Search for: All records

Abstract Nucleation and subsequent growth of new aerosol particles in the atmosphere is a major source of cloud condensation nuclei and persistent large uncertainty in climate models. Newly formed particles need to grow rapidly to avoid scavenging by pre-existing aerosols and become relevant for the climate and air quality. In the continental atmosphere, condensation of oxygenated organic molecules is often the dominant mechanism for rapid growth. However, the huge variety of different organics present in the continental boundary layer makes it challenging to predict nanoparticle growth rates from gas-phase measurements. Moreover, recent studies have shown that growth rates of nanoparticles derived from particle size distribution measurements show surprisingly little dependency on potentially condensable vapors observed in the gas phase. Here, we show that the observed nanoparticle growth rates in the sub-10 nm size range can be predicted in the boreal forest only for springtime conditions, even with state-of-the-art mass spectrometers and particle sizing instruments. We find that, especially under warmer conditions, observed growth is slower than predicted from gas-phase condensation. We show that only a combination of simple particle-phase reaction schemes, phase separation due to non-ideal solution behavior, or particle-phase diffusion limitations can explain the observed lower growth rates. Our analysis provides first insights as to why atmospheric nanoparticle growth rates above 10 nm h⁻¹are rarely observed. Ultimately, a reduction of experimental uncertainties and improved sub-10 nm particle hygroscopicity and chemical composition measurements are needed to further investigate the occurrence of such a growth rate-limiting process. 

Abstract The cardiovascular system functions under continuous cyclic mechanical stretch, with disruptions in mechanical and biochemical signals contributing to disease progression. In cardiovascular disorders, these disruptions activate cardiac fibroblasts (CFs) and promote cellular senescence, yet it remains unclear whether mechanical stimuli alone can initiate this phenotype. Here, primary murine CFs are exposed to uniaxial stretch, and systematically varied mechanical parameters assessed their role in senescence induction. Loss of stretch magnitude and increase in frequency, mimicking a pathologic hypertrophy and fibrosis, led to a senescence phenotype, identified through cell cycle arrest, decreased lamin B expression, and DNA damage. Mechanically‐induced CF senescence depends on p53/p21, whereas senescence triggered by oxidative stress or lamin A/C mutation proceeded via p16. Notably, mechanically‐induced premature senescence is accompanied by reduced levels of the nuclear envelope protein emerin. These findings demonstrate that altered mechanical signals are sufficient to trigger premature senescence and implicate compromised nuclear integrity in the underlying mechanism. 

ABSTRACT Mediation analysis is widely utilized in neuroscience to investigate the role of brain image phenotypes in the neurological pathways from genetic exposures to clinical outcomes. However, it is still difficult to conduct mediation analyses with whole genome‐wide exposures and brain subcortical shape mediators due to several challenges including (i) large‐scale genetic exposures, that is, millions of single‐nucleotide polymorphisms (SNPs); (ii) nonlinear Hilbert space for shape mediators; and (iii) statistical inference on the direct and indirect effects. To tackle these challenges, this paper proposes a genome‐wide mediation analysis framework with brain subcortical shape mediators. First, to address the issue caused by the high dimensionality in genetic exposures, a fast genome‐wide association analysis is conducted to discover potential genetic variants with significant genetic effects on the clinical outcome. Second, the square‐root velocity function representations are extracted from the brain subcortical shapes, which fall in an unconstrained linear Hilbert subspace. Third, to identify the underlying causal pathways from the detected SNPs to the clinical outcome implicitly through the shape mediators, we utilize a shape mediation analysis framework consisting of a shape‐on‐scalar model and a scalar‐on‐shape model. Furthermore, the bootstrap resampling approach is adopted to investigate both global and spatial significant mediation effects. Finally, our framework is applied to the corpus callosum shape data from the Alzheimer's Disease Neuroimaging Initiative. 

Abstract Repairing a damaged body part is critical for the survival of any organism. In plants, tissue damage induces rapid responses that activate defense, regeneration and wound healing. While early wound signaling mediated by phytohormones, electrical signals and reactive oxygen species is well-characterized, the mechanisms governing the final stages of wound healing remain poorly understood. Here, we show that wounding in Arabidopsis leaves induces localized cooling, likely due to evaporative water loss, accompanied by the activation of cold-responsive genes. The subsequent disappearance of localized cooling and deactivation of cold-responsive genes serve as a quantitative marker of wound healing. Based on these observations, we developed a workflow by leveraging computer vision and deep learning to monitor the dynamics of wound healing. We found that CBFs transcription factors relay injury-induced cooling signal to wound healing. Thus, our work advances our understanding of tissue repair and provides a tool to quantify wound healing in plants. 

Label ranking is introduced as a conceptually new means for prioritizing experiments. Their simplicity, ease of application, and the use of ranking aggregation facilitate their ability to make accurate predictions with small datasets. 

Scholars have long questioned whether and how courts influence society. We contribute to this debate by investigating the ability of judicial decisions to shape issue attention and affect toward courts in media serving the LGBTQ+ community. To do so, we compiled an original database of LGBTQ+ magazine coverage of court cases over an extended period covering major decisions, includingLawrence v. Texas(2003),Goodridge v. Massachusetts Department of Public Health(2003), andLofton v. Secretary of Department of Children & Family Services(2004). We argue these cases influence the volume and tone of LGBTQ+ media coverage. Combining computational social science techniques with qualitative analysis, we find increased attention to same-sex marriage after the decisions inLawrence,Goodridge, andLofton,and the coalescence of discussions of courts around same-sex marriage afterLawrence.We also show how LGBTQ+ media informed readers about the political and legal implications of struggles over marriage equality.