Insulin signaling and its application

The discovery of insulin in 1921 introduced a new branch of research into insulin activity and insulin resistance. Many discoveries in this field have been applied to diagnosing and treating diseases related to insulin resistance. In this mini-review, the authors attempt to synthesize the updated discoveries to unravel the related mechanisms and inform the development of novel applications. Firstly, we depict the insulin signaling pathway to explain the physiology of insulin action starting at the receptor sites of insulin and downstream the signaling of the insulin signaling pathway. Based on this, the next part will analyze the mechanisms of insulin resistance with two major provenances: the defects caused by receptors and the defects due to extra-receptor causes, but in this study, we focus on post-receptor causes. Finally, we discuss the recent applications including the diseases related to insulin resistance (obesity, cardiovascular disease, Alzheimer’s disease, and cancer) and the potential treatment of those based on insulin resistance mechanisms

An Application of the Super-SBM MAX and LTS(A,A,A) Models to Analyze the Business Performance of Hydropower Suppliers in Vietnam

As Vietnam continues to industrialize and modernize, such economic development and high-tech will require a major electrical energy source to operate the electrical equipment; hence, the hydropower plants are established and growing up to demand. Therefore, the purpose of this study is to evaluate the business performance of Vietnamese hydropower suppliers by integrating the LTS(A,A,A) model of the Additive Holt-winters method in Tableau and a super-slacks-based measure (super-SBM) max model in data envelopment analysis (DEA). The LTS(A,A,A) model is applied to forecast future valuation from 2022 to 2025 based on historical time series from 2012 to 2021. Next, with the actual and predicted data, the researcher uses the super-SBM max model to calculate the business performance of these hydropower suppliers from past to future. The empirical result reveals efficient and inefficient cases to explore which hydropower suppliers can achieve the business performance in their operational process. The position of hydropower suppliers in Vietnam from past to future time is determined particularly based on their scores every year. Further, the empirical result recommends a solution to deal with inefficient cases by deducting the input excesses and raising the output shortages based on the principle of the super-SBM Max model in DEA. The finding results create an overview of the operational process with the continuing variations in each period to equip hydropower suppliers in Vietnam which will determine their future and operational orientation

An Application of the Super-SBM MAX and LTS(A,A,A) Models to Analyze the Business Performance of Hydropower Suppliers in Vietnam

As Vietnam continues to industrialize and modernize, such economic development and high-tech will require a major electrical energy source to operate the electrical equipment; hence, the hydropower plants are established and growing up to demand. Therefore, the purpose of this study is to evaluate the business performance of Vietnamese hydropower suppliers by integrating the LTS(A,A,A) model of the Additive Holt-winters method in Tableau and a super-slacks-based measure (super-SBM) max model in data envelopment analysis (DEA). The LTS(A,A,A) model is applied to forecast future valuation from 2022 to 2025 based on historical time series from 2012 to 2021. Next, with the actual and predicted data, the researcher uses the super-SBM max model to calculate the business performance of these hydropower suppliers from past to future. The empirical result reveals efficient and inefficient cases to explore which hydropower suppliers can achieve the business performance in their operational process. The position of hydropower suppliers in Vietnam from past to future time is determined particularly based on their scores every year. Further, the empirical result recommends a solution to deal with inefficient cases by deducting the input excesses and raising the output shortages based on the principle of the super-SBM Max model in DEA. The finding results create an overview of the operational process with the continuing variations in each period to equip hydropower suppliers in Vietnam which will determine their future and operational orientation

ẢNH HƯỞNG CỦA THỜI KỲ THU CẮT ĐẾN NĂNG SUẤT, THÀNH PHẦN HOÁ HỌC VÀ TỶ LỆ PHÂN GIẢI Ở DẠ CỎ BÒ CỦA CÂY NGÔ HQ2000 TRỒNG TRÊN VÙNG CÁT PHA Ở TỈNH THỪA THIÊN HUẾ

This study consisted of two experiments. Experiment 1 determined the yield and chemical composition of maize cut at kernel milking (CSU), doughing (CSA), and denting (RNG) stages. The results show that the highest yield at the CSA stage and the dry matter (DM) content tend to increase, but crude protein, neutral detergent fibre, and acid detergent fibre tend to decrease when the development stages are extended (p < 0.05). Experiment 2 determined the in sacco degradability of maize cut in the three stages as experiment 1 for four cows with a cannula. The findings reveal that the DM degradation rate of maize cut at different stages is not significantly different (p > 0.05), but the fermentation time of CSU maize in the rumen is shorter than that of CSA and RNG maize. The effective degradation rate of at different rumen exit rates is not different regarding the harvested stages (p > 0.05). The metabolisable energy (ME) of RNG maize is similar to that of the CSA maize but higher than that of the CSU maize (p < 0.05). Therefore, it is advisable to harvest HQ2000 maize at the doughing stage.Nghiên cứu này gồm hai thí nghiệm. Thí nghiệm 1 xác định năng suất và thành phần hoá học của ngô cắt lúc chín sữa (CSU), chín sáp (CSA) và răng ngựa (RNG). Kết quả cho thấy, năng suất cao nhất lúc chín sữa và hàm lượng chất khô có xu hướng tăng, nhưng protein thô, xơ không hoà tan trong chất tẩy trung tính và xơ không hoà tan trong chất tẩy axit có xu hướng giảm khi kéo dài thời gian thu cắt (p < 0,05). Thí nghiệm 2 xác định tỷ lệ phân giải dạ cỏ của ngô cắt theo ba thời kỳ như thí nghiệm 1 trên bốn con bò đặt cannula dạ cỏ. Kết quả cho thấy, tỷ lệ phân giải chất khô của ngô cắt ở các thời kỳ không sai khác có ý nghĩa thống kê (p > 0,05), nhưng thời gian lên men ở dạ cỏ của ngô CSU ngắn hơn của ngô CSA và của ngô RNG. Tỷ lệ phân giải hữu hiệu ở các tốc độ thoát qua dạ cỏ khác nhau không khác giữa các thời điểm thu cắt (p > 0,05). Giá trị năng lượng trao đổi (ME) của ngô RNG giống của ngô CSA và cao hơn của ngô CSU (p < 0,05). Vì vậy, nên thu cắt ngô HQ2000 khi hạt vào kỳ chín sáp

Evaluation of a nutrition model in predicting performance of Vietnamese cattle

The objective of this study was to evaluate the predictions of dry matter intake (DM) and average daily gain (ADG) of Vietnamese Yellow (Vang) purebred and crossbred (Vang with Red Sindhi or Brahman) bulls fed under Vietnamese conditions using two levels of solution (1 and 2) of the large ruminant nutrition system (LRNS) model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW) of the animals was 186, with standard deviation ±33.2 kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP), accuracy (Cb), coefficient of determination (r2), and mean bias (MB). When all treatment means were used, both levels of solution predicted DMI similarly with low precision (r2 of 0.389 and 0.45 for level 1 and 2, respectively) and medium accuracy (Cb of 0.827 and 0.859, respectively). The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively), the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively). The accuracy (Cb) was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively). The RMSEP indicated that both levels were on average under- or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb) was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known

Evaluation of a Nutrition Model in Predicting Performance of Vietnamese Cattle

The objective of this study was to evaluate the predictions of dry matter intake (DM) and average daily gain (ADG) of Vietnamese Yellow (Vang) purebred and crossbred (Vang with Red Sindhi or Brahman) bulls fed under Vietnamese conditions using two levels of solution (1 and 2) of the large ruminant nutrition system (LRNS) model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW) of the animals was 186, with standard deviation ±33.2 kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP), accuracy (Cb), coefficient of determination (r2), and mean bias (MB). When all treatment means were used, both levels of solution predicted DMI similarly with low precision (r2 of 0.389 and 0.45 for level 1 and 2, respectively) and medium accuracy (Cb of 0.827 and 0.859, respectively). The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively), the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively). The accuracy (Cb) was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively). The RMSEP indicated that both levels were on average under- or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb) was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known