bluuebunny/crossref_metadata_2025 huggingface dataset:
Observational Study on Lameness Recovery in 10 Dogs Affected by Iliopsoas Injury and Submitted to a Physiotherapeutic Approach
Authors: Spinella Giuseppe , Davoli Benedetta , Musella Vincenzo , Dragone Ludovica
Published: February 2021

This preliminary study has investigated the outcome of physiotherapy in 10 dogs affected by a primary lesion of the iliopsoas muscle and the potential effects of sex, age, and breed on recovery. Ten dogs with primary injury of the iliopsoas muscle were retrospectively included in this study. Dogs were submitted to a rehabilitation program, characterized by a multimodal approach, including physical therapies and modalities. After recovery, patients were submitted to a further support period of rehabilitation to promote muscle strengthening and limit injury recurrence during their return to normal functional and sports activity. Border collies were highly represented. The recovery of lameness occurred after a mean of 22.6 ± 14.7 (median 18) days with a median number of five sessions. None of the examined variables affected the recovery time, except for the border collie breed, which showed a significantly faster recovery time; however, there was no difference between the breeds with regard to the number of sessions. Multimodal rehabilitation therapy may promote lameness recovery of mild-to-moderate iliopsoas lesions within 3 weeks. This preliminary study reports a clinical approach and recovery of primary iliopsoas lesions, findings that can provide clinicians with useful prognostic information for dogs involved in sports activities.
Glycolysis Combined with Core Pluripotency Factors to Promote the Formation of Chicken Induced Pluripotent Stem Cells
Authors: Yuan Xia , Zhang Chen , Zhao Ruifeng , Jiang Jingyi , Shi Xiang , Zhang Ming , Sun Hongyan , Zuo Qisheng , Zhang Yani , Song Jiuzhou , Chen Guohong , Li Bichun
Published: February 2021

Somatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) in vitro. Previously, a lentivirus induction strategy of introducing Oct4, Sox2, Nanog and Lin28 (OSNL) into the iPSC process has been shown as a possible way to produce chicken iPSCs from chicken embryonic fibroblasts, but the induction efficiency of this method was found to be significantly limiting. In order to help resolve this efficiency obstacle, this study seeks to clarify the associated regulation mechanisms and optimizes the reprogramming strategy of chicken iPSCs. This study showed that glycolysis and the expression of glycolysis-related genes correlate with a more efficient reprogramming process. At the same time, the transcription factors Oct4, Sox2 and Nanog were found to activate the expression of glycolysis-related genes. In addition, we introduced two small-molecule inhibitors (2i-SP) as a “glycolysis activator” together with the OSNL cocktail, and found that this significantly improved the induction efficiency of the iPSC process. As such, the study identifies direct molecular connections between core pluripotency factors and glycolysis during the chicken iPSC induction process and, with its results, provides a theoretical basis and technical support for chicken somatic reprogramming.
Population Density and Driving Factors of North China Leopards in Tie Qiao Shan Nature Reserve
Authors: Zhu Mengyan , Zaman Muhammad , Wang Meng , Vitekere Kasereka , Ma Jianzhang , Jiang Guangshun
Published: February 2021

The North China leopard (Panthera pardus japonesis) is a rare leopard subspecies distributed only in China. In this study, we conducted camera-trap surveys of a North China Leopard population in Tie Qiao Shan Nature Reserve, Shanxi Province, China. We estimated population abundance and density distribution, and explored the effects of distribution of different prey populations, habitat, and anthropogenic factors on the spatial distribution of North China leopard density. Our results suggested that the North China leopard density was 4.23 individuals/100 km2, and that 17.98 individuals might live within the study area. The population density of the North China leopard increased with the distribution of wild boars, and, on the contrary, decreased with the distribution of roe deer. We found that habitat environmental factors and anthropogenic interference also significantly affected the population density and spatial distribution of the North China leopard. These insights informed us that in order to protect this predator, which is only distributed in China, we should adopt a comprehensive customized adaptive landscape protection strategy.
Thermal Mechanisms Preventing or Favoring Multiple Ovulations in Dairy Cattle
Authors: De Rensis Fabio , Morini Giorgio , Garcia-Ispierto Irina , López-Gatius Fernando
Published: February 2021

While cows are predominantly monovular, over the past 30 years the incidence of multiple ovulations and thus twinning has increased considerably alongside milk production. Multiple pregnancies are not desirable as they negatively affect the health of cows and the herd economy. Although causal mechanisms associated with multiple ovulations have been extensively revised, the process of multiple ovulations is not well understood. Recent studies on the thermal biology of the reproductive system have shown how thermal mechanisms may prevent or favor multiple ovulations. This review focuses on this relationship between thermal dynamics and multiple pregnancies. Cooling of the pre-ovulatory follicle is able to regulate ovulation. In effect, pre-ovulatory local cooling of the female reproductive system favors male and female gamete maturation and promotes fertilization. Thermal stress is proposed here as a model of stress. Periods of high ambient temperature affect the processes of pre-ovulatory follicular cooling and multiple ovulations. While the ratio between unilateral and bilateral multiple pregnancies is normally close to one, under heat stress conditions, this ratio may be 1.4 favoring unilateral multiple pregnancies. A ratio approaching unity is here proposed as an indicator of cow wellbeing.
Antioxidant Activities and Protective Effects of Dendropachol, a New Bisbibenzyl Compound from Dendrobium pachyglossum, on Hydrogen Peroxide-Induced Oxidative Stress in HaCaT Keratinocytes
Authors: Warinhomhoun Sakan , Muangnoi Chawanphat , Buranasudja Visarut , Mekboonsonglarp Wanwimon , Rojsitthisak Pornchai , Likhitwitayawuid Kittisak , Sritularak Boonchoo
Published: February 2021

Five compounds including a new bisbibenzyl named dendropachol (1) and four known compounds (2–5) comprising 4,5-dihydroxy-2,3-dimethoxy-9,10-dihydrophenanthrene (2), gigantol (3), moscatilin (4) and 4,5,4′-trihydroxy-3,3′-dimethoxybibenzyl (5) were isolated from a methanolic extract of Dendrobium pachyglossum (Orchidaceae). The chemical structures of the isolated compounds were characterized by spectroscopic methods. Dendropachol (1) was investigated for its protective effects on hydrogen peroxide (H2O2)-induced oxidative stress in HaCaT keratinocytes. Compound 1 showed strong free radical scavenging compared to the positive control. For the cytoprotective effect, compound 1 increased the activities of GPx and CAT and the level of GSH but reduced intracellular reactive oxygen species (ROS) generation and accumulation. In addition, compound 1 significantly diminished the expression of p53, Bax, and cytochrome C proteins, decreased the activities of caspase-3 and caspase-9, and increased Bcl-2 protein. The results suggested that compound 1 exhibited antioxidant activities and protective effects in keratinocytes against oxidative stress induced by H2O2.
Intelligent Discrimination Method Based on Digital Twins for Analyzing Sensitivity of Mechanical Parameters of Prestressed Cables
Authors: Liu Zhansheng , Shi Guoliang , Jiang Antong , Li Wenjie
Published: February 2021

The information collected on large-span prestressed cables by field sensors is susceptible to interference, which leads to inaccurate collection of structural and mechanical parameters of large-span prestressed cables, resulting in misjudgment of structural safety performance. This paper proposes an intelligent judgment method for improving the sensitivity of analyzing mechanical parameters of prestressed cables based on digital twins (DTs). The safety performance of the structure was evaluated by analyzing the mechanical parameters. First, the information during prestressed cable tensioning is dynamically sensed, thereby establishing a multidimensional model of structural analysis. The virtual model is processed by the model modification rule to improve the robustness of the simulation; thus, a DT framework for the sensitivity judgment of the mechanical parameters of the cable is built. In the twin model, the simulation data of the real structure were extracted. Probabilistic analysis was performed using the Dempster–Shafer(D–S) evidence theory to discriminate the sensitivity of mechanical parameters of each cable node under the action of external forces with high accuracy and intelligence. Sensitivity analysis provides a reliable basis for the safety performance assessment of structures. Taking the wheel–spoke-type cable truss as an example, the application of DTs and D–S evidence theory to the sensitivity determination of cable mechanical parameters under temperature fully verified that the proposed intelligent method can effectively evaluate the safety performance of the actual structure.
Effective Implementation of Elastohydrodynamic Lubrication of Rough Surfaces into Multibody Dynamics Software
Authors: Dlugoš Jozef , Novotný Pavel
Published: February 2021

Currently, multibody dynamics simulations are moving away from issues exclusive to dynamics to more multiphysical problems. Most mechanical systems contain contact pairs that influence the dynamics of the entire mechanism, such as friction loss, wear, vibration and noise. In addition, deformation often affects the interaction between the contact bodies. If that is the case, this effect must be considered. However, a major disadvantage arises in that it leads to an increase in the number of degrees of freedom and the computational time. Often, the general-purpose multibody dynamics software does not take into account advanced phenomena, such as a lubricated contact pair. This paper can serve as a guide to implementing the elastohydrodynamic lubrication of rough surfaces into general-purpose multibody dynamics software (in this case MSC Adams), which remains challenging. In this paper, the deformation shape reconstruction of the reduced flexible bodies is described, as well as a solution to the increase in the computational speed issues thereby caused. To alleviate this burden, advanced sensitivity analysis techniques are used. In this paper, parallel computing has been employed. The proposed method leads to reasonable computational times for the multibody dynamics simulations, including elastohydrodynamic lubrication. The proposed method is applied to the multibody dynamics simulation of the piston–liner interaction of an internal combustion engine.
Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
Authors: Gao Lihao , Zheng Yu , Wang Yaqiang , Xia Jiangjiang , Chen Xunlai , Li Bin , Luo Ming , Guo Yuchen
Published: February 2021

Missing data in weather radar image sequences may cause bias in quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF) studies, and also the obtainment of corresponding high-quality QPE and QPF products. The traditional approaches that are used to reconstruct missing weather radar images replace missing frames with the nearest image or with interpolated images. However, the performance of these approaches is defective, and their accuracy is quite limited due to neglecting the intensification and disappearance of radar echoes. In this study, we propose a deep neuron network (DNN), which combines convolutional neural networks (CNNs) and bi-directional convolutional long short-term memory networks (CNN-BiConvLSTMs), to address this problem and establish a deep-learning benchmark. The model is trained to be capable of dealing with arbitrary missing patterns by using the proposed training schedule. Then the performances of the model are evaluated and compared with baseline models for different missing patterns. These baseline models include the nearest neighbor approach, linear interpolation, optical flow methods, and two DNN models three-dimensional CNN (3DCNN) and CNN-ConvLSTM. Experimental results show that the CNN-BiConvLSTM model outperforms all other baseline models. The influence of data quality on interpolation methods is further investigated, and the CNN-BiConvLSTM model is found to be basically uninfluenced by less qualified input weather radar images, which reflects the robustness of the model. Our results suggest good prospects for applying the CNN-BiConvLSTM model to improve the quality of weather radar datasets.
Composting Hydrochar-OFMSW Digestate Mixtures: Design of Bioreactors and Preliminary Experimental Results
Authors: Scrinzi Donato , Andreottola Gianni , Fiori Luca
Published: February 2021

An increasing number of industrial plants integrate the anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW) with a subsequent composting phase. To improve the plant productivity, a fraction of OFMSW digestate can be converted into a carbonaceous material, called hydrocar (HC), through Hydrothermal Carbonization (HTC), and then composted together with the OFMSW digestate itself, to produce “hydrochar co-compost”. The aim of this paper is to present the design and assembly of batch bioreactors, built in-house to investigate the co-composting process of OFMSW digestate and its HC, and to provide some preliminary results. The OFMSW digestate from an industrial plant was carbonized at 200 °C for 3 h in a 2 L HTC reactor, to produce wet HC after filtration. The ratio of OFMSW digestate and green waste (1:1) used as bulking medium was reproduced in four bioreactors with an increasing percentage of HC substituting the OFMSW digestate (0, 25, 50, 75%). The bioreactors managed to effectively compost the solid wet biomasses in a wet environment with temperature and oxygen control, while measuring online the oxygen consumption and thus the dynamic respirometric index (DRI). The DRI24,max measured with AIR-nl solid respirometer (standardized offline measurement) started from values above 800 mg O2 kgVS−1 h−1 before composting and dropped at the end of the process to values in the range 124–340 mg O2 kgVS−1 h−1 for the four mixes, well below the recommended limit of 500 mg O2 kgVS−1 h−1 for high-quality compost stability. These offline DRI values were confirmed by the online DRI measurements. This research is part of the international C2Land Project funded by the European Institute of Innovation and Technology Climate Knowledge and Innovation Community (EIT Climate-KIC), which is greatly acknowledged.
A Polygonal Model to Design and Fabricate Ion-Exchanged Diffraction Gratings
Authors: Montero-Orille Carlos , Prieto-Blanco Xesús , González-Núñez Héctor , Liñares Jesús
Published: February 2021

We propose a simple polygonal model to describe the phase profile of ion-exchanged gratings. This model enables the design of these gratings, as well as the characterization of the ion-exchange process itself. Several ion-exchanged gratings were fabricated to validate the model and to characterize the process involved in their fabrication. From this characterization, we show the practical utility of the model by designing and fabricating both a grating that removes the zero order and a three splitter. The performance of these two elements was good, although the first one stood out especially because only 0.5% of the power remained in the zero order after diffraction. This polygonal model could be useful to design more complex diffractive elements.
Combined Effects of Optimized Heat Treatment and Nickel Coating for the Improvement of Interfacial Bonding in Aluminum–Iron Alloys Hybrid Structures
Authors: Moon Gihoon , Lee Eunkyung
Published: February 2021

The effects of nickel coating and heat treatment on the interfacial bonds of aluminum–iron (Al/Fe) alloys hybrid structures were investigated using microstructural analysis. The application of a nickel coating successfully suppressed the formation of defects such as gaps and oxide scale, improving the physical bonding of the interface. Optimizing the heat treatment conditions generated superior chemical bonding at the interface and facilitated the formation of a nickel-bearing phase in the Al matrix. Also, the types of nickel-bearing phase were influenced by solution treatment and proximity to the interface. By analyzing the isopleth phase diagram of the aluminum system for the ranges of nickel present in the Al, it was confirmed that the Ni:Cu ratio affected the precipitation characteristics of the system. However, when heated under conditions that were optimized for chemical bonding, the Al matrix decreased by approximately 40% (from 100 HV to 60 HV), due to grain growth. The effect of artificial aging increased the hardness of the Al matrix away from the interface by 35% (from 63 HV to 90 HV). On the other hand, this did not occur in the Al matrix near the interface. These results indicate that the nickel that diffused into the Al matrix interfered with the precipitation hardening effect.
Print and Try Technique: 3D-Printing of Teeth with Complex Anatomy a Novel Endodontic Approach
Authors: Tonini Riccardo , Xhajanka Edit , Giovarruscio Massimo , Foschi Federico , Boschi Giulia , Atav-Ates Ayfer , Cicconetti Andrea , Seracchiani Marco , Gambarini Gianluca , Testarelli Luca , Pacifici Luciano
Published: February 2021

The purpose of this report is to evaluate the applicability of a novel Print and Try technique in the presence of aberrant endodontic anatomies and to achieve a predictable treatment with improved outcome. According to guidelines, cone beam computed tomography (CBCT) is recommended in the presence of complex anatomies. The volumes were utilized to produce a stereo lithographic (STL) file, from which a 3D-plastic tooth model including a hollow root canal system was obtained. The 3D-patient-specific model facilitates the treatment planning and the trial of therapeutic approaches. Using a transparent material, all the treatment steps could be directly visualized. Subsequently, endodontic therapy could be performed in vivo with reduced operating time and with a better overview. Clinicians benefit from the Print and Try technique when facing a complicated root canal system with reduced stress and higher chances of success. In aberrant endodontic anatomies, the use of a transparent 3D plastic model, derived from the CBCT dicom, provides an exact preview of clinical challenges that will be met intra-operatively. A patient centered tailored approach to shaping, cleaning, and filling strategies can be applied.
Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques
Authors: Simal Pérez Noelia , Alonso-Montesinos Joaquín , Batlles Francisco Javier
Published: February 2021

Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE).
Moment Resisting Connection with Curved Endplates: Behaviour Study
Authors: Mudrov Andrej , Šapalas Antanas , Šaučiuvėnas Gintas , Urbonas Kęstutis
Published: February 2021

This article provides a behaviour analysis of moment resisting joints with curved endplates. This is a new type of connection that can be used for joining steel beams to the circular hollow section (CHS) columns by means of bolts. Some researchers apply the Eurocode model without considering the differences in calculation schemes and assumptions, such as by using the general model of an equivalent T-stub in tension. Consequently, many of the existing behaviour studies are somewhat misleading, thus there is a need for further research. Apart from the absence of analytical methods that are devoted to predicting the initial stiffness and strength of the curved T-stub, other technical difficulties were encountered, such as gaps between the endplate and the column, as well as the initial pre-loading force of the bolts. In the previous studies, endplates were manufactured by rolling flat plates to the precise curvature which resulted in firm contact. In contrast, in this study, endplates were manufactured from a standard CHS tube, which led to significant initial gaps. Meanwhile, in terms of preloading force, it was found that it affected the moment resistance of the joint. This paper discusses problems associated with ongoing researches and presents experimental tests of the two connections. The obtained results were further used in the parametric finite element analysis (FEA) to determine the effect of the gaps and preloading force of the bolts on the moment resistance and initial rotational stiffness of the joint. The results indicate that the behaviour of curved plated connections is exceedingly complex and that the preloading force is the key factor, therefore, it should be controlled.
Deliberative and Conceptual Inference in Service Robots
Authors: Pineda Luis A. , Hernández Noé , Rodríguez Arturo , Cruz Ricardo , Fuentes Gibrán
Published: February 2021

Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios, the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here, we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making, and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot’s knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem-space that is explored through heuristic search, and the latter is based on conceptual knowledge, including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot’s communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios.
Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle
Authors: Nowakowski Mirosław , Sibilski Krzysztof , Sibilska-Mroziewicz Anna , Żyluk Andrzej
Published: February 2021

Non-linear phenomena are particularly important in -flight dynamics of micro-class unmanned aerial vehicles. Susceptibility to atmospheric turbulence and high manoeuvrability of such aircraft under critical flight conditions cover non-linear aerodynamics and inertia coupling. The theory of dynamical systems provides methodology for studying systems of non-linear ordinary differential equations. The bifurcation theory forms part of this theory and deals with stability changes leading to qualitatively different system responses. These changes are called bifurcations. There is a number of papers, the authors of which applied the bifurcation theory for analysing aircraft flight dynamics. This article analyses the dynamics of critical micro aerial vehicle flight regimes. The flight dynamics under such conditions is highly non-linear, therefore the bifurcation theory can be applied in the course of the analysis. The application of the theory of dynamical systems enabled predicting the nature of micro aerial vehicle motion instability caused by bifurcations and analysing the post-bifurcation microdrone motion. This article presents the application of bifurcation analysis, complemented with time-domain simulations, to understand the open-loop dynamics of strake-wing micro aerial vehicle model by identifying the attractors of the dynamic system that manages upset behaviour. A number of factors have been identified to cause potential critical states, including non-oscillating spirals and oscillatory spins. The analysis shows that these spirals and spins are connected in a one-parameter space and that due to improper operation of the autopilot on the spiral, it is possible to enter the oscillatory spin.
Assessing the Technical Specifications of Predictive Maintenance: A Case Study of Centrifugal Compressor
Authors: Nordal Helge , El-Thalji Idriss
Published: February 2021

Dependability analyses in the design phase are common in IEC 60300 standards to assess the reliability, risk, maintainability, and maintenance supportability of specific physical assets. Reliability and risk assessment uses well-known methods such as failure modes, effects, and criticality analysis (FMECA), fault tree analysis (FTA), and event tree analysis (ETA)to identify critical components and failure modes based on failure rate, severity, and detectability. Monitoring technology has evolved over time, and a new method of failure mode and symptom analysis (FMSA) was introduced in ISO 13379-1 to identify the critical symptoms and descriptors of failure mechanisms. FMSA is used to estimate monitoring priority, and this helps to determine the critical monitoring specifications. However, FMSA cannot determine the effectiveness of technical specifications that are essential for predictive maintenance, such as detection techniques (capability and coverage), diagnosis (fault type, location, and severity), or prognosis (precision and predictive horizon). The paper proposes a novel predictive maintenance (PdM) assessment matrix to overcome these problems, which is tested using a case study of a centrifugal compressor and validated using empirical data provided by the case study company. The paper also demonstrates the possible enhancements introduced by Industry 4.0 technologies.
Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification
Authors: Liu Jie , Liu Peiyu , Zhu Zhenfang , Li Xiaowen , Xu Guangtao
Published: February 2021

Aspect-based sentiment classification aims at determining the corresponding sentiment of a particular aspect. Many sophisticated approaches, such as attention mechanisms and Graph Convolutional Networks, have been widely used to address this challenge. However, most of the previous methods have not well analyzed the role of words and long-distance dependencies, and the interaction between context and aspect terms is not well realized, which greatly limits the effectiveness of the model. In this paper, we propose an effective and novel method using attention mechanism and graph convolutional network (ATGCN). Firstly, we make full use of multi-head attention and point-wise convolution transformation to obtain the hidden state. Secondly, we introduce position coding in the model, and use Graph Convolutional Networks to obtain syntactic information and long-distance dependencies. Finally, the interaction between context and aspect terms is further realized by bidirectional attention. Experiments on three benchmarking collections indicate the effectiveness of ATGCN.
The Feasibility of Dynamic Musculoskeletal Function Analysis of the Vastus Lateralis in Endurance Runners Using Continuous, Hands-Free Ultrasound
Authors: Sjoerdsma Marloes , Caresio Cristina , Tchang Benjamin , Meeder Amber , van de Vosse Frans , Lopata Richard
Published: February 2021

Dynamic imaging of the skeletal muscles used to be strenuous and often impossible to perform manually. Accordingly, long-term dynamic musculoskeletal imaging has not been performed. The feasibility of long-term dynamic musculoskeletal functional analysis using hands-free ultrasound will be demonstrated in ten healthy endurance runners. After every kilometer, the vastus lateralis muscle was imaged whilst running using a fixated probe connected to a smart phone. The image quality was quantified by estimation of the probe-skin contact preservation and the field-of-view stability. Moreover, the pennation angles and muscle thicknesses were computed automatically. Long-term dynamic acquisition was successful in nine out of ten runners. Probe-skin contact loss ranged between 0 and 57% of the gait cycle. The biggest change in field-of-view occurred during the first kilometer with an average decline in complex-wavelet structural similarity index of 0.21, followed by an onward total decrease of 0.09, on average. The mean pennation angle and thickness were approximately constant, with the average fluctuation being 0.94 degrees and 0.11 cm, respectively. The feasibility of long-term musculoskeletal function analysis has been demonstrated, with probe-skin contact loss the main limiting factor. Dynamic, hands-free ultrasound might enable research for a more profound insight in the prevention and rehabilitation of musculoskeletal injuries.
Can Trunk Acceleration Differentiate Stroke Patient Gait Patterns Using Time- and Frequency-Domain Features?
Authors: Hsu Wei-Chun , Sugiarto Tommy , Liao Ying-Yi , Lin Yi-Jia , Yang Fu-Chi , Hueng Dueng-Yuan , Sun Chi-Tien , Chou Kuan-Nien
Published: February 2021

This study classified the gait patterns of normal and stroke participants by using time- and frequency-domain features obtained from data provided by an inertial measurement unit sensor placed on the subject’s lower back (L5). Twenty-three participants were included and divided into two groups: healthy group (young and older adults) and stroke group. Time- and frequency-domain features from an accelerometer were extracted, and a feature selection method comprising statistical analysis and signal-to-noise ratio (SNR) calculation was used to reduce the number of features. The features were then used to train four Support Vector Machine (SVM) kernels, and the results were subsequently compared. The quadratic SVM kernel had the highest accuracy (93.46%), as evaluated through cross-validation. Moreover, when different datasets were used on model testing, both the quadratic and cubic kernels showed the highest accuracy (96.55%). These results demonstrated the effectiveness of this study’s classification method in distinguishing between normal and stroke gait patterns, with only using a single sensor placed on the L5.
Hyperspectral Molecular Orientation Mapping in Metamaterials
Authors: Ryu Meguya , Nishijima Yoshiaki , Morimoto Shinya , To Naoki , Hashizume Tomoki , Matsubara Ryosuke , Kubono Atsushi , Hu Jingwen , Ng Soon Hock , Juodkazis Saulius , Morikawa Junko
Published: February 2021

The four polarisation method is adopted for measurement of molecular orientation in dielectric nanolayers of metal-insulator-metal (MIM) metamaterials composed of gold nanodisks on polyimide and gold films. Hyperspectral mapping at the chemical finger printing spectral range of 4–20 μμm was carried out for MIM patterns of 1–2.5 μμm period (sub-wavelength). Overlay images taken at 0,π4,π2,3π4 orientation angles and subsequent baseline compensation are shown to be critically important for the interpretation of chemical mapping results and reduction of spurious artefacts. Light field enhancement in the 60-nm-thick polyimide (I in MIM) was responsible for strong absorption at the characteristic polyimide bands. Strong absorbance A at narrow IR bands can be used as a thermal emitter (emittance E=1−R), where R is the reflectance and A=1−R−T, where for optically thick samples the transmittance is T=0.
Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation
Authors: Chang Han-Jui , Zhang Guang-Yi , Su Zhi-Ming , Mao Zhong-Fa
Published: February 2021

One of the important values of Industry 4.0 is to integrate people’s needs into the manufacture of enhanced products, systems, and services to achieve greater levels of product customization. This paper presents a prediction method for predicting screw process parameters; taking crystalline and non-crystalline polymers as the molding material, when there is a lack of sufficient historical screw process data to establish a data-driven method, using various screws and polymer materials to predict tool life under different cutting conditions is a challenge. A screw life prediction method is proposed based on the mixed compound screw process parameters method using a dynamic iteration work. To meet the requirements of mass production, this work proposes the combined application of the automatic virtual metrology (AVM) system with the recognizable performance evaluation (RPE) program. The method predicts the injection of compound screws by extracting given cutting conditions and related process parameters characteristics from the senor data by converting sampling inspections with measurement delays from real-time and online routine inspections to automatically and quickly complete method creation production goals.
Nitrous Oxide Emission from Grazing Is Low across a Gradient of Plant Functional Diversity and Soil Conditions
Authors: Nyameasem John Kormla , Malisch Carsten S. , Loges Ralf , Taube Friedhelm , Kluß Christof , Vogeler Iris , Reinsch Thorsten
Published: February 2021

Nitrous oxide (N2O) emissions from pastures can vary significantly depending on soil and environmental conditions, nitrogen (N) input, as well as the plant’s ability to take up the N. We tested the hypothesis that legume-based N sources are characterized by significantly lower emission factors than mineral N based dairy systems. Therefore, this study monitored N2O emissions for a minimum of 100 days and up to two growing seasons across a gradient of plant species diversity. Emissions were measured from both grazed pastures and a controlled application of urine and dung using the static chamber method. About 90% of the simulated experiments’ accumulated N2O emissions occurred during the first 60–75 days. The average accumulated N2O emissions were 0.11, 0.87, 0.99, and 0.21 kg ha−1 for control, dung, urine patches, and grazed pastures, respectively. The N uptake efficiency at the excreta patch scale was about 70% for both dung and urine. The highest N2O-N emission factor was less than half compared with the IPCC default (0.3 vs. 0.77), suggesting an overestimation of N2O-N emissions from organically managed pastures in temperate climates. Plant diversity showed no significant effect on N2O emission. However, functional groups were significant (p < 0.05). We concluded that legume-containing pasture systems without a fertilizer addition generally appear capable of utilizing nitrogen inputs from excreta patches efficiently, resulting in low N2O emissions.
Spatio-Temporal Characteristics of PM2.5, PM10, and AOD over the Central Line Project of China’s South-North Water Diversion in Henan Province (China)
Authors: Su Bo , Wu Dongyu , Zhang Miao , Bilal Muhammad , Li Yuying , Li Bai-Lian , Atique Luqman , Zhang Ziyue , Howari Fares M.
Published: February 2021

The spatio-temporal characteristics of particulate matter with a particle size less than or equal to 2.5 μm (PM2.5), particulate matter with a particle size less than or equal to 10 μm (PM10), meteorological parameters from September 2018 to September 2019, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol optical depth (AOD) from 2007 to 2019 were investigated over the Central Line Project of China’s South-North Water Diversion (CSNWD) in Henan Province. To better understand the characteristics of the atmospheric environment over the CSNWD, air quality monitoring stations were installed in Nanyang (in the upper reaches), Zhengzhou (in the middle reaches), and Anyang (in the lower reaches). In this study, daily, monthly, and seasonal statistical analyses of PM2.5 and PM10 concentrations were performed and their relationship with meteorological parameters was investigated. The results show extremely poor air quality conditions over the Zhengzhou Station compared with the Nanyang and Anyang Stations. The annual average PM2.5 concentration did not meet China’s ambient air secondary standard (35 μg/m3 annual mean) over all the stations, while the annual average PM10 concentration satisfied China’s ambient air secondary standard (100 μg/m3 annual mean) over the Anyang and Nanyang Stations, except for the Zhengzhou Station. The highest PM2.5 and PM10 concentrations were observed during winter compared with the other seasons. The results show that PM2.5 and PM10 concentrations were negatively correlated with wind speed and temperature at the Nanyang and Zhengzhou Stations, but positively correlated with relative humidity. However, no significant negative or positive correlation was observed at Anyang Station. There is a strong linear positive correlation between PM2.5 and PM10 (R = 0.99), which indicates that the particulate matter at the three stations was mainly caused by local emissions. Additionally, the AOD values at the three stations were the highest in summer, which may be related to the residues of crops burned in Henan Province in summer.
Assessment of the COVID-19 Lockdown Effects on Spectral Aerosol Scattering and Absorption Properties in Athens, Greece
Authors: Kaskaoutis Dimitris G. , Grivas Georgios , Liakakou Eleni , Kalivitis Nikos , Kouvarakis Giorgos , Stavroulas Iasonas , Kalkavouras Panayiotis , Zarmpas Pavlos , Dumka Umesh Chandra , Gerasopoulos Evangelos , Mihalopoulos Nikolaos
Published: February 2021

COVID-19 is evolving into one of the worst pandemics in recent history, claiming a death toll of over 1.5 million as of December 2020. In an attempt to limit the expansion of the pandemic in its initial phase, nearly all countries imposed restriction measures, which resulted in an unprecedented reduction of air pollution. This study aims to assess the impact of the lockdown effects due to COVID-19 on in situ measured aerosol properties, namely spectral-scattering (bsca) and absorption (babs) coefficients, black carbon (BC) concentrations, single-scattering albedo (SSA), scattering and absorption Ångström exponents (SAE, AAE) in Athens, Greece. Moreover, a comparison is performed with the regional background site of Finokalia, Crete, for a better assessment of the urban impact on observed differences. The study examines pre-lockdown (1–22 March 2020), lockdown (23 March–3 May 2020) and post-lockdown (4–31 May 2020) periods, while the aerosol properties are also compared with a 3–4 year preceding period (2016/2017–2019). Comparison of meteorological parameters in Athens, between the lockdown period and respective days in previous years, showed only marginal variation, which is not deemed sufficient in order to justify the notable changes in aerosol concentrations and optical properties. The largest reduction during the lockdown period was observed for babs compared to the pre-lockdown (−39%) and to the same period in previous years (−36%). This was intensified during the morning traffic hours (−60%), reflecting the large decrease in vehicular emissions. Furthermore, AAE increased during the lockdown period due to reduced emissions from fossil-fuel combustion, while a smaller (−21%) decrease was observed for bsca along with slight increases (6%) in SAE and SSA values, indicating that scattering aerosol properties were less affected by the decrease in vehicular emissions, as they are more dependent on regional sources and atmospheric processing. Nighttime BC emissions related to residential wood-burning were slightly increased during the lockdown period, with respect to previous-year means. On the contrary, aerosol and pollution changes during the lockdown period at Finokalia were low and highly sensitive to natural sources and processes.
Metabolic Reprogramming, Questioning, and Implications for Cancer
Authors: Jacquet Pierre , Stéphanou Angélique
Published: February 2021

The expression “metabolic reprogramming” has been encountered more and more in the literature since the mid-1990s. It seems to encompass several notions depending on the author, but the lack of a clear definition allows it to be used as a “catch-all” expression. Our first intention is to point out the inconsistencies in the use of the reprogramming terminology for cancer metabolism. The second is to address the over-focus of the role of mutations in metabolic adaptation. With the increased interest in metabolism and, more specifically, in the Warburg effect in cancer research, it seems appropriate to discuss this terminology and related concepts in detail.
Three-Dimensional Transesophageal Echocardiography as an Alternative to Multidetector Computed Tomography in Aortic Annular Diameter Measurements for Transcatheter Aortic Valve Implantation
Authors: Dima Ciprian , Gaspar Marian , Mornos Cristian , Mornos Aniko , Deutsch Petru , Cioloca Horia , Cerbu Simona , Dinu Mihai , Hoinoiu Bogdan , Luca Constantin , Petrescu Lucian
Published: February 2021

Background and objectives: Transcatheter aortic valve implantation (TAVI) is a therapeutic choice for high surgical risk patients, serving as an alternative to open-heart surgery. Correct measurement of the aortic annulus, which leads to the selection of a suitable prosthesis and accurate outcome prediction, is essential for the success of TAVI. The objective of this study is to evaluate the accuracy of novel imaging te chniques in measuring the aortic annulus by comparing multi-detector computer tomography (MDCT) and three-dimensional transesophageal echocardiography (3D TEE) for the selection of the optimal prosthesis. Materials and Methods: Measurements of the aortic annulus have been performed on 25 patients using MDCT and TEE, and the correlation and agreement levels between the two measuring techniques were analyzed. MDCT measurements were used for the sizing of the prostheses. Results: MDCT and TEE measurements of aortic annular diameters were significantly correlated, with a mean difference of 0.001 cm. Conclusions: 3D TEE measurements have been in good agreement with MDCT and, therefore, 3D TEE can be used as an alternative in cases where MDCT is contraindicated or not available.
IGF1R Deficiency Modulates Brain Signaling Pathways and Disturbs Mitochondria and Redox Homeostasis
Authors: Cardoso Susana , López Icíar , Piñeiro-Hermida Sergio , Pichel José , Moreira Paula
Published: February 2021

Insulin-like growth factor 1 receptor (IGF1R)-mediated signaling pathways modulate important neurophysiological aspects in the central nervous system, including neurogenesis, synaptic plasticity and complex cognitive functions. In the present study, we intended to characterize the impact of IGF1R deficiency in the brain, focusing on PI3K/Akt and MAPK/ERK1/2 signaling pathways and mitochondria-related parameters. For this purpose, we used 13-week-old UBC-CreERT2; Igf1rfl/fl male mice in which Igf1r was conditionally deleted. IGF1R deficiency caused a decrease in brain weight as well as the activation of the IR/PI3K/Akt and inhibition of the MAPK/ERK1/2/CREB signaling pathways. Despite no alterations in the activity of caspases 3 and 9, a significant alteration in phosphorylated GSK3β and an increase in phosphorylated Tau protein levels were observed. In addition, significant disturbances in mitochondrial dynamics and content and altered activity of the mitochondrial respiratory chain complexes were noticed. An increase in oxidative stress, characterized by decreased nuclear factor E2-related factor 2 (NRF2) protein levels and aconitase activity and increased H2O2 levels were also found in the brain of IGF1R-deficient mice. Overall, our observations confirm the complexity of IGF1R in mediating brain signaling responses and suggest that its deficiency negatively impacts brain cells homeostasis and survival by affecting mitochondria and redox homeostasis.
Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine
Authors: Moldogazieva Nurbubu , Mokhosoev Innokenty , Zavadskiy Sergey , Terentiev Alexander
Published: February 2021

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.