The Effect Of Alpha-Cyclodextrin On Acute Blood Lipid And Glycemic Responses To A Fat Containg Meal

Obesity and hyperlipidemia have become major concerns in the United States over the past 30 years. Alpha-Cyclodextrin (á-CD), a naturally occurring soluble dietary fiber, has been shown to reduce dietary fat absorption and improve blood lipid levels in an animal model (mouse and rat) and in human studies. In the current double blind study, 34 healthy male and female participants were recruited to test if á-CD had any acute effect on blood lipid and glycemic responses to a fat containing meal. The participants received the á-CD on one occasion and a placebo the other to determine if there was any difference in the resulting acute blood lipid and glycemic responses. When the á-CD was consumed with the meal, the blood triglyceride levels showed a significant reduction post meal (placebo=419 ± 97 vs. á-CD= 359 ± 107) when compared to the placebo phase. The results for blood glucose, total cholesterol, low-density lipoprotein, and high-density lipoprotein did not show any significant difference between the two phases. These results suggest that á-CD may be beneficial for individuals who suffer from hypertriglyceridemia

Hybrid approximate message passing

Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with the weak edges representing interactions through aggregates of small, linearizable couplings of variables. AMP approximations based on the Central Limit Theorem can be readily applied to aggregates of many weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (HyGAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition of strong and weak edges, a performance--complexity trade-off can be achieved. Group sparsity and multinomial logistic regression problems are studied as examples of the proposed methodology.The work of S. Rangan was supported in part by the National Science Foundation under Grants 1116589, 1302336, and 1547332, and in part by the industrial affiliates of NYU WIRELESS. The work of A. K. Fletcher was supported in part by the National Science Foundation under Grants 1254204 and 1738286 and in part by the Office of Naval Research under Grant N00014-15-1-2677. The work of V. K. Goyal was supported in part by the National Science Foundation under Grant 1422034. The work of E. Byrne and P. Schniter was supported in part by the National Science Foundation under Grant CCF-1527162. (1116589 - National Science Foundation; 1302336 - National Science Foundation; 1547332 - National Science Foundation; 1254204 - National Science Foundation; 1738286 - National Science Foundation; 1422034 - National Science Foundation; CCF-1527162 - National Science Foundation; NYU WIRELESS; N00014-15-1-2677 - Office of Naval Research

Longitudinal analysis of the developing rhesus monkey brain using magnetic resonance imaging: birth to adulthood.

We have longitudinally assessed normative brain growth patterns in naturalistically reared Macaca mulatta monkeys. Postnatal to early adulthood brain development in two cohorts of rhesus monkeys was analyzed using magnetic resonance imaging. Cohort A consisted of 24 rhesus monkeys (12 male, 12 female) and cohort B of 21 monkeys (11 male, 10 female). All subjects were scanned at 1, 4, 8, 13, 26, 39, and 52 weeks; cohort A had additional scans at 156 weeks (3 years) and 260 weeks (5 years). Age-specific segmentation templates were developed for automated volumetric analyses of the T1-weighted magnetic resonance imaging scans. Trajectories of total brain size as well as cerebral and subcortical subdivisions were evaluated over this period. Total brain volume was about 64 % of adult estimates in the 1-week-old monkey. Brain volume of the male subjects was always, on average, larger than the female subjects. While brain volume generally increased between any two imaging time points, there was a transient plateau of brain growth between 26 and 39 weeks in both cohorts of monkeys. The trajectory of enlargement differed across cortical regions with the occipital cortex demonstrating the most idiosyncratic pattern of maturation and the frontal and temporal lobes showing the greatest and most protracted growth. A variety of allometric measurements were also acquired and body weight gain was most closely associated with the rate of brain growth. These findings provide a valuable baseline for the effects of fetal and early postnatal manipulations on the pattern of abnormal brain growth related to neurodevelopmental disorders

Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging

Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability

Wound infection in clinical practice : principles of best practice

The International Wound Infection Institute (IWII) is an organisation of volunteer interdisciplinary health professionals dedicated to advancing and improving practice relating to prevention and control of wound infection. This includes acute wounds (surgical, traumatic and burns) and chronic wounds of all types, although principally chronic wounds of venous, arterial, diabetic and pressure aetiologies. Wound infection is a common complication of wounds. It leads to delays in wound healing and increases the risk of loss of limb and life. Implementation of effective strategies to prevent, diagnose and manage, is important in reducing mortality and morbidity rates associated with wound infection. This second edition of Wound Infection in Clinical Practice is an update of the first edition published in 2008 by the World Union of Wound Healing Societies (WUWHS). The original document was authored by leading experts in wound management and endorsed by the WUWHS. The intent of this edition is to provide a practical, updated resource that is easy-to-use and understand. For this edition, the IWII collaborative team has undertaken a comprehensive review of contemporary literature, including systematic reviews and meta-analyses when available. In addition, the team conducted a formal Delphi process to reach consensus on wound infection issues for which scientific research is minimal or lacking. This rigorous process provides an update on the science and expert opinion regarding prevention, diagnosis and control of wound infection. This edition outlines new definitions relevant to wound infection, presents new paradigms and advancements in the management and diagnosis of a wound infection, and highlights controversial areas of discussion

Unsupervised Deep Representation Learning Enables Phenotype Discovery For Genetic association Studies of Brain Imaging

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants\u27 T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes

Four-year-old Cantonese-speaking children's online processing of relative clauses: a permutation analysis

We report on an eye-tracking study that investigated four-year-old Cantonese-speaking children's online processing of subject and object relative clauses (RCs). Children's eye-movements were recorded as they listened to RC structures identifying a unique referent (e.g. “Can you pick up the horse that pushed the pig?”). Two RC types, classifier (CL) and ge3 RCs, were tested in a between-participants design. The two RC types differ in their syntactic analyses and frequency of occurrence, providing an important point of comparison for theories of RC acquisition and processing. A permutation analysis showed that the two structures were processed differently: CL RCs showed a significant object-over-subject advantage, whereas ge3 RCs showed the opposite effect. This study shows that children can have different preferences even for two very similar RC structures within the same language, suggesting that syntactic processing preferences are shaped by the unique features of particular constructions both within and across different linguistic typologies. CopyrightDepartment of Chinese and Bilingual Studies2016-2017 > Academic research: refereed > Publication in refereed journalbcr

The ubiquity of frequency effects in first language acquisition

This review article presents evidence for the claim that frequency effects are pervasive in children's first language acquisition, and hence constitute a phenomenon that any successful account must explain. The article is organized around four key domains of research: children's acquisition of single words, inflectional morphology, simple syntactic constructions, and more advanced constructions. In presenting this evidence, we develop five theses. (i) There exist different types of frequency effect, from effects at the level of concrete lexical strings to effects at the level of abstract cues to thematic-role assignment, as well as effects of both token and type, and absolute and relative, frequency. High-frequency forms are (ii) early acquired and (iii) prevent errors in contexts where they are the target, but also (iv) cause errors in contexts in which a competing lower-frequency form is the target. (v) Frequency effects interact with other factors (e.g. serial position, utterance length), and the patterning of these interactions is generally informative with regard to the nature of the learning mechanism. We conclude by arguing that any successful account of language acquisition, from whatever theoretical standpoint, must be frequency sensitive to the extent that it can explain the effects documented in this review, and outline some types of account that do and do not meet this criterion