Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning

PURPOSE: To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning. METHODS: We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images. At each count level, patches extracted from 23 pairs of SD/SLD PET images were used to train three IQT models - global linear, single tree, and random forest regressions with cubic patch sizes of 3 and 5 voxels. The models were then used to estimate SD images from LD images at each count level for 10 unseen subjects. Lesion-detection task was carried out on matched lesion-present and lesion-absent images. RESULTS: LD PET-CT protocol yielded lesion detectability with sensitivity of 0.98 and specificity of 1. Random forest algorithm with cubic patch size of 5 allowed further 11.7% reduction in the effective PETCT dose without compromising lesion detectability, but underestimated SUV by 30%. CONCLUSION: LD PET-CT protocol was validated for lesion detection using ALD PET scans. Substantial image quality improvement or additional dose reduction while preserving clinical values can be achieved using machine learning methods though SUV quantification may be biased and adjustment of our research protocol is required for clinical use

Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts

Temporal-Difference Reinforcement Learning with Distributed Representations

Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the believed state of the world to distribute across sets of equivalent states. Distributed exponential discounting factors produce hyperbolic discounting in the behavior of the agent itself. We examine these issues in the context of a TD RL model in which state-belief is distributed over a set of exponentially-discounting “micro-Agents”, each of which has a separate discounting factor (γ). Each µAgent maintains an independent hypothesis about the state of the world, and a separate value-estimate of taking actions within that hypothesized state. The overall agent thus instantiates a flexible representation of an evolving world-state. As with other TD models, the value-error (δ) signal within the model matches dopamine signals recorded from animals in standard conditioning reward-paradigms. The distributed representation of belief provides an explanation for the decrease in dopamine at the conditioned stimulus seen in overtrained animals, for the differences between trace and delay conditioning, and for transient bursts of dopamine seen at movement initiation. Because each µAgent also includes its own exponential discounting factor, the overall agent shows hyperbolic discounting, consistent with behavioral experiments

Clinical research without consent in adults in the emergency setting: a review of patient and public views

<p>Abstract</p> <p>Background</p> <p>In emergency research, obtaining informed consent can be problematic. Research to develop and improve treatments for patients admitted to hospital with life-threatening and debilitating conditions is much needed yet the issue of research without consent (RWC) raises concerns about unethical practices and the loss of individual autonomy. Consistent with the policy and practice turn towards greater patient and public involvement in health care decisions, in the US, Canada and EU, guidelines and legislation implemented to protect patients and facilitate acute research with adults who are unable to give consent have been developed with little involvement of the lay public. This paper reviews research examining public opinion regarding RWC for research in emergency situations, and whether the rules and regulations permitting research of this kind are in accordance with the views of those who ultimately may be the most affected.</p> <p>Methods</p> <p>Seven electronic databases were searched: Medline, Embase, CINAHL, Cochrane Database of Systematic Reviews, Philosopher's Index, Age Info, PsychInfo, Sociological Abstracts and Web of Science. Only those articles pertaining to the views of the public in the US, Canada and EU member states were included. Opinion pieces and those not published in English were excluded.</p> <p>Results</p> <p>Considering the wealth of literature on the perspectives of professionals, there was relatively little information about public attitudes. Twelve studies employing a range of research methods were identified. In five of the six questionnaire surveys around half the sample did <it>not </it>agree generally with RWC, though paradoxically, a higher percentage would <it>personally </it>take part in such a study. Unfortunately most of the studies were not designed to investigate individuals' views in any depth. There also appears to be a level of mistrust of medical research and some patients were more likely to accept an experimental treatment 'outside' of a research protocol.</p> <p>Conclusion</p> <p>There are too few data to evaluate whether the rules and regulations permitting RWC protects – or is acceptable to – the public. However, any attempts to engage the public should take place in the context of findings from further basic research to attend to the apparently paradoxical findings of some of the current surveys.</p

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards

Cell-Cell Transmission Enables HIV-1 to Evade Inhibition by Potent CD4bs Directed Antibodies

HIV is known to spread efficiently both in a cell-free state and from cell to cell, however the relative importance of the cell-cell transmission mode in natural infection has not yet been resolved. Likewise to what extent cell-cell transmission is vulnerable to inhibition by neutralizing antibodies and entry inhibitors remains to be determined. Here we report on neutralizing antibody activity during cell-cell transmission using specifically tailored experimental strategies which enable unambiguous discrimination between the two transmission routes. We demonstrate that the activity of neutralizing monoclonal antibodies (mAbs) and entry inhibitors during cell-cell transmission varies depending on their mode of action. While gp41 directed agents remain active, CD4 binding site (CD4bs) directed inhibitors, including the potent neutralizing mAb VRC01, dramatically lose potency during cell-cell transmission. This implies that CD4bs mAbs act preferentially through blocking free virus transmission, while still allowing HIV to spread through cell-cell contacts. Thus providing a plausible explanation for how HIV maintains infectivity and rapidly escapes potent and broadly active CD4bs directed antibody responses in vivo

Floral odors and the interaction between pollinating Ceratopogonid midges and Cacao

Most plant species depend upon insect pollination services, including many cash and subsistence crops. Plants compete to attract those insects using visual cues and floral odor which pollinators associate with a reward. The cacao tree, Theobroma cacao, has a highly specialized floral morphology permitting pollination primarily by Ceratopogonid midges. However, these insects do not depend upon cacao flowers for their life cycle, and can use other sugar sources. To understand how floral cues mediate pollination in cacao we developed a method for rearing Ceratopogonidae through several complete lifecycles to provide material for bioassays. We carried out collection and analysis of cacao floral volatiles, and identified a bouquet made up exclusively of saturated and unsaturated, straight-chain hydrocarbons, which is unusual among floral odors. The most abundant components were tridecane, pentadecane, (Z)-7-pentadecene and (Z)-8-heptadecene with a heptadecadiene and heptadecatriene as minor components. We presented adult midges, Forcipomyia sp. (subgen. Forcipomyia), Culicoides paraensis and Dasyhelea borgmeieri, with natural and synthetic cacao flower odors in choice assays. Midges showed weak attraction to the complete natural floral odor in the assay, with no significant evidence of interspecific differences. This suggests that cacao floral volatiles play a role in pollinator behavior. Midges were not attracted to a synthetic blend of the above four major components of cacao flower odor, indicating that a more complete blend is required for attraction. Our findings indicate that cacao pollination is likely facilitated by the volatile blend released by flowers, and that the system involves a generalized odor response common to different species of Ceratopogonidae

Selective augmentation of striatal functional connectivity following NMDA receptor antagonism: implications for psychosis

The psychotomimetic effect of the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine is thought to arise from a functional modulation of the brain's fronto-striato-thalamic (FST) circuits. Animal models suggest a pronounced effect on ventral ‘limbic' FST systems, although recent work in patients with psychosis and high-risk individuals suggests specific alterations of dorsal ‘associative' FST circuits. Here, we used functional magnetic resonance imaging to investigate the effects of a subanesthetic dose of ketamine on measures of functional connectivity as indexed by the temporal coherence of spontaneous neural activity in both dorsal and ventral FST circuits, as well as their symptom correlates. We adopted a placebo-controlled, double-blind, randomized, repeated-measures design in which 19 healthy participants received either an intravenous saline infusion or a racemic mixture of ketamine (100 ng/ml) separated by at least 1 week. Compared with placebo, ketamine increased functional connectivity between the dorsal caudate and both the thalamus and midbrain bilaterally. Ketamine additionally increased functional connectivity of the ventral striatum/nucleus accumbens and ventromedial prefrontal cortex. Both connectivity increases significantly correlated with the psychosis-like and dissociative symptoms under ketamine. Importantly, dorsal caudate connectivity with the ventrolateral thalamus and subthalamic nucleus showed inverse correlation with ketamine-induced symptomatology, pointing to a possible resilience role to disturbances in FST circuits. Although consistent with the role of FST in mediating psychosis, these findings contrast with previous research in clinical samples by suggesting that acute NMDAR antagonism may lead to psychosis-like experiences via a mechanism that is distinct from that implicated in frank psychotic illness

Human Non-neutralizing HIV-1 Envelope Monoclonal Antibodies Limit the Number of Founder Viruses during SHIV Mucosal Infection in Rhesus Macaques

HIV-1 mucosal transmission begins with virus or virus-infected cells moving through mucus across mucosal epithelium to infect CD4+ T cells. Although broadly neutralizing antibodies (bnAbs) are the type of HIV-1 antibodies that are most likely protective, they are not induced with current vaccine candidates. In contrast, antibodies that do not neutralize primary HIV-1 strains in the TZM-bl infection assay are readily induced by current vaccine candidates and have also been implicated as secondary correlates of decreased HIV-1 risk in the RV144 vaccine efficacy trial. Here, we have studied the capacity of anti-Env monoclonal antibodies (mAbs) against either the immunodominant region of gp41 (7B2 IgG1), the first constant region of gp120 (A32 IgG1), or the third variable loop (V3) of gp120 (CH22 IgG1) to modulate in vivo rectal mucosal transmission of a high-dose simian-human immunodeficiency virus (SHIV-BaL) in rhesus macaques. 7B2 IgG1 or A32 IgG1, each containing mutations to enhance Fc function, was administered passively to rhesus macaques but afforded no protection against productive clinical infection while the positive control antibody CH22 IgG1 prevented infection in 4 of 6 animals. Enumeration of transmitted/founder (T/F) viruses revealed that passive infusion of each of the three antibodies significantly reduced the number of T/F genomes. Thus, some antibodies that bind HIV-1 Env but fail to neutralize virus in traditional neutralization assays may limit the number of T/F viruses involved in transmission without leading to enhancement of viral infection. For one of these mAbs, gp41 mAb 7B2, we provide the first co-crystal structure in complex with a common cyclical loop motif demonstrated to be critical for infection by other retroviruses