From typical electrical appliances to thriving intelligent robots, the exchange of information between humans and machines has mainly relied on the contact sensor medium. However, this kind of ...contact interaction can cause severe problems, such as inevitable mechanical wear and cross‐infection of bacteria or viruses between the users, especially during the COVID‐19 pandemic. Therefore, revolutionary noncontact human–machine interaction (HMI) is highly desired in remote online detection and noncontact control systems. In this study, a flexible high‐sensitivity humidity sensor and array are presented, fabricated by anchoring multilayer graphene (MG) into electrospun polyamide (PA) 66. The sensor works in noncontact mode for asthma detection, via monitoring the respiration rate in real time, and remote alarm systems and provides touchless interfaces in medicine delivery for bedridden patients. The physical structure of the large specific surface area and the chemical structure of the abundant water‐absorbing functional groups of the PA66 nanofiber networks contribute to the high performance synergistically. This work can lead to a new era of noncontact HMI without the risk of contagiousness and provide a general and effective strategy for the development of smart electronics that require noncontact interaction.
Flexible noncontact sensing based on a high‐sensitivity humidity sensor is realized by anchoring multilayer graphene (MG) into electrospun polyamide (PA) 66 for human–machine interaction systems, which can achieve not only asthma detection, via monitoring the respiration rate in real time, and remote alarm systems, but also touchless interfaces in medicine delivery for bedridden patients.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Objective To assess the incidence and clinical characteristics of acute kidney injury （AKI） and explore the associated risk factors in type 1 diabetes mellitus （T1DM） children complicated with ...diabetic ketoacidosis （DKA）. Methods Clinical data of 76 T1DM children presenting with DKA were retrospectively analyzed. According to the incidence of AKI， all patients were divided into the AKI and non-AKI groups. Clinical characteristics of DKA children complicated with AKI were summarized. The differences between two groups were analyzed by independent sample t-test， Mann-Whitney U test or Chi-square test， followed by binary Logistic regression. The associated risk factors were analyzed by the receiver operating characteristic （ROC） curve. Results Among 76 children， AKI occurred during DKA in 22 cases （28.9%）， including 4 cases（18.2%） of mild DKA，2 cases（9.1%） of moderate DKA and 16 cases （72.7%） of severe DKA. Among 22 children， 15 cases （68.2%） were diagnosed with stage 1 AKI， 3 （13.6%） with stage 2 AKI， and 4 （18
Deep convolutional neural networks have recently proven extremely effective for difficult face recognition problems in uncontrolled settings. To train such networks, very large training sets are ...needed with millions of labeled images. For some applications, such as near-infrared (NIR) face recognition, such large training data sets are not publicly available and difficult to collect. In this paper, we propose a method to generate very large training data sets of synthetic images by compositing real face images in a given data set. We show that this method enables to learn models from as few as 10 000 training images, which perform on par with models trained from 500 000 images. Using our approach, we also obtain state-of-the-art results on the CASIA NIR-VIS2.0 heterogeneous face recognition data set.
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•Microalgal-bacterial granular sludge (MBGS) could adapt to sulfamethoxazole (SMX).•SMX had little effect on organics, nitrogen and phosphorus removal by MBGS process.•5 and 10 mg/L ...SMX was effectively removed by MBGS in both light and dark cycles.•Eukaryotes appeared to be less sensitive to SMX than prokaryotes in MBGS systems.•SMX might be disposed by Scenedesmaceae, Rhodocyclaceae and Burkholderiaceae.
The presence of widely used sulfamethoxazole (SMX) in wastewater poses a threat to aquatic organisms and humans. Here, the responses of the emerging microalgal-bacterial granular sludge (MBGS) process in treating SMX-containing wastewater were investigated. The results indicated that 1, 5 and 10 mg/L SMX had little effect on the removals of organics and nutrients after an acclimation period of three to five days. SMX reduced intracellular glycogen content of MBGS, while the production of chlorophyll and extracellular polymeric substances tended to be promoted. Furthermore, the potential mechanisms on how MBGS adapted to SMX were deciphered to be the alterations of microbial community structure and function of MBGS. SMX might be degraded intracellularly into a carbon source for microbial metabolism and the SMX degraders were suspected to be Scenedesmaceae, Rhodocyclaceae and Burkholderiaceae. This study suggests that the MBGS process can handle SMX-containing wastewater, advancing knowledge on MBGS for antibiotics degradation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Long non-coding RNAs (lncRNAs) play critical roles in tumorigenesis and progression of colorectal cancer (CRC). However, functions of most lncRNAs in CRC and their molecular mechanisms remain ...uncharacterized. Here we found that lncRNA ITGB8-AS1 was highly expressed in CRC. Knockdown of ITGB8-AS1 suppressed cell proliferation, colony formation, and tumor growth in CRC, suggesting oncogenic roles of ITGB8-AS1. Transcriptomic analysis followed by KEGG analysis revealed that focal adhesion signaling was the most significantly enriched pathway for genes positively regulated by ITGB8-AS1. Consistently, knockdown of ITGB8-AS1 attenuated the phosphorylation of SRC, ERK, and p38 MAPK. Mechanistically, ITGB8-AS1 could sponge miR-33b-5p and let-7c-5p/let-7d-5p to regulate the expression of integrin family genes ITGA3 and ITGB3, respectively, in the cytosol of cells. Targeting ITGB8-AS1 using antisense oligonucleotide (ASO) markedly reduced cell proliferation and tumor growth in CRC, indicating the therapeutic potential of ITGB8-AS1 in CRC. Furthermore, ITGB8-AS1 was easily detected in plasma of CRC patients, which was positively correlated with differentiation and TNM stage, as well as plasma levels of ITGA3 and ITGB3. In conclusion, ITGB8-AS1 functions as a competing endogenous RNA (ceRNA) to regulate cell proliferation and tumor growth of CRC via regulating focal adhesion signaling. Targeting ITGB8-AS1 is effective in suppressing CRC cell growth and tumor growth. Elevated plasma levels of ITGB8-AS1 were detected in advanced-stage CRC. Thus, ITGB8-AS1 could serve as a potential therapeutic target and circulating biomarker in CRC.
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Functions of most lncRNAs in CRC and their molecular mechanisms remain uncharacterized. This work characterized a specific lncRNA, ITGB8-AS1, highly expressed in CRC. This lncRNA functioned as a ceRNA to target integrins and promote CRC growth and metastasis. The work identified a novel therapeutic target and circulating biomarker for CRC.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A large amount of training data is usually crucial for successful supervised learning. However, the task of providing training samples is often time-consuming, involving a considerable amount of ...tedious manual work. In addition, the amount of training data available is often limited. As an alternative, in this paper, we discuss how best to augment the available data for the application of automatic facial landmark detection. We propose the use of a 3D morphable face model to generate synthesized faces for a regression-based detector training. Benefiting from the large synthetic training data, the learned detector is shown to exhibit a better capability to detect the landmarks of a face with pose variations. Furthermore, the synthesized training data set provides accurate and consistent landmarks automatically as compared to the landmarks annotated manually, especially for occluded facial parts. The synthetic data and real data are from different domains; hence the detector trained using only synthesized faces does not generalize well to real faces. To deal with this problem, we propose a cascaded collaborative regression algorithm, which generates a cascaded shape updater that has the ability to overcome the difficulties caused by pose variations, as well as achieving better accuracy when applied to real faces. The training is based on a mix of synthetic and real image data with the mixing controlled by a dynamic mixture weighting schedule. Initially, the training uses heavily the synthetic data, as this can model the gross variations between the various poses. As the training proceeds, progressively more of the natural images are incorporated, as these can model finer detail. To improve the performance of the proposed algorithm further, we designed a dynamic multi-scale local feature extraction method, which captures more informative local features for detector training. An extensive evaluation on both controlled and uncontrolled face data sets demonstrates the merit of the proposed algorithm.
Facial pose variation is one of the major factors making face recognition (FR) a challenging task. One popular solution is to convert non-frontal faces to frontal ones on which FR is performed. ...Rotating faces causes facial pixel value changes. Therefore, existing CNN-based methods learn to synthesize frontal faces in color space. However, this learning problem in a color space is highly non-linear, causing the synthetic frontal faces to lose fine facial textures. In this paper, we take the view that the nonfrontal-frontal pixel changes are essentially caused by geometric transformations (rotation, translation, and so on) in space. Therefore, we aim to learn the nonfrontal-frontal facial conversion in the spatial domain rather than the color domain to ease the learning task. To this end, we propose an appearance-flow-based face frontalization convolutional neural network (A3F-CNN). Specifically, A3F-CNN learns to establish the dense correspondence between the non-frontal and frontal faces. Once the correspondence is built, frontal faces are synthesized by explicitly "moving" pixels from the non-frontal one. In this way, the synthetic frontal faces can preserve fine facial textures. To improve the convergence of training, an appearance-flow-guided learning strategy is proposed. In addition, generative adversarial network loss is applied to achieve a more photorealistic face, and a face mirroring method is introduced to handle the self-occlusion problem. Extensive experiments are conducted on face synthesis and pose invariant FR. Results show that our method can synthesize more photorealistic faces than the existing methods in both the controlled and uncontrolled lighting environments. Moreover, we achieve a very competitive FR performance on the Multi-PIE, LFW and IJB-A databases.
Modified asphalt with high content SBS is widely used in asphalt pavement due to its excellent high and low temperature performance. However, its anti-aging performance is insufficient. In order to ...improve the anti-aging performance of SBS modified asphalt, nano-ZnO, nano-TiO
, nano-SiO
and polyphosphoric acid (PPA) were added to high content (6.5 wt%) linear SBS modified asphalt as anti-aging agents in this study. Moreover, Dynamic Shear Rheometer (DSR), Fluorescence Microscope, and Fourier Transform Infrared Spectroscopy were employed to reveal the mechanism, through the investigation of the rheological and microscopic properties of modified asphalt before and after aging. The results showed that the influence of nanoparticles on the rutting resistance and fatigue resistance of high content SBS modified asphalt is weak, mainly because there is only weak physical interaction between nanoparticles and the SBS modifier, but no obvious chemical reaction. The significant cross-networking structure of high content SBS modified asphalt even has an adverse effect on the anti-aging performance of nano-modifiers. However, PPA obviously makes the cross-linked network structure of SBS modified asphalt more compact, and significantly improves the performance after short-term aging and long-term aging, mainly due to the chemical reaction between PPA and the active groups in SBS modified asphalt.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Real‐time telemedicine detection can solve the problem of the shortage of public medical resources caused by the coming aging society. However, the development of such an integrated monitoring system ...is hampered by the need for high‐performance sensors and the strict‐requirement of long‐distance signal transmission and reproduction. Here, a bionic crack‐spring fiber sensor (CSFS) inspired by spider leg and cirrus whiskers for stretchable and weavable electronics is reported. Trans‐scale conductive percolation networks of multilayer graphene around the surface of outer spring‐like Polyethylene terephthalate (PET) fibers and printing Ag enable a high sensitivity of 28475.6 and broad sensing range over 250%. The electromechanical changes in different stretching stages are simulated by Comsol to explain the response mechanism. The CSFS is incorporated into the fabric and realized the human‐machine interactions (HMIs) for robot control. Furthermore, the 5G Narrowband Internet of Things (NB‐IoT) system is developed for human healthcare data collection, transmission, and reproduction together with the integration of the CSFS, illustrating the huge potential of the approach in human–machine communication interfaces and intelligent telemedicine rehabilitation and diagnosis monitoring.
Trans‐scale conductive percolation networks are constructed on the high‐performance bionic crack‐spring fiber sensor (CSFS) with high sensitivity and broad sensing range for human–machine communication interfaces and long‐distance information transmission with the assistance of 5G NB‐IoT system.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The paper presents a dictionary integration algorithm using 3D morphable face models (3DMM) for pose-invariant collaborative-representation-based face classification. To this end, we first fit a 3DMM ...to the 2D face images of a dictionary to reconstruct the 3D shape and texture of each image. The 3D faces are used to render a number of virtual 2D face images with arbitrary pose variations to augment the training data, by merging the original and rendered virtual samples to create an extended dictionary. Second, to reduce the information redundancy of the extended dictionary and improve the sparsity of reconstruction coefficient vectors using collaborative-representation-based classification (CRC), we exploit an on-line class elimination scheme to optimise the extended dictionary by identifying the training samples of the most representative classes for a given query. The final goal is to perform pose-invariant face classification using the proposed dictionary integration method and the on-line pruning strategy under the CRC framework. Experimental results obtained for a set of well-known face data sets demonstrate the merits of the proposed method, especially its robustness to pose variations.
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