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To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients' readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients. [ABSTRACT FROM AUTHOR]

An efficient electrocatalyst is indispensable to significantly reduce energy consumption and accelerate the conversion efficiency of water splitting. In this work, the honeycomb-like porous MoCo alloy on nitrogen-doped three-dimensional (3D) porous graphene substrate (Mo0.3Co0.7@NPG) has been synthesized from the annealing of layered double hydroxide (MoCo-LDH@NPG). Especially, the Mo0.3Co0.7@NPG exhibits low hydrogen evolution overpotential of 75 mV (10 mA·cm–2) and a Tafel slope of 69.9 mV·dec–1, which can be attributed to highly conductive NPG substrate, the unique nanostructure and the synergistic effect of Mo and Co. Moreover, the Mo0.3Co0.7@NPG can maintain the original morphology and high catalytic activity after 50-h stability test. This work proposes a general strategy to synthesize a multi-element alloy on conductive substrates with high porosity for electrocatalytic reaction. 摘要: 高效的电催化剂可以显著降低电解水的能耗和提高转化效率。本项工作以MoCo双金属氢氧化物作为前驱体, 在三维(3D) 氮掺杂多孔石墨烯衬底上合成了蜂窝状多孔MoCo合金(Mo0.3Co0.7@NPG)。在析氢反应中, Mo0.3Co0.7@NPG的析氢过电位为75 mV (10 mA cm–2), Tafel斜率为69.9 mV dec–1。Mo0.3Co0.7@NPG具有优异的电化学性能主要归功于NPG衬底的高导电性、MoCo合金独特的纳米结构以及Mo和Co的协同作用。此外, 经过50 h的稳定性测试, Mo0.3Co0.7@NPG仍能保持原有的形貌和较高的催化活性。因此, 本项工作对于在高孔隙率导电基板上合成高效的多元素合金电催化剂提供了一种思路。 [ABSTRACT FROM AUTHOR]

It was proposed recently that the fine-grained entropy of the Hawking radiation can be expressed by the semiclassical island formula, which reproduces the unitary Page curve. In this paper, we choose the "in" vacuum state and apply the quantum extremal surface construction to study the Page curve for the step-function Vaidya model of evaporating black holes in four dimensions, which is produced by the spherical null shells. Metrics of the three regions of this spacetimes are obtained. In addition, the entanglement islands for the step-function Vaidya model of evaporating black holes at very late times are studied. When cutoff surface A is located in Minkowski region III with uA< uH at very late times, we find that the location of the boundary of island ∂I depends on the value of 8M − vA + vI. Specifically, ∂I is inside, at or outside the horizon when 8M − vA + vI is less than, equal to or larger than zero respectively. Moreover, when cutoff surface A is located in Minkowski region III with uA> uH after the black hole evaporates completely, we find that entanglement island still exists and ∂I is located on an equal-time Cauchy surface of the observer A when r A 2 ≥ 64GNκc. [ABSTRACT FROM AUTHOR]

Au-Fe3O4 multifunctional nanoparticles (NPs) were synthesized and integrated with lateral flow immunoassay (LFIA) for dual-mode detection of Salmonella typhimurium. The Au-Fe3O4 NPs not only combined excellent local surface plasmon resonance characteristics and superparamagnetic properties, but also exhibited good photothermal effect. In the detection, antibody-conjugated Au-Fe3O4 NPs first captured S. typhimurium from complex matrix, which was then loaded on the LFIA strip and trapped by the T-line. By observing the color bands with the naked eyes, qualitative detection was performed free of instrument. By measuring the photothermal signal, quantification was achieved with a portable infrared thermal camera. The introduction of magnetic separation achieved the enrichment and purification of target bacteria, thus enhancing the detection sensitivity and reducing interference. This dual-mode LFIA achieved a visual detection limit of 5 × 105 CFU/mL and a photothermal detection limit of 5 × 104 CFU/mL. Compared with traditional Au-based LFIA, this dual-mode LFIA increased the detection sensitivity by 2 orders of magnitude and could be directly applied to unprocessed milk sample. Besides, this dual-mode LFIA showed good reproducibility and specificity. The intra-assay and inter-assay variation coefficients were 3.0% and 7.9%, and with this dual-mode LFIA, other bacteria hardly produced distinguishable signals. Thus, the Au-Fe3O4 NPs-based LFIA has potential to increase the efficiency of pandemic prevention and control. Au-Fe3O4 nanoparticle proved to be a promising alternative reporter for LFIA, achieving multifunctions: target purification, target enrichment, visual qualitation, and instrumental quantification, which improved the limitations of traditional LFIA. [ABSTRACT FROM AUTHOR]

Based on the daily precipitation data of 96 stations in Xinjiang, China, from 1970 to 2021, the trend of summer extreme precipitation indices and their regional characteristics are analyzed. The generalized extreme value (GEV) model is used to investigate the probability distribution characteristics of summer extreme precipitation indices in northern, southern, and eastern Xinjiang. The results show that (1) The summer maximum 1-day precipitation (RX1day) and maximum 5-day precipitation (RX5day) of most stations in Xinjiang showed an increasing trend, while the summer consecutive dry days (CDD) showed a decreasing trend. (2) The climatology (mean intensity) of RX1day, RX5day, and CDD at most stations in northern Xinjiang were more than 10 mm, more than 15 mm, and less than 25 days, respectively, while those at most stations in southern and eastern Xinjiang were less than 10 mm, less than 15 mm, and more than 25 days. The regional averaged climatology and inter-annual variability of RX1day/RX5day (CDD) in southern and eastern Xinjiang were smaller (larger) than that in northern Xinjiang. (3) The 20-year return level (RL20) of RX1day, RX5day, and CDD at stations in northern Xinjiang were 19.38–56.57 mm, 28.05–70.91 mm, and 22.51–51.05 days, respectively. The RL20 of RX1day, RX5day, and CDD at stations in southern Xinjiang were 21.31–46.07 mm, 23.99–72.89 mm, and 14.94–89.80 days, respectively. The RL20 of RX1day, RX5day, and CDD at stations in eastern Xinjiang were 8.89–36.36 mm, 10.13–50.66 mm, and 26.75–92.00 days, respectively. Compared with northern Xinjiang, there were lesser RX1day and RX5day events, with weaker intensity and smaller variability in southern and eastern Xinjiang. And the CDD events were opposite. [ABSTRACT FROM AUTHOR]

The stone-carving robotic manipulators (SCRM) find a broad range of applications due to their high efficiency, wide range of processing and strong flexibility. In stone carving process, high response speed and high robustness are required to realize the high precision tracking control of SCRM. This paper proposes an adaptive terminal sliding mode control strategy based on radial basis function neural network (RBFNN-based TSM). First, we deduced the dynamic model of SCRM system with the Lagrange method. Then the dynamic model with uncertainties was further considered, the radial basis function neural network (RBFNN) was employed to approximate the manipulator dynamic model. Finally, in order to improve the response speed and tracking accuracy of SCRM, the RBFNN-based TSM control strategy was designed for SCRM, and the high-gain observer was used to estimate the joint velocity information online. The Lyapunov's theory proved the stability of the algorithm, and the experimental results show that the model-free and chattering-free control was achieved. [ABSTRACT FROM AUTHOR]

Great breakthrough in solving black hole information paradox took place when semiclassical island rule for entanglement entropy of Hawking radiation was proposed in recent years. Up to now, most papers which discussed island rule of asymptotic flat black hole with D ≥ 4 focus on eternal black hole. In this paper, we take one more step further by discussing island of "in" vacuum state which describes one-sided asymptotically flat black hole formed by gravitational collapse in D ≥ 4. We find that island I emerges at late time and saves entropy bound. And boundary of island ∂I depends on the position of cutoff surface. When cutoff surface is far from horizon, ∂I is inside and near horizon. When cutoff surface is set to be near horizon, ∂I is outside and near horizon. This is different from the case of eternal black hole in which ∂I is always outside horizon no matter cutoff surface is far from or near horizon. We will see that different states will manifestly affect Sent in island formula when cutoff surface is far from horizon and thus have different result for Page time. [ABSTRACT FROM AUTHOR]

The archaeal phylum Woesearchaeota, within the DPANN superphylum, includes phylogenetically diverse microorganisms that inhabit various environments. Their biology is poorly understood due to the lack of cultured isolates. Here, we analyze datasets of Woesearchaeota 16S rRNA gene sequences and metagenome-assembled genomes to infer global distribution patterns, ecological preferences and metabolic capabilities. Phylogenomic analyses indicate that the phylum can be classified into ten subgroups, termed A–J. While a symbiotic lifestyle is predicted for most, some members of subgroup J might be host-independent. The genomes of several Woesearchaeota, including subgroup J, encode putative [FeFe] hydrogenases (known to be important for fermentation in other organisms), suggesting that these archaea might be anaerobic fermentative heterotrophs. The biology of the archaeal phylum Woesearchaeota is poorly understood due to the lack of cultured isolates. Here, the authors analyze datasets of Woesearchaeota 16 S rRNA gene sequences and metagenome-assembled genomes to infer global distribution patterns, ecological preferences and metabolic capabilities. [ABSTRACT FROM AUTHOR]

Objectives: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. Methods: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. Results: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2–93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0–92.9%) and the general AUC value was 0.955 (p < 0.001). Conclusions: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription–polymerase chain reaction (RT-PCR) test. Key Points: • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations. [ABSTRACT FROM AUTHOR]

Improving the storage capacities of electrode materials is one of the most critical points for ion batteries. Two-dimensional (2D) topological semimetals with high carrier mobility are naturally suitable as electrode materials. Herein, using the first-principle calculations, 2D BP monolayer with Dirac-type band structure is predicted to be a superior anode material with ultrahigh capacity for both Li/Na-ion batteries. The BP monolayer remains metallic after the adsorption of Li/Na ions, ensuring a good conductivity. Furthermore, BP owns low diffusion barriers (0.35 eV for Li ions and 0.16 eV for Na ions) and a moderate lattice change (3%) during the process of charging and discharging. Remarkably, the storage capacity of monolayer BP is enhanced to 1924 mAh/g by multilayer adsorption of both Li/Na ions, much higher than those of most previous 2D anode materials. All these characteristics strongly suggest that BP has great potential as a superior anode material in Li/Na-ion batteries. [ABSTRACT FROM AUTHOR]

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Asgard is a recently discovered superphylum of archaea that appears to include the closest archaeal relatives of eukaryotes1–5. Debate continues as to whether the archaeal ancestor of eukaryotes belongs within the Asgard superphylum or whether this ancestor is a sister group to all other archaea (that is, a two-domain versus a three-domain tree of life)6–8. Here we present a comparative analysis of 162 complete or nearly complete genomes of Asgard archaea, including 75 metagenome-assembled genomes that—to our knowledge—have not previously been reported. Our results substantially expand the phylogenetic diversity of Asgard and lead us to propose six additional phyla that include a deep branch that we have provisionally named Wukongarchaeota. Our phylogenomic analysis does not resolve unequivocally the evolutionary relationship between eukaryotes and Asgard archaea, but instead—depending on the choice of species and conserved genes used to build the phylogeny—supports either the origin of eukaryotes from within Asgard (as a sister group to the expanded Heimdallarchaeota–Wukongarchaeota branch) or a deeper branch for the eukaryote ancestor within archaea. Our comprehensive protein domain analysis using the 162 Asgard genomes results in a major expansion of the set of eukaryotic signature proteins. The Asgard eukaryotic signature proteins show variable phyletic distributions and domain architectures, which is suggestive of dynamic evolution through horizontal gene transfer, gene loss, gene duplication and domain shuffling. The phylogenomics of the Asgard archaea points to the accumulation of the components of the mobile archaeal ‘eukaryome’ in the archaeal ancestor of eukaryotes (within or outside Asgard) through extensive horizontal gene transfer.Comparative analysis of 162 genomes of Asgard archaea results in six newly proposed phyla, including a deep branch that is provisionally named Wukongarchaeota, and sheds light on the evolutionary history of this clade. [ABSTRACT FROM AUTHOR]

Aim: To observe the therapeutic effect of low-dose amitriptyline (AMT) on epigastric pain syndrome (EPS) in patients with functional dyspepsia. Methods: Sixty patients with EPS were randomly divided into the following two groups for a four-week clinical trial: routine treatment with pantoprazole (RT group) and the AMT group. The RT group was treated with 40 mg of pantoprazole once daily. The AMT group received 25 mg of AMT once daily before bedtime. The Nepean Dyspepsia Index (NDI) checklist, Hamilton Rating Scale of Anxiety/Depression (HAMA/HAMD), and Pittsburgh Sleep Quality Index (PSQI) were employed to evaluate dyspepsia symptoms, psychological distress, and sleep, respectively. Results: All items were similar between the two groups before treatment (0 week). After 4 weeks of treatment, the NDI–symptom checklist score as well as the severity and bothersomeness of EPS in the AMT group was significantly decreased compared with those in the RT group (p < 0.05). However, no differences were found in the frequency of NDI checklist, psychological status (HAMD/HAMA scores) of EPS, or sleep quality (PSQI score) between the two groups after treatment. In addition, the time to fall asleep was shorter in the AMT group compared with the RT group after 4 weeks of treatment (p < 0.05). Conclusion: Low-dose AMT effectively improved the dyspepsia symptoms and the time to fall asleep in the EPS patients, compared with pantoprazole, although it did not reduce the psychological distress. Therefore, AMT could be considered as a good candidate for EPS treatment in the clinic. [ABSTRACT FROM AUTHOR]

The genus Paenibacillus was originally recognized based on the 16S rRNA gene phylogeny. Recently, a standardized bacterial taxonomy approach based on a genome phylogeny has substantially revised the classification of Paenibacillus, dividing it into 23 genera. However, the metabolic differences among these groups remain undescribed. Here, genomes of 41 Paenibacillus strains comprising 25 species were sequenced, and a comparative genomic analysis was performed considering these and 187 publicly available Paenibacillus genomes to understand their phylogeny and metabolic differences. Phylogenetic analysis indicated that Paenibacillus clustered into 10 subgroups. Core genome and pan-genome analyses revealed similar functional categories among the different Paenibacillus subgroups; however, each group tended to harbor specific gene families. A large proportion of genes in the subgroups A, E, and G are related to carbohydrate metabolism. Among them, genes related to the glycoside hydrolase family were most abundant. Metabolic reconstruction of the newly sequenced genomes showed that the Embden–Meyerhof–Parnas pathway, pentose phosphate pathway, and citric acid cycle are central pathways of carbohydrate metabolism in Paenibacillus. Further, the genomes of the subgroups A and G lack genes involved in glyoxylate cycle and d-galacturonate degradation, respectively. The current study revealed the metabolic diversity of Paenibacillus subgroups assigned based on a genomic phylogeny and could inform the taxonomy of Paenibacillus. Key points: • Paenibacillus clustered into 10 subgroups. • Genomic content variation and metabolic diversity in the subgroup A, E, and G were described. • Carbohydrate transport and metabolism is important for Paenibacillus survival. [ABSTRACT FROM AUTHOR]

Recent interest in the control of bone metabolism has focused on a specialized subset of CD31hiendomucinhi vessels, which are reported to couple angiogenesis with osteogenesis. However, the underlying mechanisms that link these processes together remain largely undefined. Here we show that the zinc-finger transcription factor ZEB1 is predominantly expressed in CD31hiendomucinhi endothelium in human and mouse bone. Endothelial cell-specific deletion of ZEB1 in mice impairs CD31hiendomucinhi vessel formation in the bone, resulting in reduced osteogenesis. Mechanistically, ZEB1 deletion reduces histone acetylation on Dll4 and Notch1 promoters, thereby epigenetically suppressing Notch signaling, a critical pathway that controls bone angiogenesis and osteogenesis. ZEB1 expression in skeletal endothelium declines in osteoporotic mice and humans. Administration of Zeb1-packaged liposomes in osteoporotic mice restores impaired Notch activity in skeletal endothelium, thereby promoting angiogenesis-dependent osteogenesis and ameliorating bone loss. Pharmacological reversal of the low ZEB1/Notch signaling may exert therapeutic benefit in osteoporotic patients by promoting angiogenesis-dependent bone formation. An endothelial cell subtype, expressing endomucin and CD31, has been reported to couple angiogenesis with osteogenesis. Here, the authors show that loss of ZEB1 in these cells epigenetically suppresses Notch signaling, leading to impaired angiogenesis and osteogenesis, and that Zeb1 delivery via liposomes ameliorates bone loss in osteoporotic mice [ABSTRACT FROM AUTHOR]

Materials informatics employs techniques, tools, and theories drawn from the emerging fields of data science, internet, computer science and engineering, and digital technologies to the materials science and engineering to accelerate materials, products and manufacturing innovations. Manufacturing is transforming into shorter design cycles, mass customization, on-demand production, and sustainable products. Additive manufacturing or 3D printing is a popular example of such a trend. However, the success of this manufacturing transformation is critically dependent on the availability of suitable materials and of data on invertible processing–structure–property–performance life cycle linkages of materials. Experience suggests that the material development cycle, i.e. the time to develop and deploy new material, generally exceeds the product design and development cycle. Hence, there is a need to accelerate materials innovation in order to keep up with product and manufacturing innovations. This is a major challenge considering the hundreds of thousands of materials and processes, and the huge amount of data on microstructure, composition, properties, and functional, environmental, and economic performance of materials. Moreover, the data sharing culture among the materials community is sparse. Materials informatics is key to the necessary transformation in product design and manufacturing. Through the association of material and information sciences, the emerging field of materials informatics proposes to computationally mine and analyze large ensembles of experimental and modeling datasets efficiently and cost effectively and to deliver core materials knowledge in user-friendly ways to the designers of materials and products, and to the manufacturers. This paper reviews the various developments in materials informatics and how it facilitates materials innovation by way of specific examples. [ABSTRACT FROM AUTHOR]

Accumulating evidence indicates that the zinc-finger transcription factor ZEB1 is predominantly expressed in the stroma of several tumours. However, the role of stromal ZEB1 in tumour progression remains unexplored. In this study, while interrogating human databases, we uncover a remarkable decrease in relapse-free survival of breast cancer patients expressing high ZEB1 levels in the stroma. Using a mouse model of breast cancer, we show that ZEB1 inactivation in stromal fibroblasts suppresses tumour initiation, progression and metastasis. We associate this with reduced extracellular matrix remodeling, immune cell infiltration and decreased angiogenesis. ZEB1 deletion in stromal fibroblasts increases acetylation, expression and recruitment of p53 to FGF2/7, VEGF and IL6 promoters, thereby reducing their production and secretion into the surrounding stroma. Importantly, p53 ablation in ZEB1 stroma-deleted mammary tumours sufficiently recovers the impaired cancer growth and progression. Our findings identify the ZEB1/p53 axis as a stroma-specific signaling pathway that promotes mammary epithelial tumours. In epithelial cells Zeb1 is involved in the epithelial to mesenchymal transition. In this study, the authors show in a mouse model of breast cancer, that Zeb1 expression in stromal cells is required for tumour formation and metastasis. [ABSTRACT FROM AUTHOR]

Considering the quantum effects of diffraction and the collective screening effects, the potential of test charge in semiclassical plasmas is derived. It is generalized exponential screened Coulomb potential. Using the Ritz variational method incorporating this potential, the effects of semiclassical plasma on the energy levels and radiative transitions are investigated systematically, taking highly charged H-like ion as an example. The Debye plasma model is also employed for comparison purposes. Comparisons and analysis are made between these two sets of results and the differences are discussed. Graphical abstract: [ABSTRACT FROM AUTHOR]

Gold nanoparticles (AuNPs) were in-situ deposited onto the ordered mesoporous carbon (OMC) to function as a novel SPME coating. The specific area, pore volumes and average pore diameter for OMC and AuNP/OMC were calculated to be 411 and 281 m g, 0.93 and 0.69 cm g, 7.1 and 6.8 nm, respectively. The results indicate that the introduction of AuNPs greatly enhances the extraction of anilines, probably due to the strong interaction between gold and amino groups. The composite material was coated onto a fiber which then gave enrichment factors in the range from 1143 to 3420 for anilines, 45 to 845 for alcohols and 86 to 545 for phenols, indicating its extraction selectivity towards aniline. Following desorption, four anilines were quantified by gas chromatography with flame ionization detection. The method has low limits of detection (0.072-0.18 μg L) and a good precision (1.9-3.8%). It was successfully applied to the analysis of anilines in river and drinking samples. Recoveries range from 83.9% to 107.7% and 94.0 to 113.1%, respectively. [ABSTRACT FROM AUTHOR]

In order to describe the characteristics of dynamic traffic flow and improve the robustness of its multiple applications, a dynamic traffic temporal-spatial model (DTTS) is established. With consideration of the temporal correlation, spatial correlation and historical correlation, a basic DTTS model is built. And a three-stage approach is put forward for the simplification and calibration of the basic DTTS model. Through critical sections pre-selection and critical time pre-selection, the first stage reduces the variable number of the basic DTTS model. In the second stage, variable coefficient calibration is implemented based on basic model simplification and stepwise regression analysis. Aimed at dynamic noise estimation, the characteristics of noise are summarized and an extreme learning machine is presented in the third stage. A case study based on a real-world road network in Beijing, China, is carried out to test the efficiency and applicability of proposed DTTS model and the three-stage approach. [ABSTRACT FROM AUTHOR]

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm (HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages (MSA), the middle level model is solved by non-dominated sorting genetic algorithm II (NSGA II), and the upper level model is solved by genetic algorithm (GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method. [ABSTRACT FROM AUTHOR]

Human Angiostrongylus cantonensis ( A. cantonensis) is a food-borne parasitic disease and can cause optic neuritis. Increasing clinical angiostrongyliasis cases with optic neuritis have been reported, but the pathogenesis has not been fully understood until now. Here, we applied rats with A. cantonensis infection as an animal model to study the pathogenesis of optic neuritis caused by the infection. We observed that the optic disk of experimental rats appeared hyperemic, the retina vein became thick, and the visual evoked potential (VEP) latency was prolonged. There were obvious inflammatory cell infiltration in the retina and optic nerve adventitia followed with obvious optic nerve fiber demyelination and retina ganglion swelling. We also evaluated the effect of dexamethasone combined with albendazole on optic neuritis of rats infected with A. cantonensis. The results showed it had no obvious effect to prevent progressive visual deterioration for optic neuritis caused by A. cantonensis. The studies provided evidence that the pathogenesis of optic neuritis in infected rats was correlated to optic nerve demyelination and ganglion cell damage caused by optic nerve inflammation, and the common therapy to this disease was not so effective. Based on the above results, it may be necessary to combine neuroprotective agents with common therapy to treat and protect optic nerve and ganglion cells from their secondary injury. [ABSTRACT FROM AUTHOR]

Effects of gas temperature on the conversion of benzene and the selectivity of CO and CO2 are analyzed and compared in dielectric barrier discharges. Gas temperature is determined by the method of the optical emission spectroscopy. The result is found that the conversion of benzene increases linearly with gas temperature. The selectivity of CO and CO2 and carbon balance are also investigated. [ABSTRACT FROM AUTHOR]

The support vector classification (SVC) was employed to make a model for classification of antifungal activities of 1-(1H-1,2,4-triazole-1-yl)-2-(2,4-difluorophenyl)-3-substituted-2-propanols triazole derivatives. The compounds with high antifungal activities and those with low antifungal activities were compared on the basis of the following molecular descriptors: net atomic charge on the atom N connecting with R, dipole moment and heat of formation. By using the SVC, a mathematical model was constructed, which can predict the antifungal activities of the triazole derivatives, with an accuracy of 91% on the basis of the leave-one-out cross-validation (LOOCV) test. The results indicate that the performance of the SVC model can exceed that of the principal component analysis (PCA) and K-Nearest Neighbor (KNN) models for this real world data set. [ABSTRACT FROM AUTHOR]

Aim: To discriminate 32 phenethyl-amines between antagonists and agonists, and predict the activities of these compounds. Methods: The support vector machine (SVM) is employed to investigate the structure-activity relationship (SAR)/quantitative structure-activity relationship (QSAR) of phenethyl-amines based on molecular descriptors. Results: By using the leave-one-out cross-validation (LOOCV) test, 1 optimal SAR and 2 optimal QSAR models for agonists and antagonists were attained. The accuracy of prediction for the classification of phenethyl-amines by using the LOOCV test is 91.67%, and the accuracy of prediction for teh classification of phenethyl-amines by using the independent test is 100%; the results are better than those of the Fisher, the artificial neural network (ANN), and the K-nearest neighbor models for this real world data. The RMSE (root mean square error) of antagonists' QSAR model is 0.5881, and the RMSE of agonists' QSAR model is 0.4779, which are better than those of the multiple linear regression, partial least squares, and ANN models for this real world data. Conclusion: The SVM can be used to investigate the SAR and QSAR of phenethyl-amines and could be a promising tool in the field of SAR/QSAR research. [ABSTRACT FROM AUTHOR]

Alumina membranes without pinholes and cracks were prepared by the sol-gel process using ammonium aluminium sulphate as the starting material. The effects of different preparing conditions on morphology characteristics of the membrane were investigated by scanning electron microscopy and 3D rotational microscopy. The preparing conditions include the amounts of drying control chemical additives (DCCA), sintering procedure and sol-gel concentration. The results showed that PVA is a good crack-preventing reagent and the morphology of supported membranes was affected by many factors, including Al2O3 concentration, PVA/Al2O3 ratio, heating rate, membrane thickness and intrinsic defects of the substrate surface. [ABSTRACT FROM AUTHOR]

In this work, support vector classification (SVC) algorithm was used to build structure-activity relationship (SAR) model of the 5-hydroxytryptamine type 3 (5-HT3) receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network (BP ANN), K-nearest neighbor (KNN) and Fisher methods. [ABSTRACT FROM AUTHOR]

To discriminate between fentanyl derivatives with high and low activities.The support vector classification (SVC) method, a novel approach, was employed to investigate structure-activity relationship (SAR) of fentanyl derivatives based on the molecular descriptors, which were quantum parameters includingΔ E[energy difference between highest occupied molecular orbital energy (HOMO) and lowest empty molecular orbital energy (LUMO)], MR (molecular refractivity) andMr (molecular weight).By using leave-one-out cross-validation test, the accuracies of prediction for activities of fentanyl derivatives in SVC, principal component analysis (PCA), artificial neural network (ANN) and K-nearest neighbor (KNN) models were 93%, 86%, 57%, and 71%, respectively. The results indicated that the performance of the SVC model was better than those of PCA, ANN, and KNN models for this data.SVC can be used to investigate SAR of fentanyl derivatives and could be a promising tool in the field of SAR research. [ABSTRACT FROM AUTHOR]

For a pair of incompatible quantum measurements, the total uncertainty can be bounded by a state-independent constant. However, such a bound can be violated if the quantum system is entangled with another quantum system (called memory); the quantum correlation between the systems can reduce the measurement uncertainty. On the other hand, in a curved spacetime, the presence of the Hawking radiation can reduce quantum correlation. The interplay of quantum correlation in the curved spacetime has become an interesting arena for studying uncertainty relations. Here we demonstrate that the bounds of the entropic uncertainty relations, in the presence of memory, can be formulated in terms of the Holevo quantity, which limits how much information can be encoded in a quantum system. Specifically, we considered examples with Dirac fields with and without spin, near the event horizon of a Schwarzschild black hole, the Holevo bound provides a better bound than the previous bound based on the mutual information. Furthermore, as the memory moves away from the black hole, the difference between the total uncertainty and the new lower bound remains a constant, not depending on any property of the black hole. [ABSTRACT FROM AUTHOR]