bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time Point Recognition for Seedlings Using Kernel Density Estimators and Pyramid Histogram of Oriented Gradients
Authors: Asadi Shirzi Moteaal , Kermani Mehrdad R.
Published: February 2024

This paper introduces a new real-time method based on a combination of kernel density estimators and pyramid histogram of oriented gradients for identifying a point of interest along the stem of seedlings suitable for stem–stake coupling, also known as the ‘clipping point’. The recognition of a clipping point is a required step for automating the stem–stake coupling task, also known as the clipping task, using the robotic system under development. At present, the completion of this task depends on the expertise of skilled individuals that perform manual clipping. The robotic stem–stake coupling system is designed to emulate human perception (in vision and cognition) for identifying the clipping points and to replicate human motor skills (in dexterity of manipulation) for attaching the clip to the stem at the identified clipping point. The system is expected to clip various types of vegetables, namely peppers, tomatoes, and cucumbers. Our proposed methodology will serve as a framework for automatic analysis and the understanding of the images of seedlings for identifying a suitable clipping point. The proposed algorithm is evaluated using real-world image data from propagation facilities and greenhouses, and the results are verified by expert farmers indicating satisfactory performance. The precise outcomes obtained through this identification method facilitate the execution of other autonomous functions essential in precision agriculture and horticulture.
A Study on Robust Finite-Time Visual Servoing with a Gyro-Stabilized Surveillance System
Authors: Huynh Thinh , Kim Young-Bok
Published: February 2024

This article presents the design and validation of a novel visual servoing scheme for a surveillance system. In this system, a two-axis gimbal mechanism operates the rotation of a camera which is able to provide visual information on the tracked target for the control system. The control objective is to bring the target’s projection to the center of the image plane with the smallest steady-state error and a smooth transient response, even with the unpredictable motion of the target and the influence of external disturbances. To fulfill these tasks, the proposed control scheme is designed consisting of two parts: (1) an observer estimates simultaneously the matched and unmatched disturbances; and (2) a motion control law guarantees the finite-time stability and visual servoing performance. Finally, experiments are conducted for validation and evaluation. The proposed control system shows its consistency and ought to perform better than previous approaches.
Research on the Methods for Obtaining Droplet Impingement Characteristics in the Lagrangian Framework
Authors: Shen Xiaobin , Xiao Chunhua , Ning Yijun , Wang Huanfa , Lin Guiping , Wang Liangquan
Published: February 2024

The impact of supercooled water droplets is the cause of aircraft icing, and the acquisition of impingement characteristics is the key to icing prediction and the design of ice protection systems. The introduction of water droplet collection efficiency is required to obtain the characteristics for the Lagrangian method. In this work, a traditional flow tube method, a local flow tube method, and a statistical method are established to calculate the local collection efficiency, based on Lagrangian droplet trajectories. Through the numerical simulations of the air–droplet flow field around an NACA 0012 airfoil, the accuracies of the three methods in regard to collection efficiency are verified. Then, these three methods are applied to obtain the results for water droplet trajectories and the collection efficiency of an S-shaped duct, a 2D engine cone section and an icing wind tunnel. The results show that the distributions of water droplet collection efficiency obtained by the three methods are consistent and the three methods are all feasible when the water droplets do not overlap or cross before hitting the aircraft surfaces. When the water droplets are shadowed by upstream surfaces or blown by air injection, the droplet trajectories might overlap or even cross, and the local collection efficiencies obtained by the traditional flow tube method, local flow tube method, and statistical method might differ. The statistical method is relatively accurate. However, not all the droplet impingement characteristics obtained by the three methods are different due to these effects, and the non-crossing of the droplet trajectories is not a necessary condition for the use of the flow tube method. The effects of trajectory crossings are analyzed and discussed in detail in different situations for the three methods. This work is helpful for understanding and accurately calculating the droplet impingement characteristics and is of great significance for simulations of the aircraft icing process and anti/de-icing range.
Numerical Simulation of the Chemical Reaction on Faraday MHD Accelerator
Authors: Liao Yingke , Zhu Guiping , Wang Guang , Wang Jie , Ding Yanchao
Published: February 2024

Magnetohydrodynamic (MHD) is one of the most promising novel propulsion technologies with the advantages of no pollution, high specific impulse, and high acceleration efficiency. As the carrier of this technology, the MHD accelerator has enormous potential for applications in hypersonic wind tunnels, supersonic ramjet engines, and deep space propulsion. In this study, a three-dimensional numerical simulation of an ideal Faraday magnetohydrodynamic (MHD) accelerator is conducted to assess the effect on performance with respect to applied potential and magnetic field intensity. The study is performed by employing a low magnetic Reynolds number MHD model coupled with a 7-component chemical reaction model to simplify the impact of real gas effects. The chemical reaction exhibits an increasing trend with rising applied potential and a decreasing trend with diminishing magnetic field strength. This variation influences the gas conductivity, subsequently affecting the velocity and thrust of the system. Specifically, at a magnetic field intensity of 2.0 T and an applied potential of 600 V, the accelerator exhibits maximum velocity and thrust growth rates of 18.6% and 59.8%, respectively.
New Agricultural Tractor Manufacturer’s Suggested Retail Price (MSRP) Model in Europe
Authors: Herranz-Matey Ivan , Ruiz-Garcia Luis
Published: February 2024

Research investigating models for assessing new tractor pricing is notably scarce, despite its fundamental importance in conducting comprehensive cost analyses. This study aims to identify a model that is both user-friendly and robust, evaluating both parametric and Machine Learning-optimized non-parametric models. Among parametric models, the second-order polynomial model demonstrated superior performance in terms of R-squared (R2) of 0.97469 and a Root Mean Square Error (RMSE) of 15,633. Conversely, Machine Learning-optimized Gaussian Processes Regressions exhibited the most favorable overall R-squared (R2) of 0.99951 and a Root Mean Square Error (RMSE) of 2321. While the parametric polynomial model offers a solution with minimal mathematical and computational complexity, the non-parametric GPR model delivers highly robust outcomes, presenting stakeholders involved in new agriculture tractor transactions with superior data-driven decision-making capabilities.
Effects of Different Agricultural Waste Composts on Cabbage Yield and Rhizosphere Environment
Authors: Wang Jue , Zhang Bo , Wang Jie , Zhang Guobin , Yue Zhibin , Hu Linli , Yu Jihua , Liu Zeci
Published: February 2024

The return of agricultural waste to the field is one of the most effective strategies of increasing crop yield, improving the soil’s physicochemical properties, and improving the soil rhizosphere environment. In the present study, sheep manure (SM), cow manure (CM), tail vegetable (TV), mushroom residue (MR), and corn straw (CS) were used as raw materials, and no fertilization (CK1) and local commercial organic fertilizer (CK2) treatments were used as controls. Eight composts were set up using specific mass ratios of different compost materials. After fermentation, field experiments were conducted to determine the cabbage yield, soil’s physicochemical properties, and soil rhizosphere conditions. The eight composts increased the soil organic matter and nutrient contents significantly. Among the eight fermentation formulas, T6 (CM:CS:TV:SM = 1:1:2:6), T7 (MR:CS:TV:SM = 1:1:2:6), and T8 (CM:MR:CS:TV:SM = 1:1:1:2:5) were relatively effective. Therefore, high-throughput sequencing was performed on T6, T7, T8, CK1, and CK2. T6, T7, and T8 exhibited increased relative abundance of Proteobacteria, Actinomycetes, and Firmicutes, while the Acidobacteria abundance was decreased. In addition, Ascomycota’s and Basidiomycetes’ relative abundance decreased, and the oil chytrid and mortierella increased. The microbial community structure was affected significantly by pH, electrical conductivity, available potassium, available nitrogen, and organic matter. In general, the three composts increased yield by improving the soil’s physicochemical properties, fertility, and microbial community structure. Among them, T6 had the most significant effect and is the optimal formula for use as a local organic cabbage fertilizer, and it could facilitate sustainable agricultural development.
Effects of Organic Substitution on the Yield and Quality of Apples and Residual Nitrate-N Leaching in Soil
Authors: Li Qian , Chen Yanan , Zhu Jingdi , Liu Lizhi , Liu Jian , Cheng Chunzhen , Li Lei
Published: February 2024

The substitution of chemical nitrogen (N) fertilizer with organic fertilizer (organic substitution, OS) is increasingly applied in crop production, due to its environmentally friendly characteristics, low price, and high crop and soil improvement efficacies. Here, we studied the effects of chemical N fertilizer with organic fertilizer treatment at different proportions (no organic substitution (NOS), 20% (OS-20), 40% (OS-40), 60% (OS-60), 100% (OS-100), and 200% (OS-200, double the organic fertilizer application amount of OS-100) on the yield and quality of apples in the Shanxi Province of China. The results revealed that, compared to the NOS, the total apple yields of OS treatments, especially the OS-60 and OS-100 treatments, decreased. However, all OS treatments, except OS-200, increased the yield of large-sized fruits (transverse diameter ≥ 85 mm) and the mean mass of apple fruits, and significantly decreased yield of small-sized fruits (transverse diameter < 75 mm). All OS treatments, especially OS-40, promoted the total sugar and vitamin C (Vc) contents and fruit hardness of apples, and OS-40, OS-60, and OS-200 resulted in significantly decreased titratable acid contents in apples. The influence of organic substitutions on soil quality was further investigated in a two-year field experiment. The results showed that the influence of organic substitution on soil chemical properties differed between the two years. Notably, 40% OS increased the soil organic carbon (SOC) content and the C/N ratio in the upper 20 cm of the soil in both years. Additionally, OS treatments reduced the residual nitrate (NO3−)-N (RN) content in deep soil layers, suggesting that OS has the potential to alleviate N leaching. Moreover, redundancy analysis (RDA) of the soil, fruit yield, and fruit quality parameters revealed that the SOC content in the 0–20 cm soil layer and the RN content in the 0–100 cm soil layer had the greatest impact on the fruit quality and yield variables, respectively. This study showed that the proper substitution (40%) of chemical N fertilizer with organic fertilizer could improve the yield of large-sized fruits, the mean mass and fruit quality of apples, and soil chemical properties. Our study will provide a basis for rational organic substitution in apple orchards.
Evaluating Time-Series Prediction of Temperature, Relative Humidity, and CO2 in the Greenhouse with Transformer-Based and RNN-Based Models
Authors: Ahn Ju Yeon , Kim Yoel , Park Hyeonji , Park Soo Hyun , Suh Hyun Kwon
Published: February 2024

In greenhouses, plant growth is directly influenced by internal environmental conditions, and therefore requires continuous management and proper environmental control. Inadequate environmental conditions make plants vulnerable to pests and diseases, lower yields, and cause impaired growth and development. Previous studies have explored the combination of greenhouse actuator control history with internal and external environmental data to enhance prediction accuracy, using deep learning-based models such as RNNs and LSTMs. In recent years, transformer-based models and RNN-based models have shown good performance in various domains. However, their applications for time-series forecasting in a greenhouse environment remain unexplored. Therefore, the objective of this study was to evaluate the prediction performance of temperature, relative humidity (RH), and CO2 concentration in a greenhouse after 1 and 3 h, using a transformer-based model (Autoformer), variants of two RNN models (LSTM and SegRNN), and a simple linear model (DLinear). The performance of these four models was compared to assess whether the latest state-of-the-art (SOTA) models, Autoformer and SegRNN, are as effective as DLinear and LSTM in predicting greenhouse environments. The analysis was based on four external climate data samples, three internal data samples, and six actuator data samples. Overall, DLinear and SegRNN consistently outperformed Autoformer and LSTM. Both DLinear and SegRNN performed well in general, but were not as strong in predicting CO2 concentration. SegRNN outperformed DLinear in CO2 predictions, while showing similar performance in temperature and RH prediction. The results of this study do not provide a definitive conclusion that transformer-based models, such as Autoformer, are inferior to linear-based models like DLinear or certain RNN-based models like SegRNN in predicting time series for greenhouse environments.
Farm Animal Welfare—From the Farmers’ Perspective
Authors: Phillips Clive J. C.
Published: February 2024

Improvements in the welfare of animals in the intensive production industries are increasingly being demanded by the public. Scientific methods of welfare improvement have been developed and are beginning to be used on farms, including those provided by precision livestock farming. The number of welfare challenges that animals are facing in the livestock production industries is growing rapidly, and farmers are a key component in attempts to improve welfare because their livelihood is at stake. The challenges include climate change, which not only exposes animals to heat stress but also potentially reduces forage and water availability for livestock production systems. Heat-stressed animals have reduced welfare, and it is important to farmers that they convert feed to products for human consumption less efficiently, their immune system is compromised, and both the quality of the products and the animals’ reproduction are adversely affected. Livestock farmers are also facing escalating feed and fertiliser costs, both of which may jeopardise feed availability for the animals. The availability of skilled labour to work in livestock industries is increasingly limited, with rural migration to cities and the succession of older farmers uncertain. In future, high-energy and protein feeds are unlikely to be available in large quantities when required for the expanding human population. It is expected that livestock farming will increasingly be confined to marginal land offering low-quality pasture, which will favour ruminant livestock, at the expense of pigs and poultry unable to readily digest coarse fibre in plants. Farmers also face disease challenges to their animals’ welfare, as the development of antibiotic resistance in microbes has heralded an era when we can no longer rely on antibiotics to control disease or improve the feed conversion efficiency of livestock. Farmers can use medicinal plants, pro-, pre- and synbiotics and good husbandry to help maintain a high standard of health in their animals. Loss of biodiversity in livestock breeds reduces the availability of less productive genotypes that survive better on nutrient-poor diets than animals selected for high productivity. Farmers have a range of options to help address these challenges, including changing to less intensive diets, diversification from livestock farming to other enterprises, such as cereal and pseudocereal crops, silvopastoral systems and using less highly selected breeds. These options may not always produce good animal welfare, but they will help to give farm animals a better life.
Diversity and Mechanisms of Action of Plant, Animal, and Human Antimicrobial Peptides
Authors: Satchanska Galina , Davidova Slavena , Gergova Alexandra
Published: February 2024

Antimicrobial peptides (AMPs) are usually made up of fewer than 100 amino acid residues. They are found in many living organisms and are an important factor in those organisms’ innate immune systems. AMPs can be extracted from various living sources, including bacteria, plants, animals, and even humans. They are usually cationic peptides with an amphiphilic structure, which allows them to easily bind and interact with the cellular membranes of viruses, bacteria, fungi, and other pathogens. They can act against both Gram-negative and Gram-positive pathogens and have various modes of action against them. Some attack the pathogens’ membranes, while others target their intracellular organelles, as well as their nucleic acids, proteins, and metabolic pathways. A crucial area of AMP use is related to their ability to help with emerging antibiotic resistance: some AMPs are active against resistant strains and are susceptible to peptide engineering. This review considers AMPs from three key sources—plants, animals, and humans—as well as their modes of action and some AMP sequences.
Beneficial Effects of Sideritis clandestina Extracts and Sideridiol against Amyloid β Toxicity
Authors: Gioran Anna , Paikopoulos Yiorgos , Panagiotidou Eleni , Rizou Aikaterini E. I. , Nasi Georgia I. , Dimaki Virginia D. , Vraila Konstantina D. , Bezantakou Dimitra S. , Spatharas Panagiotis M. , Papandreou Nikos C. , Magafa Vassiliki , Lamari Fotini N. , Iconomidou Vassiliki A. , Chondrogianni Niki
Published: February 2024

Alzheimer’s disease (AD) is the most common form of dementia. Given the link between oxidative stress and AD, many studies focus on the identification of natural antioxidants against AD. Although their antioxidant capacity is important, increasing data suggest that additional activities are related to their beneficial effects, including properties against amyloid beta (Aβ) aggregation. Sideritis spp. (mountain tea) extracts possess not only antioxidant activity but also other bioactivities that confer neuroprotection. Although various Sideritis spp. extracts have been extensively studied, there are scarce data on S. clandestina subsp. peloponnesiaca (SCP) phytochemical composition and neuroprotective potential, while nothing is known of the responsible compounds. Given that SCP is a weaker antioxidant compared to other Sideritis spp., here, we investigated its potential beneficial properties against Aβ aggregation. We characterized different SCP extracts and revealed their anti-aggregation activity by taking advantage of established C. elegans AD models. Importantly, we identified two pure compounds, namely, sideridiol and verbascoside, being responsible for the beneficial effects. Furthermore, we have revealed a potential anti-Aβ aggregation mechanism for sideridiol. Our results support the use of mountain tea in the elderly against dementia and demonstrate the activity of sideridiol against Aβ aggregation that could be exploited for drug development.
Microheater Topology for Advanced Gas Sensor Applications with Carbyne-Enriched Nanomaterials
Authors: Aleksandrova Mariya , Ustova Belgina , Tsanev Tsvetozar , Raptis Ioannis , Tserepi Angeliki , Gogolides Evangelos , Kolev Georgi
Published: February 2024

The response characteristics of carbyne-enriched surface-acoustic-wave (SAW)-based gas sensors utilizing meander and rectangular microheater topologies were investigated to assess their desorption and recovery properties. Comparative analysis of contact resistance and interface capacitance before and after heating revealed minimal deviation in contact resistance, signifying strong thermal stability in the carbyne-enriched layer. However, the interface capacitance varied with the microheater size. Our analysis reveals that a small meander microheater configuration (line width: 300 µm) facilitates efficient sensor recovery at ethanol concentration measurements in the range of 180–680 ppm, maintaining a low deviation in time delay across different concentrations (~2.3%), resulting in a narrow hysteresis and linear sensor response. Conversely, the large meander microheater (line width: 450 µm) and rectangular dense microheater induce irreversible changes in the sensing structure, leading to a widened hysteresis at higher concentrations and increased power consumption. Recovery patterns display substantial deviations from initial values at different concentration levels. Higher concentrations exhibit broader hysteresis, while lower concentrations show narrower hysteresis loops, compared to the small meander microheater. The study offers insights into desorption rates, power consumption variations, and recovery behaviors related to different microheater configurations. It demonstrates the importance of microheater topology selection in tailoring recovery properties and response characteristics, contributing to the advancement of carbyne-based sensor technology.
Can Machine Learning Predict Running Kinematics Based on Upper Trunk GPS-Based IMU Acceleration? A Novel Method of Conducting Biomechanical Analysis in the Field Using Artificial Neural Networks
Authors: Lawson Michael , Naemi Roozbeh , Needham Robert A. , Chockalingam Nachiappan
Published: February 2024

This study aimed to investigate whether running kinematics can be accurately estimated through an artificial neural network (ANN) model containing GPS-based accelerometer variables and anthropometric data. Thirteen male participants with extensive running experience completed treadmill running trials at several speeds. Participants wore a GPS device containing a triaxial accelerometer, and running kinematics were captured by an 18-camera motion capture system for each trial. Multiple multilayer perceptron neural network models were constructed to estimate participants’ 3D running kinematics. The models consisted of the following input variables: 3D peak accelerometer acceleration during foot stance (g), stance time (s), running speed (km/h), participant height (cm), leg length (cm), and mass (kg). Pearson’s correlation coefficient (r), root mean squared error (RMSE), and relative root mean squared error (rRMSE) showed that ANN models provide accurate estimations of joint/segment angles (mean rRMSE = 13.0 ± 4.3%) and peak segment velocities (mean rRMSE = 22.1 ± 14.7%) at key gait phases across foot stance. The highest accuracies were achieved for flexion/extension angles of the thorax, pelvis, and hip, and peak thigh flexion/extension and vertical velocities (rRMSE < 10%). The current findings offer sports science and medical practitioners working with this data a method of conducting field-based analyses of running kinematics using a single IMU.
Remote Sensing Applications in Almond Orchards: A Comprehensive Systematic Review of Current Insights, Research Gaps, and Future Prospects
Authors: Guimarães Nathalie , Sousa Joaquim J. , Pádua Luís , Bento Albino , Couto Pedro
Published: February 2024

Almond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security. The present systematic literature review, conducted according to the PRISMA protocol, is devoted to the applications of remote sensing technologies in almond orchards, a relatively new field of research. The study includes 82 articles published between 2010 and 2023 and provides insights into the predominant remote sensing applications, geographical distribution, and platforms and sensors used. The analysis shows that water management has a pivotal focus regarding the remote sensing application of almond crops, with 34 studies dedicated to this subject. This is followed by image classification, which was covered in 14 studies. Other applications studied include tree segmentation and parameter extraction, health monitoring and disease detection, and other types of applications. Geographically, the United States of America (USA), Australia and Spain, the top 3 world almond producers, are also the countries with the most contributions, spanning all the applications covered in the review. Other studies come from Portugal, Iran, Ecuador, Israel, Turkey, Romania, Greece, and Egypt. The USA and Spain lead water management studies, accounting for 23% and 13% of the total, respectively. As far as remote sensing platforms are concerned, satellites are the most widespread, accounting for 46% of the studies analyzed. Unmanned aerial vehicles follow as the second most used platform with 32% of studies, while manned aerial vehicle platforms are the least common with 22%. This up-to-date snapshot of remote sensing applications in almond orchards provides valuable insights for researchers and practitioners, identifying knowledge gaps that may guide future studies and contribute to the sustainability and optimization of almond crop management.
An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
Authors: Tian Xueqing , Hou Dongyang , Wang Siyuan , Liu Xuanyou , Xing Huaqiao
Published: February 2024

Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically determined manually or through a large number of experimental iterations, and even the weighting parameters change as the dataset and the noisy rate change. This can lead to suboptimal results and be time-consuming. Therefore, we propose an adaptively weighted method for the combined active passive loss (APL) in remote sensing image retrieval with noisy labels. First, two metrics are selected to measure the noisy samples: the ratio of the entropy to the standard deviation and the difference of the predicted probabilities. Then, an adaptive weighted learning network with a hidden layer is designed to dynamically learn the weighting parameters. The network takes the above two metrics as inputs and is trained concurrently with the feature extraction network in each batch, without significantly increasing the computational complexity. Extensive experiments demonstrate that our improved APL method outperforms the original manually weighted APL method and other state-of-the-art robust loss methods while saving the time on manual parameter selection.
Underwater Degraded Image Restoration by Joint Evaluation and Polarization Partition Fusion
Authors: Cai Changye , Fan Yuanyi , Li Ronghua , Cao Haotian , Zhang Shenghui , Wang Mianze
Published: February 2024

Images of underwater environments suffer from contrast degradation, reduced clarity, and information attenuation. The traditional method is the global estimate of polarization. However, targets in water often have complex polarization properties. For low polarization regions, since the polarization is similar to the polarization of background, it is difficult to distinguish between target and non-targeted regions when using traditional methods. Therefore, this paper proposes a joint evaluation and partition fusion method. First, we use histogram stretching methods for preprocessing two polarized orthogonal images, which increases the image contrast and enhances the image detail information. Then, the target is partitioned according to the values of each pixel point of the polarization image, and the low and high polarization target regions are extracted based on polarization values. To address the practical problem, the low polarization region is recovered using the polarization difference method, and the high polarization region is recovered using the joint estimation of multiple optimization metrics. Finally, the low polarization and the high polarization regions are fused. Subjectively, the experimental results as a whole have been fully restored, and the information has been retained completely. Our method can fully recover the low polarization region, effectively remove the scattering effect and increase an image’s contrast. Objectively, the results of the experimental evaluation indexes, EME, Entropy, and Contrast, show that our method performs significantly better than the other methods, which confirms the feasibility of this paper’s algorithm for application in specific underwater scenarios.
Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
Authors: Song Jinha , Kim Juntae , Nang Jongho
Published: February 2024

The proliferation of media-sharing platforms has led to issues with illegally edited content and the distribution of pornography. To protect personal information, de-identification technologies are being developed to prevent facial identification. Existing de-identification methods directly alter the pixel values in the face region, leading to reduced feature representation and identification accuracy. This study aims to develop a method that minimizes the possibility of personal identification while effectively preserving important features for image- and video-copy-detection tasks, proposing a new deep-learning-based de-identification approach that surpasses traditional pixel-based alteration methods. We introduce two de-identification models using different approaches: one emphasizing the contours of the original face through feature inversion and the other generating a blurred version of the face using D2GAN (Dual Discriminator Generative Adversarial Network). Both models were evaluated on their performance in image- and video-copy-detection tasks before and after de-identification, demonstrating effective feature preservation. This research presents new possibilities for personal-information protection and digital-content security, contributing to digital-rights management and law enforcement.
Advancing Care in Severe Asthma: The Art of Switching Biologics
Authors: Dragonieri Silvano , Portacci Andrea , Quaranta Vitaliano Nicola , Carpagnano Giovanna Elisiana
Published: February 2024

Biologics targeting IgE, IL-5, IL-4/IL-13, and TSLP are crucial in severe asthma treatment. Research, including randomized controlled trials and real-world studies, has been conducted to assess their efficacy and identify patient characteristics that may predict positive responses. The effectiveness of switching biologics, especially given overlaps in treatment eligibility, and the clinical outcomes post-cessation are critical areas of investigation. This work reviews the effects of switching between these biologics and the indicators of treatment success or failure. Insights are primarily derived from real-world experiences, focusing on patients transitioning from one monoclonal antibody to another. Moreover, this review aims to provide insights into the effectiveness, safety, and broader implications of switching biologics, enhancing understanding for clinicians to optimize severe asthma management. The article underlines the importance of a patient-centered approach, biomarker assessment, and the evolving nature of asthma treatment in making informed decisions about biologic therapy.
A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting
Authors: Chen Tairu , Yu Tao , Zhang Lili , Zhang Wenhao , Mi Xiaofei , Liu Yan , Zhan Yulin , Wang Chunmei , Li Juan , Yang Jian
Published: February 2024

Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.
Exploring the Interactive Influences of Climate Change and Urban Development on the Fraction of Absorbed Photosynthetically Active Radiation
Authors: Nwokolo Samuel Chukwujindu , Meyer Edson L. , Ahia Chinedu Christian
Published: February 2024

This study aimed to utilize hybrid physics-based models that allow for a comprehensive evaluation of how solar–meteorological variables influence the fraction of absorbed photosynthetically active radiation (FAPAR). By integrating both physical and statistical approaches, these models provided a novel perspective on understanding the complex relationship between solar radiation and FAPAR dynamics. The integration of URE and CLC in studying ecosystem efficiency in the nations of China and India is crucial, owing to their interdependent nature. To achieve this, the coupled model intercomparison project phase 6 (CMIP6) was utilized, which involved interpolating data from the most recent five GCMs from the six-phase CMIP6. By integrating advanced modeling techniques with observational data, this innovative approach enabled a holistic assessment of the intricate connections between urban development, climate dynamics, and the productivity of surrounding ecosystems in these regions. The reported decline in FAPAR productivity in China and India highlights the growing concern over the impact of climate change on the health and resilience of vegetation. China experienced negative changes of −1.425, −0.414, and −0.018, while India reported negative changes of −2.463, 2.199, and 2.915 under different scenarios. Interestingly, despite these variations, both countries observed similar tidal variations in their seasonal bases, suggesting a consistent pattern in the fluctuations of vegetation productivity throughout the year. The authors of this study collected and analyzed extensive data on CLC and URE in order to investigate their impact on FAPAR fluctuations in China and India. The higher impact of URE compared to CLC in terms of effective contributions suggests that URE plays a crucial role in shaping the atmospheric physics of both China, accounting for 45.26%, and India, registering 33.95%. Conversely, the effects of CLC and PAR residual factor (PRF) were more pronounced in India compared to China, with impacts of 8.21% and 53.03% compared to 4.21% and 46.70%, respectively.
An Index for Snowmelt-Induced Landslide Prediction for Zavoj Lake, Serbia
Authors: Marković Rastko , Mudelsee Manfred , Radaković Milica G. , Radivojević Aleksandar R. , Schaetzl Randall J. , Basarin Biljana , Nikolić Jugoslav , Marković Slobodan B. , Spalević Velibor , Antić Aleksandar , Marjanović Miloš , Lukić Tin
Published: February 2024

In February 1963, a huge landslide (ca. 1,950,000 m3) blocked the Visočica River and, thus, formed Zavoj Lake. The primary objective of this research was to investigate the importance of snowmelt in relation to landslide occurrence and to define the critical climatic conditions that may trigger massive winter landslides. We used monthly precipitation and average monthly maximum temperature data from meteorological and precipitation stations in the Visočica River basin (Dojkinci) and in the immediate proximity of Lake Zavoj (Pirot, Dimitrovgrad and Topli Do) as data inputs to the Snow-Melt Landslide (SML) index. It considers the summed monthly precipitation for previous months that continuously have an average maximum temperature below 0 °C. According to this method, the event at Zavoj Lake stands out among all other precipitation and snowmelt values for the past 72 years. After applying the SML index, all stations showed values of >300 mm for February 1963, which we consider as the threshold value for potential landslides appearance. In addition to meteorological data, we applied the SML index to data from the Coordinated Regional Downscaling Experiment (CORDEX) regional climate model outputs for the region from 2022 to 2100. As expected, climate change will have influenced the temperature values, especially during the winter. Conversely, the study area is experiencing drastic changes in land use caused by depopulation, leading to a reduced risk of winter landslides in the Visočica basin. We suggest that future climatic conditions in the area will make it more likely to experience extreme summer precipitation events, which might trigger large landslides. The SML method can be implemented for all landscapes that experience snowy winters, providing information in a timely manner so that local residents can react properly when the probability of landslide occurrence rises. The SML index, grounded in essential meteorological principles, provides a tailor-made, data-driven methodology applicable across varied geographical settings. Its utility extends to mitigating hydro-meteorological hazards on scales ranging from local to national scales, offering diverse and effective early warning solutions.
Craft Brewery Wastewater Treatment in a Scalable Microbial Fuel Cell Stack
Authors: Zapata-Martínez Olivia , Villa-Gomez Denys , Tapia-Tussell Raul , Dominguez-Maldonado Jorge , Hernández-Zárate Galdy , España-Gamboa Elda , Valdez-Ojeda Rubí , Alzate-Gaviria Liliana
Published: February 2024

Craft breweries release wastewater into the environment, posing serious environmental concerns. Microbial fuel cells (MFCs) are an attractive technology that has been used in industrial wastewater treatment. This study used a scalable system of nine MFCs (stacked) to treat 150 L of craft brewery wastewater (CBW). The CBW had 1831 ± 85 mg COD (chemical oxygen demand) L−1. The hydraulic retention time was 5 days, with a COD removal percentage of 93 ± 1.8%. The total internal resistance of the stack was 204.8 ± 5.2 Ω at 26 ± 2 °C without the use of a metal catalyst; the reduction of oxygen was the limiting process. Finally, the sequence of treatments applied with this proposed system demonstrated its self-sustainability, which could be a viable option for the real-life conditions of this kind of wastewater. Further research is needed.
Engineered Dual Antioxidant Enzyme Complexes Targeting ICAM-1 on Brain Endothelium Reduce Brain Injury-Associated Neuroinflammation
Authors: Leonard Brian , Shuvaev Vladimir , Bullock Trent , Galpayage Dona Kalpani , Muzykantov Vladimir , Andrews Allison , Ramirez Servio
Published: February 2024

The neuroinflammatory cascade triggered by traumatic brain injury (TBI) represents a clinically important point for therapeutic intervention. Neuroinflammation generates oxidative stress in the form of high-energy reactive oxygen and nitrogen species, which are key mediators of TBI pathology. The role of the blood–brain barrier (BBB) is essential for proper neuronal function and is vulnerable to oxidative stress. Results herein explore the notion that attenuating oxidative stress at the vasculature after TBI may result in improved BBB integrity and neuroprotection. Utilizing amino-chemistry, a biological construct (designated “dual conjugate” for short) was generated by covalently binding two antioxidant enzymes (superoxide dismutase 1 (SOD-1) and catalase (CAT)) to antibodies specific for ICAM-1. Bioengineering of the conjugate preserved its targeting and enzymatic functions, as evaluated by real-time bioenergetic measurements (via the Seahorse-XF platform), in brain endothelial cells exposed to increasing concentrations of hydrogen peroxide or a superoxide anion donor. Results showed that the dual conjugate effectively mitigated the mitochondrial stress due to oxidative damage. Furthermore, dual conjugate administration also improved BBB and endothelial protection under oxidative insult in an in vitro model of TBI utilizing a software-controlled stretching device that induces a 20% in mechanical strain on the endothelial cells. Additionally, the dual conjugate was also effective in reducing indices of neuroinflammation in a controlled cortical impact (CCI)-TBI animal model. Thus, these studies provide proof of concept that targeted dual antioxidant biologicals may offer a means to regulate oxidative stress-associated cellular damage during neurotrauma.
Understanding the Role of Biofilms in Acute Recurrent Tonsillitis through 3D Bioprinting of a Novel Gelatin-PEGDA Hydrogel
Authors: Denton Oliver , Wan Yifei , Beattie Laura , Jack Téa , McGoldrick Preston , McAllister Holly , Mullan Cara , Douglas Catriona M. , Shu Wenmiao
Published: February 2024

Acute recurrent tonsillitis is a chronic, biofilm-related infection that is a significant burden to patients and healthcare systems. It is often treated with repeated courses of antibiotics, which contributes to antimicrobial resistance. Studying biofilms is key to understanding this disease. In vitro modelling using 3D bioprinted hydrogels is a promising approach to achieve this. A novel gelatin-PEGDA pseudomonas fluorescens-laden bioink was developed and bioprinted in a 3D hydrogel construct fabricated using computer-aided design to mimic the tonsillar biofilm environment. The bioprinted constructs were cultured at 37 °C in lysogeny broth for 12 days. Bacterial growth was assessed by spectrophotometry. Cellular viability analysis was conducted using optical fluorescence microscopy (FDA/PI staining). A biocompatible 3D-printed bacteria-laden hydrogel construct was successfully fabricated. Bacterial growth was observed using optical fluorescence microscopy. A live/dead cellular-staining protocol demonstrated bacterial viability. Results obtained after the 12-day culture period showed higher bacterial growth in the 1% gelatin concentration construct compared to the 0% control. This study demonstrates the first use of a bacteria-laden gelatin-PEGDA hydrogel for biofabrication of a 3D-printed construct designed to model acute recurrent tonsillitis. Initiating a study with clinically relevant ex vivo tonsil bacteria will be an important next step in improving treatment of this impactful but understudied disease.
Copines, a Family of Calcium Sensor Proteins and Their Role in Brain Function
Authors: Khvotchev Mikhail , Soloviev Mikhail
Published: February 2024

The Copines are a family of evolutionary conserved calcium-binding proteins found in most eukaryotic organisms from protists to humans. They share a unique architecture and contain tandem C2 domains and a Von Willebrand factor type A (VWA) domain. C2 domains in Copines bind calcium, phospholipids, and other proteins and mediate the transient association of these proteins with biological membranes at elevated calcium levels. The VWA domain also binds calcium and is involved in protein–protein interactions. Here, we provide a comprehensive review of the sequences, structures, expression, targeting, and function of the entire family of known Copine proteins (Copine 1–9 in mammals) with a particular emphasis on their functional roles in the mammalian brain. Neuronal Copines are implicated in a wide array of processes from cell differentiation to synaptic transmission and plasticity and are also linked to several pathological conditions from cancers to brain diseases. This review provides the most up-to-date insights into the structure and function of Copines, with an emphasis on their role in brain function.
The Dysferlinopathies Conundrum: Clinical Spectra, Disease Mechanism and Genetic Approaches for Treatments
Authors: Anwar Saeed , Yokota Toshifumi
Published: February 2024

Dysferlinopathies refer to a spectrum of muscular dystrophies that cause progressive muscle weakness and degeneration. They are caused by mutations in the DYSF gene, which encodes the dysferlin protein that is crucial for repairing muscle membranes. This review delves into the clinical spectra of dysferlinopathies, their molecular mechanisms, and the spectrum of emerging therapeutic strategies. We examine the phenotypic heterogeneity of dysferlinopathies, highlighting the incomplete understanding of genotype-phenotype correlations and discussing the implications of various DYSF mutations. In addition, we explore the potential of symptomatic, pharmacological, molecular, and genetic therapies in mitigating the disease’s progression. We also consider the roles of diet and metabolism in managing dysferlinopathies, as well as the impact of clinical trials on treatment paradigms. Furthermore, we examine the utility of animal models in elucidating disease mechanisms. By culminating the complexities inherent in dysferlinopathies, this write up emphasizes the need for multidisciplinary approaches, precision medicine, and extensive collaboration in research and clinical trial design to advance our understanding and treatment of these challenging disorders.
Association of BRAF V600E Mutant Allele Proportion with the Dissemination Stage of Papillary Thyroid Cancer
Authors: Blazekovic Ivan , Samija Ivan , Perisa Josipa , Gall Troselj Koraljka , Regovic Dzombeta Tihana , Radulovic Petra , Romic Matija , Granic Roko , Sisko Markos Ines , Frobe Ana , Kusic Zvonko , Jukic Tomislav
Published: February 2024

The early identification of aggressive forms of cancer is of high importance in treating papillary thyroid cancer (PTC). Disease dissemination is a major factor influencing patient survival. Mutation status of BRAF oncogene, BRAF V600E, is proposed to be an indicator of disease recurrence; however, its influence on PTC dissemination has not been deciphered. This study aimed to explore the association of the frequency of BRAF V600E alleles in PTC with disease dissemination. In this study, 173 PTC samples were analyzed, measuring the proportion of BRAF V600E alleles by qPCR, which was then normalized against the proportion of tumor cells. Semiquantitative analysis of BRAF V600E mutant protein was performed by immunohistochemistry. The BRAF V600E mutation was present in 60% of samples, while the normalized frequency of mutated BRAF alleles ranged from 1.55% to 92.06%. There was no significant association between the presence and/or proportion of the BRAF V600E mutation with the degree of PTC dissemination. However, the presence of the BRAF mutation was significantly linked with angioinvasion. This study’s results suggest that there is a heterogeneous distribution of the BRAF mutation and the presence of oligoclonal forms of PTC. It is likely that the BRAF mutation alone does not significantly contribute to PTC aggressiveness.