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The chondrogenic differentiation of BMSCs in collagen hydrogels and the effect of MMPs among cell-material interactions

Journal of Leather Science and Engineering

Pub Date : 2024-12-01 DOI: 10.1186/s42825-024-00176-4

Yajun Tang, Jing Wang, He Qiu, Yang Xu, Zhanhong Liu, Lu Song, Hai Lin, Xingdong Zhang

The purpose of this study was to investigate the influence of the advanced structure of collagen on the chondrogenic differentiation of BMSCs encapsulated in collagen hydrogels, with an emphasis on MMPs which might affect the cell-material interactions. Collagen and gelatin-based hydrogels with comparable physicochemical properties but mainly distinctive in molecular structure were prepared and further utilized to load BMSCs to study the chondrogenesis. The detection results of MMPs in hydrogels with and without TIMP at both gene and protein levels suggested that MMPs were involved in cell recognition, adhesion, migration, proliferation and further remodeling of cell microenvironment. The chondrogenic gene detection, histological observation and extracellular matrix analysis indicated that the BMSCs were well differentiated into chondrocytes and maintained the phenotypes in collagen hydrogels (C group) which preserved the native structures, comparing with those results acquired from gelatin hydrogels (G group). Finally, the expression of several integrin subunits was investigated to analyze the connection of these cell membrane surface proteins and microenvironment remodeled by MMPs in collagen and gelatin hydrogels. The conclusion was drawn that the advanced structure of collagen affected the chondrogenesis of BMSCs via the cell-material interactions, among which MMPs were one of the major factors crucial to form appropriate microenvironment to modulate the BMSCs fate.

Graphical abstract

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引用次数: 0

Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY

Artificial Intelligence in Agriculture

Pub Date : 2024-12-01 DOI: 10.1016/j.aiia.2024.11.005

Mohammad Amin Razavi , A. Pouyan Nejadhashemi , Babak Majidi , Hoda S. Razavi , Josué Kpodo , Rasu Eeswaran , Ignacio Ciampitti , P.V. Vara Prasad

In this study, we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal. We analyze how these features influence crop yields by utilizing remotely sensed data. Our methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies, offering a comprehensive understanding of the factors affecting agricultural productivity in Senegal. To optimize the model's performance and identify the optimal hyperparameters, we implemented a comprehensive grid search across four distinct machine learning regressors: Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient-Boosting Machine (LightGBM). Each regressor offers unique functionalities, enhancing our exploration of potential model configurations. The top-performing models were selected based on evaluating multiple performance metrics, ensuring robust and accurate predictive capabilities. The results demonstrated that XGBoost and CatBoost perform better than the other two. We introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural datasets. By achieving high similarity scores with real-world data, our synthetic samples enhance model robustness, mitigate overfitting, and provide a viable solution for small dataset issues in agriculture. Our approach distinguishes itself by creating a flexible model applicable to various crops together. By integrating five crop datasets and generating high-quality synthetic data, we improve model performance, reduce overfitting, and enhance realism. Our findings provide crucial insights for productivity drivers in key cropping systems, enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in data-scarce regions.

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引用次数: 0

Application of Multi-Model Adaptive Statistical Iterative Reconstruction-Veo in Ultra-Low Dose Chest CT Examination of Children in Plateau Area. 多模型自适应统计迭代重建-Veo在高原地区儿童超低剂量胸部CT检查中的应用

Q4 Medicine

中国医学科学院学报

Pub Date : 2024-11-26 DOI: 10.3881/j.issn.1000-503X.16068

Xian-Tao Wang, Rui-Ting Bai, Cidanwangjiu, Suolangnima, Nimazhuoga, Bai-Yan Su

Objective To explore the application value of multi-model adaptive statistical iterative reconstruction-Veo (ASiR-V) in ultra-low dose chest CT examination of children in the plateau area. Methods The children who underwent chest CT examination in Xizang Autonomous Region People's Hospital were enrolled in this study and assigned into two groups according to the scanning conditions.Group A underwent scanning at a tube voltage of 100 kV and ASiR-V 50% reconstruction,and group B underwent scanning at a tube voltage of 80 kV and ASiR-V 0 (Group B1) and ASiR-V 50% (Group B2) reconstruction.The image quality of each group was evaluated objectively and subjectively.The radiation dose and image quality were compared between groups. Results Groups A and B showed the volume CT dose indexes of (2.33±0.62) mGy and (0.86±0.01) mGy and the dose length products of (65.01±25.12) mGy·cm and (23.55±3.38) mGy·cm,respectively,which presented differences between groups (both P<0.001).The image noise in the bilateral upper and middle lung areas in group B2 was lower than that in group B1 but higher than that in group A (all P<0.001).There was no significant difference in image quality score of the lung window among groups (all P>0.05).Groups A,B1,and B2 had no significant differences in ascending aorta (P=0.538) or liver CT value (P=0.175) in the mediastinal window.The signal-to-noise ratios and contrast-to-noise ratios of ascending aorta and liver in group B2 were higher than those in group B1 (all P<0.001) and lower than those in group A (all P<0.05).The image quality score of the mediastinal window followed a descending order of group A>group B2>group B1 (all P<0.001). Conclusion ASiR-V combined with low tube voltage can effectively reduce the radiation dose and guarantee the image quality of chest CT of children in the plateau area.

目的探讨多模型自适应统计迭代重建-Veo（ASiR-V）在高原地区儿童超低剂量胸部CT检查中的应用价值。方法将在西藏自治区人民医院接受胸部 CT 检查的患儿作为研究对象，根据扫描条件分为两组，A 组在 100 kV 管电压下扫描，ASiR-V 50%重建，B 组在 80 kV 管电压下扫描，ASiR-V 0（B1 组）和 ASiR-V 50%（B2 组）重建，对各组图像质量进行客观和主观评价，比较组间辐射剂量和图像质量。结果 A 组和 B 组的容积 CT 剂量指数分别为（2.33±0.62）mGy 和（0.86±0.01）mGy，剂量长度乘积分别为（65.01±25.12）mGy-cm 和（23.55±3.38）mGy-cm，组间差异均为 P0.05。A组、B1组和B2组纵隔窗升主动脉（P=0.538）和肝脏CT值（P=0.175）差异无学意义。

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引用次数: 0

Effects of exposure to micro/nanoplastics of polystyrene on neuronal oxidative stress, neuroinflammation, and anxiety-like behavior in mice: A Systematic Review

IF 5.3 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2024-11-23 DOI: 10.1016/j.emcon.2024.100442

Lafram Asmaa , Krami Al Mehdi , Akarid Khadija , Laadraoui Jawad , Roky Rachida

Polystyrene is a polymer widely used across various industrial and commercial sectors. Upon degradation, it fragments into microplastics (MP) and nanoplastics (NP), whose accumulation in the environment raises significant ecological and health concerns. These particles can disrupt digestive, reproductive, and other functions in exposed organisms. In this systematic review, the effects of mice exposure to PS-NP or PS-MP (PS-MP/NP) were systematically examined, focusing on neuronal oxidative stress, neuroinflammation, and anxiety-like behavior. Three databases (PubMed, Web of Science, and Scopus) were searched without any time filters until July 20, 2024. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Two independent reviewers assessed the quality of studies using the Systematic Review Centre for Laboratory Animal Experimentation tool (SYRCLE). A total of 24 original articles were included of 332 citations. Articles were published between 2021 and 2024. Out of the studies reviewed, 12 used PS-NP, 10 used PS-MP, and two used both PS-NP and PS-MP separately. The particle sizes ranged from 0.023 to 50 μm, with the majority exhibiting a spherical shape. Seven studies reported results that the exposure to PS-MP/NP elevated reactive oxygen species (ROS) levels, and/or increased lipid peroxidation-Malondialdehyde (LPO-MDA), and/or decreased antioxidants, especially superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH). Eight studies demonstrated an increase in neuroinflammation markers, including TNF-α, IL-1β, IL-6, GFAP, and Iba1. Regarding anxiety-like behavior, 10 studies confirmed its induction. Furthermore, maternal exposure induced neurotoxic responses in offspring. Overall, exposure to PS-MP/NP induced oxidative stress, neuroinflammation, and anxiety-like behavior in mice. These results highlighted complex interactions between PS-MP/NP and the nervous system, emphasizing the need for a more thorough exploration of involved mechanisms.

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引用次数: 0

Environmental occurrence of antibiotic resistance, control measures and challenges in finding therapeutic management 环境中出现的抗生素耐药性、控制措施和寻找治疗方法的挑战

IF 5.3 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2024-11-20 DOI: 10.1016/j.emcon.2024.100440

Rajendra Singh, Keugtae Kim

Antimicrobial resistance represents a growing threat to public health, with the World Health Organization placing it among the top ten global health threats. Inappropriate use of antibiotics in healthcare and agriculture contributes to their dispersal into the environment, thereby accelerating bacterial resistance through the transfer of genetic elements and antibiotic resistance genes. The widespread prevalence of antibiotic-resistance genes and bacteria has been confirmed in both soil and water across various climates. Additionally, the detection of these resistant genes and bacteria in polar regions is particularly alarming due to their potential to spread and persist in pristine environments. The impact of antibiotic resistance is considerable, and addressing the associated challenges necessitates a collaborative effort involving healthcare professionals, government entities, scientists, and the general public. Most of the recent scholarly reviews regarding antibiotic resistance predominantly concentrate on either the overarching concept of antimicrobial resistance or they delve into particular sources, pathways, or environmental ramifications. In contrast, this extensive state-of-the-art review aspires to furnish an updated synthesis of the distribution of antibiotic resistance, its repercussions on human health and economy, as well as innovative therapeutic strategies that exhibit potential in combating antibiotic-resistant bacteria. Furthermore, it scrutinizes the challenges that impede the progression of novel therapeutic modalities and the implementation of effective control strategies. The investigation provides a comprehensive overview of the classifications of antibiotic resistance genes, systematically organized by antibiotic categories. Additionally, it underscores global management strategies, particularly through stewardship programs, promising combination therapies aimed at augmenting drug efficacy, and alternative methodologies to antibiotics for the treatment of bacterial infections.

抗生素耐药性对公共卫生的威胁日益严重，世界卫生组织已将其列为全球十大健康威胁之一。医疗保健和农业中抗生素的不当使用导致抗生素扩散到环境中，从而通过遗传因子和抗生素耐药性基因的转移加速细菌耐药性的产生。抗生素耐药基因和细菌在不同气候条件下的土壤和水中的广泛存在已得到证实。此外，在极地地区检测到这些抗药性基因和细菌尤其令人担忧，因为它们有可能在原始环境中传播并持续存在。抗生素耐药性的影响是巨大的，应对相关挑战需要医疗保健专业人员、政府机构、科学家和公众的共同努力。近期有关抗生素耐药性的学术评论大多集中在抗菌素耐药性的总体概念上，或深入探讨特定的来源、途径或环境影响。与此相反，这本内容广泛的最新综述力图对抗生素耐药性的分布、其对人类健康和经济的影响以及在抗击抗生素耐药细菌方面表现出潜力的创新治疗策略进行最新综述。此外，它还仔细研究了阻碍新型治疗模式发展和有效控制战略实施的挑战。调查全面概述了抗生素耐药基因的分类，并按抗生素类别进行了系统整理。此外，它还强调了全球管理策略，特别是通过管理计划、旨在增强药物疗效的前景看好的联合疗法以及治疗细菌感染的抗生素替代方法。

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引用次数: 0

A comprehensive review of cellulose nanomaterials for adsorption of wastewater pollutants: focus on dye and heavy metal Cr adsorption and oil/water separation 全面评述用于吸附废水污染物的纤维素纳米材料：重点关注染料和重金属 Cr 的吸附以及油/水分离

Journal of Leather Science and Engineering

Pub Date : 2024-11-20 DOI: 10.1186/s42825-024-00179-1

Yadong Yang, Xuanze Li, Caichao Wan, Zhe Zhang, Wenzhe Cao, Guanyu Wang, Yiqiang Wu

Cellulose is widely distributed in higher plants and constitutes the most abundant natural biopolymer on Earth. Nanocellulose is a cellulose material with nanoscale dimensions, obtained through special processing and treatment. Up to now, nanocellulose has been widely investigated as a biosorbent to absorb various types of pollutants in wastewater due to its excellent properties, such as large specific surface area, antifouling behaviour, high aspect ratio, high heat resistance, excellent mechanical properties, biodegradability and biocompatibility. In addition, nanocellulose can be rationally structured by different recombination techniques such as membranes, sponges, aerogels, hydrogels and microspheres and provide specialised functionality for the adsorption of various types of pollutants from wastewater. This review introduces the basic properties, classification and modification methods of nanocellulose; discusses the preparation strategies of nanocellulose-based recombinant materials (including vacuum/pressurised filtration, sol–gel and electrospinning); reviews research progress in the adsorption of organic dyes and heavy metal Cr, as well as the separation of oil/water using nanocellulose-based recombinant materials; and explores the potential of nanocellulose in treating tannery wastewater. Finally, the problems faced by nanocellulose-based recombinant materials and future prospects are presented.

Graphical Abstract

纤维素广泛分布于高等植物中，是地球上最丰富的天然生物聚合物。纳米纤维素是一种通过特殊加工和处理获得的具有纳米尺寸的纤维素材料。纳米纤维素具有比表面积大、防污、高纵横比、高耐热性、优异的机械性能、生物可降解性和生物相容性等优良特性，迄今为止，纳米纤维素作为一种生物吸附剂吸收废水中的各种污染物已得到广泛研究。此外，纳米纤维素还可以通过不同的重组技术（如膜、海绵、气凝胶、水凝胶和微球）进行合理的结构化，并为吸附废水中的各类污染物提供专门的功能。本综述介绍了纳米纤维素的基本特性、分类和改性方法；讨论了基于纳米纤维素的重组材料的制备策略（包括真空/加压过滤、溶胶-凝胶和电纺丝）；回顾了利用基于纳米纤维素的重组材料吸附有机染料和重金属 Cr 以及分离油/水的研究进展；并探讨了纳米纤维素在处理制革废水方面的潜力。最后，介绍了基于纳米纤维素的重组材料面临的问题和未来前景。图文摘要

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引用次数: 0

Northern fur seal whisker bioaccumulation and partitioning of polybrominated diphenyl ethers and organochlorines 北部海狗须中多溴联苯醚和有机氯的生物累积与分配

IF 5.3 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2024-11-17 DOI: 10.1016/j.emcon.2024.100439

Courtney M. White , Dimitrios G. Giarikos , Amy C. Hirons

The eastern Pacific stock of northern fur seals (Callorhinus ursinus) on the Pribilof Islands, Alaska represents over 50 % of the global breeding population, but it has experienced population declines of unknown cause(s) since 1980. One contributing factor to the decline may be exposure to anthropogenic contaminants such as persistent organic pollutants (POPs) which are biologically accumulative in nature, toxic to organisms, and environmentally persistent. This study conducted a decadal comparison of 21 organochlorine (OC) and 39 polybrominated diphenyl ether (PBDE) analyte concentrations utilizing archived vibrissae (whiskers) from individual fur seals sampled in 1993 (n = 30) and 2013 (n = 41) during subsistence harvests on the Pribilof Islands. The recently phased out PBDEs had values five times greater than the legacy status OCs, reflecting both the global shift away from chlorinated chemicals over the past 50 years and the widespread use of brominated flame retardants within the past two decades. No significant mean concentration differences were detected between 1993 and 2013 for total organochlorines (ΣOCs) (266 and 294 ng/g, respectively), or total polybrominated diphenyl ethers (ΣPBDEs) (1377 and 1521 ng/g, respectively), indicating the enduring environmental presence of these pollutants. Recently phased out PBDEs were detected at five times greater concentrations than phased out OCs. The presence of all analytes in vibrissae indicate that there is an adaptive advantage to partitioning contaminants into this inert tissue, suggesting keratinous tissue is a reliable matrix for assessing long-term for POP exposure and introducing the potential for less invasive sampling for future monitoring efforts.

阿拉斯加州 Pribilof 群岛上的东太平洋北部海狗（Callorhinus ursinus）种群占全球繁殖种群的 50%以上，但自 1980 年以来，其种群数量一直在下降，原因不明。导致数量下降的一个因素可能是暴露于人为污染物，如持久性有机污染物（POPs），这些污染物具有生物累积性，对生物有毒，并在环境中持久存在。本研究利用 1993 年（n = 30）和 2013 年（n = 41）在普里比罗夫群岛（Pribilof Islands）自给性捕捞期间采样的海狗个体的存档鳍（须），对 21 种有机氯 (OC) 和 39 种多溴联苯醚 (PBDE) 分析物的浓度进行了十年期比较。最近淘汰的多溴联苯醚的浓度值是传统状态 OCs 的五倍，这反映了全球在过去 50 年中不再使用氯化化学品，以及过去 20 年中溴化阻燃剂的广泛使用。在 1993 年和 2013 年之间，未检测到有机氯总量（ΣOCs）（分别为 266 和 294 纳克/克）或多溴联苯醚总量（ΣPBDEs）（分别为 1377 和 1521 纳克/克）有明显的平均浓度差异，这表明这些污染物在环境中持续存在。最近淘汰的多溴联苯醚的检测浓度是淘汰的氯化石蜡的五倍。所有分析物在振膜中的存在表明，将污染物分配到这种惰性组织中具有适应优势，这表明角质组织是评估持久性有机污染物长期暴露的可靠基质，并为未来的监测工作带来了低侵入性采样的可能性。

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引用次数: 0

Associations between per- and polyfluoroalkyl substances and menstrual cycle regularity in reproductive-aged female: A cross-sectional study 育龄女性体内全氟和多氟烷基物质与月经周期规律性之间的关系：一项横断面研究

IF 5.3 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Emerging Contaminants

Pub Date : 2024-11-15 DOI: 10.1016/j.emcon.2024.100438

Aiqin Qiu , Kai Luo , Huan Liu , Jialing Huang , Xiaotu Liu , Jun Zhang , Da Chen , Weiwei Cheng , Wei Huang

Menstrual cycle characteristics constitute one of the significant female fertility indicators. Previous epidemiological studies have shown that exposure to environmental chemicals could affect menstrual cycle characteristics, but the knowledge remains limited overall. Per- and polyfluoroalkyl substances (PFAS) have been identified as potential reproductive toxicants, while previous studies mainly focused on several legacy PFAS chemicals but generally failed to explore the outcomes from exposure to a complex mixture of both legacy and emerging PFAS. Besides, the modification effect of physical activity is rarely considered. In the present study, we explored the associations of exposure to a suite of legacy and emerging PFAS and menstrual cycle regularity as well as the potential modification by physical activity based on a pre-conception cohort in Shanghai (China) with the participation of 1001 reproductive-aged women. A total of 20 PFAS chemicals with detection frequency >80 %, which were derived from the PFAS exposure profile of the same population in our previous study, were included in the confounder-adjusted logistic regression and Bayesian kernel machine regression (BKMR) analysis. In individual PFAS analysis, after adjustment of the covariates, ∑2m-PFOS (the sum of all perfluoro-dimethylhexane sulfonates) was significantly associated with menstrual cycle irregularity with an odds ratio (OR) of 1.35 (95 % confidence interval, CI: 1.09, 1.67) as well as long cycles (OR = 1.37; 95 % CI: 1.08, 1.70). In addition, a significant positive association was also found between perfluoro-n-nonanoic acid (PFNA) and long cycles (OR = 1.40; 95 % CI: 1.06, 1.86). No significant associations were found between the PFAS mixture and the menstrual cycle characteristics as revealed by BKMR analysis, while the significant association between ∑2m-PFOS and menstrual cycle irregularity was also observed in the mixture exposure model. Subgroup analysis stratified by physical activity level showed that the associations between ∑2m-PFOS and menstrual cycle irregularity as well as long cycles were more pronounced in the inactive physical activity subgroup. This study suggested that branched PFOS (i.e., ∑2m-PFOS) might act as the predominant risk factor for menstrual cycle irregularity, and physical activity could influence the risks.

月经周期特征是女性生育能力的重要指标之一。以往的流行病学研究表明，暴露于环境化学物质会影响月经周期特征，但总体而言，这方面的知识仍然有限。全氟烷基和多氟烷基物质（PFAS）已被确定为潜在的生殖毒性物质，而以往的研究主要集中于几种传统的全氟烷基和多氟烷基物质，但普遍未能探讨暴露于传统和新出现的全氟烷基和多氟烷基物质复杂混合物的结果。此外，体育锻炼的调节作用也很少被考虑。在本研究中，我们以中国上海的一个孕前队列为基础，在 1001 名育龄妇女的参与下，探讨了暴露于一系列原有和新出现的 PFAS 与月经周期规律性的关系，以及体育锻炼对月经周期规律性的潜在调节作用。在混杂因素调整的逻辑回归和贝叶斯核机器回归（BKMR）分析中，共纳入了20种检测频率为80%的PFAS化学物质，这些化学物质来自于我们之前研究中同一人群的PFAS暴露概况。在单个全氟辛烷磺酸分析中，经协变因素调整后，∑2m-PFOS（所有全氟二甲基己烷磺酸盐的总和）与月经周期不规则显著相关，其几率比（OR）为 1.35（95 % 置信区间：1.09, 1.67），与长周期也显著相关（OR = 1.37; 95 % 置信区间：1.08, 1.70）。此外，全氟壬酸（PFNA）与长周期之间也存在明显的正相关关系（OR = 1.40；95 % 置信区间：1.06，1.86）。通过 BKMR 分析，未发现全氟辛烷磺酸混合物与月经周期特征之间存在明显关联，而在混合物暴露模型中也观察到∑2m-PFOS 与月经周期不规则之间存在明显关联。按体育锻炼水平进行的亚组分析表明，∑2m-PFOS 与月经周期不规则和月经周期长之间的关系在不参加体育锻炼的亚组中更为明显。这项研究表明，支链全氟辛烷磺酸（即∑2m-全氟辛烷磺酸）可能是月经周期不规则的主要风险因素，而体育锻炼可能会影响风险。

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引用次数: 0

Neural network architecture search enabled wide-deep learning (NAS-WD) for spatially heterogenous property awared chicken woody breast classification and hardness regression 用于空间异质属性感知鸡木质胸脯分类和硬度回归的神经网络架构搜索（NAS-WD）

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY

Artificial Intelligence in Agriculture

Pub Date : 2024-11-15 DOI: 10.1016/j.aiia.2024.11.003

Chaitanya Pallerla , Yihong Feng , Casey M. Owens , Ramesh Bahadur Bist , Siavash Mahmoudi , Pouya Sohrabipour , Amirreza Davar , Dongyi Wang

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In this study, 250 raw chicken breast fillet samples (normal, mild, severe) were taken, and spatially heterogeneous hardness distribution was first considered when designing HSI processing models. The study not only classified the WB levels from HSI but also built a regression model to correlate the spectral information with sample hardness data. To achieve a satisfactory classification and regression model, a neural network architecture search (NAS) enabled a wide-deep neural network model named NAS-WD, which was developed. In NAS-WD, NAS was first used to automatically optimize the network architecture and hyperparameters. The classification results show that NAS-WD can classify the three WB levels with an overall accuracy of 95 %, outperforming the traditional machine learning model, and the regression correlation between the spectral data and hardness was 0.75, which performs significantly better than traditional regression models.

近年来，由于对快速生长率和高产肉鸡进行了密集的遗传选育，全球家禽业面临着一个具有挑战性的问题，即鸡胸木质化（WB）问题。这种病症每年造成高达 2 亿美元的重大经济损失，而 WB 的根本原因尚未查明。人体触诊是区分 WB 的最常用方法。然而，这种方法既费时又主观。高光谱成像（HSI）与机器学习算法相结合，能以无创、客观和高通量的方式评估鸡排的 WB 状况。本研究采集了 250 个生鸡胸肉片样本（正常、轻度、重度），在设计 HSI 处理模型时首先考虑了空间异质硬度分布。研究不仅对 HSI 中的 WB 级别进行了分类，还建立了一个回归模型，将光谱信息与样本硬度数据相关联。为了获得令人满意的分类和回归模型，研究人员利用神经网络架构搜索（NAS）开发了名为 NAS-WD 的宽深度神经网络模型。在 NAS-WD 中，NAS 首先用于自动优化网络架构和超参数。分类结果表明，NAS-WD 可以对三个 WB 级别进行分类，总体准确率达到 95%，优于传统的机器学习模型，而且光谱数据与硬度之间的回归相关性为 0.75，明显优于传统的回归模型。

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引用次数: 0

Utility-based regression and meta-learning techniques for modeling actual ET: Comparison to (METRIC-EEFLUX) model 基于效用回归和元学习技术的实际蒸散发建模：与（METRIC-EEFLUX）模型的比较

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY

Artificial Intelligence in Agriculture

Pub Date : 2024-11-14 DOI: 10.1016/j.aiia.2024.11.001

Fatima K. Abu Salem , Sara Awad , Yasmine Hamdar , Samer Kharroubi , Hadi Jaafar

Estimating actual evapotranspiration (ETₐ) is crucial for water resource management, yet existing methods face limitations. Traditional approaches, including eddy covariance and remote sensing-based energy balance methods, often struggle with high costs, limited spatial and temporal coverage, and reduced predictive accuracy, particularly for classical empirical models. While machine learning has emerged as a promising alternative, it still presents challenges, notably in underestimating ETₐ during periods of high heat. We attribute this to insufficient learning on the rare but highly relevant ETₐ values of interest, or the not-so-big climatic datasets available for use. In this manuscript, we demonstrate how few-shot, meta-learning models (MAML) that are specifically designed for enhanced generalizability on not-so-big datasets can outperform basic machine learning models in upscaling ETₐ from two major in-situ towers, the Ameriflux and Euroflux. Using limited remotely sensed land surface data from the METRIC-EEFlux and limited climatic variables, we demonstrate that the chosen models can attain quantifiable utility within the utility-based-regression paradigm towards impactful practical considerations. Our initial explorations reveal that EEflux ETₐ deviates significantly from in-situ observations measured through the Ameriflux and EEflux towers (

R^{2} = 39 %

). Instead, MAML shows best performance in approximating ETₐ than basic machine learning algorithms and EEFlux (

R^{2} = 71 %

on entire testing dataset,

R^{2} = 0.88

on the Csa climate,

R^{2} = 0.79

on the Cfa climate, and

R^{2} = 0.78

on the CSH vegetation class), and continues to improve without overfitting even when exposed to a relatively small training dataset. Its high F2 score (96 %) indicates that MAML has very high precision and recall for rare cases, which is significant for irrigation. Of independent interest, this study confirms that limited remotely sensed EEflux products contribute significantly to knowledge about ground truth ETₐ and can thus be of valuable use in settings where access to good quality and high-volume data is compromised.

估算实际蒸散量（ETₐ）对水资源管理至关重要，但现有方法存在局限性。传统方法，包括涡度协方差法和基于遥感的能量平衡法，往往成本高昂、时空覆盖范围有限、预测精度较低，尤其是对于经典的经验模型而言。虽然机器学习已成为一种很有前途的替代方法，但它仍然面临挑战，尤其是在高温期间低估了蒸散发。我们将其归咎于对罕见但高度相关的 ETₐ值学习不足，或可利用的气候数据集不大。在本手稿中，我们展示了在对两个主要原位塔--美国流量塔和欧洲流量塔--进行 ETₐ升级时，专为增强在不大的数据集上的泛化能力而设计的少镜头元学习模型（MAML）如何优于基本的机器学习模型。利用来自 METRIC-EEFlux 的有限遥感地表数据和有限的气候变量，我们证明了所选模型可以在基于效用的回归范式中获得可量化的效用，从而实现有影响力的实际考量。我们的初步探索表明，EEflux ETₐ与通过 Ameriflux 塔和 EEflux 塔测得的现场观测数据（R2=39%）有很大偏差。相反，与基本的机器学习算法和 EEFlux 相比，MAML 在近似 ETₐ 方面表现最佳（在整个测试数据集上 R2=71% ，在 Csa 气候上 R2=0.88 ，在 Cfa 气候上 R2=0.79 ，在 CSH 植被类别上 R2=0.78 ）。其较高的 F2 分数（96 %）表明，MAML 对罕见情况具有很高的精确度和召回率，这对灌溉意义重大。这项研究还证实，有限的遥感 EEflux 产品对了解地面真实蒸散发有很大帮助，因此在无法获得高质量、高容量数据的情况下也能发挥重要作用。

{"title":"Utility-based regression and meta-learning techniques for modeling actual ET: Comparison to (METRIC-EEFLUX) model","authors":"Fatima K. Abu Salem , Sara Awad , Yasmine Hamdar , Samer Kharroubi , Hadi Jaafar","doi":"10.1016/j.aiia.2024.11.001","DOIUrl":"10.1016/j.aiia.2024.11.001","url":null,"abstract":"<div><div>Estimating actual evapotranspiration (ETₐ) is crucial for water resource management, yet existing methods face limitations. Traditional approaches, including eddy covariance and remote sensing-based energy balance methods, often struggle with high costs, limited spatial and temporal coverage, and reduced predictive accuracy, particularly for classical empirical models. While machine learning has emerged as a promising alternative, it still presents challenges, notably in underestimating ETₐ during periods of high heat. We attribute this to insufficient learning on the rare but highly relevant ETₐ values of interest, or the not-so-big climatic datasets available for use. In this manuscript, we demonstrate how <em>few-shot, meta-learning models (MAML)</em> that are specifically designed for enhanced generalizability on not-so-big datasets can outperform basic machine learning models in upscaling ETₐ from two major in-situ towers, the Ameriflux and Euroflux. Using limited remotely sensed land surface data from the METRIC-EEFlux and limited climatic variables, we demonstrate that the chosen models can attain quantifiable utility within the <em>utility-based-regression</em> paradigm towards impactful practical considerations. Our initial explorations reveal that EEflux ETₐ deviates significantly from in-situ observations measured through the Ameriflux and EEflux towers (<span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>39</mn><mo>%</mo></math></span>). Instead, MAML shows best performance in approximating ETₐ than basic machine learning algorithms and EEFlux (<span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>71</mn><mo>%</mo></math></span> on entire testing dataset, <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.88</mn></math></span> on the Csa climate, <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.79</mn></math></span> on the Cfa climate, and <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.78</mn></math></span> on the CSH vegetation class), and continues to improve without overfitting even when exposed to a relatively small training dataset. Its high F2 score (96 %) indicates that MAML has very high precision and recall for rare cases, which is significant for irrigation. Of independent interest, this study confirms that limited remotely sensed EEflux products contribute significantly to knowledge about ground truth ETₐ and can thus be of valuable use in settings where access to good quality and high-volume data is compromised.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"14 ","pages":"Pages 43-55"},"PeriodicalIF":8.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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