A computational cognition model of perception, memory, and judgment

The mechanism of human cognition and its computability provide an important theoretical foundation to intelligent computation of visual media. This paper focuses on the intelligent processing of massive data of visual media and its corresponding processes of perception, memory, and judgment in cognition. In particular, both the human cognitive mechanism and cognitive computability of visual media are investigated in this paper at the following three levels: neurophysiology, cognitive psychology, and computational modeling. A computational cognition model of Perception, Memory, and Judgment (PMJ model for short) is proposed, which consists of three stages and three pathways by integrating the cognitive mechanism and computability aspects in a unified framework. Finally, this paper illustrates the applications of the proposed PMJ model in five visual media research areas. As demonstrated by these applications, the PMJ model sheds some light on the intelligent processing of visual media, and it would be innovative for researchers to apply human cognitive mechanism to computer science.</p

Types of Control in Acupuncture Clinical Trials Might Affect the Conclusion of the Trials: A Review of Acupuncture on Pain Management

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A retrospective study on the impact of comorbid depression or anxiety on healthcare resource use and costs among diabetic neuropathy patients

<p>Abstract</p> <p>Background</p> <p>Diabetic neuropathy (DN) is a common complication of diabetes that has significant economic burden, especially for patients with comorbid depression or anxiety. This study examines and quantifies factors associated with healthcare costs among patients diagnosed with diabetic neuropathy (DN) with or without a comorbid diagnosis of depression or anxiety (DA) using retrospective administrative claims data. No study has examined the differences in economic outcomes depending on the presence of comorbid DA disorders.</p> <p>Methods</p> <p>Over-age-18 individuals with 1+ diagnosis of DN in 2005 were selected. The first observed DN claim was considered the "index date." All individuals had a 12-month pre-index and follow-up period. For both under-age-65 commercially insured and over-age-65 individuals with employer-sponsored Medicare supplemental insurance, we constructed 2 subgroups for individuals with DA (DN-DA) or without (DN-only). Patients' clinical characteristics over pre-index period were compared. Multivariate regressions were performed to assess whether DN-DA patients had higher utilization of healthcare resources and costs than DN-only patients, controlling for demographic and clinical characteristics.</p> <p>Results</p> <p>We identified 16,831 DN-only and 1,699 DN-DA patients in the Medicare supplemental cohort, as well as 17,205 and 3,105 in the commercially insured. DN-DA patients had higher prevalence of diabetes-related comorbidities for cardiovascular disease, cerebrovascular/peripheral vascular disease, nephropathy, obesity, and hypoglycemic events than DN-only patients (all p < 0.05). Controlling for differences in demographic and clinical characteristics, DN-DA patients had $9,235 (p < 0.05) higher total healthcare costs than patients with DN-only among those with Medicare supplemental coverage ($26,718 vs. $17,483), and$10,389 (p < 0.05) more total costs among commercially insured ($29,775 vs.$19,386). Factors associated with increased costs included insurance type, geographical region, diabetes-related comorbidities, and insulin therapy.</p> <p>Conclusion</p> <p>These findings indicate that the healthcare costs were significantly higher for DN patients with depression or anxiety relative to those without such comorbid disorders.</p

A characteristics framework for Semantic Information Systems Standards

Semantic Information Systems (IS) Standards play a critical role in the development of the networked economy. While their importance is undoubted by all stakeholders—such as businesses, policy makers, researchers, developers—the current state of research leaves a number of questions unaddressed. Terminological confusion exists around the notions of “business semantics”, “business-to-business interoperability”, and “interoperability standards” amongst others. And, moreover, a comprehensive understanding about the characteristics of Semantic IS Standards is missing. The paper addresses this gap in literature by developing a characteristics framework for Semantic IS Standards. Two case studies are used to check the applicability of the framework in a “real-life” context. The framework lays the foundation for future research in an important field of the IS discipline and supports practitioners in their efforts to analyze, compare, and evaluate Semantic IS Standard

Loss-size and Reliability Trade-offs Amongst Diverse Redundant Binary Classifiers

Many applications involve the use of binary classifiers, including applications where safety and security are critical. The quantitative assessment of such classifiers typically involves receiver operator characteristic (ROC) methods and the estimation of sensitivity/specificity. But such techniques have their limitations. For safety/security critical applications, more relevant measures of reliability and risk should be estimated. Moreover, ROC techniques do not explicitly account for: 1) inherent uncertainties one faces during assessments, 2) reliability evidence other than the observed failure behaviour of the classifier, and 3) how this observed failure behaviour alters one's uncertainty about classifier reliability. We address these limitations using conservative Bayesian inference (CBI) methods, producing statistically principled, conservative values for risk/reliability measures of interest. Our analyses reveals trade-offs amongst all binary classifiers with the same expected loss { the most reliable classifiers are those most likely to experience high impact failures. This trade-off is harnessed by using diverse redundant binary classifiers

Granulocyte-Colony Stimulating Factor (G-CSF) Improves Motor Recovery in the Rat Impactor Model for Spinal Cord Injury

Granulocyte-colony stimulating factor (G-CSF) improves outcome after experimental SCI by counteracting apoptosis, and enhancing connectivity in the injured spinal cord. Previously we have employed the mouse hemisection SCI model and studied motor function after subcutaneous or transgenic delivery of the protein. To further broaden confidence in animal efficacy data we sought to determine efficacy in a different model and a different species. Here we investigated the effects of G-CSF in Wistar rats using the New York University Impactor. In this model, corroborating our previous data, rats treated subcutaneously with G-CSF over 2 weeks show significant improvement of motor function

Natural coagulates for wastewater treatment; a review for application and mechanism

The increase of water demand and wastewater generation is among the global concerns in the world. The less effective management of water sources leads to serious consequences, the direct disposal of untreated wastewater is associated with the environmental pollution, elimination of aquatic life and the spread of deadly epidemics. The flocculation process is one of the most important stages in water and wastewater treatment plants, wherein this phase the plankton, colloidal particles, and pollutants are precipitated and removed. Two major types of coagulants are used in the flocculation process included the chemical and natural coagulants. Many studies have been performed to optimize the flocculation process while most of these studies have confirmed the hazardous effects of chemical coagulants utilization on the ecosystem. This chapter reviews a summary of the coagulation/flocculation processes using natural coagulants as well as reviews one of the most effective natural methods of water and wastewater treatment

Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data

BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). Therefore, novel methods are urgently needed for these new data types, especially for individual-specific genome data. METHODOLOGY: In this study, we proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate along with the gene length. Taking the mutation rate as a weight for each gene, our weighted resampling strategy builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. PRINCIPAL FINDINGS/CONCLUSIONS: We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method performs as well as the standard methods for significant pathways with strong evidence. Importantly, our method could effectively reject many marginally significant pathways detected by standard methods, including several long-gene-based, cancer-unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, and provides an alternative way for accurate analysis of cancer-personalized genomes. It can be extended to other types of genomic data (genotyping and methylation) that have similar bias problems

Risk-Return Relationship in a Complex Adaptive System

For survival and development, autonomous agents in complex adaptive systems involving the human society must compete against or collaborate with others for sharing limited resources or wealth, by using different methods. One method is to invest, in order to obtain payoffs with risk. It is a common belief that investments with a positive risk-return relationship (namely, high risk high return and vice versa) are dominant over those with a negative risk-return relationship (i.e., high risk low return and vice versa) in the human society; the belief has a notable impact on daily investing activities of investors. Here we investigate the risk-return relationship in a model complex adaptive system, in order to study the effect of both market efficiency and closeness that exist in the human society and play an important role in helping to establish traditional finance/economics theories. We conduct a series of computer-aided human experiments, and also perform agent-based simulations and theoretical analysis to confirm the experimental observations and reveal the underlying mechanism. We report that investments with a negative risk-return relationship have dominance over those with a positive risk-return relationship instead in such a complex adaptive systems. We formulate the dynamical process for the system's evolution, which helps to discover the different role of identical and heterogeneous preferences. This work might be valuable not only to complexity science, but also to finance and economics, to management and social science, and to physics

A Deadenylase Assay by Size-Exclusion Chromatography

The shortening of the 3′-end poly(A) tail, also called deadenylation, is crucial to the regulation of mRNA processing, transportation, translation and degradation. The deadenylation process is achieved by deadenylases, which specifically catalyze the removal of the poly(A) tail at the 3′-end of eukaryotic mRNAs and release 5′-AMP as the product. To achieve their physiological functions, all deadenylases have numerous binding partners that may regulate their catalytic properties or recruit them into various protein complexes. To study the effects of various partners, it is important to develop new deadenylase assay that can be applied either in vivo or in vitro. In this research, we developed the deadenylase assay by the size-exclusion chromatography (SEC) method. The SEC analysis indicated that the poly(A) or oligo(A) substrate and the product AMP could be successfully separated and quantified. The enzymatic parameters of deadenylase could be obtained by quantifying the AMP generation. When using the commercial poly(A) as the substrate, a biphasic catalytic process was observed, which might correlate to the two distinct states of poly(A) in the commercial samples. Different lots of commercial poly(A) had dissimilar size distributions and were dissimilar in response to the degradation of deadenylase. The deadenylation pattern, processive or distributive, could also be investigated using the SEC assay by monitoring the status of the substrate and the generation kinetics of AMP and A2. The SEC assay was applicable to both simple samples using the purified enzyme and complex enzyme reaction conditions such as using protein mixtures or crude cell extracts as samples. The influence of solutes with absorption at 254 nm could be successfully eliminated by constructing the different SEC profiles