Temporal and partial inhibition of GLI1 in neural stem cells (NSCs) results in the early maturation of NSC derived oligodendrocytes in vitro.

BackgroundOligodendrocytes are a type of glial cells that synthesize the myelin sheath around the axons and are critical for the nerve conduction in the CNS. Oligodendrocyte death and defects are the leading causes of several myelin disorders such as multiple sclerosis, progressive multifocal leukoencephalopathy, periventricular leukomalacia, and several leukodystrophies. Temporal activation of the Sonic Hedgehog (SHH) pathway is critical for the generation of oligodendrocyte progenitors, and their differentiation and maturation in the brain and spinal cord during embryonic development in mammals.MethodsOur protocol utilized adherent cultures of human induced pluripotent stem cells (iPSC) and human embryonic stem cells (hESCs) with a green fluorescent protein (GFP) reporter knocked into one allele of the OLIG2 gene locus, dual SMAD inhibition, and transient partial inhibition of glioma-associated oncogene 1 (GLI1) by the small molecule GANT61 during the formation of the SOX2/PAX6-positive neural stem cells (NSCs). The SHH pathway was later restimulated by a Smoothened agonist purmorphamine to induce the generation of OLIG2 glial precursors. One hundred ninety-two individual oligodendrocyte precursor cells (OPCs) from GANT61 and control group were analyzed by single-cell RNA sequencing (RNA-Seq).ResultsWe demonstrate here that transient and partial inhibition of the SHH pathway transcription factor GLI1 in NSCs by a small molecule inhibitor GANT61 was found to generate OPCs that were more migratory and could differentiate earlier toward myelin-producing oligodendrocytes. Single-cell transcriptomic analysis (RNA-Seq) showed that GANT61-NSC-derived oligodendrocyte precursor cells (OPCs) had differential activation of some of the genes in the cytoskeleton rearrangement pathways that are involved in OPC motility and induction of maturation. At the protein level, this was also associated with higher levels of myelin-specific genes in the GANT61 group compared to controls. GANT61-NSC-derived OPCs were functional and could generate compact myelin in vitro and in vivo after transplantation in myelin-deficient shiverer mice.ConclusionsThis is a small molecule-based in vitro protocol that leads to the faster generation of functional oligodendrocytes. The development of protocols that lead to efficient and faster differentiation of oligodendrocytes from progenitors provides important advances toward the development of autologous neural stem cell-based therapies using human iPSCs

First-Principles Investigation of Anistropic Hole Mobilities in Organic Semiconductors

We report a simple first-principles-based simulation model (combining quantum mechanics with Marcus−Hush theory) that provides the quantitative structural relationships between angular resolution anisotropic hole mobility and molecular structures and packing. We validate that this model correctly predicts the anisotropic hole mobilities of ruberene, pentacene, tetracene, 5,11-dichlorotetracene (DCT), and hexathiapentacene (HTP), leading to results in good agreement with experiment

Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning

As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users. Mobile users in FL systems typically communicate with base stations (BSs) via wireless channels, where training performance could be degraded due to unreliable access caused by user mobility. However, existing work only investigates a static scenario or random initialization of user locations, which fail to capture mobility in real-world networks. To tackle this issue, we propose a practical model for user mobility in FL across multiple BSs, and develop a user scheduling and resource allocation method to minimize the training delay with constrained communication resources. Specifically, we first formulate an optimization problem with user mobility that jointly considers user selection, BS assignment to users, and bandwidth allocation to minimize the latency in each communication round. This optimization problem turned out to be NP-hard and we proposed a delay-aware greedy search algorithm (DAGSA) to solve it. Simulation results show that the proposed algorithm achieves better performance than the state-of-the-art baselines and a certain level of user mobility could improve training performance

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model

Adrenomedullin expression in epithelial ovarian cancers and promotes HO8910 cell migration associated with upregulating integrin α5β1 and phosphorylating FAK and paxillin

<p>Abstract</p> <p>Background</p> <p>Epithelial ovarian cancer (EOC) is one of the leading causes of cancer deaths in women worldwide. Adrenomedullin (AM) is a multifunctional peptide which presents in various kinds of tumors.</p> <p>Methods</p> <p>In this study, we characterized the expression and function of AM in epithelial ovarian cancer using immunohistochemistry staining. Exogenous AM and small interfering RNA (siRNA) specific for AM receptor CRLR were treated to EOC cell line HO8910. Wound healing assay and flow cytometry were used to measure the migration ability and expression of integrin α5 of HO8910 cells after above treatments. Western blot was used to examine the phosphorylation of FAK and paxillin.</p> <p>Results</p> <p>We found that patients with high AM expression showed a higher incidence of metastasis, larger residual size of tumors after cytoreduction and shorter disease-free and overall survival time. Exogenous AM induced ovarian cancer cell migration in time- and dose- dependent manners. AM upregulated the expression of integrin α5 and phosphorylation of FAK, paxillin as well.</p> <p>Conclusions</p> <p>Our results suggested that AM contributed to the progression of EOC and had additional roles in EOC cell migration by activating the integrin α5β1 signaling pathway. Therefore, we presumed that AM could be a potential molecular therapeutic target for ovarian carcinoma.</p

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization

Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. However, FL in wireless networks still faces two major challenges, i.e., large communication overhead and high energy consumption, which are exacerbated by client heterogeneity in dataset sizes and wireless channels. While model quantization is effective for energy reduction, existing works ignore adapting quantization to heterogeneous clients and FL convergence. To address these challenges, this paper develops an energy optimization problem of jointly designing quantization levels, scheduling clients, allocating channels, and controlling computation frequencies (QCCF) in wireless FL. Specifically, we derive an upper bound identifying the influence of client scheduling and quantization errors on FL convergence. Under the longterm convergence constraints and wireless constraints, the problem is established and transformed into an instantaneous problem with Lyapunov optimization. Solving Karush-Kuhn-Tucker conditions, our closed-form solution indicates that the doubly adaptive quantization level rises with the training process and correlates negatively with dataset sizes. Experiment results validate our theoretical results, showing that QCCF consumes less energy with faster convergence compared with state-of-the-art baselines

Metformin improves the angiogenic functions of endothelial progenitor cells via activating AMPK/eNOS pathway in diabetic mice

Additional file 3: Figure S3. BM-EPC functions under the osmotic pressure equal to that of high glucose (HG). Compared with the normal glucose (NG), BM-EPCs treated by mannitol to make equal osmotic pressure with HG showed no significant changes in tube formation and migration.**P < 0.01, vs NG; # P < 0.05 vs HG. Values are mean ± SEM (n = 5 per group)