Optimisation criterion for pulsatile timing: observation in the human fetus

Objectives: Pulsatile cardiac action is an energy consuming process. During pulse wave (PW) travel to the periphery, reflection back to the LV occurs. The concept of wave condition number, WCN, provides evidence that energy consumption of cardiac action is minimised when time of return Tr to LV takes a certain percentage of the cardiac cycle T. Our objective was to assess WCN and reflection timing Tr/T in the human fetus. Methods: Based on the WCN relation: WCN = HR×L/PWV, energy consumption of pulsatile LV action is optimised for WCN = 0.1 (HR: heart rate, L: effective aortic length, PWV: aortic PW velocity; Pahlevan 2014, 2020). Rearranging with Tr = 2L/PWV (figure) yield Tr/T = 0.2 as optimal reflection timing. To obtain Tr in the fetus by Doppler, hemodynamic modelling is required (figure): PWs arrive twice at cerebral circulation: 1st as a primary wave and 2nd after reflection and return. A systolic shoulder (S) in MCA Doppler (MCA‐S) represents this 2nd impulse and delay δt corresponds to Tr (Mills 1970). Results: Tr data of IUGR fetuses with MCA‐S were obtained by this method (Gonser 2018): Tr = 96 ± 15ms (GA 31 ± 3w). T = 423ms (FHR 140bpm) yield Tr/T = 96ms/423ms = 0.23, showing good agreement with optimal reflection timing of 20%, as suggested by the WCN. Conclusions: In spite of circulatory stress, IUGR fetuses maintain nearly optimal pulsatile timing, probably due to the priority of minimal energy consumption. Thus appearance of MCA‐S is not an artifact, but a sign of nearly optimal timed PW reflection

Effect of Magnesium Administration on Passive Avoidance Memory and Formalin-Induced Nociception in Diabetic Rats

Purpose: To investigate the effect of oral consumption of magnesium on the memory and pain sensation of diabetic rats.Methods: A total of 48 rats were divided into four groups - untreated control, untreated diabetic, magnesium-treated control and magnesium-treated diabetic. Plasma magnesium and glucose concentrations were measured after induction of diabetes with streptozotocin (STZ; 60 mg/kg). Four weeks after the administration of oral magnesium (10 g/L, MgSO4), the animals were subjected to passive avoidance test whereby latency time (LT) was assessed. This was followed by formalin test which entailed the determination of licking and flinching scoresResults: Increased level of glucose and decreased concentration of magnesium in untreated diabetic group compared to untreated control group (p < 0.001) were observed. There was also a significant reduction in mean LT of untreated diabetic group (p < 0.001) as indicated by the increased number of animals that entered the dark compartment. Plasma glucose and magnesium levels in magnesium treated diabetic rats returned to normal 4 weeks after oral magnesium consumption. There was no significant change in mean total pain score despite elevated licking in diabetic animals after oral magnesium consumption. Significant elevation of flinching scores of untreated diabetic rats was observed in the last 20 min of the 2nd chronic phase, compared with the untreated control group.Conclusion: It seems that magnesium treatment either restores rat memory performance that is impaired by diabetes or that it affects the aversive responses evoked by electrical shock.Keywords: Diabetes, Magnesium, Glucose, Passive avoidance memory, Formalin test

Sensitivity of remotely sensed pigment concentration via Mixture Density Networks (MDNs) to uncertainties from atmospheric correction

Lake Erie, the shallowest of the five North American Laurentian Great Lakes, exhibits degraded water quality associated with recurrent phytoplankton blooms. Optical remote sensing of these optically com�plex inland waters is challenging due to the uncertainties stemming from atmospheric correction (AC) procedures. In this study, the accuracy of remote sensing reflectance (Rrs) derived from three different AC algorithms applied to Ocean and Land Colour Instrument (OLCI) observations of western Lake Erie (WLE) is evaluated through comparison to a regional radiometric dataset. The effects of uncertainties in Rrs products on the retrieval of near-surface concentration of pigments, including chlorophyll-a (Chla) and phycocyanin (PC), from Mixture Density Networks (MDNs) are subsequently investigated. Results show that iCOR contained the fewest number of processed (unflagged) days per pixel, compared to ACOLITE and POLYMER, for parts of the lake. Limiting results to the matchup dataset in common between the three AC algorithms shows that iCOR and ACOLITE performed closely at 665 nm, while out�performing POLYMER, with the Median Symmetric Accuracy (MdSA) of �30 %, 28 %, and 53 %, respec�tively. MDN applied to iCOR- and ACOLITE-corrected data (MdSA < 37 %) outperformed MDN applied to POLYMER-corrected data in estimating Chla. Large uncertainties in satellite-derived Rrs propagated to uncertainties �100 % in PC estimates, although the model was able to recover concentrations along the 1:1 line. Despite the need for improvements in its cloud-masking scheme, we conclude that iCOR combined with MDNs produces adequate OLCI pigment products for studying and monitoring Chla across WL

Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery

Retrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆Rrs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrations (Chla), PCs, and remote sensing reflectance (Rrs) measurements to estimate PC from all relevant spectral bands. The performance of the developed model is demonstrated via PC maps produced from select images of the Hyperspectral Imager for the Coastal Ocean (HICO) and Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) using a matchup dataset. As input to the MDN, we incorporate a combination of widely used band ratios (BRs) and line heights (LHs) taken from existing multispectral algorithms, that have been proven for both Chla and PC esti�mation, as well as novel BRs and LHs to increase the overall cyanobacteria biomass estimation accuracy and reduce the sensitivity to ∆Rrs. When trained on a random half of the dataset, the MDN achieves uncertainties of 44.3%, which is less than half of the uncertainties of all viable optimized multispectral PC algorithms. The MDN is notably better than multispectral algorithms at preventing overestimation on low (10 mg m− 3). According to our extensive assessments, the developed model is anticipated to enable practical PC products from PRISMA and HICO, therefore the model is promising for planned hyperspectral missions, such as the Plankton Aerosol and Cloud Ecosystem (PACE). This advancement will enhance the complementary roles of hyperspectral radiometry from satellite and low-altitude platforms for quantifying and monitoring cyanobacteria harmful algal blooms at both large and local spatial scales

Detection of Mycolactone A/B in Mycobacterium ulcerans–Infected Human Tissue

Skin infection with bacteria called Mycobacterium ulcerans causes Buruli ulcer, a disease common in West Africa and mainly affecting children. M. ulcerans is the only mycobacterium to cause disease by production of a toxin. This lipid molecule called mycolactone diffuses from the site of infection, killing surrounding cells and, at low concentration, suppressing the immune response. The aim of this study was to show that mycolactone can be detected among lipids extracted from human M. ulcerans lesions in order to study its role in the pathogenesis of M. ulcerans disease. Lipids were extracted from skin biopsies and tested for the presence of mycolactone using thin layer chromatography and mass spectrometry. The extracts were shown to kill cultured cells in a cytotoxicity assay. Mycolactone was detected in both pre-ulcerative and ulcerative forms of the disease and also in lesions during antibiotic treatment but with reduced bioactivity, suggesting a lower concentration compared to untreated lesions. These findings indicate that there is mycolactone in affected skin at all stages of M. ulcerans disease and it could be used as a biomarker for monitoring the clinical response to antibiotic treatment

Phytoplankton composition from sPACE: Requirements, opportunities, and challenges

Ocean color satellites have provided a synoptic view of global phytoplankton for over 25 years through near surface measurements of the concentration of chlorophyll a. While remote sensing of ocean color has revolutionized our understanding of phytoplankton and their role in the oceanic and freshwater ecosystems, it is important to consider both total phytoplankton biomass and changes in phytoplankton community composition in order to fully understand the dynamics of the aquatic ecosystems. With the upcoming launch of NASA\u27s Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission, we will be entering into a new era of global hyperspectral data, and with it, increased capabilities to monitor phytoplankton diversity from space. In this paper, we analyze the needs of the user community, review existing approaches for detecting phytoplankton community composition in situ and from space, and highlight the benefits that the PACE mission will bring. Using this three-pronged approach, we highlight the challenges and gaps to be addressed by the community going forward, while offering a vision of what global phytoplankton community composition will look like through the “eyes” of PACE

Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3

Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications

Identification of a Novel Gene Product That Promotes Survival of Mycobacterium smegmatis in Macrophages

BACKGROUND: Bacteria of the suborder Corynebacterineae include significant human pathogens such as Mycobacterium tuberculosis and M. leprae. Drug resistance in mycobacteria is increasingly common making identification of new antimicrobials a priority. Mycobacteria replicate intracellularly, most commonly within the phagosomes of macrophages, and bacterial proteins essential for intracellular survival and persistence are particularly attractive targets for intervention with new generations of anti-mycobacterial drugs. METHODOLOGY/PRINCIPAL FINDINGS: We have identified a novel gene that, when inactivated, leads to accelerated death of M. smegmatis within a macrophage cell line in the first eight hours following infection. Complementation of the mutant with an intact copy of the gene restored survival to near wild type levels. Gene disruption did not affect growth compared to wild type M. smegmatis in axenic culture or in the presence of low pH or reactive oxygen intermediates, suggesting the growth defect is not related to increased susceptibility to these stresses. The disrupted gene, MSMEG_5817, is conserved in all mycobacteria for which genome sequence information is available, and designated Rv0807 in M. tuberculosis. Although homology searches suggest that MSMEG_5817 is similar to the serine:pyruvate aminotransferase of Brevibacterium linens suggesting a possible role in glyoxylate metabolism, enzymatic assays comparing activity in wild type and mutant strains demonstrated no differences in the capacity to metabolize glyoxylate. CONCLUSIONS/SIGNIFICANCE: MSMEG_5817 is a previously uncharacterized gene that facilitates intracellular survival of mycobacteria. Interference with the function of MSMEG_5817 may provide a novel therapeutic approach for control of mycobacterial pathogens by assisting the host immune system in clearance of persistent intracellular bacteria