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[Display omitted] • Effective utilization of the "synchronous etching" effect caused by the preparation of polyphosphazene coating to induce structural remodeling of ZIF-67. • A series of ZIF-derived cyclomatrix polyphosphazene flame retardants were prepared. • Polyphosphazene serves to enhance the compatibility between flame retardant and the matrix, creating increased mechanical strength. Cyclomatrix polyphosphazenes have attracted widespread attention in the field of polymer flame retardancy. Nevertheless, the optimal manifestation of their distinctive structural attributes and flame-retardant properties necessitates a judicious selection of condensation monomers and synergistic templates during the fabrication of polyphosphazene flame retardants. In our previous studies, it was discovered that when ZIF-67 is functionalized with polyphosphazene, the by-product HCl from phosphazene polycondensation causes etching on ZIF-67. Based on this "synchronous etching" effect, a series of hybrid materials comprising cyclomatrix polyphosphazene and ZIF-67, denoted as ZIF-67@PDS (PDS, poly-(cyclotriphosphazene-co-4,4'-diaminodiphenyl sulfone)), ZIF-67@PBS (PBS, poly-(cyclotriphosphazene-co-Bisphenol A)), and ZIF-67@PZS (PZS, poly-(cyclotriphosphazene-co-4,4'-sulfonyldiphenol)), was synthesized utilizing DDS (4,4'-diaminodiphenyl sulfone), BPA (Bisphenol A), and BPS (4,4'-sulfonyldiphenol) monomers as precursors, respectively. Upon the incorporation of 2.0 wt.% of ZIF-67@PDS, ZIF-67@PBS, and ZIF-67@PZS, the flame retardant and mechanical characteristics of EP composites exhibited marked enhancement. The unique structural characteristics of hybrid and the synergistic effects of Co-P-N contribute to the improvement of comprehensive properties. Compared with pure EP, EP/ZIF-67@PZS has the best enhancement effect, and its pHRR, THR, and TSP decreased by 34.0%, 30.0%, and 40.5%, respectively. In terms of mechanical strength, ZIF-67@PZS also increases the flexural strength of EP by 37.42%. Relying on the "synchronous etching" effect, this study explores and verifies the effective combination of ZIF-67 and different types of polyphosphazenes, and obtains a series of ZIF-67-derived cyclomatrix polyphosphazene hybrids with different morphologies and properties in one step. It provides a new idea and strategy for the simultaneous modification of polyphosphazene materials and the preparation of multifunctional flame retardants in the future. [ABSTRACT FROM AUTHOR]

This letter published in the Annals of Hematology discusses the impact of paraskeletal plasmacytoma on the survival of patients with transplant-ineligible newly diagnosed multiple myeloma (TI-NDMM). The authors conducted a retrospective study involving 181 TI-NDMM patients and found that plasmacytoma, specifically paraskeletal plasmacytoma, was associated with worse progression-free survival (PFS) and overall survival (OS) in both high-risk and standard-risk groups. The study contributes to the existing literature on the relationship between plasmacytoma and survival outcomes in multiple myeloma patients. It suggests that plasmacytoma is a risk factor for poor outcomes in newly diagnosed multiple myeloma patients. [Extracted from the article]

The soil dielectric constant model is essential for retrieving soil properties based on microwave remote sensing. However, the existing saline soil dielectric constant models perform poorly in simulating the dielectric constant of soil with high water content and salinity. In this study, the Wang Yueru (WYR) saline soil dielectric constant model, which was demonstrated to perform well in describing the effect of salinity and moisture on the dielectric constant, was validated based on experimental measurements of soil samples under different water content and salinity degrees. Furthermore, we adjusted the model form, refitted the empirical coefficient in the model, and finally acquired a two-stage model for simulating the soil dielectric constant. The enhanced model was validated under different soil moisture and salinity ranges using experimental measurements of soil samples. Compared to the original model, the proposed model exhibits a larger improvement in simulating the soil dielectric constant, and the RMSE of the simulated results dramatically decreased from 7.3 to 1.6, especially for soil with high salinity and water content. On this basis, a model suitable for L-band microwave was established. This model is of great significance for studying soil dielectric characteristics and retrieving soil parameters based on L-band data. Furthermore, this model can be used to retrieve soil salinity and water content using microwave remote sensing under a broadened application situation, such as in saline-alkali soils, wetlands, and salt marshes. [ABSTRACT FROM AUTHOR]

[Display omitted] • ZIF-67 was etched to produce CoCo-LDH while coated by organic polydopamine (PDA) concomitantly. A PDA modified hollow cage lamellar LDH nanostructure loaded with TPP was constructed (TPP@LDH@Co-PDA). • Polydopamine enhanced compatibility between flame retardants and the matrix, boosting mechanical strength. • An inspiration for enhancing the thermal stability and compatibility of LDH, and to solve the migration problem of phosphorous small molecules. Layered double hydroxides (LDH) have irreplaceable advantages in the field of polymer flame retardancy, but their thermal stability and compatibility with matrix still need to be improved. In this paper, the bottom-up method is adopted, and the phosphorus series flame retardant triphenyl phosphate (TPP) was first encapsulated inside ZIF-67. On this basis, ZIF-67 was etched to produce LDH while modified by polydopamine (PDA) concomitantly. An organic coated polydopamine hollow cage lamellar LDH microstructure loaded with TPP was constructed, and its structure-performance relationship was verified. When 2 wt% TPP@LDH@Co-PDA was added to the epoxy resin, the LOI value of the composite was increased to 29.4 %, the peak heat release was reduced by 43.1 %, and the smoke release was significantly reduced. The unique microstructure endows epoxy composites with good flame retardancy, improves mechanical properties, and provides a new solution to the migration problem of phosphorous based flame retardants. [ABSTRACT FROM AUTHOR]

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors. [ABSTRACT FROM AUTHOR]

Motivation Recently, deep learning has become the mainstream methodology for drug–target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug–target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers. Results We propose a novel Multi-Functional and Robust Drug–Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements. Then, we propose a new Elem-feature fusion block to refine the extracted features. After that, we construct a Mix-Decoder block that extracts drug–target interaction information and predicts their binding regions simultaneously. Last, we evaluate MFR-DTA on two benchmarks consistently with the existing methods and propose a new dataset, sc-PDB, to better measure the accuracy of binding region prediction. We also visualize some samples to demonstrate the locations of their binding sites and the predicted multi-scale interaction regions. The proposed method achieves excellent performance on these datasets, demonstrating its merits and superiority over the state-of-the-art methods. Availability and implementation https://github.com/JU-HuaY/MFR. [ABSTRACT FROM AUTHOR]

• A new target-cognisant siamese-based anchor-free tracker. • The proposed method computes cross-spatial attention for refining the measurement of spatial similarity. • Two tracking mechanisms are used to promote the precision of bounding box prediction. • A max filtering module is proposed to filter out similar distractors. • Our method achieves competitive performance on several tracking datasets. Siamese trackers have become the mainstream framework for visual object tracking in recent years. However, the extraction of the template and search space features is disjoint for a Siamese tracker, resulting in a limited interaction between its classification and regression branches. This degrades the model capacity accurately to estimate the target, especially when it exhibits severe appearance variations. To address this problem, this paper presents a target-cognisant Siamese network for robust visual tracking. First, we introduce a new target-cognisant attention block that computes spatial cross-attention between the template and search branches to convey the relevant appearance information before correlation. Second, we advocate two mechanisms to promote the precision of obtained bounding boxes under complex tracking scenarios. Last, we propose a max filtering module to utilise the guidance of the regression branch to filter out potential interfering predictions in the classification map. The experimental results obtained on challenging benchmarks demonstrate the competitive performance of the proposed method. [ABSTRACT FROM AUTHOR]

Viral myocarditis (VMC), which is most prevalently caused by Coxsackievirus B3 (CVB3) infection, is a serious clinical condition characterized by cardiac inflammation. Dapagliflozin, a kind of sodium glucose co-transporters 2(SGLT-2) inhibitor, exhibited protective effects on plenty of inflammatory diseases, while its effect on viral myocarditis has not been studied. Recently, we found the protective effect of dapagliflozin on VMC. After CVB3 infection, dapagliflozin and STATTIC (a kind of stat3 inhibitor) were given to Balb/c male mice for 8 days, and then the severity of myocarditis was assessed. Our results indicated that dapagliflozin significantly alleviated the severity of viral myocarditis, elevated the survival rate, and ameliorated cardiac function. Besides, dapagliflozin can decrease the level of pro-inflammatory cytokines including IL-1β, IL-6, and TNF-α. Furthermore, dapagliflozin can inhibit macrophages differentiate to classically activated macrophages (M1) in cardiac tissue and activate the Stat3 signal pathway which is reported to promote polarization of the alternatively activated macrophage (M2). And STATTIC can reverse these changes caused by dapagliflozin. In conclusion, we found that dapagliflozin treatment increased anti-inflammatory macrophage polarization and reduced cardiac injury following VMC via activating Stat3 signal pathway. [ABSTRACT FROM AUTHOR]

[Display omitted] • The relationships between network topological characteristics and the different ESVs were analyzed. • The important sources and corridors are mainly located along the sides of the Wei River and the Fen River. • Higher ESVs related to water resources have better network connectivity. • The "one central source and three forested belts" radially ecological arrangement is planned. There are numerous ecological challenges that are of considerable concern caused by urbanization and natural calamities. The spatial pattern of the landscape is of great significance in maintaining species diversity and regulating material cycles and individual ecophysiological processes as a medium for ecosystem service functions. Resolving the conflicting relationships between people and land necessitates the precise identification of essential ecological sources. Therefore, there is an urgent need to investigate the relationship between ecological sources and ecosystem service values (ESVs) and to optimize the matter and energy.connectivity network. The study optimizes ecological network simulation using a penetration threshold, population density inverse distance formula, and geodetector weight analysis, constructs ecological spatial networks and evaluates their structure using the minimum cumulative resistance (MCR) model and complex network analysis, and discusses the relationship between network topological characteristics (NTCs) and the spatial pattern of the various ESVs. The results show that the major sources and corridors are primarily along the banks of the Wei River and the Fen River. The clustering coefficient has a significant negative correlation with both water supply and water regulation service values. It is found that the sources with higher ecological service values related to water resources have better network connectivity. This result plays a major reference role for the identification of several crucial ecological components, such as sources, nodes and stepping stones, in the middle Yellow River basin (MYRB). Ultimately, we propose strengthening the ecological protection of the eastern part of the Qinling Mountains and increasing the ecological communication capacity of the forestland on both sides of the river valley to create an optimized ecological pattern. [ABSTRACT FROM AUTHOR]

Spray drying is a promising technique for embedding and stabilizing lactic acid bacteria. However, bacteria are unable to withstand high-temperature environment. Naturally small molecules fall short in meeting the evolving demands of production. Therefore, it is imperative to explore non-chemical pretreatment methods urgently, with the aim of enhancing their protective efficacy. In this study, natural deep eutectic solvents (NADES) were synthesized by glycerin-betaine to improve the survival rates of spray-dried Lactobacillus bulgaricus sp1.1. Results revealed that the spray drying survival rate of L. bulgaricus sp1.1 after adding NADES was 30.54%, which was 4.72 times higher than adding equal amounts of untreated glycerin and betaine, and the number of viable bacteria decreased less than 1 log after 6 months of storage. The investigation revealed that the impact of NADES was localized external to the cell rather than within. Through crosslinking with the peptidoglycan on the cell wall, NADES bolstered the cell wall structure thereby creating a protective barrier. This resulted in a reduction of damage to the membrane and ribosome induced by high temperatures during the lactic acid bacteria’s exposure. This study synthesized NADES using current protective agents as an effective method to enhance heat resistance of lactic acid bacteria for spray drying for the first time, and the lactic acid bacteria powder with high survival rate and high storage stability was successfully prepared, which provides a new approach for its industrial application.Graphical Abstract: Spray drying is a promising technique for embedding and stabilizing lactic acid bacteria. However, bacteria are unable to withstand high-temperature environment. Naturally small molecules fall short in meeting the evolving demands of production. Therefore, it is imperative to explore non-chemical pretreatment methods urgently, with the aim of enhancing their protective efficacy. In this study, natural deep eutectic solvents (NADES) were synthesized by glycerin-betaine to improve the survival rates of spray-dried Lactobacillus bulgaricus sp1.1. Results revealed that the spray drying survival rate of L. bulgaricus sp1.1 after adding NADES was 30.54%, which was 4.72 times higher than adding equal amounts of untreated glycerin and betaine, and the number of viable bacteria decreased less than 1 log after 6 months of storage. The investigation revealed that the impact of NADES was localized external to the cell rather than within. Through crosslinking with the peptidoglycan on the cell wall, NADES bolstered the cell wall structure thereby creating a protective barrier. This resulted in a reduction of damage to the membrane and ribosome induced by high temperatures during the lactic acid bacteria’s exposure. This study synthesized NADES using current protective agents as an effective method to enhance heat resistance of lactic acid bacteria for spray drying for the first time, and the lactic acid bacteria powder with high survival rate and high storage stability was successfully prepared, which provides a new approach for its industrial application. [ABSTRACT FROM AUTHOR]

In the realm of the video captioning field, acquiring large amounts of high-quality aligned video–text pairs remains laborious, impeding its practical applications. Therefore, we explore the modelling techniques for unsupervised video captioning. Using text inputs similar to the video representation to generate captions has been a successful unsupervised video captioning generation strategy in the past. However, this setting relies solely on the textual data for training, neglecting vital visual cues related to the spatio-temporal appearance within the video. The absence of visual information increases the risk of generating erroneous video captions. In view of this, we propose a novel unsupervised video captioning method that introduces visual information related to text features keywords to implicitly enhance training for text generation tasks. Simultaneously, our method incorporates sentence to explicitly augment the training process. our method injects additional implicit visual features and explicit keywords into the model, Which can inject the generated captions with more accurate semantics. the experimental analysis demonstrates the merit of the proposed formulation, achieving superior performance against the state-of-the-art unsupervised studies. • Contrast learning is used to minimise the disparities between pseudo-text labels and video features. • Visual clues are aligned with the text generator for consistent semantic enhancement. • Leveraging the Found within the sentences for semantic preservation. • Outperforming existing unsupervised video captioning approaches. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influence of meteorological and land surface hydrothermal on LAI in mountainous areas was assessed. • A clear greening trend was observed in Yan Mountains, and the greening trend was the largest in summer. • LAI was negatively correlated with land surface temperature and soil moisture but positively correlated with precipitation and air temperature. • The hysteresis of LAI of different vegetation types to changes in hydrothermal conditions was explored. Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection. [ABSTRACT FROM AUTHOR]

• A quadratic optimisation method with dimension reduction for fast sparse representation based classification. • The linear discriminative analysis is used to further improve the discriminative capability of the proposed method. • State-of-the-art deep CNN features is used for performance boosting. Extended sparse representation-based classification (ESRC) has shown interesting results on the problem of under-sampled face recognition by generating an auxiliary intraclass variant dictionary for the representation of possible appearance variations. However, the method has high computational complexity due to the l 1 -minimization problem. To address this issue, this paper proposes two strategies to speed up SRC using quadratic optimisation in downsized coefficient solution subspace. The first one, namely Fast SRC using Quadratic Optimisation (FSRC-QO), applies PCA and LDA hybrid constrained optimisation method to achieve compressed linear representations of test samples. By design, more accurate and discriminative reconstruction of a test sample can be achieved for face classification, using the downsized coefficient space. Secondly, to explore the positive impact of our proposed method on deep-learning-based face classification, we enhance FSRC-QO using CNN-based features (FSRC-QO-CNN), in which we replace the original input image using robust CNN features in our FSRC-QO framework. Experimental results conducted on a set of well-known face datasets, including AR, FERET, LFW and FRGC, demonstrate the merits of the proposed methods, especially in computational efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Design of MOF-derived hollow hybrids covered with a polyphosphazene coating. • Cobalt-phosphorus-nitrogen flame retardant elements synergistic effect. • Mismatch structure significantly improves the dispersity of fillers in EP. • The hybrids endow EP with better flame retardancy and mechanical performance. Polyphosphazene has drawn an abundance of attention as a flame retardant due to its eco-friendliness and phosphorus-nitrogen synergistic effect. ZIF-67 and cobalt compounds derived from cobalt ions released by ZIF-67 were employed as dual templates for loading polyphosphazene. We skillfully designed a polyphosphazene hollow hybrid material via the polymerization of phosphazene accompanied by the etching of ZIF-67 and cobalt compounds by generated HCl. The hybrid material (CPPHS) was composed of cobalt-doped polyphosphazene hollow sub-micron polyhedrons (CP) and polyphosphazene hollow nanospheres (PHNS) with different dimensions and magnitudes of sizes. After adding 2.0 wt% CPPHS, the limiting oxygen index of epoxy resin increased to 27.6%, while the peak heat release rate and total smoke production decreased by 40.1% and 38.8%, respectively. The formation of a continuous and strongly extended dense char layer in the EP composites was fostered by the synergistic effect of Co-P-N flame retardant components, significantly decreasing the heat flow and combustible gas exchange rate and effectively hindering the combustion chain reaction. Furthermore, the unique dimensional mismatch structure of CPPHS enhanced its dispersion in the EP matrix, enabling the EP to have superior mechanical properties that match realistic application requirements, consequently expanding the variety of applications for fire-resistant EP composites. [ABSTRACT FROM AUTHOR]

Abstract Traditional collaborative representation based classification (CRC) method usually faces the challenge of data uncertainty hence results in poor performance, especially in the presence of appearance variations in pose, expression and illumination. To overcome this issue, this paper presents a CRC-based face classification method by jointly using block weighted LBP and analysis dictionary learning. To this end, we first design a block weighted LBP histogram algorithm to form a set of local histogram-based feature vectors instead of using raw images. By this means we are able to effectively decrease data redundancy and uncertainty derived from image noises and appearance variations. Second, we adopt an analysis dictionary learning model as the projection transform to construct an analysis subspace, in which a new sample is characterized with the improved sparsity of its reconstruction coefficient vector. The crucial role of the analysis dictionary learning method in CRC is revealed by its capacity of the collaborative representation in an analytic coefficient space. Extensive experimental results conducted on a set of well-known face databases demonstrate the merits of the proposed method. [ABSTRACT FROM AUTHOR]

Active-targeting nanocarriers can significantly improve the transcytosis of poorly water-soluble or bio-macromolecular drugs across biological barrier. However, reasons for the improvement are not understood enough, which hampered the reasonable design of active targeting nanocarriers. To illustrate how different factors influence the transport of active-targeting nanocarriers, we established ligand-decorated micelles targeting different receptors to study how the decorations influence the transcytosis of the micelles by comparing the endocytosis, transport pathway and exocytosis process. Three different kinds of receptors, Neonatal Fc receptor (FcRn), transferrin receptor (TfR) and αvβ3 integrin were selected. They presented three different transport pathways, mainly mediating transcytosis, recycling pathway and cell binding, respectively. Their corresponding ligand FcBP, 7pep and c(RGDfK) decorated micelles with different ligand densities were prepared first. Then the effects of receptor and ligand density on the transcytosis across biological barrier were investigated. The results showed that the uptake rate of active micelles was higher than passive micelles and an optimum ligand density with most endocytosis appeared in all functional micelles. Transport pathway study showed 7pep decorated micelles were transferred into apical recycling endosome (ARE) and exocytosed to apical plasma membrane in a ligand depended way. c(RGDfK) decorated micelles were transferred through common recycling endosome (CRE) and Golgi complex to basolateral plasma membrane instead of ARE. While FcBP decorated micelles took both the recycling pathway and transcytosis through CRE, but not Golgi complex. Proper ligand density, not the higher the better, led the most uptake. Also the apical to basolateral transcytosis ratio may not be in accordance with the uptake. Among all the itineraries, transcytosis through CRE is the best itinerary for transcytosis. So, in the design of active targeting nanocarriers to overcome biological barrier, receptor character should be considered priorly, and then ligand density should be optimized. [ABSTRACT FROM AUTHOR]

• IL-37 reverses ox-LDL-induced decreased cell survival in HUVECs. • IL-37 inhibits ox-LDL-induced NLRP3 inflammasome activation in HUVECs. • IL-37 alleviates increased apoptosis and ROS production in ox-LDL-induced HUVECs. • IL-37 alleviates ox-LDL-induced endothelial dysfunction by enhancing autophagy. Atherosclerosis is a lipid-driven chronic inflammatory disease. Endothelial dysfunction is the initiating factor of atherosclerosis. Although much work has been done on the antiatherosclerotic effects of interleukin-37 (IL-37), the exact mechanism is still not fully understood. The aim of this study was to investigate whether IL-37 attenuates atherosclerosis by protecting endothelial cells and to confirm whether autophagy plays a role in this effect. In apolipoprotein E knockout (ApoE−/−) mice fed with a high fat diet, IL-37 treatment significantly attenuated progression of atherosclerotic plaques, reduced endothelial cell apoptosis and inflammasome activation. Human umbilical vein endothelial cells (HUVECs) were treated with oxidized low-density lipoprotein (ox-LDL) to establish an endothelial dysfunction model. We observed that IL-37 alleviated ox-LDL-induced endothelial cell inflammation and dysfunction, as evidenced by decreased nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome activation, ROS production, apoptosis rate and secretion of inflammatory cytokines IL-1β and TNF-α. Furthermore, IL-37 could activate autophagy in endothelial cells, which is characterized by the upregulation of LC3II/LC3I, the downregulation of p62 and an increase in autophagosomes. The autophagy inhibitor 3-Methyladenine (3-MA) dramatically reversed the promotion of autophagy and the protective effect of IL-37 against endothelial injury. Our data illustrate that IL-37 alleviated inflammation and apoptosis of atherosclerotic endothelial cells by enhancing autophagy. The current study provides new insights and promising therapeutic strategies for atherosclerosis. [ABSTRACT FROM AUTHOR]

Abstract: A palladium‐catalyzed cross‐coupling of α‐oxo ketene dithioacetals and bromodifluoroacetate derivatives has been developed for the synthesis of a class of CF2‐containing tetra‐substituted olefins, which has potential to extend to drug design and material application. The process is proposed to involve two single electron transfer processes accompanied by an alternative loop from palladium(0) to palladium(I), likely due to unique structural properties of ketene dithioacetals with β,β‐dialkylthiol substituents on the olefin double bond. [ABSTRACT FROM AUTHOR]

Sparse representation-based classification (SRC) has been a breakthrough of face recognition and signal reconstruction recently. However, few face images from the same subject provide insufficient observations. Meanwhile, owing to uncertainty of training images with variations in the appearance of facial illumination, pose and facial expression, it is difficult to successfully address the issues of image recognition and signal reconstruction. Thus, how to construct an optimized comprehensive dictionary is still a non-trivial task. In this paper, we develop a generalized virtual extended dictionary of SRC (V-SRC) to deal with data uncertainty problem, establishing a weighted mechanism which assigns different coefficients between arbitrary two within-class samples to obtain the virtual samples. More specifically, we firstly construct a set of synthesized training samples by means of weighted combination of existing training samples. Secondly, we combine the original and synthesized training samples as an initial extended dictionary. Thirdly, in order to decrease the constructed error in sparse representation, we exploit an elimination scheme to gain the robust training samples with respect to the initial extended training dictionary. The final goal of the proposed method is to utilize the linear combination of competitive training samples to perform face classification. Experimental results conducted on the AR, FERET and Georgia Tech databases demonstrate the effectiveness of the proposed method especially in the case of small sample size problem. [ABSTRACT FROM AUTHOR]

gem -Difluorocyclopropanes are an important fluorinated class of compounds with applications in medicinal chemistry, material sciences and organic synthesis. The transformations based on their ring-opening reactions have been recognized to be useful methods for rapidly synthesizing various fluorinated organic molecules. In this digest paper, we describe these efforts and highlight their powerfully potential and applications as fluorine-containing synthons in organic chemistry. [ABSTRACT FROM AUTHOR]

This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSMmeasurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. Thesemodel coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSMmeasurements demonstrated that SSMvariation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R²: 0.419-0.379, root mean square error: 0.046-0.051m³m-3, Bias: -0.020 to -0.025m³m-3) was found for the mid-morningmodel and the daytimemodel with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R² (0.614-0.675), a better accuracy (Bias: 0.032-0.044 m³m-3, RMSE: 0.043-0.050 m³m-3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is a critical variable for understanding the energy and water exchange between the land and atmosphere. A multi-linear model was recently developed to determine SSM using ellipse variables, namely, the center horizontal coordinate ( x 0 ), center vertical coordinate ( y 0 ), semi-major axis ( a ) and rotation angle ( θ ), derived from the elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). However, the multi-linear model has a major disadvantage. The model coefficients are calculated based on simulated data produced by a land surface model simulation that requires sufficient meteorological measurements. This study aims to determine the model coefficients directly using limited meteorological parameters rather than via the complicated simulation process, decreasing the dependence of the model coefficients on meteorological measurements. With the simulated data, a practical algorithm was developed to estimate SSM based on combined optical and thermal infrared data. The results suggest that the proposed approach can be used to determine the coefficients associated with all ellipse variables based on historical meteorological records, whereas the constant term varies daily and can only be determined using the daily maximum solar radiation in a prediction model. Simulated results from three FLUXNET sites over 30 cloud-free days revealed an average root mean square error (RMSE) of 0.042 m 3 /m 3 when historical meteorological records were used to synchronously determine the model coefficients. In addition, estimated SSM values exhibited generally moderate accuracies (coefficient of determination R 2 = 0.395, RMSE = 0.061 m 3 /m 3 ) compared to SSM measurements at the Yucheng Comprehensive Experimental Station. [ABSTRACT FROM AUTHOR]

The changes of face images with poses and polarized illuminations increase data uncertainty in face recognition. In fact, synthesized mirror samples can be recognized as representations of the left-right deflection of poses or illuminations of the face. Symmetrical face images generated from the original face images also provide more observations of the face which is useful for improving the accuracy of face recognition. In this paper, to the best of our knowledge, it is the first time that the well-known minimum squared error classification (MSEC) algorithm is used to perform face recognition on an extended face database using synthesized mirror training samples, which is titled as extended minimum squared error classification (EMSEC). By modifying the MSE classification rule, we append the mirror samples to the training set for gaining better classification performance. First, we merge original training samples and mirror samples synthesized from original training samples per subject as mixed training samples. Second, EMSEC algorithm exploits mixed training samples to obtain the projection matrix that can best transform the mixed training samples into predefined class labels. Third, the projection matrix is exploited to simultaneously obtain transform results of the test sample and its nearest neighbor from the mixed training sample set. Finally, we ultimately classify the test sample by combining the transform results of the test sample and the nearest neighbor. As an extension of MSEC, EMSEC reduces the uncertainty of the face observation by auxiliary mirror samples, so that it has better robustness classification performance than traditional MSEC. Experimental results on the ORL, GT, and FERET databases show that EMSEC has better generalization ability than traditional MSEC. [ABSTRACT FROM AUTHOR]

Traditional discriminant analysis (DA) methods are usually not amenable to being studied only with a few or even single facial image per subject. The fundamental reason lies in the fact that the traditional DA approaches cannot fully reflect the variations of a query sample with illumination, occlusion and pose variations, especially in the case of small sample size. In this paper, we develop a multi-scale fuzzy sparse discriminant analysis using a local third-order tensor model to perform robust face classification. More specifically, we firstly introduced a local third-order tensor model of face images to exploit a set of multi-scale characteristics of the Ridgelet transform. Secondly, a set of Ridgelet transformed coefficients with respect to each block from a face image are respectively generated. We then merge all these coefficients to form a new representative vector for the image. Lastly, we evaluate the sparse similarity grade between each training sample and class by constructing a sparse similarity metric, and redesign the traditional discriminant criterion that contains considerable fuzzy sparse similarity grades to perform robust classification. Experimental results conducted on a set of well-known face databases demonstrate the merits of the proposed method, especially in the case of insufficient training samples. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is one of the most variable components of the atmosphere in both space and time. In this study, a passive microwave-based retrieval algorithm for PWV over land without land surface temperature (LST) data was developed. To build the algorithm, two assumptions exist: (1) land surface emissivities (LSE) at two adjacent frequencies are equal and (2) there are simple parameterizations that relate transmittance, atmospheric effective radiating temperature, and PWV. Error analyses were performed using radiosonde sounding observations from Zhangye, China, and CE318 measurements of Dalanzadgad (43°34′37′′N, 104°25′8′′E) and Singapore (1°17′52′′N, 103°46′48′′E) sites from Aerosol Robotic Network (AERONET), respectively. In Zhangye, the algorithm had a Root Mean Square Error (RMSE) of 4.39 mm and a bias of 0.36 mm on cloud-free days, while on cloudy days there was an RMSE of 4.84 mm and a bias of 0.52 mm because of the effect of liquid water in clouds. The validations in Dalanzadgad and Singapore sites showed that the retrieval algorithm had an RMSE of 4.73 mm and a bias of 0.84 mm and the bigger errors appeared when the water vapor was very dry or very moist. [ABSTRACT FROM AUTHOR]

The horizontal axis-symmetrical nature of faces is useful and interesting, which has successfully applied in face detection. In general, images of faces are not strictly captured under a frontal and natural pose. It implies that extra training samples could be generated by means of the symmetry of the face. In this paper, we develop a framework that fuses virtual mirror synthesized training samples as bases and hierarchical multi-scale local binary patterns (LBP) features for classification. More specifically, in the first stage of proposed method, sampling uncertainty of the linear approximation model is alleviated effectively by constructing extra synthesized mirror training samples which generated from original images. Subsequently, in the second stage, features are extracted from the above dictionary using a hierarchical multi-scale LBP scheme. In the third stage, we combine the synthesized samples and original ones together to describe a test sample and simultaneously determine the relatively informative training samples by means of exploiting the reconstruction deviation of all the dictionary atoms. Besides, the introduced sparse coding process with weak l constraint has superior competitiveness that the accuracy has been improved, while the complexity has been fallen. Ultimately, the following step determines again a new decomposition coefficient of remaining samples, which makes final decision of the classification. Experimental results that are engaged in various benchmark face databases have demonstrated the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

A direct method for the synthesis of o-fluoronaphthols and o/p-fluorophenols has been developed by a catalytic ring expansion of indanones and 2-cyclopentenones, in which TMSCF2Br was used as a unique :CF2 source, a TMS transfer agent, as well as the Br− and F− releaser for the enolization, difluorocyclopropanation, desilylation, ring opening, defluorination, and aromatization sequence. [ABSTRACT FROM AUTHOR]

To retrieve surface soil moisture (SSM) content over natural surfaces quantitatively, the effects of vegetation and soil texture on a previously developed bare SSM retrieval model are evaluated using simulated data from the common land model (CoLM). The results indicate that (1) both the accuracy and the five model parameters of the previous SSM retrieval model show relatively consistent variations when the fractional vegetation cover (FVC) varies from 0 to 0.7; and (2) the SSM exhibits a generally significant and exponential relationship with the rotation angle when the clay content is lower than 30%, with the FVC ranging from 0 to 0.7. These findings make it possible to estimate SSM directly under the conditions that the underlying surface is in the presence of spatially variable FVC and soil texture. On this basis, we further confirm the feasibility of using the previous bare SSM retrieval model to estimate SSM for FVC varying from 0 to 0.7 with a clay content lower than 30%. For the simulated data on eight cloud-free days, the total root mean square error (RMSE) of the retrieved SSM and the coefficient of determination (R2) are 0.033 m3m−3and 0.758, respectively. Ultimately, a preliminary validation is conducted using the ground measurements at the Bondville site; anR2 = 0.328 and a RMSE = 0.058 m3m−3are obtained for 14 cloud-free days. [ABSTRACT FROM PUBLISHER]

Classification of high-dimensional data is usually not amenable to standard pattern recognition techniques owing to lack of necessary, underlying structured information of data. In this paper, we propose a new discriminant analysis based on three-step sparse residuals measurement called DA-TSSR to address this problem. Specifically, in the first stage of the proposed method, the contribution in presenting the test sample of any chosen class is respectively calculated by adding up the total contributions of all the training samples of this class, and then a certain class with the smallest contribution score is eliminated from the set of the training samples. This procedure is iteratively carried out for the set of the training samples of the remaining classes till the predefined termination condition is satisfied. The second stage of DA-TSSR seeks to represent the test sample as a linear combination of all the remaining training samples and exploits the representation ability of each training sample to determine M 'nearest neighbors' for the test sample. By this means, it generates unequal number of training samples on each candidate class. The third stage of DA-TSSR again determines a new weighted sum of all unequal numbers of training samples from candidate classes, which is approximately equal to the test sample. We use the new weighted sum to perform the designing of sparse residuals grades, which can be incorporated into the typical discriminant analysis criterion. The proposed method not only has a high accuracy but also can be clearly interpreted. Experimental results conducted on the ORL, XM2VTS, FERET and AR face databases demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As is well known, most of the sparse representation based classification (SRC) schemes are not the standard form of SR model, that is, the dictionary in SRC model is not a strict one compare to sparse representation model. Actually, a strict and standard dictionary will create sparser coefficient, but it might not achieve better classification accuracy in SRC model. Therefore, constructing a rational and optimal dictionary for SRC model is a challenging task for us. Understanding the good performance of such unconventional dictionaries demands new algorithmic and analytical techniques. In this paper, we propose a novel SRC fusion method using hierarchical multi-scale local binary patterns (LBP) and greedy search strategy (SRC-GSLBP) for face recognition. The proposed method involves three aspects: dictionary optimization, training variables selection and classification strategy, sparse coding coefficient decomposing. First of all, we get an optimized dictionary through extracting hierarchical multi-scale LBP features from the original training samples. Second, the training variables selection and classification strategy aim to represent a query sample as a linear combination of the most informative training samples, and exploits an optimal representation of training samples from the classes with major relevant contributions. Instead of eliminating several classes at one time, we choose eliminating classes one by one with greedy search (GS) sparse coding process until the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to perform classification. In the context of the proposed method, an important goal is to select a subset of variables that accomplishes one objective: the provision of a descriptive representation for sparse category knowledge structure. We develop a heuristic learning strategy to achieve this goal. Experimental results conducted on the ORL, FERET and AR face databases demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparse representation-based classification method using iterative class elimination strategy for face recognition. The proposed method aims to represent a test sample as a linear combination of the most competitive training samples and exploits an optimal representation of training samples from the classes with major relevant contributions. We interpret the sparse representation problem as an information fidelity problem. In the context of our proposed method, an important goal is to select a subset of variables for accomplishing one objective: the provision of a descriptive representation for sparse class knowledge structure. We develop an iterative class elimination algorithm to achieve this goal. First, the contribution in presenting the test sample of any of the specified classes is, respectively, calculated by adding up the total contribution of all training samples of this class, and then a certain class that meets the smallest score requirement to this test sample is eliminated. Second, a similar procedure is iteratively carried out for the set of remaining training samples from rest classes, and this procedure is repeatedly performed till the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to classify it. Therefore, the proposed algorithm is an iterative method that alternates between sparse representation and a process of updating the training atoms to better fit the test data. This is helpful to accurately classify the test sample. Experimental results conducted on the ORL, FERET, and AR face databases demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Hesitant fuzzy set is a generalization of the classical fuzzy set by returning a family of the membership degrees for each object in the universe. Since how to use the rough set model to solve fuzzy problems plays a crucial role in the development of the rough set theory, the fusion of hesitant fuzzy set and rough set is then firstly explored in this paper. Both constructive and axiomatic approaches are considered for this study. In constructive approach, the model of the hesitant fuzzy rough set is presented to approximate a hesitant fuzzy target through a hesitant fuzzy relation. In axiomatic approach, an operators-oriented characterization of the hesitant fuzzy rough set is presented, that is, hesitant fuzzy rough approximation operators are defined by axioms and then, different axiom sets of lower and upper hesitant fuzzy set-theoretic operators guarantee the existence of different types of hesitant fuzzy relations producing the same operators. [ABSTRACT FROM AUTHOR]

Land surface soil moisture (SSM) is crucial to research and applications in hydrology, ecology, and meteorology. To develop a SSM retrieval model for bare soil, an elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) is described and further verified using data that were simulated with the Common Land Model (CoLM) simulation. In addition, with a stepwise linear regression, a multi-linear model is developed to retrieve daily average SSM in terms of the ellipse parametersx0(horizontal coordinate of the ellipse centre),y0(vertical coordinate of the ellipse centre),a(semi-major axis), andθ(rotation angle), which were acquired from the elliptical relationship. The retrieval model for daily average SSM proved to be independent of soil type for a given atmospheric condition. Compared with the simulated daily average SSM, the proposed model was found to be of higher accuracy. For eight cloud-free days, the root mean square error (RMSE) ranged from 0.003 to 0.031 m3 m−3, while the coefficient of determination (R2) ranged from 0.852 to 0.999. Finally, comparison and validation were conducted using simulated and measured data, respectively. The results indicated that the proposed model showed better accuracy than a recently reported model using simulated data. A simple calibration decreased RMSE from 0.088 m3 m−3to 0.051 m3 m−3at Bondville Companion site, and from 0.126 m3 m−3to 0.071 m3 m−3at the Bondville site. Coefficients of determinationR2 = 0.548 and 0.445 were achieved between the estimated daily average SSM and the measured values at the two sites, respectively. This paper suggests a promising avenue for retrieving regional SSM using LST and NSSR derived from geostationary satellites in future developments. [ABSTRACT FROM PUBLISHER]

Abstract: Sparse representations using over complete dictionaries has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a pre-specified set of linear transforms or adapting the dictionary to a set of training signals. The K-SVD algorithm is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. However, the existing K-SVD algorithm is employed to dwell on the concept of a binary class assignment, which means that the multi-classes samples are assigned to the given classes definitely. The work proposed in this paper provides a novel parameterized fuzzy adaptive way to adapting dictionaries. In order to achieve the fuzzy sparse signal representations, the update of the dictionary columns is combined with an update of the sparse representations by embedding a new mechanism of fuzzy set, which is called parameterized fuzzy K-SVD. Experimental results conducted on the ORL, Yale and FERET face databases demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, we develop a novel fuzzy supervised learning algorithm based on the dynamical parameter estimation. First, a reformative supervised fuzzy LDA algorithm (RF-LDA) for the training samples is proposed. Compared with the conventional fuzzy LDA algorithm, the presented algorithm computes the discriminant vectors associated with the membership grade from each training sample, which is theoretically effective in overcoming the classification limitation originating from the imprecise samples. Second, when a novel fuzzy LDA model is required in order to take some decision about the feature extraction and classification, the dynamical parameter estimation method of the fuzzy LDA model should recursively process the measured data as they become available. In the line of previous arguments, we approach the problem of control parameter estimation of RF-LDA by considering the formulation of a Hopfield Neural Network (HNN), which is named HRF-LDA. Third, considering the fact that the Kernel Fisher Discriminant (KFD) is effective in extracting the nonlinear discriminative information of the feature space by using kernel trick, a kernel version of HRF-LDA is presented subsequently, which has the potential to outperform the traditional fuzzy learning algorithms, especially in the cases of nonlinear small sample sizes. The advantage of this learning algorithm is that it successfully utilizes the improved kernel fuzzy LDA algorithm as a supervised feature extraction tool. Meanwhile, by means of the control parameter estimation, we address the problem that the particular value of offset in the calculation of the grade of membership is dynamically assigned. Extensive experimental studies conducted on the ORL, NUST603, FERET, Yale and XM2VTS face images show the effectiveness of the proposed fuzzy integrated algorithm. [Copyright &y& Elsevier]

In this paper, a knowledge distance approach is proposed to study the hierarchy under the frame of multigranulation. In our approach, the finest granulation structure is considered as the frame of reference, and then a knowledge distances algebraic lattice is constructed. Through such algebraic lattice, a partial order is derived, which can be used to characterize the finer or coarser relationships among multigranulation structures. It is also shown that the uncertainty measurements are monotonic in terms of the obtained partial order. [ABSTRACT FROM AUTHOR]

Face anti-spoofing (FAS) is crucial for safe and reliable biometric systems. In recent years, deep neural networks have been proven to be very effective for FAS as compared with classical approaches. However, deep learning-based FAS methods are data-driven and use learning-based features only. It is a legitimate question to ask whether hand-crafted features can provide any complementary information to a deep learning-based FAS method. To answer this question, we propose a two-stream network that consists of a convolutional network and a local difference network. To be specific, we first build a texture extraction convolutional block to calculate the gradient magnitude at each pixel of an input image. Our experiments demonstrate that additional liveness cues can be captured by the proposed method. Second, we design an attention fusion module to combine the features obtained from the RGB domain and gradient magnitude domain, aiming for discriminative information mining and information redundancy elimination. Finally, we advocate a simple binary facial mask supervision strategy for further performance boost. The proposed network has only 2.79M parameters and the inference speed is up to 118 frames per second, which makes it very convenient for real-time FAS systems. The experimental results obtained on several well-known benchmarking datasets demonstrate the merits and superiority of the proposed method over the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

• A drought index was obtained for drought monitoring based on soil moisture series. • Drought characteristics (e.g., return period) were presented on a regional scale. • Wavelet analysis was adopted to study the area extent characteristics of drought. As the critical parameter of the agricultural drought process, soil moisture (SM) has the potential to monitor agricultural drought, which can provide practical support for water management. In this study, a drought index, namely Soil Dryness Index (SDI), was proposed based on long-term gridded ERA-5 land SM, and its performance was evaluated. Furthermore, we employed the run theory and copula to study the spatiotemporal drought characteristics based on this index in Inner Mongolia. Although the return period of drought events has been assessed in many studies, the drought tends to occur with a nonstationary frequency. The wavelet transform was then applied to drought area proportion time series in sub-regions to study the temporal characteristics of droughts. The results show that SDI performs well in monitoring agricultural drought. There is strong heterogeneity in the spatial distribution of drought events. Frequent and low-severity droughts dominated arid and semi-arid regions, while some high-severity droughts mainly occurred in humid and semi-humid regions (mainly in Hulunbuir and Xing'an). The droughts, especially those with high severity, have become much more frequent in the last two decades. The period from 2000 to 2010 had seen more frequent droughts in recent 40 years, and most 20-year return period droughts occurred during this period. These findings could contribute to a better understanding of spatiotemporal patterns of agricultural droughts and provide a reference for future drought trends in Inner Mongolia. [ABSTRACT FROM AUTHOR]

Soil moisture is a key parameter in water balance, and it serves as the core and link in atmosphere–vegetation–soil–groundwater systems. Soil moisture directly affects the accuracy of the simulation and prediction conducted by hydrological and atmospheric models. This article aims to develop a new model to retrieve the daily evolution of soil moisture with time series of land surface temperature (LST) and net surface shortwave radiation (NSSR). First, for the time series of soil moisture, LST and NSSR daytime data were simulated by the common land model (CoLM) with different soil types in bare soil areas. Based on these data, the variations between soil moisture and LST-NSSR during the daytime with different soil types were analysed, and a plane function was used to fit the daily evolution of soil moisture and the time series of LST and NSSR data. Further study proved that the coefficients of the soil moisture retrieval model are not sensitive to soil type. Then, a relationship model between the daily evolution of soil moisture and the time series of LST-NSSR was developed and validated using the data simulated by CoLM with different soil types and different atmospheric conditions. To demonstrate the feasibility of the soil moisture retrieval method proposed in this study, it was applied to the African continent with data from the METEOSAT Second Generation Spinning Enhanced Visible and Infrared Imager (MSG–SEVIRI) geostationary satellite. The results show that the variation of soil moisture content can be quantitatively estimated directly by the method at the regional scale with some reasonable assumptions. This study can provide a new method for monitoring the variation of soil moisture, and it also indicates a new direction for deriving the daily variation of soil moisture using the information from the time series of the land surface variables. [ABSTRACT FROM PUBLISHER]

Spray drying is an efficient, rapid and relatively low-cost method for preparing microencapsulated lactic acid bacteria (LAB) in industry, but high temperature reduces the survival rate of LAB. Oil can polymerise with LAB by hydrophobicity and reduce the thermal and oxidative damage on bacteria during spray drying. However, oil is easily oxidised during spray drying, and its insolubility makes it difficult to evenly disperse the emulsion in the oil phase. In this comprehensive review, the oils used for LAB microcapsule and its protectional mechanism were discussed. And then wall materials and modification methods, which stabilizes co-microcapsule containing lactic acid bacteria and oil, is presented. Oils were a promising material for spray-dried LAB. Liquid oils with more unsaturated fatty acids reduced oxidative stress. Solid oils with high saturated fatty acid content are often added in the form of solid small particles, reducing the bacterial thermal damage by absorbing heat during spray drying. Appropriate wall materials and pre-treatment on oil-LAB co-microcapsule can reduce lipid oxidation, stabilize the emulsion and enhance the survival rate of lactic acid bacteria after spray drying. [Display omitted] • Oil provides a protective barrier to oxygen and heat. • The polysaccharide matrix forms a mesh gel that stabilizes bacteria. • Proteins unfold and reorient at the water-oil interface to stabilize the emulsion. • The survival rate of bacteria could be improved by wall material modification. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, some studies have been made on the essence of a novel fuzzy discriminant analysis (FDA) on the fourfold-objective model (FOM). First, a fourfold-objective model on the discriminant analysis is developed, by which a set of integrated subspaces derived from within-class and between-class scatter matrices are constructed, respectively. Second, an improved FDA (IFDA) algorithm based on the relaxed normalized condition is proposed to achieve the distribution information of each sample represented with fuzzy membership grade, which is incorporated into the redefinition of Fisher''s scatter matrices. Therefore, the presented algorithm has the potential to outperform the traditional subspace learning algorithms, especially in the cases of small sample size. Experimental results conducted on the ORL, NUST603, FERET and Yale face image databases demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Land surface temperature (LST) is a crucial biophysical parameter related closely to the land–atmosphere interface. Satellite thermal infrared measurement provides an effective method to derive LST on regional and global scales, but it is very hard to acquire simultaneously high spatiotemporal resolution LST due to its limitation in the sensor design. Recently, many LST downscaling and spatiotemporal image fusion methods have been widely proposed to solve this problem. However, most methods ignored the spatial heterogeneity of LST distribution, and there are inconsistent image textures and LST values over heterogeneous regions. Thus, this study aims to propose one framework to derive high spatiotemporal resolution LSTs in heterogeneous areas by considering the optimal selection of LST predictors, the downscaling of MODIS LST, and the spatiotemporal fusion of Landsat 8 LST. A total of eight periods of MODIS and Landsat 8 data were used to predict the 100-m resolution LST at prediction time tp in Zhangye and Beijing of China. Further, the predicted LST at tp was quantitatively contrasted with the LSTs predicted by the regression-then-fusion strategy, STARFM-based fusion, and random forest-based regression, and was validated with the actual Landsat 8 LST product at tp. Results indicated that the proposed framework performed better in characterizing LST texture than the referenced three methods, and the root mean square error (RMSE) varied from 0.85 K to 2.29 K, and relative RMSE varied from 0.18 K to 0.69 K, where the correlation coefficients were all greater than 0.84. Furthermore, the distribution error analysis indicated the proposed new framework generated the most area proportion at 0~1 K in some heterogeneous regions, especially in artificial impermeable surfaces and bare lands. This means that this framework can provide a set of LST dataset with reasonable accuracy and a high spatiotemporal resolution over heterogeneous areas. [ABSTRACT FROM AUTHOR]

The spatiotemporal variation characteristics of the Normalized Difference Vegetation Index (NDVI) and its climate response patterns are of significance in deepening our understanding of regional vegetation and climate change. The response of vegetation to climate factors varies spatially and may have lag periods. In this paper, we studied the spatiotemporal responses of vegetation to climatic factors on an ecosystem-dependent scale using GIMMS NDVI3g data and climatic parameters. Pure pixels with a single vegetation type were firstly extracted to reduce the influence of mixed vegetation types. Then, a lag correlation analysis was used to explore the lag effects of climatic parameters affecting NDVI. Finally, the stepwise regression method was adopted to calculate the regression equation for NDVI and meteorological data with the consideration of effect lag times. The results show that precipitation has significant lag effects on vegetation. Temperature is the main climatic factor that affects most vegetation types at the start of growing season. At the end of growing season, the temperate desert, temperate steppe, and temperate desert steppe are greatly affected by precipitation. Moreover, the alpine steppe, alpine desert, alpine meadow, and alpine sparse vegetation are greatly affected by temperature. The needleleaf forest, subalpine scrub, and broadleaf evergreen forest are sensitive to sunshine percentage during almost the whole growing season. These findings could contribute to a better understanding of the drivers and mechanisms of vegetation degradation on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

To enhance char formation of flame retardant epoxy (EP) composites, carboxymethyl β-cyclodextrin (CM-β-CD) is employed as an etchant for or ZIF-67 derivatives. In the early stage, etching plays a dominant role. The mismatch in size between CM-β-CD opening and ZIF-67 pore leads to the stacking of carboxyl cobalt complexes on the shell. When the reaction time is prolonged, crosslinking occurs between carboxyl and hydroxyl groups. Crosslinked CM-β-CD weakens and eventually stops the etching process. Triethyl phosphate (TEP), an additive to improve flame retardancy, is also absorbed on the shell in this one-pot synthesis. Herin, the synthesis of metal-organic framework (MOF) derivatives can impart multiple functions to MOF. This novel nanohybrid significantly improved flame retardancy of EP composites with only 2.0 wt% loading. The peak heat release rate (pHRR) and total smoke production (TSP) were reduced by 54.8 and 46.9%, respectively. The integrated multi-element system resulted in an expanded and reinforced char layer. This study proposes a simple and precise method for controlling the structure of MOF-carbohydrate hybrids through competition between chemical reactions. [Display omitted] [ABSTRACT FROM AUTHOR]

The Hindu Kush Himalayan (HKH) region is one of the most ecologically vulnerable regions in the world. Several studies have been conducted on the dynamic changes of grassland in the HKH region, but few have considered grassland net ecosystem productivity (NEP). In this study, we quantitatively analyzed the temporal and spatial changes of NEP magnitude and the influence of climate factors on the HKH region from 2001 to 2018. The NEP magnitude was obtained by calculating the difference between the net primary production (NPP) estimated by the Carnegie–Ames Stanford Approach (CASA) model and the heterotrophic respiration (Rh) estimated by the geostatistical model. The results showed that the grassland ecosystem in the HKH region exhibited weak net carbon uptake with NEP values of 42.03 gC∙m−2∙yr−1, and the total net carbon sequestration was 0.077 Pg C. The distribution of NEP gradually increased from west to east, and in the Qinghai–Tibet Plateau, it gradually increased from northwest to southeast. The grassland carbon sources and sinks differed at different altitudes. The grassland was a carbon sink at 3000–5000 m, while grasslands below 3000 m and above 5000 m were carbon sources. Grassland NEP exhibited the strongest correlation with precipitation, and it had a lagging effect on precipitation. The correlation between NEP and the precipitation of the previous year was stronger than that of the current year. NEP was negatively correlated with temperature but not with solar radiation. The study of the temporal and spatial dynamics of NEP in the HKH region can provide a theoretical basis to help herders balance grazing and forage. [ABSTRACT FROM AUTHOR]

Biological tissues possess high stretchability and nonlinear mechanical response owing to their wavy and crimped microstructure. The metamaterials exhibit unusual physical and mechanical properties, dominated by the configuration of artificial periodic microstructures. However, the development of bio-inspired designs and three-dimensional (3D) soft metamaterials that can achieve a variety of unusual mechanical properties remains challenging. Here, we reported a class of 3D horseshoe-shaped soft network metamaterials with tunable nonlinear mechanical response and tension-torsion coupling effect. Based on the 3D horseshoe-shaped microstructure and octahedron lattice arrangement, the designed 3D soft network metamaterials can exhibit J-shaped stress-strain curve and large angle of torsion with large levels of deformation under tension. The mechanical properties of the 3D soft network metamaterials in different characteristic directions are investigated, where a negative Poisson's ratio appears. Furthermore, the extended design that the 3D horseshoe-shaped microstructures are replaced by the semi-elliptic and quarter-arc-straight joint microstructures can tune a wide range of desired J-shaped stress-strain curves. Thus, the multi-unusual mechanical properties provide potential uses in multi-functional metamaterials and flexible bio-integrated devices. [ABSTRACT FROM AUTHOR]

The hydrogenation of carbon dioxide (CO2) to produce light olefins is one of the most promising ways to utilize CO2 in power plant flue gas. However, the low concentration of CO2 (~10%) and the existence of water steam in the flue gas pose great challenges for the catalyst design. To address these problems, we introduced a Mg promoter and hydrophobic component into the Fe-based catalyst to improve the CO2 adsorption capacity and weaken the negative effects of water. The yield of light olefins on an optimized multifunctional Fe-based catalyst increased by 37% in low-concentration CO2 hydrogenation with water steam. A variety of characterizations proved that the Mg promoter played critical roles in regulating the adsorption capacity of CO2, increasing the surface electron density of Fe species, and promoting the formation of iron carbide active sites. The hydrophobic component mainly contributed to constraining the oxidation of iron carbides via water steam. It benefited from the rational design of the catalyst, showing how our multifunctional Fe-based catalyst has great potential for practical application in CO2 utilization. [ABSTRACT FROM AUTHOR]