Using long-term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models

Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea-Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best-performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO2 parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type.

[The Validity of the Poisson Distribution to Analyze Microbial Colony Counts on Agar Plates for Food Samples]

Microbial colony counts of food samples in microbiological examinations are one of the most important items. The probability distributions for the colony counts per agar plate at the dilution of counting had not been intensively studied so far. Recently we analyzed the colony counts of food samples with several probability distributions using the Pearson's chi-square value by the "traditional" statistics as the index of fit [Fujikawa and Tsubaki, Food Hyg.Saf.Sc., 60, 88-95 (2019)]. As a result, the selected probability distributions depended on the samples. In this study we newly selected a probability distribution, namely a statistical model, suitable for the above data with the method of maximum likelihood from the probabilistic point of view. The Akaike's Information Criterion (AIC) was used as the index of fit. Consequently, the Poisson model were better than the negative binomial model for all of four food samples. The Poisson model was also better than the binomial for three of four microbial culture samples. With Baysian Information Criterion (BIC), the Poisson model was also better than these two models for all the samples. These results suggested that the Poisson distribution would be the best model to estimate the colony counts of food samples. The present study would be the first report on the statistical model selection for the colony counts of food samples with AIC and BIC.

A Stepwise Assessment of Parsimony and Fuzzy Entropy in Species Distribution Modelling

Entropy is intrinsic to the geographical distribution of a biological species. A species distribution with higher entropy involves more uncertainty, i.e., is more gradually constrained by the environment. Species distribution modelling tries to yield models with low uncertainty but normally has to reduce uncertainty by increasing their complexity, which is detrimental for another desirable property of the models, parsimony. By modelling the distribution of 18 vertebrate species in mainland Spain, we show that entropy may be computed along the forward-backwards stepwise selection of variables in Logistic Regression Models to check whether uncertainty is reduced at each step. In general, a reduction of entropy was produced asymptotically at each step of the model. This asymptote could be used to distinguish the entropy attributable to the species distribution from that attributable to model misspecification. We discussed the use of fuzzy entropy for this end because it produces results that are commensurable between species and study areas. Using a stepwise approach and fuzzy entropy may be helpful to counterbalance the uncertainty and the complexity of the models. The model yielded at the step with the lowest fuzzy entropy combines the reduction of uncertainty with parsimony, which results in high efficiency.

Analysis of steady-state carbon tracer experiments using akaike information criteria

INTRODUCTION

Carbon isotope tracers have been used to determine relative rates of tricarboxylic acid cycle (TCA) cycle pathways since the 1950s. Steady-state experimental data are typically fit to a single mathematical model of metabolism to determine metabolic fluxes. Whether the chosen model is appropriate for the biological system has generally not been evaluated systematically. An overly-simple model omits known pathways while an overly-complex model may produce incorrect results due to overfitting.

OBJECTIVES

The objectives were to develop and study a method that systematically evaluates multiple TCA cycle mathematical models as part of the fitting process.

METHODS

The problem of choosing overly-simple or overly-complex models was approached by developing software that automatically explores all possible combinations of flux through pyruvate dehydrogenase, pyruvate kinase, pyruvate carboxylase and anaplerosis at propionyl-CoA carboxylase, and equivalent pathways, all relative to TCA cycle flux. Typical TCA cycle metabolic tracer experiments that use ¹³C nuclear magnetic resonance for detection and quantification of ¹³C-enriched glutamate products were simulated and analyzed. By evaluating the multiple model fits with both the conventional sum-of-squares residual error (SSRE) and the Akaike Information Criterion (AIC), the software helps the investigator understand the interaction between model complexity and goodness of fit.

RESULTS

When fitting alternative models of the TCA cycle metabolism, the SSRE may identify more than one model that fits the data well. Among those models, the AIC provides guidance as to which is the simplest of the candidate models is sufficient to describe the observed data. However under some conditions, AIC used alone inappropriately discriminates against necessary metabolic complexity.

CONCLUSION

In combination, the SSRE and AIC help the investigator identify the model that best describes the metabolism of a biological system.

An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation

A reliance on null hypothesis significance testing (NHST) and misinterpretations of its results are thought to contribute to the replication crisis while impeding the development of a cumulative science. One solution is a data-analytic approach called Information-Theoretic (I-T) Model Selection, which builds upon Maximum Likelihood estimates. In the I-T approach, the scientist examines a set of candidate models and determines for each one the probability that it is the closer to the truth than all others in the set. Although the theoretical development is subtle, the implementation of I-T analysis is straightforward. Models are sorted according to the probability that they are the best in light of the data collected. It encourages the examination of multiple models, something investigators desire and that NHST discourages. This article is structured to address two objectives. The first is to illustrate the application of I-T data analysis to data from a virtual experiment. A noisy delay-discounting data set is generated and seven quantitative models are examined. In the illustration, it is demonstrated that it is not necessary to know the "truth" is to identify the one that is closest to it and that the most likely models conform to the model that generated the data. Second, we examine claims made by advocates of the I-T approach using Monte Carlo simulations in which 10,000 different data sets are generated and analyzed. The simulations showed that 1) the probabilities associated with each model returned by the single virtual experiment approximated those that resulted from the simulations, 2) models that were deemed close to the truth produced the most precise parameter estimates, and 3) adding a single replicate sharpens the ability to identify the most probable model.

Temporal Variation and Statistical Assessment of the b Value off the Pacific Coast of Tokachi, Hokkaido, Japan

The Gutenberg-Richter Law describes the frequency-magnitude distribution of earthquakes. A number of studies have shown that the slope (b value) of the relationship between frequency and magnitude decreased before large earthquakes. In this paper, we investigate the temporal variation of the b value off the Pacific coast of Tokachi, Hokkaido, Japan, during 1990–2014. The magnitude of completeness (Mc) in the catalog is evaluated by combining the maximum curvature (MAXC) technique and the bootstrap approach. Then, the b value, and its uncertainty, is computed by using the maximum likelihood estimation. The Akaike Information Criterion (AIC) with the bootstrap approach is introduced to statistically assess the temporal variation of b values and quantify the significance level. The results show a decrease in trends of the b value prior to two large earthquakes (26 September 2003 (M8.0) and 11 September 2008 (M7.1)) in the analyzed area. In addition, the decrease of b values shows certain statistical significance three months before the 2003 Earthquake off the Pacific coast of Tokachi (M8.0). It is concluded that the b value with statistical assessment may contain potential information for future large earthquake preparation off the Pacific coast of Tokachi, Hokkaido, Japan.

Efficacy of control measures in the control of Ebola, Liberia 2014-2015

The largest outbreak of Ebola to date is the 2014 West Africa Ebola outbreak, with more than 10,000 cases and over 4000 deaths reported in Liberia alone. To control the spread of the outbreak, multiple interventions were implemented: identification and isolation of cases, contact tracing, quarantining of suspected contacts, proper personal protection, safely conducted burials, improved education, social awareness and individual protective measures. Devising rigorous methodologies for the evaluation of the effectiveness of the control measures implemented to stop an outbreak is of paramount importance. In this paper, we evaluate the effectiveness of the 2014 Ebola outbreak interventions. We rely on model selection to determine the best model that explains the 2014 Ebola outbreak data in Liberia which is the simplest model with a social distancing term. We couple structural and practical identifiability analysis with the computation of confidence intervals to pinpoint the uncertainty in the parameter estimations. Finally, we evaluate the efficacy of control measures using the Ebola model with social distancing. Among all the control measures, we find that social distancing had the most impact on the control of the 2014 Ebola epidemic in Libreria followed by isolation and quarantining.

Infection and Predictors of Outcome of Cirrhotic Patients after Emergency Care Hospital Admission

INTRODUCTION AND AIMS

We aimed to explore the impact of infection diagnosed upon admission and of other clinical baseline parameters on mortality of cirrhotic patients with emergency admissions.

MATERIAL AND METHODS

We performed a prospective observational monocentric study in a tertiary care center. The association of clinical parameters and established scoring systems with short-term mortality up to 90 days was assessed by univariate and multivariable Cox regression analysis. Akaike's Information Criterion (AIC) was used for automated variable selection. Statistical interaction effects with infection were also taken into account.

RESULTS

218 patients were included. 71.2% were male, mean age was 61.1 ± 10.5 years. Mean MELD score was 16.2 ± 6.5, CLIF-consortium Acute on Chronic Liver Failure-score was 34 ± 11. At 28, 90 and 365 days, 9.6%, 26.0% and 40.6% of patients had died, respectively. In multivariable analysis, respiratory organ failure [Hazard Ratio (HR) = 0.15], albumin substitution (HR = 2.48), non-HCC-malignancy (HR = 4.93), CLIF-C-ACLF (HR = 1.10), HCC (HR = 3.70) and first episode of ascites (HR = 0.11) were significantly associated with 90-day mortality. Patients with infection had a significantly higher 90-day mortality (36.3 vs. 20.1%, p = 0.007). Cultures were positive in 32 patients with resistance to cephalosporins or quinolones in 10, to ampicillin/sulbactam in 14 and carbapenems in 6 patients.

CONCLUSION

Infection is common in cirrhotic ED admissions and increases mortality. The proportion of resistant microorganisms is high. The predictive capacity of established scoring systems in this setting was low to moderate.

Using weighted power mean for equivalent square estimation

PURPOSE

Equivalent Square (ES) enables the calculation of many radiation quantities for rectangular treatment fields, based only on measurements from square fields. While it is widely applied in radiotherapy, its accuracy, especially for extremely elongated fields, still leaves room for improvement. In this study, we introduce a novel explicit ES formula based on Weighted Power Mean (WPM) function and compare its performance with the Sterling formula and Vadash/Bjärngard's formula.

METHODS

The proposed WPM formula is ESWPMa,b=waα+1-wbα1/α for a rectangular photon field with sides a and b. The formula performance was evaluated by three methods: standard deviation of model fitting residual error, maximum relative model prediction error, and model's Akaike Information Criterion (AIC). Testing datasets included the ES table from British Journal of Radiology (BJR), photon output factors (S_cp ) from the Varian TrueBeam Representative Beam Data (Med Phys. 2012;39:6981-7018), and published S_cp data for Varian TrueBeam Edge (J Appl Clin Med Phys. 2015;16:125-148).

RESULTS

For the BJR dataset, the best-fit parameter value α = -1.25 achieved a 20% reduction in standard deviation in ES estimation residual error compared with the two established formulae. For the two Varian datasets, employing WPM reduced the maximum relative error from 3.5% (Sterling) or 2% (Vadash/Bjärngard) to 0.7% for open field sizes ranging from 3 cm to 40 cm, and the reduction was even more prominent for 1 cm field sizes on Edge (J Appl Clin Med Phys. 2015;16:125-148). The AIC value of the WPM formula was consistently lower than its counterparts from the traditional formulae on photon output factors, most prominent on very elongated small fields.

CONCLUSION

The WPM formula outperformed the traditional formulae on three testing datasets. With increasing utilization of very elongated, small rectangular fields in modern radiotherapy, improved photon output factor estimation is expected by adopting the WPM formula in treatment planning and secondary MU check.

Predicting Productivity Losses from Health-Related Quality of Life Using Patient Data

OBJECTIVE

This paper estimates productivity loss using the health of the patient in order to allow indirect estimation of these costs for inclusion in economic evaluation.

METHODS

Data from two surveys of inpatients [Health outcomes data repository (HODaR) sample (n = 42,442) and health improvement and patient outcomes (HIPO) sample (n = 6046)] were used. The number of days off paid employment or normal activities (excluding paid employment) was modelled using the health of the patients measured by the EQ-5D, international classification of diseases (ICD) chapters, and other health and sociodemographic data. Two-part models (TPMs) and zero-inflated negative binomial (ZINB) models were identified as the most appropriate specifications, given large spikes at the minimum and maximum days for the dependent variable. Analysis was undertaken separately for the two datasets to account for differences in recall period and identification of those who were employed.

RESULTS

Models were able to reflect the large spike at the minimum (zero days) but not the maximum, with TPMs doing slightly better than the ZINB model. The EQ-5D was negatively associated with days off employment and normal activities in both datasets, but ICD chapters only had statistically significant coefficients for some chapters in the HODaR.

CONCLUSIONS

TPMs can be used to predict productivity loss associated with the health of the patient to inform economic evaluation. Limitations include recall and response bias and identification of who is employed in the HODaR, while the HIPO suffers from a small sample size. Both samples exclude some patient groups.

Using Parameter Constraints to Choose State Structures in Cost-Effectiveness Modelling

BACKGROUND

This article addresses the choice of state structure in a cost-effectiveness multi-state model. Key model outputs, such as treatment recommendations and prioritisation of future research, may be sensitive to state structure choice. For example, it may be uncertain whether to consider similar disease severities or similar clinical events as the same state or as separate states. Standard statistical methods for comparing models require a common reference dataset but merging states in a model aggregates the data, rendering these methods invalid.

METHODS

We propose a method that involves re-expressing a model with merged states as a model on the larger state space in which particular transition probabilities, costs and utilities are constrained to be equal between states. This produces a model that gives identical estimates of cost effectiveness to the model with merged states, while leaving the data unchanged. The comparison of state structures can be achieved by comparing maximised likelihoods or information criteria between constrained and unconstrained models. We can thus test whether the costs and/or health consequences for a patient in two states are the same, and hence if the states can be merged. We note that different structures can be used for rates, costs and utilities, as appropriate.

APPLICATION

We illustrate our method with applications to two recent models evaluating the cost effectiveness of prescribing anti-depressant medications by depression severity and the cost effectiveness of diagnostic tests for coronary artery disease.

CONCLUSIONS

State structures in cost-effectiveness models can be compared using standard methods to compare constrained and unconstrained models.

Nutrients and Other Environmental Factors Influence Virus Abundances across Oxic and Hypoxic Marine Environments

Virus particles are highly abundant in seawater and, on average, outnumber microbial cells approximately 10-fold at the surface and 16-fold in deeper waters; yet, this relationship varies across environments. Here, we examine the influence of a suite of environmental variables, including nutrient concentrations, salinity and temperature, on the relationship between the abundances of viruses and prokaryotes over a broad range of spatial and temporal scales, including along a track from the Northwest Atlantic to the Northeast Pacific via the Arctic Ocean, and in the coastal waters of British Columbia, Canada. Models of varying complexity were tested and compared for best fit with the Akaike Information Criterion, and revealed that nitrogen and phosphorus concentrations, as well as prokaryote abundances, either individually or combined, had significant effects on viral abundances in all but hypoxic environments, which were only explained by a combination of physical and chemical factors. Nonetheless, multivariate models of environmental variables showed high explanatory power, matching or surpassing that of prokaryote abundance alone. Incorporating both environmental variables and prokaryote abundances into multivariate models significantly improved the explanatory power of the models, except in hypoxic environments. These findings demonstrate that environmental factors could be as important as, or even more important than, prokaryote abundance in describing viral abundance across wide-ranging marine environments.

Recruitment and diversification of an ecdysozoan family of neuropeptide hormones for black widow spider venom expression

Venoms have attracted enormous attention because of their potent physiological effects and dynamic evolution, including the convergent recruitment of homologous genes for venom expression. Here we provide novel evidence for the recruitment of genes from the Crustacean Hyperglycemic Hormone (CHH) and arthropod Ion Transport Peptide (ITP) superfamily for venom expression in black widow spiders. We characterized latrodectin peptides from venom gland cDNAs from the Western black widow spider (Latrodectus hesperus), the brown widow (Latrodectus geometricus) and cupboard spider (Steatoda grossa). Phylogenetic analyses of these sequences with homologs from other spider, scorpion and wasp venom cDNAs, as well as CHH/ITP neuropeptides, show latrodectins as derived members of the CHH/ITP superfamily. These analyses suggest that CHH/ITP homologs are more widespread in spider venoms, and were recruited for venom expression in two additional arthropod lineages. We also found that the latrodectin 2 gene and nearly all CHH/ITP genes include a phase 2 intron in the same position, supporting latrodectin's placement within the CHH/ITP superfamily. Evolutionary analyses of latrodectins suggest episodes of positive selection along some sequence lineages, and positive and purifying selection on specific codons, supporting its functional importance in widow venom. We consider how this improved understanding of latrodectin evolution informs functional hypotheses regarding its role in black widow venom as well as its potential convergent recruitment for venom expression across arthropods.

Temporal and spatial variability of traffic-related noise in the City of Toronto, Canada

The majority of studies that assessed population-level exposure to traffic-related noise were conducted in European countries and less is known about the exposure to traffic noise in North America, particularly in Canadian cities. This study explored the temporal and spatial variability of traffic noise in the City of Toronto, the largest city in Canada. We conducted two cycles of intensive field measurement campaign to collect real-time measurements of traffic noise at 554 locations across Toronto between June 2012 and January 2013. At each site, we collected measurements for a period of 30 min during daytime. Repeated measurements were made in cycle two at 62 locations randomly selected from cycle one, which exhibited high correlation (Pearson's correlation coefficient (r): 0.79). In addition, continuous measurements of noise were recorded for seven days at ten sites. We observed that noise variability was predominantly spatial in nature, rather than temporal: spatial variability accounted for 60% of the total observed variations in traffic noise. Traffic volume, length of arterial road, and industrial area were three most important variables, explaining the majority of the spatial variability of noise (R(2)=0.68 to 0.74, depending on the cycle). In comparison to the 16-h equivalent sound level guideline for outdoor locations set out by the Ministry of the Environment of the Province of Ontario, 80% of our sampled locations exceeded this guideline (i.e. 55 dBA,16 h). These findings suggested ubiquitous traffic noise exposure across Toronto and that noise variability was explained mostly by spatial characteristics.

Common genetic variants regulating ADD3 gene expression alter biliary atresia risk

BACKGROUND & AIMS

Biliary atresia (BA) is a rare and most severe cholestatic disease in neonates, but the pathogenic mechanisms are unknown. Through a previous genome wide association study (GWAS) on Han Chinese, we discovered association of the 10q24.2 region encompassing ADD3 and XPNPEP1 genes, which was replicated in Chinese and Thai populations. This study aims to fully characterize the genetic architecture at 10q24.2 and to reveal the link between the genetic variants and BA.

METHODS

We genotyped 107 single nucleotide polymorphisms (SNPs) in 10q24.2 in 339 Han Chinese patients and 401 matched controls using Sequenom. Exhaustive follow-up studies of the association signals were performed.

RESULTS

The combined BA-association p-value of the GWAS SNP (rs17095355) achieved 6.06×10(-10). Further, we revealed the common risk haplotype encompassing 5 tagging-SNPs, capturing the risk-predisposing alleles in 10q24.2 [p=5.32×10(-11); odds ratio, OR: 2.38; confidence interval, CI: (2.14-2.62)]. Through Sanger sequencing, no deleterious rare variants (RVs) residing in the risk haplotype were found, dismissing the theory of "synthetic" association. Moreover, in bioinformatics and in vivo genotype-expression investigations, the BA-associated potentially regulatory SNPs correlated with ADD3 gene expression (n=36; p=0.0030). Remarkably, the risk haplotype frequency coincides with BA incidences in the population, and, positive selection (favoring the derived alleles that arose from mutations) was evident at the ADD3 locus, suggesting a possible role for the BA-associated common variants in shaping the general population diversity.

CONCLUSIONS

Common genetic variants in 10q24.2 can alter BA risk by regulating ADD3 expression levels in the liver, and may exert an effect on disease epidemiology and on the general population.

Newborn length predicts early infant linear growth retardation and disproportionately high weight gain in a low-income population

BACKGROUND

Stunting is prevalent by the age of 6 months in the indigenous population of the Western Highlands of Guatemala.

AIM

The objective of this study was to determine the time course and predictors of linear growth failure and weight-for-age in early infancy.

STUDY DESIGN AND SUBJECTS

One hundred and forty eight term newborns had measurements of length and weight in their homes, repeated at 3 and 6 months. Maternal measurements were also obtained.

RESULTS

Mean ± SD length-for-age Z-score (LAZ) declined from newborn -1.0 ± 1.01 to -2.20 ± 1.05 and -2.26 ± 1.01 at 3 and 6 months respectively. Stunting rates for newborn, 3 and 6 months were 47%, 53% and 56% respectively. A multiple regression model (R(2) = 0.64) demonstrated that the major predictor of LAZ at 3 months was newborn LAZ with the other predictors being newborn weight-for-age Z-score (WAZ), gender and maternal education∗maternal age interaction. Because WAZ remained essentially constant and LAZ declined during the same period, weight-for-length Z-score (WLZ) increased from -0.44 to +1.28 from birth to 3 months. The more severe the linear growth failure, the greater WAZ was in proportion to the LAZ.

CONCLUSION

The primary conclusion is that impaired fetal linear growth is the major predictor of early infant linear growth failure indicating that prevention needs to start with maternal interventions.

Paclitaxel-loaded polymeric microparticles: quantitative relationships between in vitro drug release rate and in vivo pharmacodynamics

Intraperitoneal therapy (IP) has demonstrated survival advantages in patients with peritoneal cancers, but has not become a widely practiced standard-of-care in part due to local toxicity and sub-optimal drug delivery. Paclitaxel-loaded, polymeric microparticles were developed to overcome these limitations. The present study evaluated the effects of microparticle properties on paclitaxel release (extent and rate) and in vivo pharmacodynamics. In vitro paclitaxel release from microparticles with varying physical characteristics (i.e., particle size, copolymer viscosity and composition) was evaluated. A method was developed to simulate the dosing rate and cumulative dose released in the peritoneal cavity based on the in vitro release data. The relationship between the simulated drug delivery and treatment outcomes of seven microparticle compositions was studied in mice bearing IP human pancreatic tumors, and compared to that of the intravenous Cremophor micellar paclitaxel solution used off-label in previous IP studies. Paclitaxel release from polymeric microparticles in vitro was multi-phasic; release was greater and more rapid from microparticles with lower polymer viscosities and smaller diameters (e.g., viscosity of 0.17 vs. 0.67 dl/g and diameter of 5-6 vs. 50-60 μm). The simulated drug release in the peritoneal cavity linearly correlated with treatment efficacy in mice (r(2)>0.8, p<0.001). The smaller microparticles, which distribute more evenly in the peritoneal cavity compared to the large microparticles, showed greater dose efficiency. For single treatment, the microparticles demonstrated up to 2-times longer survival extension and 4-times higher dose efficiency, relative to the paclitaxel/Cremophor micellar solution. Upon repeated dosing, the paclitaxel/Cremophor micellar solution showed cumulative toxicity whereas the microparticle that yielded 2-times longer survival did not display cumulative toxicity. The efficacy of IP therapy depended on both temporal and spatial factors that were determined by the characteristics of the drug delivery system. A combination of fast- and slow-releasing microparticles with 5-6 μm diameter provided favorable spatial distribution and optimal drug release for IP therapy.

Jumping and gliding rodents: mitogenomic affinities of Pedetidae and Anomaluridae deduced from an RNA-Seq approach

An RNA-Seq strategy was used to obtain the complete set of protein-coding mitochondrial genes from two rodent taxa. Thanks to the next generation sequencing (NGS) 454 approach, we determined the complete mitochondrial DNA genome from Graphiurus kelleni (Mammalia: Rodentia: Gliridae) and partial mitogenome from Pedetes capensis (Pedetidae), and compared them with published rodent and outgroup mitogenomes. We finished the mitogenome sequencing by a series of amplicons using conserved PCR primers to fill the gaps corresponding to tRNA, rRNA and control regions. Phylogenetic analyses of the mitogenomes suggest a well-supported rodent phylogeny in agreement with nuclear gene trees. Pedetes groups with Anomalurus into the clade Anomaluromorpha, while Graphiurus branches within the squirrel-related clade. Moreover, Pedetes+Anomalurus branch with Castor into the mouse-related clade. Our study demonstrates the utility of NGS for obtaining new mitochondrial genomes as well as the importance of choosing adequate models of sequence evolution to infer the phylogeny of rodents.

Prognostic stratification of patients with vasospastic angina: a comprehensive clinical risk score developed by the Japanese Coronary Spasm Association. J Am Coll Cardiol. 2013;62:1144-1153. [PMID: 23916938 DOI: 10.1016/j.jacc.2013.07.018]

OBJECTIVES

The present study aimed to develop a comprehensive clinical risk score for vasospastic angina (VSA) patients.

BACKGROUND

Previous studies demonstrated various prognostic factors of future adverse events in VSA patients. However, to apply these prognostic factors in clinical practice, the assessment of their accumulation in individual patients is important.

METHODS

The patient database of the multicenter registry study by the Japanese Coronary Spasm Association (JCSA) (n = 1,429; median 66 years; median follow-up 32 months) was utilized for score derivation.

RESULTS

Multivariable Cox proportional hazard model selected 7 predictors of major adverse cardiac events (MACE). The integer score was assigned to each predictors proportional to their respective adjusted hazard ratio; history of out-of-hospital cardiac arrest (4 points), smoking, angina at rest alone, organic coronary stenosis, multivessel spasm (2 points each), ST-segment elevation during angina, and beta-blocker use (1 point each). According to the total score in individual patients, 3 risk strata were defined; low (score 0 to 2, n = 598), intermediate (score 3 to 5, n = 639) and high (score 6 or more, n = 192). The incidences of MACE in the low-, intermediate-, and high-risk patients were 2.5%, 7.0%, and 13.0%, respectively (p < 0.001). The Cox model for MACE between the 3 risk strata also showed prognostic utility of the scoring system in various clinical subgroups. The average prediction rate of the scoring system in the internal training and validation sets were 86.6% and 86.5%, respectively.

CONCLUSIONS

We developed a novel scoring system, the JCSA risk score, which may provide the comprehensive risk assessment and prognostic stratification for VSA patients.

MH2c: Characterization of major histocompatibility α-helices - an information criterion approach

Major histocompatibility proteins share a common overall structure or peptide binding groove. Two binding groove domains, on the same chain for major histocompatibility class I or on two different chains for major histocompatibility class II, contribute to that structure that consists of two α-helices ("wall") and a sheet of eight anti-parallel beta strands ("floor"). Apart from the peptide presented in the groove, the major histocompatibility α-helices play a central role for the interaction with the T cell receptor. This study presents a generalized mathematical approach for the characterization of these helices. We employed polynomials of degree 1 to 7 and splines with 1 to 2 nodes based on polynomials of degree 1 to 7 on the α-helices projected on their principal components. We evaluated all models with a corrected Akaike Information Criterion to determine which model represents the α-helices in the best way without overfitting the data. This method is applicable for both the stationary and the dynamic characterization of α-helices. By deriving differential geometric parameters from these models one obtains a reliable method to characterize and compare α-helices for a broad range of applications.

PROGRAM SUMMARY

Program title: MH²c (MH helix curves) Catalogue identifier: AELX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AELX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 327 565 No. of bytes in distributed program, including test data, etc.: 17 433 656 Distribution format: tar.gz Programming language: Matlab Computer: Personal computer architectures Operating system: Windows, Linux, Mac (all systems on which Matlab can be installed) RAM: Depends on the trajectory size, min. 1 GB (Matlab) Classification: 2.1, 4.9, 4.14 External routines: Curve Fitting Toolbox and Statistic Toolbox of Matlab Nature of problem: Major histocompatibility (MH) proteins share a similar overall structure. However, identical MH alleles which present different peptides differ by subtle conformational alterations. One hypothesis is that such conformational differences could be another level of T cell regulation. By this software package we present a reliable and systematic way to compare different MH structures to each other. Solution method: We tested several fitting approaches on all available experimental crystal structures of MH to obtain an overall picture of how to describe MH helices. For this purpose we transformed all complexes into the same space and applied splines and polynomials of several degrees to them. To draw a general conclusion which method fits them best we employed the "corrected Akaike Information Criterion". The software is applicable for all kinds of helices of biomolecules. Running time: Depends on the data, for a single stationary structure the runtime should not exceed a few seconds.