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Contains fulltext : 305368.pdf (Publisher’s version ) (Closed access), Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

On February 28, 2021, a fireball dropped ∼0.6 kg of recovered CM2 carbonaceous chondrite meteorites in South-West England near the town of Winchcombe. We reconstruct the fireball's atmospheric trajectory, light curve, fragmentation behavior, and pre-atmospheric orbit from optical records contributed by five networks. The progenitor meteoroid was three orders of magnitude less massive (∼13 kg) than any previously observed carbonaceous fall. The Winchcombe meteorite survived entry because it was exposed to a very low peak atmospheric dynamic pressure (∼0.6 MPa) due to a fortuitous combination of entry parameters, notably low velocity (13.9 km s−1). A near-catastrophic fragmentation at ∼0.07 MPa points to the body's fragility. Low entry speeds which cause low peak dynamic pressures are likely necessary conditions for a small carbonaceous meteoroid to survive atmospheric entry, strongly constraining the radiant direction to the general antapex direction. Orbital integrations show that the meteoroid was injected into the near-Earth region ∼0.08 Myr ago and it never had a perihelion distance smaller than ∼0.7 AU, while other CM2 meteorites with known orbits approached the Sun closer (∼0.5 AU) and were heated to at least 100 K higher temperatures.

We investigated 634 crater clusters on Mars detected between 2007 and 2021, which represent more than half of all impacts discovered in this period. Crater clusters form when meteoroids in the 10 kg–10 ton mass range break up in Mars' atmosphere to produce a few to a few hundred fragments that hit the ground. The properties of the clusters can inform our understanding of meteoroid properties and the processes that govern their fragmentation. We mapped individual craters >1 m within each cluster and defined a range of cluster properties based on the spatial and size distributions of the craters. The large data set, with over eight times more cluster observations than previous work, provides a more robust statistical investigation of crater cluster parameters and their correlations. Trends in size, dispersion, and large crater fraction with elevation support weak atmospheric filtering of material. The diversity in the number of individual craters within a cluster, and their size-frequency distributions, may reflect either a diversity in fragmentation style, fragility, or internal particle sizes.

Seismic activity generated by impacts depends on impact conditions and properties of the impact site. Here, we combined mapping of the regolith thickness with numerical impact simulations to better estimate the seismic efficiency and seismic moment generated in small impact events in the uppermost crust on Mars. We used mapping of crater morphology to determine the regolith thickness that craters formed in. We found that local regolith thickness in the late Amazonian units is between 4 and 9 m. Combined with previous estimates for the NASA InSight landing site, we composed a more realistic uppermost crust analog and implemented it in numerical impact simulations. We estimated the seismic efficiency and seismic moment for small craters on Mars impacting a non-porous or fractured bedrock overlaid by 5, 10, or 15 m thick regolith. Seismic energy showed more dependence on target properties. Three orders of magnitude more energy were produced in stronger targets. The seismic moment does not depend on target properties, and we confirm that seismic moment is almost proportional to impact momentum. The resulting seismic moment is in agreement up to a factor of 4 between different target types. We improved the scaling relationships developed from numerical simulations used in seismic moment approximations by constraining its dependence on more realistic target properties.

Introduction: Fusion crusts form during the atmospheric entry heating of meteorites and preserve a record of the conditions that occurred in the last few seconds of their deceleration in the atmosphere [1]. Although fusion crusts are ubiquitous they are rarely characterised and studied because they obscure the primary features of meteorites. Here we report the results of a study of the fusion crust of the Winchcombe CM2 chondrite. The Winchcombe meteorite fell at 21:54 hours on 28 February 2021 in Gloucestershire in the UK and was recovered over the next week. The fall was observed on UKFAll network cameras and recorded by CCTV. The meteoroid had a low entry velocity compared to other observed falls of 13.5 km/s. Study of the fusion crust reveals unique textural features that testify to previously unknown processes related to vigorous degassing of this intensely altered CM2 chondrite. Methods: Six polished blocks of Winchcombe were studied using backscattered electron imaging, elemental mapping, energy dispersive spectroscopy (EDS), electron backscatter diffraction (EBSD) and micro-X-ray fluorescence (XRF). Apparent size distributions and abundances were obtained by threshold analysis using ImageJ. Results: The fusion crust consists of an inner thermally altered substrate and outer melted crust. The altered substrate exhibits unusually abundant dehydration cracks extending up to 5 mm into the meteorite. The crack network encompasses fragments up to 70 µm in diameter (dense rock equivalent) with increasing abundance with decreasing size. Loss of sheet-like habits for phyllosilicates and tochilinite testifies to progressive dehydration towards the exterior. The outer melted crust has a vesicular porphyritic texture with olivine phenocrysts and magnetite in a glassy mesostasis. Grain-size and magnetite abundance increase outwards similar to other CI/CM2 fusion crusts [2]. High Ni (<80 wt%) sulphide-metal droplets occur – often as menisci on vesicles. A magnetite rim oc

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Item does not contain fulltext, A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Contains fulltext : 251202.pdf (Publisher’s version ) (Open Access), A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Direct links between carbonaceous chondrites and their parent bodies in the solar system are rare. The Winchcombe meteorite is the most accurately recorded carbonaceous chondrite fall. Its pre-atmospheric orbit and cosmic-ray exposure age confirm that it arrived on Earth shortly after ejection from a primitive asteroid. Recovered only hours after falling, the composition of the Winchcombe meteorite is largely unmodified by the terrestrial environment. It contains abundant hydrated silicates formed during fluid-rock reactions, and carbon- and nitrogen-bearing organic matter including soluble protein amino acids. The near-pristine hydrogen isotopic composition of the Winchcombe meteorite is comparable to the terrestrial hydrosphere, providing further evidence that volatile-rich carbonaceous asteroids played an important role in the origin of Earth’s water.

We present a catalog of new impacts on Mars. These craters formed in the last few decades, constrained with repeat orbital imaging. Crater diameters range from 58 m down to <1 m. For each impact, we report whether it formed a single crater or a cluster (58% clusters); albedo features of the blast zone (88% halos; 64% linear rays; 10% arcuate rays; majority dark-toned; 4% light-toned; 14% dual-toned); and exposures of ice (4% definite; 2% possible). We find no trends in the occurrences of clusters with latitude, elevation, or impact size. Albedo features do not depend on atmospheric fragmentation. Halos are more prevalent at lower elevations, indicating an atmospheric pressure dependence; and around smaller impacts, which could be an observational bias. Linear rays are more likely to form from larger impacts into more consolidated material and may be enhanced by lower atmospheric pressure at higher elevations. Light- and dual-toned blast zones occur in specific regions and more commonly around larger impacts, indicating excavation of compositionally distinct material. Surfaces covered with bright dust lacking cohesion are favored to form detectable surface features. The slope of the cumulative size frequency distribution for this data set is 2.2 for diameters >8 m (differential slope 2.9), significantly shallower than the slope of new lunar craters. We believe that no systematic biases exist in the Martian data set sufficient to explain the discrepancy. This catalog is complete at the time of writing, although observational biases exist, and new discoveries continue.

Meteoroid impacts shape planetary surfaces by forming new craters and alter atmospheric composition. During atmospheric entry and impact on the ground, meteoroids excite transient acoustic and seismic waves. However, new crater formation and the associated impact-induced mechanical waves have yet to be observed jointly beyond Earth. Here we report observations of seismic and acoustic waves from the NASA InSight lander’s seismometer that we link to four meteoroid impact events on Mars observed in spacecraft imagery. We analysed arrival times and polarization of seismic and acoustic waves to estimate impact locations, which were subsequently confirmed by orbital imaging of the associated craters. Crater dimensions and estimates of meteoroid trajectories are consistent with waveform modelling of the recorded seismograms. With identified seismic sources, the seismic waves can be used to constrain the structure of the Martian interior, corroborating previous crustal structure models, and constrain scaling relationships between the distance and amplitude of impact-generated seismic waves on Mars, supporting a link between the seismic moment of impacts and the vertical impactor momentum. Our findings demonstrate the capability of planetary seismology to identify impact-generated seismic sources and constrain both impact processes and planetary interiors.

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.

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.

The entry, descent, and landing (EDL) sequence of NASA's Mars 2020 Perseverance Rover will act as a seismic source of known temporal and spatial localization. We evaluate whether the signals produced by this event will be detectable by the InSight lander (3,452 km away), comparing expected signal amplitudes to noise levels at the instrument. Modeling is undertaken to predict the propagation of the acoustic signal (purely in the atmosphere), the seismoacoustic signal (atmosphere-to-ground coupled), and the elastodynamic seismic signal (in the ground only). Our results suggest that the acoustic and seismoacoustic signals, produced by the atmospheric shock wave from the EDL, are unlikely to be detectable due to the pattern of winds in the martian atmosphere and the weak air-to-ground coupling, respectively. However, the elastodynamic seismic signal produced by the impact of the spacecraft's cruise balance masses on the surface may be detected by InSight. The upper and lower bounds on predicted ground velocity at InSight are 2.0 × 10−14 and 1.3 × 10−10 m s−1. The upper value is above the noise floor at the time of landing 40% of the time on average. The large range of possible values reflects uncertainties in the current understanding of impact-generated seismic waves and their subsequent propagation and attenuation through Mars. Uncertainty in the detectability also stems from the indeterminate instrument noise level at the time of this future event. A positive detection would be of enormous value in constraining the seismic properties of Mars, and in improving our understanding of impact-generated seismic waves.

The first seismometer operating on the surface of another planet was deployed by the NASA InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission to Mars. It gives us an opportunity to investigate the seismicity of Mars, including any seismic activity caused by small meteorite bombardment. Detectability of impact generated seismic signals is closely related to the seismic efficiency, defined as the fraction of the impactor's kinetic energy transferred into the seismic energy in a target medium. This work investigated the seismic efficiency of the Martian near surface associated with small meteorite impacts on Mars. We used the iSALE-2D (Impact-Simplified Arbitrary Lagrangian Eulerian) shock physics code to simulate the formation of the meter-size impact craters, and we used a recently formed 1.5 m diameter crater as a case study. The Martian crust was simulated as unfractured nonporous bedrock, fractured bedrock with 25% porosity, and highly porous regolith with 44% and 65% porosity. We used appropriate strength and porosity models defined in previous works, and we identified that the seismic efficiency is very sensitive to the speed of sound and elastic threshold in the target medium. We constrained the value of the impact-related seismic efficiency to be between the order of ∼10-7 to 10-6 for the regolith and ∼10-4 to 10-3 for the bedrock. For new impacts occurring on Mars, this work can help understand the near-surface properties of the Martian crust, and it contributes to the understanding of impact detectability via seismic signals as a function of the target media.

The third stage of the Saturn IV rocket used in the five Apollo missions made craters on the Moon ∼30 m in diameter. Their initial impact conditions were known, so they can be considered controlled impacts. Here, we used the iSALE-2D shock physics code to numerically simulate the formation of these craters, and to calculate the vertical component of seismic moment (∼4 × 1010 Nm) and seismic efficiency (∼10−6) associated with these impacts. The irregular booster shape likely caused the irregular crater morphology observed. To investigate this, we modeled six projectile geometries, with footprint area between 3 and 105 m2, keeping the mass and velocity of the impactor constant. We showed that the crater depth and diameter decreased as the footprint area increased. The central mound observed in lunar impact sites could be a result of layering of the target and/or low density of the projectile. Understanding seismic signatures from impact events is important for planetary seismology. Calculating seismic parameters and validating them against controlled experiments in a planetary setting will help us understand the seismic data received, not only from the Moon, but also from the InSight Mission on Mars and future seismic missions.

Contains fulltext : 229510.pdf (publisher's version ) (Open Access), BACKGROUND: Placebo or sham controls are the standard against which the benefits and harms of many active interventions are measured. Whilst the components and the method of their delivery have been shown to affect study outcomes, placebo and sham controls are rarely reported and often not matched to those of the active comparator. This can influence how beneficial or harmful the active intervention appears to be. Without adequate descriptions of placebo or sham controls, it is difficult to interpret results about the benefits and harms of active interventions within placebo-controlled trials. To overcome this problem, we developed a checklist and guide for reporting placebo or sham interventions. METHODS AND FINDINGS: We developed an initial list of items for the checklist by surveying experts in placebo research (n = 14). Because of the diverse contexts in which placebo or sham treatments are used in clinical research, we consulted experts in trials of drugs, surgery, physiotherapy, acupuncture, and psychological interventions. We then used a multistage online Delphi process with 53 participants to determine which items were deemed to be essential. We next convened a group of experts and stakeholders (n = 16). Our main output was a modification of the existing Template for Intervention Description and Replication (TIDieR) checklist; this allows the key features of both active interventions and placebo or sham controls to be concisely summarised by researchers. The main differences between TIDieR-Placebo and the original TIDieR are the explicit requirement to describe the setting (i.e., features of the physical environment that go beyond geographic location), the need to report whether blinding was successful (when this was measured), and the need to present the description of placebo components alongside those of the active comparator. CONCLUSIONS: We encourage TIDieR-Placebo to be used alongside TIDieR to assist the reporting of placebo or sham components and the

Contains fulltext : 229510.pdf (publisher's version ) (Open Access), BACKGROUND: Placebo or sham controls are the standard against which the benefits and harms of many active interventions are measured. Whilst the components and the method of their delivery have been shown to affect study outcomes, placebo and sham controls are rarely reported and often not matched to those of the active comparator. This can influence how beneficial or harmful the active intervention appears to be. Without adequate descriptions of placebo or sham controls, it is difficult to interpret results about the benefits and harms of active interventions within placebo-controlled trials. To overcome this problem, we developed a checklist and guide for reporting placebo or sham interventions. METHODS AND FINDINGS: We developed an initial list of items for the checklist by surveying experts in placebo research (n = 14). Because of the diverse contexts in which placebo or sham treatments are used in clinical research, we consulted experts in trials of drugs, surgery, physiotherapy, acupuncture, and psychological interventions. We then used a multistage online Delphi process with 53 participants to determine which items were deemed to be essential. We next convened a group of experts and stakeholders (n = 16). Our main output was a modification of the existing Template for Intervention Description and Replication (TIDieR) checklist; this allows the key features of both active interventions and placebo or sham controls to be concisely summarised by researchers. The main differences between TIDieR-Placebo and the original TIDieR are the explicit requirement to describe the setting (i.e., features of the physical environment that go beyond geographic location), the need to report whether blinding was successful (when this was measured), and the need to present the description of placebo components alongside those of the active comparator. CONCLUSIONS: We encourage TIDieR-Placebo to be used alongside TIDieR to assist the reporting of placebo or sham components and the

Contains fulltext : 229510.pdf (publisher's version ) (Open Access), BACKGROUND: Placebo or sham controls are the standard against which the benefits and harms of many active interventions are measured. Whilst the components and the method of their delivery have been shown to affect study outcomes, placebo and sham controls are rarely reported and often not matched to those of the active comparator. This can influence how beneficial or harmful the active intervention appears to be. Without adequate descriptions of placebo or sham controls, it is difficult to interpret results about the benefits and harms of active interventions within placebo-controlled trials. To overcome this problem, we developed a checklist and guide for reporting placebo or sham interventions. METHODS AND FINDINGS: We developed an initial list of items for the checklist by surveying experts in placebo research (n = 14). Because of the diverse contexts in which placebo or sham treatments are used in clinical research, we consulted experts in trials of drugs, surgery, physiotherapy, acupuncture, and psychological interventions. We then used a multistage online Delphi process with 53 participants to determine which items were deemed to be essential. We next convened a group of experts and stakeholders (n = 16). Our main output was a modification of the existing Template for Intervention Description and Replication (TIDieR) checklist; this allows the key features of both active interventions and placebo or sham controls to be concisely summarised by researchers. The main differences between TIDieR-Placebo and the original TIDieR are the explicit requirement to describe the setting (i.e., features of the physical environment that go beyond geographic location), the need to report whether blinding was successful (when this was measured), and the need to present the description of placebo components alongside those of the active comparator. CONCLUSIONS: We encourage TIDieR-Placebo to be used alongside TIDieR to assist the reporting of placebo or sham components and the

Since landing in late 2018, the InSight lander has been recording seismic signals on the surface of Mars. Despite nominal prelanding estimates of one to three meteorite impacts detected per Earth year, none have yet been identified seismically. To inform revised detectability estimates, we simulated numerically a suite of small impacts onto Martian regolith and characterized their seismic source properties. For the impactor size and velocity range most relevant for InSight, crater diameters are 1–30 m. We found that in this range scalar seismic moment is 106–1010 Nm and increases almost linearly with impact momentum. The ratio of horizontal to vertical seismic moment tensor components is ∼1, implying an almost isotropic P wave source, for vertical impacts. Seismic efficiencies are ∼10−6, dependent on the target crushing strength and impact velocity. Our predictions of relatively low seismic efficiency and seismic moment suggest that meteorite impact detectability on Mars is lower than previously assumed. Detection chances are best for impacts forming craters of diameter >10 m.

A new 1.5 m diameter impact crater was discovered on Mars only ~40 km from the InSight lander. Context camera images constrained its formation between 21 February and 6 April 2019; follow-up High Resolution Imaging Science Experiment images resolved the crater. During this time period, three seismic events were identified in InSight data. We derive expected seismic signal characteristics and use them to evaluate each of the seismic events. However, none of them can definitively be associated with this source. Atmospheric perturbations are generally expected to be generated during impacts; however, in this case, no signal could be identified as related to the known impact. Using scaling relationships based on the terrestrial and lunar analogs and numerical modeling, we predict the amplitude, peak frequency, and duration of the seismic signal that would have emanated from this impact. The predicted amplitude falls near the lowest levels of the measured seismometer noise for the predicted frequency. Hence, it is not surprising this impact event was not positively identified in the seismic data. Finding this crater was a lucky event as its formation this close to InSight has a probability of only ~0.2, and the odds of capturing it in before and after images are extremely low. We revisit impact-seismic discriminators in light of real experience with a seismometer on the Martian surface. Using measured noise of the instrument, we revise our previous prediction of seismic impact detections downward, from ~a few to tens, to just ~2 per Earth year, still with an order of magnitude uncertainty.

The world's meteorite collections contain a very rich picture of what the early Solar System would have been made of, however the lack of spatial context with respect to their parent population for these samples is an issue. The asteroid population is equally as rich in surface mineralogies, and mapping these two populations (meteorites and asteroids) together is a major challenge for planetary science. Directly probing asteroids achieves this at a high cost. Observing meteorite falls and calculating their pre-atmospheric orbit on the other hand, is a cheaper way to approach the problem. The Global Fireball Observatory (GFO) collaboration was established in 2017 and brings together multiple institutions (from Australia, USA, Canada, Morocco, Saudi Arabia, the UK, and Argentina) to maximise the area for fireball observation time and therefore meteorite recoveries. The members have a choice to operate independently, but they can also choose to work in a fully collaborative manner with other GFO partners. This efficient approach leverages the experience gained from the Desert Fireball Network (DFN) pathfinder project in Australia. The state-of-the art technology (DFN camera systems and data reduction) and experience of the support teams is shared between all partners, freeing up time for science investigations and meteorite searching. With all networks combined together, the GFO collaboration already covers 0.6% of the Earth's surface for meteorite recovery as of mid-2019, and aims to reach 2% in the early 2020s. We estimate that after 5 years of operation, the GFO will have observed a fireball from virtually every meteorite type. This combined effort will bring new, fresh, extra-terrestrial material to the labs, yielding new insights about the formation of the Solar System.

© 2020 Elsevier Ltd Hydrothermal activity is a common phenomenon in the wake of impact events, yet identifying and dating impact hydrothermal systems can be challenging. This study provides the first detailed assessment of the effects of shock microstructures and impact-related alteration on the U-Pb systematics and trace elements of titanite (CaTiSiO5), focusing on shocked granite target rocks from the peak ring of the Chicxulub impact structure, Mexico. A > 1 mm long, shock-twinned titanite grain preserves a dense network of irregular microcracks, some of which exploit shock twin interfaces. Secondary microcrystalline anatase and pyrite are heterogeneously distributed along some microcracks. In situ laser ablation multi-collector inductively-coupled plasma mass spectrometry (LA-MC-ICPMS) analysis reveals a mixture of three end-member Pb components. The Pb components are: 1) common Pb, consistent with the Pb isotopic signature of adjacent alkali feldspar; 2) radiogenic Pb accumulated since magmatic crystallization; and 3) a secondary, younger Pb signature due to impact-related complete radiogenic Pb loss. The youngest derived ages define a regression from common Pb that intersects Concordia at 67 ± 4 Ma, in agreement with the established age of 66.04 ± 0.05 Ma for the Chicxulub impact event. Contour maps of LA-MC-ICPMS data reveal that the young ages are spatially restricted to microstructurally-complex domains that correlate with significant depletion in trace elements (REE-Y-Zr-Nb-Mo-Sn-Th) and reduction in magnitude of the Eu/Eu* anomaly. Mapping by time-of-flight secondary ion mass spectrometry (ToF-SIMS) show that patterns of localised element depletion in titanite are spatially related to microcracks, which are enriched in Al. The spatial correlation of ages and trace element abundance is consistent with localised removal of Pb and other trace elements from a pervasive network of fast fluid pathways in fractured domains via a fluid-mediated element transport

Item does not contain fulltext, BACKGROUND: Poorly described placebo/sham controls make it difficult to appraise active intervention benefits and harms. The 12-item Template for Intervention Description and Replication (TIDieR) checklist was developed to improve the reporting of active interventions. The extent to which TIDieR has been used to improve description of placebo or sham control is not known. MATERIALS AND METHODS: We systematically identified and examined all placebo/sham-controlled randomised trials published in 2018 in the top six general medical journals. We reported how many of the TIDieR checklist items were used to describe the placebo/sham control(s). We supplemented this with a sample of 100 placebo/sham-controlled trials from any journal and searched Google Scholar to identify placebo/sham-controlled trials citing TIDieR. RESULTS: We identified 94 placebo/sham-controlled trials published in the top journals in 2018. None reported using TIDieR, and none reported placebo or sham components completely. On average eight TIDieR items were addressed, with placebo/sham control name (100%) and when and how much was administered (97.9%) most commonly reported. Some items (rationale, 8.5%, whether there were modifications, 25.5%) were less often reported. In our sample of less well-cited journals, reporting was poorer (average of six items) and followed a similar pattern. Since TIDieR's first publication, six placebo-controlled trials have cited it according to Google Scholar. Two of these used the checklist to describe placebo controls; neither one completely desribed the placebo intervention. CONCLUSIONS: Placebo and sham controls are poorly described within randomised trials, and TIDieR is rarely used to guide these descriptions. We recommend developing guidelines to promote better descriptions of placebo/sham control components within clinical trials.

Item does not contain fulltext, BACKGROUND: Poorly described placebo/sham controls make it difficult to appraise active intervention benefits and harms. The 12-item Template for Intervention Description and Replication (TIDieR) checklist was developed to improve the reporting of active interventions. The extent to which TIDieR has been used to improve description of placebo or sham control is not known. MATERIALS AND METHODS: We systematically identified and examined all placebo/sham-controlled randomised trials published in 2018 in the top six general medical journals. We reported how many of the TIDieR checklist items were used to describe the placebo/sham control(s). We supplemented this with a sample of 100 placebo/sham-controlled trials from any journal and searched Google Scholar to identify placebo/sham-controlled trials citing TIDieR. RESULTS: We identified 94 placebo/sham-controlled trials published in the top journals in 2018. None reported using TIDieR, and none reported placebo or sham components completely. On average eight TIDieR items were addressed, with placebo/sham control name (100%) and when and how much was administered (97.9%) most commonly reported. Some items (rationale, 8.5%, whether there were modifications, 25.5%) were less often reported. In our sample of less well-cited journals, reporting was poorer (average of six items) and followed a similar pattern. Since TIDieR's first publication, six placebo-controlled trials have cited it according to Google Scholar. Two of these used the checklist to describe placebo controls; neither one completely desribed the placebo intervention. CONCLUSIONS: Placebo and sham controls are poorly described within randomised trials, and TIDieR is rarely used to guide these descriptions. We recommend developing guidelines to promote better descriptions of placebo/sham control components within clinical trials.

Item does not contain fulltext, BACKGROUND: Poorly described placebo/sham controls make it difficult to appraise active intervention benefits and harms. The 12-item Template for Intervention Description and Replication (TIDieR) checklist was developed to improve the reporting of active interventions. The extent to which TIDieR has been used to improve description of placebo or sham control is not known. MATERIALS AND METHODS: We systematically identified and examined all placebo/sham-controlled randomised trials published in 2018 in the top six general medical journals. We reported how many of the TIDieR checklist items were used to describe the placebo/sham control(s). We supplemented this with a sample of 100 placebo/sham-controlled trials from any journal and searched Google Scholar to identify placebo/sham-controlled trials citing TIDieR. RESULTS: We identified 94 placebo/sham-controlled trials published in the top journals in 2018. None reported using TIDieR, and none reported placebo or sham components completely. On average eight TIDieR items were addressed, with placebo/sham control name (100%) and when and how much was administered (97.9%) most commonly reported. Some items (rationale, 8.5%, whether there were modifications, 25.5%) were less often reported. In our sample of less well-cited journals, reporting was poorer (average of six items) and followed a similar pattern. Since TIDieR's first publication, six placebo-controlled trials have cited it according to Google Scholar. Two of these used the checklist to describe placebo controls; neither one completely desribed the placebo intervention. CONCLUSIONS: Placebo and sham controls are poorly described within randomised trials, and TIDieR is rarely used to guide these descriptions. We recommend developing guidelines to promote better descriptions of placebo/sham control components within clinical trials.

Highly expanded Cretaceous-Paleogene (K-Pg) boundary section from the Chicxulub peak ring, recovered by International Ocean Discovery Program (IODP)-International Continental Scientific Drilling Program (ICDP) Expedition 364, provides an unprecedented window into the immediate aftermath of the impact. Site M0077 includes ∼130 m of impact melt rock and suevite deposited the first day of the Cenozoic covered by <1 m of micrite-rich carbonate deposited over subsequent weeks to years. We present an interpreted series of events based on analyses of these drill cores. Within minutes of the impact, centrally uplifted basement rock collapsed outward to forma peak ring capped in melt rock. Within tens of minutes, the peak ring was covered in ∼40 m of brecciated impact melt rock and coarsegrained suevite, including clasts possibly generated by melt-water interactions during ocean resurge. Within an hour, resurge crested the peak ring, depositing a 10-m-thick layer of suevite with increased particle roundness and sorting.Within hours, the full resurge deposit formed through settling and seiches, resulting in an 80-m-thick fining-upward, sorted suevite in the flooded crater. Within a day, the reflected rim-wave tsunami reached the crater, depositing a cross-bedded sand-to-fine gravel layer enriched in polycyclic aromatic hydrocarbons overlain by charcoal fragments. Generation of a deep crater open to the ocean allowed rapid flooding and sediment accumulation rates among the highest known in the geologic record. The high-resolution section provides insight into the impact environmental effects, including charcoal as evidence for impactinduced wildfires and a paucity of sulfur-rich evaporites from the target supporting rapid global cooling and darkness as extinction mechanisms.

[No abstract available], Funding Agencies|Flinders University; RU, Radboud Universiteit; NIU, Northern Illinois University; University of Nottingham; Universität zu Köln; Charité – Universitätsmedizin Berlin; SLU, Saint Louis University; TU/e, Technische Universiteit Eindhoven; University of Leeds; IUPUI, Indiana University-Purdue University Indianapolis; UiT, Universitetet i Tromsø; EUR, Erasmus Universiteit Rotterdam; UvT, Universiteit van Tilburg; University of Oxford; UNIL, Université de Lausanne; SEBM, Society for Experimental Biology and Medicine; NYU, New York University; UiT, Universitetet i Tromsø; RWTH Aachen University; KU Leuven; Universität Bielefeld; University of Louisiana at Lafayette; UA, University of Arkansas; Genentech Foundation for Biomedical Sciences; University of Bristol; UV, University of Virginia; City of Mobile; Université de Fribourg; Universiteit Gent; Yale University; VT, Virginia Polytechnic Institute and State University; DIBS, Duke Institute for Brain Sciences, Duke University; UCB UK; University of Leeds; BIBS, Brown Institute for Brain Science; UU, Universiteit Utrecht; Keele University; HRD, Division of Human Resource Development; WVU, West Virginia University; King’s College London; NTU, Nottingham Trent University; Rijksuniversiteit Groningen; KACST, King Abdulaziz City for Science and Technology; University of Edinburgh; UCONN, University of Connecticut; University of Kent; University of Cambridge; UNLV, University of Nevada, Las Vegas; Universiteit Leiden; UBC, University of British Columbia; HGS, Human Genome Sciences; AV CR, Akademie Ved Ceské Republiky; IUPUI, Indiana University-Purdue University Indianapolis; UK, University of Kentucky; University of Texas at Austin; DRS, Dahlem Research School, Freie Universität Berlin; Bangor University; RAND Corporation; University of Glasgow; University of Chester; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas; Massey University; UC, University of California; NIH, National Institutes

[No abstract available], Funding Agencies|Flinders University; RU, Radboud Universiteit; NIU, Northern Illinois University; University of Nottingham; Universität zu Köln; Charité – Universitätsmedizin Berlin; SLU, Saint Louis University; TU/e, Technische Universiteit Eindhoven; University of Leeds; IUPUI, Indiana University-Purdue University Indianapolis; UiT, Universitetet i Tromsø; EUR, Erasmus Universiteit Rotterdam; UvT, Universiteit van Tilburg; University of Oxford; UNIL, Université de Lausanne; SEBM, Society for Experimental Biology and Medicine; NYU, New York University; UiT, Universitetet i Tromsø; RWTH Aachen University; KU Leuven; Universität Bielefeld; University of Louisiana at Lafayette; UA, University of Arkansas; Genentech Foundation for Biomedical Sciences; University of Bristol; UV, University of Virginia; City of Mobile; Université de Fribourg; Universiteit Gent; Yale University; VT, Virginia Polytechnic Institute and State University; DIBS, Duke Institute for Brain Sciences, Duke University; UCB UK; University of Leeds; BIBS, Brown Institute for Brain Science; UU, Universiteit Utrecht; Keele University; HRD, Division of Human Resource Development; WVU, West Virginia University; King’s College London; NTU, Nottingham Trent University; Rijksuniversiteit Groningen; KACST, King Abdulaziz City for Science and Technology; University of Edinburgh; UCONN, University of Connecticut; University of Kent; University of Cambridge; UNLV, University of Nevada, Las Vegas; Universiteit Leiden; UBC, University of British Columbia; HGS, Human Genome Sciences; AV CR, Akademie Ved Ceské Republiky; IUPUI, Indiana University-Purdue University Indianapolis; UK, University of Kentucky; University of Texas at Austin; DRS, Dahlem Research School, Freie Universität Berlin; Bangor University; RAND Corporation; University of Glasgow; University of Chester; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas; Massey University; UC, University of California; NIH, National Institutes

[No abstract available], Funding Agencies|Flinders University; RU, Radboud Universiteit; NIU, Northern Illinois University; University of Nottingham; Universität zu Köln; Charité – Universitätsmedizin Berlin; SLU, Saint Louis University; TU/e, Technische Universiteit Eindhoven; University of Leeds; IUPUI, Indiana University-Purdue University Indianapolis; UiT, Universitetet i Tromsø; EUR, Erasmus Universiteit Rotterdam; UvT, Universiteit van Tilburg; University of Oxford; UNIL, Université de Lausanne; SEBM, Society for Experimental Biology and Medicine; NYU, New York University; UiT, Universitetet i Tromsø; RWTH Aachen University; KU Leuven; Universität Bielefeld; University of Louisiana at Lafayette; UA, University of Arkansas; Genentech Foundation for Biomedical Sciences; University of Bristol; UV, University of Virginia; City of Mobile; Université de Fribourg; Universiteit Gent; Yale University; VT, Virginia Polytechnic Institute and State University; DIBS, Duke Institute for Brain Sciences, Duke University; UCB UK; University of Leeds; BIBS, Brown Institute for Brain Science; UU, Universiteit Utrecht; Keele University; HRD, Division of Human Resource Development; WVU, West Virginia University; King’s College London; NTU, Nottingham Trent University; Rijksuniversiteit Groningen; KACST, King Abdulaziz City for Science and Technology; University of Edinburgh; UCONN, University of Connecticut; University of Kent; University of Cambridge; UNLV, University of Nevada, Las Vegas; Universiteit Leiden; UBC, University of British Columbia; HGS, Human Genome Sciences; AV CR, Akademie Ved Ceské Republiky; IUPUI, Indiana University-Purdue University Indianapolis; UK, University of Kentucky; University of Texas at Austin; DRS, Dahlem Research School, Freie Universität Berlin; Bangor University; RAND Corporation; University of Glasgow; University of Chester; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas; Massey University; UC, University of California; NIH, National Institutes

[No abstract available], Funding Agencies|Flinders University; RU, Radboud Universiteit; NIU, Northern Illinois University; University of Nottingham; Universität zu Köln; Charité – Universitätsmedizin Berlin; SLU, Saint Louis University; TU/e, Technische Universiteit Eindhoven; University of Leeds; IUPUI, Indiana University-Purdue University Indianapolis; UiT, Universitetet i Tromsø; EUR, Erasmus Universiteit Rotterdam; UvT, Universiteit van Tilburg; University of Oxford; UNIL, Université de Lausanne; SEBM, Society for Experimental Biology and Medicine; NYU, New York University; UiT, Universitetet i Tromsø; RWTH Aachen University; KU Leuven; Universität Bielefeld; University of Louisiana at Lafayette; UA, University of Arkansas; Genentech Foundation for Biomedical Sciences; University of Bristol; UV, University of Virginia; City of Mobile; Université de Fribourg; Universiteit Gent; Yale University; VT, Virginia Polytechnic Institute and State University; DIBS, Duke Institute for Brain Sciences, Duke University; UCB UK; University of Leeds; BIBS, Brown Institute for Brain Science; UU, Universiteit Utrecht; Keele University; HRD, Division of Human Resource Development; WVU, West Virginia University; King’s College London; NTU, Nottingham Trent University; Rijksuniversiteit Groningen; KACST, King Abdulaziz City for Science and Technology; University of Edinburgh; UCONN, University of Connecticut; University of Kent; University of Cambridge; UNLV, University of Nevada, Las Vegas; Universiteit Leiden; UBC, University of British Columbia; HGS, Human Genome Sciences; AV CR, Akademie Ved Ceské Republiky; IUPUI, Indiana University-Purdue University Indianapolis; UK, University of Kentucky; University of Texas at Austin; DRS, Dahlem Research School, Freie Universität Berlin; Bangor University; RAND Corporation; University of Glasgow; University of Chester; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas; Massey University; UC, University of California; NIH, National Institutes

[No abstract available], Funding Agencies|Flinders University; RU, Radboud Universiteit; NIU, Northern Illinois University; University of Nottingham; Universität zu Köln; Charité – Universitätsmedizin Berlin; SLU, Saint Louis University; TU/e, Technische Universiteit Eindhoven; University of Leeds; IUPUI, Indiana University-Purdue University Indianapolis; UiT, Universitetet i Tromsø; EUR, Erasmus Universiteit Rotterdam; UvT, Universiteit van Tilburg; University of Oxford; UNIL, Université de Lausanne; SEBM, Society for Experimental Biology and Medicine; NYU, New York University; UiT, Universitetet i Tromsø; RWTH Aachen University; KU Leuven; Universität Bielefeld; University of Louisiana at Lafayette; UA, University of Arkansas; Genentech Foundation for Biomedical Sciences; University of Bristol; UV, University of Virginia; City of Mobile; Université de Fribourg; Universiteit Gent; Yale University; VT, Virginia Polytechnic Institute and State University; DIBS, Duke Institute for Brain Sciences, Duke University; UCB UK; University of Leeds; BIBS, Brown Institute for Brain Science; UU, Universiteit Utrecht; Keele University; HRD, Division of Human Resource Development; WVU, West Virginia University; King’s College London; NTU, Nottingham Trent University; Rijksuniversiteit Groningen; KACST, King Abdulaziz City for Science and Technology; University of Edinburgh; UCONN, University of Connecticut; University of Kent; University of Cambridge; UNLV, University of Nevada, Las Vegas; Universiteit Leiden; UBC, University of British Columbia; HGS, Human Genome Sciences; AV CR, Akademie Ved Ceské Republiky; IUPUI, Indiana University-Purdue University Indianapolis; UK, University of Kentucky; University of Texas at Austin; DRS, Dahlem Research School, Freie Universität Berlin; Bangor University; RAND Corporation; University of Glasgow; University of Chester; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas; Massey University; UC, University of California; NIH, National Institutes