A systematic review and meta-analysis of Northern Hemisphere season of birth studies in schizophrenia

Based on the epidemiological finding that individuals with schizophrenia tend to be born in winter/spring when compared to the general population, we examined (1) the strength and timing of this effect in Northern Hemisphere sites, and (2) the correlation between the season of birth effect size and latitude. Studies were located via electronic data sources, published citations, and letters to authors. Inclusion criteria were that studies specify the diagnostic criteria used, that studies specify the counts of schizophrenia and general population births for each month, and that subjects and the general population be drawn from the same birth years and catchment area. We extracted data from eight studies based on 126,196 patients with schizophrenia and 86,605,807 general population births and drawn from 27 Northern Hemisphere sites. Comparing winter/spring versus summer/autumn births, we found a significant excess for winter/spring births (pooled odds ratio = 1.07; 95% confidence interval 1.05, 1.08; population attributable risk = 3.3%). There was a small but significant positive correlation between the odds ratios for the season of birth comparison and latitude (r = 0.271, p < 0.005). Furthermore, the shape of the seasonality in schizophrenia births varied by latitude band. These variations may encourage researchers to generate candidate seasonally fluctuating exposures.

Science of assessment

Psoriatic arthritis is a multisystem disorder which, from a measurement standpoint, demands consideration of its cutaneous manifestations and both axial and peripheral musculoskeletal involvement. Measurements of various aspects of impairment, ability/disability, and participation/ handicap are feasible using existing measurement techniques, which are for the most part valid, reliable, and responsive. Nevertheless, there remain opportunities for the further development of consensus around core set measures and responder criteria, as well as for instrument development and refinement, standardised assessor training, cross-cultural adaptation of health status questionnaires, electronic data capture, and the introduction of standardised quantitative measurement into routine clinical care.

Electronic nose evaluation of onion headspace volatiles and bulb quality as affected by nitrogen, sulphur and soil type

Edaphic factors affect the quality of onions (Allium cepa). Two experiments were carried out in the field and glasshouse to investigate the effects of N (field: 0, 120 kg ha(-1); glasshouse: 0, 108 kg ha(-1)), S (field: 0, 20 kg ha(-1); glasshouse: 0, 4.35 kg ha(-1)) and soil type (clay, sandy loam) on onion quality. A conducting polymer sensor electronic nose (E-nose) was used to classify onion headspace volatiles. Relative changes in the E-nose sensor resistance ratio (%dR/R) were reduced following N and S fertilisation. A 2D Principal Component Analysis (PCA) of the E-nose data sets accounted for c. 100% of the variations in onion headspace volatiles in both experiments. For the field experiment, E-nose data set clusters for headspace volatiles for no N-added onions overlapped (D-2 = 1.0) irrespective of S treatment. Headspace volatiles of N-fertilised onions for the glasshouse sandy loam also overlapped (D-2 = 1.1) irrespective of S treatment as compared with distinct separations among clusters for the clay soil. N fertilisation significantly (P < 0.01) reduced onion bulb pyruvic acid concentration (flavour) in both experiments. S fertilisation increased pyruvic acid concentration significantly (P < 0.01) in the glasshouse experiment, especially for the clay soil, but had no effect on pyruvic acid concentration in the field. N and S fertilisation significantly (P < 0.01) increased lachrymatory potency (pungency), but reduced total soluble solids (TSS) content in the field experiment. In the glasshouse experiment, N and S had no effect on TSS. TSS content was increased on the clay by 1.2-fold as compared with the sandy loam. Onion tissue N:water-soluble SO42- ratios of between five and eight were associated with greater %dR/R and pyruvic acid concentration values. N did not affect inner bulb tissue microbial load. In contrast, S fertilisation reduced inner bulb tissue microbial load by 80% in the field experiment and between 27% (sandy loam) and 92% (clay) in the glasshouse experiment. Overall, onion bulb quality discriminated by the E-nose responded to N, S and soil type treatments, and reflected their interactions. However, the conventional analytical and sensory measures of onion quality did not correlate with %dR/R.

Theory of strongly correlated electron systems. II. Including correlation effects into electronic structure calculations

We have previously shown that a division of the f-shell into two subsystems gives a better understanding of the cohesive properties as well the general behavior of lanthanide systems. In this article, we present numerical computations, using the suggested method. We show that the picture is consistent with most experimental data, e.g., the equilibrium volume and electronic structure in general. Compared with standard energy band calculations and calculations based on the self-interaction correction and LIDA + U, the f-(non-f)-mixing interaction is decreased by spectral weights of the many-body states of the f-ion. (c) 2005 Wiley Periodicals, Inc.

Evaluation of eight spring onion genotypes, sulphur nutrition and soil-type effects with an electronic nose

Genotype, sulphur (S) nutrition and soil-type effects on spring onion quality were assessed using a 32-conducting polymer sensor E-nose. Relative changes in sensor resistance ratio (% dR/R) varied among eight spring onion genotypes. The % dR/R was reduced by S application in four of the eight genotypes. For the other four genotypes, S application gave no change in % dR/R in three, and increased % dR/R in the other. E-nose classification of headspace volatiles by a two-dimensional principal component analysis (PCA) plot for spring onion genotypes differed for S fertilisation vs. no S fertilisation. Headspace volatiles data set clusters for cv. 'White Lisbon' grown on clay or on sandy loam overlapped when 2.9 [Mahalanobis distance value (D2) = 1.6], or 5.8-(D2 = 0.3) kg S ha-1 was added. In contrast, clear separation (D2 = 7.5) was recorded for headspace volatile clusters for 0 kg S hd-1 on clay vs. sandy loam. Addition of 5.8 kg S ha-1 increased pyruvic acid content (mmole g-1 fresh weight) by 1.7-fold on average across the eight genotypes. However, increased S from 2.9 to 5.8 kg ha-1 did not significantly (P > 0.05) influence % dR/R, % dry matter (DM) or total soluble solids (TSS) contents, but significantly (P < 0.05) increased pyruvic acid content. TSS was significantly (P < 0.05) reduced by S addition, while % DM was unaffected. In conclusion, the 32-conducting polymer E-nose discerned differences in spring onion quality that were attributable to genotype and to variations in growing conditions as shown by the significant (P < 0.05) interaction effects for % dR/R.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

Approximate processing of massive continuous quantile queries over high-speed data streams

Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.