Parallel approach of Schrödinger-based quantum corrections for ultrascaled semiconductor devices

In the current technology node, purely classical numerical simulators lack the precision needed to obtain valid results. At the same time, the simulation of fully quantum models can be a cumbersome task in certain studies such as device variability analysis, since a single simulation can take up to weeks to compute and hundreds of device configurations need to be analyzed to obtain statistically significative results. A good compromise between fast and accurate results is to add corrections to the classical simulation that are able to reproduce the quantum nature of matter. In this context, we present a new approach of Schrödinger equation-based quantum corrections. We have implemented it using Message Passing Interface in our in-house built semiconductor simulation framework called VENDES, capable of running in distributed systems that allow for more accurate results in a reasonable time frame. Using a 12-nm-gate-length gate-all-around nanowire FET (GAA NW FET) as a benchmark device, the new implementation shows an almost perfect agreement in the output data with less than a 2% difference between the cases using 1 and 16 processes. Also, a reduction of up to 98% in the computational time has been found comparing the sequential and the 16 process simulation. For a reasonably dense mesh of 150k nodes, a variability study of 300 individual simulations can be now performed with VENDES in approximately 2.5 days instead of an estimated sequential execution of 137 days.

Chemical characterization of extracellular vesicles of mesenchymal stromal cells: TOF-SIMS and BCARS approach

Paracrine functions of mesenchymal stem (stromal) cells (MSCs) rely, at least partly, on membrane-bound extracellular vesicles (EVs) with rich composition of lipids, nucleic acids and signaling proteins. Elucidation the underlying chemistry could potentially lead to MSCs-free therapy. However, the secretome of MSCs (EVs’ composition) is non-static and depends on many other factors including surrounding cells and medium. Thus, the research techniques must be able to provide not only bulk but microscopy-scale data within a reasonable time frame. Two of these label-free techniques are subject of this work toward the question of chemical composition of the EVs.

Review—Single-Molecule Sensors Based on Protein Nanopores

The recent development of single-molecule sensors (SMS), which detect individual targets one at a time, allows determination of ultra-low concentrations of structurally similar compounds from a complex matrix. Protein nanopores are one of the earliest methods able to resolve the signal from a single molecule, and have already been successfully employed in commercial DNA sequencers. The protein nanopore based SMS, however, remains challenging, largely because the quantitative single-molecule analysis requires recording a sufficient number of signals for statistical significance within a reasonable time frame, thus restricting the lower limit of detection. This review aims to critically evaluate the strategies developed in this field over the last two decades. The measurement principle of nanopore SMS is first elucidated, followed by a systematic examination of the eight common protein pores, and a comprehensive assessment of the major types of sensing applications. A particular emphasis is placed on the intrinsic relationship between the size and charge of protein nanopores and their sensing capabilities for different kinds of analytes. Innovative approaches to lift the performance of nanopore SMS are also analyzed in detail, with a prediction at the end of the most promising future applications.

Predictors for time to awake in patients undergoing awake craniotomies

OBJECTIVE Awake craniotomies are often characterized by alternating asleep-awake-asleep periods. Preceding the awake phase, patients are weaned from anesthesia and mechanical ventilation. Although clinicians aim to minimize the time to awake for patient safety and operating room efficiency, in some patients, the time to awake exceeds 20 minutes. The goal of this study was to determine the average time to awake and the factors associated with prolonged time to awake (> 20 minutes) in patients undergoing awake craniotomy. METHODS Records of patients who underwent awake craniotomy between 2003 and 2020 were evaluated. Time to awake was defined as the time between discontinuation of propofol and remifentanil infusion and the time of extubation. Patient and perioperative characteristics were explored as predictors for time to awake using logistic regression analyses. RESULTS Data of 307 patients were analyzed. The median (IQR) time to awake was 13 (10–20) minutes and exceeded 20 minutes in 17% (95% CI 13%–21%) of the patients. In both univariate and multivariable analyses, increased age, nonsmoker status, and American Society of Anesthesiologists (ASA) class III versus II were associated with a time to awake exceeding 20 minutes. BMI, as well as the use of alcohol, drugs, dexamethasone, or antiepileptic agents, was not significantly associated with the time to awake. CONCLUSIONS While most patients undergoing awake craniotomy are awake within a reasonable time frame after discontinuation of propofol and remifentanil infusion, time to awake exceeded 20 minutes in 17% of the patients. Increasing age, nonsmoker status, and higher ASA classification were found to be associated with a prolonged time to awake.

A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks

Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision within a reasonable time frame has become an important issue. In this paper, we investigate the problem of building effective Consumer Health Search (CHS) systems that use query variations to achieve high recall and fulfill the information needs of health consumers. In particular, we study an intent-aware gain metric used to estimate the amount of missing information and make a prediction about the achievable recall for each query reformulation during a search session. We evaluate and propose alternative formulations of this metric using standard test collections of the CLEF 2018 eHealth Evaluation Lab CHS.

Cocaine Detection by a Laser-Induced Immunofluorometric Biosensor

The trafficking of illegal drugs by criminal networks at borders, harbors, or airports is an increasing issue for public health as these routes ensure the main supply of illegal drugs. The prevention of drug smuggling, including the installation of scanners and other analytical devices to detect small traces of drugs within a reasonable time frame, remains a challenge. The presented immunosensor is based on a monolithic affinity column with a large excess of immobilized hapten, which traps fluorescently labeled antibodies as long as the analyte cocaine is absent. In the presence of the drug, some binding sites of the antibody will be blocked, which leads to an immediate breakthrough of the labeled protein, detectable by highly sensitive laser-induced fluorescence with the help of a Peltier-cooled complementary metal-oxide-semiconductor (CMOS) camera. Liquid handling is performed with high-precision syringe pumps and microfluidic chip-based mixing devices and flow cells. The biosensor achieved limits of detection of 7 ppt (23 pM) of cocaine with a response time of 90 s and a total assay time below 3 min. With surface wipe sampling, the biosensor was able to detect 300 pg of cocaine. This immunosensor belongs to the most sensitive and fastest detectors for cocaine and offers near-continuous analyte measurement.

Parallel Approach of Schr¨Odinger Based Quantum Corrections for Ultrascaled Semiconductor Devices

In the current technology node, purely classical numerical simulators lack the precision needed to obtain valid results. At the same time, the simulation of fully quantum models can be a cumbersome task in certain studies such as device variability analysis, since a single simulation can take up to weeks to compute and hundreds of device configurations need to be analyzed to obtain statistically significative results. A good compromise between fast and accurate results is to add corrections to the classical simulation that are able to reproduce the quantum nature of matter. In this context, we present a new approach of Schrödinger equation-based quantum corrections. We have implemented it using Message Passing Interface (MPI) in our in-house built semiconductor simulation framework called VENDES, capable of running in distributed systems that allow for more accurate results in a reasonable time frame. Using a 12 nm gate length Gate-AllAround Nanowire FET (GAA NW FET) as an application example, the new implementation shows an almost perfect agreement in the output data with less than a 2% difference between the cases using 1 and 16 processes. Also, a reduction of up to 98% in the computational time has been found comparing the sequential and the 16 process simulation. For a reasonably dense mesh of 150k nodes, a variability study of 300 individual simulations, can be now performed with VENDES in approximately 2.5 days instead of an estimated sequential execution of 137 days.

A NEAT Based Two Stage Neural Network Approach to Generate a Control Algorithm for a Pultrusion System

Controlling complex systems by traditional control systems can sometimes lead to sub-optimal results since mathematical models do often not completely describe physical processes. An alternative approach is the use of a neural network based control algorithm. Neural Networks can approximate any function and as such are able to control even the most complex system. One challenge of this approach is the necessity of a high speed training loop to facilitate enough training rounds in a reasonable time frame to generate a viable control network. This paper overcomes this problem by employing a second neural network to approximate the output of a relatively slow 3D-FE-Pultrusion-Model. This approximation is by orders of magnitude faster than the original model with only minor deviations from the original models behaviour. This new model is then employed in a training loop to successfully train a NEAT based genetic control algorithm.

Cocaine Detection by a Laser-induced Immunofluorometric Biosensor

The trafficking of illegal drugs by criminal networks at borders, harbors, or airports is an increasing issue in public health as these routes ensure the main supply of illegal drugs. The prevention of drug smuggling, including the installation of scanners and other analytical devices to detect ultra-small traces of drugs within a reasonable time frame, remains a challenge. The presented immunosensor is based on a monolithic affinity column with a large excess of immobilized hapten, which traps fluorescently labeled antibodies as long as the analyte cocaine is absent. In the presence of the drug, some binding sites of the antibody will be blocked, which leads to an immediate breakthrough of the labeled protein, detectable by highly sensitive laser-induced fluorescence with the help of a Peltier-cooled complementary metal-oxide-semiconductor (CMOS) camera. Liquid handling is performed with high-precision syringe pumps and microfluidic chip-based mixing devices and flow cells. The biosensor achieved limits of detection of 23 pM (7 ppt) of cocaine with a response time of 90 seconds and a total assay time below 3 minutes. With surface wipe sampling, the biosensor was able to detect 300 pg of cocaine. This immunosensor belongs to the most sensitive and fastest detectors for cocaine and offers near-continuous analyte measurement.

A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data

The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learning techniques in order to identify characteristic k-mers of the input dataset across a range of different k-values and within a reasonable time frame. We use these k-mers as features for clustering the input sequences and identifying differences between the distributions of k-mers across the dataset. The developed algorithm is part of an innovative and much promising approach both to the problem of grouping sequence data based on their inherent characteristic features, as well as for the study of changes in the distributions of k-mers, as the k-value is fluctuating within a range of values. Our framework is fully developed in Python language as an open source software licensed under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.