Representations of Refugees and Asylum Seekers in the Irish Print Media

This study examines media representation of asylum seekers and refugees in Ireland. The research is based on a detailed content analysis and discourse analysis of media coverage of the issues involved in immigration in five national newspapers over selected periods. The contention of this work is that much of new public opinion has originated from, and gradually gained strength through, the ideology of the Irish print media

Effect of Progressive Weight Loss on Lactate Metabolism: A Randomized Controlled Trial.

OBJECTIVE:Lactate is an intermediate of glucose metabolism that has been implicated in the pathogenesis of insulin resistance. This study evaluated the relationship between glucose kinetics and plasma lactate concentration ([LAC]) before and after manipulating insulin sensitivity by progressive weight loss. METHODS:Forty people with obesity (BMI = 37.9 ± 4.3 kg/m2 ) were randomized to weight maintenance (n = 14) or weight loss (n = 19). Subjects were studied before and after 6 months of weight maintenance and before and after 5%, 11%, and 16% weight loss. A hyperinsulinemic-euglycemic clamp procedure in conjunction with [6,6-2 H2 ]glucose tracer infusion was used to assess glucose kinetics. RESULTS:At baseline, fasting [LAC] correlated positively with endogenous glucose production rate (r = 0.532; P = 0.001) and negatively with insulin sensitivity, assessed as the insulin-stimulated glucose disposal (r = -0.361; P = 0.04). Progressive (5% through 16%) weight loss caused a progressive decrease in fasting [LAC], and the decrease in fasting [LAC] after 5% weight loss was correlated with the decrease in endogenous glucose production (r = 0.654; P = 0.002) and the increase in insulin sensitivity (r = -0.595; P = 0.007). CONCLUSIONS:This study demonstrates the interrelationships among weight loss, hepatic and muscle glucose kinetics, insulin sensitivity, and [LAC], and it suggests that [LAC] can serve as an additional biomarker of glucose-related insulin resistance

Reviews

The 1990 J.R.R. Tolkien Calendar. Illus. by Ted Nasmith. Reviewed by Paula DiSante. Letters: A Study in Friendship. C.S. Lewis and Don Giovanni Calabria. Reviewed by Nancy-Lou Patterson. Joseph Campbell, An Introduction. Robert A Segal. Reviewed by Nancy-Lou Patterson. The Magic Code: The Use of Magical Patterns in Fantasy for Children. Maria Nikolajeva. Reviewed by Pat Reynolds. Dorothy L. Sayers: A Biography. James Brabazon. Reviewed by Nancy-Lou Patterson

Deep Learning for Automated Image Classification of Seismic Damage to Built Infrastructure

The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. The proposed solution is to automate post-earthquake reconnaissance image tagging activities by training a computer algorithm to classify each occurrence of damage per building material and structural member type. The approach is based on deep learning (DL), a subset of machine learning loosely based on the operation of a biologic neural system, which aims to learn and extract accurate representations from large data sets. DL algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, DL has become one of the most popular image classification algorithms producing results comparable to and in some cases superior to human experts. The authors implemented a DL algorithm to automatically identify multiple damage types and associated structural members in a single image by adapting a pre-trained deep residual network. The algorithm was tested as follows: (i) binning building images as damage-no damage (88% accuracy), (ii) drawing a bounding box around damage in buildings (85% accuracy) and short/captive reinforced concrete columns with shear damage (77% accuracy). The lower accuracy of correctly identifying a target region in an image (test ii) compared to simple binning (test i) is anticipated since it is a more complex problem and there is a more limited number of expertly tagged training images (200 count) for shear damage-short column condition being studied. The research team expects algorithm accuracy will improve with training on additional images tagged for certain damage-structure pairs by a diverse set of experts

CHIP(-/-)-Mouse Liver: Adiponectin-AMPK-FOXO-Activation Overrides CYP2E1-Elicited JNK1-Activation, Delaying Onset of NASH: Therapeutic Implications.

Genetic ablation of C-terminus of Hsc70-interacting protein (CHIP) E3 ubiquitin-ligase impairs hepatic cytochrome P450 CYP2E1 degradation. Consequent CYP2E1 gain of function accelerates reactive O2 species (ROS) production, triggering oxidative/proteotoxic stress associated with sustained activation of c-Jun NH2-terminal kinase (JNK)-signaling cascades, pro-inflammatory effectors/cytokines, insulin resistance, progressive hepatocellular ballooning and microvesicular steatosis. Despite this, little evidence of nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) was found in CHIP(-/-)-mice over the first 8-9-months of life. We herein document that this lack of tissue injury is largely due to the concurrent up-regulation and/or activation of the adiponectin-5'-AMP-activated protein kinase (AMPK)-forkhead box O (FOXO)-signaling axis stemming from at the least three synergistic features: Up-regulated expression of adipose tissue adiponectin and its hepatic adipoR1/adipoR2 receptors, stabilization of hepatic AMPKα1-isoform, identified herein for the first time as a CHIP-ubiquitination substrate (unlike its AMPKα2-isoform), as well as nuclear stabilization of FOXOs, well-known CHIP-ubiquitination targets. Such beneficial predominance of the adiponectin-AMPK-FOXO-signaling axis over the sustained JNK-elevation and injurious insulin resistance in CHIP(-/-)-livers apparently counteracts/delays rapid progression of the hepatic microvesicular steatosis to the characteristic macrovesicular steatosis observed in clinical NASH and/or rodent NASH-models