• Cruz Kamp posted an update 6 months, 3 weeks ago

    In the process of fault diagnosis and the health and safety operation evaluation of modern industrial processes, it is crucial to measure important state variables, which cannot be directly detected due to limitations of economy, technology, environment and space. Therefore, this paper proposes a data-driven soft sensor approach based on an echo state network (ESN) optimized by an improved genetic algorithm (IGA). Firstly, with an ESN, a data-driven model (DDM) between secondary variables and dominant variables is established. Secondly, in order to improve the prediction performance, the IGA is utilized to optimize the parameters of the ESN. Then, the immigration strategy is introduced and the crossover and mutation operators are changed adaptively to improve the convergence speed of the algorithm and address the problem that the algorithm falls into the local optimum. Finally, a soft sensor model of an ESN optimized by an IGA is established (IGA-ESN), and the advantages and performance of the proposed method are verified by estimating the alumina concentration in an aluminum reduction cell. The experimental results illustrated that the proposed method is efficient, and the error was significantly reduced compared with the traditional algorithm.Chinese cabbage (Brassica campestris) is an economically important leaf vegetable crop worldwide. Mounting studies have shown that cysteine-cysteine-cysteine-histidine (CCCH) zinc-finger protein genes are involved in various plant growth and development processes. However, research on the involvement of these genes in male reproductive development is still in its infancy. Here, we identified 11 male fertility-related CCCH genes in Chinese cabbage. Among them, a pair of paralogs encoding novel non-tandem CCCH zinc-finger proteins, Brassica campestris Male Fertility 30a (BcMF30a) and BcMF30c, were further characterized. They were highly expressed in pollen during microgametogenesis and continued to express in germinated pollen. Further analyses demonstrated that both BcMF30a and BcMF30c may play a dual role as transcription factors and RNA-binding proteins in plant cells. Functional analysis showed that partial bcmf30a bcmf30c pollen grains were aborted due to the degradation of pollen inclusion at the microgametogenesis phase, and the germination rate of viable pollen was also greatly reduced, indicating that BcMF30a and BcMF30c are required for both pollen development and pollen germination. This research provided insights into the function of CCCH proteins in regulating male reproductive development and laid a theoretical basis for hybrid breeding of Chinese cabbage.(1) Background Ehlers-Danlos syndrome is a heterogeneous group of connective tissue disorders causing pain, fatigue, and disabilities; it has several implications for patients who suffer from this disease. The major clinical manifestations of EDS include joint hypermobility, skin hyperextensibility, and generalized conjunctive tissue fragility. This research aims to explore their perceptions and experiences about the phycological and social spheres. (2) Methods Semistructured interviews were carried out. Participants were encouraged to talk about issues related to their disease by asking open-ended questions in one to one interview. The interview guide included questions to identify the syndrome’s influence on the social and psychological life of patients All interviews were audio recorded, fully transcribed, and analyzed using the phenomenological theoretical framework. The method of analysis was the thematic interpreting of perspectives and approaches. (3) Results 31 individuals were proposed to participate in this study. Five patients refused to participate, so a total of 26 interviews were performed. Six themes ((1) Pain and its consequences on a daily basis; (2) The need to name the problem the diagnosis; (3) Restructuring leisure and social relationships; (4) Limitations due to economic conditions; (5) Psychological impact of the disease situation; (6) Professional limitations) and four subthemes ((1) The value of partner support; (2) The weather influence on social plans; (3) Physical exercise and illness; (4) Support groups) emerged from the data. (4) Conclusions This study revealed the impact of the syndrome on the social and daily life of patients, and not only in a physical level, but also in a psychological and social approach. These findings allow healthcare providers to know more about this disease in order to support and give advice to patients about the changes they will have to make.Cancer is one of the leading causes of premature death and overall death in the world. On the other hand, fine particulate matter, which is less than 2.5 microns in aerodynamic diameter, is a global health problem due to its small diameter but high toxicity. Accumulating evidence has demonstrated the positive associations between this pollutant with both lung and non-lung cancer processes. However, the underlying mechanisms are yet to be elucidated. The present review summarizes and analyzes the most recent findings on the relationship between fine particulate matter and various types of cancer along with the oxidative stress mechanisms as its possible carcinogenic mechanisms. Also, promising antioxidant therapies against cancer induced by this poison factor are discussed.The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host’s lifetime in response to various factors. this website The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25-50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla.

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