Targeting neuronal PAS domain protein 2 and KN motif/ankyrin repeat domains 1:Advances in type 2 diabetes therapy

This editorial summarizes the latest literature on the roles of neuronal PAS domain protein 2 and KN motif/ankyrin repeat domain 1 in type 2 diabetes(T2D).We highlight their involvement inβ-cell dysfunction,explore their potential as therapeutic targets,and discuss the implications for new treatment strategies.We offer valuable insights into relevant gene regulation and cellular mechanisms relevant for the targeted management of T2D.

C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 pathway as a therapeutic target and regulatory mechanism for spinal cord injury

Spinal cord injury involves non-reversible damage to the central nervous system that is characterized by limited regenerative capacity and secondary inflammatory damage.The expression of the C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 axis exhibits significant differences before and after injury.Recent studies have revealed that the C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 axis is closely associated with secondary inflammatory responses and the recruitment of immune cells following spinal cord injury,suggesting that this axis is a novel target and regulatory control point for treatment.This review comprehensively examines the therapeutic strategies targeting the C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 axis,along with the regenerative and repair mechanisms linking the axis to spinal cord injury.Additionally,we summarize the upstream and downstream inflammatory signaling pathways associated with spinal cord injury and the C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 axis.This review primarily elaborates on therapeutic strategies that target the C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 axis and the latest progress of research on antagonistic drugs,along with the approaches used to exploit new therapeutic targets within the C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 axis and the development of targeted drugs.Nevertheless,there are presently no clinical studies relating to spinal cord injury that are focusing on the C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 axis.This review aims to provide new ideas and therapeutic strategies for the future treatment of spinal cord injury.

The Citron homology domain of MAP4Ks improves outcomes of traumatic brain injury

The mitogen-activated protein kinase kinase kinase kinases(MAP4Ks)signaling pathway plays a pivotal role in axonal regrowth and neuronal degeneration following insults.Whether targeting this pathway is beneficial to brain injury remains unclear.In this study,we showed that adeno-associated virus-delivery of the Citron homology domain of MAP4Ks effectively reduces traumatic brain injury-induced reactive gliosis,tauopathy,lesion size,and behavioral deficits.Pharmacological inhibition of MAP4Ks replicated the ameliorative effects observed with expression of the Citron homology domain.Mechanistically,the Citron homology domain acted as a dominant-negative mutant,impeding MAP4K-mediated phosphorylation of the dishevelled proteins and thereby controlling the Wnt/β-catenin pathway.These findings implicate a therapeutic potential of targeting MAP4Ks to alleviate the detrimental effects of traumatic brain injury.

Interaction of major facilitator superfamily domain containing 2A with the blood-brain barrier

The functional and structural integrity of the blood-brain barrier is crucial in maintaining homeostasis in the brain microenvironment;however,the molecular mechanisms underlying the formation and function of the blood-brain barrier remain poorly understood.The major facilitator superfamily domain containing 2A has been identified as a key regulator of blood-brain barrier function.It plays a critical role in promoting and maintaining the formation and functional stability of the blood-brain barrier,in addition to the transport of lipids,such as docosahexaenoic acid,across the blood-brain barrier.Furthermore,an increasing number of studies have suggested that major facilitator superfamily domain containing 2A is involved in the molecular mechanisms of blood-brain barrier dysfunction in a variety of neurological diseases;however,little is known regarding the mechanisms by which major facilitator superfamily domain containing 2A affects the blood-brain barrier.This paper provides a comprehensive and systematic review of the close relationship between major facilitator superfamily domain containing 2A proteins and the blood-brain barrier,including their basic structures and functions,cross-linking between major facilitator superfamily domain containing 2A and the blood-brain barrier,and the in-depth studies on lipid transport and the regulation of blood-brain barrier permeability.This comprehensive systematic review contributes to an in-depth understanding of the important role of major facilitator superfamily domain containing 2A proteins in maintaining the structure and function of the blood-brain barrier and the research progress to date.This will not only help to elucidate the pathogenesis of neurological diseases,improve the accuracy of laboratory diagnosis,and optimize clinical treatment strategies,but it may also play an important role in prognostic monitoring.In addition,the effects of major facilitator superfamily domain containing 2A on blood-brain barrier leakage in various diseases and the research progress on cross-blood-brain barrier drug delivery are summarized.This review may contribute to the development of new approaches for the treatment of neurological diseases.

Central role of Yes-associated protein and WW-domain-containing transcriptional co-activator with PDZ-binding motif in pancreatic cancer development

Pancreatic ductal adenocarcinoma(PDAC) remains a deadly disease with no efficacious treatment options. PDAC incidence is projected to increase, which may be caused at least partially by the obesity epidemic. Significantly enhanced efforts to prevent or intercept this cancer are clearly warranted. Oncogenic KRAS mutations are recognized initiating events in PDAC development, however, they are not entirely sufficient for the development of fully invasive PDAC.Additional genetic alterations and/or environmental, nutritional, and metabolic signals, as present in obesity, type-2 diabetes mellitus, and inflammation, are required for full PDAC formation. We hypothesize that oncogenic KRAS increases the intensity and duration of the growth-promoting signaling network.Recent exciting studies from different laboratories indicate that the activity of the transcriptional co-activators Yes-associated protein(YAP) and WW-domaincontaining transcriptional co-activator with PDZ-binding motif(TAZ) play a critical role in the promotion and maintenance of PDAC operating as key downstream target of KRAS signaling. While initially thought to be primarily an effector of the tumor-suppressive Hippo pathway, more recent studies revealed that YAP/TAZ subcellular localization and co-transcriptional activity is regulated by multiple upstream signals. Overall, YAP has emerged as a central node of transcriptional convergence in growth-promoting signaling in PDAC cells. Indeed, YAP expression is an independent unfavorable prognostic marker for overall survival of PDAC. In what follows, we will review studies implicating YAP/TAZ in pancreatic cancer development and consider different approaches to target these transcriptional regulators.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

基于Motif的图采样算法

图采样通过对图数据进行约简操作,获得比原图的规模更小的图结构,进而服务于图谱分析、图可视化等下游任务.现有的图采样算法侧重于保留图中显著的结构特征而忽略了节点属性,导致采样图在许多下游任务如频繁模式挖掘等,难以取得预期效果.为此,提出基于Motif的节点有偏采样算法(Motif-Based Node Biased Sampling,MNBS),利用频繁Motif结构重新定义图中节点的重要性,随后进行有偏节点采样,实现融合节点属性与结构特征的采样.为了快速识别频繁Motif模式,设计了具有“提前终止”特性的Motif模式快速发现算法(Fast Motif-Pattern Discovery,FMPD),能高效且准确地发现Motif模式以支持图采样.实验表明,MNBS采样算法在多项指标上优于其他基线算法,其对数归一化累积组相关性指标平均降低0.54,使用包含“提前终止”特性策略的FMPD算法的时间消耗和内存消耗比基线算法分别降低56.1%和29.8%.

基于3D-motif法的刮研表面形貌表征

针对刮研表面微观特性和功能机理研究中缺少表面形貌量化表征方法的问题,采用3D-motif法对刮研表面形貌进行表征。使用LI-3型接触式三维表面形貌测量仪对刮研表面进行测量,并利用三维点云数据生成刮研表面的二维灰度图像。然后,根据3D-motif法中集水盆地的定义,采用分水岭算法对刮研表面的灰度图像进行motif分割与合并。以不同精度等级的刮研表面的整体纹理区域motif分割结果为对象,定义了特征显著度并提取计算了刮研表面在2种不同面积尺度(25 mm^(2)和0.25 mm^(2))上的深度、面积、方向角、各向异性率、扁平系数和特征显著度等6项motif参数。结合部分motif参数的分布情况和motif数量的变化趋势,从形貌特征的尺寸和形态两个方面对刮研表面进行了表征和分析,实现了以较少参数完整表征刮研表面的三维形貌。研究结果可为后续刮研表面微观特性的深入分析提供理论基础。

Delineating homogeneous domains of fractured rocks using topological manifolds and deep learning

Determining homogeneous domains statistically is helpful for engineering geological modeling and rock mass stability evaluation.In this text,a technique that can integrate lithology,geotechnical and structural information is proposed to delineate homogeneous domains.This technique is then applied to a high and steep slope along a road.First,geological and geotechnical domains were described based on lithology,faults,and shear zones.Next,topological manifolds were used to eliminate the incompatibility between orientations and other parameters(i.e.trace length and roughness)so that the data concerning various properties of each discontinuity can be matched and characterized in the same Euclidean space.Thus,the influence of implicit combined effect in between parameter sequences on the homogeneous domains could be considered.Deep learning technique was employed to quantify abstract features of the characterization images of discontinuity properties,and to assess the similarity of rock mass structures.The results show that the technique can effectively distinguish structural variations and outperform conventional methods.It can handle multisource engineering geological information and multiple discontinuity parameters.This technique can also minimize the interference of human factors and delineate homogeneous domains based on orientations or multi-parameter with arbitrary distributions to satisfy different engineering requirements.

Docking of Human Band 3 Anion Transporter Proteins with Their Plasmodium falciparum Interactors Based on Short Linear Motifs

Plasmodium (P.) falciparum is a pathogen that causes severe forms of malaria. Protein interactions have been shown to occur between P. falciparum and human erythrocytes in human blood. The Band 3 Anion Transporter (B3AT) protein is considered the main invasive pathway for the parasite in erythrocytes that causes clinical symptoms for malaria in humans. The interactions between P. falciparum parasites and erythrocytes along this receptor have previously been explored. Short linear motifs (SLIMs) are short linear mediator sequences that involve several biological processes, acting as mediators of protein interactions identifiable by computational tools such as SLiMFinder. For a given protein, the identification of SLIMs allows predicting its interactors. Using the SLIMs approach, protein-protein interaction network analyses between P. falciparum and its human host, were used to identify a tryptophan-rich protein, A5K5E5_PLAVS as an essential interactor of B3AT. To better understand the interaction mechanism, a guided protein-protein docking approach based on SLIM motifs was performed for human B3AT and A5K5E5_PLAVS. The highlights of this important interaction between P. falciparum and its human host have the potential to pave the way to identify new therapeutic candidates.

Multi-layer network embedding on scc-based network with motif

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

Consistency between domain wall oscillation modes and spin wave modes in nanostrips

Investigations on domain wall(DW) and spin wave(SW) modes in a series of nanostrips with different widths and thicknesses have been carried out using micromagnetic simulation. The simulation results show that the frequencies of SW modes and the corresponding DW modes are consistent with each other if they have the same node number along the width direction. This consistency is more pronounced in wide and thin nanostrips, favoring the DW motion driven by SWs.Further analysis of the moving behavior of a DW driven by SWs is also carried out. The average DW speed can reach a larger value of ~ 140 m/s under two different SW sources. We argue that this study is very meaningful for the potential application of DW motion driven by SWs.

Mapping the antiparallel aligned domain rotation by microwave excitation

The evolution process of magnetic domains in response to external fields is crucial for the modern understanding and application of spintronics.In this study,we investigated the domain rotation in stripe domain films of varying thicknesses by examining their response to microwave excitation in four different orientations.The resonance spectra indicate that the rotation field of stripe domain film under an applied magnetic field approaches the field where the resonance mode of sample changes.The saturation field of the stripe domain film corresponds to the field where the resonance mode disappears when measured in the stripe direction parallel to the microwave magnetic field.The results are reproducible and consistent with micromagnetic simulations,providing additional approaches and techniques for comprehending the microscopic mechanisms of magnetic domains and characterizing their rotation.

Low-Rank Optimal Transport for Robust Domain Adaptation

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

Multiple Matching Attenuation Based on Curvelet Domain Extended Filtering

The paper develops a multiple matching attenuation method based on extended filtering in the curvelet domain,which combines the traditional Wiener filtering method with the matching attenuation method in curvelet domain.Firstly,the method uses the predicted multiple data to generate the Hilbert transform records,time derivative records and time derivative records of Hilbert transform.Then,the above records are transformed into the curvelet domain and multiple matching attenuation based on least squares extended filtering is performed.Finally,the attenuation results are transformed back into the time-space domain.Tests on the model data and field data show that the method proposed in the paper effectively suppress the multiples while preserving the primaries well.Furthermore,it has higher accuracy in eliminating multiple reflections,which is more suitable for the multiple attenuation tasks in the areas with complex structures compared to the time-space domain extended filtering method and the conventional curvelet transform method.

Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.

BIG HANKEL OPERATORS ON HARDY SPACES OF STRONGLY PSEUDOCONVEX DOMAINS

In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f ) is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO and we obtain some characterizations for Hf on H^(2)(Ω) of other pseudoconvex domains.In these arguments,Amar's L^(p)-estimations and Berndtsson's L^(2)-estimations for solutions of the ■_(b)-equation play a crucial role.In addition,we solve Gleason's problem for Hardy spaces H^(p)(Ω)(1 ≤p≤∞) of bounded strongly pseudoconvex domains.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

ON THE SOBOLEV DOLBEAULT COHOMOLOGY OF A DOMAIN WITH PSEUDOCONCAVE BOUNDARIES

In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded pseudoconvex domains in ℂ^(n) with smooth boundaries,and Ω_(1),…,Ω_(m) are mutually disjoint.The main results can also be quickly obtained by virtue of[5].