bluuebunny/crossref_metadata_2025 huggingface dataset:
Communication-Efficient Federated Learning with Adaptive Consensus ADMM
Authors: He Siyi , Zheng Jiali , Feng Minyu , Chen Yixin
Published: April 2023

This paper proposes utilizing federated learning (FL), a distributed learning paradigm, to process large, decentralized, and heterogeneous edge data in the context of Internet of Things (IoT) devices. However, heterogeneity and high communication costs are two primary challenges that hinder the efficacy of federated learning. To overcome these challenges, we have designed an algorithm, FedACADMM, which applies the adaptive consensus alternating direction method of multipliers (ACADMM) to federated learning clients (i.e., the edge mobile devices) to tackle the heterogeneity problem in federated networks. Importantly, the cost per round of communication for FedACADMM remains consistent with FedAvg and FedProx without adding any extra workload. Furthermore, our experimental results demonstrate that FedACADMM outperforms baseline methods with a realistic set of federated datasets, displaying enhanced convergence robustness. Notably, in highly heterogeneous scenarios, FedACADMM exhibits significant stability and rapid convergence, requiring at least 75% fewer communication rounds to achieve the target accuracy.
A Deep Reinforcement Learning Approach for Efficient, Safe and Comfortable Driving
Authors: Selvaraj Dinesh Cyril , Hegde Shailesh , Amati Nicola , Deflorio Francesco , Chiasserini Carla Fabiana
Published: April 2023

Sensing, computing, and communication advancements allow vehicles to generate and collect massive amounts of data on their state and surroundings. Such richness of information fosters data-driven decision-making model development that considers the vehicle’s environmental context. We propose a data-centric application of Adaptive Cruise Control employing Deep Reinforcement Learning (DRL). Our DRL approach considers multiple objectives, including safety, passengers’ comfort, and efficient road capacity usage. We compare the proposed framework’s performance to traditional ACC approaches by incorporating such schemes into the CoMoVe framework, which realistically models communication, traffic, and vehicle dynamics. Our solution offers excellent performance concerning stability, comfort, and efficient traffic flow in diverse real-world driving conditions. Notably, our DRL scheme can meet the desired values of road usage efficiency most of the time during the lead vehicle’s speed-variation phases, with less than 40% surpassing the desirable headway. In contrast, its alternatives increase headway during such transient phases, exceeding the desired range 85% of the time, thus degrading performance by over 300% and potentially contributing to traffic instability. Furthermore, our results emphasize the importance of vehicle connectivity in collecting more data to enhance the ACC’s performance.
A Phishing-Attack-Detection Model Using Natural Language Processing and Deep Learning
Authors: Benavides-Astudillo Eduardo , Fuertes Walter , Sanchez-Gordon Sandra , Nuñez-Agurto Daniel , Rodríguez-Galán Germán
Published: April 2023

Phishing is a type of cyber-attack that aims to deceive users, usually using fraudulent web pages that appear legitimate. Currently, one of the most-common ways to detect these phishing pages according to their content is by entering words non-sequentially into Deep Learning (DL) algorithms, i.e., regardless of the order in which they have entered the algorithms. However, this approach causes the intrinsic richness of the relationship between words to be lost. In the field of cyber-security, the innovation of this study is to propose a model that detects phishing attacks based on the text of suspicious web pages and not on URL addresses, using Natural Language Processing (NLP) and DL algorithms. We used the Keras Embedding Layer with Global Vectors for Word Representation (GloVe) to exploit the web page content’s semantic and syntactic features. We first performed an analysis using NLP and Word Embedding, and then, these data were introduced into a DL algorithm. In addition, to assess which DL algorithm works best, we evaluated four alternative algorithms: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). As a result, it can be concluded that the proposed model is promising because the mean accuracy achieved by each of the four DL algorithms was at least 96.7%, while the best performer was BiGRU with 97.39%.
A Novel Classification Method Using the Takagi–Sugeno Model and a Type-2 Fuzzy Rule Induction Approach
Authors: Tabakov Martin , Chlopowiec Adrian B. , Chlopowiec Adam R.
Published: April 2023

The main purpose of this research was to introduce a classification method, which combines a rule induction procedure with the Takagi–Sugeno inference model. This proposal is a continuation of our previous research, in which a classification process based on interval type-2 fuzzy rule induction was introduced. The research goal was to verify if the Mamdani fuzzy inference used in our previous research could be replaced with the first-order Takagi–Sugeno inference system. In the both cases to induce fuzzy rules, a new concept of a fuzzy information system was defined in order to deal with interval type-2 fuzzy sets. Additionally, the introduced rule induction assumes an optimization procedure concerning the footprint of uncertainty of the considered type-2 fuzzy sets. A key point in the concept proposed is the generalization of the fuzzy information systems’ attribute information to handle uncertainty, which occurs in real data. For experimental purposes, the classification method was tested on different classification benchmark data and very promising results were achieved. For the data sets: Breast Cancer Data, Breast Cancer Wisconsin, Data Banknote Authentication, HTRU 2 and Ionosphere, the following F-scores were achieved, respectively: 97.6%, 96%, 100%, 87.8%, and 89.4%. The results proved the possibility of applying the Takagi–Sugeno model in the classification concept. The model parameters were optimized using an evolutionary strategy.
Intelligent Assembly Method of the Profiled Thermal Battery Pack Based on Improved DE Algorithm
Authors: Zhou Yingyu , He Ling , Zhong Jiangchuan , Liu Dan
Published: April 2023

An intelligent assembly method was designed to realize the intelligent assembly of the profiled thermal battery pack and improve its assembly accuracy. Firstly, as the number and size of different monomer batteries vary, this paper takes the monomer thermal battery assembly as the object, with a common shape circle assembly screw arrangement and an established process model. Then, the assembly also has an improved differential evolution algorithm for assembly arrangement and process on the number, location, tightening of the screw assembly, torque, and the order of solutions. According to this scheme, the assembly and the flatness test were carried out. The results showed that the bottom plate of the assembly frame was “concave in the middle and warped around”, and the flatness error was large. The scheme was optimized by numerical simulation analysis. After optimization, the average offset of the floor plane was 0.04 mm, and the offset accounted for 0.028% of the overall height; the maximum offset was 0.094 mm and the offset was reduced by 0.312%.
Multi-Source HRRP Target Fusion Recognition Based on Time-Step Correlation
Authors: Lu Jianbin , Yue Zhibin , Wan Lu
Published: April 2023

Aiming at the limitations of a single High Resolution Range Profile (HRRP) in recognition, this paper proposes a Time step Correlation-based Feature Fusion (TCFF) method. This method calculates the covariance matrix at the time step of the two features extracted by the two channels, and assigns different weights to the time step according to the strength of the covariance correlation for feature fusion. The experimental results on the simulated ship target HRRP dataset show that the feature fusion method can achieve better recognition performance than the single channel model. Compared with simple feature fusion such as element addition and element contacting, it can also achieve better recognition results.
Experimental Study on the Dip Angle Effect of Layered Cemented Fillers: A Sandblasting Treatment Applied to Mine Filling
Authors: Hai Long , Bao Rongtao
Published: April 2023

In order to study the effect of interlayer dip angle on the mechanical properties of tail sand cemented filler and to improve the deterioration of the mechanical properties of the filler caused by delamination, this study introduces the sandblasting method for treating the interface between old and new concretes in concrete science. Two types of delamination, natural delamination and sandblasting delamination, were produced for the tailing sand cemented filler specimens, and the interlayer dip angles were set to 0°, 10°, 20°, and 30°, respectively. Uniaxial compression tests were conducted at maintenance ages of 3 d, 7 d, 14 d, and 28 d, and damage models were established. The results show that (1) the uniaxial compressive strength and modulus of elasticity of the naturally delaminated specimens decrease and then increase with the increase of interlayer dip angle, and reach the lowest at the interlayer dip angle of about 20°. The uniaxial compressive strength and modulus of elasticity of sandblasted delaminated specimens with interlayer dip angle, not more than 20°, can bring positive gains in uniaxial compressive strength and modulus of elasticity, and the stable gains at high age can reach 6.4% and 39.7%, respectively. (2) The post-peak ductility of the two-layered types of specimens increases and then decreases with the increase of the dip angle between layers and reaches the optimum ductility at about 20° of dip angle. (3) The damage of the delaminated filling body is mainly in the form of tensile damage and conjugate shear damage, and the sandblasting treatment can improve the force transmission mode on the delamination surface. (4) Based on the damage evolution of the soil body with Weibull distribution, the damage instantiation model of the layered colluvial filler is constructed, which has high reliability for different interlayer dip angles. Accordingly, the findings of this study demonstrate that the addition of sandblasting can significantly reduce the degradation of the mechanical properties of the tailing sand cemented fill brought on by delamination at a slow dip angle (below 20°), and offer a helpful damage ontology model for use in engineering practice.
Influence of Rainfall Intensity and Slope on the Slope Erosion of Longling Completely Weathered Granite
Authors: Tian Haojun , Kong Zhigang
Published: April 2023

Serious slope erosion occurs in the distribution areas of fully weathered granites, and rainfall intensity and slope gradient are important factors affecting slope erosion. In this study, we investigate the erosion characteristics of Longling completely weather granites with a focus on the effects of rainfall intensity and slope gradient. Based on an indoor 60-min simulated rainfall test, we selected four slope gradients (10°, 20°, 30°, and 40°) and three rainfall intensities (50, 80, and 110 mm/h) for evaluation. A total of 12 groups of tests were conducted to analyze the erosion and surface hydrodynamic characteristics of the completely weathered granite slope. The results indicate a significant positive correlation between rainfall intensity and slope gradient, and the correlation between rainfall intensity and flow velocity became stronger as the slope gradient increased. The peak sediment yield rate represents the moment at which the change in slope shape is maximized. After the peak appears, the slope will no longer undergo great deformation, and the sediment yield rate will decrease and then become stable. Finally, rainfall intensity and slope gradient, which are the two key factors that determine slope flow velocity, are described using a binary function. The findings provide a reference for the study of slope erosion in completely weathered granites.
Machine Learning for Short-Term Prediction of Ship Motion Combined with Wave Input
Authors: Tian Ximin , Song Yang
Published: April 2023

There is a response relationship between wave and ship motion. Based on the LSTM neural network, the mapping relationship between the wave elevation and ship roll motion is established. The wave elevation and ship motion duration data obtained by the CFD simulation are used to predict ship roll motion with different input data schemes. The results show that the prediction scheme considering the wave elevation input can predict ship roll motion. Compared with the direct prediction scheme based on the roll data input, the prediction scheme considering the wave elevation input factor can greatly improve the prediction accuracy and effective advance prediction time. Different wave elevation data inputs have different prediction effects. The advance prediction duration will increase with the increase in the input wave elevation position and the ship distance. The simultaneous input of multi-point wave elevation greatly increases the amount of data, allowing the trained model to utilize a greater data depth. This not only improves the advance prediction duration of the prediction model, but it also enhances the robustness of the model, making the prediction results more stable.
Reliable Frequency Control Support Scheme Based on Wind Power Generator Combined with Rechargeable Energy Storage System Applying Adaptive Power Reference
Authors: Tagayi Roland Kobla , Han Seungyun , Lee Hyewon , Kim Jonghoon
Published: April 2023

To reduce carbon emissions in the atmosphere, the utilization of renewable energy sources has been on the rise. However, as their integration level increases, grid system operators require higher performance of the frequency response service for renewable energy sources, especially wind power generators (WPGs). Conventional frequency control schemes release kinetic energy depending on the fixed and adjustable gains in the system difference loop between the standard and current system frequency; however, these conventional schemes cannot provide frequency support outside of the rotor’s speed operating region. In this work, a frequency regulation support strategy employing a WPG and lithium-ion battery based on an adaptable power reference is implemented. This is accomplished by assigning different roles to the WPG and battery. As the primary frequency control support, the WPG uses a frequency deviation loop with adaptable gain which depends on the speed of the rotor and the difference in frequency. Additionally, to assist with the frequency control support, the battery operates based on its state-of-charge (SOC) and rotor speed of the WPG. For investigating the capability of the suggested technique, an IEEE 14-bus system is employed. Qualitative wake effect analysis is further presented in the study to determine the feasibility of the proposed approach which consists of the hybrid WPP–battery system for frequency regulation. The main limitations of this study and further research studies that can be performed in the future to improve the performance of the proposed technique are presented. The scenario study results show that the minimum frequency point during a synchronous generator trip obtains a higher value than conventional ones in the suggested strategy by releasing more stored energy from the WPG and the battery.
Heat Transfer Optimization of an Electronic Control Unit Immersed in Forced Liquid Coolant
Authors: Lates Cristina Georgiana , Dumitras Catalin Gabriel , Vizureanu Petrica , Sandu Andrei Victor
Published: April 2023

The current paper aims to present a cooling concept for future centralized platforms of ECUs (Electronic Control Units) from the automotive industry that involves grouping multiple electronic devices into a single system and cooling them with forced convection dielectric coolant. The enhancement consists of replacing the inside air of the module with a dielectric coolant that has a higher thermal conductivity than air and employing an additional prototype system that aids in forced liquid cooling. To meet automotive requirements, the experiments were exposed to an ambient temperature of 85 °C. Temperature measurements on these solutions’ hot spots were compared to those on a thermal paste-only reference electronic module. This study used DFSS (Design for Six Sigma) techniques to determine the ideal pump flow rate, fan air flow rate, and liquid volume in the housing, leading to an optimization in heat dissipation. Finding a trustworthy transfer function that could forecast the impact of the crucial design parameters that had been found was the main goal. The electronics cooled by forced convection coolant improved heat dissipation by up to 60% when compared to the reference module. This demonstrates that the DoE (Design of Experiments) method, which is based on a limited number of measurements, can estimate the behavior of the ECU without the need for a more involved theoretical framework.
Three Processor Allocation Approaches towards EDF Scheduling for Performance Asymmetric Multiprocessors
Authors: Wu Peng , Li Zhi , Yan Tao , Li Yingchun
Published: April 2023

With the rapid development of high-performance computing and parallel computing technology, by virtue of its cost-effectiveness, strong scalability, and easy programming, the multiprocessor system has gradually become the mainstream computing platform. Meanwhile, a growing number of researchers pay attention to the performance of multiprocessor systems, especially the task scheduling problem, which has an important impact on the system performance. Most of the current research works on task scheduling algorithms are based on the homogeneous computing environment. On the contrary, research works focusing on more complex performance asymmetric multiprocessor environments still remain rare. In this paper, we compare the effects of three earliest deadline first algorithms under different processor allocation strategies on performance asymmetric multiprocessors. We propose an efficient schedulability analysis for an allocation strategy that assigns high-priority tasks to the slowest idle processor. Experimental results show that the strategy of allocating processors with optimum speeds for high-priority tasks can schedule more task sets than the other two allocation strategies. The strategy that prioritizes the slowest processors for high-priority tasks has the smallest number of task migrations, and the strategy has the highest effective processor utilization.
Impacts of Tillage Practices on Growth, Phosphorus Uptake, and Yield of Maize in Controlled and Field-Based Studies in Relation to Arbuscular Mycorrhizal Fungi
Authors: Tatewaki Yuya , Higo Masao , Isobe Katsunori
Published: April 2023

This study investigated the effects of arbuscular mycorrhizal fungi (AMF) on the growth, phosphorus (P) uptake, and yield of maize in the presence or absence of tillage. The two-year field experiment was conducted in Kanagawa, Japan. Firstly, we investigated whether the presence of indigenous AMF communities in the roots, as determined by amplicon sequencing analysis, contributed to maize growth in Experiment 1, a 2-year field-based study. The findings revealed that the maize (Zea mays L.) in rotary tillage had higher P uptake, growth at the six-leaves collar stage, and yield, compared to no tillage. The AMF communities colonizing maize roots were altered by the presence or absence of tillage; specifically, tillage increased the dominance of the Gigasporaceae, whereas no tillage increased the dominance of the Acaulosporaceae. Based on these findings, we confirmed whether the inoculation of similar AMF strains, as analyzed in the field study of tillage practices on maize roots, produces growth-promoting effects for maize growth in a controlled pot experiment consistent with the results of the field experiment. For experiment 2, Dentiscutata cerradensis TK-1, Cetraspora pellucida SZ-3 (Gigasporaceae), Acaulospora morrowiae AP-5, and A. longula F-1 (Acaulosporaceae) were inoculated as AMF inocula for a pot experiment. The results showed that aboveground biomass did not change with any inoculum compared to the control. The P concentration in maize was higher for D. cerradensis TK-1 and C. pellucida SZ-3 inoculation than for the control. However, inoculation with A. morrowiae AP-5 and A. longula F-1 did not change P concentrations from the control. This indicates that D. cerradensis TK-1 and C. pellucida SZ-3 are more effective in promoting P uptake in maize than in A morrowiae AP-5 and A. longula F-1. Based on field and pot experiments, our findings suggest that tillage practices lead to alterations in the AMF communities that colonize the roots, and this shift may also contribute to changes in P uptake and crop growth.
Simulating Atmospheric Organic Aerosol in the Boreal Forest Using Its Volatility-Oxygen Content Distribution
Authors: Karnezi Eleni , Heikkinen Liine , Kulmala Markku , Pandis Spyros N.
Published: April 2023

Various parameterizations of organic aerosol (OA) formation and its subsequent evolution in the two-dimensional Volatility Basis Set (2D-VBS) framework are evaluated using ground measurements collected in the 2013 PEGASOS field campaign in the boreal forest station of Hyytiälä in southern Finland. A number of chemical aging schemes that performed well in the polluted environment of the Po Valley in Italy during the PEGASOS 2012 campaign are examined, taking into account various functionalization and fragmentation pathways for biogenic and anthropogenic OA components. All seven aging schemes considered have satisfactory results, consistent with the ground measurements. Despite their differences, these schemes predict similar contributions of the various OA sources and formation pathways for the periods examined. The highest contribution comes from biogenic secondary OA (bSOA), as expected, contributing 40–63% depending on the modeling scheme. Anthropogenic secondary OA (aSOA) is predicted to contribute 11–18% of the total OA, while SOA from intermediate-volatility compounds (SOA-iv) oxidation contributes another 18–27%. The fresh primary OA (POA) contributes 4%, while the SOA resulting from the oxidation of the evaporated semivolatile POA (SOA-sv) varies between 4 and 6%. Finally, 5–6% is predicted to be due to long-range transport from outside the modeling domain.
Fragmentation Dynamics of CO2q+ (q = 2, 3) in Collisions with 1 MeV Proton
Authors: Duley Avijit , Kelkar Aditya. H.
Published: April 2023

The fragmentation dynamics of the CO2q+ (q = 2, 3) molecular ions formed under the impact of 1 MeV protons is studied using a recoil ion momentum spectrometer equipped with a multi-hit time- and position-sensitive detector. Both two-body and three-body fragmentation channels arising from the doubly and triply ionized molecular ions of CO2 are identified and analyzed. Kinetic energy release (KER) distributions have been obtained for various channels. With the help of Dalitz plots and Newton diagrams concerted and sequential processes have been assigned to observed fragmentation channels. In addition, angular correlations are used to determine the molecular geometry of the precursor molecular ion. It is found that the symmetric breakup into C+ + O+ + O+ involves asymmetric stretching of the molecular bonds in CO23+ prior to dissociation via concerted decay implying the fact that collisions with 1 MeV proton induces an asynchronous decay in CO2.
Online High-Resolution EIS of Lithium-Ion Batteries by Means of Compact and Low Power ASIC
Authors: Ria Andrea , Manfredini Giuseppe , Gagliardi Francesco , Vitelli Michele , Bruschi Paolo , Piotto Massimo
Published: April 2023

A compact electronic circuit capable of performing Electrochemical Impedance Spectroscopy (EIS) on either single Lithium-ion cells or modules formed by the series of two cells is presented. The proposed device, named Double Cell Management Unit (DCMU), constitutes an important improvement to a recently proposed cell management unit, which combined EIS acquisition functions with a multichannel sensor interface compatible with thermistors, strain-gauges and moisture detectors. The proposed circuit maintains the versatility of the previous version and significantly extends the EIS frequency range, allowing vector impedance measurements from 0.1 Hz to about 15 kHz. The capability of handling both single Lithium-ion cells or series of two cells is obtained by adding a few external components to the previous version. This also allowed increasing the stimulation current to a maximum amplitude of 200 mA, resulting in improved resolution. Experiments consisting in EIS acquisition performed on batteries of different capacity at different temperatures and states of charge are described. Estimated impedance resolution (standard deviation) is 20 μΩ obtained at 1 kHz with a stimulation current of 100 mA amplitude.
Analysis of 2D and 3D Convolution Models for Volumetric Segmentation of the Human Hippocampus
Authors: Toh You Sheng , Hargreaves Carol Anne
Published: April 2023

Extensive medical research has revealed evidence of a strong association between hippocampus atrophy and age-related diseases such as Alzheimer’s disease (AD). Therefore; segmentation of the hippocampus is an important task that can help clinicians and researchers in diagnosing cognitive impairment and uncovering the mechanisms behind hippocampal changes and diseases of the brain. The main aim of this paper was to provide a fair comparison of 2D and 3D convolution-based architectures for the specific task of hippocampus segmentation from brain MRI volumes to determine whether 3D convolution models truly perform better in hippocampus segmentation and also to assess any additional costs in terms of time and computational resources. Our optimized model, which used 50 epochs and a mini-batch size of 2, achieved the best validation loss and Dice Similarity Score (DSC) of 0.0129 and 0.8541, respectively, across all experiment runs. Based on the model comparisons, we concluded that 2D convolution models can surpass their 3D counterparts in terms of both hippocampus segmentation performance and training efficiency. Our automatic hippocampus segmentation demonstrated potential savings of thousands of clinician person-hours spent on manually analyzing and segmenting brain MRI scans
AI Model for Detection of Abdominal Hemorrhage Lesions in Abdominal CT Images
Authors: Park Young-Jin , Cho Hui-Sup , Kim Myoung-Nam
Published: April 2023

Information technology has been actively utilized in the field of imaging diagnosis using artificial intelligence (AI), which provides benefits to human health. Readings of abdominal hemorrhage lesions using AI can be utilized in situations where lesions cannot be read due to emergencies or the absence of specialists; however, there is a lack of related research due to the difficulty in collecting and acquiring images. In this study, we processed the abdominal computed tomography (CT) database provided by multiple hospitals for utilization in deep learning and detected abdominal hemorrhage lesions in real time using an AI model designed in a cascade structure using deep learning, a subfield of AI. The AI model was used a detection model to detect lesions distributed in various sizes with high accuracy, and a classification model that could screen out images without lesions was placed before the detection model to solve the problem of increasing false positives owing to the input of images without lesions in actual clinical cases. The developed method achieved 93.22% sensitivity and 99.60% specificity.
Immunomagnetic Delivery of Adipose-Derived Endothelial Progenitor Cells for the Repair of Renal Ischemia–Reperfusion Injury in a Rat Model
Authors: Wu Di , Liu Jingyu , Zhou Changcheng , Ma Wenjie , Zhou Liuhua , Ge Yuzheng , Jia Ruipeng
Published: April 2023

Renal ischemia–reperfusion injury (IRI) is a significant cause of acute kidney injury (AKI) and usually brings severe public health consequences. Adipose-derived endothelial progenitor cell (AdEPCs) transplantation is beneficial for AKI but suffers from low delivery efficiency. This study was conducted to explore the protective effects of magnetically delivered AdEPCs on the repair of renal IRI. Two types of magnetic delivery methods, namely the endocytosis magnetization (EM) method and the immunomagnetic (IM) method were fabricated using PEG@Fe3O4 and CD133@Fe3O4, and their cytotoxicities in AdEPCs were assessed. In the renal IRI rat model, magnetic AdEPCs were injected via the tail vein and a magnet was placed beside the injured kidney for magnetic guidance. The distribution of transplanted AdEPCs, renal function, and tubular damage were evaluated. Our results suggested that CD133@Fe3O4 had the minimum negative effects on the proliferation, apoptosis, angiogenesis, and migration of AdEPCs compared with PEG@Fe3O4. Renal magnetic guidance could significantly enhance the transplantation efficiency and the therapeutic outcomes of AdEPCs–PEG@Fe3O4 and AdEPCs–CD133@Fe3O4 in the injured kidneys. However, under renal magnetic guidance, AdEPCs–CD133@Fe3O4 had stronger therapeutic effects than PEG@Fe3O4 after renal IRI. The immunomagnetic delivery of AdEPCs with CD133@Fe3O4 could be a promising therapeutic strategy for renal IRI.
Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data
Authors: Dasgupta Abhishek , Sharma Rahul , Mishra Challenger , Nagaraja Vikranth Harthikote
Published: April 2023

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) techniques are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one typically uses an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, an ML approach is presented that maps experimentally recorded IMC input data to the human upper-extremity MSK model outputs computed from (‘gold standard’) OMC input data. Essentially, this proof-of-concept study aims to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and a comprehensive search for the best-fit model in the hyperparameters space in both subject-exposed (SE) as well as subject-naive (SN) settings. We observed a comparable performance for both FFNN and RNN models, which have a high degree of agreement (ravg,SE,FFNN=0.90±0.19, ravg,SE,RNN=0.89±0.17, ravg,SN,FFNN=0.84±0.23, and ravg,SN,RNN=0.78±0.23) with the desired OMC-driven MSK estimates for held-out test data. The findings demonstrate that mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from ‘lab to field’.
Changes in Body Temperature Patterns Are Predictive of Mortality in Septic Shock: An Observational Study
Authors: Coiffard Benjamin , Merdji Hamid , Boucekine Mohamed , Helms Julie , Clere-Jehl Raphaël , Mege Jean-Louis , Meziani Ferhat
Published: April 2023

Biological rhythms are important regulators of immune functions. In intensive care unit (ICU), sepsis is known to be associated with rhythm disruption. Our objectives were to determine factors associated with rhythm disruption of the body temperature and to assess the relationship between temperature and mortality in septic shock patients; In a cohort of septic shock, we recorded body temperature over a 24-h period on day 2 after ICU admission. For each patient, the temperature rhythmicity was assessed by defining period and amplitude, and the adjusted average (mesor) of the temperature by sinusoidal regression and cosinor analysis. Analyses were performed to assess factors associated with the three temperature parameters (period, amplitude, and mesor) and mortality. 162 septic shocks were enrolled. The multivariate analysis demonstrates that the period of temperature was associated with gender (women, coefficient −2.2 h, p = 0.031) and acetaminophen use (coefficient −4.3 h, p = 0.002). The mesor was associated with SOFA score (coefficient −0.05 °C per SOFA point, p = 0.046), procalcitonin (coefficient 0.001 °C per ng/mL, p = 0.005), and hydrocortisone use (coefficient −0.5 °C, p = 0.002). The amplitude was associated with the dialysis (coefficient −0.5 °C, p = 0.002). Mortality at day 28 was associated with lower mesor (adjusted hazard ratio 0.50, 95% CI 0.28 to 0.90; p = 0.02), and higher amplitude (adjusted hazard ratio 5.48, 95% CI 1.66 to 18.12; p = 0.005) of temperature. Many factors, such as therapeutics, influence the body temperature during septic shock. Lower mesor and higher amplitude were associated with mortality and could be considered prognostic markers in ICU. In the age of artificial intelligence, the incorporation of such data in an automated scoring alert could compete with physicians to identify high-risk patients during septic shock.
Bacterial Subcellular Architecture, Structural Epistasis, and Antibiotic Resistance
Authors: Baquero Fernando , Martínez José-Luis , Sánchez Alvaro , Fernández-de-Bobadilla Miguel D. , San-Millán Alvaro , Rodríguez-Beltrán Jerónimo
Published: April 2023

Epistasis refers to the way in which genetic interactions between some genetic loci affect phenotypes and fitness. In this study, we propose the concept of “structural epistasis” to emphasize the role of the variable physical interactions between molecules located in particular spaces inside the bacterial cell in the emergence of novel phenotypes. The architecture of the bacterial cell (typically Gram-negative), which consists of concentrical layers of membranes, particles, and molecules with differing configurations and densities (from the outer membrane to the nucleoid) determines and is in turn determined by the cell shape and size, depending on the growth phases, exposure to toxic conditions, stress responses, and the bacterial environment. Antibiotics change the bacterial cell’s internal molecular topology, producing unexpected interactions among molecules. In contrast, changes in shape and size may alter antibiotic action. The mechanisms of antibiotic resistance (and their vectors, as mobile genetic elements) also influence molecular connectivity in the bacterial cell and can produce unexpected phenotypes, influencing the action of other antimicrobial agents.
Recent Advances in Peptidoglycan Synthesis and Regulation in Bacteria
Authors: Galinier Anne , Delan-Forino Clémentine , Foulquier Elodie , Lakhal Hakima , Pompeo Frédérique
Published: April 2023

Bacteria must synthesize their cell wall and membrane during their cell cycle, with peptidoglycan being the primary component of the cell wall in most bacteria. Peptidoglycan is a three-dimensional polymer that enables bacteria to resist cytoplasmic osmotic pressure, maintain their cell shape and protect themselves from environmental threats. Numerous antibiotics that are currently used target enzymes involved in the synthesis of the cell wall, particularly peptidoglycan synthases. In this review, we highlight recent progress in our understanding of peptidoglycan synthesis, remodeling, repair, and regulation in two model bacteria: the Gram-negative Escherichia coli and the Gram-positive Bacillus subtilis. By summarizing the latest findings in this field, we hope to provide a comprehensive overview of peptidoglycan biology, which is critical for our understanding of bacterial adaptation and antibiotic resistance.
Strain Echocardiography in Acute COVID-19 and Post-COVID Syndrome: More than Just a Snapshot
Authors: Kersten Johannes , Schellenberg Jana , Jerg Achim , Kirsten Johannes , Persch Hasema , Liu Yuefei , Steinacker Jürgen M.
Published: April 2023

Speckle-tracking echocardiography (STE) has become an established, widely available diagnostic method in the past few years, making its value clear in cases of COVID-19 and the further course of the disease, including post-COVID syndrome. Since the beginning of the pandemic, many studies have been published on the use of STE in this condition, enabling, on the one hand, a better understanding of myocardial involvement in COVID-19 and, on the other, a better identification of risk to patients, although some questions remain unanswered in regard to specific pathomechanisms, especially in post-COVID patients. This review takes a closer look at current findings and potential future developments by summarising the extant data on the use of STE, with a focus on left and right ventricular longitudinal strain.
Can Cold Atmospheric Plasma Be Used for Infection Control in Burns? A Preclinical Evaluation
Authors: Bagheri Mahsa , von Kohout Maria , Zoric Andreas , Fuchs Paul C. , Schiefer Jennifer L. , Opländer Christian
Published: April 2023

Wound infection with Pseudomonas aeruginosa (PA) is a serious complication and is responsible for higher rates of mortality in burn patients. Because of the resistance of PA to many antibiotics and antiseptics, an effective treatment is difficult. As a possible alternative, cold atmospheric plasma (CAP) can be considered for treatment, as antibacterial effects are known from some types of CAP. Hence, we preclinically tested the CAP device PlasmaOne and found that CAP was effective against PA in various test systems. CAP induced an accumulation of nitrite, nitrate, and hydrogen peroxide, combined with a decrease in pH in agar and solutions, which could be responsible for the antibacterial effects. In an ex vivo contamination wound model using human skin, a reduction in microbial load of about 1 log10 level was observed after 5 min of CAP treatment as well as an inhibition of biofilm formation. However, the efficacy of CAP was significantly lower when compared with commonly used antibacterial wound irrigation solutions. Nevertheless, a clinical use of CAP in the treatment of burn wounds is conceivable on account of the potential resistance of PA to common wound irrigation solutions and the possible wound healing-promoting effects of CAP.
Neurodifferentiation and Neuroprotection Potential of Mesenchymal Stromal Cell-Derived Secretome Produced in Different Dynamic Systems
Authors: Marques Cláudia Raquel , Fuzeta Miguel de Almeida , dos Santos Cunha Raquel Medina , Pereira-Sousa Joana , Silva Deolinda , Campos Jonas , Teixeira-Castro Andreia , Sousa Rui Amandi , Fernandes-Platzgummer Ana , da Silva Cláudia L. , Salgado António José
Published: April 2023

Parkinson’s disease (PD) is the second most common neurodegenerative disorder and is characterized by the degeneration of the dopamine (DA) neurons in the substantia nigra pars compacta, leading to a loss of DA in the basal ganglia. The presence of aggregates of alpha-synuclein (α-synuclein) is seen as the main contributor to the pathogenesis and progression of PD. Evidence suggests that the secretome of mesenchymal stromal cells (MSC) could be a potential cell-free therapy for PD. However, to accelerate the integration of this therapy in the clinical setting, there is still the need to develop a protocol for the large-scale production of secretome under good manufacturing practices (GMP) guidelines. Bioreactors have the capacity to produce large quantities of secretomes in a scalable manner, surpassing the limitations of planar static culture systems. However, few studies focused on the influence of the culture system used to expand MSC, on the secretome composition. In this work, we studied the capacity of the secretome produced by bone marrow-derived mesenchymal stromal cells (BMSC) expanded in a spinner flask (SP) and in a Vertical-Wheel™ bioreactor (VWBR) system, to induce neurodifferentiation of human neural progenitor cells (hNPCs) and to prevent dopaminergic neuron degeneration caused by the overexpression of α-synuclein in one Caenorhabditis elegans model of PD. Results showed that secretomes from both systems were able to induce neurodifferentiation, though the secretome produced in the SP system had a greater effect. Additionally, in the conditions of our study, only the secretome produced in SP had a neuroprotective potential. Lastly, the secretomes had different profiles regarding the presence and/or specific intensity of different molecules, namely, interleukin (IL)-6, IL-4, matrix metalloproteinase-2 (MMP2), and 3 (MMP3), tumor necrosis factor-beta (TNF-β), osteopontin, nerve growth factor beta (NGFβ), granulocyte colony-stimulating factor (GCSF), heparin-binding (HB) epithelial growth factor (EGF)-like growth factor (HB-EGF), and IL-13. Overall, our results suggest that the culture conditions might have influenced the secretory profiles of cultured cells and, consequently, the observed effects. Additional studies should further explore the effects that different culture systems have on the secretome potential of PD.
Associated Factors of Pneumonia in Individuals with Chronic Obstructive Pulmonary Disease (COPD) Apart from the Use of Inhaled Corticosteroids
Authors: Lineros Rosario , Fernández-Delgado Lourdes , Vega-Rioja Antonio , Chacón Pedro , Doukkali Bouchra , Monteseirin Javier , Ribas-Pérez David
Published: April 2023

Inhaled corticosteroids (ICSs) are widely used in chronic obstructive pulmonary disease (COPD) and in combination with long-acting β2 agonists (LABAs) to reduce exacerbations and improve patient lung function and quality of life. However, ICSs have been associated with an increased risk of pneumonia in individuals with COPD, although the magnitude of this risk remains unclear. Therefore, it is difficult to make informed clinical decisions that balance the benefits and adverse effects of ICSs in people with COPD. There may be other causes of pneumonia in patients with COPD, and these causes are not always considered in studies on the risks of using ICSs in COPD. We consider it very useful to clarify these aspects in assessing the influence of ICSs on the incidence of pneumonia and their role in the treatment of COPD. This issue has important implications for current practice and the evaluation and management of COPD, since COPD patients may benefit from specific ICS-based treatment strategies. Many of the potential causes of pneumonia in patients with COPD can act synergistically, so they can be included in more than one section.
Metallic Nanoparticles: Their Potential Role in Breast Cancer Immunotherapy via Trained Immunity Provocation
Authors: Zarenezhad Elham , Kanaan Manal Hadi Ghaffoori , Abdollah Sura Saad , Vakil Mohammad Kazem , Marzi Mahrokh , Mazarzaei Abdulbaset , Ghasemian Abdolmajid
Published: April 2023

Owing to drawbacks in the current common cancer therapies including surgery, chemotherapy and radiotherapy, the development of more reliable, low toxic, cost-effective and specific approaches such as immunotherapy is crucial. Breast cancer is among the leading causes of morbidity and mortality with a developed anticancer resistance. Accordingly, we attempted to uncover the efficacy of metallic nanoparticles (MNPs)-based breast cancer immunotherapy emphasizing trained immunity provocation or innate immunity adaptation. Due to the immunosuppressive nature of the tumor microenvironment (TME) and the poor infiltration of immune cells, the potentiation of an immune response or direct combat is a goal employing NPs as a burgeoning field. During the recent decades, the adaptation of the innate immunity responses against infectious diseases and cancer has been recognized. Although the data is in a scarcity with regard to a trained immunity function in breast cancer cells’ elimination, this study introduced the potential of this arm of immunity adaptation using MNPs.
Acute Kidney Injury in Hospitalized Patients with COVID-19: Risk Factors and Serum Biomarkers
Authors: Shchepalina Anastasia , Chebotareva Natalia , Akulkina Larissa , Brovko Mikhail , Sholomova Viktoria , Androsova Tatiana , Korotchaeva Yulia , Kalmykova Diana , Tanaschuk Elena , Taranova Marina , Lebedeva Marina , Beketov Vladimir , Moiseev Sergey
Published: April 2023

Background. AKI is one of the COVID-19 complications with high prognostic significance. In our research, we studied the prognostic role of several biomarkers that could help us understand AKI pathogenesis in patients with COVID-19. Methods. We evaluated the medical data of 500 patients hospitalized with COVID-19 in Tareev Clinic from 5 October 2020 to 1 March 2022. The diagnosis of COVID-19 was confirmed with positive RNA PCR in nasopharyngeal swabs and/or typical radiological findings on CT scans. Kidney function was assessed in accordance with KDIGO criteria. In the selected 89 patients, we evaluated serum levels of angiopoetin-1, KIM-1, MAC, and neutrophil elastase 2 and their prognostic significance. Results. The incidence of AKI in our study was 38%. The main risk factors for kidney injury were male sex, cardiovascular diseases, and chronic kidney disease. High serum angiopoetin-1 levels and a decrease in blood lymphocyte count and fibrinogen level also increased the risk of AKI. Conclusions. AKI is an independent risk factor for death in patients with COVID-19. We propose the prognostic model of AKI development, which includes the combination of serum levels of angiopoetin-1 and KIM-1 on admission. Our model can help to prevent AKI development in patients with coronavirus disease.
Soft-Tissue Mobilization and Pain Neuroscience Education for Chronic Nonspecific Low Back Pain with Central Sensitization: A Prospective Randomized Single-Blind Controlled Trial
Authors: Song Jeongkeun , Kim Hyunjoong , Jung Jihye , Lee Seungwon
Published: April 2023

This study was conducted to demonstrate the therapeutic effect of soft-tissue mobilization (STM) combined with pain neuroscience education (PNE) for patients with chronic nonspecific low back pain with central sensitization. A total of 28 participants were recruited and randomly allocated to either the STM group (SMG) (n = 14) or the STM plus PNE group (BG; blended group) (n = 14). STM was applied twice a week for four weeks, with a total of eight sessions, and PNE was applied within four weeks, for a total of two sessions. The primary outcome was pain intensity, and the secondary outcomes were central sensitization, pressure pain, pain cognition, and disability. Measurements were made at baseline, after the test, and at 2-week and 4-week follow-ups. The BG showed significant improvement in pain intensity (p < 0.001), pressure pain (p < 0.001), disability (p < 0.001), and pain cognition (p < 0.001) compared to the SMG. This study demonstrated that STM plus PNE is more effective for all measured outcomes compared to STM alone. This finding suggests that the combination of PNE and manual therapy has a positive effect on pain, disability index, and psychological factors in the short term.