In the era of large photometric surveys, the importance of automated and accurate classification is rapidly increasing. Specifically, the separation of resolved and unresolved sources in astronomical ...imaging is a critical initial step for a wide array of studies, ranging from Galactic science to large scale structure and cosmology. Here, we present our method to construct a large, deep catalog of point sources utilizing Pan-STARRS1 (PS1) 3π survey data, which consists of ∼3 × 109 sources with m 23.5 mag. We develop a supervised machine-learning methodology, using the random forest (RF) algorithm, to construct the PS1 morphology model. We train the model using ∼5 × 104 PS1 sources with HST COSMOS morphological classifications and assess its performance using ∼4 × 106 sources with Sloan Digital Sky Survey (SDSS) spectra and ∼2 × 108 Gaia sources. We construct 11 "white flux" features, which combine PS1 flux and shape measurements across five filters, to increase the signal-to-noise ratio relative to any individual filter. The RF model is compared to three alternative models, including the SDSS and PS1 photometric classification models, and we find that the RF model performs best. By number the PS1 catalog is dominated by faint sources (m 21 mag), and in this regime the RF model significantly outperforms the SDSS and PS1 models. For time-domain surveys, identifying unresolved sources is crucial for inferring the Galactic or extragalactic origin of new transients. We have classified ∼1.5 × 109 sources using the RF model, and these results are used within the Zwicky Transient Facility real-time pipeline to automatically reject stellar sources from the extragalactic alert stream.
Thirteen essays by scholars from seven countries discuss the political use and abuse of history in the recent decades with particular focus on Central and Eastern Europe (Hungary, Poland, Estonia, ...Moldova, Ukraine, Russia as case studies), but also includes articles on Germany, Japan and Turkey, which provide a much needed comparative dimension. The main focus is on new conditions of political utilization of history in post-communist context, which is characterized by lack of censorship and political pluralism. The phenomenon of history politics became extremely visible in Central and Eastern Europe in the past decade, and remains central for political agenda in many countries of the regions. Each essay is a case study contributing to the knowledge about collective memory and political use of history, offering a new theoretical twist. The studies look at actors (from political parties to individual historians), institutions (museums, Institutes of National remembrance, special political commissions), methods, political rationale and motivations behind this phenomenon.
Highlights • Examined correlational and intervention studies of health literacy-adherence relationship. • Health literacy positively associated with adherence to non-medication regimens. • Health ...literacy positively associated with adherence in cardiovascular disease patients. • Health literacy interventions increased patients health literacy and treatment adherence. • First meta-analysis to suggest directionality of health literacy-adherence relationship.
"Super-blooms" of cyanobacteria that produce potent and environmentally persistent biotoxins (microcystins) are an emerging global health issue in freshwater habitats. Monitoring of the marine ...environment for secondary impacts has been minimal, although microcystin-contaminated freshwater is known to be entering marine ecosystems. Here we confirm deaths of marine mammals from microcystin intoxication and provide evidence implicating land-sea flow with trophic transfer through marine invertebrates as the most likely route of exposure. This hypothesis was evaluated through environmental detection of potential freshwater and marine microcystin sources, sea otter necropsy with biochemical analysis of tissues and evaluation of bioaccumulation of freshwater microcystins by marine invertebrates. Ocean discharge of freshwater microcystins was confirmed for three nutrient-impaired rivers flowing into the Monterey Bay National Marine Sanctuary, and microcystin concentrations up to 2,900 ppm (2.9 million ppb) were detected in a freshwater lake and downstream tributaries to within 1 km of the ocean. Deaths of 21 southern sea otters, a federally listed threatened species, were linked to microcystin intoxication. Finally, farmed and free-living marine clams, mussels and oysters of species that are often consumed by sea otters and humans exhibited significant biomagnification (to 107 times ambient water levels) and slow depuration of freshwater cyanotoxins, suggesting a potentially serious environmental and public health threat that extends from the lowest trophic levels of nutrient-impaired freshwater habitat to apex marine predators. Microcystin-poisoned sea otters were commonly recovered near river mouths and harbors and contaminated marine bivalves were implicated as the most likely source of this potent hepatotoxin for wild otters. This is the first report of deaths of marine mammals due to cyanotoxins and confirms the existence of a novel class of marine "harmful algal bloom" in the Pacific coastal environment; that of hepatotoxic shellfish poisoning (HSP), suggesting that animals and humans are at risk from microcystin poisoning when consuming shellfish harvested at the land-sea interface.
•Selecting the appropriate machine learning method depends on the digital soil mappers’ purpose.•Artificial neural network is strong with large sample sizes, but is black box.•Cubist produces ...interpretable results; however, Random Forests’ results are semi interpretable.•R2 is more sensitive to outliers than RMSE.•Independent validation is necessary to evaluate the predictive power of the model.
Digital soil mapping (DSM) increasingly makes use of machine learning algorithms to identify relationships between soil properties and multiple covariates that can be detected across landscapes. Selecting the appropriate algorithm for model building is critical for optimizing results in the context of the available data. Over the past decade, many studies have tested different machine learning (ML) approaches on a variety of soil data sets. Here, we review the application of some of the most popular ML algorithms for digital soil mapping. Specifically, we compare the strengths and weaknesses of multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), Cubist, random forest (RF), and artificial neural networks (ANN) for DSM. These algorithms were compared on the basis of five factors: (1) quantity of hyperparameters, (2) sample size, (3) covariate selection, (4) learning time, and (5) interpretability of the resulting model. If training time is a limitation, then algorithms that have fewer model parameters and hyperparameters should be considered, e.g., MLR, KNN, SVR, and Cubist. If the data set is large (thousands of samples) and computation time is not an issue, ANN would likely produce the best results. If the data set is small (<100), then Cubist, KNN, RF, and SVR are likely to perform better than ANN and MLR. The uncertainty in predictions produced by Cubist, KNN, RF, and SVR may not decrease with large datasets. When interpretability of the resulting model is important to the user, Cubist, MLR, and RF are more appropriate algorithms as they do not function as “black boxes.” There is no one correct approach to produce models for predicting the spatial distribution of soil properties. Nonetheless, some algorithms are more appropriate than others considering the nature of the data and purpose of mapping activity.
Abstract
WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community ...developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.
Graphical Abstract
Graphical Abstract
WikiPathways enables research communities to collaborate on molecular pathway curation to create reusable, machine-readable pathway models. The pathway collections are freely available and integrated with many analysis tools and resources.
Abstract
One aspect of earthquake physics not adequately addressed is why some earthquakes generate thousands of aftershocks while other earthquakes generate few, if any, aftershocks. It also remains ...unknown why aftershock rates decay as ~1/time. Here, I show that these two are linked, with a dearth of aftershocks reflecting the absence of high-pressure fluid sources at depth, while rich and long-lasting aftershock sequences reflect tapping high-pressure fluid reservoirs that drive aftershock sequences. Using a physical model that captures the dominant aspects of permeability dynamics in the crust, I show that the model generates superior fits to observations than widely used empirical fits such as the Omori-Utsu Law, and find a functional relationship between aftershock decay rates and the tectonic ability to heal the co- and post-seismically generated fracture networks. These results have far-reaching implications, and can help interpret other observations such as seismic velocity recovery, attenuation, and migration.
Given the manifold ways that depression impairs Darwinian fitness, the persistence in the human genome of risk alleles for the disorder remains a much debated mystery. Evolutionary theories that view ...depressive symptoms as adaptive fail to provide parsimonious explanations for why even mild depressive symptoms impair fitness-relevant social functioning, whereas theories that suggest that depression is maladaptive fail to account for the high prevalence of depression risk alleles in human populations. These limitations warrant novel explanations for the origin and persistence of depression risk alleles. Accordingly, studies on risk alleles for depression were identified using PubMed and Ovid MEDLINE to examine data supporting the hypothesis that risk alleles for depression originated and have been retained in the human genome because these alleles promote pathogen host defense, which includes an integrated suite of immunological and behavioral responses to infection. Depression risk alleles identified by both candidate gene and genome-wide association study (GWAS) methodologies were found to be regularly associated with immune responses to infection that were likely to enhance survival in the ancestral environment. Moreover, data support the role of specific depressive symptoms in pathogen host defense including hyperthermia, reduced bodily iron stores, conservation/withdrawal behavior, hypervigilance and anorexia. By shifting the adaptive context of depression risk alleles from relations with conspecifics to relations with the microbial world, the Pathogen Host Defense (PATHOS-D) hypothesis provides a novel explanation for how depression can be nonadaptive in the social realm, whereas its risk alleles are nonetheless represented at prevalence rates that bespeak an adaptive function.
Summary
Ultrasound imaging of the lung and associated tissues may play an important role in the management of patients with COVID‐19–associated lung injury. Compared with other monitoring modalities, ...such as auscultation or radiographic imaging, we argue lung ultrasound has high diagnostic accuracy, is ergonomically favourable and has fewer infection control implications. By informing the initiation, escalation, titration and weaning of respiratory support, lung ultrasound can be integrated into COVID‐19 care pathways for patients with respiratory failure. Given the unprecedented pressure on healthcare services currently, supporting and educating clinicians is a key enabler of the wider implementation of lung ultrasound. This narrative review provides a summary of evidence and clinical guidance for the use and interpretation of lung ultrasound for patients with moderate, severe and critical COVID‐19–associated lung injury. Mechanisms by which the potential lung ultrasound workforce can be deployed are explored, including a pragmatic approach to training, governance, imaging, interpretation of images and implementation of lung ultrasound into routine clinical practice.
Abstract Obesity is a growing problem worldwide and is associated with a range of comorbidities, including cognitive dysfunction. In this review we will address the evidence that obesity and high fat ...feeding can lead to cognitive dysfunction. We will also examine the idea that obesity-associated systemic inflammation leads to inflammation within the brain, particularly the hypothalamus, and that this is partially responsible for these negative cognitive outcomes. Thus, obesity, and high fat feeding, lead to systemic inflammation and excess circulating free fatty acids. Circulating cytokines, free fatty acids and immune cells reach the brain at the level of the hypothalamus and initiate local inflammation, including microglial proliferation. This local inflammation likely causes synaptic remodeling and neurodegeneration within the hypothalamus, altering internal hypothalamic circuitry and hypothalamic outputs to other brain regions. The result is disruption to cognitive function mediated by regions such as hippocampus, amygdala, and reward-processing centers. Central inflammation is also likely to affect these regions directly. Thus, central inflammation in obesity leads not just to disruption of hypothalamic satiety signals and perpetuation of overeating, but also to negative outcomes on cognition.
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