Trends in marijuana and heavy alcohol use by cigarette smoking status among US adults: An analysis of the 2002-2019 NSDUH.

BACKGROUND: Polysubstance use is associated with adverse health outcomes, yet little research has measured changes in polysubstance use. We aimed to 1) estimate trends in marijuana and heavy alcohol use by cigarette smoking and demographic subgroups, and 2) examine patient factors associated with concurrent use among adults who were smoking. METHODS: We conducted a repeated cross-sectional analysis of 687,225 non-institutionalized US adults ≥18 years from the 2002-2019 National Survey on Drug Use and Health. Participants were stratified into current, former, and never smoking groups. Main outcomes were prevalence of heavy alcohol use, marijuana use, and concurrent use of both substances. RESULTS: From 2002-2019, heavy alcohol use declined from 7.8 % to 6.4 %, marijuana use rose from 6.0 % to 11.8 %, and concurrent use of alcohol and marijuana remained stable. Among adults who were smoking from 2005 to 2019, higher education was associated with higher odds of heavy alcohol use, while older ages, female gender, non-White race/ethnicity, and government-provided health insurance were associated with lower odds. The odds of marijuana use decreased in females, older ages, and higher incomes while increasing in people with poorer health status, higher education, government-provided or no health insurance, and serious mental illness. Compared to White adults who were smoking, Black counterparts had higher odds of marijuana use (OR=1.23; 95 %CI: 1.15-1.29), while Hispanic (OR=0.68; 95 %CI: 0.63-0.72) and other racial/ethnic identities (OR=0.83; 95 %CI: 0.77-0.90) had lower odds. CONCLUSIONS: Our study suggests marijuana use might not be sensitive to changes in the use of tobacco and alcohol.

Evolution of laughter from play.

In this hypothesis, I discuss how laughter from physical play could have evolved to being induced via visual or even verbal stimuli, and serves as a signal to highlight incongruity that could potentially pose a threat to survival. I suggest how laughter's induction could have negated the need for physical contact in play, evolving from its use in tickling, to tickle-misses, and to taunting, and I discuss how the application of deep learning neural networks trained on images of spectra of a variety of laughter types from a variety of individuals or even species, could be used to determine such evolutionary pathways via the use of latent space exploration.

Single-step phase identification and phase locking for coherent beam combination using deep learning.

Coherent beam combination offers a solution to the challenges associated with the power handling capacity of individual fibres, however, the combined intensity profile strongly depends on the relative phase of each fibre. Optimal combination necessitates precise control over the phase of each fibre channel, however, determining the required phase compensations is challenging because phase information is typically not available. Additionally, the presence of continuously varying phase noise in fibre laser systems means that a single-step and high-speed correction process is required. In this work, we use a spatial light modulator to demonstrate coherent combination in a seven-beam system. Deep learning is used to identify the relative phase offsets for each beam directly from the combined intensity pattern, allowing real-time correction. Furthermore, we demonstrate that the deep learning agent can calculate the phase corrections needed to achieve user-specified target intensity profiles thus simultaneously achieving both beam combination and beam shaping.

Live imaging of laser machining via plasma deep learning.

Real-time imaging of laser materials processing can be challenging as the laser generated plasma can prevent direct observation of the sample. However, the spatial structure of the generated plasma is strongly dependent on the surface profile of the sample, and therefore can be interrogated to indirectly provide an image of the sample. In this study, we demonstrate that deep learning can be used to predict the appearance of the surface of silicon before and after the laser pulse, in real-time, when being machined by single femtosecond pulses, directly from camera images of the generated plasma. This demonstration has immediate impact for real-time feedback and monitoring of laser materials processing where direct observation of the sample is not possible.

Comparative Diversity and Functional Traits of Fungal Endophytes in Response to Elevated Mineral Content in a Mangrove Ecosystem.

This study investigates the impact of water quality, specifically elevated phosphate and zinc content, on the diversity and functional properties of mangrove fungal endophytes in two distinct mangrove forests. Mangrove plant performance is directly related to the presence of fungal leaf endophytes as these fungi could enhance plant health, resilience, and adaptability under stressed environmental conditions. Two distinct mangrove forest sites, one non-disturbed (ND) and one disturbed by aquaculture practices (D), were assessed for differences in water quality parameters. We further analyzed the fungal endophyte diversity associated with the leaves of a target host mangrove, Rhizophora mucronata Lamk., with the aim to elucidate whether fungal diversity and functional traits are linked to disturbances brought about by aquaculture practices and to characterize functional traits of selected fungal isolates with respect to phosphate (PO4) and zinc (Zn) solubilization. Contrary to expectations, the disturbed site exhibited a higher fungal diversity, challenging assumptions about the relationship between contamination and fungal community dynamics. Water quality, as determined by nutrient and mineral levels, emerged as a crucial factor in shaping both microbial community compositions in the phyllosphere of mangroves. From both sites, we isolated 188 fungal endophytes, with the ND site hosting a higher number of isolates and a greater colonization rate. While taxonomic diversity marginally differed (ND: 28 species, D: 29 species), the Shannon (H' = 3.19) and FAI (FA = 20.86) indices revealed a statistically significant increase in species diversity for fungal endophytes in the disturbed mangrove site as compared to the non-disturbed area (H' = 3.10, FAI = 13.08). Our chosen mangrove fungal endophytes exhibited remarkable phosphate solubilization capabilities even at elevated concentrations, particularly those derived from the disturbed site. Despite their proficiency in solubilizing zinc across a wide range of concentrations, a significant impact on their mycelial growth was noted, underscoring a crucial aspect of their functional dynamics. Our findings revealed a nuanced trade-off between mycelial growth and enzymatic production in fungal endophytes from ostensibly less contaminated sites, highlighting the relationship between nutrient availability and microbial activities. These insights provide a foundation for understanding the impact of anthropogenic pressures, specifically nutrient pollution, on mangrove-associated fungal endophytes.

Acoustic and plasma sensing of laser ablation via deep learning.

Monitoring laser ablation when using high power lasers can be challenging due to plasma obscuring the view of the machined sample. Whilst the appearance of the generated plasma is correlated with the laser ablation conditions, extracting useful information is extremely difficult due to the highly nonlinear processes involved. Here, we show that deep learning can enable the identification of laser pulse energy and a prediction for the appearance of the ablated sample, directly from camera images of the plasma generated during single-pulse femtosecond ablation of silica. We show that this information can also be identified directly from the acoustic signal recorded during this process. This approach has the potential to enhance real-time feedback and monitoring of laser materials processing in situations where the sample is obscured from direct viewing, and hence could be an invaluable diagnostic for laser-based manufacturing.

A Description and Analysis of Injury and Exercise in Collegiate Rodeo Athletes.

INTRODUCTION: Rodeo constitutes an exciting sporting spectacle enjoyed worldwide by competitors of all ages. College rodeo encompasses nine core events: bull riding, saddle bronc riding, bareback riding, team roping, tie-down roping, breakaway roping, steer wrestling, goat tying, and barrel racing. There is little research on rodeo athletes regarding training habits, injuries during competition and/or practice, or effective injury prevention strategies. The objectives of this study were to 1) characterize the injury profile of collegiate rodeo athletes, 2) describe training practices, and 3) determine if demographic or training factors influence injury risk. METHODS: Demographic, injury, and training data from 71 National Intercollegiate Rodeo Association members was collected via a SurveyMonkey survey. Data was analyzed to determine any association with injury risk, utilizing an ANOVA test for continuous categories and chi-square test for categorical variables. RESULTS: Competing in "roughstock" events (bull riding, saddle bronc riding, and bareback riding) and wearing protective equipment were associated with increased injury risk. Injuries mirrored previous studies of contact sport athletes. Student-athletes spent most of their training in activities directly related to their event but also engaged in exercise not related to their event for a considerable amount of time each week. CONCLUSION: Rodeo competitors constitute a versatile athletic cohort at high injury risk. These findings further define the injury profile of collegiate rodeo athletes and, for the first time, describe their training habits. This information will guide event preparation and rehabilitation of injured rodeo athletes.

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment.

The purpose of the review is to evaluate wearable sensor placement, their impact and validation of wearable sensors on analyzing gait, primarily the postural instability in people with stroke. Databases, namely PubMed, Cochrane, SpringerLink, and IEEE Xplore were searched to identify related articles published since January 2005. The authors have selected the articles by considering patient characteristics, intervention details, and outcome measurements by following the priorly set inclusion and exclusion criteria. From a total of 1077 articles, 142 were included in this study and classified into functional fields, namely postural stability (PS) assessments, physical activity monitoring (PA), gait pattern classification (GPC), and foot drop correction (FDC). The review covers the types of wearable sensors, their placement, and their performance in terms of reliability and validity. When employing a single wearable sensor, the pelvis and foot were the most used locations for detecting gait asymmetry and kinetic parameters, respectively. Multiple Inertial Measurement Units placed at different body parts were effectively used to estimate postural stability and gait pattern. This review article has compared results of placement of sensors at different locations helping researchers and clinicians to identify the best possible placement for sensors to measure specific kinematic and kinetic parameters in persons with stroke.

Single-frame 3D lensless microscopic imaging via deep learning.

Since the pollen of different species varies in shape and size, visualizing the 3-dimensional structure of a pollen grain can aid in its characterization. Lensless sensing is useful for reducing both optics footprint and cost, while the capability to image pollen grains in 3-dimensions using such a technique could be truly disruptive in the palynology, bioaerosol sensing, and ecology sectors. Here, we show the ability to employ deep learning to generate 3-dimensional images of pollen grains using a series of 2-dimensional images created from 2-dimensional scattering patterns. Using a microscope to obtain 3D Z-stack images of a pollen grain and a 520 nm laser to obtain scattering patterns from the pollen, a single scattering pattern per 3D image was obtained for each position of the pollen grain within the laser beam. In order to create a neural network to transform a single scattering pattern into different 2D images from the Z-stack, additional Z-axis information is required to be added to the scattering pattern. Information was therefore encoded into the scattering pattern image channels, such that the scattering pattern occupied the red channel, and a value indicating the position in the Z-axis occupied the green and blue channels. Following neural network training, 3D images were formed from collated generated 2D images. The volumes of the pollen grains were generated with a mean accuracy of ∼84%. The development of airborne-pollen sensors based on this technique could enable the collection of rich data that would be invaluable to scientists for understanding mechanisms of pollen production climate change and effects on the wider public health.

Motion control for laser machining via reinforcement learning.

Laser processing techniques such as laser machining, marking, cutting, welding, polishing and sintering have become important tools in modern manufacturing. A key step in these processes is to take the intended design and convert it into coordinates or toolpaths that are useable by the motion control hardware and result in efficient processing with a sufficiently high quality of finish. Toolpath design can require considerable amounts of skilled manual labor even when assisted by proprietary software. In addition, blind execution of predetermined toolpaths is unforgiving, in the sense that there is no compensation for machining errors that may compromise the quality of the final product. In this work, a novel laser machining approach is demonstrated, utilizing reinforcement learning (RL) to control and supervise the laser machining process. This autonomous RL-controlled system can laser machine arbitrary pre-defined patterns whilst simultaneously detecting and compensating for incorrectly executed actions, in real time.

Single step phase optimisation for coherent beam combination using deep learning.

Coherent beam combination of multiple fibres can be used to overcome limitations such as the power handling capability of single fibre configurations. In such a scheme, the focal intensity profile is critically dependent upon the relative phase of each fibre and so precise control over the phase of each fibre channel is essential. Determining the required phase compensations from the focal intensity profile alone (as measured via a camera) is extremely challenging with a large number of fibres as the phase information is obfuscated. Whilst iterative methods exist for phase retrieval, in practice, due to phase noise within a fibre laser amplification system, a single step process with computational time on the scale of milliseconds is needed. Here, we show how a neural network can be used to identify the phases of each fibre from the focal intensity profile, in a single step of ~ 10 ms, for a simulated 3-ring hexagonal close-packed arrangement, containing 19 separate fibres and subsequently how this enables bespoke beam shaping. In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for coherent beam combination.

Deep-Learning-Assisted Focused Ion Beam Nanofabrication.

Focused ion beam (FIB) milling is an important rapid prototyping tool for micro- and nanofabrication and device and materials characterization. It allows for the manufacturing of arbitrary structures in a wide variety of materials, but establishing the process parameters for a given task is a multidimensional optimization challenge, usually addressed through time-consuming, iterative trial-and-error. Here, we show that deep learning from prior experience of manufacturing can predict the postfabrication appearance of structures manufactured by focused ion beam (FIB) milling with >96% accuracy over a range of ion beam parameters, taking account of instrument- and target-specific artifacts. With predictions taking only a few milliseconds, the methodology may be deployed in near real time to expedite optimization and improve reproducibility in FIB processing.

Modelling of fibre laser cutting via deep learning.

Laser cutting is a materials processing technique used throughout academia and industry. However, defects such as striations can be formed while cutting, which can negatively affect the final quality of the cut. As the light-matter interactions that occur during laser machining are highly non-linear and difficult to model mathematically, there is interest in developing novel simulation methods for studying these interactions. Deep learning enables a data-driven approach to the modelling of complex systems. Here, we show that deep learning can be used to determine the scanning speed used for laser cutting, directly from microscope images of the cut surface. Furthermore, we demonstrate that a trained neural network can generate realistic predictions of the visual appearance of the laser cut surface, and hence can be used as a predictive visualisation tool.

The future of bone regeneration: integrating AI into tissue engineering.

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning.

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.

Comparing invasiveness of native and non-native species under changing climate in North-East India: ecological niche modelling with plant types differing in biogeographic origin.

We assess the invasive potential of Ageratum conyzoides, Hevea brasiliensis, Urena lobata and Imperata cylindrica differing in habit and biogeographic origin through ecological niche modelling in the context of the 2000 and 2050 climates of North-East (NE) India. Out of these four species, Ageratum conyzoides, Urena lobata and Imperata cylindrica are naturally occurring weed species and Hevea brasiliensis is a cultivated tree species. This study tries to address a basic question whether species with similarity in biogeographic origin may have some uniform strategy to succeed in invasion process. Ecological niche models predicted that Ageratum conyzoides (a shrub) and Hevea brasiliensis (a tree) of South American origin have greater potential to invade/distribute in NE region of India by 2050 than two other species, Urena lobata and Imperata cylindrica, of South-Asian origin. The latter two species show lower potential to invade in NE India in 2050 compared with their extent of distribution in 2000. A set of major contributing bioclimatic factors responsible for distribution of two South-Asian species (Urena and Imperata sp.) remain more or less constant between 2000 and 2050 climates. However, the distribution of Ageratum sp. and Hevea sp. with respect to two climate scenarios is attributed by two different sets of major bioclimatic factors. This indicates the robustness of the species to get adapted to different set of climatic variables over time.

Online Learning and Residents' Acquisition of Mechanical Ventilation Knowledge: Sequencing Matters.

OBJECTIVE: Rapid advancements in medicine and changing standards in medical education require new, efficient educational strategies. We investigated whether an online intervention could increase residents' knowledge and improve knowledge retention in mechanical ventilation when compared with a clinical rotation and whether the timing of intervention had an impact on overall knowledge gains. DESIGN: A prospective, interventional crossover study conducted from October 2015 to December 2017. SETTING: Multicenter study conducted in 33 PICUs across eight countries. SUBJECTS: Pediatric categorical residents rotating through the PICU for the first time. We allocated 483 residents into two arms based on rotation date to use an online intervention either before or after the clinical rotation. INTERVENTIONS: Residents completed an online virtual mechanical ventilation simulator either before or after a 1-month clinical rotation with a 2-month period between interventions. MEASUREMENTS AND MAIN RESULTS: Performance on case-based, multiple-choice question tests before and after each intervention was used to quantify knowledge gains and knowledge retention. Initial knowledge gains in residents who completed the online intervention (average knowledge gain, 6.9%; SD, 18.2) were noninferior compared with those who completed 1 month of a clinical rotation (average knowledge gain, 6.1%; SD, 18.9; difference, 0.8%; 95% CI, -5.05 to 6.47; p = 0.81). Knowledge retention was greater following completion of the online intervention when compared with the clinical rotation when controlling for time (difference, 7.6%; 95% CI, 0.7-14.5; p = 0.03). When the online intervention was sequenced before (average knowledge gain, 14.6%; SD, 15.4) rather than after (average knowledge gain, 7.0%; SD, 19.1) the clinical rotation, residents had superior overall knowledge acquisition (difference, 7.6%; 95% CI, 2.01-12.97;p = 0.008). CONCLUSIONS: Incorporating an interactive online educational intervention prior to a clinical rotation may offer a strategy to prime learners for the upcoming rotation, augmenting clinical learning in graduate medical education.

Real-time particle pollution sensing using machine learning.

Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion.

Predictive capabilities for laser machining via a neural network.

The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target. This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data.