Should I keep studying? Consequences of a decision to stop learning in young and older adults

In situations of cognitive overload, the role of a metacognitive decision to stop learning is of utmost importance. We investigated how young and older adults decide to stop learning as a strategy for maximizing memory performance when they face to-be-learned material exceeding their memory capability. People may decide to stop learning for two main reasons: they experience a growing feeling of disfluency as a learning episode progresses and/or they perceive such a decision to be beneficial for future memory performance. In Experiments 1 and 2, participants studied lists of 50 words. The majority of young and older adults stopped learning in conditions where they were allowed to do so. This decision, counterintuitively, decreased the number of recalled words. Crucially, a similar number of young and older adults stopped the presentation of to-be-remembered material, and both age groups suffered comparable consequences in their memory performance. In Experiments 3a and 3b, participants read an experimental scenario and decided whether they would stop learning based on this description alone. People in different age groups predicted their metacognitive decisions similarly. However, participants’ forecasted performance did not reflect the negative influence of these decisions. Regardless of their age, people tend to make a suboptimal decision to stop learning, unaware of its negative consequences. Together, our results suggest that young and older adults can exert metamemory control to similar degrees even though their decisions may not be beneficial for memory performance. (PsycInfo Database Record (c) 2021 APA, all rights reserved

A modeling study of the impact of treatment policies on the evolution of resistance in sea lice on salmon farms

Salmonid aquaculture is an important source of nutritious food with more than 2 million tonnes of fish produced each year. In most salmon producing countries, sea lice represent a major barrier to the sustainability of salmonid aquaculture. This issue is exacerbated by widespread resistance to chemical treatments on both sides of the Atlantic. Regulation for sea lice management mostly involves reporting lice counts and treatment thresholds, which depending on interpretation may encourage preemptive treatments. We have developed a stochastic simulation model of sea lice infestation including the lice life-cycle, genetic resistance to treatment, a wildlife reservoir, salmon growth and stocking practices in the context of infestation, and coordination of treatment between farms. Farms report infestation levels to a central organisation, and may then cooperate or not when coordinated treatment is triggered. Treatment practice then impacts the level of resistance in the surrounding sea lice population. Our simulation finds that treatment drives selection for resistance and coordination between managers is key. We also find that position in the hydrologically-derived network of farms can impact individual farm infestation levels and the topology of this network can impact overall infestation and resistance. We show how coordination and triggering of treatment alongside varying hydrological topology of farm connections affects the evolution of lice resistance, and thus optimise salmon quality within socioeconomic and environmental constraints. Network topology drives infestation levels in cages, treatments, and hence treatment-driven resistance. Thus farmer behaviour may be highly dependent on hydrologically position and local level of infestation

A modeling study of the impact of treatment policies on the evolution of resistance in sea lice on salmon farms

Salmonid aquaculture is an important source of nutritious food with more than 2 million tonnes of fish produced each year. In most salmon producing countries, sea lice represent a major barrier to the sustainability of salmonid aquaculture. This issue is exacerbated by widespread resistance to chemical treatments on both sides of the Atlantic. Regulation for sea lice management mostly involves reporting lice counts and treatment thresholds, which depending on interpretation may encourage preemptive treatments. We have developed a stochastic simulation model of sea lice infestation including the lice life-cycle, genetic resistance to treatment, a wildlife reservoir, salmon growth and stocking practices in the context of infestation, and coordination of treatment between farms. Farms report infestation levels to a central organisation, and may then cooperate or not when coordinated treatment is triggered. Treatment practice then impacts the level of resistance in the surrounding sea lice population. Our simulation finds that treatment drives selection for resistance and coordination between managers is key. We also find that position in the hydrologically-derived network of farms can impact individual farm infestation levels and the topology of this network can impact overall infestation and resistance. We show how coordination and triggering of treatment alongside varying hydrological topology of farm connections affects the evolution of lice resistance, and thus optimise salmon quality within socioeconomic and environmental constraints. Network topology drives infestation levels in cages, treatments, and hence treatment-driven resistance. Thus farmer behaviour may be highly dependent on hydrologically position and local level of infestation

Evaluation of acidogenesis products’ effect on biogas production performed with metagenomics and isotopic approaches

Background: During the acetogenic step of anaerobic digestion, the products of acidogenesis are oxidized to substrates for methanogenesis: hydrogen, carbon dioxide and acetate. Acetogenesis and methanogenesis are highly interconnected processes due to the syntrophic associations between acetogenic bacteria and hydrogenotrophic methanogens, allowing the whole process to become thermodynamically favorable. The aim of this study is to determine the influence of the dominant acidic products on the metabolic pathways of methane formation and to find a core microbiome and substrate-specific species in a mixed biogas-producing system. Results: Four methane-producing microbial communities were fed with artificial media having one dominant component, respectively, lactate, butyrate, propionate and acetate, for 896 days in 3.5-L Up-flow Anaerobic Sludge Blanket (UASB) bioreactors. All the microbial communities showed moderately different methane production and utilization of the substrates. Analyses of stable carbon isotope composition of the fermentation gas and the substrates showed differences in average values of δ13C(CH4) and δ13C(CO2) revealing that acetate and lactate strongly favored the acetotrophic pathway, while butyrate and propionate favored the hydrogenotrophic pathway of methane formation. Genome-centric metagenomic analysis recovered 234 Metagenome Assembled Genomes (MAGs), including 31 archaeal and 203 bacterial species, mostly unknown and uncultivable. MAGs accounted for 54%–67% of the entire microbial community (depending on the bioreactor) and evidenced that the microbiome is extremely complex in terms of the number of species. The core microbiome was composed of Methanothrix soehngenii (the most abundant), Methanoculleus sp., unknown Bacteroidales and Spirochaetaceae. Relative abundance analysis of all the samples revealed microbes having substrate preferences. Substrate-specific species were mostly unknown and not predominant in the microbial communities. Conclusions: In this experimental system, the dominant fermentation products subjected to methanogenesis moderately modified the final effect of bioreactor performance. At the molecular level, a different contribution of acetotrophic and hydrogenotrophic pathways for methane production, a very high level of new species recovered, and a moderate variability in microbial composition depending on substrate availability were evidenced. Propionate was not a factor ceasing methane production. All these findings are relevant because lactate, acetate, propionate and butyrate are the universal products of acidogenesis, regardless of feedstock

Large needle aspiration biopsy and galectin-3 determination in selected thyroid nodules with indeterminate FNA-cytology

Thyroid fine-needle aspiration biopsy (FNA)-cytology is widely used for the preoperative characterisation of thyroid nodules but this task is difficult for follicular lesions, which often remain undefined. We propose a strategy for improving the preoperative characterisation of selected follicular thyroid proliferations, which is based on large needle aspiration biopsy (LNAB) and galectin-3 expression analysis. Eighty-five thyroid specimens were obtained by LNAB (20-gauge needles) from thyroid nodules with indeterminate follicular FNA-cytology. Aspirated material was processed as a tissue microbiopsy to obtain cell blocks for both cyto/histo-morphological evaluation and galectin-3 expression analysis, by using a purified monoclonal antibody to galectin-3 and a biotin-free immunoperoxidase staining method. Preoperative diagnosis was compared to the final histology. LNAB and cell-block technique allow a preliminary distinction between nodules with a homogeneous microfollicular/trabecular structure, as frequently observed in tumours, and lesions with mixed normo–micro–macrofollicular architecture, as observed in goitre. Furthermore, LNAB provides optimal substrates for galectin-3 expression analysis. Among 85 cases tested, 14 galectin-3-positive cases were discovered preoperatively (11 thyroid cancers and three adenomas confirmed at the final histology), whereas galectin-3-negative cases were 71 (one carcinoma and 70 benign proliferations at the final histology). Sensitivity, specificity and diagnostic accuracy of this integrated morphologic and phenotypic diagnostic approach were 91.6, 97.2 and 95.3%, respectively. In conclusion, LNAB plus galectin-3 expression analysis when applied preoperatively to selected thyroid nodules candidate to surgery can potentially reduce unnecessary thyroid resections