The Art of Detection

The objective of this work is to recognize object categories in paintings, such as cars, cows and cathedrals. We achieve this by training classifiers from natural images of the objects. We make the following contributions: (i) we measure the extent of the domain shift problem for image-level classifiers trained on natural images vs paintings, for a variety of CNN architectures; (ii) we demonstrate that classificationby-detection (i.e. learning classifiers for regions rather than the entire image) recognizes (and locates) a wide range of small objects in paintings that are not picked up by image-level classifiers, and combining these two methods improves performance; and (iii) we develop a system that learns a region-level classifier on-the-fly for an object category of a user’s choosing, which is then applied to over 60 million object regions across 210,000 paintings to retrieve localised instances of that category

Simple estimators of the intensity of seasonal occurrence

<p>Abstract</p> <p>Background</p> <p>Edwards's method is a widely used approach for fitting a sine curve to a time-series of monthly frequencies. From this fitted curve, estimates of the seasonal intensity of occurrence (i.e., peak-to-low ratio of the fitted curve) can be generated.</p> <p>Methods</p> <p>We discuss various approaches to the estimation of seasonal intensity assuming Edwards's periodic model, including maximum likelihood estimation (MLE), least squares, weighted least squares, and a new closed-form estimator based on a second-order moment statistic and non-transformed data. Through an extensive Monte Carlo simulation study, we compare the finite sample performance characteristics of the estimators discussed in this paper. Finally, all estimators and confidence interval procedures discussed are compared in a re-analysis of data on the seasonality of monocytic leukemia.</p> <p>Results</p> <p>We find that Edwards's estimator is substantially biased, particularly for small numbers of events and very large or small amounts of seasonality. For the common setting of rare events and moderate seasonality, the new estimator proposed in this paper yields less finite sample bias and better mean squared error than either the MLE or weighted least squares. For large studies and strong seasonality, MLE or weighted least squares appears to be the optimal analytic method among those considered.</p> <p>Conclusion</p> <p>Edwards's estimator of the seasonal relative risk can exhibit substantial finite sample bias. The alternative estimators considered in this paper should be preferred.</p

Gaviscon® vs. omeprazole in symptomatic treatment of moderate gastroesophageal reflux. a direct comparative randomised trial

<p>Abstract</p> <p>Background</p> <p>Medical management of GERD mainly uses proton pump inhibitors. Alginates also have proven efficacy. The aim of this trial was to compare short-term efficacy of an alginate (Gaviscon^®, 4 × 10 mL/day) and omeprazole (20 mg/day) on GERD symptoms in general practice.</p> <p>Methods</p> <p>A 14-day multicentre randomised double-blind double-dummy non-inferiority trial compared Gaviscon^®(4 × 10 mL/day) and omeprazole (20 mg/day) in patients with 2-6 day heartburn episodes weekly without alarm signals. The primary outcome was the mean time to onset of the first 24-h heartburn-free period after initial dosing. Secondary outcomes were the proportion of patients without heartburn by D7, pain relief by D7, and reduction in pain intensity by D7 and D14.</p> <p>Results</p> <p>278 patients were recruited; 120 were included in the Gaviscon^®group and 121 in the omeprazole group for the per protocol non-inferiority analysis. The mean time to onset of the first 24-h heartburn-free period after initial dosing was 2.0 (± 2.2) days for Gaviscon^®and 2.0 (± 2.3) days for omeprazole (<it>p </it>= 0.93); mean intergroup difference was 0.01 ± 1.55 days (95% CI = -0.41 to 0.43): i.e., less than the lower limit of the 95% CI of -0.5 days predetermined to demonstrate non-inferiority. The mean number of heartburn-free days by D7 was significantly greater in the omeprazole group: 3.7 ± 2.3 days vs. 3.1 ± 2.1 (<it>p </it>= 0.02). On D7, overall quality of pain relief was slightly in favour of omeprazole (<it>p </it>= 0.049). There was no significant difference in the reduction in pain intensity between groups by D7 (<it>p = </it>0.11) or D14 (<it>p = </it>0.08). Tolerance and safety were good and comparable in both groups.</p> <p>Conclusion</p> <p>Gaviscon^®was non-inferior to omeprazole in achieving a 24-h heartburn-free period in moderate episodic heartburn, and is a relevant effective alternative treatment in moderate GERD in primary care.</p> <p>Trial registration</p> <p><a href="http://www.controlled-trials.com/ISRCTN62203233">ISRCTN62203233</a>.</p

CAGO: A Software Tool for Dynamic Visual Comparison and Correlation Measurement of Genome Organization

CAGO (Comparative Analysis of Genome Organization) is developed to address two critical shortcomings of conventional genome atlas plotters: lack of dynamic exploratory functions and absence of signal analysis for genomic properties. With dynamic exploratory functions, users can directly manipulate chromosome tracks of a genome atlas and intuitively identify distinct genomic signals by visual comparison. Signal analysis of genomic properties can further detect inconspicuous patterns from noisy genomic properties and calculate correlations between genomic properties across various genomes. To implement dynamic exploratory functions, CAGO presents each genome atlas in Scalable Vector Graphics (SVG) format and allows users to interact with it using a SVG viewer through JavaScript. Signal analysis functions are implemented using R statistical software and a discrete wavelet transformation package waveslim. CAGO is not only a plotter for generating complex genome atlases, but also a platform for exploring genome atlases with dynamic exploratory functions for visual comparison and with signal analysis for comparing genomic properties across multiple organisms. The web-based application of CAGO, its source code, user guides, video demos, and live examples are publicly available and can be accessed at http://cbs.ym.edu.tw/cago

Serum 25-hydroxyvitamin D is inversely associated with body mass index in cancer

<p>Abstract</p> <p>Background</p> <p>The association between vitamin D deficiency and obesity in healthy populations and different disease states remains unsettled with studies reporting conflicting findings. Moreover, current dietary recommendations for vitamin D do not take into account a person's body mass index (BMI). We investigated the relationship between serum 25-hydroxy-vitamin D [25(OH)D] and BMI in cancer.</p> <p>Methods</p> <p>A consecutive case series of 738 cancer patients. Serum 25(OH)D was measured at presentation to the hospital. The cohort was divided into 4 BMI groups (underweight: <18.5, normal weight: 18.5-24.9, overweight: 25-29.9, and obese: >30.0 kg/m²). Mean 25(OH)D was compared across the 4 BMI groups using ANOVA. Linear regression was used to quantify the relationship between BMI and 25(OH)D.</p> <p>Results</p> <p>303 were males and 435 females. Mean age at diagnosis was 55.6 years. The mean BMI was 27.9 kg/m²and mean serum 25(OH)D was 21.9 ng/ml. Most common cancers were lung (134), breast (131), colorectal (97), pancreas (86) and prostate (45). Obese patients had significantly lower serum 25(OH)D levels (17.9 ng/ml) as compared to normal weight (24.6 ng/ml) and overweight (22.8 ng/ml) patients; p < 0.001. After adjusting for age, every 1 kg/m²increase in BMI was significantly associated with 0.42 ng/ml decline in serum 25(OH)D levels.</p> <p>Conclusions</p> <p>Obese cancer patients (BMI >= 30 kg/m²) had significantly lower levels of serum 25(OH)D as compared to non-obese patients (BMI <30 kg/m²). BMI should be taken into account when assessing a patient's vitamin D status and more aggressive vitamin D supplementation should be considered in obese cancer patients.</p

Utilizing individual fish biomass and relative abundance models to map environmental niche associations of adult and juvenile targeted fishes

Many fishes undergo ontogenetic habitat shifts to meet their energy and resource needs as they grow. Habitat resource partitioning and patterns of habitat connectivity between conspecific fishes at different life-history stages is a significant knowledge gap. Species distribution models were used to examine patterns in the relative abundance, individual biomass estimates and environmental niche associations of different life stages of three iconic West Australian fishes. Continuous predictive maps describing the spatial distribution of abundance and individual biomass of the study species were created as well predictive hotspot maps that identify possible areas for aggregation of individuals of similar life stages of multiple species (i.e. spawning grounds, fisheries refugia or nursery areas). The models and maps indicate that processes driving the abundance patterns could be different from the body size associated demographic processes throughout an individual's life cycle. Incorporating life-history in the spatially explicit management plans can ensure that critical habitat of the vulnerable stages (e.g. juvenile fish, spawning stock) is included within proposed protected areas and can enhance connectivity between various functional areas (e.g. nursery areas and adult populations) which, in turn, can improve the abundance of targeted species as well as other fish species relying on healthy ecosystem functioning

Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali

BACKGROUND: Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. METHODOLOGY/PRINCIPAL FINDINGS: In this longitudinal retrospective (01/1996-06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. CONCLUSIONS/SIGNIFICANCE: The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel

Overview of data-synthesis in systematic reviews of studies on outcome prediction models

Background: Many prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of reviews synthesize and report the results of primary outcome prediction studies. Methods: Outcome prediction reviews published in MEDLINE between October 2005 and March 2011 were eligible and 127 Systematic reviews with the aim to summarize outcome prediction studies written in English were identified for inclusion. Characteristics of the reviews and the primary studies that were included were independently assessed by 2 review authors, using standardized forms. Results: After consensus meetings a total of 50 systematic reviews that met the inclusion criteria were included. The type of primary studies included (prognostic factor or outcome prediction) was unclear in two-thirds of the reviews. A minority of the reviews reported univariable or multivariable point estimates and measures of dispersion from the primary studies. Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear. In most reviews there was no information about model performance. Quantitative analysis was performed in 10 reviews, and 49 reviews assessed the primary studies qualitatively. In both analyses types a range of different methods was used to present the results of the outcome prediction studies. Conclusions: Different methods are applied to synthesize primary study results but quantitative analysis is rarely performed. The description of its objectives and of the primary studies is suboptimal and performance parameters of the outcome prediction models are rarely mentioned. The poor reporting and the wide variety of data synthesis strategies are prone to influence the conclusions of outcome prediction reviews. Therefore, there is much room for improvement in reviews of outcome prediction studies. (aut.ref.

Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration

The REMARK “elaboration and explanation” guideline, by Doug Altman and colleagues, provides a detailed reference for authors on important issues to consider when designing, conducting, and analyzing tumor marker prognostic studies