Meteoroid Fragmentation in the Martian Atmosphere and the Formation of Crater Clusters.

The current rate of small impacts on Mars is informed by more than one thousand impact sites formed in the last 20 years, detected in images of the martian surface. More than half of these impacts produced a cluster of small craters formed by fragmentation of the meteoroid in the martian atmosphere. The spatial distributions, number and sizes of craters in these clusters provide valuable constraints on the properties of the impacting meteoroid population as well as the meteoroid fragmentation process. In this paper, we use a recently compiled database of crater cluster observations to calibrate a model of meteoroid fragmentation in Mars' atmosphere and constrain key model parameters, including the lift coefficient and fragment separation velocity, as well as meteoroid property distributions. The model distribution of dynamic meteoroid strength that produces the best match to observations has a minimum strength of 10-90 kPa, a maximum strength of 3-6 MPa and a median strength of 0.2-0.5 MPa. An important feature of the model is that individual fragmentation events are able to produce fragments with a wide range of dynamic strengths as much as 10 times stronger or weaker than the parent fragment. The calibrated model suggests that the rate of small impacts on Mars is 1.5-4 times higher than recent observation-based estimates. It also shows how impactor properties relevant to seismic wave generation, such as the total impact momentum, can be inferred from cluster characteristics.

Largest recent impact craters on Mars: Orbital imaging and surface seismic co-investigation.

Two >130-meter-diameter impact craters formed on Mars during the later half of 2021. These are the two largest fresh impact craters discovered by the Mars Reconnaissance Orbiter since operations started 16 years ago. The impacts created two of the largest seismic events (magnitudes greater than 4) recorded by InSight during its 3-year mission. The combination of orbital imagery and seismic ground motion enables the investigation of subsurface and atmospheric energy partitioning of the impact process on a planet with a thin atmosphere and the first direct test of martian deep-interior seismic models with known event distances. The impact at 35°N excavated blocks of water ice, which is the lowest latitude at which ice has been directly observed on Mars.

Machine learning and statistical prediction of fastball velocity with biomechanical predictors.

In recent years, one of the most important factors for success among baseball pitchers is fastball velocity. The purpose of this study was to (1) to develop statistical and machine learning models of fastball velocity, (2) to identify the strongest predictors of fastball velocity, and (3) to compare the models' prediction performances. Three dimensional biomechanical analyses were performed on high school (n = 165) and college (n = 62) baseball pitchers. A total of 16 kinetic and kinematic predictors from the entire pitching sequence were included in regression and machine learning models. All models were internally validated through ten-fold cross-validation. Model performance was evaluated through root mean square error (RMSE) and calibration with 95% confidence intervals. Gradient boosting machines demonstrated the best prediction performance [RMSE: 0.34; Calibration: 1.00 (95% CI: 0.999, 1.001)], while regression demonstrated the greatest prediction error [RMSE: 2.49; Calibration: 1.00 (95% CI: 0.85, 1.15)]. Maximum elbow extension velocity (relative influence: 19.3%), maximum humeral rotation velocity (9.6%), maximum lead leg ground reaction force resultant (9.1%), trunk forward flexion at release (7.9%), time difference of maximum pelvis rotation velocity and maximum trunk rotation velocity (7.8%) demonstrated the greatest influence on pitch velocity. Gradient boosting machines demonstrated better calibration and reduced RMSE compared to regression. The influence of lead leg ground reaction force resultant and trunk and arm kinematics on pitch velocity demonstrates the interdependent relationship of the entire kinetic chain during the pitching motion. Coaches, players, and performance professionals should focus on the identified metrics when designing pitch velocity improvement programs.

A steeply-inclined trajectory for the Chicxulub impact.

The environmental severity of large impacts on Earth is influenced by their impact trajectory. Impact direction and angle to the target plane affect the volume and depth of origin of vaporized target, as well as the trajectories of ejected material. The asteroid impact that formed the 66 Ma Chicxulub crater had a profound and catastrophic effect on Earth's environment, but the impact trajectory is debated. Here we show that impact angle and direction can be diagnosed by asymmetries in the subsurface structure of the Chicxulub crater. Comparison of 3D numerical simulations of Chicxulub-scale impacts with geophysical observations suggests that the Chicxulub crater was formed by a steeply-inclined (45-60° to horizontal) impact from the northeast; several lines of evidence rule out a low angle (<30°) impact. A steeply-inclined impact produces a nearly symmetric distribution of ejected rock and releases more climate-changing gases per impactor mass than either a very shallow or near-vertical impact.

The association between type 2 diabetes mellitus, hip fracture, and post-hip fracture mortality: a multi-state cohort analysis.

Type 2 diabetes mellitus (T2DM) is associated with an excess risk of fractures and overall mortality. This study compared hip fracture and post-hip fracture mortality in T2DM and non-diabetic subjects. The salient findings are that subjects in T2DM are at higher risk of dying after suffering a hip fracture. INTRODUCTION: Previous research suggests that individuals with T2DM are at an excess risk of both fractures and overall mortality, but their combined effect is unknown. Using multi-state cohort analyses, we estimate the association between T2DM and the transition to hip fracture, post-hip fracture mortality, and hip fracture-free all-cause death. METHODS: Population-based cohort from Catalonia, Spain, including all individuals aged 65 to 80 years with a recorded diagnosis of T2DM on 1 January 2006; and non-T2DM matched (up to 2:1) by year of birth, gender, and primary care practice. RESULTS: A total of 44,802 T2DM and 81,233 matched controls (53% women, mean age 72 years old) were followed for a median of 8 years: 23,818 died without fracturing and 3317 broke a hip, of whom 838 subsequently died. Adjusted HRs for hip fracture-free mortality were 1.32 (95% CI 1.28 to 1.37) for men and 1.72 (95% CI 1.65 to 1.79) for women. HRs for hip fracture were 1.24 (95% CI 1.08 to 1.43) and 1.48 (95% CI 1.36 to 1.60), whilst HRs for post-hip fracture mortality were 1.28 (95% CI 1.02 to 1.60) and 1.57 (95% CI 1.31 to 1.88) in men and women, respectively. CONCLUSION: T2DM individuals are at increased risk of hip fracture, post-hip fracture mortality, and hip fracture-free death. After adjustment, T2DM men were at a 28% higher risk of dying after suffering a hip fracture and women had 57% excess risk of post-hip fracture mortality.

Key steps and common pitfalls in developing and validating risk models.

Models for estimating an individual's risk of having or developing a disease are abundant in the medical literature, yet many do not meet the methodological standards that have been set to maximise generalisability and utility. This paper presents an overview of ten steps from the conception of the study to the implementation of the risk model and discusses common pitfalls. We discuss crucial aspects of study design, data collection, model development and performance evaluation, and discuss how to bring the model to clinical practice. TWEETABLE ABSTRACT: We present an overview of ten key steps for the development of risk models and discuss common pitfalls.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

The rise of big clinical databases.

BACKGROUND: The routine collection of large amounts of clinical data, 'big data', is becoming more common, as are research studies that make use of these data source. The aim of this paper is to provide an overview of the uses of data from large multi-institution clinical databases for research. METHODS: This article considers the potential benefits, the types of data source, and the use to which the data is put. Additionally, the main challenges associated with using these data sources for research purposes are considered. RESULTS: Common uses of the data include: providing population characteristics; identifying risk factors and developing prediction (diagnostic or prognostic) models; observational studies comparing different interventions; exploring variation between healthcare providers; and as a supplementary source of data for another study. The main advantages of using such big data sources are their comprehensive nature, the relatively large number of patients they comprise, and the ability to compare healthcare providers. The main challenges are demonstrating data quality and confidently applying a causal interpretation to the study findings. CONCLUSION: Large clinical database research studies are becoming ubiquitous and offer a number of potential benefits. However, the limitations of such data sources must not be overlooked; each research study needs to be considered carefully in its own right, together with the justification for using the data for that specific purpose.

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement.

BACKGROUND: Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. METHODS: An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. RESULTS: The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. CONCLUSION: The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. A complete checklist is available at http://www.tripod-statement.org.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study, regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Pressure-temperature evolution of primordial solar system solids during impact-induced compaction.

Prior to becoming chondritic meteorites, primordial solids were a poorly consolidated mix of mm-scale igneous inclusions (chondrules) and high-porosity sub-µm dust (matrix). We used high-resolution numerical simulations to track the effect of impact-induced compaction on these materials. Here we show that impact velocities as low as 1.5 km s(-1) were capable of heating the matrix to >1,000 K, with pressure-temperature varying by >10 GPa and >1,000 K over ~100 µm. Chondrules were unaffected, acting as heat-sinks: matrix temperature excursions were brief. As impact-induced compaction was a primary and ubiquitous process, our new understanding of its effects requires that key aspects of the chondrite record be re-evaluated: palaeomagnetism, petrography and variability in shock level across meteorite groups. Our data suggest a lithification mechanism for meteorites, and provide a 'speed limit' constraint on major compressive impacts that is inconsistent with recent models of solar system orbital architecture that require an early, rapid phase of main-belt collisional evolution.

Comparing disease activity indices in ulcerative colitis.

BACKGROUND: Comparisons between disease activity indices for ulcerative colitis (UC) are few. This study evaluates three indices, to determine the potential impact of inter-observer variation on clinical trial recruitment or outcome as well as their clinical relevance. METHODS: One hundred patients with UC were prospectively evaluated, each by four specialists, followed by videosigmoidoscopy, which was later scored by each specialist. The Simple Clinical Colitis Activity (SCCAI), Mayo Clinic and Seo indices were compared by assigning a disease activity category from published thresholds for remission, mild, moderate and severe activity. Inter-observer variation was evaluated using Kappa statistics and its effect for each patient on recruitment and outcome measures for representative clinical trials calculated. Clinical relevance was assessed by comparing an independently assigned clinical category, taking all information into account as if in clinic, with the disease activity assigned by the indices. RESULTS: Inter-observer agreement for SCCAI (κ=0.75, 95% CI 0.70-0.81), Mayo Clinic (κ=0.72, 95% CI 0.67-0.78) and Seo (κ=0.89, 95% CI 0.83-0.95) indices was good or very good as was the agreement for rectal bleeding (κ=0.77) and stool frequency (κ=0.90). Endoscopy in the Mayo Clinic index had the greatest variation (κ=0.38). Inter-observer variation alone would have excluded up to 1 in 5 patients from recruitment or remission criteria in representative trials. Categorisation by the SCCAI, Mayo Clinic and Seo indices agreed with the independently assigned clinical category in 61%, 67% and 47% of cases respectively. CONCLUSIONS: Trial recruitment and outcome measures are affected by inter-observer variation in UC activity indices, and endoscopic scoring was the component most susceptible to variation.

Identifying women with undetected ovarian cancer: independent and external validation of QCancer(®) (Ovarian) prediction model.

Early identification of ovarian cancer is an unresolved challenge and the predictive value of single symptoms is limited. We evaluated the performance of QCancer(®) (Ovarian) prediction model for predicting the risk of ovarian cancer in a UK cohort of general practice patients. A total of 1.1 million patients registered with a general practice surgery between 1 January 2000 and 30 June 2008, aged 30-84 years with 735 ovarian cancer cases, were included in the analysis. Ovarian cancer was defined as incident diagnosis of ovarian cancer during the 2 years after study entry. The results from this independent and external validation of QCancer(®) (Ovarian) prediction model demonstrated good performance on a large cohort of general practice patients. QCancer(®) (Ovarian) had very good discrimination with an area under the receiver operating characteristic curve of 0.86 and explained 59.9% of the variation. QCancer(®) (Ovarian) was well calibrated across all tenths of risk and over all age. The 10% of women with the highest predicted risks included 64% of all ovarian cancer diagnoses over the next 2 years. QCancer(®) (Ovarian) appears to be a useful tool for identifying undetected cases of ovarian cancer in primary care in the UK for early referral and investigation.