The COVID - Curated and Open aNalysis aNd rEsearCh plaTform (CO-CONNECT)

Objectives CO-CONNECT is making UK COVID-19 data Findable, Accessible, Interoperable and Reusable (FAIR) through a federated platform, which supports secure, anonymised research at scale and pace. This interdisciplinary project, spanning 22 organisations, is connecting data from >50 large research cohorts and data collected through routine healthcare provision across the UK. Approach Across the UK, data has been collected that can help us answer key questions about COVID-19. As the data are in many places with many different processes it is difficult and complex for public health groups, researchers, policymakers, and government to find and access lots of high-quality data quickly and efficiently to make decisions. In collaboration with Health Data Research UK, CO-CONNECT is streamlining processes of accessing data for research. Results 1) Discovering data and meta-analysis: CO-CONNECT enables researchers to determine how many people meet their research criteria within the various datasets across the UK through the Health Data Research Innovation Gateway Cohort Discovery tool e.g. “How many people in each dataset have had a PCR test which was positive and were under the age of 40?” Only summary level, anonymous data are provided so researchers can answer such questions rapidly without requiring multiple data governance permissions and directly contacting each data source. The tool also supports aggregate level meta-analysis of the data. 2) Detailed analysis: With data governance approvals, researchers can analyse detailed level, standardised, linked, pseudonymised data in a Trusted Research Environment. The common format reduces the effort on each research project, supporting rapid research. Conclusion Providing data in this de-identifiable, safe way enables rapid, robust research e.g., COVID-19 results from a test centre can be linked to hospital records along with prescriptions from pharmacies enabling researchers to understand whether people with different existing health conditions are more or less susceptible to COVID-19. If you want to know more visit https://co-connect.ac.uk

Ibuprofen Blunts Ventilatory Acclimatization to Sustained Hypoxia in Humans.

Ventilatory acclimatization to hypoxia is a time-dependent increase in ventilation and the hypoxic ventilatory response (HVR) that involves neural plasticity in both carotid body chemoreceptors and brainstem respiratory centers. The mechanisms of such plasticity are not completely understood but recent animal studies show it can be blocked by administering ibuprofen, a nonsteroidal anti-inflammatory drug, during chronic hypoxia. We tested the hypothesis that ibuprofen would also block the increase in HVR with chronic hypoxia in humans in 15 healthy men and women using a double-blind, placebo controlled, cross-over trial. The isocapnic HVR was measured with standard methods in subjects treated with ibuprofen (400 mg every 8 hrs) or placebo for 48 hours at sea level and 48 hours at high altitude (3,800 m). Subjects returned to sea level for at least 30 days prior to repeating the protocol with the opposite treatment. Ibuprofen significantly decreased the HVR after acclimatization to high altitude compared to placebo but it did not affect ventilation or arterial O2 saturation breathing ambient air at high altitude. Hence, compensatory responses prevent hypoventilation with decreased isocapnic ventilatory O2-sensitivity from ibuprofen at this altitude. The effect of ibuprofen to decrease the HVR in humans provides the first experimental evidence that a signaling mechanism described for ventilatory acclimatization to hypoxia in animal models also occurs in people. This establishes a foundation for the future experiments to test the potential role of different mechanisms for neural plasticity and ventilatory acclimatization in humans with chronic hypoxemia from lung disease

Spatial patterns and source attribution of urban methane in the Los Angeles Basin

Urban areas are increasingly recognized as a globally important source of methane to the atmosphere; however, the location of methane sources and relative contributions of source sectors are not well known. Recent atmospheric measurements in Los Angeles, California, USA, show that more than a third of the city's methane emissions are unaccounted for in inventories and suggest that fugitive fossil emissions are the unknown source. We made on-road measurements to quantify fine-scale structure of methane and a suite of complementary trace gases across the Los Angeles Basin in June 2013. Enhanced methane levels were observed across the basin but were unevenly distributed in space. We identified 213 methane hot spots from unknown emission sources. We made direct measurements of ethane to methane (C_2H_6/CH_4) ratios of known methane emission sources in the region, including cattle, geologic seeps, landfills, and compressed natural gas fueling stations, and used these ratios to determine the contribution of biogenic and fossil methane sources to unknown hot spots and to local urban background air. We found that 75% of hot spots were of fossil origin, 20% were biogenic, and 5% of indeterminate source. In regionally integrated air, we observed a wider range of C_2H_6/CH_4 values than observed previously. Fossil fuel sources accounted for 58–65% of methane emissions, with the range depending on the assumed C_2H_6/CH_4 ratio of source end-members and model structure. These surveys demonstrated the prevalence of fugitive methane emissions across the Los Angeles urban landscape and suggested that uninventoried methane sources were widely distributed and primarily of fossil origin

Ventilation–perfusion heterogeneity measured by the multiple inert gas elimination technique is minimally affected by intermittent breathing of 100% O2

Proton magnetic resonance (MR) imaging to quantify regional ventilation–perfusion ((Formula presented.)) ratios combines specific ventilation imaging (SVI) and separate proton density and perfusion measures into a composite map. Specific ventilation imaging exploits the paramagnetic properties of O2, which alters the local MR signal intensity, in an FIO2-dependent manner. Specific ventilation imaging data are acquired during five wash-in/wash-out cycles of breathing 21% O2 alternating with 100% O2 over ~20 min. This technique assumes that alternating FIO2 does not affect (Formula presented.) heterogeneity, but this is unproven. We tested the hypothesis that alternating FIO2 exposure increases (Formula presented.) mismatch in nine patients with abnormal pulmonary gas exchange and increased (Formula presented.) mismatch using the multiple inert gas elimination technique (MIGET).The following data were acquired (a) breathing air (baseline), (b) breathing alternating air/100% O2 during an emulated-SVI protocol (eSVI), and (c) 20 min after ambient air breathing (recovery). MIGET heterogeneity indices of shunt, deadspace, ventilation versus (Formula presented.) ratio, LogSD (Formula presented.), and perfusion versus (Formula presented.) ratio, LogSD (Formula presented.) were calculated. LogSD (Formula presented.) was not different between eSVI and baseline (1.04 ± 0.39 baseline, 1.05 ± 0.38 eSVI, p =.84); but was reduced compared to baseline during recovery (0.97 ± 0.39, p =.04). There was no significant difference in LogSD (Formula presented.) across conditions (0.81 ± 0.30 baseline, 0.79 ± 0.15 eSVI, 0.79 ± 0.20 recovery; p =.54); Deadspace was not significantly different (p =.54) but shunt showed a borderline increase during eSVI (1.0% ± 1.0 baseline, 2.6% ± 2.9 eSVI; p =.052) likely from altered hypoxic pulmonary vasoconstriction and/or absorption atelectasis. Intermittent breathing of 100% O2 does not substantially alter (Formula presented.) matching and if SVI measurements are made after perfusion measurements, any potential effects will be minimized

The UV-Optical Color Dependence of Galaxy Clustering in the Local Universe

We measure the UV-optical color dependence of galaxy clustering in the local universe. Using the clean separation of the red and blue sequences made possible by the NUV - r color-magnitude diagram, we segregate the galaxies into red, blue and intermediate "green" classes. We explore the clustering as a function of this segregation by removing the dependence on luminosity and by excluding edge-on galaxies as a means of a non-model dependent veto of highly extincted galaxies. We find that \xi (r_p, \pi) for both red and green galaxies shows strong redshift space distortion on small scales -- the "finger-of-God" effect, with green galaxies having a lower amplitude than is seen for the red sequence, and the blue sequence showing almost no distortion. On large scales, \xi (r_p, \pi) for all three samples show the effect of large-scale streaming from coherent infall. On scales 1 Mpc/h < r_p < 10 Mpc/h, the projected auto-correlation function w_p(r_p) for red and green galaxies fits a power-law with slope \gamma ~ 1.93 and amplitude r_0 ~ 7.5 and 5.3, compared with \gamma ~ 1.75 and r_0 ~ 3.9 Mpc/h for blue sequence galaxies. Compared to the clustering of a fiducial L* galaxy, the red, green, and blue have a relative bias of 1.5, 1.1, and 0.9 respectively. The w_p(r_p) for blue galaxies display an increase in convexity at ~ 1 Mpc/h, with an excess of large scale clustering. Our results suggest that the majority of blue galaxies are likely central galaxies in less massive halos, while red and green galaxies have larger satellite fractions, and preferentially reside in virialized structures. If blue sequence galaxies migrate to the red sequence via processes like mergers or quenching that take them through the green valley, such a transformation may be accompanied by a change in environment in addition to any change in luminosity and color.Comment: accepted by MNRA

Evolution of the Stellar Mass Tully-Fisher Relation in Disk Galaxy Merger Simulations

There is a large observational scatter toward low velocities in the stellar mass Tully-Fisher relation if disturbed and compact objects are included. However, this scatter can be eliminated if one replaces rotation velocity with $\rm S_{\rm 0.5}$, a quantity that includes a velocity dispersion term added in quadrature with the rotation velocity. In this work we use a large suite of hydrodynamic N-body galaxy merger simulations to explore a possible mechanism for creating the observed relations. Using mock observations of the simulations, we test for the presence of observational effects and explore the relationship between $\rm S_{\rm 0.5}$ and intrinsic properties of the galaxies. We find that galaxy mergers can explain the scatter in the TF as well as the tight $\rm S_{\rm 0.5}$-stellar mass relation. Furthermore, $\rm S_{\rm 0.5}$ is correlated with the total central mass of a galaxy, including contributions due to dark matter.Comment: ApJ accepte

Radiocarbon-Based Partitioning of Soil Respiration in an Old-Growth Coniferous Forest

Temperate forests play an important role in the global carbon cycle, and are thought to currently be a sink for atmospheric CO₂. However, we lack understanding of the drivers of forest carbon accumulation and loss, hampering our ability to predict carbon cycle responses to global change. In this study, we used CO₂ flux and radiocarbon (¹⁴C) measurements to investigate the role of seasonal drivers on soil respiration. Radiocarbon measurements of CO₂ evolved during incubation of fine roots and root-free soils at the beginning and end of the growing season (April and August) showed that these two soil respiration sources (fine roots vis-a`-vis soils) have different mean residence times that stayed constant between seasons. Radiocarbon measurements show that root respiration was made up of carbon fixed 3–5 years prior to sampling, and that heterotrophic respiration was made up of carbon fixed 7–10 years prior. The difference in radiocarbon signature between the two sources allowed us to partition autotrophic and heterotrophic respiration sources for soil respiration measurements in the field. We observed a small but significant increase in Δ¹⁴C of soil respiration between April and August, suggesting an increase in heterotrophic respiration sources over the growing season. Using a two end-member mixing model, we estimate that 55 ± 22% of soil respiration originated from autotrophic (root) sources in April, but their contribution dropped to 38 ± 21% in August. These findings suggest that the contribution of root respiration increases at a time of high productivity and/or as a result of relatively low microbial respiration in the early spring in this old-growth coniferous forest.Keywords: root respiration, stored carbon, residence time of soil carbon, photosynthates, soil CO₂ flux, radiocarbo

3-Phosphoinositide–Dependent Kinase 1 Potentiates Upstream Lesions on the Phosphatidylinositol 3-Kinase Pathway in Breast Carcinoma

Lesions of ERBB2, PTEN, and PIK3CA activate the phosphati- dylinositol 3-kinase (PI3K) pathway during cancer development by increasing levels of phosphatidylinositol-3,4,5-triphosphate (PIP3). 3-Phosphoinositide-dependent kinase 1 (PDK1) is the first node of the PI3K signal output and is required for activation of AKT. PIP3 recruits PDK1 and AKT to the cell membrane through interactions with their pleckstrin homology domains, allowing PDK1 to activate AKT by phosphorylating it at residue threonine-308. We show that total PDK1 protein and mRNA were overexpressed in a majority of human breast cancers and that 21% of tumors had five or more copies of the gene encoding PDK1, PDPK1. We found that increased PDPK1 copy number was associated with upstream pathway lesions (ERBB2 amplification, PTEN loss, or PIK3CA mutation), as well as patient survival. Examination of an independent set of breast cancers and tumor cell lines derived from multiple forms of human cancers also found increased PDK1 protein levels associated with such upstream pathway lesions. In human mammary cells, PDK1 enhanced the ability of upstream lesions to signal to AKT, stimulate cell growth and migration, and rendered cells more resistant to PDK1 and PI3K inhibition. After orthotopic transplantation, PDK1 overexpression was not oncogenic but dramatically enhanced the ability of ERBB2 to form tumors. Our studies argue that PDK1 overexpression and increased PDPK1 copy number are common occurrences in cancer that potentiate the oncogenic effect of upstream lesions on the PI3K pathway. Therefore, we conclude that alteration of PDK1 is a critical component of oncogenic PI3K signaling in breast cancer

Solving the conundrum of intra-specific variation in metabolic rate: A multidisciplinary conceptual and methodological toolkit

Researchers from diverse disciplines, including organismal and cellular physiology, sports science, human nutrition, evolution and ecology, have sought to understand the causes and consequences of the surprising variation in metabolic rate found among and within individual animals of the same species. Research in this area has been hampered by differences in approach, terminology and methodology, and the context in which measurements are made. Recent advances provide important opportunities to identify and address the key questions in the field. By bringing together researchers from different areas of biology and biomedicine, we describe and evaluate these developments and the insights they could yield, highlighting the need for more standardisation across disciplines. We conclude with a list of important questions that can now be addressed by developing a common conceptual and methodological toolkit for studies on metabolic variation in animals