Institute of Scientific and Technical Information

In recent years there has been enormous interest in researching oxidative stress. Reactive oxygen species (ROS) are derived from the metabolism of oxygen as by-products of cell respiration, and are continuously produced in all aerobic organisms. Oxidative stress occurs as a consequence of an imbalance between ROS production and the available antioxidant defence against them. Nowadays, a variety of diseases and degenerative processes such as cancer, Alzheimer's and autoimmune diseases are mediated by oxidative stress. Heat stress was suggested to be an environmental factor responsible for stimulating ROS production because of similarities in responses observed following heat stress compared with that occurring following exposure to oxidative stress. This manuscript describes the main mitochondrial sources of ROS and the antioxidant defences involved to prevent oxidative damage in all the mitochondrial compartments. It also deals with discussions concerning the cytotoxic effect of heat stress, mitochondrial heat-induced alterations, as well as heat shock protein (HSP) expression as a defence mechanism.

The sequence x is statistically convergent to L provided that for each > 0, lim ~" 1 {the number of k < n: \x^ -L\ > } = 0.

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.

Text classification is the task of assigning predefined categories to natural language documents, and it can provide conceptual views of document collections. The Naïve Bayes (NB) classifier is a family of simple probabilistic classifiers based on a common assumption that all features are independent of each other, given the category variable, and it is often used as the baseline in text classification. However, classical NB classifiers with multinomial, Bernoulli and Gaussian event models are not fully Bayesian. This study proposes three Bayesian counterparts, where it turns out that classical NB classifier with Bernoulli event model is equivalent to Bayesian counterpart. Finally, experimental results on 20 newsgroups and WebKB data sets show that the performance of Bayesian NB classifier with multinomial event model is similar to that of classical counterpart, but Bayesian NB classifier with Gaussian event model is obviously better than classical counterpart.

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of user's concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing.

This review summarizes recent advances in blue OLEDs based on fluorescence emitters, especially focusing on the different mechanisms involving the emitters and devices.

Electromagnetic waves constitute an essential element of societal progress, and the environmental impact resulting from using electromagnetic waves warrants significant considerations. Carbon materials have garnered considerable attention in the functional domain owing to their remarkable electrical conductivity and dielectric characteristics. Notably, spherical carbon materials, characterized by their substantial specific surface area and tunable dielectric properties, have emerged as efficient additives for microwave absorbers. These materials excel in absorbing electromagnetic energy while minimizing energy dissipation. When incorporated into absorbers of varying compositions, sizes, and morphologies, these carbon spheres facilitate the synergistic operation of multiple loss mechanisms, containing conductive loss, magnetic loss, and polarization loss. This concerted action significantly enhances the electromagnetic wave absorption performance. This paper offers a comprehensive review of the advancements in carbon sphere-based materials designed for absorbing electromagnetic waves. It also furnishes an intricate exposition of the methodologies employed in their preparation and a meticulous analysis of their performance. The paper summarizes the microstructural attributes and mechanisms governing electromagnetic wave absorption in various carbon sphere configurations, considering factors such as composition, morphology, size, and structure. In conclusion, this study forecasts the potentials of carbon sphere-based nanomaterials in the realm of electromagnetic waves, along with an assessment of forthcoming research focal points and conceivable challenges.

Pulmonary breathing sound plays a key role in the prevention and diagnosis of the lung diseases. Its correlation with pathology and physiology has become an important research topic in the pulmonary acoustics and the clinical medicine. However, it is difficult to fully describe lung sound information with the traditional features because lung sounds are complex and nonstationary signals. And the traditional convolutional neural network cannot also extract the temporal features of the lung sounds. To solve the problem, a lung sound recognition algorithm based on VGGish-BiGRU is proposed on the basis of transfer learning, which combines VGGish network with the bidirectional gated recurrent unit neural network (BiGRU). In the proposed algorithm, VGGish network is pretrained using audio set, and the parameters are transferred to VGGish network layer of the target network. The temporal features of the lung sounds are extracted through retraining BiGRU network with the lung sound data. During retraining BiGRU network, the parameters in VGGish layers are frozen, and the parameters of BiGRU network are fine-tuned. The experimental results show that the proposed algorithm effectively improves the recognition accuracy of the lung sounds in contrast with the state-of-the-art algorithms, especially the recognition accuracy of asthma.

Abstract Gross primary productivity (GPP) is the largest flux in the global terrestrial carbon cycle. Drought has significantly impacted global terrestrial GPP in recent decades, and has been projected to occur with increasing frequency and intensity. However, the drought risk of global terrestrial GPP has not been well investigated. In this study, global terrestrial GPP during 1981–2016 was simulated with the process‐based Boreal Ecosystem Productivity Simulator model. Then, the drought risk of GPP was quantified as the product of drought probability and reduction of GPP caused by drought, which was determined using the standardized precipitation evapotranspiration index. During the study period, the drought risk of GPP was high in the southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. It was low at some high latitudes of the Northern Hemisphere and in part of tropical South America, where terrestrial GPP increased slightly in drought years. The drought risk of terrestrial GPP was greater during 2000–2016 than during 1981–1999 in 21 out of 24 climatic zones. The global mean drought risk of GPP increased from 13.6 g C m −2 yr −1 during 1981–1999 to 19.3 g C m −2 yr −1 during 2000–2016. The increase in drought risk of GPP was mainly caused by the increase in drought vulnerability. Simulation experiments indicated that the drought vulnerability of GPP was mainly induced by climatic variability. This study advances our understanding on the impact of drought on GPP over the globe.

Notice of Violation of IEEE Publication Principles“Single-Image Super-Resolution Algorithm Based on Structural Self-Similarity and Deformation Block Features”by Yuantao Chen, Jin Wang, Xi Chen, Mingwei Zhu, Kai Yang, Zhi Wang, and Runlong Xia in IEEE Access, April 2019After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.This paper is a translation and duplication of the content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:“Single image super resolution algorithm based on structural self-similarity and deformation block feature”by Wen Xiang, Ling Zhang, Yunhua Chen, Qiumin Jiin the Journal of Computer Applications (39) 1, June 2018To solve the problem of insufficient sample resources and poor noise immunity in single-image super-resolution (SR) restoration procedure, the paper has proposed the single-image SR algorithm based on structural self-similarity and deformation block features (SSDBF). First, the proposed method constructs a scale model, expands the search space as much as possible, and overcomes the shortcomings caused by the lack of a single-image SR training sample; Second, the limited internal dictionary size is increased by the geometric deformation of the sample block; Finally, in order to improve the anti-noise performance of the reconstructed picture, a group sparse learning dictionary is used to reconstruct the pending image. The experimental results show that, compared with state-of-the-art algorithms such as bicubic interpolation (BI), sparse coding (SC), deep recursive convolutional network (DRCN), multi-scale deep SR network (MDSR), super-resolution convolutional neural network (SRCNN) and second-order directional total generalized variation (DTGV). The SR images with more subjective visual effects and higher objective evaluation can be obtained through the proposed method. Compared with existing algorithms, the structural network converges more rapidly, the image edge and texture reconstruction effects are obviously improved, and the image quality evaluation, such as peak signal-noise ratio (PSNR), root mean square error (RMSE), and structural similarity (SSIM), are also superior and popular in image evaluation.

Efficient and excellent nanoparticles are required for the degradation of organic dyes in photocatalysis. In this study, silver-manganese oxide nanoparticles (Ag-Mn-NPs) were synthesized through a wet chemical precipitation method and characterized as an advanced catalyst that has enhanced photocatalytic activity under sunlight irradiation. The nanoparticles were characterized using scanning electron microscopy (SEM), XRD, UV-vis light spectra, and energy-dispersive X-ray (EDX) spectroscopy, revealing their spherical and agglomerated form. The EDX spectra confirmed the composition of the nanoparticles, indicating their presence in oxide form. These bimetallic oxide nanoparticles were employed as photocatalysts for the degradation of malachite green (MG) dye under sunlight irradiation in an aqueous medium. The study investigated the effects of various parameters, such as irradiation time, catalyst dosage, recovered catalyst dosage, dye concentration, and pH, on the dye's photodegradation. The results showed that Ag-Mn oxide nanoparticles exhibited high photocatalytic activity, degrading 92% of the dye in 100 min. A longer irradiation time led to increased dye degradation. Moreover, a higher catalyst dosage resulted in a higher dye degradation percentage, with 91% degradation achieved using 0.0017 g of the photocatalyst in 60 min. Increasing the pH of the medium also enhanced the dye degradation, with 99% degradation achieved at pH 10 in 60 min. However, the photodegradation rate decreased with increasing dye concentration. The Ag-Mn oxide nanoparticles demonstrate excellent potential as a reliable visible-light-responsive photocatalyst for the efficient degradation of organic pollutants in wastewater treatment.

Purpose Facing turbulent environments, firms have strived to achieve greater supply chain resilience (SCR) to leverage the resources and knowledge of supply chain members. Both SCR and supply chain integration (SCI) require digitization in the supply chain, but their interrelationships have rarely been researched empirically. This paper aims to uncover the impact of digital technology (DT) on SCR and SCI and the role of SCI in mediating between DT and SCR. Design/methodology/approach China manufacturing enterprises were surveyed through a Web-based questionnaire, and 96 responses were received. Structural equation modeling was used to test the conceptual model. Findings The level of enterprise digitization is not directly related to supply chain resilience, but the level of enterprise digitization has a positive impact on the improvement of SCI and SCI also has a positive effect on SCR. Therefore, SCI has a complete intermediary effect between the level of DT and SCR. Originality/value This is a pioneer study to examine the relationships among DT, SCI and SCR. The findings of this study present that firms need to improve DT, SCI and SCR consequently.

Descreve o processo de seleção de revistas científicas adotado pelo Institute for Scientific Information (ISI) para incorporar publicações em sua base de dados, abordando critérios como periodicidade, conteúdo editorial, internacionalidade e análise de citação.

Through a three-year revision involving various stakeholders, China has enacted a new Environmental Protection Law ( EPL ). The new law seeks to harmonize economic and social development with environmental protection and for the first time establishes clear requirements for the construction of an ecological civilization. It toughens the penalties for environmental offences with specific articles and provisions for raising public awareness. It also places greater responsibility on local government and law enforcement for the protection of China’s environment. However, many of the problems identified in the old EPL and especially the obstacles to its implementation have not been fully addressed and resolved. Effective environmental governance entails not only environmental laws but also implementation mechanisms, accountability regimes, and institutional arrangements. Raising the status of the EPL and of the general environmental protection apparatus is only the first step to meeting China’s environmental challenges. More efforts in the area of enforcement and implementation will lead China to a cleaner future.

An endoglucanase (EG) from Aspergillus glaucus XC9 grown on 0.3% sugar cane bagasse as a carbon source was purified from the culture filtrate using ammonium sulfate, an anion exchange DEAE Sepharose fast flow column, and a Sephadex G-100 column, with a purification fold of 21.5 and a recovery of 22.3%. The ideal time for EG production is on the fourth day at 30 degrees C using bagasse as a substrate. Results obtained indicate that the enzyme was a monomer protein, and the molecular weight was determined to be 31 kDa. The optimum pH and temperature of EG for the hydrolysis of carboxymethylcellulose sodium (CMC-Na) were pH 4.0 and 50 degrees C, respectively. EG was stable over the pH range from 3.5 to 7.5 and at temperatures below 55 degrees C. Kinetic behavior of EG in the hydrolysis of CMC-Na followed Michaelis-Menten kinetics with constant K(m) of 5.0 mg/mL at pH 4.0 and 50 degrees C. The enzyme activity was stimulated by Fe(2+) and Mn(2+) but inhibited by Cd(2+), Pb(2+), and Cu(2+). The EDC chemical modification suggested that at least one carboxyl group probably acted as a proton donor in the enzyme active site.

geographic distribution of fishery resources, to understand and predict fluctuations in the quantity and distribution of these resources, and to establish levels for optimum use of the resources. NMFS is also charged with the

Summary Retraction: Multiscale fast correlation filtering tracking algorithm based on a feature fusion model Yuantao Chen, Jin Wang, Songjie Liu, Xi Chen, Jie Xiong, Jingbo Xie, Kai Yang, 2021, 33 (15), ( https://doi.org/10.1002/cpe.5533

BACKGROUND: Urinary proteins are predictive and prognostic markers for diabetes nephropathy. Conventional methods for the quantification of urinary proteins, however, are time-consuming, and most require radioactive labeling. We designed a label-free piezoelectric quartz crystal microbalance (QCM) immunosensor array to simultaneously quantify 4 urinary proteins. METHODS: We constructed a 2 x 5 model piezoelectric immunosensor array fabricated with disposable quartz crystals for quantification of microalbumin, alpha1-microglobulin, beta2-microglobulin, and IgG in urine. We made calibration curves after immobilization of antibodies at an optimal concentration and then evaluated the performance characteristics of the immunosensor with a series of tests. In addition, we measured 124 urine samples with both QCM immunosensor array and immunonephelometry to assess the correlation between the 2 methods. RESULTS: With the QCM immunosensor array, we were able to quantify 4 urinary proteins within 15 min. This method had an analytical interval of 0.01-60 mg/L. The intraassay and interassay imprecisions (CVs) were <10%, and the relative recovery rates were 90.3%-109.1%. Nonspecificity of the immunosensor was insignificant (frequency shifts <20 Hz). ROC analyses indicated sensitivities were > or =95.8% and, specificities were > or =76.3%. Bland-Altman difference plots showed the immunosensor array to be highly comparable to immunonephelometry. CONCLUSIONS: The QCM system we designed has the advantages of being rapid, label free, and highly sensitive and thus can be a useful supplement to commercial assay methods in clinical chemistry.

Inflammatory and neuroinflammatory processes are increasingly recognized as critical pathophysiologic steps in the development of multiple chronic diseases and in the etiology of persistent pain and depression. Mast cells are immune cells now viewed as cellular sensors in inflammation and immunity. When stimulated, mast cells release an array of mediators to orchestrate an inflammatory response. These mediators can directly initiate tissue responses on resident cells, and may also regulate the activity of other immune cells, including central microglia. New evidence supports the involvement of peripheral and central mast cells in the development of pain processes as well as in the transition from acute, to chronic and neuropathic pain. That behavioral and endocrine states can increase the number and activation of peripheral and brain mast cells suggests that mast cells represent the immune cells that peripherally and centrally coordinate inflammatory processes in neuropsychiatric diseases such as depression and anxiety which are associated with chronic pelvic pain. Given that increasing evidence supports the activated mast cell as a director of common inflammatory pathways/mechanisms contributing to chronic and neuropathic pelvic pain and comorbid neuropsychiatric diseases, mast cells may be considered a viable target for the multifactorial management of both pain and depression.

geographic distribution of fishery resources, to understand and predict fluctuations in the quantity and distribution of these resources, and to establish levels for optimum use of the resources. NMFS is also charged with the