Genome Sequencing and Annotation of Bacillus subtilis UBBS-14 to Ensure Probiotic Safety.

Bacillus subtilis is a rod shaped, gram positive, spore producing bacterium. They are the normal flora of gastrointestinal tract of humans and it is the best characterized model organism for endospore formation. It has the ability to withstand environmental stress, and synthesize beneficial compounds, therefore, it is recognized as a high-quality probiotic supplement. To ensure the probiotic safety and the efficiency, we report the whole genome sequence (WGS) of Bacillus subtilis UBBS-14 strain. The draft genome sequence of Bacillus subtilis UBBS-14 consists of 4,048,984 bp and 4,017 genes, respectively. Bacillus subtilis UBBS-14 does not carry any antibiotic resistant genes containing plasmid, nor it contains any harmful putative virulence factors coding genes, therefore, it confirms the probiotic safety of the respective strain at genome level.

Whole-Genome Shotgun Sequencing and Characterization of Probiotic Strain Clostridium butyricum UBCB 70 To Assess Its Safety.

Clostridium butyricum is a strictly anaerobic, butyric acid-producing, Gram-positive, spore-forming bacillus that is commonly present in the gut of humans. The complete genome sequence of Clostridium butyricum UBCB 70 was studied to evaluate the presence of antibiotic-resistant and clostridium toxin genes. Here, we announce the draft genome sequence of Clostridium butyricum UBCB 70, isolated from healthy human feces at Unique Biotech Limited, Hyderabad, Telangana, India.

High-quality draft genome and characterization of commercially potent probiotic Lactobacillus strains.

Lactobacillus acidophilus UBLA-34, L. paracasei UBLPC-35, L. plantarum UBLP-40, and L. reuteri UBLRU-87 were isolated from different varieties of fermented foods. To determine the probiotic safety at the strain level, the whole genome of the respective strains was sequenced, assembled, and characterized. Both the core-genome and pan-genome phylogeny showed that L. reuteri was closest to L. plantarum than to L. acidophilus, which was closest to L. paracasei. The genomic analysis of all the strains confirmed the absence of genes encoding putative virulence factors, antibiotic resistance, and the plasmids.

Non-linear modeling using fuzzy principal component regression for Vidyaranyapuram sewage treatment plant, Mysore - India.

Fuzzy principal component regression (FPCR) is proposed to model the non-linear process of sewage treatment plant (STP) data matrix. The dimension reduction of voluminous data was done by principal component analysis (PCA). The PCA score values were partitioned by fuzzy-c-means (FCM) clustering, and a Takagi-Sugeno-Kang (TSK) fuzzy model was built based on the FCM functions. The FPCR approach was used to predict the reduction in chemical oxygen demand (COD) and biological oxygen demand (BOD) of treated wastewater of Vidyaranyapuram STP with respect to the relations modeled between fuzzy partitioned PCA scores and target output. The designed FPCR model showed the ability to capture the behavior of non-linear processes of STP. The predicted values of reduction in COD and BOD were analyzed by performing the linear regression analysis. The predicted values for COD and BOD reduction showed positive correlation with the observed data.

Factor analysis and discriminant analysis of wastewater quality in Vidyaranyapuram sewage treatment plant, Mysore, India: a case study.

Wastewater treatment plant monitoring is an essential part of effective wastewater management. The analysis of eight physico-chemical parameters of untreated wastewater was carried out at Vidyaranyapuram sewage treatment plant, Mysore, India. Factor analysis (FA) was applied to the untreated wastewater data matrix, and pollution was found to be the most contributing factor, explaining 22.31% of the total variance (chloride, biochemical oxygen demand, chemical oxygen demand and total dissolved solids). The second most contributing factor was found to be nitrification which explained 21.11% of the total variance (pH and nitrate), whereas the salinization factor contributed 16.98% of the total variance (total solids and total suspended solids). FA regression scores could not satisfactorily classify the data matrix with respect to the seasonal variations. Discriminant analysis (DA) was used to find the seasonal variations in the data matrix, and the standard mode DA explained 66.6% of total variance by grouping the cases with respect to seasons.