Impact of Electric Field Magnitude in the Left Dorsolateral Prefrontal Cortex on Changes in Intrinsic Functional Connectivity Using Transcranial Direct Current Stimulation: A Randomized Crossover Study.

This study investigated whether the electric field magnitude (E-field) delivered to the left dorsolateral prefrontal cortex (L-DLPFC) changes resting-state brain activity and the L-DLPFC resting-state functional connectivity (rsFC), given the variability in tDCS response and lack of understanding of how rsFC changes. Twenty-one healthy participants received either 2 mA anodal or sham tDCS targeting the L-DLPFC for 10 min. Brain imaging was conducted before and after stimulation. The fractional amplitude of low-frequency fluctuation (fALFF), reflecting resting brain activity, and the L-DLPFC rsFC were analyzed to investigate the main effect of tDCS, main effect of time, and interaction effects. The E-field was estimated by modeling tDCS-induced individual electric fields and correlated with fALFF and L-DLPFC rsFC. Anodal tDCS increased fALFF in the left rostral middle frontal area and decreased fALFF in the midline frontal area (FWE p < 0.050), whereas sham induced no changes. Overall rsFC decreased after sham (positive and negative connectivity, p = 0.001 and 0.020, respectively), with modest and nonsignificant changes after anodal tDCS (p = 0.063 and 0.069, respectively). No significant differences in local rsFC were observed among the conditions. Correlations were observed between the E-field and rsFC changes in the L-DLPFC (r = 0.385, p = 0.115), left inferior parietal area (r = 0.495, p = 0.037), and right lateral visual area (r = 0.683, p = 0.002). Single-session tDCS induced resting brain activity changes and may help maintain overall rsFC. The E-field in the L-DLPFC is associated with rsFC changes in both proximal and distally connected brain regions to the L-DLPFC.

Towards a qualitative theory of the interruption of eating behavior change.

The poor maintenance of eating behavior change is one of the main obstacles to minimizing weight regain after weight loss during diets for non-surgical care of obese or overweight patients. We start with a known informal explanation of interruption in eating behavior change during severe restriction and formalize it as a causal network involving psychological variables, which we extend with energetic variables governed by principles of thermodynamics. The three core phenomena of dietary behavior change, i.e., non-initiation, initiation followed by discontinuation and initiation followed by non-discontinuation, are expressed in terms of the value of the key variable representing mood or psychological energy, the fluctuation of which is the result of three causal relationships. Based on our experimental knowledge of the time evolution profile of the three causal input variables, we then proceed to a qualitative analysis of the resulting theory, i.e., we consider an over-approximation of it which, after discretization, can be expressed in the form of a finite integer-based model. Using Answer Set Programming, we show that our formal model faithfully reproduces the three phenomena and, under a certain assumption, is minimal. We generalize this result by providing all the minimal models reproducing these phenomena when the possible causal relationships exerted on mood are extended to all the other variables (not just those assumed in the informal explanation), with arbitrary causality signs. Finally, by a direct analytical resolution of an under-approximation of our theory, obtained by assuming linear causalities, as a system of linear ODEs, we find exactly the same minimal models, proving that they are also equal to the actual minimal models of our theory since these are framed below and above by the models of the under-approximation and the over-approximation. We determine which parameters need to be person-specific and which can be considered invariant, i.e., we explain inter-individual variability. Our approach could pave the way for universally accepted theories in the field of behavior change and, more broadly, in other areas of psychology.

Personalized Outcomes in Neuropathic Pain: A Clinical Relevance and Assay Sensitivity Analysis from a Randomized Controlled Trial.

OBJECTIVE: To explore the clinical relevance and assay sensitivity of using personalized outcomes using data from a randomized clinical trial (RCT) in people with chemotherapy induced peripheral neuropathy (CIPN). DESIGN: This study is a secondary analysis that leveraged data from a RCT of transcutaneous electrical stimulation for CIPN to test whether personalized outcomes could minimize potential floor effects and increase the assay sensitivity of pain clinical trials (ie, ability to detect a true treatment effect). SETTING: Participants were recruited for a RCT from community oncology clinics in the U.S. PARTICIPANTS: Adults with CIPN (N = 72) who reported on average ≥4 intensity (measured via a 7-day baseline diary) for at least one of the following pain qualities hot/burning pain, sharp/shooting pain and/or cramping. METHODS: Personalized outcomes were defined based on participants' unique presentation of pain qualities at baseline, measured via 0-10 numeric rating scales (NRS), or ranking of the distress caused by the pain qualities. Analysis of covariance models estimated the treatment effect as measured by personalized and non-personalized outcomes. RESULTS: The adjusted mean difference between groups was higher using personalized outcomes (ie, 1.21-1.25 NRS points) compared to a non-personalized outcome (ie, 0.97 NRS points), although the standardized effect sizes were similar between outcomes (0.49-0.54). CONCLUSIONS: These results suggest that personalized pain quality outcomes could minimize floor effects, while providing similar assay sensitivity to non-personalized pain quality outcomes. Personalized outcomes better reflect an individual's unique experience, inherently providing more clinically relevant estimates of treatment effects. Personalized outcomes may be advantageous particularly for clinical trials in populations with high inter-individual variability in pain qualities.

Discrepancy between 'contributing to' and 'sharing variance with' the effective energy for height in high jump.

In high jump, the thigh and shank rotations mainly induce the effective energy for height (Evert) by directly or indirectly (via joint work) converting horizontal-kinetic energy. Meanwhile, inter-individual differences in Evert may not only be explained by large contributors. Here we show that the Evert components due to relatively small contributor segments share variance with total Evert while those due to the two largest contributor segments do not, by analyzing high jump of 15 male jumpers (personal best: 1.90-2.31 m). The largest Evert components were from the stance-leg thigh and shank (36 ± 7%, 34 ± 7% of total Evert), but each of them did not significantly share variance with total Evert (r2 < 0.12). Meanwhile, each of the thoracic and stance-leg-foot components significantly shared variance with total increase in Evert (r2 > 0.30), despite their relatively small contributions (11 ± 2%, 4 ± 1%). The stance-leg thigh and shank components had a strongly trade-off relationship (r2 = 0.60). We reveal that large contributors to the performance variable do not directly imply by their large contribution that they explain inter-individual differences in motor performance, and vice versa. We provide an example where large contributors to the performance variable are related to individually different strategies for achieving performance rather than to performance itself.

Using Targeted Metabolomics to Unravel Phenolic Metabolites of Plant Origin in Animal Milk.

Milk holds a high nutritional value and is associated with diverse health benefits. The understanding of its composition of (poly)phenolic metabolites is limited, which necessitates a comprehensive evaluation of the subject. This study aimed at analyzing the (poly)phenolic profile of commercial milk samples from cows and goats and investigating their sterilization treatments, fat content, and lactose content. Fingerprinting of phenolic metabolites was achieved by using ultra-high-performance liquid chromatography coupled with triple-quadrupole mass spectrometry (UHPLC-QqQ-MS/MS). Two hundred and three potential microbial and phase II metabolites of the main dietary (poly)phenols were targeted. Twenty-five metabolites were identified, revealing a diverse array of phenolic metabolites in milk, including isoflavones and their microbial catabolites equol and O-desmethylangolensin, phenyl-γ-valerolactones (flavan-3-ol microbial catabolites), enterolignans, urolithins (ellagitannin microbial catabolites), benzene diols, and hippuric acid derivates. Goat's milk contained higher concentrations of these metabolites than cow's milk, while the sterilization process and milk composition (fat and lactose content) had minimal impact on the metabolite profiles. Thus, the consumption of goat's milk might serve as a potential means to supplement bioactive phenolic metabolites, especially in individuals with limited production capacity. However, further research is needed to elucidate the potential health effects of milk-derived phenolics.

Structure-Based Analysis of Cefaclor Pharmacokinetic Diversity According to Human Peptide Transporter-1 Genetic Polymorphism.

Cefaclor is a substrate of human-peptide-transporter-1 (PEPT1), and the impact of inter-individual pharmacokinetic variation due to genetic polymorphisms of solute-carrier-family-15-member-1 (SLC15A1) has been a topic of great debate. The main objective of this study was to analyze and interpret cefaclor pharmacokinetic variations according to genetic polymorphisms in SLC15A1 exons 5 and 16. The previous cefaclor bioequivalence results were integrated with additional SLC15A1 exons 5 and 16 genotyping results. An analysis of the structure-based functional impact of SLC15A1 exons 5 and 16 genetic polymorphisms was recently performed using a PEPT1 molecular modeling approach. In cefaclor pharmacokinetic analysis results according to SLC15A1 exons 5 and 16 genetic polymorphisms, no significant differences were identified between genotype groups. Furthermore, in the population pharmacokinetic modeling, genetic polymorphisms in SLC15A1 exons 5 and 16 were not established as effective covariates. PEPT1 molecular modeling results also confirmed that SLC15A1 exons 5 and 16 genetic polymorphisms did not have a significant effect on substrate interaction with cefaclor and did not have a major effect in terms of structural stability. This was determined by comprehensively considering the insignificant change in energy values related to cefaclor docking due to point mutations in SLC15A1 exons 5 and 16, the structural change in conformations confirmed to be less than 0.05 Å, and the relative stabilization of molecular dynamic simulation energy values. As a result, molecular structure-based analysis recently suggested that SLC15A1 exons 5 and 16 genetic polymorphisms of PEPT1 were limited to being the main focus in interpreting the pharmacokinetic diversity of cefaclor.

Dimensionality and optimal combination of autonomic fear-conditioning measures in humans.

Fear conditioning, also termed threat conditioning, is a commonly used learning model with clinical relevance. Quantification of threat conditioning in humans often relies on conditioned autonomic responses such as skin conductance responses (SCR), pupil size responses (PSR), heart period responses (HPR), or respiration amplitude responses (RAR), which are usually analyzed separately. Here, we investigate whether inter-individual variability in differential conditioned responses, averaged across acquisition, exhibits a multi-dimensional structure, and the extent to which their linear combination could enhance the precision of inference on whether threat conditioning has occurred. In a mega-analytic approach, we re-analyze nine data sets including 256 individuals, acquired by the group of the last author, using standard routines in the framework of psychophysiological modeling (PsPM). Our analysis revealed systematic differences in effect size between measures across datasets, but no evidence for a multidimensional structure across various combinations of measures. We derive the statistically optimal weights for combining the four measures and subsets thereof, and we provide out-of-sample performance metrics for these weights, accompanied by bias-corrected confidence intervals. We show that to achieve the same statistical power, combining measures allows for a relevant reduction in sample size, which in a common scenario amounts to roughly 24%. To summarize, we demonstrate a one-dimensional structure of threat conditioning measures, systematic differences in effect size between measures, and provide weights for their optimal linear combination in terms of maximal retrodictive validity.

Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score.

Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project N = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.

Individual consistency in the learning abilities of honey bees: cognitive specialization within sensory and reinforcement modalities.

The question of whether individuals perform consistently across a variety of cognitive tasks is relevant for studies of comparative cognition. The honey bee (Apis mellifera) is an appropriate model to study cognitive consistency as its learning can be studied in multiple elemental and non-elemental learning tasks. We took advantage of this possibility and studied if the ability of honey bees to learn a simple discrimination correlates with their ability to solve two tasks of higher complexity, reversal learning and negative patterning. We performed four experiments in which we varied the sensory modality of the stimuli (visual or olfactory) and the type (Pavlovian or operant) and complexity (elemental or non-elemental) of conditioning to examine if stable correlated performances could be observed across experiments. Across all experiments, an individual's proficiency to learn the simple discrimination task was positively and significantly correlated with performance in both reversal learning and negative patterning, while the performances in reversal learning and negative patterning were positively, yet not significantly correlated. These results suggest that correlated performances across learning paradigms represent a distinct cognitive characteristic of bees. Further research is necessary to examine if individual cognitive consistency can be found in other insect species as a common characteristic of insect brains.

Importance of cytochrome 3A4 and 2D6-mediated drug-drug interactions in oxycodone consumption among older adults hospitalized for hip fracture: a cross-sectional study.

Hip fracture is a common injury and represents a major health problem with an increasing incidence. In older adults, opioids such as oxycodone are often preferred to other analgesics such as tramadol because of a lower risk of delirium. Different parameters, such as inhibition of cytochrome P450 (CYP450) 2D6 and/or 3A4, can potentially lead to pharmacokinetic variations of oxycodone representing a risk of adverse drugs effects or lack of drug response. There is a risk of drug-drug interactions involving CYP450 in older adults due to the high prevalence of polypharmacy. This study sought to identify patient characteristics that influence oxycodone administration. A single-center observational study included 355 patients with a hip fracture hospitalized in a geriatric postoperative unit. Composite endpoint based on form, duration, and timing to intake separated patients into three groups: "no oxycodone", "low oxycodone ", and "high oxycodone ". CYP450 interactions were studied based on a composite variable defining the most involved CYP450 pathways between CYP2D6 and CYP3A4. CYP450 interactions with CYP2D6 pathway involved were associated with the risk of "high oxycodone" [odds ratio adjusted on age and the type of hip fracture (OR*) 4.52, 95% confidence interval (CI) 1.39-16.83, p = 0.02)], as well as serum albumin levels (OR* 1.09, 95% CI 1.02-1.17, p = 0.01). Cognitive impairment was negatively associated with the risk of "high oxycodone" (OR* 0.38, 95% CI 0.18-0.77, p = 0.02). This study showed an association between CYP2D6 interactions and higher oxycodone consumption indirectly reflecting the existence of uncontrolled postoperative pain.

Inter-individual variability in current direction for common tDCS montages.

The direction of applied electric current relative to the cortical surface is a key determinant of transcranial direct current stimulation (tDCS) effects. Inter-individual differences in anatomy affect the consistency of current direction at a cortical target. However, the degree of this variability remains undetermined. Using current flow modelling (CFM), we quantified the inter-individual variability in tDCS current direction at a cortical target (left primary motor cortex, M1). Three montages targeting M1 using circular electrodes were compared: PA-tDCS directed current perpendicular to the central sulcus in a posterior-anterior direction relative to M1, ML-tDCS directed current parallel to the central sulcus in a medio-lateral direction, and conventional-tDCS applied electrodes over M1 and the contralateral forehead. In 50 healthy brain scans from the Human Connectome Project, we extracted current direction and intensity from the grey matter surface in the sulcal bank (M1BANK) and gyral crown (M1CROWN), and neighbouring primary somatosensory cortex (S1BANK and S1CROWN). Results confirmed substantial inter-individual variability in current direction (50%-150%) across all montages. Radial inward current produced by PA-tDCS was predominantly located in M1BANK, whereas for conventional-tDCS it was clustered in M1CROWN. The difference in radial inward current in functionally distinct subregions of M1 raises the testable hypothesis that PA-tDCS and conventional-tDCS modulate cortical excitability through different mechanisms. We show that electrode locations can be used to closely approximate current direction in M1 and precentral gyrus, providing a landmark-based method for tDCS application to address the hypothesis without the need for MRI. By contrast, ML-tDCS current was more tangentially orientated, which is associated with weaker somatic polarisation. Substantial inter-individual variability in current direction likely contributes to variable neuromodulation effects reported for these protocols, emphasising the need for individualised electrode montages, including the control of current direction.

Genetic variants affecting chemical mediated skin immunotoxicity.

The skin is an immune-competent organ and this function may be impaired by exposure to chemicals, which may ultimately result in immune-mediated dermal disorders. Interindividual variability to chemical-induced skin immune reactions is associated with intrinsic individual characteristics and their genomes. In the last 30-40 years, several genes influencing susceptibility to skin immune reactions were identified. The aim of this review is to provide information regarding common genetic variations affecting skin immunotoxicity. The polymorphisms selected for this review are related to xenobiotic-metabolizing enzymes (CYPA1 and CYPB1 genes), antioxidant defense (GSTM1, GSTT1, and GSTP1 genes), aryl hydrocarbon receptor signaling pathway (AHR and ARNT genes), skin barrier function transepidermal water loss (FLG, CASP14, and SPINK5 genes), inflammation (TNF, IL10, IL6, IL18, IL31, and TSLP genes), major histocompatibility complex (MHC) and neuroendocrine system peptides (CALCA, TRPV1, ACE genes). These genes present variants associated with skin immune responses and diseases, as well as variants associated with protecting skin immune homeostasis following chemical exposure. The molecular and association studies focusing on these genetic variants may elucidate their functional consequences and contribution in the susceptibility to skin immunotoxicity. Providing information on how genetic variations affect the skin immune system may reduce uncertainties in estimating chemical hazards/risks for human health in the future.

Semen parameter variability among users of at-home sperm testing kits.

BACKGROUND: Despite the generally accepted World Health Organization guidelines on semen analysis, an individual's results can display significant variation when performed across time or in different laboratories. Semen parameters are in fact highly variable measures that can differ significantly between various analyses. Numerous researchers have discovered a wide range of semen parameters within each individual male, but only a few studies included the analysis of semen parameters variability in patients with infertility. The aim of this study was to evaluate the inter- and intra-individual variability of semen parameters in men of reproductive age with normozoospermia and those with oligozoospermia. METHODS: Five hundred and thirteen who provided ≥ 2 semen samples (798 samples in total) using an at-home mail-in kit over a period of about 2 years were enrolled in the study. Semen samples collection using Give Legacy at-home mail-in semen collection kit; semen analysis at a CLIA-certified laboratory. RESULTS: The degree of intra-subject variation across all semen parameters was lower in men with normozoospermia compared to men with oligozoospermia. Men with normozoospermia furthermore demonstrated a level of intra-subject variation that was lower than inter-subject variation across all measured parameters. No association was observed between intra-subject coefficients of variation in any of the semen parameters, including sperm concentration, sperm count, motile sperm count, total motility, progressive motility, the percentage of sperm with normal morphology, and the age, duration of abstinence, and BMI of the men. CONCLUSION: The results of this observational study confirm the significant variability in semen parameters in men with normozoospermia and oligozoospermia, as measured from at-home semen collection kit samples. This further underscore the importance of securing multiple samples for analysis to provide a robust assessment of male fertility.

A double-blind randomized controlled trial combining cognitive training (CoRe) and neurostimulation (tDCS) in the early stages of cognitive impairment.

BACKGROUND: The prevalence of neurodegenerative diseases is expected to increase over the next years, therefore, new methods able to prevent and delay cognitive decline are needed. AIMS: To evaluate the effectiveness of a combined treatment protocol associating a computerized cognitive training (CoRe) with anodal transcranial direct current stimulation (tDCS). METHODS: In this randomized controlled trial, 33 patients in the early stage of cognitive impairment were assigned to the experimental group (CoRE + real tDCS) or control group (CoRE + sham tDCS). In each group, the intervention lasted 3 consecutive weeks (4 sessions/week). A neuropsychological assessment was administered at baseline (T0), post-intervention (T1) and 6-months later (T2). RESULTS: The CoRE + real tDCS group only improved in working memory and attention/processing speed at both T1 and T2. It reported a stable MMSE score at T2, while the CoRE + sham tDCS group worsened. Age, mood, and T0 MMSE score resulted to play a role in predicting treatment effects. CONCLUSION: Combined multi-domain interventions may contribute to preventing or delaying disease progression. TRIAL REGISTRATION: Trial registration number (ClinicalTrials.gov): NCT04118686.

Impact of inter-individual variability on the estimation of default mode network in temporal concatenation group ICA.

Temporal concatenation group ICA (TC-GICA) is a widely used data-driven method to extract common functional brain networks among individuals. TC-GICA concatenates the time series of individual fMRI data and applies dimension reduction and ICA algorithms to decompose the data into group-level components. The default mode network (DMN) estimated using TC-GICA at relatively high model orders (i.e., large numbers of components) is split into multiple components. The split DMNs are topographically different from those estimated using other methods (e.g., seed-based correlation, clustering, graph theoretical analysis, and other ICA methods like gRAICAR and IVA-GL) and are inconsistent with the existing knowledge of DMN. We hypothesize that the "DMN-splitting'' phenomenon reflects the impact of inter-individual variability in data, which is propagated into the ICA decomposition via the data-concatenation step of TC-GICA. By systematically manipulating the amount of variability involved in the temporal concatenation in both simulated and several realistic datasets, we observed that as more variability was involved, the estimated DMN became less similar to the averaged functional connectivity (FC) pattern obtained using seed-based correlation analysis. The performance of the DMN estimation in TC-GICA also exhibited remarkable dependence on the model order settings. Further analyses revealed that the "DMN-splitting" in TC-GICA could be reproduced when involving large variability in the data-concatenation and performing ICA at high model orders. These results were replicated across multiple datasets and various software implementations. When applying ICA approaches that avoid temporal concatenation, such as gRAICAR and IVA-GL, to the same datasets, the estimated group-level DMN was more consistent with the seed-based FC pattern and was more robust to various model order settings. This study calls for caution when applying TC-GICA to datasets expected to have large inter-individual variability, such as pooling different experimental groups of subjects.

Can genetic polymorphisms predict response variability to anodal transcranial direct current stimulation of the primary motor cortex?

Genetic mediation of cortical plasticity and the role genetic variants play in previously observed response variability to transcranial direct current stimulation (tDCS) have become important issues in the tDCS literature in recent years. This study investigated whether inter-individual variability to tDCS was in-part genetically mediated. In 61 healthy males, anodal-tDCS (a-tDCS) and sham-tDCS were administered to the primary motor cortex at 1 mA for 10-min via 6 × 4 cm active and 7 × 5 cm return electrodes. Twenty-five single-pulse transcranial magnetic stimulation (TMS) motor evoked potentials (MEP) were recorded to represent corticospinal excitability (CSE). Twenty-five paired-pulse MEPs were recorded with 3 ms inter-stimulus interval (ISI) to assess intracortical inhibition (ICI) via short-interval intracranial inhibition (SICI) and 10 ms ISI for intracortical facilitation (ICF). Saliva samples were tested for specific genetic polymorphisms in genes encoding for excitatory and inhibitory neuroreceptors. Individuals were sub-grouped based on a pre-determined threshold and via statistical cluster analysis. Two distinct subgroups were identified, increases in CSE following a-tDCS (i.e. Responders) and no increase or even reductions in CSE (i.e. Non-responders). No changes in ICI or ICF were reported. No relationships were reported between genetic polymorphisms in excitatory receptor genes and a-tDCS responders. An association was reported between a-tDCS responders and GABRA3 gene polymorphisms encoding for GABA-A receptors suggesting potential relationships between GABA-A receptor variations and capacity to undergo tDCS-induced cortical plasticity. In the largest tDCS study of its kind, this study presents an important step forward in determining the contribution genetic factors play in previously observed inter-individual variability to tDCS.

Modeling and characterization of inter-individual variability in CD8 T cell responses in mice.

To develop vaccines it is mandatory yet challenging to account for inter-individual variability during immune responses. Even in laboratory mice, T cell responses of single individuals exhibit a high heterogeneity that may come from genetic backgrounds, intra-specific processes (e.g. antigen-processing and presentation) and immunization protocols.To account for inter-individual variability in CD8 T cell responses in mice, we propose a dynamical model coupled to a statistical, nonlinear mixed effects model. Average and individual dynamics during a CD8 T cell response are characterized in different immunization contexts (vaccinia virus and tumor). On one hand, we identify biological processes that generate inter-individual variability (activation rate of naive cells, the mortality rate of effector cells, and dynamics of the immunogen). On the other hand, introducing categorical covariates to analyze two different immunization regimens, we highlight the steps of the response impacted by immunogens (priming, differentiation of naive cells, expansion of effector cells and generation of memory cells). The robustness of the model is assessed by confrontation to new experimental data.Our approach allows to investigate immune responses in various immunization contexts, when measurements are scarce or missing, and contributes to a better understanding of inter-individual variability in CD8 T cell immune responses.

Physiological determinants of biogeography: The importance of metabolic depression to heat tolerance.

A quantitative understanding of physiological thermal responses is vital for forecasting species distributional shifts in response to climate change. Many studies have focused on metabolic rate as a global metric for analyzing the sublethal effects of changing environments on physiology. Thermal performance curves (TPCs) have been suggested as a viable analytical framework, but standard TPCs may not fully capture physiological responses, due in part to failure to consider the process of metabolic depression. We derived a model based on the nonlinear regression of biological temperature-dependent rate processes and built a heart rate data set for 26 species of intertidal molluscs distributed from 33°S to ~40°N. We then calculated physiological thermal performance limits with continuous heating using T 1 / 2 H , the temperature at which heart rate is decreased to 50% of the maximal rate, as a more realistic measure of upper thermal limits. Results indicate that heat-induced metabolic depression of cardiac performance is a common adaptive response that allows tolerance of harsh environments. Furthermore, our model accounted for the high inter-individual variability in the shape of cardiac TPCs. We then used these TPCs to calculate physiological thermal safety margins (pTSM), the difference between the maximal operative temperature (95th percentile of field temperatures) and T 1 / 2 H of each individual. Using pTSMs, we developed a physiological species distribution model (pSDM) to forecast future geographic distributions. pSDM results indicate that climate-induced species range shifts are potentially less severe than predicted by a simple correlative SDM. Species with metabolic depression below the optimum temperature will be more thermal resistant at their warm trailing edges. High intraspecific variability further suggests that models based on species-level vulnerability to environmental change may be problematic. This multi-scale, mechanistic understanding that incorporates metabolic depression and inter-individual variability in thermal response enables better predictions about the relationship between thermal stress and species distributions.

Evidence of cognitive specialization in an insect: proficiency is maintained across elemental and higher-order visual learning but not between sensory modalities in honey bees.

Individuals differing in their cognitive abilities and foraging strategies may confer a valuable benefit to their social groups as variability may help them to respond flexibly in scenarios with different resource availability. Individual learning proficiency may either be absolute or vary with the complexity or the nature of the problem considered. Determining whether learning ability correlates between tasks of different complexity or between sensory modalities is of high interest for research on brain modularity and task-dependent specialization of neural circuits. The honeybee Apis mellifera constitutes an attractive model to address this question because of its capacity to successfully learn a large range of tasks in various sensory domains. Here, we studied whether the performance of individual bees in a simple visual discrimination task (a discrimination between two visual shapes) is stable over time and correlates with their capacity to solve either a higher-order visual task (a conceptual discrimination based on spatial relationships between objects) or an elemental olfactory task (a discrimination between two odorants). We found that individual learning proficiency within a given task was maintained over time and that some individuals performed consistently better than others within the visual modality, thus showing consistent aptitude across visual tasks of different complexity. By contrast, performance in the elemental visual-learning task did not predict performance in the equivalent elemental olfactory task. Overall, our results suggest the existence of cognitive specialization within the hive, which may contribute to ecological social success.

Characterization of the Human Eccrine Sweat Proteome-A Focus on the Biological Variability of Individual Sweat Protein Profiles.

The potential of eccrine sweat as a bio-fluid of interest for diagnosis and personalized therapy has not yet been fully evaluated, due to the lack of in-depth sweat characterization studies. Thanks to recent developments in omics, together with the availability of accredited sweat collection methods, the analysis of human sweat may now be envisioned as a standardized, non-invasive test for individualized monitoring and personalized medicine. Here, we characterized individual sweat samples, collected from 28 healthy adult volunteers under the most standardized sampling methodology, by applying optimized shotgun proteomics. The thorough characterization of the sweat proteome allowed the identification of 983 unique proteins from which 344 were identified across all samples. Annotation-wise, the study of the sweat proteome unveiled the over-representation of newly addressed actin dynamics, oxidative stress and proteasome-related functions, in addition to well-described proteolysis and anti-microbial immunity. The sweat proteome composition correlated with the inter-individual variability of sweat secretion parameters. In addition, both gender-exclusive proteins and gender-specific protein abundances were highlighted, despite the high similarity between human female and male sweat proteomes. In conclusion, standardized sample collection coupled with optimized shotgun proteomics significantly improved the depth of sweat proteome coverage, far beyond previous similar studies. The identified proteins were involved in many diverse biological processes and molecular functions, indicating the potential of this bio-fluid as a valuable biological matrix for further studies. Addressing sweat variability, our results prove the proteomic profiling of sweat to be a promising bio-fluid analysis for individualized, non-invasive monitoring and personalized medicine.