Empirical Threshold Research Articles

The collapse of marsh fritillary (Euphydryas aurinia) populations associated with declining host plant abundance

Monophagous butterflies often have smaller ranges than their host plants, which could relate to host plant abundance. Many specialist butterflies have already gone regionally extinct and studies of the role of host plant and habitat abundance at resolutions sufficiently detailed to cover host plant abundance are urgently needed for conservation management. Here, we investigate the link between a predominantly monophagous butterfly – the critically endangered Euphydryas aurinia and its host – Succissa pratensis in Denmark using high resolution (1×1km) butterfly distribution data and an extensive national host plant frequency data set. More specifically, we assessed whether patterns in the present day distribution of S. pratensis and its habitat can explain the current distribution of E. aurinia and which ecological factors determine habitat suitability for S. pratensis. We used logistic regression to model the occurrence of E. aurinia as a function of habitat and host plant abundance as well as connectivity among habitats. We also modelled the occurrence of S. pratensis from habitat type, soil type, precipitation and indicators of soil moisture, nutrient level, and pH using generalized additive models. The remaining E. aurinia populations are confined to small regions in northern Jutland and this pattern was well predicted by our model. High modelled probabilities of E. aurinia coincided with habitat abundance exceeding 6–7% of land area and host plant frequency within habitat exceeding 40% at plot scale. Habitat connectivity was not included in the most parsimonious model. S. pratensis occurrence probability increased with pH, decreased with nutrient status and showed a unimodal response to soil moisture. The geographic partitioning of pH and nutrient effects indicated host plant scarcity due to eutrophication in Eastern Denmark and acidification in Western Denmark. Our findings highlight the importance of identifying empirical thresholds for habitat and host plant abundance in future conservation of herbivorous insects while also taking host plant habitat requirements into account.

Restoration of endangered fen communities: the ambiguity of iron–phosphorus binding and phosphorus limitation

Summary Low phosphorus (P) availability limits plant biomass production in fens, which is a prerequisite for the persistence of many endangered plant species. We hypothesized that P limitation is linked to soil iron (Fe) content and soil Fe : P ratios as iron compounds provide binding sites for dissolved P, presumably reducing P availability to plants. We sampled 30 fens in a trans‐European field survey to determine how soil Fe pools relate to pools of P and Fe‐bound P, and we measured vegetation P uptake and N : P ratio to assess where P limitation occurs. Next, we determined P uptake by Carex rostrata in experimental fen mesocosms to investigate interactive effects of soil Fe and P pools (and fractions) and water levels (drained or rewetted). The field survey revealed that soil P pools correlate positively with soil Fe pools, regardless of fen degradation level, location or sampling depth. Moreover, soil Fe and P pools correlated positively with P uptake by the vegetation and negatively with vegetation N : P ratios. Generally, N : P ratios dropped below 10 g g−1 whenever thresholds of 15 mmol Fe L−1 soil and 3·3 mmol P L−1 soil were exceeded. Endangered fen species mainly thrived in Fe‐ (and thus P‐) poor fens. The mesocosm experiment further showed that interactions between water levels and P pools determined plant P uptake: although fen rewetting led to an overall increase in P uptake, plants that had grown on drained Fe‐rich soils with large acid‐extractable P pools (>1·6 mmol Pacid L−1) could still sequester large quantities of P. Soil Fe : P ratio had no effect on P uptake. Synthesis and applications. Our findings have important implications for the management and restoration of endangered fen communities. We demonstrated the existence of an iron–phosphorus (Fe–P) binding ambiguity in fens: large Fe pools ‘trap’ mobile P, thereby enhancing overall P availability to plants rather than diminishing it. For P limitation, we suggest an empirical threshold of <3·3 mmol P L−1 soil, which is mainly found in Fe‐poor fens. Restoring fens by rewetting increases the relative availability of P and may not always result in favourable conditions for endangered fen communities. Rewetting of drained fens is most likely to be successful if soil P and Fe pools are well below 3·3 and 15 mmol L−1 respectively.

Inland thinning on the Greenland ice sheet controlled by outlet glacier geometry

Greenland’s contribution to future sea-level rise remains uncertain and a wide range of upper and lower bounds has been proposed. These predictions depend strongly on how mass loss—which is focused at the termini of marine-terminating outlet glaciers—can penetrate inland to the ice-sheet interior. Previous studies have shown that, at regional scales, Greenland ice sheet mass loss is correlated with atmospheric and oceanic warming. However, mass loss within individual outlet glacier catchments exhibits unexplained heterogeneity, hindering our ability to project ice-sheet response to future environmental forcing. Using digital elevation model differencing, we spatially resolve the dynamic portion of surface elevation change from 1985 to present within 16 outlet glacier catchments in West Greenland, where significant heterogeneity in ice loss exists. We show that the up-glacier extent of thinning and, thus, mass loss, is limited by glacier geometry. We find that 94% of the total dynamic loss occurs between the terminus and the location where the down-glacier advective speed of a kinematic wave of thinning is at least three times larger than its diffusive speed. This empirical threshold enables the identification of glaciers that are not currently thinning but are most susceptible to future thinning in the coming decades. Greenland’s ice loss depends on propagation of mass loss from the marine glacier termini to the interior. An analysis of surface elevation change in 16 glacier catchments shows that the up-glacier extent of thinning is limited by glacier geometry.

Rainfall thresholds for possible landslide occurrence in Italy

The large physiographic variability and the abundance of landslide and rainfall data make Italy an ideal site to investigate variations in the rainfall conditions that can result in rainfall-induced landslides. We used landslide information obtained from multiple sources and rainfall data captured by 2228 rain gauges to build a catalogue of 2309 rainfall events with – mostly shallow – landslides in Italy between January 1996 and February 2014. For each rainfall event with landslides, we reconstructed the rainfall history that presumably caused the slope failure, and we determined the corresponding rainfall duration D (in hours) and cumulated event rainfall E (in mm). Adopting a power law threshold model, we determined cumulated event rainfall–rainfall duration (ED) thresholds, at 5% exceedance probability, and their uncertainty. We defined a new national threshold for Italy, and 26 regional thresholds for environmental subdivisions based on topography, lithology, land-use, land cover, climate, and meteorology, and we used the thresholds to study the variations of the rainfall conditions that can result in landslides in different environments, in Italy. We found that the national and the environmental thresholds cover a small part of the possible DE domain. The finding supports the use of empirical rainfall thresholds for landslide forecasting in Italy, but poses an empirical limitation to the possibility of defining thresholds for small geographical areas. We observed differences between some of the thresholds. With increasing mean annual precipitation (MAP), the thresholds become higher and steeper, indicating that more rainfall is needed to trigger landslides where the MAP is high than where it is low. This suggests that the landscape adjusts to the regional meteorological conditions. We also observed that the thresholds are higher for stronger rocks, and that forested areas require more rainfall than agricultural areas to initiate landslides. Finally, we observed that a 20% exceedance probability national threshold was capable of predicting all the rainfall-induced landslides with casualties between 1996 and 2014, and we suggest that this threshold can be used to forecast fatal rainfall-induced landslides in Italy. We expect the method proposed in this work to define and compare the thresholds to have an impact on the definition of new rainfall thresholds for possible landslide occurrence in Italy, and elsewhere.

Formation of the 2015 Shenzhen landslide as observed by SAR shape-from-shading

The time-series topography change of a landfill site before its failure has rarely been surveyed in detail. However, this information is important for both landfill management and early warning of landslides. Here, we take the 2015 Shenzhen landslide as an example, and we use the radar shape-from-shading (SFS) technique to retrieve time-series digital elevation models of the landfill. The results suggest that the total filling volume reached 4,074,300 m3 in the one and a half years before the landslide, while 2,817,400 m3 slid down in the accident. Meanwhile, the landfill rate in most areas exceeded 2 m/month, which is the empirical upper threshold in landfill engineering. Using topography captured on December 12, 2015, the slope safety analysis gives a factor of safety of 0.932, suggesting that this slope was already hazardous before the landslide. We conclude that the synthetic aperture radar (SAR) SFS technique has the potential to contribute to landfill failure monitoring.

Optimal threshold selection in the POT method for extreme value prediction of the dynamic responses of a Spar-type floating wind turbine

This paper concerns the calculation of the short term extreme structural responses of a Spar-type floating wind turbine by applying the peak-over-threshold (POT) method. A new approach is proposed for finding an optimum threshold value by incorporating a declustering algorithm into the POT method. To select the clusters and check the Poisson character of the number of clusters we use the concept of a dispersion index. Then, the method of maximum product of spacing is utilized to estimate the parameters in the Generalized Pareto distribution of the largest values in all the selected clusters over the optimum threshold value. As examples of calculations, optimum threshold values of the out-of-plane and in-plane blade root bending moments and the tower – Spar interface bending moments of the NREL 5-MW OC3-Hywind floating wind turbine have been obtained by utilizing the new approach. The extreme response probability plot based on an optimum threshold value has been compared with the probability plot based on an empirical threshold value, and the accuracy and efficiency of the new approach have been convincingly validated. Goodness-of-fits plots have also been included in this article for testing the accuracy of the proposed new approach. Finally, in order to make a fair comparison, we have also extrapolated the short-term extreme values using the different POT methods and compared them with the reference values obtained directly from a large number of time-domain simulations.

Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data

Models for forecasting rainfall-induced landslides are mostly based on the identification of empirical rainfall thresholds obtained exploiting rain gauge data. Despite their increased availability, satellite rainfall estimates are scarcely used for this purpose. Satellite data should be useful in ungauged and remote areas, or should provide a significant spatial and temporal reference in gauged areas. In this paper, the analysis of the reliability of rainfall thresholds based on rainfall remote sensed and rain gauge data for the prediction of landslide occurrence is carried out. To date, the estimation of the uncertainty associated with the empirical rainfall thresholds is mostly based on a bootstrap resampling of the rainfall duration and the cumulated event rainfall pairs (D,E) characterizing rainfall events responsible for past failures. This estimation does not consider the measurement uncertainty associated with D and E. In the paper, we propose (i) a new automated procedure to reconstruct ED conditions responsible for the landslide triggering and their uncertainties, and (ii) three new methods to identify rainfall threshold for the possible landslide occurrence, exploiting rain gauge and satellite data. In particular, the proposed methods are based on Least Square (LS), Quantile Regression (QR) and Nonlinear Least Square (NLS) statistical approaches. We applied the new procedure and methods to define empirical rainfall thresholds and their associated uncertainties in the Umbria region (central Italy) using both rain-gauge measurements and satellite estimates. We finally validated the thresholds and tested the effectiveness of the different threshold definition methods with independent landslide information. The NLS method among the others performed better in calculating thresholds in the full range of rainfall durations. We found that the thresholds obtained from satellite data are lower than those obtained from rain gauge measurements. This is in agreement with the literature, where satellite rainfall data underestimate the “ground” rainfall registered by rain gauges.

Genetic variation underlying renal uric acid excretion in Hispanic children: the Viva La Familia Study

BackgroundReduced renal excretion of uric acid plays a significant role in the development of hyperuricemia and gout in adults. Hyperuricemia has been associated with chronic kidney disease and cardiovascular disease in children and adults. There are limited genome-wide association studies associating genetic polymorphisms with renal urate excretion measures. Therefore, we investigated the genetic factors that influence the excretion of uric acid and related indices in 768 Hispanic children of the Viva La Familia Study.MethodsWe performed a genome-wide association analysis for 24-h urinary excretion measures such as urinary uric acid/urinary creatinine ratio, uric acid clearance, fractional excretion of uric acid, and glomerular load of uric acid in SOLAR, while accounting for non-independence among family members.ResultsAll renal urate excretion measures were significantly heritable (p <2 × 10−6) and ranged from 0.41 to 0.74. Empirical threshold for genome-wide significance was set at p <1 × 10−7. We observed a strong association (p < 8 × 10−8) of uric acid clearance with a single nucleotide polymorphism (SNP) in zinc finger protein 446 (ZNF446) (rs2033711 (A/G), MAF: 0.30). The minor allele (G) was associated with increased uric acid clearance. Also, we found suggestive associations of uric acid clearance with SNPs in ZNF324, ZNF584, and ZNF132 (in a 72 kb region of 19q13; p <1 × 10−6, MAFs: 0.28–0.31).ConclusionFor the first time, we showed the importance of 19q13 region in the regulation of renal urate excretion in Hispanic children. Our findings indicate differences in inherent genetic architecture and shared environmental risk factors between our cohort and other pediatric and adult populations.

Temperature and light patterns at four reefs along the Great Barrier Reef during the 2015–2016 austral summer: understanding patterns of observed coral bleaching

ABSTRACTData from real-time sensor networks along the Great Barrier Reef (GBR) over the 2015–2016 austral summer showed that reef water temperatures exceeded empirical coral bleaching thresholds at a number of sites. Temperatures in the southern GBR were within historically normal limits with temperatures below the empirical bleaching threshold. The central GBR just reached the empirical bleaching threshold while, in the north, Lizard Island recorded four consecutive days above the bleaching threshold. Thursday Island in the far northern GBR experienced 10 days above the bleaching threshold. The in situ data predicted only slight bleaching in the southern GBR, moderate bleaching in the central GBR, widespread bleaching in the north and severe bleaching in the far north, which compares well with the initial survey data. Peak temperatures occurred later in the year in the north (mid-March 2016) than in the south (early February 2015) with temperatures remaining above the long-term mean well into the austral autumn. Comparison against satellite sea surface temperature data highlighted issues of cloud cover with data only being available for 30–40% of days over the summer. While the agreement with the in situ data was good, the satellite data missed fine-scale events and under-estimated the event at Thursday Island.

车辆荷载作用下桥梁应变极值估计的阈值选取

The selection of a reasonable threshold is critical to estimate the extreme strain under vehicle load on bridges with the peak-over-threshold method. Little information can be used if the threshold is too high, while the bias of parameters of the general Pareto distribution will be large if the threshold is too low. Common threshold selection methods are not suitable to be applied in estimation of the extreme strain under vehicle load. Based on 1-year strain data of the Taiping Lake Bridge, 3 types of mixed distributions for the strain peaks induced by vehicle load were chosen to generate a large number of samples with the Monte-Carlo method. The estimated extreme values of the samples based on the generalized Pareto distributions with different thresholds were compared and analyzed. Then, an empirical threshold selection method was proposed for the strain data induced by vehicle load. Finally, the Taiping Lake Bridge was chosen as the case verification. It is demonstrated that the estimated weekly extreme strain based on the threshold selected with the proposed method is more close to the measured results than those with the common methods.

Threshold selection for extreme strain extrapolation due to vehicles on bridges

In the design and condition assessment of bridges, the extreme vehicle load effects are necessary to be taken into consideration which may occur during the service period of bridges. In order to obtain an accurate extrapolation of the extreme value based on limited duration, threshold selection is a critical step in the peak-over-threshold method. Overly high threshold results in little information to be used and excessively low threshold leads to large bias in parameters estimation of generalized Pareto distribution. To investigate this issue, 417 days of strain data acquired from the long-term structural health monitoring system of Taiping Lake Bridge in China are employed in this paper. According to the tail distribution of the strain data induced by vehicle loads, four homothetic distributions are chosen as the parent distributions, from which lots of random samples are generated by the Monte Carlo method. For each parent distribution, the 100-yearly extreme values at different thresholds are estimated and compared with the theoretical value based on those samples. Then a simple and empirical threshold selection method is proposed, which can be an effective threshold selection tool for the extreme value estimation of vehicle load effect in future engineering practice.

Adaptive weighted moving shadow detection based on multiple features

Moving object detection is a fundamental task for automatic systems in video surveillance scenes, whose performance is downgraded by moving shadows unfortunately since moving objects and their shadows tend to present similar motion patterns and most moving object detection methods confused them frequently. To deal with this problem, we propose an adaptive weighted moving shadow detection method based on multiple features. In the proposed method, intensity, color and texture properties with neighboring information are exploited to generate feature maps, which are utilized to detect moving shadows respectively in small random selected videos to determine the weight for each feature map. Subsequently, the adaptive weighted fusion strategy is applied to fusion these feature maps for shadow detection according to the empirical threshold. At last, a series of spatial adjustment operations are implemented to correct misclassified pixels for obtaining refined detection results. By analyzing extensive experimental and comparison results, it demonstrates that the effectiveness and robustness of the proposed method for different video scenes and the superiority than some state-of-the-art methods.

The impact of spin coupling signal loss on fat content characterization in multi-echo acquisitions with different echo spacing

PurposeThis study aimed to assess the effect of echo spacing in transverse magnetization (T2) signal decay of gel and fat (oil) samples. Additionally, we assess the feasibility of using spin coupling as a determinant of fat content. MethodsPhantoms of known T2 values, as well as vegetable oil phantoms, were scanned at 1.5T scanner with a multi echo FSE sequence of variable echo spacing above and below the empirical threshold of 20ms for echo train signal modulation (6.7, 13.6, 26.8, and 40ms). T2 values were calculated from monoexponential fitting of the data. Relative signal loss between the four acquisitions of different echo spacing was calculated. ResultsAgreement in the T2 values of water gel phantom was observed in all acquisitions as opposed to fat phantom (oil) samples. Relative differences in signal intensity between two successive sequences of different echo spacing on composite fat/water regions of interest was found to be linearly correlated to fat fraction of the ROI. ConclusionThe sample specific degree of signal loss that was observed between different fat samples (vegetable oils) can be attributed to the composition of each sample in J coupled fat components. Hence, spin coupling may be used as a determinant of fat content.

Inflation in Nigeria, a Historical Fact or Consequences of Fiscal Deficit?: An Empirical Examination Using Threshold VECM

The study empirically examines the consequences of the relationship between fiscal and monetary policies in Nigeria. To do this, an Inter-temporal Budget Constrain (IBC) of the federal government was estimated using annual time series data for budget deficit, growth rate of monetary aggregates(M1 and M2), change in federal government stock of debt and directs CBN financing of budget deficit. The series were collected from the Central Bank of Nigeria’s statistical bulletin, 2015. To examine the evidence, the study assessed the univariate and multivariate stochastic properties of the series using Perron (1997); LP (1997) and Gregory and Hansen (1996); Hateemi J.(2008) respectively. The following evidences were found for the univariate stochastic properties and the results reveal the presence of unit root at levels for all the series except CBN financing of budget deficit. This finding is consistent across the two tests. For the multivariate counterpart, the evidence reveals that seigniorage and debt are the two components used in long run budget deficit financing in Nigeria. In terms of the short run dynamics of the relationship, Threshold VECM was estimated and the result reveals that both debt and seigniorage components, except growth rate of broad money supply, affect budget deficit in the short run. Therefore the study made the following conclusions: firstly, seigniorage and borrowings are the two important components used in budget deficit financing both in the short and long run in Nigeria. Secondly, the evidence found by the study is in favor of fiscal policy dominance which has an implication of affecting interest rates and/or prices. Therefore, inflation in Nigeria is both historical fact and consequences of budget deficit.

Inter-rater reliability of distal ureteral diameter ratio compared to grade of VUR

Management of vesicoureteral reflux (VUR) remains controversial, and reflux grade constitutes an important prognostic factor. Recent work has suggested that distal ureteral diameter ratio (UDR) is a predictive factor relative to clinical outcome independent of grade. Previous studies have noted significant inter-rater variability with grading of VUR. The present study compared inter-rater reliability of reflux grade and UDR in children with primary VUR. Four pediatric urologists independently reviewed, in a blinded fashion, voiding cystourethrograms. For each renal unit, grade was assigned according to the standardized international scale. The UDR was calculated by dividing the largest ureteral diameter within the false pelvis by the distance between L1-L3 vertebral bodies. Correlation within each rater was determined using Pearson's correlation coefficient. Reliability of VUR grade and UDR were calculated using two-way ANOVA model inter-rater agreement. Four independent raters reliably measured VUR grade (ICC=0.87, 95% CI=0.78-0.93) and UDR (ICC=0.95, 95% CI=0.92-0.97). While UDR and grade were equally reliable measures, UDR had a tighter confidence interval. For each rater, grade and UDR were well correlated (r=0.73-0.84; P<0.0001). For higher-grade reflux, grade was more variable than UDR (Summary Figure). Using empirical thresholds, theincreased variability of grade compared with UDR maylead to significant differences in clinical decision-making among physicians (P=0.022). Known discordance with grading reflux emphasizes the need for a more objective VUR measurement, as clinicians and parents often opt for clinical intervention based on both clinical course and the likelihood of spontaneous resolution. While ICC for UDR and grade were not significantly different, the confidence intervals for grade were wider due to greater variability among grade measurements. This suggests that using UDR measurements may lead to more accurate characterization of VUR and ultimately more consistent clinical decision-making across providers. Ureteral diameter ratio has good inter-rater reliability among pediatric urologists, with less clinically relevant variability than VUR grade. Ureteral diameter ratio is a more objective and reliable measure than grade, and may be more useful in clinical decision-making.

Spatial estimation of debris flows-triggering rainfall and its dependence on rainfall return period

Forecasting the occurrence of debris flows is fundamental for issuing hazard warnings, and often focuses on rainfall as a triggering agent and on the use of empirical rainfall thresholds based on rain gauge observations. A recognized component of the uncertainty associated with the use of rainfall thresholds is related to the sampling of strongly varying rainfall variability with sparse rain gauge networks. In this work we examine the spatial distribution of rainfall depth in areas up to 10km from the debris flow initiation points as a function of return period, and we exploit this information to analyze the errors expected in the estimation of debris flow triggering rainfall when rain gauge data are used. In particular, we investigate the impact of rain gauge density and of the use of different interpolation methods. High-resolution, adjusted radar rainfall estimates, representing the best available spatially-distributed rainfall estimates at the debris flows initiation point and in the surrounding area, are sampled by stochastically generated rain gauge networks characterized by varying densities. Debris flow triggering rainfall is estimated by means of three rainfall interpolation methods: nearest neighbor, inverse distance weighting and ordinary kriging. On average, triggering rainfall shows a local peak corresponding to the debris flow initiation point, with a decay of rainfall with distance which increases with the return period of the triggering rainfall. Interpolation of the stochastically generated rain gauge measurements leads to an underestimation of the triggering rainfall that, irrespective of the interpolation methods, increases with the return period and decreases with the rain gauge density. For small return period events and high rain gauge density, the differences among the methods are minor. With increasing the return period and decreasing the rain gauge density, the nearest neighbor method is less biased, because it makes use only of the closest rain gauge to the debris flow initiation point. On the contrary, inverse distance weighting and ordinary kriging, which are using rain gauges located farther from the debris flows in addition to the closest one, exhibit negative biases that increase with return period. The standard deviation of the interpolated values is larger when the nearest neighbor is used with respect to inverse distance weighting and ordinary kriging. For large return period and low rain gauge density, the differences among the methods are minor.

SHIELD: NEUTRAL GAS KINEMATICS AND DYNAMICS

ABSTRACT We present kinematic analyses of the 12 galaxies in the “Survey of H i in Extremely Low-mass Dwarfs” (SHIELD). We use multi-configuration interferometric observations of the H i 21 cm emission line from the Karl G. Jansky Very Large Array (VLA)22 to produce image cubes at a variety of spatial and spectral resolutions. Both two- and three-dimensional fitting techniques are employed in an attempt to derive inclination-corrected rotation curves for each galaxy. In most cases, the comparable magnitudes of velocity dispersion and projected rotation result in degeneracies that prohibit unambiguous circular velocity solutions. We thus make spatially resolved position–velocity cuts, corrected for inclination using the stellar components, to estimate the circular rotation velocities. We find 30 km s−1 for the entire survey population. Baryonic masses are calculated using single-dish H i fluxes from Arecibo and stellar masses derived from HST and Spitzer imaging. Comparison is made with total dynamical masses estimated from the position–velocity analysis. The SHIELD galaxies are then placed on the baryonic Tully–Fisher relation. There exists an empirical threshold rotational velocity, V &lt; 15 km s−1, below which current observations cannot differentiate coherent rotation from pressure support. The SHIELD galaxies are representative of an important population of galaxies whose properties cannot be described by current models of rotationally dominated galaxy dynamics.

Empirical Safety Thresholds for Liquidity and Indebtedness Ratios on the Polish Capital Market

One of the elements of company's evaluation is ratio analysis. It includes computation of bankruptcy risk metrics. There are multiple such measures, of which two seem to be quite universal and commonly applied. These are current ratio and indebtedness ratio. In this study, the accuracy of bankruptcy predictions based on these two ratios is evaluated within a sample of data from the Polish market. Also, the safety thresholds (meant as values at which the probability of bankruptcy exceeds fifty percent) are estimated. The study is based on a sample of 84 companies, in which case a bankruptcy filing was announced in a period between the beginning of 2009 and the end of the first half of 2015. This sample of bankrupt firms is compared to the counter-sample of companies in which case no any bankruptcy filing occurred. The statistical analysis has confirmed the usefulness of both ratios. Even though the sample covers wide variety of businesses, the logit models with only one ratio used as an explanatory variable are capable of identifying bankrupt firms in about 70-73% of cases. Our research has also shown that the estimated safety thresholds lie near the typically assumed "rules of thumb".

Alcohol-related genes show an enrichment of associations with a persistent externalizing factor.

Research using twins has found that much of the variability in externalizing phenotypes-including alcohol and drug use, impulsive personality traits, risky sex, and property crime-is explained by genetic factors. Nevertheless, identification of specific genes and variants associated with these traits has proven to be difficult, likely because individual differences in externalizing are explained by many genes of small individual effect. Moreover, twin research indicates that heritable variance in externalizing behaviors is mostly shared across the externalizing spectrum rather than specific to any behavior. We use a longitudinal, "deep phenotyping" approach to model a general externalizing factor reflecting persistent engagement in a variety of socially problematic behaviors measured at 11 assessment occasions spanning early adulthood (ages 18 to 28). In an ancestrally homogenous sample of non-Hispanic Whites (N = 337), we then tested for enrichment of associations between the persistent externalizing factor and a set of 3,281 polymorphisms within 104 genes that were previously identified as associated with alcohol-use behaviors. Next, we tested for enrichment among domain-specific factors (e.g., property crime) composed of residual variance not accounted for by the common factor. Significance was determined relative to bootstrapped empirical thresholds derived from permutations of phenotypic data. Results indicated significant enrichment of genetic associations for persistent externalizing, but not for domain-specific factors. Consistent with twin research findings, these results suggest that genetic variants are broadly associated with externalizing behaviors rather than unique to specific behaviors. (PsycINFO Database Record

Identifying rare and common variants with Bayesian variable selection.

BackgroundRecent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Yet, the statistical aspect of testing disease association of rare variants is quite challenging as the typical assumptions fail to hold owing to low minor allele frequency (<0.5 or 1 %).MethodsI present a Bayesian variable selection approach to detect associations with both rare and common genetic variants for quantitative traits simultaneously. In my model, I frame the problem of identifying disease-associated variants as a problem of variable selection in a sparse space, that is, how best to model the relationship between phenotypes and a set of genetic variants. By constructing a risk index score for a group of rare variants, my method can effectively consider all variants in a multivariate model. I also use a within-chain permutation to generate the empirical thresholds to detect true-positive variants.ResultsI apply our method to study the association between increases in baseline systolic and diastolic blood pressure (SBP and DBP, respectively) and genetic variants in the data from Genetic Analysis Workshop 19 unrelated samples. I identify several rare and common variants in the gene MAP4 that are potentially associated with SBP and DBP.ConclusionsThe application shows that my method is powerful in identifying disease-associated variants even with the extreme rarity.