The wealth of biological databases provides a valuable asset to understand evolution at a molecular level. This research presents the machine learning approach, an unsupervised agglomerative hierarchical clustering analysis of invariant solvent accessible surface areas and conserved structural features of Amycolatopsis eburnea lipases to exploit the enzyme stability and evolution. Amycolatopsis eburnea lipase sequences were retrieved from biological database. Six structural conserved regions and their residues were identified. Total Solvent Accessible Surface Area (SASA) and structural conserved-SASA with unsupervised agglomerative hierarchical algorithm were clustered lipases in three distinct groups (99/96%). The minimum SASA of nucleus residues was related to Lipase-4. It is clearly shown that the overall side chain of SASA was higher than the backbone in all enzymes. The SASA pattern of conserved regions clearly showed the evolutionary conservation areas that stabilized Amycolatopsis eburnea lipase structures. This research can bring new insight in protein design based on structurally conserved SASA in lipases with the help of a machine learning approach.

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Microbiome plays vital role in the life. Study the microbiome of plants with great impact in the planet can provide significant information to solve many problems. Therefore, finding structural population of plant microbiome needs scientific approach. Revealing the specific biochemical and genetical approaches towards identification of specific population provided the growing bodies of methods and procedures to study and analysis the plant microbiomes. Thus, this mini-review paper presents the summarized of scientific methods for study, identify and structural population analysis of plant microbiome.

Cellulose production of aerobic bacteria with its very unique physiochemical properties attracted many researchers. The biosynthetic of Bacterial Cellulose (BC) was produced by low-cost media recently. BC has been used as biomaterials and food ingredient these days. Moreover, the capacity of BC composite gives the numerous application opportunities in other fields. Bacterial Cellulose (BC) development is differentiated from suspension planktonic culture by their Extracellular Polymeric Substances (EPS), down-regulation of growth rate and up-down the expression of genes. The attachment of microorganisms is highly dependent on their cell membrane structures and growth medium. This is a very complicated phenomenon that optimal conditions defined the specific architecture. This architecture is made of microbial cells and EPS. Cell growth and cell communication mechanisms effect biofilm development and detachment. Understandings of development and architecture mechanisms and control strategies have a great impact on the management of BC formation with beneficial microorganisms. This mini-review paper presents the overview of outstanding findings from isolating and characterizing the diversity of bacteria to BC's future application, from food to biosensor products. The review would help future researchers in the sustainable production of BC, applications advantages and opportunities in food industry, biomaterial and biomedicine.

Background: Solving many health issues needs accurate and significant information in food consumption. Recently, data analysis and communication provided outstanding and robust approaches to fulfill the necessity of scientific information and help in decision making in many fields. Many evidence reported that with little information better decisions can be achieved. Objective: This research aimed to develop the Decision Support System (DSS) for the daily dietary plan to practically help users in food consumption and health care. Methods: The system consists of 1,940 cuisine items, including Thai and English menus. In this system, the user can set the daily dietary plan by selecting menu items with food specific and total calories. Overall calories of selected menu items would be calculated automatically. The user can see the normal range of calories required based on gender with the help of the baseline (normal office person). Results: This system can help users to become familiar with a better daily dietary plan, food calories, and health care easily. Furthermore, experts (doctors) can improve their learning experiences by formulating and adjusting the Decision Support System (DSS) for patients in special need. The easiness and usefulness of this system were evaluated by 119 users on Likert scale (1=least, 5=most). The result on average was 4.58. Conclusion: The Decision Support System (DSS) for the daily dietary plan was developed. The accessibility to the system is via personal computer (PC), smartphone, and tablet with internet connection. For future work, this DSS can improve by connecting the platform with health care providers via sharing the data for more online support.