Neural mechanism underlying depressive-like state associated with social status loss.

Downward social mobility is a well-known mental risk factor for depression, but its neural mechanism remains elusive. Here, by forcing mice to lose against their subordinates in a non-violent social contest, we lower their social ranks stably and induce depressive-like behaviors. These rank-decline-associated depressive-like behaviors can be reversed by regaining social status. In vivo fiber photometry and single-unit electrophysiological recording show that forced loss, but not natural loss, generates negative reward prediction error (RPE). Through the lateral hypothalamus, the RPE strongly activates the brain's anti-reward center, the lateral habenula (LHb). LHb activation inhibits the medial prefrontal cortex (mPFC) that controls social competitiveness and reinforces retreats in contests. These results reveal the core neural mechanisms mutually promoting social status loss and depressive behaviors. The intertwined neuronal signaling controlling mPFC and LHb activities provides a mechanistic foundation for the crosstalk between social mobility and psychological disorder, unveiling a promising target for intervention.

Efficient Trifluoromethylation of Halogenated Hydrocarbons Using Novel [(bpy)Cu(O2CCF2SO2F)2] Reagent.

This study introduces a novel trifluoromethylating reagent, [(bpy)Cu(O2CCF2SO2F)2], notable for not only its practical synthesis from cost-effective starting materials and scalability but also its nonhygroscopic nature. The reagent demonstrates high efficiency in facilitating trifluoromethylation reactions with various halogenated hydrocarbons, yielding products in good yields and exhibiting broad functional group compatibility. The development of [(bpy)Cu(O2CCF2SO2F)2] represents an advancement in the field of organic synthesis, potentially serving as a valuable addition to the arsenal of existing trifluoromethylating agents.

Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation.

White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.

SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.

PURPOSE: To develop an ultrafast and robust MR parameter mapping network using deep learning. THEORY AND METHODS: We design a deep learning framework called SuperMAP that directly converts a series of undersampled (both in k-space and parameter-space) parameter-weighted images into several quantitative maps, bypassing the conventional exponential fitting procedure. We also present a novel technique to simultaneously reconstruct T1rho and T2 relaxation maps within a single scan. Full data were acquired and retrospectively undersampled for training and testing using traditional and state-of-the-art techniques for comparison. Prospective data were also collected to evaluate the trained network. The performance of all methods is evaluated using the parameter qualification errors and other metrics in the segmented regions of interest. RESULTS: SuperMAP achieved accurate T1rho and T2 mapping with high acceleration factors (R = 24 and R = 32). It exploited both spatial and temporal information and yielded low error (normalized mean square error of 2.7% at R = 24 and 2.8% at R = 32) and high resemblance (structural similarity of 97% at R = 24 and 96% at R = 32) to the gold standard. The network trained with retrospectively undersampled data also works well for the prospective data (with a slightly lower acceleration factor). SuperMAP is also superior to conventional methods. CONCLUSION: Our results demonstrate the feasibility of generating superfast MR parameter maps through very few undersampled parameter-weighted images. SuperMAP can simultaneously generate T1rho and T2 relaxation maps in a short scan time.

Efficient continuous wave and broad tunable lasers with the Tm:GdScO3 crystal.

The spectroscopic properties and tunable laser performances of the orthorhombic perovskite Tm:GdScO3 crystal grown by the Czochralski method are comparatively studied for polarization along different crystallographic axes. The polarized emission spectrum of Tm:GdScO3 along the b-axis exhibits, to the best of our knowledge, the broadest bandwidth among all the single Tm3+-doped bulk gain media, indicating the strong inhomogeneous line broadening of Tm3+ ions in GdScO3 and thus leads to a broad and smooth gain spectrum. Tunable laser operation with a tuning range as broad as 321 nm from 1824 nm to 2145 nm is achieved, which indicates its potential for few-optical-cycle pulse generation in the 2-µm spectral range.

Robust Hand Gesture Recognition Using a Deformable Dual-Stream Fusion Network Based on CNN-TCN for FMCW Radar.

Hand Gesture Recognition (HGR) using Frequency Modulated Continuous Wave (FMCW) radars is difficult because of the inherent variability and ambiguity caused by individual habits and environmental differences. This paper proposes a deformable dual-stream fusion network based on CNN-TCN (DDF-CT) to solve this problem. First, we extract range, Doppler, and angle information from radar signals with the Fast Fourier Transform to produce range-time (RT) and range-angle (RA) maps. Then, we reduce the noise of the feature map. Subsequently, the RAM sequence (RAMS) is generated by temporally organizing the RAMs, which captures a target's range and velocity characteristics at each time point while preserving the temporal feature information. To improve the accuracy and consistency of gesture recognition, DDF-CT incorporates deformable convolution and inter-frame attention mechanisms, which enhance the extraction of spatial features and the learning of temporal relationships. The experimental results show that our method achieves an accuracy of 98.61%, and even when tested in a novel environment, it still achieves an accuracy of 97.22%. Due to its robust performance, our method is significantly superior to other existing HGR approaches.

Interleukin-6 mediates lipopolysaccharide-inhibited irisin secretion in Nile tilapia (Oreochromis niloticus).

Irisin is a novel immunomodulatory adipomyokine released upon cleavage of the fibronectin type III domain-containing protein 5 (FNDC5). We aimed to examine interleukin-6 (IL-6) role in mediating irisin secretion in immunologically challenged animal and primary head kidney leukocytes cultured from tilapia. Intraperitoneal injection of lipopolysaccharide (LPS) increased plasma IL-6 levels and decreased irisin secretion, suggesting a causal relationship between the induction of IL-6 and irisin. To address this relationship, we further produced recombinant tilapia IL-6 and the anti-tilapia IL-6 polyclonal antiserum. Intraperitoneal injection of recombinant tilapia IL-6 inhibited plasma irisin levels. Consistent with this observation, LPS-induced inhibition of plasma irisin was significantly attenuated by neutralizing circulating IL-6 using an IL-6 antiserum. Besides, IL-6 treatment could inhibit irisin secretion and FNDC5 gene expression in primary cultures of tilapia head kidney leukocytes. In parallel experiments, both LPS and IL-6 blockade of irisin secretion could be reverted by IL-6 receptor antagonism. At the level of the leukocyte, IL-6 treatment also triggered rapid phosphorylation of Janus kinase 2 (JAK2) and signal transducer and activator of transcription 3 (STAT3), whereas IL-6-reduced irisin secretion could be negated by inhibiting the JAK2 and STAT3 signaling pathways. These results, as a whole, provide the first evidence that IL-6 is the mediator of LPS-inhibited irisin secretion via activation of the JAK2/STAT3 signaling pathway.

DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning.

MOTIVATION: The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity. RESULTS: In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen (http://yishi.sjtu.edu.cn/deepAntigen) that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy. AVAILABILITY AND IMPLEMENTATION: Data and implementation are available on webserver: http://yishi.sjtu.edu.cn/deepAntigen. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SuperDTI: Ultrafast DTI and fiber tractography with deep learning.

PURPOSE: To develop a deep learning-based reconstruction framework for ultrafast and robust diffusion tensor imaging and fiber tractography. METHODS: SuperDTI was developed to learn the nonlinear relationship between DWIs and the corresponding diffusion tensor parameter maps. It bypasses the tensor fitting procedure, which is highly susceptible to noises and motions in DWIs. The network was trained and tested using data sets from the Human Connectome Project and patients with ischemic stroke. Results from SuperDTI were compared against widely used methods for tensor parameter estimation and fiber tracking. RESULTS: Using training and testing data acquired using the same protocol and scanner, SuperDTI was shown to generate fractional anisotropy and mean diffusivity maps, as well as fiber tractography, from as few as six raw DWIs, with a quantification error of less than 5% in all white-matter and gray-matter regions of interest. It was robust to noises and motions in the testing data. Furthermore, the network trained using healthy volunteer data showed no apparent reduction in lesion detectability when directly applied to stroke patient data. CONCLUSIONS: Our results demonstrate the feasibility of superfast DTI and fiber tractography using deep learning with as few as six DWIs directly, bypassing tensor fitting. Such a significant reduction in scan time may allow the inclusion of DTI into the clinical routine for many potential applications.

Hepatocyte-derived MANF alleviates hepatic ischaemia-reperfusion injury via regulating endoplasmic reticulum stress-induced apoptosis in mice.

BACKGROUND: Endoplasmic reticulum (ER) perturbations are novel subcellular effectors involved in the ischaemia-reperfusion injury. As an ER stress-inducible protein, mesencephalic astrocyte-derived neurotrophic factor (MANF) has been proven to be increased during ischaemic brain injury. However, the role of MANF in liver ischaemia reperfusion (I/R) injury has not yet been studied. METHODS: To investigate the role of MANF in the process of liver ischaemia-reperfusion, Hepatocyte-specific MANF knockout (MANFhep-/- ) mice and their wild-type (WT) littermates were used in our research. Mice partial (70%) warm hepatic I/R model was established by vascular occlusion. We detected the serum levels of MANF in both liver transplant patients and WT mice before and after liver I/R injury. Recombinant human MANF (rhMANF) was injected into the tail vein before 1 hour occlusion. AST, ALT and Suzuki score were used to evaluate the extent of I/R injury. OGD/R test was performed on primary hepatocytes to simulate IRI in vitro. RNA sequence and RT-PCR were used to detect the cellular signal pathway activation while MANF knockout. RESULTS: We found that MANF expression and secretion are dramatically up-regulated during hepatic I/R. Hepatocyte-specific MANF knockout aggravates the I/R injury through the over-activated ER stress. The systemic administration of rhMANF before ischaemia has the potential to ameliorate I/R-triggered UPR and liver injury. Further study showed that MANF deficiency activated ATF4/CHOP and JNK/c-JUN/CHOP pathways, and rhMANF inhibited the activation of the two proapoptotic pathways caused by MANF deletion. CONCLUSION: Collectively, our study unravels a previously unknown relationship among MANF, UPR and hepatic I/R injury.

Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method.

Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple diffusion weighted images (DWIs). Although parallel imaging technique has improved the speed of dMRI acquisition, attaining high resolution three dimensional (3D) dMRI on preclinical MRI systems remained still time consuming. In this paper, kernel principal component analysis, a machine learning approach, was employed to estimate the correlation among DWIs. We demonstrated the feasibility of such correlation estimation from low-resolution training DWIs and used the correlation as a constraint to reconstruct high-resolution DWIs from highly under-sampled k-space data, which significantly reduced the scan time. Using full k-space 3D dMRI data of post-mortem mouse brains, we retrospectively compared the performance of the so-called kernel low rank (KLR) method with a conventional compressed sensing (CS) method in terms of image quality and ability to resolve complex fiber orientations and connectivity. The results demonstrated that the KLR-CS method outperformed the conventional CS method for acceleration factors up to 8 and was likely to enhance our ability to investigate brain microstructure and connectivity using high-resolution 3D dMRI.

Self-Supervised Deep Unrolled Reconstruction Using Regularization by Denoising.

Deep learning methods have been successfully used in various computer vision tasks. Inspired by that success, deep learning has been explored in magnetic resonance imaging (MRI) reconstruction. In particular, integrating deep learning and model-based optimization methods has shown considerable advantages. However, a large amount of labeled training data is typically needed for high reconstruction quality, which is challenging for some MRI applications. In this paper, we propose a novel reconstruction method, named DURED-Net, that enables interpretable self-supervised learning for MR image reconstruction by combining a self-supervised denoising network and a plug-and-play method. We aim to boost the reconstruction performance of Noise2Noise in MR reconstruction by adding an explicit prior that utilizes imaging physics. Specifically, the leverage of a denoising network for MRI reconstruction is achieved using Regularization by Denoising (RED). Experiment results demonstrate that the proposed method requires a reduced amount of training data to achieve high reconstruction quality among the state-of-the-art approaches utilizing Noise2Noise.

TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Expression and Antibody Preparation of GP5a Gene of Porcine Reproductive and Respiratory Syndrome Virus.

Porcine reproductive and respiratory syndrome (PRRS) is considered one of the most important infectious diseases to affect the swine industry and characterized by reproductive failure in late term gestation in sows and respiratory disease in pigs of all ages. The GP5a gene, encoding RNA-dependent RNA polymerase, is generally regarded as fairly conserved when compared to other viral proteins. It plays an important role in the process of duplication and transcription carried out by Porcine reproductive and respiratory syndrome virus (PRRSV). We firstly expressed and purified the GP5a protein of PRRSV. This provides a good method for the purification of expressed proteins and the preparation of the corresponding antibodies.

Measurement, data analysis and modeling of electromagnetic wave propagation gain in a typical vegetation environment.

This paper takes the specific environment covered by vegetation as the research object, carries out modeling and analysis, takes the large-scale fading model of wireless channel as the basis of data processing, researches the transmission law of electromagnetic wave in a typical vegetation environment, which can be divided into four situations. The signal attenuation in each case is theoretically derived and numerically simulated. From the view point of supporting vegetation environment channel, the large-scale channel measurement system is built to meet the actual needs, such as bandwidth, frequency, vegetation coverage, etc. the final vegetation environment channel model under the large-scale fading model is obtained. The results show that the path gain of four scenarios respectively are 81.3 dB, 36.5 dB, 1.6 dB, 1.5 dB, the value of path gain index is within the range of 2~3.5, four scenarios shadow fading standard deviation values are 7.1, 4.8, 10.1, 9.2, reflects the change of received power at the point caused by random factors such as reflection, absorption and scattering. In addition, the proposed channel model improves the gain about 15% compared with the tradition SUI model within vegetation coverage scene. The design process of the proposed model is carried out in the order of "studied the existing foundation → analyzed the existing problems → proposed the optimization scheme → simulation and verification results → actual measurement system". The advantage of paper's method is that, when the signal frequency, transceiver distance, antenna height and vegetation environment characteristic parameters are given, the statistical analysis results of wireless channel data are obtained. The purpose of the proposed work establishes a signal propagation prediction model under the vegetation environment, realizes a theoretical basis for channel simulation, and provides the basis of anti-fading technologies.

Light response and adsorption interaction of black phosphorus quantum dots and single-layer graphene phototransistor.

Black phosphorus quantum dots (BPQDs) are synthesized and combined with graphene sheet. The fabricated BPQDs/graphene devices are capable of detecting visible and near infrared radiation. The adsorption effect of BPQDs in graphene is clarified by the relationship of the photocurrent and the shift of the Dirac point with different substrate. The Dirac point moves toward a neutral point under illumination with both SiO2/Si and Si3N4/Si substrates, indicating an anti-doped feature of photo-excitation. To our knowledge, this provides the first observation of photoresist induced photocurrent in such systems. Without the influence of the photoresist the device can respond to infrared light up to 980 nm wavelength in vacuum in a cryostat, in which the photocurrent is positive and photoconduction effect is believed to dominate the photocurrent. Finally, the adsorption effect is modeled using a first-principle method to give a picture of charge transfer and orbital contribution in the interaction of phosphorus atoms and single-layer graphene.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions.

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.

Mid-Infrared Bipolar and Unipolar Linear Polarization Detections in Nb2 GeTe4 /MoS2 Heterostructures.

Detecting and distinguishing light polarization states, one of the most basic elements of optical fields, have significant importance in both scientific studies and industry applications. Artificially fabricated structures, e.g., metasurfaces with anisotropic absorptions, have shown the capabilities of detecting polarization light and controlling. However, their operations mainly rely on resonant absorptions based on structural designs that are usually narrow bands. Here, a mid-infrared (MIR) broadband polarization photodetector with high PRs and wavelength-dependent polarities using a 2D anisotropic/isotropic Nb2 GeTe4 /MoS2 van der Waals (vdWs) heterostructure is demonstrated. It is shown that the photodetector exhibits high PRs of 48 and 34 at 4.6  and 11.0 µm wavelengths, respectively, and even a negative PR of -3.38 for 3.7 µm under the zero bias condition at room temperature. Such interesting results can be attributed to the superimposed effects of a photovoltaic (PV) mechanism in the Nb2 GeTe4 /MoS2 hetero-junction region and a bolometric mechanism in the MoS2 layer. Furthermore, the photodetector demonstrates its effectiveness in bipolar and unipolar polarization encoding communications and polarization imaging enabled by its unique and high PRs.