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PubMed2006

Multiple alignment tensors from a denatured protein.

The structural content of the denatured state has yet to be fully characterized. In recent years, large residual dipolar couplings (RDCs) from denatured proteins have been observed under alignment conditions produced by bicelles and strained polyacrylamide gels. In this report, we describe efforts to extend our picture of the residual structure in denatured nuclease by measuring RDCs with multiple alignment tensors. Backbone amide 15N-1H RDCs were collected from 4 M urea for a total of eight RDC data sets. The RDCs were analyzed by singular value decomposition (SVD) to determine the number of independent alignment tensors present in the data. On the basis of the resultant singular values and propagated error estimates, it is clear that there are at least three independent alignment tensors. These three independent RDC datasets can be reconstituted as orthogonal linear combinations, (OLC)-RDC datasets, of the eight actually recorded. The first, second, and third OLC-RDC datasets are highly robust to the removal of any single experimental RDC dataset, establishing the presence of three independent alignment tensors, sampled well above the level of experimental uncertainty. The observation that the RDC data span three or more dimensions of the five-dimensional parameter space demonstrates that the ensemble average structure of denatured nuclease must be asymmetric with respect to these three orthogonal principal axes, which is not inconsistent with earlier work demonstrating that it has a nativelike topology.

Bacteriophage Pf1↗

PubMed2002

Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods.

We report the development of rigorously validated quantitative structure-activity relationship (QSAR) models for 48 chemically diverse functionalized amino acids with anticonvulsant activity. Two variable selection approaches, simulated annealing partial least squares (SA-PLS) and k nearest neighbor (kNN), were employed. Both methods utilize multiple descriptors such as molecular connectivity indices or atom pair descriptors, which are derived from two-dimensional molecular topology. QSAR models with high internal accuracy were generated, with leave-one-out cross-validated R(2) (q(2)) values ranging between 0.6 and 0.8. The q(2) values for the actual dataset were significantly higher than those obtained for the same dataset with randomly shuffled activity values, indicating that models were statistically significant. The original dataset was further divided into several training and test sets, with highly predictive models providing q(2) values greater than 0.5 for the training sets and R(2) values greater than 0.6 for the test sets. These models were capable of predicting with reasonable accuracy the activity of 13 novel compounds not included in the original dataset. The successful development of highly predictive QSAR models affords further design and discovery of novel anticonvulsant agents.

Amino Acids↗

PubMed2002

New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors.

A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form predictive regression equations. We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the well-studied thermolysin zinc protease inhibitor dataset and a glycogen phosphorylase inhibitor dataset. In each case the addition of ILP variables produced statistically better results (P < 0.01 for thermolysin and P < 0.05 for GP datasets) than the CoMFA analysis. Moreover, the new ILP variables were not found to increase the complexity of the final QSAR equations and gave possible insight into the binding mechanism of the ligand-protein complex under study.

Algorithms↗

PubMed2005

Derivation and validation of toxicophores for mutagenicity prediction.

Mutagenicity is one of the numerous adverse properties of a compound that hampers its potential to become a marketable drug. Toxic properties can often be related to chemical structure, more specifically, to particular substructures, which are generally identified as toxicophores. A number of toxicophores have already been identified in the literature. This study aims at increasing the current degree of reliability and accuracy of mutagenicity predictions by identifying novel toxicophores from the application of new criteria for toxicophore rule derivation and validation to a considerably sized mutagenicity dataset. For this purpose, a dataset of 4337 molecular structures with corresponding Ames test data (2401 mutagens and 1936 nonmutagens) was constructed. An initial substructure-search of this dataset showed that most mutagens were detected by applying only eight general toxicophores. From these eight, more specific toxicophores were derived and approved by employing chemical and mechanistic knowledge in combination with statistical criteria. A final set of 29 toxicophores containing new substructures was assembled that could classify the mutagenicity of the investigated dataset with a total classification error of 18%. Furthermore, mutagenicity predictions of an independent validation set of 535 compounds were performed with an error percentage of 15%. Since these error percentages approach the average interlaboratory reproducibility error of Ames tests, which is 15%, it was concluded that these toxicophores can be applied to risk assessment processes and can guide the design of chemical libraries for hit and lead optimization.

Databases, Factual↗

PubMed2003

Is adherence to drug treatment correlated with health-related quality of life?

BACKGROUND: Adherence to drug treatment and health-related quality of life (HRQL) are two distinct concepts. Generally one would expect a positive relationship between the two. OBJECTIVES: The purpose of this study was to assess the relationship between adherence and HRQL. METHODS: HRQL was measured using the physical and mental summary measures of the RAND-12 (PHC-12, MHC-12), the SF-12 (PCS-12, MCS-12), HUI-2 and HUI-3. Adherence was assessed using Morisky's instrument. Three longitudinal datasets were used. One dataset included 100 hypertensive patients. Another dataset covered 199 high risk community-dwelling individuals. The third dataset consisted of 365 elderly patients. Spearman's correlation coefficients were used to assess association. Subgroup analyses by type of medication and inter-temporal analyses were also performed. RESULTS: Correlation between adherence and PHC-12 ranged from 0.08 (p = 0.26) to 0.22 (p < 0.01). Correlations between adherence and MHC-12 ranged from 0.11 (p = 0.11) to 0.15 (p < 0.01). Similar results were observed using HUI-2, HUI-3, and SF-12 as well as by type of medication and in the lagged analyses. CONCLUSIONS: Correlations between HRQL and adherence were positive but typically weak or negligible in magnitude.

Aged↗

PubMed2004

Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods.

Several quantitative models for the prediction of aqueous solubility of organic compounds were developed based on a diverse dataset with 2084 compounds by using multi-linear regression analysis and backpropagation neural networks. The compounds were described by two different structure representation methods: (1) with 18 topological descriptors; and (2) with 32 radial distribution function codes representing the 3D structure of a molecule and eight additional descriptors. The dataset was divided into a training and a test set based on Kohonen's self-organizing neural network. Good prediction results were obtained for backpropagation neural network models: with 18 topological descriptors, for the 936 compounds in the test set, a correlation coefficient of 0.92, and a standard deviation of 0.62 were achieved; with 3D descriptors, for the 866 compounds in the test set, a correlation coefficient of 0.90, and a standard deviation of 0.73 were achieved. The models were also tested by using another dataset, and the relationship of the two datasets was examined by Kohonen's self-organizing neural network.

Molecular Structure↗

PubMed2026

Systematic background selection with BasCoD enhances contrastive dimension reduction in single cell genomics.

In single-cell experiments spanning diverse conditions, distinguishing variation specific to one condition (e.g., treatment) from shared or background variation (e.g., control) is critical for uncovering treatment-specific molecular responses. However, these studies typically yield ultra-high-dimensional data, necessitating effective dimension reduction for reliable biological interpretation. Contrastive dimension reduction methods address this challenge by identifying low-dimensional features enriched in a target dataset relative to a background dataset that captures shared variation. Despite their growing utility, the success of such methods critically depends on the choice of background, yet no formal criterion exists for evaluating or selecting backgrounds. To address this gap, we introduce BasCoD, a statistical testing framework based on spectral subspace inclusion theory, that enables rigorous evaluation and systematic selection of background datasets. Applying BasCoD across a range of single-cell datasets, we show that it effectively identifies suitable backgrounds, substantially improving the contrast and interpretability of the resulting target representations. We further demonstrate how BasCoD can guide the design of contrastive analyses in large-scale single-cell experiments conducted under heterogeneous conditions and elucidate potential interaction effects in perturbation studies.

Single-Cell Analysis↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2026

A reinforcement learning-enhanced fuzzy multi-objective equilibrium optimization framework for multiple sequence alignment.

Multiple sequence alignment (MSA) is a fundamental task in bioinformatics, underpinning comparative genomics, structural analysis, and evolutionary inference. However, MSA remains a challenging multi-objective optimization problem due to the need to simultaneously maximize alignment accuracy, preserve conserved regions, and control gap proliferation, particularly in large and heterogeneous sequence collections. In this work, we propose MOFSACEO-MSA, a novel hybrid optimization framework for multiple sequence alignment that integrates a fuzzy multi-objective evaluation scheme with the Equilibrium Optimizer (EO) and a Soft Actor-Critic (SAC)-based adaptive control mechanism. The proposed framework formulates MSA as a dynamic multi-objective optimization problem, in which alignment quality is assessed using complementary residue-level and column-level criteria, including Sum-of-Pairs score, column conservation, entropy, and gap statistics. Fuzzy membership functions are employed to harmonize competing objectives into a unified optimization landscape, while EO provides robust global exploration. To further enhance adaptability, SAC dynamically regulates key EO parameters during the search process, enabling an effective balance between exploration and exploitation across datasets of varying size and heterogeneity. Extensive experiments werew conducted on diverse biological sequence datasets, with a primary focus on RNA benchmarks, including structured families from Rfam, large-scale repositories from RNAcentral and GenBank, and organism-specific tRNA datasets from GtRNAdb. Comparative evaluations against classical alignment tools (ClustalW, MAFFT, MUSCLE, PRANK, KAlign, and T-Coffee), metaheuristic methods (SAGA, Sequoya and EAFSA), and a reinforcement learning-based approach (RLALIGN) demonstrate that MOFSACEO-MSA consistently achieves competitive or superior Sum-of-Pairs scores while significantly reducing gap proportions and maintaining compact alignment lengths. Notably, the proposed framework exhibits improved robustness on large and highly heterogeneous datasets, where existing methods often suffer from excessive gap insertion or unstable convergence. Overall, MOFSACEO-MSA provides a flexible and extensible optimization paradigm that effectively bridges evolutionary search and reinforcement learning for high-quality multiple sequence alignment, with demonstrated effectiveness on challenging RNA alignment tasks.

Sequence Alignment↗

PubMed2022

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Epidermal Growth Factor↗

PubMed2002

The reproducibility of endometrial volume acquisition and measurement with the VOCAL-imaging program.

OBJECTIVES: To determine the intraobserver reproducibility of volume acquisition and repeatability of endometrial volume measurements using the VOCAL-imaging program (Virtual Organ Computer-aided AnaLysis). METHODS: Ten three-dimensional (3D) ultrasound datasets of the same endometrium were obtained from one patient during one scan session. For every 3D dataset, the endometrial volume was calculated by four different measurement techniques; three rotational methods and one conventional method. A single observer acquired each dataset and conducted all measurements. For the rotational technique, the manual mode was used to trace the myometrial-endometrial border through three different predefined rotations of 30 degrees, 15 degrees and 9 degrees . Endometrial volume was also measured in a conventional manner by scrolling through one of the multiplanar images and outlining the contour in another. Ten consecutive measurements were taken for each of the four measurement techniques so that 40 endometrial volume readings were obtained for each of the 10 3D-ultrasound datasets. RESULTS: Endometrial volume measurements were more reproducible with the rotational technique. Rotational methods employing a rotation step of less than 30 degrees were associated with coefficients of variation below 1% and intraclass correlation coefficients above 0.94. Within each of the 10 different scans, the homogeneity of the variance of measurements was significantly different according to the method of measurement (P = 0.022 to < 0.001). Post-hoc tests revealed that both the conventional and 30 degrees rotational methods resulted in a significantly smaller mean endometrial volume than both the 15 degrees and the 9 degrees rotational methods. CONCLUSIONS: Endometrial volume acquisition is reproducible and endometrial volume measurements are repeatable but they depend upon the technique of volume calculation in so much as rotational methods employing a rotation step of less than 30 degrees are associated with a significantly smaller variance in measurements and a significantly greater mean endometrial volume.

Adult↗

PubMed2001

Laboratory enhanced surveillance for meningococcal disease in Victoria.

OBJECTIVE: To describe the epidemiological and microbiological characteristics and notification patterns of invasive meningococcal disease (IMD) in Victoria between 1990 and 1999. METHODS: Cases of IMD occurring between 1990 and 1995 identified in any of three databases were combined, matching where possible. Statistical modelling provided estimates of cases missing from all datasets. Notification sources for 1999 and 2000 cases were identified. Cases identified from notification and laboratory results provided the data to describe IMD epidemiology between 1990 and 1999. RESULTS: Between 1990 and 1995, 479 cases of IMD were identified. Three individual datasets each identified between 62 and 82% of cases and 47% of cases were identified in all three datasets. Statistical modelling estimated that between 37 and 83 additional cases were not identified by any dataset. Serogroup B and C strains caused 63 and 33% of culture-positive cases, respectively, with a substantial rise in serogroup C cases in 1999. Epidemiological characteristics remained relatively constant between 1990 and 1998, but an increase in patient age was seen in cases with serogroup C disease in 1999. In addition to three clonal strains seen elsewhere, an additional strain was identified that was unique to Victoria. Since January 1999, only 72% of notifications have come from treating doctors. CONCLUSIONS: Meningococcal disease is of increasing public health significance in Victoria. Laboratory enhanced notification has improved case identification and detailed microbiological information has improved our understanding of the changing epidemiology of this disease. Collaboration with laboratories and other agencies, active investigation of putative cases and microbiological monitoring are important elements in supporting public health decisions about the control of IMD.

Age Distribution↗

PubMed2003

Assessing optimal neural network architecture for identifying disease-associated multi-marker genotypes using a permutation test, and application to calpain 10 polymorphisms associated with diabetes.

Biallelic markers, such as single nucleotide polymorphisms (SNPs), provide greater information for localising disease loci when treated as multilocus haplotypes, but often haplotypes are not immediately available from multilocus genotypes in case-control studies. An artificial neural network allows investigation of association between disease phenotype and tightly linked markers without requiring haplotype phase and without modelling any evolutionary history for the disease-related haplotypes. The network assesses whether marker haplotypes differ between cases and controls to the extent that classification of disease status based on multi-marker genotypes is achievable. The network is "trained" to "recognise" affection status based on supplied marker genotypes, and then for each multi-marker genotype it produces outputs which aim to approximate the associated affection status. Next, the genotypes are permuted relative to affection status to produce many random datasets and the process of training and recording of outputs is repeated. The extent to which the ability to predict affection for the real dataset exceeds that for the random datasets measures the statistical significance of the association between multi-marker genotype and affection. This permutation test performs well with simulated case-control datasets, particularly when major gene effects are present. We have explored the effects of systematically varying different network parameters in order to identify their optimal values. We have applied the permutation test to 4 SNPs of the calpain 10 (CAPN10) gene typed in a case-control sample of subjects with type 2 diabetes, impaired glucose tolerance, and controls. We show that the neural network produces more highly significant evidence for association than do single marker tests corrected for the number of markers genotyped. The use of a permutation test could potentially allow conditional analyses which could incorporate known risk factors alongside marker genotypes. Permuting only the marker genotypes relative to affection status and these risk factors would allow the contribution of the markers to disease risk to be independently assessed.

Calpain↗

PubMed2005

Visualization of the pyramidal tract in glioma surgery by integrating diffusion tensor imaging in functional neuronavigation.

OBJECT: The aim of this study was to investigate whether diffusion tensor imaging (DTI) can be integrated into functional navigation for the intraoperative visualization of the pyramidal tract. METHODS: A single-shot spin-echo diffusion-weighted echo planar imaging sequence on a 1.5 T magnetic resonance (MR) scanner was used for DTI. One null image and six diffusion-weighted images (high B value 1 000 mm/s (2)) were obtained. Color-encoded fractional anisotropy maps of the principal eigenvector rendered as a boxoid within each voxel were used for segmentation of the pyramidal tract. The segmented images were rigidly registered with a T(1)-weighted gradient echo 3D dataset for navigation in 16 patients with gliomas. In tumors adjacent to the motor cortex (n = 6) data from functional MR imaging were co-registered. RESULTS: The whole DTI processing lasted about 25-30 minutes in each case. In all cases DTI could be integrated into the navigational dataset resulting in an intraoperative visualization of the pyramidal tract by microscope-based navigation. Navigational accuracy measured as the target registration error was 1.2 +/- 0.46 mm. Registration of fractional anisotropy maps with the 3D navigational dataset was possible with an error of less than 2 mm. Co-registration with fMRI was consistent with DTI data. A neurological deterioration was observed only in one patient. CONCLUSIONS: DTI can be reliably integrated into navigational datasets. Thus, microscope-based neuronavigation can be used for an intraoperative visualization of the course of the pyramidal tract. However, a possible shifting of the pyramidal tract has to be taken into account after major tumor parts are removed.

Adolescent↗

PubMed2006

[Quantification of pulmonary emphysema in multislice-CT using different software tools].

PURPOSE: The data records of thin-section MSCT of the lung with approx. 300 images are difficult to use in manual evaluation. A computer-assisted pre-diagnosis can help with reporting. Furthermore, post-processing techniques, for instance, for quantification of emphysema on the basis of three-dimensional anatomical information might be improved and the workflow might be further automated. MATERIALS AND METHODS: The results of 4 programs (Pulmo, Volume, YACTA and PulmoFUNC) for the quantitative analysis of emphysema (lung and emphysema volume, mean lung density and emphysema index) of 30 consecutive thin-section MSCT datasets with different emphysema severity levels were compared. The classification result of the YACTA program for different types of emphysema was also analyzed. RESULTS: Pulmo and Volume have a median operating time of 105 and 59 minutes respectively due to the necessity for extensive manual correction of the lung segmentation. The programs PulmoFUNC and YACTA, which are automated to a large extent, have a median runtime of 26 and 16 minutes, respectively. The evaluation with Pulmo and Volume using 2 different datasets resulted in implausible values. PulmoFUNC crashed with 2 other datasets in a reproducible manner. Only with YACTA could all graphic datasets be evaluated. The lung volume, emphysema volume, emphysema index and mean lung density determined by YACTA and PulmoFUNC are significantly larger than the corresponding values of Volume and Pulmo (differences: Volume: 119 cm(3)/65 cm(3)/1 %/17 HU, Pulmo: 60 cm(3)/96 cm(3)/1 %/37 HU). Classification of the emphysema type was in agreement with that of the radiologist in 26 panlobular cases, in 22 paraseptalen cases and in 15 centrilobular emphysema cases. CONCLUSION: The substantial expenditure of time obstructs the employment of quantitative emphysema analysis in the clinical routine. The results of YACTA and PulmoFUNC are affected by the dedicated exclusion of the tracheobronchial system. These fully automatic tools enable not only fast quantification without manual interaction, but also a reproducible measurement without user dependence.

Adolescent↗

PubMed2024

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes.

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

Arabidopsis↗

PubMed2005

Improving the performance of SOMFA by use of standard multivariate methods.

Self-Organizing Molecular Field Analysis (SOMFA) comes with a built-in regression methodology, the Self-Organizing Regression (SOR), instead of relying on external methods such as PLS. In this article we present a proof of the equivalence between SOR and SIMPLS with one principal component. Thus, the modest performance of SOMFA on complex datasets can be primarily attributed to the low performance of the SOMFA regression methodology. A multi-component extension of the original SOR methodology (MCSOR) is introduced, and the performances of SOR, MCSOR and SIMPLS are compared using several datasets. The results indicate that in general the performance of SOMFA models is greatly improved if SOR is replaced with a more sophisticated regression method. The results obtained for the Cramer (CBG) dataset further underline the fact that it is a very poor benchmark dataset and should not be used to evaluate the performance of QSAR techniques.

Multivariate Analysis↗

PubMed2005

Evaluation of the Texas 0.08 BAC law.

AIMS: The purpose of the present study was to assess the effects on alcohol-involved traffic crashes and fatalities of the 0.08 blood alcohol concentration (BAC) per se law introduced in the state of Texas in 1999. METHOD: Data pertaining to alcohol-involved traffic crashes and fatalities were extracted from two datasets: the Fatality Analysis Reporting System (FARS) compiled by the National Highway Traffic Safety Administration (for the period January 1995-September 2002), and the Texas Department of Public Safety (DPS) reports of Alcohol Related Motor Vehicle Traffic Accidents and Casualties (for the period January 1995-December 2000). The data were analysed using time-series methods (ARIMA routines). The effects of the law on all drivers were assessed, along with the effects among gender, racial, and age subgroups and crash location (urban vs rural). RESULTS: Separate time-series analyses were conducted with all alcohol-involved and fatal alcohol-involved crashes from the DPS dataset and fatal alcohol-involved crashes from the FARS dataset as the outcome variables. None of the effects for either the total sample or any of the subgroups analysed was statistically significant (this was true of both the FARS and DPS datasets). CONCLUSIONS: While there is a growing body of evidence that indicates that 0.08 BAC laws can be effective in reducing alcohol-involved traffic accidents and fatalities, the present study shows that this was not the case in Texas. Future research should move beyond the simple question of whether or not 0.08 BAC laws 'work' and instead explore in more detail the conditions, such as publicity and enforcement, under which the law does or does not contribute to a decline in alcohol-involved accidents and fatalities.

Accidents, Traffic↗