bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Two QTL Conferring Seedling Resistance to Fusarium Crown Rot in Barley on Reducing Grain Yield Loss under Field Environments
Authors: Zheng Zhi , Powell Jonathan , Gao Shang , Percy Cassandra , Kelly Alison , Macdonald Bethany , Zhou Meixue , Davies Philip , Liu Chunji
Published: May 2022

Fusarium crown rot (FCR) is one of the most damaging cereal diseases in semi-arid regions worldwide. Genetic studies on FCR resistance have mainly focused on disease symptoms measured by the browning of either leaf sheaths in seedlings or stems of mature plants. Two major QTLs conferring FCR resistance in barley, Qcsr.cpi-1H and Qcrs.cpi-4H, were previously identified in the growth room. They could explain up to 33.4 and 45.3% of phenotypic variance, respectively. This is the first study where the possible effects of FCR-resistant loci identified in the previous studies based on seedling assay are tested for their abilities to reduce grain yield loss. Near isogenic lines (NILs) and backcross (BC) lines targeting these two loci were assessed in the 2017 and 2018 crop seasons. Results from the NILs showed that the presence of a resistance allele at either the 1HL or 4HL locus reduced grain yield loss by an average of 12.0% and 10.7%, respectively. Grain yields of the top BC lines containing resistance alleles at both loci were 34.4% higher than the average of the commercial varieties under FCR inoculation. These lines will be highly valuable in breeding barley varieties with enhanced resistance to FCR.
Pathological and Tissue-Based Molecular Investigation of Granulomas in Cichlids Reared as Ornamental Fish
Authors: Mandrioli Luciana , Codotto Victorio , D’Annunzio Giulia , Volpe Enrico , Errani Francesca , Eishi Yoshinobu , Uchida Keisuke , Morini Maria , Sarli Giuseppe , Ciulli Sara
Published: May 2022

Cichlids include hundreds of species with a high economic value for aquaculture. These fish are subjected to intensive trade and farming that expose them to the risk of infectious diseases. This work focuses on ornamental cichlids held in an aquarium commercial facility presenting emaciation, in order to evaluate the presence of lesions in fish skin and organs. The fish were sampled during routine management activities and subjected to pathological and molecular investigations. The presence of lymphocystis disease virus, typically associated with cutaneous nodular disease, was ruled out. Histologically, they presented granulomas in the spleen, sometimes extending to the other visceral organs. Bacterial heat-shock protein 65 PCR products were detected in tissues associated, in the majority of cases, with granulomas; molecular investigation identified Mycobacterium spp. in two cases and Cutibacterium acnes in seven cases. Immunoreactivity to anti-Mycobacterium and anti-C. acnes antibodies was detected within granulomas. The presence of C. acnes within granuloma is elucidated for the first time in fish; however, similarly to what is found in humans, this bacterium could be harmless in normal conditions, whereas other contributing factors would be required to trigger a granulomatogenous response. Further confirmation by bacterial culture, as well as using large-scale studies in more controlled situations, is needed.
Assessment of a Diterpene-Rich Rosemary (Rosmarinus officinalis L.) Extract as a Natural Antioxidant for Salmon Pâté Formulated with Linseed
Authors: Cedeño-Pinos Cristina , Martínez-Tomé Magdalena , Mercatante Dario , Rodríguez-Estrada María Teresa , Bañón Sancho
Published: May 2022

The use of natural plant extracts with standardised antioxidant properties is a growing strategy to stabilise food products. The use of a rosemary lipophilic extract (RLE), obtained from the by-product of high-yield selected plants and rich in polyphenols (334 mg/g, with diterpenes such as carnosic acid and carnosol as main compounds), is here proposed. Four RLE doses (0, 0.21, 0.42 and 0.63 g/kg) were tested in a salmon pâté formulated with sunflower oil and linseed, which was pasteurised (70 °C for 30 min) and subjected to storage at 4 °C and 600 lux for 42 days. Rosemary diterpenes resisted pasteurisation without degrading and showed antioxidant activities during the shelf-life of pasteurised pâté. RLE addition led to increased peroxide value (from 3.9 to 5.4 meq O2/kg), but inhibited formation of secondary oxidised lipids such as malondialdehyde (from 1.55 to 0.89 mg/g) and cholesterol oxidation products (from 286 to 102 µg/100 g) and avoided discolouration (slight brownness) in the refrigerated pâté. However, this did not entail relevant changes in fatty acid content or in the abundance of volatile organic compounds from oxidised lipids. Increasing the RLE dose only improved its antioxidant efficacy for some oxidation indexes. Thus, the oxidative deterioration of these types of fish emulsion can be naturally controlled with rosemary extracts rich in diterpenes.
Valorization of Aloe vera Skin By-Products to Obtain Bioactive Compounds by Microwave-Assisted Extraction: Antioxidant Activity and Chemical Composition
Authors: Solaberrieta Ignacio , Jiménez Alfonso , Garrigós María Carmen
Published: May 2022

Aloe vera skin (AVS) is a major by-product of Aloe processing plants all over the world. In this study, response surface methodology was used to optimize microwave-assisted extraction (MAE) of bioactive compounds from AVS. The influence of extraction parameters, such as ethanol concentration (%Et), extraction temperature (T), time (t) and solvent volume (V), on extraction yield (Y), total phenolic content (TPC), antioxidant activity (DPPH and FRAP methods) and aloin content, was studied. Optimum extraction conditions were determined as 80% ethanol, 80 °C, 36.6 min and 50 mL and optimized extracts showed interesting contents of polyphenols and antioxidant performance. The phenolic profile was determined by HPLC-DAD/MS and some major phenolic compounds, such as aloin A, aloin B, aloesin, aloe-emodin, aloeresin D, orientin, cinnamic acid and chlorogenic acid, were quantified while eight other compounds were tentatively identified. Moreover, structural and thermal properties were studied by FTIR and TGA analyses, respectively. The obtained results suggested the potential of AVS as a promising source of bioactive compounds, thus increasing the added value of this agricultural waste.
Osteomyelitis, Oxidative Stress and Related Biomarkers
Authors: Massaccesi Luca , Galliera Emanuela , Pellegrini Antonio , Banfi Giuseppe , Corsi Romanelli Massimiliano
Published: May 2022

Bone is a very dynamic tissue, subject to continuous renewal to maintain homeostasis through bone remodeling, a process promoted by two cell types: osteoblasts, of mesenchymal derivation, are responsible for the deposition of new material, and osteoclasts, which are hematopoietic cells, responsible for bone resorption. Osteomyelitis (OM) is an invasive infectious process, with several etiological agents, the most common being Staphylococcus aureus, affecting bone or bone marrow, and severely impairing bone homeostasis, resulting in osteolysis. One of the characteristic features of OM is a strong state of oxidative stress (OS) with severe consequences on the delicate balance between osteoblastogenesis and osteoclastogenesis. Here we describe this, analyzing the effects of OS in bone remodeling and discussing the need for new, easy-to-measure and widely available OS biomarkers that will provide valid support in the management of the disease.
A Lightweight Multi-Source Fast Android Malware Detection Model
Authors: Peng Tao , Hu Bochao , Liu Junping , Huang Junjie , Zhang Zili , He Ruhan , Hu Xinrong
Published: May 2022

Most of the current malware detection methods running on Android are based on signature and cloud technologies leading to poor protection against new types of malware. Deep learning techniques take Android malware detection to a new level. Still, most deep learning-based Android malware detection methods are too inefficient or even unworkable on Android devices due to their high resource consumption. Therefore, this paper proposes MSFDroid, a lightweight multi-source fast Android malware detection model, which uses information from the internal files of the Android application package in several dimensions to build base models for ensemble learning. Meanwhile, this paper proposes an adaptive soft voting method by dynamically adjusting the weights of each base model to overcome the noise generated by traditional soft voting and thus improves the performance. It also proposes adaptive shrinkage convolutional unit that can dynamically adjust the convolutional kernel’s weight and the activation function’s threshold to improve the expressiveness of the CNN. The proposed method is tested on public datasets and on several real devices. The experimental results show that it achieves a better trade-off between performance and efficiency by significantly improving the detection speed while achieving a comparable performance compared to other deep learning methods.
Full-Frequency Vibroacoustic Modeling of a Ballistic Re-Entry Aeroshell and Validation through Diffuse Field Acoustic Testing
Authors: Claeys Maxence , Valle Canas Hélène , Alcoverro Benoit
Published: May 2022

During ballistic flight, a re-entry vehicle is subjected to high-level structural vibrations due to pressure fluctuations on its bounding surface. The aim of this work is to simulate this structural vibration response. The first step, which is not covered in this study, is the modeling of pressure fluctuations using aerodynamic simulation results. The second step is the simulation of the vibroacoustic response. In this study, the full-frequency vibroacoustic modeling of a metal shell representing a re-entry vehicle aeroshell is developed. The low-frequency response is computed using a FEM–BEM model while Statistical Energy Analysis is used for high-frequency behavior. The validation of these models is based on a ground experiment with controlled diffuse-field acoustic loading. A dedicated reverberation chamber was developed with loudspeaker excitation. The simulation results are compared with the experimental results. In the low-frequency range, simulation helps to understand the measured response spectra by highlighting the acoustic resonances and scattering phenomena. In the high-frequency range, an experimental identification of the damping loss factors and SEA modeling of each subsystem using an FE–SEA approach provides a predictive simulation of the vibration-response spectrum. In this application, FEM–BEM and SEA models are complementary in simulating full-frequency vibroacoustic responses.
Performance Evaluation of Track Curvature Sensor for Curvature Estimation in a Curved Section of Railway Tracks
Authors: Hur Hyun Moo , Shin Yu Jeong , Seo Jung Won , Moon Kyung Ho
Published: May 2022

Active railway vehicles such as tilting trains and active steering bogies are being developed to improve the curve running performance of railway vehicles. In this case, a technology for sensing a curve and extracting curve information such as a curvature in real time is required. The existing railway curve detection technology has the disadvantage of requiring many calibration processes through test driving and being affected by running speed. In order to overcome these disadvantages, this paper proposes a very simple and efficient curvature estimation method. A curvature sensor prototype was manufactured and a test run was performed on the test track. As a test result compared with the track inspection data, the minimum deviation was 0.8%, the maximum deviation was 4.0% and the average deviation was 2.8%, showing good curvature measurement performance. It was also confirmed that the effect of the running speed was very small and the accuracy was high in the small curve with a large curvature. Therefore, the developed curvature measurement sensor is expected to be used very practically in the field of curvature detection and curvature extraction to control the vehicle in the curve section.
An Outline of Fused Deposition Modeling: System Models and Control Strategies
Authors: Martini Michele , Scaccia Massimiliano , Marchello Gabriele , Abidi Haider , D’Imperio Mariapaola , Cannella Ferdinando
Published: May 2022

Fused Deposition Modeling (FDM) is a type of Additive Manufacturing (AM) technology that is becoming increasingly common in numerous applications thanks to its versatility and reduced material waste. However, the complex physical phenomena occurring during extrusion, including the dynamics of non-Newtonian fluids, viscoelastic behaviors and rheology, make the use of heuristic observations preferable to that of analytical approaches. Consequently, engineers have focused on optimizing materials and hardware rather than control algorithms. The limited knowledge about extrusion and deposition dynamics usually confines the control action to the motion of the printing head while keeping a constant flow rate. Existing attempts to synchronize motion and extrusion consists of open loop compensations, which, however, require identified transfer functions or need to be tuned manually. This article aims to compactly review FDM technologies from a control perspective by presenting (i) the models of extrusion and deposition and (ii) the control strategies currently adopted in industry.
An Efficient Detection Approach for Unmanned Aerial Vehicle (UAV) Small Targets Based on Group Convolution
Authors: Cheng Jianghao , Liu Yanyan , Li Guoning , Li Jin , Peng Jiantao , Hong Jintao
Published: May 2022

To solve the problem that small drones in the sky are easily confused with background objects and difficult to detect, according to the characteristics of irregular movement, small size, and changeable shape of drones, using a regional target recognition algorithm, the structure characteristics of Group Convolution (GC) in Resnext50 are absorbed. The optimized GC-faster-RCNN is obtained by improving the Fast-RCNN algorithm and the following methods are performed. First, a clustering method is used to analyze the dataset, and appropriate prior bounding box types are obtained. Second, the Resnext50 is used to replace the original feature extraction network, and the improved channel attention mechanism is integrated into its network output to enhance its feature map information. Then, we calculate its effective receptive field according to the Feature Pyramid Network (FPN) structure and redesign the prior bounding box of the corresponding size to construct a multi-scale detection network for small targets. Experiments show that the algorithm has a recognition accuracy of up to 94.8% under 1080 P image quality, and a recognition speed of 8FPS, which can effectively detect the positions of 1–5 small UAVs in a picture. This method provides an effective positioning detection for low-altitude UAVs.
Language of Driving for Autonomous Vehicles
Authors: Kalda Krister , Pizzagalli Simone-Luca , Soe Ralf-Martin , Sell Raivo , Bellone Mauro
Published: May 2022

Environmental awareness and technological advancements for self-driving cars are close to making autonomous vehicles (AV) a reality in everyday scenarios and a part of smart cities’ transportation systems. The perception of safety and trust towards AVs of passengers and other agents in the urban scenario, being pedestrians, cyclists, scooter drivers or car drivers, is of primary importance and the theme of investigation of many research groups. Driver-to-driver communication channels as much as car-to-driver human–machine interfaces (HMI) are well established and part of normal training and experience. The situation is different when users must cope with driverless and autonomous vehicles, both as passengers and as agents sharing the same urban domain. This research focuses on the new challenges of connected driverless vehicles, investigating an emerging topic, namely the language of driving (LoD) between these machines and humans participating in traffic scenarios. This work presents the results of a field study conducted at Tallinn University Technology campus with the ISEAUTO autonomous driving shuttle, including interviews with 176 subjects communicating using LoD. Furthermore, this study combines expert focus group interviews to build a joint base of needs and requirements for AVs in public spaces. Based on previous studies and questionnaire results, we established the hypotheses that we can enhance physical survey results using experimental scenarios with VR/AR tools to allow the fast prototyping of different external and internal HMIs, facilitating the assessment of communication efficacy, evaluation of usability, and impact on the users. The aim is to point out how we can enhance AV design and LoD communications using XR tools. The scenarios were chosen to be inclusive and support the needs of different demographics while at the same time determining the limitations of surveys and real-world experimental scenarios in LoD testing and design for future pilots.
Marfusion: An Attention-Based Multimodal Fusion Model for Human Activity Recognition in Real-World Scenarios
Authors: Zhao Yunhan , Guo Siqi , Chen Zeqi , Shen Qiang , Meng Zhengyuan , Xu Hao
Published: May 2022

Human Activity Recognition(HAR) plays an important role in the field of ubiquitous computing, which can benefit various human-centric applications such as smart homes, health monitoring, and aging systems. Human Activity Recognition mainly leverages smartphones and wearable devices to collect sensory signals labeled with activity annotations and train machine learning models to recognize individuals’ activity automatically. In order to deploy the Human Activity Recognition model in real-world scenarios, however, there are two major barriers. Firstly, sensor data and activity labels are traditionally collected using special experimental equipment in a controlled environment, which means fitting models trained with these datasets may result in poor generalization to real-life scenarios. Secondly, existing studies focus on single or a few modalities of sensor readings, which neglect useful information and its relations existing in multimodal sensor data. To tackle these issues, we propose a novel activity recognition model for multimodal sensory data fusion: Marfusion, and an experimental data collection platform for HAR tasks in real-world scenarios: MarSense. Specifically, Marfusion extensively uses a convolution structure to extract sensory features for each modality of the smartphone sensor and then fuse the multimodal features using the attention mechanism. MarSense can automatically collect a large amount of smartphone sensor data via smartphones among multiple users in their natural-used conditions and environment. To evaluate our proposed platform and model, we conduct a data collection experiment in real-life among university students and then compare our Marfusion model with several other state-of-the-art models on the collected datasets. Experimental Results do not only indicate that the proposed platform collected Human Activity Recognition data in the real-world scenario successfully, but also verify the advantages of the Marfusion model compared to existing models in Human Activity Recognition.
Health Assessment of Landing Gear Retraction/Extension Hydraulic System Based on Improved Risk Coefficient and FCE Model
Authors: Duan Shixuan , Li Yanjun , Cao Yuyuan , Wang Xingye , Li Xudong , Zhao Zejian
Published: May 2022

The health of the landing gear retraction/extension(R/E) hydraulic system may be assessed using fuzzy comprehensive evaluation (FCE), however the traditional FCE method depends solely on human assessment by specialists, which is excessively subjective. To address the issue of excessive human subjective variables in the assessment, an improved FCE model based on enhanced risk coefficient is provided, which includes four consideration indexes: failure probability, failure severity, failure detection difficulty, and failure repair difficulty. To reduce subjective human judgment errors entirely due to expert experience, the improved FCE takes into account the likelihood of failure using a statistical method, the severity of failure using a fault simulation analysis based on the LMS Imagine.Lab AMESim simulation platform, and the difficulty of fault detection and repair using the aircraft manufacturer’s professional maintenance information. As part of the evaluation model, the range of health assessment values and accompanying treatment methods are included, making it easier to implement on a daily basis in aircraft maintenance. As a final step, the simulation is evaluated, and the simulated faults are calculated.
EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer’s Disease
Authors: Pirrone Daniele , Weitschek Emanuel , Di Paolo Primiano , De Salvo Simona , De Cola Maria Cristina
Published: May 2022

Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). In the last years, EEG signal analysis has become an important topic of research to extract suitable biomarkers to determine the subject’s cognitive impairment. In this work, we propose a novel simple and efficient method able to extract features with a finite response filter (FIR) in the double time domain in order to discriminate among patients affected by AD, MCI, and healthy controls (HC). Notably, we compute the power intensity for each high- and low-frequency band, using their absolute differences to distinguish among the three classes of subjects by means of different supervised machine learning methods. We use EEG recordings from a cohort of 105 subjects (48 AD, 37 MCI, and 20 HC) referred for dementia to the IRCCS Centro Neurolesi “Bonino-Pulejo” of Messina, Italy. The findings show that this method reaches 97%, 95%, and 83% accuracy when considering binary classifications (HC vs. AD, HC vs. MCI, and MCI vs. AD) and an accuracy of 75% when dealing with the three classes (HC vs. AD vs. MCI). These results improve upon those obtained in previous studies and demonstrate the validity of our approach. Finally, the efficiency of the proposed method might allow its future development on embedded devices for low-cost real-time diagnosis.
Effect of High Content of Waste Tire Rubber and Sulfur on the Aging Behavior of Bitumen
Authors: Wang Sheng , Huang Weidong , Liu Xueyan , Lin Peng , Ren Shisong , Li Yi
Published: May 2022

High content rubber modified bitumen (HCRMB) prepared from the high content of waste tire rubber and bitumen has good performance while allowing greater use of the waste tires. However, HCRMB is subject to aging during use, which can affect its performance. The purpose of this paper was to investigate the effect of high content of waste tire rubber and sulfur on the aging behavior of bitumen. The properties of all bitumen were tested using rolling thin film oven aging (RTFOT) test, pressure aging vessel (PAV) test, frequency sweep tests, temperature sweep (TS) test, multiple stress creep recovery (MSCR) test, and attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) test. Test results show that the addition of sulfur to HCRMB leads to an improvement in the elasticity of HCRMB. The elasticity of HCRMB with different amounts of sulfur increases with aging. In addition, the increase in the amount of sulfur can improve the RTFOT aging resistance and the PAV aging resistance of HCRMB. Sulfur cannot reduce the degree of oxidation of HCRMB after aging, but can inhibit the degree of desulfurization of HCRMB. Furthermore, the aging process of HCRMB with different amounts of sulfur is dominated by the degradation of polybutadiene.
Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
Authors: Yan Kai , Liang Lanyue , Zheng Ziqiang , Wang Guoqing , Yang Yang
Published: May 2022

Low illumination, light reflections, scattering, absorption, and suspended particles inevitably lead to critically degraded underwater image quality, which poses great challenges for recognizing objects from underwater images. The existing underwater enhancement methods that aim to promote underwater visibility heavily suffer from poor image restoration performance and generalization ability. To reduce the difficulty of underwater image enhancement, we introduce the media transmission map as guidance for image enhancement. Different from the existing frameworks, which also introduce the medium transmission map for better distribution modeling, we formulate the interaction between the underwater visual images and the transmission map explicitly to obtain better enhancement results. At the same time, our network only requires supervised learning of the media transmission map during training, and the corresponding prediction map can be generated in subsequent tests, which reduces the operation difficulty of subsequent tasks. Thanks to our formulation, the proposed method with a very lightweight network configuration can produce very promising results of 22.6 dB on the challenging Test-R90 with an impressive 30.3 FPS, which is faster than most current algorithms. Comprehensive experimental results have demonstrated the superiority on underwater perception.
Multi-Output Sequential Deep Learning Model for Athlete Force Prediction on a Treadmill Using 3D Markers
Authors: Candela-Leal Milton Osiel , Gutiérrez-Flores Erick Adrián , Presbítero-Espinosa Gerardo , Sujatha-Ravindran Akshay , Ramírez-Mendoza Ricardo Ambrocio , Lozoya-Santos Jorge de Jesús , Ramírez-Moreno Mauricio Adolfo
Published: May 2022

Reliable and innovative methods for estimating forces are critical aspects of biomechanical sports research. Using them, athletes can improve their performance and technique and reduce the possibility of fractures and other injuries. For this purpose, throughout this project, we proceeded to research the use of video in biomechanics. To refine this method, we propose an RNN trained on a biomechanical dataset of regular runners that measures both kinematics and kinetics. The model will allow analyzing, extracting, and drawing conclusions about continuous variable predictions through the body. It marks different anatomical and reflective points (96 in total, 32 per dimension) that will allow the prediction of forces (N) in three dimensions (Fx, Fy, Fz), measured on a treadmill with a force plate at different velocities (2.5 m/s, 3.5 m/s, 4.5 m/s). In order to obtain the best model, a grid search of different parameters that combined various types of layers (Simple, GRU, LSTM), loss functions (MAE, MSE, MSLE), and sampling techniques (down-sampling, up-sampling) helped obtain the best performing model (LSTM, MSE, down-sampling) achieved an average coefficient of determination of 0.68, although when excluding Fz it reached 0.92.
Population Symmetrization in Genetic Algorithms
Authors: Kusztelak Grzegorz , Lipowski Adam , Kucharski Jacek
Published: May 2022

The paper presents a memetic modification of the classical genetic algorithm by introducing a cyclic symmetrization of the population, symmetrizing the parental points around the current population leader. Such an operator provides a more spherical distribution of the population around the current leader, which significantly improves exploitation. The proposed algorithm was described, illustrated by examples, and theoretically analyzed. Its effectiveness was examined using a recognized benchmark, which includes the continuous functions test set on a multidimensional cube, to be minimized.
Effect of Wood Properties and Building Construction on Thermal Performance of Radiant Floor Heating Worldwide
Authors: Rodríguez Jara Enrique Ángel , Ruiz-Pardo Álvaro , García Marta Conde , Ríos José Antonio Tenorio
Published: May 2022

Due to its relatively lower thermal conductivity, the suitability of wood is called into question when selecting the flooring material best suited to radiant heating systems. The European standard EN 1264 considers floorings with a thermal resistance over 0.15 m2 K/W to be out of scope. This belief was partially disproved in a previous article that studied wooden floors for Madrid’s climate. However, the effect of climate still needs to be addressed. The present study extends the previous research to worldwide climates and aimed to answer the following questions: (1) Do the lowest thermal conductivity woods present good thermal performance when used in radiant floors? (2) Should the flooring have a maximum thermal resistance value? (3) Is the standard thermal resistance limit of 0.15 m2 K/W objectively justified? And (4) Do the answers of the preceding questions depend on the climate and the construction characteristics? To answer these questions, 28 cities were selected according to the Köppen–Geiger climate classification. In each city, 216 different dwellings were simulated with 60 wood floorings and one of low thermal resistance as a reference, comprising a total of 368,928 cases. Thermal performance was evaluated in terms of three parameters: energy demand, thermal comfort, and start-up lag time. Consequently, the answers to the previous questions were: (1) The lowest thermal conductivity woods can be used efficiently worldwide in radiant floor heating systems with start-up lag times close to that of the reference flooring; (2) There is no limit value for thermal resistance for floorings that can be applied to all dwellings and climates; (3) No objective justification was found for establishing a thermal resistance limit for flooring of 0.15 m2 K/W; and (4) Climate and construction characteristics can play an important role in the correct selection of flooring properties, especially in severe winters and dwellings with the greatest outdoor-exposed envelope and the worst insulation.
Descriptive Time Series Analysis for Downtime Prediction Using the Maintenance Data of a Medical Linear Accelerator
Authors: Kim Kwang Hyeon , Sohn Moon-Jun , Lee Suk , Koo Hae-Won , Yoon Sang-Won , Madadi Ahmad Khalid
Published: May 2022

A medical linear accelerator (LINAC) delivers high-energy X-rays or electrons to the patient’s tumor. In this study, we categorized failures and predicted downtime leading to discontinuous radiation treatment using a descriptive time series analysis of a 20-year maintenance dataset of a medical LINAC. A LINAC dataset of failure records for 359 instances was collected from 2001 to 2021. Next, we performed institution-specific seasonal autoregressive integrated moving average (ARIMA) modeling to analyze the causes of the failure categories and predict the downtime. Furthermore, we evaluated the performance of the predictive model using standard error metrics and statistical methods. Our results show that the downtime will increase by 95 h/year after 2022 and 100 h/year after 2023. The accumulated downtime in 2029 is predicted to be a maximum of 2820 h. The modeled seasonal ARIMA showed statistical significance (p < 0.001) with a residual error of σ2 (328.33 ± 9.4). In addition, the forecasting performance of the model was assessed using the mean absolute percentage error (MAPE). The failure parts where the major downtime occurred were the multileaf collimator (25.2%), gantry and couch motion part (15.4%), dosimetric part (11.7%), and computer console (10.0%). Using the development of the ARIMA model specific to our institution, the downtime is predicted to reach up to 2820 h.
GAN-Based Training of Semi-Interpretable Generators for Biological Data Interpolation and Augmentation
Authors: Tsourtis Anastasios , Papoutsoglou Georgios , Pantazis Yannis
Published: May 2022

Single-cell measurements incorporate invaluable information regarding the state of each cell and its underlying regulatory mechanisms. The popularity and use of single-cell measurements are constantly growing. Despite the typically large number of collected data, the under-representation of important cell (sub-)populations negatively affects down-stream analysis and its robustness. Therefore, the enrichment of biological datasets with samples that belong to a rare state or manifold is overall advantageous. In this work, we train families of generative models via the minimization of Rényi divergence resulting in an adversarial training framework. Apart from the standard neural network-based models, we propose families of semi-interpretable generative models. The proposed models are further tailored to generate realistic gene expression measurements, whose characteristics include zero-inflation and sparsity, without the need of any data pre-processing. Explicit factors of the data such as measurement time, state or cluster are taken into account by our generative models as conditional variables. We train the proposed conditional models and compare them against the state-of-the-art on a range of synthetic and real datasets and demonstrate their ability to accurately perform data interpolation and augmentation.
Suitability of Porous Inorganic Materials from Industrial Residues and Bioproducts for Use in Horticulture: A Multidisciplinary Approach
Authors: Righi Claudia , Barbieri Francesco , Sgarbi Elisabetta , Maistrello Lara , Bertacchini Alessandro , Andreola Fernanda Nora , D’Angelo Antonio , Catauro Michelina , Barbieri Luisa
Published: May 2022

This study follows a circular economy approach through the preliminary implementation of a coated porous inorganic material (PIM), studied as sustainable controlled release fertilizer, and its application for lettuce Lactuca sativa L. cultivar Chiara growth. The PIM was made of pumice scraps that partially replaced clay as a natural raw material, spent coffee grounds as a porous agent, bovine bone ash and potassium carbonate to provide phosphorus (P) and potassium (K) nutrients, respectively. A coating made with defatted black soldier fly prepupae biomass was used as a nitrogen (N) source. Most of the ingredients used were industrial residues, with the aim of valorizing the raw waste materials present locally. The suitability of PIMs as a fertilizer was investigated with an interdisciplinary approach, which included the first chemical and physical characterization of the material, the evaluation of its antibacterial properties and of its use in horticulture through lettuce growth tests. As tests were carried out indoors, a specific LED lighting device was used to grow the lettuce. The release of nutrients into the soil was estimated by measuring the main elements in the fertilizers before and after their use in the soil. The first results from this characterization study support PIMs’ suitability for agronomic applications. The use of the PIMs suggested average higher dry weight (49%), fresh weight (112%), and leaf area (48%), compared to those with the use of a standard fertilizer soil, without the release of any dangerous element for the plant in the soil. These results are a promising beginning for the development of further studies already in progress on sustainable controlled-release fertilizers.
Semi-Analytical Solution for Elastoplastic Deflection of Non-Prismatic Cantilever Beams with Circular Cross-Section
Authors: Zorić Andrija , Trajković-Milenković Marina , Zlatkov Dragan , Vacev Todor
Published: May 2022

A solution for the elastoplastic deflection of cantilever beams with linearly variable circular cross-section loaded by shear force at the free end, which is suitable for practical use, has not yet been developed. A semi-analytical solution for such a problem is proposed in this paper. The solution involves beams made of homogenous and isotropic materials with bilinear elastoplastic strain hardening behavior. The Bernoulli–Euler formula is used for determining the elastic deflection. However, for the plastic domain of material behavior, the differential equation of beam bending does not have a solution in closed form. Therefore, an incremental procedure for determining the curvature of the plastified region of the beam is suggested. Deflection of the cantilever beam is calculated via integration of the approximated function of the beam curvature. The proposed semi-analytical solution is validated using experimental results of the seismic energy dissipation device components which have been selected as a sample of a real engineering system. Also, validation is done via finite element analysis of six different cantilever beam models with varying geometric and material characteristics. A satisfying agreement between the proposed semi-analytical results and the subsequent experimental and numerical results is herein achieved, confirming its reliability.
“Atoms” Special Issue (Electron Scattering in Gases—From Cross Sections to Plasma Modeling)
Authors: Karwasz Grzegorz P.
Published: May 2022

Experimental studies of electron scattering in gases, under the name of “cathode rays”, started before the “official” discovery of the electron by J [...]
Dynamical Analysis of a Modified Epidemic Model with Saturated Incidence Rate and Incomplete Treatment
Authors: Beay Lazarus Kalvein , Anggriani Nursanti
Published: May 2022

This paper addresses a modified epidemic model with saturated incidence and incomplete treatment. The existence of all equilibrium points is analyzed. A reproduction number R0 is determined. Next, it is found that the non-endemic point P0 is stable in case R0<1, but unstable in case R0>1. The special conditions to analyze the local and global stability of the non-endemic and endemic points are investigated. Globally, the sensitivity analysis of the system is studied by combining the Latin Hypercube Sampling and Partial Rating Correlation Coefficients methods. By using the Pontryagins maximum principle, the optimal control problem is studied. Various numerical results are given to support our analysis.
Comparison of Sweet–Sour Taste Interactions between Cold Brewed Coffee and Water
Authors: Junge Jonas Yde , Mielby Line Ahm , Kidmose Ulla
Published: May 2022

Most beverages are complex matrices. Different taste compounds within these matrices interact, and thus affect the perception of the tastes. Sweetness and sourness have generally been known to suppress each other, but often such investigations have focused on aqueous solutions. Investigations into what happens when these known interactions are transferred to more complex solutions are scarce. In this study, we investigated the differences in taste interactions between an aqueous matrix and a cold-brewed coffee matrix. Two sub-studies were conducted. In one, six aqueous samples were evaluated by 152 naïve consumers; in the other six cold-brewed coffee samples were evaluated by 115 naïve consumers. In both studies participants tasted samples with no addition or with addition of either sucrose, citric acid, tartaric acid, or a mix of sucrose and either of the acids. Results showed that the sweetness of sucrose was suppressed by both citric acid and tartaric acid in both matrices. The sourness of both citric acid and tartaric acid was suppressed in the aqueous matrix, but only the sourness of tartaric was suppressed in the coffee matrix. Generally, the suppression was lower in the coffee matrix compared to the aqueous matrix. In conclusion, results from taste interaction studies conducted on aqueous matrices can to some extent, with caution, be interpolated to more complex matrices. Importantly, suppression effects might diminish with an increase in matrix complexity.
In Vitro Anthelmintic Activity of Sea Buckthorn (Hippophae rhamnoides) Berry Juice against Gastrointestinal Nematodes of Small Ruminants
Authors: Maestrini Michela , Forzato Cristina , Mancini Simone , Pieracci Ylenia , Perrucci Stefania
Published: May 2022

Gastrointestinal nematodes are one of the major threats in small ruminant breeding. Their control is difficult due to the development of anthelmintic resistance, and the search for new molecules endowed with anthelmintic activity (AH) is considered a priority. In this context, we evaluated the in vitro AH activity of two commercial sea buckthorn (Hippophae rhamnoides) berry juices, namely SBT and SBF. The in vitro evaluation was based on the egg-hatch test and larval exsheathment assay at different concentrations. Data were statistically analysed, and the EC50 was calculated. Chemical analyses were performed to evaluate the total polyphenol content of the juices and chemical profile of the most represented compounds. The role of the polyphenolic fraction in the anthelmintic activity of the juices was also assessed. At the highest concentrations, the activity of SBT was high in both tests and comparable to that observed in the thiabendazole-treated positive controls, while SBF showed a lower efficacy. Glycosylated isorhamnetin and quercetin were the most represented polyphenolic compounds in both juices. In conclusion, both H. rhamnoides berry juices tested in this study showed interesting anthelmintic properties in vitro.
One Molecule for Mental Nourishment and More: Glucose Transporter Type 1—Biology and Deficiency Syndrome
Authors: Vulturar Romana , Chiș Adina , Pintilie Sebastian , Farcaș Ilinca Maria , Botezatu Alina , Login Cristian Cezar , Sitar-Taut Adela-Viviana , Orasan Olga Hilda , Stan Adina , Lazea Cecilia , Al-Khzouz Camelia , Mager Monica , Vințan Mihaela Adela , Manole Simona , Damian Laura
Published: May 2022

Glucose transporter type 1 (Glut1) is the main transporter involved in the cellular uptake of glucose into many tissues, and is highly expressed in the brain and in erythrocytes. Glut1 deficiency syndrome is caused mainly by mutations of the SLC2A1 gene, impairing passive glucose transport across the blood–brain barrier. All age groups, from infants to adults, may be affected, with age-specific symptoms. In its classic form, the syndrome presents as an early-onset drug-resistant metabolic epileptic encephalopathy with a complex movement disorder and developmental delay. In later-onset forms, complex motor disorder predominates, with dystonia, ataxia, chorea or spasticity, often triggered by fasting. Diagnosis is confirmed by hypoglycorrhachia (below 45 mg/dL) with normal blood glucose, 18F-fluorodeoxyglucose positron emission tomography, and genetic analysis showing pathogenic SLC2A1 variants. There are also ongoing positive studies on erythrocytes’ Glut1 surface expression using flow cytometry. The standard treatment still consists of ketogenic therapies supplying ketones as alternative brain fuel. Anaplerotic substances may provide alternative energy sources. Understanding the complex interactions of Glut1 with other tissues, its signaling function for brain angiogenesis and gliosis, and the complex regulation of glucose transportation, including compensatory mechanisms in different tissues, will hopefully advance therapy. Ongoing research for future interventions is focusing on small molecules to restore Glut1, metabolic stimulation, and SLC2A1 transfer strategies. Newborn screening, early identification and treatment could minimize the neurodevelopmental disease consequences. Furthermore, understanding Glut1 relative deficiency or inhibition in inflammation, neurodegenerative disorders, and viral infections including COVID-19 and other settings could provide clues for future therapeutic approaches.
Unveiling the Native Morphology of Extracellular Vesicles from Human Cerebrospinal Fluid by Atomic Force and Cryogenic Electron Microscopy
Authors: Kurtjak Mario , Kereïche Sami , Klepac Damir , Križan Hrvoje , Perčić Marko , Krušić Alić Vedrana , Lavrin Teja , Lenassi Metka , Wechtersbach Karmen , Kojc Nika , Vukomanović Marija , Zrna Siniša , Biberić Maša , Domitrović Robert , Grabušić Kristina , Malenica Mladenka
Published: May 2022

Extracellular vesicles (EVs) are membranous structures in biofluids with enormous diagnostic/prognostic potential for application in liquid biopsies. Any such downstream application requires a detailed characterization of EV concentration, size and morphology. This study aimed to observe the native morphology of EVs in human cerebrospinal fluid after traumatic brain injury. Therefore, they were separated by gravity-driven size-exclusion chromatography (SEC) and investigated by atomic force microscopy (AFM) in liquid and cryogenic transmission electron microscopy (cryo-TEM). The enrichment of EVs in early SEC fractions was confirmed by immunoblot for transmembrane proteins CD9 and CD81. These fractions were then pooled, and the concentration and particle size distribution were determined by Tunable Resistive Pulse Sensing (around 1010 particles/mL, mode 100 nm) and Nanoparticle Tracking Analysis (around 109 particles/mL, mode 150 nm). Liquid AFM and cryo-TEM investigations showed mode sizes of about 60 and 90 nm, respectively, and various morphology features. AFM revealed round, concave, multilobed EV structures; and cryo-TEM identified single, double and multi-membrane EVs. By combining AFM for the surface morphology investigation and cryo-TEM for internal structure differentiation, EV morphological subpopulations in cerebrospinal fluid could be identified. These subpopulations should be further investigated because they could have different biological functions.
Identification of LOC101927355 as a Novel Biomarker for Preeclampsia
Authors: Peñailillo Reyna , Monteiro Lara J. , Acuña-Gallardo Stephanie , García Felipe , Velásquez Victoria , Correa Paula , Díaz Pilar , Valdebenito Patricia P. , Navarro Cristina , Romero Roberto , Sánchez Mario , Illanes Sebastián E. , Nardocci Gino
Published: May 2022

Preeclampsia, a disorder with a heterogeneous physiopathology, can be attributed to maternal, fetal, and/or placental factors. Long non-coding RNAs (lncRNAs) refer to a class of non-coding RNAs, the essential regulators of biological processes; their differential expression has been associated with the pathogenesis of multiple diseases. The study aimed to identify lncRNAs, expressed in the placentas and plasma of patients who presented with preeclampsia, as potential putative biomarkers of the disease. In silico analysis was performed to determine lncRNAs differentially expressed in the placentas of patients with preeclampsia, using a previously published RNA-Seq dataset. Seven placentas and maternal plasma samples collected at delivery from preterm preeclamptic patients (≤37 gestational weeks of gestation), and controls were used to validate the expression of lncRNAs by qRT-PCR. Six lncRNAs were validated and differentially expressed (p < 0.05) in the preeclampsia and control placentas: UCA1 and HCG4 were found upregulated, and LOC101927355, LINC00551, PART1, and NRAD1 downregulated. Two of these lncRNAs, HCG4 and LOC101927355, were also detected in maternal plasma, the latter showing a significant decrease (p = 0.03) in preeclamptic patients compared to the control group. In silico analyses showed the cytoplasmic location of LOC101927355, which suggests a role in post-transcriptional gene regulation. The detection of LOC101927355 in the placenta and plasma opens new possibilities for understanding the pathogenesis of preeclampsia and for its potential use as a biomarker.