Contemporary Datasets Research Articles

29 Mapping Recent and Ongoing Polygenic Selection in Beef Cattle Populations

Abstract Understanding the underlying effects of ongoing selection is of interest to livestock breeding programs. Commercial livestock genotyping has generated datasets well-suited to explore how strong artificial selection has impacted the genome over very short time periods. We have developed a method for mapping ongoing polygenic selection in populations with temporally stratified genomic data called Generation Proxy Selection Mapping (GPSM). GPSM utilizes a genome-wide linear mixed model to identify allelic associations with an animal's generation number or some proxy (i.e., birth date). We applied GPSM to two large contemporary beef cattle datasets from Red Angus (n = 46,454) and Simmental (n = 90,580) breeds, with 11,759,568 high-quality imputed variants per animal. This analysis identified 294 distinct loci actively under selection. Due to the high power of these datasets, we found that GPSM could detect very small and recent(< 10 years) allele frequency changes consistent with polygenic selection. These analyses identified variants within genomic regions associated with known breed characteristics, like fertility and maternal ability in Red Angus and carcass merit and coat color in Simmental. Over 60% of GPSM loci resided in or near (< 50kb) annotated genes. We leveraged the resolution of our imputed sequence variants to overlay known epigenetic marks and possibly functional regions. 36% of GPSM loci overlapped these putatively functional regions genomic intervals. In addition to GPSM, we used two other more traditional selective sweep methods, the number of segregating loci (nSL) and Raised Accuracy in Sweep Detecting (RAiSD), to detect selection in this data. We observed minimal overlap (< 10% of loci) between traditional selective sweep mapping and GPSM. This suggests that selection mapping with GPSM can complement traditional sweep mapping methods when temporal genomic data exists. In addition to better understanding the biological processes and features that underlie artificial selection, GPSM signatures might serve as important genomic annotations.

Predictors of 30-day and 12-month mortality in left main stem percutaneous coronary intervention 2016-2020: A study from two UK centers.

Left main stem percutaneous coronary intervention (LMS-PCI) is a complex high-risk procedure which can be performed as an alternative to coronary artery bypass graft (CABG) procedure in surgical turn-down patients or where there is equipoise in percutaneous versus surgical strategies. Current guidelines suggest that PCI is an appropriate alternative to CABG in patients with unprotected LMS disease and low SYNTAX score. However, "real world"data on outcomes of LMS-PCI remain limited. This study aims to quantify and determine predictors of mortality following LMS-PCI. Using local coronary angioplasty registries from two UK centers, all LMS-PCI cases were identified from 2016 to 2020. Descriptive statistics and multivariate logistic regressions were used to examine the association between baseline and procedural characteristics with 30-day and 12-month mortality. We identified 484 cases of LMS-PCI between 2016 and 2020. There was a year-on-year increase in the number of LMS-PCI, the highest being in 2020. Covariates associated with higher 30-day mortality were age (OR 1.07, 95% CI: 1.02-1.12) and shock preprocedure (OR 23.88, 95% CI: 7.90-72.20). Covariates associated with higher 12-month mortality were age (OR 1.04, 95% CI: 1.01-1.08), acute coronary syndrome (ACS)(OR 2.50, 95% CI: 1.08-5.80), renal disease (OR 5.24, 95% CI: 1.47-18.68), and shock preprocedure (OR 7.93, 95% CI: 3.30-19.05). Overall, 30-day and 12-month mortality in this contemporary data set were 9.5% and 16.7%, respectively, with significantly lower rates in elective cases (p < 0.01). Older age and cardiogenic shock preprocedure were associated with increased 30-day mortality after LMS-PCI.Twelve-month mortality was associated with older age, ACS presentation, preexistingrenal disease, and cardiogenic shock preprocedure.

Evaluating Software Tools to Orthorectify Archival Aerial Photographs

Archival aerial photographs are among the most common, voluminous, and frequently used collections in map and geography libraries because of the unique and important spatial information that they contain. They are, however, an underutilized resource because they are challenging to work with and due to misconceptions about their quality. Therefore, developing efficient workflows to convert archival aerial photographs into research-quality digital spatial data is an important next step in making them accessible to a broad range of potential users. The present study tested three software to georeference, orthorectify, and mosaic 15 digitized archival photographs. Using positional accuracy and aesthetics of output mosaics as measures of software performance, all three completed the task impressively well. Output image spatial resolution and positional accuracy were found to be comparable to common public-domain contemporary aerial photography datasets like those produced by the National Agricultural Imagery Program. We also assessed software ease of use by geospatial professionals who were not trained photogrammetrists and found all three to be accessible with care and training resources. The software tools to efficiently convert archival aerial photographs into research-quality digital spatial data have finally matured to the point where collection managers can reasonably consider collection scale conversion projects.

Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of Whale Optimization Algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading more computational complexity. Also, to make the proposed algorithm as light as possible, this paper considers K-nearest neighbor algorithm as the main classifier. The proposed light feature selection algorithm is run on different data sets. Experimental results prove that this algorithm is able to reduce the number of features meanwhile it retains, and in some cases even increases, the accuracy of classification.

The Variations and Influences of the Channel Centerline of the Zhenjiang-Yangzhou Reach of the Yangtze River Based on Archival and Contemporary Data Sets

The Zhenjiang-Yangzhou reach of the Yangtze River is located at the top of the Yangtze River Delta, which is one of the most dramatic changes in the lower reaches of the Yangtze River. The study on the migration characteristics of the channel centerline is crucial for a comprehensive understanding of the river channel changes in the Zhenjiang-Yangzhou reach. In this study, a detailed calculation method is proposed to extract the channel centerline of the Zhenjiang-Yangzhou reach by using old maps and remote sensing satellite map and decompose it into seven parts. The spatial and temporal changes of Net Shift Distance (NSD), Cumulative Moving Distance (CMD), Migration Rate of Channel Centerline (MRCC) and Linear Regression Change Rate of channel centerline (LRCR) from 1865 to 2019 on the cross-section scale are studied. The results show that: (1) from 1868 to 2019, the channel centerline of the Zhenjiang-Yangzhou reach kept shifting. The average net displacement distance of the section is 1103.47 m on the right bank, and the average cumulative displacement distance of the section is 2790.51 m. (2) According to the NSD and CMD data of each part, the long-term movement direction of the channel centerline is basically the same, and a small part of the channel centerline has periodic reverse swing. The probability of channel centerline moving right is about twice that of moving left. At the same time, some rivers have high erosion risk. (3) Through MRCC and LRCR data, the total number of channel centerline moving left and right is 156 and 329, respectively, and the erosion risk level of the near half of the shoreline is high. (4) The change of river boundary conditions and hydrodynamic force will affect the migration rate and direction of channel centerline. (5) This study proposes a method to extract channel centerline from a braided reach and study its changes, which can be applied to other similar reaches with a long history of human activities and high density. The results enrich people’s understanding of the long-term changes of a braided reach in the lower reaches of the Yangtze River and have certain guiding significance for river regulation, navigation safety, and revetment construction.

A low-cost pavement-rating system, based on machine learning, utilising smartphone sensors

One of the key innovations of this study is the development of a cost-efficient approach for pavement monitoring. This work aims to develop a system that evaluates roadway pavement surface conditions with improved frequency by utilising unsupervised machine learning algorithms and smartphone sensors. The evaluation of roadways utilising complex contemporary data sets is currently conducted periodically because of the high cost of collection methods. For this purpose, the study presents a data-driven framework on the use of a vehicle, a smartphone, an on-board diagnostic device and machine learning for the rating of pavement surfaces, while statistical features are considered in both time- and frequency-domain forms. The selection of features is performed utilising unsupervised classification algorithms. Moreover, the proposed system architecture has been field-tested for the detection of pavement anomalies and the classification of five rating categories. Furthermore, the proposed system may provide daily information on roadway pavement surface conditions, which can be used by agencies for automating the planning of pavement maintenance operations and for improving driving safety.

Best of Both Worlds: Detecting Application Layer Attacks through 802.11 and Non-802.11 Features

Intrusion detection in wireless and, more specifically, Wi-Fi networks is lately increasingly under the spotlight of the research community. However, the literature currently lacks a comprehensive assessment of the potential to detect application layer attacks based on both 802.11 and non-802.11 network protocol features. The investigation of this capacity is of paramount importance since Wi-Fi domains are often used as a stepping stone by threat actors for unleashing an ample variety of application layer assaults. In this setting, by exploiting the contemporary AWID3 benchmark dataset along with both shallow and deep learning machine learning techniques, this work attempts to provide concrete answers to a dyad of principal matters. First, what is the competence of 802.11-specific and non-802.11 features when used separately and in tandem in detecting application layer attacks, say, website spoofing? Second, which network protocol features are the most informative to the machine learning model for detecting application layer attacks? Without relying on any optimization or dimensionality reduction technique, our experiments, indicatively exploiting an engineered feature, demonstrate a detection performance up to 96.7% in terms of the Area under the ROC Curve (AUC) metric.

Transforming Medical Cucumbers into Sunbeams

A recent report from Johns Hopkins University estimated that more than 250,000 people in the U.S. die annually from medical errors. There are surely many contributing causes for this horrendous statistic, but one of them, we conjecture, is faulty communication between medical caregivers when a patient is passed between them. In this essay we suggest that replacing the often complex tables of patient information into a more comprehensible graphic format can reduce errors of information and make clerical errors easier to pick out. We illustrate this with both a contemporary data set and a famous historical one drawn from the 18th century.

Prior Coronary Artery Bypass Graft Surgery Impacts 30-day Quality of Life after Percutaneous Coronary Intervention: Evidence from the Victorian Cardiac Outcomes Registry (VCOR)

Quality of life following percutaneous coronary intervention (PCI) in patients with coronary artery bypass graft surgery (CABG) has been reported as lower than non-CABG patients, however previous reports pre-date modern developments in PCI and cardiac surgery. This study aimed to examine the 30-day QoL after PCI between patients with and without prior CABG using a contemporary dataset. A retrospective analysis of the Victorian Cardiac Outcomes Registry was undertaken. This study included 36,799 patients who completed the EQ-5D questionnaire that was used to assess the 30-day QoL and was compared between groups with and without prior CABG at baseline. Most of the participants were older than 65 years, more than half were male and had PCI due to acute coronary symptoms (ACS) and nearly 90% of patients received drug eluting stents. Compared to the ‘no prior CABG’ group, the ‘CABG’ group had a significantly higher rate of reporting a health problem (OR 1.30, 95% CI 1.10–1.53), presence of a problem in mobility (OR 1.42, 95% CI 1.15–1.75), personal care (OR 1.49, 95%CI 1.13–1.97) and usual activities (OR 1.39, 95%CI 1.15–1.68), pain/discomfort (OR 1.31, 95%CI 1.11–1.54), and anxiety/depression (OR 1.20, 95%CI 1.02–1.42). Despite modern developments in both PCI and CABG, our study showed a consistent negative association between prior CABG status and 30-day QoL following PCI. There is a need for better targeted cardiac rehabilitation in patients with prior CABG to address their greater relative risk of experiencing poor health.

Assessment of appropriate species-specific time intervals to integrate GPS telemetry data in ecological niche models

Ecological niche models use presence-only data, which is often affected by lack of true absences leading to sampling bias. Over the last decade, there has been an uptick in the integration of occurrence data from global positioning systems telemetry data in ecological niche models and/or species distribution models. These data types can be affected by serial autocorrelation at high relocation frequencies yet have been used in ecological niche models using geographic filters and subsampling techniques. Yet, no study to date has attempted to discern a method to identify the appropriate time interval for a particular species if integrating GPS telemetry occurrence data in a MaxEnt framework. We demonstrate a rigorous spatial technique using a robust contemporary dataset from ocelots (Leopardus pardalis) to assess the appropriate time intervals to use in a species-specific ecological niche model. We assessed a range of daily time intervals (every 0.5, 1–4, 6, 8, and 12 h) commonly used in teresstrial mammalian carnivore studies. We observed the predictive performance of shorter time intervals every 2 h was comparable to much longer intervals every 12 h. These shorter intervals under/overestimated the least amount of data compared to 12 h. This study demonstrates that by accounting for serial autocorrelation and conducting rigorous spatial analyses, scientists can identify the appropriate time interval to integrate GPS telemetry data use in ecological niche models in MaxEnt. These results can also be transferable across highly mobile terrestrial taxa at different spatial scales, which can help inform species management or conservation strategies.

Overutilization of Atherectomy in Ambulatory and Office Based Centers

Atherectomy use has become increasingly prevalent in lower extremity peripheral vascular intervention (PVI), particularly in ambulatory and office based centers. Our goal was to quantify atherectomy use by site of service using a contemporary real world dataset.

Flowering time advances since the 1970s in a sagebrush steppe community: Implications for management and restoration.

Climate change is widely known to affect plant phenology, but little is known about how these impacts manifest in the widespread sagebrush ecosystem of the Western United States, which supports a number of wildlife species of concern. Shifts in plant phenology can trigger consequences for the plants themselves as well as the communities of consumers that depend upon them. We assembled historical observations of first-flowering dates for 51 species collected in the 1970s and 1980s in a montane sagebrush community in the Greater Yellowstone Ecosystem and compared these to contemporary phenological observations targeting the same species and locations (2016-2019). We also assembled regional climate data (average spring temperature, day of spring snowmelt, and growing degree days) and tested the relationship between first-flowering time and these variables for each species. We observed the largest change in phenology in early-spring flowers, which, as a group, bloomed on average 17 days earlier, and as much as 36 days earlier, in the contemporary data set. Mid-summer flowers bloomed on average 10 days earlier, nonnative species 15 days earlier, and berry-producing shrubs 5 days earlier, while late summer flowering plants did not shift. The greatest correlates of early-spring and mid-summer flowering were average spring temperature and day of snowmelt, which was 21 days earlier, on average, in 2016-2019 relative to the 1973-1978 observations. The shifts in flowering phenology that we observed could indicate developing asynchronies or novel synchronies of these plant resources and wildlife species of conservation concern, including Greater Sage-grouse, whose nesting success is tied to availability of spring forbs; grizzly bears, which rely heavily on berries for their fall diet; and pollinators. This underscores the importance of maintaining a diverse portfolio of native plants in terms of species composition, genetics, phenological responsiveness to climatic cues, and ecological importance to key wildlife and pollinator species. Redundancy within ecological niches may also be important considering that species roles in the community may shift as climate change affects them differently. These considerations are particularly relevant to restoration and habitat-enhancement projects in sagebrush communities across western North America.

External validation of the VENUSS prognostic model to predict recurrence after surgery in non-metastatic papillary renal cell carcinoma: A multi-institutional analysis.

Recently, VENUSS (VEnous extension, NUclear Grade, Size, Stage), as a prognostic model, was defined to predict disease recurrence (DR) after curative surgery of non-metastatic papillary renal cell carcinoma (papRCC). This study aimed to validate the VENUSS prognostic model in a large multi-institutional European cohort of patients with histopathologically proven papRCC after curative surgery for non-metastatic disease. Overall, 980 patients undergoing partial or radical nephrectomy for sporadic, unilateral and non-metastatic papRCC between 1987 and 2020 were included from 7 European tertiary institutions. The primary outcome was the prediction of DR by VENUSS score and VENUSS risk groups. Chi-square, Kruskal-Wallis, Cox-regression and Kaplan-Meier survival analyses were used in statistical methods. The Concordance (C) Index was calculated to assess model's discriminatory power. The median age was 64 (IQR:55-70) years and 82.6 % (n = 809) of patients were male. Median VENUSS score was 2 (IQR: 0-4), and 62.9 % (n = 617), 23.9 % (n = 234) and 13.2 % (n = 129) of patients was classified into low, intermediate and high risk according to the VENUSS model, respectively. At a median follow-up of 48 (IQR:23-88) months, the disease recurred in 6.6%, 18.8% and 63.8%, and the 5-year recurrence-free survival was 93.8%, 80.7% and 26.7% in low, intermediate and high-risk groups, respectively. (P < 0.001) Each increase in VENUSS score had 1.52-fold (95%CI:1.45-1.60, P < 0.001) DR risk. Compared with the VENUSS low risk, the intermediate risk had a 2.91-fold increased DR risk (95%CI:1.90-4.46, P < 0.001) and 17.9-fold (95%CI:12.25-26.25, P < 0.001) in high risk, while it was 6.07-fold greater in high risk vs. intermediate risk (95%CI:4.17-8.83, P < 0.001). The discrimination was 81.2% (95%CI:77.5%-84.8%) for the VENUSS score, and 78.6% (95%CI:74.8%-82.4%) for VENUSS risk groups, respectively. Both the VENUSS score and groups were well calibrated. This contemporary multi-institutional European large dataset validated the use of VENUSS score and VENUSS risk groups on the prediction of DR after curative surgery in patients with non-metastatic papRCC. The VENUSS prognostic model can provide valuable information for patient counselling, follow-up and patient selection for adjuvant trials.

The role of body height as a co-factor of excess weight in Switzerland.

ObjectExcess weight (Body Mass Index [BMI] ≥25.0 kg/m2) is a major health issue worldwide, including in Switzerland. For high‐income countries, little attention has been paid to body height in context of excess weight. The aim of this study is to assess the importance of body height as a co‐factor for excess weight in multiple large nationwide data sets.Data and methodsIn this comparative study, we included the largest nationwide and population‐based studies in the fields of public health, nutrition and economics for Switzerland, as well as data of the medical examination during conscription for the Swiss Armed Forces, which contained information on BMI and, if possible, waist‐to‐height‐ratio (WHtR) and waist‐to‐hip‐ratio (WHR).ResultsThe multinomial logistic regressions show that the probability of belonging to the excess weight category (BMI ≥25.0 kg/m2) decreased with increasing height in both sexes inall contemporary data sets. This negative association was shown to be constant, only among conscripts measured in the 1870s the association was positive, when increasing height was associated with a higher BMI. The negative association not only emerge in BMI, but also in WHtR and WHR.ConclusionOur results emphasize the importance of body height as a co‐factor of excess weight, suggesting a clear negative association between height and BMI, WHtR and WHR. Evidence indicates that both early‐life environmental exposures and alleles associated with height may contribute to these associations. This knowledge could serve as further starting points for prevention programs in the field of public health.

A Comprehensive Review of Deep Learning Techniques for the Detection of (Distributed) Denial of Service Attacks

(Distributed) Denial of Service (DoS/DDoS) attacks are performed to bring down a target by flooding it withuseless traffic. Because the DoS/DDoS attackers often change their styles and attack patterns, the nature andcharacteristics of these attacks need to be examined cautiously. Developing mechanisms to detect this menaceis a challenging task. Recently, deep learning has played a major role in the growth of intrusion detection solutions. In recent years, significant attempts have been made to construct deep learning models for counteringDoS/DDoS threats. In this review, we provide a taxonomy of DoS/DDoS attacks and deep learning-based DoS/DDoS detection approaches. Then, the article focuses on the recent (from 2016 onwards) defensive methodsagainst DoS/DDoS attacks that exploit the advantages of deep learning techniques and discusses the key features of each of them. As datasets are imperative for deep learning techniques, we also review the traditional and contemporary datasets that contain traces of DoS/DDoS attacks. The findings from the review articles are as well summarized and they urge that more effort be made to strengthen the existing state-of-the-art approaches to coping with the dynamic behavior of the attackers. The imbalances in the surveyed articles are also highlighted. Finally, we outline a few key research directions that will need additional focus in the near future to ensure good security against DoS/DDoS attacks using deep learning approaches.

The Greenland Firn Compaction Verification and Reconnaissance (FirnCover) dataset, 2013–2019

Abstract. Assessing changes in the density of snow and firn is vital to convert volume changes into mass changes on glaciers and ice sheets. Firn models simulate this process but typically rely upon steady-state assumptions and geographically and temporally limited sets of field measurements for validation. Given rapid changes recently observed in Greenland's surface mass balance, a contemporary dataset measuring firn compaction in a range of climate zones across the Greenland ice sheet's accumulation zone is needed. To fill this need, the Firn Compaction Verification and Reconnaissance (FirnCover) dataset comprises daily measurements from 48 strainmeters installed in boreholes at eight sites on the Greenland ice sheet between 2013 and 2019. The dataset also includes daily records of 2 m air temperature, snow height, and firn temperature from each station. The majority of the FirnCover stations were installed in close proximity to automated weather stations that measure a wider suite of meteorological measurements, allowing the user access to auxiliary datasets for model validation studies using FirnCover data. The dataset can be found here: https://doi.org/10.18739/A25X25D7M (MacFerrin et al., 2021).

Introducing the party-interest group relationships in contemporary democracies datasets

Few existing datasets on parties and interest groups include data from both sides and a wide variety of interest groups and parties. We contribute to filling this gap by making several interconnected new datasets publicly available. The Party-Interest Group Relationships in Contemporary Democracies (PAIRDEM) datasets include cross-national data from three different surveys of (1) central party organizations, (2) legislative party groups, and (3) interest groups. A fourth dataset based on coding of party statutes and party finance data was established together with the Political Party Database. The datasets contain novel indicators on party-group relationships in up to 21 mature democracies. In this research note, we first present the main content of the datasets and the research design. Second, we present descriptive statistics documenting the extent of organizational ties between parties and groups in contemporary democracies. Third, we illustrate more advanced usage through a simple application.

Flash flood warnings in context: combining local knowledge and large-scale hydro-meteorological patterns

Abstract. The small spatial and temporal scales at which flash floods occur make predicting events challenging, particularly in data-poor environments where high-resolution weather models may not be available. Additionally, the uptake of warnings may be hampered by difficulties in translating the scientific information to the local context and experiences. Here we use social science methods to characterise local knowledge of flash flooding among vulnerable communities along the flat Lake Malawi shoreline in the district of Karonga, northern Malawi. This is then used to guide a scientific analysis of the factors that contribute to flash floods in the area using contemporary global datasets, including geomorphology, soil and land-use characteristics, and hydro-meteorological conditions. Our results show that communities interviewed have detailed knowledge of the impacts and drivers of flash floods (deforestation and sedimentation), early warning signs (changes in clouds, wind direction, and rainfall patterns), and distinct hydro-meteorological processes that lead to flash flood events at the beginning and end of the wet season. Our analysis shows that the scientific data corroborate this knowledge and that combining local and scientific knowledge provides improved understanding of flash flood processes within the local context. We highlight the potential of linking large-scale global datasets with local knowledge to improve the usability of flash flood warnings.

Prior Coronary Artery Bypass Graft Surgery Impacts 30-Day Quality of Life After Percutaneous Coronary Intervention: Evidence From the Victorian Cardiac Outcomes Registry (VCOR)

Quality of life following percutaneous coronary intervention (PCI) in patients with coronary artery bypass graft surgery (CABG) has been reported as lower than non-CABG patients, however previous reports pre-date modern developments in PCI and cardiac surgery. This study aimed to examine the 30-day QoL after PCI between patients with and without prior CABG using a contemporary dataset.

Analysis of traffic accidents with fuzzy and crisp data mining techniques to identify factors affecting injury severity

Injury severity in motor vehicle traffic accidents is determined by a number of factors including driver, vehicle, and environment. Airbag deployment, vehicle speed, manner of collusion, atmospheric and light conditions, degree of ejection of occupant’s body from the crash, the use of equipment or other forces to re-move occupants from the vehicle, model and type of vehicle have been considered as important risk factors affecting accident severity as well as driver-related conditions such as age, gender, seatbelt use, alcohol and drug involvement. In this study, we aim to identify important variables that contribute to injury severity in the traffic crashes. A contemporary dataset is obtained from National Highway Traffic Safety Administration’s (NHTSA) Fatality Analysis Reporting System (FARS). To identify accident severity groups, we performed different clustering algorithms including fuzzy clustering. We then assessed the important factors affecting injury severity by using classification and regression trees (CRT). The results which would guide car manufacturers, policy makers and insurance companies indicate that the most important factor in defining injury severity is deployment of air-bag, followed by extrication, ejection occurrences, and travel speed and alcohol involvement.