Search

Propaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.

Previous studies suggested that anti-inflammatory microglia/macrophages (Mi/MΦ) play a role in “normal phagocytosis,” which promoted the rapid clearance of necrotic substances and apoptotic cells. More recently, a few studies have found that Mi/MΦ also play a role in “pathological phagocytosis” in the form of excessive or reduced phagocytosis, thereby worsening damage induced by CNS diseases. However, the underlying mechanisms and the Mi/MΦ subtypes related to this pathological phagocytosis are still unknown. Salt-inducible kinase 3 (SIK3), a member of the 5’ adenosine monophosphate-activated protein kinase (AMPK) family, has been shown to regulate inflammation in several peripheral diseases. Whether SIK3 also regulates the inflammatory response in CNS diseases is currently unknown. Therefore, in this study, we created a transgenic tamoxifen-induced Mi/MΦ-specific SIK3 conditional knockout (SIK3-cKO) mouse to examine SIK3’s role in phagocytotic function induced by transient focal cerebral ischemia (tFCI). By single-cell RNA-seq, we found the pro-inflammatory Mi/MΦ phenotype performed an excessive phagocytotic function, but the anti-inflammatory Mi/MΦ phenotype performed a normal phagocytotic function. We found that SIK3-cKO caused Mi/MΦ heterogenization from the transitional phenotype to the anti-inflammatory phenotype after tFCI. This phenotypic shift corresponded with enhanced phagocytosis of both apoptotic and live neurons. Interestingly, SIK3-cKO enhanced normal phagocytosis of myelin debris but attenuating excessive phagocytosis of non-damaged myelin sheath, thereby protecting white matter integrity after tFCI. CD16, a pro-inflammation marker, was decreased significantly by SIK3-cKO and correlated with “excessive phagocytosis.” SIK3-cKO promoted long-term recovery of white matter function and neurological function as assessed with electrophysiological compound action potential (CAPs) and behavioral analysis. This study is the first to show a role of SIK3 in Mi/MΦ phagocytosis in CNS diseases, and reveals that promoting Mi/MΦ anti-inflammatory heterogenization inhibits “excessive phagocytosis” of live cells and facilitates “normal phagocytosis” of apoptotic cells. Therefore, inhibition of SIK3 in Mi/MΦ may be a potential therapeutic target in stroke and other CNS diseases with accompanying white matter destruction. Graphical abstract In the acute stage of tFCI, Mi/MΦ polarized into different phenotypes. The pro-inflammatory Mi/MΦ phenotype performed an excessive phagocytotic function. In contrast, the anti-inflammatory Mi/MΦ phenotype performed a normal phagocytotic function. After tFCI, SIK3-cKO promoted anti-inflammatory phenotypic heterogenization of Mi/MΦ. SIK3-cKO promoted Mi/MΦ phagocytosis of apoptotic (normal phagocytosis) and living neuronal cell bodies (excessive phagocytosis) in gray matter. Interestingly, SIK3-cKO specifically increased normal phagocytosis of myelin debris concurrent with an attenuation of excessive phagocytosis of myelin sheath in white matter. These changes induced by SIK3-cKO were associated with protection of white matter integrity and long-term neurofunctional recovery after tFCI.

Demyelination occurs in response to brain injury and is observed in many neurodegenerative diseases. Myelin is synthesized from oligodendrocytes in the central nervous system, and oligodendrocyte death‐induced demyelination is one of the mechanisms involved in white matter damage after stroke and neurodegeneration. Oligodendrocyte precursor cells (OPCs) exist in the brain of normal adults, and their differentiation into mature oligodendrocytes play a central role in remyelination. Although the differentiation and maturity of OPCs drive endogenous efforts for remyelination, the failure of axons to remyelinate is still the biggest obstacle to brain repair after injury or diseases. In recent years, studies have made attempts to promote remyelination after brain injury and disease, but its cellular or molecular mechanism is not yet fully understood. In this review, we discuss recent studies examining the demyelination process and potential therapeutic strategies for remyelination in aging and stroke. Based on our current understanding of the cellular and molecular mechanisms underlying remyelination, we hypothesize that myelin and oligodendrocytes are viable therapeutic targets to mitigate brain injury and to treat demyelinating‐related neurodegeneration diseases., Reviews of cellular or molecular mechanism of demyelination process and therapeutic strategies for remyelination via OPCs and other potential targets in aging and stroke.

CAPTCHA, or Completely Automated Public Turing Tests to Tell Computers and Humans Apart, is a common mechanism used to protect commercial accounts from malicious computer bots, and the most widely used scheme is text-based CAPTCHA. In recent years, newly emerged deep learning techniques have achieved high accuracy and speed in attacking text-based CAPTCHAs. However, most of the existing attacks have various disadvantages, the attack process made high complexity or manually collecting and labeling a large number of samples to train a deep learning recognition model is time-consuming and expensive. In this paper, we propose a transfer learning-based approach that greatly reduces the attack complexity and the cost of labeling samples, specifically, by pre-training the model with randomly generated samples and fine-tuning the pre-trained model with a small number of real-world samples. To evaluate our attack, we tested 25 online CAPTCHAs achieving success rates ranging from 36.3% to 96.9%. To further explore the effect of the training sample characteristics on the attack accuracy, we elaborately imitate some samples and apply a generative adversarial network to refine the samples, sequentially we use these two kinds of generated samples to pre-train the models, respectively. The experimental results demonstrate that the similarity between randomly generated samples and elaborately imitated samples has a negligible impact on the attack accuracy. Instead, transfer learning is the key factor; it reduces the cost of data preparation while preserving the model's attack accuracy.

Antibiotics abuse has caused various problems threatening human health and ecological environment. Monitoring antibiotics residual levels is of great significance, yet still challenging for quantitative point-of-need testing with high-sensitivity and visual capability. Here we developed a competitive lateral flow immunoassay (CLFIA) platform with flexible readout for enrofloxacin (ENR), a regularly added antibiotic. To overcome the limitation of low sensitivity of traditional colloidal gold-based CLFIA, the three-dimensionally assembled gold nanoparticles (AuNPs) within dendritic silica scaffold were fabricated as signal reporters. The assembly structure effectively retained the intrinsic absorption features of hydrophobic AuNPs and greatly enhanced the light extinction ability of a single label for signal amplification. The obtained CLFIA strips can not only achieve qualitative screening of ENR at a very low concentration by naked eye (cutoff value: 0.125 ng/mL), but also enable ultrasensitive quantification of ENR by an optical scanner (limit of detection: 0.00195 ng/mL) or a smartphone (limit of detection: 0.0078 ng/mL). Moreover, to elaborate the visual inspection degree of CLFIA against traditional yes/no interpretation, a novel multirange gradient CLFIA strip was prepared for visually semiquantitative identification of ENR with four concentration ranges. The novel CLFIA platform demonstrated sensitive, specific, and reliable determination of ENR with flexible signal readout and provides a potential and invigorating pathway to point-of-need immunoassay of antibiotics.

Microglia are important phagocytes of the central nervous system (CNS). They play an important role in protecting the CNS by clearing necrotic tissue and apoptotic cells in many CNS diseases. However, recent studies have found that microglia can phagocytose parts of neurons excessively, such as the neuronal cell body, synapse, or myelin sheaths, before or after the onset of CNS diseases, leading to aggravated injury and impaired tissue repair. Meanwhile, reduced phagocytosis of synapses and myelin results in abnormal circuit connections and inhibition of remyelination, respectively. Previous studies focused primarily on the positive effects of microglia phagocytosis, whereas only a few studies have focused on the negative effects. In this review, we use the term "pathological microglial phagocytosis" to refer to excessive or reduced phagocytosis by microglia that leads to structural or functional abnormalities in target cells and brain tissue. The classification of pathological microglial phagocytosis, the composition, and activation of related signaling pathways, as well as the process of pathological phagocytosis in various kinds of CNS diseases, are described in this review. We hypothesize that pathological microglial phagocytosis leads to aggravation of tissue damage and negative functional outcome. For example, excessive microglial phagocytosis of synapses can be observed in Alzheimer's disease and schizophrenia, leading to significant synapse loss and memory impairment. In Parkinson's disease, ischemic stroke, and traumatic brain injury, excessive microglial phagocytosis of neuronal cell bodies causes impaired gray matter recovery and sensory dysfunction. We therefore believe that more studies should focus on the mechanism of pathological microglial phagocytosis and activation to uncover potential targets of therapeutic intervention., Microglial excessive and insufficient phagocytosis of neuronal cell body, synapses, or myelin sheaths can aggravate CNS diseases.

To date, lanthanide-doped upconversion nanoparticles (UCNPs) have been widely reported as a promising CT contrast agent because they have high atomic numbers and big X-ray attenuation coefficient values. However, it is still a challenge to fabricate a simple multimodal imaging probe with improved image quality for early cancer diagnosis in clinical medicine. Herein, ultra-small, uniform and monodisperse β-NaGdF4:Yb,Er,X% Lu (X = 0, 1, 2.5, 4, 6, 7.5) UCNPs were prepared through a solvothermal method with high-level modulation of both the phase and morphology. Meanwhile, a remarkably enhanced red upconversion luminescence (UCL) in the β-NaGdF4:Yb,Er,X% Lu NPs was successfully realized via Lu3+ doping. It is found that as the content of Lu3+ increases from 0 to 7.5 mol%, the UCL intensity of the red emission first increases and then decreases, with the optimum doping content of Lu3+ ions of 2.5 mol%. The red UCL enhancement is ascribed to the change of the Yb–Er interionic distance controlling the Yb–Er energy transfer rate and the distortion of the local environment of Er3+ ions influencing the 4f–4f transition rates of Er3+ ions, which has been further confirmed by the experimental check of the crystallographic phase and by photoluminescence spectroscopy employing Eu3+ as the structural probe, respectively. More importantly, after being modified with the HS-PEG2000-NH2 ligand, the NH2-PEGylated-NaGdF4:Yb,Er,X% Lu NPs exhibited low cytotoxicity, high biocompatibility, and remarkably enhanced contrast performance in in vitro UCL and in vivo CT imaging. On the basis of our findings, the as-obtained functionalized UCNPs could be considered as a promising versatile dual-mode imaging probe for bioimaging, tumor diagnosis, and cancer therapy.

With the development of deep learning technologies, object detection algorithms have made significant progress in terms of detection speed and detection performance. However, the detection speed of current detection networks still does not meet the requirements of real-world applications in some scenarios. In this paper, we propose a faster non-maximum suppression (FNMS) algorithm that reduces the processing time by a large margin while achieving the same detection precision compared with the traditional non-maximum suppression (NMS) algorithm. Moreover, an attempt is made to adopt additional lightweight network structures to improve the speed of the detection network. By combining our FNMS algorithm with other network optimization strategies, we are able to improve the detection speed of YOLO v3 on the DOTA dataset by 165%.

Captcha, which can prevent computer programs from attacking websites, has been the most important security technology for many years. The most popularly deployed Captcha is the text-based scheme. The vast majority of the existing text Captchas are designed with English letters and Arabic numerals. Recently, text Captchas with large character sets are being increasingly popular. From the perspective of attackers, larger character set means greater solution space and better theoretical security. However, the security of Captchas with large character sets in real world has never been studied comprehensively. In this article, we introduce a simple, fast, and effective deep learning method to attack these newly emerging Captchas. Taking 11 Chinese Captchas as representatives, we ran our experimental attack on each of them. Our attack achieved high success rates, ranging from 34.7 to 86.9 percent at an average speed of 0.175 seconds on these schemes. All of the results show that the Chinese text Captcha can be easily broken, demonstrating that text Captchas with large character sets are also insecure in existing forms. As a substitute, we proposed a 3D image-based scheme combining semantic comprehension and dragging action. The preliminary experimental results show that it is more robust than current text-based schemes.

To date, lanthanide-doped upconversion nanoparticles (UCNPs) have been widely reported as a promising CT contrast agent because they have high atomic numbers and big X-ray attenuation coefficient values. However, it is still a challenge to fabricate a simple multimodal imaging probe with improved image quality for early cancer diagnosis in clinical medicine. Herein, ultra-small, uniform and monodisperse β-NaGdF4:Yb,Er,X% Lu (X = 0, 1, 2.5, 4, 6, 7.5) UCNPs were prepared through a solvothermal method with high-level modulation of both the phase and morphology. Meanwhile, a remarkably enhanced red upconversion luminescence (UCL) in the β-NaGdF4:Yb,Er,X% Lu NPs was successfully realized via Lu3+ doping. It is found that as the content of Lu3+ increases from 0 to 7.5 mol%, the UCL intensity of the red emission first increases and then decreases, with the optimum doping content of Lu3+ ions of 2.5 mol%. The red UCL enhancement is ascribed to the change of the Yb-Er interionic distance controlling the Yb-Er energy transfer rate and the distortion of the local environment of Er3+ ions influencing the 4f-4f transition rates of Er3+ ions, which has been further confirmed by the experimental check of the crystallographic phase and by photoluminescence spectroscopy employing Eu3+ as the structural probe, respectively. More importantly, after being modified with the HS-PEG2000-NH2 ligand, the NH2-PEGylated-NaGdF4:Yb,Er,X% Lu NPs exhibited low cytotoxicity, high biocompatibility, and remarkably enhanced contrast performance in in vitro UCL and in vivo CT imaging. On the basis of our findings, the as-obtained functionalized UCNPs could be considered as a promising versatile dual-mode imaging probe for bioimaging, tumor diagnosis, and cancer therapy.

The location and recognition of scene text has been a difficult topic in many computer vision applications. In scene text location, which differ from the general objects detection and location task, scene text always suffers from large variances of the aspect ratio, scale, and orientation. As for scene text recognition, the changing character types, sharpness, sizes, and font families make it difficult to accurately recognize the text. In this paper, we have carried out work on both scene text location and recognition. We apply the general objects location methods to the scene text location task. We use two methods, one based on Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Connectionist Temporal Classifier (CTC), the other based on CNN, RNN and the Attention mechanism. They perform well both in recognition speed and accuracy rate compared with previous methods.

Heart regeneration remains a critical question in current basic research and clinical practice. The adult mammalian heart exhibits a very limited regeneration capacity. In contrast, adult zebrafish and neonatal mice retain a remarkable ability of heart regeneration after damage. Understanding the mechanisms of heart regeneration would be very valuable to help design efficient treatment strategies against myocardial damage and heart failure. While inherent regeneration of the heart occurs after damage with varying efficiency among species, regeneration may also be induced exogenously. In this study, we briefly review the different approaches and current progress in improving heart regeneration.