Two multimodal neuroimaging subtypes of obsessive-compulsive disorder disclosed by semi-supervised machine learning.

BACKGROUND: Obsessive-compulsive disorder (OCD) is a highly heterogeneous mental condition with a diverse symptom. Existing studies classified OCD on the basis of conventional phenomenology-based taxonomy ignoring the fact that the same subtype identified in accordance with clinical symptom may have different mechanisms and treatment responses. METHODS: This research involved 50 medicine-free patients with OCD and 50 matched healthy controls (HCs). All the participants were subjected to structural and functional magnetic resonance imaging (MRI). Voxel-based morphometry (VBM) and amplitude of low frequency fluctuation (ALFF) were used to evaluate gray matter volume (GMV) and spontaneous neuronal activities at rest respectively. Similarity network fusion (SNF) was utilized to integrate GMVs and spontaneous neuronal activities, and heterogeneity by discriminant analysis was applied to characterise OCD subtypes. RESULTS: Two OCD subtypes were identified: Subtype 1 exhibited decreased GMVs (i.e., left inferior temporal gyrus, right supplementary motor area and right lingual gyrus) and increased ALFF value (i.e., right orbitofrontal cortex), whereas subtype 2 exhibited increased GMVs (i.e., left cuneus, right precentral gyrus, left postcentral gyrus and left hippocampus) and decreased ALFF value (i.e., right caudate nucleus). Furthermore, the altered GMVs was negatively correlated with abnormal ALFF values in both subtype 1 and 2. LIMITATIONS: This study requires further validation via a larger, independent dataset and should consider the potential influences of psychotropic medication on OCD patients' brain activities. CONCLUSIONS: Results revealed two reproducible subtypes of OCD based on underlying multimodal neuroimaging and provided new perspectives on the classification of OCD.

Thermal controlled multi-functional metasurface for freely switching of absorption, reflection, and transmission.

Metasurfaces have garnered significant attention in recent years due to their substantial electromagnetic (EM) wave manipulation capabilities. However, most previously documented metasurfaces have been limited to controlling just a single EM wave mode, encompassing transmission, reflection, or absorption. Such limitations have impeded the broader applications of metasurfaces. To address this issue, this study introduces a multi-functional metasurface (MFM) in the utilization of Ge2Sb2Te5 (GST), vanadium dioxide (VO2), and graphene. This novel design enables real-time control over the transmission, absorption, and reflection of EM waves as necessitated through thermal control, allowing for seamless transitions from complete transmission to complete reflection. Furthermore, this configuration achieves extensive broadband perfect absorption, spanning up to 1.83 THz. The optical response mechanism of this MFM across distinct operational modes is meticulously analyzed through electric field distribution. Remarkably, this proposed MFM exhibits polarization insensitivity and maintains good optical performance even under conditions of wide-angle incidence. With the ability to switch to different operating modes according to the needs of different environments, the proposed MFM has the potential to be used in a wide range of scenarios, including radar stealth, wireless communications, and military search.

Metasurface inverse designed by deep learning for quasi-entire terahertz wave absorption.

Ultra-broadband and efficient terahertz (THz) absorption is of paramount importance for the development of high-performance detectors. These detectors find applications in next-generation wireless communications, military radar systems, security detection, medical imaging, and various other domains. In this study, we present an ultra-wideband THz wave metasurface absorber (UTWMA) featuring a composite surface microstructure and a multilayer absorbing material (graphene). This UTWMA demonstrates remarkable capabilities by achieving highly efficient absorption levels, reaching 96.33%, within the 0.5-10 THz frequency range. To enhance the efficiency and precision of the design process, we have incorporated artificial neural networks, which enable rapid and accurate parameter selection. Moreover, we have conducted a comprehensive analysis of the absorption mechanism exhibited by the UTWMA at different frequencies. This analysis combines insights from the electric field distribution and effective medium theory. The findings presented in this paper are expected to catalyze further research in the domain of broadband THz technology, particularly in the context of metasurfaces and related fields. Additionally, this work paves the way for the development of compact, supercontinuous THz photovoltaic or photothermal electrical devices.

Establishment and Validation of the LI-RADS Morphologic Type II Hepatocellular Carcinoma Early Recurrence Risk Scoring System.

BACKGROUND: Tumor morphology links to early recurrence of hepatocellular carcinoma. Controversy exists regarding the recurrence risk of Liver Imaging Reporting and Data System morphologic Type II hepatocellular carcinoma. This study aims to explore risk factors for early recurrence of Type II hepatocellular carcinoma. METHODS: Retrospective analysis of hepatocellular carcinoma patients who underwent curative resection and preoperative contrast-enhanced MRI from June 2016 to June 2020. Our patients formed the development set, and hepatocellular carcinoma patients from the TCIA database served as validation. Univariable and multivariable Cox regression identified independent risk factors for early recurrence. A risk scoring system was established for risk stratification, and an early recurrence prediction model was developed and validated. RESULTS: 95 Type II hepatocellular carcinoma patients were in the development set, and 29 cases were in the validation set. Early recurrence rates were 33.7% and 37.9%, respectively. Multivariate analysis revealed age, histological grade, AFP, and intratumoral hemorrhage as independent risk factors for early recurrence. The model's diagnostic performance for early recurrence was AUC = 0.817 in the development set. A scoring system classified patients into low-risk (scores ≤ 3) and high-risk (scores > 3) groups. The high-risk group had significantly lower recurrence-free survival (40.0% vs 73.2%, P = 0.001), consistent with the validation set (25.0% vs 73.3%, P = 0.028). CONCLUSIONS: The risk scoring system demonstrated excellent discrimination and predictive ability, aiding clinicians in assessing early recurrence risk and identifying high-risk individuals effectively.

Cavitation Erosion Prevention Using Laser Shock Peening: Development of a Predictive Evaluation System.

Marine flow-passing components are susceptible to cavitation erosion (CE), and researchers have worked to find ways to reduce its effects. Laser Shock Peening (LSP), a material strengthening method, has been widely used in aerospace and other cutting-edge fields. In recent years, LSP has been used in cavitation resistance research. However, the current LSP research does not realize a comprehensive predictive assessment of the material's CE resistance. This paper uses m stresses to develop a comprehensive set of strengthening effect prediction models from LSP to CE using finite element analysis (FEA). Results show that the LSP-1 sample (4 mm spot, 10 J energy) introduced a compressive residual stress value of 37.4 MPa, better than that of 16.6 MPa with the LSP-2 sample (6 mm spot, 10 J energy), which is generally consistent with the experimental findings; the model predicts a 16.35% improvement in the resistance of LSP-1 sample to water jet damage, which is comparable to the experimental result of 14.02%; additionally, interactions between micro-jets do not predominate the cavitation erosion process and the final CE effect of the material is mainly due to the accumulation of jet-material interaction.

An electrical/thermal dual-controlled quad-functional terahertz metasurface absorber.

Although the design of graphene-based tunable broadband terahertz (THz) absorbers has attracted much attention, improving the functionality of the absorbers to adapt to different scenarios is still worth studying. This paper presents an innovative design of a quad-functional metasurface absorber (QMA) in the THz region, which can switch the absorption frequency/band by means of dual voltage/thermal manipulation. By electrically manipulating the chemical potential of graphene, the QMA can switch freely between the narrowband absorption mode ("NAM") and the broadband absorption mode ("BAM"), while thermally manipulating the phase transition of VO2 allows switching between the low-frequency absorption mode ("LAM") and the high-frequency absorption mode ("HAM"). Detailed mechanistic analysis shows that the "NAM" and "BAM" are due to the switching of the fundamental and second order graphene surface plasmon polariton (SPP) resonances, respectively, and the switching between "LAM" and "HAM" is due to the phase transformation of VO2. Furthermore, the QMA is polarization insensitive in all absorption modes and maintains excellent absorption performance at large angular incidence of TE- and TM-polarized waves. All the results indicate that the proposed QMA has great potential for stealth, sensing, switching, and filtering applications.

Decreased gray matter volume and dynamic functional alterations in medicine-free obsessive-compulsive disorder.

BACKGROUND: Previous studies discovered the presence of abnormal structures and functions in the brain regions of patients with obsessive-compulsive disorder (OCD). Nevertheless, whether structural changes in brain regions are coupled with alterations in dynamic functional connectivity (dFC) at rest in medicine-free patients with OCD remains vague. METHODS: Three-dimensional T1-weighed magnetic resonance imaging (MRI) and resting-state functional MRI were performed on 50 medicine-free OCD and 50 healthy controls (HCs). Firstly, the differences in gray matter volume (GMV) between OCD and HCs were compared. Then, brain regions with aberrant GMV were used as seeds for dFC analysis. The relationship of altered GMV and dFC with clinical parameters in OCD was explored using partial correlation analysis. Finally, support vector machine was applied to examine whether altered multimodal imaging data might be adopted to distinguish OCD from HCs. RESULTS: Our findings indicated that GMV in the left superior temporal gyrus (STG) and right supplementary motor area (SMA) was reduced in OCD, and the dFC between the left STG and the left cerebellum Crus I and left thalamus, and between the right SMA and right dorsolateral prefrontal cortex (DLPFC) and left precuneus was decreased at rest in OCD. The brain regions both with altered GMV and dFC values could discriminate OCD from HCs with the accuracy of 0.85, sensitivity of 0.90 and specificity of 0.80. CONCLUSION: The decreased gray matter structure coupling with dynamic function in the left STG and right SMA at rest may be crucial in the pathophysiology of OCD. TRIAL REGISTRATION: Study on the mechanism of brain network in obsessive-compulsive disorder with multi-model magnetic resonance imaging (registration date: 08/11/2017; registration number: ChiCTR-COC-17,013,301).

A multimedia knowledge discovery-based optimal scheduling approach considering visual behavior in smart education.

Nowadays, the convergence of intelligent computing technique and education has been a hot concern for both academia and industry, producing the conception of smart education. It is predictable that automatic planning and scheduling for course contents are the most practical important task for smart education. As online and offline educational activities are visual behaviors, it remains challenging to capture and extract principal features. To breakthrough current barriers, this paper combines the visual perception technology and data mining theory, and proposes a multimedia knowledge discovery-based optimal scheduling approach in smart education about painting. At first, the data visualization is carried out to analyze the adaptive design of visual morphologies. On this basis, it is supposed to formulate a multimedia knowledge discovery framework which can implement multimodal inference tasks, so as to calculate specific course contents for specific individuals. At last, some simulation works are also conducted to obtain analysis results, showing that the proposed optimal scheduling scheme can work well in contents planning of smart education scenarios.

Thermally activated delayed fluorescence with dual-emission and pressure-induced bidirectional shifting: cooperative effects of intramolecular and intermolecular energy transfer.

Different from the conventional piezochromic materials with a mono-redshift of single emission, our well-designed molecule demonstrates a sensitive turn-on and color-tunable piezochromic luminescence in response to the hydrostatic pressure. The molecule PXZ-W-SOF possesses dual-emission and pressure-induced bidirectional shifting characteristics. On the basis of in-depth experimental studies, on one hand, it is confirmed that the origin of the dual-emission behavior is the intramolecular charge transfer, namely thermally activated delayed fluorescence (TADF), and the intermolecular excimer; on the other hand, the emission of the excimer exhibits three-step variations with increasing pressure, which is mainly attributed to the molecular structure and its crystal packing state. The remarkable color change of PXZ-W-SOF from sky-blue to green to deep-blue during the whole process of boosting and releasing pressure is a result of intramolecular and intermolecular energy-transfer interactions. The PXZ-W-SOF molecular model is an extremely rare example of highly sensitive fluorescence tuning driven by TADF and excimer conversion under mechanical stimulation, thus providing a novel mechanism for the field of piezochromism. The unique molecular design also offers a new idea for rare deep-blue and ultraviolet TADF materials.

FOXG1 drives transcriptomic networks to specify principal neuron subtypes during the development of the medial pallium.

The medial pallium (MP) is the major forebrain region underlying learning and memory, spatial navigation, and emotion; however, the mechanisms underlying the specification of its principal neuron subtypes remain largely unexplored. Here, by postmitotic deletion of FOXG1 (a transcription factor linked to autism spectrum disorders and FOXG1 syndrome) and single-cell RNA sequencing of E17.5 MP in mice, we found that FOXG1 controls the specification of upper-layer retrosplenial cortical pyramidal neurons [RSC-PyNs (UL)], subiculum PyNs (SubC-PyNs), CA1-PyNs, CA3-PyNs, and dentate gyrus granule cells (DG-GCs) in the MP. We uncovered subtype-specific and subtype-shared FOXG1-regulated transcriptomic networks orchestrating MP neuron specification. We further demonstrated that FOXG1 transcriptionally represses Zbtb20, Prox1, and Epha4 to prevent CA3-PyN and DG-GC identities during the specification of RSC-PyNs (UL) and SubC-PyNs; FOXG1 directly activates Nr4a2 to promote SubC-PyN identity. We showed that TBR1, controlled by FOXG1 during CA1-PyN specification, was down-regulated. Thus, our study illuminates MP principal neuron subtype specification and related neuropathogenesis.

Magnetic resonance imaging radiomics modeling predicts tumor deposits and prognosis in stage T3 lymph node positive rectal cancer.

PURPOSE: To develop a magnetic resonance imaging radiomics model to predict tumor deposits (TDs) and prognosis in stage T3 lymph node positive (T3N+) rectal cancer (RC). METHODS: This retrospective study included 163 patients with pathologically confirmed T3N + RC from December 2013 to December 2015. The patients were divided into two groups for training and testing. Extracting radiomic features from MR images and selecting features using principal component analysis (PCA), then radiomic scores (rad-scores) were obtained by logistic regression analysis. Finally, a combined TDs prediction model containing rad-scores and clinical features was developed. A receiver operating characteristic (ROC) curve was used to assess the prediction performance. The overall survival (OS) rate in patients with high-risk and low-risk TDs predicted by rad-scores was validated by Kaplan-Meier survival curves. RESULTS: Of the 163 patients included, histological TDs was diagnosed in 45 patients. The area under the curve (AUC) of the final model was 0.833 (training) and 0.844 (testing). The patients with rad-scores predicted high-risk were associated with OS. In addition, postoperative adjuvant therapy improved the OS of the high-risk TDs group (P < 0.05). CONCLUSION: MRI-based radiomics modeling helps in the preoperative prediction of patients with TDs+ in T3N + RC and provides risk stratification for neoadjuvant therapy. In addition, the rad-scores of TDs could suggest different survival benefits of postoperative adjuvant therapy for T3N + RC patients and guide clinical treatment.

Aptamer-mediated hollow MnO2 for targeting the delivery of sorafenib.

Sorafenib (SRF) presents undesirable effects in clinical treatment, due to the lack of targeting, poor water solubility, and obvious side effects. In this study, we constructed a novel nanodrug carrier system for accurate and efficient delivery of SRF, improving its therapeutic effects and achieving tumor-specific imaging. The hollow mesoporous MnO2 (H-MnO2) nanoparticles equipped with target substance aptamers (APT) on the surface were used to load SRF for the first time. The resulting H-MnO2-SRF-APT could specifically bound to glypican-3 (GPC3) receptors on the surface of hepatocellular carcinoma (HCC), rapidly undergoing subsequent degradation under decreased pH conditions in the tumor microenvironment (TME) and releasing the loaded SRF. In this process, Mn2+ ions were used for T1-weighted magnetic resonance imaging simultaneously. The in vitro cell experiments indicated that H-MnO2-SRF-APT showed much more effects on the inhibition in the proliferation of Huh7 and HepG2 HCC cells than that of the non-targeted H-MnO2-SRF and free SRF. Besides, the in vivo results further confirmed that H-MnO2-SRF-APT could effectively inhibit the growth of xenograft tumors Huh7 in the naked mouse with good biosafety. In conclusion, H-MnO2-SRF-APT could significantly enhance the therapeutic effect of SRF and is expected to be a new way of diagnosis and treatment of HCC.

A ball-milling synthesis of N-graphyne with controllable nitrogen doping sites for efficient electrocatalytic oxygen evolution and supercapacitors.

Low-cost and efficient multifunctional electrodes play an important part in promoting the practical application of energy conversion and storage. Herein, we report the facile synthesis of N-graphyne, with a novel structure, by one-step ball milling of CaC2 and pyrazine. The accurate doping of nitrogen atoms at the controllable sites of the molecular skeleton of γ-graphyne was achieved using the nitrogenous precursor (pyrazine) as a reactant. Various techniques were adopted for the investigation of the composition, structure, and morphology of the obtained samples. The electrochemical measurements demonstrated that N-graphyne can serve as an excellent electrode material for both electrocatalysis and supercapacitors. As an electrocatalyst, N-graphyne exhibited an overpotential of 280 mV at 100 mA cm-2 and a Tafel slope of 122 mV dec-1 for the oxygen evolution reaction with highly stable morphology and electrocatalytic performance. As a supercapacitor electrode, N-graphyne showed a maximum capacitance of 235 F g-1 at 1 A g-1, and capacitance retention of 87% after 3000 cycles. The superior electrochemical performance of N-graphyne is due to the nitrogen heteroatomic defects, large electrochemical active surface areas and fast electron migration. Our studies provide a facile synthesis of novel N-graphyne with controllable doping sites and promote its potential applications in electrocatalysis and supercapacitors.

Discovery of Small Molecule Inhibitors of Huntingtin Exon 1 Aggregation by FRET-Based High-Throughput Screening in Living Cells.

Huntington's disease (HD) is the most common inherited neurodegenerative disorder and one of the nine polyglutamine (polyQ) diseases. HD is characterized by the pathological aggregation of the misfolded huntingtin exon 1 protein (Httex1) with abnormally long polyQ expansion due to genetic mutation. While there is currently no effective treatment for HD, inhibition of aggregate formation represents a direct approach in mediating the toxicity associated with Httex1 misfolding. To exploit this therapeutic window, we engineered two fluorescence resonance energy transfer (FRET) based biosensors that monitor the aggregation of Httex1 with different expanded Q-lengths (Q39 and Q72) in living cells. These FRET biosensors, together with a high-precision fluorescence lifetime detection platform, enable high-throughput screening of small molecules that target Httex1 aggregation. We found six small molecules that decreased the FRET of the biosensors and reduced Httex1-Q72-induced neuronal cytotoxicity in N2a cells with nanomolar potency. Using advanced SPR and EPR techniques, we confirmed that the compounds directly bind to Httex1 fibrils and inhibit aggregate formation. This strategy in targeting the Httex1 aggregates can be applicable to other proteins involved in polyQ related diseases.

VOTENET+ : AN IMPROVED DEEP LEARNING LABEL FUSION METHOD FOR MULTI-ATLAS SEGMENTATION.

In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF. Motivated by this finding, we propose VoteNet+, an improved deep network to locally predict the probability of an atlas label to differ from the label of the target image. Furthermore, we show that JLF is more suitable for the VoteNet framework as a label fusion method than plurality voting. Lastly, we use Platt scaling to calibrate the probabilities of our new model. Results on LPBA40 3D MR brain images show that our proposed method can achieve better performance than VoteNet.

Targeting the ensemble of heterogeneous tau oligomers in cells: A novel small molecule screening platform for tauopathies.

OBJECTIVE: Understanding the heterogeneous pathology in Alzheimer's disease and related tauopathies is one of the most urgent and fundamental challenges facing the discovery of novel disease-modifying therapies. Through monitoring ensembles of toxic and nontoxic tau oligomers spontaneously formed in cells, our biosensor technology can identify tool compounds that modulate tau oligomer structure and toxicity, providing much needed insight into the nature and properties of toxic tau oligomers. BACKGROUND: Tauopathies are a group of neurodegenerative disorders characterized by pathologic aggregation of the microtubule binding protein tau. Recent studies suggest that tau oligomers are the primary toxic species in tauopathies. NEW/UPDATED HYPOTHESIS: We hypothesize that tau biosensors capable of monitoring tau oligomer conformation are able to identify tool compounds that modulate the structure and conformation of these tau assemblies, providing key insight into the unique structural fingerprints of toxic tau oligomers. These fingerprints will provide gravely needed biomarker profiles to improve staging of early tauopathy pathology and generate lead compounds for potential new therapeutics. Our time-resolved fluorescence resonance energy transfer biosensors provide us an exquisitely sensitive technique to monitor minute structural changes in monomer and oligomer conformation. In this proof-of-concept study, we identified a novel tool compound, MK-886, which directly binds tau, perturbs the conformation of toxic tau oligomers, and rescues tau-induced cytotoxicity. Furthermore, we show that MK-886 alters the conformation of tau monomer at the proline-rich and microtubule binding regions, stabilizing an on-pathway oligomer. MAJOR CHALLENGES FOR THE HYPOTHESIS: Our approach monitors changes in the ensemble of assemblies that are spontaneously formed in cells but does not specifically isolate or enrich unique toxic tau species. However, time-resolved fluorescence resonance energy transfer does not provide high-resolution, atomic scale information, requiring additional experimental techniques to resolve the structural features stabilized by different tool compounds. LINKAGE TO OTHER MAJOR THEORIES: Our biosensor technology is broadly applicable to other areas of tauopathy therapeutic development. These biosensors can be readily modified for different isoforms of tau, specific post-translational modifications, and familial Alzheimer's disease-associated mutations. We are eager to explore tau interactions with chaperone proteins, monitor cross-reactivity with other intrinsically disordered proteins, and target seeded oligomer pathology.

Fast predictive simple geodesic regression.

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.

VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation.

Deep learning (DL) approaches are state-of-the-art for many medical image segmentation tasks. They offer a number of advantages: they can be trained for specific tasks, computations are fast at test time, and segmentation quality is typically high. In contrast, previously popular multi-atlas segmentation (MAS) methods are relatively slow (as they rely on costly registrations) and even though sophisticated label fusion strategies have been proposed, DL approaches generally outperform MAS. In this work, we propose a DL-based label fusion strategy (VoteNet) which locally selects a set of reliable atlases whose labels are then fused via plurality voting. Experiments on 3D brain MRI data show that by selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct DL segmentation approach. We also provide an experimental analysis of the upper performance bound achievable by our method. While unlikely achievable in practice, this bound suggests room for further performance improvements. Lastly, to address the runtime disadvantage of standard MAS, all our results make use of a fast DL registration approach.