Gut flora in multiple sclerosis:implications for pathogenesis and treatment

Multiple sclerosis is an inflammatory disorder chara cterized by inflammation,demyelination,and neurodegeneration in the central nervous system.Although current first-line therapies can help manage symptoms and slow down disease progression,there is no cure for multiple sclerosis.The gut-brain axis refers to complex communications between the gut flo ra and the immune,nervous,and endocrine systems,which bridges the functions of the gut and the brain.Disruptions in the gut flora,termed dys biosis,can lead to systemic inflammation,leaky gut syndrome,and increased susceptibility to infections.The pathogenesis of multiple sclerosis involves a combination of genetic and environmental factors,and gut flora may play a pivotal role in regulating immune responses related to multiple scle rosis.To develop more effective therapies for multiple scle rosis,we should further uncover the disease processes involved in multiple sclerosis and gain a better understanding of the gut-brain axis.This review provides an overview of the role of the gut flora in multiple scle rosis.

A Review on Vibration Control of Multiple Cylinders Subjected to FlowInduced Vibrations

The fatigue damage caused by flow-induced vibration(FIV)is one of the major concerns for multiple cylindrical structures in many engineering applications.The FIV suppression is of great importance for the security of many cylindrical structures.Many active and passive control methods have been employed for the vibration suppression of an isolated cylinder undergoing vortex-induced vibrations(VIV).The FIV suppression methods are mainly extended to the multiple cylinders from the vibration control of the isolated cylinder.Due to the mutual interference between the multiple cylinders,the FIV mechanism is more complex than the VIV mechanism,which makes a great challenge for the FIV suppression.Some efforts have been devoted to vibration suppression of multiple cylinder systems undergoing FIV over the past two decades.The control methods,such as helical strakes,splitter plates,control rods and flexible sheets,are not always effective,depending on many influence factors,such as the spacing ratio,the arrangement geometrical shape,the flow velocity and the parameters of the vibration control devices.The FIV response,hydrodynamic features and wake patterns of the multiple cylinders equipped with vibration control devices are reviewed and summarized.The FIV suppression efficiency of the vibration control methods are analyzed and compared considering different influence factors.Further research on the FIV suppression of multiple cylinders is suggested to provide insight for the development of FIV control methods and promote engineering applications of FIV control methods.

C634Y mutation in RET-induced multiple endocrine neoplasia type 2A:A case report

BACKGROUND Multiple endocrine neoplasia type 2(MEN2)is a rare,autosomal dominant endocrine disease.Currently,the RET proto-oncogene is the only gene implicated in MEN2A pathogenesis.Once an RET carrier is detected,family members should be screened to enable early detection of medullary thyroid carcinoma,pheochromocytoma,and hyperparatitity.Among these,medullary thyroid carcinoma is the main factor responsible for patient mortality.Accordingly,delineating strategies to inform clinical follow-up and treatment plans based on genes is paramount for clinical practitioners.CASE SUMMARY Herein,we present RET proto-oncogene mutations,clinical characteristics,and treatment strategies in a family with MEN2A.A family study was conducted on patients diagnosed with MEN2A.DNA was extracted from the peripheral blood of family members,and first-generation exon sequencing of the RET protooncogene was conducted.The C634Y mutation was identified in three family members spanning three generations.Two patients were sequentially diagnosed with pheochromocytomas and bilateral medullary thyroid carcinomas.A 9-yearold child harboring the gene mutation was diagnosed with medullary thyroid carcinoma.Surgical resection of the tumors was performed.All family members were advised to undergo complete genetic testing related to the C634Y mutation,and the corresponding treatments administered based on test results and associated clinical guidelines.CONCLUSION Advancements in MEN2A research are important for familial management,assessment of medullary thyroid cancer invasive risk,and deciding surgical timing.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

In situ formation of multiple catalysts for enhancing the hydrogen storage of MgH_(2) by adding porous Ni_(3)ZnC_(0.7)/Ni loaded carbon nanotubes microspheres

MgH_(2) is considered one of the most promising hydrogen storage materials because of its safety,high efficiency,high hydrogen storage quantity and low cost characteristics.But some shortcomings are still existed:high operating temperature and poor hydrogen absorption dynamics,which limit its application.Porous Ni_(3)ZnC_(0.7)/Ni loaded carbon nanotubes microspheres(NZC/Ni@CNT)is prepared by facile filtration and calcination method.Then the different amount of NZC/Ni@CNT(2.5,5.0 and 7.5 wt%)is added to the MgH_(2) by ball milling.Among the three samples with different amount of NZC/Ni@CNT(2.5,5.0 and 7.5 wt%),the MgH_(2)-5 wt%NZC/Ni@CNT composite exhibits the best hydrogen storage performances.After testing,the MgH_(2)-5 wt%NZC/Ni@CNT begins to release hydrogen at around 110℃ and hydrogen absorption capacity reaches 2.34 wt%H_(2) at 80℃ within 60 min.Moreover,the composite can release about 5.36 wt%H_(2) at 300℃.In addition,hydrogen absorption and desorption activation energies of the MgH_(2)-5 wt%NZC/Ni@CNT composite are reduced to 37.28 and 84.22 KJ/mol H_(2),respectively.The in situ generated Mg_(2)NiH_(4)/Mg_(2)Ni can serve as a"hydrogen pump"that plays the main role in providing more activation sites and hydrogen diffusion channels which promotes H_(2) dissociation during hydrogen absorption process.In addition,the evenly dispersed Zn and MgZn2 in Mg and MgH_(2) could provide sites for Mg/MgH_(2) nucleation and hydrogen diffusion channel.This attempt clearly proved that the bimetallic carbide Ni_(3)ZnC_(0.7) is a effective additive for the hydrogen storage performances modification of MgH_(2),and the facile synthesis of the Ni_(3)ZnC_(0.7)/Ni@CNT can provide directions of better designing high performance carbide catalysts for improving MgH_(2).

SMSTracker:A Self-Calibration Multi-Head Self-Attention Transformer for Visual Object Tracking

Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information.However,current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information.In this paper,we introduce self-calibration multi-head self-attention Transformer(SMSTracker)as a solution to these challenges.It employs a hybrid tensor decomposition self-organizing multihead self-attention transformermechanism,which not only compresses and accelerates Transformer operations but also significantly reduces redundant data,thereby enhancing the accuracy and efficiency of tracking.Additionally,we introduce a self-calibration attention fusion block to resolve common issues of attention ambiguities and inconsistencies found in traditional trackingmethods,ensuring the stability and reliability of tracking performance across various scenarios.By integrating a hybrid tensor decomposition approach with a self-organizingmulti-head self-attentive transformer mechanism,SMSTracker enhances the efficiency and accuracy of the tracking process.Experimental results show that SMSTracker achieves competitive performance in visual object tracking,promising more robust and efficient tracking systems,demonstrating its potential to providemore robust and efficient tracking solutions in real-world applications.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

An Aerial Target Recognition Algorithm Based on Self-Attention and LSTM

In the application of aerial target recognition,on the one hand,the recognition error produced by the single measurement of the sensor is relatively large due to the impact of noise.On the other hand,it is difficult to apply machine learning methods to improve the intelligence and recognition effect due to few or no actual measurement samples.Aiming at these problems,an aerial target recognition algorithm based on self-attention and Long Short-Term Memory Network(LSTM)is proposed.LSTM can effectively extract temporal dependencies.The attention mechanism calculates the weight of each input element and applies the weight to the hidden state of the LSTM,thereby adjusting the LSTM’s attention to the input.This combination retains the learning ability of LSTM and introduces the advantages of the attention mechanism,making the model have stronger feature extraction ability and adaptability when processing sequence data.In addition,based on the prior information of the multidimensional characteristics of the target,the three-point estimation method is adopted to simulate an aerial target recognition dataset to train the recognition model.The experimental results show that the proposed algorithm achieves more than 91%recognition accuracy,lower false alarm rate and higher robustness compared with the multi-attribute decision-making(MADM)based on fuzzy numbers.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

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 selfattention 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.

The Extended Hybrid Carrier-Based Multiple Access Technology for High Mobility Scenarios

The hybrid carrier(HC)system rooted in the carrier fusion concept is gradually garnering attention.In this paper,we study the extended hybrid carrier(EHC)multiple access scheme to ensure reliable wireless communication.By employing the EHC modulation,a power layered multiplexing framework is realized,which exhibits enhanced interference suppression capability owing to the more uniform energy distribution design.The implementation method and advantage mechanism are explicated respectively for the uplink and downlink,and the performance analysis under varying channel conditions is provided.In addition,considering the connectivity demand,we explore the non-orthogonal multiple access(NOMA)method of the EHC system and develop the EHC sparse code multiple access scheme.The proposed scheme melds the energy spread superiority of EHC with the access capacity of NOMA,facilitating superior support for massive connectivity in high mobility environments.Simulation results have verified the feasibility and advantages of the proposed scheme.Compared with existing HC multiple access schemes,the proposed scheme exhibits robust bit error rate performance and can better guarantee multiple access performance in complex scenarios of nextgeneration communications.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

Stroke Electroencephalogram Data Synthesizing through Progressive Efficient Self-Attention Generative Adversarial Network

Early and timely diagnosis of stroke is critical for effective treatment,and the electroencephalogram(EEG)offers a low-cost,non-invasive solution.However,the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning.To address this issue,our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention(PCGAN-EASA),which incrementally improves the quality of generated EEG features.This network can yield full-scale,fine-grained EEG features from the low-scale,coarse ones.Specially,to overcome the limitations of traditional generative models that fail to generate features tailored to individual patient characteristics,we developed an encoder with an effective approximating self-attention mechanism.This encoder not only automatically extracts relevant features across different patients but also reduces the computational resource consumption.Furthermore,the adversarial loss and reconstruction loss functions were redesigned to better align with the training characteristics of the network and the spatial correlations among electrodes.Extensive experimental results demonstrate that PCGAN-EASA provides the highest generation quality and the lowest computational resource usage compared to several existing approaches.Additionally,it significantly improves the accuracy of subsequent stroke classification tasks.

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

Multiple factors influencing high-purity indium electrolytic refining

The effects of various contaminants in the electrolytic refinement of indium were investigated using a glow discharge mass spectrometer(GDMS).The effects of several factors such as the indium ion(In3+)concentration,the sodium chloride(NaCl)concentration,the current density,the gelatin concentration,the pH,and the electrode distance,were examined.Significant variations in impurity levels concerning gelatin concentration were observed.Both the gelatin and In3+concentration were moderately positively correlated with the Pb content.The Sb concentration was associated positively with the NaCl concentration,while the Ti concentration had an adverse correlation with the NaCl concentration.The Bi element content was positively linked to the electrode distance.As the current density increased,Cu,Pb,and Bi impurities initially rose and then eventually declined.Notably,a critical current density of 45 A·m^(-2) was identified in this behavior.

Clinical features and possible pathogenesis of multiple evanescent white dot syndrome with different retinal diseases and events:a narrative review

●Multiple evanescent white dot syndrome(MEWDS)is a rare fundus disease,characterized by acute vision loss and visual field defects.Many previous studies have explained the possible pathogenesis and clinical features of primary MEWDS.However,as the number of reported cases increases,secondary MEWDS occurs in other related retinal diseases and injuries,exhibiting some special characteristics.The associated retinal diseases include multifocal choroiditis/punctate inner choroidopathy(MFC/PIC),acute zonal occult outer retinopathy,best vitelliform macular dystrophy,pseudoxanthoma elasticum,and ocular toxoplasmosis.The related retinal injury is laser photocoagulation,surgery,and trauma.Although primary MEWDS often have a self-limiting course,secondary MEWDS may require treatment in some cases,according to the severity of concomitant diseases and complications.Notably,MEWDS secondary to MFC/PIC that is prone to forming choroidal neovascularization and focal choroidal excavation,needs positive treatment with corticosteroids.The possible underlying pathogenesis of secondary MEWDS is the exposure of choroidal antigen after the disruption of Bruch’s membrane.The MEWDS-related features in secondary MEWDS are still evanescent under most circumstances.Its prognosis and treatment depend on the severity of complications.Current studies propose that the etiology is associated with immune factors,including viral infection,inflammation in choroid and Bruch’s membrane,and antigen exposure caused by retinal and/or choroidal insults.More pathogenic studies should be conducted in the future.Accurate diagnosis for secondary MEWDS could benefit patients in aspects of management and prognosis.

Infection in Multiple Myeloma: Microbiological Profile and Prognosis in Senegalese Patients

Introduction: Infections are additional factors of morbidity and mortality in multiple myeloma (MM), and the current recommendation is antibiotic prophylaxis. In sub-Saharan Africa, few data on infectious complications of MM are available. We aim to describe the microbiological features of infections in MM, and their impact on survival in Senegalese patients. Methods: A retrospective (January 2005-January 2022), analytic, multicenter study on infections in patients followed for MM (IMWG criteria) in Senegalese clinical hematology services. The socio-epidemiological, diagnostic, microbiological, evolutionary and survival aspects were analyzed. Results: The study included 106 patients with multiple myeloma who had an infection at admission or during the treatment. Ten patients have the comorbidity (hypertension, lupus, type 2 diabetes). These patients had 136 infectious events identified at diagnosis (79.2%) or during chemotherapy (20.8%). The sites of infection are lung (42.6%), urinary (29.4%), dermatological (6.6%), digestive (5.2%), osteoarticular (4.4%), ear, nose and throat (3.7%), central nervous system (1.5%), or without site. We recorded 26.4% of patients with multi-site infections. The causal pathogens are bacteria (Gram-negative bacilli: 22.1%;Gram positive bacilli: 9.5%, Mycobacterium tuberculosis: 13.3%), parasitique (plasmodium falciparum 6.6%), viruses (SARS-COV2: 2.9%, VZV: 2.2%) and fungal (2.9%). Survival was reduced in patients who had an infection at the time of multiple myeloma diagnosis (p: 0.189) and those who had multiple infectious foci (p: 0.011). Conclusion: Infections in multiple myeloma are more frequent at diagnosis. The germs are varied and mostly bacteria, particularly gram-negative bacteria, and Kochs bacillus. Our study reveals that multiple infectious foci are a poor prognosis factor. It is necessary to evaluate the infectious risk early, and to adopt an antibiotic prophylaxis based on our tropical environment.

Three-dimensional magnetic reconnection in complex multiple X-point configurations in an ancient solar-lunar terrestrial system

Magnetic reconnection processes in three-dimensional(3D)complex field configurations have been investigated in different magneto-plasma systems in space,laboratory,and astrophysical systems.Two-dimensional(2D)features of magnetic reconnection have been well developed and applied successfully to systems with symmetrical property,such as toroidal fusion plasmas and laboratory experiments with an axial symmetry.But in asymmetric systems,the 3D features are inevitably different from those in the 2D case.Magnetic reconnection structures in multiple celestial body systems,particularly star-planet-Moon systems,bring fresh insights to the understanding of the 3D geometry of reconnection.Thus,we take magnetic reconnection in an ancient solar-lunar terrestrial magneto-plasma system as an example by using its crucial parameters approximately estimated already and also some specific applications in pathways for energy and matter transports among Earth,ancient Moon,and the interplanetary magnetic field(IMF).Then,magnetic reconnection of the ancient lunar-terrestrial magnetospheres with the IMF is investigated numerically in this work.In a 3D simulation for the Earth-Moon-IMF system,topological features of complex magnetic reconnection configurations and dynamical characteristics of magnetic reconnection processes are studied.It is found that a coupled lunar-terrestrial magnetosphere is formed,and under various IMF orientations,multiple X-points emerge at distinct locations,showing three typical magnetic reconnection structures in such a geometry,i.e.,the X-line,the triple current sheets,and the A-B null pairs.The results can conduce to further understanding of reconnection physics in 3D for plasmas in complex magnetic configurations,and also a possible mechanism for energy and matters transport in evolutions of similar astrophysical systems.

Development of a cell adhesion-based prognostic model for multiple myeloma:Insights into chemotherapy response and potential reversal of adhesion effects

Multiple myeloma(MM)is a hematologic malignancy notorious for its high relapse rate and development of drug resistance,in which cell adhesion-mediated drug resistance plays a critical role.This study integrated four RNA sequencing datasets(CoMMpass,GSE136337,GSE9782,and GSE2658)and focused on analyzing 1706 adhesionrelated genes.Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes,including KIF14,TROAP,FLNA,MSN,LGALS1,PECAM1,and ALCAM,which demonstrated the strongest associations with poor overall survival(OS)in MM patients.To comprehensively evaluate the impact of cell adhesion on MM prognosis,an adhesion-related risk score(ARRS)model was constructed using Lasso Cox regression analysis.The ARRS model emerged as an independent prognostic factor for predicting OS.Furthermore,our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs,protein kinase inhibitors,and drugs targeting the PI3K/Akt/mTOR signaling pathway.Nevertheless,we identified promising drug candidates,such as tirofiban,pirenzepine,erlotinib,and bosutinib,which exhibit potential in reversing this resistance.In vitro,experiments employing NCIH929,RPMI8226,and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs.By employing siRNA-mediated knockdown of the key ARRS model gene KIF14,we observed suppressed proliferation of NCIH929 cells,along with decreased adhesion to BMSCs and fibronectin.This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM.Additionally,potential molecular mechanisms underlying adhesion-related resistance are proposed,along with viable strategies to overcome such resistance.These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.

MULTIPLE INTERSECTIONS OF SPACE-TIME ANISOTROPIC GAUSSIAN FIELDS

Let X={X(t)∈R^(d),t∈R^(N)}be a centered space-time anisotropic Gaussian field with indices H=(H_(1),…,H_(N))∈(0,1)~N,where the components X_(i)(i=1,…,d)of X are independent,and the canonical metric√(E(X_(i)(t)-X_(i)(s))^(2))^(1/2)(i=1,…,d)is commensurate with■for s=(s_(1),…,s_(N)),t=(t_(1),…,t_(N))∈R~N,α_(i)∈(0,1],and with the continuous functionγ(·)satisfying certain conditions.First,the upper and lower bounds of the hitting probabilities of X can be derived from the corresponding generalized Hausdorff measure and capacity,which are based on the kernel functions depending explicitly onγ(·).Furthermore,the multiple intersections of the sample paths of two independent centered space-time anisotropic Gaussian fields with different distributions are considered.Our results extend the corresponding results for anisotropic Gaussian fields to a large class of space-time anisotropic Gaussian fields.