealed that the anticancer effect of NISM@Chi-Se-DEC can be significantly improved in combination with X-ray irradiation treatment.It can be concluded that NISM@Chi-Se-DEC nanocarriers have the potential as a therapeutic agent for cancer treatment,particularly in combination with radiation therapy and in-vivo experiments are necessary to confirm the efficacy of this nano-drug.

Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Soil erosion has been recognized as a critical environmental issue worldwide.While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective,there is a notable gap in understanding the intricate interplay between natural and socio-economic factors,especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions.To address this,our study evaluates the soil erosion vulnerability at a provincial scale,taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors.We developed an evaluation index system based on 15 indicators of soil erosion vulnerability:exposure,sensitivity,and adaptability.In addition,the combination weighting method was applied to determine index weights,and the spatial interaction was analyzed using spatial autocorrelation,geographical temporally weighted regression and geographical detector.The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020.The soil erosion vulnerability increased before 2000 and then.The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province(Xiantao,Tianmen,Qianjiang and Ezhou)with obvious spatial aggregation that intensified over time.Natural factors(habitat quality index)had negative impacts on soil erosion vulnerability,whereas socio-economic factors(population density)showed substantial spatial variability in their influences.There was a positive correlation between soil erosion vulnerability and erosion intensity,with the correlation coefficients ranging from-0.41 and 0.93.The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity.

LociScan,a tool for screening genetic marker combinations for plant variety discrimination

To reduce the cost and increase the efficiency of plant genetic marker fingerprinting for variety discrimination,it is desirable to identify the optimal marker combinations.We describe a marker combination screening model based on the genetic algorithm(GA)and implemented in a software tool,Loci Scan.Ratio-based variety discrimination power provided the largest optimization space among multiple fitness functions.Among GA parameters,an increase in population size and generation number enlarged optimization depth but also calculation workload.Exhaustive algorithm afforded the same optimization depth as GA but vastly increased calculation time.In comparison with two other software tools,Loci Scan accommodated missing data,reduced calculation time,and offered more fitness functions.In large datasets,the sample size of training data exerted the strongest influence on calculation time,whereas the marker size of training data showed no effect,and target marker number had limited effect on analysis speed.