bluuebunny/crossref_metadata_2025_split huggingface dataset:
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
Authors: Wang Hong , Du Hongwei , Hui Jian , zhang lanting 
Published: April 2024

Abstract

Modern deep learning-driven generative models have made it possible to design millions of hypothetical materials. However, to sift through these candidate materials and identify promising new materials, we need fast and accurate models for predicting material properties. Graph neural networks (GNNs) have emerged as a current research hotspot due to their ability to directly operate on the graph representations of molecules and materials, enabling comprehensively capturing key information and exhibiting outstanding performance in predicting material properties. Nevertheless, GNNs still face several key problems in practical applications: firstly, existing nested graph network strategies, while able to incorporate critical structural information such as bond angles, significantly increase the number of trainable parameters in the model, leading to a substantial rise in training costs; secondly, extending GNN models to broader fields such as molecules, crystalline materials, and catalysis, as well as adapting to small datasets, remains a challenge; finally, the scalability of GNN models are limited by the over-smoothing problem. To address these problems, we propose the DenseGNN model, which combines dense connectivity network (DCN), hierarchical node-edge-graph residual networks (HSN), and Local structure Order Parameters Embedding (LOPE) strategies, aiming to create a universal, scalable and efficient GNN model. We have achieved state-of-the-art (SOAT) performance on multiple datasets including JARVIS-DFT, Materials Project, QM9, Lipop, FreeSolv, ESOL, and OC22, demonstrating the generality and scalability of our approach. By fusing DCN and LOPE strategies into GNN models in the fields of computer, crystal materials, and molecules, we have significantly enhanced the performance of models such as GIN, Schnet, and Hamnet on material datasets like Matbench. The LOPE strategy optimizes the embedding representation of atoms, enabling our model to train efficiently at a minimal level of edge connections, significantly reducing computational costs, shortening the time required to train large GNNs, while maintaining accuracy. Our technique not only supports the construction of deeper GNNs, avoiding performance degradation problems seen in other models, but is also applicable to a wide range of applications requiring large deep learning models. Furthermore, our study demonstrates that by utilizing structural embeddings from pre-trained models, our model not only outperforms other GNNs in crystal structure distinguishment, but also approaches the standard X-ray diffraction (XRD) method.
Infrared Drying of Bocaiuva (Acrocomia Aculeata) Slices: Drying Kinetics, Energy Consumption, and Quality Characteristics
Authors: Junqueira João Renato Jesus , Carmo Juliana Rodrigues , Miyagusku Luciana , Balbinoti Thaisa Carvalho Volpe , Junqueira Mariel de Carvalho Rafael Salgado , Lucena Reinaldo Farias Paiva 
Published: April 2024

Abstract

Bocaiuva is the fruit of the palm tree Acrocomia aculeata (Jacq.) Lodd, native to various regions of Brazil, particularly in the Cerrado and Pantanal biomes. However, its commercialization is hindered by its fibrous nature and short shelf life, leading to post-harvest losses. This study aimed to obtain bocaiuva slices at different infrared drying temperatures (60, 70 and 80 ºC). It was found that a shortening in the drying time at 80 ºC caused an increase in the drying rate. Fick’s second law and Page’s equation were suitable for describing the process behavior. The thermodynamics and energetic analysis demonstrated higher energy efficiency at 80 ºC. Lower temperature (60 ºC) promoted lower total color difference and hygroscopicity, and higher volumetric shrinkage. The results suggested that IRD at 80 ºC was able to produce bocaiuva slices with suitable physical characteristics. Furthermore, the production of dried bocaiuva contributes to the regional development of the Cerrado biome, thereby enhancing the bioeconomy.
Life cycle cost (LCC) for waste to compost process: Evaluation of life cycle cost for the comparison of decentralized waste to compost and landfill facility in Dhaka North City Corporation (DNCC)
Authors: Ashraf Azad Ibn , Mohareb Eugene , Vahdati Maria 
Published: April 2024

Abstract

Background
Rapid urbanization and economic development in developing countries, such as Dhaka and Bangladesh, have led to an increase in municipal refuse. Traditional waste management practises, such as landfilling and conventional composting, have been shown to be ineffective and expensive owing to the large amounts of land required.
Objective
The purpose of this research is to do a Life Cycle Cost Analysis (LCC) of a Dhaka-based waste-to-composting operation. The objective is to examine how much money it would save in comparison to current practices so that urban waste management problems may be solved in the long run.
Methodology:
In order to evaluate the potential of the planned decentralized compost plant, LCC methods are used in the study. This includes an analysis of both upfront and ongoing expenses, as well as a comparison to more traditional approaches.
Result
The research found that the overall cost of the decentralized compost facility was $5,226,616, much less than the expenditures associated with landfilling and conventional composting methods in Dhaka.
Conclusion
This study shows that a decentralized waste-to-compost plant may be a profitable option for dealing with city trash. Its potential to ease stress on municipal governments is highlighted by its much lower price tag. Insightful for policymakers and urban planners in emerging nations confronting comparable waste management difficulties, this research stresses the need to implement such creative, cost-effective approaches in quickly rising metropolitan centres.
Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration
Authors: Secer Gorkem , Knierim James , Cowan Noah 
Published: April 2024

Abstract
Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)---a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.
Autopolyploidization and in vitro regeneration of three blueberry cultivars from leaves and microstems.
Authors: Tauler Gabriela Jarpa , Martínez-Barradas Vera , Romero-Romero Jesús Lucina , Arce-Johnson Patricio 
Published: April 2024

Abstract
Blueberries are a fruit with an increasing global demand due to their phytochemical and bioactive compounds content. They are promoted worldwide because of their health benefits. For optimal growth and productivity, blueberry crops need acidic soil pH, specific chilling hours, and an adequate atmospheric temperature. This delicate production equilibrium is under severe threat from climate change, potentially leading to reduced yields and increased cultivation costs unless new cultivars are developed for each edafoclimatic zone. Therefore, considering varietal replacements with more productive cultivars offering higher quality and better adaptability to local conditions is imperative. In this study, we employ polyploidization and in vitro tissue culture to promote variability and lay the foundation for new cultivar development. We report the successful induction of octoploids in three blueberry cultivars, namely 'Biloxi,' 'Legacy,' and 'Duke', through whole-genome duplication. Leaves and microstem explants were exposed to 0.1% colchicine for 24 and 48 hours in in vitro culture. After analyzing the polyploid level of 160 regenerated shoots using DNA flow cytometry, we obtained a total of 18 mutants, consisting of 8 mixoploids and 10 octoploids. The number of chloroplasts in the stomata was analysed by fluorescence microscopy, revealing the duplication of these organelles in the induced octoploid plants. To our knowledge, this represents the first successful induction of octoploids in three blueberry cultivars -'Biloxi,' 'Legacy,' and 'Duke'- achieved by exposing leaves and microstem explants to colchicine in in vitro culture. This technique holds promise as a valuable tool for the development of improved blueberry cultivars.
Effects of low-temperature stress during heading stage on carbon and nitrogen allocation in paddy eco-system of northeastern China
Authors: Sun Tao , Ruan Junmei , Cao Tiehua , Gao Xinhao , Yao Li , Zhao Zichao , Zhang Jun , Li Jiarui , Deng Aixing , Zhang Weijian , Song Zhenwei 
Published: April 2024

Abstract

Background and aims
In high-latitude area, climate change has resulted in frequent chilling stress which affects rice sustainable production as well as changes carbon (C) and nitrogen (N) allocation in paddy ecosystem. However, the response characteristics of C and N allocation in paddy ecosystem to low-temperature stress during key growth stage are not fully understood.
Methods
A rice pot experiment of two varieties combined with 13C and 15N isotope labelling method was conducted to evaluate how low-temperature stress at heading stage affects rice yield, above- and below-ground C and N partitioning, and soil C and N changes.
Results
Low-temperature stress significantly reduced rice grain yield of JN809 (sensitive to low-temperature stress) and J88 (resistant to low-temperature stress) varieties 27.6% and 21.4%, respectively, Low-temperature stress was prone to increase C and N accumulation in stems and leaves of rice and soil. The low temperature tolerance variety (J88) reduced the effects of low temperature stress on rice yield and the allocation of C and N between soil and rice
Conclusion
Low-temperature during rice heading stage significantly hindered transportation of C assimilate and absorbed N from soil. Low temperature tolerant variety reduced the effect of low temperature chilling stress on rice yield, and C and N allocation. Present study provides a basis for rice breeding and cultivation techniques that can enhance rice resilience and adaptability to climate change, as well as optimize C and N sequestration practices in rice fields to ensure high yields and resource use efficiency.
Genome-wide identification and analyses of ZmAPY genes reveal their roles involved in maize development and abiotic stress responses
Authors: He Zhenghua , Zhang jie , Jia Haitao , Zhang Shilong , Sun Xiaopeng , Zhang Hui , Dai Mingqiu 
Published: April 2024

Abstract

Apyrase is a class of enzyme that catalyzes the hydrolysis of nucleoside triphosphates/diphosphates (NTP/NDP), which widely involved in regulation of plant growth and stress responses. However, apyrase family genes in maize have not been identified, and their characteristics and functions are largely unknown. In this study, we identified 16 apyrases (named as ZmAPY-ZmAPY16) in maize genome, and analyzed their phylogenetic relationships, gene structures, chromosomal distribution, upstream regulatory transcription factors and expression patterns. Analysis of the transcriptome database unveiled tissue-specific and abiotic stress-responsive expression of ZmAPY genes in maize. qPCR analysis further confirmed their responsiveness to drought, heat, and cold stresses. Association analyses indicated that variations of ZmAPY genes may regulate maize agronomic traits and drought responses. Our findings shed light on the molecular characteristics and evolutionary history of maize apyrase genes, highlighting their roles in various biological processes and stress responses. This study forms a basis for further exploration of apyrase functions in maize.
The study findings demonstrated a significant association between C-reactive protein levels and trabecular bone score : NHANES 2005-2008
Authors: Yan Huangze , Wang Shenjie , Cao Huikun , Zhong Haobo , Sun Chunhan 
Published: April 2024

Abstract

Objectives: The association between C-reactive protein and bone density has been primarily investigated in previous studies., with little to no research investigating its relationship with total bone trabecular score.
Methods: Data from the NHANES database (500 males and 633 females) were utilized in this study to perform a multiple weighted linear regression analysis to estimate this relationship of CRP and TBS. Subsequently, population characterization, univariate logistic regression analysis, subgroup and interaction analysis were in progress.
Results: Upon covariate adjustment, the analysis revealed a notable negative correlation between CRP and TBS(β=-0.0081; 95% CI (-0.0142, -0.0019); P=0.009). Furthermore, no interactions were detected within any subgroups.
Conclusion: This finding enhances our comprehension of the relationship in inflammation and bone health, offering the novel research outlook for the treatment and prevention of osteoporosis and osteoporotic fractures.
On Solution of Tropical Discrete Best Approximation Problems
Authors: Krivulin Nikolai 
Published: April 2024

Abstract

We consider a discrete best approximation problem formulated in the framework of tropical algebra, which deals with the theory and applications of algebraic systems with idempotent operations. Given a set of samples of input and output of an unknown function, the problem is to construct a generalized tropical Puiseux polynomial that best approximates the function in the sense of a tropical distance function. The construction of an approximate polynomial involves the evaluation of both unknown coefficient and exponent of each monomial in the polynomial. To solve the approximation problem, we first reduce the problem to an equation in unknown vector of coefficients, which is given by a matrix with entries parameterized by unknown exponents. We derive a best approximate solution of the equation, which yields both vector of coefficients and approximation error parameterized by the exponents. Optimal values of exponents are found by minimization of the approximation error, which is transformed into minimization of a function of exponents over all partitions of a finite set. We solve this minimization problem in terms of max-plus algebra (where addition is defined as maximum and multiplication as arithmetic addition) by using a computational procedure based on the agglomerative clustering technique. This solution is extended to the minimization problem of finding optimal exponents in the polynomial in terms of max-algebra (where addition is defined as maximum). The results obtained are applied to develop new solutions for conventional problems of discrete best Chebyshev approximation of real functions by piecewise linear functions and piecewise Puiseux polynomials. We discuss computational complexity of the proposed solution and estimate upper bounds on the computational time. We demonstrate examples of approximation problems solved in terms of max-plus and max-algebra, and give graphical illustrations.
MSC Classification: 15A80 , 90C24 , 41A50 , 41A65 , 65D15
Ghost particle suppression multiplicative algebraic reconstruction technique for tomographic PIV
Authors: Lei Peng , Yang Hua , Yin Zhouping , Shan Feng 
Published: April 2024

Abstract

The exponential distribution law of the intensity of tomographic particle image velocimetry (Tomo-PIV) reconstructed particles is validated via a probabilistic approach. Moreover, based on the distribution law of ghost particles and the self-similarity of particles, a new Tomo-PIV particle reconstruction method is proposed. In this method, ghost particles are treated as reconstruction noise. Furthermore, a combination of variational denoising methods and the inversediffusion equation with regularization constraint for ghost particles suppression. This method is called ghost particle suppression multiplicative algebraic reconstruction technique(GS-MART). The proposed algorithm is evaluated numerically on cylindrical wake simulation data, and the reconstruction quality, intensity distribution of true particles and ghost particles, and velocity calculation accuracy are analyzed under different particle densities. To verify the application effect of GS-MART, experiments were carried out in jet flows, and the results showed that the high-precision 3D particle reconstruction results of GS-MART significantly improved the velocity field estimation results.
Fire, land use, and the long-term dynamics of a pitch pine woodland in coastal Maine, USA
Authors: Barton Andrew , Nurse Andrea , Lalemand Laura , Fowke Thomas , Grenier Daniel 
Published: April 2024

Abstract

Background. Pitch pine is a globally rare tree species that occurs on xeric sites in the largely mesic landscape of eastern North America. Supporting a rich assemblage of rare species, pitch pine communities are threatened by succession to more mesophytic species in the absence of natural and anthropogenic disturbance. We examined multi-millennial dynamics of pitch pine woodland in relation to fire and land use across a local moisture gradient in coastal Maine, USA.
Results. Fossil pollen from a forest hollow revealed that pitch pine has been a long-time inhabitant of the land that today is called The Basin Preserve, extending back at least seven millennia before present. Fossil charcoal demonstrated the long historical importance of fire in these pitch pine stands. Euro-American settlement left a pronounced imprint on sediment records and current forest patterns, including an expansion of pitch pine populations in response to land clearing. Age structure and current regeneration patterns of tree species across the moisture gradient revealed ongoing succession of pitch pine to more mesophytic species, such as red oak and red maple, except on the most xeric sites, where the species appears to be self-perpetuating.
Conclusions. The results reveal succession, mesophication, and contraction of pitch pine woodland in the Basin Preserve, a pattern also found in more southern parts of this species range. Given the importance of these rare xeric ecosystems as biodiversity hotspots in a largely mesic landscape, active management to reduce vegetation in this preserve cover should be considered.
Influenza virus infection activates TAK1 to suppress RIPK3- independent apoptosis and RIPK1-dependent necroptosis
Authors: Sun Yuling , Ji Lei , Liu Wei , Sun Jing , Liu Penggang , Wang Xiaoquan , Liu Xiufan , Xu Xiulong 
Published: April 2024

Abstract

Many DNA viruses develop various strategies to inhibit cell death to facilitate their replication. However, whether influenza A virus (IAV), a fast-replicating RNA virus, attenuates cell death remains unknown. Here, we report that IAV infection induces TAK1 phosphorylation in a murine alveolar epithelial cell line (LET1) and a murine fibroblastoma cell line (L929). The TAK1-specific inhibitor 5Z-7-Oxzeneonal (5Z) and TAK1 knockout significantly enhance IAV-induced apoptosis, as evidenced by increased PARP, caspase-8, and caspase-3 cleavage. TAK1 inhibition also increases necroptosis as evidenced by increased RIPK1S166, RIPK3T231/S232, and MLKLS345 phosphorylation. Mechanistically, TAK1 activates IKK, which phosphorylates RIPK1S25 and inhibits its activation. TAK1 also activates p38 and its downstream kinase MK2, which phosphorylates RIPK1S321 but does not affect RIPK1 activation. Further investigation revealed that the RIPK1 inhibitor Nec-1 and RIPK1 knockout abrogate IAV-induced apoptosis and necroptosis; re-expression of wild-type but not kinase-dead (KD)-RIPK1 restores IAV-induced cell death. ZBP1 knockout abrogates IAV-induced cell death, whereas RIPK3 knockout inhibits IAV-induced necroptosis but not apoptosis. 5Z treatment enhances IAV-induced cell death and slightly reduces the inflammatory response in the lungs of H1N1 virus-infected mice and prolongs the survival of IAV-infected mice. Our study provides evidence that IAV activates TAK1 to suppress RIPK1-dependent apoptosis and necroptosis, and that RIPK3 is required for IAV-induced necroptosis but not apoptosis,.
SRLA - High Secure Optimised routing protocol for Wireless ad hoc networks using Log likelihood ratio enabled Ant Colony Optimization
Authors: KHAGGA VIJAYALAKSHMI , N SANGEETHA PRIYA , A.M. Prasad 
Published: April 2024

Abstract

In wireless ad hoc networks, where conventional infrastructure is lacking, effective routing algorithms, security mechanisms, and energy efficiency are critical challenges. While existing cluster-based routing protocols demonstrate efficiency, the absence of a comprehensive approach for selecting cluster heads based on multiple parameters remains a gap. Addressing this multi-objective challenge is pivotal in optimizing cluster-based routing. Ant Colony Optimization (ACO) emerges as a key algorithm for path selection, while the integration of Log Likelihood Ratio (LLR) enhances route longevity and reliability. The proposed system introduces cluster-based region formation coupled with the selection of trusted cluster heads, enriching routing paths. It prioritizes paths with heavy weights and fewer links, optimizing efficiency. Implementation of the proposed system yields a notable increase in delivery ratio to 96% marking a significant advancement in wireless ad hoc network performance.