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Machine learning for enumeration of cell colony forming units

Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 315-322页

作者： Louis Zhang Department of Molecular Biosciences College of Natural SciencesUniversity of Texas at AustinAustin TX 78713-8058USA

As one of the most widely used assays in biological research,an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive *** speed up the colony counting,a machine learning method is presented for counting the colony forming units(CFUs),which is referred to as *** cellcounting program processes digital images and segments bacterial *** algorithm combines unsupervised machine learning,iterative adaptive thresholding,and local-minima-based watershed segmentation to enable an accurate and robust cell *** to a manual counting method,CFUCounter supports color-based CFU classification,allows plates containing heterologous colonies to be counted individually,and demonstrates overall performance(slope 0.996,SD 0.013,95%CI:0.97–1.02,p valuecolony-counting tools.

关键词： colony forming unit Enumeration Image analysis Blue/white screen colony count

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Evaluating mechanism of banana pseudo-stem retting using seawater:A cost-effective surface pre-treatment approach

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Journal of Bioresources and Bioproducts 2024年第3期9卷 322-335页

作者： Prince Hotor Ahmed H.Hassanin Osbert Akatwijuka Mohamed A.H.Gepreel Mitsuo Yamamoto Yukie Saito Ahmed Abdel-Mawgood Egypt-Japan University of Science and Technology Biotechnology ProgramAlexandria GovernorateNew Borg Al Arab 21934Egypt Alexandria University Department of Textile EngineeringAlexandria GovernorateBab Sharqi 5424041Egypt Egypt-Japan University of Science and Technology Material Science and EngineeringAlexandria GovernorateNew Borg Al Arab 21934Egypt Graduate School of Agricultural and Life Sciences The University of TokyoBunkyo-KuTokyo 113-8657Japan

Retting has been employed to extract natural fibers from agricultural wastes as a biological and cost-effective approach for *** its global abundance,banana pseudo-stem is a promising agro-waste for lignocellulosic fiber *** this study,fibers were extracted from the pseudo-stems after being pre-treated under four conditions using seawater at room temperature for up to 35 d Bacterial isolation from the fresh seawater sample and screening for ligninolytic ability were *** load as well as laccase and manganese peroxidase enzyme activity profile assay during the retting duration were *** transform infrared(FT-IR)and X-day diffraction(XRD)analyses were also examined for both pre-treated and untreated extracted *** results shows that six out of the eight bacterial isolates had the ability to degrade *** treatments(Raw stem+Raw seawater)and(Autoclaved stem+Raw seawater)recorded the highest viable bacterial load of 9.24×102 and 4.46×102 CFU,respectively,on the 14th day of the retting ***,the highest laccase and manganese peroxidase enzymes activity was recorded for(Raw stem+Raw seawater)and(Autoclaved stem+Raw seawater)treatments in the second to the third *** FT-IR spectra of the pre-treated fibers revealed relative reductions in peaks attributed to polysaccharides and other amorphous substances for all retting *** XRD diffractogram revealed that the crystallinity index(CI)of pre-treated fibers increased in all seawater retting treatment ***,the CI for fibers pre-treated under enzymatic conditions were enhanced even after five *** analysis for selected bacterial isolates showed homology to sequences of Bacillus velezensis,Shewanella sp.L8–5,and Citrobacter amalonaticus and Bacillus subtilis j8 *** these findings,it was suggested that physical,biological,and chemical actions were collectively involved in the seawater retting process of banana ps

关键词： Seawater retting Banana pseudo-stem Natural fiber Enzyme activity colony forming unit

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Microbial Analysis of Drinking Water from Randomly Selected Boreholes and Shallow Wells around Hargeisa, Somaliland

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Advances in Microbiology 2022年第1期12卷 1-9页

作者： Mohamud Ahmed Farah Essam Abdulmuhsin Afifi Nadira Harir Omar Ahmed Hussein Essa Department of Microbiology Faculty of Medical Laboratory Science Haldoor Medical University Hargeisa Somaliland Hargeisa Water Quality Control Laboratory Hargeisa Water Agency Hargeisa Somaliland Department of Health Science Faculty of Medicine and Health Science Abaarso Tech University Hargeisa Somaliland

Background: Shallow wells and boreholes are vital sources of potable water in Hargeisa. This water can be polluted by runoff, in particular during the rainy season, causing outbreaks of waterborne infections. Objectives: This research aimed at evaluating the microbial quality of shallow wells and boreholes water around Hargeisa, Somaliland. Methods: The total coliform and Escherichia coli count were done by using the membrane filtration method. Overall, 100 ml of each water sample was filtered via a 0.45 μm membrane filter, and then the filters were put on m-Endo agar plates that were incubated at 37°C for 24 to 48 hours. Results: The mean value of total coliform counts for the boreholes and shallow wells ranged from 1.288 × 103 to 8.8 × 103 CFU/100ml, while the mean value of total E. coli counts also ranged from 3.5 × 102 to 4.429 × 103 CFU/100ml. Results from this study have demonstrated that all water sources (Arabsiyo, Dararweyne, Darasalaam, Dabaraqas, and Jaleelo) don’t comply with the WHO guideline for drinking water. Results from the analysis of water samples of 28 wells demonstrated a significant correlation between total coliform and E. coli counts (P = 0.01). Therefore, this water is not fit for human consumption unless it is treated. Conclusion: This study has demonstrated that all results of both mean values of total coliform and E. coli counts from groundwater of selected shallow wells and boreholes were beyond WHO standards, so water from Arabsiyo, Jaleelo, Dabaraqas, Dararweyne, and Darasalaam requires treatment before human consumption.

关键词： Membrane Filtration Method Escherichia Coli Coliform Shallow Wells Boreholes Indole colony forming unit Indole Citrate Triple Sugar Iron

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