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The architecture of software‐defined network (SDN)enhances the openness of the network by separating the control and forwarding functions, but the centralized SDN control form is susceptible to distributed denial of service (DDoS) attacks. In this paper, entropy value and back‐propagation neural network (BPNN) were applied to the DDoS attack detection of SDN, and then the two detection algorithms were simulated in MATLAB software and compared with the K‐means algorithm. The results showed that in the face of four DDoS attacks, SYN Flood, ACK Flood, UDP Flood and ICMP Flood, the BPNN‐based DDoS detection had higher accuracy and less detection time; the switch that adopted the BPNN‐based DDoS detection algorithm adjusted the traffic ratio back to normal level faster when facing DDoS attacks, reducing the impact on other switches and maintaining the traffic stability of the network. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In the past decade, intestinal organoid technology has paved the way for reproducing tissue or organ morphogenesis during intestinal physiological processes in vitro and studying the pathogenesis of various intestinal diseases. Intestinal organoids are favored in drug screening due to their ability for high-throughput in vitro cultivation and their closer resemblance to patient genetic characteristics. Furthermore, as disease models, intestinal organoids find wide applications in screening diagnostic markers, identifying therapeutic targets, and exploring epigenetic mechanisms of diseases. Additionally, as a transplantable cellular system, organoids have played a significant role in the reconstruction of damaged epithelium in conditions such as ulcerative colitis and short bowel syndrome, as well as in intestinal material exchange and metabolic function restoration. The rise of interdisciplinary approaches, including organoid-on-chip technology, genome editing techniques, and microfluidics, has greatly accelerated the development of organoids. In this review, VOSviewer software is used to visualize hot co-cited journal and keywords trends of intestinal organoid firstly. Subsequently, we have summarized the current applications of intestinal organoid technology in disease modeling, drug screening, and regenerative medicine. This will deepen our understanding of intestinal organoids and further explore the physiological mechanisms of the intestine and drug development for intestinal diseases. [ABSTRACT FROM AUTHOR]

Neuroinflammation contributes to neurological dysfunction in the patients who suffer from subarachnoid hemorrhage (SAH). Isoliquiritigenin (ISL) is a bioactive component extracted from Genus Glycyrrhiza. This work is to investigate whether ISL ameliorates neuroinflammation after SAH. In this study, intravascular perforation of male Sprague–Dawley rats was used to establish a SAH model. ISL was administered by intraperitoneal injection 6 h after SAH in rats. The mortality, SAH grade, neurological score, brain water content, and blood–brain barrier (BBB) permeability were examined at 24 h after the treatment. Expressions of tumor necrosis factor‐α, interleukin‐6, Iba‐1, and MPO were measured by quantitative real‐time polymerase chain reaction (qRT‐PCR). Besides, the expression levels of NF‐κB p65 and NLRP3, ASC, caspase‐1, IL‐1β, and IL‐18 were analyzed by western blot. The experimental data suggested that ISL treatment could ameliorate neurological impairment, attenuate brain edema, and ameliorate BBB injury after SAH in rats. ISL treatment repressed the expression of proinflammatory cytokines TNF‐α and IL‐6, and meanwhile inhibited the expression of Iba‐1 and MPO. ISL also repressed NF‐κB p65 expression as well as the transport from the cytoplasm to the nucleus. In addition, ISL significantly suppressed the expression levels of NLR family pyrin domain containing 3 (NLRP3), ASC, caspase‐1, IL‐1β, and IL‐18. These findings suggest that ISL inactivates NLRP3 pathway by inhibiting NF‐κB p65 translocation, thereby repressing the neuroinflammation after SAH, and it is a potential drug for the treatment of SAH. [ABSTRACT FROM AUTHOR]

As the number of patients suffering from cardiovascular diseases and peripheral vascular diseases rises, the constraints of autologous transplantation remain unavoidable. As a result, artificial vascular grafts must be developed. Adhesion of proteins, platelets and bacteria on implants can result in stenosis, thrombus formation, and postoperative infection, which can be fatal for an implantation. Polyurethane, as a commonly used biomaterial, has been modified in various ways to deal with the adhesions of proteins, platelets, and bacteria and to stimulate endothelium adhesion. In this review, we briefly summarize the mechanisms behind adhesions, overview the current strategies of surface modifications of polyurethane biomaterials used in vascular grafts, and highlight the challenges that need to be addressed in future studies, aiming to gain a more profound understanding of how to develop artificial polyurethane vascular grafts with an enhanced implantation success rate and reduced side effect. [ABSTRACT FROM AUTHOR]

• Designed a framework to capture epidemic risk perceptions' effects on travel mode choice. • Identified latent constructs among risk perception indicators. • Estimated latent class models to capture the heterogenous risk perception effects. • Proposed targeted strategies for population subgroups to affect travel mode choice. Coronavirus disease 2019 (COVID-19) has influenced and is still influencing people's daily lives worldwide. The infection rate and health and safety countermeasures may influence people's epidemic risk perception, thus influencing their travel mode choice. It is critical to understand epidemic risk perception and its impact on travel mode choice behavior to ensure the timeliness and effectiveness of health and safety policy implementation. In this study, we conducted an online survey to investigate how commuters' epidemic risk perception affects travel mode choice behavior in Shenzhen. The online survey mainly includes Likert scale questions and a stated preference experiment with three scenarios to capture commuters' epidemic risk perceptions and travel mode choice behaviors. First, confirmatory factor analysis was used to obtain three dimensions of epidemic risk perception from the Likert scale questions. Then, three subpopulation groups, including risk-sensitive commuters, risk-insensitive commuters and cautious commuters, were classified based on their risk perception using clustering analysis. Finally, a latent class choice model was estimated to capture the influencing factors of mode choice behavior. Three classes of mode choice behavior were identified, including those who can accept using public transportation, those who avoid using public transportation and tend to bicycles/electric bicycles and those who avoid using public transportation and tend to personal cars. The obtained results can provide valuable support for decision-making during and after an epidemic. Firstly, by offering insights into the intricate risk perception characteristics of Shenzhen residents, these findings can contribute to the effective management of epidemic risks during an epidemic. Secondly, by revealing the factors that influence individuals' preferences for travel modes, these results play a pivotal role in enhancing the market share of the public transportation system after the epidemic. [ABSTRACT FROM AUTHOR]

Two sets of 1D/2D lanthanide coordination polymers with formulas of Ln(oqa)3·2H2O [Hoqa = 2-(4-oxoquinolin-1(4H)-yl) acetic acid, Ln = Dy (1), Yb (2)] and Ln(oaa)2(HCOO)(H2O) [Hoaa = 2-(9-oxoacridin-10(9H)-yl) acetic acid, Ln = Dy (3), Yb (4)] have been synthesized and their physical properties were investigated. All four complexes are constructed from seven-coordinate lanthanide ions and corresponding organic linkers. The lanthanide ions in 1 and 2 adopt a pentagonal bipyramid coordination geometry, whereas the coordination geometry of lanthanide ions in 3 and 4 can be described as a capped octahedron. Slow magnetic relaxation behaviors were observed in these four products at a zero/non-zero static magnetic field. Complexes 1, 2 and 4 exhibit the characteristic emission of Ln(III) ions, whereas complex 3 shows ligand-based emission. Bright yellow light emission was also observed when a voltage was applied, demonstrating the potential of 1 for application in light-emitting diodes (LEDs). Compounds 3 and 4 are the first examples of lanthanide complexes based on Hoaa ligands. [ABSTRACT FROM AUTHOR]

Accurate forecasting of wind speed holds significant importance for the economic and social development of humanity. However, existing numerical weather predictions have certain inaccuracies due to various reasons. Therefore, it is highly necessary to perform statistical post-processing on forecasted results. However, traditional linear statistical post-processing methods possess inherent limitations. Hence, in this study, we employed two deep learning methods, namely the convolutional neural network (CNN) and the U-Net neural network, to calibrate the forecast of the Global Ensemble Forecast System (GEFS) in predicting 10-m surface wind speed in Southwest China with a forecast lead time of one to seven days. Two traditional linear statistical post-processing methods, the decaying average method (DAM) and unary linear regression (ULR), are conducted in parallel for comparison. Results show that original GEFS forecasts yield poorer wind speed forecasting performance in the western and eastern Sichuan provinces, the eastern Yunnan province, and within the Guizhou province. All four methods provided certain correction effects on the GEFS wind speed forecasts in the study area, with U-Net demonstrating the best correction performance. After correction using the U-Net, for a 1-day forecast lead time, the proportion of the 10-m U-component of wind with errors less than 0.5 m/s has increased by 46% compared to GEFS. Similarly, for the 10-m V-component of wind, the proportion of errors less than 0.5 m/s has increased by 50% compared to GEFS. Furthermore, we employed the mean square error-based error decomposition method to further diagnose the sources of forecast errors for different prediction models and reveal their calibration capabilities for different error sources. The results indicate that DAM and ULR perform best in correcting the Bias2, while the correction effects of all methods were variable for the distribution with the forecast lead time. U-Net demonstrated the best correction performance for the sequence. [ABSTRACT FROM AUTHOR]

Genomic prediction, which is based on solving linear mixed‐model (LMM) equations, is the most popular method for predicting breeding values or phenotypic performance for economic traits in livestock. With the need to further improve the performance of genomic prediction, nonlinear methods have been considered as an alternative and promising approach. The excellent ability to predict phenotypes in animal husbandry has been demonstrated by machine learning (ML) approaches, which have been rapidly developed. To investigate the feasibility and reliability of implementing genomic prediction using nonlinear models, the performances of genomic predictions for pig productive traits using the linear genomic selection model and nonlinear machine learning models were compared. Then, to reduce the high‐dimensional features of genome sequence data, different machine learning algorithms, including the random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and convolutional neural network (CNN) algorithms, were used to perform genomic feature selection as well as genomic prediction on reduced feature genome data. All of the analyses were processed on two real pig datasets: the published PIC pig dataset and a dataset comprising data from a national pig nucleus herd in Chifeng, North China. Overall, the accuracies of predicted phenotypic performance for traits T1, T2, T3 and T5 in the PIC dataset and average daily gain (ADG) in the Chifeng dataset were higher using the ML methods than the LMM method, while those for trait T4 in the PIC dataset and total number of piglets born (TNB) in the Chifeng dataset were slightly lower using the ML methods than the LMM method. Among all the different ML algorithms, SVM was the most appropriate for genomic prediction. For the genomic feature selection experiment, the most stable and most accurate results across different algorithms were achieved using XGBoost in combination with the SVM algorithm. Through feature selection, the number of genomic markers can be reduced to 1 in 20, while the predictive performance on some traits can even be improved compared to using the full genome data. Finally, we developed a new tool that can be used to execute combined XGBoost and SVM algorithms to realize genomic feature selection and phenotypic prediction. [ABSTRACT FROM AUTHOR]

Porous Ti–Zr–Cu–Pd–Sn bulk metallic glass (BMG) with bone-like mechanical properties, created using the one-step spark plasma sintering (SPS) process, offers a significant deal of potential for usage as orthopedic biomaterials. However, due to a lack of bioactivity and an inability to manage the antibacterial ability, its therapeutic usefulness is severely limited. Here, a bioactive titanate layer was synthesized in situ on the inner and outer surfaces of porous Ti-based BMG by a chemical treatment method. This research not only significantly confers a bone-bonding capability to porous Ti-based BMG, but it also achieves the modulation of Cu2+ ion release rate, substantially lowering the potential biological toxicity of porous Ti-based BMG. In parallel, a thorough analysis of the structure, morphology, mechanical characteristics, corrosion resistance, and antibacterial properties of the porous BMG before and after chemical treatment was conducted. It is anticipated that this discovery will assist to further advance the use of porous Ti-based BMG as orthopedic biomaterials. [Display omitted] [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) directly learn from an unknown real distribution through adversarial training. However, training the generator only by the feedback of the discriminator cannot make GANs learn adaptively from the unknown complex real distribution, and for this reason the quality of generated images is unsatisfactory sometimes. To address this problem, we propose a framework for training GANs with an adaptive learning strategy from simpleness to complexity. First, we employ a pre-trained encoder and a generator to construct a simple task that looks like a real image. Second, an adaptive learning strategy is designed based on the mathematical expectation of the discriminating results of the real image and the simple task. The designed adaptive learning strategy is well compatible with various GANs architectures. Experimental results demonstrate the proposed method can improve the performance of existing GANs. [ABSTRACT FROM AUTHOR]

To investigate the application of combined audiological examination and a self-reported symptoms survey in the evaluation of hyperacusis. Patients who visited the outpatient department of Otolaryngology Head and Neck Surgery and Otological medicine, Affiliated Hospital of Southwest Medical University, from January 2019 to March 2021 were divided into a hyperacusis group and a normal control group. We measured the loudness discomfort level (LDL) and hearing threshold (HT) of the subjects and investigated their self-reported symptoms. We compared the demographic characteristics, loudness discomfort level, and hearing threshold of the two groups and analyzed the self-reported symptoms and audiological characteristics of hyperacusis. We considered 87 subjects, comprising 40 patients with hyperacusis and 47 healthy individuals. Among the hyperacusis patients, bilateral disease was predominant. Among them, 33 were females, 23 had hearing loss, and 20 had tinnitus. Patients are mainly in the 21–60 age group. Patients with hyperacusis had low discomfort thresholds at all frequencies except 500 Hz (P ≤ 0.05, mean LDL decreased by 6.14–1.37 dB HL for all frequencies). The incidences of feeling upset, pain, and anxiety or fear were 95%, 65%, and 82.5%, respectively, in patients with hyperacusis. The severity of symptoms varies between patients with hyperacusis and healthy individuals. A combination of LDL measurements and self-reported symptom surveys allows for an accurate and comprehensive assessment of hyperacusis. Trial registration: This study was retrospectively registered (TRN: ChiCTR2100047391) on June 13, 2021. [ABSTRACT FROM AUTHOR]

Although generative adversarial networks (GANs) show great prospects for the task of image synthesis, the quality of synthesized images by existing GANs is sometimes inferior to real images because their discriminators cannot effectively learn robust identification features from input images. In addition, the training process of discriminator is prone to be unstable. To this end, inspired by the denoising auto-encoders, we propose a learning-aware feature denoising discriminator. It is designed to pay attention to robust features of input images, so as to improve its robustness in identifying features and recognition ability in training process. First, we use a decoder to generate perturbing noise and add it to real image to get corrupted image. Then, we get the encodings of the corrupted image and real image through an encoder. Finally, we minimize both types of encoding to constitute a denoising penalty and add it to the loss of the discriminator. We also show that our method is compatible with most existing GANs for three image synthesis tasks. Extensive experimental results show that compared with baseline models, our proposed method not only improves the quality of synthesized images, but also stabilizes the training process of discriminator. • We propose a learning-aware feature denoising discriminator. • Our method is compatible with most existing GAN models. • Our method outperforms the baseline models for three tasks on six databases. [ABSTRACT FROM AUTHOR]

Correction for 'Single molecule magnet features in luminescent lanthanide coordination polymers with heptacoordinate Dy/Yb(III) ions as nodes' by Xiang-Tao Dong et al., Dalton Trans., 2023, 52, 12686–12694, https://doi.org/10.1039/D3DT02106H. [ABSTRACT FROM AUTHOR]

Public key encryption with keyword search (PEKS) allows users to search on encrypted data without leaking the keyword information from the ciphertexts. But it does not preserve keyword privacy within the trapdoors, because an adversary (e.g., untrusted server) might launch inside keyword-guessing attacks (IKGA) to guess keywords from the trapdoors. In recent years, public key authenticated encryption with keyword search (PAEKS) has become a promising primitive to counter the IKGA. However, existing PAEKS schemes focus on the concrete construction of PAEKS, making them unable to support modular construction, intuitive proof, or flexible extension. In this paper, our proposal called "StopGuess" is the first elegant framework to achieve the above-mentioned features. StopGuess provides a general solution to eliminate IKGA, and we can construct a bundle of PAEKS schemes from different cryptographic assumptions under the framework. To show its feasibility, we present two generic constructions of PAEKS and their (pairing-based and lattice-based) instantiations in a significantly simpler and more modular manner. Besides, without additional costs, we extend PAEKS to achieve anonymity which preserves the identity of users; we integrate it with symmetric encryption to support data retrieval functionality which makes it practical in resource-constrained applications. • We present StopGuess for PAEKS to give a general solution to eliminate IKGA. • We present two generic constructions of PAEKS with StopGuess to demonstrate its the superiority. • We extend PAEKS to support further properties or features without additional assumptions on the underlying modules. [ABSTRACT FROM AUTHOR]

Noise-to-image synthesis continues to be challenging, despite the application of the advanced loss functions in Generative Adversarial Networks (GANs). The main issue lies in the fact that discriminators employ hard margin classification, which is susceptible to misclassification. Moreover, the features of real image distribution learned by the generator are limited during adversarial training, thus the generated images are visually inferior to the real images. To tackle these challenges, a GAN method based on Siamese projection network (abbreviated as SPGAN) is proposed to learn a similarity measurement of features for image synthesis. Then, the similarity measurement is incorporated into the loss functions of the generator and discriminator, forming a similar feature adversarial learning. Through similar feature adversarial learning, SPGAN encourages the discriminator to maximize the dissimilarity between the features of real and generated images during recognition process. Simultaneously, it encourages the generator to synthesize images that contain more features resembling those of real images. Furthermore, we extend the SPGAN method by rewriting five representative loss functions, showcasing its compatibility with different loss functions. Experimental results demonstrate that the performance of SPGAN outperforms the advanced loss functions. [ABSTRACT FROM AUTHOR]

This study examines the interaction between random waves and an oyster reef, both in its plain state and when enhanced by energy dissipating macro-roughness. The investigation was carried out using wave flume experiments, incorporating different water levels and wave conditions. The primary focus of the study revolves around two key parameters: freeboard and wave steepness. Additionally, the study explores the influence of macro-roughness arrangement on the wave energy transmission. Empirical relationships were established to determine the wave transmission coefficient for both the plain reef and the reef enhanced by macro-roughness. These relationships highlight the significance of the relative freeboard and wave steepness for the plain oyster reef. Moreover, a correction factor was introduced to account for the impact of macro-roughness configuration on the wave transmission coefficient. The findings of this study suggest that the incorporation of macro-roughness can effectively attenuate wave energy through dissipation and reflection, thereby serving as an additional measure for wave energy mitigation through nature-based solutions. • Interactions of random waves with oyster reefs were studied experimentally. • Oyster reefs were enhanced with macro-roughness for further energy dissipation. • Empirical formulae were developed for both plain oyster reef and that enhanced with macro-roughness. • The gridded array of macro-roughness elements was found to be more effective in reducing transmitted wave height. • Macro-roughness configuration was more important in terms of wave transmission when water depths were larger. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the changes and clinical significance of thrombomodulin (TM) and plasma protein C (PC) in patients with cancer before and after peripherally inserted central catheter placement (PICC).Materials and Methods: The levels of plasma TM and PC in 35 patients with cancer before and after PICC were measured by enzyme-linked immunosorbent assay, and the significance of the differences was analyzed.Results: TM was 3.57 ± 1.01 μg/L at 1 day after catheterization, which was significantly lower than the value of 4.41 ± 1.26 μg/L before catheterization; these values were 4.30 ± 1.81 and 4.73 ± 0.97 μg/L at 30 and 90 days after catheterization, respectively (F = 4.14,P < 0.05). PC was 3.32 ± 1.35 μg/L at 1 day after catheterization, which was significantly lower than the value of 5.32 ± 2.12 μg/L before catheterization; these values were 4.64 ± 2.44 and 5.83 ± 3.14 μg/L at 30 and 90 days after catheterization, respectively (F = 6.28,P < 0.01). There were no significant differences in platelet (PLT) counts, plasma D-D, and coagulation parameters among the four time points before and after catheterization. There was a positive correlation between TM and PC (r = 0.5420,P < 0.01) on day 1 after PICC line insertion. The levels of TM and PC were not related to PLT, plasma D-dimer, or various coagulation parameters.Conclusions: The levels of TM and PC in the patients 1 day after PICC were significantly decreased and showed a positive correlation, but were not related to PLT, plasma D-dimer, or coagulation function. [ABSTRACT FROM AUTHOR]

The visual security index (VSI) is a quantized indicator for objective visual security evaluation of selectively encrypted images. One challenging problem in current research is that the performance of VSIs is highly sensitive to the extracted features and the method of similarity measurement, and it is hard to choose appropriate handcrafted features from encrypted images, as well as to find an effective similarity measurement. In this paper, we make the first attempt to present a novel convolutional neural network-based visual security index (CNNVSI). Our proposed CNNVSI is purely data-driven and trained end-to-end. We propose three specialized designs to make the approach work for encrypted low-quality images without any handcrafted features or prior knowledge about the human vision system (HVS). First, we present a patch labeling algorithm to assign each encrypted patch a visual security score. Second, we design a multiscale attention residual network (MARNet) for feature learning. Last, we propose to fuse the learned features from plain images, encrypted images and their discrepancy images. Extensive and systematic experiments are conducted on five publicly available image databases to analyze the performance of our proposed CNNVSI, and the experimental results and their analysis demonstrate that our proposed CNNVSI significantly outperforms the existing state-of-the-art methods in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Background: Enoxaparin is an anticoagulant that falls in the class of medications called low molecular weight heparins (LMWHs), and is used to prevent or treat patients with deep vein thrombosis (DVT) and pulmonary embolism. Enoxaparin is the most widely used LMWH for DVT prophylaxis following knee or hip replacement surgery. Common side effects of enoxaparin include bleeding, petechiae at the injection site, and thrombocytopenia. However, reactive thrombocytosis is a rarely reported adverse reaction. We managed a patient who developed enoxaparin-associated thrombocytosis, which was completely resolved after treatment cessation. Case presentation: A 78-year-old female was hospitalized for post-hip replacement rehabilitation. Low molecular weight heparin 40 mg/day was administered subcutaneously to prevent deep venous thrombosis (DVT). At admission, her platelet count was normal (228 × 109/L) and her white blood cell count was slightly elevated (12.91 × 109/L). Seven days after admission, the patient developed thrombocytosis, which peaked on the 14th day (836 × 109/L), while her white blood cell count had returned to normal (8.86 × 109/L). Her therapeutic regimen was reviewed, and enoxaparin was identified as a potentially reversible cause of reactive thrombocytosis. Switching from enoxaparin to rivaroxaban lead to a gradual decrease in the patient's platelet count, which eventually returned to normal levels 16 days after enoxaparin was discontinued. No complications secondary to thrombocytosis was observed, and no conclusion was reached on the use of small doses of aspirin for antithrombotic therapy under these circumstances. Conclusion: Enoxaparin-induced reactive thrombocytosis should be suspected in patients with thrombocytosis following enoxaparin administration as an anticoagulant to prevent certain complications. [ABSTRACT FROM AUTHOR]

Objective: To investigate the clinical manifestations, magnetic resonance imaging (MRI) features, and prognosis of patients diagnosed with bilateral cerebral peduncular infarction (BCPI) with the 'Mickey Mouse ears' sign. Methods: This study, retrospectively summarized the clinical features, MRI and magnetic resonance angiography (MRA) manifestations, and prognosis of 5 patients diagnosed with BCPI and with the 'Mickey Mouse ears' sign from our hospital. In the meantime, we also systematically analyzed and summarized the clinical and imaging features, and prognosis in combination with relevant cases from the national and international literature that had been reported. Results: In our study, five cases presented tetraplegia whereas 4 cases presented disturbance of consciousness. In all cases, the 'Mickey Mouse ears' sign was observed in the mid-brain level using a diffusion-weighted imaging (DWI) cross-section. The MRA images showed thrombosis on either the basilar artery (BA), posterior cerebral artery (PCA), or superior cerebellar artery (SCA). All the studied patients received standardized treatment for cerebral infarction. However, three patients died and the remaining two were left in a locked-in syndrome state. Conclusions: BCPI is a very rare stroke disease and its main clinical manifestations are locked-in syndrome and persistent vegetative states. However, the obtained imaging features on the brain-MRI of patients diagnosed with BCPI with the 'Mickey Mouse ears' sign can predict a bad prognosis. [ABSTRACT FROM AUTHOR]

Due to global changes, e.g., climate change and trade globalization, China is facing an increasingly severe threat from invasive freshwater fish species, which have the potential to cause negative impacts across various aspects and pose significant challenges for their eradication once established. Therefore, prioritizing the understanding of invasive species' potential ranges and their determinants is vital for developing more targeted management strategies. Moreover, it is equally essential to consider the transitory range dynamics of invasive species that reflect changes in habitat availability and accessibility. Here, we used species distribution models (the maximum entropy algorithm) to assess the potential distributions of six notorious invasive fish species (i.e., Coptodon zillii , Cyprinus carpio , Gambusia affinis , Hemiculter leucisculus , Oreochromis mossambicus , and Oreochromis niloticus) in current and future (i.e., the 2030s, 2050s, and 2070s) periods along with their determinants, under two Shared Socio-economic Pathways scenarios (SSP1-2.6 and SSP5-8.5; global climate model: MRI-ESM2-0). Our results showed that the habitat suitability for the six species substantially benefited from temperature conditions (i.e., annual mean temperature or maximum temperature of warmest month). Throughout the given time periods, dramatic range expansions would occur for C. zillii , G. affinis , O. mossambicus , and O. niloticus , ranging from 38.61% to 291.90%. In contrast, the range of C. carpio would change slightly and irregularly, while H. leucisculus would contract marginally, with losses ranging from 1.06% to 12.60%. By the 2070s, species richness of these species would be relatively high in South, Central, and East China and parts of Southwest China. Furthermore, transitory fluctuations in the species ranges for all six species were observed throughout the entire time period (the 2030s–2070s). Given the range shifts for each species during different time periods, as well as time costs and budgets, adaptation strategies should be developed and implemented in the areas where they are most needed in each time period. [Display omitted] • Potential distributions of six notorious invasive fish species were assessed. • Areas of transitory fluctuations of the six species were identified. • Species richness of these six species would be high in South, Central, and East China and Sichuan Basin. • Habitat suitability for the six species benefited greatly from temperature conditions. • Adaptation management strategies were suggested for the six invasive species. [ABSTRACT FROM AUTHOR]

Over the past centuries, freshwater fish introductions and extinctions have been the major environmental and ecological crises in various water bodies in China. However, consequences of such crises on freshwater fish biodiversity in China remain only partially or locally studied. Furthermore, identifications of relatively sensitive areas along with stressors (i.e., environmental and anthropogenic drivers) influencing freshwater fish biodiversity patterns are still pending. Taxonomic, functional, and phylogenetic facets of biodiversity can well describe and evaluate the underlying processes affecting freshwater fish biodiversity patterns under different dimensionalities. Here we thus evaluated temporal changes in these facets of freshwater fish biodiversity as well as a new developed biodiversity index, multifaceted changes in fish biodiversity, for over a century at the basin level throughout China using both alpha and beta diversity approaches. We also identified the drivers influencing the changes in fish biodiversity patterns using random forest models. The results showed that fish assemblages in Northwest and Southwest China (e.g., Ili River basin, Tarim basin, and Erhai Lake basin) experienced extreme temporal and multifaceted changes in the facets of biodiversity compared with other regions, and environmental factors (e.g., net primary productivity, average annual precipitation, and unit area) largely drove these changes. Since fish faunas in over 80% of China's water bodies covering more than 80% of China's surface were currently undergoing taxonomic, functional, and phylogenetic homogenization, targeted conservation and management strategies should be proposed and implemented, especially for the areas with relatively high changes in biodiversity. [Display omitted] • A new multifaceted biodiversity index was developed to assess biodiversity impacts. • We comprehensively evaluated changes in freshwater fish biodiversity throughout China. • Extreme changes in fish biodiversity mostly occurred in Northwest and Southwest China. • Environmental factors largely drove the changes in fish biodiversity in China. • Adapted conservation and management strategies were proposed for vulnerable areas. [ABSTRACT FROM AUTHOR]

Perceptual/selective encryption has been gaining widespread attention as an emerging technology for image privacy protection. However, few studies focus on the visual security evaluation of perceptually encrypted images, which has a significant impact on measuring the effectiveness and practicality of these encryption methods. In this paper, we propose an image importance-based visual security index (IIBVSI) by leveraging spatial contrast and texture features. Based on the characteristics of perceptually encrypted images, we present an averaged high-order gradient magnitude map to describe the spatial contrast feature and introduce a combined local amplitude map of multiple log-Gabor filters to represent the texture feature. Specifically, the multiresolution representation of an image is first created by downsampling to simulate the hierarchical property of the human visual system. Next, for each scale of image resolution, the spatial contrast and the texture feature maps are extracted from both plain and encrypted images. Similarity measurements are then conducted on these feature maps to generate the contrast and the texture similarity maps. An image importance-based pooling strategy is subsequently proposed to combine these measurements and generate a visual security score. The final IIBVSI score is computed by averaging the visual security scores of all scales of image resolution. Extensive experiments are conducted on several publicly available databases, and the results demonstrate the superiority and robustness of our proposed IIBVSI compared with existing state-of-the-art work in the low and moderate image quality ranges. [ABSTRACT FROM AUTHOR]

Fluid impact forces on a bridge superstructure in the horizontal and vertical directions due to tsunami inundation, represented by a solitary wave, were investigated through a large-scale laboratory experiment and numerical simulations. The experiment was conducted in a two-dimensional large wave flume using a 1:5 scale steel girder and concrete slab bridge model, tested under shallow water waves at two water depths. Wave forces measured in the experiment were used to validate and assess the numerical predictive capability of a nonlinear hydrodynamic model. A numerical parametric study was conducted to determine the effect of variations in bridge elevation and wave height on the magnitude of the horizontal and vertical forces and overturning moment. Finally, an improved simplified design procedure based on the experimental and numerical results is proposed. [ABSTRACT FROM AUTHOR]

In recent years, visual question answering (VQA) has become topical. The premise of VQA's significance as a benchmark in AI, is that both the image and textual question need to be well understood and mutually grounded in order to infer the correct answer. However, current VQA models perhaps ‘understand’ less than initially hoped, and instead master the easier task of exploiting cues given away in the question and biases in the answer distribution. In this paper we propose the inverse problem of VQA (iVQA). The iVQA task is to generate a question that corresponds to a given image and answer pair. We propose a variational iVQA model that can generate diverse, grammatically correct and content correlated questions that match the given answer. Based on this model, we show that iVQA is an interesting benchmark for visuo-linguistic understanding, and a more challenging alternative to VQA because an iVQA model needs to understand the image better to be successful. As a second contribution, we show how to use iVQA in a novel reinforcement learning framework to diagnose any existing VQA model by way of exposing its belief set: the set of question-answer pairs that the VQA model would predict true for a given image. This provides a completely new window into what VQA models ‘believe’ about images. We show that existing VQA models have more erroneous beliefs than previously thought, revealing their intrinsic weaknesses. Suggestions are then made on how to address these weaknesses going forward. [ABSTRACT FROM AUTHOR]

Objective: To review and meta-analyze the efficacy of educational counseling alone in tinnitus.Methods: We collected randomized controlled trials (RCTs) adhered to PRISMA guidelines. Analyzed the effect of educational counseling alone versus other forms of therapy (psychological or combination) with RevMan 5.3.Results: In nine trials, 582 patients receiving educational counseling alone and 759 patients receiving other psychological or combination therapies. During the 3-6 months follow-up, there was no significant difference in the tinnitus recovery rate between these two groups (OR 0.62, 95% CI 0.34-1.16, P = 0.14; I2 = 71%, P = 0.00, random-effects model). The tinnitus symptom severity rates were also similar during 1-12 months follow-up (mean difference, 3.59, 95% CI -0.56-7.74, P = 0.09) with heterogeneity among studies (I2 = 74%, P = 0.00; random-effects model). Sensitivity analysis indicated that a single trial containing almost 40% of the patients was the cause of heterogeneity. There was no significant change in tinnitus loudness at the 3 months follow-up (OR 0.84, 95% CI 0.42-1.66, P = 0.61), with no significant heterogeneity (I2 = 0%, P = 0.60).Conclusion: Educational counseling alone helps to improve tinnitus and related problems, and has the same effect as other psychological or combination therapies.Practice Implications: The results of the current analysis may help to develop evidence-based cost-effective treatment(s) for tinnitus, which will be minimally burdensome for the patients. [ABSTRACT FROM AUTHOR]

• We propose a randomness-based privacy-preserving face recognition scheme. • We design a novel face encryption algorithm to protect the privacy of face images. • We propose an optimization technique to increase the efficiency of face owner and the cloud. Face recognition (FR) has become increasingly significant in many computer vision applications. However, with the rapid deployment of FR, the privacy of face images has been a growing concern, especially when FR is performed in cloud platforms. Several privacy-preserving face recognition (PPFR) schemes have been proposed to outsource FR without divulging private information. However, most existing schemes are built by employing partially or fully homomorphic cryptosystems, which contain a series of computationally expensive operations and require interactions between users and service providers. In this paper, we propose a novel and efficient scheme to achieve privacy-preserving face recognition in the cloud. Using randomness techniques, we propose an affine transformation, which consists of permutation, diffusion and shift transformations, to protect the privacy of faces. Both projection (feature extraction) and recognition procedures are performed in encrypted domain without interaction. We also propose an optimization technique to increase the efficiency of encryption. Extensive experimental analysis is performed to demonstrate the correctness and effectiveness of the proposed scheme. Theoretical comparison is also conducted to illustrate the efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

Based on a hyper-chaotic system, an image compression and encryption scheme is presented by adopting mean error control to improve the quantization compression performance of the discrete cosine transform (DCT) coefficients. The transform coefficients of DCT forming a structure similar to the sub-band can be classified and encoded. Then the image is scanned with Zigzag operation in different directions to achieve sufficiently scrambled effects. The cyclic shift matrix for image encryption is generated by the hyper-chaotic system. The four initial inputs for generating the cyclic shift matrix serve as the key. Simulation results demonstrate the security and the effectiveness of the proposed image compression and encryption scheme. [ABSTRACT FROM AUTHOR]

The article emphasizes urgent action to save China's endangered gaur population due to habitat loss, hunting, and illegal trade, suggesting measures like habitat restoration, increased penalties for poaching, farmer incentives for reforestation, and assisted migration to prevent extinction.

Resveratrol, found in variety of plants, is a natural stilbene structure polyphenol. It has various pharmacological effects, such as antioxidation, anti-aging, anti-inflammation, anti-cancer, antiobesity, anti-diabetes, cardioprotection, neuroprotection. Recently, anti-leukemia activities of resveratrol has been studied extensively via its effects on a variety of biological processes involving cell proliferation, apoptosis, autophagy. Current treatments of leukemia mainly rely on intensive chemotherapy or hematopoietic stem cell transplantation, however, these treatments are still with poor survival and high treatment-related mortality. Therefore, it is extremely needed to find relatively non-toxic medicines with minimal side effects but sufficient therapeutic efficacy. Resveratrol is one such potential candidate owing to its reported anti-leukemia effect. In this review, we summarized resveratrol's discovery, sources and isolation methods, administration methods, effects in different types of leukemia, pharmacokinetics and toxicities, aiming to exploit resveratrol as a potential drug candidate for anti-leukemia. • The therapeutic efficacy of leukemia needs improvement. • Resveratrol is a natural polyphenol that can be extracted from various plants. • Resveratrol shows obvious anti-leukemia effects in vitro and in vivo. • Resveratrol is a potential anti-leukemia agent in consideration of pharmacokinetics. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Recent experiment has shown that the ABC-stacked trilayer graphene-boron nitride Moire super-lattice at half-filling is a Mott insulator. Based on symmetry analysis and effective band structure calculation, we propose a valley-contrasting chiral tight-binding model with local Coulomb interaction to describe this Moire super-lattice system. By matching the positions of van Hove points in the low-energy effective bands, the valley-contrasting staggered flux per triangle is determined around π / 2. When the valence band is half-filled, the Fermi surfaces are found to be perfectly nested between the two valleys. Such an effect can induce an inter-valley spiral order with a gap in the charge excitations, indicating that the Mott insulating behavior observed in the trilayer graphene-boron nitride Moire super-lattice results predominantly from the inter-valley scattering. [ABSTRACT FROM AUTHOR]

Zero-Shot Learning (ZSL) for visual recognition is typically achieved by exploiting a semantic embedding space. In such a space, both seen and unseen class labels as well as image features can be embedded so that the similarity among them can be measured directly. In this work, we consider that the key to effective ZSL is to compute an optimal distance metric in the semantic embedding space. Existing ZSL works employ either euclidean or cosine distances. However, in a high-dimensional space where the projected class labels (prototypes) are sparse, these distances are suboptimal, resulting in a number of problems including hubness and domain shift. To overcome these problems, a novel manifold distance computed on a semantic class prototype graph is proposed which takes into account the rich intrinsic semantic structure, i.e., semantic manifold, of the class prototype distribution. To further alleviate the domain shift problem, a new regularisation term is introduced into a ranking loss based embedding model. Specifically, the ranking loss objective is regularised by unseen class prototypes to prevent the projected object features from being biased towards the seen prototypes. Extensive experiments on four benchmarks show that our method significantly outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

An ultra-high strength TiZrNbTa (TZNT) high-entropy alloy (HEA) with good plasticity was attempted as a new reinforcing phase, which is introduced in the highly conductive Cu matrix to fabricate high-strength conductive composites. This work utilized a combination of a two-step ball milling method (first HEA mechanical alloying followed by fabrication of TZNT HEA/Cu hybrid powder) and a spark plasma sintering process to prepare the bulk TZNT HEA/Cu metal matrix composites (MMCs). Remarkably enhanced mechanical properties of the designed TZNT HEA/Cu MMCs with increasing TZNT contents were characterized while the electrical conductivity declined. Compared to the strength of the copper matrix, the strength of the fabricated 50 wt% TZNT/Cu specimens increased up to 340% induced by the second phase strengthening effect. The combination of grain boundary strengthening and dislocation strengthening contributed relatively less to the overall strength improvement of the composites. The TZNT/Cu composites were found to be a new insight into the high-strength conductive material preparation by choosing TZNT HEA with superior strength as hard secondary phase in copper matrix. [ABSTRACT FROM AUTHOR]

Highly accurate and sensitive hydrogen detection particularly at (sub)ppb level is crucial for large-scale use of green hydrogen energy. However, state-of-the-art hydrogen sensors usually have a ppm-level limit of detection (LOD). In this work, SnO 2 nanofibers were decorated with Pd single atoms using a scalable two-step annealing method, which remarkably boosted the H 2 sensing properties. The Pd-SnO 2 nanofiber sensor exhibited a response of 224 to 1000 ppm H 2 at the optimum operation temperature of 300°C, which was 107 times higher than that of SnO 2 , achieving an LOD as low as 0.6 ppb. Moreover, a fast response time of 8.4 s, excellent selectivity, and humidity tolerance were observed. The superior H 2 sensing performance of Pd-SnO 2 nanofiber was ascribed to excellent catalysis of Pd single atoms and formation of heterojunction. • SnO 2 nanofibers decorated with Pd single atoms were fabricated. • Pd-SnO 2 exhibited 107-fold higher response to 1000 ppm H 2 at 300 °C than SnO 2. • A limit of detection down to 0.6 ppb H 2 was obtained. • Superior performance was ascribed to Pd catalysis and heterojunction formation. [ABSTRACT FROM AUTHOR]

In the present study, the method of phase reversion process is adopted to obtain nanograined/ultrafine‐grained (NG/UFG) structure in 18Cr–8Ni austenitic stainless steel. Severe cold deformation (86% reduction) at room temperature and (64%, 86% reduction) at −50 °C is carried out, followed by annealing in the conditions of 650 °C–50 min and 700 °C–15 min. The influence of different cold rolling and annealing conditions on the microstructure and mechanical properties of stainless steel is studied. The decrease of rolling temperature and the increase of reduction lead to the volume fraction of strain‐induced martensite increase. Lath‐type martensite, dislocation cell‐type martensite, and untransformed austenite in 64% reduction (at −50 °C) and mainly dislocation cell‐type structure in 86% reduction (at room temperature and −50 °C) are observed. Increasing reduction or rolling temperature promotes the cold‐rolled α texture of strain‐induced martensite to the stable orientations. The grain size obtained is in the range of 0.4–0.89 μm, with yield strength in the range of 684–1017 MPa and high ductility of 23–54%. Deformation behavior shows that the amount of twins and stacking faults at 5 mm distance from the fracture is higher than the fracture. [ABSTRACT FROM AUTHOR]

In this paper, we explore the verification problem of outsourcing constrained nonlinear programming (NLP) when it is required to be solved by particle swarm optimization (PSO) algorithm, i.e., making sure that the cloud runs PSO algorithm faithfully and returns an acceptable solution. An efficient verification scheme without any cryptographic tool is proposed. The proposed scheme involves approximate KKT conditions with the ε-KKT point in verifying the optimality of the result returned by PSO algorithm. Extensive experiments on PSO benchmarks and NLP test problems demonstrate that our proposed scheme is effective and efficient at verifying the cloud’s honesty. [ABSTRACT FROM AUTHOR]

Smart materials with controllable responses to various stimulations offer considerable potential in many applications. Herein we report a simple and efficient method to fabricate ionic strength- and thermo-responsive polyethersulfone composite membranes using zwitterionic copolymers of sulfobetaine methacrylamide (SBMI) and sulfobetaine methacrylate (SBMA). The permeability of the modified membranes could be adjusted by varying the ionic strength from 0 mol kg−1 to 1.0 mol kg−1 and/or varying the surrounding temperature from 15 °C to 90 °C, and both the responses were reversible. The thermo-response transition temperature could be regulated by changing the chemical compositions of the zwitterionic copolymers. Furthermore, the zwitterionic copolymer modified membranes exhibited excellent antifouling resistances to protein solutions and surfactant-stabilized oil-in-water emulsions. Their ionic strength- and thermo-responsive flux, separation ability, and excellent antifouling properties endowed the membranes with a wide range of applications in the field of separation. [ABSTRACT FROM AUTHOR]

With the help of cloud computing, users can outsource their data to a cloud service provider (CSP) and enjoy the high-quality on-demand services from the cloud. On the other hand, once owners no longer have physical possession of the outsourced data, the protection of data integrity in cloud computing becomes a critical and challenging task. In this paper, we propose a novel and verifiable auditing scheme for outsourced database without a third party auditor (TPA). It can simultaneously authenticate the correctness and completeness of query results, preventing the dishonest CSP from returning fake or incomplete results to the users. Our scheme also supports partial attribute retrieval and flexible data dynamics. The security proof and the performance analysis show that our proposed scheme is secure and efficient for practical deployment. [ABSTRACT FROM AUTHOR]

Ensemble learning plays an important role in big data analysis. A great limitation is that multiple parties cannot share their knowledge extracted from ensemble learning model with privacy guarantee, therefore it is a great demand to develop privacy-preserving collaborative ensemble learning. This paper proposes a privacy-preserving collaborative ensemble learning framework under differential privacy. In the framework, multiple parties can independently build their local ensemble models with personalized privacy budgets, and collaboratively share their knowledge to obtain a stronger classifier with the help of central agent in a privacy-preserving way. Under this framework, this paper presents the differentially private versions of two widely-used ensemble learning algorithms: collaborative random forests under differential privacy (CRFsDP) and collaborative adaptive boosting under differential privacy (CAdaBoostDP). Theoretical analysis and extensive experimental results show that our proposed framework achieves a good balance between privacy and utility in an efficient way. [ABSTRACT FROM AUTHOR]

In order to obtain high-quality color images, it is important to keep the hue component unchanged while emphasize the intensity or saturation component. As a public color model, Hue-Saturation Intensity (HSI) model is commonly used in image processing. A new single channel quantum color image encryption algorithm based on HSI model and quantum Fourier transform (QFT) is investigated, where the color components of the original color image are converted to HSI and the logistic map is employed to diffuse the relationship of pixels in color components. Subsequently, quantum Fourier transform is exploited to fulfill the encryption. The cipher-text is a combination of a gray image and a phase matrix. Simulations and theoretical analyses demonstrate that the proposed single channel quantum color image encryption scheme based on the HSI model and quantum Fourier transform is secure and effective. [ABSTRACT FROM AUTHOR]

Achieving bimodal grain structure via cold rolling remains a challenge for Al–Mg alloys. In the present work, a high tensile strength (∼525 MPa) and ductile (∼14%) Al–9Mg alloy was fabricated by a novel hard-plate rolling (HPR) route with a single-pass thickness reduction of 75%. Increasing solute Mg content promotes a bimodal structure and a high dislocation density in the HPRed Al–Mg alloys due to the severely retarding effect of Mg solutes on dynamic recovery/recrystallization. The simultaneous high ductility and strength is due mainly to the strong work hardening ability deriving from the bimodal grain structure and the high solute Mg content. [ABSTRACT FROM AUTHOR]

Cerebral amyloid angiopathy (CAA) is characterized by the cerebrovascular amyloid-β (Aβ) accumulation, and always accompanied by Alzheimer's disease (AD). The mechanisms revealing CAA pathogenesis are still unclear, and it is challenging to develop an efficient therapeutic strategy for its treatment. Vascular endothelial growth factor (VEGF) and its receptors including VEGFR-1,-2,-3 activation are involved in Aβ processing, and modulate numerous cellular events associated with central nervous system (CNS) diseases. In the present study, we attempted to explore the regulatory function of fruquintinib (also named as HMPL-013), a highly selective inhibitor of VEGFR-1,-2,-3 tyrosine kinases, on CAA progression in Tg-SwDI mice. Here, we found that HMPL-013-rich diet consumption for 12 months significantly improved the behavioral performances and cerebral blood flow (CBF) of Tg-SwDI mice compared with the vehicle group. Importantly, HMPL-013 administration considerably reduced Aβ 1-40 and Aβ 1-42 burden in cortex and hippocampus of Tg-SwDI mice through regulating Aβ metabolism process. Congo red staining confirmed Aβ deposition in vessel walls, reflecting CAA formation, which was, however, strongly ameliorated after HMPL-013 treatment. Neuron death, aberrant glial activation and pro-inflammatory response in brain tissues of Tg-SwDI mice were dramatically alleviated after HMPL-013 consumption. More studies showed that the protective effects of HMPL-013 against CAA might be partially attributed to its regulation on the expression of genes associated with blood vasculature. Intriguingly, VEGF and phosphorylated VEGFR-1,-2 protein expression levels were remarkably decreased by HMPL-013 in cortex and hippocampus of Tg-SwDI mice, which were validated in HMPL-013-treated brain vascular endothelial cells (BVECs) under hypoxia. Finally, we found that VEGF-induced human umbilical vein endothelial cells (HUVEC) proliferation and tube formation were strongly abolished upon HMPL-013 exposure. Collectively, all these findings demonstrated that oral administration of HMPL-013 had therapeutic potential against CAA by reducing Aβ deposition, inflammation and neuron death via suppressing VEGF/VEGFR-1,-2 signaling. [Display omitted] • HMPL-013 mitigates impaired behavioral performance in Tg-SwDI mice with CAA. • HMPL-013 consumption reduces Aβ accumulation in Tg-SwDI mice. • HMPL-013 ameliorates Aβ metabolism in vitro and in vivo via suppression of ROS and inflammation. • HMPL-013 improves neuron survival in Tg-SwDI mice. • HMPL-013 reduces glial cell activation in Tg-SwDI mice. [ABSTRACT FROM AUTHOR]

Microstructure and mechanical properties of TiZrNbTa high-entropy alloy (HEA) in the as-cast and annealed states are systematically investigated. The yield strength of TiZrNbTa HEA strongly depends on the heat treatment temperatures. With the increase of heat treatment temperature, the yield strength of TiZrNbTa increases first and then decreases. When the heat treatment temperature reaches 600 ℃, the yield strength and uniform strain of TiZrNbTa HEA are 1583 ± 55 MPa and 20.5% ± 1.8%, respectively. Compared with the as-cast state TiZrNbTa, the yield strength of TiZrNbTa HEA after simple heat treatment, i.e. , 600 ℃ for 1 h improves by 47%, which is attributed to the spinodal decomposition strengthening. By comparing the contribution of grain boundary strengthening for the TiZrNbTa HEA before and after annealing, the significant decrease in the yield strength of the TiZrNbTa HEA is not caused by grain coarsening after heat treatment at 800 ℃, but by the formation of a vein-like phase in grain boundaries. The Ti(Zr)-rich and Nb(Ta)-rich phases in TiZrNbTa HEA exhibit different performances in large compressive strain conditions. The Ti(Zr)-rich phase exhibits good compressive plastic deformation ability, and the Nb(Ta)-rich phase has a higher strength. The TiZrNbTa HEA after one-step simple heat treatment displays excellent mechanical properties, which significantly improves the possibility of clinical application as a new orthopedic-implant alloy. [Display omitted] • The yield strength of annealed TiZrNbTa increases by 47% more than the as-cast due to spinodal decomposition strengthening. • Based on the deformation behaviors, Ti(Zr)-rich and Nb(Ta)-rich phases benefit plasticity and yield strength, respectively. • The decreased yield strength of TiZrNbTa annealed at 800℃ is caused by the vein-like phase formation in grain boundaries. [ABSTRACT FROM AUTHOR]

Although a great number of objective methods for assessing perceptual image quality have been proposed, their performances on different distortion types are still unsatisfactory. It is a great challenge for image quality assessment (IQA) that distorted images may have similar visual perception even if their distortion types and magnitudes are totally different, because some structural changes caused by distortions are visually imperceptible. In this paper, we propose a novel full-reference IQA (FR-IQA) scheme based on multiscale fuzzy gradient similarity deviation (MFGSD), where fuzzy inference system is introduced to reduce the negative impact of imperceptible distortions, and the standard deviation of fuzzy gradient similarity is utilized to measure their quality distinction. Extensive experiments are conducted on two publicly available image databases, and compared with many existing state-of-art FR-IQA schemes, and the proposed MFGSD has a better performance on different distortion types and strengths. [ABSTRACT FROM AUTHOR]

Background: As the fastest growing plant, duckweed can thrive on anthropogenic wastewater. The purple-backed duckweed, Landoltia punctata, is rich in starch and flavonoids. However, the molecular biological basis of high flavonoid and low lignin content remains largely unknown, as does the best method to combine nutrients removed from sewage and the utilization value improvement of duckweed biomass. Results: A combined omics study was performed to investigate the biosynthesis of flavonoid and the metabolic flux changes in L. punctata grown in different culture medium. Phenylalanine metabolism related transcripts were identified and carefully analyzed. Expression quantification results showed that most of the flavonoid biosynthetic transcripts were relatively highly expressed, while most lignin-related transcripts were poorly expressed or failed to be detected by iTRAQ based proteomic analyses. This explains why duckweed has a much lower lignin percentage and higher flavonoid content than most other plants. Growing in distilled water, expression of most flavonoid-related transcripts were increased, while most were decreased in uniconazole treated L. punctata (1/6 × Hoagland + 800 mg•L-1 uniconazole). When L. punctata was cultivated in full nutrient medium (1/6 × Hoagland), more than half of these transcripts were increased, however others were suppressed. Metabolome results showed that a total of 20 flavonoid compounds were separated by HPLC in L. punctata grown in uniconazole and full nutrient medium. The quantities of all 20 compounds were decreased by uniconazole, while 11 were increased and 6 decreased when grown in full nutrient medium. Nutrient starvation resulted in an obvious purple accumulation on the underside of each frond. Conclusions: The high flavonoid and low lignin content of L. punctata appears to be predominantly caused by the flavonoid-directed metabolic flux. Nutrient starvation is the best option to obtain high starch and flavonoid accumulation simultaneously in a short time for biofuels fermentation and natural products isolation. [ABSTRACT FROM AUTHOR]

CYP2B subfamily accounts for 2–10% of total hepatic CYP450 enzymes and participate in the metabolism of around 8% of clinical drugs. Borneol has been widely used in traditional Chinese medicine for thousands of years. There are many studies about borneol-induced promoting penetration role for a number of drugs through various physiologic barriers, whereas there is no report involved the effect of borneol on hepatic CYP2B. The present work studied the in vivo effect of borneol on the expression and activity of rat hepatic CYP2B. The results indicated that the oral administration of borneol (33, 100 and 300 mg/kg/d) to rats for consecutive 7 days increased the hepatic CYP2B1/2 activity by 1.4-, 1.7- and 2.8-fold, hepatic CYP2B1 mRNA expression by 6.3-, 8.7- and 18.1-fold, and hepatic CYP2B1/2 protein expression by 1.2-, 1.9- and 2.6-fold, respectively compared to the control. Additionally, in the borneol pre-dosing (300 mg/kg/d for consecutive 7 days) rats, the increased Cl int and decreased AUC 0–24 of bupropion were observed as compared to the control. Moreover, there were no obvious effects on CAR protein level in rat liver microsome and nucleus following the borneol treatment. Taken together, our observations indicate that borneol is an in vivo inducer of rat hepatic CYP2B with different regulatory mechanism from phenobarbital-like inducers which caused CYP2B induction with CAR activation. [ABSTRACT FROM AUTHOR]