A probiotic based product using multi-strain Bacillus species and predictive models for shrimp growth following probiotic intervention

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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Isolation of Stenotrophomonas maltophilia from Clinical Samples in Some Hospitals in Shiraz, Iran

Clinical and Experimental Investigations ◽

10.31487/j.cei.2020.03.01 ◽

2020 ◽

pp. 1-3

Author(s):

Majid Baserisalehi ◽

Samira Zarezadeh ◽

Majid Baserisalehi ◽

Saeed Shoa

Keyword(s):

Stenotrophomonas Maltophilia ◽

Enterobacter Aerogenes ◽

Late Summer ◽

Early Spring ◽

Clinical Samples ◽

Bacillus Species ◽

Microbiological Analysis ◽

Blood Samples ◽

Healthcare Facilities ◽

Phenotypic Methods

Stenotrophomonas maltophilia is an emerging pathogenic non-fermentative Gram-negative Bacillus species. It has caused many nosocomial infections and can be isolated from various hospital wards and healthcare facilities. Research has shown that most of its strains are inherently resistant to many antibiotics and have multidrug resistance. This research intended to determine its occurrence frequency at some Hospitals in shiraz, Iran. The present study was conducted in six months (from early spring to late summer 2019). Clinical samples (Blood, Urine and cerebrospinal fluid (CSF)) collected from 120 patients afflicted with various infections. The samples were transferred to the Laboratory and subjected to microbiological analysis. Identification of the isolates was carried out by phenotypic methods and Stenotrophomonas maltophilia isolates verified using molecular methods. In total, various bacteria were isolated from 84 clinical samples. The isolates were Escherichia coli, Enterobacter aerogenes, Klebsiella pneumoniae, Stenotrophomonas maltophilia, Staphylococcus aureus and Pseudomonas aeruginosa. Stenotrophomonas maltophilia was isolated from 17 (20.2%) positive samples and most of them were isolated from blood samples. Our finding indicated that Stenotrophomonas maltophilia isolated more from blood samples follow by CSF sample. In addition, our finding illustrated that Stenotrophomonas maltophilia can be considered as the common nosocomial agent at hospitals in Shiraz, Iran.

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Faculty Opinions recommendation of Comparison of predictive models for hepatitis C co-infection among HIV patients in Cambodia.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.737547793.793572444 ◽

2020 ◽

Author(s):

Philip Rosenthal

Keyword(s):

Hepatitis C ◽

Predictive Models ◽

Hiv Patients

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Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

Automation. Modern Techologies ◽

10.36652/0869-4931-2020-74-3-129-134 ◽

2020 ◽

pp. 129-134

Keyword(s):

Predictive Model ◽

Unmanned Aerial Vehicle ◽

Predictive Models ◽

Navigation System ◽

Error Compensation ◽

Compensation Scheme ◽

Radar Images ◽

Aerial Vehicle ◽

System Errors ◽

Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

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