Altered empathy processing in frontotemporal dementia A task-based fMRI study.

A lack of empathy, and particularly its affective components, is a core symptom of behavioural variant frontotemporal dementia (bvFTD). Visual exposure to images of a needle pricking a hand (pain condition) and Q-tips touching a hand (control condition) is an established functional magnetic resonance imaging (fMRI) paradigm used to investigate empathy for pain (EFP; pain condition minus control condition). EFP has been associated with increased blood oxygen level dependent (BOLD) signal in regions known to become atrophic in the early stages in bvFTD, including the anterior insula and the anterior cingulate. We therefore hypothesized that patients with bvFTD would display altered empathy processing in the EFP paradigm. Here we examined empathy processing using the EFP paradigm in 28 patients with bvFTD and 28 sex and age matched controls. Participants underwent structural MRI, task-based and resting-state fMRI. The Interpersonal Reactivity Index (IRI) was used as a measure of different facets of empathic function outside the scanner. The EFP paradigm was analysed at a whole brain level and using two regions-of-interest approaches, one based on a metanalysis of affective perceptual empathy versus cognitive evaluative empathy and one based on the controls activation pattern. In controls, EFP was linked to an expected increase of BOLD signal that displayed an overlap with the pattern of atrophy in the bvFTD patients (insula and anterior cingulate). Additional regions with increased signal were the supramarginal gyrus and the occipital cortex. These latter regions were the only ones that displayed increased BOLD signal in bvFTD patients. BOLD signal increase under the affective perceptual empathy but not the cognitive evaluative empathy region of interest was significantly greater in controls than in bvFTD patients. The controls rating on their empathic concern subscale of the IRI was significantly correlated with the BOLD signal in the EFP paradigm, as were an informants ratings of the patients empathic concern subscale. This correlation was not observed on other subscales of the IRI or when using the patient's self-ratings. Finally, controls and patients showed different connectivity patterns in empathy related networks during resting-state fMRI, mainly in nodes overlapping the ventral attention network. Our results indicate that reduced neural activity in regions typically affected by pathology in bvFTD is associated with reduced empathy processing, and a predictor of patients capacity to experience affective empathy.

CHANGES IN FUNCTIONAL CONNECTIVITY FOLLOWING INTENSIVE ATTENTION TRAINING IN PATIENTS WITH TRAUMATIC BRAIN INJURY. A PILOT STUDY.

Objective: To explore functional connectivity after intensive attention training in the chronic phase after traumatic brain injury as clinical evidence indicates that intensive attention training improves attention dysfunction in persons with traumatic brain injury. Design and subjects: A case series study. Two young adults, 13- and 18-months post traumatic brain injury, with traumatic brain injury induced attention deficits were assigned to 20 h of intensive attention training and neuroimaging. Methods: Functional magnetic resonance imaging during a psychomotor vigilance test was conducted pre- and post-intervention. Results: The neuroimaging indicated both increased and decreased connectivity density in frontal, posterior and subcortical brain regions, for some regions with separate change patterns for left and right hemisphere respectively, and an overall reduction in variability in functional connectivity. Conclusion: The changed and decreased variability of functional connectivity in various brain regions, captured by fMRI during a psychomotor vigilance test after direct attention training in a small sample of persons with traumatic brain injury, suggests further studies of functional connectivity changes in neural networks.

Age-dependent effects of oxytocin in brain regions enriched with oxytocin receptors.

Although intranasal oxytocin administration to tap into central functions is the most commonly used non-invasive means for exploring oxytocin's role in human cognition and behavior, the way by which intranasal oxytocin acts on the brain is not yet fully understood. Recent research suggests that brain regions densely populated with oxytocin receptors may play a central role in intranasal oxytocin's action mechanisms in the brain. In particular, intranasal oxytocin may act directly on (subcortical) regions rich in oxytocin receptors via binding to these receptors while only indirectly affecting other (cortical) regions via their neural connections to oxytocin receptor-enriched regions. Aligned with this notion, the current study adopted a novel approach to test 1) whether the connections between oxytocin receptor-enriched regions (i.e., the thalamus, pallidum, caudate nucleus, putamen, and olfactory bulbs) and other regions in the brain were responsive to intranasal oxytocin administration, and 2) whether oxytocin-induced effects varied as a function of age. Forty-six young (24.96 ± 3.06 years) and 44 older (69.89 ± 2.99 years) participants were randomized, in a double-blind procedure, to self-administer either intranasal oxytocin or placebo before resting-state fMRI. Results supported age-dependency in the effects of intranasal oxytocin administration on connectivity between oxytocin receptor-enriched regions and other regions in the brain. Specifically, compared to placebo, oxytocin decreased both connectivity density and connectivity strength of the thalamus for young participants while it increased connectivity density and connectivity strength of the caudate for older participants. These findings inform the mechanisms underlying the effects of exogenous oxytocin on brain function and highlight the importance of age in these processes.

CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism.

Compressed sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically generative adversarial networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning reconstruction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study, we present a novel GAN-based model that delivers superior performance without the model complexity escalating. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby focusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies affirmed the efficacy of the modified modules within the network. Incorporating DR networks and CAM elevated the peak signal-to-noise ratios (PSNR) of the reconstructed images by about 1.2 and 0.8 dB, respectively, on average, even at 10× CS acceleration. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent stability but also outperforming most of the compared networks in terms of PSNR and SSIM. When compared with U-net, DR-CAM-GAN's average gains in SSIM and PSNR were 14% and 15%, respectively. Its MSE was reduced by a factor that ranged from two to seven. The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Understanding the Interfacial Reactions and Band Alignment for Efficient and Stable Perovskite Solar Cells Built on Metal Substrates with Reduced Upscaling Losses.

Conventional perovskite solar cells (PSC) built on transparent conductive oxide (TCO) glass face a fundamental challenge to retain fill factor (FF) for large-area upscaling due to series resistance loss. Building a perovskite solar cell on metal has the potential to reduce this FF loss and is promising for flexible applications. However, their efficiency and stability lag far behind their TCO counterparts. Herein, findings on the complex chemical reactions and degradation-promoting processes at different perovskite/metal (Cu, Au, Ag, and Mo) interfaces, which are closely linked with the inherent stability; and the interlayer engineering for perovskite/metal interface's band alignment, which plays an essential role in achieving high efficiency, are reported. Leveraging these findings, 21% power conversion efficiency (PCE) is achieved for 1 cm2 perovskite solar cells using a p-i-n top-illumination structure on a molybdenum substrate, the highest reported for a PSC built on metal. Notably, the FF and PCE losses due to area upscaling are remarkably reduced by one order of magnitude relative to the counterparts on conventional TCO glass, highlighting an alternative pathway for PSC upscaling and module design.

Reducing nonradiative recombination in perovskite solar cells with a porous insulator contact.

Inserting an ultrathin low-conductivity interlayer between the absorber and transport layer has emerged as an important strategy for reducing surface recombination in the best perovskite solar cells. However, a challenge with this approach is a trade-off between the open-circuit voltage (Voc) and the fill factor (FF). Here, we overcame this challenge by introducing a thick (about 100 nanometers) insulator layer with random nanoscale openings. We performed drift-diffusion simulations for cells with this porous insulator contact (PIC) and realized it using a solution process by controlling the growth mode of alumina nanoplates. Leveraging a PIC with an approximately 25% reduced contact area, we achieved an efficiency of up to 25.5% (certified steady-state efficiency 24.7%) in p-i-n devices. The product of Voc × FF was 87.9% of the Shockley-Queisser limit. The surface recombination velocity at the p-type contact was reduced from 64.2 to 9.2 centimeters per second. The bulk recombination lifetime was increased from 1.2 to 6.0 microseconds because of improvements in the perovskite crystallinity. The improved wettability of the perovskite precursor solution allowed us to demonstrate a 23.3% efficient 1-square-centimeter p-i-n cell. We demonstrate here its broad applicability for different p-type contacts and perovskite compositions.

Quantifying the impact of Pyramid Squeeze Attention mechanism and filtering approaches on Alzheimer's disease classification.

Brain medical imaging and deep learning are important foundations for diagnosing and predicting Alzheimer's disease. In this study, we explored the impact of different image filtering approaches and Pyramid Squeeze Attention (PSA) mechanism on the image classification of Alzheimer's disease. First, during the image preprocessing, we register MRI images and remove skulls, then apply median filtering, Gaussian blur filtering, and anisotropic diffusion filtering to obtain different experimental images. After that, we add the Squeeze and Excitation (SE) mechanism and Pyramid Squeeze Attention (PSA) mechanism to the Fully Convolutional Network (FCN) model respectively, to obtain each MRI image's corresponding feature information of disease probability map. Besides, we also construct Multi-Layer Perceptron (MLP) model's framework, combining feature information of disease probability map with age, gender, and Mini-Mental State Examination (MMSE) of each sample, to get the final classification performance of model. Among them, the accuracy of the MLP-C model combining anisotropic diffusion filtering with the Pyramid Squeeze Attention mechanism can reach 98.85%. The corresponding quantitative experimental results show that different image filtering approaches and attention mechanisms provide effective assistance for the diagnosis and classification of Alzheimer's disease.

Accelerating Dynamic MRI Reconstruction Using Adaptive Sequentially Truncated Higher-Order Singular Value Decomposition.

BACKGROUND: Dynamic magnetic resonance imaging (dMRI) plays an important role in cardiac perfusion and functional clinical exams. However, further applications are limited by the speed of data acquisition. OBJECTIVE: A low-rank plus sparse decomposition approach is often introduced for reconstructing dynamic magnetic resonance imaging (dMRI) from highly under-sampling K-space data. In this paper, the reconstruction problem of DMR is transformed into a low-rank tensor plus sparse tensor recovery problem. METHODS: A sequentially truncated higher-order singular value decomposition method is proposed to quickly approximate the low-rank tensor space structure and learn sparse components by adding a tensor kernel norm to the low-rank tensor and a l1 norm to the sparse tensor to constrain the two parts at the same time. The optimization problem is solved by using the iterative soft-thresholding algorithm; therefore, under the premise of ensuring the accuracy of the data, the amount of computation can be effectively reduced. RESULTS: Compared with the state-of-the-art methods, the experimental results show that the proposed method can achieve better performance in terms of reconstruction speed and reconstruction quality on 3D and 4D dMRI datasets. CONCLUSION: The multidimensional MRI time series is represented by the tensor tool and decomposed into low rank tensor terms and sparse tensor terms. The low rank spatial structure is captured by the adaptive ST-HOSVD for fast approximation and the sparse component is constrained efficiently with a sparsity transform and l1 norm. The optimization problem is solved by an iterative soft-thresholding algorithm. Through extensive 3D and 4D dMRI experiments, it is demonstrated that our method can achieve superior reconstruction performance and efficiency compared with the other three state-of-theart methods reported in the literature.

Functional connectivity in reward-related networks is associated with individual differences in gambling strategies in male Lister hooded rats.

Individuals with gambling disorder display deficits in decision-making in the Iowa Gambling Task. The rat Gambling Task (rGT) is a rodent analogue that can be used to investigate the neurobiological mechanisms underlying gambling behaviour. The aim of this explorative study was to examine individual strategies in the rGT and investigate possible behavioural and neural correlates associated with gambling strategies. Thirty-two adult male Lister hooded rats underwent behavioural testing in the multivariate concentric square field™ (MCSF) and the novel cage tests, were trained on and performed the rGT and subsequently underwent resting-state functional magnetic resonance imaging (R-fMRI). In the rGT, stable gambling strategies were found with subgroups of rats that preferred the suboptimal safest choice as well as the disadvantageous choice, that is, the riskiest gambling strategy. R-fMRI results revealed associations between gambling strategies and brain regions central for reward networks. Moreover, rats with risky gambling strategies differed from those with strategic and intermediate strategies in brain functional connectivity. No differences in behavioural profiles, as assessed with the MCSF and novel cage tests, were observed between the gambling strategy groups. In conclusion, stable individual differences in gambling strategies were found. Intrinsic functional connectivity using R-fMRI provides novel evidence to support the notion that individual differences in gambling strategies are associated with functional connectivity in brain regions important for reward networks.

Image Classification of Alzheimer's Disease Based on External-Attention Mechanism and Fully Convolutional Network.

Automatic and accurate classification of Alzheimer's disease is a challenging and promising task. Fully Convolutional Network (FCN) can classify images at the pixel level. Adding an attention mechanism to the Fully Convolutional Network can effectively improve the classification performance of the model. However, the self-attention mechanism ignores the potential correlation between different samples. Aiming at this problem, we propose a new method for image classification of Alzheimer's disease based on the external-attention mechanism. The external-attention module is added after the fourth convolutional block of the fully convolutional network model. At the same time, the double normalization method of Softmax and L1 norm is introduced to obtain a better classification performance and richer feature information of the disease probability map. The activation function Softmax can increase the degree of fitting of the neural network to the training set, which transforms linearity into nonlinearity, thereby increasing the flexibility of the neural network. The L1 norm can avoid the attention map being affected by especially large (especially small) eigenvalues. The experiments in this paper use 550 three-dimensional MRI images and use five-fold cross-validation. The experimental results show that the proposed image classification method for Alzheimer's disease, combining the external-attention mechanism with double normalization, can effectively improve the classification performance of the model. With this method, the accuracy of the MLP-A model is 92.36%, the accuracy of the MLP-B model is 98.55%, and the accuracy of the fusion model MLP-C is 98.73%. The classification performance of the model is higher than similar models without adding any attention mechanism, and it is better than other comparison methods.

Estimated gray matter volume rapidly changes after a short motor task.

Skill learning induces changes in estimates of gray matter volume (GMV) in the human brain, commonly detectable with magnetic resonance imaging (MRI). Rapid changes in GMV estimates while executing tasks may however confound between- and within-subject differences. Fluctuations in arterial blood flow are proposed to underlie this apparent task-related tissue plasticity. To test this hypothesis, we acquired multiple repetitions of structural T1-weighted and functional blood-oxygen level-dependent (BOLD) MRI measurements from 51 subjects performing a finger-tapping task (FTT; á 2 min) repeatedly for 30-60 min. Estimated GMV was decreased in motor regions during FTT compared with rest. Motor-related BOLD signal changes did not overlap nor correlate with GMV changes. Nearly simultaneous BOLD signals cannot fully explain task-induced changes in T1-weighted images. These sensitive and behavior-related GMV changes pose serious questions to reproducibility across studies, and morphological investigations during skill learning can also open new avenues on how to study rapid brain plasticity.

A Quantitative Data-Driven Analysis Framework for Resting-State Functional Magnetic Resonance Imaging: A Study of the Impact of Adult Age.

The objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC). Whole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N = 227, aged 18-76 years old, male/female = 99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index and connectivity density index utilizing the convolutions of the cross-correlation histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately. With the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyri, posterior cingulate cortex (PCC), right insula and inferior parietal lobule of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: (1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects' age; (2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; and (3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age. The proposed QDA framework can produce threshold-free and voxel-wise RFC metrics from R-fMRI data. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.

Limbic Perfusion Is Reduced in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is an illness characterized by a diverse range of debilitating symptoms including autonomic, immunologic, and cognitive dysfunction. Although neurological and cognitive aberrations have been consistently reported, relatively little is known regarding the regional cerebral blood flow (rCBF) in ME/CFS. In this study, we studied a cohort of 31 ME/CSF patients (average age: 42.8 ± 13.5 years) and 48 healthy controls (average age: 42.9 ± 12.0 years) using the pseudo-continuous arterial spin labeling (PCASL) technique on a whole-body clinical 3T MRI scanner. Besides routine clinical MRI, the protocol included a session of over 8 min-long rCBF measurement. The differences in the rCBF between the ME/CSF patients and healthy controls were statistically assessed with voxel-wise and AAL ROI-based two-sample t-tests. Linear regression analysis was also performed on the rCBF data by using the symptom severity score as the main regressor. In comparison with the healthy controls, the patient group showed significant hypoperfusion (uncorrected voxel wise p ≤ 0.001, FWE p ≤ 0.01) in several brain regions of the limbic system, including the anterior cingulate cortex, putamen, pallidum, and anterior ventral insular area. For the ME/CFS patients, the overall symptom severity score at rest was significantly associated with a reduced rCBF in the anterior cingulate cortex. The results of this study show that brain blood flow abnormalities in the limbic system may contribute to ME/CFS pathogenesis.

Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN.

Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN architecture for the detection of abnormal cervical cells in cytology images from a cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cervical image dataset of "Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using traditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.

Dataset of whole-brain resting-state fMRI of 227 young and elderly adults acquired at 3T.

To investigate the impact of adult age on the brain functional connectivity, whole-brain resting-state functional magnetic resonance imaging (R-fMRI) data were acquired on a 3T clinical MRI scanner in a cohort of 227, right-handed, native Swedish-speaking, healthy adult volunteers (N=227, aged 18-74 years old, male/female=99/128). The dataset is mainly consisted of a younger (18-30 years old n=124, males/females=51/73) and elderly adult (n=76, 60-76 years old, males/females=35/41) subgroups. The dataset was analyzed using a new data-driven analysis (QDA) framework. With QDA two types of threshold-free voxel-wise resting-state functional connectivity (RFC) metrics were derived: the connectivity strength index (CSI) and connectivity density index (CDI), which can be utilized to assess the brain changes in functional connectivity associated with adult age. The dataset can also be useful as a reference to identify abnormal changes in brain functional connectivity resulted from neurodegenerative or neuropsychiatric disorders.

Long Non-coding RNA IRAIN Inhibits VEGFA Expression via Enhancing Its DNA Methylation Leading to Tumor Suppression in Renal Carcinoma.

Aims: Long non-coding RNA IRAIN (lncRNA IRAIN) plays a critical role in numerous malignancies. However, the function of lncRNA IRAIN in renal carcinoma (RC) remains enigmatic. The purpose of this study is to characterize the effects of lncRNA IRAIN on RC progression. Methods: The expression pattern of lncRNA IRAIN and the vascular endothelial growth factor A (VEGFA) in RC tissues and cells was characterized by RT-qPCR and Western blot analysis. The roles of lncRNA IRAIN and VEGFA in the progression of RC were studied by gain- or loss-of-function experiments. Bioinformatics data analysis was used to predict CpG islands in the VEGFA promoter region. MSP was applied to detect the level of DNA methylation in RC cells. The interaction between lncRNA IRAIN and VEGFA was identified by RNA immunoprecipitation and RNA-protein pull down assays. Recruitment of DNA methyltransferases (Dnmt) to the VEGFA promoter region was achieved by chromatin immunoprecipitation. The subcellular localization of lncRNA IRAIN was detected by fractionation of nuclear and cytoplasmic RNA. Cell viability was investigated by CCK-8 assay, cell migration was tested by transwell migration assay, and apoptosis was analyzed by flow cytometry. The expression of epithelial-mesenchymal transition-related and apoptotic factors was evaluated by Western blot analysis. Finally, the effect of the lncRNA IRAIN/VEGFA axis was confirmed in an in vivo tumor xenograft model. Results: LncRNA IRAIN was poorly expressed in RC tissues and cells with a primary localization in the nucleus, while VEGFA was highly expressed. Overexpression of lncRNA IRAIN or knockdown of VEGFA inhibited cell proliferation and migration and induced the apoptosis of RC cells. Bioinformatics analysis indicated the presence of CpG islands in the VEGFA promoter region. Lack of methylation at specific sites in the VEGFA promoter region was detected through MSP assay. We found that lncRNA IRAIN was able to inhibit VEGFA expression through recruitment of Dnmt1, Dnmt3a, and Dnmt3b to the VEGFA promoter region. LncRNA IRAIN was also able to suppress RC tumor growth via repression of VEGFA in an in vivo mouse xenograft model. Conclusion: Our data shows that by downregulating VEGFA expression in RC, the lncRNA IRAIN has tumor-suppressive potential.

Rare variants in dynein heavy chain genes in two individuals with situs inversus and developmental dyslexia: a case report.

BACKGROUND: Developmental dyslexia (DD) is a neurodevelopmental learning disorder with high heritability. A number of candidate susceptibility genes have been identified, some of which are linked to the function of the cilium, an organelle regulating left-right asymmetry development in the embryo. Furthermore, it has been suggested that disrupted left-right asymmetry of the brain may play a role in neurodevelopmental disorders such as DD. However, it is unknown whether there is a common genetic cause to DD and laterality defects or ciliopathies. CASE PRESENTATION: Here, we studied two individuals with co-occurring situs inversus (SI) and DD using whole genome sequencing to identify genetic variants of importance for DD and SI. Individual 1 had primary ciliary dyskinesia (PCD), a rare, autosomal recessive disorder with oto-sino-pulmonary phenotype and SI. We identified two rare nonsynonymous variants in the dynein axonemal heavy chain 5 gene (DNAH5): a previously reported variant c.7502G > C; p.(R2501P), and a novel variant c.12043 T > G; p.(Y4015D). Both variants are predicted to be damaging. Ultrastructural analysis of the cilia revealed a lack of outer dynein arms and normal inner dynein arms. MRI of the brain revealed no significant abnormalities. Individual 2 had non-syndromic SI and DD. In individual 2, one rare variant (c.9110A > G;p.(H3037R)) in the dynein axonemal heavy chain 11 gene (DNAH11), coding for another component of the outer dynein arm, was identified. CONCLUSIONS: We identified the likely genetic cause of SI and PCD in one individual, and a possibly significant heterozygosity in the other, both involving dynein genes. Given the present evidence, it is unclear if the identified variants also predispose to DD and further studies into the association between laterality, ciliopathies and DD are needed.

Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet).

In this study, we proposed a novel convolutional neural network (CNN) architecture for classification of benign and malignant breast cancer (BC) in histological images. To improve the delivery and use of feature information, we chose the DenseNet as the basic building block and interleaved it with the squeeze-and-excitation (SENet) module. We conducted extensive experiments with the proposed framework by using the public domain BreakHis dataset and demonstrated that the proposed framework can produce significantly improved accuracy in BC classification, compared with the state-of-the-art CNN methods reported in the literature.

Viewing Pictures Triggers Rapid Morphological Enlargement in the Human Visual Cortex.

Measuring brain morphology with non-invasive structural magnetic resonance imaging is common practice, and can be used to investigate neuroplasticity. Brain morphology changes have been reported over the course of weeks, days, and hours in both animals and humans. If such short-term changes occur even faster, rapid morphological changes while being scanned could have important implications. In a randomized within-subject study on 47 healthy individuals, two high-resolution T1-weighted anatomical images were acquired (á 263 s) per individual. The images were acquired during passive viewing of pictures or a fixation cross. Two common pipelines for analyzing brain images were used: voxel-based morphometry on gray matter (GM) volume and surface-based cortical thickness. We found that the measures of both GM volume and cortical thickness showed increases in the visual cortex while viewing pictures relative to a fixation cross. The increase was distributed across the two hemispheres and significant at a corrected level. Thus, brain morphology enlargements were detected in less than 263 s. Neuroplasticity is a far more dynamic process than previously shown, suggesting that individuals' current mental state affects indices of brain morphology. This needs to be taken into account in future morphology studies and in everyday clinical practice.

miR-204 Negatively Regulates Cell Growth And Metastasis By Targeting ROBO4 In Human Bladder Cancer.

BACKGROUND: MicroRNAs (miRNAs) are well characterized for their important roles in human cancers by influencing various aspects of malignancy. Till now, the function and mechanism of miR-204, a tumor suppressor in several cancers, remain unclear in bladder cancer (BC). Here, we intend to explore its roles in BC progression. METHODS: qRT-PCR was applied to determine miR-204 and ROBO4 expression in BC tissues and cell lines. miR-204 expression with clinicopathological features was analyzed. The impacts of miR-204 on BC cell growth and metastasis in vitro were evaluated by both loss-of-function and gain-of-function assays (CCK-8, crystal violet staining, wound healing and transwell assays). Furthermore, qRT-PCR, Western blot and luciferase reporter assays were used to validate the targeting of ROBO4 by miR-204. Finally, linear regression was performed to analyze the correlation of miR-204 and ROBO4 in BC tissues. RESULTS: Expression of miR-204 was markedly decreased in BC tissues and cell lines were compared with respective controls. Low miR-204 expression was associated with positive advanced T stage and lymph node metastasis. Cellular function studies revealed that miR-204 inhibited BC cell growth, migration and invasion. Mechanistic exploration found that miR-204 directly targeted ROBO4. Rescue assays indicated that ROBO4 restoration could reverse the antitumor effects of miR-204 in BC. Finally, ROBO4 was significantly correlated with miR-204 levels inversely. CONCLUSION: miR-204 might serve as a tumor suppressor in BC by targeting ROBO4.