The gut bacterial microbiome of Nile tilapia (Oreochromis niloticus) from lakes across an altitudinal gradient.

BACKGROUND: Microorganisms inhabiting the gut play a significant role in supporting fundamental physiological processes of the host, which contributes to their survival in varied environments. Several studies have shown that altitude affects the composition and diversity of intestinal microbial communities in terrestrial animals. However, little is known about the impact of altitude on the gut microbiota of aquatic animals. The current study examined the variations in the gut microbiota of Nile tilapia (Oreochromis niloticus) from four lakes along an altitudinal gradient in Ethiopia by using 16S rDNA Illumina MiSeq high-throughput sequencing. RESULTS: The results indicated that low-altitude samples typically displayed greater alpha diversity. The results of principal coordinate analysis (PCoA) showed significant differences across samples from different lakes. Firmicutes was the most abundant phylum in the Lake Awassa and Lake Chamo samples whereas Fusobacteriota was the dominant phylum in samples from Lake Hashengie and Lake Tana. The ratio of Firmicutes to Bacteroidota in the high-altitude sample (Lake Hashengie, altitude 2440 m) was much higher than the ratio of Firmicutes to Bacteroidota in the low altitude population (Lake Chamo, altitude 1235 m). We found that the relative abundances of Actinobacteriota, Chloroflexi, Cyanobacteria, and Firmicutes were negatively correlated with altitude, while Fusobacteriota showed a positive association with altitude. Despite variability in the abundance of the gut microbiota across the lakes, some shared bacterial communities were detected. CONCLUSIONS: In summary, this study showed the indirect influence of altitude on gut microbiota. Altitude has the potential to modulate the gut microbiota composition and diversity of Nile tilapia. Future work will be needed to elucidate the functional significance of gut microbiota variations based on the geographical environment. SIGNIFICANCE AND IMPACT OF THE STUDY: Our study determined the composition and diversity of the gut microbiota in Nile tilapia collected from lakes across an altitude gradient. Our findings greatly extend the baseline knowledge of fish gut microbiota in Ethiopian lakes that plays an important role in this species sustainable aquaculture activities and conservation.

A plant cell division algorithm based on cell biomechanics and ellipse-fitting.

BACKGROUND AND AIMS: The importance of cell division models in cellular pattern studies has been acknowledged since the 19th century. Most of the available models developed to date are limited to symmetric cell division with isotropic growth. Often, the actual growth of the cell wall is either not considered or is updated intermittently on a separate time scale to the mechanics. This study presents a generic algorithm that accounts for both symmetrically and asymmetrically dividing cells with isotropic and anisotropic growth. Actual growth of the cell wall is simulated simultaneously with the mechanics. METHODS: The cell is considered as a closed, thin-walled structure, maintained in tension by turgor pressure. The cell walls are represented as linear elastic elements that obey Hooke's law. Cell expansion is induced by turgor pressure acting on the yielding cell-wall material. A system of differential equations for the positions and velocities of the cell vertices as well as for the actual growth of the cell wall is established. Readiness to divide is determined based on cell size. An ellipse-fitting algorithm is used to determine the position and orientation of the dividing wall. The cell vertices, walls and cell connectivity are then updated and cell expansion resumes. Comparisons are made with experimental data from the literature. KEY RESULTS: The generic plant cell division algorithm has been implemented successfully. It can handle both symmetrically and asymmetrically dividing cells coupled with isotropic and anisotropic growth modes. Development of the algorithm highlighted the importance of ellipse-fitting to produce randomness (biological variability) even in symmetrically dividing cells. Unlike previous models, a differential equation is formulated for the resting length of the cell wall to simulate actual biological growth and is solved simultaneously with the position and velocity of the vertices. CONCLUSIONS: The algorithm presented can produce different tissues varying in topological and geometrical properties. This flexibility to produce different tissue types gives the model great potential for use in investigations of plant cell division and growth in silico.

Optimization of spicy red pepper paste (Awaze) formulation by D-optimal mixture design.

The aim of this study was to produce spicy red pepper paste (Awaze) by addition of various sources of antioxidant, mineral, and fiber and optimize better processing methods of the paste. For this purpose, D-optimal mixture design was used, Awaze considering color value (a*), antioxidant content, mineral content, fiber content, chewiness, and viscosity of the paste as the main parameters. Various properties of the optimized formulation were evaluated. The optimal formulation contained 65.66% red pepper, 10% garlic, 19.08% red onion, and 5.25% ginger. In the optimized formulation, the redness value (a*) increased by more than 3.12 times compared to the control with the average antioxidant activity (44.6%). The optimal formulation had a higher fiber content, chewiness, and viscosity value compared to control which is probably related to the ingredient proportion difference. Due to the higher nutritional and processing quality obtained, this formulation can be suggested for commercial and household producers as a guide to manufacture Awaze. The results obtained indicate that it is possible to production of Awaze with high nutritional value and improved processing quality by utilizing a blend of red pepper, garlic, red onion, and ginger. Therefore, this formulation stands as a viable recommendation for both commercial enterprises and household producers due to its demonstrated ability to yield Awaze with enhanced nutritional content and superior processing quality.

Optimization of Awaze paste formulations: The effects of using spices through a mixture design approach.

Previous studies have revealed the microbial quality of Awaze paste. However, limited reports describe the effect of individual spices on Awaze paste quality. A mixture design approach was used to determine the appropriate proportions, with 15 experimental points for independent variables including RP (60-90 %), GA (10-30 %), RO (5-20 %), and GI (5-10 %). The techno-functional properties, particle size, antioxidant activity (DDPH radical assay), proximate composition, iron (Fe), zinc (Zn) content, viscosity, hardness, and microbiological quality of Awaze paste were assessed. The prepared Awaze paste showed a range of characteristics, with antioxidant activity (DDPH radical assay) ranging from 11.86 % to 62.5 %, crude protein content from 6.18 % to 16.22 %, crude fat from 5.7 % to 12.6 %, crude fiber from 16.86 % to 29.06 %, total ash content from 6.32 % to 9.94 %, total carbohydrate from 41.79 % to 60.61 %, energy from 264.3 to 329.2 k cal. , iron (Fe) content from 35.59 to 108.82 mg/100g, zinc (Zn) content from 1.72 to 26.93 mg/100g, viscosity from 65.5 to 125.5 cps, hardness from 8.48 to 55.09 g, yeast and mold count from 0.83 to 2.04 log cfu/g, and total bacterial count from 1.53 to 2.61 log cfu/g. Significant differences (p < 0.05) were observed in proximate composition, techno-functional properties, particle size, antioxidant activity, physicochemical properties, and microbiological characteristics among the formulations of Awaze paste. The selected formula showed a statistically significant difference (p < 0.05) compared to the control sample. The formulation containing 74.79 % RP, 10 % GA, 10.2 % RO, and 5.0 % GI was determined to be the optimal formula with a desirability of 0.73, based on the evaluated parameters. This preferred Awaze paste had a porosity of 28.12 %, particle size of 16.49 µm, antioxidant activity of 63.63 %, crude protein content of 17.28 %, iron (Fe) content of 98.06 mg/100g, and zinc (Zn) content of 15.04 mg/100g. Therefore, this optimal blend of ingredients could be used to produce a consumer accepted Awaze paste.

Development and comparative analysis of ANN and SVR-based models with conventional regression models for predicting spray drift.

As monitoring of spray drift during application can be expensive, time-consuming, and labor-intensive, drift predicting models may provide a practical complement. Several mechanistic models have been developed as drift prediction tool for various types of application equipment. Nevertheless, mechanistic models are quite often intricate and complex with a large number of input parameters required. Quite often, the detailed data needed for such models are not readily available. In this study, two advanced machine learning models (artificial neural network (ANN) and support vector regression (SVR)) were developed for pesticide drift prediction and compared with three conventional regression-based models: multiple linear regression (MLR), generalized linear model (GLM), and generalized nonlinear least squares (GNLS). The models were evaluated in fivefold cross-validation and by external validation using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute bias (MAB). From regression-based models, GLM and GNLS models performed very well when evaluated by cross-validation with R2 = 0.96 and 0.95 and RMSE = 0.70 and 0.82 respectively, while MLR performed less with R2 of 0.65 and RMSE of 2.25. Simultaneously, ANN and SVR models performed very well with R2 = 0.98 and 0.97 and RMSE = 0.58 and 0.71 respectively. Overall, ANN model performed best compared to the other four models followed by SVR. A comparison was also made between the high-performing model, ANN, and two previously published empirical models. The ANN model outperformed the two previously published empirical models and can be used to predict pesticide drift. Therefore, the ANN model is a potentially promising new approach for predicting ground drift that merits further study. In conclusion, our work demonstrated that the new approach, ANN and SVR-based models, for pesticide drift modeling has better predictive power than conventional regression models. Their ability to model complex relationships is a clear benefit in pesticide drift modeling where the variability in pesticide drift is often affected by a number of variables and the relationships between drift and predictors are very complicated. We believe such insights will pave better way for the application of machine learning towards spray drift modeling.

Effects of storage bags type and storage duration on seed quality and proximate composition of emmer wheat (Triticum dicoccum L.) in Ethiopia.

This study was conducted to evaluate the effect of storage materials (Storage bags) and durations on seed quality and proximate composition of emmer wheat in farmer storage practices. The emmer wheat samples were stored for nine consecutive months in PICS (Purdue Improved Crop Storage) bag (PB), Grainpro® Bag (GPB), Polypropylene Bag (PPB), emmer wheat treated with Filter Cake (a byproduct of Aluminum Sulphate factory) (FC), stored in Polypropylene bag (PPBFC) and Emmer wheat treated with triplex (a by-product of soap factory) (TX) stored in Polypropylene bag (PPBTX). Data on seed quality and proximate composition were evaluated every three months' interval for 9 months. As storage period increased from three to nine months; Germination Percentage, Speed of Germination, Vigour, Thousand Seed Weight (TSW), Bulk Density (BD), Seed Damage%, Seed Weight Loss, Protein and Carbohydrate contents were significantly influenced by the interaction effect of storage period and storage bag used. The highest germination (98%) was recorded from seeds stored in GPB for three months. The protein content of grains stored in GPB for three months showed the highest (8.3%) whereas, the lowest (6.5%) was for PPB at nine months of storage. Minimal insect incidence and lower seed weight loss were observed in emmer wheat stored in bags such as BP, GPB, PPBFC, and PPBTX. The use of PB and GPB, as well as the application of FC and TX maintained the proximate and seed quality of emmer wheat.

The Impact of Sampling Season and Catching Site (Wild and Aquaculture) on Gut Microbiota Composition and Diversity of Nile Tilapia (Oreochromis niloticus).

The gut microbiota of fishes is known to play an essential role in diverse aspects of host biology. The gut microbiota of fish is affected by various environmental parameters, including temperature changes, salinity and diet. Studies of effect of environment on gut microbiota enables to have a further understanding of what comprises a healthy microbiota under different environmental conditions. However, there is insufficient understanding regarding the effects of sampling season and catching site (wild and aquaculture) on the gut microbiota of Nile tilapia. This study characterised gut microbial composition and diversity from samples collected from Lake Tana and the Bahir Dar aquaculture facility centre using 16S rDNA Illumina MiSeq platform sequencing. Firmicutes and Fusobacteria were the most dominant phyla in the Lake Tana samples, while Proteobacteria was the most dominant in the aquaculture samples. The results of differential abundance testing clearly indicated significant differences for Firmicutes, Fusobacteria, Bacteroidetes and Cyanobacteria across sampling months. However, Proteobacteria, Chloroflexi, Fusobacteria and Cyanobacteria were significantly enriched in the comparison of samples from the Lake Tana and aquaculture centre. Significant differences were observed in microbial diversity across sampling months and between wild and captive Nile tilapia. The alpha diversity clearly showed that samples from the aquaculture centre (captive) had a higher diversity than the wild Nile tilapia samples from Lake Tana. The core gut microbiota of all samples of Nile tilapia used in our study comprised Firmicutes, Proteobacteria and Fusobacteria. This study clearly showed the impact of sampling season and catching site (wild and aquaculture) on the diversity and composition of bacterial communities associated with the gut of Nile tilapia. Overall, this is the first study on the effects of sampling season and catching site on the gut microbiota of Nile tilapia in Ethiopia. Future work is recommended to precisely explain the causes of these changes using large representative samples of Nile tilapia from different lakes and aquaculture farms.

Metabarcoding Analyses of Gut Microbiota of Nile Tilapia (Oreochromis niloticus) from Lake Awassa and Lake Chamo, Ethiopia.

The Nile tilapia (Oreochromis niloticus) gut harbors a diverse microbial community; however, their variation across gut regions, lumen and mucosa is not fully elucidated. In this study, gut microbiota of all samples across gut regions and sample types (luminal content and mucosa) were analyzed and compared from two Ethiopian lakes. Microbiota were characterized using 16S rRNA Illumina MiSeq platform sequencing. A total of 2061 operational taxonomic units (OTUs) were obtained and the results indicated that Nile tilapia from Lake Chamo harbored a much more diversified gut microbiota than Lake Awassa. In addition, the gut microbiota diversity varied significantly across the gut region based on the Chao1, Shannon and Simpson index. The microbiome analyses of all samples in the midgut region showed significantly higher values for alpha diversity (Chao 1, Shannon and Simpson). Beta diversity analysis revealed a clear separation of samples according to sampling areas and gut regions. The most abundant genera were Clostridium_sensu_stricto and Clostridium_XI genera across all samples. Between the two sampling lakes, two phyla, Phylum Fusobacteria and Cyanobacteria, were found to be significantly different. On the other hand, six phyla (Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Proteobacteria and Cyanobacteria) were significantly different across gut regions. In this study, we found that all samples shared a large core microbiota, comprising a relatively large number of OTUs, which was dominated by Proteobacteria, Firmicutes, Cyanobacteria, Fusobacteria and Actinobacteria. This study has established the bases for future large-scale investigations of gut microbiota of fishes in Ethiopian lakes.