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Joint effect of uncertainty-of-outcome and calorie content on food preference
Abstract In today’s market, mystery boxes (where the contents are uncertain) introduce uncertainty concerning the outcome, offering the consumer the chance to receive one of several possible foods. However, the role of uncertainty-of-outcome induced in food preference is unclear. This research investigated how uncertainty-of-outcome affects food preference for high- versus low-calorie food products, and its cognitive and neural mechanisms. Fifty-eight participants completed a binary-choice task, deciding between a certain option (a known food) and an uncertain option (an unknown food hidden in a mystery box), and rated the expected tastiness (subjective value) of each food. The participants exhibited a stronger preference for uncertain options in the low- (vs. high-) calorie case. Using the drift-diffusion model, the drift rate was higher in the low- (vs. high-) calorie condition. Moreover, a larger frontal N2 amplitude and smaller frontal P3 and LPP amplitudes were detected in the low- (vs. high-) calorie condition during the binary-choice task. Besides, frontal LPP amplitudes were negatively correlated with drift rate, suggesting that more cognitive effort was required to accumulate evidence to make a decision. Additionally, high-calorie foods elicited larger frontal alpha event-related desynchronization than low-calorie foods, thus suggesting participants require more evidence and effort in value comparison during the decision-making. This research highlights how uncertainty-of-outcome enhances the reward value of food, especially low-calorie foods, and thus impacts food preference, providing insights for developing marketing and public health strategies.
Genetic Improvement of grass pea (Lathyrus sativus L.) through gamma-ray-induced mutagenesis: evaluation of M₄ progenies for yield, agronomic traits, and low ODAP content
Abstract Grass pea ( Lathyrus sativus L.) is a protein-rich legume widely cultivated in drought-prone areas of Asia and Africa. Despite its resilience and nutritional value, Lathyrus suffers from limited genetic variability and the persistent problem of β-ODAP toxicity, which restricts consumption and warrants focused breeding initiatives. Developing high-yielding, low-ODAP varieties is critical for food safety and agricultural productivity. The present study employed gamma irradiation (250, 300, 350 Gy) to induce mutagenesis in seeds of cultivar NLK-73. Through successive generational selection (up to M₄), 29 promising mutants were evaluated in a randomized block design. Phenotypic and yield attributes were measured, along with ODAP quantification using spectrophotometry. Data analysis included ANOVA, estimation of genetic parameters, heritability, and genetic advance. Significant genetic variability was observed among M₄ mutants for all evaluated traits. The analysis of variance indicated highly significant differences ( p < 0.01) among genotypes for days to flowering, maturity, plant height, branches/plant, pods/plant, 100-seed weight, seed yield, and ODAP content. High heritability (> 60%) and substantial genetic advance were found for key traits such as branches and pods per plant, suggesting additive genetic action. Ten mutants (notably NLM-12, NLM-20, NLM-23) surpassed checks in seed yield (23–24.5 g/plant vs. 13.9 g/plant) with proportionately lower ODAP content, marking them as candidates for breeding programs and further evaluation. Gamma ray mutagenesis effectively broadened variability in Lathyrus sativus , enabling selection of superior M₄ mutants with enhanced yield and reduced ODAP content. The results suggest the feasibility of developing safer, high-yielding grass pea cultivars, warranting further validation. Adoption of mutation breeding should continue for rapid improvement of grass pea, focusing on reducing β-ODAP to trace levels while maximizing germplasm diversity and yield. Multi-location field trials are recommended to confirm stability of desirable traits. Molecular characterization and marker-assisted selection to expedite breeding for low-ODAP, high-protein lines is warranted. Exploration of alternative mutagens and advanced genomic tools will facilitate precise genetic improvement.
Development and evaluation of a multistage transfer learning framework for robust medical image analysis
miR-320a enhances radiosensitivity in non-small cell lung cancer by targeting RAD51 and modulating ferroptosis via GPX4
Association of pre-endoscopic fresh frozen plasma transfusion with clinical outcomes in patients with acute upper gastrointestinal bleeding and mild coagulopathy: a two-center retrospective cohort study
A cost-optimized medical digital twin framework for secure and efficient patient data management in smart healthcare
Patient vs. physician narratives on refractive surgery in Turkish YouTube videos: a comparative reflexive thematic analysis
Optimized battery energy management using an improved type-2 fuzzy logic approach
A new late Cretaceous squamate from Patagonia sheds light on Gondwanan diversity
Influence of fruit maturity and slimy seed coat on seed traits and germination of horned melon (Cucumis metuliferus E. Mey. Ex Schrad.)
Theoretical analysis of prestressed unequal-walled rectangular concrete-filled steel beams
Design and implementation of a deep learning framework for automated crop classification and health diagnosis in precision agriculture
Machine learning-based MPPT integration with quadratic double-extended DC-DC converter for grid-connected PV-powered BLDC electric vehicles
Auto-arrange buildings in urban planning with DQN
Experimental study on layered cemented tailings backfill damage and failure mechanisms under blast loading
Life cycle assessment of MSW-to-biofuel conversion pathways: a comparative analysis
Abstract Rapidly increasing municipal solid waste (MSW) generation, reaching 160,039 tonnes per day in India, and the environmental burdens of conventional disposal highlight the need for efficient waste-to-biofuel solutions. This study conducts a comparative Life Cycle Assessment (LCA) of seven MSW-to-biofuel pathways: open landfilling, landfill gas recovery, incineration, torrefaction, gasification, hydrothermal carbonization, and integrated gasification. Using a functional unit of 1 tonne of MSW, the assessment quantifies environmental impacts across five midpoint categories (GWP, SOD, FEP, LU, WC) following ISO 14040/44 guidelines. The methodology integrates experimental MSW characterization, national waste statistics, and ± 10% sensitivity analysis to address uncertainties in methane capture, energy recovery, and grid displacement. Results show substantial differences across pathways, with integrated gasification (MIG) emerging as the most sustainable option, achieving an avoided GWP of − 1095 kg CO 2 eq, water savings of − 1125.61 m 3 , and the lowest land-use requirement (− 32.39 m 2 ·a). Material Flow Analysis further validates MIG’s superior mass-energy conversion when combined with recycling. The study’s novelty lies in its first holistic comparison of seven thermochemical and conventional MSW pathways tailored to India, integrating LCA and MFA evidence. the findings support prioritizing advanced thermochemical routes, particularly MIG, for climate-resilient, resource-efficient, and circular MSW management.