Abstract: Image segmentation is a fundamental component of vision-based agricultural robotics, enabling accurate fruit localization, disease detection, and automated harvesting. However, real-world strawberry fields present significant challenges due to irregular fruit morphology, dense foliage occlusions, variable ripeness, and strong illumination variability. Moreover, segmentation models trained on a single dataset often fail to generalize across domains, limiting their practical deployment. This paper presents a comprehensive benchmark of classical computer vision methods, convolutional neural networks, instance-based models, and transformer-based architectures across three heterogeneous public strawberry datasets: Db1 (instance segmentation), Db2 (lesion segmentation), and Db3 (semantic segmentation). A unified preprocessing and evaluation framework is adopted to ensure fair comparison using standard metrics, including Intersection-over-Union (IoU), Dice coefficient, Precision, and Recall. Extensive in-domain experiments demonstrate that deep learning models significantly outperform classical approaches, with U-Net and SegFormer achieving IoU values above 0.95 on Db1 and up to 0.83 on Db3. Cross-domain zero-shot evaluations reveal a substantial generalization gap, with U-Net suffering IoU drops of up to 100\%, while SegFormer consistently exhibits improved robustness and reduced cross-domain degradation across most transfer scenarios. To our knowledge, these results establish the first systematic multi-dataset benchmark for strawberry segmentation under domain shift, highlighting the importance of transformer-based architectures for robust agricultural perception and providing practical insights for real-world robotic deployment.

Abstract: Gestational diabetes mellitus (GDM), one of the most common metabolic complications of gestation, affects approximately 10–15% of pregnancies and represents a significant challenge for obstetrics and diabetologists in the attempt of reducing adverse maternal and fetal outcomes. Medical nutrition therapy remains the cornerstone of GDM management, alongside lifestyle modification and pharmacological treatment in presence of unachieved glycemic targets. However, current dietary recommendations primarily emphasize nutrient composition and caloric intake, often without fully considering the temporal aspects of food intake. Chrono-nutrition is an emerging field that investigates the interaction between meal timing, circadian rhythms, and metabolic regulation. Increasing evidence indicates that glucose metabolism and insulin sensitivity exhibit marked diurnal variations, which may be further amplified in women with GDM, resulting in time-dependent differences in postprandial glycemic responses. The narrative review summarizes current evidence on the role of chrono-nutrition in GDM by integrating mechanistic insights with findings from observational and interventional human studies. Although the available literature is limited by heterogeneity and a paucity of well-designed randomized controlled trials, the convergence of biological plausibility and emerging clinical data supports chrono-nutrition as a low-risk refinement of standard medical nutrition therapy. Incorporating temporal aspects of eating into dietary counseling may enhance glycemic management and contribute to more physiologically aligned and personalized nutritional strategies for pregnancies complicated by GDM.

Abstract: Background and Objectives: Procalcitonin (PCT) kinetics have emerged as a promising prognostic marker in sepsis; however, their interpretation is complicated by dynamic changes in renal function during acute illness. Most previous studies relied on a single baseline estimated glomerular filtration rate (eGFR), which may lead to misclassification in patients with evolving acute kidney injury. This study aimed to evaluate the prognostic value of procalcitonin kinetics (ΔPCT) for 30-day mortality in critically ill patients with sepsis or septic shock by incorporating serial kinetic eGFR measurements and renal function–adapted ΔPCT cut-off values based on the mean kinetic eGFR during the first 72 hours of intensive care unit (ICU) admission. Materials and Methods: This retrospective cohort study included 106 adult patients admitted to a general ICU with sepsis or septic shock. Procalcitonin levels were measured serially, and ΔPCT was calculated as the logarithmic ratio of follow-up to baseline values. Renal function was assessed using kinetic eGFR calculated at serial time points from ICU admission, and the mean kinetic eGFR over the first 72 hours was used for renal function stratification. Multivariable logistic regression models incorporating ΔPCT and severity scores (APACHE II and SOFA) were constructed, and discriminative performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Thirty-day mortality was 43.4%. ΔPCT was a strong independent predictor of mortality across all models. When stratified according to mean kinetic eGFR, optimal ΔPCT cut-off values expressed as absolute proportional PCT decline differed markedly by renal function: an 81.2% decrease in PCT best discriminated mortality in the overall cohort, whereas renal function–specific thresholds were 63.7% for patients with mean kinetic eGFR <30 mL/min, 87.6% for those with kinetic eGFR 30–59 mL/min, and 92.6% for patients with kinetic eGFR ≥60 mL/min. The combination of APACHE II and ΔPCT demonstrated the highest discriminative performance (AUC 0.946). Conclusions: Procalcitonin kinetics provide robust prognostic information in sepsis when interpreted alongside dynamic renal function. Using serial kinetic eGFR measurements and the 72-hour mean renal function enables renal function–adapted ΔPCT cut-off determination and may improve mortality risk stratification in critically ill septic patients.

Abstract: The classical scientific paradigm, centered on the ideal of a passive observer discovering pre-existing facts, is fundamentally challenged by insights from quantum mechanics and complex systems, where measurement is inherently interventional (Heisenberg, 1927; Barad, 2007). We propose the Ze System framework, a radical epistemological shift that redefines scientific inquiry as the active engineering of predictive conflicts to provoke latent reality into manifesting observable phenomena. The framework posits that a substantial portion of reality exists not as localized facts but as a high-entropy "wave" state of unactualized potentialities. By deploying competing, precise predictive models (e.g., P1 and P2 and applying a minimal, targeted intervention—the Ze probe (π)—this methodology forces a system into a crisis of choice. This forced localization is an entropic transaction: it expends energy, increases disorder, and irrevocably annihilates alternative potentials. Crucially, truth is not found in a model's confirmation but is forged in the structured, interpretable residual error (ϵL) that persists when a "greedy" model, equipped with a "cheating lever" to shape its own data, encounters an unyielding latent structure (L) (Tkemaladze, 2026). This paper details the ontological, methodological, and ethical foundations of this second-order science, framing Ze systems as entropy engines that strategically invest disorder to purchase certainty, thereby recasting the scientist's role from detached observer to accountable architect of co-created facts.

Abstract: Poly(lactic acid) (PLA) is extensively used in food-contact applications due to its bio-based origin, compostability, and transparency; however, its limited resistance to thermo-oxidative degradation remains an obstacle for applications involving repeated thermal exposure. The moderate but repetitive heating conditions commonly encountered during food use and pre-recycling stages were analyzed for the samples filled with algal biomass and rosemary extract, aditives accepted for use in food industry. In this context, the present study introduces a comparative and application-driven approach by evaluating the effect of food-grade fillers—rosemary extract, spirulina biomass, and kelp biomass—incorporated at low loadings (0.5–3 wt%) on the thermal and oxidative behavior of PLA subjected to repeated heating at 80 °C. The presented results show algal biomasses as multifunctional fillers and benchmarks their performance against a well-established natural extract. By combining DSC, FTIR, and chemiluminescence analyses, the study aims to clarify whether such bio-fillers act as stabilizing or destabilizing factors under realistic service-life thermal stress. This strategy provides insight into the suitability of algae-based fillers for food-contact PLA materials from both performance and recyclability perspectives.

Abstract: The quality of governance is a key driver of resource mobilisation in a context marked by successive shocks that exacerbate fiscal imbalances. This study aims to analyse the role of institutional quality in the relationship between public expenditure and tax revenue in a panel of 162 countries, broken down into developed and emerging economies between 2000 and 2023. Using Dumitrescu and Hurlin's (2012) causality tests and the cross-sectional autoregressive model with staggered lags (CS-ARDL) to control for cross-sectional heterogeneity and cross-dependence, the results reveal a bidirectional causality linking expenditure and revenue for the entire panel; emerging countries are more sensitive to fiscal policies; public expenditure significantly stimulates tax revenue in the short and long term, with an effect amplified by institutional quality; long-term sustainability depends crucially on the institutional framework. This study highlights the need for targeted institutional reforms and fiscal rules differentiated according to countries' level of economic development.

Abstract: Acute Myeloid leukemia (AML) is characterized by differentiation arrest, driving blast proliferation and abnormal blood formation. While differentiation therapy revolu-tionized acute promyelocytic leukemia (APL) with all-trans retinoic acid (ATRA) and arsenic trioxide (ATO), its extension into non-APL AML has been limited until recent targeted agents. This narrative review synthesizes preclinical and clinical evidence in-to differentiation-inducing therapy, with a focus on IDH1/2, FLT3 and menin inhibi-tors. Following SANRA guidelines, we searched pubmed (2010-sep,2025) for clinical trials and key preclinical studies, with particular attention to the molecular mecha-nism of differentiation induction, clinical efficacy and management of differentiation syndrome (DS). IDH1/2 inhibitors (ivosidenib, enasidenib, olutasidenib) yield overall response rate (ORR) of 30-94% in AML with DS in 10-19%. Menin inhibitors (re-vumenib, ziftomenib, enzomenib, bleximenib) achieve an ORR of 33-88% in KMT2A-rearranged or NPM1-mutated AML, with DS in 10-25% and QT prolongation as key toxicities. FLT3-inhibitors (gilteritinib, quizartinib) improve survival in FLT3-mutated AML with DS in 1-5%. Resistance mutations limit durability and com-binations enhance efficacy. Differentiation therapy represents a paradigm shift to-wards non-cytotoxic AML management. Improved recognition of DS and rational combination approaches will be essential to maximize therapeutic benefit. Future re-search should address mechanisms of resistance and biomarkers to achieve cure be-yond APL.

Abstract: Plastic manufacturing depends heavily on petroleum-derived monomers like terephthalic acid, the main component of polyethylene terephthalate (PET). However, the depletion of fossil resources and increasing environmental concerns have heightened the need for sustainable alternatives. Lignocellulosic biomass has emerged as a promising resource due to its renewable, abundant, and eco-friendly nature. Understanding its chemical composition enables conversion of this biomass into platform chemicals, such as 2,5-furandicarboxylic acid (FDCA) and lactic acid, derived from cellulose and hemicellu-lose. These can be polymerized into bioplastics such as polyethylene furanoate (PEF) and polylactic acid (PLA), offering greener alternatives to fossil-based plastics. PEF features rigid furan rings that enhance thermal stability, mechanical strength, and barrier proper-ties, and reduce gas permeability compared to PET. PLA is a renewable, biodegradable plastic widely used in packaging and medical applications. This review covers the chem-ical makeup of lignocellulosic biomass cellulose, hemicellulose, and lignin, and various pretreatment strategies, chemical, physicochemical, and physical, to overcome biomass recalcitrance and improve conversion efficiency. It also highlights recent catalytic ad-vances in transforming lignocellulosic carbohydrates into bioplastic precursors such as FDCA and lactic acid. Lastly, the review discusses polymerization pathways for produc-ing PEF and PLA, emphasizing their role in reducing the environmental impact of poly-mer manufacturing and promoting green chemistry principles.

Abstract: Nearest-neighbour classifiers are simple and effective, but their performance and inference cost depend strongly on the size and quality of the reference (design) set. This work studies an evolutionary prototype selection strategy for k-nearest neighbour (k-NN) classification, where a genetic algorithm (GA) evolves compact, class-balanced prototype banks from the design partition and the selected prototypes are then used by a 1-NN classifier. Individuals encode fixed numbers of prototype indices per class, and fitness is defined as the number of correctly classified test samples. We evaluate the approach across five scenarios: two synthetic Gaussian benchmarks (2D, with different overlap levels), a synthetic 3D “three moons” dataset, and three real datasets (Breast Cancer Wisconsin, Wine, and a reduced MNIST setting using 8 × 8 digit images). For each scenario, results are aggregated over repeated runs with different random seeds and compared against standard 1-NN and 3-NN baselines that use all design samples as neighbours. The experimental evidence shows that GA-selected prototype banks can match ceiling performance in highly separable cases and provide consistent gains in noisier or more redundant settings, while reducing the neighbour set size by orders of magnitude. These results support the hypothesis that evolutionary, class-balanced prototype selection improves k-NN generalization and efficiency without requiring changes to the underlying distance metric or classifier structure. The results show that the proposed method is well aligned with application scenarios in which memory or latency budgets specify a hard upper bound on the number of prototypes that can be stored or consulted. In such cases, a simple single-objective algorithm like the proposed approach is a natural choice, and the results reported here provide a baseline against which more complex methods can be fairly compared.

Abstract: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a type 2 inflammatory disease effectively treated with dupilumab, a monoclonal antibody that inhibits IL-4 and IL-13 signaling. Although efficacy of dupilumab in controlling upper airway inflammation is well established, concerns have emerged regarding its potential cardiovascular effects. Emerging evidence suggests that IL-4/IL-13 signaling plays a protective role in post-myocardial infarction remodeling by promoting anti-inflammatory macrophage polarization, angiogenesis, and controlled fibrosis, especially during the early healing phase. Pharmacological blockade of the IL-4/IL-13 signaling pathway, such as that induced by dupilumab, may theoretically impair myocardial repair mechanisms, particularly in male patients who appear more responsive to these cytokines. Although rare, dupilumab-associated hypereosinophilia and myocarditis have been reported. In patients with pre-existing ischemic heart disease or heart failure, a multidisciplinary risk–benefit evaluation should be considered. Concomitant use of cardioprotective agents such as sacubitril/valsartan or SGLT2 inhibitors may help mitigate potential cardiac risks. Future studies are needed to clarify the safety and therapeutic implications of combining dupilumab with cardiovascular therapies in patients with coexisting CRSwNP and heart disease. The aim of this review is to explore the available data on the cardiovascular impact of dupilumab and to provide possible future perspectives.

Abstract: Large language models (LLMs) often generate fluent but incorrect or unsupported statements, commonly referred to as hallucinations. We propose a hallucination detection framework based on a Labeled Logic Program (LLP) architecture that integrates multiple reasoning paradigms, including logic programming, argumentation, probabilistic inference, and abductive explanation. By enriching symbolic rules with semantic, epistemic, and contextual labels and applying discourse-aware weighting, the system prioritizes nucleus claims over peripheral statements during verification. Experiments on three benchmark datasets and a challenging clinical narrative dataset show that LLP consistently outperforms classical symbolic validators, achieving the highest detection accuracy when combined with discourse modeling. A human evaluation further demonstrates that logic-assisted explanations improve both hallucination detection accuracy and user trust. The results suggest that labeled symbolic reasoning with discourse awareness provides a robust and interpretable approach to LLM verification in safety-critical domains.

Abstract: Background: In recent years, the state of Rio Grande do Sul, Brazil, has experienced increasingly severe flooding, culminating in the unprecedented 2024 disaster. This study examines how the 2024 flooding impacted key dimensions of healthy aging among Quilombola older adults, with attention to quality of life, health disruptions, coping strategies, and governance processes shaping recovery. Methods: We conducted a community-based, mixed-methods study with 32 Quilombola adults aged 55 years and older in flood-affected territories of Southern Brazil. Data included structured interviews using the WHOQOL-BREF, a tailored Quilombola Flood Impact and Governance Module, in-depth narrative interviews, and field observations. Analyses were guided by critical gerontology and environmental justice frameworks. Results: Participants reported poor physical, psychological, and environmental quality of life, marked disruptions to health care access, prolonged displacement, and extensive territorial loss. Social relationships emerged as a protective domain, reflecting strong community solidarity. Qualitative findings revealed how environmental degradation, fragmented disaster governance, and cumulative life-course inequalities intensified health vulnerabilities, while traditional knowledge and collective agency supported coping and resilience. Conclusions: Flooding constitutes a socially produced risk that undermines healthy aging in Quilombola ter-ritories. Integrating critical gerontology and environmental justice highlights the need for equity-centered disaster risk reduction, territorial protection, and age-sensitive public health policies to support aging with dignity in climate-vulnerable contexts.

Abstract: Terpenolol is a newly developed hydrophilic derivative of cannabidiol (CBD) designed to overcome the solubility, stability, and pharmacokinetic limitations of conventional lipophilic CBD formulations. This study presents its physicochemical profile, 30‑day stability, and preliminary pharmacokinetic behavior in humans. Terpenolol forms a stable aqueous micellar‑like dispersion with neutral pH (7.0 ± 0.1), consistent viscosity, and minimal variation in tintometric and colloidal parameters over the observation period.In a single‑dose, two‑period crossover study involving five healthy volunteers, sublingual administration of Terpenolol resulted in markedly higher plasma CBD concentrations at 30 minutes compared with an oil‑based reference formulation. Observational data from more than 200 individuals using Terpenolol‑based preparations across oral, nasal, cutaneous, and transdermal routes indicate good tolerability and reported improvements in anxiety, panic symptoms, insomnia, psychotic‑spectrum disturbances, and musculoskeletal or joint pain.The compound’s complete solubility in aqueous media, combined with its neutral taste and odorless profile, also supports its incorporation into functional beverages, as demonstrated by a prototype mineral water formulation containing 50 mg of CBD in 500 ml.Overall, Terpenolol demonstrates physicochemical stability, improved early systemic exposure to CBD, and broad formulation compatibility. Controlled studies are planned to further define its pharmacological relevance and potential application domains.

Abstract: Reputational risk in textual narratives is a vital aspect of understanding stakeholder perceptions of megaprojects; however, formal computational methods for measuring it remain underdeveloped. This study introduces a computational model for senti-ment-based reputational risk, defined as a feature-based supervised classification task. The proposed model combines sentiment polarity, polarity intensity, topic distribu-tions, content length, and structural textual features into a structured mathematical model, enabling systematic evaluation and reproducible predictions. Data were col-lected from online news and social media, then processed through cleaning, tokenisa-tion, lemmatisation, and sentiment annotation. An ensemble model, merging Random Forests, Gradient Boosting, Logistic Regression, and Support Vector Machines via soft voting, was trained and assessed using accuracy, precision, recall, F1-score, and Co-hen’s Kappa. Results suggest that reputational risk can be reliably inferred from the interaction between sentiment, topics, and textual structure. Analyses of feature im-portance highlight polarity intensity, risk scoring, content length, and topic distribu-tion as key predictors. These findings demonstrate the potential of formal computa-tional models to quantify and predict risk within textual data. Future research could expand this model with transformer models and multilingual datasets to improve context-aware insights, explainability, and scalability, thereby laying a foundation for generalised computational approaches to reputational risk modelling.

Abstract: We prove the irrationality of the odd zeta values \( \zeta(2n+1),\,n\in\mathbb{N} \). Our approach is based on constructing explicit integer linear forms in \( \zeta(2n+1) \), and applying a refinement of Dirichlet's approximation theorem. We prove that the sequence of denominators produced by successive rational approximations yields infinitely many nontrivial integer relations of the type \( \Lambda_m^{(q)}=A_m^{(q)}\zeta(2n+1)-B_m^{(q)} \), with \( |\Lambda_m^{(q)}| \) (\( q \) being a parameter) decaying towards zero as \( m \) approaches infinity. This permits us to invoke a general irrationality criterion and thereby deduce that each \( \zeta(2n+1) \) is irrational. Our method combines ideas from probability theory and Diophantine approximation, and complements earlier work of Apéry, Beukers, Rivoal, and Zudilin.

Abstract: Background: Currently, the duration of tourniquet time in total knee arthroplasty is chosen by the surgeons and varies between 0 and 120 minutes. Studies evaluating the effect of tourniquet time in this operation are lacking. The purpose of this study was, therefore, to determine whether the duration of tourniquet-induced limb ischemia during total knee arthroplasty influences reperfusion injury, resulting in pain, swelling and the release of proinflammatory markers. Methods: In 40 patients undergoing total knee arthroplasty, tourniquet was applied for up to 30 minutes (group A, short tourniquet) or 90-120 minutes (group B, long tourniquet). Postoperative pain and swelling served as primary outcome parameters. The levels of pro- and anti-inflammatory markers (D-dimers, C3a, C5a, TAT, fetuin-A, PAI-I/tPA complexes, CK-MM, IP10, M-CSF, MIG, MIP-1α, and sC5b9) before surgery and 4 hours, 24 hours and 48 hours after surgery, were used as secondary outcome parameters. Results: Patients in group B, with the long tourniquet time, required patient-controlled intravenous analgesia more frequently than group A patients (47% versus 5%, group B vs. group A, p < 0.0001). However, there were no differences in numeric rating pain scale (NRS) scores and calf circumference between groups A and B. In group B, a significantly higher increase of C3a levels between 4 h and 48 h, a significantly higher increase for MIG between 4 h and 48 h as well as 24 h and 48 h, and a significantly higher increase in M-CSF levels between 24 h and 48 h were observed when compared to group A. Conclusions: Tourniquet times between 90 and 120 minutes, despite currently being accepted in the clinical setting, were associated with an increased need for intravenous analgesia and higher increase of the pro-inflammatory markers C3a, MIG, and M-CSF, suggesting a more pronounced ischemia/reperfusion injury with tourniquet times longer than 90 minutes.

Abstract: Background: Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune demyelinating disease with important disability accumulation. Early-onset NMOSD, defined as disease onset before age 50, exhibits distinct clinical characteristics compared to late-onset disease. We present a case series of patients with first symptom onset before age 30. Methods: Retrospective review of 10 patients diagnosed with NMOSD at our center in San Luis Potosí, Mexico, with disease onset before age 30. Clinical presentation, imaging findings, AQP4 antibody status, treatment response, and disability outcomes were analyzed. Results: Mean age at onset was 18.6 years (range 6-30). Area postrema syndrome was the most common presentation (40%), followed by acute myelitis and optic neuritis (30% each). All tested patients were AQP4-positive. Mean EDSS at follow-up was 6.6, indicating severe disability. Most patients received rituximab with variable response rates. Conclusions: Our cohort showed higher disability than reported in other early-onset series, emphasizing the need for prompt diagnosis and aggressive treatment in this population.

Abstract: Educational sustainability necessitates a holistic development paradigm where academic resilience and physical literacy are mutually reinforcing. Within the framework of the United Nations Sustainable Development Goals (specifically SDG 3: Good Health and Well-being and SDG 4: Quality Education), this study investigates the predictive capacity of kinesthetic profiles—encompassing both intelligence and learning styles—on sports at-titudes and academic achievement among adolescents. Employing a quantitative cross-sectional design, data were collected from a substantial sample of 695 adolescents. The regression analyses revealed a critical pedagogical distinction: unlike kinesthetic in-telligence, the kinesthetic learning style emerged as the paramount predictor of sports at-titudes (β=.612), explaining a substantial 42.3% of the total variance. Furthermore, a sig-nificant positive correlation was identified between kinesthetic traits and academic per-formance, challenging the traditional dichotomy between physical and cognitive devel-opment. These findings advocate for a strategic paradigm shift from "one-size-fits-all" in-struction to kinesthetic-based pedagogies that align with students' sensory preferences. Consequently, integrating movement-oriented strategies into curricula is proposed not merely as an instructional choice, but as a vital sustainable education strategy to foster both academic excellence and the lifelong physical and mental well-being of the next generation.

Abstract: Bladder cancer (BCa) is a major global urinary tract malignancy characterized by high incidence, frequent recurrence, and significant mortality. Early diagnosis is crucial for improving prognosis and minimizing invasive procedures; however, current standard techniques, cystoscopy and urine cytology, are limited by invasiveness, cost, low sensitivity, and subjectivity. This has spurred the development of non‑invasive diagnostic strategies based on urine analysis. This review highlights five emerging approaches: AI‑augmented urine cytology, genomic biomarker assays (e.g., PCR and NGS for mutations and copy‑number variations), DNA methylation profiling, RNA biomarkers (mRNA, miRNA, lncRNA), and protein/peptide/metabolite detection utilizing ELISA, SERS, nanozymes, and mass spectrometry. We assess the diagnostic accuracy, innovations, and clinical potential of each, while addressing persisting issues such as lack of standardization, high costs, and insufficient sensitivity for early‑stage lesions. Future directions include integrating multi‑omics data with AI, advancing point‑of‑care devices, and conducting large‑scale multicenter trials. Together, these developments promise to shift BCa management toward molecular‑based early detection, enabling more precise, non‑invasive, and personalized patient care.

Abstract: This review examines the potential impact of potato biofortification on boosting climate resilience and enhancing the nutritional content of potato tubers to combat hidden hunger. It also explores future possibilities for biofortified potatoes as a food source during space travel or colonization. Widespread mineral deficiencies are prevalent globally, particularly in developing countries. Additionally, climate change could adversely affect potato production and soil nutrient absorption. In this context, developing breeding methods to develop cultivars that respond better to biofortification amid climate change is essential. These cultivars may be physiologically efficient at absorbing and transporting minerals into tubers. The review covers various approaches, including identifying germplasm accessions with enhanced micronutrient storage, understanding mechanisms of micronutrient uptake and translocation, and pinpointing genes related to micronutrient, oligopeptide transport, and lignads. It also discusses in vitro selection and screening of calli with improved capacity for micronutrient absorption and transport.