bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data
Authors: Kaushik Saurabh , Singh Tejpal , Bhardwaj Anshuman , Joshi Pawan K. , Dietz Andreas J.
Published: March 2022

High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km2 and 1710 km2 of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data.
Edge Guided Context Aggregation Network for Semantic Segmentation of Remote Sensing Imagery
Authors: Liu Zhiqiang , Li Jiaojiao , Song Rui , Wu Chaoxiong , Liu Wei , Li Zan , Li Yunsong
Published: March 2022

Semantic segmentation of remote sensing imagery (RSI) has obtained great success with the development of deep convolutional neural networks (DCNNs). However, most of the existing algorithms focus on designing end-to-end DCNNs, but neglecting to consider the difficulty of segmentation in imbalance categories, especially for minority categories in RSI, which limits the performance of RSI semantic segmentation. In this paper, a novel edge guided context aggregation network (EGCAN) is proposed for the semantic segmentation of RSI. The Unet is employed as backbone. Meanwhile, an edge guided context aggregation branch and minority categories extraction branch are designed for a comprehensive enhancement of semantic modeling. Specifically, the edge guided context aggregation branch is proposed to promote entire semantic comprehension of RSI and further emphasize the representation of edge information, which consists of three modules: edge extraction module (EEM), dual expectation maximization attention module (DEMA), and edge guided module (EGM). EEM is created primarily for accurate edge tracking. According to that, DEMA aggregates global contextual features with different scales and the edge features along spatial and channel dimensions. Subsequently, EGM cascades the aggregated features into the decoder process to capture long-range dependencies and further emphasize the error-prone pixels in the edge region to acquire better semantic labels. Besides this, the exploited minority categories extraction branch is presented to acquire rich multi-scale contextual information through an elaborate hybrid spatial pyramid pooling module (HSPP) to distinguish categories taking a small percentage and background. On the Tianzhi Cup dataset, the proposed algorithm EGCAN achieved an overall accuracy of 84.1% and an average cross-merge ratio of 68.1%, with an accuracy improvement of 0.4% and 1.3% respectively compared to the classical Deeplabv3+ model. Extensive experimental results on the dataset released in ISPRS Vaihingen and Potsdam benchmarks also demonstrate the effectiveness of the proposed EGCAN over other state-of-the-art approaches.
Investigation of Underwater Shoulder Muscle Activity during Manikin-Carrying in Young Elite Lifesaving Athletes
Authors: Tse Daniel Hon-Ting , Kwok Wan-Yu , So Billy Chun-Lung
Published: March 2022

Manikin carrying is a lifesaving sports technique, in which athletes stroke with one arm and carry a manikin of 60 kg with the other arm as they swim. Stabilizing the manikin exerts great demand on the shoulder muscles of the carrying arm; thus, this study aimed to investigate the muscle activation of the carrying shoulder and the possible factors associated with it. This was a cross-sectional study, in which 20 young elite lifesaving athletes were recruited from the Hong Kong Lifesaving Society. The muscle activity of the posterior deltoid (PD), teres major (TM), and middle trapezius (MT) were recorded with wireless surface electromyography (sEMG) during the performance of 25-m manikin carrying in a swimming pool. The 25-m manikin-carrying was divided into and analyzed in 3 phases: initial, middle, and end phase. The initial phase was defined as the period from the athlete’s first swimming stroke to the end of the third stroke; the middle phase was defined as the period between the initial and the end phase; and the end phase was defined as the period from the last third stroke to the last stroke at the 25-m finishing line. The first web space and grip strength were measured. The speed and number of inhalations were calculated. PD showed muscle activity of 55.73% of maximal voluntary isometric contraction (MVIC) in the initial phase and 40.21% MVIC in middle phase. TM showed a muscle activity of 65.26% MVIC in the initial phase and 64.35% MVIC in the middle phase. MT showed 84.54% MVIC in the initial phase and 68.54% MVIC in the middle phase. Young elite athletes showed significant use of PD, TM, and MT during manikin-carrying. The muscle activity levels correlated with the first web space, grip strength, speed, and number of inhalations of the athletes.
The Short-Range, High-Accuracy Compact Pulsed Laser Ranging System
Authors: Ma Hongbin , Luo Yuan , He Yan , Pan Shiguang , Ren Lihong , Shang Jianhua
Published: March 2022

A short-range, compact, real-time pulsed laser rangefinder is constructed based on pulsed time-of-flight (ToF) method. In order to reduce timing discrimination error and achieve high ranging accuracy, gray-value distance correction and temperature correction are proposed, and are realized with a field programmable gate array (FPGA) in a real-time application. The ranging performances—such as the maximum ranging distance, the range standard deviation, and the ranging accuracy—are theoretically calculated and experimentally studied. By means of these proposed correction methods, the verification experimental results show that the achievable effective ranging distance can be up to 8.08 m with a ranging accuracy of less than ±11 mm. The improved performance shows that the designed laser rangefinder can satisfy on-line ranging applications with high precision, fast ranging speed, small size, and low implementation cost, and thus has potential in the areas of robotics, manufacturing, and autonomous navigation.
Wearable IMMU-Based Relative Position Estimation between Body Segments via Time-Varying Segment-to-Joint Vectors
Authors: Lee Chang June , Lee Jung Keun
Published: March 2022

In biomechanics, estimating the relative position between two body segments using inertial and magnetic measurement units (IMMUs) is important in that it enables the capture of human motion in unconstrained environments. The relative position can be estimated using the segment orientation and segment-to-joint center (S2J) vectors where the S2J vectors are predetermined as constants under the assumption of rigid body segments. However, human body segments are not rigid bodies because they are easily affected by soft tissue artifacts (STAs). Therefore, the use of the constant S2J vectors is one of the most critical factors for the inaccurate estimation of relative position. To deal with this issue, this paper proposes a method of determining time-varying S2J vectors to reflect the deformation of the S2J vectors and thus to increase the estimation accuracy, in IMMU-based relative position estimation. For the proposed method, first, reference S2J vectors for learning needed to be collected. A regression method derived a function outputting S2J vectors based on specific physical quantities that were highly correlated with the deformation of S2J vectors. Subsequently, time-varying S2J vectors were determined from the derived function. The validation results showed that, in terms of the averaged root mean squared errors of four tests performed by three subjects, the proposed method (15.08 mm) provided a higher estimation accuracy than the conventional method using constant vectors (31.32 mm). This indicates the proposed method may effectively compensate for the effects of STAs and ultimately estimate more accurate relative positions. By providing STA-compensated relative positions between segments, the proposed method applied in a wearable motion tracking system can be useful in rehabilitation or sports sciences.
Human Gait Modeling, Prediction and Classification for Level Walking Using Harmonic Models Derived from a Single Thigh-Mounted IMU
Authors: Abhayasinghe Nimsiri , Murray Iain
Published: March 2022

The majority of human gait modeling is based on hip, foot or thigh acceleration. The regeneration accuracy of these modeling approaches is not very high. This paper presents a harmonic approach to modeling human gait during level walking based on gyroscopic signals for a single thigh-mounted Inertial Measurement Unit (IMU) and the flexion–extension derived from a single thigh-mounted IMU. The thigh angle can be modeled with five significant harmonics, with a regeneration accuracy of over 0.999 correlation and less than 0.5° RMSE per stride cycle. Comparable regeneration accuracies can be achieved with nine significant harmonics for the gyro signal. The fundamental frequency of the harmonic model can be estimated using the stride time, with an error level of 0.0479% (±0.0029%). Six commonly observed stride patterns, and harmonic models of thigh angle and gyro signal for those stride patterns, are presented in this paper. These harmonic models can be used to predict or classify the strides of walking trials, and the results are presented herein. Harmonic models may also be used for activity recognition. It has shown that human gait in level walking can be modeled with a harmonic model of thigh angle or gyro signal, using a single thigh-mounted IMU, to higher accuracies than existing techniques.
Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices
Authors: Xue Bing , Shi Wen , Chotirmall Sanjay H. , Koh Vivian Ci Ai , Ang Yi Yang , Tan Rex Xiao , Ser Wee
Published: March 2022

Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm’s low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.
A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance
Authors: Domingos Lucas C. F. , Santos Paulo E. , Skelton Phillip S. M. , Brinkworth Russell S. A. , Sammut Karl
Published: March 2022

This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships). Not only is the contribution of this work to provide a systematic description of the state of the art of this field, but also to identify five main ingredients in its current development: the application of deep-learning methods using convolutional layers alone; deep-learning methods that apply biologically inspired feature-extraction filters as a preprocessing step; classification of data from frequency and time–frequency analysis; methods using machine learning to extract features from original signals; and transfer learning methods. This paper also describes some of the most important datasets cited in the literature and discusses data-augmentation techniques. The latter are used for coping with the scarcity of annotated sonar datasets from real maritime missions.
Experiences of Inclusive Action and Social Design Research with Social Workers and People with Intellectual Disabilities
Authors: Knevel Jeroen , Wilken Jean Pierre , Schippers Alice
Published: March 2022

In this study, we report on a two-year experience of inclusive participative action and social design research consisting of intensive collaboration between social workers, people with intellectual disabilities and researchers. Action research and design research are attunable and lend themselves to an inclusive approach aimed at knowledge development and change in practice. Social workers and people with intellectual disabilities were involved in a community of development. They became owners of the subject matter and the answers and solutions they designed. We conclude that an inclusive approach lends itself well to combining or even merging action research and social design research. Inclusive participative action and social design research cannot be standardized since it contains a particularly emergent process. Hence, it requires flexibility and creativity in finding ways to create an inclusive process of co-creation.
Does the Government’s Environmental Attention Affect Ambient Pollution? Empirical Research on Chinese Cities
Authors: Huang Shan , Ding Yan , Failler Pierre
Published: March 2022

Environmental pollution has attracted growing government attention. We employ a series of panel data regression models to measure and analyze the impact of environmental attention of 284 prefecture-level municipal governments on ambient pollution in China. The results show that: (1) The improvement of government environmental attention can curb ambient pollution. (2) The impact of government environmental attention on ambient pollution is heterogeneous in the difference of regional and local environmental pollution severity. (3) Government environmental attention inhibits ambient pollution through green development and industrial upgrading. The conclusions of this paper provide evidence and implications for environmental regulation in developing countries and cities.
Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data
Authors: Antzoulatos Gerasimos , Kouloglou Ioannis-Omiros , Bakratsas Marios , Moumtzidou Anastasia , Gialampoukidis Ilias , Karakostas Anastasios , Lombardo Francesca , Fiorin Roberto , Norbiato Daniele , Ferri Michele , Symeonidis Andreas , Vrochidis Stefanos , Kompatsiaris Ioannis
Published: March 2022

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into 70%, used for training, and 30%, employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.
Aquatic Plants and Aquatic Animals in the Context of Sustainability: Cultivation Techniques, Integration, and Blue Revolution
Authors: Mansour Abdallah Tageldein , Ashour Mohamed , Alprol Ahmed E. , Alsaqufi Ahmed Saud
Published: March 2022

The aquaculture industry has rapidly increased in response to the increasing world population, with the appreciation that aquaculture products are beneficial for human health and nutrition. Globally, aquaculture organisms are mainly divided into two divisions, aquatic animals (finfish, crustaceans, and molluscs) and aquatic plants (microalgae and seaweed). Worldwide aquaculture production has reached more than 82 million tonnes (MTs) in 2018 with more than 450 cultured species. The development of economical, environmentally friendly, and large-scale feasible technologies to produce aquaculture organisms (even aquatic animals and/or aquatic plants) is an essential need of the world. Some aquaculture technologies are related to aquatic animals or aquatic plants, as well as some technologies have an integrated system. This integration between aquatic plants and aquatic animals could be performed during early larvae rearing, on-growing and/or mass production. In the context of the blue revolution, the current review focuses on the generations of integration between aquatic plants and aquatic animals, such as live feeds, biomass concentrates, water conditioners “green water technique”, aqua-feed additives, co-culturing technologies, and integrated multi-trophic aquaculture (IMTA). This review could shed light on the benefit of aquatic animals and plant integration, which could lead future low-cost, highly efficient, and sustainable aquaculture industry projects.
Emotions, Illness Symptoms, and Job Satisfaction among Kindergarten Teachers: The Mediating Role of Emotional Exhaustion
Authors: Zhang Keshun , Cui Xinxin , Wang Rundong , Mu Chenchen , Wang Fang
Published: March 2022

Kindergarten teachers’ emotions are an essential factor in their physical and psychological wellbeing. Previous studies mainly focused on the relationship between kindergarten teachers’ emotions and their students’ emotions while ignoring the important relationships between kindergarten teachers’ emotions and their own wellbeing (e.g., teachers’ health, job satisfaction, burnout). Therefore, this study explores teacher emotions as predictor variables, illness symptoms, and job satisfaction as criterion variables, and emotional exhaustion as a mediator. In total, 1997 kindergarten teachers completed the Teacher’s Emotion Scale, the Occupational Emotional Exhaustion Scale, the Illness Symptoms Scale, and the Job Satisfaction Scale. Results revealed that enjoyment negatively predicted illness symptoms and positively predicted job satisfaction via the mediating role of emotional exhaustion. The opposite relationships were found with anger, also confirming the mediating role of emotional exhaustion. Anxiety positively predicted illness symptoms, completely mediated by emotional exhaustion, but no relationship was found with job satisfaction. The function of emotions in teachers’ physical and mental health, implications for kindergartens’ research and practice, and suggestions for future research are discussed.
Multiperiod Dynamic Programming Algorithm for Optimizing a Nature Reserve
Authors: Lin Chih-Wei , Hong Yu , Tu Weihao , Liu Jinfu
Published: March 2022

Zoning adjustments are a key method of improving the conservation efficiency of a nature reserve. Existing studies typically consider the one-period programming method and ignore dynamic ecological changes during the programming of a nature reserve. In this study, a scientific method for nature reserve (NR) programming, namely the multiperiod dynamic programming (MDP) algorithm, is proposed. The MDP algorithm designs an NR over three periods and does so by using ecological suitability values for each grid area. Ecological suitability values for each period were determined based on existing data on rare aquatic animals with Maxent software and cellular automata (CA). CA were used to determine the actual protection effect and to adjust each period’s ecological suitability values through comparisons with the sites’ surroundings. The maximization of ecological suitability values was used as an objective function; these values were assumed to indicate protection benefits. The objective function of the MDP also includes grid perimeters and numerical minimization for spatial compactness. Moreover, we designed three MDP constraints for the dynamic programming, including base constraints, distinguishing constraints, and multiperiod constraints. In the base and distinguishing constraints, we require a grid square to be a core, buffer, or unselected square, and we require the core and buffer grids to be spatially connected. For the multiperiod constraints, we used virtual points to ensure spatial continuity in different periods while attaining high ecological suitability. Our main contributions are as follows: (1) the novel MDP algorithm combining ecological attributes and multiperiod dynamic planning to optimize NR planning; (2) the use of virtual points to avoid selecting invalid grids and to ensure spatial continuity with significant protection benefits; and (3) the definition of ecological suitability values and use of CA to simulate dynamic changes over the three periods. The results reveal that the MDP algorithm results in a reserve with greater protection benefits than current reserves with superior spatial distribution due to multiperiod programming. The proposed MDP algorithm is a novel method for the scientific optimization and adjustment of nature reserves.
Attention-Based Distributed Deep Learning Model for Air Quality Forecasting
Authors: Mengara Mengara Axel Gedeon , Park Eunyoung , Jang Jinho , Yoo Younghwan
Published: March 2022

Air quality forecasting has become an essential factor in facilitating sustainable development worldwide. Several countries have implemented monitoring stations to collect air pollution particle data and meteorological information using parameters such as hourly timespans. This research focuses on unravelling a new framework for air quality prediction worldwide and features Busan, South Korea as its model city. The paper proposes the application of an attention-based convolutional BiLSTM autoencoder model. The proposed deep learning model has been trained on a distributed framework, referred to data parallelism, to forecast the intensity of particle pollution (PM2.5 and PM10). The algorithm automatically learns the intrinsic correlation among the particle pollution in different locations. Each location’s meteorological and traffic data is extensively exploited to improve the model’s performance. The model has been trained using air quality particle data and car traffic information. The traffic information is obtained by a device which counts cars passing a specific area through the YOLO algorithm, and then sends the data to a stacked deep autoencoder to be encoded alongside the meteorological data before the final prediction. In addition, multiple one-dimensional CNN layers are used to obtain the local spatial features jointly with a stacked attention-based BiLSTM layer to figure out how air quality particles are correlated in space and time. The evaluation of the new attention-based convolutional BiLSTM autoencoder model was derived from data collected and retrieved from comprehensive experiments conducted in South Korea. The results not only show that the framework outperforms the previous models both on short- and long-term predictions but also indicate that traffic information can improve the accuracy of air quality forecasting. For instance, during PM2.5 prediction, the proposed attention-based model obtained the lowest MAE (5.02 and 22.59, respectively, for short-term and long-term prediction), RMSE (7.48 and 28.02) and SMAPE (17.98 and 39.81) among all the models, which indicates strong accuracy between observed and predicted values. It was also found that the newly proposed model had the lowest average training time compared to the baseline algorithms. Furthermore, the proposed framework was successfully deployed in a cloud server in order to provide future air quality information in real time and when needed.
Simulation of Freshwater Ecosystem Service Flows under Land-Use Change: A Case Study of Lianshui River Basin, China
Authors: Zou Yang , Mao Dehua
Published: March 2022

The service function of freshwater ecosystem is of great significance for ensuring the water security and the sustainable development of the social economy. However, it is vague how land-use change can influence freshwater ecosystem service flows. In this paper, we analyzed the land-use changes in the Lianshui River Basin from 2000 to 2018, built an ecosystem service flow model, and quantified the supply, demand, and flow of freshwater ecosystems under land-use change. The most intensified shifts of land-use change were the transfer of woodland to arable land and the transfer of arable land to built-up land. Urbanization and deforestation have increased water output by 0.06 billion m3, but water demand has increased by 2.42 billion m3, resulting in a 6% reduction in the flow of freshwater ecosystem services. Our study provides detailed information on freshwater ecosystem services flow from providers to beneficiaries within a watershed, showing how land-use change and ecosystem service flows can be integrated at the watershed scale to provide information for land-use management and the availability of freshwater ecosystems. Sustained development provides a scientific basis.
Subjective and Objective Quality Evaluation for Underwater Image Enhancement and Restoration
Authors: Li Wenxia , Lin Chi , Luo Ting , Li Hong , Xu Haiyong , Wang Lihong
Published: March 2022

Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed. However, many underwater image enhancement and restoration methods produce over-enhancement or under-enhancement, which affects their application. To better design underwater image enhancement and restoration methods, it is necessary to research the underwater image quality evaluation (UIQE) for underwater image enhancement and restoration methods. Therefore, a subjective evaluation dataset for an underwater image enhancement and restoration method is constructed, and on this basis, an objective quality evaluation method of underwater images, based on the relative symmetry of underwater dark channel prior (UDCP) and the underwater bright channel prior (UBCP) is proposed. Specifically, considering underwater image enhancement in different scenarios, a UIQE dataset is constructed, which contains 405 underwater images, generated from 45 different underwater real images, using 9 representative underwater image enhancement methods. Then, a subjective quality evaluation of the UIQE database is studied. To quantitatively measure the quality of the enhanced and restored underwater images with different characteristics, an objective UIQE index (UIQEI) is used, by extracting and fusing four groups of features, including: (1) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater dark channel map; (2) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater bright channel map; (3) the saturation and colorfulness features; (4) the fog density feature; (5) the global contrast feature; these features capture key aspects of underwater images. Finally, the experimental results are analyzed, qualitatively and quantitatively, to illustrate the effectiveness of the proposed UIQEI method.
Mycotoxins Exposure in Cabinda, Angola—A Pilot Biomonitoring Survey of Breastmilk
Authors: Duarte Sofia , Silva Liliana J. G. , Pereira André M. P. T. , Gimbi Marta , Cesar Cristiane , Vidal Vanessa , Basílio Rita , Almeida Anabela , Lino Celeste , Pena Angelina
Published: March 2022

Breast milk is considered the ideal form of nutrition for newborns and infants. However, it can carry over contaminants, namely mycotoxins, with biological effects to which this population is particularly vulnerable. Human biomonitoring and surveillance programs are particularly scarce in low-income countries, where food security is a more urgent priority in comparison with food safety. This pilot survey aims to assess exposure of breastfed infants to aflatoxin M1 (AFM1), zearalenone (ZEN), and ochratoxin A (OTA) in Angola, and to evaluate the main socio-demographical and food consumption determinants of lactating mothers. All 37 breast milk samples analyzed are found to be contaminated with ZEN and OTA, although none are found contaminated with AFM1. Contamination levels are lower than previously reported for ZEN but higher in the case of OTA. A significant association between ZEN levels in breast milk and the consumption of cookies by the lactating mothers is found. As for OTA, higher levels are observed in the milk from mothers with younger infants, for which high estimated daily intake (EDI) is determined. As far as the authors are aware, this is the first survey of the occurrence of mycotoxins in breast milk in Angola, so further human biomonitoring works should follow, given that mycotoxins are a global health issue that directly impact the health of populations.
The Management of Urinary Tract Infections during the COVID-19 Pandemic: What Do We Need to Know?
Authors: Cai Tommaso , Tascini Carlo , Novelli Andrea , Anceschi Umberto , Bonkat Gernot , Wagenlehner Florian , Bjerklund Johansen Truls E.
Published: March 2022

The landscape of management of urinary tract infections (UTI) is changing rapidly. The COVID-19 pandemic draws our attention to the SARS-CoV-2 management with a subsequent reduced attention on bacterial infections. The COVID-19 diffusion containing procedures, such as use of facemasks and handwashing, have reduced spreading of bacteria and bacterial lung infections. However, a brief analysis of UTI management during the COVID-19 pandemic reveals that the pandemic has changed our management of UTI in a way that violates the principles of antimicrobial stewardship. We therefore remind all urologists and other physicians who manage patients affected by UTI about the importance of continued adherence to antimicrobial stewardship principles during the COVID-19 pandemic.
Special Issue: “Viral Genetic Diversity”
Authors: Heraud Jean-Michel , Lavergne Anne , Njouom Richard
Published: March 2022

Some say that small is beautiful, and if beauty could be measured by levels of diversity and complexity, we could definitely say that viruses are beautiful [...]
StREM1.3 REMORIN Protein Plays an Agonistic Role in Potyvirus Cell-to-Cell Movement in N. benthamiana
Authors: Rocher Marion , Simon Vincent , Jolivet Marie-Dominique , Sofer Luc , Deroubaix Anne-Flore , Germain Véronique , Mongrand Sébastien , German-Retana Sylvie
Published: March 2022

REMORIN proteins belong to a plant-specific multigene family that localise in plasma membrane nanodomains and in plasmodesmata. We previously showed that in Nicotiana benthamiana, group 1 StREM1.3 limits the cell-to-cell spread of a potexvirus without affecting viral replication. This prompted us to check whether an effect on viral propagation could apply to potyvirus species Turnip mosaic virus (TuMV) and Potato virus A (PVA). Our results show that StREM1.3 transient or stable overexpression in transgenic lines increases potyvirus propagation, while it is slowed down in transgenic lines underexpressing endogenous NbREMs, without affecting viral replication. TuMV and PVA infection do not alter the membranous localisation of StREM1.3. Furthermore, StREM1.3-membrane anchoring is necessary for its agonist effect on potyvirus propagation. StREM1.3 phosphocode seems to lead to distinct plant responses against potexvirus and potyvirus. We also showed that StREM1.3 interacts in yeast and in planta with the key potyviral movement protein CI (cylindrical inclusion) at the level of the plasma membrane but only partially at plasmodesmata pit fields. TuMV infection also counteracts StREM1.3-induced plasmodesmata callose accumulation at plasmodesmata. Altogether, these results showed that StREM1.3 plays an agonistic role in potyvirus cell-to-cell movement in N. benthamiana.
Effectiveness and Safety of SARS-CoV-2 Vaccines among Children and Adolescents: A Systematic Review and Meta-Analysis
Authors: Gao Peng , Cai Shan , Liu Qiao , Du Min , Liu Jue , Liu Min
Published: March 2022

Background: The proportion of children and adolescents with COVID-19 had gradually increased according to data reported by WHO. However, there was no meta-analysis of effectiveness and safety of SARS-CoV-2 vaccines in children and adolescents. We aimed to provide investigation-based medical evidence and reference recommendations for children and adolescents in regard to SARS-CoV-2 vaccines. Methods: We systematically searched PubMed, Embase, and Web of Science from inception to 5 January 2022. RCTs and observational studies that examined the effectiveness and safety were included. Results: A total of 13 eligible studies were included for analysis. For the first dose, the effectiveness of SARS-CoV-2 vaccines against SARS-CoV-2 infection and COVID-19 was 88.5% (95% CI:15.7–98.4%, p = 0.033) and 84.3% (95% CI: 66.6–92.6%, p < 0.001) separately. For the second dose, the effectiveness against SARS-CoV-2 infection and COVID-19 was 91.6% (95% CI: 37.8–99.5%, p = 0.083) and 92.7 (95% CI: 82.2–97.0, p < 0.001) separately. Injection-site pain, fatigue, headache, anorexia, and axillary swelling were the top five adverse events after the first dose of SARS-CoV-2 vaccines. Fatigue, injection-site pain, headache, chills, and myalgia/muscle pain were the top five adverse events after the second dose of SARS-CoV-2 vaccines. Conclusions: SARS-CoV-2 vaccines had good effectiveness and safety in children and adolescents. We suggest that children and adolescents should get vaccinated as soon as possible to protect themselves and slow the spread of the pandemic.
Association of Vaccine Confidence and Hesitancy in Three Phases of COVID-19 Vaccine Approval and Introduction in Japan
Authors: Tokiya Mikiko , Hara Megumi , Matsumoto Akiko , Ashenagar Mohammad Said , Nakano Takashi , Hirota Yoshio
Published: March 2022

Understanding vaccine hesitancy, considering the target region and phase, is an urgent issue to quell the coronavirus disease (COVID-19) pandemic. This study aimed to monitor COVID-19 vaccine hesitancy in the Japanese population during the three phases of vaccine approval and introduction, and evaluate the association of vaccine hesitancy with vaccine confidence and literacy. We conducted web-based cross-sectional surveys during the three phases of COVID-19 vaccine introduction: January 2021, before approval; June, start of vaccination of the elderly; and September, when about 70% of the target population was vaccinated with at least one dose. There were 7210 participants, aged 20–80 years. We evaluated the association of vaccine hesitancy with vaccine confidence and literacy in the three phases using multivariate logistic regression analysis. The proportion of hesitancy in January, June, and September was 17.5%, 65.3%, and 19.4%, respectively. In any phase, lower vaccine confidence and literacy showed a higher adjusted odds ratio (AOR) of vaccine hesitancy in most items (AOR > 1, p < 0.001). Vaccine hesitancy in June had a different trend in perception of COVID-19 compared to that in the January and September surveys. The findings suggested that hesitancy increases transiently during vaccination introduction phases, and changes as the vaccination program progressed or waves of epidemic. Careful risk communication to increase vaccine confidence and literacy is essential to reduce vaccine hesitancy, especially in the introduction phase.
COVID-19 Vaccine Hesitancy and Associated Factors among People with HIV in the United States: Findings from a National Survey
Authors: Shrestha Roman , Meyer Jaimie P. , Shenoi Sheela , Khati Antoine , Altice Frederick L. , Mistler Colleen , Aoun-Barakat Lydia , Virata Michael , Olivares Miriam , Wickersham Jeffrey A.
Published: March 2022

Introduction: Scaling up vaccination against COVID-19 is central to controlling the COVID-19 epidemic in the United States. Several vaccines are now approved for the prevention of COVID-19, but public concerns over safety and efficacy have heightened distrust and vaccine hesitancy. This is particularly concerning among people with HIV (PWH) who may be vulnerable to more severe COVID-19 disease. Here, we aimed to identify and understand COVID-19 vaccine hesitancy in a sample of PWH in the U.S. Methods: We conducted a cross-sectional online survey among PWH in the U.S. between 6 December 2020 and 8 January 2021. Measures included demographics, participants’ HIV and health-related attributes, COVID-19 history and experiences, COVID-19 vaccine-related concerns, and standardized measures of attitudes towards COVID-19 vaccines. Multivariate linear regression was used to identify factors associated with vaccine hesitancy in this sample. Results: Among the 1030 respondents, most were male (89.7%), White (66.0%), and identified as gay or lesbian (84.5%). Participants’ mean time living with HIV was 17.0 years (standard deviation (SD) = 11.1). The mean score for vaccine hesitancy was 1.5 (SD = 0.5; range: 1–5); 935 participants (90.8%) had a score greater than 1.0, indicating most participants had some degree of vaccine hesitancy. The final multivariate linear regression showed that greater vaccine hesitancy was associated with being Black (b = 0.149, p = 0.005), single (b = 0.070, p = 0.018), politically conservative (b = 0.157, p = 0.010), “anti-vaxxer” (b = 1.791, p < 0.001), concern about side effects (b = 0.226, p < 0.001), concern about safety (b = 0.260, p < 0.001), and being worried that the vaccine will not be effective (b = 0.169, p = 0.008) and they were being experimented on (b = 0.287, p < 0.001). Participants who were male White (b = −0.093, p = 0.008) and university graduates (b = −0.093, p < 0.001) and had a CD4 count of 200 cells/mm3 (b = −0.082, p = 0.048) and a liberal political orientation (b = −0.131, p < 0.001) were associated with lower vaccine hesitancy. Conclusions: Our findings provide important insights regarding COVID-19 vaccine hesitancy among PWH. Further efforts are required to understand how various social, political, and psychological factors contribute to COVID-19 vaccine hesitancy among key populations.
Risk Analysis of 24 Residual Antibiotics in Poultry Eggs in Shandong, China (2018–2020)
Authors: Ma Xiaoyu , Chen Ling , Yin Lingling , Li Youzhi , Yang Xiuzhen , Yang Zhiguo , Li Guihua , Shan Hu
Published: March 2022

Although antibiotics have played a certain positive role in the prevention and treatment of poultry diseases, as well as the promotion of poultry growth, some farmers use antibiotics in an incorrect way in the breeding process, resulting in antibiotic residues in poultry tissues, organs and edible products. Residual antibiotics enter the human body through the food chain and accumulate, which not only causes poisoning and allergic reactions, but also drug resistance of pathogenic microorganisms, thus endangering the health of consumers. In this investigation, the residues of 24 antibiotics, including fluoroquinolones, sulfonamides, macrolides, tetracyclines, antivirals, lincomycin and florfenicol, were analyzed in 1211 poultry egg samples in Shandong, China, from 2018 to 2020. Then, based on the per capita intake of poultry eggs recommended in the dietary guidelines of Chinese residents, the maximum residue limit of veterinary drugs specified in Chinese regulations and the average weight of males and females aged 18 and over in 2020, the risk of residual antibiotics was evaluated by International Food Safety indices (IFS). The detection results showed that 104 of 1211 samples were detected with antibiotic residues, with a detection rate of 8.58%. Among them, the main residues were enrofloxacin, sulfonamides and florfenicol. The IFS calculation results showed that the IFS of residual antibiotics ranged from 1.44 × 10−7 to 0.102. Therefore, although enrofloxacin, sarafloxacin, danofloxacin, sulfonamides, tilmicosin, doxycycline, florfenicol, which are banned during egg laying, were detected in poultry eggs in Shandong, these residues did not pose a threat to the health of Chinese adult consumers, according to the daily dietary habits of Chinese people. However, it is strongly suggested that Shandong should strengthen the monitoring of antibiotic use during egg laying.
Inherited Sensory and Autonomic Neuropathy in a Border Collie, Interest of Oclacitinib for the Control of Self-Mutilation
Authors: Leonard Caroline , Van Soens Iris , Fontaine Jacques
Published: March 2022

Sensory and autonomic neuropathy was diagnosed in a five-month-old Border Collie puppy, who presented with progressive self-mutilation, proprioceptive ataxia and urinary incontinence. In the Border Collie, sensory neuropathy is different from what is observed in acral mutilation syndrome, as the genetic mutation is linked to an inversion disrupting the FAM134B gene. Diagnosis was based on history, clinical signs and genetic testing. The prognosis of sensory neuropathies is poor and no curative treatment is available. In the present case, oclacitinib was started for symptomatic treatment of the self-mutilation. A good control of the self-mutilation was quickly observed with an improvement in quality of life for five months. Unfortunately, progression of neurological signs with severe proprioceptive deficits, ataxia, muscular atrophy and urinary/fecal incontinence was observed. Five months after diagnosis, the owner elected for euthanasia.
Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran
Authors: Goudarzi Fatemeh , Hedayatiaghmashhadi Amir , Kazemi Azadeh , Fürst Christine
Published: March 2022

The economic development, livelihood and drinking water of millions of people in the central plateau of Iran depend on the Qarah-Chay River, but due to a lack of inappropriate monitoring, it has been exposed to destruction and pollution. Consequently, an assessment of the river’s water quality is of utmost importance for both the management of human health and the maintenance of a safe environment, which can be achieved by determining the best locations for pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) has been used to optimize the locations for Qarah-Chay River monitoring stations in Markazi province, Iran. The data are collected based on the Iranian Water Quality Index (IRWQI), the US National Sanitation Foundation Water Quality Index (NSFWQI) and the Oregon Water Quality Index (OWQI). The database is given to a multilayer perceptron (MLP) neural network along with a geographic information system (GIS). The output of this study identified six pollution monitoring stations on the river, which are mainly downstream due to the accumulation of land uses and the concentration of pollution. The gradient of the MLP network training courses model from the proposed monitoring stations is 0.062299. In addition, the performance evaluation criteria of the proposed MLP model for F1-score, recall, precision and accuracy were 0.85, 0.84, 0.88 and 0.88, respectively. The results obtained help managers to properly monitor the river’s water resources with accuracy, efficiency and lower cost; furthermore, the findings were able to provide scientific references for river water quality monitoring and river ecosystem protection.
Genotypic Diversity Improves Photosynthetic Traits of Hydrocotyle vulgaris and Alters Soil Organic Matter and N2O Emissions of Wetland Microecosystems
Authors: Cai Jing-Fang , Sun Kai , Qin Tian-Jian , Bu Xiang-Qi , Wang Mo-Zhu , Li Hong-Li
Published: March 2022

In plant communities, genotypic diversity can impact the plant community structure and ecosystem functions, but related research has focused on native plants. Therefore, whether genotypic diversity affects the growth of invasive plants and then changes the wetland microecosystem remains unresolved. In this study, six different genotypes of Hydrocotyle vulgaris, a common invasive plant in China, were selected to construct populations with three different genotypic diversity levels (one, three, and six genotype combinations, respectively) to explore the effects of different genotypic diversity levels on the growth and physiological traits of H. vulgaris, and soil nutrients and greenhouse gas emissions of the wetland microecosystem under flooding conditions. We found that genotypic diversity improved the leaf area, root to shoot ratio and photosynthetic physiological traits of H. vulgaris, especially under flooding. Moreover, genotypic diversity increased soil organic matter (SOM) contents in the wetland microecosystem, while it reduced the cumulative nitrous oxide emissions under flooding conditions. Overall, genotype diversity improved photosynthetic traits of H. vulgaris, further increased SOM, and reduced the N2O emissions of the wetland microecosystem. The results of this study can provide a theoretical basis for exploring how genotypic diversity levels affect the invasiveness of invasive plants and ecosystems in wetland microecosystems.
Sustainable Polymers: New 4-H STEM Curricula
Authors: Worker Steven , McCambridge Jennifer , Stevenson Anne , Fields Jane , Maille Alexa , Meehan C. L. , Mondl Amie , Malone Charles , Smith Martin
Published: March 2022

There are many environmental issues surrounding the global production and use of plastics. Three science curricula (Grades K-2, 3-5, and 6-8) were developed to introduce youth to the past, present, and future of plastics. Designed using research-based methods and grounded in effective science pedagogy, the curricula provide young people opportunities to explore viable alternatives to plastics and develop knowledge and skills necessary to help mitigate environmental impacts associated with the production, use and disposal of plastics. Evaluation results demonstrated that youth improved their understanding of polymers and intention to help reduce impacts of plastics on the environment.
Community Engagement and Programming Models for the 21st Century Extension Professional
Authors: Downey Laura
Published: March 2022

In this article, I review Donaldson's (2020) Community Engagement for Extension Professionals: 21st Century Program Planning, Evaluation, and Professionalism. This guidebook is relevant to several audiences including undergraduate and graduate students, Extension professionals, and faculty who work with students and advisees on program planning research and practice. This article highlights the primary contributions of the guidebook, with special emphasis on proactive and reactive Extension programming models.