Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials

BACKGROUND: Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. METHODS: Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. FINDINGS: No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. INTERPRETATION: Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation. FUNDING: NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC)

A Comprehensive Approach to Improve Performance and Stability of State-of-the- Art Air Electrodes for Intermediate Temperature Reversible Cells: An Impedance Spectroscopy Analysis

Solid oxide fuel cells (SOFC) are devices for the transformation of chemical energy in electrical energy. SOFC appear very promising for their very high efficiency, in addition to the capability to work in reverse mode, which makes them suitable for integration in production units powered with renewables. Research efforts are currently addressed to find chemically and structurally stable materials, in order to improve performance stability during long-term operation. In this work, we examine different approaches for improving stability of two state-of-the-art perovskite materials, La0.6Sr0.4Co0.2Fe0.8O3-\uf064 (LSCF) and Ba0.5Sr0.5Co0.8Fe0.2O3-\uf064 (BSCF), very promising as air electrodes. Two different systems are considered: (i) LSCF and BSCF porous electrodes impregnated by a nano-sized La0.8Sr0.2MnO3-\uf064 layer and (ii) LSCF-BSCF composites with the two phases in different volume proportions. The study considers the results obtained by electrochemical impedance spectroscopy investigation, observing the polarisation resistance (Rp) of each system to evaluate performance in typical SOFC operating conditions. Furthermore, the behaviour of polarisation resistance under the effect of a net current load (cathodic) circulating for hundreds of hours is examined, as parameter to evaluate long-term performance stability

Intoxicación por Brugmansia arborea (Solanaceae) en un canino

Los animales de compañía pueden ser víctimas de intoxicaciones de diferente origen, constituyendo las plantas un 10-15% de las etiologías reportadas. Brugmansia arborea sin. Datura arborea (floripondio, trompeta de ángel) es un arbusto o árbol pequeño, perenne, con flores blancas, cónicas y pendulares, que se encuentra distribuido mundialmente, utilizándose como planta ornamental en jardines. El género Brugmansia posee un alto contenido de alcaloides del tropano como escopolamina, hiosciamina y atropina, que antagonizan las acciones de la acetilcolina y los receptores colinérgicos muscarínicos. En la bibliografía se encuentran documentados numerosos casos de intoxicación por esta planta en seres humanos, provocando un síndrome anticolinérgico, parálisis flácida, convulsiones y muerte. En el presente trabajo se describe por primera vez el diagnóstico de un caso de intoxicación por Brugmansia arborea en un perro de dos meses de edad. Se describen los signos clínicos observados y su correlación con los reportados en casos humanos

Allocating the Burdens of Climate Action: Consumption-Based Carbon Accounting and the Polluter-Pays Principle

Action must be taken to combat climate change. Yet, how the costs of climate action should be allocated among states remains a question. One popular answer—the polluter-pays principle (PPP)—stipulates that those responsible for causing the problem should pay to address it. While intuitively plausible, the PPP has been subjected to withering criticism in recent years. It is timely, following the Paris Agreement, to develop a new version: one that does not focus on historical production-based emissions but rather allocates climate burdens in proportion to each state’s annual consumption-based emissions. This change in carbon accounting results in a fairer and more environmentally effective principle for distributing climate duties

Smoking abstinence-related expectancies among American Indians, African Americans, and women: Potential mechanisms of disparities in cigarette use

Research has documented tobacco-related health disparities by race and gender. Prior research, however, has not examined expectancies about the smoking cessation process (i.e., abstinence-related expectancies) as potential contributors to tobacco-related disparities in special populations. This cross-sectional study compared abstinence-related expectancies between American Indian (n = 87), African American (n = 151), and White (n = 185) smokers, and between women (n = 231) and men (n = 270) smokers. Abstinence-related expectancies also were examined as mediators of race and gender relationships with motivation to quit and abstinence self efficacy. Results indicated that American Indians and African Americans were less likely than Whites to expect withdrawal effects, and more likely to expect that quitting would be unproblematic. African Americans also were less likely than Whites to expect smoking cessation interventions to be effective. Compared with men, women were more likely to expect withdrawal effects and weight gain. These expectancy differences mediated race and gender relationships with motivation to quit and abstinence self-efficacy. Findings emphasize potential mechanisms underlying tobacco-related health disparities among American Indians, African Americans, and women and suggest a number of specific approaches for targeting tobacco dependence interventions to these populations

Sentient Spaces: Intelligent Totem Use Case in the ECSEL FRACTAL Project

The objective of the FRACTAL project is to create a novel approach to reliable edge computing. The FRACTAL computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes). The node will also have the capability of learning how to improve its performance against the uncertainty of the environment. In such a context, this paper presents in detail one of the key use cases: an Internet-of-Things solution, represented by intelligent totems for advertisement and wayfinding services, within advanced ICT-based shopping malls conceived as a sentient space. The paper outlines the reference scenario and provides an overview of the architecture and the functionality of the demonstrator, as well as a roadmap for its development and evaluation

Fine sediment reduces vertical migrations of Gammarus pulex (Crustacea: Amphipoda) in response to surface water loss

Surface and subsurface sediments in river ecosystems are recognized as refuges that may promote invertebrate survival during disturbances such as floods and streambed drying. Refuge use is spatiotemporally variable, with environmental factors including substrate composition, in particular the proportion of fine sediment (FS), affecting the ability of organisms to move through interstitial spaces. We conducted a laboratory experiment to examine the effects of FS on the movement of Gammarus pulex Linnaeus (Crustacea: Amphipoda) into subsurface sediments in response to surface water loss. We hypothesized that increasing volumes of FS would impede and ultimately prevent individuals from migrating into the sediments. To test this hypothesis, the proportion of FS (1–2 mm diameter) present within an open gravel matrix (4–16 mm diameter) was varied from 10 to 20% by volume in 2.5% increments. Under control conditions (0% FS), 93% of individuals moved into subsurface sediments as the water level was reduced. The proportion of individuals moving into the subsurface decreased to 74% at 10% FS, and at 20% FS no individuals entered the sediments, supporting our hypothesis. These results demonstrate the importance of reducing FS inputs into river ecosystems and restoring FS-clogged riverbeds, to promote refuge use during increasingly common instream disturbances