The fuzzy rat, a genetic mutant between hairless and hairy albino rats, expresses androgen-dependent hypersecretion of sebum and hyperplastic sebaceous glands. Using this model for human acne, we examined the effects of inhibitors of human steroid 5α-reductase isozymes, type I (MK386) and type II (finasteride), and an androgen receptor blocker (RU58841) on regression of glandular and ductal hyperplasia. The above three agents, 1 % weight volume, were dissolved into the vehicle (propy-lene glycol, alcohol and water) and applied on the backs of peripubertal male rats for 2 months. Control and castrate groups received vehicle alone. At 8 weeks, we examined the size of the sebaceous glandular lobules and ducts in split epidermal preparations as well as in frozen sections of skin stained with osmium-potassium dichromate solution. The number of bro-modeoxyuridine (BrdU)-positive cells was counted in the glandular lobes in split-skin tissues stained with BrdU immunochemistry. The results revealed that the sizes of both lobes and ducts in castrates were 40-60% smaller than in controls. RU58841 induced glandular and ductal regression equivalent to that in castrates. Finasteride induced a moderate degree of lobular and ductal reduction, whereas MK386 caused only ductal regression. Reduction of BrdU-positive cells in the sebaceous lobes was found in the skin treated with finasteride and RU58841. Serum concentrations of testosterone and dihydrotestosterone showed no significant changes in all drug-treated rats. The weight of the prostatic lobes was reduced significantly in rats treated with finasteride but not by the other two agents. RU58841 effectively counteracted endogenous androgens resulting in a suppression of growth of the sebaceous glands but not the prostate. This rodent model for androgen-dependent hyperplasia of the sebaceous glands is useful for the study of many pharmacological aspects comprising the rate of percutaneous absorption, stability and affinity to target organs of the testing compounds, and selection of adequate vehicles for topical application.

Objective. Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects. Approach. This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture the hidden representations related to different MI mode of each people. Main results. Experiments on BCI Competition 2a dataset and actual collected dataset achieve high accuracy near 99.40% and 92.56%, and the standard deviation is 0.34 and 1.35 respectively. Results demonstrate that the proposed method outperforms the advanced methods and baseline models. Significance. Experimental results show that the proposed method improves the accuracy of multi-classification and overcomes the impact of individual differences on classification by training neural network subject-dependent, which promotes the development of actual brain-computer interface systems.

Green redevelopment (GR) is a promising strategy to deal with industrial brownfields, this sustainable initiation usually fails to be implemented practically in China. Thus, investigating the driving mechanism of developer’s GR behavior, as executors of renovation project, is quite essential. The study introduced formative constructs perceived risk (PR) and perceived cost (PC), integrated them with theory of planned behavior (TPB), and extended them by adding two altruistic motives, awareness of responsibility (AR) and awareness of consequence (AC), as moderation variables to explore the bridging role of altruistic motives in GR’s intention–behavior gap. Based on 156 developers-oriented field surveys, the study conducted data analysis through partial least square structural equation modeling. It interestingly showed that subjective norm could primarily affect developers’ GR behavior, while perceived behavior control is not a significant influencing factor. Meanwhile, adding PR and PC as the additional constructs significantly increased the explanatory power of standard TPB model. Furthermore, the conclusion confirmed altruistic motives AR can distinctly adjust the relationship between GR intention and behavior, whereas AC has no such effect. These findings provide a scientific theoretical basis and a targeted path reference for promoting GR of industrial brownfields.

With the gradual improvement of people’s living standards, the production and drinking of all kinds of food is increasing. People’s disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

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Green regeneration of industrial brownfields (GRIB) is an inevitable choice under the collision of industrial structure adjustment and ecological civilization construction. Due to vegetation destruction and industrial pollution, the integrity and health of the ecosystem in the industrial brownfield have been destroyed and ecological security has become a primary factor in restricting GRIB. In order to explore the impact mechanism of GRIB under ecological security constraints, based on the original data obtained from in-depth interviews with 21 professionals, this study examines the applicability of DPSIR model in GRIB by using the grounded theory method to sort the determinants and explore the impact mechanism of GRIB under ecological security constraints from five dimensions: driving forces (incentive factor), pressure (external factor), state (internal factor), influence (produced comprehensive result), and response (substantive response of human society). Suggestions are made to strengthen the investigation and remediation of environmental pollution in industrial brownfield, cultivate the concept and awareness of green regeneration, and formulate incentive policies. The research conclusions effectively improve the problems existing in the reconstruction of industrial brownfield as well as provide a theoretical basis and targeted reference for the promotion of GRIB.

A Gram-negative, aerobic, motile rod strain, designated Ma-20(T), was isolated from a pool of marine Spirulina platensis cultivation, Sanya, China, and was subjected to a polyphasic taxonomy study. Strain Ma-20(T) can grow in the presence of 0.5-11 % (w/v) NaCl, 10-43 °C and pH 6-10, and grew optimally at 30 °C, pH 7.5-9.0 in natural seawater medium. The polar lipids were composed of phosphatidylethanolamine, three unidentified phospholipids and three unidentified polar lipids. The respiratory quinone was ubiquinone 8 (Q-8) and the major fatty acids were C18:1ω6c/C18:1ω7c (summed feature 8, 32.84 %), C16:1ω6c/C16:1ω7c (summed feature 3, 30.76 %), C16:0 (13.54 %), C12:03-OH (4.63 %), and C12:0 (4.09 %). The DNA G+C content of strain Ma-20(T) was 58 mol %. Phylogenetic analyses based on 16S rRNA gene sequences showed that strain Ma-20(T) belonging to Gammaproteobacteria, it shared 88.46-91.55 and 89.21-91.26 % 16S rRNA gene sequence similarity to the type strains in genus Hahella and Marinobacter, respectively. In addition to the large 16S rRNA gene sequence difference, Ma-20(T) can also be distinguished from the reference type strains Hahella ganghwensis FR1050(T) and Marinobacter hydrocarbonoclasticus sp. 17(T) by several phenotypic characteristics and chemotaxonomic properties. On the basis of phenotypic, chemotaxonomic and phylogenetic properties, strain Ma-20(T) is suggested to represent a novel species of a new genus in Gammaproteobacteria, for which the name Nonhongiella spirulinensis gen. nov., sp. nov. is proposed. The type strain is Ma-20(T) (=KCTC 32221(T)=LMG 27470(T)).

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