Polyglutamine diseases comprise a cluster of genetic disorders involving neurodegeneration and movement disabilities. In polyglutamine diseases, the target proteins become aberrated due to polyglutamine repeat formation. These aberrant proteins form the root cause of associated complications. The metabolic regulation during polyglutamine diseases is not well studied and needs more attention. We have brought to light the significance of regulating glutamine metabolism during polyglutamine diseases, which could help in decreasing the neuronal damage associated with excess glutamate and nucleotide generation. Most polyglutamine diseases are accompanied by symptoms that occur due to excess glutamate and nucleotide accumulation. Along with a dysregulated glutamine metabolism, the Nicotinamide adenine dinucleotide (NAD+) levels drop down, and, under these conditions, NAD+ supplementation is the only achievable strategy. NAD+ is a major co-factor in the glutamine metabolic pathway, and it helps in maintaining neuronal homeostasis. Thus, strategies to decrease excess glutamate and nucleotide generation, as well as channelizing glutamine toward the generation of ATP and the maintenance of NAD+ homeostasis, could aid in neuronal health. Along with understanding the metabolic dysregulation that occurs during polyglutamine diseases, we have also focused on potential therapeutic strategies that could provide direct benefits or could restore metabolic homeostasis. Our review will shed light into unique metabolic causes and into ideal therapeutic strategies for treating complications associated with polyglutamine diseases. Full article

Ecosystem carbon use efficiency (CUE) is a key parameter in the carbon cycling of terrestrial ecosystems. The magnitude of CUE reflects the ecosystem’s potential for CO₂ sequestration. China’s grasslands play an important role in the carbon cycle. Here, we aimed to investigate the comparation of CUE and its environmental regulation among different grassland in Northern China based on eddy covariance carbon fluxes measurements of 31 grassland sites. The results showed that the average CUE of grassland in Northern China was 0.05 ± 0.22, with a range from −0.42 to 0.66. It was demonstrated that there were significant differences in CUE among different grassland types, and CUE values were ranked by type as follows: alpine grassland > temperate meadow steppe > temperate typical steppe > temperate desert steppe, driven by a combination of climatic, soil, and biological factors, with net ecosystem productivity (NEP) having the greatest impact on them. Except for meadow steppes, moisture had a greater impact on grassland CUE in Northern China than temperature. While temperate desert grassland CUE decreased with increasing soil water content (SWC), the CUE of other grassland types increased with higher precipitation and SWC. These findings will advance our ability to predict future grassland ecosystem carbon cycle scenarios. Full article

SMYD4 is a member of the SMYD family that has lysine methyltransferase function. Little is known about the roles of SMYD4 in cancer. The aim of this study is to investigate genetic alterations in the SMYD4 gene across the most prevalent solid tumors and determine its potential as a biomarker. We performed an integrative multi-platform analysis of the most common mutations, copy number alterations (CNAs), and mRNA expression levels of the SMYD family genes using cohorts available at the Cancer Genome Atlas (TCGA), cBioPortal, and the Catalogue of Somatic Mutations in Cancer (COSMIC). SMYD genes displayed a lower frequency of mutations across the studied tumors, with none of the SMYD4 mutations detected demonstrating sufficient discriminatory power to serve as a biomarker. In terms of CNAs, SMYD4 consistently exhibited heterozygous loss and downregulation across all tumors evaluated. Moreover, SMYD4 showed low expression in tumor samples compared to normal samples, except for stomach adenocarcinoma. SMYD4 demonstrated a frequent negative correlation with other members of the SMYD family and a positive correlation between CNAs and mRNA expression. Additionally, patients with low SMYD4 expression in STAD and LUAD tumors exhibited significantly poorer overall survival. SMYD4 demonstrated its role as a tumor suppressor in the majority of tumors evaluated. The consistent downregulation of SMYD4, coupled with its association with cancer progression, underscores its potential usefulness as a biomarker. Full article

Skeletal muscle grows in response to a combination of genetic and environmental factors, and its growth and development influence the quality of pork. Elucidating the molecular mechanisms regulating the growth and development of skeletal muscle is of great significance to both animal husbandry and farm management. The Jiangquan black pig is an excellent pig breed based on the original Yimeng black pig, importing the genes of the Duroc pig for meat traits, and cultivated through years of scientific selection and breeding. In this study, full-length transcriptome sequencing was performed on three growth stages of Jiangquan black pigs, aiming to study the developmental changes in Jiangquan black pigs at different developmental stages at the molecular level and to screen the key genes affecting the growth of skeletal muscle in Jiangquan black pigs. We performed an enrichment analysis of genes showing differential expression and constructed a protein–protein interaction network with the aim of identifying core genes involved in the development of Jiangquan black pigs. Notably, genes such as TNNI2, TMOD4, PLDIM3, MYOZ1, and MYH1 may be potential regulators of muscle development in Jiangquan black pigs. Our results contribute to the understanding of the molecular mechanisms of skeletal muscle development in this pig breed, which will facilitate molecular breeding efforts and the development of pig breeds to meet the needs of the livestock industry. Full article

Compared to other identity verification systems applications, vein patterns have the lowest potential for being used fraudulently. The present research examines the practicability of gathering vascular data from NIR images of veins. In this study, we propose a self-supervision learning algorithm that envisions an automated process to retrieve vascular patterns computationally using unsupervised approaches. This new self-learning algorithm sorts the vascular patterns into clusters and then uses 2D image data to recuperate the extracted vascular patterns linked to NIR templates. Our work incorporates multi-scale filtering followed by multi-scale feature extraction, recognition, identification, and matching. We design the ORC, GPO, and RDM algorithms with these inclusions and finally develop the vascular pattern mining model to visualize the computational retrieval of vascular patterns from NIR imageries. As a result, the developed self-supervised learning algorithm shows a 96.7% accuracy rate utilizing appropriate image quality assessment parameters. In our work, we also contend that we provide strategies that are both theoretically sound and practically efficient for concerns such as how many clusters should be used for specific tasks, which clustering technique should be used, how to set the threshold for single linkage algorithms, and how much data should be excluded as outliers. Consequently, we aim to circumvent Kleinberg’s impossibility while attaining significant clustering to develop a self-supervised learning algorithm using unsupervised methodologies. Full article

The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features. Full article

This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset. Full article

We develop a model of cheap talk with transparent and monotone motives from a seller to an informed buyer. By transparent and monotone motives, we mean that the seller’s preference does not depend on the state of the world and is increasing in the choice(s) of the buyer regardless of the state of the world. We first show that if the buyer is completely uninformed, only the babbling equilibrium exists. Then, we obtain our main result that even if the buyer has the slightest information, full revelation can be supported by using the crosschecking strategy of the buyer if and only if the seller has a CARA (constant absolute risk aversion) utility function unless the buyer has too much information. In this equilibrium, the buyer can punish the seller who sends a message far above the buyer’s information by ignoring the seller’s message. Paradoxically, no information and too much information of the buyer both eliminate the fully revealing equilibrium with the crosschecking strategy. We also obtain a counterintuitive result that the seller prefers a more informed buyer than a less informed buyer. Full article

Hepatocellular carcinoma (HCC) is a disease with a poor prognosis, often diagnosed at an advanced stage. Therapeutic options have developed considerably in recent years, particularly with trans-arterial treatments. Systemic treatments have also evolved significantly, with the rise of immune checkpoint inhibitors (ICI) as first-line treatment for advanced HCC. The combination of loco-regional treatments and ICI is opening up new prospects and is the subject of numerous clinical trials. Recently, two global phase 3 trials investigating ICI-based adjuvant combinations have demonstrated improvements in recurrence-free survival or progression-free survival in patients treated with resection, ablation, or trans-arterial chemoembolization. However, mature data and overall survival results are still awaited but will be difficult to interpret. We are at the start of a new era of combinations of loco-regional treatments and immunotherapy. The identification of the best therapeutic strategies and predictive biomarkers is a crucial issue for future standards in clinical practice. Full article

Aluminum is produced from its primary bauxite ore through the Bayer process. Although Al is important nowadays in the development of humanity, its production leads to the generation of a huge amount of waste, called red mud. Globally, the estimation of the stock of red mud is about 4 billion tons, with about 10 million tons located in Turkey. The presence of rare-earth elements (REEs) in crucial materials such as red mud makes it a major source of these elements. A number of methods have been developed for treating red mud, which are employed globally to recover valuable products. The application of a suitable method for REE extraction from red mud is a way to overcome the supply risk, contributing to reducing the environmental issues linked to red mud pollution. The current review summarizes the research on red mud processing and examines the viability of recovering REEs from red mud sustainably, utilizing hydrometallurgy and biohydrometallurgy. Full article

CBCT (cone beam computed tomography) is an imaging investigation that provides three-dimensional (3D) images of craniofacial structures. The purpose of this study is to determine the mechanical behavior of an orthodontic system where the lingual treatment technique was used in a 25-year-old female patient from whom a set of CBCT scans was used. CBCT images were processed through software programs such as Invesalius, Geomagic, and Solid Works, to create models containing virtual solids. These models were then imported into Ansys Workbench 2019 R3 (a finite element method software program) for successive simulations to generate displacement maps, deformations, stress distributions, and diagrams. We observed that in the lingual technique, the lowest force occurring on the maxillary teeth is at 1.1, while the highest force appears at 2.3. In the mandible, the lowest force occurs at 4.6, and the highest force at 3.1. The values of the forces and the results of the finite element method can represent a basis for the innovation of new orthodontic springs and also of bracket elements. Thus, by using new technologies, orthodontic practice can be significantly improved for the benefit of patients. Other virtual methods and techniques can be used in future studies, including the application of virtual reality for orthodontic diagnosis. Full article

Utility as-built plans, which typically provide information about underground utilities’ position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipment has increased in an attempt to resolve utility as-built inaccuracies and mitigate the high rate of accidental underground utility strikes during excavation activities. Adapting data fusion into utility engineering and investigation practices has been shown to be effective in generating information with improved accuracy. However, the complexities in data interpretation and associated prohibitive costs, especially for large-scale projects, are limiting factors. This paper addresses the problem of data interpretation, costs, and large-scale utility mapping with a novel framework that generates probabilistic inferences by fusing data from an automatically generated initial map with as-built data. The probabilistic inferences expose regions of high uncertainty, highlighting them as prime targets for further investigations. The proposed model is a collection of three main processes. First, the automatic initial map creation is a novel contribution supporting rapid utility mapping by subjecting identified utility appurtenances to utility inference rules. The second and third processes encompass the fusion of the created initial utility map with available knowledge from utility as-builts or historical satellite imagery data and then evaluating the uncertainties using confidence value estimators. The proposed framework transcends the point estimation of buried utility locations in previous works by producing a final probabilistic utility map, revealing a confidence level attributed to each segment linking aboveground features. In this approach, the utility infrastructure is rapidly mapped at a low cost, limiting the extent of more detailed utility investigations to low-confidence regions. In resisting obsolescence, another unique advantage of this framework is the dynamic nature of the mapping to automatically update information upon the arrival of new knowledge. This ultimately minimizes the problem of utility as-built accuracies dwindling over time. Full article

The speed of sound in seawater plays a crucial role in determining the accuracy of multibeam bathymetric measurements. In deep-sea multibeam measurements, the challenge of inadequate longitudinal coverage of sound speed profiles arises from variations in seafloor topography, meteorological conditions, measurement equipment, and operational efficiency, resulting in diminished measurement precision. Building upon the EOF (Empirical Orthogonal Function), a method employed to analyze spatiotemporal data such as sound speeds, this paper addresses the limitations of the EOF method caused by the shallowest sampling depth of the sound speed profile samples. It proposes two methods for EOF reconstruction of measured sound speed profiles extended to full water depth by splicing measured sound speed profiles at non-full water depths with historical average sound speed profiles of the surveyed sea area. Specially, Method 2 introduces the latest metaheuristic optimization algorithm, CPO (Crested Porcupine Optimizer), which exhibited superior performance on multiple standard test functions in 2024. The study reconstructs randomly sampled measured sound speed profiles using the two proposed methods and commonly employed substitution and splicing methods, followed by a comparative analysis of the experimental outcomes. At a sampling depth of 200 m, Method 2 demonstrates performance superior to other methods, with RMSE, MAE, MAPE, and R² values of 0.9511 m/s, 0.8492 m/s, 0.0566%, and 0.9963, respectively. Method 1 yields corresponding values of 0.9594 m/s, 0.8492 m/s, 0.0568%, and 0.9962, respectively. Despite its slightly inferior performance compared with Method 2, it offers substantial advantages over the substitution and splicing methods. Varying the sampling depth of measured sound speed profiles reveals that Methods 1 and 2 exhibit inferior reconstruction performance in shallow water compared with the substitution and splicing methods. Nevertheless, when the sampling depth surpasses the depth range of initial spatial modes with abrupt variations, both methods achieve notably higher reconstruction accuracy compared with the substitution and splicing methods, reaching a stabilized state. Sound ray tracing reveals that the reconstructed sound speed profiles from both methods meet the stringent accuracy standards for bathymetric measurements, achieving an effective beam ratio of 100%. The proposed methods not only provide rapid reconstruction of sound speed profiles, thereby improving the efficiency of multibeam bathymetric surveys, but also provide references for the reasonable determination of sampling depths of sound speed profiles to ensure reconstruction accuracy, demonstrating practical application value. Full article

This paper introduces a software tool developed within the MATLAB environment, called Tremors, aimed at streamlining the pre-processing and analysis of seismic catalogues, with a particular emphasis on determining the Magnitude of Completeness. It will outline the criteria for event selection, as well as various techniques to derive the Magnitude of Completeness values, including the recent and widely used Lilliefors statistical method. The study also addresses the important issue of short-term aftershock incompleteness and proposes solutions for managing it. Moreover, the software generates high-quality, customizable figures, and georeferenced raster images in .tif format as output. A standalone version of the App is also available (i.e., the users do not need a MATLAB license on their PC/laptop). Full article

Paclitaxel (PTX) is a chemotherapeutic agent affecting microtubule polymerization. The efficacy of PTX depends on the type of tumor, and its improvement would be beneficial in patients’ treatment. Therefore, we tested the effect of slow sulfide donor GYY4137 on paclitaxel sensitivity in two different breast cancer cell lines, MDA-MB-231, derived from a triple negative cell line, and JIMT1, which overexpresses HER2 and is resistant to trastuzumab. In JIMT1 and MDA-MB-231 cells, we compared IC50 and some metabolic (apoptosis induction, lactate/pyruvate conversion, production of reactive oxygen species, etc.), morphologic (changes in cytoskeleton), and functional (migration, angiogenesis) parameters for PTX and PTX/GYY4137, aiming to determine the mechanism of the sensitization of PTX. We observed improved sensitivity to paclitaxel in the presence of GYY4137 in both cell lines, but also some differences in apoptosis induction and pyruvate/lactate conversion between these cells. In MDA-MB-231 cells, GYY4137 increased apoptosis without affecting the IP₃R1 protein, changing the morphology of the cytoskeleton. A mechanism of PTX sensitization by GYY4137 in JIMT1 cells is distinct from MDA-MB-231, and remains to be further elucidated. We suggest different mechanisms of action for H₂S on the paclitaxel treatment of MDA-MB-231 and JIMT1 breast cancer cell lines. Full article

Key long-lived greenhouse gases (CO₂, CH₄, and N₂O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO₂, CH4, and N₂O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO₂, CH₄, and N₂O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr⁻¹ for CO₂; (0.46 ± 0.02) % yr⁻¹ for CH₄; (0.28 ± 0.01) % yr⁻¹ for N₂O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH₄ and N₂O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory. Full article

In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm. Full article

Titanium dioxide nanoparticles (TiO₂ NPs) have been investigated as one of the potential dose enhancement agents for radiation therapy. The role of TiO₂ NPs as a photodynamic sensitiser has been well documented, but its sensitisation with X-rays is not highlighted. Unlike other metal NPs, such as gold NPs, the main challenge for TiO₂ NPs as radiosensitisers is their low atomic number, resulting in a small cross-section for X-rays. This review summarises the results of current research in this area to explore the dose enhancement inflicted by TiO₂ NPs, which could potentially be of great value in improving radiation therapy efficiency. Full article

Risk-stratified breast screening has been proposed as a strategy to overcome the limitations of age-based screening. A prospective cohort study was undertaken within the PERSPECTIVE I&I project, which will generate the first Canadian evidence on multifactorial breast cancer risk assessment in the population setting to inform the implementation of risk-stratified screening. Recruited females aged 40–69 unaffected by breast cancer, with a previous mammogram, underwent multifactorial breast cancer risk assessment. The adoption of multifactorial risk assessment, the effectiveness of methods for collecting risk factor information and the costs of risk assessment were examined. Associations between participant characteristics and study sites, as well as data collection methods, were assessed using logistic regression; all p-values are two-sided. Of the 4246 participants recruited, 88.4% completed a risk assessment, with 79.8%, 15.7% and 4.4% estimated at average, higher than average and high risk, respectively. The total per-participant cost for risk assessment was CAD 315. Participants who chose to provide risk factor information on paper/telephone (27.2%) vs. online were more likely to be older (p = 0.021), not born in Canada (p = 0.043), visible minorities (p = 0.01) and have a lower attained education (p < 0.0001) and perceived fair/poor health (p < 0.001). The 34.4% of participants requiring risk factor verification for missing/unusual values were more likely to be visible minorities (p = 0.009) and have a lower attained education (p ≤ 0.006). This study demonstrates the feasibility of risk assessment for risk-stratified screening at the population level. Implementation should incorporate an equity lens to ensure cancer-screening disparities are not widened. Full article

The need to develop advanced wastewater treatment techniques and their use has become a priority, the main goal being the efficient removal of pollutants, especially those of organic origin. This study presents the photo-degradation of a pharmaceutical wastewater containing Kabi cytarabine, using ultraviolet (UV) radiation, and a synthesized catalyst, a composite based on bismuth and iron oxides (BFO). The size of the bandgap was determined by UV spectroscopy, having a value of 2.27 eV. The specific surface was determined using the BET method, having a value of 0.7 m² g⁻¹. The material studied for the photo-degradation of cytarabine presents a remarkable photo-degradation efficiency of 97.9% for an initial concentration 0f 10 mg/L cytarabine Kabi when 0.15 g of material was used, during 120 min of interaction with UV radiation at 3 cm from the irradiation source. The material withstands five photo-degradation cycles with good results. At the same time, through this study, it was possible to establish that pyrimidine derivatives could be able to combat infections caused by Escherichia coli and Candida parapsilosis. Full article

Abstract: Artificial intelligence (AI) is transforming much of society in a short time. Regardless of whether we know it, we interact with AI systems when we seek information online, shop, work, and engage with social media. AI has massive potential to promote human wellbeing but also poses considerable risks, as set out in an open letter signed by leaders in the field, such as Geoffrey Hinton, the “Godfather of AI”. This paper examines how AI can be used as a powerful tool to change pro-social behaviors as part of social marketing programs. We examine opportunities to build on existing efforts to use AI for pro-social behavior changes and the challenges and potential risks that AI may pose. The specific aims of the paper are to explore how AI can be used in social marketing policy, strategy development, and operational delivery. We also explore what this means for future social marketing practice. We present an overview of case studies from the social marketing field and the application of AI in the past, present, and future. We examine the following key question: can these new technologies can be used to promote social good, and if so, how? Through examples from policy, strategy development, operations, and research in social marketing, we examine how AI has been used and successfully applied to improve consumer outcomes and analyze its implications for social marketing. We conclude that AI has substantial promise but also poses some challenges and has potential negative impacts on efforts to promote pro-social behavior changes. Used well, AI may enable social marketers to more rapidly assess how to modify programs of action to ensure maximum efficiency and effectiveness. We suggest future research and programs within this field. Full article

Drought limits the growth and development of Phaseolus vulgaris L. (known as common bean). Common bean plants contain various phenylpropanoids, but it is not known whether the levels of these metabolites are altered by drought. Here, BT6 and BT44, two white bean recombinant inbred lines (RILs), were cultivated under severe drought. Their respective growth and phenylpropanoid profiles were compared to those of well-irrigated plants. Both RILs accumulated much less biomass in their vegetative parts with severe drought, which was associated with more phaseollin and phaseollinisoflavan in their roots relative to well-irrigated plants. A sustained accumulation of coumestrol was evident in BT44 roots with drought. Transient alterations in the leaf profiles of various phenolic acids occurred in drought-stressed BT6 and BT44 plants, including the respective accumulation of two separate caftaric acid isomers and coutaric acid (isomer 1) relative to well-irrigated plants. A sustained rise in fertaric acid was observed in BT44 with drought stress, whereas the greater amount relative to well-watered plants was transient in BT6. Apart from kaempferol diglucoside (isomer 2), the concentrations of most leaf flavonol glycosides were not altered with drought. Overall, fine tuning of leaf and root phenylpropanoid profiles occurs in white bean plants subjected to severe drought. Full article