A Smart Window with Passive Radiative Cooling and Switchable Near-Infrared Light Transmittance via Molecular Engineering.

Smart windows with synergetic light modulation have heightened demands for applications in smart cars and novel buildings. However, improving the on-demand energy-saving efficiency is quite challenging due to the difficulty of modulating sunlight with a broad bandwidth in an energy-saving way. Herein, a smart window with switchable near-infrared light transmittance and passive radiative cooling is prepared via a monomer design strategy and photoinduced polymerization. The effects of hydrogen bonds and fluorine groups in acrylate monomers on the electro-optical properties as well as microstructures of polymer-dispersed liquid crystal films have been systematically studied. Some films show a high contrast ratio of 90.4 or a low threshold voltage (Vth) of 2.0 V, which can be roll-to-roll processed in a large area. Besides, the film has a superior indoor temperature regulation ability due to its passive radiative cooling and controllable near-infrared light transmittance properties. Its radiative cooling efficiency is calculated to be 142.69 W/m2 and NIR transmittance could be switched to below 10%. The introduction of a carboxylic monomer and fluorinated monomer into the system endows the film with a highly efficient temperature management capability. The film has great potential for applications in fields such as flexible smart windows, camouflage materials, and so on.

Study on the Effect of α-Substituted Acrylate Monomers on the Electro-Optical Properties of Polymer-Dispersed Liquid Crystal Films.

Polymer-dispersed liquid crystals (PDLCs) show great application potential in the areas of displays and smart windows. However, their electro-optical (E-O) properties such as contrast ratio and threshold voltage still need further improvement. In this study, the effects of α-substituted acrylate monomers on the morphology and E-O properties of PDLC composite films were systematically studied. It was found that the large substituent tended to increase the void size of the polymer matrix, while the small fluorine substitution led to a microsphere-type polymer morphology, which deteriorated the E-O performance. Finally, a largely improved E-O performance of low threshold voltage (0.437 V/µm), low saturation voltage (1.012 V/µm), and high contrast ratio (27) was achieved in an 8 µm-thick film by the addition of a chlorine-substituted monomer. This study provides a new approach for optimizing PDLCs from a material perspective.

Representation of Imprecision in Deep Neural Networks for Image Classification.

Quantification and reduction of uncertainty in deep-learning techniques have received much attention but ignored how to characterize the imprecision caused by such uncertainty. In some tasks, we prefer to obtain an imprecise result rather than being willing or unable to bear the cost of an error. For this purpose, we investigate the representation of imprecision in deep-learning (RIDL) techniques based on the theory of belief functions (TBF). First, the labels of some training images are reconstructed using the learning mechanism of neural networks to characterize the imprecision in the training set. In the process, a label assignment rule is proposed to reassign one or more labels to each training image. Once an image is assigned with multiple labels, it indicates that the image may be in an overlapping region of different categories from the feature perspective or the original label is wrong. Second, those images with multiple labels are rechecked. As a result, the imprecision (multiple labels) caused by the original labeling errors will be corrected, while the imprecision caused by insufficient knowledge is retained. Images with multiple labels are called imprecise ones, and they are considered to belong to meta-categories, the union of some specific categories. Third, the deep network model is retrained based on the reconstructed training set, and the test images are then classified. Finally, some test images that specific categories cannot distinguish will be assigned to meta-categories to characterize the imprecision in the results. Experiments based on some remarkable networks have shown that RIDL can improve accuracy (AC) and reasonably represent imprecision both in the training and testing sets.

Orientational Distribution Learning With Hierarchical Spatial Attention for Open Set Recognition.

Open set recognition (OSR) aims to correctly recognize the known classes and reject the unknown classes for increasing the reliability of the recognition system. The distance-based loss is often employed in deep neural networks-based OSR methods to constrain the latent representation of known classes. However, the optimization is usually conducted using the nondirectional euclidean distance in a single feature space without considering the potential impact of spatial distribution. To address this problem, we propose orientational distribution learning (ODL) with hierarchical spatial attention for OSR. In ODL, the spatial distribution of feature representation is optimized orientationally to increase the discriminability of decision boundaries for open set recognition. Then, a hierarchical spatial attention mechanism is proposed to assist ODL to capture the global distribution dependencies in the feature space based on spatial relationships. Moreover, a composite feature space is constructed to integrate the features from different layers and different mapping approaches, and it can well enrich the representation information. Finally, a decision-level fusion method is developed to combine the composite feature space and the naive feature space for producing a more comprehensive classification result. The effectiveness of ODL has been demonstrated on various benchmark datasets, and ODL achieves state-of-the-art performance.

Deep Hyperspherical Clustering for Skin Lesion Medical Image Segmentation.

Diagnosis of skin lesions based on imaging techniques remains a challenging task because data (knowledge) uncertainty may reduce accuracy and lead to imprecise results. This paper investigates a new deep hyperspherical clustering (DHC) method for skin lesion medical image segmentation by combining deep convolutional neural networks and the theory of belief functions (TBF). The proposed DHC aims to eliminate the dependence on labeled data, improve segmentation performance, and characterize the imprecision caused by data (knowledge) uncertainty. First, the SLIC superpixel algorithm is employed to group the image into multiple meaningful superpixels, aiming to maximize the use of context without destroying the boundary information. Second, an autoencoder network is designed to transform the superpixels' information into potential features. Third, a hypersphere loss is developed to train the autoencoder network. The loss is defined to map the input to a pair of hyperspheres so that the network can perceive tiny differences. Finally, the result is redistributed to characterize the imprecision caused by data (knowledge) uncertainty based on the TBF. The proposed DHC method can well characterize the imprecision between skin lesions and non-lesions, which is particularly important for the medical procedures. A series of experiments on four dermoscopic benchmark datasets demonstrate that the proposed DHC yields better segmentation performance, increasing the accuracy of the predictions while can perceive imprecise regions compared to other typical methods.

A New Progressive Multisource Domain Adaptation Network With Weighted Decision Fusion.

Multisource unsupervised domain adaptation (MUDA) is an important and challenging topic for target classification with the assistance of labeled data in source domains. When we have several labeled source domains, it is difficult to map all source domains and target domain into a common feature space for classifying the targets well. In this article, a new progressive multisource domain adaptation network (PMSDAN) is proposed to further improve the classification performance. PMSDAN mainly consists of two steps for distribution alignment. First, the multiple source domains are integrated as one auxiliary domain to match the distribution with the target domain. By doing this, we can generally reduce the distribution discrepancy between each source and target domains, as well as the discrepancy between different source domains. It can efficiently explore useful knowledge from the integrated source domain. Second, to mine assistance knowledge from each source domain as much as possible, the distribution of the target domain is separately aligned with that of each source domain. A weighted fusion method is employed to combine the multiple classification results for making the final decision. In the optimization of domain adaption, weighted hybrid maximum mean discrepancy (WHMMD) is proposed, and it considers both the interclass and intraclass discrepancies. The effectiveness of the proposed PMSDAN is demonstrated in the experiments comparing with some state-of-the-art methods.

A novel label-free photoelectrochemical immunosensor based on CdSe quantum dots sensitized Ho3+/Yb3+-TiO2 for the detection of Vibrio parahaemolyticus.

A novel photoelectrochemical (PEC) immunosensor based on CdSe quantum dots (QDs) sensitized Ho3+/Yb3+-TiO2 for the detection of Vibrio parahaemolyticus (VP) was assembled. The working electrode was constructed via the layer-by-layer (LBL) method with the Ho3+/Yb3+-TiO2, CdSe QDs, NHS/EDC, antibody of VP (anti-VP), bovine serum albumin (BSA) modified on the surface of the FTO in sequence. Ascorbic acid (AA) acts as an electron donor to combine photogenerated holes in order to provide a stable current system. Ho3+ and Yb3+ co-doping TiO2 broadened the spectral response range of TiO2 to the infrared region and improved the photocurrent responsiveness of TiO2. The PEC immunosensor, with Ho3+/Yb3+ ratio of 1:5, Ho3+/Yb3+-TiO2 of 2 mg/mL and PBS solution of pH 7.4, had an optimal photocurrent responsiveness. Immobilization of anti-VP was by classical NHS/EDC coupling reactions between COOH groups of CdSe QDs and NH2 groups of the anti-VP. The results indicated that PEC immunosensors had a low detection limit of 25 CFU/mL, a wide detection range of 102-108 CFU/mL, high stability, low price, and short detection time. This method could be promising for the rapid and ultrasensitive detection of pathogenic microorganisms in the food.

Celecoxib derivative OSU-03012 inhibits the proliferation and activation of hepatic stellate cells by inducing cell senescence.

Liver fibrosis may lead to portal hypertension, liver failure or hepatocellular carcinoma, and predominantly results from the proliferation and activation of hepatic stellate cells. OSU­03012, a non­cyclooxygenase­inhibiting celecoxib derivative, has been previously demonstrated to promote apoptosis in certain cell types, however, its function in hepatic fibrosis remains unclear. In the current study, the inhibitory effect of OSU­03012 on the proliferation of the LX2 human hepatic stellate cell line was evaluated by cell counting kit­8 assay. Reverse transcription­quantitative polymerase chain reaction was performed in order to examine the expression of α­smooth muscle actin and type I collagen, which are representative of LX2 cell activation. The senescence of LX2 cells was measured by senescence­associated ß­galactosidase staining, and the cell cycle and apoptosis levels were assessed by flow cytometry. The impact of senescence­associated signaling on protein expression was assessed by western blot analysis. OSU­03012 was observed to inhibit cell proliferation and prevent the secretion of profibrotic factors in LX2 cells in a dose­dependent manner. Furthermore, the results demonstrated that OSU­03012 inhibited the proliferation and activation of LX2 via the induction of cell senescence at the G1 phase, rather than via cell apoptosis. The induction of senescence may be via the upregulation of p16, p21 and p27. In conclusion, the current study provided insight into the pharmacological mechanisms of OSU­03012 in preventing the proliferation and activation of hepatic stellate cells through cell senescence. The current study supports the theory that OSU­03012 is a novel agent for potential use against liver fibrosis.

An aptamer targets HBV core protein and suppresses HBV replication in HepG2.2.15 cells.

Hepatitis B virus (HBV)-related hepatitis is a major health concern worldwide. As current anti-HBV therapies are limited, it is essential to develop new strategies. Aptamer, a newly developed adaptive molecule (single-strand DNA or RNA also known as nucleotide antibody), is a new strategy for clinical diagnosis and therapy due to its high affinity and specificity. In the present study, by systematic evolution of ligand by exponential enrichment (SELEX), aptamers were screened against the core protein of HBV (HBc) from a random ssDNA library. Quantitative PCR (qPCR) results showed that the binding proportions of the SELEX-enriched aptamer pools were increased with the SELEX rounds, until round seven. Thus, the pool of round seven was cloned. Following the sequence analysis of a total of 90 clones by Macaw software, five aptamer candidates were selected and their affinity to HBc was tested by dot blot. Apt.No.28, which had sequence replicates in the clones, was shown to have a high affinity. Furthermore, by agarose gel electrophoresis-capillary transfer-blotting and qPCR, Apt.No.28 was shown to inhibit the assembly of the nucleocapsid, reducing extracellular HBV DNA whose synthesis relied on the formation of the nucleocapsid, indicating its role in suppressing HBV replication. The results provided a new ideal targeting molecule and may facilitate the strategy for targeted therapy as well as drug development of HBV-related diseases.

A novel splicing mutation alters DSPP transcription and leads to dentinogenesis imperfecta type II.

Dentinogenesis imperfecta (DGI) type II is an autosomal dominant disease characterized by a serious disorders in teeth. Mutations of dentin sialophosphoprotein (DSPP) gene were revealed to be the causation of DGI type II (DGI-II). In this study, we identified a novel mutation (NG_011595.1:g.8662T>C, c.135+2T>C) lying in the splice donor site of intron 3 of DSPP gene in a Chinese Han DGI-II pedigree. It was found in all affected subjects but not in unaffected ones or other unrelated healthy controls. The function of the mutant DSPP gene, which was predicted online and subsequently confirmed by in vitro splicing analysis, was the loss of splicing of intron 3, leading to the extended length of DSPP mRNA. For the first time, the functional non-splicing of intron was revealed in a novel DSPP mutation and was considered as the causation of DGI-II. It was also indicated that splicing was of key importance to the function of DSPP and this splice donor site might be a sensitive mutation hot spot. Our findings combined with other reports would facilitate the genetic diagnosis of DGI-II, shed light on its gene therapy and help to finally conquer human diseases.