New Plasma Separation Glucose Oxidase-based Glucometer in Monitoring of Blood With Different PO2 Levels

BackgroundThe PalmLab glucometer is a newly designed plasma separation glucose oxidase (GO)-based glucometer. Past studies have shown that the accuracy of GO-based glucometers is compromised when measurements are taken in patients with high PO2 levels. We performed a two-arm study comparing the fitness of the PalmLab blood glucometer with that of a standard glucose analyzer in monitoring blood glucose levels in pediatric patients, especially when arterial partial pressure of oxygen (PO2) was high.MethodsIn the first arm of the study, arterial blood samples from pediatric patients were measured by the PalmLab blood glucometer and the YSI 2302 Plus Glucose/Lactate analyzer. In the second arm of the study, venous blood samples from adult volunteers were spiked with glucose water to prepare three different levels of glucose (65, 150, and 300mg/dL) and then oxygenated to six levels of PO2 (range, 40–400mmHg). The biases of the PalmLab glucometer were calculated.ResultsA total of 162 samples were collected in the first arm of the study. Results of linear regression showed that the coefficient of determination (R2) between PalmLab glucometer and standard glucose analyzer was 0.9864. Error grid analysis revealed that all the results were within Zone A (clinically accurate estimate zone). The biases between the two systems were low at different PO2 levels. In the second arm of the study, the results were also unaffected by changes in PO2.ConclusionThe PalmLab glucometer provides accurate results in samples with high PO2 and is suitable for measuring arterial glucose levels in pediatric patients

106GBaud (200G PAM4) CWDM EML for 800G/1.6T Optical Networks and AI Applications

We report ultrahigh speed 106GBaud (200G PAM4) electro-absorption modulated laser (EML) for 800G and 1.6T optical transmission. Four CWDM EMLs of 1271, 1291, 1311 and 1331nm in 800G FR4 optical transceivers show clear eye diagram after 2km. Our 106GBaud EMLs show high bandwidth, high extinction ratio, low threshold current and high power, making it a suitable source laser for 800G/1.6T and AI applications.&nbsp

Identification of Anchor Genes during Kidney Development Defines Ontological Relationships, Molecular Subcompartments and Regulatory Pathways

The development of the mammalian kidney is well conserved from mouse to man. Despite considerable temporal and spatial data on gene expression in mammalian kidney development, primarily in rodent species, there is a paucity of genes whose expression is absolutely specific to a given anatomical compartment and/or developmental stage, defined here as ‘anchor’ genes. We previously generated an atlas of gene expression in the developing mouse kidney using microarray analysis of anatomical compartments collected via laser capture microdissection. Here, this data is further analysed to identify anchor genes via stringent bioinformatic filtering followed by high resolution section in situ hybridisation performed on 200 transcripts selected as specific to one of 11 anatomical compartments within the midgestation mouse kidney. A total of 37 anchor genes were identified across 6 compartments with the early proximal tubule being the compartment richest in anchor genes. Analysis of minimal and evolutionarily conserved promoter regions of this set of 25 anchor genes identified enrichment of transcription factor binding sites for Hnf4a and Hnf1b, RbpJ (Notch signalling), PPARγ:RxRA and COUP-TF family transcription factors. This was reinforced by GO analyses which also identified these anchor genes as targets in processes including epithelial proliferation and proximal tubular function. As well as defining anchor genes, this large scale validation of gene expression identified a further 92 compartment-enriched genes able to subcompartmentalise key processes during murine renal organogenesis spatially or ontologically. This included a cohort of 13 ureteric epithelial genes revealing previously unappreciated compartmentalisation of the collecting duct system and a series of early tubule genes suggesting that segmentation into proximal tubule, loop of Henle and distal tubule does not occur until the onset of glomerular vascularisation. Overall, this study serves to illuminate previously ill-defined stages of patterning and will enable further refinement of the lineage relationships within mammalian kidney development

Dynamic chromatin organization and regulatory interactions in human endothelial cell differentiation

Vascular endothelial cells are a mesoderm-derived lineage with many essential functions, including angiogenesis and coagulation. The gene-regulatory mechanisms underpinning endothelial specialization are largely unknown, as are the roles of chromatin organization in regulating endothelial cell transcription. To investigate the relationships between chromatin organization and gene expression, we induced endothelial cell differentiation from human pluripotent stem cells and performed Hi-C and RNA-sequencing assays at specific time points. Long-range intrachromosomal contacts increase over the course of differentiation, accompanied by widespread heteroeuchromatic compartment transitions that are tightly associated with transcription. Dynamic topologically associating domain boundaries strengthen and converge on an endothelial cell state, and function to regulate gene expression. Chromatin pairwise point interactions (DNA loops) increase in frequency during differentiation and are linked to the expression of genes essential to vascular biology. Chromatin dynamics guide transcription in endothelial cell development and promote the divergence of endothelial cells from cardiomyocytes

Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations

Heterogeneity of cell states represented in pluripotent cultures have not been described at the transcriptional level. Since gene expression is highly heterogeneous between cells, single-cell RNA sequencing can be used to identify how individual pluripotent cells function. Here, we present results from the analysis of single-cell RNA sequencing data from 18,787 individual WTC CRISPRi human induced pluripotent stem cells. We developed an unsupervised clustering method, and through this identified four subpopulations distinguishable on the basis of their pluripotent state including: a core pluripotent population (48.3%), proliferative (47.8%), early-primed for differentiation (2.8%) and late-primed for differentiation (1.1%). For each subpopulation we were able to identify the genes and pathways that define differences in pluripotent cell states. Our method identified four discrete predictor gene sets comprised of 165 unique genes that denote the specific pluripotency states; and using these sets, we developed a multigenic machine learning prediction method to accurately classify single cells into each of the subpopulations. Compared against a set of established pluripotency markers, our method increases prediction accuracy by 10%, specificity by 20%, and explains a substantially larger proportion of deviance (up to 3-fold) from the prediction model. Finally, we developed an innovative method to predict cells transitioning between subpopulations, and support our conclusions with results from two orthogonal pseudotime trajectory methods

Groups and protocluster candidates in the CLAUDS and HSC-SSP joint deep surveys

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMUsing the extended halo-based group finder developed by Yang et al., which is able to deal with galaxies via spectroscopic and photometric redshifts simultaneously, we construct galaxy group and candidate protocluster catalogs in a wide redshift range (0 2.0. By checking the galaxy number distributions within a 5-7 h -1Mpc projected separation and a redshift difference Δz ≤ 0.1 around those richest groups at redshift z > 2, we identify lists of 761, 343, and 43 protocluster candidates in the redshift bins 2 ≤ z < 3, 3 ≤ z < 4, and z ≥ 4, respectively. In general, these catalogs of galaxy groups and protocluster candidates will provide useful environmental information in probing galaxy evolution along cosmic tim

Genotype-free demultiplexing of pooled single-cell RNA-seq.

A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit