Osteoarthritis (OA) is a highly prevalent joint disorder characterized by progressive degeneration of articular cartilage, subchondral bone remodeling, osteophyte formation, synovial inflammation, ...and meniscal damage. Although the etiology of OA is multifactorial, pro-inflammatory processes appear to play a key role in disease pathogenesis. Previous studies indicate that electroacupuncture (EA) exerts chondroprotective, anti-inflammatory, and analgesic effects in preclinical models of OA, but the mechanisms underlying these potential therapeutic benefits remain incompletely defined. This study aimed to investigate the effects of EA on OA development in a rat model, as well as to explore associated molecular mechanisms modulated by EA treatment. Forty rats were divided into OA, EA, antagomiR-214, and control groups. Following intra-articular injection of monosodium iodoacetate to induce OA, EA and antagomiR-214 groups received daily EA stimulation at acupoints around the knee joint for 21 days. Functional pain behaviors and chondrocyte apoptosis were assessed as outcome measures. The expression of microRNA-214 (miR-214) and its downstream targets involved in apoptosis and nociception, BAX and TRPV4, were examined. Results demonstrated that EA treatment upregulated miR-214 expression in OA knee cartilage. By suppressing pro-apoptotic BAX and pro-nociceptive TRPV4, this EA-induced miR-214 upregulation ameliorated articular pain and prevented chondrocyte apoptosis. These findings suggested that miR-214 plays a key role mediating EA's therapeutic effects in OA pathophysiology, and represents a promising OA treatment target for modulation by acupuncture.
Distributed Denial of Service (DDoS) attacks are increasingly harmful to the cyberspace nowadays. The attackers can now easily launch a bigger and more challenging DDoS attack both towards and with ...Internet-of-Things (IoT) devices, due to the fast popularization of them. Because of the characteristic of fast overwhelming, it is important to make fast as well as accurate response to DDoS attacks, and the real-time performance can be even more important to prevent and legitimate the attacks. Among the methods proposed by researchers, the entropy-based detection method provides a sensitive and reliable performance. However, the balance between computational complexity and recognition accuracy remains a challenge. In this paper, we propose a detection method that consists of 3 main parts in different aspects: a sliding time window to fasten the entropy calculation, a single-directional filter to realize early detection during the DDoS progress but not after the crash, and a quintile deviation check algorithm to optimize the detection result. These will eventually lead to a real-time and high-efficient performance to recognize IoT DDoS attacks as soon as possible.
With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, ...WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSIndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.
By imitating the exceptional compositions, structures, formations and functions of biological or natural materials, a myriad of biomimetic and bioinspired membranes have been designed and prepared ...using cell membrane, lotus, mussel as representative prototypes and biomineralization, bioadhesion, self-assembly as major tools. These membranes have displayed fascinating properties and outstanding performances such as multiple interactions, hierarchical organizations, multiple selective transport mechanisms, superior stability/resistance and distinct adaptability. Meanwhile, these membranes have made tremendous contributions in coping with energy and water stress, environment threats. Biomimetics focuses on the basic science by fundamentally exploring the principles of biological systems, while bioinspiration focuses on the applied engineering by technologically implementing the principles from biological systems. Biomimetics and bioinspiration, as the complementary and interchangeable strategies for sustainable innovation and development of membrane technology, have great implications in exploring membrane materials and intensifying membrane processes. This review will present a brief overview on the prototypes, preparation, application as well as perspective of biomimetic and bioinspired membranes.
The composite nanofiltration membranes were prepared via interfacial polymerization of tetraethylenepentamine (TEPA) and 1,3,5-benzenetricarbonyl trichloride (TMC). The improved performance of the ...composite nanofiltratiom membranes by adding calcium chloride in aqueous phase during the interfacial polymerization was verified in terms of pure water permeability, rejection of different solutes including dyes and inorganic salts and chlorine resistance. The results showed that the pure water permeability of composite nanofiltration membranes would acquire a remarkable increase with a slight decrease in solute rejections under the optimized addition of CaCl2. The addition of CaCl2 also dramatically enhanced the chloride resistance of composite nanofiltration membranes in comparison with the control membranes prepared without CaCl2 added. Therefore, inorganic salts like CaCl2 could be considered as a potential additive to enhance the performance of composite nanofiltration membranes.
•Composite nanofiltration membrane was prepared via the interfacial polymerization.•Calcium chloride was used to mediate the interfacial polymerization process.•The water permeation flux of nanofiltration membranes was enhanced.•The chlorine resistance of nanofiltration membranes was enhanced.
A new kind of fluorinated polyamine was successfully synthesized and grafted onto the polyamide membrane surface to fabricate an antifouling nanofiltration membrane with low surface free energy. The ...surface composition of the fluorinated polyamide nanofiltration membrane was confirmed by Fourier transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS). The membrane cross-section morphology was observed by a field emission scanning electron microscopy (FESEM). The presence of perfluoroalkyl groups on the membrane surface significantly lowered the surface free energy from 60.0 to 44.4mJ/m2. The filtration experiment results indicated that the surface fluorination did not lower the separation performance of the polyamide nanofiltration membrane significantly. The antifouling experiment results demonstrated that the fluorinated polyamide nanofiltration membranes exhibited superior antifouling property, that is, high flux recovery ratio (~98.5%) and low total flux decline ratio (~11%) during protein aqueous solution and humic acid aqueous solution filtration.
•Nanofiltration membranes with low surface free energy were prepared.•Fluorinated polyamine was synthesized and grafted onto polyamide membrane surface.•The resultant nanofiltration membranes exhibited superior antifouling property.
Polyvinyl chloride (PVC) and polyvinyl formal (PVF) blend ultrafiltration membranes were fabricated by non-solvent induced phase separation (NIPS) method with different casting solution composition. ...The PVC/PVF membranes were characterized and evaluated by scanning electron microscopy (SEM), Fourier Transform Infrared (FT-IR), X-ray photoelectron spectroscopy (XPS), water contact angle measurement and performance measurement. The results showed that PVF played the role of pore formation agent during the NIPS process, and both of porosity and the mean pore size of the membranes were increased with the increased dosage of PVF. Simultaneously, PVF was enriched to membrane surface via spontaneous surface segregation and the membrane surface hydrophilicity was greatly elevated, which implied the remarkably enhanced antifouling property. The robust residence of PVF on the membrane surface was confirmed by a long-term test of incubating membranes in deionized water, which revealed the stable antifouling property of PVC/PVF membranes. Therefore, PVF could be explored as a potential versatile modifier for fabricating high performance ultrafiltration membranes.
Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and ...diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a Fine-grained Target Focusing Network (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global–Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.
Chromatin-associated RNA (caRNA) has been proposed as a type of epigenomic modifier. Here, we test whether environmental stress can induce cellular dysfunction through modulating RNA-chromatin ...interactions. We induce endothelial cell (EC) dysfunction with high glucose and TNFα (H + T), that mimic the common stress in diabetes mellitus. We characterize the H + T-induced changes in gene expression by single cell (sc)RNA-seq, DNA interactions by Hi-C, and RNA-chromatin interactions by iMARGI. H + T induce inter-chromosomal RNA-chromatin interactions, particularly among the super enhancers. To test the causal relationship between H + T-induced RNA-chromatin interactions and the expression of EC dysfunction-related genes, we suppress the LINC00607 RNA. This suppression attenuates the expression of SERPINE1, a critical pro-inflammatory and pro-fibrotic gene. Furthermore, the changes of the co-expression gene network between diabetic and healthy donor-derived ECs corroborate the H + T-induced RNA-chromatin interactions. Taken together, caRNA-mediated dysregulation of gene expression modulates EC dysfunction, a crucial mechanism underlying numerous diseases.
Identifying tiny objects from extremely low-resolution (LR) unmanned-aerial-vehicle-based remote sensing images is generally considered as a very challenging task, because of very limited information ...in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this article, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on low-frequency (LF) components and high-frequency (HF) components. To address this LF-HF inconsistency problem, we propose a dual-stream representation learning generative adversarial network (DRL-GAN). The core idea is to produce enhanced image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected `WIDER-SHIP' dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average.
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