High Throughput Research Articles

Automated production of nerve repair constructs containing endothelial cell tube-like structures.

Engineered neural tissue (EngNT) is a stabilised aligned cellular hydrogel that offers a potential alternative to the nerve autograft for the treatment of severe peripheral nerve injury. This work aimed to automate the production of EngNT, to improve feasibility of scalable manufacture for clinical translation. Endothelial cells were used as the cellular component, with the formation of endothelial cell tube-like structures mimicking the polarised vascular structures formed early on in the natural regenerative process. Gel aspiration-ejection (GAE) for the production of EngNT was automated by integrating a syringe pump with a robotic positioning system, using software coded in Python to control both devices. Having established the production method and tested mechanical properties, the EngNT containing human umbilical vein endothelial cells (EngNT-HUVEC) was characterised in terms of viability and alignment, compatibility with neurite outgrowth from rat dorsal root ganglia and formation of endothelial cell networks in vitro. EngNT-HUVEC manufactured using the automated system contained viable and aligned endothelial cells with a network of multinucleated, endothelial cell tube-like structures inside the constructs and an outer layer of endothelialisation. The EngNT-HUVEC constructs could be made in various sizes within minutes and provided support and guidance to regenerating neurites in vitro. This work automated the formation of EngNT, facilitating high throughput manufacture at scale. The formation of endothelial cell tube-like structures within stabilised hydrogels provides an engineered tissue with potential for use in nerve repair.

Targeting GPR39 in structure-based drug discovery reduces Ang II-induced hypertension.

The endothelium-dependent vascular injury, a primary pathological feature of angiotensin II (Ang II)-induced hypertension. This study aimed to explore the role and underlying mechanisms of G protein-coupled receptor 39 (GPR39) in the pathogenesis of Ang II-induced hypertension. For in vivo studies, GPR39 knockout (KO) mice (C57BL/6 J, male) were generated and administered Ang II for 4 weeks. GPR39 expression was upregulated in the aorta of hypertensive patients and mice. The ablation of GPR39 mitigated vascular fibrosis, augmented endothelium-dependent vasodilation, and inhibited endothelial inflammation, oxidative stress, and apoptosis in mice. Additionally, GPR39 KO decreased NOD-like receptor protein 3 (Nlrp3) gene expression in Ang II-stimulated endothelial cells. Notably, Nlrp3 activation counteracted the therapeutic benefits of GPR39 KO. We identified the potential ligand of GPR39 using structure-based high throughput virtual screening (HTVS) and validated its antihypertensive function in vitro and in vivo. The small molecule ligand Z1780628919 of GPR39 can also reduce Ang II-induced hypertension and improve vascular function. GPR39 KO and the small molecule ligand Z1780628919 potentially downregulates Nlrp3, thereby mitigating vascular fibrosis, endothelial inflammation, oxidative stress, and apoptosis. This effect contributes to the alleviation of Ang II-induced hypertension and the rectification of vascular dysfunctions. These findings suggest new avenues for therapeutic intervention.

Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach

Abstract This study explored the application of black box machine learning (ML) to build high throughput models that predict the creep response of Ni-based Alloy 617. Black box ML refers to highly complex machine learning algorithms that generate outputs from inputs without an interpretable internal process. The rapid implementation of suitable heat of alloys into targeted service is impeded by the extended qualification process involving chemistry-processing-structure-performance assessment. The ASME B&PV Code III outlines the requirement of 10,000 h of creep testing before each heat can be qualified for service and 30,000 h for heats that exhibit metastable phases. There is a critical need to shorten the development-to-deployment timeline for heats of an alloy at specific applications. Recent advancement in ML offers the ability to identify correlations in data which is difficult to elucidate by other approaches. To that end, black box ML is employed to expedite the HEAT qualification of Alloy 617 and predict performance from HEAT chemistry out to an unprecedented timescale. In this study, creep data for Ni-based Alloy 617—a solid solution strengthened material is gathered from a wide range of data sources. The alloy chemistry, phases, precipitates, and microstructural features are analyzed to obtain the key strengthening mechanism. Service conditions, mechanical properties, chemistry, and chemical ratios are provided as potential input features. The Pearson correlation coefficient with a 95% confidence bound is employed for input feature screening where poorly correlated inputs are eliminated to speed up the ML process and prevent under- and/or over-fitting of predictions. In the ML algorithm, the selected input features are regarded as predictors, and rupture is regarded as the response. An algorithm evaluation is performed where 20 ML algorithms are trained with the training set. The three-layered neural network (NN) was observed to be the best algorithm for the given data based on statistical rationale. The NN accurately predicts rupture across a range of isotherms and data sources. The interpolative and extrapolative predictions are in compliance with ECCC V5 guidelines. A post-audit validation exhibits neither under- nor over-fitting and confirms the applicability of NN algorithms to unseen data. The black box ML provides a pathway to predict the performance directly from chemistry and opens avenues to rapid heat qualification.

Explainable artificial intelligence for machine learning prediction of bandgap energies

The bandgap is an inherent property of semiconductors and insulators, significantly influencing their electrical and optical characteristics. However, theoretical calculations using the density functional theory (DFT) are time-consuming and underestimate bandgaps. Machine learning offers a promising approach for predicting bandgaps with high precision and high throughput, but its models face the difficulty of being hard to interpret. Hence, an application of explainable artificial intelligence techniques to the bandgap prediction models is necessary to enhance the model's explainability. In our study, we analyzed the support vector regression, gradient boosting regression, and random forest regression models for reproducing the experimental and DFT bandgaps using the permutation feature importance (PFI), the partial dependence plot (PDP), the individual conditional expectation plot, and the accumulated local effects plot. Through PFI, we identified that the average number of electrons forming covalent bonds and the average mass density of the elements within compounds are particularly important features for bandgap prediction models. Furthermore, PDP visualized the dependency relationship between the characteristics of the constituent elements of compounds and the bandgap. Particularly, we revealed that there is a dependency where the bandgap decreases as the average mass density of the elements of compounds increases. This result was then theoretically interpreted based on the atomic structure. These findings provide crucial guidance for selecting promising descriptors in developing high-precision and explainable bandgap prediction models. Furthermore, this research demonstrates the utility of explainable artificial intelligence methods in the efficient exploration of potential inorganic semiconductor materials.

Microfluidics for foodborne bacteria analysis: Moving toward multiple technologies integration.

Food security related to bacterial pathogens has seriously threatened human life and caused public health problems. Most of the reported methods are targeted at known major pathogens commonly found in food samples, but to some extent, they have the disadvantage of lacking simplicity, speed, high throughput, and high sensitivity. Microfluidics has become a promising tool for foodborne bacteria analysis and addresses the above limitations. In this perspective, we briefly discussed the ongoing research and development in this field. We outline the major types of microfluidics, the strategies of target biorecognition, and signal amplification technologies in the microfluidic system for the foodborne bacteria analysis. We also proposed the future directions of microfluidics for foodborne bacterial analysis, which aims to integrate multiple technologies toward intelligent analysis with high selectivity and sensitivity for unknown samples, multiple bacterial detection, and simultaneous detection of multiple food pollutants.

A Data Driven Black Box Approach for the Inverse Quantification of Set-Theoretical Uncertainty

Abstract Inverse uncertainty quantification commonly uses the well established Bayesian framework. Recently, alternative interval methodologies have been introduced. However, in their current state of the art implementation, both techniques suffer from a large and usually unpredictable computational effort. Thus, both techniques are not applicable in a real-time context. To achieve a low-cost, real-time solution to this inverse problem, we introduce a deep-learning framework consisting of unsupervised auto-encoders and a shallow neural network. This framework is trained by means of a numerically generated dataset that captures typical relations between the model parameters and selected measured system responses. The performance and efficacy of the technique is illustrated using two distinct case studies. The first case involves the DLR AIRMOD, a benchmark case that has served as reference case for the inverse uncertainty quantification problem. The results demonstrate that the achieved accuracy is on par with the existing interval method found in literature, while requiring only a fraction of its computational resources. The second case study examines a resistance pressure welding process, which is known to require extremely fast monitoring and control due to the high process throughput. Based on the proposed method, and with only a limited selection of simulated responses of the process, it is possible to identify the interval uncertainty of the crucial parameters of the process. The computational cost in this case makes it possible for an inverse uncertainty quantification in a real-time setting.

Urban Pigeons as Reservoirs of Critical Pathogens: Improved protocol for sequencing pigeon faeces in disease monitoring

The spread of pathogens by animals is a serious issue around the world that causes a severe threat to human health. Feral pigeons (Columba livia forma domestica) that live in urban areas are zoonotic carriers of various pathogens that can be transmitted to humans by faecal contamination. This study aimed to detect the presence of bacterial, viral, and specifically fungal pathogens in pigeon faeces based on the World Health Organization's (WHO) priority pathogen list published in 2022. Fresh faecal samples were collected at Uppsala, Central Station, and Svandammen, the pigeons' most relevant gathering spots and feeding sites. Genomic DNA was directly extracted from these samples, and ScilifeLab performed High throughput sequencing through Oxford Nanopore Technologies(ONT) (PromethION). Metagenomic analysis revealed that most of the critically prioritized viral and bacterial pathogens listed by WHO were present in pigeon faeces. Regarding fungal pathogens, which were the main objective of this study, samples from both studied locations contained all critical, high and, medium-important fungal pathogens published in the WHO list, such as Aspergillus fumigatus, Candida albicans, Candida auris, Cryptococcus neoformans, Nakaseomyces glabratus, Candida tropicalis, and Cryptococcus gattii. These fungal pathogens pose the risk of invasive fungal diseases and severe infections in low-immunity individuals and vulnerable populations. The findings indicate the importance of conducting further research to comprehensively understand potential exposure to feral pigeons. Furthermore, keeping pigeons away from sensitive areas, such as hospitals, and implementing measures to control pigeon populations can significantly decrease the spread of pathogens.

A bottom-up approach identifies the antipsychotic and antineoplastic trifluoperazine and the ribose derivative deoxytubercidin as novel microglial phagocytosis inhibitors.

Phagocytosis is an indispensable function of microglia, the brain professional phagocytes. Microglia is particularly efficient phagocytosing cells that undergo programmed cell death (apoptosis) in physiological conditions. However, mounting evidence suggests microglial phagocytosis dysfunction in multiple brain disorders. These observations prompted us to search for phagocytosis modulators (enhancers or inhibitors) with therapeutic potential. We used a bottom-up strategy that consisted on the identification of phagocytosis modulators using phenotypic high throughput screenings (HTSs) in cell culture and validation in organotypic cultures and in vivo. We performed two complementary HTS campagnes: at Achucarro, we used primary cultures of mouse microglia and compounds of the Prestwick Chemical Library; at Roche, we used human iPSC derived macrophage-like cells and a proprietary chemo-genomic library with 2200 compounds with known mechanism-of-action. Next, we validated the more robust compounds using hippocampal organotypic cultures and identified two phagocytosis inhibitors: trifluoperazine, a dopaminergic and adrenergic antagonist used as an antipsychotic and antineoplastic; and deoxytubercidin, a ribose derivative. Finally, we tested whether these compounds were able to modulate phagocytosis of apoptotic newborn cells in the adult hippocampal neurogenic niche in vivo by administering them into the mouse hippocampus using osmotic minipumps. We confirmed that both trifluoperazine and deoxytubercidin have anti-phagocytic activity in vivo, and validated our bottom-up strategy to identify novel phagocytosis modulators. These results show that chemical libraries with annotated mechanism of action are an starting point for the pharmacological modulation of microglia in drug discovery projects aiming at the therapeutic manipulation of phagocytosis in brain diseases.

How to Find a Fragment: Methods for Screening and Validation in Fragment-Based Drug Discovery.

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

Performance enhancement in blockchain based IoT data sharing using lightweight consensus algorithm

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

A Well‐Advanced High‐Throughput Test System for Electrocatalytic Screening Applications Under Industrial Relevant Conditions – A Perspective to Accelerate Electrolysis Research and Development

ABSTRACTElectrolysis is a dynamic research area in which both mature and new promising processes, such as alkaline water electrolysis and electrochemical CO2 reduction, are under enormous development pressure due to their high relevance for the energy sector. High‐throughput (HT) technologies are efficient screening platforms that can accelerate research activities and significantly shorten development times. Over the past 25 years, various HT platforms have found their way into electrochemical research. These typically have one or more major disadvantages: they are characterized by abstract experimental conditions, designed for a specific application or process, or generate insufficiently comparable data. In this publication, we present a newly developed HT test system that enables the parallel operation of 16 electrochemical bench‐scale flow cells under industry‐relevant test conditions. The specially developed modular flow cell can be operated variably in the fully automated system and allows research into the most common applications in electrochemistry for many different processes with a focus on all relevant variants of water electrolysis and electrochemical CO2 reduction. Both the HT system and the developed flow cell are designed to accelerate the generation of reliably reproducible data with high comparability in order to strengthen scientific exchange. The fully automated process control, online analysis and programmable feedback loops of the HT test system provide great potential for the design of experiment strategies. The implementation of Design of Experiment strategies will maximize the testing efficiency of this innovative research system.

Maximizing energy efficiency and system throughput using a self-adaptive penalty function with a whale optimization algorithm in social-aware device-to-device communications

Device-to-device (D2D) communication is an emerging approach for 5 G networks. It allows for high capacity, low latency, and increased bandwidth. These features have prompted several research studies to focus on meeting customers’ expectations. This research concentrates on obtaining optimal resource and power allocation, better quality of service, and reliable connectivity between D2D clients through a whale optimization algorithm (WOA). The WOA possesses greater stability, faster integration speeds, a powerful global search ability, and greater accuracy than traditional methods. The WOA utilizes an unconstrained objective function obtained through a self-adaptive penalty function. This unconstrained objective function yields optimal solutions for the resource as well as power allocation, higher throughputs, and energy and spectral efficiencies in D2D communications. For D2D communications, the energy efficiency of the WOA is 47.12 % higher than a genetic algorithm (GA) and 70.33 % higher than particle swarm optimization (PSO), and it yields high spectral efficiency and system throughput. Obviously, the WOA in this proposal is more efficient than a GA or PSO and shows the advantages of combining physical and social parameters for improved wireless network performance.

UAVs Revolutionizing Efficiency: Integrating DRL and Random Walrus for Enhanced Energy and Spectral Resource Management

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

Digital PCR in Virology: Current Applications and Future Perspectives.

Digital PCR (dPCR) has been used in the field of virology since its inception. Technological innovations in microfluidics more than a decade ago caused a sharp increase in its use. There is an emerging consensus that dPCR now outperforms quantitative PCR (qPCR) in the basic parameters such as precision, sensitivity, accuracy, repeatability and resistance to inhibitors. These strengths have led to several current applications in quantification, mutation detection and environmental DNA and RNA samples. In high throughput scenarios, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the cost and throughput still significantly hampered the adaption of dPCR. There is much unexplored potential within the multiplexing capabilities of dPCR. This will allow simultaneous multi-target quantification and can also partially alleviate the throughput and cost drawback. In this review, we discuss the strengths and weaknesses of dPCR with a focus on virology applications and we discuss future applications. Finally, we discuss recent evolutions of the technology in the form of real-time dPCR and digital high-resolution melting.

Moving objects detection based on histogram of oriented gradient algorithm chip for hazy environment

<span>The most important aspects of computer vision are moving object detection (MOD) and tracking. Many signal-processing applications use regional image statistics. Compute-intensive video and image processing with low latency and high throughput is done with field programmable gate array (FPGA) image processing. Local image statistics are used for edge identification and filtering. The histogram of oriented gradients (HoG) algorithm extracts local shape characteristics by equalizing histograms. The objective of the work is to design the hardware chip of the algorithm and perform the simulation in the Xilinx ISE 14.7 simulation environment. The performance of the chip is evaluated in Modelsim 10.0 simulation software to check its feasibility. The performance of the chip design is estimated on Viretx-5 FPGA and compared with the MATLAB-2020 image processing tool-based response time. This form of tracking typically deals with identifying, anchoring, and tracking images and videos. A mask made from a cut-out of the object can then determine the plane's coordinates depending on its position. This type of object tracking is frequently utilized in the field of augmented reality (AR). The algorithm is most suited for object detection using hardware controllers in haze and foggy environments.</span>

The development of a novel high-throughput membrane potential assay and a solid-supported membrane (SSM)-based electrophysiological assay to study the pharmacological inhibition of GLUT9/SLC2A9 isoforms in a drug discovery program

GLUT9/SLC2A9 is a urate transporter and takes a fundamental role in the maintenance of normal serum urate levels. GLUT9 is the sole transporter of reabsorbed urate from renal epithelial cells to blood, thus making it an ideal pharmacological target for the development of urate-lowering drugs. None of the three currently available assays for studying GLUT9 pharmacological inhibition can support a high throughput drug discovery screening campaign. In this manuscript we present two novel assay technologies which can be used in a drug discovery screening cascade for GLUT9: a GLUT9 membrane potential assay for primary screening; and a solid-supported membrane (SSM)-based supported electrophysiological assay for secondary screening.

Development of a Spectrophotometric Flow-Based System for the Determination of Total Polyphenol Content in Legume Flours.

Conventional nutritional characterization of legume flours comprises costly and laborious analytical methods for nutrient quantification that establish the quality of seeds, including their macronutrient quantification, fiber, mineral, antioxidant content, and more. The quantification of total polyphenol content (TPC) in legumes is performed using different analytic methods, namely the Folin-Ciocalteu (FC) method, which is lengthy and employs potentially toxic reagents. Additionally, it is time-consuming and prone to human error if carried out in a conventional batch mode. To develop a semi-automatic method for a faster, greener, and more precise TPC quantification, resorting to flow injection analysis (FIA). The development of the FIA method was based on the FC method. The flow manifold was structured by performing an array of preliminary studies, which led to the establishment of the method that displayed the highest sensitivity (highest slope of the calibration curve). The method was applied to determine the TPC in various legume flours. This novel method resulted in a throughput of 1 analysis per minute, allowing to analyze 20 samples in triplicate within an hour, with a LOD of 4.32 mg L-1, a LOQ of 14.4 mg L-1, and an RSD of 4.4%. The results calculated from the proposed FIA method agreed with those of the reference procedure. By using the FIA system, a lower consumption of reagents was observed. Additionally, as there is no need to reach chemical equilibrium, a high throughput has been achieved, resulting in a faster and greener method for the determination of polyphenols in various types of legumes.

Targeting MurG Enzyme in Klebsiella pneumoniae: An In Silico Approach to Novel Antimicrobial Discovery

Antibiotic resistance poses a global crisis fuelled by widespread antibiotic use, particularly against Gram-negative bacteria like Klebsiella pneumoniae, a leading cause of hospital-acquired infections with high mortality rates. Urgent identification of effective drug targets is imperative, with a focus on metabolic pathways to inhibit bacterial growth. Targeting the crucial metabolic pathways of K. pneumoniae would be a more efficient way to prevent its growth and the diseases that it causes. The present study focused on inhibiting the UDP-N-acetylglucosamine--N-acetylmuramyl-(pentapeptide)pyrophosphoryl-undecaprenol N-acetylglucosamine transferase (MurG) enzyme, which is a key enzyme in peptidoglycan synthesis pathway. A high throughput virtual screening was used to find possible lead molecules from Enamine -High-Throughput Screening Centre library. The resulting high binding affinity ligands were further assessed for their drug-likeness and other pharmacokinetic properties. Based on these analyses, the three ligands Z95813755_1, Z324718246_1 and Z324718246_2 were selected for further molecular dynamic simulation studies. The molecular dynamic simulation results and MM/PBSA analysis predicted that both Z95813755_1 and Z324718246_2, molecules show higher affinity towards MurG. For the first time we are reporting potential inhibitors against MurG from K. pneumoniae, providing new insights in management of multi drug resistant K. pneumoniae infections.

CaviPlasma: parametric study of discharge parameters of high-throughput water plasma treatment technology in glow-like discharge regime

Abstract AC discharge in a dense hydrodynamic cavitation cloud in water, called CaviPlasma, has been studied at different discharge parameters. CaviPlasma stands for cavitation and plasma, which are two coupled basic phenomena of the novel technology enabling very high throughput of plasma water processing compared to other current technologies. In this article, the diagnostics and the properties of CaviPlasma discharge are discussed based on optical and electric characterization of the discharge phenomena together with the physico-chemical characterization of the plasma-treated water. The so-called unbridged mode of CaviPlasma operation is described, where the discharge propagates from a metal electrode towards a liquid electrode at the collapsing end of the cavitation cloud. The production of H, O and OH species in the discharge was proven by optical emission spectroscopy. The formation of hydrogen peroxide (H2O2) in water was determined by chemical methods. The energy yield for H2O2 generation is as high as 9.6 g kWh−1 and the generation rate is up to 2.4 g h−1. The degradation of phenol admixture in water was also studied. The article covers a parametric study enabling the development of tailored applications.

Piezo-traces of ferroelectrics and multiferroics of BiFeO3 -embedded substrate-free Ti3C2Tx MXene films for high throughput data storage application

Piezo-traces of ferroelectrics and multiferroics of BiFeO3 -embedded substrate-free Ti3C2Tx MXene films for high throughput data storage application