ALBERT

Description: As one kind of readily available renewable energy sources, wind is widely used in power generation where wind speed plays an important role. Generally speaking, we need to forecast the wind speed for improving the controllability of wind power generation. However, there exists considerable randomness and instabilities in wind speed data so that it is difficult to obtain accurate forecasting results. In this paper, we propose a novel fuzzy inference method based hybrid model for accurate wind speed forecasting. In this hybrid model, we adopt two strategies to enhance the estimation performance. On one hand, we propose the purification machine which utilize the Irregular Information Reduction Module (IIRM) and the Irrelevant Variable Reduction Module (IVRM) to reduce the randomness and instabilities of the data and to eliminate the variables with zero or negative effect in the wind speed time series. On the other hand, we adopt the developed Single-Input-Rule-Modules based Fuzzy Inference System (SIRM-FIS), the functionally weighted SIRM-FIS (FWSIRM-FIS) to realize the prediction of wind speed. This FWSIRM-FIS utilizes the multi-variable functional weights to dynamically measure the importance of the input variables so that the input-output mapping can be strengthened and more accurate forecasting results can be achieved. Furthermore, detailed experiments and comparisons are given. Experimental results demonstrate that the proposed FWSIRM-FIS and purification machine contributes greatly to deal with the randomness and instability in the wind speed data and yield more accurate forecasting results than those existing excellent forecasting models.

Description: Glycogen is a highly-branched polysaccharide that is widely distributed across the three life domains. It has versatile functions in physiological activities such as energy reserve, osmotic regulation, blood glucose homeostasis, and pH maintenance. Recent research also confirms that glycogen plays important roles in longevity and cognition. Intrinsically, glycogen function is determined by its structure that has been intensively studied for many years. The recent association of glycogen α-particle fragility with diabetic conditions further strengthens the importance of glycogen structure in its function. By using improved glycogen extraction procedures and a series of advanced analytical techniques, the fine molecular structure of glycogen particles in human beings and several model organisms such as Escherichia coli, Caenorhabditis elegans, Mus musculus, and Rat rattus have been characterized. However, there are still many unknowns about the assembly mechanisms of glycogen particles, the dynamic changes of glycogen structures, and the composition of glycogen associated proteins (glycogen proteome). In this review, we explored the recent progresses in glycogen studies with a focus on the structure of glycogen particles, which may not only provide insights into glycogen functions, but also facilitate the discovery of novel drug targets for the treatment of diabetes mellitus.

Description: Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

Description: Bovine milk-derived extracellular vesicles (BM-EVs) are recognized as promising nanoscale delivery vectors owing to their large availability. However, few isolation methods can achieve high purity and yield simultaneously. Therefore, we developed a novel and cost-effective procedure to separate BM-EVs via “salting-out.” First, BM-EVs were isolated from skimmed milk using ammonium sulfate. The majority of BM-EVs were precipitated between 30 and 40% saturation and 34% had a relatively augmented purity. The separated BM-EVs showed a spherical shape with a diameter of 60–150 nm and expressed the marker proteins CD63, TSG101, and Hsp70. The purity and yield were comparable to the BM-EVs isolated via ultracentrifugation while ExoQuick failed to separate a relatively pure fraction of BM-EVs. The uptake of BM-EVs into endothelial cells was dose- and time-dependent without significant cytotoxicity. The levels of endothelial nitric oxide syntheses were regulated by BM-EVs loaded with icariside II and miRNA-155-5p, suggesting their functions as delivery vehicles. These findings have demonstrated that it is an efficient procedure to isolate BM-EVs via “salting-out,” holding great promise toward therapeutic applications.