Observing temporally varying synoptic‐scale total alkalinity and dissolved inorganic carbon in the Arctic Ocean

This is the final version. Available from Wiley via the DOI in this record. Data Availability Statement: The matchup database “OceanSODA-MDB” which was used in the algorithm evaluation is available at https:// data-cersat.ifremer.fr/data/ocean-carbonate/oceansoda-mmdb/ (Land et al., 2023; Land & Piollé, 2022). The python code used to run the analysis can be found at https://doi.org/10.5281/zenodo.10067204 (Green et al., 2023) and was adapted from DOI: https://doi.org/10.5281/zenodo.10069611 (Sims et al., 2022).The long-term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). The Arctic is a challenging region to gather enough data to examine the changes in carbonate chemistry over sufficient scales. However, algorithms that calculate carbonate chemistry parameters from more frequently measured parameters, such as temperature and salinity, can be used to fill in data gaps. Here, these published algorithms were evaluated against in situ measurements using different data input types (data from satellites or in situ re-analysis climatologies) across the Arctic Ocean. With the lowest uncertainties in the Atlantic influenced Seas (AiS), where re-analysis inputs achieved total alkalinity estimates with Root Mean Squared Deviation (RMSD) of 21 μmol kg−1 and a bias of 2 μmol kg−1 (n = 162) and dissolved inorganic carbon RMSD of 24 μmol kg−1 and bias of −14 μmol kg−1 (n = 262). AiS results using satellite observation inputs show similar bias but larger RMSD, although due to the shorter time span of available satellite observations, more contemporary in situ data would provide further assessment and improvement. Synoptic-scale observations of surface water carbonate conditions in the Arctic are now possible to monitor OA, but targeted in situ data collection is needed to enable the full exploitation of satellite observation-based approaches.European Space AgencyAXA XLJoint Transnational Call on Next Generation Climate Science in Europe for the Ocea

Tidal mixing of estuarine and coastal waters in the western English Channel is a control on spatial and temporal variability in seawater CO<sub>2</sub>

Surface ocean carbon dioxide (CO2) measurements are used to compute the oceanic air–sea CO2 flux. The CO2 flux component from rivers and estuaries is uncertain due to the high spatial and seasonal heterogeneity of CO2 in coastal waters. Existing high-quality CO2 instrumentation predominantly utilises showerhead and percolating style equilibrators optimised for open-ocean observations. The intervals between measurements made with such instrumentation make it difficult to resolve the fine-scale spatial variability of surface water CO2 at timescales relevant to the high frequency variability in estuarine and coastal environments. Here we present a novel dataset with unprecedented frequency and spatial resolution transects made at the Western Channel Observatory in the south-west of the UK from June to September 2016, using a fast-response seawater CO2 system. Novel observations were made along the estuarine– coastal continuum at different stages of the tide and reveal distinct spatial patterns in the surface water CO2 fugacity (fCO2) at different stages of the tidal cycle. Changes in salinity and fCO2 were closely correlated at all stages of the tidal cycle and suggest that the mixing of oceanic and riverine endmembers partially determines the variations in fCO2. The correlation between salinity and fCO2 was different in Cawsand Bay, which could be due to enhanced gas exchange or to enhanced biological activity in the region. The observations demonstrate the complex dynamics determining spatial and temporal patterns of salinity and fCO2 in the region. Spatial variations in observed surface salinity were used to validate the output of a regional high-resolution hydrodynamic model. The model enables a novel estimate of the air–sea CO2 flux in the estuarine–coastal zone. Air–sea CO2 flux variability in the estuarine–coastal boundary region is influenced by the state of the tide because of strong CO2 outgassing from the river plume. The observations and model output demonstrate that undersampling the complex tidal and mixing processes characteristic of estuarine and coastal environment biases quantification of air–sea CO2 fluxes in coastal waters. The results provide a mechanism to support critical national and regional policy implementation by reducing uncertainty in carbon budgets

Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis

<p>Abstract</p> <p>Background</p> <p>Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis.</p> <p>Results</p> <p>Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets.</p> <p>Conclusion</p> <p>Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.</p

GAD1 mRNA Expression and DNA Methylation in Prefrontal Cortex of Subjects with Schizophrenia

Dysfunction of prefrontal cortex in schizophrenia includes changes in GABAergic mRNAs, including decreased expression of GAD1, encoding the 67 kDa glutamate decarboxylase (GAD67) GABA synthesis enzyme. The underlying molecular mechanisms remain unclear. Alterations in DNA methylation as an epigenetic regulator of gene expression are thought to play a role but this hypothesis is difficult to test because no techniques are available to extract DNA from GAD1 expressing neurons efficiently from human postmortem brain. Here, we present an alternative approach that is based on immunoprecipitation of mononucleosomes with anti-methyl-histone antibodies differentiating between sites of potential gene expression as opposed to repressive or silenced chromatin. Methylation patterns of CpG dinucleotides at the GAD1 proximal promoter and intron 2 were determined for each of the two chromatin fractions separately, using a case-control design for 14 schizophrenia subjects affected by a decrease in prefrontal GAD1 mRNA levels. In controls, the methylation frequencies at CpG dinucleotides, while overall higher in repressive as compared to open chromatin, did not exceed 5% at the proximal GAD1 promoter and 30% within intron 2. Subjects with schizophrenia showed a significant, on average 8-fold deficit in repressive chromatin-associated DNA methylation at the promoter. These results suggest that chromatin remodeling mechanisms are involved in dysregulated GABAergic gene expression in schizophrenia

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data

Anaesthesia and airway management in mucopolysaccharidosis

Abstract This paper provides a detailed overview and dis-cussion of anaesthesia in patients with mucopolysacchari-dosis (MPS), the evaluation of risk factors in these patients and their anaesthetic management, including emergency airway issues. MPS represents a group of rare lysosomal storage disorders associated with an array of clinical mani-festations. The high prevalence of airway obstruction and restrictive pulmonary disease in combination with cardio-vascular manifestations poses a high anaesthetic risk to these patients. Typical anaesthetic problems include airway obstruction after induction or extubation, intubation diffi-culties or failure [can’t intubate, can’t ventilate (CICV)], possible emergency tracheostomy and cardiovascular and cervical spine issues. Because of the high anaesthetic risk, the benefits of a procedure in patients with MPS shoul

Ins and Outs of Cerebellar Modules

The modular concept of cerebellar connections has been advocated in the lifetime work of Jan Voogd. In this concept, a cerebellar module is defined as the conglomerate of one or multiple and non-adjacent, parasagittally arranged zones of Purkinje cells, their specific projection to a well-defined region of the cerebellar nuclei, and the climbing fiber input to these zones by a well-defined region of the inferior olivary complex. The modular organization of these olivo-cortico-nuclear connections is further exemplified by matching reciprocal connections between inferior olive and cerebellar nuclei. Because the different regions of the cerebellar nuclei show highly specific output patterns, cerebellar modules have been suggested to constitute functional entities. This idea is strengthened by the observation that anatomically defined modules adhere to the distribution of chemical markers in the cerebellar cortex suggesting that modules not only differ in their input and output relations but also may differ in operational capabilities. Here, I will briefly review some recent data on the establishment of cerebellar modules in rats. Furthermore, some evidence will be shown suggesting that the other main afferent system (i.e., mossy fibers), at least to some extent, also adheres to the modular organization. Finally, using retrograde transneuronal tracing with rabies virus, some evidence will be provided that several cerebellar modules may be involved in the control of individual muscles

Involvement of Cyclin K Posttranscriptional Regulation in the Formation of Artemia Diapause Cysts

Background: Artemia eggs tend to develop ovoviviparously to yield nauplius larvae in good rearing conditions; while under adverse situations, they tend to develop oviparously and encysted diapause embryos are formed instead. However, the intrinsic mechanisms regulating this process are not well understood. Principal Finding: This study has characterized the function of cyclin K, a regulatory subunit of the positive transcription elongation factor b (P-TEFb) in the two different developmental pathways of Artemia. In the diapause-destined embryo, Western blots showed that the cyclin K protein was down-regulated as the embryo entered dormancy and reverted to relatively high levels of expression once development resumed, consistent with the fluctuations in phosphorylation of position 2 serines (Ser2) in the C-terminal domain (CTD) of the largest subunit (Rpb1) of RNA polymerase II (RNAP II). Interestingly, the cyclin K transcript levels remained constant during this process. In vitro translation data indicated that the template activity of cyclin K mRNA stored in the postdiapause cyst was repressed. In addition, in vivo knockdown of cyclin K in developing embryos by RNA interference eliminated phosphorylation of the CTD Ser2 of RNAP II and induced apoptosis by inhibiting the extracellular signal-regulated kinase (ERK) survival signaling pathway. Conclusions/Significance: Taken together, these findings reveal a role for cyclin K in regulating RNAP II activity during diapause embryo development, which involves the post-transcriptional regulation of cyclin K. In addition, a further role wa