Tongqiao Huoxue Decoction inhibits ferroptosis by facilitating ACSL4 ubiquitination degradation for neuroprotection against cerebral ischemia-reperfusion injury.

BACKGROUND: Cerebral ischemia-reperfusion injury (CIRI) refers to brain tissue injury caused by the temporary interruption of cerebral blood flow ischemia followed by the restoration of reperfusion, which is the main cause of post-stroke brain injury. A traditional Chinese herbal preparation called Tongqiao Huoxue Decoction (TQHX) has shown promise in reducing CIRI in rats. However, the mechanism of this herbal preparation for CIRI remains unclear. PURPOSE: This study aimed to evaluate the therapeutic effect of TQHX extract on rats with CIRI and to further explore the underlying mechanisms. METHODS: The active ingredients of TQHX extract were quantified by the high-performance liquid chromatography (HPLC) condition. We conducted thorough investigations to assess the effects of TQHX on CIRI and ferroptosis using oxygen-glucose deprivation/reperfusion (OGD/R)-treated PC12 cells as an in vitro model and transient middle cerebral artery occlusion (tMCAO) animals as an in vivo model. The neurological score assessment was performed to evaluate the neuroprotective effects of TQHX extract on tMCAO rats. Using histologic methods to study the extent of cerebral infarction, blood-brain barrier, and rat brain tissue. We examined the impact of TQHX on ferroptosis-related markers of Fe2+, superoxide dismutase (SOD), reactive oxygen species (ROS), and malondialdehyde (MDA) in the brain tissue. In addition, the expression of key proteins and markers of ferroptosis, as well as key factors associated with Acyl-CoA synthetase long-chain family member 4 (ACSL4) were detected by Western blot and quantitative real-time PCR (RT-qPCR). RESULTS: TQHX extract could decrease the Longa score and extent of cerebral infarction of tMCAO rats, which exerted the function of neuroprotection. Additionally, TQHX treatment efficiently decreased levels of MDA and ROS while increasing the expression of SOD and ferroptosis-related proteins including ferritin heavy chain 1 (FTH1) and glutathione peroxidase 4 (GPX4) at the transcription and translation level. Meanwhile, TQHX provided strong protection against oxidative stress and ferritin accumulation by increasing the ubiquitination and degradation of ACSL4. The injection of OE-ACSL4 reversed the effects of TQHX on neuroprotection and ferroptosis inhibition in PC12 cells. The injection of shACSL4 reversely validate the crucial role of ACSL4 in CIRI rat treatment. CONCLUSION: This work shows that TQHX promotes the ubiquitination-mediated degradation of ACSL4, which improves oxidative stress and inhibits the beginning of ferroptosis in cells. TQHX provides a possible path for additional research in CIRI therapies, advancing translational investigations.

Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis.

The brain development during the perinatal period is characterized by rapid changes in both structure and function, which have significant impact on the cognitive and behavioral abilities later in life. Accurate assessment of brain age is a crucial indicator for brain development maturity and can help predict the risk of neonatal pathology. However, evaluating neonatal brains using magnetic resonance imaging (MRI) is challenging due to its complexity, multi-dimension, and noise with subtle alterations. In this paper, we propose a multi-modal deep learning framework based on transformers for precise post-menstrual age (PMA) estimation and brain development analysis using T2-weighted structural MRI (T2-sMRI) and diffusion MRI (dMRI) data. First, we build a two-stream dense network to learn modality-specific features from T2-sMRI and dMRI of brain individually. Then, a transformer module based on self-attention mechanism integrates these features for PMA prediction and preterm/term classification. Finally, saliency maps on brain templates are used to enhance the interpretability of results. Our method is evaluated on the multi-modal MRI dataset of the developing Human Connectome Project (dHCP), which contains 592 neonates, including 478 term-born and 114 preterm-born subjects. The results demonstrate that our method achieves a 0.5-week mean absolute error (MAE) in PMA estimation for term-born subjects. Notably, preterm-born subjects exhibit delayed brain development, worsening with increasing prematurity. Our method also achieves 95% accuracy in classification of term-born and preterm-born subjects, revealing significant group differences.

Higher cortical excitability to negative emotions involved in musculoskeletal pain in Parkinson's disease.

OBJECTIVE: Changes in brain structure and neurotransmitter systems are involved in pain in Parkinson's disease (PD), and emotional factors are closely related to pain. Our study applied electroencephalography (EEG) to investigate the role of emotion in PD patients with chronic musculoskeletal pain. METHODS: Forty-two PD patients with chronic musculoskeletal pain and 38 without were enrolled. EEG data were recorded under resting conditions, and while viewing pictures with neutral, positive, and negative content. We compared spectrum power, functional connectivity, and late positive potential (LPP), an event-related potential (ERP), between the groups. RESULTS: PD patients with pain tended to have higher scores for the Hamilton Rating Scale for Depression (HRSD). In the resting EEG, mean ß-band amplitude was significantly higher in patients with pain than in those without. Logistic regression analysis showed that higher HRSD scores and higher mean ß-band amplitude were associated with pain. ERP analysis revealed that the amplitudes of LPP difference waves (the absolute difference between positive and negative condition LPP and neutral condition LPP) at the central-parietal region were significantly reduced in patients with pain (P = 0.029). Spearman correlation analysis showed that the amplitudes of late (700-1000 ms) negative versus neutral condition LPP difference waves were negatively correlated with pain intensity, assessed by visual analogue scale, (r = -0.393, P = 0.010) and HRSD scores (r = -0.366, P = 0.017). CONCLUSION: Dopaminergic and non-dopaminergic systems may be involved in musculoskeletal pain in PD by increasing ß-band activity and weakening the connection of the θ-band at the central-parietal region. PD patients with musculoskeletal pain have higher cortical excitability to negative emotions. The changes in pain-related EEG may be used as electrophysiological markers and therapeutic targets in PD patients with chronic pain.

REG3A promotes proliferation and DDP resistance of ovarian cancer cells by activating the PI3K/Akt signaling pathway.

This study explored the effect of Regenerating Islet-Derived 3-Alpha (REG3A) on ovarian cancer (OC) progression. REG3A expression was scrutinized in clinical tissues of 97 OC cases by quantitative real-time polymerase chain reaction (qRT-PCR). REG3A expression in OC cells and cisplatin (DDP) resistance OC cells was regulated by transfection. LY294002 (10 µM, inhibitor of the PI3K/Akt signaling pathway) was used to treat OC cells and DDP resistance OC cells. Cell counting kit-8 and methyl-thiazolyl-tetrazolium assays were applied for proliferation and DDP resistance detection. Flow cytometry was utilized for cell cycle and apoptosis analysis. The effect of REG3A on the OC cell in vivo growth was researched by establishing xenograft tumor model via using nude mice using nude mice. The expression of genes in clinical samples, cells and xenograft tumor tissues was investigated by qRT-PCR, Western blot and immunohistochemistry. As a result, REG3A was over-expressed in OC patients and cells, associating with dismal prognosis of patients. REG3A knockdown repressed proliferation, DDP resistance, induced cell cycle arrest and apoptosis of OC cells, and reduced the expression MDR-1, Cyclin D1, Cleaved caspase 3 proteins and the PI3K/Akt signaling pathway activity in OC cells. LY294002 treatment abrogated the promotion effect of REG3A on OC cell proliferation, apoptosis inhibition and DDP resistance. REG3A knockdown suppressed the in vivo growth of OC cells. Thus, REG3A promoted proliferation and DDP resistance of OC cells by activating the PI3K/Akt signaling pathway. REG3A might be a promising target for the clinical treatment of OC.

Imaging process matched neural network for complex wavefront retrieval with a higher space-bandwidth product.

Recently, deep learning (DL) has shown great potential in complex wavefront retrieval (CWR). However, the application of DL in CWR does not match well with the physical diffraction process. The state-of-the-art DL-based CWR methods crop full-size diffraction patterns down to a smaller size to save computational resources. However, cropping reduces the space-bandwidth product (SBP). In order to solve the trade-off between computational resources and SBP, we propose an imaging process matched neural network (IPMnet). IPMnet accepts full-size diffraction patterns with a larger SBP as inputs and retrieves a higher resolution and a larger field of view of the complex wavefront. We verify the effectiveness of the proposed IPMnet through simulations and experiments.

How sleep quality affects activities of daily living in Parkinson's disease: the mediating role of disease severity and the moderating role of cognition.

Objective: The aim of this study was to explore the influential mechanism of the relationship between sleep quality and activities of daily living (ADL) in patients with Parkinson's disease (PD), we hypothesized disease severity as a mediator and assumed the mediating process was regulated by cognition. Methods: 194 individuals with PD (95 women and 99 men) were enrolled in study. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality of PD patients. Patients' ADL, disease severity and cognition were measured by the Unified Parkinson's Disease Rating Scale-II (UPDRSII), Hoehn-Yahr (H-Y) Scale, and Mini-Mental State Examination (MMSE). We investigated the mediating role of disease severity and the moderating effect of cognition on the association between sleep quality and ADL in PD patients. Results: The score of UPDRSII was positively correlated with the score of PSQI and H-Y stage, while the score of MMSE was negatively correlated with the score of H-Y stage and UPDRSII. Sleep quality predicts disease severity, and disease severity predicts ADL. Disease severity mediated the relationship between sleep quality and ADL, and the mediating effect was 0.179. Cognition alone did not affect ADL, but the interaction between disease severity and cognition was significantly affected ADL, confirming the moderating effect of cognition in PD patients. Conclusion: Disease severity mediated the association between sleep quality and ADL, good cognition significantly reduced disease severity's mediating influence on the relationship between sleep quality and ADL. Our study indicated a close relationship between ADL and sleep and cognition in PD, and also provided new insights into the overall management of PD and a better quality of life of PD patients.

Multimodal transformer network for incomplete image generation and diagnosis of Alzheimer's disease.

Multimodal images such as magnetic resonance imaging (MRI) and positron emission tomography (PET) could provide complementary information about the brain and have been widely investigated for the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD). However, multimodal brain images are often incomplete in clinical practice. It is still challenging to make use of multimodality for disease diagnosis with missing data. In this paper, we propose a deep learning framework with the multi-level guided generative adversarial network (MLG-GAN) and multimodal transformer (Mul-T) for incomplete image generation and disease classification, respectively. First, MLG-GAN is proposed to generate the missing data, guided by multi-level information from voxels, features, and tasks. In addition to voxel-level supervision and task-level constraint, a feature-level auto-regression branch is proposed to embed the features of target images for an accurate generation. With the complete multimodal images, we propose a Mul-T network for disease diagnosis, which can not only combine the global and local features but also model the latent interactions and correlations from one modality to another with the cross-modal attention mechanism. Comprehensive experiments on three independent datasets (i.e., ADNI-1, ADNI-2, and OASIS-3) show that the proposed method achieves superior performance in the tasks of image generation and disease diagnosis compared to state-of-the-art methods.

Brain Status Transferring Generative Adversarial Network for Decoding Individualized Atrophy in Alzheimer's Disease.

Deep learning has been widely investigated in brain image computational analysis for diagnosing brain diseases such as Alzheimer's disease (AD). Most of the existing methods built end-to-end models to learn discriminative features by group-wise analysis. However, these methods cannot detect pathological changes in each subject, which is essential for the individualized interpretation of disease variances and precision medicine. In this article, we propose a brain status transferring generative adversarial network (BrainStatTrans-GAN) to generate corresponding healthy images of patients, which are further used to decode individualized brain atrophy. The BrainStatTrans-GAN consists of generator, discriminator, and status discriminator. First, a normative GAN is built to generate healthy brain images from normal controls. However, it cannot generate healthy images from diseased ones due to the lack of paired healthy and diseased images. To address this problem, a status discriminator with adversarial learning is designed in the training process to produce healthy brain images for patients. Then, the residual between the generated and input images can be computed to quantify pathological brain changes. Finally, a residual-based multi-level fusion network (RMFN) is built for more accurate disease diagnosis. Compared to the existing methods, our method can model individualized brain atrophy for facilitating disease diagnosis and interpretation. Experimental results on T1-weighted magnetic resonance imaging (MRI) data of 1,739 subjects from three datasets demonstrate the effectiveness of our method.

Extraction of the Microstructure of Wool Fabrics Based on Structure Tensor.

The trends of "fashionalization", "personalization" and "customization" of wool fabrics have prompted the textile industry to change the original processing design based on the experience of engineers and trial production. In order to adapt to the promotion of intelligent production, the microstructure of wool fabrics is introduced into the finishing process. This article presents an automated method to extract the microstructure from the micro-CT data of woven wool fabrics. Firstly, image processing was performed on the 3D micro-CT images of the fabric. The raw grayscale data were converted into eigenvectors of the structure tensor to segment the individual yarns. These data were then used to calculate the three parameters of diameter, spacing and the path of the center points of the yarn for the microstructure. The experimental results showed that the proposed method was quite accurate and robust on woven single-ply tweed fabrics.

Multimodal Transformer of Incomplete MRI Data for Brain Tumor Segmentation.

Accurate segmentation of brain tumors plays an important role for clinical diagnosis and treatment. Multimodal magnetic resonance imaging (MRI) can provide rich and complementary information for accurate brain tumor segmentation. However, some modalities may be absent in clinical practice. It is still challenging to integrate the incomplete multimodal MRI data for accurate segmentation of brain tumors. In this paper, we propose a brain tumor segmentation method based on multimodal transformer network with incomplete multimodal MRI data. The network is based on U-Net architecture consisting of modality specific encoders, multimodal transformer and multimodal shared-weight decoder. First, a convolutional encoder is built to extract the specific features of each modality. Then, a multimodal transformer is proposed to model the correlations of multimodal features and learn the features of missing modalities. Finally, a multimodal shared-weight decoder is proposed to progressively aggregate the multimodal and multi-level features with spatial and channel self-attention modules for brain tumor segmentation. A missing-full complementary learning strategy is used to explore the latent correlation between the missing and full modalities for feature compensation. For evaluation, our method is tested on the multimodal MRI data from BraTS 2018, BraTS 2019 and BraTS 2020 datasets. The extensive results demonstrate that our method outperforms the state-of-the-art methods for brain tumor segmentation on most subsets of missing modalities.

DEPDC1B is involved in the proliferation, metastasis, cell cycle arrest and apoptosis of colon cancer cells by regulating NUP37.

It has been reported that DEP domain protein 1B (DEPDC1B) serves several roles in the occurrence and development of various types of cancer. Nevertheless, the effect of DEPDC1B on colorectal cancer (CRC), as well as its particular underlying molecular mechanism remain to be elucidated. In the present study, the mRNA and protein expression levels of DEPDC1B and nucleoporin 37 (NUP37) in CRC cell lines were assessed by reverse transcription­quantitative PCR and western blotting, respectively. Cell Counting Kit­8 and 5­Ethynyl­2'­deoxyuridine assays were carried out to determine cell proliferation. In addition, the migration and invasion abilities of cells were evaluated using wound healing and Transwell assays. The changes in cell apoptosis and cell cycle distribution were assessed by flow cytometry and western blotting. Bioinformatics analysis and co­immunoprecipitation assays were performed to predict and verify, respectively, the binding capacity of DEPDC1B on NUP37. The expression levels of Ki­67 were detected by immunohistochemical assay. Finally, the activation of phosphoinositide 3­kinase (PI3K)/protein kinase B (AKT) signaling was measured using western blotting. The results showed that DEPDC1B and NUP37 were upregulated in CRC cell lines. DEPDC1B and NUP37 silencing both inhibited the proliferation, migration and invasion capabilities of CRC cells and promoted cell apoptosis and cell cycle arrest. Furthermore, NUP37 overexpression reversed the inhibitory effects of DEPDC1B silencing on the behavior of CRC cells. Animal experiments demonstrated that DEPDC1B knockdown inhibited the growth of CRC in vivo by targeting NUP37. In addition, DEPDC1B knockdown inhibited the expression levels of the PI3K/AKT signaling­related proteins in CRC cells and tissues by also binding to NUP37. Overall, the current study suggested that DEPDC1B silencing could alleviate the progression of CRC via targeting NUP37.

A Hybrid Multi-Scale Attention Convolution and Aging Transformer Network for Alzheimer's Disease Diagnosis.

Deep neural networks have been successfully investigated in the computational analysis of structural magnetic resonance imaging (sMRI) data for the diagnosis of dementia, such as Alzheimer's disease (AD). The disease-related changes in sMRI may be different in local brain regions, which have variant structures but with some correlations. In addition, aging increases the risk of dementia. However, it is still challenging to capture the local variations and long-range correlations of different brain regions and make use of the age information for disease diagnosis. To address these problems, we propose a hybrid network with multi-scale attention convolution and aging transformer for AD diagnosis. First, to capture the local variations, a multi-scale attention convolution is proposed to learn the feature maps with multi-scale kernels, which are adaptively aggregated by an attention module. Then, to model the long-range correlations of brain regions, a pyramid non-local block is used on the high-level features to learn more powerful features. Finally, we propose an aging transformer subnetwork to embed the age information into image features and capture the dependencies between subjects at different ages. The proposed method can learn not only the subject-specific rich features but also the inter-subject age correlations in an end-to-end framework. Our method is evaluated with T1-weighted sMRI scans from a large cohort of subjects on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that our method has achieved promising performance for AD-related diagnosis.

The relationship between depressive symptoms and cognitive function in Alzheimer's disease: The mediating effect of amygdala functional connectivity and radiomic features.

BACKGROUND: Depressive symptoms are common in Alzheimer's disease (AD) and are associated with cognitive function. Amygdala functional connectivity (FC) and radiomic features related to depression and cognition. However, studies have yet to explore the neural mechanisms underlying these associations. METHODS: We enrolled eighty-two AD patients with depressive symptoms (ADD) and 85 healthy controls (HCs) in this study. We compared amygdala FC using the seed-based approach between ADD patients and HCs. The least absolute shrinkage and selection operator (LASSO) was used to select amygdala radiomic features. A support vector machine (SVM) model was constructed based on the identified radiomic features to distinguish ADD from HCs. We used mediation analyses to explore the mediating effects of amygdala radiomic features and amygdala FC on cognition. RESULTS: We found that ADD patients showed decreased amygdala FC with posterior cingulate cortex, middle frontal gyrus (MFG), and parahippocampal gyrus involved in the default mode network compared to HCs. The area under the receiver operating characteristic curve (AUC) of the amygdala radiomic model was 0.95 for ADD patients and HCs. Notably, the mediation model demonstrated that amygdala FC with the MFG and amygdala-based radiomic features mediated the relationship between depressive symptoms and cognitive function in AD. LIMITATIONS: This study is a cross-sectional study and lacks longitudinal data. CONCLUSION: Our findings may not only expand existing biological knowledge of the relationship between cognition and depressive symptoms in AD from the perspective of brain function and structure but also may ultimately provide potential targets for personalized treatment strategies.

Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study.

Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients' interests well with effective treatment, which is important to them and their family. Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to characterize the abnormal brain function by computing the voxel-wise measures and Pearson's correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. However, exploring the powerful measures of rsfMRI to improve ADHD diagnosis remains a particular challenge. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based high-order FC (tHOFC) and dynamics-based high-order FC (dHOFC), the sparse representation (SR)-derived FCs including the group SR (GSR), the strength and similarity guided GSR (SSGSR), and sparse low-rank (SLR). Finally, these measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is applied to the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to those from the single measure and the previous studies. We have identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.

FAM122A Is Required for Mesendodermal and Cardiac Differentiation of Embryonic Stem Cells.

Mesendodermal specification and cardiac differentiation are key issues for developmental biology and heart regeneration medicine. Previously, we demonstrated that FAM122A, a highly conserved housekeeping gene, is an endogenous inhibitor of protein phosphatase 2A (PP2A) and participates in multifaceted physiological and pathological processes. However, the in vivo function of FAM122A is largely unknown. In this study, we observed that Fam122 deletion resulted in embryonic lethality with severe defects of cardiovascular developments and significantly attenuated cardiac functions in conditional cardiac-specific knockout mice. More importantly, Fam122a deficiency impaired mesendodermal specification and cardiac differentiation from mouse embryonic stem cells but showed no influence on pluripotent identity. Mechanical investigation revealed that the impaired differentiation potential was caused by the dysregulation of histone modification and Wnt and Hippo signaling pathways through modulation of PP2A activity. These findings suggest that FAM122A is a novel and critical regulator in mesendodermal specification and cardiac differentiation. This research not only significantly extends our understanding of the regulatory network of mesendodermal/cardiac differentiation but also proposes the potential significance of FAM122A in cardiac regeneration.

Graph Transformer Geometric Learning of Brain Networks Using Multimodal MR Images for Brain Age Estimation.

Brain age is considered as an important biomarker for detecting aging-related diseases such as Alzheimer's Disease (AD). Magnetic resonance imaging (MRI) have been widely investigated with deep neural networks for brain age estimation. However, most existing methods cannot make full use of multimodal MRIs due to the difference in data structure. In this paper, we propose a graph transformer geometric learning framework to model the multimodal brain network constructed by structural MRI (sMRI) and diffusion tensor imaging (DTI) for brain age estimation. First, we build a two-stream convolutional autoencoder to learn the latent representations for each imaging modality. The brain template with prior knowledge is utilized to calculate the features from the regions of interest (ROIs). Then, a multi-level construction of the brain network is proposed to establish the hybrid ROI connections in space, feature and modality. Next, a graph transformer network is proposed to model the cross-modal interaction and fusion by geometric learning for brain age estimation. Finally, the difference between the estimated age and the chronological age is used as an important biomarker for AD diagnosis. Our method is evaluated with the sMRI and DTI data from UK Biobank and Alzheimer's Disease Neuroimaging Initiative database. Experimental results demonstrate that our method has achieved promising performances for brain age estimation and AD diagnosis.

Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images.

It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of brain structure and function. The brain maturity is an important indicator for evaluating the normal development of children. In this paper, we propose a multimodal regression framework to combine the features from structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) data for age prediction of children. First, three types of features are extracted from sMRI and DTI data. Second, we propose to combine the sparse coding and Q-Learning for feature selection from each modality. Finally, the ensemble regression is performed by random forest based on proximity measures to fuse multimodal features for age prediction. The proposed method is evaluated on 212 participants, including 76 young children less than 2 years old and 136 children aged from 2-15 years old recruited from Shanghai Children's Hospital. The results show that integrating multimodal features has achieved the highest accuracies with the root mean squared error (RMSE) of 0.208 years and mean absolute error (MAE) of 0.150 years for age prediction of young children (0-2), and RMSE of 1.666 years and MAE of 1.087 years for older children (2-15). We have shown that the selected features by Q-Learning can consistently improve the prediction accuracy. The comparison of prediction results demonstrates that the proposed method performs better than other competing methods.

Classification of breast cancer histology images using MSMV-PFENet.

Deep learning has been used extensively in histopathological image classification, but people in this field are still exploring new neural network architectures for more effective and efficient cancer diagnosis. Here, we propose multi-scale, multi-view progressive feature encoding network (MSMV-PFENet) for effective classification. With respect to the density of cell nuclei, we selected the regions potentially related to carcinogenesis at multiple scales from each view. The progressive feature encoding network then extracted the global and local features from these regions. A bidirectional long short-term memory analyzed the encoding vectors to get a category score, and finally the majority voting method integrated different views to classify the histopathological images. We tested our method on the breast cancer histology dataset from the ICIAR 2018 grand challenge. The proposed MSMV-PFENet achieved 93.0[Formula: see text] and 94.8[Formula: see text] accuracies at the patch and image levels, respectively. This method can potentially benefit the clinical cancer diagnosis.

Sex-specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adults.

Although there are pronounced sex differences for psychiatric disorders, relatively little has been published on the heterogeneity of sex-specific genetic effects for these traits until very recently for adults. Much less is known about children because most psychiatric disorders will not manifest until later in life and existing studies for children on psychiatric traits such as cognitive functions are underpowered. We used results from publicly available genome-wide association studies for six psychiatric disorders and individual-level data from the Adolescent Brain Cognitive Development (ABCD) study and the UK Biobank (UKB) study to evaluate the associations between the predicted polygenic risk scores (PRS) of these six disorders and observed cognitive functions, behavioral and brain imaging traits. We further investigated the mediation effects of the brain structure and function, which showed heterogeneity between males and females on the correlation between genetic risk of schizophrenia and fluid intelligence. There was significant heterogeneity in genetic associations between the cognitive traits and psychiatric disorders between sexes. Specifically, the PRSs of schizophrenia of boys showed stronger correlation with eight of the ten cognitive functions in the ABCD data set; whereas the PRSs of autism of females showed a stronger correlation with fluid intelligence in the UKB data set. Besides cognitive traits, we also found significant sexual heterogeneity in genetic associations between psychiatric disorders and behavior and brain imaging. These results demonstrate the underlying early etiology of psychiatric disease and reveal a shared and unique genetic basis between the disorders and cognition traits involved in brain functions between the sexes.

Urethane Improves the Response of Auditory Neurons to Tone.

Urethane has little effect on nervous system and is often used in neuroscience studies. However, the effect of urethane in neurons is not thoroughly clear. In this study, we investigated changes in neuron responses to tones in inferior colliculus during urethane anesthesia. As urethane was metabolized, the best and characteristic frequencies did not obviously change, but the minimal threshold (MT) remained relatively stable or was elevated. The frequency tuning bandwidth at 60 dB SPL (BW60dBSPL) remained unchanged or decreased, and the average evoked spike of effective frequencies at 60 dB SPL (ES60dBSPL) gradually decreased. Although the average evoked spike of effective frequencies at a tone intensity of 20 dB SPL above MT (ES20dBSPLaboveMT) decreased, the frequency tuning bandwidth at a tone intensity of 20 dB SPL above MT (BW20dBSPLaboveMT) did not change. In addition, the changes in MT, ES60dBSPL, BW60dBSPL, and ES20dBSPLaboveMT increased with the MT in pre-anesthesia awake state (MTpre-anesthesiaawake). In some neurons, the MT was lower, BW60dBSPL was broader, and ES60dBSPL and ES20dBSPLaboveMT were higher in urethane anesthesia state than in pre-anesthesia awake state. During anesthesia, the inhibitory effect of urethane reduced the ES20dBSPLaboveMT, but did not change the MT, characteristic frequency, or BW20dBSPLaboveMT. In the recording session with the strongest neuron response, the first spike latency did not decrease, and the spontaneous spike did not increase. Therefore, we conclude that urethane can reduce/not change the MT, increase the evoked spike, or broaden/not change the frequency tuning range, and eventually improve the response of auditory neurons to tone with or without "pushing down" the tonal receptive field in thresholding model. The improved effect increases with the MTpre-anesthesiaawake of neurons. The changes induced by the inhibitory and improved effects of urethane abide by similar regularities, but the change directions are contrary. The improvement mechanism may be likely due to the increase in the ratio of excitatory/inhibitory postsynaptic inputs to neurons.