Comparative evaluation of interpretation methods in surface-based age prediction for neonates.

Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods-regional age prediction and the perturbation-based saliency map approach-for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.

Tandem mass tag-based proteomics reveals the antiepileptic mechanism of steroidal saponins from Anemarrhena asphodeloides in Kainic acid induced epileptic rat model.

Epilepsy (EP) is one of the most common neurological diseases in the world. Anemarrhena asphodeloides Bunge. (AA), as a typical heat-cleaning medicine, has been proven to possess the antiepileptic effect in clinical and experimental studies. Anemarrhena asphodeloides steroidal saponins (AAS) are main components. However, the therapeutic effects and underlying mechanisms of AAS against EP are not been fully elucidated. In this study, 63 steroidal saponins were discovered in AAS by UPLC-Q-TOF/MS analysis. Pharmacological and behavioral analysis demonstrated that AAS could significantly lower the Racine classification and reduce the frequency of generalized spike rhythm the rate of tetanic seizures in kainic acid-induced epileptic rats. Hematoxylin and eosin and Nissl staining-indicated AAS could significantly improve hippocampal injury and neuron loss in epileptic rats. TMT proteomic analysis discovered 26 different expressed proteins (DEPs), which were identified as the rescue proteins. After bioinformatic analysis, Heat Shock Protein 90 Alpha Family Class B Member 1 (Hsp90ab1) and Tyrosine 3-Monooxygenase (Ywhab) were screened as key DEPs and verified by western blotting. AAS could significantly inhibited the up-regulation of Hsp90ab1 and Ywhab in EP rats; these two proteins might be the key targets of AAS in treating EP.

Dynamic chromatin accessibility profiling reveals changes in host genome organization in response to baculovirus infection.

DNA viruses can hijack and manipulate the host chromatin state to facilitate their infection. Multiple lines of evidences reveal that DNA virus infection results in the host chromatin relocation, yet there is little known about the effects of viral infection on the architecture of host chromatin. Here, a combination of epigenomic, transcriptomic and biochemical assays were conducted to investigate the temporal dynamics of chromatin accessibility in response to Bombyx mori nucleopolyhedrovirus (BmNPV) infection. The high-quality ATAC-seq data indicated that progressive chromatin remodeling took place following BmNPV infection. Viral infection resulted in a more open chromatin architecture, along with the marginalization of host genome and nucleosome disassembly. Moreover, our results revealed that chromatin accessibility in uninfected cells was regulated by euchromatic modifications, whereas the viral-induced highly accessible chromatin regions were originally associated with facultative heterochromatic modification. Overall, our findings illustrate for the first time the organization and accessibility of host chromatin in BmNPV-infected cells, which lay the foundation for future studies on epigenomic regulation mediated by DNA viruses.

Protein-protein interactions of the baculovirus per os infectivity factors (PIFs) in the PIF complex.

After ingestion of occlusion bodies, the occlusion-derived viruses (ODVs) of the baculoviruses establish the first round of infection within the larval host midgut cells. Several ODV envelope proteins, called per os infectivity factors (PIFs), have been shown to be essential for oral infection. Eight PIFs have been identified to date, including P74, PIFs 1-6 and Ac110. At least six PIFs, P74, PIFs 1-4 and PIF6, together with three other ODV-specific proteins, Ac5, P95 (Ac83) and Ac108, have been reported to form a complex on the ODV surface. In this study, in order to understand the interactions of these PIFs, the direct protein-protein interactions of the nine components of the Autographa californica multiple nucleopolyhedrovirus PIF complex were investigated using yeast two-hybrid (Y2H) screening combined with bimolecular fluorescence complementation (BiFC) assay. Six direct interactions, comprising PIF1-PIF2, PIF1-PIF3, PIF1-PIF4, PIF1-P95, PIF2-PIF3 and PIF3-PIF4, were identified in the Y2H analysis, and these results were further verified by BiFC. For P74, PIF6, Ac5 and Ac108, no direct interaction was identified. P95 (Ac83) was identified to interact with PIF1, and further Y2H analysis of the truncation and deletion mutants showed that the predicted P95 chitin-binding domain and amino acids 100-200 of PIF1 were responsible for P95 interaction with PIF1. Furthermore, a summary of the protein-protein interactions of PIFs reported so far, comprising 10 reciprocal interactions and two self-interactions, is presented, which will facilitate our understanding of the characteristics of the PIF complex.

Identification of Bombyx mori nucleopolyhedrovirus bm58a as an auxiliary gene and its requirement for cell lysis and larval liquefaction.

Bombyx mori nucleopolyhedrovirus orf58a (bm58a) and its homologues are highly conserved in genomes of all sequenced group I alphabaculoviruses and its function is still unknown. Transcriptional analysis revealed that bm58a is a very late gene initiated from a late transcriptional start motif TAAG. To examine its role in the virus, a bm58a knockout virus (vBmbm-58a-KO-PH-GFP) was generated through homologous recombination in Escherichia coli. Analysis of fluorescence microscopy, titration assays and electron microscopy examination showed that the deletion of bm58a did not affect viral replication and occlusion bodies formation in vitro, indicating that bm58a is not required for viral propagation. However, vBmbm-58a-KO-PH-GFP did not result in cell lysis when wild-type virus infected cells began to lyse, and the vBmbm-58a-KO-PH-GFP infected cells remained intact until 2 weeks post-infection. Quantification of polyhedra release from cells confirmed this observation. Accordingly, though deletion of bm58a did not reduce Bombyx mori nucleopolyhedrovirus infectivity in vivo in bioassays, it did significantly disrupt the larval liquefaction, reducing the level of polyhedra release from infected host. Immunofluorescence analysis demonstrated that Bm58a was predominantly localized on the cellular membrane at the late stage of infection, which may contribute to its function of facilitating cell lysis and larval liquefaction. Our results suggest that although bm58a is not essential for viral propagation as an auxiliary gene, it is a key factor of virus-induced cell lysis and larval liquefaction in vitro and in vivo.

Transcriptome analysis of the brain of the silkworm Bombyx mori infected with Bombyx mori nucleopolyhedrovirus: A new insight into the molecular mechanism of enhanced locomotor activity induced by viral infection.

Baculoviruses have been known to induce hyperactive behavior in their lepidopteran hosts for over a century. As a typical lepidopteran insect, the silkworm Bombyx mori displays enhanced locomotor activity (ELA) following infection with B. mori nucleopolyhedrovirus (BmNPV). Some investigations have focused on the molecular mechanisms underlying this abnormal hyperactive wandering behavior due to the virus; however, there are currently no reports about B. mori. Based on previous studies that have revealed that behavior is controlled by the central nervous system, the transcriptome profiles of the brains of BmNPV-infected and non-infected silkworm larvae were analyzed with the RNA-Seq technique to reveal the changes in the BmNPV-infected brain on the transcriptional level and to provide new clues regarding the molecular mechanisms that underlies BmNPV-induced ELA. Compared with the controls, a total of 742 differentially expressed genes (DEGs), including 218 up-regulated and 524 down-regulated candidates, were identified, of which 499, 117 and 144 DEGs could be classified into GO categories, KEGG pathways and COG annotations by GO, KEGG and COG analyses, respectively. We focused our attention on the DEGs that are involved in circadian rhythms, synaptic transmission and the serotonin receptor signaling pathway of B. mori. Our analyses suggested that these genes were related to the locomotor activity of B. mori via their essential roles in the regulations of a variety of behaviors and the down-regulation of their expressions following BmNPV infection. These results provide new insight into the molecular mechanisms of BmNPV-induced ELA.

Wavelet-Improved Score-Based Generative Model for Medical Imaging.

The score-based generative model (SGM) has demonstrated remarkable performance in addressing challenging under-determined inverse problems in medical imaging. However, acquiring high-quality training datasets for these models remains a formidable task, especially in medical image reconstructions. Prevalent noise perturbations or artifacts in low-dose Computed Tomography (CT) or under-sampled Magnetic Resonance Imaging (MRI) hinder the accurate estimation of data distribution gradients, thereby compromising the overall performance of SGMs when trained with these data. To alleviate this issue, we propose a wavelet-improved denoising technique to cooperate with the SGMs, ensuring effective and stable training. Specifically, the proposed method integrates a wavelet sub-network and the standard SGM sub-network into a unified framework, effectively alleviating inaccurate distribution of the data distribution gradient and enhancing the overall stability. The mutual feedback mechanism between the wavelet sub-network and the SGM sub-network empowers the neural network to learn accurate scores even when handling noisy samples. This combination results in a framework that exhibits superior stability during the learning process, leading to the generation of more precise and reliable reconstructed images. During the reconstruction process, we further enhance the robustness and quality of the reconstructed images by incorporating regularization constraint. Our experiments, which encompass various scenarios of low-dose and sparse-view CT, as well as MRI with varying under-sampling rates and masks, demonstrate the effectiveness of the proposed method by significantly enhanced the quality of the reconstructed images. Especially, our method with noisy training samples achieves comparable results to those obtained using clean data. Our code at https://zenodo.org/record/8266123.

Two-and-a-half order score-based model for solving 3D ill-posed inverse problems.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial technologies in the field of medical imaging. Score-based models demonstrated effectiveness in addressing different inverse problems encountered in the field of CT and MRI, such as sparse-view CT and fast MRI reconstruction. However, these models face challenges in achieving accurate three dimensional (3D) volumetric reconstruction. The existing score-based models predominantly concentrate on reconstructing two-dimensional (2D) data distributions, resulting in inconsistencies between adjacent slices in the reconstructed 3D volumetric images. To overcome this limitation, we propose a novel two-and-a-half order score-based model (TOSM). During the training phase, our TOSM learns data distributions in 2D space, simplifying the training process compared to working directly on 3D volumes. However, during the reconstruction phase, the TOSM utilizes complementary scores along three directions (sagittal, coronal, and transaxial) to achieve a more precise reconstruction. The development of TOSM is built on robust theoretical principles, ensuring its reliability and efficacy. Through extensive experimentation on large-scale sparse-view CT and fast MRI datasets, our method achieved state-of-the-art (SOTA) results in solving 3D ill-posed inverse problems, averaging a 1.56 dB peak signal-to-noise ratio (PSNR) improvement over existing sparse-view CT reconstruction methods across 29 views and 0.87 dB PSNR improvement over existing fast MRI reconstruction methods with × 2 acceleration. In summary, TOSM significantly addresses the issue of inconsistency in 3D ill-posed problems by modeling the distribution of 3D data rather than 2D distribution which has achieved remarkable results in both CT and MRI reconstruction tasks.

Adaptive and Iterative Learning With Multi-Perspective Regularizations for Metal Artifact Reduction.

Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image domains or both. However, their performance is limited by the inherent characteristics of the two domains, i.e., the errors introduced by local manipulations in the sinogram domain would propagate throughout the whole image during backprojection and lead to serious secondary artifacts, while it is difficult to distinguish artifacts from actual image features in the image domain. To alleviate these limitations, this study analyzes the desirable properties of wavelet transform in-depth and proposes to perform MAR in the wavelet domain. First, wavelet transform yields components that possess spatial correspondence with the image, thereby preventing the spread of local errors to avoid secondary artifacts. Second, using wavelet transform could facilitate identification of artifacts from image since metal artifacts are mainly high-frequency signals. Taking these advantages of the wavelet transform, this paper decomposes an image into multiple wavelet components and introduces multi-perspective regularizations into the proposed MAR model. To improve the transparency and validity of the model, all the modules in the proposed MAR model are designed to reflect their mathematical meanings. In addition, an adaptive wavelet module is also utilized to enhance the flexibility of the model. To optimize the model, an iterative algorithm is developed. The evaluation on both synthetic and real clinical datasets consistently confirms the superior performance of the proposed method over the competing methods.

Wavelet-Inspired Multi-Channel Score-Based Model for Limited-Angle CT Reconstruction.

Score-based generative model (SGM) has demonstrated great potential in the challenging limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of the ground truth data and generates reconstruction results by sampling from it. Nevertheless, direct application of the existing SGM methods to LA-CT suffers multiple limitations. Firstly, the directional distribution of the artifacts attributing to the missing angles is ignored. Secondly, the different distribution properties of the artifacts in different frequency components have not been fully explored. These drawbacks would inevitably degrade the estimation of the probability density and the reconstruction results. After an in-depth analysis of these factors, this paper proposes a Wavelet-Inspired Score-based Model (WISM) for LA-CT reconstruction. Specifically, besides training a typical SGM with the original images, the proposed method additionally performs the wavelet transform and models the probability density in each wavelet component with an extra SGM. The wavelet components preserve the spatial correspondence with the original image while performing frequency decomposition, thereby keeping the directional property of the artifacts for further analysis. On the other hand, different wavelet components possess more specific contents of the original image in different frequency ranges, simplifying the probability density modeling by decomposing the overall density into component-wise ones. The resulting two SGMs in the image-domain and wavelet-domain are integrated into a unified sampling process under the guidance of the observation data, jointly generating high-quality and consistent LA-CT reconstructions. The experimental evaluation on various datasets consistently verifies the superior performance of the proposed method over the competing method.

Multi-Channel Optimization Generative Model for Stable Ultra-Sparse-View CT Reconstruction.

Score-based generative model (SGM) has risen to prominence in sparse-view CT reconstruction due to its impressive generation capability. The consistency of data is crucial in guiding the reconstruction process in SGM-based reconstruction methods. However, the existing data consistency policy exhibits certain limitations. Firstly, it employs partial data from the reconstructed image of the iteration process for image updates, which leads to secondary artifacts with compromising image quality. Moreover, the updates to the SGM and data consistency are considered as distinct stages, disregarding their interdependent relationship. Additionally, the reference image used to compute gradients in the reconstruction process is derived from the intermediate result rather than ground truth. Motivated by the fact that a typical SGM yields distinct outcomes with different random noise inputs, we propose a Multi-channel Optimization Generative Model (MOGM) for stable ultra-sparse-view CT reconstruction by integrating a novel data consistency term into the stochastic differential equation model. Notably, the unique aspect of this data consistency component is its exclusive reliance on original data for effectively confining generation outcomes. Furthermore, we pioneer an inference strategy that traces back from the current iteration result to ground truth, enhancing reconstruction stability through foundational theoretical support. We also establish a multi-channel optimization reconstruction framework, where conventional iterative techniques are employed to seek the reconstruction solution. Quantitative and qualitative assessments on 23 views datasets from numerical simulation, clinical cardiac and sheep's lung underscore the superiority of MOGM over alternative methods. Reconstructing from just 10 and 7 views, our method consistently demonstrates exceptional performance.

Constructing hierarchical attentive functional brain networks for early AD diagnosis.

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.

Asynchronous Functional Brain Network Construction with Spatiotemporal Transformer for MCI Classification.

Construction and analysis of functional brain networks (FBNs) with resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method to diagnose functional brain diseases. Nevertheless, the existing methods suffer from several limitations. First, the functional connectivities (FCs) of the FBN are usually measured by the temporal co-activation level between rs-fMRI time series from regions of interest (ROIs). While enjoying simplicity, the existing approach implicitly assumes simultaneous co-activation of all the ROIs, and models only their synchronous dependencies. However, the FCs are not necessarily always synchronous due to the time lag of information flow and cross-time interactions between ROIs. Therefore, it is desirable to model asynchronous FCs. Second, the traditional methods usually construct FBNs at individual level, leading to large variability and degraded diagnosis accuracy when modeling asynchronous FBN. Third, the FBN construction and analysis are conducted in two independent steps without joint alignment for the target diagnosis task. To address the first limitation, this paper proposes an effective sliding-window-based method to model spatiotemporal FCs in Transformer. Regarding the second limitation, we propose to learn common and individual FBNs adaptively with the common FBN as prior knowledge, thus alleviating the variability and enabling the network to focus on the individual disease-specific asynchronous FCs. To address the third limitation, the common and individual asynchronous FBNs are built and analyzed by an integrated network, enabling end-to-end training and improving the flexibility and discriminativity. The effectiveness of the proposed method is consistently demonstrated on three data sets for mild cognitive impairment (MCI) diagnosis.

Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction.

The multi-source stationary CT, where both the detector and X-ray source are fixed, represents a novel imaging system with high temporal resolution that has garnered significant interest. Limited space within the system restricts the number of X-ray sources, leading to sparse-view CT imaging challenges. Recent diffusion models for reconstructing sparse-view CT have generally focused separately on sinogram or image domains. Sinogram-centric models effectively estimate missing projections but may introduce artifacts, lacking mechanisms to ensure image correctness. Conversely, image-domain models, while capturing detailed image features, often struggle with complex data distribution, leading to inaccuracies in projections. Addressing these issues, the Dual-domain Collaborative Diffusion Sampling (DCDS) model integrates sinogram and image domain diffusion processes for enhanced sparse-view reconstruction. This model combines the strengths of both domains in an optimized mathematical framework. A collaborative diffusion mechanism underpins this model, improving sinogram recovery and image generative capabilities. This mechanism facilitates feedback-driven image generation from the sinogram domain and uses image domain results to complete missing projections. Optimization of the DCDS model is further achieved through the alternative direction iteration method, focusing on data consistency updates. Extensive testing, including numerical simulations, real phantoms, and clinical cardiac datasets, demonstrates the DCDS model's effectiveness. It consistently outperforms various state-of-the-art benchmarks, delivering exceptional reconstruction quality and precise sinogram.

A rapid localization and analysis method for isoquinoline alkaloids with fluorescence in Coptis chinensis Franch. By fabricating the nano-silver sol as a substrate for surface-enhanced Raman spectroscopy.

BACKGROUND: The quality of traditional Chinese medicines (TCMs) directly impacts their clinical efficacy and drug safety, making standardization a critical component of modern TCMs. Surface-enhanced Raman spectroscopy (SERS) is an effective physical detection method with speed, sensitivity, and suitability for large sample analyses. In this study, a SERS analysis method was developed using a nano-silver sol as the matrix to address the interference of fluorescence components in TCMs and overcome the limitations of traditional detection methods. RESULTS: The higher sensitivity and efficiency of SERS was used, enabling detection of a single sample within 30 s. Coptis chinensis Franch. (CCF) was chosen as the model medicine, the nano-silver sol was used as the matrix, and CCF's fourteen main fluorescent alkaloids were tested as index components. Typical signal peaks of the main components in CCF corresponded to the bending deformation of the nitrogen-containing ring plane outer ring system, methoxy stretching vibration, and isoquinoline ring deformation vibration. Through SERS detection of different parts, the distribution content of the main active components in the cortex of CCF was found to be lower than that in the xylem and phloem. Additionally, rapid quality control analyses indicated that among the nine batches of original medicinal materials purchased from Emei and Guangxi, the main active ingredient showed a higher content. SIGNIFICANCE: A SERS-based method for the rapid localization and analysis of multiple components of TCMs was established. The findings highlight the potential of SERS as a valuable tool for the analysis and quality control of TCMs, especially for fluorescent components.

Spectrum learning for super-resolution tomographic reconstruction.

Objective. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information.Approach. The study proposes a new deep learning based method called spectrum learning (SPEAR) network for CT images super-resolution. This approach leverages both global information in the image domain and high-frequency information in the frequency domain. The SPEAR network is designed to reconstruct high-resolution images from low-resolution inputs by considering not only the main body of the images but also the small structures and other details. The symmetric property of the spectrum is exploited to reduce weight parameters in the frequency domain. Additionally, a spectrum loss is introduced to enforce the preservation of both high-frequency components and global information.Main results. The network is trained using pairs of low-resolution and high-resolution CT images, and it is fine-tuned using additional low-dose and normal-dose CT image pairs. The experimental results demonstrate that the proposed SPEAR network outperforms state-of-the-art networks in terms of image reconstruction quality. The approach successfully preserves high-frequency information and small structures, leading to better results compared to existing methods. The network's ability to generate high-resolution images from low-resolution inputs, even in cases of low-dose CT images, showcases its effectiveness in maintaining image quality.Significance. The proposed SPEAR network's ability to simultaneously capture global information and high-frequency details addresses the limitations of existing methods, resulting in more accurate and informative image reconstructions. This advancement can have substantial implications for various industries and medical diagnoses relying on accurate imaging.

Dataset-Driven Unsupervised Object Discovery for Region-Based Instance Image Retrieval.

Instance image retrieval could greatly benefit from discovering objects in the image dataset. This not only helps produce more reliable feature representation but also better informs users by delineating query-matched object regions. However, object classes are usually not predefined in a retrieval dataset and class label information is generally unavailable in image retrieval. This situation makes object discovery a challenging task. To address this, we propose a novel dataset-driven unsupervised object discovery framework. By utilizing deep feature representation and weakly-supervised object detection, we explore supervisory information from within an image dataset, construct class-wise object detectors, and assign multiple detectors to each image for detection. To efficiently construct object detectors for large image datasets, we propose a novel "base-detector repository" and derive a fast way to generate the base detectors. In addition, the whole framework is designed to work in a self-boosting manner to iteratively refine object discovery. Compared with existing unsupervised object detection methods, our framework produces more accurate object discovery results. Different from supervised detection, we need neither manual annotation nor auxiliary datasets to train object detectors. Experimental study demonstrates the effectiveness of the proposed framework and the improved performance for region-based instance image retrieval.

Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization.

It is challenging to obtain good image quality in spectral computed tomography (CT) as the photon-number for the photon-counting detectors is limited for each narrow energy bin. This results in a lower signal to noise ratio (SNR) for the projections. To handle this issue, we first formulate the weight bidirectional image gradient with L0-norm constraint of spectral CT image. Then, as a new regularizer, bidirectional image gradient with L0-norm constraint is introduced into the tensor decomposition model, generating the Spectral-Image Tensor and Bidirectional Image-gradient Minimization (SITBIM) algorithm. Finally, the split-Bregman method is employed to optimize the proposed SITBIM mathematical model. The experiments on the numerical mouse phantom and real mouse experiments are designed to validate and evaluate the SITBIM method. The results demonstrate that the SITBIM can outperform other state-of-the-art methods (including TVM, TV + LR, SSCMF and NLCTF). INDEX TERMS: -spectral CT, image reconstruction, tensor decomposition, unidirectional image gradient, image similarity.

Diffusion Kernel Attention Network for Brain Disorder Classification.

Constructing and analyzing functional brain networks (FBN) has become a promising approach to brain disorder classification. However, the conventional successive construct-and-analyze process would limit the performance due to the lack of interactions and adaptivity among the subtasks in the process. Recently, Transformer has demonstrated remarkable performance in various tasks, attributing to its effective attention mechanism in modeling complex feature relationships. In this paper, for the first time, we develop Transformer for integrated FBN modeling, analysis and brain disorder classification with rs-fMRI data by proposing a Diffusion Kernel Attention Network to address the specific challenges. Specifically, directly applying Transformer does not necessarily admit optimal performance in this task due to its extensive parameters in the attention module against the limited training samples usually available. Looking into this issue, we propose to use kernel attention to replace the original dot-product attention module in Transformer. This significantly reduces the number of parameters to train and thus alleviates the issue of small sample while introducing a non-linear attention mechanism to model complex functional connections. Another limit of Transformer for FBN applications is that it only considers pair-wise interactions between directly connected brain regions but ignores the important indirect connections. Therefore, we further explore diffusion process over the kernel attention to incorporate wider interactions among indirectly connected brain regions. Extensive experimental study is conducted on ADHD-200 data set for ADHD classification and on ADNI data set for Alzheimer's disease classification, and the results demonstrate the superior performance of the proposed method over the competing methods.

BmNPV-induced hormone metabolic disorder in silkworm leads to enhanced locomotory behavior.

Many parasites alter the host locomotory behaviors in a way that increases their fitness and progeny transmission. Baculoviruses can manipulate host physiology and alter the locomotory behavior by inducing 'hyperactivity' (increased locomotion) or 'tree-top disease' (climbing high up to the top before dying). However, the detailed molecular mechanism underlying virus-induced this hyperactive behavior remains elusive. In the present study, we showed that BmNPV invaded into silkworm brain tissue, resulting in severe brain damage. Moreover, BmNPV infection disturbed the insect hormone balance. The content of 20-hydroxyecdysone (20E) in hemolymph was much lower during the hyperactive stage, while the dopamine (DA) titer was higher than mock infection. Exogenous hormone treatment assays demonstrated that 20E inhibits virus-induced ELA (enhanced locomotory activity), while dopamine stimulates this behavior. More specificity, injection of dopamine or its agonist promote this hyperactive behavior in BmNPV-infected larvae. Taking together, our findings revealed the important role of hormone metabolism in BmNPV-induced ELA.