Search

Research of the main university stakeholders has only been of a cross-sectional or short-term nature thereby limiting our understanding of how universities have evolved as a result of stakeholder influence. Indeed, neglect of stakeholders in strategic planning may result in both companies and universities becoming less successful and less competitive. For this reason, a temporal perspective was adopted to enable a consideration of events, their antecedents and subsequent effects thereby identifying emerging evolutionary trends and responding to them so that there can be appropriate decision making and accountability. This paper uses historical organisational studies to provide a longitudinal overview of internal and external stakeholder influence on university evolution and change from their foundations in the early Medieval period. Five university generations are described: Medieval, Humboldtian, Civic/Land Grant, Mass, and Stakeholder. This investigation reveals a number of strategic shifts in stakeholders as their voices have become increasingly prominent or have declined. Over time, the number of stakeholders have grown as their salience has been acknowledged through concepts such as the third mission; corporate social responsibility and helix structure; and, although some of the main stakeholders have remained constant such as learners and faculty, their influence has fluctuated.

The distribution of surface particulate matter (PM2.5) exhibits significant spatiotemporal patterns, especially with a spatial clustering effect. Therefore, resolving spatial characteristics is essential in the modeling process. This study proposes a space–time varying coefficients (STVC-LG) model by considering spatial effects from the perspective of eigenvector spatial filtering and incorporating spatial effects and temporal features into the LightGBM model to estimate ground PM2.5. A site-based cross-validation result of the estimated daily 1 km PM2.5 concentration in China from 2016 to 2020 shows that the proposed model offers high accuracy, an R2 of 0.88, and an RMSE of 12.21 μg/m3 (17% and 32% enhanced compared to the original LightGBM model). It uses a series of spatial eigenvectors to describe different spatial patterns. It then constructs spatial interaction terms by combining the eigenvectors and influencing variables, allowing the values of the variable to vary spatially with the eigenvectors. The obtained PM2.5, with a decreasing trend, had a clear distributional consistency with NO2 and SO2 according to the co-occurrence distribution maps, particularly in winter. In areas where high concentrations co-occur, both NO2 and SO2 concentrations are Granger-causing PM2.5 concentrations from a statistical perspective.

Abstract Global climate patterns fundamentally shape the distribution of species and ecosystems. For example, Bergmann’s rule predicts that homeothermic animals, including birds and mammals, inhabiting cooler climates are generally larger than close relatives from warmer climates. The modern world, however, lacks the comparative data needed to evaluate such macroecological rules rigorously. Here, we test for Bergmann’s rule in Mesozoic dinosaurs and mammaliaforms that radiated within relatively temperate global climate regimes. We develop a phylogenetic model that accounts for biases in the fossil record and allows for variable evolutionary dispersal rates. Our analysis also includes new fossil data from the extreme high-latitude Late Cretaceous Arctic Prince Creek Formation. We find no evidence for Bergmann’s rule in Mesozoic dinosaurs or mammaliaforms, the ancestors of extant homeothermic birds and mammals. When our model is applied to thousands of extant dinosaur (bird) and mammal species, we find that body size evolution remains independent of latitude. A modest temperature effect is found in extant, but not in Mesozoic, birds, suggesting that body size evolution in modern birds was influenced by Bergmann’s rule during Cenozoic climatic change. Our study provides a general approach for studying macroecological rules, highlighting the fossil record’s power to address longstanding ecological principles.

Forest canopy height (FCH) is crucial for monitoring forest structure and aboveground biomass. Light detecting and ranging (LiDAR), as a promising remote sensing technology, provides various forms of data for measuring and mapping FCH. Airborne laser scanning (ALS) could accurately measure FCH at the plot-level. Spaceborne lidar system (SLS) allows for global sampling of FCH at the footprint-level. However, ALS data has limited spatial coverage, while SLS data has relatively lower estimation accuracy. To this end, we proposed a two-step FCH mapping framework by combining ALS, SLS and auxiliary data. Firstly, using the ALS-derived FCH as reference, the SLS-derived relative height metrics were calibrated at the footprint-level using a regression method. Secondly, to further address the spatial discontinuities in SLS-derived FCH maps, a site-level FCH model was built using a weighted ensemble multi-machine learning model incorporating spatial effects (WEML_SE). The calibrated footprint-level calibration FCH model was used as a reference, and multiple remote sensing data metrics were selected and subjected to important variable selection. Specifically, a spatial adjacency matrix was established based on the spatial locations of SLS footprints, and spatial feature vectors were extracted. The result indicated that the correlation coefficient between the SLS-derived FCH and the ALS-derived FCH (r = 0.39–0.73, MRE=10.6–25.9 %, and RMSE=2.58–9.37 m) improved at footprint-level (r = 0.71–0.84, MRE=7.7–18.7 %, RMSE=1.96–7.68 m). Moreover, the WEML_SE exhibited better performance (r = 0.59–0.75, MRE=8.8–14.8 %, RMSE=2.12–5.4 m) compared to the model without incorporating spatial effects (r = 0.45–0.71, MRE=9.4–15.8 %, RMSE=2.28–5.89 m). This study emphasizes the advantages of integrating spaceborne and airborne LiDAR data to construct footprint-level estimation of FCH. The proposed WEML_SE model provides new possibilities for accurately generating wall-to-wall estimates of forest biomass.

Agricultural land use and temperature change can profoundly affect soil organic carbon (SOC), but their combined effects on regulating cropland SOC fractions over broad geographic scales remain poorly understood. Based on 871 topsoil (0–20 cm) samples collected from the Sichuan Basin of China, we used random forest model and partial correlations analysis to explore how the functionally distinct SOC fractions, including particulate and mineral-associated organic C (POC and MAOC), are affected by both agricultural land use and temperature over a large scale. The results show that POC accounts for 32.54 % of the total cropland SOC and soils with high SOC contents have larger proportions of POC. Cropland type is the dominant control of the two SOC fractions and regulates the impacts of other factors. The contents of soil POC and MAOC in dryland are much lower than that in paddy field and dryland-paddy rotation land. Significant correlations between mean annual temperature and the two SOC fractions are only found in dryland and dryland-paddy rotation land, while mean annual temperature has no impact on both POC and MAOC in paddy field. Meanwhile, the correlation coefficients between mean annual temperature and POC and MAOC for dryland decreased by 61.90 % and 69.23 %, respectively, and for dryland-paddy rotation land decreased by 28.00 % and 14.29 %, respectively, after removing correlations with net primary productivity. Additionally, correlation between mean annual temperature and POC is slightly higher than that between mean annual temperature and MAOC in dryland, while the reverse in dryland-paddy rotation land. These results suggest that the temperature effects on POC and MAOC are cropland type-dependent. To accurately predicting future cropland SOC change and reliably evaluate the potential contribution of cropland soil C sequestration, fraction-specific control mechanisms related to cropland type should be sufficiently considered in carbon models.

Chronic pelvic pain (CPP) affects 2.1-24% of women. Frequently, no underlying pathology is identified, and the pain is difficult to manage. Gabapentin is prescribed for CPP despite no robust evidence of efficacy. We performed a pilot trial in two UK centres to inform the planning of a future multicentre RCT to evaluate gabapentin in CPP management. Our primary objective was to determine levels of participant recruitment and retention. Secondary objectives included estimating potential effectiveness, acceptability to participants of trial methodology, and cost-effectiveness of gabapentin. Women with CPP and no obvious pelvic pathology were assigned to an increasing regimen of gabapentin (300-2700 mg daily) or placebo. We calculated the proportion of eligible women randomised, and of randomised participants who were followed up to six months. The analyses by treatment group were by intention-to-treat. Interviews were conducted to evaluate women's experiences of the trial. A probabilistic decision analytical model was used to estimate cost-effectiveness. Between September 2012-2013, 47 women (34% of those eligible) were randomised (22 to gabapentin, 25 to placebo), and 25 (53%) completed six-month follow-up. Participants on gabapentin had less pain (BPI difference 1.72 points, 95% CI:0.07-3.36), and an improvement in mood (HADS difference 4.35 points, 95% CI:1.97-6.73) at six months than those allocated placebo. The majority of participants described their trial experience favorably. At the UK threshold for willingness-to-pay, the probabilities of gabapentin or no treatment being cost-effective are similar. A pilot trial assessing gabapentin for CPP was feasible, but uncertainty remains, highlighting the need for a large definitive trial.

Summary: Efficient metabolism, or the means by which cells produce energy resources, is critical for proper effector function. Here, we present a protocol for examining the bioenergetics and mitochondrial fuel utilization of primary murine autoreactive immunocytes using cellular metabolism-modulating drugs. We describe steps for plate calibration, isolation of primary immunocytes, and Seahorse assay plate preparation. We then detail procedures for performing the XF Cell Mito Stress Test followed by bioenergetics calculations and statistics.For complete details on the use and execution of this protocol, please refer to Wilson et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

A key global change challenge is to significantly reduce the risks of alien taxa causing harmful impacts without compromising the rights of citizens. As part of efforts to address this challenge, South Africa promulgated comprehensive regulations and lists of alien taxa in 2014. In this paper, we review how the lists developed, changed over time, and how they have been implemented. As of March 2021, 560 taxa were listed under four broad regulatory categories, and between 2014 and 2020, almost 3000 permits were issued to regulate the continued use of listed taxa. The full lists of regulated taxa, permits issued, and corresponding regulations are available in the Supplementary material. A proposed standardised, transparent, and science-informed process to revise the regulatory lists is also presented – as of 30 April 2024, risk analyses have been developed for 140 taxa using the Risk Analysis for Alien Taxa (RAAT) framework and reviewed by an independent scientific body [the Alien Species Risk Analysis Review Panel (ASRARP)] with input from taxon-specific experts. These recommendations are being considered by an interdepartmental governmental decision-making body established in March 2023 [the Risk Analysis Review Committee (RARC)]. Finally, key issues with the listing of alien taxa in South Africa that remain to be resolved are presented. As South Africa’s regulatory framework continues to develop, the process of listing and regulating alien taxa will, we believe, become more transparent, consistent, and acceptable to stakeholders, and ultimately facilitate efforts to reduce the harmful impacts of alien taxa. Significance: The regulation of alien species is a major part of how South Africa addresses biological invasions. For this process to be effective, relevant stakeholders need to be engaged and involved. This paper outlines how species have been regulated in the past, provides regulatory lists in accessible formats, and analyses how the lists have changed over time. A transparent, science-informed process to update the regulatory lists is presented and progress to date reviewed. This process aims to engage interested and affected parties in efforts to preserve the benefits of alien species while reducing the harmful impacts of invasions. Open data set: https://zenodo.org/records/10809766

Abstract The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people’s homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals’ obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people’s use of dynamic food environments and links to diet and health.

Abstract Objective: The COVID-19 pandemic increased food insufficiency: a severe form of food insecurity. Drawing on an ecological framework, we aimed to understand factors that contributed to changes in food insufficiency from April to December 2020, in a large urban population hard hit by the pandemic. Design: We conducted internet surveys every 2 weeks in April–December 2020, including a subset of items from the Food Insecurity Experience Scale. Longitudinal analysis identified predictors of food insufficiency, using fixed effects models. Setting: Los Angeles County, which has a diverse population of 10 million residents. Participants: A representative sample of 1535 adults in Los Angeles County who are participants in the Understanding Coronavirus in America tracking survey. Results: Rates of food insufficiency spiked in the first year of the pandemic, especially among participants living in poverty, in middle adulthood and with larger households. Government food assistance from the Supplemental Nutrition Assistance Program was significantly associated with reduced food insufficiency over time, while other forms of assistance such as help from family and friends or stimulus funds were not. Conclusions: The findings highlight that during a crisis, there is value in rapidly monitoring food insufficiency and investing in government food benefits.

Iron is an essential mineral that supports biological functions like growth, oxygen transport, cellular function, and hormone synthesis. Insufficient dietary iron can lead to anemia and cause fatigue, cognitive impairment, and poor immune function. Animal-based foods provide heme iron, which is more bioavailable to humans, while plant-based foods typically contain less bioavailable non-heme iron. Edible insects vary in their iron content and may have heme or non-heme forms, depending on their diet. Edible insects have been proposed as a protein source that could address issues of food insecurity and malnutrition in low resource contexts; therefore, it is important to understand the bioavailability of iron from insect-based foods. In this study, we used Inductively Coupled Plasma and Mass Spectrometry (IPC-MS) and Caco-2 cell culture models to compare the soluble and bioavailable iron among five different lab-produced tempeh formulations featuring Tenebrio molitor (mealworm) with their non-fermented raw ingredient combinations. Finally, we compared the iron bioavailability of a mealworm tempeh with two sources of conventional beef (ground beef and sirloin steaks) and two commercially available plant-based meat alternatives. The results show that while plant-based meat alternatives had higher amounts of soluble iron, particularly in the Beyond Burger samples, the fermented mealworm-based tempeh had greater amounts of bioavailable iron than the other samples within the set. While all the samples presented varying degrees of iron bioavailability, all products within the sample set would be considered good sources of dietary iron.

Terrestrial gross primary productivity (GPP) is a critical part of land carbon fluxes. Accurately quantifying GPP in terrestrial ecosystems and understanding its spatiotemporal dynamics are essential for assessing the capability of vegetation to absorb carbon from the atmosphere. Nevertheless, traditional remote sensing estimation models often require complex parameters and data inputs, and they do not account for spatial effects resulting from the distribution of monitoring sites. This can lead to biased parameter estimation and unstable results. To address these challenges, we have raised a spatial autocorrelation light gradient boosting machine model (SA-LGBM) to enhance GPP estimation. SA-LGBM combines reflectance information from remote sensing observations with eigenvector spatial filtering (ESF) methods to create a set of variables that capture continuous spatiotemporal variations in plant functional types and GPP. SA-LGBM demonstrates promising results when compared to existing GPP products. With the inclusion of eigenvectors, we observed an 8.5% increase in R2 and a 20.8% decrease in RMSE. Furthermore, the residuals of the model became more random, reducing the inherent spatial effects within them. In summary, SA-LGBM represents the first attempt to quantify the impact of spatial autocorrelation and addresses the limitations of underestimation present in existing GPP products. Moreover, SA-LGBM exhibits favorable applicability across various vegetation types.

We present a simple and effective method to create highly entangled spin states on a faster timescale than that of the commonly employed one-axis twisting (OAT) model. We demonstrate that by periodically driving the Dicke Hamiltonian at a resonance frequency, the system effectively becomes a two-axis countertwisting Hamiltonian, which is known to quickly create Heisenberg limit scaled entangled states. For these states we show that simple quadrature measurements can saturate the ultimate precision limit for parameter estimation determined by the quantum Cramér-Rao bound. An example experimental realization of the periodically driven scheme is discussed with the potential to quickly generate momentum entanglement in a recently described experimental vertical cavity system. We analyze effects of collective dissipation in this vertical cavity system and find that our squeezing protocol can be more robust than the previous realization of OAT.

Estimating a quantum phase is a necessary task in a wide range of fields of quantum science. To accomplish this task, two well-known methods have been developed in distinct contexts, namely, Ramsey interferometry (RI) in atomic and molecular physics and quantum phase estimation (QPE) in quantum computing. We demonstrate that these canonical examples are instances of a larger class of phase estimation protocols, which we call reductive quantum phase estimation (RQPE) circuits. Here, we present an explicit algorithm that allows one to create an RQPE circuit. This circuit distinguishes an arbitrary set of phases with a smaller number of qubits and unitary applications, thereby solving a general class of quantum hypothesis testing to which RI and QPE belong. We further demonstrate a tradeoff between measurement precision and phase distinguishability, which allows one to tune the circuit to be optimal for a specific application.

The intricate interplay between climate and tectonics profoundly shapes landscapes over time frames surpassing 10 million years. Active tectonic processes and climatic shifts unsettle established drainage systems, instigating fragmentation or amalgamation of watersheds. These activities yield substantial transformation in surface hydrologic connectivity, thereby underlining the profound influence of these tectonic and climatic forces on the evolution of both landscape and hydrology. Such transformations within the hydrological landscape have direct implications for the evolution of aquatic species. As connections among aquatic habitats undergo reconfiguration, they incite shifts in species distribution and adaptive responses. These findings underscore the role of tectonics and climate in not only sculpting the physical landscape but also steering the course of biological evolution within these dynamically changing aquatic ecosystems relying on hydrologic connections. Despite the significance of these interactions, scholarly literature seldom examines alterations in hydrologic connectivity over tectonic, or orogen-scale, timescales. This study aims to bridge this gap, exploring changes in hydrologic connectivity over extended periods by simulating a continental rift system akin to the Rio Grande Rift, USA, subject to various tectonoclimatic scenarios. Multiple rift basins hosting large lakes, brought into existence by active tectonic extension, are further molded by tectonic extension and post-rift climatic changes. The study focuses on phenomena such as interbasin river breakthroughs and knickpoint generation, assembling a time-series of connectivity metrics based on stream network characteristics such as flow rate, flow distance, and captured drainage areas. We anticipate that the insights gleaned from this study will enhance our comprehension of the enduring impact of tectonic and climate processes on hydrologic connectivity and the subsequent evolution of aquatic species.

Abstract Objective: Comprehensive studies examining longitudinal predictors of dietary change during the coronavirus disease 2019 pandemic are lacking. Based on an ecological framework, this study used longitudinal data to test if individual, social and environmental factors predicted change in dietary intake during the peak of the coronavirus 2019 pandemic in Los Angeles County and examined interactions among the multilevel predictors. Design: We analysed two survey waves (e.g. baseline and follow-up) of the Understanding America Study, administered online to the same participants 3 months apart. The surveys assessed dietary intake and individual, social, and neighbourhood factors potentially associated with diet. Lagged multilevel regression models were used to predict change from baseline to follow-up in daily servings of fruits, vegetables and sugar-sweetened beverages. Setting: Data were collected in October 2020 and January 2021, during the peak of the coronavirus disease 2019 pandemic in Los Angeles County. Participants: 903 adults representative of Los Angeles County households. Results: Individuals who had depression and less education or who identified as non-Hispanic Black or Hispanic reported unhealthy dietary changes over the study period. Individuals with smaller social networks, especially low-income individuals with smaller networks, also reported unhealthy dietary changes. After accounting for individual and social factors, neighbourhood factors were generally not associated with dietary change. Conclusions: Given poor diets are a leading cause of death in the USA, addressing ecological risk factors that put some segments of the community at risk for unhealthy dietary changes during a crisis should be a priority for health interventions and policy.

ObjectiveGlycolytic inhibition via 2-deoxy-D-glucose (2DG) has potential therapeutic benefits for a range of diseases, including cancer, epilepsy, systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA), and COVID-19, but the systemic effects of 2DG on gene function across different tissues are unclear.MethodsThis study analyzed the transcriptional profiles of nine tissues from C57BL/6J mice treated with 2DG to understand how it modulates pathways systemically. Principal component analysis (PCA), weighted gene co-network analysis (WGCNA), analysis of variance, and pathway analysis were all performed to identify modules altered by 2DG treatment.ResultsPCA revealed that samples clustered predominantly by tissue, suggesting that 2DG affects each tissue uniquely. Unsupervised clustering and WGCNA revealed six distinct tissue-specific modules significantly affected by 2DG, each with unique key pathways and genes. 2DG predominantly affected mitochondrial metabolism in the heart, while in the small intestine, it affected immunological pathways.ConclusionsThese findings suggest that 2DG has a systemic impact that varies across organs, potentially affecting multiple pathways and functions. The study provides insights into the potential therapeutic benefits of 2DG across different diseases and highlights the importance of understanding its systemic effects for future research and clinical applications.

Intraoperative navigation is critical during spine surgery to ensure accurate instrumentation placement. From the early era of fluoroscopy to the current advancement in robotics, spinal navigation has continued to evolve. By understanding the variations in system protocols and their respective usage in the operating room, the surgeon can use and maximize the potential of various image guidance options more effectively. At the same time, maintaining navigation accuracy throughout the procedure is of the utmost importance, which can be confirmed intraoperatively by using an internal fiducial marker, as demonstrated herein. This technology can reduce the need for revision surgeries, minimize postoperative complications, and enhance the overall efficiency of operating rooms.

Abstract Purpose To determine the role of magnetic resonance imaging (MRI)-based metrics to quantify myocardial toxicity following radiotherapy (RT) in human subjects through review of current literature. Methods Twenty-one MRI studies published between 2011-2022 were identified from available databases. Patients received chest irradiation with/without other treatments for various malignancies including breast, lung, esophageal cancer, Hodgkin’s, and non-Hodgkin’s lymphoma. In 11 longitudinal studies, the sample size, mean heart dose, and follow-up times ranged from 10-81 patients, 2.0-13.9 Gy, and 0-24 months after RT (in addition to a pre-RT assessment), respectively. In 10 cross-sectional studies, the sample size, mean heart dose, and follow-up times ranged from 5-80 patients, 2.1-22.9 Gy, and 2-24 years from RT completion, respectively. Global metrics of left ventricle ejection fraction (LVEF) and mass/dimensions of cardiac chambers were recorded, along with global/regional values of T1/T2 signal, extracellular volume (ECV), late gadolinium enhancement (LGE), and circumferential/radial/longitudinal strain. Results LVEF tended to decline at >20 years follow-up and in patients treated with older RT techniques. Changes in global strain were observed after shorter follow-up (13±2 months) for concurrent chemoradiotherapy. In concurrent treatments with longer follow-up (8.3 years), increases in left ventricle (LV) mass index were correlated with LV mean dose. In pediatric patients, increases in LV diastolic volume were correlated with heart/LV dose at 2 years post-RT. Regional changes were observed earlier post-RT. Dose-dependent responses were reported for several parameters, including: increased T1 signal in high-dose regions, a 0.136% increase of ECV per Gy, progressive increase of LGE with increasing dose at regions receiving >30 Gy, and correlation between increases in LV scarring volume and LV mean/V10/V25 Gy dose. Conclusion Global metrics only detected changes over longer follow-up, in older RT techniques, in concurrent treatments, and in pediatric patients. In contrast, regional measurements detected myocardial damage at shorter follow-up and in RT treatments without concurrent treatment and had greater potential for dose-dependent response. The early detection of regional changes suggests the importance of regional quantification of RT-induced myocardial toxicity at early stages, before damage becomes irreversible. Further works with homogeneous cohorts are required to examine this matter.

Intraoperative neurophysiological monitoring (IONM) provides data on the state of neurological functionality. However, the current state of technology impedes the reliable and timely extraction and communication of relevant information. Advanced signal processing and machine learning (ML) technologies can develop a robust surveillance system that can reliably monitor the current state of a patient's nervous system and promptly alert the surgeons of any imminent risk. Various ML and signal processing tools can be utilized to develop a real-time, objective, multi-modal IONM based-alert system for spine surgery. Next generation systems should be able to obtain inputs from anesthesiologists on vital sign disturbances and pharmacological changes, as well as being capable of adapting patient baseline and model parameters for patient variability in age, gender, and health. It is anticipated that the application of automated decision guiding of checklist strategies in response to warning criteria can reduce human work-burden, improve accuracy, and minimize errors.

Abstract Extracellular vesicle (EV)‐carried miRNAs can influence gene expression and functional phenotypes in recipient cells. Argonaute 2 (Ago2) is a key miRNA‐binding protein that has been identified in EVs and could influence RNA silencing. However, Ago2 is in a non‐vesicular form in serum and can be an EV contaminant. In addition, RNA‐binding proteins (RBPs), including Ago2, and RNAs are often minor EV components whose sorting into EVs may be regulated by cell signaling state. To determine the conditions that influence detection of RBPs and RNAs in EVs, we evaluated the effect of growth factors, oncogene signaling, serum, and cell density on the vesicular and nonvesicular content of Ago2, other RBPs, and RNA in small EV (SEV) preparations. Media components affected both the intravesicular and extravesicular levels of RBPs and miRNAs in EVs, with serum contributing strongly to extravesicular miRNA contamination. Furthermore, isolation of EVs from hollow fiber bioreactors revealed complex preparations, with multiple EV‐containing peaks and a large amount of extravesicular Ago2/RBPs. Finally, KRAS mutation impacts the detection of intra‐ and extra‐vesicular Ago2. These data indicate that multiple cell culture conditions and cell states impact the presence of RBPs in EV preparations, some of which can be attributed to serum contamination.

Abstract This article begins by briefly examining the multitude of ways in which climate and climate change affect human health and wellbeing. It then proceeds to present a quick overview of how geospatial data, methods and tools are playing key roles in the measurement, analysis and modelling of climate change and its effects on human health. Geospatial techniques are proving indispensable for making more accurate assessments and estimates, predicting future trends more reliably, and devising more optimised climate change adaptation and mitigation plans.

The Differential Speckle Survey Instrument (DSSI) was relocated to the Astrophysical Research Consortium 3.5 m telescope at Apache Point Observatory (APO) in early 2022. Here we present results from the first year of observations along with an updated instrument description for DSSI at APO, including a detailed description of a new internal slit mask assembly used to measure the instrument plate scale from first principles. Astrometric precision for DSSI at APO during this time was measured to be 2.06 ± 0.11 mas, with a photometric precision of 0.14 ± 0.04 mag. Results of 40 resolved binary systems are reported, including two that were previously unknown to be binaries: HIP 7535 and HIP 9603. We also present updated orbital fits for two systems: HIP 93903 and HIP 100714. Finally, we report updated or confirmed dispositions for five Kepler Objects of Interest (KOIs) that were previously explored in Colton et al., using speckle imaging to discern common proper motions pairs from line of sight companions: KOI-270, KOI-959, KOI-1613, KOI-1962, and KOI-3214AB.

Summary: Aberrant metabolic demand is observed in immune/inflammatory disorders, yet the role in pathogenesis remains unclear. Here, we discover that in lupus, activated B cells, including germinal center B (GCB) cells, have remarkably high glycolytic requirement for survival over T cell populations, as demonstrated by increased metabolic activity in lupus-activated B cells compared to immunization-induced cells. The augmented reliance on glucose oxidation makes GCB cells vulnerable to mitochondrial ROS-induced oxidative stress and apoptosis. Short-term glycolysis inhibition selectively reduces pathogenic activated B in lupus-prone mice, extending their lifespan, without affecting T follicular helper cells. Particularly, BCMA-expressing GCB cells rely heavily on glucose oxidation. Depleting BCMA-expressing activated B cells with APRIL-based CAR-T cells significantly prolongs the lifespan of mice with severe autoimmune disease. These results reveal that glycolysis-dependent activated B and GCB cells, especially those expressing BCMA, are potentially key lupus mediators, and could be targeted to improve disease outcomes.

In order to effectively control the microstructure of steel strip during manufacture it is vital that techniques be developed to monitor microstructural changes in the steel at high temperatures. Electromagnetic non-destructive testing is increasingly being used in these applications as it is non-contact, robust, relatively inexpensive and has the potential to provide real-time feedback to control the manufacturing process. However, if the data produced by these systems is to be fully exploited it is necessary to accurately measure the magnetic properties of steel grades of interest at temperatures up to the Curie point. This work details measurements carried out to assess the magnetic properties of selected steels grades, supplied by Tata Steel, up to the Curie temperature using a novel test rig based on a modified Epstein frame designed by the University of Manchester with high temperature trials at the University of Warwick. Several steel grades are studied with the full magnetic hysteresis (BH) loops reported during heating and cooling, along with extracted magnetic parameters including coercivity, magnetic saturation and magnetic permeability. The magnetic parameters of the selected steel grades are compared and implications of the results on electromagnetic monitoring of steel strip during manufacture considered. Results show the developed system has the potential to provide valuable data to inform online electromagnetic monitoring systems.

Weight savings in mobility and transport are mandatory in order to reduce CO 2 emissions and energy consumption. The steel industry offers weight saving solutions by a growing portfolio of Advanced High Strength Steel (AHSS) products. AHSS owe their strength to their largely refined and complex microstructures, containing multiple metallurgical phases. Optimal control of the thermo-mechanical processing of AHSS requires inline sensors for real-time monitoring of evolution and consistency of microstructure and material properties. To coordinate and accelerate European development activities in this domain, the project ”Online Microstructure Analytics (OMA)” was established in 2019, constituting of a consortium of 14 specialised research organisations. The EU-funded OMA project, with a total budget exceeding 6 MEU, focuses on inline sensing techniques to monitor the consistency and homogeneity of microstructure, texture and mechanical properties for automotive steels and in particular for AHSS.

Abstract Natural infection as well as vaccination with live or attenuated viruses elicit tissue resident, CD8+ memory T cell (Trm) response. Trm cells so elicited act quickly upon reencounter with the priming agent to protect the host. These Trm cells express a unique molecular signature driven by the master regulators—Runx3 and Hobit. We previously reported that intranasal instillation of a subunit vaccine in a prime boost vaccination regimen installed quick-acting, CD8+ Trm cells in the lungs that protected against lethal vaccinia virus challenge. It remains unexplored whether CD8+ Trm responses so elicited are driven by a similar molecular signature as those elicited by microbes in a real infection or by live, attenuated pathogens in conventional vaccination. We found that distinct molecular signatures distinguished subunit vaccine-elicited lung interstitial CD8+ Trm cells from subunit vaccine-elicited CD8+ effector memory and splenic memory T cells. Nonetheless, the transcriptome signature of subunit vaccine elicited CD8+ Trm resembled those elicited by virus infection or vaccination. Clues to the basis of tissue residence and function of vaccine specific CD8+ Trm cells were found in transcripts that code for chemokines and chemokine receptors, purinergic receptors, and adhesins when compared to CD8+ effector and splenic memory T cells. Our findings inform the utility of protein-based subunit vaccination for installing CD8+ Trm cells in the lungs to protect against respiratory infectious diseases that plague humankind.

Scale conversion between DEMs is an important issue in geomorphometry. There are many mature studies on the generation of low-resolution(LR) DEMs from high-resolution(HR) DEMs. However, as an important and convenient means of obtaining HR DEMs, traditional super resolution (SR) methods have shown insufficient consideration of the terrain features embedded in DEMs. Therefore, this article investigates the combination of terrain features and the use of convolutional neural networks (CNN) to reconstruct HR DEMs, and proposes a multi-terrain feature-based deep CNN for super-resolution(SR) DEMs (MTF-SR). In the experiments, from the perspective of vector and raster terrain features, we fuse raster terrain features in the input and loss functions, and fuse vector terrain features in the optimization of the output of the model. The results show that the MTF-SR model has a 30–50 % reduction in mean absolute error (MAE) compared with interpolation methods, has the lowest slope and aspect error and has a 10 to 30 % improvement in streamline matching rate (SMR). These results point to the advantages of the method in overall elevation accuracy and the preservation of terrain features.

OBJECTIVES:. Capacity planning of ICUs is essential for effective management of health safety, quality of patient care, and the allocation of ICU resources. Whereas ICU length of stay (LOS) may be estimated using patient information such as severity of illness scoring systems, ICU census is impacted by both patient LOS and arrival patterns. We set out to develop and evaluate an ICU census forecasting algorithm using the Multiple Organ Dysfunction Score (MODS) and the Nine Equivalents of Nursing Manpower Use Score (NEMS) for capacity planning purposes. DESIGN:. Retrospective observational study. SETTING:. We developed the algorithm using data from the Medical-Surgical ICU (MSICU) at University Hospital, London, Canada and validated using data from the Critical Care Trauma Centre (CCTC) at Victoria Hospital, London, Canada. PATIENTS:. Adult patient admissions (7,434) to the MSICU and (9,075) to the CCTC from 2015 to 2021. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. We developed an Autoregressive integrated moving average time series model that forecasts patients arriving in the ICU and a survival model using MODS, NEMS, and other factors to estimate patient LOS. The models were combined to create an algorithm that forecasts ICU census for planning horizons ranging from 1 to 7 days. We evaluated the algorithm quality using several fit metrics. The root mean squared error ranged from 2.055 to 2.890 beds/d and the mean absolute percentage error from 9.4% to 13.2%. We show that this forecasting algorithm provides a better fit when compared with a moving average or a time series model that directly forecasts ICU census. Additionally, we evaluated the performance of the algorithm using data during the global COVID-19 pandemic and found that the error of the forecasts increased proportionally with the number of COVID-19 patients in the ICU. CONCLUSIONS:. It is possible to develop accurate tools to forecast ICU census. This type of algorithm may be important to clinicians and managers when planning ICU capacity as well as staffing and surgical demand planning over a short time horizon.

In this study, a small sample of patients’ neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during spine surgery. Notably, this technology, when operated by neurophysiologists and surgeons familiar with proper alarm criteria, is capable of detecting neurological deficits. However, non-surgical factors, such as volatile anesthetics like sevoflurane, can negatively influence robust IONM signal generation. While sevoflurane has been shown to affect the latency and amplitude of somatosensory evoked potential (SSEP), a more complex and nuanced analysis of the SSEP waveform has not been performed. In this study, signal processing and machine learning techniques were used to more intricately characterize and predict SSEP waveform changes as a function of varying end-tidal sevoflurane concentration. With data from ten patients who underwent spinal procedures, features describing the SSEP waveforms were generated using principal component analysis (PCA), phase space curves (PSC), and time-frequency analysis (TFA). A minimum redundancy maximum relevance (MRMR) feature selection technique was then used to identify the most important SSEP features associated with changing sevoflurane concentrations. Once the features carrying the maximum amount of information about the majority of signal waveform variability were identified, ML models were used to predict future changes in SSEP waveforms. Linear regression, regression trees, support vector machines, and neural network ML models were then selected for testing. Using SSEP data from eight patients, the models were trained using a range of features selected during MRMR calculations. During the training phase of model development, the highest performing models were identified as support vector machines and regression trees. After identifying the highest performing models for each nerve group, we tested these models using the remaining two patients’ data. We compared the models’ performance metrics using the root mean square error values (RMSEs). The feasibility of the methodology described provides a general framework for the applications of machine learning strategies to further delineate the effects of surgical and non-surgical factors affecting IONM signals.

The Lamc2jeb junctional epidermolysis bullosa (EB) mouse model has been used to demonstrate that significant genetic modification of EB symptoms is possible, identifying as modifiers Col17a1 and six other quantitative trait loci, several with strong candidate genes including dystonin (Dst/Bpag1). Here, CRISPR/Cas9 was used to alter exon 23 in mouse skin specific isoform Dst-e (Ensembl GRCm38 transcript name Dst-213, transcript ID ENSMUST00000183302.5, protein size 2639AA) and validate a proposed arginine/glutamine difference at amino acid p1226 in B6 versus 129 mice as a modifier of EB. Frame shift deletions (FSD) in mouse Dst-e exon 23 (Dst-eFSD/FSD) were also identified that cause mice carrying wild-type Lamc2 to develop a phenotype similar to human EB simplex without dystonia musculorum. When combined, Dst-eFSD/FSD modifies Lamc2jeb/jeb (FSD+jeb) induced disease in unexpected ways implicating an altered balance between DST-e (BPAG1e) and a rarely reported rodless DST-eS (BPAG1eS) in epithelium as a possible mechanism. Further, FSD+jeb mice with pinnae removed are found to provide a test bed for studying internal epithelium EB disease and treatment without severe skin disease as a limiting factor while also revealing and accelerating significant nasopharynx symptoms present but not previously noted in Lamc2jeb/jeb mice.