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The influences of two red mud (RM) samples (ZZ and ZM) on fusion behaviors of three high ash fusion temperature (AFT) coals (FZ, JC and JZ) were investigated. It was evaluated by determining AFTs of coals, RMs, and the mixtures of coal ash with RM, as well as their compositions of ashes and mineral. The results showed that the contents of CaO, Fe2O3, and Na2O in RMs were higher than those in coal ashes, which caused RMs’ AFTs lower than those of coals. The formation of high melting-point mullite might be the main factor causing coal AFTs higher. With the increase in RM mass ratio, the increasing amount of low-melting eutectic (anorthite (CaAl2Si2O8), fayalite (Fe2SiO4), and anorthite (Na-rich) (Ca,Na)(Si,Al)4O8)) and amorphous matter made AFTs lower. ZZ reduced AFTs obviously than ZM because the content of Na2O in ZZ was higher than that in ZM. Owing to lower ion potential of Na+ than those of Ca2+ and Fe2+, Na+ is easier to enter into the lattice of mullite and results in the reduction of AFTs.

Titanium dioxide has been widely used as an efficient transition metal oxide photocatalyst. However, its photocatalytic activity is limited to the ultraviolet spectrum range due to the large bandgap beyond 3 eV. Efforts to reduce the bandgap to achieve a broader spectrum range of light absorption have been successfully attempted via the experimental synthesis of dopant-free metastable surface structures of rutile-type TiO2 (011) 2 × 1. This new surface phase possesses a reduced bandgap of ∼2.1 eV, showing great potential for an excellent photocatalyst covering a wide range of visible light. There is a need to establish the atomistic structure of this metastable surface to understand the physical cause for the bandgap reduction and to improve the future design of photocatalysts. Here, we report computational investigations in an effort to unravel this surface structure via swarm structure-searching simulations. The established structure adopts the anatase (101)-like structure model, where the topmost 2-fol...

Motivated by the high superconducting transition temperature (TC) shown by monolayer FeSe on cubic perovskite SrTiO3(001) and SrTiO3(001)-2×1 reconstructed surfaces, in this study, we explore the atomic and electronic structures of monolayer FeSe on various SrTiO3(001)-2×1 surface reconstructions using the CALYPSO method and first-principles calculations. Our search reveals two new Ti2O2 and Ti2O reconstructed surface structures, besides the Ti2O3 and double TiO2 layer reconstructed surfaces, and the two new Ti2O2 and Ti2O reconstructed surface structures are more stable under Ti-rich conditions than under Ti-poor conditions. The Fermi-surface topology of an FeSe monolayer on Ti2O3- and Ti2O2-type reconstructed STO surfaces is different from that of an FeSe monolayer on a Ti2O-type STO reconstructed surface. The established structure of monolayer FeSe on a Ti2O-type STO(001) reconstructed surface can naturally explain the experimental observation of the electronic band structure on the monolayer FeSe superconductor and obtained electrons counting per Fe atom. Surface states in the mid-gap induced by various STO surface reconstructions will result in band bending. The surface-state-induced band bending is also responsible for the electron transfer from the STO substrate to the FeSe films.

The ash adhesion and deposition (AD)characteristics of three low-rank coal (LRC) were investigated by self-made AD analyzer, X-ray fluorescence spectrometry, scanning electron microscopy equipped with energy dispersive X-ray detector, and X-ray diffraction. Under a given condition, the deposition mass ratios of different LRC ashes are different, which mainly due to the difference in iron content. The mass ratios of ash deposition under oxidizing atmospheres are higher than those under reducing atmospheres because of the effects of different valence of iron. The mass ratio of AD increases with the increase of temperature, while it decreases with gas velocity increases. The possible formation mechanism of AD is proposed by adhesive amorphous matter formation, iron enrichment and the generation of ferro-aluminate and ferro-alumino silicate, substitution of Fe 2 + by Ca 2 + , and mullite adhesion.

Pentalenolactone is a microbial sesquiterpenoid with antibiotic activity. Its biosynthetic pathway was elucidated by a combination of genetic and biochemical characterizations of all genes involved. For the related neopentalenoketolactone biosynthetic gene cluster from Streptomyces avermitilis, an α-ketoglutarate-dependent mononuclear nonheme iron enzyme, PtlD, was proposed to catalyze both desaturation and olefin epoxidation reactions. Yet, these activities remained to be validated by in vitro biochemical evidence. In this report, we demonstrated that PtlD has multiple activities, including hydroxylation, desaturation, and epoxidation, and confirmed the presence of the elusive epoxide intermediate in a neopentalenoketolactone pathway.

Pressure is a fundamental tool that can induce structural and electronic transformations, which is helpful to search for exotic materials not accessible at ambient conditions. Here, we have performed an extensive structural study on cubic Mg

High pressure can drastically alter chemical bonding and produce exotic compounds that defy conventional wisdom. Especially significant are compounds pertaining to oxygen cycles inside Earth, which hold key to understanding major geological events that impact the environment essential to life on Earth. Here we report the discovery of pressure-stabilized divalent ozonide CaO3 crystal that exhibits intriguing bonding and oxidation states with profound geological implications. Our computational study identifies a crystalline phase of CaO3 by reaction of CaO and O2 at high pressure and high temperature conditions; ensuing experiments synthesize this rare compound under compression in a diamond anvil cell with laser heating. High-pressure x-ray diffraction data show that CaO3 crystal forms at 35 GPa and persists down to 20 GPa on decompression. Analysis of charge states reveals a formal oxidation state of −2 for ozone anions in CaO3. These findings unravel the ozonide chemistry at high pressure and offer insights for elucidating prominent seismic anomalies and oxygen cycles in Earth’s interior. We further predict multiple reactions producing CaO3 by geologically abundant mineral precursors at various depths in Earth’s mantle., Calcium and oxygen are abundant elements in the Earth’s mantle, largely present as calcium oxide. Here the authors show, by experiments and computations, that calcium ozonide (CaO3) is stabilized at the high pressures and temperatures characteristic of the lower mantle, with implications for the deep Earth’s chemistry.

Differential evolution for multi-objective optimization is a useful and straightforward evolutionary optimization algorithm. Since the algorithm randomly searches in problem space, it may get into trouble when we adapt it to solve multi-objective problems. With regard to the limitation of DE, we propose a new algorithm PSDE to solve multi-objective functions based on the PSO operator and DE operator. In this algorithm, we utilize DE operator to promote the uniform spread of solutions and use PSO operator to obtain good local search ability by retaining the best solutions obtained so far. Experimental results on a set of benchmark test functions sustain the effectiveness of the proposed algorithm.

For the development of high-performance spintronic nanodevices, one of the most urgent and challenging tasks is the preparation of two-dimensional materials with room-temperature ferromagnetism and a large magnetic anisotropic energy (MAE). Through first-principles swarm-intelligence structural search calculations, we identify an ideal ferromagnetic Fe

Mechanical properties of Q370qE carbon steel can be improved by cladding it with 316L stainless steel. After rolling these materials together, microstructure, hardness, shear strength, and corrosion properties of the cladded metals were evaluated. Hardness and shear tests were performed according to appropriate standards to evaluate the bonds. The results show that the remarkable diffusion of Ni and Cr formed a hard transition zone. The width of this diffusion zone increases with increasing temperature. The shear strength of the clad interface reached a minimum of 385 MPa. In addition, the surfaces of samples were examined by neutral salt spray (NSS) tests and potentiodynamic polarization tests to examine corrosion behavior. The 316L side primarily exhibited pitting corrosion, while the Q370qE side was dominated by uniform corrosion.

Titanium dioxide has been widely used in modern industrial applications, especially as an effective photocatalyst. Recently, freestanding TiO2 films with a markedly reduced bandgap of ∼1.8 eV have been synthesized, indicating that the dimension has a considerable influence on the bulk band gap (>∼3 eV) and enhances the adsorption range of visible light. Titanium oxide compounds have various stoichiometries and versatile properties. Therefore, it is very necessary to explore the electronic properties and functionalities of other titanium oxide films with different stoichiometries. Here, we combined structure searches with first-principle calculations to explore candidate Ti–O films with different stoichiometries. In addition to the experimentally synthesized TiO2 film, the structure searches identified three new energetically and dynamically stable Ti–O films with stoichiometries of Ti3O5, Ti3O2, and Ti2O. Calculations show that the Ti–O films undergo several interesting electronic transformations as the Ti fraction increases, namely, from a wide-gap semiconductor (TiO2, 3.2 eV) to a narrow-gap semiconductor (Ti3O5, 1.80 eV) and then to metals (Ti3O2 and Ti2O) due to the abundance of unpaired Ti_d electrons. In addition to the electronic transformations, we observed nonmagnetic (TiO2) to ferromagnetic (Ti3O5, Ti3O2, and Ti2O) transformations. Notably, the Ti3O5 film possesses both narrow-gap semiconductive and ferromagnetic properties, with a large magnetic moment of 2.0 µB per unit cell; therefore, this film has high potential for use in applications such as spintronic devices. The results highlight metal fraction-induced electronic and magnetic transformations in transition metal oxide films and provide an alternative route for the design of new, functional thin-film materials.Titanium dioxide has been widely used in modern industrial applications, especially as an effective photocatalyst. Recently, freestanding TiO2 films with a markedly reduced bandgap of ∼1.8 eV have been synthesized, indicating that the dimension has a considerable influence on the bulk band gap (>∼3 eV) and enhances the adsorption range of visible light. Titanium oxide compounds have various stoichiometries and versatile properties. Therefore, it is very necessary to explore the electronic properties and functionalities of other titanium oxide films with different stoichiometries. Here, we combined structure searches with first-principle calculations to explore candidate Ti–O films with different stoichiometries. In addition to the experimentally synthesized TiO2 film, the structure searches identified three new energetically and dynamically stable Ti–O films with stoichiometries of Ti3O5, Ti3O2, and Ti2O. Calculations show that the Ti–O films undergo several interesting electronic transformations as the T...

A better tradeoff between robustness and invisibility will be realized by using the just noticeable (JND) model into the quantization-based watermarking scheme. The JND model is usually used to describe the perception characteristics of human visual systems (HVS). According to the research of cognitive science, HVS can adaptively extract the structure features of an image. However, the existing JND models in the watermarking scheme do not consider the structure features. Therefore, a novel JND model is proposed , which includes three aspects: contrast sensitivity function, luminance adaptation, and contrast masking (CM). In this model, the CM effect is modeled by analyzing the direction features and texture complexity, which meets the human visual perception characteristics and matches well with the spread transform dither modulation (STDM) watermarking framework by employing a new method to measure edge intensity. Compared with the other existing JND models, the proposed JND model based on structural regularity is more efficient and applicable in the STDM watermarking scheme. In terms of the experimental results, the proposed scheme performs better than the other watermarking scheme based on the existing JND models.

The search for new inorganic electrides has attracted significant attention due to their potential applications in transparent conductors, battery electrodes, electron emitters, as well as catalysts for chemical synthesis. However, only a few inorganic electrides have been successfully synthesized thus far, limiting the variety of electride examples. Here, we show the stabilization of inorganic electrides in the Ti-rich Ti–O system through first-principles calculations in conjunction with swarm-intelligence-based CALYPSO method for structure prediction. Besides the known Ti-rich stoichiometries of Ti2O, Ti3O, and Ti6O, two hitherto unknown Ti4O and Ti5O stoichiometries are predicted to be thermodynamically stable at certain pressure conditions. We found that these Ti-rich Ti–O compounds are primarily zero-dimensional electrides with excess electrons confined in the atom-sized lattice voids or between the cationic layers playing the role as anions. The underlying mechanism behind the stabilization of electrides has been rationalized in terms of the excess electrons provided by Ti atoms and their accommodation of excess electrons by multiple cavities and layered atomic packings. The present results provide a viable direction for searching for practical electrides in the technically important Ti–O system. Computer simulations predict that compounds made of oxygen and titanium atoms form cages that can host electrons in their interstices. A team led by Hanyu Liu and Yanming Ma from Jilin University, China, have investigated the possibility for materials belonging to the family of titanium dioxide, a pigment used in paints and sunscreens, to behave as electrides, materials in which electrons resemble larger negatively charged atoms in the way in which they localize and interact with the surrounding atomic structure. Their calculations show that this occurs if the amount of titanium atoms in the material exceeds that of oxygen atoms by at least a factor of 2, which allows the formation of larger cages in which electrons can be confined. The synthesis of these materials will allow to assess their significance for catalysis or other applications.

Time series prediction is an important branch of mathematical statistics, that uses stochastic process theory and machine learning method to study the data sequence. It is based on the research of structural models, with the purpose of predicting the future developing trend. The method proposed in this paper is based on ARMA model and Lasso regression, ARMA model is an effective model for time series prediction, and Lasso regression is a kind of compression estimation method to refine the ARMA model. First, we build an ARMA model. Second, we use the extend autocorrelation function to do the model specification. Third, we use Lasso to calculate the model parameters. Finally, the method presented in this paper is compared with some other methods on two real-world datasets. Experimental results demonstrate its effectiveness for time series prediction.

Hydrogen-rich materials have attracted great interest since the recent discovery of superconductivity at 203 K in highly compressed hydrogen sulfide. To probe the role of covalent bonding in determining the ${T}_{\mathrm{c}}$ of hydrogen-related superconductors, we systematically studied the crystal structure and superconductivity of ${\mathrm{H}}_{6}\mathrm{SSe}$, a hypothetical compound derived from ${\mathrm{H}}_{3}\mathrm{S}$ with half its S atoms replaced by group neighbor Se. First-principles structure searches identify three dynamically stable structures for ${\mathrm{H}}_{6}\mathrm{SSe}$ at 200 GPa. Interestingly, all three structures keep the main feature of the cubic $Im\overline{3}m$ structure of ${\mathrm{H}}_{3}\mathrm{S}$, but with different Se substitution positions. Electron-phonon coupling calculations reveal the superconductive potential of the three phases of ${\mathrm{H}}_{6}\mathrm{SSe}$, with ${T}_{\mathrm{c}}$ decreasing (from 195 to 115 K) upon the declining strength of the weakest covalent H-S or H-Se bonds in each structure, thereby highlighting the key role of covalent bonding in determining ${T}_{\mathrm{c}}$. For comparison, O-substituted ${\mathrm{H}}_{6}\mathrm{SO}$ was predicted to assume a semiconducting phase with entirely different structural features from ${\mathrm{H}}_{6}\mathrm{SSe}$. We attribute this difference to the much stronger electronegativity of O (3.44) compared with S (2.58) or Se (2.55).

Background The majority of ruptured intracranial aneurysms are combined with subarachnoid hemorrhage, but patients with only intracerebral hematoma without any subarachnoid hemorrhage are extremely rare. Case presentation The patient was hospitalized due to sudden dizziness combined with slurred speech. The patient showed considerable decreased physical activity without any nuchal rigidity. Head CT showed hematoma in the left temporal lobe, and the shape of hematoma was extremely irregular. MRI indicated the absence of any vascular malformations. The patient was diagnosed with middle cerebral artery bifurcation aneurysm in the left by head CTA. Intracranial aneurysm clip and removal of hematoma in the left temporal lobe were performed under general anesthesia. The patient did not show any significant neurological dysfunction after the surgery and was followed up for 4 months after discharge with GOS score of 5 points. Conclusions Intracranial hematoma with irregular morphology around the lateral fissure of the brain should be considered critical in order to avoid misdiagnosis and any possibility of missed diagnosis of vascular lesions, so as to ensure an exact therapeutic strategy with good prognosis for the patients.

Background Drug resistance in clinical cancer treatment has become an issue. Objective We focus on abnormally expressed lncRNAs in glioma and investigating the function of PVT1. Methods The paclitaxel-resistant glioma cells SHG-44 RE was obtained through screening the SHG 44 cells that were cultured in medium containing a certain concentration of paclitaxel. Cell survival of SHG 44 RE and SHG 44 cells under the treatment of paclitaxel was detected by MTT assay. The aberrant expressed lncRNAs were screened out with microarray analysis. Further qRT-PCR was utilized to validate the expression of lncRNA PVT1 in the two cells. After manipulating the expression of PVT1, cell viability and apoptosis were measured by MTT and flow cytometry respectively. Results LncRNA PVT1 was overexpressed in glioma cells SHG-44 RE compared with parent SHG-44 cells. Down-regulation of lncRNA PVT1 inhibited the SHG-44 RE cell viability and increased glioma SHG-44 RE cells apoptosis after paclitaxel treatment, suggesting that inhibition of lncRNA PVT1 improved paclitaxel sensibility in human glioma cells. Conclusion Down-regulation of PVT1 could enhance chemosensitivity of paclitaxel, induce apoptosis of glioma cells and noteworthy inhibit glioma cells proliferation. Our findings of PVT1 could contribute to attenuate paclitaxel resistance in clinical medicine.

Antibiotic resistance and rising CO2 levels are considered among the most significant challenges we will face in terms of global development over the following decades. However, the impact of elevated CO2 on soil antibiotic resistance has rarely been investigated. We used a free-air CO2 enrichment system to investigate the potential risks posed by applying mineral and organic fertilizers to paddy soil at current CO2 concentration (370 ppm) and future elevated CO2 (eCO2, 570 ppm predicted for 2100). Organic fertilizer substitution (substituting the mineral fertilizer by 50% N) alone increased the plant uptake and soil residue of sulfamethazine, and enriched sulfonamide resistance genes (sul1, sul2), tetracycline resistance genes (tetG, tetM) and class 1 integron (intl1). But it decreased the rice grain yield (by 7.6%). Comparatively, eCO2 decreased the sul2, tetG and intl1 gene abundances by organic fertilizer substitution, and meanwhile increased grain yield (by 8.4%). Proteobacteria and Nitrospirae were potential hosts of antibiotic resistance genes (ARGs). Horizontal gene transfer via intl1 may play an important role in ARGs spread under eCO2. Results indicated that future elevated CO2 concentration could modify the effects of organic fertilizer substitution on rice yield and soil ARGs, with unknown implications for future medicine and human health.

Considering that pressure-induced formation of short, strong covalent bonds in light-element compounds can produce superhard materials, we employ structure searching and first-principles calculations to predict a new class of boron nitrides with a stoichiometry of BN2, which are stable relative to alpha-B and alpha-N2 at ambient pressure. At ambient pressure, the most stable phase has a layered structure (h-BN2) containing hexagonal BN layers between which there are intercalated N2 molecules. At 25 GPa, a three-dimensional P42/mmc structure with single N–N bonds becomes the most stable. Dynamical, thermal, and mechanical stability calculations reveal that this structure can be recovered under ambient conditions. Its calculated stress-strain relations demonstrate an intrinsic superhard nature with an estimated Vickers hardness of ∼43 GPa. This structure has a potentially high energy density of ∼4.19 kJ/g.

Nitrogen (N)-doped graphitic carbons with one-dimensional hollow/porous structures were synthesized via a sacrificial template of CdTe@ZIF-8 nanofibers. The N-doped graphitic carbons exhibited better electrocatalytic activity for ORR based on higher diffusion-limited current density and more positive half-wave potential as compared to carbons derived from ZIF-8.

Artificially polluted surface water was treated using magnetic-seeding coagulation, with polyaluminum chloride (PAC) as the coagulant and a ferromagnetic material as the magnetic seed (MS). A novel approach using a combination of response surface methodology (RSM) and the Box–Behnken design (BBD) was employed to evaluate the effects and interactions of four main factors, the dosages and dosing points of PAC and MS. The three response variables selected to evaluate the effectiveness of the treatment were supernatant turbidity removal rate, floc size, and floc strength. The optimal operating conditions were obtained by constraining the three desirable responses using Minitab software. The PAC and MS dosages (22.7 mg/L and 0.6 g/L) and their dosing points (1.5 and 1.2 min) were determined to be the optimum conditions for the maximum turbidity removal rate, floc size, and floc strength of 98.0%, 154.7 μm, and 57.8%, respectively. Confirmation experiments demonstrated that such a combination of RSM and...

Catalytically active graphene-based hollow TiO2 composites(TiO2/RGO) were successfully synthesized via the solvothermal method. Hollow TiO2 microspheres are uniformly dispersed on RGO. X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), UV–vis diffuse reflectance spectroscopy (DRS) and photoluminescence (PL) were used for the characterization of prepared photocatalysts. The mass of GO was optimized in the photocatalytic removal of rhodamine B (RhB) as a model dye pollutants. The results showed that graphene-based hollow TiO2 composites exhibit a significantly enhanced photocatalytic activity in degradation of RhB under either UV or visible light irradiation. The formation of the graphene-based hollow TiO2 composites and the photocatalytic mechanisms under UV and visible light were also discussed.

The fusion characteristics of three low-rank coals and cornstalk (CS) mixtures were investigated by ash fusion temperature (AFT) analysis, X-ray fluorescence spectrometry, scanning electron microscopy, and X-ray diffractometry. The increasing trend of CS ash fusion temperature (AFT) is not the same with the additions of different coals at the same mass ratio, due to the difference of the ash content and component of coal samples. Variations in AFTs depended on the mineral composition, which derived from mineral interaction. As the coal mass ratio increased at high temperature, Ca 2+ replaced K + in semi-molten alumino-silicate in CS ashes and led to formation of higher-melting-point anorthite, which also increased the AFT of the ash mixture. The AFT fluctuation of CS/Husheng lignite (HS) mixture during addition of HS resulted from formation of higher-MP mullite.

The sacred lotus (Nelumbo nucifera Gaertn.) can produce heat autonomously and maintain a relatively stable floral chamber temperature for several days when blooming. Floral thermogenesis is critical for flower organ development and reproductive success. However, the regulatory role of microRNA (miRNA) underlying floral thermogenesis in N. nucifera remains unclear. To comprehensively understand the miRNA regulatory mechanism of thermogenesis, we performed small RNA sequencing and transcriptome sequencing on receptacles from five different developmental stages. In the present study, a total of 172 known miRNAs belonging to 39 miRNA families and 126 novel miRNAs were identified. Twenty-nine thermogenesis-related miRNAs and 3024 thermogenesis-related mRNAs were screened based on their expression patterns. Of those, seventeen differentially expressed miRNAs (DEMs) and 1765 differentially expressed genes (DEGs) had higher expression during thermogenic stages. The upregulated genes in the thermogenic stages were mainly associated with mitochondrial function, oxidoreductase activity, and the energy metabolism process. Further analysis showed that miR156_2, miR395a_5, miR481d, and miR319p may play an important role in heat-producing activity by regulating cellular respiration-related genes. This study provides comprehensive miRNA and mRNA expression profile of receptacle during thermogenesis in N. nucifera, which advances our understanding on the regulation of floral thermogenesis mediated by miRNA.

The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series. A sparse autoencoder is used to extract the features from the input instead of the randomly initialized weights. In this way, the RBLS retains the merit of fast computing and fits for processing sequential data. Motivated by the idea of “fine-tuning” in deep learning, the weights in the RBLS can be updated by conjugate gradient methods if the prediction errors are large. We exhibit the merits of our proposed model on several chaotic time series. Experimental results substantiate the effectiveness of the RBLS. For chaotic benchmark datasets, the RBLS achieves very small errors, and for the real-world dataset, the performance is satisfactory.

Multivariate chaotic time series prediction is a hot research topic, the goal of which is to predict the future of the time series based on past observations. Echo state networks (ESNs) have recently been widely used in time series prediction, but there may be an ill-posed problem for a large number of unknown output weights. To solve this problem, we propose a hybrid regularized ESN, which employs a sparse regression with the ${L}_{1}/{2}$ regularization and the ${L}_{2}$ regularization to compute the output weights. The ${L}_{1}/{2}$ penalty shows many attractive properties, such as unbiasedness and sparsity. The ${L}_{2}$ penalty presents appealing ability on shrinking the amplitude of the output weights. After the output weights are calculated, the input weights, internal weights, and output weights are fine-tuning by a Hessian-free optimization method—conjugate gradient backpropagation algorithm. The fine-tuning helps to bubble up the input information toward the output layer. Besides, the largest Lyapunov exponent is used to calculate the predictable horizon of a chaotic time series. Experimental results on benchmark and real-world datasets show that our proposed method is superior to other ESN-based models, as sparser, smaller-absolute-value, and more informative output weights are obtained. All of the predictions within the predictable horizon of the proposed model are accurate.

Improving universality and robustness of the control method is one of the most challenging problems in the field of complex networks (CNs) synchronization. In this paper, a special unilateral coupling finite-time synchronization (UCFTS) method for uncertain CNs is proposed for this challenging problem. Multiple influencing factors are considered, so that the proposed method can be applied to a variety of situations. First, two kinds of drive-response CNs with different sizes are introduced, each of which contains two types of nonidentical nodes and time-varying coupling delay. In addition, the node parameters and topological structure are unknown in drive network. Then, an effective UCFTS control technique is proposed to realize the synchronization of drive-response CNs and identify the unknown parameters and topological structure. Second, the UCFTS of uncertain CNs with four types of nonidentical nodes is further studied. Moreover, both the networks are of unknown parameters, time-varying coupling delay and uncertain topological structure. Through designing corresponding adaptive updating laws, the unknown parameters are estimated successfully and the weight of uncertain topology can be automatically adapted to the appropriate value with the proposed UCFTS. Finally, two experimental examples show the correctness of the proposed scheme. Furthermore, the method is compared with the other three synchronization methods, which shows that our method has a better control performance.

It has been known that human visual systems (HVSs) can be applied to describe the underlying masking properties for the image processing. In general, HVS can only perceive small changes in a scene when they are greater than the just noticeable distortion (JND) threshold. Recently, the cognitive resources of huma visual attention mechanisms are limited, which can not concentrate on all stimuli. To be specific, only more important stimuli will react from the mechanisms. When it comes to visual attention mechanisms, we need to introduce the visual saliency to model the human perception more accurately. In this paper, we presents a new wavelet-based JND estimation method that takes into account the interrelationship between visual saliency and JND threshold. In the experimental part, we verify it from both subjective and objective aspects. In addition, the experimental results show that extracting the saliency map of the image in the discrete wavelet transform (DWT) domain and then modulating its JND threshold is better than the non-modulated JND effect.

Recently, the scale of observed time series becomes bigger and bigger, and the learning ability of conventional models is limited to depict the characteristics of large-scale time series. We put forward a deep model that uses the sparse autoencoder to extract the characteristics of the time series layer by layer. Sparse autoencoder can provide a simple explanation of the data by means of a reduced dimensionality of parts and extract the hidden architecture in the data. By this method, on the one hand, the dimension of the matrix that describes the problem is reduced, on the other hand, a large number of data can be compressed and summarized. Then we calculate the output weights by the method of pseudo inverse. Experiment results on Lorenz series and PM2.5 concentration in Shanghai dataset demonstrate the effectiveness of the proposed method.

As one of the most abundant elements on earth, sulfur is part of many small molecular metabolites and is key to their biological activities. Over the past few decades, some general strategies have been discovered for the incorporation of sulfur into natural products. In this review, we summarize recent efforts in elucidating the biosynthetic details for two sulfur-containing metabolites, ergothioneine and ovothiol. Their biosyntheses involve an unprecedented trans-sulfur strategy, a combination of a mononuclear non-heme iron enzyme-catalyzed oxidative C–S bond formation reaction and a PLP enzyme-mediated C–S lyase reaction.

In this study, hollow core/shell CeO2@TiO2 photocatalysts were prepared via precipitation-co-hydrothermal method. X-ray diffraction, transmission electron microscopy, UV−Vis diffuse reflectance spectroscopy, thermo-gravimetric–differential thermal analysis, and photoluminescence were used for the characterization of prepared photocatalysts. The synthesis variables were optimized in the photocatalytic removal of rhodamine B (rhB) as a model dye pollutants. The results showed that hollow core/shell CeO2@TiO2 exhibit a significantly enhanced photocatalytic activity in degradation of rhB under either UV or visible light irradiation. The formation of the hollow core/shell CeO2@TiO2 and the photocatalytic mechanisms under UV and visible light were also discussed.

The present study aimed to investigate whether miR-30b plays a pivotal role in the progression of esophageal squamous cell carcinoma (ESCC) as well as to elucidate its possible regulatory mechanism. The expression levels of miR-30b in the ESCC tissues and cells were determined. The TE-1 cells were transfected with the miR-30b mimic, the mimic control, the miR-30b inhibitor, the inhibitor control, the pCDNA3.1-Chromobox 3 (pCDNA3.1-CBX3) and/or the blank vector, while the TE-2 cells were transfected with the miR-30b mimic and/or the mimic control. Cell proliferation, cell apoptosis, and cell migration of the different transfected groups were evaluated. The luciferase reporter assay was performed to detect the relationship between miR-30b, and CBX3. Furthermore, the relationship between miR-30b, CBX3, and the JAK2/STAT3 signaling pathway was explored. Significant downregulation of miR-30b was observed in the ESCC tissues and cells, while CBX3 was upregulated in the ESCC tissues and cells. The inhibition of miR-30b promoted cell proliferation, inhibited cell apoptosis, and enhanced cell migration in ESCC, which was similar to the effects of CBX3 overexpression. In fact, CBX3 was confirmed to be a direct target of miR-30b. The overexpression of miR-30b decreased the expression levels of p-JAK2/JAK2 and p-STAT3/JAK3 significantly, which was obviously reversed after the simultaneous overexpression of CBX3. Our results revealed that the downregulation of miR-30b may increase cell proliferation, inhibit cell apoptosis, and promote cell migration in ESCC by targeting CBX3 and activating the JAK2/STAT3 signaling pathway. Thus, miR-30b may serve as a useful marker for predicting the progression of ESCC.

The just noticeable distortion (JND) model plays an important role in measuring the visual visibility for spread transform dither modulation (STDM) watermarking. However, the existing JND model characterizes the suprathreshold distortions with an equal saliency level. Visual saliency (VS) has been widely studied by psychologists and computer scientists during the last decade, where the distortions are more likely to be noticeable to any viewer. With this consideration, we proposed a novel STDM watermarking method for a monochrome image by exploiting a visual saliency-based JND model. In our proposed JND model, a simple VS model is employed as a feature to reflect the importance of a local region and compute the final JND map. Extensive experiments performed on the classic image databases demonstrate that the proposed watermarking scheme works better in terms of the robustness than other related methods.

Over the last few years, the combination synchronization of chaotic system receives a special attention. This paper introduces the idea of combination synchronization into complex networks. Based on sliding mode control (SMC) principle, the finite-time combination synchronization (FTCS) of four uncertain complex networks which combining as the form of A+B+C-D is investigated. And there are unknown parameters and uncertain disturbances in all the network models. By using finite time stability theory and linear matrix inequality, designing appropriate network sliding mode surface and control input, then obtain the synchronization condition of the FTCS and corresponding update laws of unknown parameters. The proposed method has a strong robustness which can overcome the influence of uncertain disturbances and identify the unknown parameters of nodes. Furthermore, the method can estimate the high bounds of the synchronous settling time. Simulation experiment proves the feasibility and effectiveness of the proposed method.

For extreme learning machine (ELM), it is difficult to select an appropriate number of hidden layer nodes, which has a great influence on the prediction performance of ELM. Therefore, in order to overcome the influence of a large number of hidden layer nodes to prediction and enhance the prediction performance of ELM, an improved ELM is proposed based on orthogonal projections to latent structures (O-PLS) method. The improved ELM contains two steps. Firstly, the hidden layer of ELM is used to build the nonlinear mapping for input variables and get the output matrix H, where the number of bidden layer nodes can be random assigned. Then O-PLS is used to build the linear regression. Compared with least squares regression (LSR), O-PLS can effectively reduce the high dimensions, overcome the multicollinearity among H, which are caused by an improper number of hidden layer nodes. The simulation experiment results based on Lorenz time series and sunspots-runoff of Yellow river time series demonstrate that the improved ELM can effectively overcome the influence of hidden layer nodes to prediction performance and improve the stability performance and prediction performance of ELM.

An anatase TiO2(001) surface has shown great potential as an ideal and powerful photocatalyst due to its chemical stability, nontoxicity, and high reactivity. However, the fundamental atomic structure of the reconstructed anatase (001)-(1 × 4) surface is still under debate, which greatly impedes further exploration of its chemical activity. Herein, the anatase (001)-(1 × 4) surface reconstruction and the photocatalytic reactivity have been extensively studied using an effective surface structure searching method in combination with the first-principles calculations. Our study reveals that there exist two stable (1 × 4) reconstructed surfaces, i.e., the previously proposed "ad-molecule" (ADM) and oxidized ridge (OR) surface structures, and their simulated STM images are in good agreement with experimental observations. Moreover, we find that the ADM surface has superior photocatalytic reactivity to the OR surface and a small number of water can be dissociated at the terrace at one water monolayer coverage, which has never been found before. These findings can not only be applied to solve the experimental controversies about the atomic structure of the reconstructed anatase (001)-(1 × 4) surface but also provide a theoretical basis for exploring the intrinsic properties of the surface.

A recent study sequenced the full coding region of SORL1 in 1,255 early-onset Alzheimer's disease (EOAD) cases and 1,938 control individuals, and investigated the contribution of genetic variability in SORL1 to EOAD risk in a European cohort. This study identified six common variants and five low frequency variants in the SORL1 coding sequence. However, none of these 11 variants was significantly associated with EOAD risk after adjusting for multiple testing. We consider whether these 11 SORL1 variants identified in European EOAD contribute to late-onset Alzheimer's disease (LOAD) risk in individuals of European ancestry. Here, we investigated these 11 SORL1 variants identified in European EOAD and LOAD risk in individuals of European ancestry using a large-scale LOAD GWAS. Our results indicate that three genetic variants rs2070045, rs2276412, and rs17125548 as well as their tagged genetic variants contribute to LOAD risk in European population. We further investigate whether these variants could affect SORL1 expression using multiple expression quantitative trait loci (eQTLs) datasets. Our findings suggest that three genetic variants rs2070045, rs1699102, and rs3824968 could significantly regulate SORL1 expression in human brain tissues. We believe that our findings further provide important supplementary information about the involvement of the SORL1 variants in LOAD risk.

Motivated by the high superconducting transition temperature (T

Titanium dioxide has been widely used as an efficient transition metal oxide photocatalyst. However, its photocatalytic activity is limited to the ultraviolet spectrum range due to the large bandgap beyond 3 eV. Efforts to reduce the bandgap to achieve a broader spectrum range of light absorption have been successfully attempted via the experimental synthesis of dopant-free metastable surface structures of rutile-type TiO

The perceptual just noticeable distortion (JND) model has attracted increasing attention in the field of the quantization-based watermarking framework. The JND model can provide a superior tradeoff between robustness and fidelity. However, the conventional JND models are not fit for the quantization-based watermarking, as the image has been altered by watermarking embedding. In this paper, we present an improved spread transform dither modulation (STDM) watermarking scheme, which is based on the image primitive features produced according to JND mechanism. The procedures include the contrast masking effect by utilizing a new measurement of edge strength which represent semantic information. What’s more, the proposed semantic information-based JND model can be theoretically invariant to the changes in the watermark-embedding processing. The newly proposed JND model is very simple but more effective in the STDM watermarking. Experiments results demonstrate that the proposed watermarking scheme can bring about better performance compared with previously proposed perceptual STDM schemes.

In the presence of sulfonated polystyrene (sPS) template, sPS@SnO 2 core shell particles formed via the interaction between the functional group of -SO 3 H on the template surface and ions of Sn 2+ from the precursor of SnSO 4 which were in ethanol-aqueous medium. After high-temperature calcination treatment for removal of sPS, the sPS@SnO 2 changed into SnO 2 hollow spheres. With the further carbonization of the sPS@SnO 2 @glucose composite microspheres, SnO 2 @C composite hollow spheres were fabricated. Using SEM, TEM, and N 2 adsorption - desorption technology, the structure, specific surface area, and the core-shell structure formation mechanism were determined. Cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) properties of SnO 2 hollow spheres and SnO 2 @C composites were investigated, respectively, in the foam nickel electrode under alkaline condition. The specific capacitance of SnO 2 @C composite hollow spheres could reach 25.8 F g −1 in 1 mol L −1 KOH aqueous solution and showed excellent charge-discharge behavior.

Rationale: Multiple primary central nervous system lymphoma (MPCNSL) is a rare disease with differential diagnosis and treatment. As the underlying pathogenesis is not yet clarified, the early-stage clinical manifestations are occult and atypical. Also, the imaging manifestations are not specific, which is challenging for the clinical diagnosis and treatment. Therefore, additional clinical research is essential to understand the etiology of the disease. Patient concerns: A 63-year-old male patient suffered from MPCNSLs but without typical clinical manifestations. The findings of the imaging examination were as follows. Magnetic resonance imaging (MRI) showed long T1 and T2 signal shadows in the right frontal lobe, right hippocampus, right cerebellar hemisphere, and the left occipital lobe. In addition, patchy T1-enhanced signal shadows were observed in the right frontal lobe and around the midline. Frontal lesions were detected in the magnetic resonance spectroscopy (MRS), Cho peak increased, and N-acetylaspartate (NAA) peak decreased. On the other hand, in the diffusion weighted imaging (DWI), apparent dispersion coefficient (ADC) showed low-value changes and high signal changes. The positron emission tomography-computed tomography (PET-CT) displayed radioactive accumulation in the right frontal lobe. Diagnosis: Multiple primary central nervous system lymphoma. Interventions: The patient received some conservative medical treatment, but his condition continued to worsen. Finally, he received a pathological biopsy, and refused further treatment after the result was reported. Outcomes: The patient died 1 week after biopsy, and the course of disease was about 100 days. Lessons: PCNSL is a primary intracranial malignancy with low incidence and a high degree of malignancy and specificity in clinical manifestations. To facilitate early clinical treatment and improve the long-term survival of patients, it is necessary to master the imaging diagnostic methods and its features. The comprehensive application of multiple imaging examinations, such as CT, MRI, PET/CT, and PET/MRI, as well as, cerebrospinal fluid cytology can greatly improve the diagnosis of PCNSL.

In order to effectively utilize the network teaching resources, a teaching resource classification method based on the improved KNN (K-Nearest Neighbor) algorithm was proposed. Taking the text class primary and secondary school teaching resources as the research object, combined with the domain characteristics, the KNN algorithm was improved. By measuring the sample space density, the text of the high-density area was found. Different clipping methods were proposed for both intra-class and inter-class regions. The problem of cropping in the space of multiple class boundaries was considered. Results showed that the method ensured uniform distribution of samples and reduced the time of classification. Therefore, under the Weka platform, the improved KNN algorithm is effective.

Hollow SnO2 microspheres are prepared from resorcinol–formaldehyde gel and different tin compound precursors, including stannous sulfate (SnSO4), stannous chloride dihydrate (SnCl2·2H2O), and stannic chloride pentahydrate (SnCl4·5H2O) via chemically induced self-assembly in hydrothermal environment. Morphological and structural characterizations of as-prepared hollow SnO2 microspheres are carried out using scanning electron microscopy, X-ray diffraction, and nitrogen adsorption–desorption method. Their electrochemical properties as the supercapacitor electrode materials for application are also investigated using cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) measurement in 1 M H2SO4 electrolyte. There are redox peaks in CV curves and a large number of Faradic plateaus in GCD curves. At different scan rates, all the obtained samples have excellent electrochemical properties. The hollow SnO2 microspheres obtained from SnSO4 and SnCl2·2H2O as precursors show relatively lower specific capacitances of 395 and 347 F g−1, respectively. However, the specific capacitance of SnO2 from SnCl4·5H2O is up to 663 F g−1. The high specific surface area and hollow structure of SnO2 microspheres are due to facilitating the rapid transport of electrolyte ions and improving the electrochemical performance. It is expected that hollow SnO2 microspheres are the promising redox supercapacitor materials.

Echo state network (ESN) is a novel kind of recurrent neural networks, where a reservoir is generated randomly and only readout layer is adaptable. It outperforms conventional recurrent neural networks in the field of multivariate time series prediction. Often ESN works beautifully. But sometimes it works poorly because of ill-posed problem. To solve it, we propose a new model on the basis of ESNs, termed as fast subspace decomposition echo state network (FSDESN). The core of the model is to utilize fast subspace decomposition algorithm for extracting a compact subspace out of a redundant large-scale reservoir matrix in order to remove approximate collinear components, overcome the ill-posed problem, and improve generalization performance. Experimental results on two multivariate benchmark datasets substantiate the effectiveness and characteristics of FSDESN.

A robust and sensitive high performance liquid chromatography-electrospray tandem mass spectrometry (HPLC-ESI MS/MS) method was developed with solid phase extraction (SPE) sample preparation. It was optimized for the analysis of various amino acids in rat plasma. Silica-based strong cationic exchange SPE cartridges were used to extract amino acids and minimize the ion-suppression effect. The SPE cleanup protocol was optimized and it was found that the sample loading pH was very critical for the recovery and reproducibility. When the plasma sample was mixed with 0.1 mol/L acetic acid (pH 2.8) and loaded on SPE cartridge, the recoveries and repeatability for most of the amino acids were satisfactory. The method was fully validated. The analytical characteristics such as total recoveries (33.6%-107.7%, except lysine and ornithine), linearities (r > 0.99 except arginine), intra-day precisions (relative standard deviation (RSD) < 9.0%) and inter-day precisions (RSD < 19.1%) were satisfactory for most of the amino acids. Furthermore, the method was successfully applied to study the ionizing radiation exposure induced changes in amino acid concentration of plasma samples from rats. Experimental results showed that the ionization radiation could lead to metabolism disorder of plasma amino acids, and the degree of disorder was related with the degree of injury by the ionization radiation injury. Some of the amino acids may be potential biomarkers of ionization radiation injury. These results provide experimental basis for screening of new markers of the acute radiation damage.

Considering the problem that for multivariate time series prediction, the adaptation of a single reservoir may not be sufficient to improve the prediction accuracy, we propose a novel hierarchical neural network herein. In the first hierarchy, several simplified echo state networks - simple cycle reservoirs (SCRs) are used to extract the dynamical features of the multivariate time series. Particle swarm optimization method is conducted in the pre-training stage to optimize the free parameters of SCRs. The reservoir states of SCRs are collected as dynamical features. In the second hierarchy, a feature selection method based on mutual information is used to select a compact feature set as the input for the extreme learning machine (ELM). In order to further improve the prediction accuracy, the optimal number of hidden nodes of the ELM is chosen by a modified recursive algorithm. Simulation results on monthly average temperature and rainfall series in Dalian China sustain that the proposed model is effective for multivariate time series.

Accurate gene quantification depends on the use of an appropriate internal control gene, which should be verified before its use for normalizing data. Housekeeping genes, which are expressed at relatively constant levels, are generally regarded as candidate internal control genes. To determine the ideal internal control for gene expression profiles for Porphyra haitanensis T. J. Chang et B. F. Zheng (Bangiales, Rhodophyta) at different life-history stages, we used absolute quantification to assess the expression levels of six housekeeping genes (18S ribosomal RNA, 30S ribosomal protein, glyceraldehyde-3-phosphate dehydrogenase, elongation factor 3, alpha-tubulin, and beta-tubulin) at the sporophyte and gametophyte stages. Housekeeping genes were selected by comparing the differences of observed copy numbers in sporophytes and in gametophytes. TubB (beta-tubulin) was found to be the optimal internal control gene, because it showed the smallest difference of gene expression. Compared with TubB, other housekeeping genes had greater variation of expression to different degrees.

Accurate gene quantification depends on the use of an appropriate internal control gene, which should be verified before its use for normalizing data. Housekeeping genes, which are expressed at relatively constant levels, are generally regarded as candidate internal control genes. To determine the ideal internal control for gene expression profiles for Porphyra haitanensis T. J. Chang et B. F. Zheng (Bangiales, Rhodophyta) at different life-history stages, we used absolute quantification to assess the expression levels of six housekeeping genes (18S ribosomal RNA, 30S ribosomal protein, glyceraldehyde-3-phosphate dehydrogenase, elongation factor 3, alpha-tubulin, and beta-tubulin) at the sporophyte and gametophyte stages. Housekeeping genes were selected by comparing the differences of observed copy numbers in sporophytes and in gametophytes. TubB (beta-tubulin) was found to be the optimal internal control gene, because it showed the smallest difference of gene expression. Compared with TubB, other housekeeping genes had greater variation of expression to different degrees.