Tilapia nilotica Head Lipids Improved Bone Loss by Regulating Inflammation and Serum Metabolism Through Gut Microbiota in Ovariectomized Rats

Osteoporosis is a global health problem, and it is of great significance to replace the drugs with natural functional factors. In this study, we investigated the antiosteoporotic activity of lipids prepared from Tilapia nilotica fish head lipids (THLs) in the ovariectomized osteoporosis rats. THLs are composed of neutral lipids (NL, 77.84%), phospholipids (PL, 11.86%), and glycolipids (GL, 6.47%). There were apparent differences in the fatty acid composition of disparate components, and PL contains the most abundant Ω-3 polyunsaturated fatty acids. The results proved that THLs could improve bone microstructure, increase bone mineral density, and decrease bone resorption. To illustrate the antiosteoporotic mechanism, we analyzed the changes in gut microbial communities, proinflammation factors, serum metabolites, and metabolic pathways. Further study on gut microbiota showed that THLs significantly decreased the content of Alistipes in the gut and dramatically increased the beneficial bacteria such as Oscillospira, Roseburia, and Dubosiella. Meanwhile, proinflammation factors of serum in OVX rats decreased significantly, and metabolites were changed. Therefore, we speculated that THLs improved bone loss through reducing inflammation and changing the metabolites and metabolic pathways such as arachidonic acid metabolism and primary bile acid metabolism, etc., by altering gut microbiota. The results indicated that THLs could be a functional factor with antiosteoporotic activity.

Intelligent aquaculture system for pisciculture simulation using deep learning algorithm

The project aims to develop an intelligent system for simulating pisciculture in Taal Lake in the Philippines through geographical information system and deep learning algorithm. Records of 2018-2020 from the database of Bureau of fisheries and aquatic resources IV-A-protected area management board (BFAR IVA-PAMB) was collected for model development. Deep learning algorithm model was developed and integrated to the system for time series analysis and simulation. Different technologies including tensorflow.js were used to successfully developed the intelligent system. It is found on this paper that recurrent neural network (RNN) is a good deep learning algorithm for predicting pisciculture in Taal lake. Further, it is also shown in the initial visualization of the system that barangay Sampaloc in Taal has highest rate of fish production in Taal while Tilapia nilotica sp. is the major product of the latter.

Effects of Some Pesticides on Tilapia nilotica and Daphnia magna Life

Application of pesticides creates hazards to the aquatic organisms. This study aimed to determined influence of bromacil, thiabendazole, malathion and mixtures of it on Tilapia nilotica (fish) and D magna. Effects included determination of mortality rate, losing sensation and behavior change (jump attempts) of fish and mortality rate for D. magna using concentration range from 0-6 mg/l. Normal growth of fish and D. Magana was observed under suitable laboratory conditions. The most toxic compound to D. magna was malathion, has an EC50 (Effective concentration that affect 50% of experimental animal) value of 0.43 (mg/l) compared to that of bromacil 0.59 (mg/l). Mixture of bromacil and malathion was less toxic than separately and have larger EC50 value 0.64 in toxic unit (TU) indicating potential antagonistic effect. Moreover, thiabendazole was more effective to fish than malathion. Relative toxicity (RT) indicates that thiabendazole has a lower value (0.276) than malathion indicating higher toxicity of thiabendazole. Furthermore, fish and D. magna were sensitive to low concentrations. These data indicate potent threats to aquatic organisms.