Partially reconfigurable TVWS transceiver for use in UK and US markets

With more and more countries opening up sections of unlicensed spectrum for use by TV White Space (TVWS) devices, the prospect of building a device capable of operating in more than one world region is appealing. The difficulty is that the locations of TVWS bands within the radio spectrum are not globally harmonised. With this problem in mind, the purpose of this paper is to present a TVWS transceiver design which is capable of being reconfigured to operate in both the UK and US spectrum. We present three different configurations: one covering the UK TVWS spectrum and the remaining two covering the various locations of the US TVWS bands

Regression DCM for fMRI

The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in 'hidden' physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Evidence for surprise minimization over value maximization in choice behavior

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents' to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus 'keep their options open'. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations

The novel mTOR inhibitor RAD001 (Everolimus) induces antiproliferative effects in human pancreatic neuroendocrine tumor cells

Background/Aim: Tumors exhibiting constitutively activated PI(3) K/Akt/mTOR signaling are hypersensitive to mTOR inhibitors such as RAD001 (everolimus) which is presently being investigated in clinical phase II trials in various tumor entities, including neuroendocrine tumors (NETs). However, no preclinical data about the effects of RAD001 on NET cells have been published. In this study, we aimed to evaluate the effects of RAD001 on BON cells, a human pancreatic NET cell line that exhibits constitutively activated PI(3) K/Akt/mTOR signaling. Methods: BON cells were treated with different concentrations of RAD001 to analyze its effect on cell growth using proliferation assays. Apoptosis was examined by Western blot analysis of caspase-3/PARP cleavage and by FACS analysis of DNA fragmentation. Results: RAD001 potently inhibited BON cell growth in a dose-dependent manner which was dependent on the serum concentration in the medium. RAD001-induced growth inhibition involved G0/G1-phase arrest as well as induction of apoptosis. Conclusion: In summary, our data demonstrate antiproliferative and apoptotic effects of RAD001 in NET cells in vitro supporting its clinical use in current phase II trials in NET patients. Copyright (c) 2007 S. Karger AG, Basel

Convincing the crowd: entrepreneurial storytelling in crowdfunding campaigns

This study examines the structure of entrepreneurial stories in pursuit of mobilizing resources from crowds. Based on a comparative analysis of Kickstarter crowdfunding campaigns, we examine in particular how, across different project types, project histories and potential futures are framed and interlinked in narratives to appeal to funders. We find that projects are narrated in different styles—as ongoing journeys” or “results-inprogress”— to convey project value. The former style narrates projects as longer-term endeavors powered by creative initial ideas and a bold vision, inviting audiences to “join the journey”; the latter narrates projects more narrowly as a progression of accomplishments, engaging the audience instrumentally to support next steps. We find that styles are used and combined in different ways, reflecting the tangibility of project outcomes, the sophistication of technology, and the social orientation of projects. Also, successful differ from unsuccessful campaigns in using narratives more coherently. Findings inform research on narrative processes in entrepreneurship and innovation, and research on the mobilization of crowds

Single Gene Deletions of Orexin, Leptin, Neuropeptide Y, and Ghrelin Do Not Appreciably Alter Food Anticipatory Activity in Mice

Timing activity to match resource availability is a widely conserved ability in nature. Scheduled feeding of a limited amount of food induces increased activity prior to feeding time in animals as diverse as fish and rodents. Typically, food anticipatory activity (FAA) involves temporally restricting unlimited food access (RF) to several hours in the middle of the light cycle, which is a time of day when rodents are not normally active. We compared this model to calorie restriction (CR), giving the mice 60% of their normal daily calorie intake at the same time each day. Measurement of body temperature and home cage behaviors suggests that the RF and CR models are very similar but CR has the advantage of a clearly defined food intake and more stable mean body temperature. Using the CR model, we then attempted to verify the published result that orexin deletion diminishes food anticipatory activity (FAA) but observed little to no diminution in the response to CR and, surprisingly, that orexin KO mice are refractory to body weight loss on a CR diet. Next we tested the orexigenic neuropeptide Y (NPY) and ghrelin and the anorexigenic hormone, leptin, using mouse mutants. NPY deletion did not alter the behavior or physiological response to CR. Leptin deletion impaired FAA in terms of some activity measures, such as walking and rearing, but did not substantially diminish hanging behavior preceding feeding time, suggesting that leptin knockout mice do anticipate daily meal time but do not manifest the full spectrum of activities that typify FAA. Ghrelin knockout mice do not have impaired FAA on a CR diet. Collectively, these results suggest that the individual hormones and neuropepetides tested do not regulate FAA by acting individually but this does not rule out the possibility of their concerted action in mediating FAA

Embodied neurology: an integrative framework for neurological disorders

From a systems biology perspective, the brain and spinal cord are interwoven with the body, through afferent and efferent synaptic connections—they are literally ‘embodied’ (Adams et al., 2013). Neurologists appreciate the embodied nature of neurological disorders in terms of diagnosis, classification and their understanding of the underlying pathophysiology. They routinely use a combination of physical examinations (e.g. scales that test motor, sensory and autonomic function) in conjunction with physiological, biochemical and anatomical measures (e.g. electrophysiology, serum and CSF, and radiology) of the peripheral and central nervous system. These measures often produce combinations of symptoms and signs that translate into conventional nosological classifications. While therapeutics focus on the ‘treatable’ cause of a disorder, it is difficult to separate out the impact on the patient due to the primary effects of a lesion/insult etc. and the effects of (possibly delayed) secondary processes that may be reasonable targets for interventions on their own. Moreover, standard neurological assessments often fail to distinguish between pathogenic and compensatory processes. This state of affairs calls for a better understanding of neurological disease within a formal framework that links pathology to phenomenology (i.e. symptoms, impairment and physical signs). We suggest that such a framework should pay special attention to the embodied nature of the nervous system and the implicit pathophysiological and compensatory processes that can be present throughout the neuroaxis. In particular, we postulate that reciprocal information flows, between the body and the nervous system, are crucial for understanding and treating neurological disorders. This framework aims to link pathology to phenomenology, while respecting the ‘embodied’ nature of the nervous system. If fully realized, the framework of embodied neurology has the potential to improve functional outcome following individualized treatment (i.e. precision neurology), promote successful translation of novel therapeutics into clinical use, and refine nosology in the context of disease heterogeneity.Our description of embodied neurology is largely theoretical and is based on a series of focused workshops. It draws on recent advances in biophysical modelling of functional (Deco et al., 2008) and microstructural processes and neuroimaging (Weiskopf et al., 2015). These advances—together with preclinical research—constitute the three tenets of embodied neurology: biophysical modelling, quantitative physiological measures (with an emphasis on non-invasive neuroimaging) and preclinical research on basic mechanisms. These three have a particular focus on the entire nervous system

The statistical neuroanatomy of frontal networks in the macaque

We were interested in gaining insight into the functional properties of frontal networks based upon their anatomical inputs. We took a neuroinformatics approach, carrying out maximum likelihood hierarchical cluster analysis on 25 frontal cortical areas based upon their anatomical connections, with 68 input areas representing exterosensory, chemosensory, motor, limbic, and other frontal inputs. The analysis revealed a set of statistically robust clusters. We used these clusters to divide the frontal areas into 5 groups, including ventral-lateral, ventral-medial, dorsal-medial, dorsal-lateral, and caudal-orbital groups. Each of these groups was defined by a unique set of inputs. This organization provides insight into the differential roles of each group of areas and suggests a gradient by which orbital and ventral-medial areas may be responsible for decision-making processes based on emotion and primary reinforcers, and lateral frontal areas are more involved in integrating affective and rational information into a common framework

The price of tumor control: an analysis of rare side effects of anti-CTLA-4 therapy in metastatic melanoma from the ipilimumab network

Background: Ipilimumab, a cytotoxic T-lymphocyte antigen-4 (CTLA-4) blocking antibody, has been approved for the treatment of metastatic melanoma and induces adverse events (AE) in up to 64% of patients. Treatment algorithms for the management of common ipilimumab-induced AEs have lead to a reduction of morbidity, e.g. due to bowel perforations. However, the spectrum of less common AEs is expanding as ipilimumab is increasingly applied. Stringent recognition and management of AEs will reduce drug-induced morbidity and costs, and thus, positively impact the cost-benefit ratio of the drug. To facilitate timely identification and adequate management data on rare AEs were analyzed at 19 skin cancer centers. Methods and Findings: Patient files (n = 752) were screened for rare ipilimumab-associated AEs. A total of 120 AEs, some of which were life-threatening or even fatal, were reported and summarized by organ system describing the most instructive cases in detail. Previously unreported AEs like drug rash with eosinophilia and systemic symptoms (DRESS), granulomatous inflammation of the central nervous system, and aseptic meningitis, were documented. Obstacles included patientś delay in reporting symptoms and the differentiation of steroid-induced from ipilimumab-induced AEs under steroid treatment. Importantly, response rate was high in this patient population with tumor regression in 30.9% and a tumor control rate of 61.8% in stage IV melanoma patients despite the fact that some patients received only two of four recommended ipilimumab infusions. This suggests that ipilimumab-induced antitumor responses can have an early onset and that severe autoimmune reactions may reflect overtreatment. Conclusion: The wide spectrum of ipilimumab-induced AEs demands doctor and patient awareness to reduce morbidity and treatment costs and true ipilimumab success is dictated by both objective tumor responses and controlling severe side effects