bluuebunny/crossref_metadata_2025 huggingface dataset:
Measurable Residual Disease in Acute Myeloid Leukemia Using Flow Cytometry: A Review of Where We Are and Where We Are Going
Authors: Dix Caroline , Lo Tsun-Ho , Clark Georgina , Abadir Edward
Published: June 2020

The detection of measurable residual disease (MRD) has become a key investigation that plays a role in the prognostication and management of several hematologic malignancies. Acute myeloid leukemia (AML) is the most common acute leukemia in adults and the role of MRD in AML is still emerging. Prognostic markers are complex, largely based upon genetic and cytogenetic aberrations. MRD is now being incorporated into prognostic models and is a powerful predictor of relapse. While PCR-based MRD methods are sensitive and specific, many patients do not have an identifiable molecular marker. Immunophenotypic MRD methods using multiparametric flow cytometry (MFC) are widely applicable, and are based on the identification of surface marker combinations that are present on leukemic cells but not normal hematopoietic cells. Current techniques include a “different from normal” and/or a “leukemia-associated immunophenotype” approach. Limitations of MFC-based MRD analyses include the lack of standardization, the reliance on a high-quality marrow aspirate, and variable sensitivity. Emerging techniques that look to improve the detection of leukemic cells use dimensional reduction analysis, incorporating more leukemia specific markers and identifying leukemic stem cells. This review will discuss current methods together with new and emerging techniques to determine the role of MFC MRD analysis.
Hippocampal Volume in Provisional Tic Disorder Predicts Tic Severity at 12-Month Follow-up
Authors: Kim Soyoung , Greene Deanna , D’Andrea Carolina , Bihun Emily , Koller Jonathan , O’Reilly Bridget , Schlaggar Bradley , Black Kevin
Published: June 2020

Previous studies have investigated differences in the volumes of subcortical structures (e.g., caudate nucleus, putamen, thalamus, amygdala, and hippocampus) between individuals with and without Tourette syndrome (TS), as well as the relationships between these volumes and tic symptom severity. These volumes may also predict clinical outcome in Provisional Tic Disorder (PTD), but that hypothesis has never been tested. This study aimed to examine whether the volumes of subcortical structures measured shortly after tic onset can predict tic symptom severity at one-year post-tic onset, when TS can first be diagnosed. We obtained T1-weighted structural MRI scans from 41 children with PTD (25 with prospective motion correction (vNavs)) whose tics had begun less than 9 months (mean 4.04 months) prior to the first study visit (baseline). We re-examined them at the 12-month anniversary of their first tic (follow-up), assessing tic severity using the Yale Global Tic Severity Scale. We quantified the volumes of subcortical structures using volBrain software. Baseline hippocampal volume was correlated with tic severity at the 12-month follow-up, with a larger hippocampus at baseline predicting worse tic severity at follow-up. The volumes of other subcortical structures did not significantly predict tic severity at follow-up. Hippocampal volume may be an important marker in predicting prognosis in Provisional Tic Disorder.
Fatal Outcomes of COVID-19 in Patients with Severe Acute Kidney Injury
Authors: Lim Jeong-Hoon , Park Sun-Hee , Jeon Yena , Cho Jang-Hee , Jung Hee-Yeon , Choi Ji-Young , Kim Chan-Duck , Lee Yong-Hoon , Seo Hyewon , Lee Jaehee , Kwon Ki Tae , Kim Shin-Woo , Chang Hyun-Ha , Kim Yong-Lim
Published: June 2020

The outcome of coronavirus disease 2019 (COVID-19) is associated with organ damage; however, the information about the relationship between acute kidney injury (AKI) and COVID-19 is still rare. We evaluated the clinical features and prognosis of COVID-19 patients with AKI according to the AKI severity. Medical data of hospitalized COVID-19 patients in two university-based hospitals during an outbreak in Daegu, South Korea, were retrospectively analyzed. AKI and its severity were defined according to the Acute Kidney Injury Network. Of the 164 hospitalized patients with COVID-19, 30 patients (18.3%) had AKI; 14, 4, and 12 patients had stage 1, 2, and 3, respectively. The median age was significantly higher in AKI patients than in non-AKI patients (75.5 vs. 67.0 years, p = 0.005). There were 17 deaths (56.7%) among AKI patients; 4 (28.6%), 1 (25.0%), and 12 (100.0%), respectively. In-hospital mortality was higher in AKI patients than in non-AKI patients (56.7% vs. 20.8%, p < 0.001). After adjusting for potential confounding factors, stage 3 AKI was associated with higher mortality than either non-AKI or stage 1 AKI (hazard ratio (HR) = 3.62 (95% confidence interval (CI) = 1.75–7.48), p = 0.001; HR = 15.65 (95% CI = 2.43–100.64), p = 0.004). Among the AKI patients, acute respiratory distress syndrome and low serum albumin on admission were considered independent risk factors for stage 3 AKI (both p < 0.05). Five patients with stage 3 AKI underwent dialysis and eventually died. In conclusion, COVID-19 patients with severe AKI had fatal outcomes.
Acupuncture Treatment Modulates the Connectivity of Key Regions of the Descending Pain Modulation and Reward Systems in Patients with Chronic Low Back Pain
Authors: Yu Siyi , Ortiz Ana , Gollub Randy L. , Wilson Georgia , Gerber Jessica , Park Joel , Huang Yiting , Shen Wei , Chan Suk-Tak , Wasan Ajay D. , Edwards Robert R. , Napadow Vitaly , Kaptchuk Ted J. , Rosen Bruce , Kong Jian
Published: June 2020

Chronic low back pain (cLBP) is a common disorder with unsatisfactory treatment options. Acupuncture has emerged as a promising method for treating cLBP. However, the mechanism underlying acupuncture remains unclear. In this study, we investigated the modulation effects of acupuncture on resting state functional connectivity (rsFC) of the periaqueductal gray (PAG) and ventral tegmental area (VTA) in patients with cLBP. Seventy-nine cLBP patients were recruited and assigned to four weeks of real or sham acupuncture. Resting state functional magnetic resonance imaging data were collected before the first and after the last treatment. Fifty patients completed the study. We found remission of pain bothersomeness in all treatment groups after four weeks, with greater pain relief after real acupuncture compared to sham acupuncture. We also found that real acupuncture can increase VTA/PAG rsFC with the amygdala, and the increased rsFC was associated with decreased pain bothersomeness scores. Baseline PAG-amygdala rsFC could predict four-week treatment response. Our results suggest that acupuncture may simultaneously modulate the rsFC of key regions in the descending pain modulation (PAG) and reward systems (VTA), and the amygdala may be a key node linking the two systems to produce antinociceptive effects. Our findings highlight the potential of acupuncture for chronic low back pain management.
Optical Coherence Tomography Angiography in Diabetes and Diabetic Retinopathy
Authors: Chua Jacqueline , Sim Ralene , Tan Bingyao , Wong Damon , Yao Xinwen , Liu Xinyu , Ting Daniel S. W. , Schmidl Doreen , Ang Marcus , Garhöfer Gerhard , Schmetterer Leopold
Published: June 2020

Diabetic retinopathy (DR) is a common complication of diabetes mellitus that disrupts the retinal microvasculature and is a leading cause of vision loss globally. Recently, optical coherence tomography angiography (OCTA) has been developed to image the retinal microvasculature, by generating 3-dimensional images based on the motion contrast of circulating blood cells. OCTA offers numerous benefits over traditional fluorescein angiography in visualizing the retinal vasculature in that it is non-invasive and safer; while its depth-resolved ability makes it possible to visualize the finer capillaries of the retinal capillary plexuses and choriocapillaris. High-quality OCTA images have also enabled the visualization of features associated with DR, including microaneurysms and neovascularization and the quantification of alterations in retinal capillary and choriocapillaris, thereby suggesting a promising role for OCTA as an objective technology for accurate DR classification. Of interest is the potential of OCTA to examine the effect of DR on individual retinal layers, and to detect DR even before it is clinically detectable on fundus examination. We will focus the review on the clinical applicability of OCTA derived quantitative metrics that appear to be clinically relevant to the diagnosis, classification, and management of patients with diabetes or DR. Future studies with longitudinal design of multiethnic multicenter populations, as well as the inclusion of pertinent systemic information that may affect vascular changes, will improve our understanding on the benefit of OCTA biomarkers in the detection and progression of DR.
Randomized Trial of General Strength and Conditioning versus Motor Control and Manual Therapy for Chronic Low Back Pain on Physical and Self-Report Outcomes
Authors: Tagliaferri Scott D. , Miller Clint T. , Ford Jon J. , Hahne Andrew J. , Main Luana C. , Rantalainen Timo , Connell David A. , Simson Katherine J. , Owen Patrick J. , Belavy Daniel L.
Published: June 2020

Exercise and spinal manipulative therapy are commonly used for the treatment of chronic low back pain (CLBP) in Australia. Reduction in pain intensity is a common outcome; however, it is only one measure of intervention efficacy in clinical practice. Therefore, we evaluated the effectiveness of two common clinical interventions on physical and self-report measures in CLBP. Participants were randomized to a 6-month intervention of general strength and conditioning (GSC; n = 20; up to 52 sessions) or motor control exercise plus manual therapy (MCMT; n = 20; up to 12 sessions). Pain intensity was measured at baseline and fortnightly throughout the intervention. Trunk extension and flexion endurance, leg muscle strength and endurance, paraspinal muscle volume, cardio-respiratory fitness and self-report measures of kinesiophobia, disability and quality of life were assessed at baseline and 3- and 6-month follow-up. Pain intensity differed favoring MCMT between-groups at week 14 and 16 of treatment (both, p = 0.003), but not at 6-month follow-up. Both GSC (mean change (95%CI): −10.7 (−18.7, −2.8) mm; p = 0.008) and MCMT (−19.2 (−28.1, −10.3) mm; p < 0.001) had within-group reductions in pain intensity at six months, but did not achieve clinically meaningful thresholds (20mm) within- or between-group. At 6-month follow-up, GSC increased trunk extension (mean difference (95% CI): 81.8 (34.8, 128.8) s; p = 0.004) and flexion endurance (51.5 (20.5, 82.6) s; p = 0.004), as well as leg muscle strength (24.7 (3.4, 46.0) kg; p = 0.001) and endurance (9.1 (1.7, 16.4) reps; p = 0.015) compared to MCMT. GSC reduced disability (−5.7 (−11.2, −0.2) pts; p = 0.041) and kinesiophobia (−6.6 (−9.9, −3.2) pts; p < 0.001) compared to MCMT at 6-month follow-up. Multifidus volume increased within-group for GSC (p = 0.003), but not MCMT or between-groups. No other between-group changes were observed at six months. Overall, GSC improved trunk endurance, leg muscle strength and endurance, self-report disability and kinesiophobia compared to MCMT at six months. These results show that GSC may provide a more diverse range of treatment effects compared to MCMT.
Design of Chatter-Resistant Damped Boring Bars Using a Receptance Coupling Approach
Authors: Yadav Ajay , Talaviya Devangkumar , Bansal Ankit , Law Mohit
Published: June 2020

Deep hole boring using slender bars that have tuned mass dampers integrated within them make the boring process chatter vibration resistant. Dampers are usually designed using classical analytical solutions that presume the (un)damped boring bar which can be approximated by a single degree of freedom system, and the damper is placed at the free end. Since the free end is also the cutting end, analytical models may result in infeasible design solutions. To place optimally tuned dampers within boring bars, but away from the free end, this paper presents a receptance coupling approach in which the substructural receptances of the boring bar modelled as a cantilevered Euler–Bernoulli beam are combined with the substructural receptances of a damper modelled as a rigid mass integrated anywhere within the bar. The assembled and damped system response thus obtained is used to predict the chatter-free machining stability limit. Maximization of this limit is treated as the objective function to find the optimal mass, stiffness and damping of the absorber. Proposed solutions are first verified against other classical solutions for assumed placement of the absorber at the free end. Verified models then guide prototyping of a boring bar integrated with a damper placed away from its free end. Experiments demonstrate a ~100-fold improvement in chatter vibration free machining capability. The generalized methods presented herein can be easily extended to design and develop other damped and chatter-resistant tooling systems.
Surface Characteristics and Color Stability of Gingiva-Colored Resin Composites
Authors: Petropoulou Aikaterini , Dimitriadi Maria , Zinelis Spiros , Sarafianou Aspasia , Eliades George
Published: June 2020

The purpose of this study was to investigate the surface characteristics and color stability of gingiva-colored composite restorative materials (Anaxgum—ANG, Ceramage—CMG and Gradia Gum—GRG). The microstructure, composition, degree of conversion (DC %) and 3D roughness (Sa, Sz, Sdr, Sc) were examined by LV-SEM/EDS, ATR-FTIR and optical profilometry, respectively. For the color stability (CIE L*, a*, b* system) and hardness (HV), measurements were performed at baseline and after 30 days storage in distilled water, coffee and red wine. The ANG and GRG contain prepolymerized particles in aromatic and aliphatic resin matrices, respectively, whereas CMG contains inorganic zirconia silicate/silica particles, in an aromatic resin matrix, with a smaller particle size and a higher surface area fraction. Urethane monomers were mainly identified in CMG and GRG. The DC% showed statistically insignificant differences between the materials. The same applied for the roughness parameters, except for the greatest Sdr in CMG. ANG showed a color difference (ΔE) of > 3.3 after immersion in all media, CMG in coffee and wine and GRG only in coffee. Sc was the only roughness parameter demonstrating correlations with the ΔL*, Δb* and ΔE*. The HV values showed insignificant differences between the storage conditions per material. There are important differences in the color stability of the materials tested, which were mostly affected by the roughness parameters due to variations in their microstructure.
Non-Deterministic Assessment of Surface Roughness as Bond Strength Parameters between Concrete Layers Cast at Different Ages
Authors: Kozubal Janusz , Wróblewski Roman , Muszyński Zbigniew , Wyjadłowski Marek , Stróżyk Joanna
Published: June 2020

The importance of surface roughness and its non-destructive examination has often been emphasised in structural rehabilitation. The presented innovative procedure enables the estimation of concrete-to-concrete strength based on a combination of low-cost, area-limited tests and geostatistical methods. The new method removes the shortcomings of the existing one, i.e., it is neither qualitative nor subjective. The interface strength factors, cohesion and friction, can be estimated accurately based on the collected data on a surface texture. The data acquisition needed to create digital models of the concrete surface can be performed by terrestrial close-range photogrammetry or other methods. In the presented procedure, limitations to the availability of concrete surfaces are overcome by the generation of subsequential Gaussian random fields (via height profiles) based on the semivariograms fitted to the digital surface models. In this way, the randomness of the surface texture is reproduced. The selected roughness parameters, such as mean valley depth and, most importantly, the geostatistical semivariogram parameter sill, were transformed into contact bond strength parameters based on the available strength tests. The proposed procedure estimates the interface bond strength based on the geostatistical methods applied to the numerical surface model and can be used in practical and theoretical applications.
Correction: Elduque, A., et al. Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application
Authors: Elduque Ana , Elduque Daniel , Pina Carmelo , Clavería Isabel , Javierre Carlos
Published: June 2020

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Coordination Supply Chain Management Under Flexible Manufacturing, Stochastic Leadtime Demand, and Mixture of Inventory
Authors: Malik Asif Iqbal , Sarkar Biswajit
Published: June 2020

The necessity of coordination among entities is essential for the success of any supply chain management (SCM). This paper focuses on coordination between two players and cost-sharing in an SCM that considers a vendor and a buyer. For random demand and complex product production, a flexible production system is recommended. The study aims to minimize the total SCM cost under stochastic conditions. In the flexible production systems, the production rate is introduced as the decision variable and the unit production cost is minimum at the obtained optimal value. The setup cost of flexible systems is higher and to control this, a discrete investment function is utilized. The exact information about the probability distribution of lead time demand is not available with known mean and variance. The issue of unknown distribution of lead time demand is solved by considering a distribution-free approach to find the amount of shortages. The game-theoretic approach is employed to obtain closed-form solutions. First, the model is solved under decentralized SCM based on the Stackelberg model, and then solved under centralized SCM. Bargaining is the central theme of any business nowadays among the players of an SCM to make their profit within a centralized and decentralized setup. For this, a cost allocation model for lead time crashing cost based on the Nash bargaining model with the satisfaction level of SCM members is proposed. The cost allocation model under Nash bargaining achieves exciting results in SCM coordination.
In-Situ Biofabrication of Silver Nanoparticles in Ceiba pentandra Natural Fiber Using Entada spiralis Extract with Their Antibacterial and Catalytic Dye Reduction Properties
Authors: Wan Mat Khalir Wan Khaima Azira , Shameli Kamyar , Jazayeri Seyed Davoud , Othman Nor Azizi , Che Jusoh Nurfatehah Wahyuny , Mohd Hassan Norazian
Published: June 2020

It is believed of great interest to incorporate silver nanoparticles (Ag-NPs) into stable supported materials using biological methods to control the adverse properties of nanoscale particles. In this study, in-situ biofabrication of Ag-NPs using Entada spiralis (E. spiralis) aqueous extract in Ceiba pentandra (C. pentandra) fiber as supporting material was used in which, the E. spiralis extract acted as both reducing and stabilizing agents to incorporate Ag-NPs in the C. pentandra fiber. The properties of Ag-NPs incorporated in the C. pentandra fiber (C. pentandra/Ag-NPs) were characterized using UV-visible spectroscopy (UV-vis), X-ray Diffraction (XRD), Field Emission Transmission Electron Microscope (FETEM), Scanning Electron Microscope (Scanning Electron Microscope (SEM), Energy Dispersive X-ray (EDX), Brunauer-Emmett-Teller (BET), Thermogravimetric (TGA) and Fourier Transform Infrared (FTIR) analyses. The average size of Ag-NPs measured using FETEM image was 4.74 nm spherical in shape. The C. pentandra/Ag-NPs was easily separated after application, and could control the release of Ag-NPs to the environment due to its strong attachment in C. pentandra fiber. The C. pentandra/Ag-NPs exposed good qualitative and quantitative antibacterial activities against Staphylococcus aureus (ATCC 25923), Enterococcus faecalis (ATCC 29212), Escherichia coli (ATCC 25922) and Proteus vulgaris (ATCC 33420). The dye catalytic properties of C. pentandra/Ag-NPs revealed the dye reduction time in which it was completed within 4 min for 20 mg/L rhodamine B and 20 min for 20 mg/L methylene blue dye, respectively. Based on the results, it is evident that C. pentandra/Ag-NPs are potentially promising to be applied in wound healing, textile, wastewater treatment, food packaging, labeling and biomedical fields.
Hot-Melt Extrusion Process Fluctuations and Their Impact on Critical Quality Attributes of Filaments and 3D-Printed Dosage Forms
Authors: Ponsar Hanna , Wiedey Raphael , Quodbach Julian
Published: June 2020

Fused deposition modeling (FDMTM) is a 3D-printing technology of rising interest for the manufacturing of customizable solid dosage forms. The coupling of hot-melt extrusion with FDMTM is favored to allow the production of pharma-grade filaments for the printing of medicines. Filament diameter consistency is a quality of great importance to ensure printability and content uniformity of 3D-printed drug delivery systems. A systematical process analysis referring to filament diameter variations has not been described in the literature. The presented study aimed at a process setup optimization and rational process analysis for filament fabrication related to influencing parameters on diameter inhomogeneity. In addition, the impact of diameter variation on the critical quality attributes of filaments (mechanical properties) and uniformity of mass of printed drug-free dosage forms was investigated. Process optimization by implementing a winder with a special haul-off unit was necessary to obtain reliable filament diameters. Subsequently, the optimized setup was used for conduction of rational extrusion analysis. The results revealed that an increased screw speed led to diameter fluctuations with a decisive influence on the mechanical resilience of filaments and mass uniformity of printed dosage forms. The specific feed load was identified as a key parameter for filament diameter consistency.
Genomic Insight into Differentiation and Selection Sweeps in the Improvement of Upland Cotton
Authors: Nazir Mian Faisal , Jia Yinhua , Ahmed Haris , He Shoupu , Iqbal Muhammad Shahid , Sarfraz Zareen , Ali Mushtaque , Feng Chenfan , Raza Irum , Sun Gaofei , Pan Zhaoe , Du Xiongming
Published: June 2020

Upland cotton is the most economically important fibre crop. The human-mediated selection has resulted in modern upland cultivars with higher yield and better fibre quality. However, changes in genome structure resulted from human-mediated selection are poorly understood. Comparative population genomics offers us tools to dissect the genetic history of domestication and helps to understand the genome-wide effects of human-mediated selection. Hereby, we report a comprehensive assessment of Gossypium hirsutum landraces, obsolete cultivars and modern cultivars based on high throughput genome-wide sequencing of the core set of genotypes. As a result of the genome-wide scan, we identified 93 differential regions and 311 selection sweeps associated with domestication and improvement. Furthermore, we performed genome-wide association studies to identify traits associated with the differential regions and selection sweeps. Our study provides a genetic basis to understand the domestication process in Chinese cotton cultivars. It also provides a comprehensive insight into changes in genome structure due to selection and improvement during the last century. We also identified multiple genome-wide associations (GWAS associations) for fibre yield, quality and other morphological characteristics.
Biology and Management of Vulpia myuros—An Emerging Weed Problem in No-Till Cropping Systems in Europe
Authors: Akhter Muhammad Javaid , Jensen Peter Kryger , Mathiassen Solvejg Kopp , Melander Bo , Kudsk Per
Published: June 2020

Recently, Vulpia myuros has become a problematic grass weed species in parts of Europe. It is most common in no-till cropping systems. The inherent tolerance to several selective grass weed herbicides is of serious concern to the successful management of V. myuros in arable farming. Here, we reviewed the available knowledge about the biology of V. myuros to identify knowledge gaps and assess management efforts to identify best practices for control. V. myuros is a winter-annual species producing seeds with a short dormancy that can germinate at a wide range of conditions. Seed longevity in the soil is short. Little information is available on the influence of V. myuros on crop yield but some results suggest that yield losses can be significant. The findings provide a better understanding of the weedy characteristics of V. myuros and highlight that management strategies in Europe need to be diversified and integrate preventive and cultural control methods. Finally, we identify some of the management tools that should be considered to minimize the impact of V. myuros on European farming and future needs for research to develop sustainable integrated weed management strategies.
Time to Acceptance of 3 Days for Papers About COVID-19
Authors: Kun Ádám
Published: June 2020

Time to acceptance from submission and time to publication (publication lag) determines how quickly novel information is made available to other scientists and experts. In the medical field, the review process and revisions usually takes 3–4 months; the total time from submission to publication is 8–9 months. During the COVID-19 pandemic, information should be available much faster. The analysis of 833 documents published on SARS-CoV-2 and COVID-19 prior to 19 March 2020 shows that these times shrunk by a factor of ten. The median time to acceptance was three days for all publications, six days for research papers and reviews, four days for case studies and two days for other publication types. The median publication lag was nine days for all publications together, 11 days for research papers, nine days for case studies, 13 days for reviews and seven days for other publications. This demonstrates that the publication process—if necessary—can be sped up. For the sake of scientific accuracy, review times should not be pushed down, but the time from acceptance to actual publication could be shorter.
A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring
Authors: Akanbi Adeyinka , Masinde Muthoni
Published: June 2020

In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.
Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing
Authors: Barros Wysterlânya K. P. , Morais Daniel S. , Lopes Felipe F. , Torquato Matheus F. , Barbosa Raquel de M. , Fernandes Marcelo A. C.
Published: June 2020

This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the features from the analyzed skin, and the MLP classifies the lesion into melanoma or non-melanoma. The classification results are validated with an open-access database. Finally, analysis regarding execution time, hardware resources usage, and power consumption are performed. The results obtained through this analysis are then compared to an equivalent software implementation embedded in an ARM A9 microprocessor.
Optimization Complete Area Coverage by Reconfigurable hTrihex Tiling Robot
Authors: Le Anh Vu , Parween Rizuwana , Elara Mohan Rajesh , Nhan Nguyen Huu Khanh , Enjikalayil Abdulkader Raihan
Published: June 2020

Completed area coverage planning (CACP) plays an essential role in various fields of robotics, such as area exploration, search, rescue, security, cleaning, and maintenance. Tiling robots with the ability to change their shape is a feasible solution to enhance the ability to cover predefined map areas with flexible sizes and to access the narrow space constraints. By dividing the map into sub-areas with the same size as the changeable robot shapes, the robot can plan the optimal movement to predetermined locations, transform its morphologies to cover the specific area, and ensure that the map is completely covered. The optimal navigation planning problem, including the least changing shape, shortest travel distance, and the lowest travel time while ensuring complete coverage of the map area, are solved in this paper. To this end, we propose the CACP framework for a tiling robot called hTrihex with three honeycomb shape modules. The robot can shift its shape into three different morphologies ensuring coverage of the map with a predetermined size. However, the ability to change shape also raises the complexity issues of the moving mechanisms. Therefore, the process of optimizing trajectories of the complete coverage is modeled according to the Traveling Salesman Problem (TSP) problem and solved by evolutionary approaches Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Hence, the costweight to clear a pair of waypoints in the TSP is defined as the required energy shift the robot between the two locations. This energy corresponds to the three operating processes of the hTrihex robot: transformation, translation, and orientation correction. The CACP framework is verified both in the simulation environment and in the real environment. From the experimental results, proposed CACP capable of generating the Pareto-optimal outcome that navigates the robot from the goal to destination in various workspaces, and the algorithm could be adopted to other tiling robot platforms with multiple configurations.
Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images
Authors: Bannister Jordan J. , Crites Sebastian R. , Aponte J. David , Katz David C. , Wilms Matthias , Klein Ophir D. , Bernier Francois P. J. , Spritz Richard A. , Hallgrímsson Benedikt , Forkert Nils D.
Published: June 2020

3D facial landmarks are known to be diagnostically relevant biometrics for many genetic syndromes. The objective of this study was to extend a state-of-the-art image-based 2D facial landmarking algorithm for the challenging task of 3D landmark identification on subjects with genetic syndromes, who often have moderate to severe facial dysmorphia. The automatic 3D facial landmarking algorithm presented here uses 2D image-based facial detection and landmarking models to identify 12 landmarks on 3D facial surface scans. The landmarking algorithm was evaluated using a test set of 444 facial scans with ground truth landmarks identified by two different human observers. Three hundred and sixty nine of the subjects in the test set had a genetic syndrome that is associated with facial dysmorphology. For comparison purposes, the manual landmarks were also used to initialize a non-linear surface-based registration of a non-syndromic atlas to each subject scan. Compared to the average intra- and inter-observer landmark distances of 1.1 mm and 1.5 mm respectively, the average distance between the manual landmark positions and those produced by the automatic image-based landmarking algorithm was 2.5 mm. The average error of the registration-based approach was 3.1 mm. Comparing the distributions of Procrustes distances from the mean for each landmarking approach showed that the surface registration algorithm produces a systemic bias towards the atlas shape. In summary, the image-based automatic landmarking approach performed well on this challenging test set, outperforming a semi-automatic surface registration approach, and producing landmark errors that are comparable to state-of-the-art 3D geometry-based facial landmarking algorithms evaluated on non-syndromic subjects.
FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
Authors: Liang Yihuai , Li Yan , Shin Byeong-Seok
Published: June 2020

Crowdsensing applications provide platforms for sharing sensing data collected by mobile devices. A blockchain system has the potential to replace a traditional centralized trusted third party for crowdsensing services to perform operations that involve evaluating the quality of sensing data, finishing payment, and storing sensing data and so forth. The requirements which are codified as smart contracts are executed to evaluate the quality of sensing data in a blockchain. However, regardless of the fact that the quality of sensing data may actually be sufficient, one key challenge is that malicious requesters can deliberately publish abnormal requirements that cause failure to occur in the quality evaluation process. If requesters control a miner node or full node, they can access the data without making payment; this is because of the transparency of data stored in the blockchain. This issue promotes unfair dealing and severely lowers the motivation of workers to participate in crowdsensing tasks. We (i) propose a novel crowdsensing scheme to address this issue using Trusted Execution Environments; (ii) offer a solution for the confidentiality and integrity of sensing data, which is only accessible by the worker and corresponding requester; (iii) and finally, report on the implementation of a prototype and evaluate its performance. Our results demonstrate that the proposed solution can guarantee fairness without a significant increase in overhead.
Comparison of Gas Sensing Properties of Reduced Graphene Oxide Obtained by Two Different Methods
Authors: Drewniak Sabina , Procek Marcin , Muzyka Roksana , Pustelny Tadeusz
Published: June 2020

In this study, the sensitivity of reduced graphene oxide structures (rGO) to the action of selected gases (especially hydrogen, but also nitrogen dioxide and ammonia) was examined. Two sensing structures, based on rGO structures, obtained by different methods of oxidation (the modified Hummers, and the modified Tour’s method respectively), were investigated. We show here that the method used for the oxidation of rGO influences the sensitivity of the sensing structure during contact with various gaseous atmospheres. We performed our experiments in the atmosphere, containing hydrogen in a concentration range from 0 to 4% in nitrogen or synthetic air, both in dry and wet conditions. The temperature range was from 50 °C to 190 °C. Finally, we checked how the resistance of the samples changes when the other gases (NO2, NH3) appear in tested gas mixtures. The gas investigations were supplemented by the characterization of rGOs materials using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and N2 sorption method.
Highly Porous Platinum Electrodes for Dry Ear-EEG Measurements
Authors: Eickenscheidt Max , Schäfer Patrick , Baslan Yara , Schwarz Claudia , Stieglitz Thomas
Published: June 2020

The interest in dry electroencephalography (EEG) electrodes has increased in recent years, especially as everyday suitability earplugs for measuring drowsiness or focus of auditory attention. However, the challenge is still the need for a good electrode material, which is reliable and can be easily processed for highly personalized applications. Laser processing, as used here, is a fast and very precise method to produce personalized electrode configurations that meet the high requirements of in-ear EEG electrodes. The arrangement of the electrodes on the flexible and compressible mats allows an exact alignment to the ear mold and contributes to high wearing comfort, as no edges or metal protrusions are present. For better transmission properties, an adapted coating process for surface enlargement of platinum electrodes is used, which can be controlled precisely. The resulting porous platinum-copper alloy is chemically very stable, shows no exposed copper residues, and enlarges the effective surface area by 40. In a proof-of-principle experiment, these porous platinum electrodes could be used to measure the Berger effect in a dry state using just one ear of a test person. Their signal-to-noise ratio and the frequency transfer function is comparable to gel-based silver/silver chloride electrodes.
A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution
Authors: Guo Yangyang , Chai Lilong , Aggrey Samuel E. , Oladeinde Adelumola , Johnson Jasmine , Zock Gregory
Published: June 2020

The proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that can monitor the chicken’s floor distributions. In the current study, a machine vision-based method was developed and tested in an experimental broiler house. For the new method to recognize bird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately to avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles were extracted from images collected from 18 to 35 days of age to build a BP neural network model for floor distribution analysis, and another 200 images were used to validate the model. The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of broiler distribution. Missed detections were mainly caused by interference with the equipment (e.g., the feeder hanging chain and water line); studies are ongoing to address these issues. This study provides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution and behavior in commercial facilities.
Special Issue on Intelligent Systems in Sensor Networks and Internet of Things
Authors: Choi Chang , D’Angelo Gianni , Palmieri Francesco
Published: June 2020

This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions and face with the emerging challenges.
Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI
Authors: Khan Zia , Yahya Norashikin , Alsaih Khaled , Ali Syed Saad Azhar , Meriaudeau Fabrice
Published: June 2020

In this paper, we present an evaluation of four encoder–decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder–decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to 92.8 % . This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.
Melatonin Potentiates the Therapeutic Effects of Metformin in Women with Metabolic Syndrome
Authors: Abood Sattar J. , Abdulsahib Waleed K. , Hussain Saad A. , Ismail Sajida H.
Published: June 2020

Objective: This study evaluated the effect of melatonin on the response of patients suffering from metabolic syndrome (MEBS) treated with metformin. Design: This study used two-armed groups in a double-blind, randomized controlled clinical trial. Materials and Methods: A randomized double-blind placebo-controlled study was carried out on female patients diagnosed as having MEBS, according to the International Diabetes Federation (IDF) diagnosing criteria of MEBS (2005), from the outpatient clinic in Al-Zahraa Teaching Hospital/Kut, Iraq. They were diagnosed utilizing laboratory and clinical investigations, then randomized into two groups. The first group (group A) was treated with metformin (500 mg) twice daily, in addition to a placebo formula once daily at bedtime for three months. The second group (group B) was treated with metformin (500 mg) twice daily after meals, in addition to melatonin (10 mg) once daily at bedtime for three months. Results: The treatment of patients with MEBS using metformin–melatonin showed an improvement in most MEBS components such as fasting serum glucose (FSG), lipid profile, and body mass index (BMI), in addition to a reduction in insulin resistance and hyperinsulinemia. Simultaneously, there were increments in serum uric acid (UA), leptin, prolactin (PRL), and estradiol levels, while serum progesterone level decreased. Furthermore, patients treated with metformin–placebo showed less improvement in the studied parameters compared to that produced due to the inclusion of melatonin in the treatment protocol. Conclusion: Melatonin improves the effect of metformin on several components of MEBS such as FSG, lipid profile, and BMI, in addition to insulin resistance and hyperinsulinemia, compared to metformin alone.
Two Types of Support for Redistribution of Wealth: Consistent and Inconsistent Policy Preferences
Authors: Sudo Naoki
Published: June 2020

This article aims to clarify the latent structure of support for redistribution. To this end, the author analyzed data from the National Survey of Social Stratification and Social Mobility in 2015 (SSM 2015), which was conducted in Japan, using finite mixtures of regression models. The results revealed that the population could be categorized into two latent groups: one that determines preferences for social policies based on self-interest and another that does so based on ideology. Surprisingly, the results also showed that, compared to those who supported redistribution of wealth based on ideology, those who supported them based on self-interest were more likely to hold inconsistent preferences (e.g., simultaneous support for redistribution of wealth and free-market competition). This observation implies that, even when individuals want to determine their policy preferences rationally, they often do not have enough information to correctly assess the influence of each social policy on their self-interest.
Vaccine Advances against Venezuelan, Eastern, and Western Equine Encephalitis Viruses
Authors: Stromberg Zachary R. , Fischer Will , Bradfute Steven B. , Kubicek-Sutherland Jessica Z. , Hraber Peter
Published: June 2020

Vaccinations are a crucial intervention in combating infectious diseases. The three neurotropic Alphaviruses, Eastern (EEEV), Venezuelan (VEEV), and Western (WEEV) equine encephalitis viruses, are pathogens of interest for animal health, public health, and biological defense. In both equines and humans, these viruses can cause febrile illness that may progress to encephalitis. Currently, there are no licensed treatments or vaccines available for these viruses in humans. Experimental vaccines have shown variable efficacy and may cause severe adverse effects. Here, we outline recent strategies used to generate vaccines against EEEV, VEEV, and WEEV with an emphasis on virus-vectored and plasmid DNA delivery. Despite candidate vaccines protecting against one of the three viruses, few studies have demonstrated an effective trivalent vaccine. We evaluated the potential of published vaccines to generate cross-reactive protective responses by comparing DNA vaccine sequences to a set of EEEV, VEEV, and WEEV genomes and determining the vaccine coverages of potential epitopes. Finally, we discuss future directions in the development of vaccines to combat EEEV, VEEV, and WEEV.