clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.</div

Practical Cooling Strategies During Continuous Exercise in Hot Environments: A Systematic Review and Meta-Analysis

Background Performing exercise in thermally stressful environments impairs exercise capacity and performance. Cooling during exercise has the potential to attenuate detrimental increases in body temperature and improve exercise capacity and performance. Objective The objective of this review was to assess the effectiveness of practical cooling strategies applied during continuous exercise in hot environments on body temperature, heart rate, whole body sweat production, rating of perceived exertion (RPE), thermal perception and exercise performance. Methods Electronic database searches of MEDLINE, SPORTDiscus, Scopus and Physiotherapy Evidence Database (PEDro) were conducted using medical subject headings, indexing terms and keywords. Studies were eligible if participants were defined as ‘healthy’, the exercise task was conducted in an environment ≥25 °C, it used a cooling strategy that would be practical for athletes to use during competition, cooling was applied during a self-paced or fixed-intensity trial, participants exercised continuously, and the study was a randomised controlled trial with the comparator either a thermoneutral equivalent or no cooling. Data for experimental and comparator groups were meta-analysed and expressed as a standardised mean difference and 95 % confidence interval. Results Fourteen studies including 135 participants met the eligibility criteria. Confidence intervals for meta-analysed data included beneficial and detrimental effects for cooling during exercise on core temperature, mean skin temperature, heart rate and sweat production during fixed-intensity exercise. Cooling benefited RPE and thermal perception during fixed-intensity exercise and improved self-paced exercise performance. Conclusion Cooling during fixed-intensity exercise, particularly before a self-paced exercise trial, improves endurance performance in hot environments by benefiting RPE and thermal perception, but does not appear to attenuate increases in body temperature

Light enough to travel: migratory bats have smaller brains, but not larger hippocampi, than sedentary species

Migratory bird species have smaller brains than non-migratory species. The behavioural flexibility/migratory precursor hypothesis suggests that sedentary birds have larger brains to allow the behavioural flexibility required in a seasonally variable habitat. The energy trade-off hypothesis proposes that brains are heavy, energetically expensive and therefore, incompatible with migration. Here, we compared relative brain, neocortex and hippocampus volume between migratory and sedentary bats at the species-level and using phylogenetically independent contrasts. We found that migratory bats had relatively smaller brains and neocortices than sedentary species. Our results support the energy trade-off hypothesis because bats do not exhibit the same degree of flexibility in diet selection as sedentary birds. Our results also suggest that bat brain size differences are subtler than those found in birds, perhaps owing to bats' shorter migration distances. Conversely, we found no difference in relative hippocampus volume between migratory and sedentary species, underscoring our limited understanding of the role of the hippocampus in bats

Using a whole-body 31P birdcage transmit coil and 16-element receive array for human cardiac metabolic imaging at 7T.

PURPOSE: Cardiac phosphorus magnetic resonance spectroscopy (31P-MRS) provides unique insight into the mechanisms of heart failure. Yet, clinical applications have been hindered by the restricted sensitivity of the surface radiofrequency-coils normally used. These permit the analysis of spectra only from the interventricular septum, or large volumes of myocardium, which may not be meaningful in focal disease. Löring et al. recently presented a prototype whole-body (52 cm diameter) transmit/receive birdcage coil for 31P at 7T. We now present a new, easily-removable, whole-body 31P transmit radiofrequency-coil built into a patient-bed extension combined with a 16-element receive array for cardiac 31P-MRS. MATERIALS AND METHODS: A fully-removable (55 cm diameter) birdcage transmit coil was combined with a 16-element receive array on a Magnetom 7T scanner (Siemens, Germany). Electro-magnetic field simulations and phantom tests of the setup were performed. In vivo maps of B1+, metabolite signals, and saturation-band efficiency were acquired across the torsos of eight volunteers. RESULTS: The combined (volume-transmit, local receive array) setup increased signal-to-noise ratio 2.6-fold 10 cm below the array (depth of the interventricular septum) compared to using the birdcage coil in transceiver mode. The simulated coefficient of variation for B1+ of the whole-body coil across the heart was 46.7% (surface coil 129.0%); and the in vivo measured value was 38.4%. Metabolite images of 2,3-diphosphoglycerate clearly resolved the ventricular blood pools, and muscle tissue was visible in phosphocreatine (PCr) maps. Amplitude-modulated saturation bands achieved 71±4% suppression of phosphocreatine PCr in chest-wall muscles. Subjects reported they were comfortable. CONCLUSION: This easy-to-assemble, volume-transmit, local receive array coil combination significantly improves the homogeneity and field-of-view for metabolic imaging of the human heart at 7T

The origin of carbonate cements in the hildasay reservoir, Cambo Field, Faroe-Shetland Basin; clumped isotopic analysis and implications for reservoir performance

The early Eocene paralic sandstones of the Hildasay Member of the Flett Formation form the major oil-bearing reservoir in the Cambo Field, located in the Faroe-Shetland Basin. The sandstones locally contain calcite-cemented intervals that vary in thickness from decimetre to over 1 m. A calcite-cemented interval from well 204/10a-5 has been analysed petrographically and using clumped isotopes to determine its mode of formation and potential lateral extent. Petrographic analysis shows the cemented interval to consist of ferroan calcite, with a consistent dull red cathodoluminescence, suggesting a single phase of precipitation. The centre of the cemented interval comprises a finer grained unit with detrital kerogen and early sphaerosiderite, while the rest comprises homogeneous porosity-occluding ferroan calcite. The early sphaerosiderite in the centre is replaced by ferroan calcite with a high δ13 carbon isotopic signature (δ13 CVPDB = 7.53‰) suggesting that it formed during anaerobic methanogenesis or fermentation. Samples from the intermediate zone have a lower δ13 carbon isotopic composition (δ13 CVPDB = 0.72 to −3.68‰). In comparison the outer margin of the cemented unit has an even lower δ13 carbon isotopic signature (δ13 CVPDB = −15.5 to 15.9‰) more typical of a strong aerobic oxidation source. The oxygen isotopic signature of the cements is similar (δ18 OVPDB = −10.9 to −12.2‰). Analysis of the clumped isotopes suggest that the ferroan calcite formed at ∼40–50 °C, from pore waters that were predominantly meteoric in origin (δ18 OVSMOW = −4.8 to −5.7‰). Burial history modelling would suggest that the cements formed at a depth of ∼500–1000 m. Meteoric water was likely to have been introduced during the formation of the mid-Lutetian unconformity ∼45Mya, approximately 10 Mya after the sandstones were deposited. The model proposed is that ferroan calcite precipitation was initiated in the fine lag deposits that contained the kerogen and sphaerosiderite, and then grew outwards. If the model is correct, the cemented units should be restricted to this facies and consequently of limited lateral extent. Consequently, it is likely that the cemented intervals will have a limited impact upon the reservoir performance and are unlikely to act as major barriers to fluid flow

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets.

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets.

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function