bluuebunny/crossref_metadata_2025 huggingface dataset:
Mini-Beam Spatially Fractionated Radiation Therapy for Whole-Brain Re-Irradiation—A Pilot Toxicity Study in a Healthy Mouse Model
Authors: Yuan Hong , Rivera Judith N. , Frank Jonathan E. , Nagel Jonathan , Shen Colette , Chang Sha X.
Published: May 2024

For patients with recurrent brain metastases, there is an urgent need for a more effective and less toxic treatment approach. Accumulating evidence has shown that spatially fractionated radiation therapy (SFRT) is able to provide a significantly higher therapeutic ratio with lower toxicity compared to conventional radiation using a uniform dose. The purpose of this study was to explore the potential low toxicity benefit of mini-beam radiotherapy (MBRT), a form of SFRT, for whole-brain re-irradiation in a healthy mouse model. Animals first received an initial 25 Gy of uniform whole-brain irradiation. Five weeks later, they were randomized into three groups to receive three different re-irradiation treatments as follows: (1) uniform irradiation at 25 Gy; (2) MBRT at a 25 Gy volume-averaged dose (106.1/8.8 Gy for peak/valley dose, 25 Gy-MBRT); and (3) MBRT at a 43 Gy volume-averaged dose (182.5/15.1 Gy for peak/valley dose, 43 Gy-MBRT). Animal survival and changes in body weight were monitored for signs of toxicity. Brains were harvested at 5 weeks after re-irradiation for histologic evaluation and immunostaining. The study showed that 25 Gy-MBRT resulted in significantly less body weight loss than 25 Gy uniform irradiation in whole-brain re-irradiation. Mice in the 25 Gy-MBRT group had a higher level of CD11b-stained microglia but also maintained more Ki67-stained proliferative progenitor cells in the brain compared to mice in the uniform irradiation group. However, the high-dose 43 Gy-MBRT group showed severe radiation toxicity compared to the low-dose 25 Gy-MBRT and uniform irradiation groups, indicating dose-dependent toxicity. Our study demonstrates that MBRT at an appropriate dose level has the potential to provide less toxic whole-brain re-irradiation. Future studies investigating the use of MBRT for brain metastases are warranted.
No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning
Authors: Li Yuyan , Dong Yubo , Li Haoyong , Liu Danhua , Xue Fang , Gao Dahua
Published: May 2024

In hyperspectral image (HSI) reconstruction tasks, due to the lack of ground truth in real imaging processes, models are usually trained and validated on simulation datasets and then tested on real measurements captured by real HSI imaging systems. However, due to the gap between the simulation imaging process and the real imaging process, the best model validated on the simulation dataset may fail on real measurements. To obtain the best model for the real-world task, it is crucial to design a suitable no-reference HSI quality assessment metric to reflect the reconstruction performance of different models. In this paper, we propose a novel no-reference HSI quality assessment metric via ranking feature learning (R-NHSIQA), which calculates the Wasserstein distance between the distribution of the deep features of the reconstructed HSIs and the benchmark distribution. Additionally, by introducing the spectral self-attention mechanism, we propose a Spectral Transformer (S-Transformer) to extract the spatial-spectral representative deep features of HSIs. Furthermore, to extract quality-sensitive deep features, we use quality ranking as a pre-training task to enhance the representation capability of the S-Transformer. Finally, we introduce the Wasserstein distance to measure the distance between the distribution of the deep features and the benchmark distribution, improving the assessment capacity of our method, even with non-overlapping distributions. The experimental results demonstrate that the proposed metric yields consistent results with multiple full-reference image quality assessment (FR-IQA) metrics, validating the idea that the proposed metric can serve as a substitute for FR-IQA metrics in real-world tasks.
A Comprehensive Evaluation of Dual-Polarimetric Sentinel-1 SAR Data for Monitoring Key Phenological Stages of Winter Wheat
Authors: Wang Mo , Wang Laigang , Guo Yan , Cui Yunpeng , Liu Juan , Chen Li , Wang Ting , Li Huan
Published: May 2024

Large-scale crop phenology monitoring is critical for agronomic planning and yield prediction applications. Synthetic Aperture Radar (SAR) remote sensing is well-suited for crop growth monitoring due to its nearly all-weather observation capability. Yet, the capability of the dual-polarimetric SAR data for wheat phenology estimation has not been thoroughly investigated. Here, we conducted a comprehensive evaluation of Sentinel-1 SAR polarimetric parameters’ sensibilities on winter wheat’s key phenophases while considering the incidence angle. We extracted 12 polarimetric parameters based on the covariance matrix and a dual-pol-version H-α decomposition. All parameters were evaluated by their temporal profile and feature importance score of Gini impurity with a decremental random forest classification process. A final wheat phenology classification model was built using the best indicator combination. The result shows that the Normalized Shannon Entropy (NSE), Degree of Linear Polarization (DoLP), and Stokes Parameter g2 were the three most important indicators, while the Span, Average Alpha (α2¯), and Backscatter Coefficient σVH0 were the three least important features in discriminating wheat phenology for all three incidence angle groups. The smaller-incidence angle (30–35°) SAR images are better suited for estimating wheat phenology. The combination of NSE, DoLP, and two Stokes Parameters (g2 and g0) constitutes the most effective indicator ensemble. For all eight key phenophases, the average Precision and Recall scores were above 0.8. This study highlighted the potential of dual-polarimetric SAR data for wheat phenology estimation. The feature importance evaluation results provide a reference for future phenology estimation studies using dual-polarimetric SAR data in choosing better-informed indicators.
Range-Spread Target Detection Networks Using HRRPs
Authors: Ye Yishan , Deng Zhenmiao , Pan Pingping , He Wei
Published: May 2024

Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based detectors are proposed for RST detection using HRRPs, i.e., an NLS detector and DFCW detector. The NLS detector leverages domain knowledge from the traditional detector, treating the input HRRP as a low-level feature vector for target detection. An interpretable NLS module is designed to perform noise reduction for the input HRRP. The DFCW detector takes advantage of the extracted high-level feature map of the input HRRP to improve detection performance. It incorporates a feature cross-weighting module for element-wise feature weighting within the feature map, considering the channel and spatial information jointly. Additionally, a nonlinear accumulation module is proposed to replace the conventional noncoherent accumulation operation in the double-HRRP detection scenario. Considering the influence of the target spread characteristic on detector performance, signal sparseness is introduced as a measure and used to assist in generating two datasets, i.e., a simulated dataset and measured dataset incorporating real target echoes. Experiments based on the two datasets are conducted to confirm the contribution of the designed modules to detector performance. The effectiveness of the two proposed detectors is verified through performance comparison with traditional and deep learning-based detectors.
LED Junction Temperature Measurement: From Steady State to Transient State
Authors: Zhao Xinyu , Gong Honglin , Zhu Lihong , Zheng Zhenyao , Lu Yijun
Published: May 2024

In this review, we meticulously analyze and consolidate various techniques used for measuring the junction temperature of light-emitting diodes (LEDs) by examining recent advancements in the field as reported in the literature. We initiate our exploration by delineating the evolution of LED technology and underscore the criticality of junction temperature detection. Subsequently, we delve into two key facets of LED junction temperature assessment: steady-state and transient measurements. Beginning with an examination of innovations in steady-state junction temperature detection, we cover a spectrum of approaches ranging from traditional one-dimensional methods to more advanced three-dimensional techniques. These include micro-thermocouple, liquid crystal thermography (LCT), temperature sensitive optical parameters (TSOPs), and infrared (IR) thermography methods. We provide a comprehensive summary of the contributions made by researchers in this domain, while also elucidating the merits and demerits of each method. Transitioning to transient detection, we offer a detailed overview of various techniques such as the improved T3ster method, an enhanced one-dimensional continuous rectangular wave method (CRWM), and thermal reflection imaging. Additionally, we introduce novel methods leveraging high-speed camera technology and reflected light intensity (h-SCRLI), as well as micro high-speed transient imaging based on reflected light (μ_HSTI). Finally, we provide a critical appraisal of the advantages and limitations inherent in several transient detection methods and offer prognostications on future developments in this burgeoning field.
FEM Modeling Strategies: Application to Mechanical and Dielectric Sensitivities of Love Wave Devices in Liquid Medium
Authors: Rube Maxence , Tamarin Ollivier , Choudhari Asawari , Sebeloue Martine , Rebiere Dominique , Dejous Corinne
Published: May 2024

This paper presents an extended work on the Finite Element Method (FEM) simulation of Love Wave (LW) sensors in a liquid medium. Two models are proposed to simulate the multiphysical response of the sensor. Both are extensively described in terms of principle, composition and behavior, making their applications easily reproducible by the sensor community. The first model is a Representative Volume Element (RVE) simulating the transducer and the second focuses on the sensor’s longitudinal (OXZ) cut which simulates the multiphysical responses of the device. Sensitivity of the LW device to variations in the rheological and dielectric properties of liquids is estimated and then compared to a large set of measurements issued from LW sensors presenting different technological characteristics. This integral approach allows for a deeper insight into the multiphysical behavior of the LW sensor. This article also explores the advantages and drawbacks of each model. Both are in good accordance with the measurements and could be used for various applications, for which a non-exhaustive list is proposed in the conclusion.
Unsupervised Feature-Construction-Based Motor Fault Diagnosis
Authors: Amarbayasgalan Tsatsral , Ryu Keun Ho
Published: May 2024

Any bearing faults are a leading cause of motor damage and bring economic losses. Fast and accurate identification of bearing faults is valuable for preventing damaging the whole equipment and continuously running industrial processes without interruption. Vibration signals from a running motor can be utilized to diagnose a bearing health condition. This study proposes a detection method for bearing faults based on two types of neural networks from motor vibration data. The proposed method uses an autoencoder neural network for constructing a new motor vibration feature and a feed-forward neural network for the final detection. The constructed signal feature enhances the prediction performance by focusing more on a fault type that is difficult to detect. We conducted experiments on the CWRU bearing datasets. The experimental study shows that the proposed method improves the performance of the feed-forward neural network and outperforms the other machine learning algorithms.
An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task
Authors: Xiong Hongqiang , An Baizhou , Sun Boyang , Lu Jiayu
Published: May 2024

The S-transform is a fundamental time–frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0–3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.
IMU/Magnetometer-Based Azimuth Estimation with Norm Constraint Filtering
Authors: Yang Chuang , Zeng Qinghua , Xiong Zhi , Yang Jinxian
Published: May 2024

A typical magnetometer-based measurement-while-drilling (MWD) system determines the azimuth of the bottom hole assembly during the drilling process by employing triaxial accelerometers and magnetometers. The geomagnetic azimuth solution is susceptible to magnetic interference, especially strong magnetic interference and so a rotary norm constraint filtering (RNCF) method for azimuth estimation, designed to support a gyroscope-aided magnetometer-based MWD system, is proposed. First, a new magnetic dynamical system, one whose output is observed by the magnetometers triad, is designed based on the Coriolis equation of the desired geomagnetic vector. Second, given that the norm of the non-interfered geomagnetic vector can be approximated as a constant during a short-term drilling process, a norm constraint procedure is introduced to the Kalman filter. This is achieved by the normalization of the geomagnetic part of the state vector of the dynamical system and is undertaken in order to obtain a precise geomagnetic component. Simulation and actual drilling experiments show that the proposed RNCF method can effectively improve the azimuth measurement precision with 98.5% over the typical geomagnetic solution and 37.1% over the KF in a RMSE sense when being strong magnetic interference environment.
Optimizing Collaborative Crowdsensing: A Graph Theoretical Approach to Team Recruitment and Fair Incentive Distribution
Authors: Liu Hui , Zhang Chuang , Chen Xiaodong , Tai Weipeng
Published: May 2024

Collaborative crowdsensing is a team collaboration model that harnesses the intelligence of a large network of participants, primarily applied in areas such as intelligent computing, federated learning, and blockchain. Unlike traditional crowdsensing, user recruitment in collaborative crowdsensing not only considers the individual capabilities of users but also emphasizes their collaborative abilities. In this context, this paper takes a unique approach by modeling user interactions as a graph, transforming the recruitment challenge into a graph theory problem. The methodology employs an enhanced Prim algorithm to identify optimal team members by finding the maximum spanning tree within the user interaction graph. After the recruitment, the collaborative crowdsensing explored in this paper presents a challenge of unfair incentives due to users engaging in free-riding behavior. To address these challenges, the paper introduces the MR-SVIM mechanism. Initially, the process begins with a Gaussian mixture model predicting the quality of users’ tasks, combined with historical reputation values to calculate their direct reputation. Subsequently, to assess users’ significance within the team, aggregation functions and the improved PageRank algorithm are employed for local and global influence evaluation, respectively. Indirect reputation is determined based on users’ importance and similarity with interacting peers. Considering the comprehensive reputation value derived from the combined assessment of direct and indirect reputations, and integrating the collaborative capabilities among users, we have formulated a feature function for contribution. This function is applied within an enhanced Shapley value method to assess the relative contributions of each user, achieving a more equitable distribution of earnings. Finally, experiments conducted on real datasets validate the fairness of this mechanism.
Wearable EMG Measurement Device Using Polyurethane Foam for Motion Artifact Suppression
Authors: Takagi Takuma , Tomita Naoto , Sato Suguru , Yamamoto Michitaka , Takamatsu Seiichi , Itoh Toshihiro
Published: May 2024

We propose the use of a specially designed polyurethane foam with a plateau region in its mechanical characteristics—where stress remains nearly constant during deformation—between the electromyography (EMG) electrode and clothing to suppress motion artifacts in EMG measurement. Wearable EMG devices are receiving attention for monitoring muscle weakening due to aging. However, daily EMG measurement has been challenging due to motion artifacts caused by changes in the contact pressure between the bioelectrode and the skin. Therefore, this study aims to measure EMG signals in daily movement environments by controlling the contact pressure using polyurethane foam between the bioelectrode on the clothing and the skin. Through mechanical calculations and finite element method simulations of the polyurethane foam’s effect, we clarified that the characteristics of the polyurethane foam significantly influence contact pressure control and that the contact pressure is adjustable through the polyurethane foam thickness. The optimization of the design successfully controlled the contact pressure between the bioelectrode and skin from 1.0 kPa to 2.0 kPa, effectively suppressing the motion artifact in EMG measurement.
Development of 2400–2450 MHz Frequency Band RF Energy Harvesting System for Low-Power Device Operation
Authors: Khan Nasir Ullah , Ullah Sana , Khan Farid Ullah , Merla Arcangelo
Published: May 2024

Recently, there has been an increasing fascination for employing radio frequency (RF) energy harvesting techniques to energize various low-power devices by harnessing the ambient RF energy in the surroundings. This work outlines a novel advancement in RF energy harvesting (RFEH) technology, intending to power portable gadgets with minimal operating power demands. A high-gain receiver microstrip patch antenna was designed and tested to capture ambient RF residue, operating at 2450 MHz. Similarly, a two-stage Dickson voltage booster was developed and employed with the RFEH to transform the received RF signals into useful DC voltage signals. Additionally, an LC series circuit was utilized to ensure impedance matching between the antenna and rectifier, facilitating the extraction of maximum power from the developed prototype. The findings indicate that the developed rectifier attained a peak power conversion efficiency (PCE) of 64% when operating at an input power level of 0 dBm. During experimentation, the voltage booster demonstrated its capability to rectify a minimum input AC signal of only 50 mV, yielding a corresponding 180 mV output DC signal. Moreover, the maximum power of 4.60 µW was achieved when subjected to an input AC signal of 1500 mV with a load resistance of 470 kΩ. Finally, the devised RFEH was also tested in an open environment, receiving signals from Wi-Fi modems positioned at varying distances for evaluation.
A Deep Learning Approach for Accurate Path Loss Prediction in LoRaWAN Livestock Monitoring
Authors: Ojo Mike O. , Viola Irene , Miretti Silvia , Martignani Eugenio , Giordano Stefano , Baratta Mario
Published: May 2024

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model’s precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.
Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM
Authors: Yang Lei , Jiang Yibo , Zeng Kang , Peng Tao
Published: May 2024

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time–frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.
Comparison of the Waterlogging Tolerance and Morphological Responses of Five Urochloa spp. Grasses
Authors: Amaral Rafael Marzall , Sarango Lesly Astrid Calva , Reis Cristiano Eduardo Rodrigues , Lins Tulio Otávio Jardim D’almeida , Schultz Ericka Beatriz , Guerrero Daniel Carballo
Published: May 2024

Periods with high precipitation and temporary waterlogging in the humid tropics are challenging to the production and survival of some grasses of the genus Urochloa. This study aimed to evaluate the tolerance of five types of grass belonging to the genus Urochloa under waterlogging conditions through productive and morphological traits. The grasses [U. arrecta (Tanner), U. arrecta x U. mutica (Brachipará), U. brizantha cv. Marandú, U. hybrid cv. Cayman and U. humidicola cv. Llanero] were planted in pots and kept under field capacity for 33 days; then, half of them were submitted to (i) field capacity (33% humidity retention) and the other half were submitted to (ii) waterlogging conditions (2 cm of water above soil level) for 28 days. In this study, Tanner and Brachipará grasses showed higher dry shoot mass under waterlogging conditions, which were followed by Llanero, Cayman, and Marandú, respectively. Llanero, Tanner, and Brachipará presented higher waterlogging tolerance coefficients, 78.7, 76.5, and 64.5, respectively, being less affected than Cayman and Marandú (41.0 and 23.1, respectively). Brachipará, Tanner, and Cayman presented a higher root volume under waterlogging conditions, while Marandú root volume decreased by 88.77%. The Tanner, Brachipará, and Llanero genotypes were more tolerant to poorly drained or waterlogged soils than Cayman and Marandú genotypes.
How Indonesia’s Cities Are Grappling with Plastic Waste: An Integrated Approach towards Sustainable Plastic Waste Management
Authors: Zahrah Yunisa , Yu Jeongsoo , Liu Xiaoyue
Published: May 2024

In Indonesia, plastic constitutes the second largest component of municipal solid waste; however, 58% remains uncollected. Most plastic recycling depends on informal sectors, accounting for only 10% of the total, leaving the remainder to potentially harm the environment. This paper analyzes how cities in Indonesia have tackled their plastic waste problems from the perspective of the integrated sustainable waste management framework. This study focuses on plastic waste management (PWM) in three cities: Bandung, Yogyakarta, and Magelang. Data were collected from 41 semi-structured interviews, a plastic waste composition data analysis, and site visits. The research outcomes include a comprehensive analysis of stakeholder roles, plastic waste flow, plastic waste composition data, and various challenges in PWM. This study identifies three main stakeholders in collecting plastic wastes: the Department of Environment as the main local government body, community-based waste banks, and private waste management companies. Most recyclable plastic waste is recovered through waste banks and private collection services. In 2022, the predominant types of plastic waste in Bandung, Yogyakarta, and Magelang City were soft plastics, thick plastics, and hard plastics, respectively. The challenges for the PWM stem from various aspects in political, legal, and institutional; environmental; financial and technical; and socio-cultural spheres. Positive trends towards an integrated system are observed, although full integration has not yet been achieved. The research recommends a multi-stakeholder cooperative approach involving municipal authorities, waste banks, and private collectors, responsive to local conditions and emphasizing sustainability aspects throughout waste management stages for a sustainable resource recycling in developing countries.
From Isolated Valves to a Potential Marine Living Resource: History, Documented Distribution and Sustainable Population Enhancement Possibilities of the Smooth Scallop (Flexopecten glaber) on the Romanian Coast
Authors: Danilov Cristian , Nenciu Magda , Țiganov George , Filimon Adrian , Tănase Mihaela-Cosmina , Niță Victor
Published: May 2024

Until recently, the smooth scallop Flexopecten glaber (Linnaeus, 1758) was considered absent from Romanian Black Sea waters, as only isolated valves were documented on beaches of the southern coast. Yet, in 2020, the first living specimens were collected by dredging at 25 m depth in Mangalia. Moreover, in the past three years, a considerable number of live individuals were also retrieved from beam-trawling operations targeting the gastropod Rapana venosa (Valenciennes, 1846) along the Romanian coast, indicating that there is a stable population here. This remarkable expansion of F. glaber in Romanian waters may be explained as a consequence of the Black Sea’s recovery from eutrophication, reduced predatory pressure from the declining population of R. venosa and changing climate conditions. Our research aimed at using all this novel information to document for the first time the occurrence and distribution of this bivalve on the Romanian coast, also considering perspectives for the development of a new type of shellfish mariculture, avoiding any pressure on the natural stock. In total, 386 F. glaber individuals were sampled during 2020–2023, at depths between 22.5 and 33.3 m. The abundance and biomass per station oscillated between 5 and 319 specimens and 51.5 and 7377 g, respectively. A subsample of 122 specimens was preserved and analyzed in the laboratory, revealing an average shell length of 53.54 mm (±4.13 SD) and an average shell height of 49.54 mm (±3.64 SD). The smooth scallop has both ecological and economic value, which makes it a viable candidate for sustainable aquaculture by collecting spat from the most abundant areas and subsequently placing “seedlings” in the appropriate polygons for stock recovery. Ultimately, commercial exploitation from aquaculture installations can be considered, provided a permanent healthy F. glaber population is established on the Romanian coast.
Employing Plant Parasitic Nematodes as an Indicator for Assessing Advancements in Landfill Remediation
Authors: Zapałowska Anita , Skwiercz Andrzej , Kozacki Dawid , Puchalski Czesław
Published: May 2024

This research concentrated on the soil nematode communities inhabiting a reclaimed municipal waste landfill situated in Giedlarowa, southeastern Poland. The landfill, which was layered with natural soil in 2008 and cultivated with grass, served as the primary focus of the study. Samples for analysis were taken four times (October 2020 (Pf1), March 2021 (Pf2), October 2021 (Pf3), and March 2022 (Pf4)), with each time comprising three repetitions. The analysis was conducted employing microscopic examination, which enabled the identification of up to five trophic groups and species of plant-parasitic nematodes. During the assessment of nematode activity in the initial and subsequent growing seasons, it was found that Pratylenchus crenatus emerged as the predominant species among herbivorous nematodes in the plant-parasitic nematode (PPNs) community. Criconemoides informis, another nematode species, held a significant rank as well; their population during the third growing season formed the most substantial group among the PPN organisms dwelling in the soil. Nevertheless, interesting results were also obtained by populations of nematodes of the genus Hemicyclophora and Loofia, which were characterized by high densities. The analyzed soil environment showcased a C:N ratio spanning from 0.69 to 3.13. Furthermore, the soil samples exhibited variations in phosphorus content (P2O5), ranging from 4.02 mg/100 g to 10.09 mg/100 g. Criconemoides informis, Longidorus attenuates, Mesocriconema spp., and Bitylenchus maximus exhibited a positive correlation with soil mineral levels of calcium (Ca) and magnesium (Mg).
The Effects of Food on the Uptake and Excretion of Nano-Plastics by Daphnia magna
Authors: Liu Xiao-Jing , Zhang Yu-Hang , Gao Rong-Yao , Jia Hua-Bing , Shao Qian-Qian , Hu Ya-Wen , Fu Li-Min , Zhang Jian-Ping
Published: May 2024

The effects of nano-plastics (NPs) on aquatic organisms have drawn significant attention. Understanding the uptake and excretion of NPs by aquatic organisms can provide clearer insights into their behavior within organisms. And the effect of different food on the processes is unclear. Daphnia magna (D. magna) is considered as a model organism for assessing the ecological risks of NPs. This work observed the uptake and excretion of NPs by D. magna under different food supply conditions. The effects of three different types of foods (Chlorella sp., Euglena gracilis, and yeast powder) on the uptake and excretion of two concentrations of NPs (1 mg/L and 3 mg/L) by the D. magna were compared. A Time-Gated Imaging technique was used to quantify the NPs uptake mass by D. magna. The study results showed the inhibitory effect presented by food on the uptake of NPs by D. magna. The inhibitory ability of different foods varies, with similar levels observed in Chlorella sp. and E. gracilis, while the inhibitory effect of yeast powder was slightly weaker. The facilitating effect was presented by food on the excretion of NPs. The time constant of excretion of NPs by feeding yeast powder was about 4–5 min longer than that of two types of algae. These effects can be attributed to food occupying the intestine tract of D. magna and supplying energy. This work emphasizes the important role of food in evaluating the ecological effects of NPs and provides support for future research on the long-term risks of pollutants to aquatic organisms and environmental sustainability.
Do Natural Disasters Reduce Loans to the More CO2-Emitting Sectors?
Authors: Forte Antonio , Sahan Selay , Silipo Damiano B.
Published: May 2024

We studied the impact of major floods occurring in Turkey between 2005 and 2020 on lending and the allocation of loans between sectors that differ in their CO2 emissions. Our evidence shows that the floods are not significant determinants of lending or the allocation of loans between sectors, even though CO2 emissions contribute to the reallocation of loans from the more polluting to the less polluting sectors. Indeed, risks and returns of the sector remain the main determinants of lending and of the allocation of loans among sectors. The results are robust to alternative estimation methods and specifications of the econometric models. Since in the period of investigation no environmental regulations were implemented in Turkey, and the Paris Agreement was ratified only at end-2021, the evidence suggests that more stringent regulations and green policies are required to accelerate the green transition in Turkey.
Microplastics and Endocrine Disruptors in Typical Wastewater Treatment Plants in Megacity Shanghai
Authors: Tong Yuxiao , Xie Manjun , Xv Hanwen , Sun Ruihua , Wang Qian , Li Juanying
Published: May 2024

The fast development of China’s urbanization has led to a notable release of emerging pollutants, including microplastics (MPs) and endocrine disruptors (EDCs). Generally, these pollutants enter the coastal environment through the discharge of wastewater treatment plants (WWTPs) and finally threaten the organisms in the receiving waterbody. The study investigated the environmental behavior of MPs and EDCs in two typical WWTPs in one of the megacities in China, Shanghai. The abundance of MPs in the influent ranged from 321 to 976 items/L. Four shapes (films, fragments, fibers, and microbead) were found, while fibers and films dominated. Transparent (31–63%) and white (20–47%) MPs were more frequently observed, while polyethylene terephthalate, cellulose, and cellophane were the main polymetric materials. The size of the MPs fell between 15.8 μm and 2220 μm, and the smaller one (<500 μm) dominated. The removal efficiencies of the two WWTPs for MPs ranged from 64% to 92%, and both WWTPs performed better for large pieces of MPs (>500 μm). For EDCs, total concentrations in the influent were detected, ranging from 113 to 2780 ng/L. Two groups, including phenolic estrogens (PEs) and steroid estrogens (SEs), were detected, and PEs, especially bisphenol A (BPA), were the predominant individuals among the studied EDCs. Specifically, PEs ranged from 82.8 to 2637 ng/L, while SEs ranged from 27.3 to 143 ng/L. The removal efficiencies of the WWTPs for EDCs varied (82.8–100%) as well, possibly due to the different treatment compartments and contamination load in the influent. Seasonal variations for both MPs and EDCs were observed. Specifically, concentrations of MPs and EDCs in WWTPs influent were higher in the wet season, as well as the removal efficiency. Furthermore, there was a correlation observed between the concentrations of MPs and EDCs, suggesting that MPs and EDCs may originate from the same source and that EDCs released by MPs cannot be ignored during treatment. Finally, the study evaluated the environmental risk of the effluents. MPs led to a minor risk (Level I), while EDCs might lead to an adverse impact on algae (RQs = 0.0014–0.024) and fish (RQs = 3.4–30.2). In summary, WWTPs received considerable amounts of MPs and EDCs. Although the WWTPs removed the contaminants efficiently, the environmental risk of the effluent needs to be noted.
Influence of Four Veterinary Antibiotics on Constructed Treatment Wetland Nitrogen Transformation
Authors: Russell Matthew V. , Messer Tiffany L. , Repert Deborah A. , Smith Richard L. , Bartelt-Hunt Shannon , Snow Daniel D. , Reed Ariel P.
Published: May 2024

The use of wetlands as a treatment approach for nitrogen in runoff is a common practice in agroecosystems. However, nitrate is not the sole constituent present in agricultural runoff and other biologically active contaminants have the potential to affect nitrate removal efficiency. In this study, the impacts of the combined effects of four common veterinary antibiotics (chlortetracycline, sulfamethazine, lincomycin, monensin) on nitrate-N treatment efficiency in saturated sediments and wetlands were evaluated in a coupled microcosm/mesocosm scale experiment. Veterinary antibiotics were hypothesized to significantly impact nitrogen speciation (e.g., nitrate and ammonium) and nitrogen uptake and transformation processes (e.g., plant uptake and denitrification) within the wetland ecosystems. To test this hypothesis, the coupled study had three objectives: 1. assess veterinary antibiotic impact on nitrogen cycle processes in wetland sediments using microcosm incubations, 2. measure nitrate-N reduction in water of floating treatment wetland systems over time following the introduction of veterinary antibiotic residues, and 3. identify the fate of veterinary antibiotics in floating treatment wetlands using mesocosms. Microcosms containing added mixtures of the veterinary antibiotics had little to no effect at lower concentrations but stimulated denitrification potential rates at higher concentrations. Based on observed changes in the nitrogen loss in the microcosm experiments, floating treatment wetland mesocosms were enriched with 1000 μg L−1 of the antibiotic mixture. Rates of nitrate-N loss observed in mesocosms with the veterinary antibiotic enrichment were consistent with the microcosm experiments in that denitrification was not inhibited, even at the high dosage. In the mesocosm experiments, average nitrate-N removal rates were not found to be impacted by the veterinary antibiotics. Further, veterinary antibiotics were primarily found in the roots of the floating treatment wetland biomass, accumulating approximately 190 mg m−2 of the antibiotic mixture. These findings provide new insight into the impact that veterinary antibiotic mixtures may have on nutrient management strategies for large-scale agricultural operations and the potential for veterinary antibiotic removal in these wetlands.
Occurrence of Free-Living Amoebae in Non-Human Primate Gut
Authors: Cardoso Igor Rodrigues , de Lima Clezia Siqueira , dos Reis Rhagner Bonono , Pinto Ana Cristina Araujo , Pissinatti Thalita , Kugelmeier Tatiana , Neto Sócrates Fraga da Costa , da Silva Fabio Alves , Santos Helena Lúcia Carneiro
Published: May 2024

The gut microbiome reflects health and predicts possible disease in hosts. A holistic view of this community is needed, focusing on identifying species and dissecting how species interact with their host and each other, regardless of whether their presence is beneficial, inconsequential, or detrimental. The distribution of gut-associated eukaryotes within and across non-human primates is likely driven by host behavior and ecology. To ascertain the existence of free-living amoebae (FLA) in the gut of wild and captive non-human primates, 101 stool samples were collected and submitted to culture-dependent microscopy examination and DNA sequencing. Free-living amoebae were detected in 45.4% (46/101) of fecal samples analyzed, and their morphological characteristics matched those of Acanthamoeba spp., Vermamoeba spp., heterolobosean amoeboflagellates and fan-shaped amoebae of the family Vannellidae. Sequence analysis of the PCR products revealed that the suspected amoebae are highly homologous (99% identity and 100% query coverage) with Acanthamoeba T4 genotype and Vermamoeba vermiformis amoebae. The results showed a great diversity of amoebae in the non-human primate’s microbiome, which may pose a potential risk to the health of NHPs. To our knowledge, this is the first report of free-living amoebae in non-human primates that are naturally infected. However, it is unknown whether gut-borne amoebae exploit a viable ecological niche or are simply transient residents in the gut.
Cytokine Response of Natural Killer Cells to Hepatitis B Virus Infection Depends on Monocyte Co-Stimulation
Authors: Kupke Paul , Brucker Johanna , Wettengel Jochen M. , Protzer Ulrike , Wenzel Jürgen J. , Schlitt Hans J. , Geissler Edward K. , Werner Jens M.
Published: May 2024

Hepatitis B virus (HBV) is a major driver of chronic hepatic inflammation, which regularly leads to liver cirrhosis or hepatocellular carcinoma. Immediate innate immune cell response is crucial for the rapid clearance of the infection. Here, natural killer (NK) cells play a pivotal role in direct cytotoxicity and the secretion of antiviral cytokines as well as regulatory function. The aim of this study was to further elucidate NK cell responses triggered by an HBV infection. Therefore, we optimized HBV in vitro models that reliably stimulate NK cells using hepatocyte-like HepG2 cells expressing the Na+-taurocholate co-transporting polypeptide (NTCP) and HepaRG cells. Immune cells were acquired from healthy platelet donors. Initially, HepG2-NTCP cells demonstrated higher viral replication compared to HepaRG cells. Co-cultures with immune cells revealed increased production of interferon-γ and tumor necrosis factor-α by NK cells, which was no longer evident in isolated NK cells. Likewise, the depletion of monocytes and spatial separation from target cells led to the absence of the antiviral cytokine production of NK cells. Eventually, the combined co-culture of isolated NK cells and monocytes led to a sufficient cytokine response of NK cells, which was also apparent when communication between the two immune cell subpopulations was restricted to soluble factors. In summary, our study demonstrates antiviral cytokine production by NK cells in response to HBV+ HepG2-NTCP cells, which is dependent on monocyte bystander activation.
Prevalence of Acute Hepatitis E Virus Infections in Swiss Blood Donors 2018–2020
Authors: Niederhauser Christoph , Gowland Peter , Widmer Nadja , Amar EL Dusouqui Soraya , Mattle-Greminger Maja , Gottschalk Jochen , Frey Beat M.
Published: May 2024

Introduction: Hepatitis E virus (HEV) genotype 3 is the major cause of acute viral hepatitis in several European countries. It is acquired mainly by ingesting contaminated pork, but has also been reported to be transmitted through blood transfusion. Although most HEV infections, including those via blood products, are usually self-limiting, they may become chronic in immunocompromised persons. It is thus essential to identify HEV-infected blood donations to prevent transmission to vulnerable recipients. Aims: Prior to the decision whether to introduce HEV RNA screening for all Swiss blood donations, a 2-year nationwide prevalence study was conducted. Methods: All blood donations were screened in pools of 12–24 samples at five regional blood donation services, and HEV RNA-positive pools were subsequently resolved to the individual donation index donation (X). The viral load, HEV IgG and IgM serology, and HEV genotype were determined. Follow-up investigations were conducted on future control donations (X + 1) and previous archived donations of the donor (X − 1) where available. Results: Between October 2018 and September 2020, 541,349 blood donations were screened and 125 confirmed positive donations were identified (prevalence 1:4331 donations). At the time of blood donation, the HEV RNA-positive individuals were symptom-free. The median viral load was 554 IU/mL (range: 2.01–2,500,000 IU/mL). Men (88; 70%) were more frequently infected than women (37; 30%), as compared with the sex distribution in the Swiss donor population (57% male/43% female, p < 0.01). Of the 106 genotyped cases (85%), all belonged to genotype 3. Two HEV sub-genotypes predominated; 3h3 (formerly 3s) and 3c. The remaining sub-genotypes are all known to circulate in Europe. Five 3ra genotypes were identified, this being a variant associated with rabbits. In total, 85 (68%) X donations were negative for HEV IgM and IgG. The remaining 40 (32%) were positive for HEV IgG and/or IgM, and consistent with an active infection. We found no markers of previous HEV in 87 of the 89 available and analyzed archive samples (X − 1). Two donors were HEV IgG-positive in the X − 1 donation suggesting insufficient immunity to prevent HEV reinfection. Time of collection of the 90 (72%) analyzed X + 1 donations varied between 2.9 and 101.9 weeks (median of 35 weeks) after X donation. As expected, none of those tested were positive for HEV RNA. Most donors (89; 99%) were positive for anti-HEV lgG/lgM (i.e., seroconversion). HEV lgM-positivity (23; 26%) indicates an often-long persistence of lgM antibodies post-HEV infection. Conclusion: The data collected during the first year of the study provided the basis for the decision to establish mandatory HEV RNA universal screening of all Swiss blood donations in minipools, a vital step in providing safer blood for all recipients, especially those who are immunosuppressed.
Models of Herpes Simplex Virus Latency
Authors: Canova Paige N. , Charron Audra J. , Leib David A.
Published: May 2024

Our current understanding of HSV latency is based on a variety of clinical observations, and in vivo, ex vivo, and in vitro model systems, each with unique advantages and drawbacks. The criteria for authentically modeling HSV latency include the ability to easily manipulate host genetics and biological pathways, as well as mimicking the immune response and viral pathogenesis in human infections. Although realistically modeling HSV latency is necessary when choosing a model, the cost, time requirement, ethical constraints, and reagent availability are also equally important. Presently, there remains a pressing need for in vivo models that more closely recapitulate human HSV infection. While the current in vivo, ex vivo, and in vitro models used to study HSV latency have limitations, they provide further insights that add to our understanding of latency. In vivo models have shed light on natural infection routes and the interplay between the host immune response and the virus during latency, while in vitro models have been invaluable in elucidating molecular pathways involved in latency. Below, we review the relative advantages and disadvantages of current HSV models and highlight insights gained through each.