Oxygen Profiles Research Articles

Evaluation of leg symmetry in muscle oxygen saturation during submaximal to maximal cycling exercise.

It is unclear whether physiological responses, such as muscle oxygen saturation (SmO2), can be considered symmetrical during cycling. This knowledge has important practical implications for both training and performance assessment. The aim of this study was to determine whether oxygenation profiles in the three active muscles of both legs were symmetrical during cycling at different intensities. Twenty-six trained cyclists and triathletes completed a graded exercise test (GXT) and an 8-min functional threshold power estimation test (8MTT) on a cycle ergometer over two nonconsecutive days. SmO2 was bilaterally assessed using NIRS technology in the vastus lateralis, gastrocnemius medialis, and tibialis anterior. Symmetry was compared between legs in both tests, and reliability and agreement between the measurements were quantified. The main results were that SmO2 in the three muscles assessed did not differ between legs during the GXT and 8MTT (p>0.05). Reliability of the measures was poor to good in the vastus lateralis (ICC=0.83-0.37), moderate to excellent in the tibialis anterior (ICC=0.92-0.73), and poor to good for the gastrocnemius medialis (ICC=0.80-0.24). Overall, the group variability in SmO2 showed a narrower distribution at lower intensities, with data dispersion increasing at higher intensities. In conclusion, the SmO2 was similar and showed symmetrical responses in both the preferred and nonpreferred limb in different muscles assessed during cycling at different intensities within a range of 10%-20%. Although individual physiological differences that might be relevant in some clinical/performance settings should not be disregarded, these findings indicate that measuring a single lower limb provides an accurate approximation of the responses in both lower limbs.

In vitro/In vivo oxygen electrochemical nanosensor for bioanalysis

Direct in vivo monitoring of O2 is critical in the study of numerous biological processes, and the development of novel highly sensitive techniques for O2in vivo detection is of great potential importance. Electrochemical amperometric sensors are some of the most promising, easy to operate, inexpensive, sensitive and have a high temporal resolution. As part of this work, we have demonstrated a direct rapid method for molecular oxygen detection inside multicellular spheroids in vitro and rat brain in vivo. External and internal oxygen profiles in human adenocarcinoma MCF-7 spheroids were studied using developed nanoelectrode. The size of spheroids was shown to affect the level of hypoxia inside them, and also affected the oxygen consumption near spheroids in solution. To demonstrate the in vivo relevance of the novel sensor we used it to measure the oxygen concentrations in the superficial layers of the rat brain. This study demonstrates a minimally invasive electrochemical method for real-time oxygen profiling in vivo.

Diffusion of oxygen in amorphous HfO2

We carefully prepared 100-nm-thick amorphous hafnia (a-HfO2) thin films of a homogeneous structure, and measured the diffusion profile of oxygen using secondary-ion mass spectrometry with a stable isotope (18O) as a tracer. The diffusion coefficient of oxygen, D, in a-HfO2 was 3.5 × 10−24 to 7.8 × 10−20 m2 s−1 at annealing temperatures from 300 to 500 °C, which are smaller than all previously reported values. The Arrhenius plots of D showed a curvature, from which we determined the activation energy for oxygen diffusion to be 1.42 and 2.24 eV for fast and slow diffusivity, respectively. Our results suggest the atomic movement of oxygen in a-HfO2 is very sensitive to the local structure.

Estimating the vertical profile of water quality variables in reservoirs: Application of remotely sensed data and machine learning techniques

Water quality assessment and management of reservoirs depend on accurate, large-scale, and continuous monitoring of the vertical profile of Water Quality Variables (WQVs). Remote sensing data have been widely used to retrieve high spatiotemporal water quality data; however, their application has practically been limited to evaluating surface WQVs. In this paper, a novel and efficient approach is introduced for assessing the profile of WQVs in reservoirs that depend on stratification, by taking into account the shape of profile as prior knowledge. First, an appropriate function is fitted to the WQVs vertical profile, and second, the parameters of that function as representative of the WQVs vertical profile are estimated using machine learning techniques. The model's inputs are day, maximum depth of point, and remote sensing data. Finally, PAWN sensitivity analysis is applied to show the extent to which each input influences different parts of the vertical profile. This method is applied in the Wadi Dayqah Reservoir, the largest dam in Oman, to evaluate water temperature, dissolved oxygen, pH, and chlorophyll-a profile. The results show that the predicted profiles are properly representative of in situ measurements, with a mean absolute error of 0.28 °C, 0.25 mg/L, 0.052, and 0.33 μg/L on test data sets of water temperature, dissolved oxygen, pH, and chlorophyll-a, respectively. Finally, PAWN sensitivity analysis illustrates that satellite data not only influence the parameters representing surface WQVs but also contribute to the estimation of other curve parameters.

A neural network algorithm for quantifying seawater pH using Biogeochemical-Argo floats in the open Gulf of Mexico

Within the Gulf of Mexico (GOM), measurements of ocean pH are limited across space and time. This has hindered our ability to robustly monitor and study regional carbon dynamics, inclusive of ocean acidification, over this biogeochemically variable sea. The 2021 launch of Biogeochemical-Argo (BGC-Argo) ocean profiling floats that carry five sensors represented the entry of this particular ocean observing technology into this region. The GOM BGC-Argo floats have vastly increased the number of oxygen, nitrate, pH, chlorophyll-a fluorescence, and particulate backscattering profile observations within the “open GOM” region (>1,000 m water column depth). To circumvent a set of uncertainties associated with the collected sensor pH data, regionally trained neural network algorithms were developed to skillfully predict GOM pH (total scale, in situ temperature and pressure), which served as a secondary QC and sensor performance assessment tool. The GOM neural network pH (GOM-NNpH) algorithms were trained using a selection of climate quality CTD and bottle data (temperature, salinity, oxygen, nitrate, pressure, and location) collected as a part of NOAA’s Gulf of Mexico Ecosystems and Carbon Cruises (GOMECC). Neural network pH estimates were generated using the newly developed GOMNNpH algorithm and two widely used, globally trained neural network algorithms (Empirical Seawater Property Estimation Routines (ESPER) and CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network (CANYON-B)) to compare algorithm performance against validation data. The results demonstrate the advanced skill of the GOM-NNpH in capturing water column variability and robustly reconstructing GOM pH profiles. Using a combination of concurrent float-measured seawater values of pressure, temperature, salinity, and oxygen, a GOM-NNpH algorithm was applied to two years of BGC-Argo float data. Resulting data were used to diagnose the performance of float pH sensors and to generate a time series of neural network estimated pH based on the collected float profiles. These algorithms emphasize the value of regionally-trained neural networks and their utility by the BGC-Argo community. Further, the GOM-NNpH algorithms can also be applied by a variety of users to estimate pH values in the open GOM in the absence of direct pH observations.

Modeling future dissolved oxygen and temperature profiles in small temperate lake trout lakes

Modeling future dissolved oxygen and temperature profiles in small temperate lake trout lakes

Root-Driven Soil Reduction in Wadden Sea Salt Marshes

The soil redox potential in wetlands such as peatlands or salt marshes exerts a strong control over microbial decomposition processes and consequently soil carbon cycling. Wetland plants can influence redox by supplying both terminal electron acceptors (i.e. oxygen) and electron donors (i.e. organic matter) to the soil system. However, quantitative insight into the importance of plant effects on wetland soil redox and associated plant traits are scarce. In a combined mesocosm and field study we investigated the impact of plants on soil reduction using IRIS (Indicator of Reduction in Soils) sticks. Vegetated plots were compared to non-vegetated plots along an elevational gradient in a salt marsh of the Wadden Sea and along an artificially created gradient in a tidal tank mesocosm experiment. Our findings from the mesocosm experiment demonstrated that vegetation both enhanced and suppressed soil reduction relative to non-vegetated control pots. The direction of the plant effect (i.e., net oxidizing or net reducing) was inversely correlated with background redox conditions. Insights from high-resolution oxygen profiling via planar optode imaging corroborated these findings. In the field study, vegetation consistently reduced the comparatively well-aerated Wadden Sea salt marsh soil. Reduction correlated positively with soil organic matter content and belowground biomass, indicating that greater availability of plant-derived electron donors, in the form of organic matter, increased soil reduction. Challenging the dominant paradigm that wetland plants primarily act as soil oxidizers, our study reveals their potential to exert a net reducing effect. The documented impact of these plant-induced changes in soil redox conditions suggests a previously overlooked role in shaping the stability of soil organic carbon stocks in wetland ecosystems with variable water tables.

PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks

Abstract. Effective observation of the ocean is vital for studying and assessing the state and evolution of the marine ecosystem and for evaluating the impact of human activities. However, obtaining comprehensive oceanic measurements across temporal and spatial scales and for different biogeochemical variables remains challenging. Autonomous oceanographic instruments, such as Biogeochemical (BGC)-Argo profiling floats, have helped expand our ability to obtain subsurface and deep-ocean measurements, but measuring biogeochemical variables, such as nutrient concentration, still remains more demanding and expensive than measuring physical variables. Therefore, developing methods to estimate marine biogeochemical variables from high-frequency measurements is very much needed. Current neural network (NN) models developed for this task are based on a multilayer perceptron (MLP) architecture, trained over point-wise pairs of input–output features. Although MLPs can produce smooth outputs if the inputs change smoothly, convolutional neural networks (CNNs) are inherently designed to handle profile data effectively. In this study, we present a novel one-dimensional (1D) CNN model to predict profiles leveraging the typical shape of vertical profiles of a variable as a prior constraint during training. In particular, the Predict Profiles Convolutional (PPCon) model predicts nitrate, chlorophyll, and backscattering (bbp700) starting from the date and geolocation and from temperature, salinity, and oxygen profiles. Its effectiveness is demonstrated using a robust BGC-Argo dataset collected in the Mediterranean Sea for training and validation. Results, which include quantitative metrics and visual representations, prove the capability of PPCon to produce smooth and accurate profile predictions improving upon previous MLP applications.

Fisheries shocks provide an opportunity to reveal multiple recruitment sources of sardine in the Sea of Japan

The abrupt decline in sardine catches in the Sea of Japan and the East China Sea (SJ-ECS) in 2014 and 2019 and the recovery in the following years call into question the current assumption that sardines in the SJ-ECS form a self-recruiting subpopulation. To test this hypothesis, we analysed otolith stable oxygen and carbon isotope profiles (δ18O, δ13C) of age-0 and age-1 sardines from 2010 and 2013–2015 year-classes captured in the SJ-ECS, as geographic markers for nursery areas. Age-0 sardines generally showed a significant ontogenetic decrease in otolith δ18O from larval to juvenile stages. However, the majority of age-1 captured in spring 2011, 2015 and 2016 showed non-decreasing otolith δ18O profiles, suggesting that the age-0 off the Japanese coast were not the main source of recruitment. Different migration groups were thus indicated: the “locals” growing up off the Japanese coast and the migrating “nonlocals”. The isotope profiles of the “nonlocals” overlapped with those of age-0 captured in the subarctic North Pacific, suggesting that they may be migrants from the Pacific, or perhaps an unobserved northward migration group in the SJ-ECS. Our results highlight the considerable uncertainty in the population structure assumed in current stock assessment models for Japanese sardine.

Does polymixis complicate prediction of high‐frequency dissolved oxygen in lakes and reservoirs?

AbstractAs lake and reservoir ecosystems transition across major environmental regimes (e.g., mixing regime) resulting from anthropogenic change, setting predictive expectations is imperative. We tested the hypothesis that (dissolved) oxygen is more predictable in monomictic reservoirs that thermally stratify throughout the summer (warm) season compared to polymictic reservoirs that stratify intermittently. Using two‐hourly vertical profiles of oxygen, we compared daily‐aggregated errors of oxygen predictions from random forests across and within two monomictic and two polymictic reservoirs in the south‐central (subtropical) USA. Although one monomictic reservoir was typically more predictable than the polymictic reservoirs, the hypereutrophic, small monomictic reservoir had less predictable oxygen patterns potentially related to rapid oxygen cycling and intrusions of oxygenated waters in the hypolimnion without mixing. Daily mixing did not relate strongly to model errors. Water temperature, depth, and wind were the most important predictors, but were not clearly related to season or mixing. Lastly, we compared multiple model types (regression, neural network, and process‐based) in one polymictic reservoir to test how our interpretations of oxygen predictability were sensitive to model type, finding that the models generally agreed; however, the process‐based model poorly predicted oxygen in the middle of the vertical profiles (5 m) where most models performed poorly due to a temporally unstable, vacillating metalimnion. Our results suggest predicting reservoir oxygen dynamics may be easier in stratified reservoirs, but eutrophication and complex hydrodynamics may cause forecasting surprises especially for those who use or manage water resources in mono‐ or dimictic reservoirs.

Hidden seafloor hypoxia in coastal waters

AbstractThe expansion of transient and permanent coastal benthic anoxia is one of the most severe problems for the coastal ocean globally. We report frequent, hidden hypoxia in the bottom 5 cm of the water column of a coastal site in the central Baltic Sea by continuous high‐resolution profiling of oxygen (O2) directly above the sediment surface. This hypoxia stood in stark contrast to 30‐yr O2 monitoring records at this site that suggest apparent continuous well‐oxygenated conditions. In situ measurements showed highly dynamic conditions in the bottom 30 cm recording frequent gradual and abrupt changes between normoxic (> 63 μmol L−1) and hypoxic (< 63 μmol L−1) conditions that would remain undetectable by conventional bottom water O2 monitoring. The temporal variability of these “hidden” hypoxia is tied to the dynamic current field and to changes in O2 consumption following resuspension events. Our observations suggest that transient benthic hypoxia is much more common than routine monitoring data indicate.

Oxygen Microenvironments in E. coli Biofilm Nutrient Transport Channels: Insights from Complementary Sensing Approaches.

Chemical gradients and the emergence of distinct microenvironments in biofilms are vital to the stratification, maturation and overall function of microbial communities. These gradients have been well characterised throughout the biofilm mass but the microenvironment of recently discovered nutrient transporting channels in Escherichia coli biofilms remains unexplored. This study employs three different oxygen sensing approaches to provide a robust quantitative overview of the oxygen gradients and microenvironments throughout the biofilm transport channel networks formed by E. coli macrocolony biofilms. Oxygen nanosensing combined with confocal laser scanning microscopy established that the oxygen concentration changes along the length of biofilm transport channels. Electrochemical sensing provided precise quantification of the oxygen profile in the transport channels, showing similar anoxic profiles compared with the adjacent cells. Anoxic biosensing corroborated these approaches, providing an overview of the oxygen utilisation throughout the biomass. The discovery that transport channels maintain oxygen gradients contradicts the previous literature that channels are completely open to the environment along the apical surface of the biofilm. We provide a potential mechanism for the sustenance of channel microenvironments via orthogonal visualisations of biofilm thin sections showing thin layers of actively growing cells. This complete overview of the oxygen environment in biofilm transport channels primes future studies aiming to exploit these emergent structures for new bioremediation approaches.

Computational modelling of the therapeutic outputs of photodynamic therapy on spheroid-on-chip models

Photodynamic therapy (PDT) is a medical radio chemotherapeutic method that uses light, photosensitizing agents, and oxygen to produce cytotoxic compounds, which eliminate malignant cells. Recently, Microfluidic systems have been used to analyse photosensitizers (PSs) due to their potential to replicate in vivo environments. While prior studies have established a strong correlation between reacted singlet oxygen concentration and PDT-induced cellular death, the effects that the ambient fluid flow might have on the concentration of oxygen and PS have been disregarded in many, which limits the reliability of the results. Herein, we coupled the transport of oxygen and PS throughout the ambient medium and within the spheroidal multicellular aggregate to initially study the profiles of oxygen and PS concentration alongside PDT-induced cellular death throughout the spheroid before and after radiation. The attained results indicate that the PDT-induced cellular death initiates on the surface of the spheroids and subsequently spreads to the neighbouring regions, which is in great accordance with experimental results. Afterward, the effects that drug-light interval (DLI), fluence rate, PS composition, microchannel height, and inlet flow rate have on the therapeutic outcomes are studied. The findings show that adequate DLI is critical to ensure uniform distribution of PS throughout the medium, and a value of 5 h was found to be sufficient. The composition of PS is critical, as ALA-PpIX induces earlier cell death but accelerates oxygen consumption, especially in the outer layers, depriving the inner layers of oxygen necessary for PDT, which in turn disrupts and prolongs the exposure time compared to mTHPC and Photofrin. Despite the fluence rate directly influencing the singlet oxygen generation rate, increasing the fluence rate by 189 mW/cm2 would not significantly benefit us. Microwell height and inlet flow rate involve competing phenomena—increasing height or decreasing flow reduces oxygen supply and increases PS “washout” and its concentration.

Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model

Abstract. Biogeochemical-Argo (BGC-Argo) float profiles provide substantial information on key vertical biogeochemical dynamics and have been successfully integrated in biogeochemical models via data assimilation approaches. Although BGC-Argo assimilation results have been encouraging, data scarcity remains a limitation with respect to their effective use in operational oceanography. To address availability gaps in the BGC-Argo profiles, an observing system experiment (OSE) that combines a neural network (NN) and data assimilation (DA) was performed here. A NN was used to reconstruct nitrate profiles, starting from oxygen profiles and associated Argo variables (pressure, temperature, and salinity), while a variational data assimilation scheme (3DVarBio) was upgraded to integrate BGC-Argo and reconstructed observations in the Copernicus Mediterranean operational forecast system (MedBFM). To ensure the high quality of oxygen data, a post-deployment quality control method was developed with the aim of detecting and eventually correcting potential sensors drift. The Mediterranean OSE features three different set-ups: a control run without assimilation; a multivariate run with assimilation of BGC-Argo chlorophyll, nitrate, and oxygen; and a multivariate run that also assimilates reconstructed observations. The general improvement in the skill performance metrics demonstrated the feasibility of integrating new variables (oxygen and reconstructed nitrate). Major benefits have been observed with respect to reproducing specific biogeochemical-process-based dynamics such as the nitracline dynamics, primary production, and oxygen vertical dynamics. The assimilation of BGC-Argo nitrate corrects a generally positive bias of the model in most of the Mediterranean areas, and the addition of reconstructed profiles makes the corrections even stronger. The impact of enlarged nitrate assimilation propagates to ecosystem processes (e.g. primary production) at a basin-wide scale, demonstrating the importance of the assimilation of BGC-Argo profiles in forecasting the biogeochemical ocean state.

Modeling oxygen transport in the brain: An efficient coarse-grid approach to capture perivascular gradients in the parenchyma.

Recent progresses in intravital imaging have enabled highly-resolved measurements of periarteriolar oxygen gradients (POGs) within the brain parenchyma. POGs are increasingly used as proxies to estimate the local baseline oxygen consumption, which is a hallmark of cell activity. However, the oxygen profile around a given arteriole arises from an interplay between oxygen consumption and delivery, not only by this arteriole but also by distant capillaries. Integrating such interactions across scales while accounting for the complex architecture of the microvascular network remains a challenge from a modelling perspective. This limits our ability to interpret the experimental oxygen maps and constitutes a key bottleneck toward the inverse determination of metabolic rates of oxygen. We revisit the problem of parenchymal oxygen transport and metabolism and introduce a simple, conservative, accurate and scalable direct numerical method going beyond canonical Krogh-type models and their associated geometrical simplifications. We focus on a two-dimensional formulation, and introduce the concepts needed to combine an operator-splitting and a Green's function approach. Oxygen concentration is decomposed into a slowly-varying contribution, discretized by Finite Volumes over a coarse cartesian grid, and a rapidly-varying contribution, approximated analytically in grid-cells surrounding each vessel. Starting with simple test cases, we thoroughly analyze the resulting errors by comparison with highly-resolved simulations of the original transport problem, showing considerable improvement of the computational-cost/accuracy balance compared to previous work. We then demonstrate the model ability to flexibly generate synthetic data reproducing the spatial dynamics of oxygen in the brain parenchyma, with sub-grid resolution. Based on these synthetic data, we show that capillaries distant from the arteriole cannot be overlooked when interpreting POGs, thus reconciling recent measurements of POGs across cortical layers with the fundamental idea that variations of vascular density within the depth of the cortex may reveal underlying differences in neuronal organization and metabolic load.

High-resolution carbon and oxygen isotopic compositions of cultured brachiopods: Effect of pH, temperature and growth

Brachiopod shells are ubiquitous since the Early Cambrian up to now. As they secrete a shell made of low-magnesium calcite, more resistant to diagenesis than biocarbonates richer in Mg, their geochemical signatures are generally considered a powerful tool for paleo-environmental and paleo-climatic reconstructions. However, gaps in knowledge still remain on the underlying controls of the shell chemistry, in particular at a high spatial resolution. In this study, in situ oxygen and carbon isotope measurements by SIMS (Secondary Ion Mass Spectrometry) were performed in brachiopod shells of the cold-temperate water species Magellania venosa, constituted of a primary and a secondary layer. The individual specimens studied here grew under controlled conditions mimicking the natural environment and in experiments under low-pH (high pCO2) and high-temperature conditions. Transversal carbon and oxygen profiles showed a “brachiopod pattern” typical of extant two-layered brachiopods, with the primary layer depleted in 18O and 13C relative to equilibrium and the secondary layer showing a gradual increasing trend until reaching a near-equilibrium plateau. Overall, shells cultured at low pH were found to have δ18O and δ13C values closer to equilibrium when compared to shells from the control experiment. These near-equilibrium values may reflect a decrease in shell precipitation rate, leading to less kinetic effects, and/or a more rapid kinetics for the equilibration between DIC species and water. By close pairing of seawater δ18O and δ13C to that of shell microstructure, our study enables us to derive layer-specific C and O enrichment factors, which show the extent of pH and temperature effects superimposed on the seawater δ18O and DIC δ13C signal inherited. Finally, we show that during brachiopod shell growth, newly precipitated calcite is added to the calcite already existing, thus empirically validating the conceptual accretionary growth model proposed by Ackerly (1989).

Sustainability in Aquatic Ecosystem Restoration: Combining Classical and Remote Sensing Methods for Effective Water Quality Management

The utilization of Effective Microorganisms (EMs) for lake restoration represents a sustainable approach to enhancing water quality and rebalancing the ecology of aquatic ecosystems. The primary objective of this study was to evaluate the effects of two bioremediation treatment cycles employing EM-enriched biopreparations on water quality in the Siemiatycze lakes. Specifically, this research analyzed various parameters, including dissolved oxygen, transparency, chlorophyll-a, pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total phosphorus, total nitrogen, and suspended matter (SM), across eleven designated sampling locations. Additionally, this study employed remote sensing techniques, leveraging Sentinel-2 satellite imagery and the Maximum Chlorophyll Index (MCI), to detect and quantify algal blooms, with a particular focus on elevated chlorophyll-a concentrations. This comprehensive approach aimed to provide a holistic understanding of the impact of biotechnological reclamation on aquatic ecosystem restoration and sustainability. The study’s findings indicated a significant improvement in water quality in all lakes, with enhanced water clarity and oxygen profiles. Further, remote sensing studies indicated a reduction in algal blooms, particularly those with high chlorophyll-a concentrations. A considerable decrease in water eutrophication intensity was observed due to diminished nutrient concentrations. The improvements in water parameters are likely to enhance the living conditions of aquatic organisms. These results demonstrate the effectiveness of using EM-enriched biopreparations in the bioremediation of lakes, providing a sustainable approach to enhancing water quality and balancing aquatic ecosystems.

Profiles of oxygen and titanium point defects in ferromagnetic TiO2 films

Experimentally it is shown that without any oxygen manipulation for TiO2, a strong room temperature ferromagnetism could be expected only in ultra-thin films, with the ideal thickness below 100 nm. Both bulks and nano-powders of TiO2 are diamagnetic, indicating that the surface and its nano-sublayers play very important roles in tailoring the magnetic properties in this type of compound. To shed a new light on the defect-related magnetism in the typical case of anatase TiO2 surfaces, we have performed a series of quantum-mechanical calculations for TiO2 slabs containing Ti or O vacancies in different distances from the (001) surface. The lowest formation energies were obtained for the Ti vacancies in the first sub-surface layer and the O vacancies within the surface. The computed magnetic states reflect complicated structural relaxations of atoms influenced by both the surface and vacant atomic positions. O atoms cannot contribute much to magnetic moment when Ti vacancies are isolated and far from the surface. Ti vacancies in TiO2 are only metastable. The formation energy of Ti interstitials is lower than for Ti vacancies since high-temperature annealing, especially with a lot of O2 available that would fill up O-related defects, and as a result, eliminate most of Ti vacancies. Lower temperatures, less O2, and shorter exposure times may enable not only partial elimination of Ti vacancies but also can facilitate their diffusion into different states of aggregations. In the ferromagnetic films (i.e. thin films below 100 nm), it looks like that the O atoms are located closer to the Ti vacancies.

A mathematical model of tissue axial and radial diffusion in the microvasculature for intravascular microscopy and phosphorescence quenching data

This study aims to extend earlier Krogh Cylinder Models of an oxygen profile by considering axial diffusion and analytically solving Fick's Law Partial Differential Equation with novel boundary conditions via the separation of variables. We next prospectively collected a total of 20 animals, which were randomly assigned to receive either fresh or two-week-old stored red blood cell (RBC) transfusions and PQM oxygen data were measured acutely (90 min) or chronically (24 h). Transfusion effects were evaluated in vivo using intravital microscopy of the dorsal skinfold window chamber in Golden Syrian Hamsters. Hamsters were initially hemorrhaged by 50% of total blood volume and resuscitated 1-h post hemorrhage. PQM data were subsequently collected and fit the derived 2D Krogh cylinder model. Systemic hemodynamics (mean arterial pressure, heart rate) were similar in both pre and post-transfusion with either stored or fresh cells. Transfusion with stored cells was found to impair axial and radial oxygen gradients as quantified by our model and consistent with previous studies. Specifically, we observed a statistically significant decrease in the arteriolar tissue radial oxygen gradient after transfusion with stored RBCs at 24 h compared with fresh RBCs (0.33 ± 0.17 mmHg μ m-1 vs, 0.14 ± 0.12 mmHg μ m-1; p = 0.0280). We also observed a deficit in the arteriolar tissue oxygen gradient (0.03 ± 0.01 mmHg μ m-1 fresh vs. 0.018 ± 0.007 mmHg μ m-1 stored; p = 0.0185). We successfully derived and validated an analytical 2D Krogh cylinder model in an animal model of microhemodynamic oxygen diffusion aberration secondary to storage lesions.

Retrieval of Ar, N2, O, and CO in the Martian Thermosphere Using Dayglow Limb Observations by EMM EMUS

AbstractThe Emirates Ultraviolet Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) Hope probe images Mars at wavelengths extending from approximately 100 to 170 nm. EMUS observations began in February 2021 and cover over a full Mars year. We report the first limb scan observations at Mars of ultraviolet emissions Ar I 106.6 nm, N I 120 nm, and carbon monoxide (CO) Fourth Positive Group (A − X) band system excited by electron impact on CO. We use EMUS limb scan observations to retrieve number density profiles of argon, molecular nitrogen, atomic oxygen, and CO in the upper atmosphere of Mars from 130 to 160 km. CO is a sensitive tracer of the thermal profile and winds in Mars' middle atmosphere and the chemistry that balances CO2 in the atmosphere of Mars. EMUS insertion orbit special observations demonstrate that far ultraviolet limb measurements of the Martian thermosphere can be spectroscopically analyzed with a robust retrieval algorithm to further quantify variations of CO composition in the Martian upper atmosphere.