Target Application Research Articles

Pacybara: accurate long-read sequencing for barcoded mutagenized allelic libraries.

Long-read sequencing technologies, an attractive solution for many applications, often suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. sequencing mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, sequencing errors can interfere with correct barcode identification, and a given barcode sequence may be linked to multiple independent clones within a given library. Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use long-read sequencing of barcoded mutant libraries for accurate association of barcode with genotype. Existing long-read sequencing pipelines do not account for inaccurate sequencing or nonunique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while also detecting barcodes that have been associated with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In three example applications, we show that Pacybara identifies and correctly resolves these issues. Pacybara, freely available at https://github.com/rothlab/pacybara, is implemented using R, Python, and bash for Linux. It runs on GNU/Linux HPC clusters via Slurm, PBS, or GridEngine schedulers. A single-machine simplex version is also available.

Programming self-assembling magnetic microrobots with multiple physical and chemical intelligence

Medical microrobots represent the cutting-edge of biomedical research, showcasing their potential as versatile tools. They exhibit promise in acting as carriers for cancer cell therapy, effectively delivering drugs, and as manipulators equipped for biosensing, offering mobility and adaptability. Despite these advancements, the intricate challenge of creating a microrobot that seamlessly integrates various physical and chemical functionalities persists. This includes the fusion of selective sensing, manipulation capabilities, carrier functionality, precise time-based actuators for motion control, and adaptive shaping. Addressing these complexities remains an ongoing endeavor. In this context, our work introduces a pioneering magnetic microrobot founded on CaCO3 microparticles (MPs) synthesized alongside polyethylenimine (CaCO3-PEI), forming the core body. This is combined with Fe3O4 nanoparticles (NPs) enveloped in glutaraldehyde (Fe3O4-Glu), constituting the propulsive engine. The synergy of these elements enables the microrobot to execute multimodal motions, orchestrating its movement with finesse. This dynamic capability follows a “deliver-and-return” pattern for precise targeting applications with real-world relevance. Furthermore, the Fe3O4-Glu/CaCO3-PEI microrobots demonstrated remarkable proficiency in the targeted identification, manipulation, and transportation of cancer cells through the strategic integration of specific antibodies onto their structure. Within the realm of selective cancer cell detection, these microrobots adeptly function as dynamic mobile immunosensors. The versatile utility of the Fe3O4-Glu/CaCO3-PEI microrobots extends to their role as carriers for drugs and imaging agents, facilitated by the mediation of extracellular pH modulation in cancer cells orchestrated by CaCO3. This innovative work introduces a novel “on-the-fly” concept, revolutionizing the landscape of robotics programmed with multifaceted chemical and physical intelligences.

Experimental validation of igneous rocks as filler material for packed bed system dedicated to small CSP plants 1MWel

This study is based on an experimental evaluation of four igneous rocks as storage materials for a packed bed system. This system will be in conjunction with a 1 Mel Concentrated Solar Power (CSP) plant employing an Organic Rankine Cycle (ORC). The target temperature of the application ranges from 180 °C to 300 °C, which will be achieved through a Linear Fresnel solar field. In this endeavor, this work aims to identify the potential of locally collected rocks: dolerite, leucogabbro, wehrlite, and troctolite, for application in this medium-temperature thermal energy storage (TES) system. To assess their suitability, a prior short-term thermal stability tests were performed by applying five cycles using thermogravimetric and dilatometry techniques. Afterward, a durability test was conducted by subjecting the rocks to 380 cycles within a temperature range of 100 °C to 300 °C, under an air atmosphere. At the end of the test, an evaluation was assessed to analyze the evolution of mineralogical and structural stability. Furthermore, this study examined the development of critical thermophysical and mechanical properties for sensible storage materials. We concluded that, the hydrothermal alterations occurred in mafic rocks leucogabbro and dolerite, enhancing their thermomechanical properties. In contrast, ultra-mafic rocks, wehrlite and, troctolite maintained a stable mineralogical composition and structure, preserving their main properties after the durability test. Dolerite, wehrlite and troctolite are considered suitable for an air-packed bed system. For our target application, wehrlite presents the most potential rock as it combines high thermophysical and mechanical properties, along with stable petrographic characteristics.

A novel strategy for the design of compositionally complex alloys for advanced nuclear applications

The development of compositionally complex alloys (CCAs), including medium- and high-entropy alloys (M/HEAs), which can offer exceptional mechanical properties, thermal stability, and corrosion resistance, has largely been driven by trial-and-error, and designing CCAs for specific properties and commercial applications remains an unresolved challenge. This study introduces a novel approach for designing CCAs with specific properties for target applications. It presents a TiVCrFeZrTa high-entropy master alloy (HEMA), composed primarily of refractory elements, with low neutron capture, minimal transmutation, and reduced gamma activity, targeting nuclear applications. Utilizing a natural-mixing guided design (NMGD) methodology, the process identifies the crystalline structures of the HEMA's phases, allowing for the recreation of solid solution phases while avoiding brittle intermetallic phases. The resultant Ti55Zr30Ta6V5Fe2Cr2 CCA exhibits as-cast ductility allowing 86 % thickness reduction during cold-rolling, high tensile yield strengths up to 1360 MPa and fracture toughness up to 36 MPa m1/2, contingent on thermomechanical treatment. This approach demonstrates how tailored property calculations and NMGD can expedite the identification of CCA compositions, streamlining the development process and reducing reliance on extensive, costly trial-and-error experimentation.

Computational analysis of electrode structure and configuration for efficient and localized neural stimulation.

Neuromodulation technique using electric stimulation is widely applied in neural prosthesis, therapy, and neuroscience research. Various stimulation techniques have been developed to enhance stimulation efficiency and to precisely target the specific area of the brain which involves optimizing the geometry and the configuration of the electrode, stimulation pulse type and shapes, and electrode materials. Although the effects of electrode shape, size, and configuration on the performance of neural stimulation have individually been characterized, to date, there is no integrative investigation of how this factor affects neural stimulation. In this study, we computationally modeled the various types of electrodes with varying shapes, sizes, and configurations and simulated the electric field to calculate the activation function. The electrode geometry is then integratively assessed in terms of stimulation efficiency and stimulation focality. We found that stimulation efficiency is enhanced by making the electrode sharper and smaller. A center-to-vertex distance exceeding 100µm shows enhanced stimulation efficiency in the bipolar configuration. Additionally, the separation distance of less than 1mm between the reference and stimulation electrodes exhibits higher stimulation efficiency compared to the monopolar configuration. The region of neurons to be stimulated can also be modified. We found that sharper electrodes can locally activate the neuron. In most cases, except for the rectangular electrode shape with a center-to-vertex distance smaller than 100µm, the bipolar electrode configuration can locally stimulate neurons as opposed to the monopolar configuration. These findings shed light on the optimal selection of neural electrodes depending on the target applications.

Effect of Suboptimal Disease Control on Patient Quality of Life: Real-World Data from the Observational IBD-PODCAST Canada Trial

BackgroundThe real-world application of STRIDE-II treatment targets to identify whether disease control is optimal in Crohn’s disease (CD) and ulcerative colitis (UC) is not well known.AimsThis study aimed to estimate proportions of patients with suboptimally controlled CD and UC in real-world Canadian healthcare settings and the impact on quality of life (QoL).MethodsThe noninterventional, multicenter, observational IBD-PODCAST Canada study comprised a single study visit involving routine assessments, patient- and clinician-completed questionnaires, and a retrospective chart review. Primary outcomes were proportions of patients with STRIDE-II-based red flags indicative of suboptimal disease control and mean ± standard deviation Short Inflammatory Bowel Disease Questionnaire (SIBDQ) scores. Secondary outcomes included proportions of patients and clinicians subjectively reporting suboptimal control.ResultsAmong 163 enrolled patients from 10 sites, 45/87 patients with CD (51.7%; 95% CI: 40.8%, 62.6%) and 33/76 patients with UC (43.3%; 95% CI: 32.1%, 55.3%) had suboptimal disease control based on STRIDE-II criteria. Suboptimal control was subjectively reported at lower proportions (patients: CD, 15.0%; UC, 18.6%; clinicians: CD, 19.5%; UC, 25.0%). Numerically lower SIBDQ scores were observed with suboptimal control (CD, 43.0 ± 10.8; UC, 42.5 ± 12.0) than with optimal control (CD, 58.2 ± 7.2; UC, 57.8 ± 6.6).ConclusionsApproximately 50% (CD) and 40% (UC) of patients from real-world Canadian practices had suboptimal disease control based on STRIDE-II criteria. Suboptimal control was underestimated by patients and clinicians and accompanied by reduced QoL, suggesting further efforts to implement STRIDE-II treat-to-target strategies are needed.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound’s aggregation propensity.Graphical

Solubility of seven amino acids in two acetate ionic liquids from 298.15 K to 328.15 K

The number of studies on the solubilities of amino acids in pure ionic liquids is currently very scarce. Focusing on fundamental studies for the recovery of added-value compounds from fisheries and aquaculture discards, in this work the solubility of several amino acids in acetate ionic liquids and their mixtures with water were determined at temperatures ranging from 298.15 K to 328.15 K and atmospheric pressure. Sharing the acetate anion, solubilities were higher in the ionic liquid with 1-ethyl-3-methylimidazolium than with 1-hexyl-3-methylimidazolium cation. Increasing temperatures resulted in enhanced solubilisation. Solubilities in the ionic liquids ranked thus: Pro > OH-Pro > Lys·HCl > Ala > Gly > Cys, with an intermediate position of Arg (the amino acid most influenced by the hydrophobic environment and temperature). In the case of the mixtures with water, the ranking was Pro > Lys·HCl > Ala > Arg > OH-Pro > Gly > Cys. The van't Hoff equation adequately correlated experimental solubilities in the range of temperatures studied, and the process of dissolution was found endothermic and entropy-driven in all the cases. The ionic liquid 1-ethyl-3-methylimidazolium should be considered for the target application and the water content a design variable to analyse.

Let it stick: Strategies and applications for intracellular plasma membrane targeting of proteins in Saccharomyces cerevisiae.

Lipid binding domains and protein lipidations are essential features to recruit proteins to intracellular membranes, enabling them to function at specific sites within the cell. Membrane association can also be exploited to answer fundamental and applied research questions, from obtaining insights into the understanding of lipid metabolism to employing them for metabolic engineering to redirect fluxes. This review presents a broad catalog of membrane binding strategies focusing on the plasma membrane of Saccharomyces cerevisiae. Both lipid binding domains (pleckstrin homology, discoidin-type C2, kinase associated-1, basic-rich and bacterial phosphoinositide-binding domains) and co- and post-translational lipidations (prenylation, myristoylation and palmitoylation) are introduced as tools to target the plasma membrane. To provide a toolset of membrane targeting modules, respective candidates that facilitate plasma membrane targeting are showcased including their in vitro and in vivo properties. The relevance and versatility of plasma membrane targeting modules are further highlighted by presenting a selected set of use cases.

Systematic literature review based on the descriptive, bibliometric, and content analysis of renewable energy supply chain for a circular economy

Recently, the renewable energy supply chain has gained momentum as a vital component for clean energy, contributing to a sustainable future for the world. The development of the renewable energy supply chain is directly linked to circular economy targets and practices. This paper presents a systematic literature review of studies that analyze the roles, functions, and application objectives of the circular economy in the renewable energy supply chain. The aim of this review is to investigate key elements of renewable energy supply chains in the circular economy, shedding light on the state of research on the progress of sustainability in renewable energy supply chains, and guiding future research. Mainly, the systematic literature review demonstrates its findings by identifying and mapping the collated literature to reviews and applied studies that support and drive the renewable energy supply chain under the circular economy. This review paper is handled with a systematically descriptive, bibliometric, and content analysis hybrid approach, framed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology on the research topic. Findings show that circular economy applications have gained momentum in the renewable energy supply chain; nonetheless, some deficiencies persist. Redesign-based applications and design for recycling concepts encompass a significant gap in the renewable energy supply chain. Moreover, the current literature lacks outcomes on the applicability of circular economy 10R targets and the interrelationship and scalability of sustainable development goals in the renewable energy supply chain.

Aldehyde Dehydrogenases as Promising Targets for Treating Toxic Alde-hyde-related Diseases.

<p>Background: Mammals are exposed to various endogenous and exogenous aldehydes, and aldehyde dehydrogenases (ALDHs) function to metabolize these aldehydes to acids to counteract aldehyde over-load. ALDHs therefore play important roles in a series of physiological and pathophysiological process-es. ALDHs activators and inhibitors are not only important probes for exploring ALDHs functions, but promising for the treatment of toxic aldehyde-related diseases. </p> <p> Methods: This review comprehensively summarized the categories and characteristics of 19 human ALDHs, elaborated their related biological pathways, such as alcohol metabolism, retinoic acid (RA) production, neurotransmitter metabolism, etc. Especially, reported ALDHs activators and inhibitors were summarized by listing their target, inhibition form and clinical application. </p> <p> Results: On the one hand, summarization of the types and relative functions is useful for further re-search on aldehyde metabolic pathways and related diseases. On the other hand, review of existing acti-vators and inhibitors of ALDHs contributes to discovering new leading compounds and provide new insights. </p> <p> Conclusion: In consideration of the important role ALDH plays in toxic aldehyde-related diseases, ALDHs are promising targets for the treatment of toxic aldehyde-related diseases, and advocate more research efforts to explore their pathophysiology and to develop new regulators. </p>.

Bisection Neural Network Toward Reconfigurable Hardware Implementation.

A hardware-friendly bisection neural network (BNN) topology is proposed in this work for approximately implementing massive pieces of complex functions in arbitrary on-chip configurations. Instead of the conventional reconfigurable fully connected neural network (FC-NN) circuit topology, the proposed hardware-friendly topology performs NN behaviors in a bisection structure, in which each neuron includes two constant synapse connections for both inputs and outputs. Compared with the FC-NN one, the reconfiguration of the BNN circuit topology eliminates the remarkable amount of dummy synapse connections in hardware. As the main target application, this work aims at building a general-purpose BNN circuit topology that offers a great amount of NN regressions. To achieve this target, we prove that the NN behaviors of the FC-NN circuit topologies can be migrated to the BNN circuit topologies equivalently. We introduce two approaches including the refining training algorithm and the inverted-pyramidal strategy to further reduce the number of neurons and synapses. Finally, we conduct the inaccuracy tolerance analysis to suggest the guideline for ultra-efficient hardware implementations. Compared with the state-of-the-art FC-NN circuit topology-based TrueNorth baseline, the proposed design can achieve 17.8- 22.2× hardware reduction and less than 1% inaccuracy.

Ab initio guided atomistic modelling of nanomaterials on exascale high-performance computing platforms

The continuous development of increasingly powerful supercomputers makes theory-guided discoveries in materials and molecular sciences more achievable than ever before. On this ground, the incoming arrival of exascale supercomputers (running over 1018 floating point operations per second) is a key milestone that will tremendously increase the capabilities of high-performance computing (HPC). The deployment of these massive platforms will enable continuous improvements in the accuracy and scalability of ab initio codes for materials simulation. Moreover, the recent progress in advanced experimental synthesis and characterisation methods with atomic precision has led ab initio-based materials modelling and experimental methods to a convergence in terms of system sizes. This makes it possible to mimic full-scale systems in silico almost without the requirement of experimental inputs. This article provides a perspective on how computational materials science will be further empowered by the recent arrival of exascale HPC, going alongside a mini-review on the state-of-the-art of HPC-aided materials research. Possible challenges related to the efficient use of increasingly larger and heterogeneous platforms are commented on, highlighting the importance of the co-design cycle. Also, some illustrative examples of materials for target applications, which could be investigated in detail in the coming years based on a rational nanoscale design in a bottom-up fashion, are summarised.

Investigating the Impact of Final Finishes on the Insertion Loss in As Received and After Aging

The task of the final finish on the printed circuit board is to protect the copper surface and at the same time keep it active for the subsequent assembly steps. In high frequency applications, additionally the impact on the signal loss becomes of interest. Typically, the choice of the final finish is defined by the target application. The demands on solderability and bondability need to be considered and the acceptable price level needs to be defined. For the use in high frequency applications the signal integrity becomes of interest and adds on the reliability criteria such as solder joint reliability or corrosion resistance. In this study various surface finishes were tested towards their performance on insertion loss at high frequency. In contrast to earlier studies the frequency range was investigated up to 150 GHz and the impact of thermal or humid aging of the final finish was evaluated. Overall, seven different finishes have been selected including EPAG (Electroless palladium/Autocatalytic gold), I-Sn (Immersion tin), I-Ag (immersion silver), DIG (direct immersion gold), Au/Pd/Au (Gold/Palladium/Gold), ENIG (Electroless Nickel/Immersion Gold) with high and medium phosphorous Nickel and ENEPIG (Electroless Nickel/Electroless Palladium/Immersion Gold). The paper compiles the data for insertion loss versus frequency for the different final finishes at layer thicknesses as commonly used in the PCB industry. The results in the insertion loss performance are correlated to other properties of the different final finishes such as reliability and assembly performance to identify the best candidates for high frequency applications.

Impact of Ionic Strength (Sodium Chloride Concentration) on Homopolymerization and Copolymerization Kinetics of Acrylamide and 2‐Acrylamido‐2‐Methylpropane Sulfonic Acid

AbstractAnionic polyelectrolytes can be used for a variety of applications, including flocculation and enhanced oil recovery. While it is widely recognized that polyelectrolyte synthesis is impacted by the pre‐polymer formulation and polymerization conditions, the specific relationships between these factors and the subsequent polymer properties are not well understood. Therefore, the current work intends to improve understanding of ionic strength (IS) effects during the copolymerization of 2‐acrylamido‐2‐methylpropane sulfonic acid (AMPS) and acrylamide (AAm). The aims of the study are i) to use in situ H1 NMR to study copolymerization kinetics, and ii) to determine how increasing IS impacts copolymerization kinetics (and, by extension copolymer microstructure). It is found that altering IS prior to copolymerization has significant impacts on the reactivity ratios, and therefore impacts the microstructure through multiple mechanisms. Increasing IS causes a crowding effect, where the propagating chain develops a random coil conformation and causes steric hindrance of the large AMPS monomer, decreasing the likelihood of AMPS propagation. When the IS is increased further, the ionic shielding effect is more impactful, increasing the likelihood of AMPS propagation. Ultimately, this work will make it possible to manipulate IS to synthesize AMPS/AAm copolymers with specific desirable properties for target applications.

In vitro and in vivo modeling systems of supratentorial ependymomas.

Ependymomas are rare brain tumors that can occur in both children and adults. Subdivided by the tumors' initial location, ependymomas develop in the central nervous system in the supratentorial or infratentorial/posterior fossa region, or the spinal cord. Supratentorial ependymomas (ST-EPNs) are predominantly characterized by common driver gene fusions such as ZFTA and YAP1 fusions. Some variants of ST-EPNs carry a high overall survival rate. In poorly responding ST-EPN variants, high levels of inter- and intratumoral heterogeneity, limited therapeutic strategies, and tumor recurrence are among the reasons for poor patient outcomes with other ST-EPN subtypes. Thus, modeling these molecular profiles is key in further studying tumorigenesis. Due to the scarcity of patient samples, the development of preclinical in vitro and in vivo models that recapitulate patient tumors is imperative when testing therapeutic approaches for this rare cancer. In this review, we will survey ST-EPN modeling systems, addressing the strengths and limitations, application for therapeutic targeting, and current literature findings.

Iterative dynamics-based mesh discretisation for multi-scale coastal ocean modelling

Flow in coastal waters contains multi-scale flow features that are generated by flow separation, shear-layer instabilities, bottom roughness and topographic form. Depending on the target application, the mesh design used for coastal ocean modelling needs to adequately resolve flow features pertinent to the study objectives. We investigate an iterative mesh design strategy, inspired by hydrokinetic resource assessment, that uses modelled dynamics to refine the mesh across key flow features, and a target number of elements to constrain mesh density. The method is solver-agnostic. Any quantity derived from the model output can be used to set the mesh density constraint. To illustrate and assess the method, we consider the cases of steady and transient flow past the same idealised headland, providing dynamic responses that are pertinent to multi-scale ocean modelling. This study demonstrates the capability of an iterative approach to define a mesh density that concentrates mesh resolution across areas of interest dependent on model forcing, leading to improved predictive skill. Multiple design quantities can be combined to construct the mesh density, refinement can be applied to multiple regions across the model domain, and convergence can be managed through the number of degrees of freedom set by the target number of mesh elements. To apply the method optimally, an understanding of the processes being model is required when selecting and combining the design quantities. We discuss opportunities and challenges for robustly establishing model resolution in multi-scale coastal ocean models.

Field Sprayer with Application Rate Control Using Fast Response Proportional Valves under Variable Speed Conditions

In modern agriculture, which is characterised by dynamic field environments, challenges are faced in maintaining consistent application rates due to varying tractor speeds, field conditions, and certain calibration errors. Conventional control systems, which rely on slower valves, have difficulty adapting to these dynamic field conditions. By contrast, the integration of fast-acting proportional valves improves the precision and flexibility of flow rate adjustment during spraying applications. This research focused on evaluating the accuracy of spraying applications under different tractor speed conditions through field experiments and data analysis. This study involves a field sprayer with boom wings divided into right and left sections, where the flow rate of the liquid to each section is controlled by proportional valves with a 3 s full opening and closing time, dependent on speed information. Using a closed-loop control system consisting of a flow meter, proportional valve, and PLC, the valves are controlled by the PLC’s internal PID blocks. Observations reveal that as the tractor speed increases to a certain level, the system effectively adjusts the application rate close to the target value and maintains control against the changing ground speed during all field tests. The study included five different application tests, with target application rates of 100, 150, 200, 250, and 300 L ha−1, with each repeated three times, resulting in a total of 15 field tests at different ground speeds. During these tests, the data were meticulously recorded every second, covering the tractor speed, flow rate, and pressure values for both right and left boom sections, along with regulator pressure, proportional valve opening rates, and application rates. The durations for each application rate were documented alongside instances within specified periods where error boundaries of ±10% were exceeded. During the total test duration of 9734 s, the actual application rate value exceeded error boundaries during only 209 s. Within the application durations, the speed variation intervals ranged from 5.10 to 10.23 km h−1, 4.64 to 9.91 km h−1, 3.68 to 7.89 km h−1, 4.80 to 8.21 km h−1, and from 4.90 to 8.69 km h−1. The absolute percentage mean application errors were recorded as 2.81%, 2.68%, 2.28%, 2.14%, and 2.51% for respective application rates. Furthermore, statistically significant correlations (p &lt; 0.01) were identified among the variables (speed, valve opening rate, flow rate, pressure) in both the right and left boom sections across all application rates.

ZnO Decorated Graphene-Based NFC Tag for Personal NO2 Exposure Monitoring during a Workday.

This paper presents the integration of a sensing layer over interdigitated electrodes and an electronic circuit on the same flexible printed circuit board. This integration provides an effective technique to use this design as a wearable gas measuring system in a target application, exhibiting high performance, low power consumption, and being lightweight for on-site monitoring. The wearable system proves the concept of using an NFC tag combined with a chemoresistive gas sensor as a cumulative gas sensor, having the possibility of holding the data for a working day, and completely capturing the exposure of a person to NO2 concentrations. Three different types of sensors were tested, depositing the sensing layers on gold electrodes over Kapton substrate: bare graphene, graphene decorated with 5 wt.% zinc oxide nanoflowers, or nanopillars. The deposited layers were characterized using FESEM, EDX, XRD, and Raman spectroscopy to determine their crystalline structure, morphological and chemical compositions. The gas sensing performance of the sensors was analyzed against NO2 (dry and humid conditions) and other interfering species (dry conditions) to check their sensitivity and selectivity. The resultant-built wearable NFC tag system accumulates the data in a non-volatile memory every minute and has an average low power consumption of 24.9 µW in dynamic operation. Also, it can be easily attached to a work vest.

Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.

Spiking neural network simulations are a central tool in Computational Neuroscience, Artificial Intelligence, and Neuromorphic Engineering research. A broad range of simulators and software frameworks for such simulations exist with different target application areas. Among these, PymoNNto is a recent Python-based toolbox for spiking neural network simulations that emphasizes the embedding of custom code in a modular and flexible way. While PymoNNto already supports GPU implementations, its backend relies on NumPy operations. Here we introduce PymoNNtorch, which is natively implemented with PyTorch while retaining PymoNNto's modular design. Furthermore, we demonstrate how changes to the implementations of common network operations in combination with PymoNNtorch's native GPU support can offer speed-up over conventional simulators like NEST, ANNarchy, and Brian 2 in certain situations. Overall, we show how PymoNNto's modular and flexible design in combination with PymoNNtorch's GPU acceleration and optimized indexing operations facilitate research and development of spiking neural networks in the Python programming language.