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.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

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.

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.

Optimizing trade-offs between light transmittance and intraspecific competition under varying crop layouts in a maize-soybean strip relay cropping system

Light is one of the most important environmental factors for plant growth and development.In relay cropping systems,crop layouts influence light distribution,affecting light use efficiency(LUE).However,the response of light interception,light conversion,and LUE for relay maize and relay soybean to different crop layouts remains unclear.We aimed to quantify the effect of crop layout on intraspecific and interspecific competition,light interception,light conversion,LUE,and land productivity between relay maize and relay soybean.We conducted a field experiment for four consecutive years from 2017 to 2020 in Sichuan province,China,comparing different crop layouts(bandwidth 2.0 m,row ratio 2:2;bandwidth 2.4 m,row ratio 2:3;bandwidth 2.8 m,row ratio 2:4),with sole maize and sole soybean as controls.The results showed that relay maize in the 2.0 m bandwidth layout had the largest leaf area index and plant biomass,the lowest intraspecific competitive intensity and the highest aggressiveness.Compared to a bandwidth of 2.0 m,a bandwidth of 2.8 m significantly decreased relay maize leaf area index by 11%and plant biomass by 24%,while a 2.4 m bandwidth caused roughly half these reductions.The 2.0 m bandwidth layout also significantly improved crop light interception and LUE compared to sole maize.The light interception,light interception rate,light conversion rate and LUE in relay maize all decreased significantly with increasing bandwidth,but they increased in relay soybean.The increased light transmittance to the lower and middle canopy with increasing bandwidth did not compensate for the loss of relay maize yield caused by increased intraspecific competition.However,it enhanced the yield of relay soybeans.Increasing the bandwidth by 80 cm increased the relay maize intraspecific competition by 580%,and reduced maize yield by 33%,light interception by 12%,and LUE by 18%.In contrast,the relay soybean intraspecific competition was reduced by 64%,and the soybean yield was increased by 26%,light interception by 32%and LUE by 46%.Relay cropping systems with a 2.0 m bandwidth optimize the trade-off between light transmittance and intraspecific competition of relay crops.These systems achieve the highest LUE,group yield and economic benefits,making them a recommended crop layout for the southwest regions of China.Our study offers valuable insights for developing strip relay cropping systems that maximize light utilization and contributes to the theoretical understanding of efficient sunlight use in relay cropping practices.

Development of Fixture Layout Optimization for Thin-Walled Parts:A Review

An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing literature,this paper summarizes the process of fixture layout optimization and the methods applied.The process of optimization is made up of optimization objective setting,assembly variation/deformation modeling,and fixture layout optimization.This paper makes a review of the fixture layout for thin-walled parts according to these three steps.First,two different kinds of optimization objectives are introduced.Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives.Then,modeling methods for assembly variation and deformation are divided into two categories:Mechanism-based and data-based methods.Several common methods are discussed respectively.After that,optimization algorithms are reviewed systematically.There are two kinds of optimization algorithms:Traditional nonlinear programming and heuristic algorithms.Finally,discussions on the current situation are provided.The research direction of fixture layout optimization in the future is discussed from three aspects:Objective setting,improving modeling accuracy and optimization algorithms.Also,a new research point for fixture layout optimization is discussed.This paper systematically reviews the research on fixture layout optimization for thin-walled parts,and provides a reference for future research in this field.

Performance-Oriented Layout Synthesis for Quantum Computing

Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits(qubits)can only interact effectively with their nearest neighbors.This constraint severely impacts the design and efficiency of quantum algorithms,as arranging qubits optimally can significantly reduce circuit depth and improve computational performance.To tackle the layout synthesis challenge,we propose an algorithm based on integer linear programming(ILP).ILP is well-suited for this problem as it can formulate the optimization objective of minimizing circuit depth while adhering to the nearest neighbor interaction constraint.The algorithm aims to generate layouts that maximize qubit connectivity within the given physical constraints of the quantum device.For experimental validation,we outline a clear and feasible setup using real quantum devices.This includes specifying the type and configuration of the quantum hardware used,such as the number of qubits,connectivity constraints,and any technological limitations.The proposed algorithm is implemented on these devices to demonstrate its effectiveness in producing depth-optimal quantum circuit layouts.By integrating these elements,our research aims to provide practical solutions to enhance the efficiency and scalability of quantum computing systems,paving the way for advancements in quantum algorithm design and implementation.

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.

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.

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.

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].

Diabetic retinopathy identification based on multi-sourcefree domain adaptation

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

Magnetic domain structures in ultrathin Bi_(2)Te_(3)/CrTe_(2) heterostructures

Chromium tellurium compounds are important two-dimensional van der Waals ferromagnetic materials with high Curie temperature and chemical stability in air,which is promising for applications in spintronic devices.Here,highquality spin-orbital-torque(SOT)device,Bi_(2)Te_(3)/CrTe_(2)heterostructure was epitaxially grown on Al_(2)O_(3)(0001)substrates.Anomalous Hall measurements indicate the existence of strong ferromagnetism in this device with the CrTe_(2)thickness down to 10 nm.In order to investigate its micromagnetic structure,cryogenic magnetic force microscope(MFM)was utilized to measure the magnetic domain evolutions at various temperatures and magnetic fields.The virgin domain state of the device shows a worm-like magnetic domain structure with the size around 0.6μm-0.8μm.Larger irregular-shape magnetic domains(>1μm)can be induced and pinned,after the field is increased to coercive field and ramped back to low fields.The temperature-dependent MFM signals exhibit a nice mean-field-like ferromagnetic transition with Curie temperature around 201.5 K,indicating a robust ferromagnetic ordering.Such a device can be potentially implemented in future magnetic memory technology.