Semiempirical Modeling of Reset Transitions in Unipolar Resistive-Switching based Memristors

We have measured the transition process from the high to low resistivity states, i.e., the reset process of resistive switching based memristors based on Ni/HfO2/Si-n+ structures, and have also developed an analytical model for their electrical characteristics. When the characteristic curves are plotted in the current-voltage (I-V) domain a high variability is observed. In spite of that, when the same curves are plotted in the charge-flux domain (Q-phi), they can be described by a simple model containing only three parameters: the charge (Qrst) and the flux (rst) at the reset point, and an exponent, n, relating the charge and the flux before the reset transition. The three parameters can be easily extracted from the Q-phi plots. There is a strong correlation between these three parameters, the origin of which is still under study

Artificial Intelligence in Higher Education during and After the COVID-19 Pandemic: Need, Transition and Transformation

The aim of this article focuses on the achievements and challenges of the application of AI (artificial intelligence) based technologies in the field of higher education. Articles on AI-based technologies and their relationship with higher education have been collected from databases such as WOS, Scopus, ProQuest, Ebsco and PudMed. It oriented the analysis to provide the various contributions about technologies, methodologies, processes and learning contexts based on AI that have been emerging during the crisis caused by the Covid-19 pandemic in the university context. This article focuses on the achievements and challenges of the application of AI based technologies in the field of higher education, and we provide a series of relevant data, examples and explicit studies on the titanic potential of AI in its adaptation to higher education, emphasising crucial aspects of the application of new technologies and their aspects in the current scenario

Daptomycin: a novel lipopeptide antibiotic against Gram-positive pathogens

The aim of this review is to summarize the historical background of drug resistance of Gram-positive pathogens as well as to describe in detail the novel lipopeptide antibiotic daptomycin. Pharmacological and pharmacokinetic aspects are reviewed and the current clinical use of daptomycin is presented. Daptomycin seems to be a reliable drug in the treatment of complicated skin and skin structure infections, infective right-sided endocarditis, and bacteremia caused by Gram-positive agents. Its unique mechanism of action and its low resistance profile, together with its rapid bactericidal action make it a favorable alternative to vancomycin in multi-drug resistant cocci. The role of daptomycin in the treatment of prosthetic material infections, osteomyelitis, and urogenital infections needs to be evaluated in randomized clinical trials

Enhancing structure relaxations for first-principles codes: an approximate Hessian approach

We present a method for improving the speed of geometry relaxation by using a harmonic approximation for the interaction potential between nearest neighbor atoms to construct an initial Hessian estimate. The model is quite robust, and yields approximately a 30% or better reduction in the number of calculations compared to an optimized diagonal initialization. Convergence with this initializer approaches the speed of a converged BFGS Hessian, therefore it is close to the best that can be achieved. Hessian preconditioning is discussed, and it is found that a compromise between an average condition number and a narrow distribution in eigenvalues produces the best optimization.Comment: 9 pages, 3 figures, added references, expanded optimization sectio

An in silico analysis identifies drugs potentially modulating the cytokine storm triggered by SARS-CoV-2 infection

The ongoing COVID-19 pandemic is one of the biggest health challenges of recent decades. Among the causes of mortality triggered by SARS-CoV-2 infection, the development of an inflammatory “cytokine storm” (CS) plays a determinant role. Here, we used transcriptomic data from the bronchoalveolar lavage fluid (BALF) of COVID-19 patients undergoing a CS to obtain gene-signatures associated to this pathology. Using these signatures, we interrogated the Connectivity Map (CMap) dataset that contains the effects of over 5000 small molecules on the transcriptome of human cell lines, and looked for molecules which effects on transcription mimic or oppose those of the CS. As expected, molecules that potentiate immune responses such as PKC activators are predicted to worsen the CS. In addition, we identified the negative regulation of female hormones among pathways potentially aggravating the CS, which helps to understand the gender-related differences in COVID-19 mortality. Regarding drugs potentially counteracting the CS, we identified glucocorticoids as a top hit, which validates our approach as this is the primary treatment for this pathology. Interestingly, our analysis also reveals a potential effect of MEK inhibitors in reverting the COVID-19 CS, which is supported by in vitro data that confirms the anti-inflammatory properties of these compounds.Open access funding provided by Karolinska Institute.S

Pipeline for recording datasets and running neural networks on the Bela embedded hardware platform

Deploying deep learning models on embedded devices is an arduous task: oftentimes, there exist no platform-specific instructions, and compilation times can be considerably large due to the limited computational resources available on-device. Moreover, many music-making applications de- mand real-time inference. Embedded hardware platforms for audio, such as Bela, offer an entry point for beginners into physical audio computing; however, the need for cross- compilation environments and low-level software develop- ment tools for deploying embedded deep learning models imposes high entry barriers on non-expert users. We present a pipeline for deploying neural networks in the Bela embedded hardware platform. In our pipeline, we include a tool to record a multichannel dataset of sen- sor signals. Additionally, we provide a dockerised cross- compilation environment for faster compilation. With this pipeline, we aim to provide a template for programmers and makers to prototype and experiment with neural networks for real-time embedded musical applications

Complicated septic shock caused by Achromobacter xylosoxidans bacteremia in a patient with acute lymphoblastic leukaemia

Infections caused by Achromobacter xylosoxidans cause significant morbidity and mortality in debilitated individuals. Eradication of these infections requires prolonged therapy with antimicrobial agents and removal of any infected central venous catheter. The outcome is usually poor in patients with high risk malignancy, septic complications, and/or multi-organ dysfunction