Search for: All records

Abstract In many modern applications, discretely-observed data may be naturally understood as a set of functions. Functional data often exhibit two confounded sources of variability: amplitude (y-axis) and phase (x-axis). The extraction of amplitude and phase, a process known as registration, is essential in exploring the underlying structure of functional data in a variety of areas, from environmental monitoring to medical imaging. Critically, such data are often gathered sequentially with new functional observations arriving over time. Despite this, existing registration procedures do not sequentially update inference based on the new data, requiring model refitting. To address these challenges, we introduce a Bayesian framework for sequential registration of functional data, which updates statistical inference as new sets of functions are assimilated. This Bayesian model-based sequential learning approach utilizes sequential Monte Carlo sampling to recursively update the alignment of observed functions while accounting for associated uncertainty. Distributed computing significantly reduces computational cost relative to refitting the model using an iterative method such as Markov chain Monte Carlo on the full data. Simulation studies and comparisons reveal that the proposed approach performs well even when the target posterior distribution has a challenging structure. We apply the proposed method to three real datasets: (1) functions of annual drought intensity near Kaweah River in California, (2) annual sea surface salinity functions near Null Island, and (3) a sequence of repeated patterns in electrocardiogram signals. 

Montane snowpack in the Sierra Nevada provides critical water resources for ecological functions and downstream communities. Forest removal allows us to manage the snowpack in montane forests and mitigate the effect of climate on water resources. Little is known about the mid- to long-term effects that changing snowpack following forest disturbance has on tree re-growth, and how tree re-growth might in turn affect snowpack accumulation and melt. We use a 1-m resolution process-based snow model (SnowPALM) coupled with a stand-scale ecohydrological model (RHESSys) that resolves water, energy and carbon cycling to represent tree growth, and to quantify how trees and snowpack co-evolve following two disturbance scenarios (thinning and clearcutting) over a period of 40 years in a small 100 m x 234 m mid-elevation forested area in the Sierra Nevada, California. We first calculate the impact of forest disturbance on the snowpack assuming no tree regrowth and then we compare it with scenarios that include the feedback of trees regrowth on the snowpack. Without tree regrowth, snow accumulation and melt volume increase on average by roughly 5 % and 13 % following thinning and clearcutting, respectively. With tree regrowth, a regrowth rate of 0.75 and 1.15 m/decade are found for thinning and clearcutting, respectively, along with a decrease of melt volumes of 2.5 to 0.9 mm/decade, respectively. About 50 % of the snowmelt volume gains from forest thinning are lost after 40 years of regrowth, whereas only about 7 % is lost from clearcutting after the same period, which are largely explained by changes to canopy interception and sublimation. This proof-of-concept study is expected to shed light into the coevolution of montane forests and snowpack response to forest disturbance. 

Abstract Understanding photoreaction dynamics in crystals is important for predicting the dynamic property changes accompanying these photoreactions. In this work, we investigate the photoreaction dynamics ofp‐phenylenediacrylic acid dimethyl ester (p‐PDAMe) in single crystals that show reaction front propagation, in which the photoreaction proceeds heterogeneously from the edge to the center of the crystal. Moreover, we find thatp‐PDAMesingle crystals exhibit a distinctive crystal shape change from a parallelogram to a distorted shape resembling a fluttering flag, then to a rectangle as the photoreaction proceeds. Density functional theory calculations predict the crystal structure after the photoreaction, providing a reasonable explanation of the distinctive crystal shape change that results from the spatially heterogeneous photoreaction. These results prove that the spatially heterogeneous photoreaction dynamics have the ability to induce novel crystal shape changes beyond what would be expected based on the equilibrium reactant and product crystal shapes. 

Abstract The relationship between genotype and phenotype is often mediated by the environment. Moreover, gene-by-environment (GxE) interactions can contribute to variation in phenotypes and fitness. In the last 500 yr, house mice have invaded the Americas. Despite their short residence time, there is evidence of rapid climate adaptation, including shifts in body size and aspects of metabolism with latitude. Previous selection scans have identified candidate genes for metabolic adaptation. However, environmental variation in diet as well as GxE interactions likely impact body mass variation in wild populations. Here, we investigated the role of the environment and GxE interactions in shaping adaptive phenotypic variation. Using new locally adapted inbred strains from North and South America, we evaluated response to a high-fat diet, finding that sex, strain, diet, and the interaction between strain and diet contributed significantly to variation in body size. We also found that the transcriptional response to diet is largely strain-specific, indicating that GxE interactions affecting gene expression are pervasive. Next, we used crosses between strains from contrasting climates to characterize gene expression regulatory divergence on a standard diet and on a high-fat diet. We found that gene regulatory divergence is often condition-specific, particularly for trans-acting changes. Finally, we found evidence for lineage-specific selection on cis-regulatory variation involved in diverse processes, including lipid metabolism. Overlap with scans for selection identified candidate genes for environmental adaptation with diet-specific effects. Together, our results underscore the importance of environmental variation and GxE interactions in shaping adaptive variation in complex traits. 

Abstract Understanding surface stability becomes critical as 2D materials like SnSe are developed for piezoelectric and optical applications. SnSe thin films deposited by molecular beam epitaxy showed no structural changes after a two-year exposure to atmosphere, as confirmed by X-ray diffraction and Raman spectroscopy. X-ray photoelectron spectroscopy and reflectivity show a stable 3.5 nm surface oxide layer, indicating a self-arresting oxidative process. Resistivity measurements show an electrical response dominated by SnSe post-exposure. This work shows that SnSe films can be used in ambient conditions with minimal risk of long-term degradation, which is critical for the development of piezoelectric or photovoltaic devices. Graphical Abstract 

Multiple myeloma is the second most hematological cancer. RUVBL1 and RUVBL2 form a subcomplex of many chromatin remodeling complexes implicated in cancer progression. As an inhibitor specific to the RUVBL1/2 complex, CB-6644 exhibits remarkable anti-tumor activity in xenograft models of Burkitt’s lymphoma and multiple myeloma (MM). In this work, we defined transcriptional signatures corresponding to CB-6644 treatment in MM cells and determined underlying epigenetic changes in terms of chromatin accessibility. CB-6644 upregulated biological processes related to interferon response and downregulated those linked to cell proliferation in MM cells. Transcriptional regulator inference identified E2Fs as regulators for downregulated genes and MED1 and MYC as regulators for upregulated genes. CB-6644-induced changes in chromatin accessibility occurred mostly in non-promoter regions. Footprinting analysis identified transcription factors implied in modulating chromatin accessibility in response to CB-6644 treatment, including ATF4/CEBP and IRF4. Lastly, integrative analysis of transcription responses to various chemical compounds of the molecular signature genes from public gene expression data identified CB-5083, a p97 inhibitor, as a synergistic candidate with CB-6644 in MM cells, but experimental validation refuted this hypothesis.