Search

In this study, accelerometric data from seven Japanese buildings under long-term monitoring were analysed to explore the variability of the buildings' co-seismic response over time and its within- and between-building components, using co-seismic capacity curves developed in acceleration-displacement-response-spectrum format. The data include the 2011 Tohoku Mw9.1 earthquake, which caused building damage of different levels of severity, and the time-varying actual capacity curves were analysed considering earthquakes before and after 2011. Result showed that the initial slope of the capacity curves reflects the amount of damage. The between-building and within-building components of the variability are discussed by comparing a single building and several buildings in the same class for several earthquakes. Finally, the epistemic uncertainty of seismic risk assessment studies is discussed in relation to the selection of a generic capacity model for all buildings in a single class. [ABSTRACT FROM AUTHOR]

Assessing or predicting seismic damage in buildings is an essential and challenging component of seismic risk studies. Machine learning methods offer new perspectives for damage characterization, taking advantage of available data on the characteristics of built environments. In this study, we aim (1) to characterize seismic damage using a classification model trained and tested on damage survey data from earthquakes in Nepal, Haiti, Serbia and Italy and (2) to test how well a model trained on a given region (host) can predict damage in another region (target). The strategy adopted considers only simple data characterizing the building (number of stories and building age), seismic ground motion (macroseismic intensity) and a traffic-light-based damage classification model (green, yellow, red categories). The study confirms that the extreme gradient boosting classification model (XGBC) with oversampling predicts damage with 60% accuracy. However, the quality of the survey is a key issue for model performance. Furthermore, the host-to-target test suggests that the model's applicability may be limited to regions with similar contextual environments (e.g., socio-economic conditions). Our results show that a model from one region can only be applied to another region under certain conditions. We expect our model to serve as a starting point for further analysis in host-to-target region adjustment and confirm the need for additional post-earthquake surveys in other regions with different tectonic, urban fabric and socio-economic contexts. [ABSTRACT FROM AUTHOR]

Assessing or forecasting seismic damage to buildings is an essential issue for earthquake disaster management. In this study, we explore the efficacy of several machine learning models for damage characterization, trained and tested on the database of damage observed after Italian earthquakes (the Database of Observed Damage – DaDO). Six models were considered: regression- and classification-based machine learning models, each using random forest, gradient boosting, and extreme gradient boosting. The structural features considered were divided into two groups: all structural features provided by DaDO or only those considered to be the most reliable and easiest to collect (age, number of storeys, floor area, building height). Macroseismic intensity was also included as an input feature. The seismic damage per building was determined according to the EMS-98 scale observed after seven significant earthquakes occurring in several Italian regions. The results showed that extreme gradient boosting classification is statistically the most efficient method, particularly when considering the basic structural features and grouping the damage according to the traffic-light-based system used; for example, during the post-disaster period (green, yellow, and red), 68 % of buildings were correctly classified. The results obtained by the machine-learning-based heuristic model for damage assessment are of the same order of accuracy (error values were less than 17 %) as those obtained by the traditional RISK-UE method. Finally, the machine learning analysis found that the importance of structural features with respect to damage was conditioned by the level of damage considered. [ABSTRACT FROM AUTHOR]

Assessing or forecasting seismic damage to buildings is an essential issue for earthquake disaster management. In this study, we explore the efficacy of several machine learning models for damage characterization, trained and tested on the database of damage observed after Italian earthquakes (DaDO). Six regression- and classification-based machine learning models were considered: random forest, gradient boosting and extreme gradient boosting. The structural features considered were divided into two groups: all structural features provided by DaDO or only those considered to be the most reliable and easiest to collect (age, number of storeys, floor area, building height). Macroseismic intensity was also included as an input feature. The seismic damage per building was determined according to the EMS-98 scale observed after seven significant earthquakes occurring in several Italian regions. The results showed that extreme gradient boosting classification is statistically the most efficient method, particularly when considering the basic structural features and grouping the damage according to the traffic-light based system used, for example, during the post-disaster period (green, yellow and red). The results obtained by the machine learning-based heuristic model for damage assessment are of the same order of accuracy as those obtained by the traditional Risk-UE method. Finally, the machine learning analysis found that the importance of structural features with respect to damage was conditioned by the level of damage considered. [ABSTRACT FROM AUTHOR]

Adrenal cortical carcinoma is a rare and aggressive cancer with poor prognosis. Cases usually present with signs and symptoms of excessive hormone production. Hyperglycemia and Cushing syndrome are common, but tumor-associated hypoglycemia due to paraneoplastic secretion of insulin-like growth factor-2 (termed Anderson's syndrome) is uncommon. Given the rarity of adrenal cortical carcinoma, diagnosis and management of associated complications is challenging. In this study, we present a case of metastatic adrenal cortical carcinoma with a myriad of hormonal abnormalities. We will also briefly review literature regarding genetic association, pathophysiology, treatment options, and prognosis. [ABSTRACT FROM AUTHOR]

Peg3 (Paternally expressed gene 3) is an imprinted gene encoding a DNA-binding protein that is a well-known transcriptional repressor. Previous studies have shown that the mutant phenotypes of Peg3 are associated with the over-expression of genes involved in lipid metabolism. In the current study, we investigated four potential downstream genes of Peg3, which were identified through ChIP-seq data: Acly, Fasn, Idh1, and Hmgcr. In vivo binding of PEG3 to the promoter region of these key genes involved in lipogenesis was subsequently confirmed through individual ChIP experiments. We observed the opposite response of Acly expression levels against the variable gene dosages of Peg3, involving 0x, 1x, and 2x Peg3. This suggests the transcriptional repressor role of Peg3 in the expression levels of Acly. Another set of analyses showed a sex-biased response in the expression levels of Acly, Fasn, and Idh1 against 0x Peg3 with higher levels in female and lower levels in male mammary glands. These results overall highlight that Peg3 may be involved in regulating the expression levels of several key genes in adipogenesis. [ABSTRACT FROM AUTHOR]

Sepsis is a global health burden that needs intensive medical care. Thrombocytopenia in sepsis is well known to increase morbidity as well as mortality. Several studies have been performed both in animal models and in humans to understand the mechanism by which sepsis causes thrombocytopenia. Recent studies have shown that inhibiting thrombocytopenia improves outcomes in sepsis patients. Understanding these mechanisms to identify targets in use of newer treatment modalities besides using resuscitation measures, antibiotics and removal of thrombocytopenia inducing agent could potentially help us improve outcomes in sepsis. [ABSTRACT FROM AUTHOR]

Over the last two decades, seismic ground motion prediction has been significantly improved thanks to the development of shared, open, worldwide databases (waveform and parametric values). Unlike seismic ground motion, earthquake data recorded in buildings are rarely shared. However, their contribution could be essential for evaluating the performance of structures. Increasing interest in deploying instrumentation in buildings gives hope for new observations, leading to better understanding of behavior. This manuscript presents a flat-file containing information on earthquake responses of instrumented buildings. Herein, we present the structure of the NDE1.0 flat-file containing structural response parameters (i.e. drift ratio, peak top values of acceleration, velocity and displacement, pre- and co-seismic fundamental frequencies) computed for several intensity measures characterizing ground motion (i.e. peak ground values of acceleration, velocity and displacement, spectral values, Arias intensity, strong motion duration, cumulative absolute velocity, destructive potential). The data are from real earthquake recordings collected in buildings over the years. Some building, site and earthquake characteristics are also included (i.e. structural design, shear wave velocity, magnitude, epicentral distance, etc.). This 1.0 version contains 8520 strong motion recordings that correspond to 118 buildings and 2737 events, providing useful information for analyses related to seismic hazard, variability of building responses, structural health monitoring, nonlinear studies, damage prediction, etc. [ABSTRACT FROM AUTHOR]

The parental allele specificity of mammalian imprinted genes has been evolutionarily well conserved, although its functional constraints and associated mechanisms are not fully understood. In the current study, we generated a mouse mutant with switched active alleles driving the switch from paternal-to-maternal expression for Peg3 and the maternal-to-paternal expression for Zim1. The expression levels of Peg3 and Zim1, but not the spatial expression patterns, within the brain showed clear differences between wild type and mutant animals. We identified putative enhancers localized upstream of Peg3 that displayed allele-biased DNA methylation, and that also participate in allele-biased chromosomal conformations with regional promoters. Most importantly, these data suggest for the first time that long-distance enhancers may contribute to allelic expression within imprinted domains through allele-biased interactions with regional promoters. [ABSTRACT FROM AUTHOR]

Pancreas divisum is the most common congenital malformation of the pancreas. Sometimes it is considered an etiology when patients present with recurrent episodes of pancreatitis. Endoscopic retrograde pancreatography (ERP) with selective cannulation of the minor papilla with sphincterotomy and stent placement are performed in these patients. Proximal migration of pancreatic stents in pancreas divisum is rare and challenging to manage. We describe a case of proximal migration of a pancreatic stent in a patient with pancreas divisum and perform a review of literature. [ABSTRACT FROM AUTHOR]

In the current study, we characterized the expression and histone modification profiles of the alternative promoters found within imprinted Igf2r, Mest, Zac1, Peg3, Snrpn and non-imprinted Myc loci. In terms of expression pattern, the alternative promoters are highly tissue-specific, which is in a stark contrast to the ubiquitous expression of the corresponding main promoters. The alternative promoters are associated with the histone modification mark H3K4me1, but not with H3K4me3, which is frequently associated with the main promoters. Phylogenetic analyses also indicated that the majority of the alternative promoters are unique to the mammalian lineage, further suggesting the recent formation of these promoters during mammalian evolution. Overall, this study suggests that the alternative promoters of imprinted loci may have been derived from enhancers in recent evolutionary times and co-evolved with the genomic imprinting mechanism. [ABSTRACT FROM AUTHOR]

Circular RNA is a newly discovered class of non-coding RNA generated through the back-splicing of linear pre-mRNA. In the current study, we characterized two circular RNAs that had been identified through NGS-based 5’RACE experiments. According to the results, the Peg3 locus contains a 214-nucleotide-long circular RNA, circPeg3, that is detected in low abundance from the neonatal brain, lung and ovary. In contrast, the Igf2r locus contains a group of highly abundant circular RNAs, circIgf2r, showing multiple forms with various exon combinations. In both cases, the expression patterns of circPeg3 and circIgf2r among individual tissues are quite different from their linear mRNA counterparts. This suggests potential unique roles played by the identified circular RNAs. Overall, this study reports the identification of novel circular RNAs specific to mammalian imprinted loci, suggesting that circular RNAs are likely involved in the function and regulation of imprinted genes. [ABSTRACT FROM AUTHOR]

Lenvatinib, an oral multikinase inhibitor, was approved by the US Food and Drug Administration in February 2015. In a pivotal phase III study of 392 patients with progressive radioiodine-refractory thyroid cancer, the overall response rate of patients receiving lenvatinib was 64.8%, with complete response in four patients. The median progression-free survival was 18.3 months in the lenvatinib arm versus 3.6 months in patients receiving placebo. Median overall survival was not reached in either arm. Lenvatinib is a promising new treatment for patients with radioiodine (iodine-131)-refractory differentiated thyroid cancer. [ABSTRACT FROM AUTHOR]

Mikania micrantha is one of the major invasive alien plant species in tropical moist forest regions of Asia including Nepal. Recently, this weed is spreading at an alarming rate in Chitwan National Park (CNP) and threatening biodiversity. This paper aims to assess the impacts of Mikania micrantha on different land cover and to predict potential invasion sites in CNP using Maxent model. Primary data for this were presence point coordinates and perceived Mikania micrantha cover collected through systematic random sampling technique. Rapideye image, Shuttle Radar Topographic Mission data and bioclimatic variables were acquired as secondary data. Mikania micrantha distribution maps were prepared by overlaying the presence points on image classified by object based image analysis. The overall accuracy of classification was 90% with Kappa coefficient 0.848. A table depicting the number of sample points in each land cover with respective Mikania micrantha coverage was extracted from the distribution maps to show the impact. The riverine forest was found to be the most affected land cover with 85.98% presence points and sal forest was found to be very less affected with only 17.02% presence points. Maxent modeling predicted the areas near the river valley as the potential invasion sites with statistically significant Area Under the Receiver Operating Curve (AUC) value of 0.969. Maximum temperature of warmest month and annual precipitation were identified as the predictor variables that contribute the most to Mikania micrantha's potential distribution. [ABSTRACT FROM AUTHOR]

B INTRODUCTION: b Ethylene glycol metabolites Glycolic and Glyoxylic Acids are structurally similar to lactate and can cause artificial elevation of lactate concentration. B DISCUSSION: b Glycolic acid and glyoxylic acid, metabolites of ethylene glycol, can both cause artificial elevation of lactate. ABG analyzer using L-lactate oxidase may have cross reaction with ethylene glycol metabolites while analyzers using lactate dehydrogenase such as standard chemistry analyzers are free of these interference.(1) By measuring a "lactate gap" with lactate values from two different analyzers, only one of which is sensitive to ethylene glycol metabolites, we can diagnose ethylene glycol poisoning before we get ethylene glycol level from laboratory testing which take some time.(2). [Extracted from the article]