OBJECTIVES: To assess the construct validity of a modified single-item measure of bother due to side effects (the GP5 item) from the Functional Assessment of Chronic Illness Therapy (FACIT) system by comparing it to current symptomatic side effects from the Patient-Reported Outcomes of the Common Terminology Criteria for Adverse Events (PROCTCAE) reported by patients with rheumatoid arthritis (RA). METHODS: Through a cross-sectional, web-based survey we collected information on the frequency of symptomatic side effects and bother from side effects related to RA medications. We applied multiple correspondence analysis (MCA) to reduce 80 symptomatic side effects into key dimensions (≥5% of the total variance each). We then examined associations among key dimensions, individual items, the sum of current side effects, and the single-item bother measure using Spearman rho. RESULTS: A total of 560 patients participated in the online survey. Our scree plot showed a clear elbow point after the first dimension, indicating that keeping just one dimension captured the most meaningful information. This overall side effect burden score appeared to reflect a broad concept influenced by a variety of symptomatic side effects, each having only a negligible to weak impact. CONCLUSIONS: Our results may indicate that individuals have diverse experiences of side effects, allowing the global index to capture these variations, even when they differ across patients. Thus, a single-item burden measure to side effects can potentially serve as a useful summary indicator, shedding light on the impact of symptomatic side effects experienced by RA patients.

Microneedle‐based access to plant phloem enables sustainable energy harvesting and in situ biochemical sensing, but its performance is limited by defense responses such as callose deposition triggered by mechanical overstimulation of cell walls. This study presents a combined numerical–experimental framework for investigation how microneedle penetration dynamics influence transient stress fields within plant cellular tissue. A 3D finite element model of tomato stem tissue was reconstructed from SEM data, incorporating elastic–plastic cell walls, compressible intracellular fluid, and an augmented‐Lagrangian contact to simulate cell‐wall rupture and middle‐lamella delamination. Simulations reveal that lower insertion velocities significantly reduce stress transients and localize stress propagation, favoring single‐cell failure over multicell delamination. This effect results from a reduced rate of energy transfer into the tissue during insertion, limiting elastic energy accumulation and mechanical loading of mechanosensory pathways associated with callose secretion. Microneedle prototypes were fabricated and tested on tomato stems. Despite the quasistatic experimental velocities, displacement‐based comparison showed good agreement with numerical predictions. Both approaches confirmed that slower penetration shifts energy partitioning toward elastic storage and rheological dissipation. Overall, the developed mesoscale FEM framework reliably captures microneedle–tissue interactions and provides a transferable tool for optimizing minimally disruptive microneedle insertion strategies.

The formation of C-N bonds by Pd-catalyzed cross-coupling is one of the most widely practiced reactions in chemical synthesis. Typical reaction conditions involve either a strong base, which limits the scope of substrates, or an insoluble, inorganic base, which complicates running reactions on a large scale. Reaction conditions for C-N couplings with a base that is both mild and soluble are needed. We report the discovery of a combination of a phosphorinane ligand (<b>L147</b>) and a soluble carboxylate base, potassium 2-ethylhexanoate (K-2-EH), which leads to the coupling of a wide range of base-sensitive coupling partners. To explore the enhanced substrate scope of the reaction with this base and catalyst, we evaluated the scope using representative reactants selected from published partners, using chemical descriptors and clustering to ensure their chemical diversity. These results show that the combination of this phosphorinane ligand and K-2-EH can couple primary aliphatic amines, amides, sulfonamides, and heteroaromatic nucleophiles as well as acidic secondary nitrogen nucleophiles, such as arylamines, heteroarylamines, and amides, with a range of electrophiles. A side-by-side comparison to form selected coupling products in the presence of a range of previously reported bases and ligands showed that the products that decomposed under standard reaction conditions were stable with K-2-EH as a base. Finally, models of quantitative structure-reactivity relationships, trained on ligand screening data, were developed to help reveal the structural features that engender reactivity.

Loss landscapes are a powerful tool for understanding neural network optimization and generalization, yet traditional low-dimensional analyses often miss complex topological features. We present Landscaper, an open-source Python package for arbitrary-dimensional loss landscape analysis. Landscaper combines Hessian-based subspace construction with topological data analysis to reveal geometric structures such as basin hierarchy and connectivity. A key component is the Saddle-Minimum Average Distance (SMAD) for quantifying landscape smoothness. We demonstrate Landscaper's effectiveness across various architectures and tasks, including those involving pre-trained language models, showing that SMAD captures training transitions, such as landscape simplification, that conventional metrics miss. We also illustrate Landscaper's performance in challenging chemical property prediction tasks, where SMAD can serve as a metric for out-of-distribution generalization, offering valuable insights for model diagnostics and architecture design in data-scarce scientific machine learning scenarios.

Specific, designed, nonperiodic arrangements of gold nanocrystals that are 5 and 10 nm in diameter can be prepared with double-stranded DNA serving as a template (see drawing; A′ and B′ denote oligonucleotide sequences complementary to sequences A and B). The methods described should be applicable to nanocrystals composed of various materials.

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Importance: The Global Burden of Disease (GBD) reports widely used estimates of mortality and disability-adjusted life-years (DALYs) and related risk factors. However, the overall reliability of these estimates between GBD iterations has not been assessed. Objective: To evaluate the instability and inconsistency of GBD risk factor estimates for mortality and DALYs across GBD iterations. Data Sources: GBD risk factor collaboration estimates extracted from the published tables of GBD iterations and the Institute for Health Metrics and Evaluation repository. Study Selection: GBD risk factor collaboration publications published for 2010 through 2023. Data Extraction and Synthesis: Death and DALY estimates were manually extracted by 1 reviewer with independent validation of a random sample of 100 by another with no discrepancies. Risk factor naming was harmonized across iterations to ensure comparability; those with inconsistent definitions were excluded. Main Outcomes and Measures: Fluctuations were calculated for numbers of deaths and DALYs for each risk factor across GBD iterations during the study period (2010-2023) and between the original and subsequently revised estimates for each year (1990-2021). Differences were expressed as a ratio of the minimum to maximum range to the mean (R:M) and coefficient of variation. Detail analyses assessed diet and low physical activity. Point estimates were compared to the previous iterations' estimates 95% uncertainty intervals (95% UI) for GBD 2019, 2021, and 2023. Results: Across GBD iterations from 2010 to 2023, the median (range) R:M was 0.8 (0-3.8) for deaths, and 0.7 (0.1-3.3) for DALYs. Level 2 dietary and child and maternal malnutrition death estimates showed high instability (R:M >1 for 9 of 16 and 4 of 8 risks, respectively). When comparing original estimates with GBD 2019, 2021, and 2023 estimates for the same years, the median R:M was 0.4 (0-2.9) for both deaths and DALYs. The coefficient of variation was greater than 0.2 for 336 of 675 death estimates (50%). Specifically, 70% to 96% of point estimates for red meat, sugar-sweetened beverages, fruits, vegetables, and seafood omega-3 fatty acids in GBD 2021 fell outside the GBD 2019 95% UI. In GBD 2023, only diet high in trans fats had more than half of point estimates outside the GBD 2021 95% UI. Conclusions and Relevance: This meta-epidemiological assessment indicates that GBD estimates are substantially unstable, particularly for behavioral risks, making them unlikely to simply reflect genuine changes over time, and warranting caution in interpretation.

Methane (CH4) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions, however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH4 emissions at the facility-level. Near-field quantification of methane (CH4) emissions from oil and gas (O&amp;amp;G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian Plume (GP) and backward Lagrangian Stochastic (bLS), by comparing calculated CH4 emissions to controlled single-point emissions of between 0.4 and 5.2 kg CH4 h-1. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. Comparison shows the bLS approach showed better predictive performance with twice as many emission estimates were within a factor of two (FAC2) of the known emission rates compared to those calculated using the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows the lateral and vertical alignment of source and sensor plays a critical role in emission estimations as measurements made closer to the plume centerline and at a distance between 40 to 80 m downwind yielded the best FAC2 agreement. High wind meander degraded ability of both approaches to generate representative emissions particularly with the GP approach as it violates the modelling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While it is likely that the results presented here are suitable for informing leak detection technology in relatively flat unvegetated environments, it is currently unknown if these findings will be applicable in more vertiginous or heavily vegetated oil and gas producing regions of the Marcellus or Uinta Basins.

Loss landscapes are a powerful tool for understanding neural network optimization and generalization, yet traditional low-dimensional analyses often miss complex topological features. We present Landscaper, an open-source Python package for arbitrary-dimensional loss landscape analysis. Landscaper combines Hessian-based subspace construction with topological data analysis to reveal geometric structures such as basin hierarchy and connectivity. A key component is the Saddle-Minimum Average Distance (SMAD) for quantifying landscape smoothness. We demonstrate Landscaper's effectiveness across various architectures and tasks, including those involving pre-trained language models, showing that SMAD captures training transitions, such as landscape simplification, that conventional metrics miss. We also illustrate Landscaper's performance in challenging chemical property prediction tasks, where SMAD can serve as a metric for out-of-distribution generalization, offering valuable insights for model diagnostics and architecture design in data-scarce scientific machine learning scenarios.

There has been witness to remarkable progress in the ability to (a) grow well-controlled dots; (b) characterize dots on a near-atomic level; and (c) theoretically describe their electronic properties using not only continuum, but also atomistic approaches. This book focuses on the broad scientific and technological interest in semiconductor quantum dots. Papers on growth and self-assembly/self-organization, as well as characterization of semiconductor quantum dots based upon various materials systems, e.g., silicon-germanium, III-V materials, and II-VI materials, are included. Discussions of technological applications, ranging from biomedical technology, microelectronics and photonics, to more esoteric applications including quantum computing, are also featured. Topics include: Si and Ge dots; II-VI and other free-standing (colloidal) dots; near-field spectroscopy of quantum dots, wires and metals; organized dots and dot arrays; transport, coulomb blockade and metallic dots; optical spectroscopy and phonons; light-emitting quantum dots; and structural characterization and growth.

Nanotechnology is the creation and utilization of materials, devices, and systems through the control of matter on the nanometer-length scale, that is, at the level of atoms, molecules, and supramolecular structures. The essence of nanotechnology is the ability to work at these levels to generate larger structures with fundamentally new molecular organization. These nanostructures, made with building blocks understood from first principles, are the smallest human-made objects, and they exhibit novel physical, chemical, and biological properties and phenomena. The aim of nanotechnology is to learn to exploit these properties and efficiently manufacture and employ the structures. Control of matter on the nanoscale already plays an important role in scientific disciplines as diverse as physics, chemistry, materials science, biology, medicine, engineering, and computer simulation. For example, it has been shown that carbon nanotubes are ten times as strong as steel with one sixth of the weight, and that nanoparticles can target and kill cancer cells. Nanoscale systems have the potential to make supersonic transport cost- effective and to increase computer efficiency by millions of times. As understanding develops of the way natural and living systems are governed by molecular behavior at nanometer scale, and as this understanding begins to be felt in science and medicine, researchers seek systematic approaches for nanoscale-based manufacturing of human- made products.

The covariates included within the multivariable models fitted by each paper. This is a data microarray in which the studies run along the Y-axis and the covariates run along the X-axis. Rows and columns are ordered in descending order, based on the total number each covariate was included in the multivariable models fitted by each study. Where patterns were similar between studies or covariates, those studies or covariates were placed next to each other. (PDF 82 kb)

fMRI research is highly prolific but raises multiple concerns. Many competing statistical methods and respective packages are available using different assumptions, none of which applies equally well to all settings. However, the most fundamental concerns are not about the statistical machinery, but about issues of reproducibility, utility, and even construct validity. One can probe how much the field would benefit by statistical refinements, the conduct of larger studies and/or improved reproducibility practices. Alternatively, maybe fMRI research should largely be abandoned with focus shifting toward developing imaging methods with construct validity for granular neuronal activity and higher potential for clinical utility.