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Highlights: This review systematically summarizes the source of electricity, the key choice of catalyst, and the potentiality of electrolyte for prospective hydrogen generation. Each section provides comprehensive overview, detailed comparison and obvious advantages in these system configurations. The problems of hydrogen generation from electrolytic water splitting and directions of next-generation green hydrogen in the future are discussed and outlooked. Direct regeneration method has been widely concerned by researchers in the field of battery recycling because of its advantages of in situ regeneration, short process and less pollutant emission. In this review, we firstly analyze the primary causes for the failure of three representative battery cathodes (lithium iron phosphate, layered lithium transition metal oxide and lithium cobalt oxide), targeting at illustrating their underlying regeneration mechanism and applicability. Efficient stripping of material from the collector to obtain pure cathode material has become a first challenge in recycling, for which we report several pretreatment methods currently available for subsequent regeneration processes. We review and discuss emphatically the research progress of five direct regeneration methods, including solid-state sintering, hydrothermal, eutectic molten salt, electrochemical and chemical lithiation methods. Finally, the application of direct regeneration technology in production practice is introduced, the problems exposed at the early stage of the industrialization of direct regeneration technology are revealed, and the prospect of future large-scale commercial production is proposed. It is hoped that this review will give readers a comprehensive and basic understanding of direct regeneration methods for used lithium-ion batteries and promote the industrial application of direct regeneration technology. [ABSTRACT FROM AUTHOR]

Machine learning methods have been widely used control and information systems. Robust learning is an important issue in machine learning field. In this work, we propose a novel robust regression framework. Specifically, we propose a robust similarity measure induced by correntropy, and its important properties are demonstrated theoretically such as symmetry, boundedness, nonnegativity, consistency, smoothness and approximation behaviors. Moreover, the proposed robust metric extends the traditional metrics such as the l 2 -norm and l 0 -norm as the kernel parameter approaches different values. Then with the proposed similarity metric and ϵ -insensitive pinball loss, a new robust twin support vector regression framework (RTSVR) is proposed to handle robust regression problems. The linear RTSVR model is first built, and a kernelled RTSVR version is developed to deal with nonlinear regressions. To handle the nonconvexity of the proposed RTSVR, we use DC (different of convex function) programming algorithm to iteratively solve the problem, and the resulting algorithm converges linearly. To test the proposed RTSVR, numerical experiments are implemented on two databases including a public benchmark database and a practical application database. Experiments on benchmark data with different types of noise illustrate that the proposed methods achieve better performance than the traditional methods in most cases. Experiments on the application database, the proposed RTSVR is combined with near-infrared (NIR) spectral technique to analyze the hardness of licorice seeds in low frequency,intermediate frequency and high frequency spectral regions respectively. Experiments on different spectral regions show that the performance of the RTSVR is better than that of the traditional methods in all spectral regions. [ABSTRACT FROM AUTHOR]

The CRISPR/Cas systems have emerged as transformative tools for precisely manipulating plant genomes and enhancement. It has provided unparalleled applications from modifying the plant genomes to resistant enhancement. This review manuscript summarises the mechanism, application, and current challenges in the CRISPR/Cas genome editing technology. It addresses the molecular mechanisms of different Cas genes, elucidating their applications in various plants through crop improvement, disease resistance, and trait improvement. The advent of the CRISPR/Cas systems has enabled researchers to precisely modify plant genomes through gene knockouts, knock-ins, and gene expression modulation. Despite these successes, the CRISPR/Cas technology faces challenges, including off-target effects, Cas toxicity, and efficiency. In this manuscript, we also discuss these challenges and outline ongoing strategies employed to overcome these challenges, including the development of novel CRISPR/Cas variants with improved specificity and specific delivery methods for different plant species. The manuscript will conclude by addressing the future perspectives of the CRISPR/Cas technology in plants. Although this review manuscript is not conclusive, it aims to provide immense insights into the current state and future potential of CRISPR/Cas in sustainable and secure plant production. [ABSTRACT FROM AUTHOR]

We conducted a systematic search across medical databases, including PubMed, Web of Science, EMBASE, and Cochrane Library, up to March 2023. A total of 1944 subjects or individuals from 17 studies were included in our final analysis. The correlation coefficient (r) between sKlotho and calcium was [0.14, (0.02, 0.26)], and a moderate heterogeneity was observed (I2 = 66%, P < 0.05). The correlation coefficient (r) between Klotho and serum phosphate was [− 0.21, (− 0.37, − 0.04)], with apparent heterogeneity (I2 = 84%, P < 0.05). The correlation coefficient (r) between sKlotho and parathyroid hormone and vascular calcification was [− 0.23,(− 0.29, − 0.17); − 0.15, (− 0.23, − 0.08)], with no significant heterogeneity among the studies. (I2 = 40%, P < 0.05; I2 = 30%, P < 0.05). A significant correlation exists between low sKlotho levels and an increased risk of CKD–MBD in patients with CKD. According to the findings, sKlotho may play a role in alleviating CKD–MBD by lowering phosphorus and parathyroid hormone levels, regulating calcium levels, and suppressing vascular calcification. As analysis showed that sKlotho has an important impact on the pathogenesis and progression of CKD–MBD in CKD patients. Nonetheless, further comprehensive and high-quality studies are needed to validate our conclusions. [ABSTRACT FROM AUTHOR]

Background: Aberrant expression of circular RNA (circRNA) has been demonstrated to be related to atherosclerosis (AS) formation. However, the mechanism of circCHMP5 (also known as hsa_circ_0003575) in AS formation remains unclear. Methods: Oxidized low-density lipoprotein (ox-LDL) was used to treat human umbilical vein endothelial cells (HUVECs) to construct a cell injury model. The expression level of circCHMP5, miR-532-5p, and Rho-associated protein kinase 2 (ROCK2) was measured using quantitative real-time PCR. Cell cycle, apoptosis, proliferation, and angiogenesis were determined by flow cytometry, 5-ethynyl-2′-deoxyuridine (EdU) assay, and tube formation assay. In addition, the protein expression of apoptosis markers, inflammation factors, and ROCK2 was detected by western blot analysis. The interaction between miR-532-5p and circCHMP5 or ROCK2 was assessed by dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Results: Our results indicated that circCHMP5 was overexpressed in ox-LDL-induced HUVECs. CircCHMP5 knockdown promoted cell cycle, proliferation, and angiogenesis while inhibiting apoptosis and inflammation in ox-LDL-induced HUVECs. MiR-532-5p could be sponged by circCHMP5, and its inhibitor reversed the negative regulation of si-circCHMP5 on ox-LDL-induced HUVECs injury. ROCK2, a target of miR-532-5p, reversed the inhibition effect of miR-532-5p on ox-LDL-induced HUVECs injury. Furthermore, we confirmed that circCHMP5 upregulated ROCK2 by sponging miR-532-5p. Conclusion: To sum up, our data showed that circCHMP5 regulated the miR-532-5p/ROCK2 axis to accelerate ox-LDL-induced HUVECs injury, confirming that circCHMP5 might be a potential target for AS treatment. [ABSTRACT FROM AUTHOR]

Multi-metric learning is important for improving performance of learners. For complex data, multi metric learning algorithms need intensive research. Moreover, the existing multi-metric learning methods may lead to the distance not being comparable. To solve these shortcomings and characterize better complexity data, we propose a novel multi-metric learning framework, where each class is divided into several clusters, and then a local metric and two concentric hypers-pheres are trained jointly in a cluster, such that the samples of the same cluster distribute within one hypersphere, and the classification margin are as large as possible simultaneously. This will leads to intra-class compactness and inter-class dispersion. During the test phase, the relative distance in learned metric space is designed to make classification decisions. A new example is classified to the class of its closest hyper-sphere center. This ensures that the comparison of distances is meaningful and avoids effectively the limitation of k-nearest neighbors (kNN) classifiers. Moreover,some important properties the proposed algorithm are analyzed theoretically. Further, an alternating iterative algorithm is developed to solve the problem. Numerical experiments are carried out on different scales and types datasets. Experiment results confirm the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A high-order gas kinetic flux solver (GKFS) is presented for simulating inviscid compressible flows. The weighted essentially non-oscillatory (WENO) scheme on a uniform mesh in the finite volume formulation is combined with the circular function-based GKFS (C-GKFS) to capture more details of the flow fields with fewer grids. Different from most of the current GKFSs, which are constructed based on the Maxwellian distribution function or its equivalent form, the C-GKFS simplifies the Maxwellian distribution function into the circular function, which ensures that the Euler or Navier-Stokes equations can be recovered correctly. This improves the efficiency of the GKFS and reduces its complexity to facilitate the practical application of engineering. Several benchmark cases are simulated, and good agreement can be obtained in comparison with the references, which demonstrates that the high-order C-GKFS can achieve the desired accuracy. [ABSTRACT FROM AUTHOR]

The massive conversion of resourceful biomass to carbon nanomaterials not only opens a new avenue to effective and economical disposal of biomass, but provides a possibility to produce highly valued functionalized carbon-based electrodes for energy storage and conversion systems. In this work, biomass is applied to a facile and scalable one-step pyrolysis method to prepare three-dimensional (3D) carbon nanotubes/mesoporous carbon architecture, which uses transition metal inorganic salts and melamine as initial precursors. The role of each employed component is investigated, and the electrochemical performance of the attained product is explored. Each component and precise regulation of their dosage is proven to be the key to successful conversion of biomass to the desired carbon nanomaterials. Owing to the unique 3D architecture and integration of individual merits of carbon nanotubes and mesoporous carbon, the assynthesized carbon nanotubes/mesoporous carbon hybrid exhibits versatile application toward lithium-ion batteries and Zn-air batteries. Apparently, a significant guidance on effective conversion of biomass to functionalized carbon nanomaterials can be shown by this work. [ABSTRACT FROM AUTHOR]

The selective capture of copper from strongly acidic solutions is of vital importance from the perspective of sustainable development and environmental protection. Metal organic frameworks (MOFs) have attracted the interest of many scholars for adsorption due to their fascinating physicochemical characteristics, including adjustable structure, strong stability and porosity. Herein, pz-UiO-66 containing a pyrazine structure is successfully synthesized for the efficient separation of copper from strongly acidic conditions. Selective copper removal at low pH values is accomplished by using this material that is not available in previously reported metal—organic frameworks. Furthermore, the material exhibits excellent adsorption capacity, with a theoretical maximum copper uptake of 247 mg/g. As proven by XPS and FT-IR analysis, the coordination of pyrazine nitrogen atoms with copper ions is the dominant adsorption mechanism of copper by pz-UiO-66. This work provides an opportunity for efficient and selective copper removal under strongly acidic conditions, and promises extensive application prospects for the removal of copper in the treatment for acid metallurgical wastewater. [ABSTRACT FROM AUTHOR]

Correntropy is a locally second-order statistical measure in kernel space. The different kernel functions induce different correntropy with different properties. In this work, we propose an asymmetric mixture kernel and the corresponding correntropy. Then, we propose a new loss function (called RCH-loss) that is induced by the correntropy with the reproducing asymmetric kernel. Some important properties of the proposed kernel and RCH-loss are demonstrated such as non-convexity, boundedness, asymmetry and asymptotic approximation. Moreover, the proposed RCH-loss satisfies Bayes optimal decision rule. With the RCH-loss function, a new robust classification framework is built to handle robust classification. Theoretically, we prove the generalization bound of the proposed model based on the Rademacher complexity. Following that, DC (difference of convex functions) programming algorithm (DCA) is developed to solve the problem iteratively, where ADMM (alternating direction method of multipliers) is used to solve the subproblem at each iteration. Moreover, we analyze the computation complexity of the algorithm and the sensitivity of parameters. Numerical experimentations are carried out on various datasets including benchmark data sets and artificial data sets with different noise levels. The experimental results display the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Multi-metric learning is important for improving classification performance since learning a single metric is usually insufficient for complex data. The existing multi-metric learning methods are based on the triplet constraints, and thus are with high computing complexity. In this work, we propose an efficient multi-metric learning framework by a pair of two-metric learning schemes (called TMML) to jointly train two local metrics and a global metric, where the distances between samples are automatically adjusted to maximize classification margin. Instead of the triplet constraints, the proposed TMML is based on the pair constraints to reduce the computational burden. Moreover, a global regularization is introduced to improve generalization and control overfitting. The proposed TMML improves the limitation of a single metric, where a pair of local metrics are interrelated to conduct adaptation for the local characteristics, while global metrics are to depict the common properties from all the data. Furthermore, we develop an alternating direction iterative algorithm to optimize the proposed TMML. The convergence of the algorithm is analyzed theoretically. Numerical experiments are carried out on different scale datasets. Under different evaluation criteria, experiments show that the proposed TMML is superior to the single metric learning methods, and achieves better performance than other state-of-the-art multi-metric learning methods in most cases. [ABSTRACT FROM AUTHOR]

The adsorbents—adsorbates interaction is critical for resourcelization in heavy metal wastewater treatment. Nevertheless, it is still indistinct to depict the impact of metal center effect on heavy metals removal performance in metal-organic frameworks (MOFs)-based adsorbents. Herein, a series of MOFs with different metal centers of Mg(II), La(III), and Zr(IV) are rationally designed, and the effect of electronic structure on the Sb(V) removal performance is systematically investigated. The obtained La-MGs achieve Sb(V) adsorption capacity of 897.6 mg/g, which is about 1.2 and 4.5 times above average than those of Zr-MGs and Mg-MGs, respectively. On account of more edge adsorption sites achieve, the sites utilization efficiency of La-MGs (92.1%) is much better than Zr-MGs (75.0%) and Mg-MGs (20.4%). Furthermore, density functional theory (DFT) calculations reveal that La-MGs are more active than Mg-MGs and Zr-MGs, owing to the lower adsorption energy, higher charge transfer, and stronger bonding interaction, which will promote the Sb(V) removal performance. The experimental results in practical water indicate that La-MGs effectively capture antimony at low concentration, reaching drinking water standard in samples from Ganjiang River. This study opens an avenue for atomic-level insight into high-efficient absorbents design for water treatment from electronic structure-modification of active centers. [ABSTRACT FROM AUTHOR]

In the assessment of the tool wear state for the spiral edge of milling cutter based on machine vision, the traditional assessment criterion is often inaccurate due to the problem of missing of information, especially near the tip area. In order to deal with this problem, different lighting-condition settings and additional non-image information compensation techniques are needed. In view of this, an integration tool wear detection method combined line laser edge detection and machine vision is proposed. Subsequently, the combined data acquirement experimental system is designed and built, which can simultaneously acquire two types of parameters: diameter and images under the same detection condition. Then, based on the above information, a multi-dimension series assessment criterion is proposed consisting of three dimensions index: The one-dimensional assessment index gives the average wear value of the spiral side, which is mainly used as data ordination and characterizes the rule of tool wear degradation; the two-dimensional assessment index describes the contour of the wear region and calculates the area value, which can predict the change of the tool wear stage more precisely; and the three-dimensional assessment index gives the 3D morphology by adding depth information within the wear region and quantifies the volume of the worn-off part, which provides clues for early warning of the deterioration caused by the tool worn. Experiments proved that the multi-dimension series assessment criterion is helpful in reflecting the trend of tool life and giving a more accurate assessment of the tool wear state for the spiral edge of a milling cutter. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) has attracted widespread attention due to its simple, quick and good performance. In this work, in order to deal with cross data quickly and efficiently, we first propose generalized eigenvalue proximal extreme learning machine (GEPELM). It takes the form of ratio into consideration to seek for two non-parallel separating hyperplanes in ELM feature space, each of which is close to the samples of its own class and far away from the others simultaneously. Then generalized eigenvalue algorithm is adopted to solve, which incurs GEPELM to enjoy faster calculation speed than TELM. Further, improved generalized eigenvalue proximal extreme learning machine (IGEPELM) is proposed, which uses minus instead of ratio to avoid singular value phenomenon and further mitigate the computational burden by solving two standard eigenvalue problems. To further improve classification performance, generalized eigenvalue proximal extreme learning machine based on inter-class graph (GGEPELM) is proposed, which incorporates the geometric structure information of dissimilar samples into the guideline of GEPELM. In addition, the proposed classifiers are all extended to kernel ELM framework to handle non-linear data more precisely. Moreover, Sherman-Morrison-Woodbury formula is utilized to reduce time complexity of matrix inversion. Simultaneously, a quick solution strategy is incorporated into GEPELMs and GGEPELMs to mitigate the burden of solving large-scale problems. The numerical simulations are carried out on three databases including a benchmark database, an artificial database and a practical application database, which demonstrates the proposed classifiers enjoy high computational speed, good generalization performance and insensitivity to parameters. [ABSTRACT FROM AUTHOR]

Semi-supervised learning is an attractive technique for using unlabeled data in classification. In this work, an efficient semi-supervised extreme learning machine (ELM) classification framework is proposed by introducing the Laplacian regularization term. It tries to build two nonparallel hyperplanes such that each hyperplane is closer to one of the two classes and farther away from the other class in ELM feature space, based on which, two new semi-supervised ELM classification algorithms are proposed. First, we formulate semi-supervised ELM as a ratio form, and reformulate it as a generalized eigenvalue problem (called Laplacian generalized eigenvalue extreme learning machine, LapGELM) to solve simply. Then we replace the form of ratio with a form of difference so that the optimization problems can be transformed into a standard eigenvalue problem (called Laplacian standard eigenvalue ELM, LapSELM) to control overfit and solve quickly. Furthermore, the proposed algorithms are implemented on various datasets with different sizes and structures. Compared with traditional methods, experiment results show the feasibility and effectiveness of the proposed algorithms due to their simplicity, rapidity, and good generalization performance. [ABSTRACT FROM AUTHOR]

The projection twin support vector machine (PTSVM) is a potential tool for classification problem. However the loss function of PTSVM is hinge loss function which is a unbounded loss and not robust enough to outliers. In this work, a robust PTSVM (termed RSHPTSVM) is proposed based on rescaled square hinge loss (RSH-loss) to handle classification problem. A close relationship between RSH-loss and correntropy is established theoretically. The RSH-loss can be viewed as a correntropy-induced loss by a reproducing piecewise kernel. As such a correntropy loss, it has vastly different properties from hinge loss such as boundedness, robustness and nonconvexity. Moreover, RSH-loss is with higher order statistical information from samples. However the nonconvexity of RSHPTSVM makes it difficult to optimize, so that an efficient iterative optimization algorithm based on semi-quadratic optimization theory is proposed to solve RSHPTSVM, which can quickly converge to the optimal solution. Furthermore, we extend our RSHPTSVM from binary classification to multi-classification and propose a robust projection multi-birth support vector machine model (termed RSHPMBSVM). The proposed method is implemented on various datasets including three artificial datasets, UCI datasets, and a practical application dataset. The experiment results under no noise and label noise circumstance confirm the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Cold-resistance pathways that operate in model plants such as Arabidopsis thaliana and Oryza sativa have been studied extensively. It has been found that CBF genes play an important role in plant cold resistance. Liriodendron chinense, a tree known for its graceful tree shape and widely spread in south China, has weak cold tolerance. However, little is known about its response to cold. To further study the function of L. chinense CBF gene family, we started by characterizing all members of this gene family in the L. chinense genome and their expression profiling. Phylogenetic analysis found that 14 CBF genes in L. chinense are more closely related to their homologues in woody plants and A. thaliana than those in O. sativa. Cis-acting elements and GO analysis showed that some LcCBF genes participated in the biological process of cold stress response. The transcriptomic and RT-qPCR data showed that most of LcCBF genes displayed an initially increasing and subsequently decreasing trend during cold stress course and the expression profile of each member was different. Some LcCBF genes exhibited a different abundance in callus, root, stem and leaf tissues. The structure and expression characteristics of LcCBF genes imply that they may have similar and different functions in response to cold stress conditions. The identification and analysis of LcCBF gene family have laid the foundation for future studies into L. chinense cold stress mechanisms and for the cultivation of cold-resistance cultivars. [ABSTRACT FROM AUTHOR]

Mutations in CSNK2B lead to Poirier-Bienvenu neurodevelopmental syndrome (POBINDS), a rare neurodevelopmental disorder. Only 14 cases of POBINDS have been reported worldwide. The main manifestations are seizures, often tonic–clonic, with or without intellectual disability, growth retardation, and developmental language retardation. We conducted a comprehensive phenotypic mining and trio-whole exome sequencing on six children with POBINDS for gene diagnosis and analyzed the different variants using bioinformatics analysis software and related experiments. This paper reviews previous literature and discusses two common missense variants that lead to structural changes. Among the six patients, four, one, and one had tonic–clonic, myoclonic, and febrile seizures, respectively. Language development disorder, motor development disorder, and developmental delay/intellectual disability (DD/ID) are the main clinical features. All children had de novo mutations in CSNK2B, including three missense variants (c.410G > T/p.(Cys137Phe), c.494A > G/p.(His165Arg), and c.3G > A/p.(Met1Ile)), two splice variants (c.292-2A > T, c.558-3 T > G), and one frameshift variant (c.499delC/p.(Leu167Serfs*60)). Three missense variants were predicted to be harmful by various software programs, and two splicing variants were found to produce new exonic splicing enhancers by the minigene assay. Western blot analysis showed that the frameshift variant resulted in decreased protein expression. According to a literature review, c.3G > A/p.(Met1Ile), c.292-2A > T, c.558-3 T > G, and c.499delC/p.(Leu167Serfs*60) are novel variants of CSNK2B. The decrease or loss of protein function caused by CSNK2B mutations may be a pathogenic factor in this cohort. The severity of the POBINDS phenotype differs, and refractory epilepsy may be accompanied by a more serious DD/ID, language disorder, and motor retardation. At present, there is no specific treatment, and antiepileptic therapy usually requires the combination of two or more anti-epileptic drugs. [ABSTRACT FROM AUTHOR]

Least squares kernel-based methods have been widely used in regression problems due to the simple implementation and good generalization performance. Among them, least squares support vector regression (LS-SVR) and extreme learning machine (ELM) are popular techniques. However, the noise sensitivity is a major bottleneck. To address this issue, a generalized loss function, called ℓ s -loss, is proposed in this paper. With the support of novel loss function, two kernel-based regressors are constructed by replacing the ℓ 2 -loss in LS-SVR and ELM with the proposed ℓ s -loss for better noise robustness. Important properties of ℓ s -loss, including robustness, asymmetry and asymptotic approximation behaviors, are verified theoretically. Moreover, iteratively reweighted least squares are utilized to optimize and interpret the proposed methods from a weighted viewpoint. The convergence of the proposal is proved, and detailed analyses of robustness are given. Experiments on both artificial and benchmark datasets confirm the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

Nowadays, artificial bone materials have been widely applied in the filling of non-weight bearing bone defects, but scarcely ever in weight-bearing bone defects. This study aims to develop an artificial bone with excellent mechanical properties and good osteogenic capability. Firstly, the collagen-thermosensitive hydrogel-calcium phosphate (CTC) composites were prepared as follows: dissolving thermosensitive hydrogel at 4 °C, then mixing with type I collagen as well as tricalcium phosphate (CaP) powder, and moulding the composites at 37 °C. Next, the CTC composites were subjected to evaluate for their chemical composition, micro morphology, pore size, Shore durometer, porosity and water absorption ability. Following this, the CTC composites were implanted into the muscle of mice while the 70% hydroxyapatite/30% β-tricalcium phosphate (HA/TCP) biomaterials were set as the control group; 8 weeks later, the osteoinductive abilities of biomaterials were detected by histological staining. Finally, the CTC and HA/TCP biomaterials were used to fill the large segments of tibia defects in mice. The bone repairing and load-bearing abilities of materials were evaluated by histological staining, X-ray and micro-CT at week 8. Both the CTC and HA/TCP biomaterials could induce ectopic bone formation in mice; however, the CTC composites tended to produce larger areas of bone and bone marrow tissues than HA/TCP. Simultaneously, bone-repairing experiments showed that HA/TCP biomaterials were easily crushed or pushed out by new bone growth as the material has a poor hardness. In comparison, the CTC composites could be replaced gradually by newly formed bone and repair larger segments of bone defects. The CTC composites trialled in this study have better mechanical properties, osteoinductivity and weight-bearing capacity than HA/TCP. The CTC composites provide an experimental foundation for the synthesis of artificial bone and a new option for orthopedic patients. [ABSTRACT FROM AUTHOR]

The insufficiency and contamination of supervision information are the main factors affecting the performance of support vector machines (SVMs) in real-world applications. To address this issue, novel correntropy loss functions and Laplacian SVM (LapSVM) are utilized for robust semi-supervised classification. It is known that correntropy loss functions have been used in robust learning and achieved promising results. However, the potential for more diverse priors has not been extensively explored. In this paper, a correntropy loss function induced from Laplace kernel function, called LK-loss, is applied to LapSVM for the construction of robust semi-supervised classifier. Properties of LK-loss are demonstrated including robustness, symmetry, boundedness, Fisher consistency and asymptotic approximation behaviors. Moreover, the asymmetric version of LK-loss is introduced to further improve the performance. Concave-convex procedure (CCCP) technique is used to handle the non-convexity of Laplace kernel-induced correntropy loss functions iteratively. Experimental results show that in most cases, the proposed methods have better generalization performance than the comparing ones, which demonstrate the feasibility and effectiveness of the proposed semi-supervised classification framework. [ABSTRACT FROM AUTHOR]

Ensemble learning is an effective technique to improve performance and stability compared to single classifiers. This work proposes a selective ensemble classification strategy to handle missing data classification, where an uncertain extreme learning machine with probability constraints is used as individual (or base) classifiers. Then, three selective ensemble frameworks are developed to optimize ensemble margin distributions and aggregate individual classifiers. The first two are robust ensemble frameworks with the proposed loss functions. The third is a sparse ensemble classification framework with the zero-norm regularization, to automatically select the required individual classifiers. Moreover, the majority voting method is applied to produce ensemble classifier for missing data classification. We demonstrate some important properties of the proposed loss functions such as robustness, convexity and Fisher consistency. To verify the validity of the proposed methods for missing data, numerical experiments are implemented on benchmark datasets with missing feature values. In experiments, missing features are first imputed by using expectation maximization algorithm. Numerical experiments are simulated in filled datasets. With different probability lower bounds of classification accuracy, experimental results under different proportion of missing values show that the proposed ensemble methods have better or comparable generalization compared to the traditional methods in handling missing-value data classifications. [ABSTRACT FROM AUTHOR]

As an important multi-classification learning tool, multi-birth support vector machine (MBSVM) has been widely studied and applied due to its low computational complexity and good generalization. In this paper, a new multi-birth support vector machine is proposed to handle multi-class classification problem, called projection multi-birth support vector machine (PMBSVM). Specifically, we intend to seek a projection direction wk for k-th class, so that the covariance of remaining samples (except the k-th class) is as small as possible, and the samples of k-th class are as far as possible from the mean of the remaining samples. The proposed PMBSVM not only inherits the advantages of MBSVM, but also can find a suitable projection direction for each class so that the sample is separable in the projection space. Additionally, a regularization term is introduced to maximize the margin of different classes in the projected space. Moreover, a recursive PMBSVM algorithm is proposed for generating multiple orthogonal projection directions for each class. Then we extend the proposed approaches to nonlinear situations through kernel technology. Simulation results on benchmark datasets show that the proposed algorithms improve the generalization in most cases. [ABSTRACT FROM AUTHOR]

The structural information of data contains useful prior knowledge and thus is important for designing classifiers. Extreme learning machine (ELM) has been a potential technique in handling classification problems. However, it only simply considers the prior class-based structural information and ignores the prior knowledge from statistics and geometry of data. In this paper, to capture more structural information of the data, we first propose a Fisher-regularized extreme learning machine (called Fisher-ELM) by applying Fisher regularization into the ELM learning framework, the main goals of which is to build an optimal hyperplane such that the output weight and within-class scatter are minimized simultaneously. The proposed Fisher-ELM reflects both the global characteristics and local properties of samples. Intuitively, the Fisher-ELM can approximatively fulfill the Fisher criterion and can obtain good statistical separability. Then, we exploit graph structural formulation to obtain semi-supervised Fisher-ELM version (called Lap-FisherELM) by introducing manifold regularization that characterizes the geometric information of the marginal distribution embedded in unlabeled samples. An efficient successive overrelaxation algorithm is used to solve the proposed Fisher-ELM and Lap-FisherELM, which converges linearly to a solution, and can process very large datasets that need not reside in memory. The proposed Fisher-ELM and Lap-FisherELM do not need to deal with the extra matrix and burden the computations related to the variable switching, which makes them more suitable for relatively large-scale problems. Experiments on several datasets verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Uncertain or missing data may occur in many practical applications. A principled strategy for handling this problem would therefore be very useful. We consider two-class and multi-class classification problems where the mean and covariance of each class are assumed to be known. With simple structure, fast speed and good performance, extreme learning machine (ELM) has been an important technology in machine learning. In this work, from the viewpoint of probability, we present a robust ELM framework (RELM) for missing data classification. Applying the Chebyshev–Cantelli inequality, the proposed RELM is reformulated as a second-order cone programming with global optimal solution. The proposed RELM only relates to the second moments of input samples and makes no assumption about the data probability distribution. Expectation maximization algorithm is used to fill in missing values and then obtain complete data. Numerical experiments are simulated in various datasets from UCI database and a practical application database. Experimental results show that the proposed method can achieve better performance than traditional methods. These results illustrate the feasibility and effectiveness of the proposed method for missing data classification. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) has demonstrated great potential in machine learning owing to its simplicity, rapidity and good generalization performance. However, the traditional ELM is sensitive to noise and outliers due to using traditional least square loss function. In this paper, we present a new mixed loss function from a combination of pinball loss and least square loss. Then three robust ELM frameworks are proposed based on rescaled hinge loss function, pinball loss function and mixed loss function respectively to enhance noise robustness. To train the proposed ELM with rescaled hinge loss, the half-quadratic optimization algorithm is used to handle nonconvexity, and we demonstrate the convergence of the resulting algorithm. Furthermore, the proposed methods are applied to various datasets including classification data and regression data, with different types of noises such as feature noise and target noise. Compared with traditional methods, experiment results on UCI benchmark datasets show that the proposed methods are less sensitive to noises and achieve better performance in classification and regression applications. [ABSTRACT FROM AUTHOR]

Emerging evidence has revealed widespread stroke-induced brain dysconnectivity, which leads to abnormal network organization. However, there are apparent discrepancies in dysconnectivity between structural connectivity and functional connectivity studies. In this work, resting-state fMRI and structural diffusion tensor imaging were obtained from 26 patients with subacute (10-14 days) intracerebral hemorrhage (ICH) and 20 matched healthy participants (patients/controls = 21/18 after head motion rejection). Graph theoretical approaches were applied to multimodal brain networks to quantitatively compare topological properties between both groups. Prominent small-world properties were found in the structural and functional brain networks of both groups. However, a significant deficit in global integration was revealed in the structural brain networks of the patient group and was associated with more severe clinical manifestations of ICH. Regarding ICH-related nodal deficits, reduced nodal interconnectivity was mainly detected in motor-related regions. Moreover, in the functional brain network, topological properties were mostly comparable between patients with ICH and healthy participants. Beyond the prominent small-world architecture in multimodal brain networks, there are dissociable alterations between structural and functional brain networks in patients with ICH. These findings highlight the potential for using aberrant network metrics as neural biomarkers for evaluation of the severity of ICH. Graphical abstract Intracerebral hemorrhage (ICH) also known as cerebral bleed, a major type of stroke, would significantly affect brain structure and function. Using multimodal neuroimaging, Zhang et al. investigate the ICH-related dysconnectivity in structural and functional brain networks and show a significantly disintegrated structural brain network with a preserved functional network topology in subacute phase (10-14 days). [ABSTRACT FROM AUTHOR]

Two extreme learning machine (ELM) frameworks are proposed to handle supervised and semi-supervised classifications. The first is called lagrangian extreme learning machine (LELM), which is based on optimality conditions and dual theory. Then LELM is extended to semi-supervised setting to obtain a semi-supervised extreme learning machine (called Lap-LELM), which incorporates the manifold regularization into LELM to improve performance when insufficient training information is available. In order to avoid the inconvenience caused by matrix inversion, Sherman-Morrison-Woodbury (SMW) identity is used in LELM and Lap-LELM, which leads to two smaller sized unconstrained minimization problems. The proposed models are solvable in a space of dimensionality equal to the number of sample points. The resulting iteration algorithms converge globally and have low computational burden. So as to verify the feasibility and effectiveness of the proposed method, we perform a series of experiments on a synthetic dataset, near-infrared (NIR) spectroscopy datasets and benchmark datasets. Compared with the traditional methods, experimental results demonstrate that the proposed methods achieve better performances than the traditional supervised and semi-supervised methods in most of considered datasets. [ABSTRACT FROM AUTHOR]

UV irradiation grafting technique was applied on a modified 316L stainless steel (SS) surface. The SS sheets were first coated with dopamine by dipping in the dopamine solution with KIO3 and ultrasonication. Then, the acrylamide (AAm) was grafted onto the dopamine-modified SS samples under UV irradiation with ultrasonication, when the ketone-diol compound was used as a photoinitiating system. For comparison, the UV irradiation grafting of AAm was also performed without ultrasonication. The structures and morphologies of the samples were examined by Fourier transform IR spectroscopy, atomic force microscopy, and scanning electronic microscopy before and after modification, respectively. Results showed that acrylamide was successfully grafted onto the poly-dopamine-coated SS. The hydrophilicity of the modified samples improved significantly, determined by the water contact angle measurement. Shown by Tafel polarization curves, the corrosion potential of modified SS was positively shifted, which demonstrated that the corrosion resistance was improved. [ABSTRACT FROM AUTHOR]

Expression of recombinant proteins in Escherichia coli often requires use of inducible promoters to shorten the lag phase and improve protein productivity and final protein titer. Synthetic molecules that cannot be metabolized by E. coli, such as isopropyl thiogalactopyranoside (IPTG), have been frequently used to trigger the protein expression during early exponential growth phase. This practice has many drawbacks, including high cost and toxicity of IPTG, complex operating procedure, and non-uniform protein expression pattern (some cells in the population do not express recombinant proteins). A few auto-inducible protein expression systems have been developed recently to overcome some of these limitations, but they required use of an additional plasmid or presence of large (a few kilobases) DNA part to be functional, making plasmid construction to be difficult, especially when multiple genes need to be expressed. In this study, by using RNA sequencing, we identified a short, endogenous promoter (PthrC) that can be auto-induced during early exponential growth phase, and improved its performance by use of native and mutated regulatory elements. We found that the developed mutants of PthrC drove uniform protein expression—close to 100% of cells were fluorescent when green fluorescence protein was used as target protein—and cells carrying them could achieve much higher cell density than those with T7 promoter (PT7), a commonly used inducible promoter. In terms of promoter strength (product protein quantity per cell), the developed promoter mutants can cover a range of strength, from 30 to 150% of maximal strength of PT7. One strong mutant (PthrC3_8) was found to work well at a large range of temperature (22, 30, 37 °C) and in various media, and was also confirmed to cause less stress to host cell than PT7 when they were used to express a toxic protein. We foresee that PthrC3 and its mutants will be useful genetic parts for various applications including metabolic engineering and biocatalysis. [ABSTRACT FROM AUTHOR]

Photoelectrocatalytic reduction of CO2 was investigated over Pd nanoparticles (NPs) and reduced graphene oxide (RGO) co-modified anodic TiO2 nanotubes arrays (TNTs) which were grown on a soft Ti wire. Under irradiation, aligned TNTs grown around Ti wire provide orientated pathways for electron transferring to Pd/RGO layers where Pd NPs act as electrons trapping centers and RGO layers function as storage interface with rich electrons for reducing CO2. It is proposed that the ionization of CO2 to CO32- in aqueous system is the vital process for the following reduction of CO2. Moreover, the integrity enables the resulting Pd/RGO/TNTs/Ti wire a superior cathode in an electrochemical cell, improving the efficiency in CO2 conversion assisted by external potential. The highest yields of methanol and ethanol were 1624 and 536 nmol/cm2 h, respectively. [ABSTRACT FROM AUTHOR]

Fe-Cr-Ni/Al-Si-Cu-Ni-Mg composite was taken as the experimental material. The chemical composition of interfacial layer was tested. The generation mechanism and influence of interfacial layer on the composite were analyzed. The results indicated that the generation of interfacial layer is sensitive to temperature. Interfacial layer will generate rapidly when temperature reaches 500 °C or above. The interfacial layer is mainly composed of Al, Si, Cu, Fe, and Cr, element Ni distributes at the outward of the interfacial layer for the precipitate of Ni later than Si and Cu, and there is almost no diffusion of Ni during the solution treatment. During heat treatment process, unequal quantity changing of metal atom results in disperse or concentrated vacancies or holes near the matrix. The existence of interfacial layer will induce a decrease of compression strength and plasticity at room temperature and an increase of strength at higher temperature comparing with composite without interfacial layer. [ABSTRACT FROM AUTHOR]

The anti-CD52 antibody has already been approved for the treatment of patients with resistant chronic lymphocytic leukemia, relapsing-remitting multiple sclerosis, and has demonstrable efficacy against stem cell transplantation rejection. A CHO cell line expressing a humanized anti-CD52 monoclonal antibody (mAb-TH) was cultivated in both fed-batch and perfusion modes, and then purified. The critical quality attributes of these mAb variants were characterized and the pharmacokinetics (PK) properties were investigated. Results showed that the perfusion culture achieved higher productivity, whereas the fed-batch culture produced more aggregates and acid components. Additionally, the perfusion culture produced similar fucose, more galactose and a higher proportion of sialic acid on the anti-CD52 mAb compared to the fed-batch culture. Furthermore, the perfusion process produced anti-CD52 mAb had higher complement-dependent cytotoxicity (CDC) efficacy than that produced by the fed-batch culture, a result probably linked to its higher galactose content. However, antibody produced by fed-batch and perfusion cultures showed similar PK profiles in vivo. In conclusion, perfusion is a more efficient method than fed-batch process in the production of functional anti-CD52 monoclonal antibody. Product quality variants of anti-CD52 mAb were found in different cell culture processes, which demonstrated different physiochemical and biological activities, but comparable PK properties. Whether these observations apply to all mAbs await further investigation. [ABSTRACT FROM AUTHOR]

The isothermal compression tests were carried out on Gleeble-3500 thermal-mechanical simulation machine in a temperature range of 298-473 K and strain rate range of 0.001-10 s. The experimental results show that the flow stress data are negatively correlated with temperature for temperature softening, and the strain rates sensitivity of this composite increases with elevating temperature. Based on the experimental data, Johnson-Cook, modified Johnson-Cook and Arrhenius constitutive models were established. The accuracy of these three constitutive models was analyzed and compared. The results show that the values predicted by Johnson-Cook model could not agree well with the experimental values. The prediction accuracy of Arrhenius model is higher than that of Johnson-Cook model but lower than that of the Modified Johnson-Cook model. [ABSTRACT FROM AUTHOR]

Brain edema following intracerebral hemorrhage (ICH) causes severe secondary brain injury, and no efficient pharmacological preventions are available. The present study was designed to demonstrate the neuroprotective effects of glibenclamide on brain edema and key factors of the blood-brain barrier (BBB). The study was divided into two parts. First, we utilized an autoblood-induced rat model to investigate the expression of sulfonylurea receptor 1 (Sur1). Second, rats were randomized into sham, vehicle, and glibenclamide groups. Neurological scores, brain water content, Evans blue extravasation, Morris water maze test, western blots, and immunofluorescence were used to study the effects of glibenclamide. The expression of the Sur1-Trpm4 channel but not the Sur1-KATP channel was increased in the perihematomal tissue following ICH. Glibenclamide administration significantly decreased the brain water content, restored the BBB, and reduced the expression of MMPs. In addition, glibenclamide improved long-term cognitive deficits following ICH. Glibenclamide protected BBB integrity and improved neurological outcomes after ICH by inhibiting the Sur1-Trpm4 channel, which reduces the expression of MMPs and thereby increases BBB tight-junction protein levels. Glibenclamide may have potential to protect the BBB after ICH. [ABSTRACT FROM AUTHOR]

Feature selection for logistic regression (LR) is still a challenging subject. In this paper, we present a new feature selection method for logistic regression based on a combination of the zero-norm and l -norm regularization. However, discontinuity of the zero-norm makes it difficult to find the optimal solution. We apply a proper nonconvex approximation of the zero-norm to derive a robust difference of convex functions (DC) program. Moreover, DC optimization algorithm (DCA) is used to solve the problem effectively and the corresponding DCA converges linearly. Compared with traditional methods, numerical experiments on benchmark datasets show that the proposed method reduces the number of input features while maintaining accuracy. Furthermore, as a practical application, the proposed method is used to directly classify licorice seeds using near-infrared spectroscopy data. The simulation results in different spectral regions illustrates that the proposed method achieves equivalent classification performance to traditional logistic regressions yet suppresses more features. These results show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Germinal matrix hemorrhage (GMH) is the most common neurological disease of premature newborns leading to detrimental neurological sequelae. Minocycline has been reported to play a key role in neurological inflammatory diseases by controlling some mechanisms that involve cannabinoid receptor 2 (CB2R). The current study investigated whether minocycline reduces neuroinflammation and protects the brain from injury in a rat model of collagenase-induced GMH by regulating CB2R activity. To test this hypothesis, the effects of minocycline and a CB2R antagonist (AM630) were evaluated in male rat pups that were post-natal day 7 (P7) after GMH. We found that minocycline can lead to increased CB2R mRNA expression and protein expression in microglia. Minocycline significantly reduced GMH-induced brain edema, microglial activation, and lateral ventricular volume. Additionally, minocycline enhanced cortical thickness after injury. All of these neuroprotective effects of minocycline were prevented by AM630. A cannabinoid CB2 agonist (JWH133) was used to strengthen the hypothesis, which showed the identical neuroprotective effects of minocycline. Our study demonstrates, for the first time, that minocycline attenuates neuroinflammation and brain injury in a rat model of GMH, and activation of CBR2 was partially involved in these processes. [ABSTRACT FROM AUTHOR]

Thrombin mediates the life-threatening cerebral edema and blood-brain barrier (BBB) damage that occurs after intracerebral hemorrhage (ICH). We previously found that the selective cannabinoid receptor 2 (CB2R) agonist JWH-133 reduced brain edema and neurological deficits following germinal matrix hemorrhage (GMH). We explored whether CB2R stimulation ameliorated thrombin-induced brain edema and BBB permeability as well as the possible molecular mechanism involved. A total of 144 Sprague-Dawley (S-D) rats received a thrombin (20 U) injection in the right basal ganglia. JWH-133 (1.5 mg/kg) or SR-144528 (3.0 mg/kg) and vehicle were intraperitoneally (i.p.) injected 1 h after surgery. Brain water content measurement, Evans blue (EB) extravasation, Western blot, and immunofluorescence were used to study the effects of a CB2R agonist 24 h after surgery. The results demonstrated that JWH-133 administration significantly decreased thrombin-induced brain edema and reduced the number of Iba-1-positive microglia. JWH-133 also decreased the number of P44/P42(+)/Iba-1(+) microglia, lowered Evans blue extravasation, and inhibited the elevated matrix metallopeptidase (MMP)-9 and matrix metallopeptidase (MMP)-12 activities. However, a selective CB2R antagonist (SR-144528) reversed these effects. We demonstrated that CB2R stimulation reduced thrombin-induced brain edema and alleviated BBB damage. We also found that matrix metalloproteinase suppression may be partially involved in these processes. [ABSTRACT FROM AUTHOR]

A colorless transparent hydrogel SSA-Ferrous-PVA was prepared as a 3D dosimeter using 5-sulfosalicylic acid. Under serial doses of irradiation by aCo source, dosimeter turned into pink with serial color densities. The resulting color densities were characterized by a UV-vis spectrophotometer at the fixed wavelength of 505 nm. Various concentrations of 5-sulfosalicylic acid and ferrous ammonium sulfate were tested by linear relevant fitting for optimal hydrogel production conditions. The effects of various formulations were evaluated on the stability and sensitivity of dosimeters. The slopes of diffusion curves were measured and used to evaluate the diffusion rates of irradiated dosimeters. Results indicated that a careful doping with 5-sulfosalicylic acid produced a hydrogel with a colorless transparent background and an optimal dosimeter with a low diffusion rate, good stability, and great linearity of dose response. [ABSTRACT FROM AUTHOR]

Main conclusion: We found the novel role of hydrogen sulfide in the adaptation of the alpine plant to altitude gradient in the Northern Tibetan Plateau. Alpine plants have developed strategies to survive the extremely cold conditions prevailing at high altitudes; however, the mechanism underlying the evolution of these strategies remains unknown. Hydrogen sulfide (HS) is an essential messenger that enhances plant tolerance to environmental stress; however, its role in alpine plant adaptation to environmental stress has not been reported until now. In this work, we conducted a comparative proteomics analysis to investigate the dynamic patterns of protein expression in Lamiophlomis rotata plants grown at three different altitudes. We identified and annotated 83 differentially expressed proteins. We found that the levels and enzyme activities of proteins involved in HS biosynthesis markedly increased at higher altitudes, and that HS accumulation increased. Exogenous HS application increased antioxidant enzyme activity, which reduced ROS (reactive oxygen species) damage, and GSNOR (S-nitrosoglutathione reductase) activity, which reduced RNS (reactive nitrogen species) damage, and activated the downstream defense response, resulting in protein degradation and proline and sugar accumulation. However, such defense responses could be reversed by applying HS biosynthesis inhibitors. Based on these findings, we conclude that L. rotata uses multiple strategies to adapt to the alpine stress environment and that HS plays a central role during this process. [ABSTRACT FROM AUTHOR]

The ABC1 atypical kinases (aPKs) have been extensively studied in bacteria, yeast and human, where their mutation causes a deficiency of ubiquinone, an isoprenoid compound in the respiratory electron transfer chain. Considerably less is known, however, about their evolution and function in higher plants. In this study, we identified 16 ABC1-domain containing genes from model plant rice by comprehensive genome analysis and gene cloning. Detection of positive selection showed that purifying selection was the major force underlying the evolution of most rice and Arabidopsis ABC1s. However, the expression profiles of plant ABC1s appear to have diverged based on microarray data analysis and realtime PCR; some of them are primarily expressed in developing leaves, some in stamens and/or mature pollen, whereas others in diverse tissues and organs. The possible functions of plant ABC1s were identified using genome-wide coexpression analysis. This analysis suggested that many of them might be involved in the regulation of isoprenoid biosynthesis. In conclusion, the plant ABC1 aPKs, which harbor distinct expression patterns, might have conserved functions in modulating isoprenoid metabolism. [ABSTRACT FROM AUTHOR]

To safely land after different drops, cats can perfectly use their limbs as dampers to dissipate impact forces. Yet, we know little about the contribution of the forelimbs and hindlimbs of cats to attenuating the impact forces during landing. In order to investigate this, we analyzed the landing impulses and energy absorption based on the ground reaction forces and high-speed video recordings of cats performing self-initiated jump downs at different heights. Our results show that the distribution of impact forces between forelimbs and hindlimbs exhibits a landing height-dependent manner. Furthermore, combining the experimental measurements and the inverted pendulum-spring model, we find that variation in landing angle is correlated with the distribution manner. This posture-dependent actuation allows the animal to tune the distribution of energy absorption between forelimbs and hindlimbs. These findings highlight how cats perfectly jump down using their limbs, providing fundamental insights into the importance of control mechanisms that attenuate landing impulses safely and efficiently. [ABSTRACT FROM AUTHOR]