bluuebunny/crossref_metadata_2025 huggingface dataset:
Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution
Authors: Li Shizhao , Yan Zhichao , Ma Boxiang , Guo Shaoru , Song Hongxia
Published: January 2025

Semantic segmentation of three-dimensional (3D) plant point clouds at the stem-leaf level is foundational and indispensable for high-throughput tomato phenotyping systems. However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. To address these challenges, we propose an innovative encoding-decoding structure, incorporating voxel sparse convolution (SpConv) and attention-based feature fusion (VSCAFF) to enhance semantic segmentation of the point clouds of high-resolution tomato seedling images. Tomato seedling point clouds from the Pheno4D dataset labeled into semantic classes of ‘leaf’, ‘stem’, and ‘soil’ are applied for the semantic segmentation. In order to reduce the number of parameters so as to further improve the inference speed, the SpConv module is designed to function through the residual concatenation of the skeleton convolution kernel and the regular convolution kernel. The feature fusion module based on the attention mechanism is designed by giving the corresponding attention weights to the voxel diffusion features and the point features in order to avoid the ambiguity of points with different semantics having the same characteristics caused by the diffusion module, in addition to suppressing noise. Finally, to solve model training class bias caused by the uneven distribution of point cloud classes, the composite loss function of Lovász-Softmax and weighted cross-entropy is introduced to supervise the model training and improve its performance. The results show that mIoU of VSCAFF is 86.96%, which outperformed the performance of PointNet, PointNet++, and DGCNN, respectively. IoU of VSCAFF achieves 99.63% in the soil class, 64.47% in the stem class, and 96.72% in the leaf class. The time delay of 35ms in inference speed is better than PointNet++ and DGCNN. The results demonstrate that VSCAFF has high performance and inference speed for semantic segmentation of high-resolution tomato point clouds, and can provide technical support for the high-throughput automatic phenotypic analysis of tomato plants.
Green Apple Detector Based on Optimized Deformable Detection Transformer
Authors: Liu Qiaolian , Meng Hu , Zhao Ruina , Ma Xiaohui , Zhang Ting , Jia Weikuan
Published: January 2025

In the process of smart orchard construction, accurate detection of target fruit is an important guarantee to realize intelligent management of orchards. Green apple detection technology greatly diminishes the need for manual labor, cutting costs and time, while enhancing the automation and efficiency of sorting processes. However, due to the complex orchard environment, the ever-changing posture of the target fruit, and the difficulty of detecting green target fruit similar to the background, they bring new challenges to the detection of green target fruit. Aiming at the problems existing in green apple detection, this study takes green apples as the research object, and proposes a green apple detection model based on optimized deformable DETR. The new method first introduces the ResNeXt network to extract image features to reduce information loss in the feature extraction process; secondly, it improves the accuracy and optimizes the detection results through the deformable attention mechanism; and finally, it uses a feed-forward network to predict the detection results. The experimental results show that the accuracy of the improved detection model has been significantly improved, with an overall AP of 54.1, AP50 of 80.4, AP75 of 58.0, APs of 35.4 for small objects, APm of 60.2 for medium objects, and APl of 85.0 for large objects. It can provide a theoretical reference for green target detection of other fruit and vegetables green target detection.
Impact of Wetting-Drying Cycles on Soil Intra-Aggregate Pore Architecture Under Different Management Systems
Authors: Pires Luiz F. , Oliveira Jocenei A. T. de , Gaspareto José V. , Posadas Adolfo N. D. , Lourenço André L. F.
Published: January 2025

In many soil processes, including solute and gas dynamics, the architecture of intra-aggregate pores is a crucial component. Soil management practices and wetting-drying (W-D) cycles, the latter having a significant impact on pore aggregation, are two key factors that shape pore structure. This study examines the effects of W-D cycles on the architecture of intra-aggregate pores under three different soil management systems: no-tillage (NT), minimum tillage (MT), and conventional tillage (CT). The soil samples were subjected to 0 and 12 W-D cycles, and the resulting pore structures were scanned using X-ray micro-computed tomography, generating reconstructed 3D volumetric data. The data analyses were conducted in terms of multifractal spectra, normalized Shannon entropy, lacunarity, porosity, anisotropy, connectivity, and tortuosity. The multifractal parameters of capacity, correlation, and information dimensions showed mean values of approximately 2.77, 2.75, and 2.75 when considering the different management practices and W-D cycles; 3D lacunarity decreased mainly for the smallest boxes between 0 and 12 W-D cycles for CT and NT, with the opposite behavior for MT. The normalized 3D Shannon entropy showed differences of less than 2% before and after the W-D cycles for MT and NT, with differences of 5% for CT. The imaged porosity showed reductions of approximately 50% after 12 W-D cycles for CT and NT. Generally, the largest pores (>0.1 mm3) contributed the most to porosity for all management practices before and after W-D cycles. Anisotropy increased by 9% and 2% for MT and CT after the cycles and decreased by 23% for NT. Pore connectivity showed a downward trend after 12 W-D cycles for CT and NT. Regarding the pore shape, the greatest contribution to porosity and number of pores was due to triaxial-shaped pores for both 0 and 12 W-D cycles for all management practices. The results demonstrate that, within the resolution limits of the microtomography analysis, pore architecture remained resilient to changes, despite some observable trends in specific parameters.
Water Management in Wheat Farming in Romania: Simulating the Irrigation Requirements with the CROPWAT Model
Authors: Constantin Dana Maria (Oprea) , Mincu Florentina Iuliana , Diaconu Daniel Constantin , Burada Cristina Doina , Băltățeanu Elena
Published: January 2025

The development of water demand analysis methodologies to maintain agricultural crops at an optimal production level, in relation to current climate changes, is a necessity for many geographical areas. The methodology used uses CROPWAT 8.0 software, in the desire to highlight for an important agricultural region in Romania the need to optimize the water requirement for winter wheat crops. The methodology used was able to highlight this fact, as major changes are needed in future technological processes in the current context of climate change. Based on the modeling of evapotranspiration, effective precipitation, and irrigation requirements, it was obtained that the winter wheat needs four additional irrigations per year (in April, May, and July). The irrigations at critical depletion led to a 100% efficiency of reducing the harvest deficit, during the middle and late vegetation stages. The irrigation required by winter wheat depends on precipitation efficiency, and it is very important for improving crop yield up to 100%. The obtained results provide a methodological framework, but also concrete information for decision-makers in the field of agriculture.
NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning
Authors: Wang Jianliang , Chen Chen , Wang Jiacheng , Yao Zhaosheng , Wang Ying , Zhao Yuanyuan , Sun Yi , Wu Fei , Han Dongwei , Yang Guanshuo , Liu Xinyu , Sun Chengming , Liu Tao
Published: January 2025

The Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. Traditional NDVI calculation methods often rely on multispectral or hyperspectral imagery, which are costly and complex to operate, thus limiting their applicability in small-scale farms and developing countries. To address these limitations, this study proposes an NDVI estimation method based on low-cost RGB (red, green, and blue) UAV (unmanned aerial vehicle) imagery combined with deep learning techniques. This study utilizes field data from five major crops (cotton, rice, maize, rape, and wheat) throughout their whole growth periods. RGB images were used to extract conventional features, including color indices (CIs), texture features (TFs), and vegetation coverage, while convolutional features (CFs) were extracted using the deep learning network ResNet50 to optimize the model. The results indicate that the model, optimized with CFs, significantly enhanced NDVI estimation accuracy. Specifically, the R2 values for maize, rape, and wheat during their whole growth periods reached 0.99, while those for rice and cotton were 0.96 and 0.93, respectively. Notably, the accuracy improvement in later growth periods was most pronounced for cotton and maize, with average R2 increases of 0.15 and 0.14, respectively, whereas wheat exhibited a more modest improvement of only 0.04. This method leverages deep learning to capture structural changes in crop populations, optimizing conventional image features and improving NDVI estimation accuracy. This study presents an NDVI estimation approach applicable to the whole growth period of common crops, particularly those with significant population variations, and provides a valuable reference for estimating other vegetation indices using low-cost UAV-acquired RGB images.
Transcriptomics and Physiological Analyses of Soybean Stay-Green Syndrome
Authors: Wang Dagang , Wang Yanan , Sun Ruidong , Yang Yong , Zhao Wei , Yu Guoyi , Wang Yueying , Wang Feng , Zhou Lin , Huang Zhiping
Published: January 2025

Stay-green syndrome (SGS) is an important factor that causes soybean (Glycine max) yield reduction. Despite progress being made, the regulatory mechanism remains largely unclear. Therefore, in this study, an SGS-sensitive soybean variety, “HD0702”, was employed to investigate the underlying mechanism. Transcriptomic analyses were performed in a tissue-specific manner to investigate differentially expressed genes (DEGs) in soybeans impacted by SGS and in those without SGS. A total of 1858 DEGs were identified in the pods, and 2814 DEGs were identified in the leaves. Further investigation revealed that SGS mainly affected the expression levels of key genes involved in the regulation of photosynthesis, starch and sucrose metabolism, and plant hormone signal transduction. To support this finding, the chlorophyll content of the pods was to be found increased by 320% for chlorophyll a and 260% for chlorophyll b. In leaves, soluble sugar levels significantly increased, whereas phytohormones IAA and ABA decreased in SGS pods. DEGs were classified using gene ontology (GO) terms, and photosynthesis-related genes α-glucosidase, β-mannosidase, β-amylase 5 (GmBAM5), and starch synthase 2 (GmSS2) were up-regulated. This study demonstrates a molecular and physiological basis for SGS that merits further investigation to allow for SGS management.
Effects of Climatic Conditions and Agronomic Practices on Health, Tuber Yield, and Mineral Composition of Two Contrasting Potato Varieties Developed for High and Low Input Production Systems
Authors: Hasanaliyeva Gultekin , Giannakopoulou Ourania , Wang Juan , Barański Marcin , Sufar Enas Khalid , Knutt Daryl , Gilroy Jenny , Shotton Peter , Leifert Halima , Średnicka-Tober Dominika , Cakmak Ismail , Ozturk Levent , Zhao Bingqiang , Iversen Per Ole , Volakakis Nikolaos , Bilsborrow Paul , Leifert Carlo , Rempelos Leonidas
Published: January 2025

Modern potato varieties from high-input, conventional farming-focused breeding programs produce substantially (up to 45%) lower yields when grown in organic production systems, and this was shown to be primarily due to less efficient fertilization and late blight (Phytophthora infestans) control methods being used in organic farming. It has been hypothesized that the breeding of potato varieties suitable for the organic/low-input sector should (i) focus on increasing nutrient (especially N) use efficiency, (ii) introduce durable late blight resistance, and (iii) be based on selection under low-input conditions. To test this hypothesis, we used an existing long-term factorial field experiment (the NEFG trials) to assess the effect of crop management practices (rotation design, fertilization regime, and crop protection methods) used in conventional and organic farming systems on crop health, tuber yield, and mineral composition parameters in two potato varieties, Santé and Sarpo mira, that were developed in breeding programs for high and low-input farming systems, respectively. Results showed that, compared to Santé, the variety Sarpo mira was more resistant to foliar and tuber blight but more susceptible to potato scab (Streptomyces scabies) and produced higher yields and tubers with higher concentrations of nutritionally desirable mineral nutrients but lower concentrations of Cd. The study also found that, compared to the Cu-fungicides permitted for late blight control in organic production, application of synthetic chemical fungicides permitted and widely used in conventional production resulted in significantly lower late blight severity in Sante but not in Sarpo mira. Results from both ANOVA and redundancy analysis (RDA) indicate that the effects of climatic (precipitation, radiation, and temperature) and agronomic (fertilization and crop protection) explanatory variables on crop health and yield differed considerably between the two varieties. Specifically, the RDA identified crop protection as a significant driver for Santé but not Sarpo mira, while precipitation was the strongest driver for crop health and yield for Sarpo mira but not Santé. In contrast, the effect of climatic and agronomic drivers on tuber mineral and toxic metal concentrations in the two varieties was found to be similar. Our results support the hypothesis that selection of potato varieties under low agrochemical input conditions can deliver varieties that combine (i) late blight resistance/tolerance, (ii) nutrient use efficiency, and (iii) yield potential in organic farming systems.
Agronomic and Nutritional Evaluation of INIA 910—Kumymarca Ryegrass (Lolium multiflorum Lam.): An Alternative for Sustainable Forage Production in Department of Amazonas (NW Peru)
Authors: Vásquez Héctor V. , Valqui Leandro , Bobadilla Leidy G. , Meseth Enrique , Trigoso Milton J. , Zagaceta Luis H. , Valqui-Valqui Lamberto , Saravia-Navarro David , Barboza Elgar , Maicelo Jorge L.
Published: January 2025

Grassland ecosystems cover about 25% of the Earth’s surface, providing essential ecosystem services that benefit nature, people, and food security. This study evaluated agronomic and nutritional parameters of ryegrass (Lolium multiflorum Lam.) based on fertilization levels and cutting frequency in the Amazonas department. The INIA 910—Kumymarca variety was used with nitrogen fertilization rates (0, 60, 120, 180 kg/ha) and cutting intervals of 30 and 45 days for agronomic traits and 30, 45, and 60 days for nutritional traits. A randomized complete block design with eight treatments and three replications was applied. Repeated measures analysis and Tukey’s mean comparison tests (p < 0.005) were performed, along with Pearson correlation and response surface analysis using the central composite design in R. The results showed that applying 180 kg/ha of nitrogen with a 45-day cutting interval provided the highest dry matter yield (460 kg/m2) and superior agronomic traits, including plant height (96.73 cm), number of tillers, and stem diameter. Non-fertilized treatments had the highest crude protein content (17.45%) and digestibility, while higher nitrogen doses increased crude fiber and acid detergent fiber, reducing digestibility. Significant correlations were observed between fresh and dry weight with plant height (p = 0.000; r = 0.84), fiber contents (p = 0.000; r = 1), and ash and protein content (p = 0.000; r = 0.85). The optimal management practice was cutting every 45 days with 180 kg/ha of nitrogen (T8), maximizing forage yield and quality. Proper fertilization and cutting management can improve ryegrass production, benefiting livestock feeding and rural economies.
Screening and Evaluation of Actinomycetes B-35 from Rhizosphere of Salvia miltiorrhiza Bunge and Its Biocontrol and Growth-Promoting Effects
Authors: Han Mingli , Wang Jing , Lin Hang , Yang Chen , Zhang Jing , Liu Jiawei , Pan Guojun , Xiao Na
Published: January 2025

Salvia miltiorrhiza Bunge (S. miltiorrhiza) is one of the most commonly used bulk herbs in China; however, root rot can seriously affect its quality and yields. To minimize the use of chemical pesticides for managing this plant fungal disease, biological control utilizing microbial bio-pesticides offers a promising alternative. This study aimed to enhance the biocontrol resources available for combating S. miltiorrhiza root rot by isolating actinomycetes with antifungal activity from the rhizosphere soil of S. miltiorrhiza and identifying biocontrol actinomycetes with a preventive effect on root rot. A total of 35 actinomycetes were successfully screened from the rhizosphere soil of S. miltiorrhizae. The strain B-35 with the strongest antifungal activity was screened out through antagonizing the pathogen Fusarium solani of S. miltiorrhiza, strain morphology and 16S rRNA analysis. The antagonistic actinomycetes fermentation filtrate and crude extract could significantly destroy the mycelium and spores of Fusarium solani; the biocontrol effect of mature S. miltiorrhiza rhizome reached 83.3%, and the number of leaves, plant height and biomass in the B-35 treatment group were significantly increased compared with the control group. B-35 has a certain application potential in the biological control of root rot and the promotion of S. miltiorrhizae. The antifungal activity of actinomycetes sourced from the rhizosphere soil of S. miltiorrhiza has been demonstrated for the first time, potentially enhancing future crop quality and production.
An Economic Model Evaluating Competitive Wheat Genotypes for Weed Suppression and Yield in a Wheat and Canola Rotation
Authors: Nordblom Thomas L. , Gurusinghe Saliya , Hendriks Pieter-Willem , Rebetzke Greg J. , Weston Leslie A.
Published: January 2025

Recurrent selection for early vigour traits in wheat (Triticum aestivum L.) has provided an opportunity to generate competitive biotypes to suppress agronomically important weeds. Quantifying the potential benefits of competitive genotypes, including yield improvement and reduced frequency of herbicide application when incorporated into a long-term rotation, is vital to increase grower adoption. In this simple economic model, we evaluated a weed-suppressive early vigour genotype utilising on-farm experimental results and simulation analysis to predict gross margins for a seven-year wheat-canola rotation in southeastern Australia. The model applied a local weather sequence and predicted wheat production potential, costs and benefits over time. An early vigour wheat genotype was compared to commercial wheat cultivars for weed control, yield and actual production cost. With respect to weed control, three scenarios were evaluated in the model: standard herbicide use with a commercial cultivar (A), herbicide use reduced moderately by inclusion of an early vigour wheat genotype and elimination of the postharvest grass herbicide (B) or inclusion of an early vigour wheat genotype and withdrawal of both postharvest grass and broadleaf herbicides (C). Cost savings for the use of a competitive wheat genotype ranged from 12 AUD/ha in scenario B to 40 AUD/ha in scenario C, for a total saving of 52 AUD/ha. The model generated annual background gross margins, which varied from 300 AUD/ha to 1400 AUD/ha based on historical weather conditions, production costs and crop prices over the 30-year period from 1992 to 2021. The benefits of lower costs for each of the three scenarios are presented with rolling seven-year average wheat–canola rotation gross margins over the 30-year period. The limitations of this model for evaluation of weed suppression and cost benefits are discussed, as well as relative opportunities for adoption of early vigour traits in wheat.
Transcriptomic Insights into the Molecular Mechanisms of Indole Analogues from the Periplaneta americana Extract and Their Therapeutic Effects on Ulcerative Colitis
Authors: Xie Yuchen , Yu Qi , Yao Shun , Peng Rui , Li Jing
Published: January 2025

Ulcerative colitis (UC) is an inflammatory disease of the intestinal mucosa, and its incidence is steadily increasing worldwide. As a traditional Chinese medicinal insect, Periplaneta americana has been broadly utilized in clinical practice to treat wound healing. The tryptophan (Trp), tryptamine (Try), and 1,2,3,4-tetrahydrogen-β-carboline-3-carboxylic acid (Thcc) identified from P. americana concentrated ethanol-extract liquid (PACEL) exhibit significant cell proliferation-promoting and anti-inflammatory effects in the treatment of UC, but the mechanism involved remains obscure. Here, a dextran sulfate sodium (DSS)-induced UC mouse model was used to investigate the efficacy of high/low doses of PACEL, Trp, Try, and Thcc. Transcriptome sequencing was employed to detect the gene expression in the mouse intestine. The results showed that high doses of PACEL, Trp, Try, and Thcc could significantly improve weight loss and diarrhea, notably in the PACEL and Trp groups. Transcriptome analysis indicated that statistically changed genes in four treatment groups were specifically enriched in the immune system. Of these, the integrated analysis identified six hub genes (IL1β, CCL4, CXCL5, CXCR2, LCN2, and MMP9) regulated by NF-κB, which were significantly downregulated. This study investigates the molecular mechanisms underlying the UC treatment properties of indole analogues from PACEL, potentially through the inhibition of the NF-κB signaling pathway.
Genome-Wide Identification and Expression Analysis of the ALDH Gene Family in Sinonovacula constricta Bivalve in Response to Acute Hypersaline Stress
Authors: Yu Jianing , Wu Biao , Dong Yinghui , Lin Zhihua , Yao Hanhan
Published: January 2025

The razor clam Sinonovacula constricta, a significant marine bivalve species, inhabits estuaries and encounters salinity stress. Despite its commercial importance, there is limited understanding of its adaptive mechanisms to high salinity. Aldehyde dehydrogenases (ALDHs), which belong to the NAD(P)+-dependent superfamily, play a crucial role in stress resilience by participating in catabolic and anabolic pathways, such as carnitine synthesis, glycolysis, and amino acid metabolism. This study presents the first comprehensive analysis of the ALDH family in S. constricta under acute high salt stress conditions and identifies 16 ScALDH genes across 10 subfamilies. These genes are located on eight chromosomes, with tandem duplications observed on chromosome 10; they encode mostly acidic and hydrophilic proteins. Among them, ScALDH18A1 contains a conserved P5CS domain that is implicated in proline synthesis and osmotic regulation. The expression of 14 ScALDH members were significantly altered under acute salt stress conditions, with ScALDH8 and ScALDH18A1 showing increased expression levels, suggesting their involvement in osmotic pressure regulation. This research provides insights into the characteristics, evolution, and response to salinity stress of the ScALDH gene family while shedding light on ALDH function in bivalves, as well as serving as a foundation for further studies on osmotic stress regulation.
High Concentrations of Tilmicosin Promote the Spread of Multidrug Resistance Gene tolC in the Pig Gut Microbiome Through Mobile Genetic Elements
Authors: Chen Tao , Zhao Minxing , Chen Majian , Tang Xiaoyue , Qian Yuliang , Li Xiaoting , Wang Yan , Liao Xindi , Wu Yinbao
Published: January 2025

The impact of antibiotic therapy on the spread of antibiotic resistance genes (ARGs) and its relationship to gut microbiota remains unclear. This study investigated changes in ARGs, mobile genetic elements (MGEs), and gut microbial composition following tilmicosin administration in pigs. Thirty pigs were randomly divided into control (CK), low-concentration (0.2 g/kg; L), and high-concentration (0.4 g/kg; H) groups. Tilmicosin concentration in manure peaked on day 16 of dosing and dropped below detectable levels by day 13 of the withdrawal period. While tilmicosin did not significantly affect the total abundance of macrolide resistance genes (MRGs) (p > 0.05), it significantly increased the abundance of the multidrug resistance gene tolC in the H group compared with the L and CK groups during the withdrawal period (p < 0.05). This increase was associated with a coincidental rise in the abundance of MGEs (e.g., int1 and int2) and the growth of potential tolC-hosting bacteria such as Paenalcaligenes and Proteiniclasticum. Redundancy analysis showed gut microbial composition as the primary driver of MRG abundance, with MGEs, tilmicosin concentration, and manure physicochemical properties playing secondary roles. These findings suggest that high-dose tilmicosin may alter the gut microbiota and promote ARG spread via MGE-mediated transfer.
Human-Caused High Direct Mortality in Birds: Unsustainable Trends and Ameliorative Actions
Authors: Kaplan Gisela
Published: January 2025

Human interaction with birds has never been more positive and supported by so many private citizens and professional groups. However, direct mortality of birds from anthropogenic causes has increased and has led to significant annual losses of birds. We know of the crucial impact of habitat loss on the survival of birds and its effects on biodiversity. Direct mortality via anthropogenic causes is an additive but biologically important cause of avian decline. This is the focus of this paper. This paper synthesises and interprets the data on direct anthropogenic causes of mortality in birds, and it also discusses emerging and relatively hidden problems, including new challenges that birds may not be able to manage. This paper points out that such deaths occur indiscriminately and have negative behavioural and reproductive consequences even for survivors. All of these factors are important to address, because any functional habitat depends on birds. This paper suggests that some of this death toll can be reduced substantially and immediately, even some of the seemingly intractable problems. This paper also proposes cross-disciplinary solutions, bearing in mind that “ecosystem services” provided by birds benefit us all, and that the continued existence of avian diversity is one cornerstone for human survival.
16S rRNA Sequencing and Metabolomics to Analyze Correlation Between Fecal Flora and Metabolites of Squabs and Parent Pigeons
Authors: Li Xiaobin , Zheng Shengchen , Li Haiying , Liu Jiajia , Yang Fan , Zhao Xiaoyu , Liang Yafei
Published: January 2025

Intestinal microorganisms are essential for maintaining homeostasis, health, and development, playing a critical role in nutrient digestion, growth, and exercise performance in pigeons. In young pigeons, the gut microbiota is primarily acquired through pigeon milk, meaning the microbial composition of parent pigeons directly influences microbial colonization in squabs. However, research on the correlation between the gut microbial diversity of parent pigeons and their offspring remains scarce. This study investigates the fecal microbiota and metabolites of 10 pairs of parent pigeons and 20 squabs raised under a 2 + 2 system. Fecal samples were collected at 15 days of age, and differences in the microbiota and metabolites between the two groups were analyzed using 16S rRNA sequencing and LC-MS/MS. The results showed the following: (1) Squabs exhibited significantly lower α diversity, with a reduction in their Chao1 index and observed OTUs compared to the parent pigeons. (2) Firmicutes dominated the fecal microbiota in both groups, but parent pigeon feces showed a notably higher abundance of Proteobacteria. At the family level, 10 distinct families were identified, with 9 at the genus level and 4 at the species level. (3) A LEfSe analysis identified 16 significantly different bacterial species in the parent pigeons and 7 in the squabs. Functional gene abundance was highest in the metabolism, genetic information processing, and environmental information processing pathways. (4) An LC-MS/MS analysis in cationic mode identified 218 metabolites, with 139 upregulated and 79 downregulated in the squabs relative to the parents. These metabolites were primarily concentrated in five functional categories and enriched in 33 pathways, 2 of which showed significant differences. In conclusion, significant differences in both the α and β diversity of fecal microbiota were observed between squabs and parent pigeons, with similar bacterial species but marked differences in abundance. Metabolite analysis revealed greater richness in the parent pigeon feces. These findings suggest that future gut modulation using beneficial bacteria, such as probiotics, could potentially enhance host health based on microbial and metabolite compositions.
A Study on the Attachment to Pets Among Owners of Cats and Dogs Using the Lexington Attachment to Pets Scale (LAPS) in the Basque Country
Authors: Egaña-Marcos Eider , Goñi-Balentziaga Olatz , Azkona Garikoiz
Published: January 2025

The relationship between humans and their pets has long fascinated researchers, particularly in exploring how attachment varies according to the type of pet. Cats and dogs exhibit unique behavioral and social traits that influence the dynamics of human–pet relationships. Moreover, specific human characteristics have been found to affect this attachment. Our study examines the human factors that influence pet attachment among cat and dog owners in the Basque Country, located in northern Spain. By investigating these elements, our research aims to enhance the understanding of how human factors shape the human–animal bond. The study included a total of 202 participants, of whom 66.8% were dog owners, and 74.8% identified as women, with ages ranging from 18 to 74 years. Consistent with many previous studies, our results indicate that attachment is generally stronger with dogs compared to cats and that owner’s characteristics such as being female, younger, not living with children, and the amount of time spent with pets on weekends are linked to stronger attachments to pets.
Whole-Genome Sequencing Reveals the Progress of Genetic Breeding in Eriocheir sinensis
Authors: Wang Xuanpeng , Zhang Gaowei , Gao Dandan , Ge Yongchun , Cheng Yongxu , Wang Xinhai
Published: January 2025

Eriocheir sinensis is an important and popular crustacean species in China, producing huge economic benefits. Large individuals of E. sinensis are preferred due to market demand. The long-term goal of our research group is to produce a new variety of E. sinensis with better growth performance and stronger abilities to resist environmental changes through mass selection. The present study aimed to evaluate the progress of the genetic breeding of E. sinensis by analyzing the genetic diversity and genetic distance between the basic breeding population (pooled population of Suqian and Yixing, G0) and generation 1 (G1) using whole-genome sequencing (WGS). The growth traits, including body weight, shell length, shell width, and third appendage length, in the G1 generation increased by 8.3%, 7.9%, 9.6%, and 9.3%, respectively, compared with those of the G0 generation, indicating that the G1 generation showed better growth performance. A total of 372,448,393 high-quality single nucleotide polymorphisms (SNPs) were detected in 40 E. sinensis individuals, with an average of 9,331,209.83 SNPs. The fixation index values were 0.007 between the Yixing and Suqian populations and 0.015 between the G0 generation and G1 generation, indicating a close genetic background between these groups, especially when considered in combination with the phylogenetic tree and principal component analysis. All of these data suggest that genetic information was stably inherited by the G1 generation, with no introduction of foreign genetic information during the genetic breeding process. In addition, the genetic diversity analysis revealed that the G0 and G1 generations showed a high level of genetic diversity and a relatively stable genetic structure. The present study evaluated the recent progress of the genetic improvement of E. sinensis by our research group, providing valuable evidence for further genetic improvement in this species. Future studies will be performed to select growth-related SNPs and genes through genome-wide association studies.
Study on the In Vitro and In Vivo Antioxidant Activity and Potential Mechanism of Polygonum viviparum L.
Authors: Yang Zhen , Man Jingyuan , Liu Haoyu , Wu Di , Gu Qiangwen , Zhang Hongjuan , Liu Yu , Shao Dan , Hao Baocheng , Wang Shengyi
Published: January 2025

Oxidative stress refers to the phenomenon in which the redox balance of the body is disrupted in response to stimuli, leading to an excessive accumulation of reactive oxygen species in vivo, which can lead to a variety of diseases. In contrast to artificial antioxidants, whose safety is controversial, natural antioxidants, which are widely available, pharmacologically active, and have little toxic side effects, are expected to be candidates for the treatment of oxidative stress-related diseases. Polygonum viviparum L. (PV) is a natural herbal medicine with antioxidant properties and is used as a traditional medicine in the Tibetan Plateau region. However, there are few studies that have focused on its antioxidant activity and mechanism of action in vitro and in vivo. Therefore, the present study firstly demonstrated that PV could exert good in vitro antioxidant effects by scavenging DPPH radicals and inhibiting the production of hydroxyl radicals through in vitro experiments. Secondly, PV was proven to attenuate the effects of oxidative stress on body weight gain and thymus development by establishing the Senna leaf-induced diarrhea model in rats, as well as to increase the activity of antioxidant enzymes and the content of non-enzymatic antioxidants in the intestinal tract and to enhance the rats’ own antioxidant defenses, to mitigate the oxidative damage caused by diarrhea. Subsequently, the application of the cellular oxidative stress model evidenced that PV could play a protective role against cellular oxidative stress by inhibiting the overaccumulation of ROS in macrophages. Furthermore, the candidate antioxidant targets of PV were analyzed and screened using a comprehensive network pharmacology method, and their expression were then examined at the mRNA level and protein level. Our results suggest that PV may protect against H2O2-induced oxidative damage in macrophages by activating BCL2L1 and inhibiting ESR1, JAK2/STAT3, and MMP2. These findings open new perspectives on the antioxidant mechanism of PV and the prospect of developing it as a novel natural antioxidant drug.
Coffee and Cocoa By-Products as Valuable Sources of Bioactive Compounds: The Influence of Ethanol on Extraction
Authors: Martínez-Inda Blanca , Jiménez-Moreno Nerea , Esparza Irene , Ancín-Azpilicueta Carmen
Published: January 2025

Cocoa and coffee are two of the world’s most important crops. Therefore, their by-products are generated in large quantities. This work proposes a simple method for the valorization of these residues by obtaining phenolic compounds and melanoidins by solid–liquid extraction using different hydroalcoholic solutions as extracting solvents (0, 25, 50, 75, 100% ethanol). Extracts of both by-products presented the highest antioxidant capacity and total phenolic and melanoidin content when using 50–75% ethanol in the solvent. Among all the extracts, those obtained from spent coffee grounds at 75% ethanol showed the highest concentrations of total phenolic compounds (13.5 ± 1.3 mmol gallic acid equivalents/g dry matter) and melanoidins (244.4 ± 20.1 mg/g dry matter). Moreover, the sun protection factor values of the coffee extracts obtained with 50 and 75% of ethanol as extraction solvent (7.8 ± 0.9 and 8.5 ± 0.7, respectively) showed their potential for use in the cosmetic sector. The most important phenolic compounds identified in the coffee by-products extracts were phenolic acids, and most of them were found in higher concentration in extracts obtained with lower percentages of ethanol (0–25%). Protocatechuic acid was the most abundant phenolic in cocoa extracts, with concentrations ranging from 18.49 ± 2.29 to 235.35 ± 5.55 µg/g dry matter, followed by 4-hydroxybenzoic acid, (-)-epicatechin and (+)-catechin. Esculetin was found in both coffee and cocoa extracts, which had not been reported to date in these residues. In summary, the use of 75% ethanol as an extraction solvent seems a good strategy to obtain extracts rich in phenolic compounds from food by-products rich in melanoidins, such as coffee and cocoa by-products. The high antioxidant potential of these extracts makes them of great interest for the cosmetic and nutraceutical industries.
Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning
Authors: Doğan Ferdi , Oyucu Saadin , Unsal Derya Betul , Aksöz Ahmet , Vafaeipour Majid
Published: January 2025

The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study’s primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sources
Simultaneous Estimation of Soil Hydraulic and Thermal Properties Based on Multiobjective Optimization Algorithms
Authors: Zhang Jiachen , Li Na
Published: January 2025

Simultaneous simulations of liquid water, water vapor, and heat transport are essential for modeling unsaturated hydrological processes, especially in semi-arid and arid regions. Modeling such coupled hydrothermal processes greatly depends on accurate estimations of soil hydraulic and thermal properties. However, many contributions for estimating these parameters using inversion methods use a single observation as the objective variable, e.g., soil water content is the most common. This study employ multiobjective algorithms to evaluate the worth of different observation types in simultaneous estimations of the soil hydraulic and thermal properties in Inner Mongolia, China. The coupled hydrothermal processes are quantified by HYDRUS-1D model, within which a multialgorithm, genetically adaptive multiobjective (AMALGAM) algorithm is employed to investigate four types of observations that may be available including soil water content, soil temperature, matrix potential, and heat flux in soil profiles. Different combinations of the four measurement types are considered as objectives, resulting single-, dual-, triple-, and quadruple-objective optimization schemes. The results demonstrate that incorporating additional observation types, such as soil water content and matrix potential, significantly improves the overall simulation accuracy of the coupled model. Particularly, the soil water movement is closely linked to the observation of water content, which plays a crucial role in the inversion process. While adding temperature or heat flux to the multi-objective optimization further refines the accuracy of inversion. Considering the cost-benefit ratio of different observation types, simultaneous measurement of water content and temperature is the most practical approach for the inversion since these two variables can be observed simultaneously by the same set of probes such as with a TDR.
Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
Authors: Kuta Abdullahi A. , Grebby Stephen , Boyd Doreen S.
Published: January 2025

The widespread oil extraction in the Niger Delta and the impacts on different types of vegetation are poorly understood due to lack of ground access. This study aims to determine the impact of oil spills on vegetation in the Niger Delta using a Landsat satellite-derived normalised difference vegetation index (NDVI). The impact of oil spill volume and time after an oil spill on the health of different types of vegetation were evaluated, and the time series of the changes in NDVI were analysed to determine the medium- and long-term responses of vegetation to oil spill exposure, using a combination of linear regression and paired t-tests. With regards to the relationship between spill volume and NDVI, a moderate correlation (R2 = 0.5018) was observed for spill volumes in the range of 401–1000 barrels for sparse vegetation, for volumes greater than 1000 barrels for dense vegetation (R2 = 0.4356), whilst no correlation was found for mangrove vegetation at any range of spill volume. Similarly, the results of the paired t-test confirmed a significant difference (p-value < 0.05) between the change in NDVI values for spill sites and non-spill sites for all vegetation types, with the sparse vegetation being the most affected of the three types. However, the impact of the oil spill on vegetation over a period is not statistically significant. The outcomes of this study provide insights into how different types of vegetation in the Niger Delta respond to oil spills, which could ultimately help in designing an oil spill clean-up program to reduce the impact on the environment.
Analysis of China’s Low-Carbon Power Transition Path Considering Low-Carbon Energy Technology Innovation
Authors: Xia Peng , Lu Gang , Yuan Bo , Gong Yichun , Chen Haitao
Published: January 2025

Innovation in key low-carbon technologies plays a supporting role in achieving a high-quality low-carbon transition in the power sector. This paper aims to integrate research on the power transition pathway under the “dual carbon” goals with key technological innovation layouts. First, it deeply analyzes the development trends of three key low-carbon technologies in the power sector—new energy storage, CCUS, and hydrogen energy—and establishes a quantitative model for their technological support in the low-carbon transition of the power sector. On this basis, the objective function and constraints of traditional power planning models are improved to create an integrated optimization model for the power transition pathway and key low-carbon technologies. Finally, a simulation analysis is conducted using China’s power industry “dual carbon” pathway as a case study. The optimization results include the power generation capacity structure, power generation mix, carbon reduction pathway, and key low-carbon technology development path for China from 2020 to 2060. Additionally, the impact of uncertainties in breakthroughs in new energy storage, CCUS, and hydrogen technologies on the power “dual carbon” pathway is analyzed, providing technological and decision-making support for the low-carbon transition of the power sector.
Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
Authors: Ding Can , Ji Yinbo , Yuan Zhao
Published: January 2025

Solid-state DC circuit breakers provide crucial support for the safe and reliable operation of low-voltage DC distribution networks. A hardware topology based on a cascaded structure with dual-stage, current-limiting, small-capacity, solid-state DC circuit breakers has been proposed. The hardware topology uses a series–parallel configuration of cascaded SCR (thyristors) and MOSFETs (metal oxide semiconductor field-effect transistors) in the transfer branch, which enhances the breaking capacity of the transfer branch. Additionally, a secondary current-limiting circuit composed of an inductor and resistor in parallel is integrated at the front end of the transfer branch to effectively improve the current-limiting performance of the circuit breaker. Meanwhile, a dissipation branch is introduced on the fault side to reduce the energy consumption burden on surge arresters. For the power supply system of the hardware part, a capacitor-powered method is adopted for safety and efficiency, with a capacitor switch serially connected to the capacitor power supply for high-precision control of the power supply. Current detection branches are introduced into each branch to provide conditions for the on–off control of semiconductor switching devices and experimental data analysis. The high-frequency control of semiconductor devices is achieved using optocoupler signal isolation chips and high-speed drive chips through a microcontroller STM32. Simulation verification based on MATLAB/SIMULINK software and experimental prototype testing have been conducted, and the results show that the hardware topology is correct and effective.
Part B: Innovative Data Augmentation Approach to Boost Machine Learning for Hydrodynamic Purposes—Computational Efficiency
Authors: Majidiyan Hamed , Enshaei Hossein , Howe Damon , Gubesch Eric
Published: January 2025

The increasing influence of AI across various scientific domains has prompted engineering to embark on new explorations. However, studies often overlook the foundational aspects of the maritime field, leading to over-optimistic or oversimplified outputs for real-world applications. We previously highlighted the sensitivity of trained models to noise, the importance of computational efficiency, and the need for feature engineering/compactness in hydrodynamic models due to the stochastic nature of waves. A novel data analysis framework was introduced with two purposes to augment data for machine learning (ML) models: transferring features from high-fidelity to low-fidelity surrogates and enhancing simulation data and increasing computational efficiency. The current issue addresses the second objectives. Wave-induced response time series data from experiments on a spherical model under various wave conditions were analyzed using continuous wavelet transform to extract spectral-temporal features. These features were then reorganized into a new feature map and augmented with additional endogenous features to enhance their uniqueness. Different ML models were trained; the new framework substantially reduced training costs while maintaining fair accuracy, with training times slashed from hours to seconds. The significance of the current study extends beyond the maritime context and can be utilized for ML applications in intrinsically stochastic data.