Generative AI for brain image computing and brain network computing: a review.

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial.

Computation-Efficient Fault Detection Framework for Partially Known Nonlinear Distributed Parameter Systems.

Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

Robust Adaptive Fault-Tolerant Control for a Riser-Vessel System With Input Hysteresis and Time-Varying Output Constraints.

Recently, with the development of the marine economy, marine risers have garnered increasing attention as they present facile and reliable methods for oil and gas transportation. However, these risers are susceptible to vibrations, which can lead to system performance degradation and fatigue damage. Therefore, effective vibration control strategies are required to address this issue. In this study, a novel adaptive fault-tolerant control (FTC) strategy is adopted to suppress the vibrations of a 3-D riser-vessel system against the effects of actuator failures, backlash-like hysteresis, and external disturbances. A barrier-based Lyapunov function is merged to eliminate the time-varying output constraints of the system. Adaptive FTC laws with projection mapping operators are designed to compensate for parameter uncertainties and consider input nonlinearities to improve system robustness. Finally, a rigorous Lyapunov analysis and numerical simulations are performed to verify the validity of the proposed controller and guarantee uniformly bounded stability of the system.

Isolation of BVDV-1a, 1m, and 1v strains from diarrheal calf in china and identification of its genome sequence and cattle virulence.

Bovine viral diarrhea virus (BVDV) is an important livestock viral pathogen responsible for causing significant economic losses. The emerging and novel BVDV isolates are clinically and biologically important, as there are highly antigenic diverse and pathogenic differences among BVDV genotypes. However, no study has yet compared the virulence of predominant genotype isolates (BVDV-1a, 1b, and 1m) in China and the emerging genotype isolate BVDV-1v. The serological relationship among these genotypes has not yet been described. In this study, we isolated three BVDV isolates from calves with severe diarrhea, characterized as BVDV-1a, 1m, and novel 1v, based on multiple genomic regions [including 5-untranslated region (5'-UTR), Npro, and E2] and the phylogenetic analysis of nearly complete genomes. For the novel genotype, genetic variation analysis of the E2 protein of the BVDV-1v HB-03 strain indicates multiple amino acid mutation sites, including potential host cell-binding sites and neutralizing epitopes. Recombination analysis of the BVDV-1v HB-03 strain hinted at the possible occurrence of cross-genotypes (among 1m, 1o, and 1q) and cross-geographical region transmission events. To compare the pathogenic characters and virulence among these BVDV-1 genotypes, newborn calves uninfected with common pathogens were infected intranasally with BVDV isolates. The calves infected with the three genotype isolates show different symptom severities (diarrhea, fever, slowing weight gain, virus shedding, leukopenia, viremia, and immune-related tissue damage). In addition, these infected calves also showed bovine respiratory disease complexes (BRDCs), such as nasal discharge, coughing, abnormal breathing, and lung damage. Based on assessing different parameters, BVDV-1m HB-01 is identified as a highly virulent strain, and BVDV-1a HN-03 and BVDV-1v HB-03 are both identified as moderately virulent strains. Furthermore, the cross-neutralization test demonstrated the antigenic diversity among these Chinese genotypes (1a, 1m, and 1v). Our findings illustrated the genetic evolution characteristics of the emerging genotype and the pathogenic mechanism and antigenic diversity of different genotype strains, These findings also provided an excellent vaccine candidate strain and a suitable BVDV challenge strain for the comprehensive prevention and control of BVDV.

HIF-1 stabilization in T cells hampers the control of Mycobacterium tuberculosis infection.

The hypoxia-inducible factors (HIFs) regulate the main transcriptional pathway of response to hypoxia in T cells and are negatively regulated by von Hippel-Lindau factor (VHL). But the role of HIFs in the regulation of CD4 T cell responses during infection with M. tuberculosis isn't well understood. Here we show that mice lacking VHL in T cells (Vhl cKO) are highly susceptible to infection with M. tuberculosis, which is associated with a low accumulation of mycobacteria-specific T cells in the lungs that display reduced proliferation, altered differentiation and enhanced expression of inhibitory receptors. In contrast, HIF-1 deficiency in T cells is redundant for M. tuberculosis control. Vhl cKO mice also show reduced responses to vaccination. Further, VHL promotes proper MYC-activation, cell-growth responses, DNA synthesis, proliferation and survival of CD4 T cells after TCR activation. The VHL-deficient T cell responses are rescued by the loss of HIF-1α, indicating that the increased susceptibility to M. tuberculosis infection and the impaired responses of Vhl-deficient T cells are HIF-1-dependent.

High Glucose and Carbonyl Stress Impair HIF-1-Regulated Responses and the Control of Mycobacterium tuberculosis in Macrophages.

Diabetes mellitus (DM) increases the risk of developing tuberculosis (TB), but the mechanisms behind diabetes-TB comorbidity are still undefined. Here, we studied the role of hypoxia-inducible factor-1 (HIF-1), a main regulator of metabolic and inflammatory responses, in the outcome of Mycobacterium tuberculosis infection of bone marrow-derived macrophages (BMM). We observed that M. tuberculosis infection of BMM increased the expression of HIF-1α and HIF-1-regulated genes. Treatment with the hypoxia mimetic deferoxamine (DFO) further increased levels of HIF-1-regulated immune and metabolic molecules and diminished the intracellular bacterial load in BMM and in the lungs of infected mice. The expression of HIF-1-regulated immunometabolic genes was reduced, and the intracellular M. tuberculosis levels were increased in BMM incubated with high-glucose levels or with methylglyoxal (MGO), a reactive carbonyl compound elevated in DM. In line with the in vitro findings, high M. tuberculosis levels and low HIF-1-regulated transcript levels were found in the lungs from hyperglycemic Leprdb/db compared with wild-type mice. The increased intracellular M. tuberculosis growth and the reduced expression of HIF-1-regulated metabolic and inflammatory genes in BMM incubated with MGO or high glucose were reverted by additional treatment with DFO. Hif1a-deficient BMM showed ablated responses of immunometabolic transcripts after mycobacterial infection at normal or high-glucose levels. We propose that HIF-1 may be targeted for the control of M. tuberculosis during DM. IMPORTANCE People living with diabetes who are also infected with M. tuberculosis are more likely to develop tuberculosis disease (TB). Why diabetic patients have an increased risk for developing TB is not well understood. Macrophages, the cell niche for M. tuberculosis, can express microbicidal mechanisms or be permissive to mycobacterial persistence and growth. Here, we showed that high glucose and carbonyl stress, which mediate diabetes pathogenesis, impair the control of intracellular M. tuberculosis in macrophages. Infection with M. tuberculosis stimulated the expression of genes regulated by the transcription factor HIF-1, a major controller of the responses to hypoxia, resulting in macrophage activation. High glucose and carbonyl compounds inhibited HIF-1 responses by macrophages. Mycobacterial control in the presence of glucose or carbonyl stress was restored by DFO, a compound that stabilizes HIF-1. We propose that HIF-1 can be targeted to reduce the risk of developing TB in people with diabetes.

Spatial Decomposition-Based Fault Detection Framework for Parabolic-Distributed Parameter Processes.

Fault detection for distributed parameter processes (heat processes, fluid processes, etc.) is vital for safe and efficient operation. On one hand, the existing data-driven methods neglect the evolution dynamics of the processes and cannot guarantee that they work for highly dynamic or transient processes; on the other hand, model-based methods reported so far are mostly based on the backstepping technique, which does not possess enough redundancy for fault detection since only the boundary measurement is considered. Motivated by these considerations, we intend to investigate the robust fault detection problem for distributed parameter processes in a model-based perspective covering both boundary and in-domain measurement cases. A real-time fault detection filter (FDF) is presented, which gets rid of a large amount of data collection and offline training procedures. Rigorous theoretic analysis is presented for guiding the parameters selection and threshold computation. A time-varying threshold is designed such that the false alarm in the transient stage can be avoided. Successful application results on a hot strip mill cooling system demonstrate the potential for real industrial applications.

Basis Function Matrix-Based Flexible Coefficient Autoregressive Models: A Framework for Time Series and Nonlinear System Modeling.

We propose, in this paper, a framework for time series and nonlinear system modeling, called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) model. It has very flexible nonlinear structure. We show that many famous nonlinear time series models can be derived under this framework by choosing the proper basis function matrices. Some probabilistic properties (the conditions of geometrical ergodicity) of the BFM-FCAR model are investigated. Taking advantage of the model structure, we present an efficient parameter estimation algorithm for the proposed framework by using the variable projection method. Finally, we show how new models are generated from the proposed framework.

Fuzzy Control Under Spatially Local Averaged Measurements for Nonlinear Distributed Parameter Systems With Time-Varying Delay.

This paper introduces a fuzzy control (FC) under spatially local averaged measurements (SLAMs) for nonlinear-delayed distributed parameter systems (DDPSs) represented by parabolic partial differential-difference equations (PDdEs), where the fast-varying time delay and slow-varying one are considered. A Takagi-Sugeno (T-S) fuzzy PDdE model is first derived to exactly describe the nonlinear DDPSs. Then, by virtue of the T-S fuzzy PDdE model and a Lyapunov-Krasovskii functional, an FC design under SLAMs, where the membership functions of the proposed FC law are determined by the measurement output and independent of the fuzzy PDdE plant model, is developed on basis of spatial linear matrix inequalities (SLMIs) to guarantee the exponential stability for the resulting closed-loop DDPSs. Lastly, a numerical example is offered to support the presented approach.

Quantized Sampled-Data Synchronization of Delayed Reaction-Diffusion Neural Networks Under Spatially Point Measurements.

This article considers the synchronization problem of delayed reaction-diffusion neural networks via quantized sampled-data (SD) control under spatially point measurements (SPMs), where distributed and discrete delays are considered. The synchronization scheme, which takes into account the communication limitations of quantization and variable sampling, is based on SPMs and only available in a finite number of fixed spatial points. By utilizing inequality techniques and Lyapunov-Krasovskii functional, some synchronization criteria via a quantized SD controller under SPMs are established and presented by linear matrix inequalities, which can ensure the exponential stability of the synchronization error system containing the drive and response dynamics. Finally, two numerical examples are offered to support the proposed quantized SD synchronization method.

Ivermectin Inhibits Bovine Herpesvirus 1 DNA Polymerase Nuclear Import and Interferes with Viral Replication.

Bovine herpesvirus1 (BoHV-1) is a major bovine pathogen. Despite several vaccines being available to prevent viral infection, outbreaks are frequent and cause important economic consequences worldwide. The development of new antiviral drugs is therefore highly desirable. In this context, viral genome replication represents a potential target for therapeutic intervention. BoHV-1 genome is a dsDNA molecule whose replication takes place in the nuclei of infected cells and is mediated by a viral encoded DNA polymerase holoenzyme. Here, we studied the physical interaction and subcellular localization of BoHV-1 DNA polymerase subunits in cells for the first time. By means of co-immunoprecipitation and confocal laser scanning microscopy (CLSM) experiments, we could show that the processivity factor of the DNA polymerase pUL42 is capable of being autonomously transported into the nucleus, whereas the catalytic subunit pUL30 is not. Accordingly, a putative classic NLS (cNLS) was identified on pUL42 but not on pUL30. Importantly, both proteins could interact in the absence of other viral proteins and their co-expression resulted in accumulation of UL30 to the cell nucleus. Treatment of cells with Ivermectin, an anti-parasitic drug which has been recently identified as an inhibitor of importin α/ß-dependent nuclear transport, reduced UL42 nuclear import and specifically reduced BoHV-1 replication in a dose-dependent manner, while virus attachment and entry into cells were not affected. Therefore, this study provides a new option of antiviral therapy for BoHV-1 infection with Ivermectin.

Reinforcement Learning-Based Optimal Sensor Placement for Spatiotemporal Modeling.

A reinforcement learning-based method is proposed for optimal sensor placement in the spatial domain for modeling distributed parameter systems (DPSs). First, a low-dimensional subspace, derived by Karhunen-Loève decomposition, is identified to capture the dominant dynamic features of the DPS. Second, a spatial objective function is proposed for the sensor placement. This function is defined in the obtained low-dimensional subspace by exploiting the time-space separation property of distributed processes, and in turn aims at minimizing the modeling error over the entire time and space domain. Third, the sensor placement configuration is mathematically formulated as a Markov decision process (MDP) with specified elements. Finally, the sensor locations are optimized through learning the optimal policies of the MDP according to the spatial objective function. The experimental results of a simulated catalytic rod and a real snap curing oven system are provided to demonstrate the feasibility and efficiency of the proposed method in solving the combinatorial optimization problems, such as optimal sensor placement.

Incremental Reinforcement Learning in Continuous Spaces via Policy Relaxation and Importance Weighting.

In this paper, a systematic incremental learning method is presented for reinforcement learning in continuous spaces where the learning environment is dynamic. The goal is to adjust the previously learned policy in the original environment to a new one incrementally whenever the environment changes. To improve the adaptability to the ever-changing environment, we propose a two-step solution incorporated with the incremental learning procedure: policy relaxation and importance weighting. First, the behavior policy is relaxed to a random one in the initial learning episodes to encourage a proper exploration in the new environment. It alleviates the conflict between the new information and the existing knowledge for a better adaptation in the long term. Second, it is observed that episodes receiving higher returns are more in line with the new environment, and hence contain more new information. During parameter updating, we assign higher importance weights to the learning episodes that contain more new information, thus encouraging the previous optimal policy to be faster adapted to a new one that fits in the new environment. Empirical studies on continuous controlling tasks with varying configurations verify that the proposed method achieves a significantly faster adaptation to various dynamic environments than the baselines.

Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples.

Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods.

Fructose-1,6-bisphosphate aldolase is involved in Mycoplasma bovis colonization as a fibronectin-binding adhesin.

Mycoplasma bovis is a common pathogenic microorganism of cattle and represents an important hazard on the cattle industry. Adherence to host cells is a significant component of mycoplasma-pathogenesis research. Fibronectin (Fn), an extracellular matrix protein, is a common host cell factor that can interact with the adhesions of pathogens. The aims of this study were to investigate the Fn-binding properties of M. bovis fructose-1,6-bisphosphate aldolase (FBA) and evaluate its role as a cell adhesion factor during mycoplasma colonization. The fba (MBOV_RS00435) gene of M. bovis was cloned and expressed, with the resulting recombinant protein used to prepare rabbit polyclonal antibodies. The purified recombinant FBA (rFBA) was shown to have fructose bisphosphate aldolase activity. Western blot indicated that FBA was an antigenically conserved protein in several M. bovis strains. Western blot combined with immunofluorescent assay (IFA) revealed that FBA was dual-localized to both cytoplasm and membrane in M. bovis. IFA showed that rFBA was able to adhere to embryonic bovine lung (EBL) cells. Meanwhile, an adhesion inhibition assay demonstrated that anti-rFBA antibodies could significantly block the adhesion of M. bovis to EBL cells. Moreover, a dose-dependent binding of rFBA to Fn was found by dot blotting and enzyme-linked immunosorbent assays. Together these results provided evidence that FBA is a surface-localized and antigenic protein of M. bovis, suggesting that it may function as a virulence determinant through interacting with host Fn.

Learning Rates of Regularized Regression With Multiple Gaussian Kernels for Multi-Task Learning.

This paper considers a least square regularized regression algorithm for multi-task learning in a union of reproducing kernel Hilbert spaces (RKHSs) with Gaussian kernels. It is assumed that the optimal prediction function of the target task and those of related tasks are in an RKHS with the same but with unknown Gaussian kernel width. The samples for related tasks are used to select the Gaussian kernel width, and the sample for the target task is used to obtain the prediction function in the RKHS with this selected width. With an error decomposition result, a fast learning rate is obtained for the target task. The key step is to estimate the sample errors of related tasks in the union of RKHSs with Gaussian kernels. The utility of this algorithm is illustrated with one simulated data set and four real data sets. The experiment results illustrate that the underlying algorithm can result in significant improvements in prediction error when few samples of the target task and more samples of related tasks are available.

Probabilistic Regularized Extreme Learning Machine for Robust Modeling of Noise Data.

The extreme learning machine (ELM) has been extensively studied in the machine learning field and has been widely implemented due to its simplified algorithm and reduced computational costs. However, it is less effective for modeling data with non-Gaussian noise or data containing outliers. Here, a probabilistic regularized ELM is proposed to improve modeling performance with data containing non-Gaussian noise and/or outliers. While traditional ELM minimizes modeling error by using a worst-case scenario principle, the proposed method constructs a new objective function to minimize both mean and variance of this modeling error. Thus, the proposed method considers the modeling error distribution. A solution method is then developed for this new objective function and the proposed method is further proved to be more robust when compared with traditional ELM, even when subject to noise or outliers. Several experimental cases demonstrate that the proposed method has better modeling performance for problems with non-Gaussian noise or outliers.

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning.

In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.

Uncertain Data Clustering in Distributed Peer-to-Peer Networks.

Uncertain data clustering has been recognized as an essential task in the research of data mining. Many centralized clustering algorithms are extended by defining new distance or similarity measurements to tackle this issue. With the fast development of network applications, these centralized methods show their limitations in conducting data clustering in a large dynamic distributed peer-to-peer network due to the privacy and security concerns or the technical constraints brought by distributive environments. In this paper, we propose a novel distributed uncertain data clustering algorithm, in which the centralized global clustering solution is approximated by performing distributed clustering. To shorten the execution time, the reduction technique is then applied to transform the proposed method into its deterministic form by replacing each uncertain data object with its expected centroid. Finally, the attribute-weight-entropy regularization technique enhances the proposed distributed clustering method to achieve better results in data clustering and extract the essential features for cluster identification. The experiments on both synthetic and real-world data have shown the efficiency and superiority of the presented algorithm.