Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20

To identify multiple sclerosis (MS) susceptibility loci, we conducted a genome-wide association study (GWAS) in 1,618 cases and used shared data for 3,413 controls. We performed replication in an independent set of 2,256 cases and 2,310 controls, for a total of 3,874 cases and 5,723 controls. We identified risk-associated SNPs on chromosome 12q13-14 (rs703842, P = 5.4 x 10(-11); rs10876994, P = 2.7 x 10(-10); rs12368653, P = 1.0 x 10(-7)) and upstream of CD40 on chromosome 20q13 (rs6074022, P = 1.3 x 10(-7); rs1569723, P = 2.9 x 10(-7)). Both loci are also associated with other autoimmune diseases. We also replicated several known MS associations (HLA-DR15, P = 7.0 x 10(-184); CD58, P = 9.6 x 10(-8); EVI5-RPL5, P = 2.5 x 10(-6); IL2RA, P = 7.4 x 10(-6); CLEC16A, P = 1.1 x 10(-4); IL7R, P = 1.3 x 10(-3); TYK2, P = 3.5 x 10(-3)) and observed a statistical interaction between SNPs in EVI5-RPL5 and HLA-DR15 (P = 0.001).

The estrogen receptor 1 G594A polymorphism is associated with migraine susceptibility in two independent case/control groups

Migraine is a painful and debilitating disorder with a significant genetic component. Steroid hormones, in particular estrogen, have long been considered to play a role in migraine, as variations in hormone levels are associated with migraine onset in many sufferers of the disorder. Steroid hormones mediate their activity via hormone receptors, which have a wide tissue distribution. Estrogen receptors have been localized to the brain in regions considered to be involved in migraine pathogenesis. Hence it is possible that genetic variation in the estrogen receptor gene may play a role in migraine susceptibility. This study thus examined the estrogen receptor 1 (ESRα) gene for a potential role in migraine pathogenesis and susceptibility. A population-based cohort of 224 migraine sufferers and 224 matched controls were genotyped for the G594A polymorphism located in exon 8 of the ESR1 gene. Statistical analysis indicated a significant difference between migraineurs and non-migraineurs in both the allele frequencies (P=0.003) and genotype distributions (P=0.008) in this sample. An independent follow-up study was then undertaken using this marker in an additional population-based cohort of 260 migraine sufferers and 260 matched controls. This resulted in a significant association between the two groups with regard to allele frequencies (P=8×10−6) and genotype distributions (P=4×10−5). Our findings support the hypothesis that genetic variation in hormone receptors, in particular the ESR1 gene, may play a role in migraine.

Pre-crash traffic flow trend analysis on motorways

Crashes on motorway contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence reduce crashes will help address congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a Short time window around the time of crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques, that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists, and that this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with traffic flow data of one hour prior to the crash using an incident detection algorithm. Traffic flow trends (traffic speed/occupancy time series) revealed that crashes could be clustered with regards of the dominant traffic flow pattern prior to the crash. Using the k-means clustering method allowed the crashes to be clustered based on their flow trends rather than their distance. Four major trends have been found in the clustering results. Based on these findings, crash likelihood estimation algorithms can be fine-tuned based on the monitored traffic flow conditions with a sliding window of 60 minutes to increase accuracy of the results and minimize false alarms.

Pre-crash and non-crash traffic flow trends analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence, reducing the frequency of crashes assists in addressing congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a short time window around the time of a crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists. We will compare them with normal traffic trends and show this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding to traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash. Using the K-Means clustering method with Euclidean distance function allowed the crashes to be clustered. Then, normal situation data was extracted based on the time distribution of crashes and were clustered to compare with the “high risk” clusters. Five major trends have been found in the clustering results for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Based on these findings, crash likelihood estimation models can be fine-tuned based on the monitored traffic conditions with a sliding window of 30 minutes to increase accuracy of the results and minimize false alarms.

High efficiency auxiliary power converters

The auxiliary load DC-DC converters of the Sunshark solar car have never been examined. An analysis of the current design reveals it is complicated, and inefficient. Some simple measures to greatly improve the efficiency are present which will achieve an overall worthwhile power saving. Two switch-mode power supply DC-DC converter designs are presented. One is a constant current supply for the LED brake and turn indicators, which allows them to be powered directly from the main DC bus, and switched only as necessary. The second is a low power flyback converter, which employs synchronous rectification among other techniques to achieve good efficiency and regulation over a large range of output powers. Practical results from both converters, and an indication of the overall improvement in system efficiency will be offered.

High rates of muscle glycogen resynthesis after exhaustive exercise when carbohydrate is coingested with caffeine

We determined the effect of coingestion of caffeine (Caff) with carbohydrate (CHO) on rates of muscle glycogen resynthesis during recovery from exhaustive exercise in seven trained subjects who completed two experimental trials in a randomized, double-blind crossover design. The evening before an experiment subjects performed intermittent exhaustive cycling and then consumed a low-CHO meal. The next morning subjects rode until volitional fatigue. On completion of this ride subjects consumed either CHO [4 g/kg body mass (BM)] or the same amount of CHO + Caff (8 mg/kg BM) during 4 h of passive recovery. Muscle biopsies and blood samples were taken at regular intervals throughout recovery. Muscle glycogen levels were similar at exhaustion [?75 mmol/kg dry wt (dw)] and increased by a similar amount (?80%) after 1 h of recovery (133 ± 37.8 vs. 149 ± 48 mmol/kg dw for CHO and Caff, respectively). After 4 h of recovery Caff resulted in higher glycogen accumulation (313 ± 69 vs. 234 ± 50 mmol/kg dw, P < 0.001). Accordingly, the overall rate of resynthesis for the 4-h recovery period was 66% higher in Caff compared with CHO (57.7 ± 18.5 vs. 38.0 ± 7.7 mmol·kg dw-1·h-1, P < 0.05). After 1 h of recovery plasma Caff levels had increased to 31 ± 11 ?M (P < 0.001) and at the end of the recovery reached 77 ± 11 ?M (P < 0.001) with Caff. Phosphorylation of CaMKThr286 was similar after exercise and after 1 h of recovery, but after 4 h CaMKThr286 phosphorylation was higher in Caff than CHO (P < 0.05). Phosphorylation of AMP-activated protein kinase (AMPK)Thr172 and AktSer473 was similar for both treatments at all time points. We provide the first evidence that in trained subjects coingestion of large amounts of Caff (8 mg/kg BM) with CHO has an additive effect on rates of postexercise muscle glycogen accumulation compared with consumption of CHO alone.

Does project culture matter? A comparative study of two major hospital projects

Purpose - Cultural issues in the construction industry have attracted growing attention from both practitioners and academia. However, there are few studies on culture issues at the project level. The influence of project culture has not traditionally been on the research radar. This research aims to investigate the impacts of project culture on the performance of construction projects. Design/methodology/approach - A case study approach, utilising questionnaire surveys, in-depth interviews and review of project documents, was employed to investigate project culture and its associated impacts in two major hospital projects. Findings - The results indicated that project culture played an important role in achieving harmonious relationships between project participants and better project outcomes in terms of schedule, functionality, satisfaction with the process, satisfaction with the relationships, environmental issues addressed commercial success, further business opportunities and overall performance. Case 1 outperformed Case 2 in these performance indicators. Similarly, it became clear that the project’s culture should be developed from the outset and sustained during the project period. Furthermore, it was also highlighted that the project culture should be translated to all levels of the supply chain i.e. sub-contractors and suppliers. Practical implications – The findings enabled the client to understand the role of project culture and actively commit towards the development and maintenance of the project culture from very early on. It also helps project teams to understand how to deal with cultural issues at the project level. Originality/value- This study is one of limited empirical studies that offer in-depth insights of how project culture affects the performance of construction projects. It is also the first study of hospital projects on the research topic.

Traffic safety risks trends and patterns analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestion. Hence, reducing the frequency of crashes assist in addressing congestion issues (Meyer, 2008). Analysing traffic conditions and discovering risky traffic trends and patterns are essential basics in crash likelihood estimations studies and still require more attention and investigation. In this paper we will show, through data mining techniques, that there is a relationship between pre-crash traffic flow patterns and crash occurrence on motorways, compare them with normal traffic trends, and that this knowledge has the potentiality to improve the accuracy of existing crash likelihood estimation models, and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash occurrence. K-Means clustering algorithm applied to determine dominant pre-crash traffic patterns. In the first phase of this research, traffic regimes identified by analysing crashes and normal traffic situations using half an hour speed in upstream locations of crashes. Then, the second phase investigated the different combination of speed risk indicators to distinguish crashes from normal traffic situations more precisely. Five major trends have been found in the first phase of this paper for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Moreover, the second phase explains that spatiotemporal difference of speed is a better risk indicator among different combinations of speed related risk indicators. Based on these findings, crash likelihood estimation models can be fine-tuned to increase accuracy of estimations and minimize false alarms.

Prognostic value of TP53, KRAS and EGFR mutations in nonsmall cell lung cancer : the EUELC cohort

Nonsmall cell lung cancer samples from the European Early Lung Cancer biobank were analysed to assess the prognostic significance of mutations in the TP53, KRAS and EGFR genes. The series included 11 never-smokers, 86 former smokers, 152 current smokers and one patient without informed smoking status. There were 110 squamous cell carcinomas (SCCs), 133 adenocarcinomas (ADCs) and seven large cell carcinomas or mixed histologies. Expression of p53 was analysed by immunohistochemistry. DNA was extracted from frozen tumour tissues. TP53 mutations were detected in 48.8% of cases and were more frequent among SCCs than ADCs (p<0.0001). TP53 mutation status was not associated with prognosis. G to T transversions, known to be associated with smoking, were marginally more common among patients who developed a second primary lung cancer or recurrence/metastasis (progressive disease). EGFR mutations were almost exclusively found in never-smoking females (p=0.0067). KRAS mutations were detected in 18.5% of cases, mainly ADC (p<0.0001), and showed a tendency toward association with progressive disease status. These results suggest that mutations are good markers of different aetiologies and histopathological forms of lung cancers but have little prognostic value, with the exception of KRAS mutation, which may have a prognostic value in ADC. Copyright©ERS 2012.

Creating a Sustainability Scorecard as a predictive tool for measuring the complex social, economic and environmental impacts of industries, a case study : assessing the viability and sustainability of the dairy industry

Sustainability is a key driver for decisions in the management and future development of industries. The World Commission on Environment and Development (WCED, 1987) outlined imperatives which need to be met for environmental, economic and social sustainability. Development of strategies for measuring and improving sustainability in and across these domains, however, has been hindered by intense debate between advocates for one approach fearing that efforts by those who advocate for another could have unintended adverse impacts. Studies attempting to compare the sustainability performance of countries and industries have also found ratings of performance quite variable depending on the sustainability indices used. Quantifying and comparing the sustainability of industries across the triple bottom line of economy, environment and social impact continues to be problematic. Using the Australian dairy industry as a case study, a Sustainability Scorecard, developed as a Bayesian network model, is proposed as an adaptable tool to enable informed assessment, dialogue and negotiation of strategies at a global level as well as being suitable for developing local solutions.

Experience, recursive awareness and understanding in autism spectrum disorders : insights of parents and teachers in Singapore

Provision of an individually responsive education requires a comprehensive understanding of the inner worlds of learners, such as their feelings and thoughts. However, this is difficult to achieve when learners, such as those with Autism Spectrum Disorders (ASD) and cognitive difficulties, have problems with communication. To address this issue, the current exploratory descriptive study sought the views of 133 Singaporean parents and teachers of school-age learners with ASD and cognitive difficulties regarding the inner experience of their children and students. The findings highlight the variety of abilities and difficulties found in how these learners experience their own mental states and understand those of others. These abilities and difficulties are characterized according to type of mental state and analysed in line with three qualia, those of experience, recursive awareness and understanding. The findings indicate that learners show a greater awareness of their own mental states compared to their ability to understand these same mental states in others. Educational implications are discussed.

A reciprocal collaborative method using relevance feedback and feature importance

In a people-to-people matching systems, filtering is widely applied to find the most suitable matches. The results returned are either too many or only a few when the search is generic or specific respectively. The use of a sophisticated recommendation approach becomes necessary. Traditionally, the object of recommendation is the item which is inanimate. In online dating systems, reciprocal recommendation is required to suggest a partner only when the user and the recommended candidate both are satisfied. In this paper, an innovative reciprocal collaborative method is developed based on the idea of similarity and common neighbors, utilizing the information of relevance feedback and feature importance. Extensive experiments are carried out using data gathered from a real online dating service. Compared to benchmarking methods, our results show the proposed method can achieve noticeable better performance.

The velocity synchronous discrete Fourier transform for order tracking in the field of rotating machinery

Diagnostics of rotating machinery has developed significantly in the last decades, and industrial applications are spreading in different sectors. Most applications are characterized by varying velocities of the shaft and in many cases transients are the most critical to monitor. In these variable speed conditions, fault symptoms are clearer in the angular/order domains than in the common time/frequency ones. In the past, this issue was often solved by synchronously sampling data by means of phase locked circuits governing the acquisition; however, thanks to the spread of cheap and powerful microprocessors, this procedure is nowadays rarer; sampling is usually performed at constant time intervals, and the conversion to the order domain is made by means of digital signal processing techniques. In the last decades different algorithms have been proposed for the extraction of an order spectrum from a signal sampled asynchronously with respect to the shaft rotational velocity; many of them (the so called computed order tracking family) use interpolation techniques to resample the signal at constant angular increments, followed by a common discrete Fourier transform to shift from the angular domain to the order domain. A less exploited family of techniques shifts directly from the time domain to the order spectrum, by means of modified Fourier transforms. This paper proposes a new transform, named velocity synchronous discrete Fourier transform, which takes advantage of the instantaneous velocity to improve the quality of its result, reaching performances that can challenge the computed order tracking.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.