Are target date funds the easy bake option?

Target date funds provide a simple, automated approach to retirement savings in defined contribution plans. The passing of the Pension Protection Act of 2006 has seen an increase in the popularity of these funds in the United States, becoming the default option for many plans. However, recent research findings have challenged the easy bake or ‘set-and-forget’ nature of target date funds. This study explores some of the critical design features of target date funds (which shifts an individual’s asset allocation from growth to defensive assets following a pre-set glidepath) against a simple balanced (or target risk) fund design. Using both time-weighted and dollar-weighted returns, our results suggest that there is more to achieving successful retirement outcomes than the investor simply selecting a proposed year of retirement. Our findings can perhaps be summarized by Einstein’s famous epithet, that in the murky world of retirement product design, everything should be made as simple as possible, but not simpler.

Surgical resection for non-small cell lung cancer : clinical features and outcomes for a consecutive series at an Irish tertiary referral centre

Background: Few patients diagnosed with lung cancer are still alive 5 years after diagnosis. The aim of the current study was to conduct a 10-year review of a consecutive series of patients undergoing curative-intent surgical resection at the largest tertiary referral centre to identify prognostic factors. Methods: Case records of all patients operated on for lung cancer between 1998 and 2008 were reviewed. The clinical features and outcomes of all patients with non-small cell lung cancer (NSCLC) stage I-IV were recorded. Results: A total of 654 patients underwent surgical resection with curative intent during the study period. Median overall survival for the entire cohort was 37 months. The median age at operation was 66 years, with males accounting for 62.7 %. Squamous cell type was the most common histological subtype, and lobectomies were performed in 76.5 % of surgical resections. Pneumonectomy rates decreased significantly in the latter half of the study (25 vs. 16.3 %), while sub-anatomical resection more than doubled (2 vs. 5 %) (p < 0.005). Clinico-pathological characteristics associated with improved survival by univariate analysis include younger age, female sex, smaller tumour size, smoking status, lobectomy, lower T and N status and less advanced pathological stage. Age, gender, smoking status and tumour size, as well as T and N descriptors have emerged as independent prognostic factors by multivariate analysis. Conclusion: We identified several factors that predicted outcome for NSCLC patients undergoing curative-intent surgical resection. Survival rates in our series are comparable to those reported from other thoracic surgery centres. © 2012 Royal Academy of Medicine in Ireland.

Utilising hospital data to inform product safety prioritisation : the application of RAPEX severity rankings in ICD-coded burn data

Background The implementation of the Australian Consumer Law in 2011 highlighted the need for better use of injury data to improve the effectiveness and responsiveness of product safety (PS) initiatives. In the PS system, resources are allocated to different priority issues using risk assessment tools. The rapid exchange of information (RAPEX) tool to prioritise hazards, developed by the European Commission, is currently being adopted in Australia. Injury data is required as a basic input to the RAPEX tool in the risk assessment process. One of the challenges in utilising injury data in the PS system is the complexity of translating detailed clinical coded data into broad categories such as those used in the RAPEX tool. Aims This study aims to translate hospital burns data into a simplified format by mapping the International Statistical Classification of Disease and Related Health Problems (Tenth Revision) Australian Modification (ICD-10-AM) burn codes into RAPEX severity rankings, using these rankings to identify priority areas in childhood product-related burns data. Methods ICD-10-AM burn codes were mapped into four levels of severity using the RAPEX guide table by assigning rankings from 1-4, in order of increasing severity. RAPEX rankings were determined by the thickness and surface area of the burn (BSA) with information extracted from the fourth character of T20-T30 codes for burn thickness, and the fourth and fifth characters of T31 codes for the BSA. Following the mapping process, secondary data analysis of 2008-2010 Queensland Hospital Admitted Patient Data Collection (QHAPDC) paediatric data was conducted to identify priority areas in product-related burns. Results The application of RAPEX rankings in QHAPDC burn data showed approximately 70% of paediatric burns in Queensland hospitals were categorised under RAPEX levels 1 and 2, 25% under RAPEX 3 and 4, with the remaining 5% unclassifiable. In the PS system, prioritisations are made to issues categorised under RAPEX levels 3 and 4. Analysis of external cause codes within these levels showed that flammable materials (for children aged 10-15yo) and hot substances (for children aged <2yo) were the most frequently identified products. Discussion and conclusions The mapping of ICD-10-AM burn codes into RAPEX rankings showed a favourable degree of compatibility between both classification systems, suggesting that ICD-10-AM coded burn data can be simplified to more effectively support PS initiatives. Additionally, the secondary data analysis showed that only 25% of all admitted burn cases in Queensland were severe enough to trigger a PS response.

Utilising hospital data to inform product safety prioritisation : the application of RAPEX severity rankings in ICD-coded Burn Data

Background The implementation of the Australian Consumer Law in 2011 highlighted the need for better use of injury data to improve the effectiveness and responsiveness of product safety (PS) initiatives. In the PS system, resources are allocated to different priority issues using risk assessment tools. The rapid exchange of information (RAPEX) tool to prioritise hazards, developed by the European Commission, is currently being adopted in Australia. Injury data is required as a basic input to the RAPEX tool in the risk assessment process. One of the challenges in utilising injury data in the PS system is the complexity of translating detailed clinical coded data into broad categories such as those used in the RAPEX tool. Aims This study aims to translate hospital burns data into a simplified format by mapping the International Statistical Classification of Disease and Related Health Problems (Tenth Revision) Australian Modification (ICD-10-AM) burn codes into RAPEX severity rankings, using these rankings to identify priority areas in childhood product-related burns data. Methods ICD-10-AM burn codes were mapped into four levels of severity using the RAPEX guide table by assigning rankings from 1-4, in order of increasing severity. RAPEX rankings were determined by the thickness and surface area of the burn (BSA) with information extracted from the fourth character of T20-T30 codes for burn thickness, and the fourth and fifth characters of T31 codes for the BSA. Following the mapping process, secondary data analysis of 2008-2010 Queensland Hospital Admitted Patient Data Collection (QHAPDC) paediatric data was conducted to identify priority areas in product-related burns. Results The application of RAPEX rankings in QHAPDC burn data showed approximately 70% of paediatric burns in Queensland hospitals were categorised under RAPEX levels 1 and 2, 25% under RAPEX 3 and 4, with the remaining 5% unclassifiable. In the PS system, prioritisations are made to issues categorised under RAPEX levels 3 and 4. Analysis of external cause codes within these levels showed that flammable materials (for children aged 10-15yo) and hot substances (for children aged <2yo) were the most frequently identified products. Discussion and conclusions The mapping of ICD-10-AM burn codes into RAPEX rankings showed a favourable degree of compatibility between both classification systems, suggesting that ICD-10-AM coded burn data can be simplified to more effectively support PS initiatives. Additionally, the secondary data analysis showed that only 25% of all admitted burn cases in Queensland were severe enough to trigger a PS response.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

The M(r) 43K major capsid protein of rice ragged stunt oryzavirus is a post-translationally processed product of a M(r) 67 348 polypeptide encoded by genome segment 8

The nucleotide sequence of DNA complementary to rice ragged stunt oryzavirus (RRSV) genome segment 8 (S8) of an isolate from Thailand was determined. RRSV S8 is 1 914 bp in size and contains a single large open reading frame (ORF) spanning nucleotides 23 to 1 810 which is capable of encoding a protein of M(r) 67 348. The N-terminal amino acid sequence of a ~43K virion polypeptide matched to that inferred for an internal region of the S8 coding sequence. These data suggest that the 43K protein is encoded by S8 and is derived by a proteolytic cleavage. Predicted polypeptide sizes from this possible cleavage of S8 protein are 26K and 42K. Polyclonal antibodies raised against a maltose binding protein (MBP)-S8 fusion polypeptide (expressed in Escherichia coli) recognised four RRSV particle associated polypeptides of M(r) 67K, 46K, 43K and 26K and all except the 26K polypeptide were also highly immunoreactive to polyclonal antibodies raised against purified RRSV particles. Cleavage of the MBP-S8 fusion polypeptide with protease Factor X produced the expected 40K MBP and two polypeptides of apparent M(r) 46K and 26K. Antibodies to purified RRSV particles reacted strongly with the intact fusion protein and the 46K cleavage product but weakly to the 26K product. Furthermore, in vitro transcription and translation of the S8 coding region revealed a post-translational self cleavage of the 67K polypeptide to 46K and 26K products. These data indicate that S8 encodes a structural polypeptide, the majority of which is auto- catalytically cleaved to 26K and 46K proteins. The data also suggest that the 26K protein is the self cleaving protease and that the 46K product is further processed or undergoes stable conformational changes to a ~43K major capsid protein.

Characterisation of the subgenomic RNAs of an Australian isolate of barley yellow dwarf luteovirus

We have characterised the subgenomic RNAs of an Australian isolate of BYDV-PAV. Northern blot analyses of infected plants and protoplasts have shown that this isolate synthesises three subgenomic RNAs. Precise mapping of the transcription start sites of all three subgenomic RNAs and translational analyses of subgenomic RNA 2 and 3 have revealed a number of features. First, the transcription start site of subgenomic RNA 1 in this isolate differs markedly from the start site determined for an Illinois isolate of BYDV-PAV. Second, the start sites of subgenomic RNA 1 and 2 occur at a sequence that closely resembles the 5' end sequence of the genomic RNA (5'AGUGAAGA). Third, subgenomic RNA 2 appears to express ORF 6 of BYDV-PAV but the gene product is truncated due to the appearance of a new stop codon in the sequence. Last, subgenomic RNA 3, which is abundantly transcribed and encapsidated by the virus particle, appears to have no coding ability. We postulate that this novel subgenomic RNA has a regulatory function.

Anticipated user experience in the early stages of product development

This research investigates users' anticipation of their future experiences with interactive products to support design for experience in the early stages of product development. This research generates new knowledge of anticipated user experience (AUX), which reveals users' tendency to perceive the pragmatic quality of products as the main determinant of their positive future experiences. The AUX Framework has been an important outcome of this study. The exploration of the components of this framework allows a better prediction and understanding of users' underlying needs and potential usage contexts valuable for the early design phases.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

How to guarantee compliance between workflows and product lifecycles?

Product Lifecycle Management (PLM) systems are widely used in the manufacturing industry. A core feature of such systems is to provide support for versioning of product data. As workflow functionality is increasingly used in PLM systems, the possibility emerges that the versioning transitions for product objects as encapsulated in process models do not comply with the valid version control policies mandated in the objects’ actual lifecycles. In this paper we propose a solution to tackle the (non-)compliance issues between processes and object version control policies. We formally define the notion of compliance between these two artifacts in product lifecycle management and then develop a compliance checking method which employs a well-established workflow analysis technique. This forms the basis of a tool which offers automated support to the proposed approach. By applying the approach to a collection of real-life specifications in a main PLM system, we demonstrate the practical applicability of our solution to the field.

Design, synthesis and spectroscopic characterisation of a focused library based on the polyandrocarpamine natural product scaffold

A focused library based on the marine natural products polyandrocarpamines A (1) and B (2) has been designed and synthesised using parallel solution-phase chemistry. In silico physicochemical property calculations were performed on synthetic candidates in order to optimise the library for drug discovery and chemical biology. A library of ten 2-aminoimidazolone products (3–12) was prepared by coupling glycocyamidine and a variety of aldehydes using a one-step stereoselective aldol condensation reaction under microwave conditions. All analogues were characterised by NMR, UV, IR and MS. The library was evaluated for cytotoxicity towards the prostate cancer cell lines, LNCaP, PC-3 and 22Rv1.

Structured feature extraction using association rules

As of today, opinion mining has been widely used to iden- tify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feed- back such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hi- erarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be ap- plied to generate features about a service such as the services in hospitals or clinics.

Integration of sentiment analysis into customer relational model : the importance of feature ontology and synonym

Online business or Electronic Commerce (EC) is getting popular among customers today, as a result large number of product reviews have been posted online by the customers. This information is very valuable not only for prospective customers to make decision on buying product but also for companies to gather information of customers’ satisfaction about their products. Opinion mining is used to capture customer reviews and separated this review into subjective expressions (sentiment word) and objective expressions (no sentiment word). This paper proposes a novel, multi-dimensional model for opinion mining, which integrates customers’ characteristics and their opinion about any products. The model captures subjective expression from product reviews and transfers to fact table before representing in multi-dimensions named as customers, products, time and location. Data warehouse techniques such as OLAP and Data Cubes were used to analyze opinionated sentences. A comprehensive way to calculate customers’ orientation on products’ features and attributes are presented in this paper.