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In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively. [ABSTRACT FROM AUTHOR]

In order to improve the light extraction for the deep ultraviolet light emitting diodes (DUV-LEDs), the surface microstructure based on a parabola cone array is used and optimized in work. In the optimization of the surface structure, inverse design based on a particle swarm optimization intelligent algorithm is applied to maximize the light extraction. The optimization results show that compared with the traditional planar structure, the optimized surface structure improves the light extraction efficiency by more than 200%. In addition, the influence of the designed surface microstructure on the light propagation is also explored by comparing the light field distribution and the light extraction process with the planar structure DUV-LEDs. It is revealed that the high aspect ratio of an array microstructure can change the light propagation and greatly expand the angle of a light escape cone. This effect can be maximized by the inverse design based on the intelligent algorithm, which has great potential in improving the light extraction of AlGaN-based DUV-LEDs. [ABSTRACT FROM AUTHOR]

Exploring the spatiotemporal patterns of urban thermal environments is crucial for mitigating the detrimental effects of urban heat islands (UHI). However, the long-term and fine-grained monitoring of UHI is limited by the temporal and spatial resolutions of various sensors. To address this limitation, this study employed the Google Earth Engine (GEE) platform and a multi-source remote sensing data fusion approach to generate a densely time-resolved Landsat-like Land Surface Temperature (LST) dataset for daytime observations spanning from 2001 to 2020 in Shanghai. A comprehensive analysis of the spatiotemporal patterns of UHI was conducted. The results indicate that over the past 20 years, the highest increase in average LST was observed during spring with a growth coefficient of 0.23, while the lowest increase occurred during autumn (growth coefficient of 0.12). The summer season exhibited the most pronounced UHI effect in the region (average proportion of Strong UHI and General UHI was 28.73%), while the winter season showed the weakest UHI effect (proportion of 22.77%). The Strong UHI areas gradually expanded outward over time, with a noticeable intensification of heat island intensity in the northwest and coastal regions, while other areas did not exhibit significant changes. Impervious surfaces contributed the most to LST, with a contribution of 0.96 °C, while water had the lowest contribution (−0.42 °C). The average correlation coefficients between LST and NDVI, NDWI, and NDBI over 20 years were −0.4236, −0.5128, and 0.5631, respectively. [ABSTRACT FROM AUTHOR]

The increased oil-spill possibilities in sea-ice areas pose a threat to the marine environment and the ice-area coverage. Contrary to being widely used in monitoring oil spills in open waters, quantitative analyses of oil-spill hyperspectral information in ice-covered sea areas are inadequate. Due to multiple propagating processes, small oil droplets tend to form and mix into ice, resulting in low background oil concentration. For a complex nonlinear mixture, accurately estimating the material abundances remains a challenging yet fundamental task, particularly with extensive detailed and low-energy information. Herein, an abundance estimation method with normalizations in a combined polynomial and sine model is proposed to resolve the over-fitting problems. An energy information–based wavelet package scheme effectively highlights the latent information of the concerned materials. The oil-contaminated sea-ice images were generated with spectral reflectance collected outdoors on sea ice in Bohai Bay, China, in the form of pure as well as mixed spectral signatures. Experimental analyses suggest that the proposed method exhibits superior unmixing performance, particularly in delivering more accurate abundance estimations of different oil concentration levels as low as 10 − 4 than classic unmixing methods, even in low signal-to-noise data, and can be used to respond to oil spills at high latitudes. [ABSTRACT FROM AUTHOR]

China in recent years has been actively promoting the policy of establishing national parks to preserve nature systems. Visitor center (VC) is one of the most important infrastructures for protection, education, and recreation purposes in national parks. However, the current design of VCs in China is mostly based on the subjective considerations of designers and lacks in-depth discussion on the requirements of visitors. This study employed the Kano Two-Dimensional Quality method to investigate the design requirements of visitors and experts and then applied the Quality Function Deployment (QFD) method to transform the requirements into design strategies. Based on these design strategies, the design of VC in a national park in southeast China was taken as an example, and two design teams were used to verify the architectural scheme design and obtain feedback from visitors through a questionnaire survey. The research results show that the design strategies proposed by this study can better meet the expectations and satisfaction of the public. In the future, it is suggested that more parties, such as national park managers and community residents, should be involved in the study to obtain better planning and design strategies. [ABSTRACT FROM AUTHOR]

In this work, a joint evaluation method for the internal quantum efficiency (IQE) and light extraction efficiency (LEE) for AlGaN-based deep ultraviolet (DUV) light emitting diodes (LEDs) is proposed by fully considering the optical polarization properties. In this method, the IQE curve varying with the forward current can be reconstructed from a few measurement data at a small injection level. The corresponding LEE can be determined using the composition of spontaneous emission light with transverse electric and transverse magnetic components at different forward currents. The proposed method is applied for the efficiency evaluation of the AlGaN-based LED sample and verified by comparing with the EQE result deduced from the measured light output power–current (L–I) characteristic. It is demonstrated that the very low EQE and significant efficiency droop is from not only IQE but also LEE for AlGaN-based DUV LEDs. [ABSTRACT FROM AUTHOR]

Marine oil spills have caused severe environmental pollution with long-term toxic effects on marine ecosystems and coastal habitants. Hyperspectral remote sensing is currently used in efforts to respond to oil spills. Spectral unmixing plays a key role in hyperspectral imaging because of its ability to extract accurate fractional abundances of constituent materials from spectrums collected by sensors. However, multiple oil-propagating processes provide different mixing states of oil and water, thereby involving complicated, nonlinear mixing effects between in-depth elements in water, especially those with a low concentration. Therefore, an accurate inversion of material abundance remains a challenging yet fundamental task. This study proposes an unmixing method with normalizers in a combined polynomial and sine model to resolve overfitting problems. An energy information-based wavelet package scheme effectively highlights the latent information of the concerned material. Experimental analyses of synthetic and real data indicate that the proposed method shows superior unmixing performance, especially in delivering more accurate abundance estimations of different background oil concentration levels as low as a fractional abundance of 10 − 5 , and can be used for long-term monitoring of oil propagation. [ABSTRACT FROM AUTHOR]

The current gold standard for the clinical diagnosis of portal hypertension (PH) is an invasive and indirect estimation of portal vein pressure (PVP). Therefore, the need for a non-invasive PVP measurement method is urgent. Subharmonic scattering of ultrasound contrast agent (UCA) microbubbles is under investigation in clinical research as a pressure indicator. However, the driving acoustic pressure must be optimized to improve the ambient pressure sensitivity of the subharmonic amplitude for different UCAs. In this study, for the first time, we obtained the relationship between the PVP and the amplitude of the subharmonic signal scattered from SonoVue microbubbles by using two canines to build the PH model. The results revealed a desirable linear correlation between the subharmonic amplitude and PVP (<20 mmHg) at the incident acoustic pressure of 453 kPa (r = -0.910, p < 0.005; sensitivity: -2.003 dB/mmHg); this was one order of magnitude higher in sensitivity than that of the in vitro case with a detectable pressure variation of approximately 1 mmHg. This indicates the feasibility of using UCA microbubbles to accurately measure low ambient pressures in vivo and further exhibits the potential of the method for non-invasive pressure estimation in clinical applications. [ABSTRACT FROM AUTHOR]

Detecting the phase and polarization singularities of optical vortex (OV) beams is essential for integrated optical systems. So far, achieving the integration and further minimizing and stabilizing the orbital angular momentum (OAM) detector in more robust ways remain goals of related researches. In this study, we propose a semicircular nano-slit array to couple an OV beam to a focused surface plasmon beam (FSPB). The electric field intensities and movement of peak positions of FSPB are found to strongly depend on the topological charges of the incident OV beam. The positions of the FSPB can be controlled by an incident OV beam, which is left-circularly polarized (LCP) or right-circularly polarized (RCP). These results can be utilized for dynamical detection of topological charges values, positive or negative, odd or even, and polarization states of OV beams by a simple and intuitive method. Also, the electric field intensities of FSPB can be dynamically controlled by changing the structural parameters. These results provide an integrated and miniaturized method for detecting the topological charges of incident OV beams. [ABSTRACT FROM AUTHOR]

• Based on the measured dataset, we propose a tunable light source using a deep neural network (DNN) spectral matching algorithm. • Improvement of matching accuracy by using neural networks to solve the instability problem of modulation and the nonlinearity problem during spectral matching. • Build a communication link between algorithms and PWM regulation to optimize the speed of spectral matching of light sources. A spectrally tunable light source based on deep neural network (DNN) model is proposed in this work, which can reproduce arbitrary spectra accurately and rapidly. After calculating the scale factors using the trained DNN model, the target spectrum can be reproduced by regulating the combined monochromatic LEDs based on the pulse width modulation (PWM) signal with corresponding duty cycle. The standard solar spectrum and measured natural spectra at different times are reproduced using the proposed spectrally tunable light source. It is demonstrated that there is a good agreement between the target spectra and the corresponding reproduced spectra, with a fitted correlation index of above 0.9. Furthermore, the proposed spectrally tunable light source is able to transform spectrum at a frequency higher than 25 Hz and occupies only 750 KB memory space. As a result, the proposed spectrally tunable light source system can offer a high accuracy, fast speed and low cost approach to the development of specially light sources. [ABSTRACT FROM AUTHOR]

To develop natural complex materials as starch-dominated emulsifiers, pregelatinization was conducted on potato flour. The effects of gelatinization degrees (GDs, 0 %–50 %) on the structural characteristics, physicochemical properties, and emulsifying potentials of potato flour were investigated. Increasing GD of potato flour promoted protein aggregation on starch granules surfaces and transformed starch semicrystalline structures into melted networks. The emulsion stabilized with 50 % GD potato flour exhibited excellent storage stability (7 d) and gel-like behavior. With increasing GD from 0 to 50 %, the respective apparent viscosities and elastic moduli of emulsion increased from 21.4 Pa to 1126.7 Pa, and from 0.133 Pa·s to 1176.6 Pa·s, promoting the formation of a stable network structure in the emulsion. Fourier transform infrared spectra from emulsions with a continuous phase of >20 % GD displayed a new peak around 1740 cm−1, suggesting improved covalent interactions between droplets, thereby facilitating emulsion stability. Confocal laser scanning microscopy images indicated that droplets could be anchored in the melted networks and broken starch granules, inhibiting droplets coalescence. These results suggest that pregelatinization is a viable strategy for customizing natural starch-dominated emulsions. [Display omitted] • Emulsions stabilized by pregelatinized potato flour were successfully fabricated. • Proteins were aggregated on the surface of starch in the gelatinized potato flour. • Increased gelatinization degree of potato flour enhanced the stability of emulsion. • The enhancement of emulsification was due to increasing interactions of components. [ABSTRACT FROM AUTHOR]

Purpose. To explore and analyze the therapeutic effect of gamma globulins (GG) on Kawasaki disease (KD) in children and the influencing factors of short-term prognosis. Methods. First, 90 pediatric KD patients admitted between January 2019 and January 2021 were selected and divided into a control group (n = 40) and a research group (n = 50) according to the difference in treatment. In addition to routine treatment and nursing given to both groups, control group was given aspirin (ASA), based on which research group was supplemented with GG therapy. The treatment outcome and adverse events (AEs) of the two cohorts of patients were analyzed and compared, and the influencing factors of children's short-term prognosis were analyzed by logistics multivariate analysis. Results. Research group had a statistical higher overall response rate than control group, with significantly fewer cases suffering from AEs such as nausea and vomiting, diarrhea, rash, dizziness and headache, and coronary artery injury. On the other hand, logistics multivariate analysis identified that gender, body mass index (BMI), onset time, platelet (PLT), and treatment mode all independently influence the short-term prognosis of children with KD. Conclusions. GG therapy is effective in treating pediatric KD patients and can effectively prevent AEs. In addition, gender, BMI, onset-to-treatment time, PLT, C-reactive protein (CRP), and treatment methods are independent risk factors for short-term prognosis of children with KD. [ABSTRACT FROM AUTHOR]

Defocus blur detection (DBD) for natural images is a challenging vision task especially in the presence of homogeneous regions and gradual boundaries. In this paper, we propose a novel image-scale-symmetric cooperative network (IS2CNet) for DBD. On one hand, in the process of image scales from large to small, IS2CNet gradually spreads the recept of image content. Thus, the homogeneous region detection map can be optimized gradually. On the other hand, in the process of image scales from small to large, IS2CNet gradually feels the high-resolution image content, thereby gradually refining transition region detection. In addition, we propose a hierarchical feature integration and bi-directional delivering mechanism to transfer the hierarchical feature of previous image scale network to the input and tail of the current image scale network for guiding the current image scale network to better learn the residual. The proposed approach achieves state-of-the-art performance on existing datasets. Codes and results are available at: https://github.com/wdzhao123/IS2CNet. [ABSTRACT FROM AUTHOR]

Dynamic detection of the orbital angular momentum (OAM) properties of optical vortex (OV) beams has shown a wide range of applications in on-chip photonics, optical communication, and optical imaging. However, conventional detection technique suffers from issues of low efficiency, limited functionality, and low noise immunity. In this paper, a dislocated circular hole array is designed to generate ring-shaped surface plasmonic bending (R-SPB) beams. The propagation characteristics of R-SPB beams are numerically simulated using the finite difference time domain (FDTD) method. By analyzing the spot shape, size, and split-to-single spot conversion of the R-SPB beams, we are able to dynamically detect the topological charge values, polarization state (left circular polarization and right circular polarization), and positive or negative of the OV beam. We have successfully detected topological charges up to l=±12\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$l{\kern 1pt} {\kern 1pt} { = }{\kern 1pt} \pm 12$$\end{document}. These results demonstrate that an intuitive and convenient method for detecting the higher-order topological charges of OV beam is realized utilizing the R-SPB beam propagation properties. In addition, we found that changing the structural parameters only affects the electric field intensity distribution of the R-SPB but do not impact the detection results. These findings are useful for the development of on-chip OV beam topological charge detectors with multiplexed and highly integrated capabilities. [ABSTRACT FROM AUTHOR]

Sepsis, characterized with high risk of life‐threatening organ dysfunction, represents a major cause of health loss and the World Health Organization (WHO) labelled sepsis as the most urgent unmet medical need in 2017. The emerging biological understanding of the role of RIPK1 in sepsis has opened up an exciting opportunity to explore potent and selective RIPK1 inhibitors as an effective therapeutic strategy for SIRS and sepsis therapy. Herein, we have synthesized a class of highly potent dual‐mode RIPK1 inhibitors occupying both the allosteric and the ATP binding pockets, exemplified by compound 21 (ZB‐R‐55) which is about 10‐fold more potent than GSK2982772, and exhibits excellent kinase selectivity, good oral pharmacokinetics and good therapeutic effects in the LPS‐induced sepsis model, suggesting that compound ZB‐R‐55 is a highly promising preclinical candidate. [ABSTRACT FROM AUTHOR]

Sepsis, characterized with high risk of life‐threatening organ dysfunction, represents a major cause of health loss and the World Health Organization (WHO) labelled sepsis as the most urgent unmet medical need in 2017. The emerging biological understanding of the role of RIPK1 in sepsis has opened up an exciting opportunity to explore potent and selective RIPK1 inhibitors as an effective therapeutic strategy for SIRS and sepsis therapy. Herein, we have synthesized a class of highly potent dual‐mode RIPK1 inhibitors occupying both the allosteric and the ATP binding pockets, exemplified by compound 21 (ZB‐R‐55) which is about 10‐fold more potent than GSK2982772, and exhibits excellent kinase selectivity, good oral pharmacokinetics and good therapeutic effects in the LPS‐induced sepsis model, suggesting that compound ZB‐R‐55 is a highly promising preclinical candidate. [ABSTRACT FROM AUTHOR]

Ultrasound contrast agent microbubbles are considered promising sensors to measure portal vein pressure noninvasively. In this study, we investigated the subharmonic scattering power and optimal incident acoustic pressure of SonoVue microbubbles (concentration: $1~\mu \text{L}$ /mL 0.9% NaCl solution) in the ambient pressure range of 10–40 mmHg with 10-mmHg increments at a temperature of 25 °C. The results demonstrated that the subharmonic response of the SonoVue microbubbles existed in three stages: the first growth stage (40–300 kPa), saturation (300–400 kPa), and the second growth stage (400–540 kPa). In the first growth stage, the subharmonic amplitude increased with ambient pressure. However, while the ambient pressure increased, the subharmonic amplitude decreased in the second growth stage. The best correlation of the subharmonic amplitudes with the ambient pressures was obtained at a high incident acoustic pressure of 520 kPa (sensitivity: 0.15 dB/mmHg, ${r}^{{{2}}} =0.99$ , and root-mean-square error = 0.49 mmHg), which indicated that the subharmonic signals in the second growth stage might be suitable for estimating low ambient pressures. The results presented in our study may pave the way for portal vein pressure estimation using SonoVue microbubbles as sensors in clinical applications. [ABSTRACT FROM AUTHOR]

Coagulation dysfunction is an evident factor in the clinical diagnosis and treatment of patients with coronavirus disease 2019 (COVID-19), appearing even in COVID-19 patients with normal inflammation indices. Therefore, this study aimed to analyze the characteristics of coagulation function indices in COVID-19 patients to investigate possible mechanisms through the comparison of non-severe and severe COVID-19 patients. We included 143 patients whose clinical characteristics, coagulation function, and other indices such as inflammatory factors were collected and compared based on disease severity. Activated partial thromboplastin time (APTT), D-dimer, and fibrinogen levels were evidently higher in the severe group than in the non-severe group. Among non-severe COVID-19 patients, the aforementioned indicators depicted increasing trends, but the fibrinogen level alone was higher than normal. However, in severe COVID-19 patients, values of all three indices were higher than normal. In severe COVID-19 patients, fibrinogen and D-dimer were correlated with several inflammation indices during the early stage of the disease. However, no correlation between fibrinogen and inflammatory factors was observed in non-severe COVID-19 patients at any time point. Results revealed that the hypercoagulability tendency of severe COVID-19 patients was more evident. The relationship between coagulation function and inflammatory factors showed that changes in coagulation function in severe COVID-19 patients may be related to abnormal increase in inflammatory factors at an early stage; however, in non-severe COVID-19 patients, there might be other factors leading to abnormal coagulation. Inflammatory factors were not the only cause of abnormal coagulation function in COVID-19 patients. [ABSTRACT FROM AUTHOR]

The multi-step reaction of CO2 over basic catalysts significantly improves the diversity of products, vividly embodying the ability of green chemistry to turn waste into treasure. However, under a Lewis acidic CO2 atmosphere, the activity maintenance of the multi-step reaction puts forward higher requirements for the reversible regeneration ability of the basic catalysts. Novel Lewis–Brønsted binary basic poly(ionic liquid)s (LB-PILs) were successfully developed through a radical copolymerization followed by ion-exchange strategy, and were applied to the two-step synthesis of dimethyl carbonate (DMC) by coupling CO2 cycloaddition and CH3OH transesterification without any cocatalyst. Relying on the activation of CO2 by Lewis basic tertiary N and the thermal recovery of nucleophilic HCO3− to stronger Brønsted basic OH− with the help of temperature control in CH3OH transesterification, the LB-PILs presented high activity, stable repeatability and linear carbonate universality. In situ IR analysis and Knoevenagel condensation, combined with theoretical calculations, provided reliable support for the ingenious incorporation of reversible regeneration of basic catalysts in a Lewis acidic CO2 atmosphere and multi-step coupling reaction process of CO2. [ABSTRACT FROM AUTHOR]

Long-term consumption of a high-fat diet increases the circulating concentration of stearic acid (SA), which has a potent toxic effect on β-cells, but the underlying molecular mechanisms of this action have not been fully elucidated. Here, we evaluated the role of long noncoding (lnc)RNA TCONS_00077866 (lnc866) in SA-induced β-cell inflammation. lnc866 was selected for study because lncRNA high-throughput sequencing analysis demonstrated it to have the largest fold-difference in expression of five lncRNAs that were affected by SA treatment. Knockdown of lnc866 by virus-mediated shRNA expression in mice or by Smart Silencer in mouse pancreatic β-TC6 cells significantly inhibited the SA-induced reduction in insulin secretion and β-cell inflammation. According to lncRNA-miRNAs-mRNA coexpression network analysis and luciferase reporter assays, lnc866 directly bound to miR-297b-5p, thereby preventing it from reducing the expression of its target serum amyloid A3 (SAA3). Furthermore, overexpression of miR-297b-5p or inhibition of SAA3 also had marked protective effects against the deleterious effects of SA in β-TC6 cells and mouse islets. In conclusion, lnc866 silencing ameliorates SA-induced β-cell inflammation by targeting the miR-297b-5p/SAA3 axis. lnc866 inhibition may represent a new strategy to protect β-cells against the effects of SA during the development of type 2 diabetes. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis can help consumers provide clear and objective sentiment recommendations through massive amounts of data and is conducive to overcoming ambiguous human weaknesses in subjective judgments. However, the robustness and accuracy of existing sentiment analysis methods must still be improved. In this article, deep learning and machine learning techniques are combined to construct a sentiment analysis model based on ensemble learning ideas. Furthermore, the proposed model is applied to a sentiment classification for user reviews about restaurants, which are the representative location-based and user-oriented applications in 5G networks. Specifically, a multi-aspect-labeling model is established, and an ensemble aspect-based model is proposed based on the concept of ensemble learning to predict the consumer's true consumption feelings and willingness to consume again, and to improve machine learning based on the developed model. The predictive performance of the algorithm lies within a single domain. [ABSTRACT FROM AUTHOR]

Immunotherapy based on γδT cells has limited efficiency in solid tumors, including colon cancer (CC). The immune evasion of tumor cells may be the main cause of the difficulties of γδT cell-based treatment. In the present study, we explored whether and how B7-H3 regulates the resistance of CC cells to the cytotoxicity of Vγ9Vδ2 (Vδ2) T cells. We observed that B7-H3 overexpression promoted, while B7-H3 knockdown inhibited, CC cell resistance to the killing effect of Vδ2 T cells in vitro and in vivo. Mechanistically, we showed that B7-H3-mediated CC cell resistance to the cytotoxicity of Vδ2 T cells involved a molecular pathway comprising STAT3 activation and decreased ULBP2 expression. ULBP2 blockade or knockdown abolished the B7-H3 silencing-induced increase in the cytotoxicity of Vδ2 T cells to CC cells. Furthermore, cryptotanshinone, a STAT3 phosphorylation inhibitor, reversed the B7-H3 overexpression-induced decrease in ULBP2 expression and attenuated the killing effect of Vδ2 T cells on CC cells. Moreover, there was a negative correlation between the expression of B7-H3 and ULBP2 in the tumor tissues of CC patients. Our results suggest that the B7-H3-mediated STAT3/ULBP2 axis may be a potential candidate target for improving the efficiency of γδT cell-based immunotherapy in CC. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing is currently being used to detect and monitor marine oil spills that cause damage to the environment. However, nonlinear interactions of oil and water make it difficult to extract their fractional abundances from the spectral response. Improving the modelling of nonlinear hyperspectral mixtures, which is required for a thorough and reliable characterization of the materials in an image, remains a challenging yet fundamental task. This study proposes a new model that combines polynomial and trigonometric systems to understand the nonlinear effects of oil and water spectral response. Although the model is nonlinear, unmixing is performed by solving a linear problem, thus allowing fast computation. Compared to classic polynomial models, the details of nonlinear interactions are better expressed and quantified, and the reconstruction accuracy and endmember abundance estimation are improved for both synthetic and real datasets. Both the polynomial and trigonometric parts of the model play important roles in characterizing nonlinearities, with statistically linear dependence areas covering more than 90% and 30%, respectively, in oil spill images sampled after the Deepwater Horizon explosion. Analysis of the experimental results suggests that the proposed model provides an efficient and accurate unmixing method that can be used to help design oil spill response plans. [ABSTRACT FROM AUTHOR]

Circulating tumor cells (CTCs) is a clinical biomarker for cancer metastasis. CTCs are cells circulating in the body of patients by being separated from primary cancer and entering into blood vessel. CTCs spread every positions in the body, and this is one of the cause of cancer metastasis. To analyze them, pathologists get information about metastasis without invasive test. CTCs test is conducted by analyzing the blood sample from patient. The fluorescence microscope generates a large number of images per each sample, and images contain a lot of cells. There are only a few CTCs in images and cells often have blurry boundaries. So CTCs identification is not an easy work for pathologists. In this paper, we develop an automatic CTCs identification method in fluorescence microscopy images. This proposed method has three section. In the first approach, we conduct the cell segmentation in images by using filtering methods. Next, we compute feature values from each CTC candidate region. Finally, we identify CTCs using artificial neural network algorithm. We apply the proposed method to 5895 microscopy images (7 samplesas), and evaluate the effectiveness of our proposed method by using leave-one-out cross validation. We achieve the result of performance tests, a true positive rate is 92.57% and false positive rate is 9.156%. [ABSTRACT FROM AUTHOR]

Human wearable helmet is a useful tool for monitoring the status of miners in the mining industry. However, there is little research regarding human emotion recognition in an extreme environment. To the best of our knowledge, this paper is the first to describe the human anxiety change rule and to propose a cloud computing platform for detecting human emotions using brain-computer interface (BCI) devices. In this paper, an emotional state evoked paradigm is designed to identify the brain area where the emotion feature is most evident. Next, the correct electrode position is determined for the collection of the negative emotion by the electroencephalograph (EEG) based on the international 10–20 system of electrode placement. Next, a fusion algorithm of the anxiety level is proposed to evaluate the person's mental state using the θ, α, and β rhythms of an EEG. Next, the human smart helmet system is designed to collect the human state, which includes the mental parameters of the anxiety level, the fatigue level, the concentration level, and the environmental parameters in the coal mine. Experiments demonstrate that the position Fp2 is the best electrode position for obtaining the anxiety level parameter. The most visible EEG changes appear within the first 2 s following stimulation. The amplitudes of the θ rhythm increase most significantly in the negative emotional state. The fusion algorithm of the anxiety level accurately measures negative emotional change. [ABSTRACT FROM AUTHOR]

Deep-sea organism automatic tracking has rarely been studied because of a lack of training data. However, it is extremely important for underwater robots to recognize and to predict the behavior of organisms. In this paper, we first develop a method for underwater real-time recognition and tracking of multi-objects, which we call "You Only Look Once: YOLO". This method provides us with a very fast and accurate tracker. At first, we remove the haze, which is caused by the turbidity of the water from a captured image. After that, we apply YOLO to allow recognition and tracking of marine organisms, which include shrimp, squid, crab and shark. The experiments demonstrate that our developed system shows satisfactory performance. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are reported to play a critical role in the regulation of cancer cell proliferation; however, the role of microRNA-25 (miR-25) in pancreatic ductal adenocarcinoma (PDAC) remains unclear. In the present study, the role of miR-25 in PDAC cell proliferation was investigated. Upregulated expression of miR-25 was found in PDAC tissues and cell lines by reverse transcription-quantitative PCR. Cell proliferation was significantly enhanced by overexpression of miR-25 as shown by CCK-8 assay results. Meanwhile, overexpression of miR-25 also promoted G1-to-S phase transition of the cell cycle in Aspc-1 cells via flow cytometry analysis. However downregulation of miR-25 inhibited the tumor cell proliferation and cell cycle transition. Online software was used to predict the target gene for miR-25 and luciferase reporter assay confirmed that Abl interactor 2 (ABI2) was a target of miR-25 via direct binding of its 3' untranslated region with miR-25. Moreover, results of the western blot analysis demonstrated that miR-25 negatively regulated the expression of ABI2 at the protein level. In addition, introduction of ABI2 mRNA into cells overexpressing miR-25 attenuated the carcinogenic effects of miR-25. In conclusion, these findings demonstrate that miR-25 plays an oncogenic role and promotive role in PDAC cell proliferation via targeting of ABI2. [ABSTRACT FROM AUTHOR]

Local descriptors play a crucial role in numerous computer vision and pattern recognition applications. This paper proposes a novel local descriptor, called the quaternionic local angular binary pattern (QLABP), for color image classification. QLABP is based on the quaternionic representation (QR) of color images such that it is able to handle all color components holistically as well as consider their relations. Using QR, the quaternionic angular information is further developed to account for more color characteristics. We provide two ways to derive the quaternionic angular information from different perspectives. A pattern encoding operation is finally conducted on the obtained angular information to obtain QLABP. The effectiveness of QLABP has successfully been evaluated by comparing with several state-of-the-art descriptors. [ABSTRACT FROM AUTHOR]

The immunoregulatory protein B7-H3, a member of the B7 family, has been confirmed to be highly expressed in colon cancer. However, the exact influence of B7-H3 on the features and antitumor ability of γδT cells in colon cancer remains unknown. In the present study, we investigated that the proportions of B7-H3+ γδT cells were distinctly increased in the peripheral blood and tumor tissues of colon cancer patients. B7-H3 blockade or knockdown promoted proliferation, inhibited cell apoptosis and induced the expression of activation markers (CD25 and CD69) on Vδ2 T cells. In contrast, treatment with the B7-H3 agonist 4H7 had the opposite effect. Furthermore, B7-H3 suppressed IFN-γ expression by inhibiting T-bet in Vδ2 T cells. Moreover, B7-H3 mediated the inhibition of Vδ2 T cell cytotoxicity via the downregulation of IFN-γ and perforin/granzyme B expression. More importantly, blocking the B7-H3 function significantly enhanced the cytotoxicity of Vδ2 T cells against colon cancer cells in vivo. Therefore, the inhibition or blockade of B7-H3 is a potential immunotherapeutic approach for colon cancer. [ABSTRACT FROM AUTHOR]

LINC01638 is a long non-coding RNA (lncRNA) with an oncogenic role in breast cancer, while its involvement in other malignancies is unknown. This study was performed to investigate the potential role of LINC01638 in pancreatic ductal adenocarcinoma (PDAC). The expression of LINC01638 was determined via reverse transcription-quantitative polymerase chain reaction analysis, whereas the levels of transforming growth factor-β1 (TGF-β1) in plasma were measured via ELISA. Receiver operating characteristic curve analysis was conducted to determine the diagnostic value of LINC01638. Additionally, the migratory and invasive abilities of cells were evaluated via Transwell migration and invasion assays. In the present study, LINC01638 was significantly upregulated in tumor tissues compared with adjacent healthy tissues in the majority of patients with PDAC. Plasma levels of LINC01638 were significantly higher in patients with PDAC compared with in healthy controls. In effect, upregulation of plasma LINC01638 distinguished patients with PDAC from healthy controls in receiver operating characteristic analysis. Plasma levels of LINC01638 and TGF-β1 were positively correlated in patients with PDAC, but not in healthy controls. LINC01638 overexpression increased TGF-β1 expression, while silencing of LINC01638 using short hairpin RNA (shRNA) led to reduced TGF-β1 expression in a PDAC cell line. LINC01638 overexpression promoted, while shRNA silencing inhibited, migration and invasion of cell of a PDAC cell line. Treatment with exogenous TGF-β1 attenuated the inhibitory effect of LINC01638 shRNA silencing on cancer cell migration and invasion. It is concluded that LINC01638 lncRNA may be involved in the migration and invasion of PDAC cells via regulation of TGF-β1. [ABSTRACT FROM AUTHOR]

In order to improve the light extraction of AlGaN-based short wavelength ultraviolet light emitting diodes (DUC-LEDs), a type of microstructure with high aspect ratio is introduced and optimized on the AlN substrate surface. And, particle swarm optimization (PSO) algorithm is used to inverse design of the surface microstructure to maximize the light extraction efficiency (LEE). Considering that the propagation characteristics of TM-polarized light are different from that of TE-polarized light, the optical field distribution and LEE is analyzed for the UVC-LEDs with different TE-polarized component when the optimized surface microstructure is applied. Furthermore, the preparation process tolerance of the high aspect ratio structure is discussed by calculating the LED's LEE when the structural deviation occurs or morphology changes. Simulation results show that, by using the optimized surface microstructure based on parabola cone array, the LEDs' LEE is increased from 4.4% to 8.7% and from 0.4% to 3.7% for TE-polarized and TM-polarized emission, respectively. In addition, it is demonstrated that the light extraction improvement by the surface microstructure has a good tolerance to the structural deviation and morphology. The results are significant for improving light extraction and realizing high efficient short wavelength AlGaN-based UVC-LEDs by designing surface microstructures. • We use the optimization algorithm to optimize the optimal structure of different polarized light. The results show that different polarized light sources will affect the optimal structure parameters and the optimal LEE value. We quantitatively calculated and analyzed the relevant results. • We have drawn the far-field diagram of the LEE optimal value under different polarization ratios. Compared with the plane structure, the influence of the microstructure on the light field can be seen directly. • Various possible process defects are discussed separately and jointly. The results show that the optimized structure has a high tolerance to the process regardless of the polarization. • In order to understand the effect of structural morphology on the results. The optimization algorithm is used to optimize the three kinds of array microstructures. The optimization results show that the three microstructures can significantly improve LEE. The optimal effect is also relatively close. It shows that the structure shape has little influence on the results, and the joint optimization of multiple parameters will have a great influence on the results. At the same time, the effectiveness of the optimization algorithm for multi-parameter objectives is proved again. [ABSTRACT FROM AUTHOR]

Using deep learning to improve the performance of finger vein recognition has become a part of mainstream research. Currently, the performance of finger vein recognition systems is limited by insufficient finger vein training samples, which leads to insufficient feature learning and weak model generalization. To solve the above problems, first, we propose a simple and effective finger vein data augmentation strategy named FV-Mix, which uses fine finger vein image region of interest (ROI) for grayscale normalization and linear mixing. It can accomplish exponential-level data augmentation on the training sample, and the augmented data will represent a more complete dataset. Second, a residual Gabor convolutional network (RGCN) is designed for finger vein recognition, which includes a residual Gabor convolutional layer (RGCL) and a dense semantic analysis module (DSAM). The RGCL is designed to replace the shallow convolutional layer in the network by using the characteristics of the Gabor filter to enhance the scale and direction of information in the shallow pattern features. And then, the enhanced deep features are further extracted and analyzed by DSAM, which is used to assist the final model in classifying and recognizing finger vein images. To verify the effectiveness of our work, five publicly available finger vein image datasets are used, and a large number of comparison experiments are designed. The proposed FV-Mix strategy, RGCL module, and DSAM module were fully validated. The experimental results show that on these five datasets, the average recognition accuracy and equal error rate (EER) of the proposed RGCN are 99.22% and 0.188%, respectively, achieves competitive performance compared with current state-of-the-art work. • Novel residual Gabor convolutional network improves recognition system performance. • Novel residual Gabor convolutional layer enhances finger vein pattern features. • Novel dense semantic analysis module assists with model classification. • Exponential level augmentation for finger vein image samples. [ABSTRACT FROM AUTHOR]

Abstract Overcoming slow kinetics of cathodic electrochemical reactions is a key factor toward achieving economical and efficient electrocatalysts for practical application of primary Al-air batteries. In this study, a hybrid catalyst with specific surface area and manganese valence controlled α-MnO 2 nanotube decorated by silver nanoparticles (Ag/MnO 2 -3) is successful synthesized. A facile hydrothermal method was used, followed by liquid phase reducing treatment. The Ag/MnO 2 -3 showed not only superior catalytic activity for the oxygen reduction reaction with more positive onset potential (0.996 V vs. RHE) and comparable half-wave potential (0.870 V vs. RHE) to the commercial Pt/C catalyst but also a higher discharge voltage plateau (∼1.1 V at discharge current density of 50 mA cm−2) with superior stability in the primary Al−air battery test. The high electrocatalytic activity of the Ag/MnO 2 -3 nanohybrid is attributed to the larger specific surface area of MnO 2 nanotubes and its ultrahigh atomic ratios of Mn3+/Mn4+ on the surface. This results in a strong synergistic effect between manganese dioxide nanotube and Ag nanoparticles to inhibit the accumulation of hydrogen peroxide. This material is promising for applications in Al-air batteries. [ABSTRACT FROM AUTHOR]

Long intergenic non-protein coding RNA, p53 induced transcript (Linc-pint) is a newly identified long non-coding RNA, which has demonstrated antitumor activities in various types of cancer. The present study aimed to investigate the role of Linc-pint in pancreatic ductal adenocarcinoma (PDAC). Plasma samples from patients with PDAC, and PDAC and normal cell lines were used in the study. Gene expression was analyzed by reverse transcription-quantitative polymerase chain reaction. Western blotting was used to assess protein level. Transforming growth factor β1 (TGF-β1) plasma level was determined by ELISA. Cancer cell proliferation was measured in vitro with the Cell Counting Kit-8 assy. The results demonstrated that Linc-pint plasma levels were significantly lower in patients with stage 0–1 PDAC compared with healthy controls. In addition, Linc-pint downregulation effectively distinguished patients with PDAC from healthy controls. Linc-pint and TGF-β1 plasma levels were positively correlated in patients with PDAC but not in healthy controls. Furthermore, Linc-pint overexpression upregulated TGF-β1 expression in PDAC cells but not in normal pancreatic ductal cells; however, exogenous TGF-β1 exhibited no significant effects on Linc-pint expression. Linc-pint overexpression and TGF-β1 both inhibited PDAC cell proliferation, whereas treatment with a TGF-β inhibitor reduced their inhibitory effects on cell proliferation. In conclusion, results from the present study suggested that Linc-pint may inhibit early stage PDAC growth through TGF-β pathway activation. [ABSTRACT FROM AUTHOR]

As it combines AI and IoT, autonomous driving has attracted a great deal of attention from both academia and industry because of its benefits to the economy and society. However, ultra-low delay and ultra-high reliability cannot be guaranteed by individual autonomous vehicles with limited intelligence and the existing architectures of the Internet of Vehicles. In this article, based on a cloud/fog-computing pattern and the IoT AI service framework, we propose a cross-domain solution for auto-driving. In contrast to existing studies, which mainly focus on communication technologies, our solution achieves intelligent and flexible autonomous driving task processing and enhances transportation performance with the help of the Cognitive Internet of Vehicles. We first present an overview of the enabling technology and the architecture of the Cognitive Internet of Vehicles for autonomous driving. Then we discuss the autonomous driving Cognitive Internet of Vehicles specifically from the perspectives of what to compute, where to compute, and how to compute. Simulations are then conducted to prove the effect of the Cognitive Internet of Vehicles for autonomous driving. Our study explores the research value and opportunities of the Cognitive Internet of Vehicles in autonomous driving. [ABSTRACT FROM AUTHOR]

To rational design non-noble catalysts with oxygen reduction reaction (ORR) activity and durability superior to that of Pt/C, we report the fabrication of iron group metal sulphides immobilized inside hierarchically porous N,S-enriched carbon architecture using a facile double-phase encapsulation approach followed by a pyrolysis process. The as-prepared hybrid architectures exhibit significantly enhanced electrocatalytic activity towards the ORR, including a positive onset potential, low Tafel slope, and high electron transfer number. In particular, the ORR activity of FeS@G/NSC is even superior to that of the Pt/C benchmark, which possesses an outstanding onset potential (0.99 V vs RHE) and a high discharge voltage plateau (∼1.47 V at discharge current density of 50 mA cm−2 in the Al-air battery test) with excellent stability. These hybrid carbon architectures provide a new direction for carbon-based ORR electrocatalysts performance optimization for next-generation air batteries. Image 1 • MS@G (M: Fe, Co, Ni) immobilized inside N,S-enriched carbon architectures. • Iron group metal sulphides-core and graphene-shell interaction. • Highly active and robust hybrid catalyst toward ORR. • Synergetic effect between active species. [ABSTRACT FROM AUTHOR]

Abstract To develop high-performance and efficient bifunctional electrocatalysts for Li O 2 batteries, a promising new material is developed in this study, which consists of hierarchical porous hollow carbon nanofibers (HCNFs) with Ag/Co@CoO nanoparticles immobilized inside. This material is prepared using a facile and convenient coaxial electrospinning method, followed by a simple heat treatment. The Ag/Co@CoO HCNF cathode shows low polarization and excellent cycle performance. A combination of structural characterizations (e.g., scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, Raman scattering spectroscopy, and the Brunauer-Emmett-Teller method) and electrochemical analyses shows that the excellent battery performance of the Ag/Co@CoO HCNF cathode can be attributed to its hierarchical porous structure, high surface area and high content of active sites combined with the synergetic interactions between Ag and Co@CoO as well as the improved electrical conductivity. Notably, the introduction of Ag may promote the Li 2 O 2 to grow and crystallize into nanosheets due to the synergetic interactions between Ag and CoO. The petal-like Li 2 O 2 discharge products formed on the Ag/Co@CoO HCNF cathode are more readily decomposed than the film-like Li 2 O 2. Our findings provide a reference for designing free-standing high-performance hollow-structured Ag/Co@CoO hybrid electrodes by coaxial electrospinning for non-aqueous Li O 2 batteries. Graphical abstract Image 1 [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to reduce artifacts on temporal difference images by improving the conventional method using a non-rigid registration method for ground glass opacification (GGO), which is light in concentration and difficult to detect early. In this method, global matching, local matching, and 3D elastic matching are performed on the current image and past image, and an initial temporal difference image is generated. After that, we use an Iris filter, which is the gradient vector concentration degree filter, to determine the initial GGO candidate regions and perform segmentation using SuperVoxel and Graph Cuts in which a superpixel is extended to three dimensions for each region of interest. For each extracted region, a support vector machine (SVM) is used to reduce the over-segmentation. Finally, in the method that greatly reduces artifacts other than the remaining GGO candidate regions, Voxel Matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact. The resulting ratio of artifacts to lung volume is 0.101 with an FWHM of 28.3, which is an improvement over the conventional method and shows the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

Therapeutic approaches aimed at curing prostate cancer are only partially successful given the occurrence of highly metastatic resistant phenotypes that frequently develop in response to therapies. Recently, we have described αvβ6, a surface receptor of the integrin family as a novel therapeutic target for prostate cancer; this epithelial-specific molecule is an ideal target since, unlike other integrins, it is found in different types of cancer but not in normal tissues. We describe a novel αvβ6-mediated signaling pathway that has profound effects on the microenvironment. We show that αvβ6 is transferred from cancer cells to monocytes, including β6-null monocytes, by exosomes and that monocytes from prostate cancer patients, but not from healthy volunteers, express αvβ6. Cancer cell exosomes, purified via density gradients, promote M2 polarization, whereas αvβ6 down-regulation in exosomes inhibits M2 polarization in recipient monocytes. Also, as evaluated by our proteomic analysis, αvβ6 down-regulation causes a significant increase in donor cancer cells, and their exosomes, of two molecules that have a tumor suppressive role, STAT1 and MX1/2. Finally, using the Pten pc −/− prostate cancer mouse model, which carries a prostate epithelial-specific Pten deletion, we demonstrate that αvβ6 inhibition in vivo causes up-regulation of STAT1 in cancer cells. Our results provide evidence of a novel mechanism that regulates M2 polarization and prostate cancer progression through transfer of αvβ6 from cancer cells to monocytes through exosomes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan’s economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called “Beyond AI”. Specifically, we plan to develop an intelligent learning model called “Brain Intelligence (BI)” that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots. [ABSTRACT FROM AUTHOR]

Optical underwater images often demonstrate low contrast, heavy scatter, and color distortion. Contrast enhancement methods have been proposed to solve these issues. However, such methods typically do not consider high-level inhomogeneous scatter removal and do not focus on real-scene color restoration. We proposed a hierarchical transmission fusion method and a color-line ambient light estimation method for image de-scattering from a single input image. Our proposed method can be summarized into three steps. Firstly, we take the dark channel as prior information to estimating the preliminary transmission and ambient light. In the second step, we then use color lines to estimate the refined ambient light in selected patches. The refined transmission is obtained by hierarchical transmission maps using maximum local energy-based fusion at different turbidity levels. We then use a joint normalized filter to obtain the final transmission. Finally, a chromatic color correction method and de-blurring algorithm are used to recover the scene color. Experimental results demonstrate that the accurate estimation of the depth map and ambient light by the proposed method can recover visually appealing images with sharp details. [ABSTRACT FROM AUTHOR]

Investigating the characteristics of urban expansion is helpful in managing the relationship between urbanization and the ecological and environmental issues related to sustainable development. The Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) collects visible and near-infrared light fromthe Earth's surface at nightwithoutmoonlight. It generates effective time series data for mapping the dynamics of urban expansion. As a major urban agglomeration in the world, the Yangtze River Delta Urban Agglomeration (YRDUA) is an important intersection zone of both the "Belt and Road Initiative" and the "Yangtze River Economic Belt" in China. Therefore, this paper analyses urban expansion characteristics of the YRDUA for 1993-2012 from urban extents extracted from the DMSP/OLS for 1993, 1997, 2002, 2007, and 2012. First, calibration procedures are applied to DMSP/OLS data, including intercalibration, intra-annual composition, and inter-annual series correction procedures. Spatial extents are then extracted from the corrected DMSP/OLS data, and a threshold is determined via the spatial comparison method. Finally, three models are used to explore urban expansion characteristics of the YRDUA from expansion rates, expansion spatial patterns, and expansion evaluations. The results show that the urban expansion of the YRDUA occurred at an increasing rate from 1993-2007 and then declined after 2007 with the onset of the global financial crisis. The Suxichang and Ningbo metropolitan circles were seriously affected by the financial crisis, while the Hefei metropolitan circle was not. The urban expansion of the YRDUA moved from the northeast to the southwest over the 20-year period. Urban expansion involved internal infilling over the first 15 years and then evolved into external sprawl and suburbanization after 2007. [ABSTRACT FROM AUTHOR]

To develop high-performance and low-cost electrocatalysts for the oxygen reduction reaction (ORR) in Al-air batteries, a promising hierarchical porous N,S-codoped carbon architecture with cobalt-nickel-sulphide nanoparticles immobilized inside was prepared using a simple double-phase encapsulation approach (DPEA), followed by a post heat treatment. The integrated hybrid architecture (Ni-Co-S@G/NSC) exhibited not only a comparable onset potential (0.94 V vs. RHE) and half-wave potential (0.86 V vs. RHE) but also a higher discharge voltage plateau (∼1.23 V at discharge current density of 150 mA cm −2 ) with superior stability to the commercial Pt/C catalyst in ORR and the full cell test. A combination of structural characterizations (e.g., XPS, RSS, XRD, SEM, HRTEM, and BET) and electrochemical analyses showed that the high ORR activity of the Ni-Co-S@G/NSC catalyst was attributed to its high surface area and pore volume, proper degree of the graphitization, and high content of active species combined with their synergetic interactions. Our findings show the advantages in utilizing a metal-organic framework with DPEA for designing well-dispersed, highly active and robust N,S-codoped porous carbons immobilized with sulphide NP catalysts for Al-air batteries. [ABSTRACT FROM AUTHOR]

Underwater cameras are widely used to observe the sea floor. They are usually included in autonomous underwater vehicles (AUVs), unmanned underwater vehicles (UUVs), and in situ ocean sensor networks. Despite being an important sensor for monitoring underwater scenes, there exist many issues with recent underwater camera sensors. Because of light's transportation characteristics in water and the biological activity at the sea floor, the acquired underwater images often suffer from scatters and large amounts of noise. Over the last five years, many methods have been proposed to overcome traditional underwater imaging problems. This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers. We present an overview of various underwater image-processing approaches, such as underwater image de-scattering, underwater image color restoration, and underwater image quality assessments. Finally, we summarize the future trends and challenges in designing and processing underwater imaging sensors. [ABSTRACT FROM AUTHOR]

Electrospinning is a common approach for the fabrication of composite carbon nanofibers (CNFs) used as cathodes in lithium-oxygen batteries (LOBs). However, the addition of a salt precursor in the synthetic procedure makes a strong impact on the final morphology and structure of composites, directly affecting the battery performance. Herein, 3D hierarchical porous CNFs with different loading amounts of metallic Co are prepared in situ by the electrospinning method combined with a thermal annealing process. Notably, we find that with the overloading of metallic Co, the resultant hybrid CNFs become rather fragile, resulting in a discontinuous nanorod structure. In contrast, an optimal addition of the Co precursor is found to create a continuous and integrated porous structure within the CNFs, leading to the highest surface area. The free-standing Co/CNFs films can not only supply a high number of active sites and facilitate reactant diffusion but also provide abundant Li 2 O 2 storage that can avoid binder-induced side reactions. Benefiting from the structural advantages and superior catalytic performance of Co, the resultant battery achieves a large capacity, outstanding rate capability and a good cycling stability and exhibits great potential for application in LOBs. [ABSTRACT FROM AUTHOR]

The online generation and dissemination of synthetic media through Internet media such as Facebook, Twitter, Snapchat, and TikTok, can easily produce "fake news" with fast spread. With the latest advances of artificial intelligence technology, images, audio, videos, and textual information can easily be transformed or synthesized in real-time. It is time to understand any possible effects that synthetic media might have on people, societies, and how psychological and social media theories apply to this new technology. [Extracted from the article]

Abstract Constipation is a prevalent gastrointestinal symptom that can considerably affect a patients’ quality of life. Although several drugs have been used to treat constipation, they are associated with high costs, side effects, and low universality. Therefore, alternative intervention strategies are urgently needed. Traditional lactic acid bacteria (LAB), such as Bifidobacterium and Lactobacillus, play a vital role in regulating intestinal microecology and have demonstrated favorable effects in constipation; however, a comprehensive review of their constipation relief mechanisms is limited. This review summarizes the pathogenesis of constipation and the relationship between intestinal motility and gut microbiota, elucidates the possible mechanism by which LAB alleviates of constipation through a systematic summary of animal and clinical research, and highlights the challenges and applications of LAB in the treatment of constipation. Our review can improve our understanding of constipation, and advance targeted microecological therapeutic agents, such as LAB. [ABSTRACT FROM AUTHOR]

Most of the existing integration and mining methods of multi-source time series assume that the data has been calibrated, which avoids the problem of time series registration. However, the inconsistent sampling frequency, different time references, and transmission delay make time and frequency mismatch in multi-source time series data, resulting in an unsatisfactory integration and mining and an even poorer effect than that of the single-source data. Time series registration has becoming an important research point in the pre-processing stage of time series integration and mining. To improve the overall integration performance and data mining accuracy, a new translation registration algorithm for multi-source time series data based on the sliding window was proposed. Firstly, the demands for time series big data mining was clarified, and the disadvantages of existing time series registration approaches was analyzed, then a structural model for the translation registration based on the sliding window was presented. Secondly, by using the slide window and the nearest neighbourhood principle, the real-time intervals offset was calculated, the low-frequency sampling of time data was translated to high-frequency, and then the proposed translation registration algorithm based on the sliding window was designed and implemented. Finally, the experimental analysis and verification of the typical algorithms for time series was completed. The experimental results demonstrated that the nearest neighbourhood method can provide a targeted time registration strategy, and the sliding window approach which does not dwell on the starting moment of time series can satisfy the registration requirements of dynamic time series big data. The experiments show that the proposed algorithm's goodness-of-fit is 96.7%, and it is slightly influenced by missing data and is easy to operate. It cannot only improve the accuracy and timeliness of multi-source time series registration, but also has important theoretical and practical values to the dynamic time series data integration and mining. [ABSTRACT FROM AUTHOR]

Vision-based underwater navigation and object detection requires robust computer vision algorithms to operate in turbid water. Many conventional methods aimed at improving visibility in low turbid water. High turbid underwater image enhancement is still an opening issue. Meanwhile, we find that the de-scattering and color correction of underwater images affect classification results. In this paper, we correspondingly propose a novel joint guidance image de-scattering and physical spectral characteristics-based color correction method to enhance high turbidity underwater images. The proposed enhancement method removes the scatter and preserves colors. In addition, as a rule to compare the performance of different image enhancement algorithms, a more comprehensive image quality assessment index Q u is proposed. The index combines the benefits of SSIM index and color distance index. We also use different machine learning methods for classification, such as support vector machine, convolutional neural network. Experimental results show that the proposed approach statistically outperforms state-of-the-art general purpose underwater image contrast enhancement algorithms. The experiment also demonstrated that the proposed method performs well for image classification. [ABSTRACT FROM AUTHOR]

• Different responses of foliar pH to variations in water availability between the common species. • The foliar pH of U. pumila decreased driven by soil pH and water availability. • A quadratic curve was observed for S. viridis , impacted by MAT, soil pH and water availability. • The mean foliar pH of S. viridi s was significantly higher than that of U. pumila in arid and semiarid regions. Water availability is one of the most pertinent factors affected by global change that regulates plant functional traits. Foliar pH is a newly recognized predictive functional trait that is indicative of plant physiological processes, carbon and nutrient cycling and plant survival under environmental change. In general, foliar pH is directly related to soil pH, while soil pH is directly related to water availability. Thus, biogeographic variation in foliar pH across water availability gradients is expected. This study investigated the foliar pH of common species (woody plant: Ulmus pumila ; herb: Setaria viridis) along a 3300 km transect characterized by a water availability gradient from northwestern to southeastern China. The results revealed that the foliar pH of U. pumila significantly decreased as the water availability increased, but for S. viridis , a quadratic curve with the knee point at a humidity index (HI) of 0.63 was observed, indicating different responses of foliar pH to variations in water availability between these common species. The mean foliar pH of S. viridis was significantly higher than that of U. pumila , especially in arid and semiarid regions. The soil pH, soil water stress coefficient (Ksoil) and HI were the three most influential factors on the foliar pH of U. pumila. For S. viridis , the variation in foliar pH was attributed to mean annual temperature (MAT), soil pH, HI, Ksoil, mean annual precipitation (MAP) and the precipitation of the wettest quarter. Our study revealed contrasting patterns of variation in foliar pH between woody species and herbs, suggesting that we should discriminate plant functional types in large-scale models when predicting plant responses to global change. [ABSTRACT FROM AUTHOR]