Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia

The accuracy of three satellite rainfall products (TMPA 3B42RT, CMORPH and PERSIANN) was investigated through comparison with grid cell average ground station rainfall data in Indonesia, with a focus on their ability to detect patterns of low rainfall that may lead to drought conditions. Each of the three products underestimated rainfall in dry season months. The CMORPH and PERSIANN data differed most from ground station data and were also very different from the TMPA 3B42RT data. It proved possible to improve TMPA 3B42RT estimates by applying a single empirical bias correction equation that was uniform in space and time. For the six regions investigated, this reduced the root mean square error for estimates of dry season rainfall totals by a mean 9% (from 44 to 40 mm) and for annual totals by 14% (from 77 to 66 mm). The resulting errors represent 10% and 3% of mean dry season and annual rainfall, respectively. The accuracy of these bias corrected TMPA 3B42RT data is considered adequate for use in real-time drought monitoring in Indonesia. Compared to drought monitoring with only ground stations, this use of satellite-based rainfall estimates offers important advantages in terms of accuracy, spatial coverage, timeliness and cost efficiency

B2.5-Eunomia simulations of Magnum-PSI detachment experiments: I. Quantitative comparisons with experimental measurements

Detachment experiments have been carried out in the linear plasma device Magnum-PSI by increasing the gas pressure near the target. In order to have a proper detailed analysis of the mechanism behind momentum and power loss in detachment, a quantitative match is pursued between B2.5-Eunomia solutions and experimental data. B2.5 is a multi fluid plasma code and Eunomia is a Monte Carlo solver for neutral particles, and they are coupled together to provide steady-state solution of the plasma and neutral distribution in space. B2.5-Eunomia input parameters are adjusted to produce a close replication of the plasma beam measured in the experiments without any gas puffing in the target chamber. Using this replication as an initial condition, the neutral pressure near the plasma beam target is exclusively increased during simulation, matching the pressures measured in the experiments. Reasonable agreement is found between the electron temperature of the simulation results with experimental measurements using laser Thomson scattering near the target. The simulations also reveal the effect of increased gas pressure on the plasma current, effectively reducing the current penetration from the plasma source. B2.5-Eunomia is capable of reproducing detachment characteristics, namely the loss of plasma pressure along the magnetic field and the reduction of particle and heat flux to the target. The simulation results for plasma and neutrals will allow future studies of the exact contribution of individual plasma-neutral collisions to momentum and energy loss in detachment in Magnum-PSI.</p

Preimplantation genetic testing for Neurofibromatosis type 1:more than 20 years of clinical experience

Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder that affects the skin and the nervous system. The condition is completely penetrant with extreme clinical variability, resulting in unpredictable manifestations in affected offspring, complicating reproductive decision-making. One of the reproductive options to prevent the birth of affected offspring is preimplantation genetic testing (PGT). We performed a retrospective review of the medical files of all couples (n = 140) referred to the Dutch PGT expert center with the indication NF1 between January 1997 and January 2020. Of the couples considering PGT, 43 opted out and 15 were not eligible because of failure to identify the underlying genetic defect or unmet criteria for in vitro fertilization (IVF) treatment. The remaining 82 couples proceeded with PGT. Fertility assessment prior to IVF treatment showed a higher percentage of male infertility in males affected with NF1 compared to the partners of affected females. Cardiac evaluations in women with NF1 showed no contraindications for IVF treatment or pregnancy. For 67 couples, 143 PGT cycles were performed. Complications of IVF treatment were not more prevalent in affected females compared to partners of affected males. The transfer of 174 (out of 295) unaffected embryos led to 42 ongoing pregnancies with a pregnancy rate of 24.1% per embryo transfer. There are no documented cases of misdiagnosis following PGT in this cohort. With these results, we aim to provide an overview of PGT for NF1 with regard to success rate and safety, to optimize reproductive counseling and PGT treatment for NF1 patients.</p

LiMeS-Lab:An Integrated Laboratory for the Development of Liquid–Metal Shield Technologies for Fusion Reactors

The liquid metal shield laboratory (LiMeS-Lab) will provide the infrastructure to develop, test, and compare liquid metal divertor designs for future fusion reactors. The main research topics of LiMeS-lab will be liquid metal interactions with the substrate material of the divertor, the continuous circulation and capillary refilling of the liquid metal during intense plasma heat loading and the retention of plasma particles in the liquid metal. To facilitate the research, four new devices are in development at the Dutch Institute for Fundamental Energy Research and the Eindhoven University of Technology: LiMeS-AM: a custom metal 3D printer based on powder bed fusion; LiMeS-Wetting, a plasma device to study the wetting of liquid metals on various substrates with different surface treatments; LiMeS-PSI, a linear plasma generator specifically adapted to operate continuous liquid metal loops. Special diagnostic protection will also be implemented to perform measurements in long duration shots without being affected by the liquid metal vapor; LiMeS-TDS, a thermal desorption spectroscopy system to characterize deuterium retention in a metal vapor environment. Each of these devices has specific challenges due to the presence and deposition of metal vapors that need to be addressed in order to function. In this paper, an overview of LiMeS-Lab will be given and the conceptual designs of the last three devices will be presented.</p

Uncovering Enhancer Functions Using the α-Globin Locus

Over the last three decades, studies of the α- and β-globin genes clusters have led to elucidation of the general principles of mammalian gene regulation, such as RNA stability, termination of transcription, and, more importantly, the identification of remote regulatory elements. More recently, detailed studies of α-globin regulation, using both mouse and human loci, allowed the dissection of the sequential order in which transcription factors are recruited to the locus during lineage specification. These studies demonstrated the importance of the remote regulatory elements in the recruitment of RNA polymerase II (PolII) together with their role in the generation of intrachromosomal loops within the locus and the removal of polycomb complexes during differentiation. The multiple roles attributed to remote regulatory elements that have emerged from these studies will be discussed

CpG binding protein (CFP1) occupies open chromatin regions of active genes, including enhancers and non-CpG islands

Additional file 1. Fig. S1: Analysis of CFP1 binding at individual loci and CpG islands (CGIs). (A-B) Analysis of CFP1 binding at the human α-globin locus in expressing and non-expressing cells. (A) Real-Time PCR analysis of immunoprecipitated chromatin using CFP1 antibody in human erythroblasts (red) and B-lymphocytes (blue). The y-axis represents enrichment over the input DNA, normalised to a control sequence in the human 18S gene. The x-axis represents the positions of Taqman probes used. The coding sequence is represented by the three exons (Promoter/Ex1, Ex2, Ex3) of the α-globin genes. 218 and hBact denote control sequences adjacent to the CpG islands of the human LUC7L (218) and ACTB promoters. Error bars correspond to ± 1 SD from at least two independent ChIPs. (B) Real-Time PCR analysis of immunoprecipitated chromatin using the CFP1 antibody indicated in humanised erythroblasts (normal, +MCS-R2 (left) and mutant, MCS-R2 (right). The y-axis represents enrichment over the input DNA, normalised to a control sequence in the mouse GAPDH gene. CpG Act denotes additional control sequence at the CGI of the mouse ACTB gene. The amplicons highlighted in red represent deleted regions in the humanised mice, for which no PCR signal is observed. Error bars correspond to ± 1 SD from at least two independent ChIPs. (C) CFP1 ChIP signal intensity in the top 200 peaks, by antibody and by cell type. Abcam, ab56035 antibody. Roeder, main antibody used in this study. (D) Analysis of CGI (green) and non-CGI (blue) transcription start sites (1-kb window, centred on TSS). Gene symbols shown with CpG content of individual loci in parentheses. Greek letters represent individual globin genes. Fig. S2: Peak overlaps of CFP1 and marks of active and repressed chromatin in transcription start sites (TSSs). Peaks were detected by MACS2. Venn diagrams show that CFP1 peaks within 1-kb of TSSs are strongly associated with H3K4me3 histone mark and poorly associated with H3K27me3 repressive histone mark. Cell types are (A) ERY and (B) EBV. Public data sets: * NCBI GEO GSE36985, ** NCBI GEO GSE50893. Fig. S3: UCSC tracks showing CFP1 and other ChIP signals in gene loci in erythroblasts (ERY) and EBV-transformed B-lymphoblasts (EBV). Hg38 coordinates for multiple genes, CpG islands (CGI, green boxes), and putative regulatory regions (blue boxes) are shown. CFP1 signals are shown in dark reds, inputs in grey, histone H3 signals in blues and open chromatin marks in greens. All ChIP pileups are scaled to 1x coverage genome-wide and shown in a range 0–50, except CFP1 (Roeder) is shown with extended range and H3K27me3 graphs scaled by 2x. (A) Tissue-specific binding of CFP1 to CGI promoters of tissue-specifically expressed genes. Left (chr16), CGI promoters of active genes in alpha globin locus are CFP1-bound in ERY, and unbound in EBV. Flanking regions are included, with known tissue-specific enhancers. Right (chr6), first seven exons of IRF4 locus, active in EBV and inactive in ERY, with CFP1 binding to CGI promoter in EBV only. (B) CGI promoters of housekeeping genes are CFP1 bound and unmarked by H3K27me3. Left (chr7), ACTB locus. Right (chr16), LUC7L locus. (C) CGI promoter of RHBDF1 locus (chr16) has H3K27me3 mark and the absence of CFP1 binding in both ERY and EBV. Fig. S4: Western blot analysis of CGBP (CFP1) expression in mouse and human erythroid and human lymphoid cell types. Whole cell extracts (20 µg) were loaded in each lane (1) mouse ES, (2) U-MEL, (3) I-MEL, (4) mouse primary erythroblasts and (5) human primary T lymphocytes and (6) human primary erythroblasts and separated on a 10% SDS-polyacrylamide gel. CFP1 antibody was used at a 1:1000 dilution. Fig. S5: Similar cell type-specific CFP1 read depth at CGI TSS of HBA1 gene and non-CGI TSS of HBB gene. Upper two tracks use the main antibody, and second two tracks use the commercial antibody. Coordinates are from the hg38 human genome build. Read depths are averaged in 50 bp bins and normalised to 1x genome-wide coverage. Blue boxes, known regulatory regions; green box, CGI. Fig. S6: Distribution of TrxG components in erythroid cells. Green indicates CGI and blue indicates other putative regulatory regions. All loci transcribed right to left. Pileups are shown scaled to 1x genome coverage, with full scale 0–50x depth. (A) Housekeeping genes ACTB, left (chr7), and LUC7L, right (chr16). (B) β-globin locus (chr11), (C) Non-expressed RHBDF1 locus (chr16). Fig. S7: Overlap of TrxG subunit ChIP peaks in a high-confidence subset of regions. SET1A complexes are represented by CFP1-SET1A colocalisation. MLL1/2 complexes are represented by Menin, and MLL3/4 complexes are represented by UTX, respectively. HCF1 is found in SET1A/B and MLL1/2 complexes, and RBBP5 is a member of SET1A/B and MLL1/2/3/4 complexes. Red outline (4220 peaks) shows strong colocalisation of Menin and CFP1-SET1A, accounting for the vast majority (99.5%) of 4242 CFP1-SET1A and half (50.0%) of 8432 Menin peak regions. Majority (87.0%, 2089/2400 peaks) of HCF1 (blue region) is accounted for by approximately half (49.5%, 2089/4220) of regions of Menin-SET1A-CFP1 colocalisation. Regions where either SET1A-CFP1 or Menin or both are colocalised with HCF1 (blue dashed line) accounts for nearly all (99.6%, 2390/2400) HCF1 regions, suggesting that HCF1 bound to DNA is primarily present as part of SET1A/B or MLL1/2 complexes. Fig. S8: Chromatin accessibility in TSSs and enhancers in erythroid cells as measured by ATAC-seq and DNase-seq. 1x-normalised, input-subtracted signals from ATAC-seq and DNase were averaged in a 2-kb window about TSSs and putative enhancers. Z-score transformed values for ATAC-seq and DNase-seq at a given locus were averaged. Fig. S9: Relationship of CFP1 signal to three predictive factors in top-decile open chromatin regions. A linear combination of CpG density and SET1A and H3K4me3 ChIP signals explains a substantial fraction of variation in CFP1 ChIP signal. Table S1: Bias of CFP1 for CGI TSSs in cell types and gene classes. Table S2: Bias of CFP1 for housekeeping gene TSSs. Table S3: Motifs associated with CFP1 peaks. Table S4: Dependence of CFP1 ChIP signal in erythroid cells on covariates putatively associated with its binding. Table S5: Analysis of variance of CFP1 signal in top-decile open chromatin regions surrounding TSSs and putative enhancers

Differential regulation of the alpha-globin locus by Kruppel-like factor 3 in erythroid and non-erythroid cells

Background: Krüppel-like Factor 3 (KLF3) is a broadly expressed zinc-finger transcriptional repressor with diverse biological roles. During erythropoiesis, KLF3 acts as a feedback repressor of a set of genes that are activated by Krüppel-like Factor 1 (KLF1). Noting that KLF1 binds α-globin gene regulatory sequences during erythroid maturation, we sought to determine whether KLF3 also interacts with the α-globin locus to regulate transcription. Results: We found that expression of a human transgenic α-globin reporter gene is markedly up-regulated in fetal and adult erythroid cells of Klf3−/− mice. Inspection of the mouse and human α-globin promoters revealed a number of canonical KLF-binding sites, and indeed, KLF3 was shown to bind to these regions both in vitro and in vivo. Despite these observations, we did not detect an increase in endogenous murine α-globin expression in Klf3−/− erythroid tissue. However, examination of murine embryonic fibroblasts lacking KLF3 revealed significant de-repression of α-globin gene expression. This suggests that KLF3 may contribute to the silencing of the α-globin locus in non-erythroid tissue. Moreover, ChIP-Seq analysis of murine fibroblasts demonstrated that across the locus, KLF3 does not occupy the promoter regions of the α-globin genes in these cells, but rather, binds to upstream, DNase hypersensitive regulatory regions. Conclusions: These findings reveal that the occupancy profile of KLF3 at the α-globin locus differs in erythroid and non-erythroid cells. In erythroid cells, KLF3 primarily binds to the promoters of the adult α-globin genes, but appears dispensable for normal transcriptional regulation. In non-erythroid cells, KLF3 distinctly binds to the HS-12 and HS-26 elements and plays a non-redundant, albeit modest, role in the silencing of α-globin expression. </p

Different mechanisms are implicated in ERBB2 gene overexpression in breast and in other cancers

The ERBB2 gene is overexpressed in 30% of breast cancers and this has been correlated with poor prognosis. ERBB2 is upregulated in other cancers such as prostate, pancreas, colon and ovary. In breast cancer cells, the mechanisms leading to ERBB2 gene overexpression are increased transcription and gene amplification. In these cancers, AP-2 transcription factors are involved in ERBB2 overexpression, and AP-2 levels are correlated with p185(c-erbB-2) levels. In this work, we wanted to know if the same molecular mechanisms are responsible for the ERBB2 upregulation in non-breast cancers. We compared ERBB2 gene copy number, p185(c-erbB-2) and mRNA levels with AP-2 levels in several ovary, prostate, colon and pancreas cancer cells. A moderate expression of erbB-2 mRNA and protein were observed in some cells without gene amplification. In contrast to breast cancer cells, AP-2 factors were absent or low in some non-breast cells which did express ERBB2. It is thus likely that AP-2 is not a major player in the increased levels of erbB-2 transcripts in non-breast cancer cells. The transcriptional activity of the ERBB2 promoter in colon and ovary cancer cells was estimated using reporter vectors. The results showed that the promoter regions involved in ERBB2 gene overexpression in breast cancer cells are different from those that lead to the gene upregulation in colon and ovary cancers. In conclusion, our results indicate that different transcriptional and post-transcriptional mechanisms are responsible for the increased levels of erbB-2 transcript and protein in breast and non-breast cancer cells