Isolation, Purification, Identification and Hypolipidemic Activity of Lipase Inhibitory Peptide from Chlorella pyrenoidosa

In this study, pancrelipase inhibitory peptides (PES) from an enzymatic protein hydrolysate of Chlorella pyrenoidosa were isolated and purified by ultrafiltration and Sephadex gel chromatography. The in vivo hypolipidemic activity of PES was evaluated by fat deposition and the levels of triglyceride (TG) and total cholesterol (TC) in Caenorhabditis elegans fed a high sugar diet. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify the peptide sequence of PES, and molecular docking was used to select potential pancreatic lipase inhibitory peptides, and the pancreatic lipase inhibitory activity of the synthesized peptides was verified. The results showed that PES had good hypolipidemic activity at a concentration of 1 mg/mL; it inhibited lipid deposition by 22.5%, and reduced the levels of TG and TC by 27.4% and 29.4%, respectively. In total, 999 peptides were identified, and four potential lipase inhibitory peptides were obtained. Among them, FLGPF had the best inhibitory effect on pancreatic lipase, with an inhibition rate of 50.12% at 8 mg/mL. The inhibition was reversible and non-competitive, with an inhibition constant of 5.23 mg/mL. Molecular docking showed that FLGPF could better bind to human pancreatic triacylglycerol lipase (PTL) via π-hydrogen, π-cation and hydrogen bond interactions. This study can provide a theoretical reference for the development and utilization of C. pyrenoidosa protein-derived hypolipidemic peptide

Whole exome sequencing of insulinoma reveals recurrent T372R mutations in YY1

Functional pancreatic neuroendocrine tumours (PNETs) are mainly represented by insulinoma, which secrete insulin independent of glucose and cause hypoglycaemia. The major genetic alterations in sporadic insulinomas are still unknown. Here we identify recurrent somatic T372R mutations in YY1 by whole exome sequencing of 10 sporadic insulinomas. Further screening in 103 additional insulinomas reveals this hotspot mutation in 30% (34/113) of all tumours. T372R mutation alters the expression of YY1 target genes in insulinomas. Clinically, the T372R mutation is associated with the later onset of tumours. Genotyping of YY1, a target of mTOR inhibitors, may contribute to medical treatment of insulinomas. Our findings highlight the importance of YY1 in pancreatic β-cells and may provide therapeutic targets for PNETs

Exploring the Relationship among Predictability, Prediction Accuracy and Data Frequency of Financial Time Series

In this paper, we aim to reveal the connection between the predictability and prediction accuracy of stock closing price changes with different data frequencies. To find out whether data frequency will affect its predictability, a new information-theoretic estimator Plz, which is derived from the Lempel–Ziv entropy, is proposed here to quantify the predictability of five-minute and daily price changes of the SSE 50 index from the Chinese stock market. Furthermore, the prediction method EEMD-FFH we proposed previously was applied to evaluate whether financial data with higher sampling frequency leads to higher prediction accuracy. It turns out that intraday five-minute data are more predictable and also have higher prediction accuracy than daily data, suggesting that the data frequency of stock returns affects its predictability and prediction accuracy, and that higher frequency data have higher predictability and higher prediction accuracy. We also perform linear regression for the two frequency data sets; the results show that predictability and prediction accuracy are positive related

Exploring the Relationship among Predictability, Prediction Accuracy and Data Frequency of Financial Time Series

In this paper, we aim to reveal the connection between the predictability and prediction accuracy of stock closing price changes with different data frequencies. To find out whether data frequency will affect its predictability, a new information-theoretic estimator Plz, which is derived from the Lempel–Ziv entropy, is proposed here to quantify the predictability of five-minute and daily price changes of the SSE 50 index from the Chinese stock market. Furthermore, the prediction method EEMD-FFH we proposed previously was applied to evaluate whether financial data with higher sampling frequency leads to higher prediction accuracy. It turns out that intraday five-minute data are more predictable and also have higher prediction accuracy than daily data, suggesting that the data frequency of stock returns affects its predictability and prediction accuracy, and that higher frequency data have higher predictability and higher prediction accuracy. We also perform linear regression for the two frequency data sets; the results show that predictability and prediction accuracy are positive related

Time series classification based on detrended partial cross-correlation

Classifying stocks by measuring the similarity between them can provide investors with a reliable reference and help them earn more profits than before. This paper attempts to explore a convincing method to measure the similarity of international stocks. We selected the daily closing prices of 18 stocks from the Americas, Asia, Europe, and Australia, and mapped them as points into a three-dimensional space. In order to measure the similarity of stocks, we recommend calculating the Hurst surface distance as a distance matrix to classify stocks through the multidimensional scaling (MDS) method. We compare the classification results with classical MDS using Euclidean distance as similarity measure and MDS based on the $\rho_{D P X A}$ (the detrending partial cross-correlation (DPXA) coefficient). The research results show that using Hurst surface distance as a reflection of similarity can not only provide more relevant information, but also distinguish the differences of economic fluctuations in different regions, while $\rho_{D P X A}$ lays more emphasis on the similarities and differences within the same region. Both the two improved techniques for MDS are superior to the classic method based on Euclidean distance. In addition, the two methods can provide more detailed and clearer information

Distribution of eigenvalues of detrended cross-correlation matrix

This letter is devoted to the cross-correlation analysis of non-stationary multivariate data, in which the detrended cross-correlation matrix based on the detrended cross-correlation coefficient is studied. The relationship between Pearson's cross-correlation coefficient and the detrended cross-correlation coefficient is analyzed. As a special case of random matrix theory, the distribution of the eigenvalues of the detrended cross-correlation matrix for purely random variables is derived

Prediction of hot spot areas of hemorrhagic fever with renal syndrome in Hunan Province based on an information quantity model and logistical regression model.

BackgroundChina's "13th 5-Year Plan" (2016-2020) for the prevention and control of sudden acute infectious diseases emphasizes that epidemic monitoring and epidemic focus surveys in key areas are crucial for strengthening national epidemic prevention and building control capacity. Establishing an epidemic hot spot areas and prediction model is an effective means of accurate epidemic monitoring and surveying. Objective: This study predicted hemorrhagic fever with renal syndrome (HFRS) epidemic hot spot areas, based on multi-source environmental variable factors. We calculated the contribution weight of each environmental factor to the morbidity risk, obtained the spatial probability distribution of HFRS risk areas within the study region, and detected and extracted epidemic hot spots, to guide accurate epidemic monitoring as well as prevention and control. Methods: We collected spatial HFRS data, as well as data on various types of natural and human social activity environments in Hunan Province from 2010 to 2014. Using the information quantity method and logistic regression modeling, we constructed a risk-area-prediction model reflecting the epidemic intensity and spatial distribution of HFRS. Results: The areas under the receiver operating characteristic curve of training samples and test samples were 0.840 and 0.816. From 2015 to 2019, HRFS case site verification showed that more than 82% of the cases occurred in high-risk areas.DiscussionThis research method could accurately predict HFRS hot spot areas and provided an evaluation model for Hunan Province. Therefore, this method could accurately detect HFRS epidemic high-risk areas, and effectively guide epidemic monitoring and surveyance

Plasticity of brain wave network interactions and evolution across physiologic states

Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of network connectivity and link strength, while at the same time each frequency-specific network is characterized by a different signature pattern of sleep-stage stratification, reflecting a remarkable flexibility in response to change in physiologic state. These new aspects of neural plasticity demonstrate that in addition to dominant brain waves, the network of brain wave interactions is a previously unrecognized hallmark of physiologic state and function