Assessing the prognostic value of KRAS mutation combined with tumor size in stage I-II non-small cell lung cancer: a retrospective analysis

BackgroundKRAS mutation status is a well-established independent prognostic factor in advanced non-small cell lung cancer (NSCLC), yet its role in early-stage disease is unclear. Here, we investigate the prognostic value of combining survival data on KRAS mutation status and tumor size in stage I-II NSCLC.MethodsWe studied the combined impact of KRAS mutational status and tumor size on overall survival (OS) in patients with stage I-II NSCLC. We performed a retrospective study including 310 diagnosed patients with early (stage I-II) NSCLCs. All molecularly assessed patients diagnosed with stage I-II NSCLC between 2016–2018 in the Västra Götaland Region of western Sweden were screened in this multi-center retrospective study. The primary study outcome was overall survival.ResultsOut of 310 patients with stage I-II NSCLC, 37% harbored an activating mutation in the KRAS gene. Our study confirmed staging and tumor size as prognostic factors. However, KRAS mutational status was not found to impact OS and there was no difference in the risk of death when combining KRAS mutational status and primary tumor size.ConclusionsIn our patient cohort, KRAS mutations in combination with primary tumor size did not impact prognosis in stage I-II NSCLC

Computationally highly efficient mixture of adaptive filters

We introduce a new combination approach for the mixture of adaptive filters based on the set-membership filtering (SMF) framework. We perform SMF to combine the outputs of several parallel running adaptive algorithms and propose unconstrained, affinely constrained and convexly constrained combination weight configurations. Here, we achieve better trade-off in terms of the transient and steady-state convergence performance while providing significant computational reduction. Hence, through the introduced approaches, we can greatly enhance the convergence performance of the constituent filters with a slight increase in the computational load. In this sense, our approaches are suitable for big data applications where the data should be processed in streams with highly efficient algorithms. In the numerical examples, we demonstrate the superior performance of the proposed approaches over the state of the art using the well-known datasets in the machine learning literature. © 2016, Springer-Verlag London

Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines

We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data-and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data. © 2016 IEEE

A Neural Circuit Arbitrates between Persistence and Withdrawal in Hungry Drosophila

In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, we show that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. We further show that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, our data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive

Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis

We introduce a novel family of adaptive robust channel estimators for highly challenging underwater acoustic (UWA) channels. Since the underwater environment is highly non-stationary and subjected to impulsive noise, we use adaptive filtering techniques based on minimization of a logarithmic cost function, which results in a better trade-off between the convergence rate and the steady state performance of the algorithm. To improve the convergence performance of the conventional first and second order linear estimation methods while mitigating the stability issues related to impulsive noise, we intrinsically combine different norms of the error in the cost function using a logarithmic term. Hence, we achieve a comparable convergence rate to the faster algorithms, while significantly enhancing the stability against impulsive noise in such an adverse communication medium. Furthermore, we provide a thorough analysis for the tracking and steady-state performances of our proposed methods in the presence of impulsive noise. In our analysis, we not only consider the impulsive noise, but also take into account the frequency and phase offsets commonly experienced in real life experiments. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels. © 2017 Elsevier Inc

Considering Trauma Exposure in the Context of Genetics Studies of Posttraumatic Stress Disorder: A Systematic Review

Background: Posttraumatic stress disorder (PTSD) is a debilitating anxiety disorder. Surveys of the general population suggest that while 50-85% of Americans will experience a traumatic event in their lifetime, only 2-50% will develop PTSD. Why some individuals develop PTSD following trauma exposure while others remain resilient is a central question in the field of trauma research. For more than half a century, the role of genetic influences on PTSD has been considered as a potential vulnerability factor. However, despite the exponential growth of molecular genetic studies over the past decade, limited progress has been made in identifying true genetic variants for PTSD. Methods: In an attempt to aid future genome wide association studies (GWAS), this paper presents a systematic review of 28 genetic association studies of PTSD. Inclusion criteria required that 1) all participants were exposed to Criterion A traumatic events, 2) polymorphisms of relevant genes were genotyped and assessed in relation to participants’ PTSD status, 3) quantitative methods were used, and 4) articles were published in English and in peer-reviewed journals. In the examination of these 28 studies, particular attention was given to variables related to trauma exposure (e.g. number of traumas, type of trauma). Results: Results indicated that most articles did not report on the GxE interaction in the context of PTSD or present data on the main effects of E despite having data available. Furthermore, some studies that did consider the GxE interaction had significant findings, underscoring the importance of examining how genotypes can modify the effect of trauma on PTSD. Additionally, results indicated that only a small number of genes continue to be studied and that there were marked differences in methodologies across studies, which subsequently limited robust conclusions. Conclusions: As trauma exposure is a necessary condition for the PTSD diagnosis, this paper identifies gaps in the current literature as well as provides recommendations for how future GWAS studies can most effectively incorporate trauma exposure data in both the design and analysis phases of studies