bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Diabetes Mellitus on Cardiovascular Risk Onset in Children and Adolescents
Authors: Pastore Ida , Bolla Andrea Mario , Montefusco Laura , Lunati Maria Elena , Rossi Antonio , Assi Emma , Zuccotti Gian Vincenzo , Fiorina Paolo
Published: July 2020

The prevalence of diabetes mellitus is rising among children and adolescents worldwide. Cardiovascular diseases are the main cause of morbidity and mortality in diabetic patients. We review the impact of diabetes on establishing, during childhood and adolescence, the premises for cardiovascular diseases later in life. Interestingly, it seems that hyperglycemia is not the only factor that establishes an increased cardiovascular risk in adolescence. Other factors have been recognized to play a role in triggering the onset of latent cardiovascular diseases in the pediatric population. Among these cardiovascular risk factors, some are modifiable: glucose variability, hypoglycemia, obesity, insulin resistance, waist circumference, hypertension, dyslipidemia, smoking alcohol, microalbuminuria and smoking. Others are unmodifiable, such as diabetes duration and family history. Among the etiological factors, subclinical endothelial dysfunction represents one of the earliest key players of atherosclerosis and it can be detected during early ages in patients with diabetes. A better assessment of cardiovascular risk in pediatric population still represents a challenge for clinicians, and thus further efforts are required to properly identify and treat pediatric patients who may suffer from cardiovascular disease later in early adulthood.
Copper Homeostasis in Mammals, with Emphasis on Secretion and Excretion. A Review
Authors: Linder Maria C.
Published: July 2020

One of the hallmarks of Cu metabolism in mammals is that tissue and fluid levels are normally maintained within a very narrow range of concentrations. This results from the ability of the organism to respond to variations in intake from food and drink by balancing excretion, which occurs mainly via the bile and feces. Although this sounds straightforward and we have already learned a great deal about aspects of this process, the balance between overall intake and excretion occurs over a high background of Cu recycling, which has generally been ignored. In fact, most of the Cu absorbed from the GI tract actually comes from digestive fluids and is constantly “re-used”. A great deal more recycling of Cu probably occurs in the interior, between cells of individual tissues and the fluid of the blood and interstitium. This review presents what is known that is pertinent to understanding these complexities of mammalian Cu homeostasis and indicates where further studies are needed.
A Confirmation for the Positive Electric Charge of Bio-Molecular Motors through Utilizing a Novel Nano-Technology Approach In Vitro
Authors: Feizabadi Mitra Shojania , Alejilat Ramiz S. , Duffy Alexis B. , Breslin Jane C. , Akintola Ibukunoluwa I.
Published: July 2020

Molecular motors are microtubule-based proteins which contribute to many cell functions, such as intracellular transportation and cell division. The details of the nature of the mutual interactions between motors and microtubules still needs to be extensively explored. However, electrostatic interaction is known as one of the key factors making motor-microtubule association possible. The association rate of molecular motors to microtubules is a way to observe and evaluate the charge of the bio-motors in vivo. Growing evidence indicates that microtubules with distinct structural compositions in terms of beta tubulin isotypes carry different charges. Therefore, the electrostatic-driven association rate of motors–microtubules, which is a base for identifying the charge of motors, can be more likely influenced. Here, we present a novel method to experimentally confirm the charge of molecular motors in vitro. The offered nanotechnology-based approach can validate the charge of motors in the absence of any cellular components through the observation and analysis of the changes that biomolecular motors can cause on the dynamic of charged microspheres inside a uniform electric field produced by a microscope slide-based nanocapacitor. This new in vitro experimental method is significant as it minimizes the intracellular factors that may interfere the electric charge that molecular motors carry.
Calmodulin-Cork Model of Gap Junction Channel Gating—One Molecule, Two Mechanisms
Authors: Peracchia Camillo
Published: July 2020

The Calmodulin-Cork gating model is based on evidence for the direct role of calmodulin (CaM) in channel gating. Indeed, chemical gating of cell-to-cell channels is sensitive to nanomolar cytosolic calcium concentrations [Ca2+]i. Calmodulin inhibitors and inhibition of CaM expression prevent chemical gating. CaMCC, a CaM mutant with higher Ca2+-sensitivity greatly increases chemical gating sensitivity (in CaMCC the NH2-terminal EF-hand pair (res. 9–76) is replaced by the COOH-terminal pair (res. 82–148). Calmodulin colocalizes with connexins. Connexins have high-affinity CaM binding sites. Several connexin mutants paired to wild-type connexins have a high gating sensitivity that is eliminated by inhibition of CaM expression. Repeated transjunctional voltage (Vj) pulses slowly and progressively close a large number of channels by the chemical/slow gate (CaM lobe). At the single-channel level, the chemical/slow gate closes and opens slowly with on-off fluctuations. The model proposes two types of CaM-driven gating: “Ca-CaM-Cork” and “CaM-Cork”. In the first, gating involves Ca2+-induced CaM-activation. In the second, gating takes place without [Ca2+]i rise. The Ca-CaM-Cork gating is only reversed by a return of [Ca2+]i to resting values, while the CaM-Cork gating is reversed by Vj positive at the gated side.
BC-Box Motif in SOCS6 Induces Differentiation of Epidermal Stem Cells into GABAnergic Neurons
Authors: Yoshizumi Tetsuya , Kubo Atsuhiko , Murata Hidetoshi , Shinonaga Masamichi , Kanno Hiroshi
Published: July 2020

The BC-box motif in suppressor of cytokine signaling 6 (SOCS6) promotes the neuronal differentiation of somatic stem cells, including epidermal stem cells. SOCS6 protein belongs to the group of SOCS proteins and inhibits cytokine signaling. Here we showed that epidermal stem cells were induced to differentiate into GABAnergic neurons by the intracellular delivery of a peptide composed of the amino-acid sequences encoded by the BC-box motif in SOCS6 protein. The BC-box motif (SLQYLCRFVI) in SOCS6 corresponded to the binding site of elongin BC. GABAnergic differentiation mediated by the BC-box motif in SOCS6 protein was caused by ubiquitination of JAK2 and inhibition of the JAK2-STAT3 pathway. Furthermore, GABAnergic neuron-like cells generated from epidermal stem cells were transplanted into the brain of a rodent ischemic model. Then, we demonstrated that these transplanted cells were GAD positive and that the cognitive function of the ischemic model rodents with the transplanted cells was improved. This study could contribute to not only elucidating the mechanism of GABAnergic neuronal differentiation but also to neuronal regenerative medicine utilizing GABAnergic neurons.
COVID-19/SARS-CoV-2 Infection: Lysosomes and Lysosomotropism Implicate New Treatment Strategies and Personal Risks
Authors: Blaess Markus , Kaiser Lars , Sauer Martin , Csuk René , Deigner Hans-Peter
Published: July 2020

In line with SARS and MERS, the SARS-CoV-2/COVID-19 pandemic is one of the largest challenges in medicine and health care worldwide. SARS-CoV-2 infection/COVID-19 provides numerous therapeutic targets, each of them promising, but not leading to the success of therapy to date. Neither an antiviral nor an immunomodulatory therapy in patients with SARS-CoV-2 infection/COVID-19 or pre-exposure prophylaxis against SARS-CoV-2 has proved to be effective. In this review, we try to close the gap and point out the likely relationships among lysosomotropism, increasing lysosomal pH, SARS-CoV-2 infection, and disease process, and we deduce an approach for the treatment and prophylaxis of COVID-19, and cytokine release syndrome (CRS)/cytokine storm triggered by bacteria or viruses. Lysosomotropic compounds affect prominent inflammatory messengers (e.g., IL-1B, CCL4, CCL20, and IL-6), cathepsin-L-dependent viral entry of host cells, and products of lysosomal enzymes that promote endothelial stress response in systemic inflammation. As supported by recent clinical data, patients who have already taken lysosomotropic drugs for other pre-existing conditions likely benefit from this treatment in the COVID-19 pandemic. The early administration of a combination of antivirals such as remdesivir and lysosomotropic drugs, such as the antibiotics teicoplanin or dalbavancin, seems to be able to prevent SARS-CoV-2 infection and transition to COVID-19.
Targeting Discoidin Domain Receptor 1 (DDR1) Signaling and Its Crosstalk with β1-Integrin Emerges as a Key Factor for Breast Cancer Chemosensitization upon Collagen Type 1 Binding
Authors: Baltes Fabian , Caspers Julia , Henze Svenja , Schlesinger Martin , Bendas Gerd
Published: July 2020

Collagen type 1 (COL1) is a ubiquitously existing extracellular matrix protein whose high density in breast tissue favors metastasis and chemoresistance. COL1-binding of MDA-MB-231 and MCF-7 breast cancer cells is mainly dependent on β1-integrins (ITGB1). Here, we elucidate the signaling of chemoresistance in both cell lines and their ITGB1-knockdown mutants and elucidated MAPK pathway to be strongly upregulated upon COL1 binding. Notably, Discoidin Domain Receptor 1 (DDR1) was identified as another important COL1-sensor, which is permanently active but takes over the role of COL1-receptor maintaining MAPK activation in ITGB1-knockdown cells. Consequently, inhibition of DDR1 and ERK1/2 act synergistically, and sensitize the cells for cytostatic treatments using mitoxantrone, or doxorubicin, which was associated with an impaired ABCG2 drug efflux transporter activity. These data favor DDR1 as a promising target for cancer cell sensitization, most likely in combination with MAPK pathway inhibitors to circumvent COL1 induced transporter resistance axis. Since ITGB1-knockdown also induces upregulation of pEGFR in MDA-MB-231 cells, inhibitory approaches including EGFR inhibitors, such as gefitinib appear promising for pharmacological interference. These findings provide evidence for the highly dynamic adaptation of breast cancer cells in maintaining matrix binding to circumvent cytotoxicity and highlight DDR1 signaling as a target for sensitization approaches.
Spiegelmer-Based Sandwich Assay for Cardiac Troponin I Detection
Authors: Tolnai Zoltán János , András Judit , Szeitner Zsuzsanna , Percze Krisztina , Simon László Ferenc , Gyurcsányi Róbert E. , Mészáros Tamás
Published: July 2020

Two subunits of the ternary troponin complex, I and C, have cardiac muscle specific isoforms, and hence could be applied as highly-selective markers of acute coronary syndrome. We aimed at paving the way for the development of a robust cardiac troponin I-detecting sandwich assay by replacing antibodies with nuclease resistant aptamer analogues, so-called spiegelmers. To complement the previously generated spiegelmers that were specific for the N-terminus of cTnI, spiegelmers were selected for an amino acid stretch in the proximity of the C-terminal part of the protein by using a D-amino acid composed peptide. Following the selection, the oligonucleotides were screened by filter binding assay, and surface plasmon resonance analysis of the most auspicious candidates demonstrated that this approach could provide spiegelmers with subnanomolar dissociation constant. To demonstrate if the selected spiegelmers are functional and suitable for cTnI detection in a sandwich type arrangement, AlphaLisa technology was leveraged and the obtained results demonstrated that spiegelmers with different epitope selectivity are suitable for specific detection of cTnI protein even in human plasma containing samples. These results suggest that spiegelmers could be considered in the development of the next generation cTnI monitoring assays.
Perspective of Dose and Response for Individualized Physical Exercise and Training Prescription
Authors: Gronwald Thomas , Törpel Alexander , Herold Fabian , Budde Henning
Published: July 2020

Physical interventions are used to increase physical (sports) performance and considered as effective low-cost strategies in the fields of healthcare, disease or injury prevention, and medical treatment. In general, a considerable amount of evidence buttress the application of physical interventions in various fields as it has been demonstrated to contribute to the maintenance and recovery of physical performance, cognitive function, and overall state of health. To implement physical interventions effectively, it is essential to provide an appropriate exercise and training prescription. Exercise and training prescription are key for “dose” specification and for the individualization (personalizing) of physical exercise and training, precisely adjusted and controlled like medication. Since the physiological response to physical interventions is demonstrably individual and dependent on many influencing factors, individualization is an emerging approach aiming to maximize the efficiency of an intervention by accounting for the interindividual heterogeneity. The present brief viewpoint article aims to distinguish and to redefine between the terms dose and response in order to improve the understanding of practitioners, the methodology of study protocols, and to relate future findings to the actual biological (interindividual) variability of acute and chronic responses.
Inventory Tracking for Unstructured Environments via Probabilistic Reasoning
Authors: Rajaraman Mabaran , Bannerman Kyle , Shimada Kenji
Published: July 2020

Workpiece location is critical to efficiently plan actions downstream in manufacturing processes. In labor-intensive heavy industries, like construction and shipbuilding, multiple stakeholders interact, stack and move workpieces in the absence of any system to log such actions. While track-by-detection approaches rely on sensing technologies such as Radio Frequency Identification (RFID) and Global Positioning System (GPS), cluttered environments and stacks of workpieces pose several limitations to their adaptation. These challenges limit the usage of such technology to presenting the last known position of a workpiece with no further guidance on a search strategy. In this work we show that a multi-hypothesis tracking approach that models human reasoning can provide a search strategy based on available observations of a workpiece. We show that inventory tracking problems under uncertainty can be approached like probabilistic inference approaches in localization to detect, estimate and update the belief of the workpiece locations. We present a practical Internet-of-Things (IoT) framework for information collection over which we build our reasoning. We also present the ability of our system to accommodate additional constraints to prune search locations. Finally, in our experiments we show that our approach can provide a significant reduction against the conventional search for missing workpieces, of up to 80% in workpieces to visit and 60% in distance traveled. In our experiments we highlight the critical nature of identifying stacking events and inferring locations using reasoning to aid searches even when direct observation of a workpiece is not available.
First-Principles Study on the Cu/Fe Interface Properties of Ternary Cu-Fe-X Alloys
Authors: Wang Yufei , Li Min , Gao Haiyan , Wang Jun , Sun Baode
Published: July 2020

The supersaturated Fe in Cu is known to reduce the electrical conductivity of Cu severely. However, the precipitation kinetics of Fe from Cu are sluggish. Alloying is one of the effective ways to accelerate the aging precipitation of Cu-Fe alloys. Nucleation plays an important role in the early stage of aging. The interface property of Cu/γ–Fe is a key parameter in understanding the nucleation mechanism of γ-Fe, which can be obviously affected with the addition of alloying elements. In this paper, first principles calculations were carried out to investigate the influence of alloying elements on the interface properties, including the geometric optimizations, interfacial energy, work of adhesion and electronic structure. Based on the previous research, 14 elements including B, Si, P, Al, Ge, S, Mg, Ag, Cd, Sn, In, Sb, Zr and Bi were selected for investigation. Results showed that all these alloying elements tend to concentrate in the Cu matrix with the specific substitution position of the atoms determined by the binding energy between Fe and alloy element (X). The bonding strength of the Cu/γ-Fe interface will decrease obviously after adding Ag, Mg and Cd, while a drop in interfacial energy of Cu/γ–Fe will happen when alloyed with Al, B, S, P, Si, Ge, Sn, Zr, Bi, Sb and In. Further study of the electronic structure found that Al and Zr were not effective alloying elements.
A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam
Authors: Wang Chia-Nan , Nguyen Minh Nhat , Le Anh Luyen , Tibo Hector
Published: July 2020

The food and beverage industry plays a significant role in the economic development of developing and emerging countries in Asia through an immense contribution to the national income, employment, value-added inducement, and foreign exchange earnings. Among the developing countries in Asia, Thailand and Vietnam have recently experienced a significant growth in the industry due to their many advantages. However, the nascent stage of this industry was found to be lacking sustainable competitiveness in both countries. Therefore, this study aims to evaluate and forecast the performance efficiency of the food and beverage industry in Thailand and Vietnam to understand how efficient the food and beverage industry to these countries is and formulate suggestions to improve their productivity in accordance with the research findings. To achieve the research objectives, the resampling method in the data envelopment analysis is applied to measure and forecast the efficiency of 20 Vietnamese companies and 20 Thailand firms over the period of 2016 to 2023. The Malmquist productivity index is deployed to calculate the efficiency change over observed periods. The results reveal that Vietnam is found to have a higher efficiency than Thailand due to the outstanding performance of one company but have performed quite poorly due to low scores in technical and productivity change. The findings of this research can give useful information and practical suggestions to improve performance for inefficient companies as well as enhance competitiveness of the efficient companies trying to operate and reach global markets.
Second Order Expansions for High-Dimension Low-Sample-Size Data Statistics in Random Setting
Authors: Christoph Gerd , Ulyanov Vladimir V.
Published: July 2020

We consider high-dimension low-sample-size data taken from the standard multivariate normal distribution under assumption that dimension is a random variable. The second order Chebyshev–Edgeworth expansions for distributions of an angle between two sample observations and corresponding sample correlation coefficient are constructed with error bounds. Depending on the type of normalization, we get three different limit distributions: Normal, Student’s t-, or Laplace distributions. The paper continues studies of the authors on approximation of statistics for random size samples.
The Influence of Quench Interruption and Direct Artificial Aging on the Hardening Response in AA6082 during Hot Deformation and In-Die Quenching
Authors: Myrold Benedikte , Jensrud Ola , Snilsberg Knut Erik
Published: July 2020

The automotive industry is searching for lightweight solutions to meet emission regulations. Development of an integrated hot forming and in-die quenching process will leverage use of age-hardenable aluminum alloys with high specific strength for applications in volume car manufacturing. Quench interruption and direct artificial aging may reduce the cycle time in a die-quenching process. However, this alters the temperature exposure of the part and results in an altered precipitation and clustering sequence during hardening. To investigate the effect of modified precipitation and clustering on mechanical properties, the process has been simulated by application of a water-cooled compression tool to control the combination of a temperature drop and simultaneous deformation prior to aging. Extruded 4.6 mm thick AA6082 profiles were deformed during different quenching rates and directly transferred to subsequent artificial aging from various temperatures between room temperature and 200 °C. The results indicate insignificant changes of strength and elongation after direct aging from 200 °C compared to specimens cooled to room temperature before aging.
Current Trends of Essential Trace Elements in Patients with Chronic Liver Diseases
Authors: Himoto Takashi , Masaki Tsutomu
Published: July 2020

Essential trace elements play crucial roles in the maintenance of health, since they are involved in many metabolic pathways. A deficiency or an excess of some trace elements, including zinc, selenium, iron, and copper, frequently causes these metabolic disorders such as impaired glucose tolerance and dyslipidemia. The liver largely regulates most of the metabolism of trace elements, and accordingly, an impairment of liver functions can result in numerous metabolic disorders. The administration or depletion of these trace elements can improve such metabolic disorders and liver dysfunction. Recent advances in molecular biological techniques have helped to elucidate the putative mechanisms by which liver disorders evoke metabolic abnormalities that are due to deficiencies or excesses of these trace elements. A genome-wide association study revealed that a genetic polymorphism affected the metabolism of a specific trace element. Gut dysbiosis was also responsible for impairment of the metabolism of a trace element. This review focuses on the current trends of four trace elements in chronic liver diseases, including chronic hepatitis, liver cirrhosis, nonalcoholic fatty liver disease, and autoimmune liver diseases. The novel mechanisms by which the trace elements participated in the pathogenesis of the chronic liver diseases are also mentioned.
Resources 2019 Best Paper Awards
Authors:   Resources Editorial Office
Published: July 2020

Resources is instituting the Best Paper Awards to recognize the outstanding papers published in the journal [...]
How Risky Are the Options? A Comparison with the Underlying Stock Using MaxVaR as a Risk Measure
Authors: Patra Saswat , Bhattacharyya Malay
Published: July 2020

This paper investigates the risk exposure for options and proposes MaxVaR as an alternative risk measure which captures the risk better than Value-at-Risk especially. While VaR is a measure of end-of-horizon risk, MaxVaR captures the interim risk exposure of a position or a portfolio. MaxVaR is a more stringent risk measure as it assesses the risk during the risk horizon. For a 30-day maturity option, we find that MaxVaR can be 40% higher than VaR at a 5% significance level. It highlights the importance of MaxVaR as a risk measure and shows that the risk is vastly underestimated when VaR is used as the measure for risk. The sensitivity of MaxVaR with respect to option characteristics like moneyness, time to maturity and risk horizons at different significance levels are observed. Further, interestingly enough we find that the MaxVar to VaR ratio is higher for stocks than the options and we can surmise that stock returns are more volatile than options. For robustness, the study is carried out under different distributional assumptions on residuals and for different stock index options.
Underwater Localization via Wideband Direction-of-Arrival Estimation Using Acoustic Arrays of Arbitrary Shape
Authors: Dubrovinskaya Elizaveta , Kebkal Veronika , Kebkal Oleksiy , Kebkal Konstantin , Casari Paolo
Published: July 2020

Underwater sensing and remote telemetry tasks necessitate the accurate geo-location of sensor data series, which often requires underwater acoustic arrays. These are ensembles of hydrophones that can be jointly operated in order to, e.g., direct acoustic energy towards a given direction, or to estimate the direction of arrival of a desired signal. When the available equipment does not provide the required level of accuracy, it may be convenient to merge multiple transceivers into a larger acoustic array, in order to achieve better processing performance. In this paper, we name such a structure an “array of opportunity” to signify the often inevitable sub-optimality of the resulting array design, e.g., a distance between nearest array elements larger than half the shortest acoustic wavelength that the array would receive. The most immediate consequence is that arrays of opportunity may be affected by spatial ambiguity, and may require additional processing to avoid large errors in wideband direction of arrival (DoA) estimation, especially as opposed to narrowband processing. We consider the design of practical algorithms to achieve accurate detections, DoA estimates, and position estimates using wideband arrays of opportunity. For this purpose, we rely jointly on DoA and rough multilateration estimates to eliminate spatial ambiguities arising from the array layout. By means of emulations that realistically reproduce underwater noise and acoustic clutter, we show that our algorithm yields accurate DoA and location estimates, and in some cases it allows arrays of opportunity to outperform properly designed arrays. For example, at a signal-to-noise ratio of –20 dB, a 15-element array of opportunity achieves lower average and median localization error (27 m and 12 m, respectively) than a 30-element array with proper λ / 2 element spacing (33 m and 15 m, respectively). We confirm the good accuracy of our approach via emulation results, and through a proof-of-concept lake experiment, where our algorithm applied to a 10-element array of opportunity achieves a 90th-percentile DoA estimation error of 4 ∘ and a 90th-percentile total location error of 5 m when applied to a real 10-element array of opportunity.
Using Sensor Data to Detect Lameness and Mastitis Treatment Events in Dairy Cows: A Comparison of Classification Models
Authors: Post Christian , Rietz Christian , Büscher Wolfgang , Müller Ute
Published: July 2020

The aim of this study was to develop classification models for mastitis and lameness treatments in Holstein dairy cows as the target variables based on continuous data from herd management software with modern machine learning methods. Data was collected over a period of 40 months from a total of 167 different cows with daily individual sensor information containing milking parameters, pedometer activity, feed and water intake, and body weight (in the form of differently aggregated data) as well as the entered treatment data. To identify the most important predictors for mastitis and lameness treatments, respectively, Random Forest feature importance, Pearson’s correlation and sequential forward feature selection were applied. With the selected predictors, various machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), Extra Trees Classifier (ET) and different ensemble methods such as Random Forest (RF) were trained. Their performance was compared using the receiver operator characteristic (ROC) area-under-curve (AUC), as well as sensitivity, block sensitivity and specificity. In addition, sampling methods were compared: Over- and undersampling as compensation for the expected unbalanced training data had a high impact on the ratio of sensitivity and specificity in the classification of the test data, but with regard to AUC, random oversampling and SMOTE (Synthetic Minority Over-sampling) even showed significantly lower values than with non-sampled data. The best model, ET, obtained a mean AUC of 0.79 for mastitis and 0.71 for lameness, respectively, based on testing data from practical conditions and is recommended by us for this type of data, but GNB, LR and RF were only marginally worse, and random oversampling and SMOTE even showed significantly lower values than without sampling. We recommend the use of these models as a benchmark for similar self-learning classification tasks. The classification models presented here retain their interpretability with the ability to present feature importances to the farmer in contrast to the “black box” models of Deep Learning methods.
Ambulatory Electrocardiographic Monitoring and Ectopic Beat Detection in Conscious Mice
Authors: Steijns Felke , Tóth Máté I. , Demolder Anthony , Larsen Lars E. , Desloovere Jana , Renard Marjolijn , Raedt Robrecht , Segers Patrick , De Backer Julie , Sips Patrick
Published: July 2020

Ambulatory electrocardiography (AECG) is a primary diagnostic tool in patients with potential arrhythmic disorders. To study the pathophysiological mechanisms of arrhythmic disorders, mouse models are widely implemented. The use of a technique similar to AECG for mice is thus of great relevance. We have optimized a protocol which allows qualitative, long-term ECG data recording in conscious, freely moving mice. Automated algorithms were developed to efficiently process the large amount of data and calculate the average heart rate (HR), the mean peak-to-peak interval and heart rate variability (HRV) based on peak detection. Ectopic beats are automatically detected based on aberrant peak intervals. As we have incorporated a multiple lead configuration in our ECG set-up, the nature and origin of the suggested ectopic beats can be analyzed in detail. The protocol and analysis tools presented here are promising tools for studies which require detailed, long-term ECG characterization in mouse models with potential arrhythmic disorders.
Selective Generation of Lamb Wave Modes in a Finite-Width Plate by Angle-Beam Excitation Method
Authors: Park Sang-Jin , Joo Young-Sang , Kim Hoe-Woong , Kim Sung-Kyun
Published: July 2020

A Lamb wave in a plate with a finite width has both thickness and width modes, whereas only thickness modes exist in an infinitely wide plate. The thickness and width modes are numerously formed in a finite-width plate, and they all have different cut-off frequencies, wave velocities, and wave structures. These different characteristics can be utilized in various applications, but a selective generation method for a particular Lamb wave mode in a finite-width plate has not been sufficiently studied, and only a method using multiple elements has been reported. This paper presents the selective generation of a certain Lamb wave mode in a finite-width plate by an angle-beam excitation method using single or dual wedges. In the proposed generation method, a specially designed wedge with grooves or a patch having insulation layers is employed for partial acoustic insulation of the ultrasonic energy incident into the plate. The feasibility of the proposed method was investigated through finite element method (FEM) simulations for Lamb wave excitation and propagation, and then experimentally demonstrated by the measurement of Lamb wave propagation using a laser scanning vibrometer.
Restriction Endonuclease-Based Assays for DNA Detection and Isothermal Exponential Signal Amplification
Authors: Smith Maria , Smith Kenneth , Olstein Alan , Oleinikov Andrew , Ghindilis Andrey
Published: July 2020

Application of restriction endonuclease (REase) enzymes for specific detection of nucleic acids provides for high assay specificity, convenience and low cost. A direct restriction assay format is based on the specific enzymatic cleavage of a target–probe hybrid that is accompanied with the release of a molecular marker into the solution, enabling target quantification. This format has the detection limit in nanomolar range. The assay sensitivity is improved drastically to the attomolar level by implementation of exponential signal amplification that is based on a cascade of self-perpetuating restriction endonuclease reactions. The cascade is started by action of an amplification “trigger”. The trigger is immobilized through a target-specific probe. Upon the target probe hybridization followed with specific cleavage, the trigger is released into the reaction solution. The solution is then added to the assay amplification stage, and the free trigger induces cleavage of amplification probes, thus starting the self-perpetuating cascade of REase-catalyzed events. Continuous cleavage of new amplification probes leads to the exponential release of new triggers and rapid exponential signal amplification. The proposed formats exemplify a valid isothermal alternative to qPCR with similar sensitivity achieved at a fraction of the associated costs, time and labor. Advantages and challenges of the approach are discussed.
Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview
Authors: Song Mingming , Behmanesh Iman , Moaveni Babak , Papadimitriou Costas
Published: July 2020

Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview of hierarchical Bayesian model updating which has been recently developed for probabilistic integration of models with measured data, while accounting for different sources of uncertainties and modeling errors. The proposed hierarchical Bayesian framework allows one to explicitly account for pertinent sources of variability such as ambient temperatures and/or excitation amplitudes, as well as modeling errors, and therefore yields more realistic predictions. The paper reports observations from applications of hierarchical approach to three full-scale civil structural systems, namely (1) a footbridge, (2) a 10-story reinforced concrete (RC) building, and (3) a damaged 2-story RC building. The first application highlights the capability of accounting for temperature effects within the hierarchical framework, while the second application underlines the effects of considering bias for prediction error. Finally, the third application considers the effects of excitation amplitude on structural response. The findings underline the importance and capabilities of the hierarchical Bayesian framework for structural identification. Discussions of its advantages and performance over classical deterministic and Bayesian model updating methods are provided.
A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors
Authors: Zhu Tiantian , Weng Zhengqiu , Chen Guolang , Fu Lei
Published: July 2020

With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.
Simultaneous Coercivity and Size Determination of Magnetic Nanoparticles
Authors: Coene Annelies , Leliaert Jonathan
Published: July 2020

Magnetic nanoparticles are increasingly employed in biomedical applications such as disease detection and tumor treatment. To ensure a safe and efficient operation of these applications, a noninvasive and accurate characterization of the particles is required. In this work, a magnetic characterization technique is presented in which the particles are excited by specific pulsed time-varying magnetic fields. This way, we can selectively excite nanoparticles of a given size so that the resulting measurement gives direct information on the size distribution without the need for any a priori assumptions or complex postprocessing procedures to decompose the measurement signal. This contrasts state-of-the-art magnetic characterization techniques. The possibility to selectively excite certain particle types opens up perspectives in “multicolor” particle imaging, where different particle types need to be imaged independently within one sample. Moreover, the presented methodology allows one to simultaneously determine the size-dependent coercivity of the particles. This is not only a valuable structure–property relation from a fundamental point of view, it is also practically relevant to optimize applications like magnetic particle hyperthermia. We numerically demonstrate that the novel characterization technique can accurately reconstruct several particle size distributions and is able to retrieve the coercivity–size relation of the particles. The developed technique advances current magnetic nanoparticle characterization possibilities and opens up exciting pathways for biomedical applications and particle imaging procedures.
Estimation for Runway Friction Coefficient Based on Multi-Sensor Information Fusion and Model Correlation
Authors: Niu Yadong , Zhang Sixiang , Tian Guangjun , Zhu Huabo , Zhou Wei
Published: July 2020

Friction is a crucial factor affecting air accident occurrence on landing or taking off. Tire–runway friction directly contributes to aircraft stability on land. Therefore, an accurate friction estimation is a rising issue for all stakeholders. This paper summarizes the existing measurement methods, and a multi-sensor information fusion scheme is proposed to estimate the friction coefficient between the tire and the runway. Acoustic sensors, optical sensors, tread sensors, and other physical sensors form a sensor system that is used to measure friction-related parameters and fuse them through a neural network. So far, many attempts have been made to link the ground friction coefficient with the aircraft braking friction coefficient. The models that have been developed include the International Runway Friction Index (IRFI), Canada Runway Friction Index (CRFI), and other fitting models. Additionally, this paper attempts to correlate the output of the neural network (estimated friction coefficient) with the correlation model to predict the friction coefficient between the tire and the runway when the aircraft brakes. The sensor system proposed in this paper can be regarded as a mobile weather–runway–tire system, which can estimate the friction coefficient by integrating the runway surface conditions and the tire conditions, and fully consider their common effects. The role of the correlation model is to convert the ground friction coefficient to the grade of the aircraft braking friction coefficient and the information is finally reported to the pilots so that they can make better decisions.
Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review
Authors: Khan Md Ashfaque Hossain , Rao Mulpuri V.
Published: July 2020

In the last two decades, GaN nanostructures of various forms like nanowires (NWs), nanotubes (NTs), nanofibers (NFs), nanoparticles (NPs) and nanonetworks (NNs) have been reported for gas sensing applications. In this paper, we have reviewed our group’s work and the works published by other groups on the advances in GaN nanostructures-based sensors for detection of gases such as hydrogen (H2), alcohols (R-OH), methane (CH4), benzene and its derivatives, nitric oxide (NO), nitrogen dioxide (NO2), sulfur-dioxide (SO2), ammonia (NH3), hydrogen sulfide (H2S) and carbon dioxide (CO2). The important sensing performance parameters like limit of detection, response/recovery time and operating temperature for different type of sensors have been summarized and tabulated to provide a thorough performance comparison. A novel metric, the product of response time and limit of detection, has been established, to quantify and compare the overall sensing performance of GaN nanostructure-based devices reported so far. According to this metric, it was found that the InGaN/GaN NW-based sensor exhibits superior overall sensing performance for H2 gas sensing, whereas the GaN/(TiO2–Pt) nanowire-nanoclusters (NWNCs)-based sensor is better for ethanol sensing. The GaN/TiO2 NWNC-based sensor is also well suited for TNT sensing. This paper has also reviewed density-functional theory (DFT)-based first principle studies on the interaction between gas molecules and GaN. The implementation of machine learning algorithms on GaN nanostructured sensors and sensor array has been analyzed as well. Finally, gas sensing mechanism on GaN nanostructure-based sensors at room temperature has been discussed.
Synthesis, Photophysical Characterization, and Sensor Activity of New 1,8-Naphthalimide Derivatives
Authors: Yordanova-Tomova Stanislava , Cheshmedzhieva Diana , Stoyanov Stanimir , Dudev Todor , Grabchev Ivo
Published: July 2020

Three new 1,8-naphthalimide derivatives M1–M3 with different substituents at the C-4 position have been synthesized and characterized. Their photophysical properties have been investigated in organic solvents of different polarity, and their fluorescence intensity was found to depend strongly on both the polarity of the solvents and the type of substituent at C-4. For compounds M1 and M2 having a tertiary amino group linked via an ethylene bridge to the chromophore system, high quantum yield was observed only in non-polar media, whereas for compound M3, the quantum efficiency did not depend on the medium polarity. The effect of different metal ions (Ag+, Ba2+, Cu2+, Co2+, Mg2+, Pb2+, Sr2+, Fe3+, and Sn2+) on the fluorescence emission of compounds M1 and M2 was investigated. A significant enhancement has been observed in the presence of Ag+, Pb2+, Sn2+, Co2+, Fe3+, as this effect is expressed more preferably in the case of M2. Both compounds have shown significant pH dependence, as the fluorescence intensity was low in alkaline medium and has been enhanced more than 20-fold in acidic medium. The metal ions and pH do not affect the fluorescence intensity of M3. Density-functional theory (DFT) and Time-dependent density-functional theory (TDDFT) quantum chemical calculations are employed in deciphering the intimate mechanism of sensor mechanism. The functional properties of M1 and M2 were compared with polyamidoamine (PAMAM) dendrimers of different generations modified with 1,8-naphthalimide.
An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm
Authors: Chowdhary Chiranji Lal , Mittal Mohit , P. Kumaresan , Pattanaik P. A. , Marszalek Zbigniew
Published: July 2020

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.
Surface Plasmon Resonance Sensor Based on Dual-Side Polished Microstructured Optical Fiber with Dual-Core
Authors: Han Haixia , Hou Donglian , Luan Nannan , Bai Zhenxu , Song Li , Liu Jianfei , Hu Yongsheng
Published: July 2020

A surface plasmon resonance (SPR) sensor based on a dual-side polished microstructured optical fiber (MOF) with a dual core is proposed for a large analyte refractive index (RI; na) detection range. Gold is used as a plasmonic material coated on the polished surface, and analytes can be directly contacted with the gold film. The special structure not only facilitates the fabrication of the sensor, but also can work in the na range of 1.42–1.46 when the background material RI is 1.45, which is beyond the reach of other traditional MOF-SPR sensors. The sensing performance of the sensor was investigated by the wavelength and amplitude interrogation methods. The detailed numerical results showed that the proposed sensor can work effectively in the na range of 1.35–1.47 and exhibits higher sensitivity in the na range of 1.42–1.43.
Dynamics of the Prefrontal Cortex during Chess-Based Problem-Solving Tasks in Competition-Experienced Chess Players: An fNIR Study
Authors: Pereira Telmo , Castro Maria António , Villafaina Santos , Carvalho Santos António , Fuentes-García Juan Pedro
Published: July 2020

This study aimed to compare the dynamics of the prefrontal cortex (PFC), between adult and adolescent chess players, during chess-based problem-solving tasks of increasing level of difficulty, relying on the identification of changes in oxygenated hemoglobin (HbO2) and hemoglobin (HHb) through the functional near-infrared spectroscopy (fNIRS) method. Thirty male federated chess players (mean age: 24.15 ± 12.84 years), divided into adults and adolescents, participated in this cross-sectional study. Participants were asked to solve three chess problems with different difficulties (low, medium, and high) while changes in HbO2 and HHb were measured over the PFC in real-time with an fNIRS system. Results indicated that the left prefrontal cortex (L-PFC) increased its activation with the difficulty of the task in both adolescents and adults. Interestingly, differences in the PFC dynamics but not in the overall performance were found between adults and adolescents. Our findings contributed to a better understanding of the PFC resources mobilized during complex tasks in both adults and adolescents.