bluuebunny/crossref_metadata_2025 huggingface dataset:
Energy Scheduling and Performance Evaluation of an e-Vehicle Charging Station
Authors: Cabrera-Tobar Ana , Blasuttigh Nicola , Massi Pavan Alessandro , Lughi Vanni , Petrone Giovanni , Spagnuolo Giovanni
Published: November 2022

This paper proposes an energy management system (EMS) for a photovoltaic (PV) grid-connected charging station with a battery energy storage system (BESS). The main objective of this EMS is to manage the energy delivered to the electric vehicle (EV), considering the price and CO2 emissions due to the grid’s connection. Thus, we present a multi-objective two-stage optimization to reduce the impact of the charging station on the environment, as well as the costs. The first stage of the optimization provides an energy schedule, taking into account the PV forecast, the hourly grid’s CO2 emissions factor, the electricity price, and the initial state of charge of the BESS. The output from this first stage corresponds to the maximum power permitted to be delivered to the EV by the grid. Then, the second stage of the optimization is based on model predictive control that looks to manage the energy flow from the grid, the PV, and the BESS. The proposed EMS is validated using an actual PV/BESS charging station located at the University of Trieste, Italy. Then, this paper presents an analysis of the performance of the charging station under the new EMS considering three main aspects, economic, environmental, and energy, for one month of data. The results show that due to the proposed optimization, the new energy profile guarantees a reduction of 32% in emissions and 29% in energy costs.
Development of Quantum Protocol Modification CSLOE–2022, Increasing the Cryptographic Strength of Classical Quantum Protocol BB84
Authors: Cherckesova Larissa V. , Safaryan Olga A. , Beskopylny Alexey N. , Revyakina Elena
Published: November 2022

Quantum cryptography protocols make it possible not only to ensure the protection of data transmitted in a communication channel from unauthorized access by intruders, but also to detect the existence of any attempted interception. This scientific direction is currently relevant, since it is related to the problem of security and data protection in current information and communication networks. The article is devoted to quantum cryptography; it describes the development of quantum protocols as quantum key distribution systems. Grounded on the laws of quantum mechanics, the elaboration of modifications of secure data transfer protocols is shown. The authors considered the best-known protocol to be BB84 of quantum key distribution; a more modern modification of this protocol is BB84 Info-Z. Comparative analysis of these has also been carried out. It has been established that the BB84-Info-Z quantum protocol works more efficiently than BB84 since its lower error threshold allows the interceptor to obtain much less information about the secret key. The authors put forward a new idea to improve the BB84 protocol (which has been quite outdated for almost 40 years), due to the increase in modern requirements for quantum cryptography protocols. The modification is called CSLOE-2022. It enables significant intensification of cryptographic strength and the entanglement degree of the interceptor (cryptanalyst), which greatly complicates the very possibility of intercepting information. The ultimate goal of the CSLOE-2022 modification is to complicate the eavesdropping process so much that it can be considered completely useless for an attacker in terms of wasting time and resources. The modification allows exceeding the known speed limit of key generation without repeaters since it uses two sources, the phases of which, in addition to the hundreds of kilometers of fiber between them, are very difficult to stabilize. Comparison of the protocols by working distance showed that for BB84, this distance does not exceed 70 km; for BB84-Info-Z it is similar, at no more than 70 km, and the modification of CSLOE-2022 proposed by the authors theoretically allows increasing the working distance of the quantum protocol to 511 km (7.3 times).
Anomaly-PTG: A Time Series Data-Anomaly-Detection Transformer Framework in Multiple Scenarios
Authors: Li Gang , Yang Zeyu , Wan Honglin , Li Min
Published: November 2022

In actual scenarios, industrial and cloud computing platforms usually need to monitor equipment and traffic anomalies through multivariable time series data. However, the existing anomaly detection methods can not capture the long-distance temporal correlations of data and the potential relationships between features simultaneously, and only have high detection accuracy for specific time sequence anomaly detection scenarios without good generalization ability. This paper proposes a time-series anomaly-detection framework for multiple scenarios, Anomaly-PTG (anomaly parallel transformer GRU), given the above limitations. The model uses the parallel transformer GRU as the information extraction module of the model to learn the long-distance correlation between timestamps and the global feature relationship of multivariate time series, which enhances the ability to extract hidden information from time series data. After extracting the information, the model learns the sequential representation of the data, conducts the sequential modeling, and transmits the data to the full connection layer for prediction. At the same time, it also uses the autoencoder to learn the potential representation of the data and reconstruct the data. The two are optimally combined to form an anomaly detection module of the model. The module combines timestamp prediction with time series data reconstruction, improving the detection rate of rare anomalies and detection accuracy. By using three public datasets of physical devices and one dataset of network traffic intrusion detection, the model’s effectiveness was verified, and the model’s generalization ability and strong robustness were demonstrated. Compared with the most advanced method, the average F1 value of the Anomaly-PTG model on four datasets was increased by 2.2%, and the F1 value on each dataset was over 94%.
Study on Frequency Stability of an Independent System Based on Wind-Photovoltaic-Energy Storage-Diesel Generator
Authors: Wu Yonghu , Huang Cun , Dong Fen , Li Guoxiang , Wang Gaowei , Zhang Sai
Published: November 2022

Wind and photovoltaic power generation connected to the independent power system can save fuel, reduce carbon emissions, and provide significant economic and environmental benefits. Influenced by the characteristics of light resources and wind resources, the wind and photovoltaic output active power is characterized by volatility and randomness, which affects the frequency stability of the independent power system. In order to evaluate the frequency stability, in this paper, the simulation model of an independent power system is established, and the simulation model of a diesel generator, wind and photovoltaic are connected. Through droop calculation and Simulink simulation, the frequency characteristics of an independent power system under different working conditions are analyzed, and the maximum absorption capacity of wind and photovoltaic is studied. In an independent power system, when the new energy output is 25% of the total output, all the new energy output is cut off, the frequency drops by 0.5 Hz, and the frequency fluctuation is within the specified range.
Experience with Using BBC Micro:Bit and Perceived Professional Efficacy of Informatics Teachers
Authors: Kvaššayová Nika , Cápay Martin , Petrík Štefan , Bellayová Magdaléna , Klimeková Eva
Published: November 2022

Our study is focused on the perceived professional efficacy of informatics in-service teachers with the experience of using micro-controller BBC micro:bit. In Slovakia, teaching using hardware is not typical. In addition, many teachers do not teach programming. BBC micro:bit is designed to be a tool for computer science (CS) teachers that should make a significant contribution to the innovation of CS teaching and enable CS teachers to implement CS lessons. The following research questions were asked. Q1: Is there a difference in the perceived efficacy to use teaching strategies based on experience with the micro:bit? Q2: Is there a relationship between the perceived efficacy of using teaching strategies and experience using the micro:bit? The research sample comprised N = 388 CS teachers employed in Slovak schools from the available selection. The research sample included CS teachers who participated in the project called ENTER. All participants have a grant, weekly online practices, supporting materials, and also consultant for implementation of a new teaching strategy. This study’s findings indicate that the use of a microcontroller such as the micro:bit has a positive impact on self-efficacy for instructional strategies, but not for classroom management.
Exploiting a Deep Learning Toolbox for Human-Machine Feedback towards Analog Integrated Circuit Placement Automation
Authors: Gusmão António , Vieira Rafael , Horta Nuno , Lourenço Nuno , Martins Ricardo
Published: November 2022

The layout design of analog integrated circuits has been defying all automation attempts, and it is still primarily a handcrafting process carried by circuit designers on traditional layout editing frameworks. This paper presents a toolbox based on deep learning techniques and a sturdy graphical user interface to assist designers during that process. The underlying mechanism of this toolbox relies on a simple pairwise device interaction circuit description, i.e., the circuits’ topological constraints, to propose valid floorplan solutions for block-level structures, including topologies and deep nanometer technology nodes not used for its training, at push-button speed. Despite its automatic functionalities, the toolbox is focused on explainable artificial intelligence, involving the designer in the synthesis flow via filtering and editing options over the candidate floorplan solutions. This constant state of human-machine feedback environment turns the designer aware of the impact of each device’s position change and inherent tradeoffs while suggesting subsequent moves, ultimately increasing the designers’ productivity in this time-consuming and iterative task. Finally, the toolbox is shown to instantly generate floorplans with similar or better constraint fulfilment than human designed solutions for state-of-the-art analog circuit blocks.
Hybrid Encryption Scheme for Medical Imaging Using AutoEncoder and Advanced Encryption Standard
Authors: Alslman Yasmeen , Alnagi Eman , Ahmad Ashraf , AbuHour Yousef , Younisse Remah , Abu Al-haija Qasem
Published: November 2022

Recently, medical image encryption has gained special attention due to the nature and sensitivity of medical data and the lack of effective image encryption using innovative encryption techniques. Several encryption schemes have been recommended and developed in an attempt to improve medical image encryption. The majority of these studies rely on conventional encryption techniques. However, such improvements have come with increased computational complexity and slower processing for encryption and decryption processes. Alternatively, the engagement of intelligent models such as deep learning along with encryption schemes exhibited more effective outcomes, especially when used with digital images. This paper aims to reduce and change the transferred data between interested parties and overcome the problem of building negative conclusions from encrypted medical images. In order to do so, the target was to transfer from the domain of encrypting an image to encrypting features of an image, which are extracted as float number values. Therefore, we propose a deep learning-based image encryption scheme using the autoencoder (AE) technique and the advanced encryption standard (AES). Specifically, the proposed encryption scheme is supposed to encrypt the digest of the medical image prepared by the encoder from the autoencoder model on the encryption side. On the decryption side, the analogous decoder from the auto-decoder is used after decrypting the carried data. The autoencoder was used to enhance the quality of corrupted medical images with different types of noise. In addition, we investigated the scores of structure similarity (SSIM) and mean square error (MSE) for the proposed model by applying four different types of noise: salt and pepper, speckle, Poisson, and Gaussian. It has been noticed that for all types of noise added, the decoder reduced this noise in the resulting images. Finally, the performance evaluation demonstrated that our proposed system improved the encryption/decryption overhead by 50–75% over other existing models.
Federated Deep Reinforcement Learning-Based Caching and Bitrate Adaptation for VR Panoramic Video in Clustered MEC Networks
Authors: Li Yan
Published: November 2022

Virtual reality (VR) panoramic video is more expressive and experiential than traditional video. With the accelerated deployment of 5G networks, VR panoramic video has experienced explosive development. The large data volume and multi-viewport characteristics of VR panoramic videos make it more difficult to cache and transcode them in advance. Therefore, VR panoramic video services urgently need to provide powerful caching and computing power over the edge network. To address this problem, this paper establishes a hierarchical clustered mobile edge computing (MEC) network and develops a data perception-driven clustered-edge transmission model to meet the edge computing and caching capabilities required for VR panoramic video services. The joint optimization problem of caching and bitrate adaptation can be formulated as a Markov Decision Process (MDP). The federated deep reinforcement learning (FDRL) algorithm is proposed to solve the problem of caching and bitrate adaptation (called FDRL-CBA) for VR panoramic video services. The simulation results show that FDRL-CBA can outperform existing DRL-based methods in the same scenarios in terms of cache hit rate and quality of experience (QoE). In conclusion, this work developed a FDRL-CBA algorithm based on a data perception-driven clustered-edge transmission model, called Hierarchical Clustered MEC Networks. The proposed method can improve the performance of VR panoramic video services.
Quantum Dynamic Optimization Algorithm for Neural Architecture Search on Image Classification
Authors: Jin Jin , Zhang Qian , He Jia , Yu Hongnian
Published: November 2022

Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks.
Location-Aware IoT-Enabled Wireless Sensor Networks for Landslide Early Warning
Authors: El Houssaini Dhouha , Khriji Sabrine , Viehweger Christian , Keutel Thomas , Kanoun Olfa
Published: November 2022

Wireless Sensor Networks (WSNs) represent an interesting technology for designing early warning systems for landslides as they can ensure real-time and continuous monitoring. Through accurate localization techniques, changes in the position of installed nodes can be detected even during the early stage of field instability. This is through an accurate detection of nodes position changes independently from systematic deviations resulting from outdoor environmental conditions. In this study, we propose an accurate measurement system for distance measurement between wireless sensor nodes based on an ultra-wideband (UWB) localization method. In particular, distance measurements at different real weather conditions were performed to identify the impact of weather changes on distance measurement deviations. A prototype for a landslide warning system has been developed realizing a localization accuracy of 98%.
A Convolutional Neural Network for COVID-19 Diagnosis: An Analysis of Coronavirus Infections through Chest X-rays
Authors: Mehta Avani Kirit , Swarnalatha R. , Subramoniam M. , Salunkhe Sachin
Published: November 2022

Coronavirus (COVID-19) disease has not only become a pandemic but also an overwhelming strain on the healthcare industry. The conventional diagnostic methods include Antigen Rapid Kits and Reverse Transcription–Polymerase Chain Reaction (RT-PCR) tests. However, they entail several drawbacks such as low precision in diagnosis, increased time in obtaining test results, increased human–patient interaction, and high inaccuracy in the diagnosis of asymptomatic individuals, thus posing a significant challenge in today’s medical practice in curbing an extremely infectious disease such as COVID-19. To overcome these shortcomings, a machine learning (ML) approach was proposed to aid clinicians in more accurate and precise infection diagnoses. A Convolutional Neural Network was built using a sample size of 1920 chest X-rays (CXR) of healthy individuals and COVID-19-infected patients. The developed CNN’s performance was further cross-checked using the clinical results of the validation dataset comprising 300 CXRs. By converting the final output to binary, an intuitive classification of whether a specific CXR is of a healthy or a COVID-infected patient was accomplished. The statistical analysis of the CNN was: Accuracy: 95%; Precision: 96%; Specificity: 95%; Recall: 95%, and F1 score: 95%, thus, proving it to be a promising diagnostic tool in comparison to the other existing ML-based models. The datasets were obtained from Kaggle, GitHub, and European Institute for Biomedical Imaging Research repositories. The prospects of the proposed CNN lie in its flexibility to be altered and extrapolated in diagnosing other lung infections, such as pneumonia and bacterial infections, with relevant training algorithms and inputs. Additionally, the usage of other bio-imaging modalities as input datasets such as CT scans, Lung Ultrasounds and Heat Maps gives the CNN immense potential to assess for better insights on the severity of infection in both infected and asymptomatic patients as well as other related medical diagnoses.
SOC Balanced Power Distribution Control Strategy of a DC–DC Converter with Virtual Synchronous Generator
Authors: Zhao Haodong , Chen Xiangyong , Wang Chunmei , Liu Xueqiang , Qiu Jianlong
Published: November 2022

The DC microgrid does not need to consider frequency when accessing distributed energy, but the distributed energy access port does not have inertia and damping characteristics, so there are problems of voltage instability and power fluctuation. In this paper, the bidirectional DC–DC converter is the main object; based on the virtual synchronous generator (VSG) control strategy, the inertia regulation is added to adjust the bus voltage dynamically. In addition, a balancing strategy is proposed to ensure the balanced distribution of the state of charge (SOC) and power for multiple batteries. Finally, a simulink is built to prove the viability and availability of the VSG control strategy.
Inhibitory Actions of Clinical Analgesics, Analgesic Adjuvants, and Plant-Derived Analgesics on Nerve Action Potential Conduction
Authors: Kumamoto Eiichi
Published: November 2022

The action potential (AP) conduction in nerve fibers plays a crucial role in transmitting nociceptive information from the periphery to the cerebral cortex. Nerve AP conduction inhibition possibly results in analgesia. It is well-known that many analgesics suppress nerve AP conduction and voltage-dependent sodium and potassium channels that are involved in producing APs. The compound action potential (CAP) recorded from a bundle of nerve fibers is a guide for knowing if analgesics affect nerve AP conduction. This entry mentions the inhibitory effects of clinically used analgesics, analgesic adjuvants, and plant-derived analgesics on fast-conducting CAPs and voltage-dependent sodium and potassium channels. The efficacies of their effects were compared among the compounds, and it was revealed that some of the compounds have similar efficacies in suppressing CAPs. It is suggested that analgesics-induced nerve AP conduction inhibition may contribute to at least a part of their analgesic effects.
Assessment of Prevalence and Heterogeneity of Meso- and Microplastic Pollution in Icelandic Waters
Authors: Ovide Belén García , Cirino Erica , Basran Charla Jean , Geertz Torsten , Syberg Kristian
Published: November 2022

Surface water samples were collected using a low-tech aquatic debris instrument (LADI) at six nearshore locations on the north and northwestern coasts of Iceland to investigate the prevalence of mesoplastic (5–10 mm) and microplastic (0.3–5 mm) in the region. This sampling strategy involved sampling each transect three times for a total of 18 samples collected in order to assess uncertainties related to heterogeneous distribution of plastic in surface waters. Samples in all six nearshore locations contained meso- and/or microplastic, though concentrations were highly variable. Visual, physical, and FTIR analyses were performed on 71 suspected plastic particles collected, confirming and identifying 40 of those particles as one of six types of plastic: polypropylene (PP), polyethylene (PE), high-density polyethylene (HDPE), polyester, low-density polyethylene (LDPE), and polyvinyl chloride (PVC). Lines originating from fishing gear were the most prevalent types of plastic detected across the samples. This study is among the first to quantify and identify microplastic particles collected in Icelandic nearshore surface waters.
Effects of Bacteriocin-Producing Lactiplantibacillus plantarum on Fermentation, Dynamics of Bacterial Community, and Their Functional Shifts of Alfalfa Silage with Different Dry Matters
Authors: Li Ziqian , Li Fuhou , Xie Dongmei , Zhang Baibing , Kharazian Zohreh Akhavan , Guo Xusheng
Published: November 2022

This study investigated the effects of two bacteriocin-producing Lactiplantibacillus plantarum strains on fermentation, bacterial communities, and their functions of alfalfa silage with two dry matter (DM) contents of 355 (moderate DM) and 428 (high DM) g/kg fresh weight. Before ensiling, alfalfa was treated with (1) distilled water (control), (2) the commercial strain L. plantarum MTD/1, (3) bacteriocin-producing L. plantarum ATCC14917, and (4) bacteriocin-producing L. plantarum LP1-4, and ensiled for 3 d, 7 d, 14 d, 60 d, and 90 d, respectively. Application of ATCC14917 promoted lactic acid production in the moderate DM silage at the early fermentation stage (3 d). Silages treated with ATCC14917 and LP1-4 showed lower DM losses and non-protein nitrogen concentrations versus the control or MDT/1-treated silage (p < 0.05). During fermentation, a high proportion of Weissella cibaria was observed in the silages with high DM content from 3 to 60 d of ensiling, and the functions of carbohydrate and amino acid metabolisms of silage bacterial community were decreased by ATCC14917 before 60 d of ensiling. In addition, ATCC14917 also inhibited the growth of Aerococcus and Enterobacter in silage. Therefore, the bacteriocin-producing L. plantarum ATCC14917 has a great potential to improve alfalfa silage quality, nutritive value, and safety as well.
Evacuation Time Estimation Model in Large Buildings Based on Individual Characteristics and Real-Time Congestion Situation of Evacuation Exit
Authors: Deng Qing , Zhang Bo , Zhou Zheng , Deng Hongyu , Zhou Liang , Zhou Zhengqing , Jiang Huiling
Published: November 2022

Fire is one of the most common and harmful disasters in real life. In 2021, firefighting teams in China reported 748,000 fires, resulting in 1987 deaths, 2225 injuries and CNY 6.75 billion of direct property losses, which account for 0.05‰ of GDP. Scientific and accurate estimation of evacuation time can provide decision support for intelligent fire evacuation. This paper aims to effectively improve the evacuation efficiency of people in large buildings, especially for a scenario with intricate evacuation passages. There are many factors that make a difference in evacuation time, such as individual behavior, occupant density, exit width, and so on. The people distribution density is introduced to effectively assess the impact of unstable pedestrian flow and unbalanced distribution in the process of evacuation. The verification results show that there is a strong positive correlation between people distribution density and evacuation time. Combining the people distribution density with many other factors, the training dataset is built by Pathfinder to learn the relationship between evacuation time and influencing factors. Finally, an evacuation time prediction model is established to estimate the consumption time that occupants spend on moving in the evacuation process based on stacking integration. The model can assist occupants in choosing different channels for evacuation in advance. After testing, the average error between the predicted evacuation consumption time and the reference time is 3.63 s. The result illustrates that the model can accurately predict the time consumed in the process of evacuation.
Experimental Investigation on the Heat Dissipation and Postfire Structural Performance of a Reinforced Concrete Column with Biomimicked Geometry
Authors: Vishal M. , Satyanarayanan K.S.
Published: November 2022

Cactus plants are prevalent in hot terrain locations. The spines in the cactus plants have an important function in preventing water evaporation. The strong pointed spines serve to distribute heat and prevent internal moisture loss owing to high heat. This paper addresses the biomimicking of a cactus plant to a reinforced concrete column. Columns are one of the most predominant elements in a structure and are responsible for maintaining the stability of the structure. Under the occurrences of fire, columns are the most affected, and the failure of the same could eventually steer to global collapse of the structure. In this study, various geometries were adopted based on the cactus plant, and the heat dissipation characteristics were studied. Finite element analysis was used to determine the optimal form based on the heat dissipation. The optimized shape was tested experimentally using a high-temperature localized heating element. Five column specimens were considered for experiments and named C (conventional nonheated column), C1 (conventional heated column), C2 (mimicked column), C3 (mimicked column with rebar in cone), and C4 (mimicked column with rebar in cone (quenching)). The heat-dissipating nature was observed, and the structural aspects were tested aftermath. The results reveal that the quenched specimen depicts better heat dissipation than the other specimens and eventually maintains the stability of the specimen throughout the height.
The Responses of the Ovary and Eyestalk in Exopalaemon carinicauda under Low Salinity Stress
Authors: Zhang Xiuhong , Wang Jiajia , Wang Chengwei , Li Wenyang , Ge Qianqian , Qin Zhen , Li Jian , Li Jitao
Published: November 2022

As a euryhaline shrimp, the ridgetail white prawn Exopalaemon carinicauda is strongly adaptable to salinity. Exploring the effect of long-term low salinity stress on ovarian development in E. carinicauda is essential to promote its culture in a non-marine environment. In this study, we performed biochemical assays and ovary histology analysis, finding that the E. carinicauda can adapt to low salinity stress through osmotic adjustment, and there was no substantial damage to the ovary of E. carinicauda under low salinity stress. Then, the ovarian development of E. carinicauda under low salt stress was further explored by RNA sequencing of eyestalk and ovarian tissues. A total of 389 differentially expressed genes (DEGs) in ovary tissue were identified under low salinity stress, and the 16 important DEGs were associated with ovarian development. The majority of the DEGs were enriched in ECM-receptor interaction, folate biosynthesis, arginine biosynthesis, insect hormone biosynthesis and lysosome which were involved in the ovarian development of E. carinicauda. A total of 1223 DEGs were identified in eyestalk tissue under low salinity stress, and the 18 important DEGs were associated with ovarian development. KEGG enrichment analysis found that ECM-receptor interaction, folate biosynthesis, lysosome, arginine biosynthesis and retinol metabolism may be involved in the ovarian development under low salinity stress. Our results provided new insights and revealed new genes and pathways involved in ovarian development of E. carinicauda under long-term low salinity stress.
Effects of Hay, Baleage, and Soybean Hulls Waste Used as Supplemental Feeds on the Nutritional Profile of Grass-Finished Beef
Authors: Krusinski Lucas , Maciel Isabella C. F. , Sergin Selin , Jambunathan Vijayashree , Garg Esha , Garmyn Andrea J. , Singh Sukhdeep , Bitler Chad A. , Rowntree Jason E. , Fenton Jenifer I.
Published: November 2022

Grass-finished beef (GFB) has demonstrated wide nutritional variations with some GFB having a considerably higher n-6:n-3 ratio compared to grain-finished beef. To better understand these variations, the current study investigated the effects of commonly used supplemental feeds on the nutritional profile of GFB. This two-year study involved 117 steers randomly allocated to one of four diets: (1) grass+hay (G-HAY), (2) grass+baleage (G-BLG), (3) grass+soybean hulls (G-SH), and (4) baleage+soybean hulls in feedlot (BLG-SH). Feed samples were analyzed for their nutritional value, and beef samples underwent analysis for fatty acids (FAs), vitamin E, minerals, lipid oxidation, and shear force. FAs were measured by GC-MS, vitamin E was analyzed chromatographically, minerals were analyzed by ICP-MS, and lipid oxidation was measured via a thiobarbituric acid reactive substances (TBARS) assay. G-SH beef had the highest n-6:n-3 ratio (p < 0.001), while BLG-SH beef contained less vitamin E (p < 0.001) and higher TBARS values (p < 0.001) compared to the other groups. G-HAY beef contained more long-chain n-3 polyunsaturated FAs compared to the other groups (p < 0.001). In conclusion, G-HAY beef had the most beneficial nutritional profile, while soybean hulls increased the n-6:n-3 ratio of beef.
Innovative Processing Technologies to Develop a New Segment of Functional Citrus-Based Beverages: Current and Future Trends
Authors: Vilas-Boas Ana A. , Magalhães Daniela , Campos Débora A. , Porretta Sebastiano , Dellapina Giovanna , Poli Giovanna , Istanbullu Yildiray , Demir Sema , San Martín Ángel Martínez , García-Gómez Presentación , Mohammed Reda S. , Ibrahim Faten M. , El Habbasha El Sayed , Pintado Manuela
Published: November 2022

The food industries are interested in developing functional products due to their popularity within nutritional and healthy circles. Functional fruit-based beverages represent one of the fast-growing markets due to the high concentrations of bioactive compounds (BCs), which can be health promoters. Hence, functional beverages based on citrus fruits are a potential way to take advantage of their nutritional and bioactive properties that could attract the interest of consumers. In order to ensure microbial and quality stability, the beverages are subjected to preservation treatment; however, the application of high temperatures leads to the loss of thermolabile BCs. Nowadays, innovative processing technologies (IPT) such as pulsed electric field (PEF), high-pressure processing (HPP), ultrasound processing (US), ohmic heating (OH), and microwave (MW) are a promising alternative due to their efficiency and low impact on juice BCs. The available literature concerning the effects of these technologies in functional fruit-based beverages is scarce; thus, this review gathers the most relevant information about the main positive and negative aspects of the IPT in functional properties, safety, and consumer acceptance of functional citrus-based beverages, as well as the use of citrus by-products to promote the circular economy in citrus processing.
Ultrasound-Assisted Slightly Acidic Electrolyzed Water in Aquatic Product Sterilization: A Review
Authors: Zhang Chenchen , Xie Jing
Published: November 2022

Ultrasound has been confirmed as the propagation process of mechanical vibrations in a medium, with a frequency significantly higher than 20 kHz. Moreover, it has an effect of sterilization on foods. In general, ultrasonic sterilization medium is manifested as a liquid. Ultrasonic treatment technology has certain advantages in aquatic product processing. It is noteworthy that this technology will have better effects of sterilization if used in combination with other treatment methods. Slightly acidic electrolyzed water (SAEW) is characterized by high-efficiency broad-spectrum sterilization operation, low cost, and environmental protection, among other properties, and has a positive effect on aquatic product sterilization and preservation. Selecting acidic electrolyzed water with a low concentration coupled with low-power ultrasonic waves for combined sterilization exerts a more potent sterilization effect, and acidic electrolyzed water combined with ultrasonic sterilization is expected to be a potentially environment-friendly alternative. In this study, the sterilization mechanisms of ultrasonic and SAEW methods used both individually and as a synergistic treatment, the effect on microbial growth, and the research progress of the application of the combined effect in the sterilization and refrigeration of aquatic products are reviewed. Furthermore, this study looks forward to the future development trend, with a view to its application in aquatic products, while providing a reference for research and application in the field of processing and safety.
Taste Compound Generation and Variation in Chinese Water Chestnut (Eleocharis dulcis (Burm.f.) Trin. ex Hensch.) Processed with Different Methods by UPLC-MS/MS and Electronic Tongue System
Authors: Li Guanli , Nie Hui , Huang Shuangquan , Li Xiaochun , Wu Shujie , Tang Xiaoxian , Song Mubo , Luo Yanghe
Published: November 2022

Chinese water chestnut (CWC) is popular among consumers due to its unique flavor and crisp and sweet taste. Thus far, the key substances affecting the taste compound of CWC are still unclear. In this study, we used UPLC-MS/MS and an electronic tongue system to study the effects of four typical steaming and cooking methods, cooking without peel for 10 min (PC), steaming without peel for 15 min (PS), cooking with peel for 30 min (WPC), steaming with peel for 30 min (WPS), on the taste compound generation and variation of CWC, and revealed the secret of its crisp and sweet taste. The results show that the electronic tongue can effectively identify the taste profile of CWC, and the effective tastes of CWC were umami, bitterness, saltiness, and sweetness. We screened 371 differential compounds from 640 metabolic species. Among them, nucleotides and their derivatives, carbohydrates, organic acids and their derivatives, and amino acids and their derivatives are closely related to the key taste of CWC, and these compounds affected the taste of CWC through six related metabolic pathways: oxidative phosphorylation and purine metabolism; alanine, aspartate, and glutamate; bile secretion; amino sugar and nucleotide sugar metabolism; the phenylpropane pathway; and toluene degradation. This study reveals the potential metabolic causes of taste compound generation and variation in the taste of CWC.
Editorial on Special Issue “Advances in Hydrogels”
Authors: Liu Yang
Published: November 2022

Hydrogels are a class of soft materials with crosslinked network structures [...]
Single-Cell Sequencing Identifies Master Regulators Affected by Panobinostat in Neuroblastoma Cells
Authors: Milazzo Giorgio , Perini Giovanni , Giorgi Federico M.
Published: November 2022

The molecular mechanisms and gene regulatory networks sustaining cell proliferation in neuroblastoma (NBL) cells are still not fully understood. In this tumor context, it has been proposed that anti-proliferative drugs, such as the pan-HDAC inhibitor panobinostat, could be tested to mitigate tumor progression. Here, we set out to investigate the effects of panobinostat treatment at the unprecedented resolution offered by single-cell sequencing. We identified a global senescence signature paired with reduction in proliferation in treated Kelly cells and more isolated transcriptional responses compatible with early neuronal differentiation. Using master regulator analysis, we identified BAZ1A, HCFC1, MAZ, and ZNF146 as the transcriptional regulators most significantly repressed by panobinostat. Experimental silencing of these transcription factors (TFs) confirmed their role in sustaining NBL cell proliferation in vitro.
Genome-Wide Identification and Expression Profiling of Heat Shock Protein 20 Gene Family in Sorbus pohuashanensis (Hance) Hedl under Abiotic Stress
Authors: Qi Xiangyu , Di Zexin , Li Yuyan , Zhang Zeren , Guo Miaomiao , Tong Boqiang , Lu Yizeng , Zhang Yan , Zheng Jian
Published: November 2022

Small heat shock proteins (HSP20s) are a significant factor in plant growth and development in response to abiotic stress. In this study, we investigated the role of HSP20s’ response to the heat stress of Sorbus pohuashanensis introduced into low-altitude areas. The HSP20 gene family was identified based on the genome-wide data of S. pohuashanensis, and the expression patterns of tissue specificity and the response to abiotic stresses were evaluated. Finally, we identified 38 HSP20 genes that were distributed on 16 chromosomes. Phylogenetic analysis of HSP20s showed that the closest genetic relationship to S. pohuashanensis (SpHSP20s) is Malus domestica, followed by Populus trichocarpa and Arabidopsis thaliana. According to phylogenetic analysis and subcellular localization prediction, the 38 SpHSP20s belonged to 10 subfamilies. Analysis of the gene structure and conserved motifs indicated that HSP20 gene family members are relatively conserved. Synteny analysis showed that the expansion of the SpHSP20 gene family was mainly caused by segmental duplication. In addition, many cis-acting elements connected with growth and development, hormones, and stress responsiveness were found in the SpHSP20 promoter region. Analysis of expression patterns showed that these genes were closely related to high temperature, drought, salt, growth, and developmental processes. These results provide information and a theoretical basis for the exploration of HSP20 gene family resources, as well as the domestication and genetic improvement of S. pohuashanensis.
ASH2L Aggravates Fibrosis and Inflammation through HIPK2 in High Glucose-Induced Glomerular Mesangial Cells
Authors: Zhong Wen , Hong Chen , Dong Yejun , Li Yuhui , Xiao Chenxi , Liu Xinhua
Published: November 2022

Diabetic nephropathy (DN) is a leading cause of end-stage renal disease and continues to be a threat to patients with diabetes. Dysfunction of glomerular mesangial cells (GMCs) is the main contributing factor to glomerulosclerosis, which is a pathological feature of DN. The epigenetic factor ASH2L has long been thought to be a transcriptional activator, but its function and involvement in diabetic nephropathy is still unclear. Here, we investigated the effect of ASH2L on the regulation of fibrosis and inflammation induced by high glucose in mouse mesangial cells (mMCs). We observed that ASH2L expression is increased in high glucose-induced mMCs, while loss of ASH2L alleviated fibrosis and inflammation. Furthermore, ASH2L-mediates H3K4me3 of the homeodomain-interacting protein kinase 2 (HIPK2) promoter region, which is a contributor to fibrosis in the kidneys and promotes its transcriptional expression. Similar to loss of ASH2L, silencing HIPK2 also inhibited fibrosis and inflammation. In addition, ASH2L and HIPK2 are upregulated in the kidneys of both streptozocin-induced and db/db mouse. In conclusion, we uncovered the crucial role of ASH2L in high glucose-induced fibrosis and inflammation, suggesting that ASH2L regulation may be an attractive approach to attenuate the progression of DN.
Unraveling Signatures of Local Adaptation among Indigenous Groups from Mexico
Authors: García-Ortiz Humberto , Barajas-Olmos Francisco , Contreras-Cubas Cecilia , Reynolds Austin , Flores-Huacuja Marlen , Snow Meradeth , Ramos-Madrigal Jazmín , Mendoza-Caamal Elvia , Baca Paulina , López-Escobar Tomás , Bolnick Deborah , Flores-Martínez Silvia , Ortiz-Lopez Rocio , Kostic Aleksandar , Villafan-Bernal José , Galaviz-Hernández Carlos , Centeno-Cruz Federico , García-Zapién Alejandra , Monge-Cázares Tulia , Lazalde-Ramos Blanca , Loeza-Becerra Francisco , Abrahantes-Pérez María , Rangel-Villalobos Héctor , Sosa-Macías Martha , Rojas-Martínez Augusto , Martínez-Hernández Angélica , Orozco Lorena
Published: November 2022

Few studies have addressed how selective pressures have shaped the genetic structure of the current Native American populations, and they have mostly limited their inferences to admixed Latin American populations. Here, we searched for local adaptation signals, based on integrated haplotype scores and population branch statistics, in 325 Mexican Indigenous individuals with at least 99% Native American ancestry from five previously defined geographical regions. Although each region exhibited its own local adaptation profile, only PPARG and AJAP1, both negative regulators of the Wnt/β catenin signaling pathway, showed significant adaptation signals in all the tested regions. Several signals were found, mainly in the genes related to the metabolic processes and immune response. A pathway enrichment analysis revealed the overrepresentation of selected genes related to several biological phenotypes/conditions, such as the immune response and metabolic pathways, in agreement with previous studies, suggesting that immunological and metabolic pressures are major drivers of human adaptation. Genes related to the gut microbiome measurements were overrepresented in all the regions, highlighting the importance of studying how humans have coevolved with the microbial communities that colonize them. Our results provide a further explanation of the human evolutionary history in response to environmental pressures in this region.
Hyperpigmentation Inhibits Early Skeletal Muscle Development in Tengchong Snow Chicken Breed
Authors: Shi Hongmei , Fu Jing , He Yang , Li Zijian , Kang Jiajia , Hu Changjie , Zi Xiannian , Liu Yong , Zhao Jinbo , Dou Tengfei , Jia Junjing , Duan Yong , Wang Kun , Ge Changrong
Published: November 2022

Tengchong snow, which has white feathers and black meat, is one of the most important black-bone chicken breeds and a genetic treasure of black food in China. Although the black meat traits are dominant, there are some chickens with white meat traits born in the process of folk selection and breeding. The purpose of this study was to compare the differences in skeletal muscle development between Tengchong snow black meat chickens (BS) and white meat chickens (WS), as well as whether excessive melanin deposition has an effect on skeletal muscle development. The BS and WS groups were selected to determine their muscle development difference at stages of 1, 7, 14, 21, and 42 days, using histological stain methods to analyze the development and composing type of breast and leg muscle fibers, as well as the count of melanin in BS muscle fibers. Finally, we were validated key candidate genes associated with muscle development and melanin synthesis. The results showed that BS breast muscle development was inhibited at 7, 14, and 21 days, while the leg muscle was inhibited at 7, 14, 21, and 42 days, compared to WS. Melanin deposition was present in a temporal migration pattern and was greater in the leg muscles than in the breast muscles, and it focused around blood vessels, as well as the epithelium, perimysium, endomysium, and connective tissue. Additionally, melanin produced an inhibitory effect similar to MSTN during skeletal muscle fiber development, and the inhibition was strongest at the stage of melanin entry between muscle fibers, but the precise mechanisms need to be confirmed. This study revealed that melanin has an inhibitory effect on the early development of skeletal muscle, which will provide new insights into the role of melanin in the black-boned chicken and theoretical references for the future conservation and utilization of black-boned chicken.
Effect of Didactic Training on Barriers and Biases to Treatment of Opioid Use Disorder: Meeting the Ongoing Needs of Patients with Opioid Use Disorder in the Emergency Department during the COVID-19 Pandemic
Authors: Johnson Emily , Bolshakova Maria , Vosooghi Aidan , Lam Chun Nok , Trotzky-Sirr Rebecca , Bluthenthal Ricky , Schneberk Todd
Published: November 2022

In the wake of COVID-19, morbidity and mortality due to Opioid Use Disorder (OUD) is beginning to emerge as a second wave of deaths of despair. Medication assisted treatment (MAT) for opioid use disorder MAT delivered by Emergency Medicine (EM) providers can decrease mortality due to OUD; however, there are numerous cited barriers to MAT delivery. We examined the impact of MAT training on these barriers among EM residents in an urban, tertiary care facility with a large EM residency. Training included the scripted and standardized content from the Provider Clinical Support System curriculum. Residents completed pre- and post-training surveys on knowledge, barriers, and biases surrounding OUD. We performed Wilcoxon matched-pairs signed-ranks test to detect statistical differences. Of 74 residents, 49 (66%) completed the pre-training survey, and 34 (69%) of these completed the follow-up survey. Residents reported improved preparedness to treat aspects of OUD across all areas queried, reported decreased perception of barriers to providing MAT, and increased comfort prescribing naloxone, counseling patients, prescribing buprenorphine, and treating opioid withdrawal. A didactic training on MAT was associated with residents reporting improved comfort providing buprenorphine and naloxone. As the wake of morbidity and mortality from both COVID and OUD continue to increase, programs should offer dedicated training on MAT.