Noncommutative generalizations of theorems of Cohen and Kaplansky

This paper investigates situations where a property of a ring can be tested on a set of "prime right ideals." Generalizing theorems of Cohen and Kaplansky, we show that every right ideal of a ring is finitely generated (resp. principal) iff every "prime right ideal" is finitely generated (resp. principal), where the phrase "prime right ideal" can be interpreted in one of many different ways. We also use our methods to show that other properties can be tested on special sets of right ideals, such as the right artinian property and various homological properties. Applying these methods, we prove the following noncommutative generalization of a result of Kaplansky: a (left and right) noetherian ring is a principal right ideal ring iff all of its maximal right ideals are principal. A counterexample shows that the left noetherian hypothesis cannot be dropped. Finally, we compare our results to earlier generalizations of Cohen's and Kaplansky's theorems in the literature.Comment: 41 pages. To appear in Algebras and Representation Theory. Minor changes were made to the numbering system, in order to remain consistent with the published versio

Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

A Bayesian non-linear method for feature selection in machine translation quality estimation

We perform a systematic analysis of the effectiveness of features for the problem of predicting the quality of machine translation (MT) at the sentence level. Starting from a comprehensive feature set, we apply a technique based on Gaussian processes, a Bayesian non-linear learning method, to automatically identify features leading to accurate model performance. We consider application to several datasets across different language pairs and text domains, with translations produced by various MT systems and scored for quality according to different evaluation criteria. We show that selecting features with this technique leads to significantly better performance in most datasets, as compared to using the complete feature sets or a state-of-the-art feature selection approach. In addition, we identify a small set of features which seem to perform well across most datasets

Reliability analysis of moment redistribution in reinforced concrete beams

Design codes allow a limited amount of moment redistribution in continuous reinforced concrete beams and often make use of lower bound values in the procedure for estimating the moment redistribution factors. Here, based on the concept of demand and capacity rotation, and by means of Monte Carlo simulation, a probabilistic model is derived for the evaluation of moment redistribution factors. Results show that in all considered cases, the evaluated mean and nominal values of moment redistribution factor are greater than the values provided by the ACI code. On the other hand, the 5th percentile value of moment redistribution factor could be lower than those specified by the code. Although the reduction of strength limit state reliability index attributable to uncertainty in moment redistribution factors is not large, it is comparable to the reduction in reliability index resulting from increasing the ratio of live to dead load

How to minimise the effect of tumour cell content in detection of aberrant genetic markers in neuroblastoma

Background:Clinical heterogeneity reflects the complexity of genetic events associated with neuroblastoma (NB). To identify the status of all described genetic loci with possible prognostic interest, high-throughput approaches have been used, but only with tumour cell content >60%. In some tumours, necrotic, haemorrhagic and/or calcification areas influence the low amount of neuroblasts. We evaluated the effect of tumour cell content in the detection of relevant aberrant genetic markers (AGM) diagnosed by fluorescence in situ hybridisation (FISH) on tissue microarrays (TMA) in NB.Methods:Two hundred and thirty-three MYCN non-amplified primary NB included in 12 TMAs were analysed.Results:Presence of AGM reduced event-free survival (EFS) (P=0.004) as well as overall survival (OS) (P=0.004) of patients in the whole cohort. There were no differences in prognostic impact of presence of AGM according to tumour cell content.Conclusion:We propose the use of FISH to diagnose AGM of all NB samples having the above-mentioned areas to determine patient risk

Accounting Problems Under the Excess Profits Tax

DNA vaccines based on subunits from pathogens have several advantages over other vaccine strategies. DNA vaccines can easily be modified, they show good safety profiles, are stable and inexpensive to produce, and the immune response can be focused to the antigen of interest. However, the immunogenicity of DNA vaccines which is generally quite low needs to be improved. Electroporation and co-delivery of genetically encoded immune adjuvants are two strategies aiming at increasing the efficacy of DNA vaccines. Here, we have examined whether targeting to antigen-presenting cells (APC) could increase the immune response to surface envelope glycoprotein (Env) gp120 from Human Immunodeficiency Virus type 1 (HIV- 1). To target APC, we utilized a homodimeric vaccine format denoted vaccibody, which enables covalent fusion of gp120 to molecules that can target APC. Two molecules were tested for their efficiency as targeting units: the antibody-derived single chain Fragment variable (scFv) specific for the major histocompatilibility complex (MHC) class II I-E molecules, and the CC chemokine ligand 3 (CCL3). The vaccines were delivered as DNA into muscle of mice with or without electroporation. Targeting of gp120 to MHC class II molecules induced antibodies that neutralized HIV-1 and that persisted for more than a year after one single immunization with electroporation. Targeting by CCL3 significantly increased the number of HIV-1 gp120-reactive CD8(+) T cells compared to non-targeted vaccines and gp120 delivered alone in the absence of electroporation. The data suggest that chemokines are promising molecular adjuvants because small amounts can attract immune cells and promote immune responses without advanced equipment such as electroporation.Funding Agencies|Research Council of Norway; Odd Fellow</p

Species identification by experts and non-experts: comparing images from field guides

Accurate species identification is fundamental when recording ecological data. However, the ability to correctly identify organisms visually is rarely questioned. We investigated how experts and non-experts compared in the identification of bumblebees, a group of insects of considerable conservation concern. Experts and non-experts were asked whether two concurrent bumblebee images depicted the same or two different species. Overall accuracy was below 60% and comparable for experts and non-experts. However, experts were more consistent in their answers when the same images were repeated, and more cautious in committing to a definitive answer. Our findings demonstrate the difficulty of correctly identifying bumblebees using images from field guides. Such error rates need to be accounted for when interpreting species data, whether or not they have been collected by experts. We suggest that investigation of how experts and non-experts make observations should be incorporated into study design, and could be used to improve training in species identification

Determining Interacting Objects in Human-Centric Activities via Qualitative Spatio-Temporal Reasoning

Abstract. Understanding the activities taking place in a video is a chal-lenging problem in Artificial Intelligence. Complex video sequences con-tain many activities and involve a multitude of interacting objects. De-termining which objects are relevant to a particular activity is the first step in understanding the activity. Indeed many objects in the scene are irrelevant to the main activity taking place. In this work, we consider human-centric activities and look to identify which objects in the scene are involved in the activity. We take an activity-agnostic approach and rank every moving object in the scene with how likely it is to be involved in the activity. We use a comprehensive spatio-temporal representation that captures the joint movement between humans and each object. We then use supervised machine learning techniques to recognize relevant objects based on these features. Our approach is tested on the challeng-ing Mind’s Eye dataset.

Identification of a predominant isolate of Mycobacterium tuberculosis using molecular and clinical epidemiology tools and in vitro cytokine responses

BACKGROUND: Tuberculosis (TB) surveillance programs in Canada have established that TB in Canada is becoming a disease of geographically and demographically distinct groups. In 1995, treaty status aboriginals from the province of Manitoba accounted for 46% of the disease burden of this sub-group in Canada. The TB incidence rates are dramatically high in certain reserves of Manitoba and are equivalent to rates in African countries. The objective of our study was to identify prevalent isolates of Mycobacterium tuberculosis in the patient population of Manitoba using molecular epidemiology tools, studying the patient demographics associated with the prevalent strain and studying the in vitro cytokine profiles post-infection with the predominant strain. METHODS: Molecular typing was performed on all isolates available between 1992 to1997. A clinical database was generated using patient information from Manitoba. THP-1 cells were infected using strains of M. tuberculosis and cytokine profiles were determined using immunoassays for cytokines IL-1β, IL-10, IL-12, IFN-γ and TNF-α. RESULTS: In Manitoba, 24% of the disease burden is due to a particular M. tuberculosis strain (Type1). The strain is common in patients of aboriginal decent and is responsible for at least 87% of these cases. Cytokine assays indicate that the Type1 strain induces comparatively lower titers of IL-1β, IFN-γ and TNF-α in infected THP-1 cells as compared to H37Ra and H37Rv strains. CONCLUSION: In Manitoba, Type1 strain is predominant in TB patients. The majority of the cases infected with this particular strain are newly active with a high incidence of respiratory disease, positive chest radiographs and pulmonary cavities. In vitro secretion of IL-1β, IFN-γ and TNF-α is suppressed in Type1 infected culture samples when compared to H37Ra and H37Rv infected cells

International consensus for neuroblastoma molecular diagnostics: report from the International Neuroblastoma Risk Group (INRG) Biology Committee

Neuroblastoma serves as a paradigm for utilising tumour genomic data for determining patient prognosis and treatment allocation. However, before the establishment of the International Neuroblastoma Risk Group (INRG) Task Force in 2004, international consensus on markers, methodology, and data interpretation did not exist, compromising the reliability of decisive genetic markers and inhibiting translational research efforts. The objectives of the INRG Biology Committee were to identify highly prognostic genetic aberrations to be included in the new INRG risk classification schema and to develop precise definitions, decisive biomarkers, and technique standardisation. The review of the INRG database (n=8800 patients) by the INRG Task Force finally enabled the identification of the most significant neuroblastoma biomarkers. In addition, the Biology Committee compared the standard operating procedures of different cooperative groups to arrive at international consensus for methodology, nomenclature, and future directions. Consensus was reached to include MYCN status, 11q23 allelic status, and ploidy in the INRG classification system on the basis of an evidence-based review of the INRG database. Standardised operating procedures for analysing these genetic factors were adopted, and criteria for proper nomenclature were developed. Neuroblastoma treatment planning is highly dependant on tumour cell genomic features, and it is likely that a comprehensive panel of DNA-based biomarkers will be used in future risk assignment algorithms applying genome-wide techniques. Consensus on methodology and interpretation is essential for uniform INRG classification and will greatly facilitate international and cooperative clinical and translational research studies