Glovertolstrup1481

Cardiac mesenchymal stem cells (C-MSCs) are endogenous cardiac stromal cells that play a crucial role in maintaining normal cardiac function. Rab27b is a member of the small GTPase Rab family that controls membrane trafficking and the secretion of exosomes. However, its role in regulating energy metabolism in C-MSC is unclear. In this study, we analyzed mitochondrial oxidative phosphorylation by quantifying cellular oxygen consumption rate (OCR) and quantified the extracellular acidification rate (ECAR) in C-MSC with/without Rab27b knockdown. Knockdown of Rab27b increased glycolysis, but significantly reduced mitochondrial oxidative phosphorylation (OXPHOS) with loss of mitochondrial membrane potential in C-MSC. Furthermore, knockdown of Rab27b reduced H3k4me3 expression in C-MSC and selectively decreased the expression of the essential genes involved in β-oxidation, tricarboxylic acid cycle (TCA), and electron transport chain (ETC). Taken together, our findings highlight a novel role for Rab27b in maintaining fatty acid oxidation in C-MSCs. Copyright © 2020 Jin, Shen, Su, Cai, Liu, Weintraub and Tang.Long non-coding RNAs (lncRNAs) can enhance plant stress resistance by regulating the expression of functional genes. Sweet sorghum is a salt-tolerant energy crop. However, little is known about how lncRNAs in sweet sorghum respond to salt stress. In this study, we identified 126 and 133 differentially expressed lncRNAs in the salt-tolerant M-81E and the salt-sensitive Roma strains, respectively. Salt stress induced three new lncRNAs in M-81E and inhibited two new lncRNAs in Roma. These lncRNAs included lncRNA13472, lncRNA11310, lncRNA2846, lncRNA26929, and lncRNA14798, which potentially function as competitive endogenous RNAs (ceRNAs) that influence plant responses to salt stress by regulating the expression of target genes related to ion transport, protein modification, transcriptional regulation, and material synthesis and transport. Additionally, M-81E had a more complex ceRNA network than Roma. This study provides new information regarding lncRNAs and the complex regulatory network underlying salt-stress responses in sweet sorghum. Copyright © 2020 Sun, Zheng, Li, Liu, Zhang and Sui.Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. read more Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases. Copyright © 2020 Rodriguez-Torres, Paredes-Hernandez, Vazquez-Mendoza, Tetlalmatzi-Montiel, Morgado-Valle, Beltran-Parrazal and Villarroel-Flores.Hip osteoarthritis patients exhibit changes in kinematics and kinetics that affect joint loading. Monitoring this load can provide valuable information to clinicians. For example, a patient's joint loading measured across different activities can be used to determine the amount of exercise that the patient needs to complete each day. Unfortunately, current methods for measuring joint loading require a lab environment which most clinicians do not have access to. This study explores employing machine learning to construct a model that can estimate joint loading based on sensor data obtained solely from a mobile phone. In order to learn such a model, we collected a dataset from 10 patients with hip osteoarthritis who performed multiple repetitions of nine different exercises. During each repetition, we simultaneously recorded 3D motion capture data, ground reaction force data, and the inertial measurement unit data from a mobile phone attached to the patient's hip. The 3D motion and ground reaction force data were used to compute the ground truth joint loading using musculoskeletal modeling. Our goal is to estimate the ground truth loading value using only the data captured by the sensors of the mobile phone. We propose a machine learning pipeline for learning such a model based on the recordings of a phone's accelerometer and gyroscope. When evaluated for an unseen patient, the proposed pipeline achieves a mean absolute error of 29% for the left hip and 36% for the right hip. While our approach is a step in the direction of using a minimal number of sensors to estimate joint loading outside the lab, developing a tool that is accurate enough to be applicable in a clinical context still remains an open challenge. It may be necessary to use sensors at more than one location in order to obtain better estimates. Copyright © 2020 De Brabandere, Emmerzaal, Timmermans, Jonkers, Vanwanseele and Davis.The growing demand for more sustainable, alternative processes leading to production of plant-derived preparations imposes the use of plants waste generated mainly by agri-food and pharmaceutical industries. These mostly unexploited but large quantities of plants waste also increase the interest in developing alternative approaches for sustainable production of therapeutic molecules. In order to reduce the amount of plant waste by further processing, different novel extraction techniques can be applied. Fruits and their industrial by-products are rich sources of different classes of compounds with therapeutic properties. The processed fruits waste can be reused and lead to novel pharmaceuticals, food supplements or functional foods. This review intends to briefly summarize recent aspects regarding the production of different active compounds from fruit by-products, and their therapeutic properties. The potential use of fruits by-products in different industries will be also discussed. Copyright © 2020 Fierascu, Sieniawska, Ortan, Fierascu and Xiao.