Synergistic anticancer effects of co-delivery of linc-RoR siRNA and curcumin using polyamidoamine dendrimers against breast cancer

Breast cancer resistance to chemotherapy necessitates novel combination therapeutic approaches. Linc-RoR is a long intergenic noncoding RNA that regulates stem cell differentiation and promotes metastasis and invasion in breast cancer. Herein, we report a dual delivery system employing polyamidoamine dendrimers to co-administer the natural compound curcumin and linc-RoR siRNA for breast cancer treatment. Polyamidoamine dendrimers efficiently encapsulated curcumin and formed complexes with linc-RoR siRNA at an optimal N/P ratio. In MCF-7 breast cancer cells, the dendriplexes were effectively internalized and the combination treatment synergistically enhanced cytotoxicity, arresting the cell cycle at the G1 phase and inducing apoptosis. Linc-RoR gene expression was also significantly downregulated. Individual treatments showed lower efficacy, indicating synergism between components. Mechanistic studies are warranted to define the molecular underpinnings of this synergistic interaction. Our findings suggest dual delivery of linc-RoR siRNA and curcumin via dendrimers merits further exploration as a personalized therapeutic approach for overcoming breast cancer resistance.

Training-Dependent Gradients of Timescales of Neural Dynamics in the Primate Prefrontal Cortex and Their Contributions to Working Memory

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, for example, from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales are inherent properties of neurons and/or depend on training in a specific task and if the latter, how their modulations contribute to task performance. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.

Dissociable Contributions of Basolateral Amygdala and Ventrolateral Orbitofrontal Cortex to Flexible Learning Under Uncertainty

Reversal learning measures the ability to form flexible associations between choice outcomes with stimuli and actions that precede them. This type of learning is thought to rely on several cortical and subcortical areas, including the highly interconnected orbitofrontal cortex (OFC) and basolateral amygdala (BLA), and is often impaired in various neuropsychiatric and substance use disorders. However, the unique contributions of these regions to stimulus- and action-based reversal learning have not been systematically compared using a chemogenetic approach particularly before and after the first reversal that introduces new uncertainty. Here, we examined the roles of ventrolateral OFC (vlOFC) and BLA during reversal learning. Male and female rats were prepared with inhibitory designer receptors exclusively activated by designer drugs targeting projection neurons in these regions and tested on a series of deterministic and probabilistic reversals during which they learned about stimulus identity or side (left or right) associated with different reward probabilities. Using a counterbalanced within-subject design, we inhibited these regions prior to reversal sessions. We assessed initial and pre-/post-reversal changes in performance to measure learning and adjustments to reversals, respectively. We found that inhibition of the ventrolateral orbitofrontal cortex (vlOFC), but not BLA, eliminated adjustments to stimulus-based reversals. Inhibition of BLA, but not vlOFC, selectively impaired action-based probabilistic reversal learning, leaving deterministic reversal learning intact. vlOFC exhibited a sex-dependent role in early adjustment to action-based reversals, but not in overall learning. These results reveal dissociable roles for BLA and vlOFC in flexible learning and highlight a more crucial role for BLA in learning meaningful changes in the reward environment.

Dissociable contributions of basolateral amygdala and ventrolateral orbitofrontal cortex to flexible learning under uncertainty

Reversal learning measures the ability to form flexible associations between choice outcomes with stimuli and actions that precede them. This type of learning is thought to rely on several cortical and subcortical areas, including highly interconnected orbitofrontal cortex (OFC) and basolateral amygdala (BLA), and is often impaired in various neuropsychiatric and substance use disorders. However, unique contributions of these regions to stimulus- and action-based reversal learning have not been systematically compared using a chemogenetic approach and particularly before and after the first reversal that introduces new uncertainty. Here, we examined the roles of ventrolateral OFC (vlOFC) and BLA during reversal learning. Male and female rats were prepared with inhibitory DREADDs targeting projection neurons in these regions and tested on a series of deterministic and probabilistic reversals during which they learned about stimulus identity or side (left or right) associated with different reward probabilities. Using a counterbalanced within-subject design, we inhibited these regions prior to reversal sessions. We assessed initial and pre-post reversal changes in performance to measure learning and adjustments to reversals, respectively. We found that inhibition of vlOFC, but not BLA, eliminated adjustments to stimulus-based reversals. Inhibition of BLA, but not vlOFC, selectively impaired action-based probabilistic reversal learning, leaving deterministic reversal learning intact. vlOFC exhibited a sex-dependent role in early adjustment to action-based reversals, but not in overall learning. These results reveal dissociable roles for BLA and vlOFC in flexible learning and highlight a more crucial role for BLA in learning meaningful changes in the reward environment.

Training-dependent gradients of timescales of neural dynamics in the primate prefrontal cortex and their contributions to working memory

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, e.g., from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales depend on training in a specific task and/or are an inherent property of neurons, and whether more fine-grained hierarchies of timescales exist within specific cortical regions. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.

Significance statement

Recent studies have demonstrated that neural responses exhibit dynamics with different timescales that follow a certain order or hierarchy across cortical areas. While the hierarchy of timescales is consistent across different tasks, it is unknown if these timescales emerge only after training or if they represent inherent properties of neurons. To answer this question, we estimated multiple timescales in neural response across five subregions of the monkeys' lateral prefrontal cortex before and after training on a working-memory task. Our results provide evidence for fine-grained gradients related to certain neural dynamics. Moreover, we show that these timescales depend on and can be modulated by training in a cognitive task, and contribute to encoding of task-relevant information at single-cell and population levels.

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

Confirmatory factor analysis of the BRIEF2 in a sample of youth with Down syndrome

BACKGROUND

The factor structure of the Behavior Rating Inventory of Executive Function, second edition (BRIEF2) has been widely examined in both typically developing children and specific clinical samples. Despite the frequent use of the BRIEF2 for measuring executive functioning in individuals with Down syndrome, no study has investigated the factorial validity or dimensionality of the BRIEF2 in this population. This study aimed to address this notable gap in the literature.

METHODS

Parents of 407 children and youth with Down syndrome aged 6-18 years completed the BRIEF2 as part of different studies led by six sites. Three competing models proposed by previous studies were analysed using Confirmatory Factor Analysis: the theoretical structure of the BRIEF2 where the scales were constrained to load on three factors labelled as Cognitive, Behavioral, and Emotional Regulation, a two-factor correlated model with the merged Behavioral and Emotional regulation, and a single-factor model.

RESULTS

The three-factor model provided a better fit than the one- and two-factor models, yet a large correlation was observed between Behavioural and Emotional regulation factors. The results provide meaningful explanatory value for the theoretical structure of the BRIEF2. However, the Behavioral and Emotional regulation factors might be less differentiated and the two-factor structure of the BRIEF2 may also make theoretical and empirical sense.

CONCLUSIONS

Although more studies are needed to further examine the factor structure of the BRIEF2 in youth with Down syndrome, this investigation provides preliminary support for the interpretation of the three executive function index scores provided by the BRIEF2: Cognitive, Behavioral, and Emotional Regulation.

Cephalexin degradation initiated by OH radicals: theoretical prediction of the mechanisms and the toxicity of byproducts

In this work, the density functional theory is used to study the local reactivity of cephalexin (CLX) to radical attack and explain the mechanism of the reaction between CLX and hydroxyl radical attack leading to degradation byproducts. The reaction between •OH and CLX is supposed to lead to either an addition of a hydroxyl radical or an abstraction of a hydrogen. The results showed that the affinity of cephalexin for addition reactions increases as it passes from the gas to the aqueous phase and decreases as it passes from the neutral to the ionized form. Thermodynamic data confirmed that OH addition radicals (R_add) are thermodynamically favored over H abstraction radicals (R_abs). The ecotoxicity assessments of CLX and its byproducts are estimated from the acute toxicities toward green algae, Daphnia, and fish. The formation of byproducts is safe for aquatic organisms, and only one byproduct is harmful to Daphnia.

Prediction of COVID-19 manipulation by selective ACE inhibitory compounds of Potentilla reptant root: In silico study and ADMET profile

In the novel SARS-CoV-2 (COVID-19) as a global emergency event, the main reason of the cardiac injury from COVID-19 is angiotensin-converting enzyme 2 (ACE2) targeting in SARS-CoV-2 infection. The inhibition of ACE2 induces an increase in the angiotensin II (Ang II) and the angiotensin II receptor type 1 (AT1R) leading to impaired cardiac function or cardiac inflammatory responses. The ethyl acetate fraction of Potentilla reptans L. root can rescue heart dysfunction, oxidative stress, cardiac arrhythmias and apoptosis. Therefore, isolated components of P. reptans evaluated to identify natural anti-SARS-CoV-2 agents via molecular docking.

In silico molecular docking study were carried out using the Auto Dock software on the isolated compounds of Potentilla reptans root. The protein targets of selective ACE and others obtained from Protein Data Bank (PDB). The best binding pose between amino acid residues involved in active site of the targets and compounds was discovered via molecular docking. Furthermore, ADMET properties of the compounds were evaluated.

The triterpenoids of P. reptans showed more ACE inhibitory potential than catechin in both domains. They were selective on the nACE domain, especially compound 5. Also, the compound 5 & 6 had the highest binding affinity toward active site of nACE, cACE, AT1R, ACE2, and TNF-α receptors. Meanwhile, compound 3 showed more activity to inhibit TXA2. Drug likeness and ADMET analysis showed that the compounds passed the criteria of drug likeness and Lipinski rules. The current study depicted that P. reptans root showed cardioprotective effect in COVID-19 infection and manipulation of angiotensin II-induced side effects.

Computational models of adaptive behavior and prefrontal cortex

The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus-action associations through rewards; (2) predictive models learning stimulus- and/or action-outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

Computational mechanisms of distributed value representations and mixed learning strategies

Learning appropriate representations of the reward environment is challenging in the real world where there are many options, each with multiple attributes or features. Despite existence of alternative solutions for this challenge, neural mechanisms underlying emergence and adoption of value representations and learning strategies remain unknown. To address this, we measure learning and choice during a multi-dimensional probabilistic learning task in humans and trained recurrent neural networks (RNNs) to capture our experimental observations. We find that human participants estimate stimulus-outcome associations by learning and combining estimates of reward probabilities associated with the informative feature followed by those of informative conjunctions. Through analyzing representations, connectivity, and lesioning of the RNNs, we demonstrate this mixed learning strategy relies on a distributed neural code and opponency between excitatory and inhibitory neurons through value-dependent disinhibition. Together, our results suggest computational and neural mechanisms underlying emergence of complex learning strategies in naturalistic settings.

Metabolomic biomarkers of low BMD: a systematic review

Due to the metabolic nature of osteoporosis, this study was conducted to identify metabolomic studies investigating the metabolic profile of low bone mineral density (BMD) and osteoporosis. A comprehensive systematic literature search was conducted through PubMed, Web of Science, Scopus, and Embase databases up to April 08, 2020, to identify observational studies with cross-sectional or case-control designs investigating the metabolic profile of low BMD in adults using biofluid specimen via metabolomic platform. The quality assessment panel specified for the "omics"-based diagnostic research (QUADOMICS) tool was used to estimate the methodologic quality of the included studies. Ten untargeted and one targeted approach metabolomic studies investigating biomarkers in different biofluids through mass spectrometry or nuclear magnetic resonance platforms were included in the systematic review. Some metabolite panels, rather than individual metabolites, showed promising results in differentiating low BMD from normal. Candidate metabolites were of different categories including amino acids, followed by lipids and carbohydrates. Besides, certain pathways were suggested by some of the studies to be involved. This systematic review suggested that metabolic profiling could improve the diagnosis of low BMD. Despite valuable findings attained from each of these studies, there was great heterogeneity regarding the ethnicity and age of participants, samples, and the metabolomic platform. Further longitudinal studies are needed to validate the results and confirm the predictive role of metabolic profile on low BMD and fracture. It is also mandatory to address and minimize the heterogeneity in future studies by using reliable quantitative methods. Summary: Due to the metabolic nature of osteoporosis, researchers have considered metabolomic studies recently. This systematic review showed that metabolic profiling including different categories of metabolites could improve the diagnosis of low BMD. However, great heterogeneity was observed and it is mandatory to address and minimize the heterogeneity in future studies.

Entropy-based metrics for predicting choice behavior based on local response to reward

For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching behavior. To address this, we developed metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct more accurate models of choice. Together, our entropy-based metrics provide a model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.

Timescales of Cognition in the Brain

We live in a world that changes on many timescales. To learn and make decisions appropriately, the human brain has evolved to integrate various types of information, such as sensory evidence and reward feedback, on multiple timescales. This is reflected in cortical hierarchies of timescales consisting of heterogeneous neuronal activities and expression of genes related to neurotransmitters critical for learning. We review the recent findings on how timescales of sensory and reward integration are affected by the temporal properties of sensory and reward signals in the environment. Despite existing evidence linking behavioral and neuronal timescales, future studies must examine how neural computations at multiple timescales are adjusted and combined to influence behavior flexibly.

Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges

Walking/gait speed is a key measure for daily mobility characterization. To date, various studies have attempted to design algorithms to estimate walking speed using an inertial sensor worn on the lower back, which is considered as a proper location for activity monitoring in daily life. However, these algorithms were rarely compared and validated on the same datasets, including people with different preferred walking speed. This study implemented several original, improved, and new algorithms for estimating cadence, step length and eventually speed. We designed comprehensive cross-validation to compare the algorithms for walking slow, normal, fast, and using walking aids. We used two datasets, including reference data for algorithm validation from an instrumented mat (40 subjects) and shanks-worn inertial sensors (88 subjects), with normal and impaired walking patterns. The results showed up to 50% performance improvements. Training of algorithms on data from people with different preferred speeds led to better performance. For the slow walkers, an average RMSE of 2.5 steps/min, 0.04 m, and 0.10 m/s were respectively achieved for cadence, step length, and speed estimation. For normal walkers, the errors were 3.5 steps/min, 0.08 m, and 0.12 m/s. An average RMSE of 1.3 steps/min, 0.05 m, and 0.10 m/s were also observed on fast walkers. For people using walking aids, the error significantly increased up to an RMSE of 14 steps/min, 0.18 m, and 0.27 m/s. The results demonstrated the robustness of the proposed combined speed estimation approach for different speed ranges. It achieved an RMSE of 0.10, 0.18, 0.15, and 0.32 m/s for slow, normal, fast, and using walking aids, respectively.

Unique features of stimulus-based probabilistic reversal learning

Reversal learning paradigms are widely used assays of behavioral flexibility with their probabilistic versions being more amenable to studying integration of reward outcomes over time. Prior research suggests differences between initial and reversal learning, including higher learning rates, a greater need for inhibitory control, and more perseveration after reversals. However, it is not well-understood what aspects of stimulus-based reversal learning are unique to reversals, and whether and how observed differences depend on reward probability. Here, we used a visual probabilistic discrimination and reversal learning paradigm where male and female rats selected between a pair of stimuli associated with different reward probabilities. We compared accuracy, rewards collected, omissions, latencies, win-stay/lose-shift strategies, and indices of perseveration across two different reward probability schedules. We found that discrimination and reversal learning are behaviorally more unique than similar: Fit of choice behavior using reinforcement learning models revealed a lower sensitivity to the difference in subjective reward values (greater exploration) and higher learning rates for the reversal phase. We also found latencies to choose the better option were greater in females than males, but only for the reversal phase. Further, animals employed more win-stay strategies during early discrimination and increased perseveration during early reversal learning. Interestingly, a consistent reward probability group difference emerged with a richer environment associated with longer reward collection latencies than a leaner environment. Future studies should systematically compare the neural correlates of fine-grained behavioral measures to reveal possible dissociations in how the circuitry is recruited in each phase. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Investigations of adsorption behavior and anti-inflammatory activity of glycine functionalized Al12N12 and Al12ON11 fullerene-like cages

The adsorption behavior of the amino acid, glycine (Gly), via the carboxyl, hydroxyl, and amino groups onto the surfaces of Al₁₂N₁₂ and Al₁₆N₁₆ fullerene-like cages were computationally evaluated by the combination of density functional theory (DFT) and molecular docking studies. It was found that Gly can chemically bond with the Al₁₂N₁₂ and Al₁₆N₁₆ fullerene-like cages as its amino group being more favorable to interact with the aluminum atoms of the adsorbents compared to carboxyl and hydroxyl groups. Oxygen and carbon doping were reported to reduce steric hindrance for Glycine interaction at Al site of Al₁₂ON₁₁/Gly and Al₁₂CN₁₁/Gly complexes. Interaction was further enhanced by oxygen doping due to its greater electron withdrawing effect. Herein, the Al₁₂ON₁₁/Gly complex where two carbonyl groups of Gly are bonded to the aluminum atoms of the Al₁₂N₁₂ fullerene-like cage is the most stable interaction configuration showing ∆_adsH and ∆_adsG values of -81.74 kcal/mol and -66.21 kcal/mol, respectively. Computational studies also revealed the frequency shifts that occurred due to the interaction process. Molecular docking analysis revealed that the Al₁₂N₁₂/Gly (-11.7 kcal/mol) and the Al₁₂ON₁₁/Gly (-9.2 kcal/mol) complexes have a good binding affinity with protein tumor necrosis factor alpha (TNF-α). TNF-α was implicated as a key cytokine in various diseases, and it has been a validated therapeutic target for the treatment of rheumatoid arthritis. These results suggest that the Al₁₂N₁₂/Gly complex in comparison with the Al₁₆N₁₆/Gly, Al₁₂ON₁₁/Gly, and the Al₁₂CN₁₁/Gly complexes could be efficient inhibitors of TNF-α.

Separable Influences of Reward on Visual Processing and Choice

Primate vision is characterized by constant, sequential processing and selection of visual targets to fixate. Although expected reward is known to influence both processing and selection of visual targets, similarities and differences between these effects remain unclear mainly because they have been measured in separate tasks. Using a novel paradigm, we simultaneously measured the effects of reward outcomes and expected reward on target selection and sensitivity to visual motion in monkeys. Monkeys freely chose between two visual targets and received a juice reward with varying probability for eye movements made to either of them. Targets were stationary apertures of drifting gratings, causing the end points of eye movements to these targets to be systematically biased in the direction of motion. We used this motion-induced bias as a measure of sensitivity to visual motion on each trial. We then performed different analyses to explore effects of objective and subjective reward values on choice and sensitivity to visual motion to find similarities and differences between reward effects on these two processes. Specifically, we used different reinforcement learning models to fit choice behavior and estimate subjective reward values based on the integration of reward outcomes over multiple trials. Moreover, to compare the effects of subjective reward value on choice and sensitivity to motion directly, we considered correlations between each of these variables and integrated reward outcomes on a wide range of timescales. We found that, in addition to choice, sensitivity to visual motion was also influenced by subjective reward value, although the motion was irrelevant for receiving reward. Unlike choice, however, sensitivity to visual motion was not affected by objective measures of reward value. Moreover, choice was determined by the difference in subjective reward values of the two options, whereas sensitivity to motion was influenced by the sum of values. Finally, models that best predicted visual processing and choice used sets of estimated reward values based on different types of reward integration and timescales. Together, our results demonstrate separable influences of reward on visual processing and choice, and point to the presence of multiple brain circuits for the integration of reward outcomes.

Learning from Others, but with What Confidence?

A recent study by Zhang and Gläscher (2020) in humans examines learning from one's own versus others' actions under reward uncertainty. Comparing findings from this and non-human studies on learning under perceptual uncertainty suggests a unified role for confidence in learning under different types of uncertainty across mammalian brains.

Learning arbitrary stimulus-reward associations for naturalistic stimuli involves transition from learning about features to learning about objects

Most cognitive processes are studied using abstract or synthetic stimuli with specific features to fully control what is presented to subjects. However, recent studies have revealed enhancements of cognitive capacities (such as working memory) when processing naturalistic versus abstract stimuli. Using abstract stimuli constructed from distinct visual features (e.g., color and shape), we have recently shown that human subjects can learn multidimensional stimulus-reward associations via initially estimating reward value of individual features (feature-based learning) before gradually switching to learning about reward value of individual stimuli (object-based learning). Here, we examined whether similar strategies are adopted during learning about naturalistic stimuli that are clearly perceived as objects (instead of a combination of features) and contain both task-relevant and irrelevant features. We found that similar to learning about abstract stimuli, subjects initially adopted feature-based learning more strongly before transitioning to object-based learning. However, there were three key differences between learning about naturalistic and abstract stimuli. First, compared with abstract stimuli, the initial learning strategy was less feature-based for naturalistic stimuli. Second, subjects transitioned to object-based learning faster for naturalistic stimuli. Third, unexpectedly, subjects were more likely to adopt feature-based learning for naturalistic stimuli, both at the steady state and overall. These results suggest that despite the stronger tendency to perceive naturalistic stimuli as objects, which leads to greater likelihood of using object-based learning as the initial strategy and a faster transition to object-based learning, the influence of individual features on learning is stronger for these stimuli such that ultimately the object-based strategy is adopted less. Overall, our findings suggest that feature-based learning is a general initial strategy for learning about reward value of all types of multi-dimensional stimuli.

Impacts of fire cues on germination of Brassica napus L. seeds with high and low secondary dormancy

Agricultural burning is used in farm management operations; however, information about the impact of fire cues on the release and/or induction of secondary dormancy in crop seeds is scarce. Seeds from two oilseed rape cultivars were induced for high (HD) or low (LD) secondary dormancy using polyethyleneglycol (PEG) pre-treatment, and their germination after exposure to various fire cues was compared to control PEG pre-treated and non-dormant seeds. Non-dormant seed germination was unaffected by various fire cues. Low doses of aerosol smoke released secondary dormancy in HD seeds, while higher doses increased dormancy of LD seeds. Dilute smoke water also released HD seed secondary dormancy, but concentrated smke water enhanced dormancy in both LD and HD seeds. The concentrated aqueous extracts from charred oilseed rape straw only promoted germination of HD seeds, while dilution inhibited LD seed germination. Heat shock (80 °C, 5 min) released secondary dormancy in HD seeds; however, higher temperatures and/or increased exposure time was associated with seed death. GC-MS analyses of smoke water revealed two butenolides and an array of monoaromatic hydroxybenzene compounds with potential germination inhibitor or promoter activity. The extent of secondary dormancy induction in seeds affects their subsequent responses to fire cues. Both aerosol smoke and smoke water have both germination promoter and inhibitor activity. Lacking any butenolides, aqueous extracts of charred straw contain a potential germination stimulating steroid, i.e. ergosterol. The significance of fire-derived cues on behaviour of oilseed rape seeds in the soil seed bank is discussed.

AB0175 INNOVATIVE PREPARATION OF CURCUMIN NANOPARTICLES TO IMPROVE ANTI-INFLAMMATORY EFFECT IN RHEUMATIC DISEASE

Background:

Curcumin (Cur) as a natural compound can be used in the wide spectrum of healthy functions and pharmacological activities [1-4]. It shows great promise for medication of various pro-inflammatory chronic illnesses [5]. In this study, we evaluate the ability of poly(lactide-co-glycolide)(PLGA) and different grads of PVA (polyvinyl alcohol) and lecithin as a drug delivery system for poorly soluble Cur

Objectives:

The goal of this study was to prepare and characterize Cur encapsulated PLGA and different grads of PVA and lecithin as an efficient nanocarrier for improve anti-inflammatory effect in rheumatic disease

Methods:

The PLGA nanospheres were formulated and then characterized for percent yield, encapsulation efficiency, surface morphology, and in vitro drug release profiles. At first, 6 mg of Cur was added to the organic phase including 24 mg of polymer dissolved in 5 mL of dichloromethane to constitute 1:4 (drug-to-polymer) ratios. Then, a mixture of PVA-lecithin (at about 5 cc) was added to maintain the stability of double emulsion droplets. The emulsion was continuously stirred at 300 rpm for 24 hours (at temperature of 37.5 ˚C) to evaporate the solvent, leaving behind the colloidal suspension of the drug-encapsulated nanoparticle in aqueous phase. The encapsulation of Cur into PLGA was characterized by Fourier transform infrared spectroscopy (FT-IR) and Transmission electron microscopy (TEM).

Results:

Our studies achieved the successful formation of smooth surface and spherical shape Cur encapsulated into PLGA nanoparticles by the TEM image confirmed. The particle size distribution demonstrated a range of 30 nm to 100 nm, with the mean particle size being 45 nm. FTIR study implies successful loading of Cur into the nanoparticles. We show high drug-loading efficiency about 98 ± 0.5% for 6% of Cur weight in total ingredients weight of PLGA (w/w). It was also seen that a slower sustained release of 10% CUR in 48 hours is observed with biocompatible PLGA in phosphate buffered saline (pH = 7.4). The MTT assay of the Cur-PLGA exhibited no cytotoxic effect on Normal mouse fibroblast cells (L-929) cell line. IC50 of Cur -PLGA increased 99.5% against Cur nanoparticles (33.57 ±0.62 µM) (P < 0.05).

Conclusion:

In this study, we constructed a novel preparation of curcumin nanoparticles with PLGA and different grads of PVA (polyvinyl alcohol) and lecithin to improve the bioavailability of CUR and PLGA exhibited no cytotoxic effect on L-929 cell line

References:

In this study, we constructed a novel preparation of curcumin nanoparticles with PLGA and different grads of PVA (polyvinyl alcohol) and lecithin to improve the bioavailability of CUR and PLGA exhibited no cytotoxic effect on L-929 cell line

Disclosure of Interests:

None declared

Adaptive learning under expected and unexpected uncertainty

The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them - at the computational and the neural level - is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.

Influence of the adsorption of toxic agents on the optical and electronic properties of B12N12 fullerene in the presence and absence of an external electric field

The interaction energies and optoelectronic properties of sarin (SF) and chlorosarin (SC) on the B₁₂N₁₂ with and without the presence of an electric field have been studied using density functional theory (DFT) calculations.

Influence of Expected Reward on Temporal Order Judgment

Perceptual decision-making has been shown to be influenced by reward expected from alternative options or actions, but the underlying neural mechanisms are currently unknown. More specifically, it is debated whether reward effects are mediated through changes in sensory processing, later stages of decision-making, or both. To address this question, we conducted two experiments in which human participants made saccades to what they perceived to be either the first or second of two visually identical but asynchronously presented targets while we manipulated expected reward from correct and incorrect responses on each trial. By comparing reward-induced bias in target selection (i.e., reward bias) during the two experiments, we determined whether reward caused changes in sensory or decision-making processes. We found similar reward biases in the two experiments indicating that reward information mainly influenced later stages of decision-making. Moreover, the observed reward biases were independent of the individual's sensitivity to sensory signals. This suggests that reward effects were determined heuristically via modulation of decision-making processes instead of sensory processing. To further explain our findings and uncover plausible neural mechanisms, we simulated our experiments with a cortical network model and tested alternative mechanisms for how reward could exert its influence. We found that our experimental observations are more compatible with reward-dependent input to the output layer of the decision circuit. Together, our results suggest that, during a temporal judgment task, reward exerts its influence via changing later stages of decision-making (i.e., response bias) rather than early sensory processing (i.e., perceptual bias).

Characterization of the anisotropic deformation of the right ventricle during open heart surgery

Digital Image Correlation (DIC) was used for studying the anisotropic behavior of the thin walled right ventricle of the human heart. Strains measured with Speckle Tracking Echocardiography (STE) were compared with the DIC data. Both DIC and STE were used to measure longitudinal strains of the right ventricle in the beginning of an open-heart surgery as well as after the cardiopulmonary bypass. Based on the results, the maximum end-systolic strains obtained with the DIC and STE change similarly during the surgery with less than 10% difference. The difference is largely due to the errors in matching the longitudinal direction in the two methods, sensitivity of the measurement to the positioning of the virtual extensometer of in both STE and DIC, and physiological difference of the measurements as the DIC measures the top surface of the heart whereas the STE obtains the data from below. The anisotropy of the RV was measured using full field principal strains acquired from the DIC displacement fields. The full field principal strains cover the entire region of interest instead of just two points as the virtual extensometer approach used by the STE. The principal strains are not direction dependent measures, and therefore are more independent of the anatomy of the patient and the exact positioning of the virtual strain gage or the STE probe. The results show that the longitudinal strains alone are not enough to fully characterize the behavior of the heart, as the deformation of the heart can be very anisotropic, and the anisotropy changes during the surgery, and from patient to patient.