Inhibition of phosphoinositide 3-kinase/protein kinase B signaling hampers the vasopressin-dependent stimulation of myogenic differentiation

Arginine-vasopressin (AVP) promotes muscle differentiation, hypertrophy, and regeneration through the combined activation of the calcineurin and Calcium/Calmodulin-dependent Protein Kinase (CaMK) pathways. The AVP system is impaired in several neuromuscular diseases, suggesting that AVP may act as a physiological factor in skeletal muscle. Since the Phosphoinositide 3-kinases/Protein Kinase B/mammalian Target Of Rapamycin (PI3K/Akt/mTOR) signaling plays a significant role in regulating muscle mass, we evaluated its role in the AVP myogenic effect. In L6 cells AKT1 expression was knocked down, and the AVP-dependent expression of mTOR and Forkhead box O3 (FoxO) was analyzed by Western blotting. The effect of the PI3K inhibitor LY294002 was evaluated by cellular and molecular techniques. Akt knockdown hampered the AVP-dependent mTOR expression while increased the levels of FoxO transcription factor. LY294002 treatment inhibited the AVP-dependent expression of Myocyte Enhancer Factor-2 (MEF2) and myogenin and prevented the nuclear translocation of MEF2. LY294002 also repressed the AVP-dependent nuclear export of histone deacetylase 4 (HDAC4) interfering with the formation of multifactorial complexes on the myogenin promoter. We demonstrate that the PI3K/Akt pathway is essential for the full myogenic effect of AVP and that, by targeting this pathway, one may highlight novel strategies to counteract muscle wasting in aging or neuromuscular disorders

Italian Law 326/2003 for The Reimbursement of Orphan and Life Saving Drugs Awaiting Market Entry: Approvals, Rejections And Methods In Aifa's Evaluation Process Between January 2013 And May 2015

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Sparse Optical Arbitrary Waveform Measurement by Compressive Sensing

We propose and experimentally demonstrate a compressive sensing scheme based on optical coherent receiver that recovers sparse optical arbitrary signals with an analog bandwidth up to 25GHz. The proposed scheme uses 16x lower sampling rate than the Nyquist theorem and spectral resolution of 24.4MHz

Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%

Performance studies of 3D-Hyper-FleX-LION for HPC applications

This paper studies the performance of 3D-Hyper-FleX-LION for HPC systems. The simulation results obtained for different HPC applications (i.e. Fill Boundary, Crystal Router, MiniFE, and MiniDFT) show up to 2.8× improvements in throughput per watt when compared with a Fat-Tree with no oversubcription

Photonic spiking neural networks with event-driven femtojoule optoelectronic neurons based on Izhikevich-inspired model

Photonic spiking neural networks (PSNNs) potentially offer exceptionally high throughput and energy efficiency compared to their electronic neuromorphic counterparts while maintaining their benefits in terms of event-driven computing capability. While state-of-the-art PSNN designs require a continuous laser pump, this paper presents a monolithic optoelectronic PSNN hardware design consisting of an MZI mesh incoherent network and event-driven laser spiking neurons. We designed, prototyped, and experimentally demonstrated this event-driven neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of two photodetectors for excitatory and inhibitory optical spiking inputs, electrical transistors’ circuits providing spiking nonlinearity, and a laser for optical spiking outputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. The nanoscale neuron designed in our monolithic PSNN utilizes quantum impedance conversion. It shows that estimated 21.09 fJ/spike input can trigger the output from on-chip nanolasers running at a maximum of 10 Gspike/second in the neural network. Utilizing the simulated neuron model, we conducted simulations on MNIST handwritten digits recognition using fully connected (FC) and convolutional neural networks (CNN). The simulation results show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. The benchmark shows our PSNN can achieve 50 TOP/J energy efficiency, which corresponds to 100 × throughputs and 1000 × energy-efficiency improvements compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid

Performance studies of evolutionary transfer learning for end-to-end QoT estimation in multi-domain optical networks [Invited]

This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD-EONs). Evol-TL exploits a broker-based MD-EON architecture that enables cooperative learning between the broker plane (end-to-end) and domain-level (local) machine learning functions while securing the autonomy of each domain. We designed a genetic algorithm to optimize the neural network architectures and the sets of weights to be transferred between the source and destination tasks. We evaluated the performance of Evol-TL with three case studies considering the QoT estimation task for lightpaths with (i) different path lengths (in terms of the numbers of fiber links traversed), (ii) different modulation formats, and (iii) different device conditions (emulated by introducing different levels of wavelength-specific attenuation to the amplifiers). The results show that the proposed approach can reduce the average amount of required training data by up to 13× while achieving an estimation accuracy above 95%

DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines