Validation and application of a multiparameter model for the densification of biochar

Densification is generally addressed to improve both the mechanical properties and the energy density of biomass feedstock. For such reason, it can also be used for biochar. However, the investigation of larger scales production by smaller academic equipment is not straightforward. It is therefore relevant to have access to models to facilitate a reliable comparison between the two different scales. In 2011, Holm et al. provided and validated a multiparameter model for analyzing industrial wood pelleting by a lab-scale single pellet press1. In 2017, the model was further verified for torrefied wood pelleting by Puig-Arnavat et al2. The intention of the authors of the present study was to prove the suitability of this model for pyrolyzed wood. It may help to understand how the densification process can be optimized at larger scales, especially regarding the forces that act through the pelleting channel, which are often cause of damages. Theoretically the model links the pelleting pressure , that the pellets undergo when ejected out of the channel, to the compression ratio c = x / 2r (where x is the length of the channel and r is its radius) 1. This is described by the equation: Please click Additional Files below to see the full abstract

A study of densified biochar as carbon source in the silicon and ferrosilicon production

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DOT1L promotes progenitor proliferation and primes neuronal layer identity in the developing cerebral cortex

Cortical development is controlled by transcriptional programs, which are orchestrated by transcription factors. Yet, stable inheritance of spatiooral activity of factors influencing cell fate and localization in different layers is only partly understood. Here we find that deletion of Dot1l in the murine telencephalon leads to cortical layering defects, indicating DOT1L activity and chromatin methylation at H3K79 impact on the cell cycle, and influence transcriptional programs conferring upper layer identity in early progenitors. Specifically, DOT1L prevents premature differentiation by increasing expression of genes that regulate asymmetric cell division (Vangl2, Cenpj). Loss of DOT1L results in reduced numbers of progenitors expressing genes including SoxB1 gene family members. Loss of DOT1L also leads to altered cortical distribution of deep layer neurons that express either TBR1, CTIP2 or SOX5, and less activation of transcriptional programs that are characteristic for upper layer neurons (Satb2, Pou3f3, Cux2, SoxC family members). Data from three different mouse models suggest that DOT1L balances transcriptional programs necessary for proper neuronal composition and distribution in the six cortical layers. Furthermore, because loss of DOT1L in the pre-neurogenic phase of development impairs specifically generation of SATB2-expressing upper layer neurons, our data suggest that DOT1L primes upper layer identity in cortical progenitors.Fil: Franz, Henriette. Universität Freiburg Im Breisgau; AlemaniaFil: Villarreal, Alejandro. Universität Freiburg Im Breisgau; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Heidrich, Stefanie. Universität Freiburg Im Breisgau; AlemaniaFil: Videm, Pavankumar. Universität Freiburg Im Breisgau; AlemaniaFil: Kilpert, Fabian. Max Planck Institute Of Immunobiology And Epigenetics; AlemaniaFil: Mestres, Ivan. Technical University Dresden; AlemaniaFil: Calegari, Federico. Technical University Dresden; AlemaniaFil: Backofen, Rolf. Universidad de Copenhagen; Dinamarca. Universität Freiburg Im Breisgau; AlemaniaFil: Manke, Thomas. Max Planck Institute Of Immunobiology And Epigenetics; AlemaniaFil: Vogel, Tanja. Universität Freiburg Im Breisgau; Alemani

MOF-associated complexes ensure stem cell identity and Xist repression

Histone acetyl transferases (HATs) play distinct roles in many cellular processes and are frequently misregulated in cancers. Here, we study the regulatory potential of MYST1-(MOF)-containing MSL and NSL complexes in mouse embryonic stem cells (ESCs) and neuronal progenitors. We find that both complexes influence transcription by targeting promoters and TSS-distal enhancers. In contrast to flies, the MSL complex is not exclusively enriched on the X chromosome, yet it is crucial for mammalian X chromosome regulation as it specifically regulates Tsix, the major repressor of Xist lncRNA. MSL depletion leads to decreased Tsix expression, reduced REX1 recruitment, and consequently, enhanced accumulation of Xist and variable numbers of inactivated X chromosomes during early differentiation. The NSL complex provides additional, Tsix-independent repression of Xist by maintaining pluripotency. MSL and NSL complexes therefore act synergistically by using distinct pathways to ensure a fail-safe mechanism for the repression of X inactivation in ESCs

Exercise training and high-sensitivity cardiac troponin T in patients with heart failure with reduced ejection fraction

Plasma cell disorders (PCDs) are identified in the clinical lab by detecting the monoclonal immunoglobulin (M-protein) which they produce. Traditionally, serum protein electrophoresis methods have been utilized to detect and isotype Mproteins. Increasing demands to detect low-level disease and new therapeutic monoclonal immunoglobulin treatments have stretched the electrophoretic methods to their analytical limits. Newer techniques based on mass spectrometry (MS) are emerging which have improved clinical and analytical performance. MS is gaining traction into clinical laboratories, and has replaced immunofixation electrophoresis (IFE) in routine practice at one institution. The International Myeloma Working Group (IMWG) Mass Spectrometry Committee reviewed the literature in order to summarize current data and to make recommendations regarding the role of mass spectrometric methods in diagnosing and monitoring patients with myeloma and related disorders. Current literature demonstrates that immune-enrichment of immunoglobulins coupled to intact light chain MALDI-TOF MS has clinical characteristics equivalent in performance to IFE with added benefits of detecting additional risk factors for PCDs, differentiating Mprotein from therapeutic antibodies, and is a suitable replacement for IFE for diagnosing and monitoring multiple myeloma and related PCDs. In this paper we discuss the IMWG recommendations for the use of MS in PCDs.publishedVersio

The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis. Availability: The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench