Nutritional evaluation of soybean meal after fermentation with two fish gut bacterial strains, Bacillus cereus LRF5 and Staphylococcus caprae CCF2 in formulated diets for Labeo rohita fingerlings

Twelve isonitrogenous (35 % crude protein) and isocaloric (18.0 kJ/g) diets were formulated incorporating raw and fermented soybean meal (SBM) at 15%, 30%, 45% and 60% levels by weight. Two phytase-producing bacterial strains, Bacillus cereus LRF5 and Staphylococcus caprae CCF2 isolated from the gut of adult Labeo rohita and Catla catla, respectively were used for fermentation of SBM. Fermentation of SBM was effective in reducing the anti-nutritional factors, trypsin inhibitor and phytic acid and enhancing protein, lipid and mineral concentration. The response of L. rohita, fingerlings (initial weight 3.33±0.07 g) fed the experimental diets for 100 days was compared with fish fed a fish meal based diet. In terms of growth, feed conversion ratio and protein efficiency ratio, diet S7 containing 45% SBM fermented with B. cereus LRF5 resulted in a significantly (P<0.05) better performance of fish. The overall performance of L. rohita fed fermented SBM incorporated diets was better in comparison to those fed raw SBM incorporated diets. The apparent digestibility of nutrients and minerals was significantly (P<0.05) higher in fish fed diet S7. The maximum deposition of protein in the carcass was recorded in fish fed diet S7. Diets containing fermented SBM reduced fecal P levels.&nbsp

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Nutritional evaluation of soybean meal after fermentation with two fish gut bacterial strains, Bacillus cereus LRF5 and Staphylococcus caprae CCF2 in formulated diets for Labeo rohita fingerlings

Twelve isonitrogenous (35 % crude protein) and isocaloric (18.0 kJ/g) diets were formulated incorporating raw and fermented soybean meal (SBM) at 15%, 30%, 45% and 60% levels by weight. Two phytase-producing bacterial strains, Bacillus cereus LRF5 and Staphylococcus caprae CCF2 isolated from the gut of adult Labeo rohita and Catla catla, respectively were used for fermentation of SBM. Fermentation of SBM was effective in reducing the anti-nutritional factors, trypsin inhibitor and phytic acid and enhancing protein, lipid and mineral concentration. The response of L. rohita, fingerlings (initial weight 3.33±0.07 g) fed the experimental diets for 100 days was compared with fish fed a fish meal based diet. In terms of growth, feed conversion ratio and protein efficiency ratio, diet S7 containing 45% SBM fermented with B. cereus LRF5 resulted in a significantly (P<0.05) better performance of fish. The overall performance of L. rohita fed fermented SBM incorporated diets was better in comparison to those fed raw SBM incorporated diets. The apparent digestibility of nutrients and minerals was significantly (P<0.05) higher in fish fed diet S7. The maximum deposition of protein in the carcass was recorded in fish fed diet S7. Diets containing fermented SBM reduced fecal P levels.

Nutritional evaluation of soybean meal after fermentation with two fish gut bacterial strains, Bacillus cereus LRF5 and Staphylococcus caprae CCF2 in for-mulated diets for Labeo rohita fingerlings

Twelve isonitrogenous (35 % crude protein) and isocaloric (18.0 kJ/g) diets were formulated incorporating raw and fermented soybean meal (SBM) at 15%, 30%, 45% and 60% levels by weight. Two phytase-producing bacterial strains, Bacillus cereus LRF5 and Staphylococcus caprae CCF2 isolated from the gut of adult Labeo rohita and Catla catla, respectively were used for fermentation of SBM. Fermentation of SBM was effective in reducing the anti-nutritional factors, trypsin inhibitor and phytic acid and enhancing protein, lipid and mineral concentration. The response of L. rohita, fingerlings (initial weight 3.33±0.07 g) fed the experimental diets for 100 days was compared with fish fed a fish meal based diet. In terms of growth, feed conversion ratio and protein efficiency ratio, diet S7 containing 45% SBM fermented with B. cereus LRF5 resulted in a significantly (P<0.05) better performance of fish. The overall performance of L. rohita fed fermented SBM incorporated diets was better in comparison to those fed raw SBM incorporated diets. The apparent digestibility of nutrients and minerals was significantly (P<0.05) higher in fish fed diet S7. The maximum deposition of protein in the carcass was recorded in fish fed diet S7. Diets containing fermented SBM reduced fecal P levels. The use of this fermented feed will definitely increase the production in fish farm. Furthermore, it will also reduce the production cost, as fish meal protein is costly in the market

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License