Prognostic factors in patients with thymoma who underwent surgery.

PURPOSE: Thymoma is the most common primary tumor in the anterior mediastinum. The prognostic factors of patients with thymoma still need to be clarified. In this study, we aimed to investigate the prognostic factors of patients with thymoma who received radical resection and establish the nomogram to predict the prognosis of these patients. MATERIALS AND METHODS: Patients who underwent radical resection for thymoma with complete follow-up data between 2005 and 2021 were enrolled. Their clinicopathological characteristics and treatment methods were retrospectively analyzed. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared by the log-rank test. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify the independent prognostic factors. According to the results of the univariate analysis in the Cox regression model, the predictive nomograms were created. RESULTS: A total of 137 patients with thymoma were enrolled. With a median follow-up of 52 months, the 5-year and 10-year PFS rates were 79.5% and 68.1%, respectively. The 5-year and 10-year OS rates were 88.4% and 73.1%, respectively. Smoking status (P = 0.022) and tumor size (P = 0.039) were identified as independent prognostic factors for PFS. Multivariate analysis showed that a high level of neutrophils (P = 0.040) was independently associated with OS. The nomogram showed that the World Health Organization (WHO) histological classification contributed more to the risk of recurrence than other factors. Neutrophil count was the most important predictor of OS in patients with thymoma. CONCLUSION: Smoking status and tumor size are risk factors for PFS in patients with thymoma. A high level of neutrophils is an independent prognostic factor for OS. The nomograms developed in this study accurately predict PFS and OS rates at 5 and 10 years in patients with thymoma based on individual characteristics.

Effects of early-life zinc deficiency on learning and memory in offspring and the changes in DNA methylation patterns.

OBJECTIVE: To investigate the effect of maternal zinc deficiency on learning and memory in offspring and the changes in DNA methylation patterns. METHODS: Pregnant rats were divided into zinc adequate (ZA), zinc deficient (ZD), and paired fed (PF) groups. Serum zinc contents and AKP activity in mother rats and offspring at P21 (end of lactation) and P60 (weaned, adult) were detected. Cognitive ability of offspring at P21 and P60 were determined by Morris water maze. The expression of proteins including DNMT3a, DNMT1, GADD45ß, MeCP2 and BDNF in the offspring hippocampus were detected by Western-blot. The methylation status of BDNF promoter region in hippocampus of offspring rats was detected by MS-qPCR. RESULTS: Compared with the ZA and PF groups, pups in the ZD group had lower zinc levels and AKP activity in the serum, spent more time finding the platform and spent less time going through the platform area. Protein expression of DNMT1 and GADD45b were downregulated in the ZD group during P0 and P21 but not P60 compared with the ZA and PF group, these results were consistent with a reduction in BDNF protein at P0 (neonate), P21. However, when pups of rats in the ZD group were supplemented with zinc ion from P21 to P60, MeCP2 and GADD45b expression were significantly downregulated compared with the ZA and PF group. CONCLUSION: Post-weaning zinc supplementation may improve cognitive impairment induced by early life zinc deficiency, whereas it may not completely reverse the abnormal expression of particular genes that are involved in DNA methylation, binding to methylated DNA and neurogenesis.

A Steep-Slope MoS2/Graphene Dirac-Source Field-Effect Transistor with a Large Drive Current.

In the continuous transistor feature size scaling down, the scaling of the supply voltage is stagnant because of the subthreshold swing (SS) limit. A transistor with a new mechanism is needed to break through the thermionic limit of SS and hold the large drive current at the same time. Here, by adopting the recently proposed Dirac-source field-effect transistor (DSFET) technology, we experimentally demonstrate a MoS2/graphene (1.8 nm/0.3 nm) DSFET for the first time, and a steep SS of 37.9 mV/dec at room temperature with nearly free hysteresis is observed. Besides, by bringing in the structure of gate-all-around (GAA), the MoS2/graphene DSFET exhibits a steeper SS of 33.5 mV/dec and a 40% increased normalized drive current up to 52.7 µA·µm/µm (VDS = 1 V) with a current on/off ratio of 108, which shows potential for low-power and high-performance electronics applications.

Changes of hippocampus proteomic profiles after blueberry extracts supplementation in APP/PS1 transgenic mice.

Objective: To examine protein changes in the hippocampus of APP/PS1 transgenic mice after blueberry extracts (BB) intervention.Methods: Eight APP/PS1 transgenic mice were randomly assigned to Alzheimer's disease (AD)+BB group (n=4) and AD+control group (n=4). After a 16-week treatment, 2-DE and MALDI-TOF-MS were used to compare the proteomic profiles of the hippocampus in the two groups and Western blot was used to confirm the important differentially expressed proteins.Results: Twelve proteins were differentially expressed between the two groups. Nine of them were identified. Cytochrome b-c1 complex subunit 6, beta-actin, dynamin 1, and heat shock cognate 71 were up-regulated in AD+BB group, while a-enolase, stress-induced-phosphoprotein 1, malate dehydrogenase (MDH), MDH 1, and T-complex protein 1 subunit beta were down-regulated, respectively. Importantly, some of the identified proteins (e.g. dynamin 1) are known to be involved in cognitive impairment. Western blot analysis of hippocampus dynamin 1 expression confirmed the proteomic findings.Conclusions: The consumption of BB modulates the expression of proteins that are linked to the improvements of cognitive dysfunction in hippocampus of APP/PS1 transgenic mice.

DNA methylation mechanism of intracellular zinc deficiency-induced injury in primary hippocampal neurons in the rat brain.

OBJECTIVE: To explore Zn2+ deficiency-induced neuronal injury in relation to DNA methylation, providing valuable data and basic information for clarifying the mechanism of Zn2+ deficiency-induced neuronal injury. METHODS: Cultured hippocampal neurons were exposed to the cell membrane-permeant Zn2+ chelator N,N,N',N'-Tetrakis (2-pyridylmethyl) ethylenediamine (TPEN) (2 µM), and to TPEN (2 µM) plus ZnSO4 (5 µM) for 24 hours. We analyzed intracellular Zn2+ levels, neuronal viability, and protein/mRNA levels for DNA (cytosine-5) methyltransferase 1 (DNMT1), DNA (cytosine-5-) methyltransferase 3 alpha (DNMT3a), methyl CpG binding protein 2 (MeCP2), Brain-derived neurotrophic factor (BDNF), and growth arrest and DNA-damage-inducible, beta (GADD45b) in the treated neurons. RESULTS: We found that exposure of hippocampal neurons to TPEN (2 µM) for 24 hours significantly reduced intracellular Zn2+ concentration and neuronal viability. Furthermore, DNMT3a, DNMT1, BDNF, and GADD45b protein levels in TPEN-treated neurons were significantly downregulated, whereas MeCP2 levels were, as expected, upregulated. In addition, DNMT3a and DNMT1 mRNA levels in TPEN-treated neurons were downregulated, while MeCP2, GADD45b, and BDNF mRNA were largely upregulated. Addition of ZnSO4 (5 µM) almost completely reversed the TPEN-induced alterations. CONCLUSION: Our data suggest that free Zn2+ deficiency-induced hippocampal neuronal injury correlates with free Zn2+ deficiency-induced changes in methylation-related protein gene expression including DNMT3a/DNMT1/MeCP2 and GADD45b, as well as BDNF gene expression.

The cognitive impairment induced by zinc deficiency in rats aged 0∼2 months related to BDNF DNA methylation changes in the hippocampus.

OBJECTIVE: This study was carried out to understand the effects of zinc deficiency in rats aged 0∼2 months on learning and memory, and the brain-derived neurotrophic factor (BDNF) gene methylation status in the hippocampus. METHODS: The lactating mother rats were randomly divided into three groups (n = 12): zinc-adequate group (ZA: zinc 30 mg/kg diet), zinc-deprived group (ZD: zinc 1 mg/kg diet), and a pair-fed group (PF: zinc 30 mg/kg diet), in which the rats were pair-fed to those in the ZD group. After weaning (on day 23), offspring were fed the same diets as their mothers. After 37 days, the zinc concentrations in the plasma and hippocampus were measured, and the behavioral function of the offspring rats was measured using the passive avoidance performance test. We then assessed the DNA methylation patterns of the exon IX of BDNF by methylation-specific quantitative real-time PCR and the mRNA expression of BDNF in the hippocampus by RT-PCR. RESULTS: Compared with the ZA and PF groups, rats in the ZD group had shorter latency period, lower zinc concentrations in the plasma and hippocampus (P < 0.05). Interestingly, the DNA methylation of the BDNF exon IX was significantly increased in the ZD group, compared with the ZA and PF groups, whereas the expression of the BDNF mRNA was decreased. In addition, the DNMT1 mRNA expression was significantly upregulated and DNMT3A was downregulated in the ZD group, but not in the ZA and PF groups. CONCLUSION: The learning and memory damage in offspring may be a result of the epigenetic changes of the BDNF genes in response to the zinc-deficient diet during 0∼2 month period. Furthermore, this work supports the speculative notion that altered DNA methylation of BDNF in the hippocampus is one of the main causes of cognitive impairment by zinc deficiency.

Cyanidin-3-O-galactoside and blueberry extracts supplementation improves spatial memory and regulates hippocampal ERK expression in senescence-accelerated mice.

OBJECTIVE: To investigate whether the antioxidation and the regulation on the Extracellular Regulated Protein Kinases (ERK) signaling pathway are involved in the protective effects of blueberry on central nervous system. METHODS: 30 Senescence-accelerated mice prone 8 (SAMP8) mice were divided into three groups and treated with normal diet, blueberry extracts (200 mg/kg•bw/day) and cyaniding-3-O-galactoside (Cy-3-GAL) (50 mg/kg•bw/day) from blueberry for 8 weeks. 10 SAMR1 mice were set as control group. The capacity of spatial memory was assessed by Passive avoidance task and Morris water maze. Histological analyses on hippocampus were completed. Malondialdehyde (MDA) levels, Superoxide Dismutase (SOD) activity and the expression of ERK were detected. RESULTS: Both Cy-3-GAL and blueberry extracts were shown effective functions to relieve cellular injury, improve hippocampal neurons survival and inhibit the pyramidal cell layer damage. Cy-3-GAL and blueberry extracts also increased SOD activity and reduced MDA content in brain tissues and plasma, and increased hippocampal phosphorylated ERK (p-ERK) expression in SAMP8 mice. Further more, the passive avoidance task test showed that both the latency time and the number of errors were improved by Cy-3-GAL treatment, and the Morris Water Maze test showed significant decreases of latency were detected by Cy-3-GAL and blueberry extracts treatment on day 4. CONCLUSION: Blueberry extracts may reverse the declines of cognitive and behavioral function in the ageing process through several pathways, including enhancing the capacity of antioxidation, altering stress signaling. Cy-3-GAL may be an important active ingredient for these biological effects.

[Applied research on the physical model for calculating optical constant of metal oxide films].

In the present paper, two metal oxide films of HfO2 and Ta2 O5 were prepared by ion beam sputtering technology. Through measuring ellipsometric parameters of HfO2 and Ta2O5 films, their optical constants can be inversion-calculated by nonlinear least squares techniques. In the fitting process, eight experiment groups were arranged by the orthogonal table L8 (2(7)). After selecting Cauchy model, the largest influencing factor for fitting optical constant of HfO2 is surface layer model and the largest influencing factor for fitting optical constant of Ta2 O5 is refractive index gradient model. The impact of different physical model on MSE and the order for selecting model in the fitting process are determined. According to the selecting model and the determined fitting order, optical constants of the two metal oxide films were inversion-calculated with adding weak absorption model, and the obtained MSE can descend 79% and 39% according to the initial value. The results indicated that the selecting model possesses definite physical significance in the fitting process. The obtained method can be applied in inversion-calculating many metal oxide films with weak absorption. It has wide application value. At 500 nm, the refractive index gradient of Ta2O5 films is greater than HfO2 films, while the extinction coefficient of HfO2 films is greater than Ta2O5. It was shown that Hf metal is easier to form stable oxide than Ta metal. And the absorption of HfO2 films is larger than Ta2O5 films.

Adaptive Cross-Modal Transferable Adversarial Attacks From Images to Videos.

The cross-model transferability of adversarial examples makes black-box attacks to be practical. However, it typically requires access to the input of the same modality as black-box models to attain reliable transferability. Unfortunately, the collection of datasets may be difficult in security-critical scenarios. Hence, developing cross-modal attacks for fooling models with different modalities of inputs would highly threaten real-world DNNs applications. The above considerations motivate us to investigate cross-modal transferability of adversarial examples. In particular, we aim to generate video adversarial examples from white-box image models to attack video CNN and ViT models. We introduce the Image To Video (I2V) attack based on the observation that image and video models share similar low-level features. For each video frame, I2V optimizes perturbations by reducing the similarity of intermediate features between benign and adversarial frames on image models. Then I2V combines adversarial frames together to generate video adversarial examples. I2V can be easily extended to simultaneously perturb multi-layer features extracted from an ensemble of image models. To efficiently integrate various features, we introduce an adaptive approach to re-weight the contributions of each layer based on its cosine similarity values of the previous attack step. Experimental results demonstrate the effectiveness of the proposed method.

Building an Open-Vocabulary Video CLIP Model With Better Architectures, Optimization and Data.

Despite significant results achieved by Contrastive Language-Image Pretraining (CLIP) in zero-shot image recognition, limited effort has been made exploring its potential for zero-shot video recognition. This paper presents Open-VCLIP++, a simple yet effective framework that adapts CLIP to a strong zero-shot video classifier, capable of identifying novel actions and events during testing. Open-VCLIP++ minimally modifies CLIP to capture spatial-temporal relationships in videos, thereby creating a specialized video classifier while striving for generalization. We formally demonstrate that training Open-VCLIP++ is tantamount to continual learning with zero historical data. To address this problem, we introduce Interpolated Weight Optimization, a technique that leverages the advantages of weight interpolation during both training and testing. Furthermore, we build upon large language models to produce fine-grained video descriptions. These detailed descriptions are further aligned with video features, facilitating a better transfer of CLIP to the video domain. Our approach is evaluated on three widely used action recognition datasets, following a variety of zero-shot evaluation protocols. The results demonstrate that our method surpasses existing state-of-the-art techniques by significant margins. Specifically, we achieve zero-shot accuracy scores of 88.1%, 58.7%, and 81.2% on UCF, HMDB, and Kinetics-600 datasets respectively, outpacing the best-performing alternative methods by 8.5%, 8.2%, and 12.3%. We also evaluate our approach on the MSR-VTT video-text retrieval dataset, where it delivers competitive video-to-text and text-to-video retrieval performance, while utilizing substantially less fine-tuning data compared to other methods.

Towards Transferable Adversarial Attacks on Image and Video Transformers.

The transferability of adversarial examples across different convolutional neural networks (CNNs) makes it feasible to perform black-box attacks, resulting in security threats for CNNs. However, fewer endeavors have been made to investigate transferable attacks for vision transformers (ViTs), which achieve superior performance on various computer vision tasks. Unlike CNNs, ViTs establish relationships between patches extracted from inputs by the self-attention module. Thus, adversarial examples crafted on CNNs might hardly attack ViTs. To assess the security of ViTs comprehensively, we investigate the transferability across different ViTs in both untargetd and targeted scenarios. More specifically, we propose a Pay No Attention (PNA) attack, which ignores attention gradients during backpropagation to improve the linearity of backpropagation. Additionally, we introduce a PatchOut/CubeOut attack for image/video ViTs. They optimize perturbations within a randomly selected subset of patches/cubes during each iteration, preventing over-fitting to the white-box surrogate ViT model. Furthermore, we maximize the L2 norm of perturbations, ensuring that the generated adversarial examples deviate significantly from the benign ones. These strategies are designed to be harmoniously compatible. Combining them can enhance transferability by jointly considering patch-based inputs and the self-attention of ViTs. Moreover, the proposed combined attack seamlessly integrates with existing transferable attacks, providing an additional boost to transferability. We conduct experiments on ImageNet and Kinetics-400 for image and video ViTs, respectively. Experimental results demonstrate the effectiveness of the proposed method.

Depletion of intracellular zinc induced apoptosis in cultured hippocampal neurons through Raf/MEK/ERK pathways.

An experiment was performed to observe the changes in Raf-1 kinase/mitogen-activated protein kinase ERK (MEK)/extracellular signal-regulated kinase (ERK) signaling pathways in cultured hippocampal neurons and its correlation with neurons apoptosis induced by intracellular zinc depletion. Cultured hippocampal neurons were exposed to a cell membrane-permeant zinc chelator TPEN (2 µM), and to TPEN plus zinc sulfate (5 µM) for 24 h. Cultures were then processed to detect neuronal viability by the methyl thiazolyl tetrazolium assay, while apoptosis rate was simultaneously observed by the flow cytometric analysis. Caspase-3, Raf-1, pMEK, pERK1/2, and pCREB protein levels were examined by Western blot assays. The viability in TPEN-incubated neurons was notably decreased, apoptosis rate and expression of caspase-3 significantly increased compared to untreated controls. The significant down-regulation of Raf/MEK/ERK signaling pathway and expression of pCREB were decreased in TPEN-treated neurons. Co-addition of zinc almost completely reversed TPEN-induced alterations described. The results demonstrated zinc-modulated apoptosis and the expression of Raf/MEK/ERK at the protein level in hippocampal neurons. It is possible that zinc depletion-induced apoptosis in cultured hippocampal neurons may be relevant to the changes of Raf/MEK/ERK signaling pathway.

Generalized Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data.

The vanilla Few-shot Learning (FSL) learns to build a classifier for a new concept from one or very few target examples, with the general assumption that source and target classes are sampled from the same domain. Recently, the task of Cross-Domain Few-Shot Learning (CD-FSL) aims at tackling the FSL where there is a huge domain shift between the source and target datasets. Extensive efforts on CD-FSL have been made via either directly extending the meta-learning paradigm of vanilla FSL methods, or employing massive unlabeled target data to help learn models. In this paper, we notice that in the CD-FSL task, the few labeled target images have never been explicitly leveraged to inform the model in the training stage. However, such a labeled target example set is very important to bridge the huge domain gap. Critically, this paper advocates a more practical training scenario for CD-FSL. And our key insight is to utilize a few labeled target data to guide the learning of the CD-FSL model. Technically, we propose a novel Generalized Meta-learning based Feature-Disentangled Mixup network, namely GMeta-FDMixup. We make three key contributions of utilizing GMeta-FDMixup to address CD-FSL. Firstly, we present two mixup modules - mixup-P and mixup-M that help facilitate utilizing the unbalanced and disjoint source and target datasets. These two novel modules enable diverse image generation for training the model on the source domain. Secondly, to narrow the domain gap explicitly, we contribute a novel feature disentanglement module that learns to decouple the domain-irrelevant and domain-specific features. By stripping the domain-specific features, we alleviate the negative effects caused by the domain inductive bias. Finally, we repurpose a new contrastive learning module, dubbed ConL. ConL prevents the model from only capturing category-related features via introducing contrastive loss. Thus, the generalization ability on novel categories is improved. Extensive experimental results on two benchmarks show the superiority of our setting and the effectiveness of our method. Code and models will be released.

A Dynamic Frame Selection Framework for Fast Video Recognition.

We introduce AdaFrame, a conditional computation framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame, which contains a Long Short-Term Memory augmented with a global memory to provide context information, operates as an agent to interact with video sequences aiming to search over time which frames to use. Trained with policy search methods, at each time step, AdaFrame computes a prediction, decides where to observe next, and estimates a utility, i.e., expected future rewards, of viewing more frames in the future. Exploring predicted utilities at testing time, AdaFrame is able to achieve adaptive lookahead inference so as to minimize the overall computational cost without incurring a degradation in accuracy. We conduct extensive experiments on two large-scale video benchmarks, FCVID and ActivityNet. With a vanilla ResNet-101 model, AdaFrame achieves similar performance of using all frames while only requiring, on average, 8.21 and 8.65 frames on FCVID and ActivityNet, respectively. We also demonstrate AdaFrame is compatible with modern 2D and 3D networks for video recognition. Furthermore, we show, among other things, learned frame usage can reflect the difficulty of making prediction decisions both at instance-level within the same class and at class-level among different categories.

Depressed hippocampal MEK/ERK phosphorylation correlates with impaired cognitive and synaptic function in zinc-deficient rats.

An experiment was performed to observe changes of mitogen-activated protein kinase ERK (MEK)/extracellular signal-regulated kinase (ERK) signaling pathways in the hippocampus of zinc-deficient (ZD) rats and the correlation with cognitive dysfunction. Forty-four male weanling Wistar rats were randomly assigned to ZD (n = 22) and control (pair-fed, n = 22) groups. After a 4-week treatment, Y-maze was used to test the spatial memory of the rats. The long-term potentiation (LTP) in rat hippocampal dentate gyrus was observed simultaneously. pMEK, pERK1/2, and pCREB protein levels were examined by Western blot assays. The results demonstrated that the latency period in Y-maze was significantly shorter for the ZD rats. LTP amplitude in the ZD group decreased significantly compared with the control group. pMEK, pERK1/2, and pCREB protein expression of hippocampus in the ZD group decreased significantly. The results implicated a possibility that zinc deficiency-induced cognitive and synaptic impairment may be relevant to the MEK/ERK signaling pathway.

Effects of intracellular zinc depletion on the expression of VDAC in cultured hippocampal neurons.

An experiment was conducted to investigate whether intracellular zinc depletion can actually change expression of voltage-dependent anion channel 1 (VDAC1) and VPAC2 in cultured hippocampal neurons as well as their significance. Hippocampal neurons were obtained by primary culture from hippocampus of newborn Wistar rats. Cultured hippocampal neurons were exposed to a cell membrane-permeable zinc chelator N,N,N',N'-tetrakis (2-pyridyl methyl) ethylenediamine (TPEN) (2 µM), and to TPEN plus zinc sulfate (5 µM) for 1 or 24 hours. Cultures were then processed to detect neuronal injury by lactate dehydrogenase (LDH) assay, intracellular Ca(2+) with the fluorescent probe fluo-3/AM, reactive oxygen species (ROS) generation using 2',7'-dichlorofluorescein diacetate (DCFH-DA) assay, nuclear morphology by Hoechst 33342, VDAC1, and VDAC2 protein levels by western blot, and VDAC1 and VDAC2 mRNA levels by RT-PCR. The results demonstrated that exposure of hippocampal neurons to TPEN (2 µM) for 24 hours induced notably neuronal injury, significantly increased the number of apoptotic nuclei, up-regulated the expression of VDAC1 protein level and down-regulated the expression of VDAC2 protein level. Significant down-regulation of mRNA levels for VDAC1 and VDAC2 were observed in TPEN-treated neurons. Co-addition of zinc almost completely reversed TPEN-induced neuronal injury and above alterations in VDAC1 and VDAC2 protein levels and mRNA levels. Present results implicate a possibility that up-regulation of VDAC1 and down-regulation of VDAC2 may participate in hippocampal neuron injury induced by zinc deficiency.

A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition.

Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composition. In reality, two dishes with the same name do not necessarily share the exact list of ingredients. Therefore, the dishes under the same food category are not mandatorily equal in nutrition content. Nevertheless, due to limited datasets available with ingredient labels, the problem of ingredient recognition is often overlooked. Furthermore, as the number of ingredients is expected to be much less than the number of food categories, ingredient recognition is more tractable in the real-world scenario. This paper provides an insightful analysis of three compelling issues in ingredient recognition. These issues involve recognition in either image-level or region level, pooling in either single or multiple image scales, learning in either single or multi-task manner. The analysis is conducted on a large food dataset, Vireo Food-251, contributed by this paper. The dataset is composed of 169,673 images with 251 popular Chinese food and 406 ingredients. The dataset includes adequate challenges in scale and complexity to reveal the limit of the current approaches in ingredient recognition.

LncRNA XIST sponges miR-199a-3p to modulate the Sp1/LRRK2 signal pathway to accelerate Parkinson's disease progression.

In vitro and in vivo models of Parkinson's disease were established to investigate the effects of the lncRNA XIST/miR-199a-3p/Sp1/LRRK2 axis. The binding between XIST and miR-199a-3p as well as miR-199a-3p and Sp1 were examined by luciferase reporter assay and confirmed by RNA immunoprecipitation analysis. Following the Parkinson's disease animal behavioural assessment by suspension and swim tests, the brain tissue injuries were evaluated by hematoxylin and eosin, TdT-mediated dUTP-biotin nick end labelling, and tyrosine hydroxylase stainings. The results indicated that miR-199a-3p expression was downregulated, whereas that of XIST, Sp1 and LRRK2 were upregulated in Parkinson's disease. Moreover, miR-199a-3p overexpression or XIST knockdown inhibited the cell apoptosis induced by MPP+ treatment and promoted cell proliferation. The neurodegenerative defects were significantly recovered by treating the cells with shXIST or shSp1, whereas miR-199a-3p inhibition or Sp1 and LRRK2 overexpression abrogated these beneficial effects. Furthermore, the results of our in vivo experiments confirmed the neuroprotective effects of shXIST and miR-199a-3p against MPTP-induced brain injuries, and the Parkinson's disease behavioural symptoms were effectively alleviated upon shXIST or miR-199a-3p treatment. In summary, the results of the present study showed that lncRNA XIST sponges miR-199a-3p to modulate Sp1 expression and further accelerates Parkinson's disease progression by targeting LRRK2.

Pixel2Mesh: 3D Mesh Model Generation via Image Guided Deformation.

In this paper, we propose an end-to-end deep learning architecture that generates 3D triangular meshes from single color images. Restricted by the nature of prevalent deep learning techniques, the majority of previous works represent 3D shapes in volumes or point clouds. However, it is non-trivial to convert these representations to compact and ready-to-use mesh models. Unlike the existing methods, our network represents 3D shapes in meshes, which are essentially graphs and well suited for graph-based convolutional neural networks. Leveraging perceptual features extracted from an input image, our network produces the correct geometry by progressively deforming an ellipsoid. To make the whole deformation procedure stable, we adopt a coarse-to-fine strategy, and define various mesh/surface related losses to capture properties of various aspects, which benefits producing the visually appealing and physically accurate 3D geometry. In addition, our model by nature can be adapted to objects in specific domains, e.g., human faces, and be easily extended to learn per-vertex properties, e.g., color. Extensive experiments show that our method not only qualitatively produces the mesh model with better details, but also achieves the higher 3D shape estimation accuracy compared against the state-of-the-arts.

Ultrafast non-volatile flash memory based on van der Waals heterostructures.

Flash memory has become a ubiquitous solid-state memory device widely used in portable digital devices, computers and enterprise applications. The development of the information age has demanded improvements in memory speed and retention performance. Here we demonstrate an ultrafast non-volatile flash memory based on MoS2/hBN/multilayer graphene van der Waals heterostructures, which achieves an ultrafast writing/erasing speed of 20 ns through two-triangle-barrier modified Fowler-Nordheim tunnelling. Using detailed theoretical analysis and experimental verification, we postulate that a suitable barrier height, gate coupling ratio and clean interface are the main reasons for the breakthrough writing/erasing speed of our flash memory devices. Because of its non-volatility this ultrafast flash memory could provide the foundation for the next generation of high-speed non-volatile memory.