bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rotational complexity across US counties is currently insufficient to observe yield gains in major crops
Authors: Burchfield Emily K , Crossley Michael S , Nelson Katherine S 
Published: March 2024

Abstract
Landscape complexity promotes ecosystem services and agricultural productivity, and often encompasses aspects of compositional or configurational land cover diversity across space. However, a key agricultural diversification practice, crop rotation, extends crop land cover complexity concurrently across space and time. Long-term experiments suggest that complex crop rotations can facilitate yield increases in major crops. Using a compiled county-annual panel dataset, we examined whether yield benefits of crop rotational complexity were apparent on a landscape scale in the conterminous US for four major crops between 2008 and 2020. We found that the benefit of rotational complexity was only apparent for cotton and winter wheat, and that the benefit for wheat was driven by one region. Corn exhibited the opposite pattern, wherein higher yields were consistently obtained with lower rotational complexity, while soybean yield appeared relatively insensitive to rotational complexity. Effects of rotational complexity were sometimes influenced by agrochemical usage. Positive effects of rotational complexity were only apparent with high fertilizer for soybean and wheat, and with low fertilizer for cotton. Corn yield in high-complexity, low-yielding counties appeared to improve with high fertilizer inputs. For the overwhelming majority of acres growing these major crops, crop rotation patterns were quite simple, which when combined with the short time span of available data, may explain the apparent discrepancy between long-term experiments and nationwide data. Current demand and incentives that promote highly intensified and specialized agriculture likely hinder realization of the benefits of rotational complexity for production of key crops in the US. Increasing rotational complexity where major crops are grown thus remains an underutilized approach to mitigate landscape simplification and to promote ecosystem services and crop yields.
Quercetin-coated biogenic selenium nanoparticles: synthesis, characterization, and in-vitro antioxidant study
Authors: Alex Ashika , Biju Tina Sara , Francis Arul Prakash , Veeraraghavan Vishnu Priya , Gayathri R , Sankaran Kavitha 
Published: March 2024

Abstract
In recent years, the interest in environmentally friendly and sustainable methods for producing nanoparticles has grown significantly due to their potential benefits. Among these nanoparticles, selenium nanoparticles (SeNPs) have gained attention for their potential applications in biomedicine. This research sheds light on the feasibility of eco-friendly synthesis approaches for generating quercetin-coated selenium nanoparticles (DQSN) and their potential applications in the field of biomedicine. The main objective of this study is to develop a green synthesis technique for SeNPs using Diospyros ebenum extract as a natural reducing agent and quercetin as a capping agent. The synthesised nanoparticles were subjected to a range of characterisation techniques including UV–vis spectroscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy, and energy-dispersive x-ray analysis. The antioxidant assay was employed to evaluate nanoparticle’s bioactivity. The results revealed the successful synthesis of DQSN with comprehensive characterisation confirming their morphology, crystallinity, functionalisation, elemental composition, size, and stability. In vitro, bioactivity studies demonstrated substantial antioxidant activity through DPPH radical scavenging. These findings underscore the potential utility of DQSN as promising candidates for biomedical applications; however, further extensive investigations are essential to ascertain their complete efficacy and toxicity profiles.
Mechanism of simultaneous photocatalytic degradation of Rhodamine B and Cr(VI) under visible light using WO3 nanotubes
Authors: Do T Anh Thu , Nguyen Duc Toan , Ho Truong Giang , Giang Hong Thai , Pham Quang Ngan , Nghiem T Ha Lien , Nguyen Trung Hieu , Man Minh Tan 
Published: March 2024

Abstract
A hydrothermal method was used to synthesise WO3 nanotubes, which were analysed using transmission electron microscopy (TEM), x-ray diffraction (XRD), Raman, and UV–Vis spectroscopy for morphological, structural, and optical properties. TEM revealed nanotubes several micrometers long with a diameter of 10–15 nm. These nanotubes effectively removed Rhodamine B (RhB) and Cr(VI) under visible light. The high photocatalytic efficiency of obtained WO3 material was attributed to the large surface area provided by the unique configuration in the form of nanotubes. The study identified reactive species through scavenger tests and proposed a photocatalytic mechanism. This approach offers efficient photocatalysts for the simultaneous sunlight-driven degradation of organic and inorganic pollutants in wastewater.
Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images
Authors: Guilenea Federico N , Casciaro Mariano E , Soulat Gilles , Mousseaux Elie , Craiem Damian 
Published: March 2024

Abstract
Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improve cardiovascular risk prediction. The aim of this study was to develop a fully automatic system to detect TAC and to evaluate its performance for classifying the patients into four TAC risk categories. The method started by segmenting the thoracic aorta, combining three UNets trained with axial, sagittal and coronal CT images. Afterwards, the surrounding lesion candidates were classified using three combined convolutional neural networks (CNNs) trained with orthogonal patches. Image datasets included 1190 non-enhanced ECG-gated cardiac CT studies from a cohort of cardiovascular patients (age 57 ± 9 years, 80% men, 65% TAC > 0). In the test set (N = 119), the combination of UNets was able to successfully segment the thoracic aorta with a mean volume difference of 0.3 ± 11.7 ml (<6%) and a median Dice coefficient of 0.947. The combined CNNs accurately classified the lesion candidates and 87% of the patients (N = 104) were accurately placed in their corresponding risk categories (Kappa = 0.826, ICC = 0.9915). TAC measurement can be estimated automatically from cardiac CT images using UNets to isolate the thoracic aorta and CNNs to classify calcified lesions.
An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients
Authors: Muccignat Dale L , Boyle Gregory G , Garland Nathan A , Stokes Peter W , White Ronald D 
Published: March 2024

Abstract
We propose improvements to the artificial neural network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the non-unique nature of its solutions, particularly when there exists multiple cross-sections that each describe similar scattering processes. Considering this, prior methods leveraged existing knowledge of a particular cross-section set to reduce the solution space of the problem. To reduce the need for prior knowledge, we propose the following modifications to the ANN method. First, we propose a multi-branch ANN (MBANN) that assigns an independent branch of hidden layers to each cross-section output. We show that in comparison with an equivalent conventional ANN, the MBANN architecture enables an efficient and physics informed feature map of each cross-section. Additionally, we show that the MBANN solution can be improved upon by successive networks that are each trained using perturbations of the previous regression. Crucially, the method requires much less input data and fewer restrictive assumptions, and only assumes knowledge of energy loss thresholds and the number of cross-sections present.
A simple and highly efficient approach towards the degradation of methylene blue and study the impact of degraded water on seed germination of cicer arietinum
Authors: Ahlawat Amit , Rana Pawan S , Solanki Pratima R 
Published: March 2024

Abstract
The scarcity of fresh air, drinking water, and soil is a matter of serious concern worldwide owing to the presence of organic pollutants in the environment. The organic dye, such as methylene blue (MB) have enormous toxic effects on the environment and human health. Therefore, the degradation of non-biodegradable dyes is very important to reduce toxicity in water and-a step towards waste water management systems. This paper focuses on the degradation of non-biodegradable MB dye using carbon quantum dots (CQDs). CQDs were synthesized by a microwave irradiation method using citric acid and L-cysteine as precursor and confirmed by X-Ray Diffraction (XRD), Raman Spectrum, Fourier Transform Infrared Spectroscopy (FTIR) and Energy Dispersive x-ray (EDX) spectroscopy techniques. The optical properties of the synthesized CQDs of 2.56 nm, were investigated by UV-visible spectroscopy technique and the absorption peak appeared at 340 nm which corresponding to n → π* transition. In photoluminescence (PL) spectra analysis, the highest emission peak was obtained at 440 nm when excited at 345 nm. The synthesized CQDs were used for the dye degradation of MB in distilled water and degradation percentage was calculated and found to be 99.17% in 90 min under UV light irradiation. Also, studied the impact of degraded water in seed germination of Cicer arietinum (black gramme) and calculated the seed germination growth rate in degraded water was found to be 15%–20% more than the seed germination growth rate in MB containing dye water.
Green synthesis of silver nanoparticles using olive mill wastewater and olive stones extract and testing their antimicrobial activities against Escherichia coli and Staphylococcus epidermidis
Authors: Rigopoulos Nickolas , Gkaliouri Christina Megetho , Ioannou Zacharias , Giaouris Efstathios , Sakavitsi Viktoria , Gournis Dimitrios 
Published: March 2024

Abstract
Plant mediated synthesis of silver nanoparticles is eco-friendly and of low cost. The synthesis involves a reduction of silver ions and is controlled by several independent factors. In this work, silver nanoparticles (AgNPs) were successfully synthesized using olive stone extract (OSE) and olive mill wastewater (OMW) extract. The nanoparticle synthesis was monitored using the plasmon resonance observed in the UV–Vis absorption spectrum, in which a Voigt profile was fitted. The peak wavelength (λ
0), the peak area (A), and the Full Width at Half Maximum (FWHM) were the fitting parameters and were used as the response. The independent factors were the incubation temperature, the incubation time, the silver nitrate, extract, and sodium hydroxide concentrations. The influence of these factors was investigated ‘two factor at a time’, using interaction plots. Strong interaction was observed between all factors, with sodium hydroxide to have a crucial role. The optimum conditions for silver nanoparticle formation were a) OSE (1% v/v), AgNO3 (2 mM), and NaOH (0.2 mM), and b) OMW (2% v/v), AgNO3 (1 mM), and NaOH (7.9 mM), showing an absorption maximum at 414 nm, and 410 nm, respectively. The mean diameter of AgNPs using OMW, measured with Transmission Electron Microscopy was

12.87
±
4.84

nm. Both types of AgNPs showed antibacterial action against Staphylococcus epidermidis (S. epidermidis) and Escherichia coli (E. coli), using the broth microdilution assay. Both nanoparticle types inhibited bacterial growth up to one dilution higher than reference samples.
Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression
Authors: Zhao Chongyue , Xu Zhongli , Wang Xinjun , Tao Shiyue , MacDonald William A , He Kun , Poholek Amanda C , Chen Kong , Huang Heng , Chen Wei 
Published: March 2024

Abstract
Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model’s accuracy, unveiling complex structure–functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.
Luminol-conjugated cyclodextrin biological nanoparticles for the treatment of severe burn-induced intestinal barrier disruption
Authors: Song Yajun , Li Yang , Hu Wengang , Li Feng , Sheng Hao , Huang Chibing , Gou Xin , Hou Jingming , Zheng Ji , Xiao Ya 
Published: March 2024

Abstract

Background
The breakdown of intestinal barrier integrity occurs after severe burn injury and is responsible for the subsequent reactions of inflammation and oxidative stress. A new protective strategy for the intestinal barrier is urgently needed due to the limitations of the traditional methods. Recently, the application of nanoparticles has become one of the promising therapies for many inflammation-related diseases or oxidative damage. Herein, we developed a new anti-inflammatory and antioxidant nanoparticle named luminol-conjugated cyclodextrin (LCD) and aimed to evaluate its protective effects in severe burn-induced intestinal injury.

Methods
First, LCD nanoparticles, engineered with covalent conjugation between luminol and β-cyclodextrin (β-CD), were synthesized and examined. Then a mouse burn model was successfully established before the mouse body weight, intestinal histopathological manifestation, permeability, tight junction (TJ) expression and pro-inflammatory cytokines were determined in different groups. The proliferation, apoptosis, migration and reactive oxygen species (ROS) of intestinal epithelial cells (IECs) were assessed. Intraepithelial lymphocytes (IELs) were isolated and cultured for analysis by flow cytometry.

Results
LCD nanoparticle treatment significantly relieved the symptoms of burn-induced intestinal injury in the mouse model, including body weight loss and intestinal permeability abnormalities. Moreover, LCD nanoparticles remarkably recovered the mechanical barrier of the intestine after severe burn, renewed TJ structures, promoted IEC proliferation and migration, and inhibited IEC apoptosis. Mechanistically, LCD nanoparticles dramatically alleviated pro-inflammation factors (tumor necrosis factor-α, IL-17A) and ROS accumulation, which could be highly involved in intestinal barrier disruption. Furthermore, an increase in IL-17A and the proportion of IL-17A+Vγ4+ γδ T subtype cells was also observed in vitro in LPS-treated Vγ4+ γδ T cells, but the use of LCD nanoparticles suppressed this increase.

Conclusions
Taken together, these findings demonstrate that LCD nanoparticles have the protective ability to ameliorate intestinal barrier disruption and provide a therapeutic intervention for burn-induced intestinal injury.
Service Function Placement Optimization For Cloud Service With End-to-End Delay Constraints
Authors: Yan Guofeng , Su Zhengwen , Tan Hengliang , Du Jiao 
Published: March 2024

Abstract
Network function virtualization (NFV) has been proposed to enable flexible management and deployment of the network service in cloud. In NFV architecture, a network service needs to invoke several service functions (SFs) in a particular order following the service chain function. The placement of SFs has significant impact on the performance of network services. However, stochastic nature of the network service arrivals and departures as well as meeting the end-to-end Quality of Service(QoS) makes the SFs placement problem even more challenging. In this paper, we firstly provide a system architecture for the SFs placement of cloud service with end-to-end QoS deadline. We then formulate the end-to-end service placement as a Markov decision process (MDP) which aims to minimize the placement cost and the end-to-end delay. In our MDP, the end-to-end delay of active services in the network is considered to be the state of the system, and the placement (nonplacement or placement) of SF is considered as the action. Also, we discuss the rationality of our analytical model by analyzing the Markov stochastic property of the end-to-end service placement. To obtain the optimal placement policy, we then propose an algorithm (Algorithm 1) for dynamic SFs placement based on our model and use successive approximations, i.e. $\epsilon $-iteration algorithm (Algorithm 2) to obtain action distribution. Finally, we evaluate the proposed MDP by comparing our optimal method with DDQP, DRL-QOR, MinPath and MinDelay for QoS optimization, including acceptance probability, average delay, resource utilization, load-balancing and reliability.
An Improved Density Peaks Clustering Algorithm Based On Density Ratio
Authors: Zou Yujuan , Wang Zhijian , Xu Pengfei , Lv Taizhi 
Published: March 2024

Abstract
Density peaks clustering (DPC) is a relatively new density clustering algorithm. It is based on the idea that cluster centers always have relatively high local densities and are relatively far from the points with higher densities. With the aforementioned idea, a decision graph can be drawn, and cluster centers will be chosen easily with the aid of the decision graph. However, the algorithm has its own weaknesses. Because the algorithm calculates local density and allocates points based on the distances between certain points, the algorithm has difficulty in classifying points into proper groups with varying densities or nested structures. This paper proposes an improved density peaks clustering algorithm called Dratio-DPC to overcome this weakness. First, Dratio-DPC adjusts the original local density with a coefficient calculated with the density ratio. Second, Dratio-DPC takes density similarity into consideration to calculate the distances between one point and other points with higher local densities. We design and perform experiments on different benchmark datasets and compare the clustering results of Dratio-DPC, traditional clustering algorithms and three improved DPC algorithms. Comparison results show that Dratio-DPC is effective and applicable to a wider range of scenarios.
Concept Drift–Based Intrusion Detection For Evolving Data Stream Classification In IDS: Approaches And Comparative Study
Authors: Seth Sugandh , Chahal Kuljit Kaur , Singh Gurvinder 
Published: March 2024

Abstract
Static machine and deep learning algorithms are commonly used in intrusion detection systems (IDSs). However, their effectiveness is constrained by the evolving data distribution and the obsolescence of the static data sources used for model training. Consequently, static classifiers lose efficacy, necessitating expensive model retraining with time. The aim is to develop a dynamic and adaptable IDS that mitigates the limitations of static models, ensuring real-time threat detection and reducing the need for frequent, resource-intensive model retraining. This research proposes an approach that amalgamates the adaptive random forest (ARF) classifier with Hoeffding’s bounds and a moving average test for the early and accurate detection of network intrusions. The ARF can adapt in real time to shifting network conditions and evolving attack patterns, constantly refining its intrusion detection capabilities. Furthermore, the inclusion of Hoeffding’s bounds and the moving average test adds a dimension of statistical rigor to the system, facilitating the timely recognition of concept drift and distinguishing benign network variations from potential intrusions. The synergy of these techniques results in reduced false positives and false negatives, thereby enhancing the overall detection rate. The proposed method delivers outstanding results, with 99.95% accuracy and an impressive 99.96% recall rate on the latest CIC-IDS 2018 dataset, outperforming the results of existing approaches.
Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs
Authors: Le Vincent , Rohmer Tom , David Ingrid 
Published: March 2024

Abstract
Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal’s robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (&gt;0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3 d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).