3,181 research outputs found
Co-existence of other copy number variations with 22q11.2 deletion or duplication: a modifier for variable phenotypes of the syndrome?
<p>Abstract</p> <p>Background</p> <p>The phenotype in patients with a 22q11.2 deletion or duplication can be extremely variable, and the causes of such as variations are not well known.</p> <p>Results</p> <p>We observed additional copy number variations (CNVs) in 2 of 15 cases with a 22q11.2 deletion or duplication. Both cases were newborn babies referred for severe congenital heart defects. The first case had a deletion with a size of approximately 1.56 Mb involving multiple genes including <it>STS </it>in the Xp22.31 region along with a 22q11.2 deletion. The second case had a duplication of 605 kb in the 15q13.3 region encompassing <it>CHRNA7 </it>and a deletion of 209 kb involving the <it>RBFOX1 </it>gene in the 16p13.2 region, in addition to 22q11.2 duplication.</p> <p>Discussion</p> <p>Our observations have shown that additional CNVs are not rare (2/15, 13%) in patients with a 22q11.2 deletion or duplication. We speculate that these CNVs may contribute to phenotype variations of 22q11.2 microdeletion/duplication syndromes as genomic modifiers.</p
Parvovirus B19 infection presenting with severe erythroid aplastic crisis during pregnancy in a woman with autoimmune hemolytic anemia and alpha-thalassemia trait: a case report
Dual-mode adaptive-SVD ghost imaging
In this paper, we present a dual-mode adaptive singular value decomposition
ghost imaging (A-SVD GI), which can be easily switched between the modes of
imaging and edge detection. It can adaptively localize the foreground pixels
via a threshold selection method. Then only the foreground region is
illuminated by the singular value decomposition (SVD) - based patterns,
consequently retrieving high-quality images with fewer sampling ratios. By
changing the selecting range of foreground pixels, the A-SVD GI can be switched
to the mode of edge detection to directly reveal the edge of objects, without
needing the original image. We investigate the performance of these two modes
through both numerical simulations and experiments. We also develop a
single-round scheme to halve measurement numbers in experiments, instead of
separately illuminating positive and negative patterns in traditional methods.
The binarized SVD patterns, generated by the spatial dithering method, are
modulated by a digital micromirror device (DMD) to speed up the data
acquisition. This dual-mode A-SVD GI can be applied in various applications,
such as remote sensing or target recognition, and could be further extended for
multi-modality functional imaging/detection
Quantitative and dark field ghost imaging with ultraviolet light
Ultraviolet (UV) imaging enables a diverse array of applications, such as
material composition analysis, biological fluorescence imaging, and detecting
defects in semiconductor manufacturing. However, scientific-grade UV cameras
with high quantum efficiency are expensive and include a complex thermoelectric
cooling system. Here, we demonstrate a UV computational ghost imaging (UV-CGI)
method to provide a cost-effective UV imaging and detection strategy. By
applying spatial-temporal illumination patterns and using a 325 nm laser
source, a single-pixel detector is enough to reconstruct the images of objects.
To demonstrate its capability for quantitative detection, we use UV-CGI to
distinguish four UV-sensitive sunscreen areas with different densities on a
sample. Furthermore, we demonstrate dark field UV-CGI in both transmission and
reflection schemes. By only collecting the scattered light from objects, we can
detect the edges of pure phase objects and small scratches on a compact disc.
Our results showcase a feasible low-cost solution for non-destructive UV
imaging and detection. By combining it with other imaging techniques, such as
hyperspectral imaging or time-resolved imaging, a compact and versatile UV
computational imaging platform may be realized for future applications.Comment: 9 pages, 5 figure
CSST forecast: impact from non-Gaussian covariances and requirements on systematics-control
The precise estimation of the statistical errors and accurate removal of the
systematical errors are the two major challenges for the stage IV cosmic shear
surveys. We explore their impact for the China Space-Station Telescope (CSST)
with survey area up to redshift . We consider
statistical error contributed from Gaussian covariance, connected non-Gaussian
covariance and super-sample covariance. We find the super-sample covariance can
largely reduce the signal-to-noise of the two-point statistics for CSST,
leading to a loss in the figure-of-merit for the matter clustering
properties ( plane) and in the dark energy
equation-of-state ( plane). We further put requirements of
systematics-mitigation on: intrinsic alignment of galaxies, baryonic feedback,
shear multiplicative bias, and bias in the redshift distribution, for an
unbiased cosmology. The to level requirements emphasize
strong needs in related studies, to support future model selections and the
associated priors for the nuisance parameters.Comment: submitted to MNRA
A case of AML characterized by a novel t(4;15)(q31;q22) translocation that confers a growth-stimulatory response to retinoid-based therapy
Here we report the case of a 30-year-old woman with relapsed acute myeloid leukemia (AML) who was treated with all-transretinoic acid (ATRA) as part of investigational therapy (NCT02273102). The patient died from rapid disease progression following eight days of continuous treatment with ATRA. Karyotype analysis and RNA-Seq revealed the presence of a novel t(4;15)(q31;q22) reciprocal translocation involving theTMEM154andRASGRF1genes. Analysis of primary cells from the patient revealed the expression ofTMEM154-RASGRF1mRNA and the resulting fusion protein, but no expression of the reciprocalRASGRF1-TMEM154fusion. Consistent with the response of the patient to ATRA therapy, we observed a rapid proliferation of t(4;15) primary cells following ATRA treatment ex vivo. Preliminary characterization of the retinoid response of t(4;15) AML revealed that in stark contrast to non-t(4;15) AML, these cells proliferate in response to specific agonists of RARα and RARγ. Furthermore, we observed an increase in the levels of nuclear RARγ upon ATRA treatment. In summary, the identification of the novel t(4;15)(q31;q22) reciprocal translocation opens new avenues in the study of retinoid resistance and provides potential for a new biomarker for therapy of AML
Chest computed tomography radiomics to predict the outcome for patients with COVID-19 at an early stage
PURPOSEEarly monitoring and intervention for patients with novel coronavirus disease-2019 (COVID-19) will benefit both patients and the medical system. Chest computed tomography (CT) radiomics provide more information regarding the prognosis of COVID-19.METHODSA total of 833 quantitative features of 157 COVID-19 patients in the hospital were extracted. By filtering unstable features using the least absolute shrinkage and selection operator algorithm, a radiomic signature was built to predict the prognosis of COVID-19 pneumonia. The main outcomes were the area under the curve (AUC) of the prediction models for death, clinical stage, and complications. Internal validation was performed using the bootstrapping validation technique.RESULTSThe AUC of each model demonstrated good predictive accuracy [death, 0.846; stage, 0.918; complication, 0.919; acute respiratory distress syndrome (ARDS), 0.852]. After finding the optimal cut-off for each outcome, the respective accuracy, sensitivity, and specificity were 0.854, 0.700, and 0.864 for the prediction of the death of COVID-19 patients; 0.814, 0.949, and 0.732 for the prediction of a higher stage of COVID-19; 0.846, 0.920, and 0.832 for the prediction of complications of COVID-19 patients; and 0.814, 0.818, and 0.814 for ARDS of COVID-19 patients. The AUCs after bootstrapping were 0.846 [95% confidence interval (CI): 0.844–0.848] for the death prediction model, 0.919 (95% CI: 0.917–0.922) for the stage prediction model, 0.919 (95% CI: 0.916–0.921) for the complication prediction model, and 0.853 (95% CI: 0.852–0.0.855) for the ARDS prediction model in the internal validation. Based on the decision curve analysis, the radiomics nomogram was clinically significant and useful.CONCLUSIONThe radiomic signature from the chest CT was significantly associated with the prognosis of COVID-19. A radiomic signature model achieved maximum accuracy in the prognosis prediction. Although our results provide vital insights into the prognosis of COVID-19, they need to be verified by large samples in multiple centers
Estimating perfluorocarbon emission factors for industrial rare earth metal electrolysis
Rare earth (RE) metals have been widely applied in new materials, leading to their drastic production increase in the last three decades. In the production process featured by the molten-fluoride electrolysis technology, perfluorocarbon (PFC) emissions are significant and therefore deserve full accounting in greenhouse gas (GHG) emission inventories. Yet, in the ‘2006 IPCC Guidelines for National Greenhouse Gas Inventories’, no method currently exists to account for PFC emissions from rare earth metal production. This research aims to determine emission factors for industrial rare earth metals production through on-site monitoring and lab analysis of PFC concentrations in the exhaust gases from rare earth metal electrolysis. Continuous FTIR measurements and time-integrated samples (analysed off-site by high-precision Medusa GC–MS) were conducted over 24–60 h periods from three rare earth companies in China, covering production of multiple rare earth metals/alloys including Pr-Nd, La and Dy-Fe. The study confirmed that PFC emissions are generated during electrolysis, typically in the form of CF4 (∼90% wt of detected PFCs), C2F6 (∼10%) and C3F8 (<1%); trace levels of c-C4F8 and C4F10 were also detected. In general, PFC emission factors vary with rare earth metal produced and from one facility to another, ranging from 26.66 to 109.43 g/t-RE for CF4 emissions, 0.26 to 10.95 g/t-RE for C2F6, and 0.03 to 0.27 g/t-RE for C3F8. Converted to 211.60 to 847.41 kg CO2-e/t-RE for total PFCs, this emissions intensity for rare earths electrolysis is of lower (for most RE production) or similar (Dy-Fe production) level of magnitude to industrial aluminium electrolysis
Local synergetic collaboration between Pd and local tetrahedral symmetric Ni oxide enables ultra-high-performance CO2 thermal methanation (vol 8, pg 12744, 2020)
Galectin-3 synergizes with CD47 to Suppress Phagocytosis and T cell immunity in Peritoneal Metastases of Gastric Adenocarcinoma
View full abstracthttps://openworks.mdanderson.org/leading-edge/1061/thumbnail.jp
- …