The advancements made in tissue culture techniques has made it possible to regenerate various horticultural species in vitro as micropropagation protocols for commercial scale multiplication are available for a wide range of crops. Clonal propagation and preservation of elite genotypes, selected for their superior characteristics, require high degree of genetic uniformity amongst the regenerated plants. However, plant tissue culture may generate genetic variability, i.e., somaclonal variations as a result of gene mutation or changes in epigenetic marks. The occurrence of subtle somaclonal variation is a drawback for both in vitro cloning as well as germplasm preservation. Therefore, it is of immense significance to assure the genetic uniformity of in vitro raised plants at an early stage. Several strategies have been followed to ascertain the genetic fidelity of the in vitro raised progenies comprising morpho-physiological, biochemical, cytological and DNA-based molecular markers approaches. Somaclonal variation can pose a serious problem in any micropropagation program, where it is highly desirable to produce true-to-type plant material. On the other hand, somaclonal variation has provided a new and alternative tool to the breeders for obtaining genetic variability relatively rapidly and without sophisticated technology in horticultural crops, which are either difficult to breed or have narrow genetic base. In the present paper, sources of variations induced during tissue culture cycle and strategies to ascertain and confirm genetic fidelity in a variety of in vitro raised plantlets and potential application of variants in horticultural crop improvement are reviewed.
While a characterization of plant-model mismatch is necessary for robust control, the mismatch usually can not be described accurately due to the lack of knowledge about the plant model or the complexity of nonlinear plants. Hence, this paper considers this problem in a data-driven way, where the mismatch is captured by parametric forms of integral quadratic constraints (IQCs) and the parameters contained in the IQC equalities are learned from sampled trajectories from the plant. To this end, a one-class support vector machine (OC-SVM) formulation is proposed, and its generalization performance is analyzed based on the statistical learning theory. The proposed approach is demonstrated by a single-input-single-output time delay mismatch and a nonlinear two-phase reactor with a linear nominal model, showing accurate recovery of frequency-domain uncertainties.
Researchers, engineers, scientists, and program designers can use the information from this publication to engage better with farmers and to ensure programs and products from research are beneficial and easily adopted. This publication shares background on the importance of engagement and several high-level strategies for consideration as you approach engagement, especially for the first time, illustrated by the work of the IoT4Ag Center. Overall, the strategies center the farmer, addressing how to design engagement around audience needs, understand externalities, and determine shared values, highlighted by a case study of how engagement has gone wrong. Written by Shiala M. Naranjo and Kathryn A. Stofer, and published by the UF/IFAS Department of Agricultural Education and Communication, September 2025.
An alternative to ranking cultivars based on mean and stability of phenotype is evaluating pairs of cultivars and for each pair estimating which cultivar is more likely to perform better across a random subset of target environments. Such pairwise probabilistic order can then be translated into probabilistic ranking of all cultivars that accounts for both mean and stability in a single metric. Mean and probabilistic order will be the same for most cultivar pairs; but the pairs that differ reflect differences in stability and should thus be at least partially explained by existing stability measures. We formulate a classification problem to predict differences between mean and probabilistic order for a pair of cultivars with the predictor variables defined as differences in stability. We then apply a feature selection method to identify the best predictors, that is, the stability measures that are most predictive of the differences in the two orders. The results from applying this method to data observed from multiple crops, namely, rapeseed, sorghum and maize, show that a) existing stability measures explain most of the differences, b) no stability measure explains all differences for all data, and c) stability measures that combine mean with specific type of stability perform the most like probabilistic order. These results support the premise that probabilistic ranking combines mean and stability; but no existing stability measure can completely replace estimating the relevant probabilities to identify the cultivars that are more likely to perform better across their target environments.
Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, we introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture that features datasets of cultural news, idioms, and poetry. It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels. Using the KULTURE Bench, we assessed the capabilities of models trained with different language corpora and analyzed the results comprehensively. The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture.
Mozzamil Mohammed, Mohammed AY Mohammed, Abdallah Alsammani
et al.
Carnivores interact with herbivores to indirectly impact plant populations, creating trophic cascades within plant-herbivore-carnivore systems. We developed and analyzed a food chain model to gain a mechanistic understanding of the critical roles carnivores play in ecosystems where plants face intense herbivory. Our model incorporates key factors such as seed production rates, seed germination probabilities, local plant interactions, herbivory rates, and carnivore predation rates. In the absence of carnivores, herbivores significantly reduce plant densities, often driving plants to extinction under high herbivory rates. However, the presence of carnivores suppresses herbivore populations, allowing plants to recover from herbivore pressure. We found that plant densities increase with carnivore predation rates, highlighting top-down effects and underscoring the importance of conserving carnivores in ecosystems where plants are at high risk of extinction from herbivory. Our results also show that carnivore density increases with seed-production rates, while herbivore density remains constant, indicating that plants benefit carnivores more than herbivores. This increase in carnivore density driven by high seed-production rates reflects bottom-up effects in the system. Overall, our study demonstrates that plants, herbivores, and carnivores can coexist even under intense predation stress. It suggests that carnivores play a crucial role in regulating plant and herbivore populations, with significant potential for maintaining biodiversity within ecosystems.
The remarkable ecological success of humans is often attributed to our ability to develop complex cultural artefacts that enable us to cope with environmental challenges. The evolution of complex culture (cumulative cultural evolution) is usually modelled as a collective process in which individuals invent new artefacts (innovation) and copy information from others (social learning). This classic picture overlooks the growing role of intelligent algorithms in the digital age (e.g. search engines, recommender systems and large language models) in mediating information between humans, with potential consequences for cumulative cultural evolution. Building on a previous model, we investigate the combined effects of network-based social learning and a simplistic version of algorithmic mediation on cultural accumulation. We find that algorithmic mediation significantly impacts cultural accumulation and that this impact grows as social networks become less densely connected. Cultural accumulation is most effective when social learning and algorithmic mediation are combined, and the optimal ratio depends on the network's density. This work is an initial step towards formalizing the impact of intelligent algorithms on cumulative cultural evolution within an established framework. Models like ours provide insights into mechanisms of human-machine interaction in cultural contexts, guiding hypotheses for future experimental testing.
Plant disease recognition is a critical task that ensures crop health and mitigates the damage caused by diseases. A handy tool that enables farmers to receive a diagnosis based on query pictures or the text description of suspicious plants is in high demand for initiating treatment before potential diseases spread further. In this paper, we develop a multimodal plant disease image retrieval system to support disease search based on either image or text prompts. Specifically, we utilize the largest in-the-wild plant disease dataset PlantWild, which includes over 18,000 images across 89 categories, to provide a comprehensive view of potential diseases relating to the query. Furthermore, cross-modal retrieval is achieved in the developed system, facilitated by a novel CLIP-based vision-language model that encodes both disease descriptions and disease images into the same latent space. Built on top of the retriever, our retrieval system allows users to upload either plant disease images or disease descriptions to retrieve the corresponding images with similar characteristics from the disease dataset to suggest candidate diseases for end users' consideration.
Electronic word-of-mouth (eWOM) is a prominent source of information that significantly influences consumer purchase decisions. Recent literature has extensively explored the impact of eWOM on consumers-generated reviews and purchase decisions. However, few studies have analyzed the role of culture on eWOM. We use a novel dataset of Airbnb eWOM messages in order to empirically extend the findings by Banerjee and Chai (2019). We find that the sentiment of individualistic customers is worse than that of their collectivistic counterparts when both groups experience the same level of negative disconfirmations. Furthermore, guests from a relatively more distant culture rely less on heuristics. In particular, quality signals, such as the "superhost" status, are more influential to consumers from a less distant cultural background.
As the scaling of Large Language Models (LLMs) has dramatically enhanced their capabilities, there has been a growing focus on the alignment problem to ensure their responsible and ethical use. While existing alignment efforts predominantly concentrate on universal values such as the HHH principle, the aspect of culture, which is inherently pluralistic and diverse, has not received adequate attention. This work introduces a new benchmark, CDEval, aimed at evaluating the cultural dimensions of LLMs. CDEval is constructed by incorporating both GPT-4's automated generation and human verification, covering six cultural dimensions across seven domains. Our comprehensive experiments provide intriguing insights into the culture of mainstream LLMs, highlighting both consistencies and variations across different dimensions and domains. The findings underscore the importance of integrating cultural considerations in LLM development, particularly for applications in diverse cultural settings. Through CDEval, we aim to broaden the horizon of LLM alignment research by including cultural dimensions, thus providing a more holistic framework for the future development and evaluation of LLMs. This benchmark serves as a valuable resource for cultural studies in LLMs, paving the way for more culturally aware and sensitive models.
Israel Felipe Lustosa da Silva, Gabriel Danilo Shimizu, Eduardo Ladislau dos Santos
et al.
The main strawberry cultivars grown in tropical regions originated from breeding programs developed in temperate countries, which limit the expression of their maximum productive potential when grown in warm climates. Additionally, strawberry cultivation in these regions depends almost entirely on imported strawberry young plants, increasing production costs. Obtaining adapted cultivars with a lower requirement for the accumulation of chilling hours is the main objective of breeding programs in the southern hemisphere. Thus, the objective of this study was to evaluate short-day strawberry genotypes, pre-selected based on yield and fruit quality components through multivariate analysis, for cultivation in tropical conditions. Two experiments were performed. In the first one, 36 pre-selected genotypes and their parents were assessed based on productive traits (total fruit mass, commercial fruit mass, and mean mass of commercial fruits), soluble solids content, and fruit firmness. For selection, multivariate analysis was applied using the genotype (ideotype) selection index and principal component analysis. In the second experiment, the fruit of the best-ranked genotypes, the parents, and the Camarosa and Dover cultivars were assessed for soluble solids (SS), titratable acidity (TA), SS/TA ratio, ascorbic acid, phenolic compounds, and total anthocyanins. To identify the most promising genotypes, the data were analyzed using principal component analysis. The RVFS06CR-105, RVDA11CR-61, RVDA11CR-125, and RVCS44CR-130 were the most promising genotypes for cultivation in tropical conditions, based on yield and fruit quality components. The means found for these genotypes were higher than the commercial cultivars Dover, Camino Real, and Camarosa. The use of multivariate analysis was efficient in selecting the genotypes, with repeatability of information between the methods guaranteeing consistency in the information. The selected genotypes had higher yields than those used as the control, which is a promising result considering the independence in relation to genetic material, mainly in the expansion of the cultivation area in the tropical regions of the southern hemisphere. In addition, these genotypes are being evaluated in different tropical climate regions with the aim of validating them for submission to the registration and release process for farmers. Additionally, they are being used as a source of genes for crossings and new selections.