Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the application of cortically coupled computer vision to rapid image search. In RSVP, images are presented to participants in a rapid serial sequence which can evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram (EEG). The contemporary approach to this problem involves supervised spatial filtering techniques which are applied for the purposes of enhancing the discriminative information in the EEG data. In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three pipelines without spatial filtering are used as baseline comparison. The Area Under Curve (AUC) is used as an evaluation metric in this paper. The results reveal that MTWLB and xDAWN spatial filtering techniques enhance the classification performance of the pipeline but CSP does not. The results also support the conclusion that LR can be effective for RSVP based BCI if discriminative features are available

TDP1/TOP1 ratio as a promising indicator for the response of small cell lung cancer to topotecan

BACKGROUND AND OBJECTIVE Small cell lung cancer (SCLC) is one of the most challenging tumors to treat due to high proliferation rate, early metastatic dissemination and rapid development of chemotherapy resistance. The current treatment protocols involve the use of topoisomerase 1 (TOP1) poisons such as irinotecan and topotecan in combination with platinum-based compounds. TOP1 poisons kill cancer cells by trapping TOP1 on DNA, generating lethal DNA double-strand breaks. A potential mechanism employed by cancer cells to resist killing by TOP1 poisons is to overexpress enzymes involved in the repair of TOP1-DNA breaks. Tyrosyl DNA phosphodiesterase 1 (TDP1) is a key player in this process and despite its importance, no data is currently available to correlate TDP1 protein and mRNA levels with catalytic activity in SCLC. In addition, it is not known if TDP1 and TOP1 protein levels correlate with the cellular response of SCLC to TOP1 based therapies. METHODS AND RESULTS We report a remarkable variation in TDP1 and TOP1 protein levels in a panel of SCLC cell lines. TDP1 protein level correlates well with TDP1 mRNA and TDP1 catalytic activity, as measured by two newly developed independent activity assays, suggesting the potential utility of immunohistochemistry in assessing TDP1 levels in SCLC tissues. We further demonstrate that whilst TDP1 protein level alone does not correlate with topotecan sensitivity, TDP1/TOP1 ratio correlates well with sensitivity in 8 out of 10 cell lines examined. CONCLUSION This study provides the first cellular analyses of TDP1 and TOP1 in SCLC and suggests the potential utility of TDP1/TOP1 ratio to assess the response of SCLC to topotecan. The establishment and validation of an easy-to-use TDP1 enzymatic assay in cell extracts could be exploited as a diagnostic tool in the clinic. These findings may help in stratifying patients that are likely to benefit from TOP1 poisons and TDP1 inhibitors currently under development

Quantum Corrections to Newton's Law

We present a new approach to quantum gravity starting from Feynman's formulation for the simplest example, that of a scalar field as the representative matter. We show that we extend his treatment to a calculable framework using resummation techniques already well-tested in other problems. Phenomenological consequences for Newton's law are described.Comment: 7 pages, 1 figure; improved fig., refs;improved discussion;more discussion; proo

Self-regulation theory and self-monitoring of blood glucose behavior in type 2 diabetes mellitus.

The present study examined self-monitoring of blood glucose (SMBG) as part of a selfregulatory process of health decision-making using the Self-Regulation Model of illness perceptions, or Common Sense Model. Participants were N=185 individuals with type 2 diabetes from a specialty diabetes clinic prescribed subcutaneous insulin or other injectable diabetes medication at least daily. Collected information included both medical chart data and self-report questionnaires completed prior receiving lab results. Self-care burden was generally high; the modal prescribed times per day of injecting insulin was 4 with modal SMBG recommendations of 3-4 times per day. Participants reported high adherence to prescribed medication regimens, varied aherence to diet recommednations, and low engagement in exercise. Specific hypotheses were developed to examine the relationship between illness coherence and illness control beliefs (IPQ), SMBG decisionmaking behavior, and outcomes including diabetes distress (PAID) and hemoglobin A1c level. These hypotheses were not supported. Supplemental analyses revealed that SMBG decision-making use was related to illness perceptions, including a positive relationship with personal control and coherence beliefs, but not treatment control, and a negative relationship with both outcome variables (A1c at baseline and PAID score). Both treatment and personal control beliefs were not associated with glucose control outcomes, suggesting that illness beliefs alone do not explain why some individuals are more successful at managing their diabetes than others. Coherence was found to differ by education level and SES and greatly vary in an otherwise relatively homogenous sample. Study findings suggest that illness perceptions play an important role in the process of SMBG use for decision-making as it relates to glucose control and diabetes distress. Results also point to possible clinical targets such as illness coherence and diabetes distress. The study provides a foundation for future research related to SMBG as a decision-making strategy

Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks

Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. Arguably the most striking results have been in the area of image synthesis. However, evaluating the performance of GANs is still an open and challenging problem. Existing evaluation metrics primarily measure the dissimilarity between real and generated images using automated statistical methods. They often require large sample sizes for evaluation and do not directly reflect human perception of image quality. In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals. Our results show that Neuroscore has superior performance to the current evaluation metrics in that: (1) It is more consistent with human judgment; (2) The evaluation process needs much smaller numbers of samples; and (3) It is able to rank the quality of images on a per GAN basis. A convolutional neural network (CNN) based neuro-AI interface is proposed to predict Neuroscore from GAN-generated images directly without the need for neural responses. Importantly, we show that including neural responses during the training phase of the network can significantly improve the prediction capability of the proposed model. Materials related to this work are provided at https://github.com/villawang/Neuro-AI-Interface

Designing a Voluntary Beef Checkoff

Recently, the U.S. Supreme Court considered whether the mandatory fees imposed by the beef checkoff violates the First Amendment. As a precaution, many states began forming voluntary beef checkoffs, where funds would be raised through voluntary contributions. This study conducted a survey of Oklahoma cattle producers to determine what type ofvoluntary checkoff design would receive the greatest support. The most popular checkoff placed a large emphasis on advertising and a slightly lower checkoff fee. The survey also tested the ability of a provision point mechanism to limit free-riding. The mechanism was not as effective as in other studies which used laboratory experiments.beef marketing, checkoff, free-rider, provision point mechanism, public good, Agricultural and Food Policy, Livestock Production/Industries,

An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data

The current gold standard for human activity recognition (HAR) is based on the use of cameras. However, the poor scalability of camera systems renders them impractical in pursuit of the goal of wider adoption of HAR in mobile computing contexts. Consequently, researchers instead rely on wearable sensors and in particular inertial sensors. A particularly prevalent wearable is the smart watch which due to its integrated inertial and optical sensing capabilities holds great potential for realising better HAR in a non-obtrusive way. This paper seeks to simplify the wearable approach to HAR through determining if the wrist-mounted optical sensor alone typically found in a smartwatch or similar device can be used as a useful source of data for activity recognition. The approach has the potential to eliminate the need for the inertial sensing element which would in turn reduce the cost of and complexity of smartwatches and fitness trackers. This could potentially commoditise the hardware requirements for HAR while retaining the functionality of both heart rate monitoring and activity capture all from a single optical sensor. Our approach relies on the adoption of machine vision for activity recognition based on suitably scaled plots of the optical signals. We take this approach so as to produce classifications that are easily explainable and interpretable by non-technical users. More specifically, images of photoplethysmography signal time series are used to retrain the penultimate layer of a convolutional neural network which has initially been trained on the ImageNet database. We then use the 2048 dimensional features from the penultimate layer as input to a support vector machine. Results from the experiment yielded an average classification accuracy of 92.3%. This result outperforms that of an optical and inertial sensor combined (78%) and illustrates the capability of HAR systems using...Comment: 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Scienc

A preliminary training guide for utilizing high-altitude, color-infrared photography in compiling soil maps

Instruction for acquiring and analytically processing small-scale color-infrared photography to perform a soil resources inventory over forests of the southern U.S. is provided. Planning the project; acquiring aerial photography, materials, equipment and supplemental data; and preparing the photography for analysis are discussed. The procedures for preparing ancillary and primary component overlays are discussed. The use of correlation charts and dichotomous keys for mountain landforms, water regime, and vegetation is explained

ECONOMIC ANALYSIS OF ALFALFA INTEGRATED MANAGEMENT PRACTICES

Integrated pest management (IMP) initially focused on insect pest control. More recently, IPM encompasses a broader concept of management, one which crosses several disciplinary boundaries. This article reports results of research dealing with four integrated management decisions for alfalfa (cultivar selection, inset control, weed control, and end-of-season harvest options.Crop Production/Industries,