Search for: All records

Abstract Study objectivesThis study aimed to compare YASA’s automated sleep staging to manual staging in the context of a multi-night experimental sleep restriction protocol. MethodsSeventy-five adults (58% female; 57% nonwhite) participated in up to seven nights of laboratory-based polysomnography measurements. The study involved one adaptation night, followed by three nights of normal sleep (9 h time in bed) and three nights of sleep restriction (5.5 h time in bed). Condition order was counterbalanced. Manual sleep staging was performed by a registered polysomnographic technician. Sleep data were exported and processed using Python 3.12, MNE 1.8.0, and YASA 0.6.5 for automated sleep staging. ResultsAcross 483 valid sleep nights, there was 82.9% overall agreement between YASA scoring and manual scoring. Stage-specific agreement was 40.3% (N1), 85.1% (N2), 86.9% (N3), and 78.7% (REM). Agreement was higher during normal sleep nights than sleep restriction nights, particularly for wake, N1, and REM sleep classifications. ConclusionsYASA-based staging exhibited good overall agreement with manual scoring during normal sleep nights. However, caution is needed when interpreting N1 estimates, as well as in interpreting data during short sleep nights. Brief summary Current knowledge/study rationaleSleep stage scoring is typically conducted manually by at least one experienced technician, but this process is laborious and subject to biases. This study investigated whether a machine-learning-based open tool—YASA—could automatically stage polysomnography data with acceptable accuracy. Study impactAcross 430,813 epochs, YASA showed acceptable accuracy in sleep staging, making it an efficient tool for the sleep community. However, caution is still needed for some applications, such as interpreting YASA’s N1 estimates as well as data from short sleep nights. 

Abstract BackgroundCancer phylogenies are key to understanding tumor evolution. However, due to the uncertainty in phylogenetic estimation, one typically infers many, equally-plausible phylogenies from bulk DNA sequencing data of tumors, hindering downstream analysis that relies on correct phylogenies. ResultsTo resolve this challenge, we introduce Sapling, a method to solve two variants of theBackbone Tree Inference from Readsproblem, which seeks a small set of backbone trees on a subset of mutations that collectively summarize the space of plausible cancer phylogenies. We prove that the problems are NP-hard. ConclusionsOn simulated and real data, we demonstrate that Sapling is capable of inferring high-quality backbone trees that adequately summarize the space of plausible cancer phylogenies. In addition, we demonstrate that Sapling is able to infer full-size trees with higher likelihoods than state-of-the-art methods. 

Abstract Key messageThe 2024 ENSO event advanced the timing of spring phenological phases of native shrubs significantly more than non-native shrubs and native trees in a temperate deciduous woodland fragment in Wisconsin, USA. This suggests that, as spring temperatures warm, shrubs will likely play a pivotal role in forest dynamics including contributing to an earlier onset to the growing period and an early start to CO₂assimilation. ContextThe 2023/2024 El Niño Southern Oscillation (ENSO) event brought warmer than average temperatures to the Midwest USA. This presented a unique opportunity to examine how short-term warming might impact the phenology of temperate deciduous forest vegetation. AimTo quantify the impact of an ENSO-driven warm spring on the phenology of temperate deciduous forest vegetation in order to assess how trees and shrubs respond to short-term temperature anomalies. MethodsSpring phenology was recorded twice weekly (2018–2024) on 5 dominant tree species and 5 native and 4 non-native shrub species, in a woodland fragment on the University of Wisconsin Milwaukee campus. In addition, phenological transition dates were extracted from daily Green Chromatic Coordinate (GCC) data from a PhenoCam installed at the site. ResultsIn 2024, the average spring (March–May) temperature (8.7 ± 0.57 °C) was significantly warmer than the 2018–2023 average (6.7 ± 0.28 °C). Compared to the average of the previous 6 years, the timing of budburst in 2024 occurred significantly (p < 0.001) earlier on DOY 76, 82, and 98 for native shrubs, non-native shrubs, and trees, representing advances of 20, 17, and 18 days, respectively. The advance was greater in shrubs than trees suggesting that any future advance to the start of the growing-season in temperate deciduous forests resulting from warmer spring temperatures will likely be driven by early leafing species like shrubs. Notably, the rise in GCC in 2024 (DOY 116) occurred following budburst, indicating that PhenoCam imagery may not fully capture early vegetation phenology. ConclusionEarly leafing shrubs, and in particular native species, were more sensitive to warmer temperatures early in the season than non-native shrubs and native trees. Therefore, as temperatures warm in the future, the onset of growth in temperate deciduous forests is likely to be driven by the early spring phenophases of early leafing species. 

Abstract BackgroundEngineered vasculature is essential for the biofabrication of functional tissue mimics. To fabricate engineered vasculature, three-dimensional (3D) bioprinting has emerged as a promising approach due to its ability to form perfusable structures with customized geometries. Sacrificial ink extrusion, where sacrificial inks are printed into a crosslinkable hydrogel precursor support bath, is a versatile bioprinting modality for fabricating interconnected perfusable networks. However, the fabrication of self-supporting structures with a vessel-like shell remains challenging using conventional sacrificial ink extrusion approaches. To enable the fabrication of self-supporting, perfusable networks, we developed a 3D bioprinting approach termed Gelation of Uniform Interfacial Diffusant in Embedded 3D Printing (GUIDE-3DP). This approach leverages the diffusion of crosslinking initiators from a printed sacrificial ink into a gel precursor support bath to generate branched, perfusable networks with precise control over channel inner and outer diameters. MethodsHere, we present an end-to-end protocol for fabricating self-supporting vascular-like networks using the GUIDE-3DP method. We describe methods for freeform print path design, support bath and sacrificial ink preparation, 3D printing of perfusable structures, and seeding of printed structures with endothelial cells. Through this protocol, perfusable structures with complex branching geometries can be designed, fabricated, and endothelialized. DiscussionTo highlight the ability of GUIDE-3DP to fabricate self-supporting, perfusable networks with complex geometries, we demonstrate the fabrication of three representative structures: (1) an interconnected retinal vasculature network, (2) a hierarchical branched vascular network, and (3) a dual-material capillary-like network. We further demonstrate the endothelialization of printed structures with one or two cell types via single- or dual-material printing. Beyond vascular-like networks, this protocol is readily adaptable to design and fabricate mimics of other perfusable structures in the human body. Clinical trial numberNot applicable. 

Abstract BackgroundEffective balance rehabilitation requires training at an appropriate level of exercise intensity given an individual’s needs and abilities. Typically balance intensity is assessed through in-clinic visual observation by physical therapists (PTs), which limits the ability to monitor and progress intensity during home-based components of training programs. The goal of this study was to train and evaluate machine learning models for estimating physical therapists’ perceived balance exercise intensity using data from full-body wearable sensors to support the development of home-based training exercise dosage monitoring. MethodsBalance exercise participants (n = 47) participated in a single-day balance training session where they were filmed performing static standing exercises at various levels of intensity. Kinematic data from 13 full-body wearable inertial measurement units (IMUs) and self-ratings of balance intensity were also collected. An additional cohort of PT participants (n = 42) was recruited to watch the videos of the balance exercise participants and provide ratings of balance intensity. The mean PT rating for each video was used as a ground truth (GT) label of balance intensity. We trained and evaluated Convolutional Neural Networks (CNN)-based models to predict balance intensity based on performance as captured through the IMUs. Model performance was evaluated by calculating the root-mean-square error (RMSE) of predications. A sensitivity analysis was also performed to assess the effect of the number of IMUs used on model performance. ResultsModels trained on orientation derived from all 13 IMUs achieved good predictive performance as indicated by a RMSE of 0.66 [0.62, 0.69], which was within the threshold defined by typical inter-rater variabilities between PTs (RMSE of 0.74 [0.72, 0.76]). Sensitivity analysis indicated that model performance stabilized at four sensors with the best performance corresponding to sensors placed on both thighs and the lower and upper back. ConclusionsFindings from this study indicated that balance intensity assessment can be achieved through wearable sensors and a CNN model, which could support the supervision and effectiveness of home-based balance rehabilitation. 

Abstract Purpose of ReviewLouisiana has one of the largest concentrations of petrochemical industry in the USA. Many studies have assessed patterns of industrial pollution and health in Louisiana; we aim to systematically review this evidence.We systematically searched PubMed, Web of Science, Embase, APA PsycInfo, and GreenFILE for peer-reviewed papers published 1999–2024 that reported geographical variation in health or industrial pollution, and/or tested for an association between the two in Louisiana. We used Covidence to support standardized review and extraction. Recent FindingsWe identified 2485 non-duplicate papers in our search; 53 met the inclusion criteria. Most reported quantitative findings. All studies of industrial pollution described air pollution (some also described other pollution). Studies described various health outcomes, including cancer, respiratory health, mortality, and COVID-19. Overall, people who lived closer to industrial activity had higher pollution exposure and worse health. Black and lower-income residents were exposed to more industrial activity than white and higher-income residents. Twenty-one studies assessed statistical associations between industrial pollution and health; many found an association. Twenty-one studies were quantitative and adjusted for confounding, 29 studies did not adjust for confounding (including qualitative studies), and three studies did not adjust for confounding and had authors with industry ties. SummaryEvidence suggests that there is a higher burden of air pollution and worse health outcomes in industrialized areas of Louisiana. While there was some evidence of significant associations between industrial pollution and health outcomes, research with larger sample sizes and improved pollution exposure measurements could be informative. 

Abstract BackgroundEstablishing continuous cell lines is hindered by limited molecular resolution of culture establishment and the transition to sustained proliferation. Here, proteome changes across passages were quantified in two independently derived California grunion (Leuresthes tenuis) embryonic-derived cell cultures, alongside morphology and growth metrics, to identify proteome dynamics associated with early establishment and subsequent stabilization of continuous proliferation under the standard conditions. ResultsMorphological analysis identified a reproducible transition window centered on passage 4 (P4), coincident with changes in growth trajectories and consolidation toward epithelial-like morphology in three replicate cell lines LtE-1, LtE-2, and LtE3. Two of these replicate lines (LtE1 and LtE2) were analyzed by quantitative cell population proteomics confirming this transition, consistent with establishment-associated selection and/or cell-state change in a mixed early culture. Marker proteins associated with epithelial identity increased in abundance while fibroblast-associated markers declined. Across the transition window and subsequent passages, LtE-1 and LtE-2 shared broad remodeling of biosynthetic, proteostatic, adhesion/ECM, and lipid-related functions, and recurrence-filtered interaction networks highlighted passage-linked module consolidation. However, LtE-1 and LtE-2 differed in their temporal trajectories (transient surges in LtE-1 versus sustained reinforcement in LtE-2). Because early cultures contained mixed morphologies and cell population proteomics integrates across subpopulations, these patterns are presented as proteome dynamics of establishment and candidate biosignatures rather than definitive cell-intrinsic mechanisms. ConclusionsPassage-resolved cell population proteomics in two replicate California grunion embryonic-derived cell cultures define a baseline of establishment-associated remodeling and identifies candidate biosignatures linked to a reproducible transition window and subsequent stabilization of proliferation. The resulting passage-resolved baseline motivates lineage-resolved validation to distinguish the relative contributions of selection, cell-state transitions, and media adaptation to the observed establishment trajectory. 

Abstract BackgroundDisturbance is a natural part of all ecosystems and often creates a balance of resistance-resilience among taxa. Grassland ecosystems, and in particular tallgrass prairie, are model systems for studying the outcomes of disturbance regime shifts because they are disturbance-dependent (i.e., maintained by fire, grazing, or climate). The effects of changing disturbance regimes, such as fire frequency in mesic grasslands, are often assessed based on one or a few taxa. However, to support diverse management goals, land managers must consider the effects of their choices on many taxa. In this study, we addressed this gap using a meta-analysis of 37 studies from tallgrass prairie to assess the effects of different fire frequencies on arthropods, birds, plants, small mammals, and soil properties (referred to here as ecological factors) and the interactive effects of fire frequency and grazing, another important disturbance. ResultsAs expected, the abundance and diversity of taxa were affected by different fire frequencies. However, the directionality of the change varied among taxonomic groups, indicating that there is no “one-size-fits-all” fire-management strategy in tallgrass prairie. Annual fires promoted small mammal abundance but decreased plant abundance and diversity. Meanwhile, intermediate fire frequencies promoted plant abundance but at the cost of plant diversity, arthropod abundance, and soil total carbon and nitrogen. Grazing promoted plant abundance while reducing arthropod and obligate grassland-bird abundance. ConclusionsOur study revealed research gaps, with critical data missing from small mammals, birds, soil properties, and eastern tallgrass prairie. However, quantifying the differential responses of ecological factors to fire frequency, as we did here, can inform tallgrass prairie management strategies, providing an example of the potential for land managers to manipulate disturbance frequencies to meet diverse management goals. We outline the important tradeoffs associated with management strategies using fire frequency and highlight the potential for fire to be used in unison with grazing to create a more heterogeneous landscape conducive to tallgrass prairie. Multi-taxonomic syntheses like this one are needed for land managers and ecologists to harness the power of prescribed fire in order to increase grassland sustainability and health worldwide. 

Abstract BackgroundClimate change is expected to alter fire return intervals in cold and wet forests in the northwestern United States. This coupled with an expected rise in prescribed fires to restore healthy forests, disproportionately increases risk to saplings of tree species adapted to colder and wetter environments that have low fire resistance. To assess this potential impact, we evaluated the impacts of increasing fire intensity onPicea engelmanniiandThuja plicatasapling physiology, morphology, and mortality. This was achieved using established pyro-ecophysiology experiments where saplings were subjected to controlled surface fires across a range of fire intensities and post-fire growth, physiology and mortality were assessed up to 7 months post-fire. ResultsIn this study we demonstrate that the probability of mortality in the saplings of these two conifer species displays a sigmoidal increase with increasing fire intensity. At fire radiative energy dosage levels < 0.6 MJ m⁻², the observed mortality in both species was lower than predicted by existing crown scorch-based models due to their limited sensitivity at small diameters. Prior to sapling death, chlorophyll fluorescence transiently recovers before a rapid decline, though the timing varies by species and fire intensity dosage. A new general sapling mortality model derived from 7 conifer species is presented. ConclusionsOur results provide predictive tools that managers could use to make informed decisions on the potential impacts of fires on conifer saplings growing in cold and wet environments. Results from both species suggest that chlorophyll fluorescence temporal trends could serve as a potential early warning indicator of fire-induced tree mortality, however, future work should explore whether similar responses are observable using remote sensing data from solar-induced chlorophyll fluorescence and assess potential mechanisms underlying this signal. The general sapling mortality model presented in this paper appears to provide an improved method of predicting conifer sapling mortality over existing approaches, however, research is needed to develop coefficients to adjust the model with tree age and environmental factors. Further studies could also explore whether phenotypic plasticity is driving observed tree responses to fire from plants grown from similar environments.