R-matrix calculations of electron impact electronic excitation of BeH

The R-matrix method is used to perform high-level calculations of electron collisions with beryllium mono-hydride at its equilibrium geometry with a particular emphasis on electron impact electronic excitation. Several target and scattering models are considered. The calculations were performed using (1) the UKRMol suite which relies on the use of Gaussian type orbitals (GTOs) to represent the continuum and (2) using the new UKRMol+ suite which allows the inclusion of B-spline type orbitals in the basis for the continuum. The final close-coupling scattering models used the UKRMol+ code and a frozen core, valence full configuration interaction, method based on a diffuse GTO atomic basis set. The calculated electronic properties of the molecule are in very good agreement with state-of-the-art electronic structure calculations. The use of the UKRMol+ suite proved critical since it allowed the use of a large R-matrix sphere (35 Bohr), necessary to contain the diffuse electronic states of the molecule. The corresponding calculations using UKRMol are not possible due to numerical problems associated with the combination of GTO-only continuum and a large R-matrix sphere. This work provides the first demonstration of the utility and numerical stability of the new UKRMol+ code. The inelastic cross sections obtained here present a significant improvement over the results of earlier studies on BeH

GLUT3 is induced during epithelial-mesenchymal transition and promotes tumor cell proliferation in non-small cell lung cancer.

BACKGROUND: Alterations in glucose metabolism and epithelial-mesenchymal transition (EMT) constitute two important characteristics of carcinoma progression toward invasive cancer. Despite an extensive characterization of each of them separately, the links between EMT and glucose metabolism of tumor cells remain elusive. Here we show that the neuronal glucose transporter GLUT3 contributes to glucose uptake and proliferation of lung tumor cells that have undergone an EMT. RESULTS: Using a panel of human non-small cell lung cancer (NSCLC) cell lines, we demonstrate that GLUT3 is strongly expressed in mesenchymal, but not epithelial cells, a finding corroborated in hepatoma cells. Furthermore, we identify that ZEB1 binds to the GLUT3 gene to activate transcription. Importantly, inhibiting GLUT3 expression reduces glucose import and the proliferation of mesenchymal lung tumor cells, whereas ectopic expression in epithelial cells sustains proliferation in low glucose. Using a large microarray data collection of human NSCLCs, we determine that GLUT3 expression correlates with EMT markers and is prognostic of poor overall survival. CONCLUSIONS: Altogether, our results reveal that GLUT3 is a transcriptional target of ZEB1 and that this glucose transporter plays an important role in lung cancer, when tumor cells loose their epithelial characteristics to become more invasive. Moreover, these findings emphasize the development of GLUT3 inhibitory drugs as a targeted therapy for the treatment of patients with poorly differentiated tumors

Optimization and deployment of CNNs at the Edge: The ALOHA experience

Deep learning (DL) algorithms have already proved their effectiveness on a wide variety of application domains, including speech recognition, natural language processing, and image classification. To foster their pervasive adoption in applications where low latency, privacy issues and data bandwidth are paramount, the current trend is to perform inference tasks at the edge. This requires deployment of DL algorithms on low-energy and resource-constrained computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage without adequate support and experience. In this paper, we present ALOHA, an integrated tool flow that tries to facilitate the design of DL applications and their porting on embedded heterogenous architectures. The proposed tool flow aims at automating different design steps and reducing development costs. ALOHA considers hardware-related variables and security, power efficiency, and adaptivity aspects during the whole development process, from pre-training hyperparameter optimization and algorithm configuration to deployment