Exploring the use of mouth guards in Muay Thai:a questionnaire survey

10.1038/s41405-020-00048-zBDJ Open612

Predicting brain activation maps for arbitrary tasks with cognitive encoding models

A deep understanding of the neural architecture of mental function should enable the accurate prediction of a specific pattern of brain activity for any psychological task, based only on the cognitive functions known to be engaged by that task. Encoding models (EMs), which predict neural responses from known features (e.g., stimulus properties), have succeeded in circumscribed domains (e.g., visual neuroscience), but implementing domain-general EMs that predict brain-wide activity for arbitrary tasks has been limited mainly by availability of datasets that 1) sufficiently span a large space of psychological functions, and 2) are sufficiently annotated with such functions to allow robust EM specification. We examine the use of EMs based on a formal specification of psychological function, to predict cortical activation patterns across a broad range of tasks. We utilized the Multi-Domain Task Battery, a dataset in which 24 subjects completed 32 ten-minute fMRI scans, switching tasks every 35 s and engaging in 44 total conditions of diverse psychological manipulations. Conditions were annotated by a group of experts using the Cognitive Atlas ontology to identify putatively engaged functions, and region-wise cognitive EMs (CEMs) were fit, for individual subjects, on neocortical responses. We found that CEMs predicted cortical activation maps of held-out tasks with high accuracy, outperforming a permutation-based null model while approaching the noise ceiling of the data, without being driven solely by either cognitive or perceptual-motor features. Hierarchical clustering on the similarity structure of CEM generalization errors revealed relationships amongst psychological functions. Spatial distributions of feature importances systematically overlapped with large-scale resting-state functional networks (RSNs), supporting the hypothesis of functional specialization within RSNs while grounding their function in an interpretable data-driven manner. Our implementation and validation of CEMs provides a proof of principle for the utility of formal ontologies in cognitive neuroscience and motivates the use of CEMs in the further testing of cognitive theories

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors’ retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGCseagrass) estimates. This study assessed atmospheric correction’s impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (Lw), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction

Short-term Clinical Outcomes of a European Training Programme for Robotic Colorectal Surgery

Background Despite there being a considerable amount of published studies on robotic colorectal surgery (RCS) over the last few years, there is a lack of evidence regarding RCS training pathways. This study examines the short-term clinical outcomes of an international RCS training programme (the European Academy of Robotic Colorectal Surgery—EARCS). Methods Consecutive cases from 26 European colorectal units who conducted RCS between 2014 and 2018 were included in this study. The baseline characteristics and short-term outcomes of cases performed by EARCS delegates during training were analysed and compared with cases performed by EARCS graduates and proctors. Results Data from 1130 RCS procedures were collected and classified into three cohort groups (323 training, 626 graduates and 181 proctors). The training cases conversion rate was 2.2% and R1 resection rate was 1.5%. The three groups were similar in terms of baseline characteristics with the exception of malignant cases and rectal resections performed. With the exception of operative time, blood loss and hospital stay (training vs. graduate vs. proctor: operative time 302, 265, 255 min, p < 0.001; blood loss 50, 50, 30 ml, p < 0.001; hospital stay 7, 6, 6 days, p = 0.003), all remaining short-term outcomes (conversion, 30-day reoperation, 30-day readmission, 30-day mortality, clinical anastomotic leak, complications, R1 resection and lymph node yield) were comparable between the three groups. Conclusions Colorectal surgeons learning how to perform RCS under the EARCS-structured training pathway can safely achieve short-term clinical outcomes comparable to their trainers and overcome the learning process in a way that minimises patient harm

The reintroduction of large carnivores to the Eastern Cape, South Africa: an assessment

Recently, conservation estate in South Africa's Eastern Cape Province has increased 10-fold resulting in large predators being increasingly reintroduced to restore ecological integrity and maximize tourism. We describe the reintroductions of large carnivores (>10 kg) that have occurred in the Eastern Cape and use various criteria to assess their success. Lion Panthera leo reintroduction has been highly successful with a population of 56 currently extant in the region and problems of overpopulation arising. The African wild dog Lycaon pictus population has increased to 24 from a founder population of 11. Preliminary results for spotted hyaenas Crocuta crocuta also indicate success. Wild populations of leopards Panthera pardus exist on several reserves and have been supplemented by translocated individuals, although deaths of known individuals have occurred and no estimate of reproduction is available. Cheetah Acinonyx jubatus reintroduction has also been less successful with 36 individuals reintroduced and 23 cubs being born but only 41 individuals surviving in 2005. Criteria for assessing the success of reintroductions of species that naturally occur in low densities, such as top predators, generally have limited value. Carrying capacity for large predators is unknown and continued monitoring and intensive management will be necessary in enclosed, and possibly all, conservation areas in the Eastern Cape to ensure conservation success

Detection of Seagrass Scars Using Sparse Coding and Morphological Filter

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve \u3e90% accuracy on the detection of seagrass scars

Personalized Genetic Risk Counseling to Motivate Diabetes Prevention: A randomized trial

OBJECTIVE To examine whether diabetes genetic risk testing and counseling can improve diabetes prevention behaviors. RESEARCH DESIGN AND METHODS We conducted a randomized trial of diabetes genetic risk counseling among overweight patients at increased phenotypic risk for type 2 diabetes. Participants were randomly allocated to genetic testing versus no testing. Genetic risk was calculated by summing 36 single nucleotide polymorphisms associated with type 2 diabetes. Participants in the top and bottom score quartiles received individual genetic counseling before being enrolled with untested control participants in a 12-week, validated, diabetes prevention program. Middle-risk quartile participants were not studied further. We examined the effect of this genetic counseling intervention on patient self-reported attitudes, program attendance, and weight loss, separately comparing higher-risk and lower-risk result recipients with control participants. RESULTS The 108 participants enrolled in the diabetes prevention program included 42 participants at higher diabetes genetic risk, 32 at lower diabetes genetic risk, and 34 untested control subjects. Mean age was 57.9 ± 10.6 years, 61% were men, and average BMI was 34.8 kg/m2, with no differences among randomization groups. Participants attended 6.8 ± 4.3 group sessions and lost 8.5 ± 10.1 pounds, with 33 of 108 (30.6%) losing ≥5% body weight. There were few statistically significant differences in self-reported motivation, program attendance, or mean weight loss when higher-risk recipients and lower-risk recipients were compared with control subjects (P > 0.05 for all but one comparison). CONCLUSIONS Diabetes genetic risk counseling with currently available variants does not significantly alter self-reported motivation or prevention program adherence for overweight individuals at risk for diabetes

From Meters to Kilometers: A Look at Ocean-Color Scales of Variability, Spatial Coherence, and the Need for Fine-Scale Remote Sensing in Coastal Ocean Optics

The physical, biological, chemical, and optical processes of the ocean operate on a wide variety of spatial and temporal scales, from seconds to decades and from micrometers to thousands of kilometers (Dickey et al., this issue; Dickey, 1991). These processes drive the accumulation and loss of living and nonliving mass constituents in the water column (e.g., nutrients, phytoplankton, detritus, sediments). These mass constituents frequently have unique optical characteristics that alter the clarity and color of the water column (e.g., Preisendorfer, 1976). This alteration of the ocean color, or more specifically the change in the spectral “water-leaving radiance,” L(λ), has led to the development of optical techniques to sample and study the change in biological and chemical constituents (Schofield et al., this issue). Thus, these optical techniques provide a mechanism to study the effects of underlying biogeochemical processes. In addition, because time- and space-dependent changes in L(λ) may be measured remotely, optical oceanography provides a way to sample ecological interactions over a wide range of spatial and temporal scales. The question often posed by scientists trying to resolve problems involving the temporal and spatial variation of oceanic properties is: “What is the optimal time/ space sampling frequency?” The obvious answer is that the sampling frequency should be one half the frequency of the variation (i.e., Nyquist frequency) of the property of interest. However, therein lies the rub for the oceanographer: the range of the relevant scales is large, and the range of available resources and/or actual engineering capabilities to sample all relevant scales is often small. Hence, the decisions affecting resource allocation become critical in order to maximize the total data information in both quantity and quality. While these scientific resource decisions are rarely discussed in explicit terms of cost-benefit analysis, such discussions should be integral parts of the scientific design of instruments, platforms, and experiments aimed at resolving oceanic processes. The practical examples of this problem in remote sensing include: “What is the optimal repeat coverage frequency?” and “What is the optimal Ground Sample Distance (GSD) or pixel size of the data?” For the optical oceanographer, there is also the issue of optimal spectral coverage needed to resolve the optical constituents of interest (Chang et al., this issue). The sum of these considerations feed into the sensor, deployment platform, and deployment schedule decisions. For polarorbiting and geo-stationary satellites that cost hundreds of millions of dollars, as well as airborne sensors that have smaller upfront costs but higher deployment costs, the decision of sampling frequency directly impacts the scientific use of the data stream, and what processes may be addressed with data streams collected by these sensors. These scientific cost-benefit analyses extend beyond the cost in dollars because the typical lifetime and replacement cycle of these sensors is on the order of years to decades, and a poorly designed sensor package is very difficult to replace. In 2001, the Office of Naval Research (ONR) sponsored the Hyperspectral Coastal Ocean Dynamics Experiment (HyCODE) (Dickey et al., this issue), which presented the opportunity to study the question of scales of variability in remote-sensing data. Hyperspectral airborne sensors were deployed on several platforms at various altitudes. This coverage was supplemented by numerous space-borne, remote-sensing satellites. The airborne instruments included two versions of the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS 1 and PHILLS 2) (Davis et al., 2002) operating at an altitude of less than 10,000 feet and 30,000 feet, respectively, as well as the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor operating at 60,000 feet. These sensors provided hyperspectral data at 2 m, 9 m, and 20 m GSDs, respectively. The satellite data collected included the multi-spectral images from Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Fengyun 1 C (FY1-C), Oceansat as well as the multispectral polarimeter Multiangle Imaging SpectroRadiometer (MISR) sensor and sea surface temperature (SST) sensor Advanced Very High Resolution Radiometer (AVHRR). These collections provided a wealth of remote- sensing and field data during a spatially and temporally intense oceanographic field campaign, and they offered the ability to begin to address the issue of optimal sampling scales for the coastal ocean. The use of these multiple remote-sensing data streams requires the calibration, validation, and atmospheric correction of the sensor signals to retrieve estimates of L(λ), or “remote sensing reflectance,” Rᵣₛ(λ), a normalized measure of the L(λ). Our goals in this paper are to illuminate some of the issues of remote sensing spatial scaling in the nearshore environment and attempt to derive some understanding of appropriate sampling scales in the nearshore environment. We will focus on the data collected by a single sensor (PHILLS 2) to reduce uncertainties in the analysis that may result from the different data processing techniques applied to each of the individual sensors’ data

SARS-CoV-2 evolution and patient immunological history shape the breadth and potency of antibody-mediated immunity

Since the emergence of SARS-CoV-2, humans have been exposed to distinct SARS-CoV-2 antigens, either by infection with different variants, and/or vaccination. Population immunity is thus highly heterogeneous, but the impact of such heterogeneity on the effectiveness and breadth of the antibody-mediated response is unclear. We measured antibody-mediated neutralisation responses against SARS-CoV-2Wuhan, SARS-CoV-2α, SARS-CoV-2δ and SARS-CoV-2ο pseudoviruses using sera from patients with distinct immunological histories, including naive, vaccinated, infected with SARS-CoV-2Wuhan, SARS-CoV-2α or SARS-CoV-2δ, and vaccinated/infected individuals. We show that the breadth and potency of the antibody-mediated response is influenced by the number, the variant, and the nature (infection or vaccination) of exposures, and that individuals with mixed immunity acquired by vaccination and natural exposure exhibit the broadest and most potent responses. Our results suggest that the interplay between host immunity and SARS-CoV-2 evolution will shape the antigenicity and subsequent transmission dynamics of SARS-CoV-2, with important implications for future vaccine design

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task

© Verbruggen et al. Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis