Accelerating Infinite Ensemble of Clustering by Pivot Features

The infinite ensemble clustering (IEC) incorporates both ensemble clustering and representation learning by fusing infinite basic partitions and shows appealing performance in the unsupervised context. However, it needs to solve the linear equation system with the high time complexity in proportion to O(d³) where d is the concatenated dimension of many clustering results. Inspired by the cognitive characteristic of human memory that can pay attention to the pivot features in a more compressed data space, we propose an acceleration version of IEC (AIEC) by extracting the pivot features and learning the multiple mappings to reconstruct them, where the linear equation system can be solved with the time complexity O(dr²) (r ? d). Experimental results on the standard datasets including image and text ones show that our algorithm AIEC improves the running time of IEC greatly but achieves the comparable clustering performance.     

The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry

This paper discusses the problems arising from the multidisciplinary nature of cognitive research and the need to conceptually unify insights from multiple fields into the phenomena that drive cognition. Specifically, the Fundamental Code Unit (FCU) is proposed as a means to better quantify the intelligent thought process at multiple levels of analysis. From the linguistic and behavioral output, FCU produces to the chemical and physical processes within the brain that drive it. The proposed method efficiently model the most complex decision-making process performed by the brain.     

A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infra<Emphasis Type="Bold">r</Emphasis>ed Spectroscopy (NIRS) Cerebral Functional Activity Data

Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analytic calorie-based cognitive studies, especially using non-invasive near infrared spectroscopy (NIRS) data that predict obesity using predictive data mining. Obesity is linked with neurodegenerative diseases, diabetes, and cardiovascular diseases. Thus, understanding, predicting, preventing, and managing obesity have the potential to save the lives of millions. Behavioral studies suggest that overeating in obese individuals is triggered by exaggerated brain reward center (BRC) activity to high-calorie food stimuli (Shefer et al., Neurosci Biobehav Rev 37(10):2489–503, 2013). In this paper, details of a novel research methodology are presented for a 24-month longitudinal study using a 44-channel NIRS device with the subjects in a natural environment. The proposed methodology consists of using visual stimuli of low/high calorie food items under fasting and satiated conditions for three types of subjects. The experiments consist of block design, longitudinal plan, data smoothing, BRC activation mapping, stereotactic normalization, generating paired t-test maps under fasting and non-fasting conditions and subsequently using Naïve Bayes modeling to generate obesity prediction maps for the control subjects. The simulated results consist of generation of Bayesian prediction maps using layers of paired t-test cerebral activity maps for the four BRC functional regions considered for three types of subjects, i.e., obese, control, and control subjects fed high calorie diet. We have demonstrated how cerebral functional activity data in response to visual food stimuli can be used to predict obesity in the non-obese, thus offering a non-invasive preventive measure.     

Meta-analysis of randomized controlled trials in dacryocystorhinostomy with and without silicone intubation

Objective

To determine the effect of bicanalicular silicone intubation (SI) on dacryocystorhinostomy (DCR) success rate in adult primary acquired nasolacrimal duct obstruction.

Chances of Renal Recovery or Liver Transplantation After Hospitalization for Alcoholic Liver Disease Requiring Dialysis

Digestive Diseases and Sciences - Acute kidney injury in the setting of alcoholic liver disease portends a poor prognosis without liver transplant. Using a tertiary care population, we aimed to...     

Is gastroesophageal reflux disease associated with primary acquired nasolacrimal duct obstruction?

Purpose: To determine if patients with primary acquired nasolacrimal duct obstruction (PANDO) have an increased prevalence of gastroesophageal reflux disease (GERD) compared to the general population.

Methods: Cross-sectional case–control study. The Mayo Clinic Reflux Disease Questionnaire (RDQ) is used to help physicians diagnose GERD. The retrospective group consisted of patients who previously had a dacryocystorhinostomy (DCR) over a 4-year period by one surgeon (JTH) at one institution. The prospective group included consecutive patients undergoing DCR over an 8-month period for PANDO. The control group consisted of patients at our institution who did not have complaints related to nasolacrimal duct obstruction (NLDO) or a known history of NLDO. The RDQ score or a previous diagnosis of GERD was recorded for each patient. The prevalence of patients with GERD in each of the groups was compared to that in the control group using the Fisher exact test.

Results: Nine (7.7%) of 117 patients in the control group were found to have GERD. Twenty-four of 65 (36.9%) patients in the retrospective cohort were found to have GERD. Twelve of 18 (66.7%) patients in the prospective cohort were found to have GERD. When the prospective and retrospective groups were compared to the control group, the presence of GERD was higher in patients with NLDO (p < .0001).

Conclusion: Based on the results of this study and compared to the general population, GERD has an increased prevalence in patients with PANDO. GERD may have a role in the development of PANDO.     

Omega‐3 fatty acids regulate plasticity in distinct hippocampal glutamatergic synapses

Dietary omega‐3 fatty acids accumulate and are actively retained in central nervous system membranes, mainly in synapses, dendrites and photoreceptors. Despite this selective enrichment, their impact on synaptic function and plasticity has not been fully determined at the molecular level. In this study, we explored the impact of omega‐3 fatty acid deficiency on synaptic function in the hippocampus. Dietary omega‐3 fatty acid deficiency for 5 months after weaning led to a 65% reduction in the concentration of docosahexaenoic acid in whole brain synaptosomal phospholipids with no impact on global dopaminergic or serotonergic turnover. We observed reduced concentrations of glutamate receptor subunits, including GluA1, GluA2 and NR2B, and synaptic vesicle proteins synaptophysin and synaptotagmin 1 in hippocampal synaptosomes of omega‐3 fatty acid‐deficient mice as compared to the omega‐3 fatty acid rich group. In contrast, an increased concentration of neuronal inositol 1,4,5‐trisphosphate‐receptor (IP₃‐R) was observed in the deficient group. Furthermore, omega‐3 fatty acid deficiency reduced the long‐term potentiation (LTP) in stratum oriens of the hippocampal CA1 area, but not in stratum radiatum. Thus, omega‐3 fatty acids seem to have specific effects in distinct subsets of glutamatergic synapses, suggesting specific molecular interactions in addition to altering plasma membrane properties on a more global scale.