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Significance: Laparoscopic surgery presents challenges in localizing oncological margins due to poor contrast between healthy and malignant tissues. Optical properties can uniquely identify various tissue types and disease states with high sensitivity and specificity, making it a promising tool for surgical guidance. Although spatial frequency domain imaging (SFDI) effectively measures quantitative optical properties, its deployment in laparoscopy is challenging due to the constrained imaging environment. Thus, there is a need for compact structured illumination techniques to enable accurate, quantitative endogenous contrast in minimally invasive surgery. Aim: We introduce a compact, two-camera laparoscope that incorporates both active stereo depth estimation and speckle-illumination SFDI (si-SFDI) to map profile-corrected, pixel-level absorption (μa), and reduced scattering (μ′s) optical properties in images of tissues with complex geometries. Approach: We used a multimode fiber-coupled 639-nm laser illumination to generate high-contrast speckle patterns on the object. These patterns were imaged through a modified commercial stereo laparoscope for optical property estimation via si-SFDI. Compared with the original si-SFDI work, which required ≥10 images of randomized speckle patterns for accurate optical property estimations, our approach approximates the DC response using a laser speckle reducer (LSR) and consequently requires only two images. In addition, we demonstrate 3D profilometry using active stereo from low-coherence RGB laser flood illumination. Sample topography was then used to correct for measured intensity variations caused by object height and surface angle differences with respect to a calibration phantom. The low-contrast RGB speckle pattern was blurred using an LSR to approximate incoherent white light illumination. We validated profile-corrected si-SFDI against conventional SFDI in phantoms with simple and complex geometries, as well as in a human finger in vivo time-series constriction study. Results: Laparoscopic si-SFDI optical property measurements agreed with conventional SFDI measurements when measuring flat tissue phantoms, exhibiting an error of 6.4% for absorption and 5.8% for reduced scattering. Profile-correction improved the accuracy for measurements of phantoms with complex geometries, particularly for absorption, where it reduced the error by 23.7%. An in vivo finger constriction study further validated laparoscopic si-SFDI, demonstrating an error of 8.2% for absorption and 5.8% for reduced scattering compared with conventional SFDI. Moreover, the observed trends in optical properties due to physiological changes were consistent with previous studies. Conclusions: Our stereo-laparoscopic implementation of si-SFDI provides a simple method to obtain accurate optical property maps through a laparoscope for flat and complex geometries. This has the potential to provide quantitative endogenous contrast for minimally invasive surgical guidance. 

Phase separation and percolation contribute to phase transitions of multivalent macromolecules. Contributions of percolation are evident through the viscoelasticity of condensates and through the formation of heterogeneous distributions of nano- and mesoscale pre-percolation clusters in sub-saturated solutions. Here, we show that clusters formed in sub-saturated solutions of FET (FUS-EWSR1-TAF15) proteins are affected differently by glutamate versus chloride. These differences on the nanoscale, gleaned using a suite of methods deployed across a wide range of protein concentrations, are prevalent and can be unmasked even though the driving forces for phase separation remain unchanged in glutamate versus chloride. Strikingly, differences in anion-mediated interactions that drive clustering saturate on the micron-scale. Beyond this length scale the system separates into coexisting phases. Overall, we find that sequence-encoded interactions, mediated by solution components, make synergistic and distinct contributions to the formation of pre-percolation clusters in sub-saturated solutions, and to the driving forces for phase separation. 

Drift and gene flow affect genetic diversity. Given that the strength of genetic drift increases as population size decreases, management activities have focused on increasing population size through preserving habitats to preserve genetic diversity. Few studies have empirically evaluated the impacts of drift and gene flow on genetic diversity.Kryptolebias marmoratus, henceforth ‘rivulus’, is a small killifish restricted to fragmented New World mangrove forests with gene flow primarily associated with ocean currents. Rivulus form distinct populations across patches, making them a well-suited system to test the extent to which habitat area, fragmentation and connectivity are associated with genetic diversity. Using over 1000 individuals genotyped at 32 microsatellite loci, high-resolution landcover data and oceanographic simulations with graph theory, we demonstrate that centrality (connectivity) to the metapopulation is more strongly associated with genetic diversity than habitat area or fragmentation. By comparing models with and without centrality standardized by the source population’s genetic diversity, our results suggest that metapopulation centrality is critical to genetic diversity regardless of the diversity of adjacent populations. While we find evidence that habitat area and fragmentation are related to genetic diversity, centrality is always a significant predictor with a larger effect than any measure of habitat configuration. 

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. 

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.