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SEH1 like nucleoporin (SEH1L) is an important component of nuclear pore complex (NPC), which is crucial in the regulation of cell division. However, the interrelation between SEH1L expression and tumor progression is less studied. In this research, we performed a systematic bioinformatic analysis about SEH1L using TCGA, Timer 2.0, Cbioportal, UCLAN and CellMiner™ databases in pan-cancer. Besides, we further validated the bioinformatic results through in vitro and in vivo experiments in HCC, including transcriptome sequencing, real-time quantitative PCR (RT-qPCR), western blotting (WB), immunohistochemistry (IHC), cell proliferation assays, clone formation, EdU, transwell, flow cytometry and subcutaneous tumor model. Our results suggested that SEH1L was significantly up-regulated and related to poor prognosis in most cancers, and may serve as a potential biomarker. SEH1L could promote HCC progression in vitro and in vivo. Besides, the next generation sequencing suggested that 684 genes was significantly up-regulated and 678 genes was down-regulated after the knock down of SEH1L. SEH1L siliencing could activate ATF3/HMOX1/GPX4 axis, decrease mitochondrial membrane potential and GSH, but increase ROS and MDA, and these effects could be reversed by the knock down of ATF3. This study indicated that SEH1L siliencing could induce ferroptosis and suppresses hepatocellular carcinoma (HCC) progression via ATF3/HMOX1/GPX4 axis. [ABSTRACT FROM AUTHOR]

Reperfusion after cerebral ischemia leads to secondary damage to the nervous system, called cerebral ischemia/reperfusion injury (CIRI). The blood–brain barrier (BBB) consists of endothelial cells and tight junction (TJ) proteins, and its disruption aggravates CIRI. Two GSE datasets identified Tissue Factor Pathway Inhibitor 2 (TFPI2) as a differentially upregulated gene (Log2FC > 1, p < 0.01) in the cerebral cortex of ischemic rats, and TFPI2 affects angiogenesis of endothelial cells. Moreover, genes (c-Jun, c-Fos, FosL1) encoding subunits of Activator Protein-1 (AP-1), a transcription factor involved in IRI, were highly expressed in ischemic samples. Thus, the effects of the AP-1/TFPI2 axis on CIRI were explored. We determined increased TFPI2 expression in the cerebral cortex of rats receiving middle cerebral artery occlusion (MCAO) for 90 min and reperfusion (R) for 48 h. Then AAV2-shTFPI2 particles (5 × 1010 vg) were injected into the right lateral ventricle of rats 3 weeks before MCAO/R. TFPI2 knockdown decreased infarct size and neuronal injury in ischemic rats. It improved BBB integrity, demonstrated by reduced FITC-dextran leakage in brain tissues of MCAO/R-operated rats. Furthermore, it increased the expression of TJ proteins (Occludin, Claudin-5, TJP-1) in the cerebral cortex of rats with CIRI. Consistently, we found that TFPI2 knockdown mitigated cell damage in mouse endothelial bEND.3 cells with oxygen and glucose deprivation (ODG) for 6 h and reoxygenation (R) for 18 h (OGD/R) treatment. High co-expression of c-Jun and c-Fos significantly elevated TFPI2 promoter activity. c-Jun knockdown inhibited TFPI2 expression in OGD/R-treated bEND.3 cell. Collectively, our findings demonstrate that inhibition of the AP-1/TFPI2 axis alleviates CIRI. [ABSTRACT FROM AUTHOR]

Several pulsar timing array (PTA) collaborations have recently reported the evidence for a stochastic gravitational-wave background (SGWB), which can unveil the formation of primordial seeds of inhomogeneities in the early universe. With the SGWB parameters inferred from PTAs data, we can make a prediction of the seeds for early galaxy formation from the domain walls in the axion-like particles (ALPs) field distribution. This also naturally provides a solution to the observation of high redshifts by the James Webb Space Telescope. The predicted photon coupling of the ALP is within the reach of future experimental searches. [ABSTRACT FROM AUTHOR]

We investigate the temperature-dependent production of feebly interacting massive dark matter particle (FIMP DM) within a Z 2 model, incorporating two Z 2 -odd scalar fields. In specific parameter regions, three distinct mechanisms individually dominate the production of the FIMP DM. These mechanisms include the semi-production process, commonly known as the “exponential growth” process, the three-body decay process, and the production from pair annihilations of the bath particles. It is crucial to consider the thermal history during the evolution of FIMPs, as it involves multiple phase transitions occurring prior to the freeze-in of dark matter. Consequently, the scalar masses experience thermal variations, leading to distinctive evolutionary trajectories for FIMPs when compared to scenarios without accounting for the thermal effects. Notably, the unique patterns of FIMP evolution are accompanied by the production of gravitational waves, presenting promising opportunities for detection using forthcoming interferometers. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) suffers from insufficient label information, as human expressions are complex and diverse, with many expressions ambiguous. Using low-quality labels or low-quantity labels will aggravate ambiguity of model predictions and reduce the accuracy of FER. How to improve the robustness of FER to ambiguous data with insufficient information remains challenging. To this end, we propose the Suppressing Ambiguity Self-Training (SAST) framework which is the first attempt to address the problem of insufficient information both label quality and label quantity containing, simultaneously. Specifically, we design an Ambiguous Relative Label Usage (ARLU) strategy that mixes hard labels and soft labels to alleviate the information loss problem caused by hard labels. We also enhance the robustness of the model to ambiguous data by means of Self-Training Resampling (STR). We further use the landmarks and Patch Branch (PB) to enhance the ability of suppressing ambiguity. Experiments on RAF-DB, FERPlus, SFEW, and AffectNet datasets show that our SAST outperforms 6 semi-supervised methods with fewer annotations, and achieves competitive accuracy to State-Of-The-Art (SOTA) FER methods. Our code is available at https://github.com/Liuxww/SAST. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is one of the popular research topics in the field of computer vision. When most of the deep learning expression recognition methods that achieve satisfactory results with a single dataset are applied to a new dataset, additional costs result from labeling the new data. FER under cross-dataset also suffers from difficulties such as data discrepancy and expression ambiguity. To address these issues, we propose an Unsupervised Self-Training Similarity Transfer (USTST) method for cross-domain FER. The Cross-Swin-Transformer (CST) module is designed to extract features and assign greater attention weight to the similar regions of the source and target domain images. The Self-Training Resampling (STR) and the Knowledge Transfer (KT) modules are then constructed to improve the confidence of the model prediction for the target domain. We also design ambiguity suppression loss and cross-domain loss to improve the ability of the model to discriminate expressions while transferring knowledge across domains. The experimental results with the RAF-DB dataset as the source domain and the CK+, JAFFE, SFEW, FER2013 and ExpW datasets as the target domains, show that our approach achieves much higher performance than the state-of-the-art cross-domain FER methods, while requiring no labels of new datasets. [ABSTRACT FROM AUTHOR]

Previous research on cross-domain Facial Expression Recognition (FER) mainly focused on metric learning or adversarial learning, which presupposes access to source domain data to find domain invariant information. However, in practical applications, due to the high privacy and sensitivity of face data, it is often impossible to directly obtain source domain data. In this case, these methods cannot be effectively applied. In order to better apply the cross-domain FER method to the real scenarios, this paper proposes a source-free FER method called Label Transfer Virtual Adversarial Learning (LTVAL), which does not need to directly access source domain data. First, we train the target domain model based on the information maximization constraint, and obtain the pseudo-labels of the target domain data through deep clustering to achieve label transfer. Secondly, the perturbation is added to the target domain samples, and the perturbed samples and the original samples are together used for virtual adversarial training with local distributed smoothing constraints. Finally, a joint loss function is constructed to optimize the target domain model. Using the source domain model trained on RAF-DB, experiments on four public datasets FER2013, JAFFE, CK+, and EXPW as target domain datasets show that our approach achieves much higher performance than the state-of-the-art cross-domain FER methods that require access to source domain data. [ABSTRACT FROM AUTHOR]

An efficient copper-catalyzed cyanoalkylation/cyclization strategy was developed for the construc-tion of indolo[2,1-a]isoquinoline derivatives from 2-aryl-N-methacryloylindoles. The cyclization involved a cascade of radical reactions promoted by commercially available 2,2′-azobis(isobutyronitrile) (AIBN) or its analogs. This strategy provides a convenient access to a variety of cyanoalkylated indolo[2,1-a]isoquinolines in reasonable yields. [ABSTRACT FROM AUTHOR]

The keV scale sterile neutrino was a qualified candidate for dark matter particles in the Dodelson-Widrow mechanism. But the mixing angle, needed to provide enough amount of dark matter, is in contradiction with the astrophysical observations. To alleviate such tension, we introduce an effective interaction, i.e. g a (ϕ / Λ) ∂ μ a ν α ¯ γ μ γ 5 ν α , among Standard Model neutrino ν α , axion a, and singlet ϕ . The axial-vector interaction form is determined by the axion shift symmetry, and the singlet ϕ with dynamically varied vacuum expectation value is introduced to reinforce the axial-vector coupling strength and evade the stringent neutrino oscillation constraints. The effective potential generated by the new interaction could cancel the SM counterpart, resulting in an enhanced converting probability between SM neutrino and sterile neutrino. Hence, the production rate of sterile neutrinos can be substantially enlarged with smaller mixing compared to the DW mechanism. [ABSTRACT FROM AUTHOR]

In this paper, we extend the Shen-Larsson's construction for Witt algebras to simple generalized Witt algebras and construct a class of modules for these algebras. We determine the irreducibility and isomorphism of these modules under certain countability conditions. Thus we obtain a large family of irreducible weight modules other than highest weight modules and many irreducible non-weight modules over the simple generalized Witt algebras. [ABSTRACT FROM AUTHOR]

Background: The significant roles of circular RNAs (circRNAs) in different cancers and diseases have been reported. We now focused on the possible role of a newly recognized circRNA, circ_0004674 in triple-negative breast cancer (TNBC), and the related downstream mechanism. Methods: The expression of circ_0004674 in TNBC tissues and cells was determined followed by analysis of the correlation between circ_0004674 and TNBC patients' prognosis. The interaction between circ_0004674, miR-377-3p, E2F6, and PNO1 was then identified using bioinformatics analysis combined with FISH, RIP, RNA pull-down, RT-qPCR, and Western blot analysis. Using gain-of-function and loss-of-function methods, we analyzed the effect of circ_0004674, miR-377-3p, E2F6, and PNO1 on TNBC in vivo and in vitro. Results: Increased circ_0004674 and E2F6 but decreased miR-377-3p were observed in TNBC tissues and MDA-MB-231 TNBC cells, all of which findings were associated with poor prognosis in patients with TNBC. Silencing of circ_0004676 remarkably suppressed the proliferation, cell cycle progression, and migration of TNBC cells in vitro, as well as inhibiting tumorigenesis and metastasis in vivo. Additionally, circ_0004676 served as a sponge of miR-377-3p which bound to the transcription factor E2F6. In the presence of overexpression of circ_0004676, E2F6 expression and its target PNO1 expression were elevated, while miR-377-3p expression was decreased. Interestingly, overexpression of E2F6 could reverse the inhibitory effect on tumor growth caused by downregulation of circ_0004676. Conclusion: Our study highlighted the carcinogenic effect of circ_0004676 on TNBC through regulation of the miR-377-3p/E2F6/PNO1 axis. 1. Circ_0004674 is highly expressed in TNBC tissues and cells. 2. Circ_0004674 upregulates the expression of E2F6 by sponging miR-377-3p. 3. E2F6 upregulates PNO1 by binding to the PNO1 promoter. 4. Circ_0004674 favors TNBC progression by regulating the miR-377-3p/E2F6/PNO1 axis. 5. This study provides a new target for the treatment of TNBC. [ABSTRACT FROM AUTHOR]

Realization problems, including the minimum realization of Boolean control networks (BCNs), are investigated by the vertex partition method. A general definition of the realization for BCNs that does not involve any subspaces is proposed. For the new and existing realization definitions, by a common concolorous perfect vertex partition (CCP-VP) of vertex-colored state transition graphs, an equivalence relation and a quotient mapping are defined, which induce a quotient system, i.e., the realization of original BCNs. In addition, if the CCP-VP is an equal partition, a realization induced by a γ-friendly controlled invariant regular subspace is constructed. Finally, an algorithm is designed to construct the minimum realization of BCNs for different realization definitions, and three examples are given to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

Graph-based dimensionality reduction methods have attracted much attention for they can be applied successfully in many practical problems such as digital images and information retrieval. Two main challenges of these methods are how to choose proper neighbors for graph construction and make use of global and local information when conducting dimensionality reduction. In this paper, we want to tackle these two challenges by presenting an improved graph optimization approach for unsupervised dimensionality reduction. Our method can deal with dimensionality reduction and graph construction at the same time, which doesn't need to construct an affinity graph beforehand. On the other hand, by integrating the advantages of the orthogonal local preserving projections and principal component analysis, both the local and global information of the original data are considered in dimensionality reduction in our approach. Eventually, we learn the sparse affinity graph by considering probabilistic neighbors, which is optimal and suitable for classification. To testify the superiority of our approach, we carry out some experiments on several publicly available UCI and image data sets, and the results have demonstrated the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Flavor-specific scalar bosons exist in various Standard Model extensions and couple to a single generation of fermions via a global flavor symmetry breaking mechanism. Given this strategy, we propose a MeV flavor-specific scalar model in dimension-5 operator series, which explains the muon g — 2 anomaly and proton radius puzzle by coupling with the muon and down-quark at the same time. The framework is consistent with the null result of high-intensity searches. Specifically, the supernova constraints for muon couplings become weakened by including the contribution of down-quark interaction. The parameter space for explaining muon g — 2 discrepancy is available when 10% energy deposition is required in the energy explosion process in the supernova, but this is ruled out by the 1% energy deposition requirement. We also investigate the searches for mediator and dark matter and the resulting constraints on viable parameter space such as nuclear physics constraints, direct detection for light boosted dark matter, and possible CMB constraints. When compared with conventional dark matter production, light dark matter production has two additional modifications: bound state formation and early kinetic equilibrium decoupling. We are now looking into the implications of these effects on the relic density of light dark matter. [ABSTRACT FROM AUTHOR]

We study the superheavy dark matter (DM) scenario in an extended B−L model, where one generation of right-handed neutrino νR is the DM candidate. If there is a new lighter sterile neutrino that co-annihilate with the DM candidate, then the annihilation rate is exponentially enhanced, allowing a DM mass much heavier than the Griest-Kamionkowski bound (∼105 GeV). We demonstrate that a DM mass MνR ≳ 1013 GeV can be achieved. Although beyond the scale of any traditional DM searching strategy, this scenario is testable via gravitational waves (GWs) emitted by the cosmic strings from the U(1)B−L breaking. Quantitative calculations show that the DM mass O (109−1013 GeV) can be probed by future GW detectors. [ABSTRACT FROM AUTHOR]

A novel marine hydrogen- and sulfur-oxidizing bacterium, designated strain S2-6 T, was isolated from the deep-sea sediment samples at the Longqi hydrothermal system, southwestern Indian Ocean. Cells were Gram-stain-negative, motile, short rods with a single polar flagellum. Growth was observed at 10–45 °C (optimum 33 °C), pH 5.0–8.0 (optimum pH 7.0) and 1.5 to 6.0% (w/v) NaCl with an optimum at 3.0% (w/v). The isolate was an obligate chemolithoautotroph capable of growth using thiosulfate, tetrathionate, elemental sulfur or sodium sulfide as the energy source, and oxygen or nitrate as the sole electron acceptor. When hydrogen was used as the energy source, strain S2-6 T could respire oxygen, nitrate or element sulfur. The major cellular fatty acids of strain S2-6 T were summed feature 3 (C16:1ω7c and/or C16:1ω6c), C16:0 and summed feature 8 (C18:1ω7c and/or C18:1ω6c). The total size of its genome was 2,320,257 bp and the genomic DNA G + C content was 37.3 mol%. Phylogenetic analysis based on 16S rRNA gene sequences and core genes showed that the novel isolate belonged to the genus Sulfurimonas and was most closely related to Sulfurimonas paralvinellae GO25T (96.8% sequence identity) and Sulfurimonas autotrophica OK10T (95.8% sequence identity). The average nucleotide identity and DNA-DNA hybridization values between strain S2-6 T and S. paralvinellae GO25T and S. autotrophica OK10T were 74.6%-81.2% and 19.1%-24.6%, respectively. Based on the polyphase taxonomical data, strain S2-6 T represents a novel species of the genus Sulfurimonas, for which the name Sulfurimonas sediminis sp. nov. is proposed, with the type strain S2-6 T (= MCCC 1A14513T = KCTC 15854 T). [ABSTRACT FROM AUTHOR]

Methyl orange (MO) is a common anionic azo dye that is harmful to the environment and biology, so it must be treated innocuously before it can be discharged. Adsorption is an effective method to remove anionic dyes. Nanotube mineral is a natural one-dimensional adsorption material, and its unique morphology and structure endow it with good adsorption capacity. Although there are many related studies, there is a lack of in-depth discussions on the influence of nanotube's composition and structure on the adsorption of dyes and other pollutants. In this paper, two kinds of natural one-dimensional silicate minerals [halloysite nanotubes (HNTs) and chrysotile nanotubes (ChNTs)] with similar morphology but slightly different compositions and crystal structures were used as adsorbents, and MO solution was used as simulate pollutants. It is the first time to discuss in depth the influence of the composition and structure of nanotube minerals on their charge properties and the adsorption performance of methyl orange dyes. It is found that HNTs and ChNTs have different adsorption capacity due to the difference of electronegativity between Al3+ and Mg2+ in the crystal, so they possess negative and positive charges respectively in near-neutral solution, which leads to the adsorption capacity of MO by ChNTs with positive charges which is greater than that of HNTs. [ABSTRACT FROM AUTHOR]

In actual line operation, the critical velocity is one of the key physical quantities of rail design owing to its great influence on the riding comfort and safety of vehicles due to the wheel-rail contact loss caused by the abrupt change of rail foundation rigidity, rail wear, or abruptness irregularities on rail. In this study, the short floating slab track (SFST) structure is regarded as a double-layer system. The Euler beam and the rigid body model are adopted for the rail and the floating slab, respectively, and the dispersion equation and the theoretical critical velocity of the rail structure under ideal conditions are deduced. Besides, this study considers the implementation of the SFST in the vehicle-structure coupling system. The alterable element method is introduced for accurately simulating the change of the wheel-rail contact state and coding a vehicle-structure dynamic analysis program (VSDAP) to calculate the critical velocity of rail structures from the dynamic response of vehicles and rail structures. The principle of its design at the beginning of the design is given on the basis of the theoretical value of the critical velocity and the simulation of the dynamic response, which can provide reference for practical engineering design. [ABSTRACT FROM AUTHOR]

In this work, we study the long-standing experimental anomaly in muon (g −2) and also recent anomalous excess in K L → π 0 + v v ¯ at the J-PARC KOTO experiment with sgoldstino. After supersymmetry breaking, the interactions between quarks and sgoldstino (s) make the decays K → π + s sizable through loop diagrams, which affects the measurements of decays K → π + invisible. Furthermore, the couplings between photons and sgoldstino contribute to ∆aμ as well as the bino-slepton contribution. With satisfying all known experimental constraints such as from NA62, E949, E137, Orsay, KTEV and CHARM experiments, these two anomalies can be explained simultaneously. The mass of CP-even sgoldstino is close to the neutral pion mass which does not violate the Grossman-Nir bound. The parameter space can be further tested in future NA62, DUNE experiments, as well as experiments in the LHC. [ABSTRACT FROM AUTHOR]

The isothermal single-component multi-phase lattice Boltzmann method (LBM) combined with the particle motion model is used to simulate the detailed process of liquid film rupture induced by a single spherical particle. The entire process of the liquid film rupture can be divided into two stages. In Stage 1, the particle contacts with the liquid film and moves into it due to the interfacial force and finally penetrates the liquid film. Then in Stage 2, the upper and lower liquid surfaces of the thin film are driven by the capillary force and approach to each other along the surface of the particle, resulting in a complete rupture. It is found that a hydrophobic particle with a contact angle of 106.7° shows the shortest rupture duration when the liquid film thickness is less than the particle radius. When the thickness of the liquid film is greater than the immersed depth of the particle at equilibrium, the time of liquid film rupture caused by a hydrophobic particle will be increased. On the other hand, a moderately hydrophilic particle can form a bridge in the middle of the liquid film to enhance the stability of the thin liquid film. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM), a major cause of sudden death in youth, is largely affected by genetic factors. The R58Q mutation in the MYL2 gene was identified in some HCM patients and was considered as a deleterious HCM mutation. However, the passing of R58Q between generations along with HCM symptoms was observed only in small families with only two or three members; thus, whether R58Q is as deleterious as previously claimed remains questionable. Here, we reported a large four-generation Chinese family, and found that R58Q existed in all six members with HCM and two healthy juveniles who had not yet developed HCM yet, and presumably in three deceased members who suffered from sudden death. In addition, we also found that compared with other mutations, R58Q had a more severe effect on the cellular level. Therefore, we confirmed that R58Q could be passed from generation to generation along with HCM symptoms and that it was indeed a deleterious mutation for HCM. However, further study is needed to identify additional factors that may determine the various symptoms shown in different family members within the same family. [ABSTRACT FROM AUTHOR]

This paper presents a novel optimization scheme using a Genetic Algorithm (GA) to design a composite connecting bracket in manned spacecraft especially for the stress concentration of the hinge pedestal. Experimental modal analysis (EMA) as well as a large bending load tests was performed on the bracket to verify the failure mode through their mechanical response. The EMA results showed the best optimization scheme is placing the reinforced sleeve 20 mm from the hinge pedestal. Experimental results were also used to validate the Finite Element Analysis (FEA) approached used to predict the bracket structural response. The method used in this paper can provide some valuable guidance to study other similar composite structures in the future. [ABSTRACT FROM AUTHOR]

Great interest has been attracted to enhancing the photoelectrochemical performances of BiVO4. However, there have rarely been reported about addressing their PEC stability. Herein, it is proposed that Fe ions doping could enhance the stability and PEC performances of BiVO4, which may because it could improve the crystalline structure and eliminates the crystal defects of BiVO4. Moreover, it is found that metal-organic framework can play a co-catalyst role during the PEC experiments. We designed and fabricated nanoporous Fe-doped BiVO4 modified with MIL-53(Fe), which exhibits much higher PEC performance and stability than pristine BiVO4 and Fe-doped BiVO4. It is believed that MIL-53(Fe) can capture the photogenerated holes and thus facilitates the charge separation efficiency.Graphical Abstract: [ABSTRACT FROM AUTHOR]

Aging is a biological process characterized by a progressive functional decline in tissues and organs, which eventually leads to mortality. Telomeres, the repetitive DNA repeat sequences at the end of linear eukaryotic chromosomes protecting chromosome ends from degradation and illegitimate recombination, play a crucial role in cell fate and aging. Due to the mechanism of replication, telomeres shorten as cells proliferate, which consequently contributes to cellular senescence and mitochondrial dysfunction. Cells are the basic unit of organismal structure and function, and mitochondria are the powerhouse and metabolic center of cells. Therefore, cellular senescence and mitochondrial dysfunction would result in tissue or organ degeneration and dysfunction followed by somatic aging through multiple pathways. In this review, we summarized the main mechanisms of cellular senescence, mitochondrial malfunction and aging triggered by telomere attrition. Understanding the molecular mechanisms involved in the aging process may elicit new strategies for improving health and extending lifespan. [ABSTRACT FROM AUTHOR]

Abstract: Great efforts have been paid to improve the photoelectrochemical performances of FeVO4 semiconductors. However, there have rarely been reported about the modifying of FeVO4 with metal-free co-catalyst for enhanced photoelectrochemical activities. Herein, we demonstrate a facile and effective immersion method for coupling ultrathin g-C3N4 nanosheets with nanoporous FeVO4 photoelectrodes. The FeVO4/g-C3N4 heterostructures demonstrate more than twice higher photocurrent density than pure nanoporous FeVO4 photoelectrodes, which should be attributed to the metal-free co-catalyst of ultrathin g-C3N4. It is believed that the ultrathin g-C3N4 nanosheets can effectively trap the photo excited holes and facilitates the charge separation efficiency and thus significantly improve the photoelectrochemical performances of FeVO4.Graphical Abstract: [ABSTRACT FROM AUTHOR]

Oxidative stress plays important role in the pathogenesis of Alzheimer's disease (AD). Edaravone is a potent free radical scavenger that exerts antioxidant effects. Therefore, in this study we aimed to investigate neuroprotective effects of edaravone for AD. Wistar rats were randomly divided into three groups (n = 15): control group, model group, and treatment group, which were injected with phosphate buffered saline, Aβ1-40, and Aβ1-40 together with 5 mg/kg edaravone, respectively, into the right hippocampal dentate gyrus. Spatial learning and memory of the rats were examined by Morris water maze test. 4-Hydroxynonenal (4-HNE) level in rat hippocampus was analyzed by immunohistochemistry. Acetylcholinesterase (AChE) and choline acetylase (ChAT) activities were assayed by commercial kits. We found that edaravone ameliorated spatial learning and memory deficits in the rats. 4-HNE level in the hippocampus as well as AChE and ChAT activities in the hippocampus was significantly lower in treatment group than in model group. In conclusion, edaravone may be developed as a novel agent for the treatment of AD for improving cholinergic system and protecting neurons from oxidative toxicity. [ABSTRACT FROM AUTHOR]

In this paper, we study weight modules over a class of graded Lie algebras, which were introduced by Olivier Mathieu when he classified simple graded Lie algebras of finite growth. We show that any weight module over such algebras with finite dimensional weight spaces can be decomposed into three parts. As a consequence, we give rough descriptions of indecomposable and simple weight modules. Some examples are also presented. [ABSTRACT FROM AUTHOR]

GM-1 ganglioside (GM-1) has been proposed as a new therapeutic agent against Alzheimer's disease (AD). Therefore, in this study we aimed to investigate the effects of GM1 on memory deficits and oxidative stress in the hippocampus of rat model of AD. Wistar rats were randomly divided into three groups ( n = 15): control group, model group, and treatment group, which were injected with vehicle, Aβ1-40, and Aβ1-40 together with GM-1, respectively. Morris water maze test was performed to evaluate spatial learning and memory of the rats. Brain malondialdehyde (MDA) content was detected by biochemical assay, and 4-hydroxynonenal (4-HNE) level in the hippocampus was examined by immunohistochemistry. The results showed that learning and memory deficits were improved in treatment group compared to model group. Brain MDA content and 4-HNE level in hippocampus CA1 were much lower in treatment group than in model group. In summary, we demonstrate that GM-1 could improve spatial learning and memory deficits in rat model of AD, and this may be mediated by the inhibition of oxidative stress and lipid peroxidation in the neurons. These data suggest that GM-1 is a potential agent for AD treatment. [ABSTRACT FROM AUTHOR]

In this paper, we study Whittaker modules over the loop Virasoro algebra relative to some total order. We give a description of all Whittaker vectors for the universal Whittaker modules. We also show that any universal Whittaker module admits a unique simple quotient modules except for a special case. [ABSTRACT FROM AUTHOR]

The expression status of CD74 in breast cancer stem cells and its clinical implications was evaluated in order to lay a foundation for managing breast cancer. Five hundred and eighty breast cancer specimens were enrolled in the study. The relationship between the CD74 protein and clinicopathological parameters as well as prognosis was subsequently determined. In total, 468 (80.69 %) of the 580 breast cases showed CD74-positive expression. After universal analysis, CD74 was observed to be related to lymph node metastasis and triple-negative breast cancer ( P = 0.01 and 0.001). Moreover, CD74 expression has a line correlation with lymph node metastasis and triple-negative breast cancer ( P = 0.02 and 0.001). Furthermore, periostin was shown to attain a significantly more distant liver metastasis and worse disease-specific survival than those with none or low-expressed CD74 protein ( P = 0.001). In the Cox regression test, CD74 protein was detected as an independent prognostic factor ( P = 0.001). CD74 is consistently expressed in triple-negative subgroups of breast cancer and might be a new potential marker for triple-negative breast cancer. [ABSTRACT FROM AUTHOR]

The transient luminescence of three kinds of ruthenium complexes [Ru(bpy)2(7-CH3-dppz)]2+, [Ru(bpy)2(7-F-dppz)]2+ and [Ru(phen)2(7-F-dppz)]2+ bound to calf thymus DNA (ctDNA) has been studied by using the time-resolved spectroscopy. The results show that the luminescence is due to the radiative decay from the charge-transfer states to the ground state. By the interaction with DNA, the radiativeless rate of the photoexcited Ru complex molecules decreases, which results in the increase of luminescence lifetime and efficiency. The structure of the Ru complex has an important impact on the interaction with DNA. The [Ru(bpy)2(7-CH3-dppz)]2+ shows the longest luminescence lifetime (about 382 ns), while the [Ru(bpy)2(7-F-dppz)]2+ shows the shortest lifetime (about 65 ns). The possible origin of the luminescence dynamics is discussed. [ABSTRACT FROM AUTHOR]

The aim of our study was to document the patterns of RLN spread by using MRI. The MR images of 294 patients with newly diagnosed NPC were reviewed retrospectively. Criteria for metastatic lymph nodes included: shortest axial diameter, nodal necrosis, extracapsular spread, and a contrast enhancing rim. RLN involvement was detected in 190 (64.6%) patients. A significantly higher incidence of metastatic RLNs was observed in patients with involvement of the oropharynx, nasal cavity, pre-styloid parapharyngeal space, post-styloid parapharyngeal space, levator muscle of the velum palatini, and tensor muscle of the velum palatine. Patients with level II, III, and V node involvement also had a higher incidence of metastatic RLNs. Of the 231 patients who had metastatic RLNs or cervical lymph nodes, 43 (18.6%) had only metastatic RLNs, 41 (17.7%) had only metastatic cervical lymph nodes, and 147 (63.6%) exhibited an involvement of both the RLNs and cervical lymph nodes. The difference between the incidence of RLN involvements (82.3%) and the incidence of cervical lymph node involvement (81.4%) was very small. Metastatic RLNs are significantly associated with early stage primary tumor involvement and supper cervical lymph node metastasis in NPC. [ABSTRACT FROM AUTHOR]

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM) represents one of the most common heritable heart diseases. However, the signalling pathways and regulatory networks underlying the pathogenesis of HCM remain largely unknown. Here, we present a strand-specific RNA-seq dataset for both coding and lncRNA profiling in myocardial tissues from 28 HCM patients and 9 healthy donors. This dataset constitutes a valuable resource for the community to examine the dysregulated coding and lncRNA genes in HCM versus normal conditions. Design Type(s) transcription profiling design • disease state design • sequence analysis objective Measurement Type(s) transcription profiling assay Technology Type(s) RNA sequencing Factor Type(s) experimental condition Sample Characteristic(s) Homo sapiens • heart Machine-accessible metadata file describing the reported data (ISA-Tab format) [ABSTRACT FROM AUTHOR]

With the development of wireless devices and the increase of mobile users, the operator's focus has shifted from the construction of the communication network to the operation and maintenance of the network. Operators are eager to know the behavior of mobile networks and the real-time experience of users, which requires the using of historical data to accurately predict future network conditions. Big data analysis and computing which is widely adopted can be used as a solution. However, there are still some challenges in data analysis and prediction for mobile network optimization, such as the timeliness and accuracy of the prediction. This paper proposes a traffic analysis and prediction system which is suitable for urban wireless communication networks by combining actual call detail record (CDR) data analysis and multivariate prediction algorithms. Firstly, a spatial-temporal modeling is used for historical traffic data extracting. After that, causality analysis is applied to communication data analysis for the first time. Based on causal analysis, multivariate long short-term memory models are used to predict future data for CDR data. Finally, the prediction algorithm is used to process real data of different scenes in the city to verify the performance of the entire system. [ABSTRACT FROM AUTHOR]

Multi-frame super-resolution recovers a high-resolution (HR) image from a sequence of low-resolution (LR) images. In this paper, we propose an algorithm that performs multi-frame super-resolution in an online fashion. This algorithm processes only one low-resolution image at a time instead of co-processing all LR images which is adopted by state-of-the-art super-resolution techniques. Our algorithm is very fast and memory efficient, and simple to implement. In addition, we employ a noise-adaptive parameter in the classical steepest gradient optimization method to avoid noise amplification and overfitting LR images. Experiments with simulated and real-image sequences yield promising results. [ABSTRACT FROM AUTHOR]

Self-healing is one of the most important parts in self-organizing mobile communication network. It focuses on detecting the decline of service quality and finding out the cause of network anomalies and repairing it with high automation. Diagnosis is a particularly important task which identifies the fault cause of problematic cells or regions. To perform the diagnosis, this paper presents two modified ensemble classifiers by using Support Vector Machine (SVM) with different kernels, i.e., SVM with the radial basis function (RBF) kernel (RBFSVM in short) and SVM with the linear kernel (LSVM in short), as component classifier in Adaptive Boosting (AdaBoost), and we call the two ensemble classifiers as Adaptive Boosting based on RBFSVM (AdaBoostRBFSVM in short) and Adaptive Boosting based on linear kernel (AdaBoostLSVM in short). Different with previous AdaBoostSVM classifiers using weak component classifiers, in this paper, the performance of the classifiers is adaptively improved by using moderately accurate SVM classifiers (the training error is less than 50%). To solve the accuracy/diversity dilemma in AdaBoost and get good classification performance, the training error threshold is regulated to adjust the diversity of classifier, and the parameters of SVM (regularization parameter C and Gaussian width σ) are changed to control the accuracy of classifier. The accuracy and diversity will be well balanced through reasonable parameter adjustment strategy. Results show that the proposed approaches outperform individual SVM approaches and show good generalization performance. The AdaBoostLSVM classifier has higher accuracy and stability than LSVM classifier. Compared with RBFSVM, the undetected rate and diagnosis error rate of AdaBoostRBFSVM decrease slightly, but the false positive rate does reduce a lot. It means that the AdaBoostRBFSVM classifier is indeed available and can greatly reduce the number of normal class samples that have been wrongly classified. Therefore, the two ensemble classifiers based on the SVM component classifier can improve the generalization performance by reasonably adjusting the parameters. To set the parameter values of component classifiers in a more reasonable and effective way, genetic algorithm is introduced to find the set of parameter values for the best classification accuracy of AdaBoostSVM, and the new ensemble classifier is called AdaboostSVM based on genetic algorithm (GA-AdaboostSVM in short) (including AdaboostLSVM based on genetic algorithm and AdaboostRBFSVM based on genetic algorithm). Results show that GA-AdaboostSVM classifiers have a lower overall error than AdaboostSVM classifiers. Genetic algorithm could help to achieve a more optimal performance of the ensemble classifiers. [ABSTRACT FROM AUTHOR]