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Rotational and vibrational energy relaxation (RER and VER) of N2O embedded in xenon and SF6 environments ranging from the gas phase to the liquid, including the supercritical regime, is studied at a molecular level. Calibrated intermolecular interactions from high-level electronic structure calculations, validated against experiments for the pure solvents, were used to carry out classical molecular dynamics simulations corresponding to experimental state points for near-critical isotherms. The computed RER rates in low-density solvents of krotXe=(3.67±0.25)×1010 s−1 M−1 and krotSF6=(1.25±0.12)×1011 s−1 M−1 compare well with the rates determined by the analysis of two-dimensional infrared experiments. Simulations find that an isolated binary collision description is successful up to solvent concentrations of ∼4 M. For higher densities, including the supercritical regime, the simulations do not correctly describe RER, probably due to the neglect of solvent–solute coupling in the analysis of the rotational motion. For VER, the near-quantitative agreement between simulations and pump–probe experiments captures the solvent density-dependent trends. 

This study examines the boundary layer wind profile and turbulence variables during the landfalls of Hurricanes Ida (2021) and Zeta (2020) using ground‐based Doppler radar observations and a nearby anemometer's wind measurements. While the radar sampled different parts of the hurricane circulation of the two cases, the observed maximum near‐surface wind and frictional velocity were similar. Radar‐retrieved wind profiles in both hurricanes revealed a boundary‐layer jet generally >1 km AGL, descending toward smaller radii as the hurricanes moved inland. A “knee‐like” structure in most wind profiles below the jet suggests an internal boundary layer (IBL) below 200 m and a log layer above it. Among the three methods for estimating near‐surface sustained winds from radar‐retrieved winds, leveraging low‐level IBL winds improves estimation accuracy and reduces the uncertainty to the selection of upstream surface roughness length. These findings offer valuable guidance for developing future probabilistic near‐surface wind products. 

The transition between the gas-, supercritical-, and liquid-phase behavior is a fascinating topic, which still lacks molecular-level understanding. Recent ultrafast two-dimensional infrared spectroscopy experiments suggested that the vibrational spectroscopy of N 2 O embedded in xenon and SF 6 as solvents provides an avenue to characterize the transitions between different phases as the concentration (or density) of the solvent increases. The present work demonstrates that classical molecular dynamics (MD) simulations together with accurate interaction potentials allows us to (semi-)quantitatively describe the transition in rotational vibrational infrared spectra from the P-/R-branch line shape for the stretch vibrations of N 2 O at low solvent densities to the Q-branch-like line shapes at high densities. The results are interpreted within the classical theory of rigid-body rotation in more/less constraining environments at high/low solvent densities or based on phenomenological models for the orientational relaxation of rotational motion. It is concluded that classical MD simulations provide a powerful approach to characterize and interpret the ultrafast motion of solutes in low to high density solvents at a molecular level. 

In recent years, technological advances in tissue preparation, high‐throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high‐resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets. 

The density dependence of rotational and vibrational energy relaxation (RER and VER) of the N 2 O ν 3 asymmetric stretch in dense gas and supercritical Xe and SF 6 solutions for near critical isotherms is measured by ultrafast 2DIR and infrared pump–probe spectroscopy. 2DIR analysis provides precise measurements of RER at all gas and supercritical solvent densities. An isolated binary collision (IBC) model is sufficient to describe RER for solvent densities ≤ ∼4M where rotational equilibrium is re-established in ∼1.5–2.5 collisions. N 2 O RER is ∼30% more efficient in SF 6 than in Xe due to additional relaxation pathways in SF 6 and electronic factor differences. 2DIR analysis revealed that N 2 O RER exhibits a critical slowing effect in SF 6 at near critical density ( ρ* ∼ 0.8) where the IBC model breaks down. This is attributable to the coupling of critical long-range density fluctuations to the local N 2 O free rotor environment. No such RER critical slowing is observed in Xe because IBC break down occurs much further from the Xe critical point. Many body interactions effectively shield N 2 O from these near critical Xe density fluctuations. The N 2 O ν 3 VER density dependence in SF 6 is different than that seen for RER, indicating a different coupling to the near critical environment than RER. N 2 O ν 3 VER is only about ∼7 times slower than RER in SF 6 . In contrast, almost no VER decay is observed in Xe over 200 ps. This VER solvent difference is due to a vibrationally resonant energy transfer pathway in SF 6 that is not possible for Xe. 

Abstract Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two‐dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over‐ and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three‐dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape‐based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open‐top light‐sheet (OTLS) microscopy of 102 cancer‐containing biopsies extractedex vivofrom the prostatectomy specimens of 46 patients. A deep learning‐based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape‐based nuclear features were extracted, and a nested cross‐validation scheme was used to train a supervised machine classifier based on 5‐year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape‐based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape‐based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision‐support tools. © 2023 The Pathological Society of Great Britain and Ireland. 

Prostate cancer prognostication largely relies on visual assessment of a few thinly sectioned biopsy specimens under a microscope to assign a Gleason grade group (GG). Unfortunately, the assigned GG is not always associated with a patient’s outcome in part because of the limited sampling of spatially heterogeneous tumors achieved by 2-dimensional histopathology. In this study, open-top light-sheet microscopy was used to obtain 3-dimensional pathology data sets that were assessed by 4 human readers. Intrabiopsy variability was assessed by asking readers to perform Gleason grading of 5 different levels per biopsy for a total of 20 core needle biopsies (ie, 100 total images). Intrabiopsy variability (Cohen k) was calculated as the worst pairwise agreement in GG between individual levels within each biopsy and found to be 0.34, 0.34, 0.38, and 0.43 for the 4 pathologists. These preliminary results reveal that even within a 1-mm-diameter needle core, GG based on 2-dimensional images can vary dramatically depending on the location within a biopsy being analyzed. We believe that morphologic assessment of whole biopsies in 3 dimension has the potential to enable more reliable and consistent tumor grading. 

Epibionts are organisms that utilize the exterior of other organisms as a living substratum. Many affiliate opportunistically with hosts of different species, but others specialize on particular hosts as obligate associates. We investigated a case of apparent host specificity between two barnacles that are epizoites of sea turtles and illuminate some ecological considerations that may shape their host relationships. The barnaclesChelonibia testudinariaandChelonibia caretta, though roughly similar in appearance, are separable by distinctions in morphology, genotype, and lifestyle. However, though each is known to colonize both green (Chelonia mydas) and hawksbill (Eretmochelys imbricata) sea turtles,C. testudinariais >5 times more common on greens, whileC. carettais >300 times more common on hawksbills. Two competing explanations for this asymmetry in barnacle incidence are either that the species’ larvae are spatially segregated in mutually exclusive host-encounter zones or their distributions overlap and the larvae behaviorally select their hosts from a common pool. We indirectly tested the latter by documenting the occurrence of adults of both barnacle species in two locations (SE Florida and Nose Be, Madagascar) where both turtle species co-mingle. For green and hawksbill turtles in both locations (Florida:n= 32 andn= 275, respectively; Madagascar:n= 32 andn= 125, respectively), we found thatC. testudinariaoccurred on green turtles only (percent occurrence – FL: 38.1%; MD: 6.3%), whereas the barnacleC. carettawas exclusively found on hawksbill turtles (FL: 82.2%; MD: 27.5%). These results support the hypothesis that the larvae of these barnacles differentially select host species from a shared supply. Physio-biochemical differences in host shell material, conspecific chemical cues, external microbial biofilms, and other surface signals may be salient factors in larval selectivity. Alternatively, barnacle presence may vary by host micro-environment. Dissimilarities in scute structure and shell growth between hawksbill and green turtles may promote critical differences in attachment modes observed between these barnacles. In understanding the co-evolution of barnacles and hosts it is key to consider the ecologies of both hosts and epibionts in interpreting associations of chance, choice, and dependence. Further studies are necessary to investigate the population status and settlement spectrum of barnacles inhabiting sea turtles.