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Terrestrial LiDAR scans (TLS) offer a rich data source for high-fidelity vegetation characterization, addressing the limitations of traditional fuel sampling methods by capturing spatially explicit distributions that have a significant impact on fire behavior. However, large volumes of complex, high resolution data are difficult to use directly in wildland fire models. In this study, we introduce a novel method that employs a voxelization technique to convert high-resolution TLS data into fine-grained reference voxels, which are subsequently aggregated into lower-fidelity fuel cells for integration into physics-based fire models. This methodology aims to transform the complexity of TLS data into a format amenable for integration into wildland fire models, while retaining essential information about the spatial distribution of vegetation. We evaluate our approach by comparing a range of aggregate geometries in simulated burns to laboratory measurements. The results show insensitivity to fuel cell geometry at fine resolutions (2–8 cm), but we observe deviations in model behavior at the coarsest resolutions considered (16 cm). Our findings highlight the importance of capturing the fine scale spatial continuity present in heterogeneous tree canopies in order to accurately simulate fire behavior in coupled fire-atmosphere models. To the best of our knowledge, this is the first study to examine the use of TLS data to inform fuel inputs to a physics based model at a laboratory scale.

 

As part of ongoing efforts to isolate biologically active fungal metabolites, a cyclic pentapeptide, sheptide A (1), was discovered from strain MSX53339 (Herpotrichiellaceae). The structure and sequence of1were determined primarily by analysis of 2D NMR and HRMS/MS data, while the absolute configuration was assigned using a modified version of Marfey’s method. In an in vitro assay for antimalarial potency,1displayed a pEC₅₀value of 5.75 ± 0.49 against malaria-causingPlasmodium falciparum. Compound1was also tested in a counter screen for general cytotoxicity against human hepatocellular carcinoma (HepG2), yielding a pCC₅₀value of 5.01 ± 0.45 and indicating a selectivity factor of ~6. This makes1the third known cyclic pentapeptide biosynthesized by fungi with antimalarial activity.

 

Hamiltonian formulations of quasilinear theory are presented for the cases of uniform and nonuniform magnetized plasmas. First, the standard quasilinear theory of Kennel and Engelmann (Kennel, Phys. Fluids, 1966, 9, 2377) is reviewed and reinterpreted in terms of a general Hamiltonian formulation. Within this Hamiltonian representation, we present the transition from two-dimensional quasilinear diffusion in a spatially uniform magnetized background plasma to three-dimensional quasilinear diffusion in a spatially nonuniform magnetized background plasma based on our previous work (Brizard and Chan, Phys. Plasmas, 2001, 8, 4762–4771; Brizard and Chan, Phys. Plasmas, 2004, 11, 4220–4229). The resulting quasilinear theory for nonuniform magnetized plasmas yields a 3 × 3 diffusion tensor that naturally incorporates quasilinear radial diffusion as well as its synergistic connections to diffusion in two-dimensional invariant velocity space (e.g., energy and pitch angle). 

Phosphorylation of Inhibitor of κB (IκB) proteins by IκB Kinase β (IKKβ) leads to IκB degradation and subsequent activation of nuclear factor κB transcription factors. Of particular interest is the IKKβ-catalyzed phosphorylation of IκBα residues Ser32 and Ser36 within a conserved destruction box motif. To investigate the catalytic mechanism of IKKβ,we performed pre–steady-state kinetic analysis of the phosphorylation of IκBα protein substrates catalyzed by constitutively active, human IKKβ. Phosphorylation of full-length IκBα catalyzed by IKKβ was characterized by a fast exponential phase followed by a slower linear phase. The maximum observed rate (kp) of IKKβ-catalyzed phosphorylation of IκBα was 0.32 s−1 and the binding affinity of ATP for the IKKβ IκBα complex (Kd) was 12 μM. Substitution of either Ser32 or Ser36 with Ala, Asp, or Cys reduced the amplitude of the exponential phase by approximately 2-fold. Thus, the exponential phase was attributed to phosphorylation of IκBα at Ser32 and Ser36, whereas the slower linear phase was attributed to phosphorylation of other residues. Interestingly, the exponential rate of phosphorylation of the IκBα(S32D) phosphomimetic amino acid substitution mutant was nearly twice that of WT IκBα and 4-fold faster than any of the other IκBα amino acid substitution mutants, suggesting that phosphorylation of Ser32 increases the phosphorylation rate of Ser36. These conclusions were supported by parallel experiments using GST-IκBα(1–54) fusion protein substrates bearing the first 54 residues of IκBα. Our data suggest a model wherein, IKKβ phosphorylates IκBα at Ser32 followed by Ser36 within a single binding event. 

A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.

 

Sequencing data—genomics, transcriptomics, epigenomics, proteomics, and metabolomics—have revolutionized biological research, enabling a more detailed study of processes, ranging from subcellular to evolutionary, that drive biological organization. These processes, collectively, are responsible for generating patterns of phenotypic variation and can operate over dramatically different timescales (milliseconds to billions of years). While researchers often study phenotypic variation at specific levels of biological organization to isolate processes operating at that particular scale, the varying types of sequence data, or ‘omics, can also provide complementary inferences to link molecular and phenotypic variation to produce an integrated view of evolutionary biology, ranging from molecular pathways to speciation. We briefly describe how ‘omics has been used across biological levels and then demonstrate the utility of integrating different types of sequencing data across multiple biological levels within the same study to better understand biological phenomena. However, single-time-point studies cannot evaluate the temporal dynamics of these biological processes. Therefore, we put forward temporal ‘omics as a framework that can better enable researchers to study the temporal dynamics of target processes. Temporal ‘omics is not infallible, as the temporal sampling regime directly impacts inferential ability. Thus, we also discuss the role the temporal sampling regime plays in deriving inferences about the environmental conditions driving biological processes and provide examples that demonstrate the impact of the sampling regime on biological inference. Finally, we forecast the future of temporal ‘omics by highlighting current methodological advancements that will enable temporal ‘omics to be extended across species and timescales. We extend this discussion to using temporal multi-omics to integrate across the biological hierarchy to evaluate and link the temporal dynamics of processes that generate phenotypic variation.

 

The synthesis, structure, and reactivity of a μ₃-SnH capped trinuclear nickel cluster, [Ni₃(dppm)₃(μ₃-H)(μ₃-SnH)], is reported. This complex undergoes oxidative addition chemistry, alkyne insertion, and subsequent hydrogenation.

 

A foundational assumption in paleomagnetism is that the Earth's magnetic field behaves as a geocentric axial dipole (GAD) when averaged over sufficient timescales. Compilations of directional data averaged over the past 5 Ma yield a distribution largely compatible with GAD, but the distribution of paleointensity data over this timescale is incompatible. Reasons for the failure of GAD include: (a) Arbitrary “selection criteria” to eliminate “unreliable” data vary among studies, so the paleointensity database may include biased results. (b) The age distribution of existing paleointensity data varies with latitude, so different latitudinal averages represent different time periods. (c) The time‐averaged field could be truly non‐dipolar. Here, we present a consistent methodology for analyzing paleointensity results and comparing time‐averaged paleointensities from different studies. We apply it to data from Plio/Pleistocene Hawai'ian igneous rocks, sampled from fine‐grained, quickly cooled material (lava flow tops, dike margins and scoria cones) and subjected to the IZZI‐Thellier technique; the data were analyzed using the Bias Corrected Estimation of Paleointensity method of Cych et al. (2021,https://doi.org/10.1029/2021GC009755), which produces accurate paleointensity estimates without arbitrarily excluding specimens from the analysis. We constructed a paleointensity curve for Hawai'i over the Plio/Pleistocene using the method of Livermore et al. (2018,https://doi.org/10.1093/gji/ggy383), which accounts for the age distribution of data. We demonstrate that even with the large uncertainties associated with obtaining a mean field from temporally sparse data, our average paleointensities obtained from Hawai'i and Antarctica (reanalyzed from Asefaw et al., 2021,https://doi.org/10.1029/2020JB020834) are not GAD‐like from 0 to 1.5 Ma but may be prior to that.