Many neurocognitive models of anxiety emphasize the importance of a hyper-responsive threat-detection system centered on the amygdala, with recent accounts incorporating a role for prefrontal ...mechanisms in regulating attention to threat. Here we investigated whether trait anxiety is associated with a much broader dysregulation of attentional control. Volunteers performed a response-conflict task under conditions that posed high or low demands on attention. High trait-anxious individuals showed reduced prefrontal activity and slower target identification in response to processing competition when the task did not fully occupy attentional resources. The relationship between trait anxiety and prefrontal recruitment remained after controlling for state anxiety. These findings indicate that trait anxiety is linked to impoverished recruitment of prefrontal attentional control mechanisms to inhibit distractor processing even when threat-related stimuli are absent. Notably, this deficit was observed when ongoing task-related demands on attention were low, potentially explaining the day-to-day difficulties in concentration that are associated with clinical anxiety.
Anxiety can be hugely disruptive to everyday life. Anxious individuals show increased attentional capture by potential signs of danger, and interpret expressions, comments and events in a negative ...manner. These cognitive biases have been widely explored in human anxiety research. By contrast, animal models have focused upon the mechanisms underlying acquisition and extinction of conditioned fear, guiding exposure-based therapies for anxiety disorders. Recent neuroimaging studies of conditioned fear, attention to threat and interpretation of emotionally ambiguous stimuli indicate common amygdala–prefrontal circuitry underlying these processes, and suggest that the balance of activity within this circuitry is altered in anxiety, creating a bias towards threat-related responses. This provides a focus for future translational research, and targeted pharmacological and cognitive interventions.
In everyday life, the outcomes of our actions are rarely certain. Further, we often lack the information needed to precisely estimate the probability and value of potential outcomes as well as how ...much effort will be required by the courses of action under consideration. Under such conditions of uncertainty, individual differences in the estimation and weighting of these variables, and in reliance on model-free versus model-based decision making, have the potential to strongly influence our behavior. Both anxiety and depression are associated with difficulties in decision making. Further, anxiety is linked to increased engagement in threat-avoidance behaviors and depression is linked to reduced engagement in reward-seeking behaviors. The precise deficits, or biases, in decision making associated with these common forms of psychopathology remain to be fully specified. In this article, we review evidence for which of the computations supporting decision making are altered in anxiety and depression and consider the potential consequences for action selection. In addition, we provide a schematic framework that integrates the findings reviewed and will hopefully be of value to future studies.
Selective serotonin reuptake inhibitors (SSRIs) are primary treatment options for major depressive and anxiety disorders. CYP2D6 and CYP2C19 polymorphisms can influence the metabolism of SSRIs, ...thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide dosing recommendations for fluvoxamine, paroxetine, citalopram, escitalopram, and sertraline based on CYP2D6 and/or CYP2C19 genotype (updates at www.pharmgkb.org).
Artificial Neural Networks have reached "grandmaster" and even "super-human" performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect ...information, such as "Starcraft". Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an "AI" brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong-an autonomous vehicle crashes, a chatbot exhibits "racist" behavior, automated credit-scoring processes "discriminate" on gender, etc.-there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that "
." The key, as Pearl suggests, is to replace "reasoning by association" with "causal reasoning" -the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the
: "
." In this paper, foregrounding what in 1949 Gilbert Ryle termed "a category mistake", I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot "grasp" causality, but that AI machinery (qua computation) cannot understand anything at all.
Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression ...databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills.
We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene-gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses.
Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR .
The molecular mechanisms underlying the response to exercise and inactivity are not fully understood. We propose an innovative approach to profile the skeletal muscle transcriptome to exercise and ...inactivity using 66 published datasets. Data collected from human studies of aerobic and resistance exercise, including acute and chronic exercise training, were integrated using meta-analysis methods (www.metamex.eu). Here we use gene ontology and pathway analyses to reveal selective pathways activated by inactivity, aerobic versus resistance and acute versus chronic exercise training. We identify NR4A3 as one of the most exercise- and inactivity-responsive genes, and establish a role for this nuclear receptor in mediating the metabolic responses to exercise-like stimuli in vitro. The meta-analysis (MetaMEx) also highlights the differential response to exercise in individuals with metabolic impairments. MetaMEx provides the most extensive dataset of skeletal muscle transcriptional responses to different modes of exercise and an online interface to readily interrogate the database.
Summary
Glioblastoma (GBM) is an aggressive cancer with a very poor prognosis. Generally viewed as weakly immunogenic, GBM responds poorly to current immunotherapies. To understand this problem more ...clearly we used a combination of natural killer (NK) cell functional assays together with gene and protein expression profiling to define the NK cell response to GBM and explore immunosuppression in the GBM microenvironment. In addition, we used transcriptome data from patient cohorts to classify GBM according to immunological profiles. We show that glioma stem‐like cells, a source of post‐treatment tumour recurrence, express multiple immunomodulatory cell surface molecules and are targeted in preference to normal neural progenitor cells by natural killer (NK) cells ex vivo. In contrast, GBM‐infiltrating NK cells express reduced levels of activation receptors within the tumour microenvironment, with hallmarks of transforming growth factor (TGF)‐β‐mediated inhibition. This NK cell inhibition is accompanied by expression of multiple immune checkpoint molecules on T cells. Single‐cell transcriptomics demonstrated that both tumour and haematopoietic‐derived cells in GBM express multiple, diverse mediators of immune evasion. Despite this, immunome analysis across a patient cohort identifies a spectrum of immunological activity in GBM, with active immunity marked by co‐expression of immune effector molecules and feedback inhibitory mechanisms. Our data show that GBM is recognized by the immune system but that anti‐tumour immunity is restrained by multiple immunosuppressive pathways, some of which operate in the healthy brain. The presence of immune activity in a subset of patients suggests that these patients will more probably benefit from combination immunotherapies directed against multiple immunosuppressive pathways.
Our manuscript challenges the current paradigm that Glioblastoma (GBM) is a poor candidate for immunotherapy, therefore impacting on clinical approaches. We identify NK cells as a target for immunosuppression in GBM, with multiple suppressive pathways within GBM human tumours. We also identify a group of GBM patients with higher immunological activity, who will benefit from immunotherapies that target these immunosuppressive pathways.
Prescribing the frequency, duration, or volume of training is simple as these factors can be altered by manipulating the number of exercise sessions per week, the duration of each session, or the ...total work performed in a given time frame (e.g., per week). However, prescribing exercise intensity is complex and controversy exists regarding the reliability and validity of the methods used to determine and prescribe intensity. This controversy arises from the absence of an agreed framework for assessing the construct validity of different methods used to determine exercise intensity. In this review, we have evaluated the construct validity of different methods for prescribing exercise intensity based on their ability to provoke homeostatic disturbances (e.g., changes in oxygen uptake kinetics and blood lactate) consistent with the moderate, heavy, and severe domains of exercise. Methods for prescribing exercise intensity include a percentage of anchor measurements, such as maximal oxygen uptake (Formula: see text), peak oxygen uptake (Formula: see text), maximum heart rate (HR
), and maximum work rate (i.e., power or velocity-Formula: see text or Formula: see text, respectively), derived from a graded exercise test (GXT). However, despite their common use, it is apparent that prescribing exercise intensity based on a fixed percentage of these maximal anchors has little merit for eliciting distinct or domain-specific homeostatic perturbations. Some have advocated using submaximal anchors, including the ventilatory threshold (VT), the gas exchange threshold (GET), the respiratory compensation point (RCP), the first and second lactate threshold (LT
and LT
), the maximal lactate steady state (MLSS), critical power (CP), and critical speed (CS). There is some evidence to support the validity of LT
, GET, and VT to delineate the moderate and heavy domains of exercise. However, there is little evidence to support the validity of most commonly used methods, with exception of CP and CS, to delineate the heavy and severe domains of exercise. As acute responses to exercise are not always predictive of chronic adaptations, training studies are required to verify whether different methods to prescribe exercise will affect adaptations to training. Better ways to prescribe exercise intensity should help sport scientists, researchers, clinicians, and coaches to design more effective training programs to achieve greater improvements in health and athletic performance.
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