bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial Prophylaxis Reduces the Rate of Surgical Site Infection in Upper Gastrointestinal Surgery: A Systematic Review
Authors: Marano Luigi , Carbone Ludovico , Poto Gianmario Edoardo , Calomino Natale , Neri Alessandro , Piagnerelli Riccardo , Fontani Andrea , Verre Luigi , Savelli Vinno , Roviello Franco , Marrelli Daniele
Published: February 2022

Surgical site infection occurs with high frequency in gastrointestinal surgery, contributing to the high incidence of morbidity and mortality. The accepted practice worldwide for the prevention of surgical site infection is providing single- or multiple-dose antimicrobial prophylaxis. However, most suitable antibiotic and optimal duration of prophylaxis are still debated. The aim of the systematic review is to assess the efficacy of antimicrobial prophylaxis in controlling surgical site infection rate following esophagogastric surgery. PubMed and Cochrane databases were systematically searched until 31 October 2021, for randomized controlled trials comparing different antimicrobial regimens in prevention surgical site infections. Risk of bias of studies was assessed with standard methods. Overall, eight studies concerning gastric surgery and one study about esophageal surgery met inclusion criteria. No significant differences were detected between single- and multiple-dose antibiotic prophylaxis. Most trials assessed the performance of cephalosporins or inhibitor of bacterial beta-lactamase. Antimicrobial prophylaxis (AMP) is effective in reducing the incidence of surgical site infection. Multiple-dose antimicrobial prophylaxis is not recommended for patients undergoing gastric surgery. Further randomized controlled trials are needed to determine the efficacy and safety of antimicrobial prophylaxis in esophageal cancer patients.
Antibiotic Use in Alpine Dairy Farms and Its Relation to Biosecurity and Animal Welfare
Authors: Menegon Francesca , Capello Katia , Tarakdjian Jacopo , Pasqualin Dario , Cunial Giovanni , Andreatta Sara , Dellamaria Debora , Manca Grazia , Farina Giovanni , Di Martino Guido
Published: February 2022

The quantification of antimicrobial usage (AMU) in food-producing animals can help identify AMU risk factors, thereby enhancing appropriate stewardship policies and strategies for a more rational use. AMU in a sample of 34 farms in the Province of Trento (north-eastern Italy) from 2018 to 2020 was expressed as defined daily doses for animals per population correction unit according to European Surveillance of Veterinary Antimicrobial Consumption guidelines (DDDvet) and according to Italian guidelines (DDDAit). A retrospective analysis was carried out to test the effects of several husbandry practices on AMU. Overall, the average AMU ranged between 6.5 DDDAit in 2018 and 5.2 DDDAit in 2020 (corresponding to 9 and 7 DDDvet, respectively), showing a significant trend of decrement (−21.3%). Usage of the highest priority critically important antimicrobials (HPCIA) was reduced by 83% from 2018 to 2020. Quarantine management, available space, water supply, animals’ cleanliness and somatic cell count had no significant association with AMU. Rather, farms with straw-bedded cubicles had lower AMU levels than those with mattresses and concrete floors (p < 0.05). In conclusion, this study evidenced a decrement in AMU, particularly regarding HPCIA, but only a few risk factors due to farm management.
Prevalence and Therapeutic Management of Infections by Multi-Drug-Resistant Organisms (MDROs) in Patients with Liver Cirrhosis: A Narrative Review
Authors: Onorato Lorenzo , Monari Caterina , Capuano Salvatore , Grimaldi Pierantonio , Coppola Nicola
Published: February 2022

Bacterial infections are common events that significantly impact the clinical course of patients with cirrhosis. As in the general population, infections caused by multi-drug-resistant organisms (MDROs) are progressively increasing in cirrhotic patients, accounting for up to 30–35% of all infections. Nosocomial acquisition and prior exposure to antimicrobial treatment or invasive procedures are well-known risk factors for MDRO infections. Several studies have demonstrated that infections due to MDROs have a poorer prognosis and higher rates of treatment failure, septic shock, and hospital mortality. Due to the increasing rate of antimicrobial resistance, the approach to empirical treatment in cirrhotic patients with life-threatening infections has become significantly more challenging. In order to ensure a prompt administration of effective antibiotic therapy while avoiding unnecessary antibiotic exposure at the same time, it is of utmost importance to choose the correct antimicrobial therapy and administration schedule based on individual clinical characteristics and risk factors and rapidly adopt de-escalation strategies as soon as microbiological data are available. In the present paper, we aimed to provide an overview of the most frequent infections diagnosed in cirrhotic patients, the prevalence and impact of antimicrobial resistance, and potential therapeutic options in this population.
Serum Paraoxonase-1 Activity and the Risk of Prostate Cancer Recurrence in Patients Treated with Radiotherapy
Authors: Jasinski Milosz , Olszewska-Slonina Dorota
Published: February 2022

The antioxidant paraoxonase-1 (PON1) may be involved in the response to radiation-induced oxidative stress and possibly prevent cell apoptosis. The correlation of PON1 with the risk of cancer recurrence after radiotherapy (RT) is not yet explored. We investigated changes in the activity of PON1 in patients with prostate cancer (PCa) undergoing RT, and the relation of PON1 activity to the risk of recurrence after RT. We included 56 men with PCa. Blood samples were obtained before irradiation and after the completion of RT. Patients were followed for an average of 51.2 months. Each case of biochemical recurrence was confirmed with biopsy. The control group was composed of 60 healthy men. There was no significant difference in PON1 activity between the control group and patients pre-radiotherapy. Irradiation was associated with a significant decrease in PON1 activity. Patients with PCa recurrence had significantly higher serum PON1 activity than those recurrence-free, both before and after RT. PON1 activity was a predictor of PCa recurrence, with sensitivity over 80% and specificity over 64%. Our results suggest that PON1 activity may be a predictor of PCa recurrence risk after RT. Studies with a larger number of patients and longer follow-up are needed to confirm this hypothesis.
Chemical Composition and Antioxidant Activity of Ammi visnaga L. Essential Oil
Authors: Kamal Fatima Zahra , Stanciu Gabriela Dumitrita , Lefter Radu , Cotea Valeriu V. , Niculaua Marius , Ababei Daniela Carmen , Ciobica Alin , Ech-Chahad Abdellah
Published: February 2022

The present study evaluated the chemical composition and the in vitro and in vivo antioxidant potential of Ammi visnaga L. essential oil to provide a scientific basis for the use of this plant in the traditional pharmacopoeia. Gas chromatography-mass spectrometry was used to identify the volatile constituents present of the oil. The in vitro antioxidant capacity was evaluated by the DPPH and the reducing power assays. For the in vivo tests, oral administration of Ammi visnaga L. oil (600 and 1200 mg/kg body weight) was performed in Swiss albino mice treated with acetaminophen (400 mg/kg). The toxic effect of acetaminophen and the action of the essential oil were measured by determining the levels of lipid peroxidation and antioxidant enzymes in liver and kidneys homogenates. The major components identified were butanoic acid, 2-methyl-, pentyl ester, (Z)-β-ocimene, D-limonene, linalool, pulegone and lavandulyl-butyrate. The in vitro DPPH and reducing power assays showed moderate to low free radical scavenging activity and the antioxidant power was positively correlated with the polyphenols’ concentration. In vivo, the Ammi visnaga L. essential oil showed a high antioxidant capacity at both concentrations (600 and 1200 mg/kg), effectively increasing the levels of reduced glutathione, superoxide dismutase, and catalase and significantly reducing the lipid peroxidation. The results obtained from this study suggest that Ammi visnaga L. could represent a source of molecules with antioxidant potential in the prevention of free radical-related diseases.
Effects of Exercise Training on the Autonomic Nervous System with a Focus on Anti-Inflammatory and Antioxidants Effects
Authors: Daniela Matei , Catalina Luca , Ilie Onu , Paula Matei , Daniel-Andrei Iordan , Ioana Buculei
Published: February 2022

Studies show that the autonomic nervous system (ANS) has an important impact on health in general. In response to environmental demands, homeostatic processes are often compromised, therefore determining an increase in the sympathetic nervous system (SNS)’s functions and a decrease in the parasympathetic nervous system (PNS)’s functions. In modern societies, chronic stress associated with an unhealthy lifestyle contributes to ANS dysfunction. In this review, we provide a brief introduction to the ANS network, its connections to the HPA axis and its stress responses and give an overview of the critical implications of ANS in health and disease—focused specifically on the immune system, cardiovascular, oxidative stress and metabolic dysregulation. The hypothalamic–pituitary–adrenal axis (HPA), the SNS and more recently the PNS have been identified as regulating the immune system. The HPA axis and PNS have anti-inflammatory effects and the SNS has been shown to have both pro- and anti-inflammatory effects. The positive impact of physical exercise (PE) is well known and has been studied by many researchers, but its negative impact has been less studied. Depending on the type, duration and individual characteristics of the person doing the exercise (age, gender, disease status, etc.), PE can be considered a physiological stressor. The negative impact of PE seems to be connected with the oxidative stress induced by effort.
NRF2-Dependent Placental Effects Vary by Sex and Dose following Gestational Exposure to Ultrafine Particles
Authors: Behlen Jonathan C. , Lau Carmen H. , Pendleton Drew , Li Yixin , Hoffmann Aline Rodrigues , Golding Michael C. , Zhang Renyi , Johnson Natalie M.
Published: February 2022

Exposure to ultrafine particles (UFPs, PM0.1) during pregnancy triggers placental oxidative stress and inflammation, similar to fine PM (PM2.5). The Nrf2 gene encodes a redox-sensitive transcription factor that is a major regulator of antioxidant and anti-inflammatory responses. Disruption of NRF2 is known to substantially enhance PM2.5-driven oxidant and inflammatory responses; however, specific responses to UFP exposure, especially during critical windows of susceptibility such as pregnancy, are not fully characterized; To investigate the role of NRF2 in regulating maternal antioxidant defenses and placental responses to UFP exposure, wildtype (WT) and Nrf2−/− pregnant mice were exposed to either low dose (LD, 100 µg/m3) or high dose (HD, 500 µg/m3) UFP mixture or filtered air (FA, control) throughout gestation; Nrf2−/− HD-exposed female offspring exhibited significantly reduced fetal and placental weights. Placental morphology changes appeared most pronounced in Nrf2−/− LD-exposed offspring of both sexes. Glutathione (GSH) redox analysis revealed significant increases in the GSH/GSSG ratio (reduced/oxidized) in WT female placental tissue exposed to HD in comparison with Nrf2−/− HD-exposed mice. The expression of inflammatory cytokine genes (Il1β, Tnfα) was significantly increased in Nrf2−/− placentas from male and female offspring across all exposure groups. Genes related to bile acid metabolism and transport were differentially altered in Nrf2−/− mice across sex and exposure groups. Notably, the group with the most marked phenotypic effects (Nrf2−/− HD-exposed females) corresponded to significantly higher placental Apoa1 and Apob expression suggesting a link between placental lipid transport and NRF2 in response to high dose UFP exposure; Disruption of NRF2 exacerbates adverse developmental outcomes in response to high dose UFP exposure in female offspring. Morphological effects in placenta from male and female offspring exposed to low dose UFPs also signify the importance of NRF2 in maternal–fetal response to UFPs.
An Update on the Exploratory Use of Curcumin in Neuropsychiatric Disorders
Authors: Lamanna-Rama Nicolás , Romero-Miguel Diego , Desco Manuel , Soto-Montenegro Maria Luisa
Published: February 2022

Curcumin is a polyphenol extracted from the rhizome of the turmeric plant. Beyond its common use as a culinary spice in Eastern Asia, curcumin has been proposed as a therapeutic compound due to its antioxidant, anti-inflammatory and neuroprotective properties. Thus, its efficacy has been evaluated in various inflammatory-based psychiatric disorders, such as schizophrenia, depression, or autism. Our aim is to review those preclinical and clinical studies carried out in psychiatric disorders whose therapeutic approach has involved the use of curcumin and, therefore, to discern the possible positive effect of curcumin in these disorders. Preclinical studies and completed clinical trials of curcumin for psychiatric disorders published from January 2005 to October 2021 were identified through searching relevant databases until 31st October 2021. Sixty-five preclinical studies and 15 clinical trials and open-label studies were selected. Results showed a bias toward studies in depression and, to a lesser extent, schizophrenia. In all disorders, the results were positive in reducing psychiatric deficits. Despite the considerable number of beneficial outcomes reported, the small number of trials and the heterogeneity of protocols make it difficult to draw solid conclusions about the real potency of curcumin in psychiatric disorders.
Cyanobacteria and Algae-Derived Bioactive Metabolites as Antiviral Agents: Evidence, Mode of Action, and Scope for Further Expansion; A Comprehensive Review in Light of the SARS-CoV-2 Outbreak
Authors: Pradhan Biswajita , Nayak Rabindra , Patra Srimanta , Bhuyan Prajna Paramita , Dash Soumya Ranjan , Ki Jang-Seu , Adhikary Siba Prasad , Ragusa Andrea , Jena Mrutyunjay
Published: February 2022

COVID-19—a severe acute respiratory syndrome disease caused by coronavirus 2 (SARS-CoV-2)—has recently attracted global attention, due to its devastating impact, to the point of being declared a pandemic. The search for new natural therapeutic drugs is mandatory, as the screening of already-known antiviral drugs so far has led to poor results. Several species of marine algae have been reported as sources of bioactive metabolites with potential antiviral and immunomodulatory activities, among others. Some of these bioactive metabolites might be able to act as antimicrobial drugs and also against viral infections by inhibiting their replication. Moreover, they could also trigger immunity against viral infection in humans and could be used as protective agents against COVID-In this context, this article reviews the main antiviral activities of bioactive metabolites from marine algae and their potential exploitation as anti-SARS-CoV-2 drugs.
The Analysis of the Aftershock Sequences of the Recent Mainshocks in Alaska
Authors: Sedghizadeh Mohammadamin , Shcherbakov Robert
Published: February 2022

The forecasting of the evolution of natural hazards is an important and critical problem in natural sciences and engineering. Earthquake forecasting is one such example and is a difficult task due to the complexity of the occurrence of earthquakes. Since earthquake forecasting is typically based on the seismic history of a given region, the analysis of the past seismicity plays a critical role in modern statistical seismology. In this respect, the recent three significant mainshocks that occurred in Alaska (the 2002, Mw 7.9 Denali; the 2018, Mw 7.9 Kodiak; and the 2018, Mw 7.1 Anchorage earthquakes) presented an opportunity to analyze these sequences in detail. This included the modelling of the frequency-magnitude statistics of the corresponding aftershock sequences. In addition, the aftershock occurrence rates were modelled using the Omori–Utsu (OU) law and the Epidemic Type Aftershock Sequence (ETAS) model. For each sequence, the calculation of the probability to have the largest expected aftershock during a given forecasting time interval was performed using both the extreme value theory and the Bayesian predictive framework. For the Bayesian approach, the Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution was performed to generate the chains of the model parameters. These MCMC chains were used to simulate the models forward in time to compute the predictive distributions. The calculation of the probabilities to have the largest expected aftershock to be above a certain magnitude after a mainshock using the Bayesian predictive framework fully takes into account the uncertainties of the model parameters. Moreover, in order to investigate the credibility of the obtained forecasts, several statistical tests were conducted to compare the performance of the earthquake rate models based on the OU formula and the ETAS model. The results indicate that the Bayesian approach combined with the ETAS model produced more robust results than the standard approach based on the extreme value distribution and the OU law.
A Theoretical Approach to Ordinal Classification: Feature Space-Based Definition and Classifier-Independent Detection of Ordinal Class Structures
Authors: Bellmann Peter , Lausser Ludwig , Kestler Hans A. , Schwenker Friedhelm
Published: February 2022

Ordinal classification (OC) is a sub-discipline of multi-class classification (i.e., including at least three classes), in which the classes constitute an ordinal structure. Applications of ordinal classification can be found, for instance, in the medical field, e.g., with the class labels order, early stage-intermediate stage-final stage, corresponding to the task of classifying different stages of a certain disease. While the field of OC was continuously enhanced, e.g., by designing and adapting appropriate classification models as well as performance metrics, there is still a lack of a common mathematical definition for OC tasks. More precisely, in general, a classification task is defined as an OC task, solely based on the corresponding class label names. However, an ordinal class structure that is identified based on the class labels is not necessarily reflected in the corresponding feature space. In contrast, naturally any kind of multi-class classification task can consist of a set of arbitrary class labels that form an ordinal structure which can be observed in the current feature space. Based on this simple observation, in this work, we present our generalised approach towards an intuitive working definition for OC tasks, which is based on the corresponding feature space and allows a classifier-independent detection of ordinal class structures. To this end, we introduce and discuss novel, OC-specific theoretical concepts. Moreover, we validate our proposed working definition in combination with a set of traditionally ordinal and traditionally non-ordinal data sets, and provide the results of the corresponding detection algorithm. Additionally, we motivate our theoretical concepts, based on an illustrative evaluation of one of the oldest and most popular machine learning data sets, i.e., on the traditionally non-ordinal Fisher’s Iris data set.
An Open Access Chamber Designed for the Acoustic Characterisation of Microbubbles
Authors: Smith Elliott , Nie Luzhen , McLaughlan James , Clegg Harry , Carpenter Thomas , Cowell David , Evans Stephen , Frangi Alejandro F. , Freear Steven
Published: February 2022

Microbubbles are used as contrast agents in clinical ultrasound for Left Ventricular Opacification (LVO) and perfusion imaging. They are also the subject of promising research in therapeutics as a drug delivery mechanism or for sonoporation and co-administration. For maximum efficacy in these applications, it is important to understand the acoustic characteristics of the administered microbubbles. Despite this, there is significant variation in the experimental procedures and equipment used to measure the acoustic properties of microbubble populations. A chamber was designed to facilitate acoustic characterisation experiments and was manufactured using additive manufacturing techniques. The design has been released to allow wider uptake in the research community. The efficacy of the chamber for acoustic characterisation has been explored with an experiment to measure the scattering of SonoVue® microbubbles at the fundamental frequency and second harmonic under interrogation from emissions in the frequency range of 1.6 to 6.4 MHz. The highest overall scattering values were measured at 1.6 MHz and decreased as the frequency increased, a result which is in agreement with previously published measurements. Statistical analysis of the acoustic scattering measurements have been performed and a significant difference, at the 5% significance level, was found between the samples containing contrast agent and the control sample containing only deionised water. These findings validate the proposed design for measuring the acoustic scattering characteristics of ultrasound contrast agents.
A Reaction Microscope for AMO Science at Shanghai Soft X-ray Free-Electron Laser Facility
Authors: Jiang Wenbin , Wang Xincheng , Zhang Shuai , Dong Ruichao , Guo Yuliang , Feng Jinze , Shen Zhenjie , Zhu Zhiyuan , Jiang Yuhai
Published: February 2022

We report on the design and capabilities of a reaction microscope (REMI) end-station at the Shanghai Soft X-ray Free-Electron Laser Facility (SXFEL). This apparatus allows high-resolution and 4π solid-angle coincidence detection of ions and electrons. The components of REMI, including a supersonic gas injection system, spectrometer, detectors and data acquisition system, are described in detail. By measuring the time of flight and the impact positions of ions and electrons on the corresponding detectors, three-dimensional momentum vectors can be reconstructed to study specific reaction processes. Momentum resolutions of ions and electrons with 0.11 a.u. are achieved, which have been measured from a single ionization experiment of oxygen molecules in an infrared (IR), femtosecond laser field, under vacuum at 1.2×10−10 torr, in a reaction chamber. As a demonstration, a Coulomb explosion experiment of oxygen molecules in the IR field is presented. These results demonstrate the performance of this setup, which provides a basic tool for the study of atomic and molecular reactions at SXFEL.
A Fiducial-Aided Reconfigurable Artefact for the Estimation of Volumetric Errors of Multi-Axis Ultra-Precision Machine Tools
Authors: Wang Shixiang , Cheung Chifai , Kong Lingbao
Published: February 2022

In this paper, a fiducial-aided reconfigurable artefact is presented for estimating volumetric errors of multi-axis machine tools. The artefact makes use of an adjustable number of standard balls as fiducials to build a 3D artefact which has been calibrated on a coordinate measuring machine (CMM). This 3D artefact demonstrates its reconfigurability in its number of fiducials and their locations according to the characteristics of workpieces and machine tools. The developed kinematics of the machine tool were employed to identify the volumetric errors occupied by the workpiece in the working space by comparing the information acquired by on-machine metrology with that acquired by the CMM. Experimental studies are conducted on a five-axis ultra-precision machine tool. A developed 3D artefact composed of five standard spheres is measured by the integrated on-machine measurement system. Factors including the gravity effect and measurement repeatability are also examined in order to optimize the geometry of the artefact. The results show that the developed 3D artefact is able to provide information about the working space occupied by the workpiece.
Human Activity Signatures Captured under Different Directions Using SISO and MIMO Radar Systems
Authors: Waqar Sahil , Muaaz Muhammad , Pätzold Matthias
Published: February 2022

In this paper, we highlight and resolve the shortcomings of single-input single-output (SISO) millimeter wave (mm-Wave) radar systems for human activity recognition (HAR). A 2×2 distributed multiple-input multiple-output (MIMO) radar framework is presented to capture human activity signatures under realistic conditions in indoor environments. We propose to distribute the two pairs of collocated transmitter–receiver antennas in order to illuminate the indoor environment from different perspectives. For the proposed MIMO system, we measure the time-variant (TV) radial velocity distribution and TV mean radial velocity to observe the signatures of human activities. We deploy the Ancortek SDR-KIT 2400T2R4 mm-Wave radar in a SISO as well as a 2×2 distributed MIMO configuration. We corroborate the limitations of SISO configurations by recording real human activities in different directions. It is shown that, unlike the SISO radar configuration, the proposed MIMO configuration has the ability to obtain superior human activity signatures for all directions. To signify the importance of the proposed 2×2 MIMO radar system, we compared the performance of a SISO radar-based passive step counter with a distributed MIMO radar-based passive step counter. As the proposed 2×2 MIMO radar system is able to detect human activity in all directions, it fills a research gap of radio frequency (RF)-based HAR systems.
Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
Authors: Gimeno Pablo , Ribas Dayana , Ortega Alfonso , Miguel Antonio , Lleida Eduardo
Published: February 2022

Speech Activity Detection (SAD) aims to accurately classify audio fragments containing human speech. Current state-of-the-art systems for the SAD task are mainly based on deep learning solutions. These applications usually show a significant drop in performance when test data are different from training data due to the domain shift observed. Furthermore, machine learning algorithms require large amounts of labelled data, which may be hard to obtain in real applications. Considering both ideas, in this paper we evaluate three unsupervised domain adaptation techniques applied to the SAD task. A baseline system is trained on a combination of data from different domains and then adapted to a new unseen domain, namely, data from Apollo space missions coming from the Fearless Steps Challenge. Experimental results demonstrate that domain adaptation techniques seeking to minimise the statistical distribution shift provide the most promising results. In particular, Deep CORAL method reports a 13% relative improvement in the original evaluation metric when compared to the unadapted baseline model. Further experiments show that the cascaded application of Deep CORAL and pseudo-labelling techniques can improve even more the results, yielding a significant 24% relative improvement in the evaluation metric when compared to the baseline system.
TorchEsegeta: Framework for Interpretability and Explainability of Image-Based Deep Learning Models
Authors: Chatterjee Soumick , Das Arnab , Mandal Chirag , Mukhopadhyay Budhaditya , Vipinraj Manish , Shukla Aniruddh , Nagaraja Rao Rajatha , Sarasaen Chompunuch , Speck Oliver , Nürnberger Andreas
Published: February 2022

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning-based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas that influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generates visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post hoc interpretations and explanations of their models, develop more explainable tools, and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-Flight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods.
Comparative Study of Roxarsone Reduction by Shewanella oneidensis MR-1 and Cellulomonas sp. Strain Cellu-2a
Authors: Khanal Anamika , Ho Cuong Tu , Hur Hor-Gil , Lee Ji-Hoon
Published: February 2022

Roxarsone, an organoarsenic compound, has been used extensively as an additive in poultry feed to promote animal growth. Although roxarsone is moderately toxic, it can be converted into more toxic compounds upon exposure to the environment, resulting in human health risks. This article reports on a comparative study of roxarsone reduction by fermentative, anaerobic Cellulomonas sp. Cellu-2a and a widely distributed metal-reducing bacterium, Shewanella oneidensis MR-1. The strain Cellu-2a showed a faster roxarsone reduction capability by fermentative growth than by MR-1 in anaerobic respiration. With an environmentally relevant mineral, hydrous ferric oxide (HFO) included in the incubations, there was a rather rapid abiotic (indirect, heterogeneous) reduction of roxarsone by the solid phase Fe(II) in HFO generated by reduction of Fe(III) of HFO by MR-1, while still direct reduction was major reaction by Cellu-2a even with HFO. This study provides evidence of direct and/or indirect bacterial reduction of roxarsone in fermentative conditions as well as in anaerobic respiration conditions, at exposure to the environment, which would assist understanding of environmental behaviors of roxarsone in biological settings with HFO, a naturally occurring ubiquitous mineral class.
PrimeNet: Adaptive Multi-Layer Deep Neural Structure for Enhanced Feature Selection in Early Convolution Stage
Authors: Khan Farhat Ullah , Aziz Izzatdin
Published: February 2022

The colossal depths of the deep neural network sometimes suffer from ineffective backpropagation of the gradients through all its depths, whereas the strong performance of shallower multilayer neural structures proves their ability to increase the gradient signals in the early stages of training, which easily gets backpropagated for global loss corrections. Shallow neural structures are always a good starting point for encouraging the sturdy feature characteristics of the input. In this research, a shallow, deep neural structure called PrimeNet is proposed. PrimeNet is aimed to dynamically identify and encourage the quality visual indicators from the input to be used by the subsequent deep network layers and increase the gradient signals in the lower stages of the training pipeline. In addition to this, the layer-wise training is performed with the help of locally generated errors, which means the gradient is not backpropagated to previous layers, and the hidden layer weights are updated during the forward pass, making this structure a backpropagation free variant. PrimeNet has obtained state-of-the-art results on various image datasets, attaining the dual objective of: (1) a compact dynamic deep neural structure, which (2) eliminates the problem of backward-locking. The PrimeNet unit is proposed as an alternative to traditional convolution and dense blocks for faster and memory-efficient training, outperforming previously reported results aimed at adaptive methods for parallel and multilayer deep neural systems.
Sub-Harmonic Response Analysis of Nonlinear Dynamic Behaviors Induced by Piecewise-Type Nonlinearities in a Torsional Vibratory System
Authors: Yoon Jong-Yun , Kim Byeongil
Published: February 2022

Piecewise-type nonlinearities, such as clutch dampers in a torsional system, induce complex nonlinear dynamic behaviors that resemble super- and sub-harmonic responses. This study focuses on investigating the sub-harmonic responses induced by piecewise-type nonlinearities in the middle of various dynamic behaviors in a torsional vibratory system. To examine the dynamic characteristics in a sub-harmonic regime, the harmonic balance method (HBM) was implemented. Its results were compared with the numerical simulation (NS). To reveal the sub-harmonic responses, the input conditions of the HBM were modified with a small number of input values. In addition, bifurcation diagrams were numerically determined and projected onto stable and unstable solutions of the HBM to examine the effective dynamic behaviors within the unstable regimes. The results of the HBM with the modified input conditions reveal the sub-harmonic effects well, and the comparisons of bifurcation diagrams under unstable conditions lead to an understanding of the complex dynamic behaviors. Overall, this study suggests the first analytical technique to determine the sub-harmonic responses with the HBM, and second investigates the complex dynamic behaviors in a practical vibratory system by considering the bifurcations in the unstable regimes.
A Blockchain-Applied Personal Health Record Application: Development and User Experience
Authors: Kim Ji Woong , Kim Su Jin , Cha Won Chul , Kim Taerim
Published: February 2022

This study aims to introduce a novel blockchain-applied personal health records (PHR) application and validate its user experience. The system transmits the part corresponding to the patient’s personal information off-chain and prevents data forgery and falsification by storing encrypted data on-chain. Patients may easily trace the opt-in and opt-out history of their consent data and dynamically store the consent system for data exchange on the blockchain. A mixed-method study using a questionnaire, in-depth interviews, and usability evaluation were conducted for 30 participants. The system usability score was 74.0, indicating the high usability of the application. Those who were familiar with blockchain showed confidence in the application, but those unfamiliar wanted their data to be safe using another way. Most of the participants were interested in exchanging and using their medical data and considered security important but those unfamiliar wanted their data to be safe using another way. We found that participants were concerned about data security and considered a blockchain-based PHR as a novel way to store and exchange their medical information securely. Blockchain is not a visible technology. However, a blockchain-applied PHR must be able to win user trust through visualizations, certificates, and system descriptions.
Effectiveness of Augmented Reality in Stroke Rehabilitation: A Meta-Analysis
Authors: Phan Huu Lam , Le Thi Huong , Lim Jung Min , Hwang Chang Ho , Koo Kyo-in
Published: February 2022

Augmented reality (AR)-based rehabilitation shows potential to improve upper and lower limb function after stroke. This study aims to review the effect of AR technology in the recovery of the upper and lower limb function in stroke patients. Published randomized controlled trials and observational investigations with adult stroke patients were retrieved from five electronic databases to analyze the effect of the AR systems in improving motor function and balance and gait function for stroke patients. The treatment effect was estimated by standardized mean difference (SMD) and 95% confidence interval (CI) using a random effect model for motor function outcomes at the body structure and function, body activity and participation level of the International Classification of Functioning, and balance and gait outcomes. In total, 13 investigations (9 for the upper limb and 4 for the lower limb) were identified. AR demonstrated a significant influence on the upper limb function (SMD = 0.657; 95% CI, 0.287 to 1.026; p = 0.000) and the lower limb function (SMD = 0.52; 95% CI, 0.039 to 1.001; p = 0.034). The present analysis suggests that AR applications could offer options for increasing treatment intensity and promoting motor recovery after a stroke. This approach can be used with the conventional rehabilitation methods as a new intervention for recovering upper and lower limb function.
Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification
Authors: Rukhsar Laiqa , Bangyal Waqas Haider , Ali Khan Muhammad Sadiq , Ag Ibrahim Ag Asri , Nisar Kashif , Rawat Danda B.
Published: February 2022

Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand.
Implementation of Predictive Models in Industrial Machines with Proposed Automatic Adaptation Algorithm
Authors: Kuric Ivan , Klačková Ivana , Domnina Kseniia , Stenchlák Vladimír , Sága Milan
Published: February 2022

This article describes in more detail the issue of using predictive models of NAR neural networks to predict the course of certain quantities, which may indicate a problem with the industrial machines or their major failures. It is very important to find sufficient size of the structure and values of parameters that directly affect the output accuracy of the model. This article presents the way in which it is possible to automatically find the settings of these NAR models so that the required final accuracy metric is achieved. This presented algorithm was tested on simulation data samples collected by using the M5StickC microcontroller device. This collected dataset presented in this article contains accelerometer and gyroscopic data only, but there is a possibility to expand and add some other sensors to this microcontroller, to collect some other relevant data. This M5-StickC microcontroller device can be used for gathering data in the first phase of the machine state analysis without interfering with the mechanical construction and electrical connections of the machine. Testing of proposed algorithm was carried out in MATLAB environment. The article also describes the way in which these predictive NAR neural network models can be implemented directly in control systems, specifically PLCs from the manufacturer SIEMENS without the use of 3rd party analytical platforms. This application can be helpful in the area of predictive maintenance tasks, especially in avoiding critical failures of industrial machines and devices, or some of their specific parts.
The Effect of Back Pressure Change on Exhaust Emissions According to the Confluence Geometry of a Dual Exhaust System in Idling
Authors: Kang Il-seok , Yang Sung-mo
Published: February 2022

In this study, a pressure transducer was installed in an exhaust system to analyze the effect of the change in back pressure according to the change of the confluence geometry of an exhaust pipe system on an exhaust emission. In addition, to perform exhaust gas measurement, the system was warmed up for about 40 s on the chassis dynamometer, and exhaust gas and back pressure measurements were performed simultaneously. In the back pressure measurement results, it was possible to confirm the difference in back pressure according to the change in the confluence shape. In addition, it was also confirmed that there was a clear difference in the exhaust emission measurement result. In particular, the H-type exhaust pipe system showed the highest pressure in the exhaust pipe due to the influence of the confluence geometry. Due to this influence, THC showed the highest measured value in the exhaust emission result. However, the X-type exhaust pipe system showed the lowest pressure due to the influence of the confluence geometry. Due to this influence, the THC showed the lowest measured value in the exhaust emission result. Therefore, through the conclusion of this study, an optimal exhaust system to reduce THC was proposed, and the importance of back pressure in exhaust system design was confirmed.
Design of Two-Mode Spectroscopic Sensor for Biomedical Applications: Analysis and Measurement of Relative Intensity Noise through Control Mechanism
Authors: Masud Usman , Amirzada Muhammad Rizwan , Elahi Hassan , Akram Faraz , Zeeshan Ahmed , Khan Yousuf , Ehsan Muhammad Khurram , Qureshi Muhammad Aasim , Ali Aasim , Nawaz Sajid , Ghafoor Usman
Published: February 2022

The design of an intracavity spectroscopy based two-mode biomedical sensor involves a thorough investigation of the system. For this purpose, the individual components that are present in the system must be examined. This work describes the principle of two very important gadgets, namely the Fibre Bragg Grating (FBG), and the tunable coupler. We adhere to a Petri network scheme to model and analyze the performance of the FBG, and the results mirror strikingly low difference in the values of Bragg Wavelength during its ascending and descending operational principle, thereby maintaining the accuracy of the sensor’s results. Next, a pseudocode is developed and implemented for the investigation of the optical coupler in LabView. The values of its maximum output power are determined, and the coupling ratio for various values of controlling voltage is determined at three different wavelengths. The hysteresis results mirror an extremely low difference between the forward and reverse values in the results. Both the results of the FBG and the coupler are thereby extremely reliable to use them in the laser system, as evident from the respective intensity noise outcomes, as well as the experimentation on substances of interest (Dichloro Methane and Propofol).
Quantitative Optical Coherence Tomography for Longitudinal Monitoring of Postnatal Retinal Development in Developing Mouse Eyes
Authors: Ma Guangying , Ding Jie , Kim Tae-Hoon , Yao Xincheng
Published: February 2022

A better study of postnatal retinal development is essential for the in-depth understanding of the nature of the vision system. To date, quantitative analysis of postnatal retinal development is primarily limited to endpoint histological examination. This study is to validate in vivo optical coherence tomography (OCT) for longitudinal monitoring of postnatal retinal development in developing mouse eyes. OCT images of C57BL/6J mice were recorded from postnatal day (P) 14 to P56. Three-dimensional (3D) frame registration and super averaging were adopted to investigate the fine structure of the retina. Quantitative OCT analysis revealed distinct outer and inner retinal layer changes, corresponding to eye development. At the outer retina, external limiting membrane (ELM) and ellipsoid zone (EZ) band intensities gradually increased with aging, and the IZ band was detectable by P28. At the inner retina, a hyporeflective layer (HRL) between the nerve fiber layer (NFL) and inner plexiform layer (IPL) was observed in developing eyes and gradually disappeared with aging. Further image analysis revealed individual RGCs within the HRL layer of the young mouse retina. However, RGCs were merged with the NFL and the IPL in the aged mouse retina. Moreover, the sub-IPL layer structure was observed to be gradually enhanced with aging. To interpret the observed retinal layer kinetics, a model based on eyeball expansion, cell apoptosis, and retinal structural modification was proposed.
Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks
Authors: Hazra Debapriya , Byun Yung-Cheol , Kim Woo , Kang Chul-Ung
Published: February 2022

Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preprocesses them, and uses a hybrid GAN architecture to generate synthetic images of the cell types containing fewer data. We prepared a single dataset with expert intervention by combining images from three different sources. The final dataset consists of 12 cell types and has 33,177 microscopic cell images. We use the discriminator architecture of auxiliary classifier GAN (AC-GAN) and combine it with the Wasserstein GAN with gradient penalty model (WGAN-GP). We name our model as WGAN-GP-AC. The discriminator in our proposed model works to identify real and generated images and classify every image with a cell type. We provide experimental results demonstrating that our proposed model performs better than existing individual and hybrid GAN models in generating microscopic cell images. We use the generated synthetic data with classification models, and the results prove that the classification rate increases significantly. Classification models achieved 0.95 precision and 0.96 recall value for synthetic data, which is higher than the original, augmented, or combined datasets.