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This article investigates finite-time stabilization of competitive neural networks with discrete time-varying delays (DCNNs). By virtue of comparison strategies and inequality techniques, finite-time stabilization of the underlying DCNNs is analyzed by designing a discontinuous state feedback controller, which simplifies the controller design and proof processes of some existing results. Meanwhile, global exponential stabilization of the DCNNs is provided under a continuous state feedback controller. In addition, global exponential stability of the DCNNs is shown as an M-matrix, which contains some published outcomes as special cases. Finally, three examples are given to illuminate the validity of the theories.

The first shallow hydrate sample off southwest Taiwan has been collected at core site MD18-3542 in cruise MD214. Attractive features are present at ~5.5 m core depth where an unconformity lies, and sediment properties are distinctively different above and below the layer. In the work, we perform rock magnetic analyses to study the core magnetic behavior, including magnetic susceptibility (MS), hysteresis parameters, and X-ray diffraction spectra. Combining the core features and rock magnetic results with the shallow hydrate formation off southwest Taiwan, we consider that variation of the core magnetic property could relate to the pyritization of magnetic minerals caused by the dissociated gas, and summarize the gas distribution at the shallow strata: Abundant methane with hydrogen sulfide trapped below the unconformity could form an anoxic setting and activate the pyritization. Dissociated gas may have also leaked to its above layer, featuring the MS anomaly at ~3.5 m core depth. Magnetite appears in addition to iron sulfides at the depth between ~6.5 and ~10.0 m, indicating a transition layer and implying the insufficiency of the dissociated gas to complete the pyritization. Iron sulfides become dominant again at the core bottom, revealing that the dissociated gas mainly migrates from deep strata.

This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stability (GAS) and global exponential stability (GES) of the underlying DBAMNNs are of concern in terms of p -norm ( p ≥ 2 ). Meanwhile, GES of the addressed DBAMNNs is also analyzed in terms of 1-norm. When distributed time delay is neglected, the GES of the corresponding bidirectional associative memory neural networks is presented as an M -matrix, which includes certain existing outcomes as special cases. Two examples are finally provided to substantiate the validity of theories.

This article investigates global exponential stabilization (GES) of Takagi-Sugeno (T-S) fuzzy memristive neural networks with multiple time-varying delays (DFMNNs) via intermittent control strategy. By resorting to differential inclusion theory, comparison means, and inequality techniques, some results are developed to ensure GES of the underlying DFMNNs via a fuzzy intermittent state feedback control law within the sense of Filippov. The outcome is generalized to GES of FMNNs with infinite distributed time delays. Additionally, the global exponential stability of FMNNs with discrete time-varying delays is explored in terms of 1-norm. The derived conditions herein contain certain existing ones as special cases. Finally, three examples are presented to illuminate the validness of the outcomes.

There is a semantic gap between people’s understanding of images and the underlying visual features of images, which makes it difficult for image retrieval results to meet the needs of individual interests. To overcome the semantic gap in image retrieval, this paper proposes a semantic image retrieval method based on interest selection. This method analyses the interest points of individual selections and gives the weight of the interest selection in different regions of an image. By extracting the underlying visual features of different regions, this paper constructs two feature vector methods after users’ interest point weighting. The two methods are called interest weighted summation and interest weighting. Finally, this paper compares the accuracy of different image classification methods using a support vector machine classification algorithm. The experimental results show that the target classification accuracy of the classification algorithm based on interest weighted summation is higher than that of the traditional and interest weighted methods. The classification algorithm based on interest weighted summation has the highest overall effect on target object classification in the four experimental scenarios. Therefore, the interest point selection method can effectively improve the overall satisfaction of image recommendation and can be used as a novel solution to overcome the semantic gap.

IntroductionCognition decline is associated with aging and certain diseases, such as neurodegenerative or neuropsychiatric disorders, diabetes and chronic kidney disease. Inflammation/neuroinflammation is considered an important causal factor, and experimental evidence suggests that anti-inflammatory natural compounds may effectively prevent cognitive decline. The goal of this study was to evaluate the effects of two natural bioactive agents, oligo-lactic acid (LAP) and fermented soy extract (ImmunBalance, IMB), on cognition in an adenine-induced cognitive impairment mouse model and to investigate the modulation of related biomarkers.MethodsMale C57 black mice were randomly assigned into the following experimental groups and received the corresponding treatments for 2 weeks before the use of adenine for model development: (1) negative control; (2) model control: injection of adenine at 50 mg/kg daily for 4 weeks; (3, 4) IMB groups: adenine injection and IMB oral gavage at 250 and 1,000 mg/kg BW, respectively; and (5) LAP group: adenine injection and LAP oral gavage at 1,000 mg/kg BW. One week after the model was developed, mice were evaluated for cognitive performances by using Y maze test, novel object recognition test, open field test, and Barnes maze tests. At the end of the experiment, brain tissues and cecum fecal samples were collected for analysis of gene expression and gut microbiota.ResultsMice treated with LAP or IMB had significantly improved spatial working memory, spatial recognition memory (LAP only), novel object recognition, and spatial learning and memory, compared with those in the model group. Gene expression analysis showed that, among a panel of cognition related genes, six of them (ELOVL2, GLUT4, Nestein, SNCA, TGFB1, and TGFB2) were significantly altered in the model group. LAP treatment significantly reversed expression levels of inflammatory/neuroinflammatory genes (SNCA, TGFB1), and IMB significantly reversed expression levels of genes related to inflammation/neuroinflammation, neurogenesis, and energy metabolism (ELOVL2, GLUT4, Nestin, TGFB1, and TGFB2). The altered microbiome was attenuated only by IMB.DiscussionIn conclusion, our data showed that LAP improved cognition associated with regulating biomarkers related to neuroinflammation and energy metabolism, whereas IMB improved cognition associated with regulating biomarkers related to neuroinflammation, energy metabolism, and neurogenesis, and modulating gut microbiota. Our results suggest that LAP and IMB may improve cognitive performance in mice via distinct mechanisms of action.

Blast furnace slag and steelmaking slag, as the main accessory products of iron and steel smelting, are piled up in large quantities due to their huge output, high treatment difficulty and low comprehensive utilization rate, which has a serious impact on the land and environment. In order to improve the comprehensive utilization of steelmaking slag, low basicity blast furnace slag was added to the existing steel slag for quenching and tempering. The influence of basicity on the chemical composition and phase precipitation of mixed slag was analyzed. In the research process, the phase composition and morphology of blast furnace slag and steel slag of Baotou Steel were analyzed using FactSage7.1 thermodynamic calculation software, ZEISS high-resolution scanning electron microscope (SEM), modern fast high-resolution Bruker energy dispersive spectrometer and AMICS-Mining automatic mineral analysis software. The results show that the mineral phase composition of blast furnace slag is mainly calcium aluminum yellow feldspar and that of steelmaking slag is mainly dicalcium silicate(C2S), magnesium-iron phase solid solution, rose pyroxene and calcium iron aluminate. When the basicity of the mixed slag is 2.0, it can effectively inhibit the formation of non-cementitious mineral anorthite and promote the formation of better cementitious mineral C2S. At the same time, it is found that the melting temperature of mixed slag decreases with the increase in Al2O3 content.

Additional file 1: Supplementary Figure 1. The expression level of SPP1 in ovarian cancer in GEPIA. (A) The expression level of SPP1 in ovarian cancer and normal tissues. (B) Representative IHC images of SPP1 expression in ovarian cancer tissue. (C) Representative IHC images of SPP1 expression in normal ovarian tissue. Supplementary Figure 2. Correlation of SPP1 expression with tumor-infiltrating immune cells in various types of cancers via the TIMER database. Supplementary Figure 3. (A) Correlation of SPP1 expression and immune subtypes (C1: wound healing, C2: IFN-gamma dominant, C3: inflammatory, C4: lymphocyte depleted) in ovarian cancer. (B) Correlation of SPP1 expression and immune subtypes (differentiated, immunoreactive, mesenchymal, proliferative) in ovarian cancer. Supplementary Table 1. Correlation between SPP1 expression and patients’ clinical characteristics. Supplementary Table 2. Spearman correlations between expression of SPP1 and TILs of ovarian cancer in TISIDB. Supplementary Table 3. Spearman correlations between expression of SPP1 and Immunoinhibitors of ovarian cancer in TISIDB. Supplementary Table 4. Correlation analysis between SPP1 and relate genes and markers of immune cells in TIMER.

X-rays play an extremely significant role in medical diagnosis, safety testing, scientific research, and other practical applications. However, as the main sources of radioactive pollution, the hazard of X-rays to human health and the environment has been a major concern. Herein, the explored perovskite scintillator of Cs

The present study aimed to explore the methods and clinical outcomes of repairing skin defects caused by knee joint trauma with a saphenous flap. Fifteen patients with skin and soft tissue defects after knee joint trauma received saphenous flap transposition, and the rotation axis point of the pedicle and transposition method were modified. The flap was transposed through a “semi-open tunnel” using a subcutaneous tissue pedicle and by parallel “bilobed” flap transposition through an “open tunnel.” The whole flap survived after the surgery in all patients. The pedicle was not bulging, had no “cat’s ear” deformity, and was flattened. The donor site was sutured directly, and the wound healed by primary intention without postoperative complications. When a saphenous flap is applied to repair the knee joint trauma skin defects with small and medium size, by modifying the pedicle transposition method, the flap rotation angle is more flexible, surgery is safer and more reliable, and flap appearance is more optimized.

This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays (DMNNs). By means of inequality techniques, theories of the M-matrix, and the comparison strategy, the Lagrange exponential stability of the underlying DMNNs is considered in the sense of Filippov, and the globally exponentially attractive set is estimated through employing the M-matrix and external input. Especially, when the external input is not concerned, the Lyapunov exponential stability of the corresponding DMNNs is developed immediately in the form of an M-matrix, which contains some published outcomes as special cases. Furthermore, by constructing an M-matrix-based differential system, the Lyapunov exponential stability of the DMNNs is studied, which is less conservative than some existing ones. Finally, three simulation examples are carried out to examine the validness of the theories.

This article investigates the global exponential stabilization (GES) of inertial memristive neural networks with discrete and distributed time-varying delays (DIMNNs). By introducing the inertial term into memristive neural networks (MNNs), DIMNNs are formulated as the second-order differential equations with discontinuous right-hand sides. Via a variable transformation, the initial DIMNNs are rewritten as the first-order differential equations. By exploiting the theories of differential inclusion, inequality techniques, and the comparison strategy, the $p$ th moment GES ( $p\geq 1$ ) of the addressed DIMNNs is presented in terms of algebraic inequalities within the sense of Filippov, which enriches and extends some published results. In addition, the global exponential stability of MNNs is also performed in the form of an M-matrix, which contains some existing ones as special cases. Finally, two simulations are carried out to validate the correctness of the theories, and an application is developed in pseudorandom number generation.

Responses of soil aggregate stability and nutrient stoichiometry to land use change are of great significance to the protection and restoration of fragile ecosystems. In order to explore the influence of the law of land use change on soil aggregate stability and C, N, and P nutrient stoichiometric characteristics in the karst ecosystem of Southwest China, a typical region of the Southwest China Karst was selected as the study area. A series of sample plots were established for the seven typical land use types, and the constitution; stability; and C, N, and P contents and stoichiometric ratios of soil aggregates were studied. The results showed that land use change significantly affected the soil aggregate stability. The soil aggregate stability of corn fields and abandoned land was relatively high, whereas that of grassland and bare land was relatively low. The soil aggregate stability generally decreased with the increase in soil layer. There were significant differences in the C, N, and P contents of soil aggregates between different land use types. The C and N contents of soil aggregates of open woodland and abandoned land were higher, the C and N contents of grassland were lower, and the P contents of soil aggregates of bare land and abandoned land were relatively higher. Land use change also significantly affected the stoichiometric ratio of soil aggregate C, N, and P. The C:N of soil aggregates of arbor forest land, open forest land, and abandoned land was relatively high, and that of grassland was the lowest. The C:P of soil aggregates of arbor woodland and open woodland was higher than that in shrub woodland and grassland. The soil aggregate N:P of arbor forest land, open forest land, and shrub forest land was higher and was significantly higher than that of bare land and abandoned land. There were significant or extremely significant correlations between the constitution and stability of soil aggregates and the stoichiometric characteristics of soil aggregate C, N, and P. Soil aggregate stability was positively correlated with the mass fraction of large soil aggregates. The mass fraction of large soil aggregates increased, and the stability of aggregates correspondingly increased, which was conducive to the maintenance and accumulation of soil C, N, and P nutrients. These results are of great significance for vegetation restoration, soil and water conservation, and land use regulation in karst ecosystems of Southwest China.

An extensive study was performed to discover a series of novel 20(R)-panaxadiol derivatives with various substituents at the 3-OH position as nontoxic, brain-permeable, multi-target leads for treating Alzheimer's disease. In vitro analysis revealed that a compound bearing benzyl-substituted carbamate, which we denoted compound 14a, exhibited the most potent neuroprotective activity, with an EC

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

This paper focuses on Lagrange exponential stability and finite-time stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks with discrete and distributed time-varying delays (DFMNNs). By resorting to theories of differential inclusions and the comparison strategy, an algebraic condition is developed to confirm Lagrange exponential stability of the underlying DFMNNs in Filippov's sense, and the exponentially attractive set is estimated. When external input is not considered, global exponential stability of DFMNNs is derived directly, which includes some existing ones as special cases. Furthermore, finite-time stabilization of the addressed DFMNNs is analyzed by exploiting inequality techniques and the comparison approach via designing a nonlinear state feedback controller. The boundedness assumption of activation functions is removed herein. Finally, two simulations are presented to demonstrate the validness of the outcomes, and an application is performed in pseudorandom number generation.

This paper investigates the $\phi $ -type stability and robust stability for a general class of stochastic reaction–diffusion neural networks (SRDNNs) with Dirichlet boundary conditions, infinite discrete time-varying delays, and infinite continuously distributed delays. By virtue of inequality techniques, properties of $\boldsymbol M$ -matrix, and theories of stochastic analysis, several sufficient criteria are obtained to guarantee the almost sure $\boldsymbol \phi $ -type stability, $\boldsymbol p$ th moment $\boldsymbol \phi $ -type stability, and $\boldsymbol \phi $ -type robust stability of the underlying SRDNNs with hybrid unbounded time delays. With appropriate choices of the function $\boldsymbol \phi $ , the $\boldsymbol \phi $ -type stability reduces to the exponential stability, polynomial stability, and logarithmic stability. Additionally, the developed results herein include some existing ones as special cases. A numerical simulation is performed to substantiate the effectiveness and superiority of the theoretical analysis.

NK-lysins, a type of broad-spectrum antimicrobial peptide (AMP), act as an essential effector of innate defense against microbial attack in higher vertebrates and so in fish. The present study delineates the structural and functional characterization of NK-lysin from yellow catfish (Pelteobagrus fulvidrac) (Pelteobagrus fulvidraco). PfNK-lysin encodes a 153-residue peptide, which displays the hallmark features of other known NK-lysins with the ordered array of six well-conserved cysteine residues and five-exon/four-intron structure. It was found to be ubiquitous in tissues, being detected most abundantly in gill and head kidney. In vivo exposure to stimuli (LPS, PolyI:C, and Edwardsiella ictaluri) induced PfNK-lysin expression in head kidney and spleen. Synthetic PfNK-lysin-derived peptide exhibited in vitro bactericidal potency against both Gram-positive and Gram-negative bacteria, with the highest inhibitory effect on pathogen Edwardsiella ictaluri. Fluorescence microscopy and scanning electron microscopy further confirmed its capacity to cause damage to the bacterial plasma membrane. Taken together, these data suggest that PfNK-lysin might participate in antimicrobial defense of yellow catfish by membrane-disruptive action.

OBJECTIVE The aim of this study was to build a convolutional neural network (CNN)–based prediction model of glioblastoma (GBM) molecular subtype diagnosis and prognosis with multimodal features. METHODS In total, 222 GBM patients were included in the training set from Sun Yat-sen University Cancer Center (SYSUCC) and 107 GBM patients were included in the validation set from SYSUCC, Xuanwu Hospital Capital Medical University, and the First Hospital of Jilin University. The multimodal model was trained with MR images (pre- and postcontrast T1-weighted images and T2-weighted images), corresponding MRI impression, and clinical patient information. First, the original images were segmented using the Multimodal Brain Tumor Image Segmentation Benchmark toolkit. Convolutional features were extracted using 3D residual deep neural network (ResNet50) and convolutional 3D (C3D). Radiomic features were extracted using pyradiomics. Report texts were converted to word embedding using word2vec. These three types of features were then integrated to train neural networks. Accuracy, precision, recall, and F1-score were used to evaluate the model performance. RESULTS The C3D-based model yielded the highest accuracy of 91.11% in the prediction of IDH1 mutation status. Importantly, the addition of semantics improved precision by 11.21% and recall in MGMT promoter methylation status prediction by 14.28%. The areas under the receiver operating characteristic curves of the C3D-based model in the IDH1, ATRX, MGMT, and 1-year prognosis groups were 0.976, 0.953, 0.955, and 0.976, respectively. In external validation, the C3D-based model showed significant improvement in accuracy in the IDH1, ATRX, MGMT, and 1-year prognosis groups, which were 88.30%, 76.67%, 85.71%, and 85.71%, respectively (compared with 3D ResNet50: 83.51%, 66.67%, 82.14%, and 70.79%, respectively). CONCLUSIONS The authors propose a novel multimodal model integrating C3D, radiomics, and semantics, which had a great performance in predicting IDH1, ATRX, and MGMT molecular subtypes and the 1-year prognosis of GBM.

Nearly half of pregnancies worldwide are unintended mainly due to failure of contraception, resulting in negative effects on women's health. Male contraception techniques, primarily condoms and vasectomy, play a crucial role in birth control, but cannot be both highly effective and reversible at the same time. Herein, an ultrasound (US)-induced self-clearance hydrogel capable of real-time monitoring is utilized foriin situ/iinjection into the vas deferens, enabling effective contraception and noninvasive recanalization whenever needed. The hydrogel is composed of (i) sodium alginate (SA) conjugated with reactive oxygen species (ROS)-cleavable thioketal (SA-tK), (ii) titanium dioxide (TiOsub2/sub), which can generate a specific level of ROS after US treatment, and (iii) calcium chloride (CaClsub2/sub), which triggers the formation of the hydrogel. For contraception, the above mixture agents are one-time injected into the vas deferens, which can transform from liquid to hydrogel within 160 s, thereby significantly physically blocking the vas deferens and inhibiting movability of sperm. When fertility is needed, a noninvasive remedial ultrasound can make TiOsub2/subgenerate ROS, which cleaves SA-tK to destroy the network of the hydrogel. Owing to the recanalization, the refertility rate is restored to 100%. Meanwhile, diagnostic ultrasound (D-US, 22 MHz) can monitor the occlusion and recanalization process in real-time. In summary, the proposed hydrogel contraception can be a reliable, safe, and reversible male contraceptive strategy that addresses an unmet need for men to control their fertility.

Covalent organic frameworks (COFs) are a new kind of porous material. Nano-COFs have a unique structure. They have the characteristics of high stability, adjustable channel structure, and excellent biocompatibility modularity. In addition to adsorption/separation, sensing, catalysis, and energy applications, COFs and their complexes have broad prospects as excellent photothermal therapy carriers in tumor therapy. In this chapter, we present the latest advances in photothermal therapy of cancer based on COFs and its complexes, in order to inspire researchers to make further contributions in this new and promising field.

Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly consider static, pair-wise interactions with limited relational reasoning. To promote more comprehensive interaction modeling and relational reasoning, we propose DynGroupNet, a dynamic-group-aware network, which can i) model time-varying interactions in highly dynamic scenes; ii) capture both pair-wise and group-wise interactions; and iii) reason both interaction strength and category without direct supervision. Based on DynGroupNet, we further design a prediction system to forecast socially plausible trajectories with dynamic relational reasoning. The proposed prediction system leverages the Gaussian mixture model, multiple sampling and prediction refinement to promote prediction diversity, training stability and trajectory smoothness, respectively. Extensive experiments show that: 1)DynGroupNet can capture time-varying group behaviors, infer time-varying interaction category and interaction strength during trajectory prediction without any relation supervision on physical simulation datasets; 2)DynGroupNet outperforms the state-of-the-art trajectory prediction methods by a significant improvement of 22.6%/28.0%, 26.9%/34.9%, 5.1%/13.0% in ADE/FDE on the NBA, NFL Football and SDD datasets and achieve the state-of-the-art performance on the ETH-UCY dataset., Comment: arXiv admin note: text overlap with arXiv:2204.08770

According to the typical Sillén-structured BiOBr, a simple solvothermal method was used to successfully synthesise Sillén-structured bimetallic oxyhalide CdBiO2Br with the existence of 1-hexadecyl-3-methylimidazolium bromide ([C16mim]Br), a kind of reactive ionic liquid. The introduction of the metal cadmium, which can form Sillén-structured bimetallic oxyhalide, made the alternating structure of BiOBr originally [Bi2O2]2+ and bilayer Br- modified to that of [CdBiO2]+ and monolayer Br-. So that the distance between layer and layer is greatly shortened, which facilitates the migration and separation of photogenerated carriers and promotes the generation of more reactive oxygen species. After modification, the band positions of CdBiO2Br materials can make more full use of visible light and more favourable utilisation of solar resources. As confirmed by radical trapping analysis and ESR analysis, superoxide radical (·O2−) and hole (h+) acted the major part during photocatalysis. The possible intermediate products that appeared during the degradation progress were analyzed by LC–MS. Moreover, the generation of superoxide ions was quantitatively analyzed by nitroblue tetrazolium chloride (NBT). In this paper, we present an ultra-thin layered material for visible light catalysis, which enlightens a feasible scheme for the research and development of new layered photocatalytic materials.

This paper reveals a data bias issue that can severely affect the performance while conducting a machine learning model for malicious URL detection. We describe how such bias can be identified using interpretable machine learning techniques, and further argue that such biases naturally exist in the real world security data for training a classification model. We then propose a debiased training strategy that can be applied to most deep-learning based models to alleviate the negative effects from the biased features. The solution is based on the technique of self-supervised adversarial training to train deep neural networks learning invariant embedding from biased data. We conduct a wide range of experiments to demonstrate that the proposed strategy can lead to significantly better generalization capability for both CNN-based and RNN-based detection models.