Near-IR narrow-band imaging with CIRCE at the Gran Telescopio Canarias: Searching for Lyα-emitters at z ∼ 9.3

Context. Identifying very high-redshift galaxies is crucial for understanding the formation and evolution of galaxies. However, many questions still remain, and the uncertainty on the epoch of reionization is large. In this approach, some models allow a double-reionization scenario, although the number of confirmed detections at very high z is still too low to serve as observational proof. Aims: The main goal of this project is studying whether we can search for Lyman-α emitters (LAEs) at z ∼ 9 using a narrow-band (NB) filter that was specifically designed by our team and was built for this experiment. Methods: We used the NB technique to select candidates by measuring the flux excess due to the Lyα emission. The observations were taken with an NB filter (full width at half minimum of 11 nm and central wavelength λc = 1.257 μm) and the CIRCE near-infrared camera for the Gran Telescopio Canarias. We describe a data reduction procedure that was especially optimized to minimize instrumental effects. With a total exposure time of 18.3 h, the final NB image covers an area of ∼6.7 arcmin2, which corresponds to a comoving volume of 1.1 × 103 Mpc3 at z = 9.3. Results: We pushed the source detection to its limit, which allows us to analyze an initial sample of 97 objects. We detail the different criteria we applied to select the candidates. The criteria included visual verifications in different photometric bands. None of the objects resembled a reliable LAE, however, and we found no robust candidate down to an emission-line flux of 2.9 × 10−16 erg s−1 cm−2, which corresponds to a Lyα luminosity limit of 3 × 1044 erg s−1. We derive an upper limit on the Lyα luminosity function at z ∼ 9 that agrees well with previous constraints. We conclude that deeper and wider surveys are needed to study the LAE population at the cosmic dawn

The alpha-galactosidase A p.Arg118Cys variant does not cause a Fabry disease phenotype: data from individual patients and family studies

Acessível em: www.ncbi.nlm.nih.gov/pmc/articles/PMC4423738/Lysosomal α-galactosidase A (α-Gal) is the enzyme deficient in Fabry disease (FD), an X-linked glycosphingolipidosis caused by pathogenic mutations affecting the GLA gene. The early-onset, multi-systemic FD classical phenotype is associated with absent or severe enzyme deficiency, as measured by in vitro assays, but patients with higher levels of residual α-Gal activity may have later-onset, more organ-restricted clinical presentations. A change in the codon 118 of the wild-type α-Gal sequence, replacing basic arginine by a potentially sulfhydryl-binding cysteine residue - GLA p.(Arg118Cys) -, has been recurrently described in large FD screening studies of high-risk patients. Although the Cys118 allele is associated with high residual α-Gal activity in vitro, it has been classified as a pathogenic mutation, mainly on the basis of theoretical arguments about the chemistry of the cysteine residue. However its pathogenicity has never been convincingly demonstrated by pathology criteria. We reviewed the clinical, biochemical and histopathology data obtained from 22 individuals of Portuguese and Spanish ancestry carrying the Cys118 allele, including 3 homozygous females. Cases were identified either on the differential diagnosis of possible FD manifestations and on case-finding studies (n=11; 4 males), or on unbiased cascade screening of probands' close relatives (n=11; 3 males). Overall, those data strongly suggest that the GLA p.(Arg118Cys) variant does not segregate with FD clinical phenotypes in a Mendelian fashion, but might be a modulator of the multifactorial risk of cerebrovascular disease. The Cys118 allelic frequency in healthy Portuguese adults (n=696) has been estimated as 0.001, therefore not qualifying for "rare" condition

Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

[EN] Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analysesThis study used computing resources from Artemisa, co-funded by the European Union through the 2014-2020 FEDER Operative Programme of the Comunitat Valenciana, project DIFEDER/2018/048. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. The NEXT collaboration acknowledges support from the following agencies and institutions: Xunta de Galicia (Centro singularde investigacion de Galicia accreditation 2019-2022), by European Union ERDF, and by the "Maria de Maeztu" Units of Excellence program MDM-2016-0692 and the Spanish Research State Agency"; the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program grants SEV-20140398 and CEX2018-000867-S; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014 and under projects UID/FIS/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under contracts number DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015 18820. JMA acknowledges support from Fundacion Bancaria "la Caixa" (ID 100010434), grant code LCF/BQ/PI19/11690012. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Kekic, M.; Adams, C.; Woodruff, K.; Renner, J.; Church, E.; Del Tutto, M.; Hernando Morata, JA.... (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. Journal of High Energy Physics (Online). (1):1-22. https://doi.org/10.1007/JHEP01(2021)189S1221NEXT collaboration, The Next White (NEW) Detector, 2018 JINST 13 P12010 [arXiv:1804.02409] [INSPIRE].NEXT collaboration, Energy calibration of the NEXT-White detector with 1% resolution near Qββ of 136Xe, JHEP 10 (2019) 230 [arXiv:1905.13110] [INSPIRE].NEXT collaboration, Demonstration of the event identification capabilities of the NEXT-White detector, JHEP 10 (2019) 052 [arXiv:1905.13141] [INSPIRE].NEXT collaboration, Radiogenic Backgrounds in the NEXT Double Beta Decay Experiment, JHEP 10 (2019) 051 [arXiv:1905.13625] [INSPIRE].G. Carleo et al., Machine learning and the physical sciences, Rev. Mod. Phys. 91 (2019) 045002 [arXiv:1903.10563] [INSPIRE].A. Aurisano et al., A Convolutional Neural Network Neutrino Event Classifier, 2016 JINST 11 P09001 [arXiv:1604.01444] [INSPIRE].MicroBooNE collaboration, Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber, 2017 JINST 12 P03011 [arXiv:1611.05531] [INSPIRE].MicroBooNE collaboration, Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber, Phys. Rev. D 99 (2019) 092001 [arXiv:1808.07269] [INSPIRE].N. Choma et al., Graph Neural Networks for IceCube Signal Classification, in proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, U.S.A., 17–20 December 2018, pp. 386–391 [arXiv:1809.06166] [INSPIRE].E. Racah et al., Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks, in proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, U.S.A., 18–20 December 2016, pp. 892–897 [arXiv:1601.07621] [INSPIRE].EXO collaboration, Deep Neural Networks for Energy and Position Reconstruction in EXO-200, 2018 JINST 13 P08023 [arXiv:1804.09641] [INSPIRE].H. Qiao, C. Lu, X. Chen, K. Han, X. Ji and S. Wang, Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation, Sci. China Phys. Mech. Astron. 61 (2018) 101007 [arXiv:1802.03489] [INSPIRE].P. Ai, D. Wang, G. Huang and X. Sun, Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous Time Projection Chamber, 2018 JINST 13 P08015 [arXiv:1803.01482] [INSPIRE].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017 JINST 12 T01004 [arXiv:1609.06202] [INSPIRE].NEXT collaboration, Sensitivity of NEXT-100 to Neutrinoless Double Beta Decay, JHEP 05 (2016) 159 [arXiv:1511.09246] [INSPIRE].D. Nygren, High-pressure xenon gas electroluminescent TPC for 0-ν ββ-decay search, Nucl. Instrum. Meth. A 603 (2009) 337 [INSPIRE].NEXT collaboration, Calibration of the NEXT-White detector using 83mKr decays, 2018 JINST 13 P10014 [arXiv:1804.01780] [INSPIRE].J. Martín-Albo, The NEXT experiment for neutrinoless double beta decay searches, Ph.D. Thesis, University of Valencia, Valencia Spain (2015) [INSPIRE].GEANT4 collaboration, GEANT4 — a simulation toolkit, Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE].A. Krizhevsky, I. Sutskever and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM 60 (2017) 84.N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014) 1929.S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167 [INSPIRE].C. Guo, G. Pleiss, Y. Sun and K.Q. Weinberger, On calibration of modern neural networks, arXiv:1706.04599.K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385 [INSPIRE].K. He, X. Zhang, S. Ren and J. Sun, Identity mappings in deep residual networks, arXiv:1603.05027.X. Li, S. Chen, X. Hu and J. Yang, Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift, in proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, U.S.A., 15–20 June 2019, pp. 2677–2685.J. Deng, W. Dong, R. Socher, L. Li, K. Li and L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, U.S.A., 20–25 June 2009, pp. 248–255.B. Graham and L. van der Maaten, Submanifold sparse convolutional networks, arXiv:1706.01307.L. Dominé and K. Terao, Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys. Rev. D 102 (2020) 012005 [arXiv:1903.05663] [INSPIRE].C. Shorten and T.M. Khoshgoftaar, A survey on image data augmentation for deep learning, J. Big Data 6 (2019) 60.G.J. Székely and M.L. Rizzo, Testing for equal distributions in high dimension, InterStat 5 (2004) 1.G. Székely and M.L. Rizzo, Energy statistics: A class of statistics based on distances, J. Stat. Plann. Infer. 8 (2013) 1249.R.A. Fisher, The Design of Experiments, Oliver and Boyd (1935).NEXT collaboration, Sensitivity of a tonne-scale NEXT detector for neutrinoless double beta decay searches, arXiv:2005.06467 [INSPIRE].NEXT collaboration, Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment, 2013 JINST 8 P04002 [arXiv:1211.4838] [INSPIRE].NEXT collaboration, Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array, 2013 JINST 8 P09011 [arXiv:1306.0471] [INSPIRE]

Age-related increase of kynurenine enhances miR29b-1-5p to decrease both CXCL12 signaling and the epigenetic enzyme Hdac3 in bone marrow stromal cells

Mechanisms leading to age-related reductions in bone formation and subsequent osteoporosis are still incompletely understood. We recently demonstrated that kynurenine (KYN), a tryptophan metabolite, accumulates in serum of aged mice and induces bone loss. Here, we report on novel mechanisms underlying KYN's detrimental effect on bone aging. We show that KYN is increased with aging in murine bone marrow mesenchymal stem cells (BMSCs). KYN reduces bone formation via modulating levels of CXCL12 and its receptors as well as histone deacetylase 3 (Hdac3). BMSCs responded to KYN by significantly decreasing mRNA expression levels of CXCL12 and its cognate receptors, CXCR4 and ACKR3, as well as downregulating osteogenic gene RUNX2 expression, resulting in a significant inhibition in BMSCs osteogenic differentiation. KYN's effects on these targets occur by increasing regulatory miRNAs that target osteogenesis, specifically miR29b-1-5p. Thus, KYN significantly upregulated the anti-osteogenic miRNA miR29b-1-5p in BMSCs, mimicking the up-regulation of miR-29b-1-5p in human and murine BMSCs with age. Direct inhibition of miR29b-1-5p by antagomirs rescued CXCL12 protein levels downregulated by KYN, while a miR29b-1-5p mimic further decreased CXCL12 levels. KYN also significantly downregulated mRNA levels of Hdac3, a target of miR-29b-1-5p, as well as its cofactor NCoR1. KYN is a ligand for the aryl hydrocarbon receptor (AhR). We hypothesized that AhR mediates KYN's effects in BMSCs. Indeed, AhR inhibitors (CH-223191 and 3',4'-dimethoxyflavone [DMF]) partially rescued secreted CXCL12 protein levels in BMSCs treated with KYN. Importantly, we found that treatment with CXCL12, or transfection with an miR29b-1-5p antagomir, downregulated the AhR mRNA level, while transfection with miR29b-1-5p mimic significantly upregulated its level. Further, CXCL12 treatment downregulated IDO, an enzyme responsible for generating KYN. Our findings reveal novel molecular pathways involved in KYN's age-associated effects in the bone microenvironment that may be useful translational targets for treating osteoporosis

The X-ray emission of local luminous infrared galaxies

We study the X-ray emission of a representative sample of 27 local luminous infrared galaxies (LIRGs). The median IR luminosity of our sample is log L_IR/L_sun = 11.2, thus the low-luminosity end of the LIRG class is well represented. We used new XMM-Newton data as well as Chandra and XMM-Newton archive data. The soft X-ray (0.5-2 keV) emission of most of the galaxies (>80%), including LIRGs hosting a Seyfert 2 nucleus, is dominated by star-formation related processes. These LIRGs follow the star-formation rate (SFR) versus soft X-ray luminosity correlation observed in local starbursts. We find that ~15% of the non-Seyfert LIRGs (3 out of 20) have an excess hard X-ray emission relative to that expected from star-formation that might indicate the presence of an obscured AGN. The rest of the non-Seyfert LIRGs follow the SFR versus hard X-ray (2-10 keV) luminosity correlation of local starbursts. The non-detection of the 6.4 keV Fe K alpha emission line in the non-Seyfert LIRGs allows us to put an upper limit to the bolometric luminosity of an obscured AGN, L_bol <1043 erg s-1 . That is, in these galaxies, if they hosted a low luminosity AGN, its contribution to total luminosity would be less than 10%. Finally we estimate that the AGN contribution to the total luminosity for our sample of local LIRGs is between 7% and 10%.Comment: Accepted for Publication in A&A, 22 pages, 9 figure

Age-associated changes in microRNAs affect the differentiation potential of human mesenchymal stem cells: Novel role of miR-29b-1-5p expression

Age-associated osteoporosis is widely accepted as involving the disruption of osteogenic stem cell populations and their functioning. Maintenance of the local bone marrow (BM) microenvironment is critical for regulating proliferation and differentiation of the multipotent BM mesenchymal stromal/stem cell (BMSC) population with age. The potential role of microRNAs (miRNAs) in modulating BMSCs and the BM microenvironment has recently gained attention. However, miRNAs expressed in rapidly isolated BMSCs that are naïve to the non-physiologic standard tissue culture conditions and reflect a more accurate in vivo profile have not yet been reported. Here we directly isolated CD271 positive (+) BMSCs within hours from human surgical BM aspirates without culturing and performed microarray analysis to identify the age-associated changes in BMSC miRNA expression. One hundred and two miRNAs showed differential expression with aging. Target prediction and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the up-regulated miRNAs targeting genes in bone development pathways were considerably enriched. Among the differentially up-regulated miRNAs the novel passenger strand miR-29b-1-5p was abundantly expressed as a mature functional miRNA with aging. This suggests a critical arm-switching mechanism regulates the expression of the miR-29b-1-5p/3p pair shifting the normally degraded arm, miR-29b-1-5p, to be the dominantly expressed miRNA of the pair in aging. The normal guide strand miR-29b-1-3p is known to act as a pro-osteogenic miRNA. On the other hand, overexpression of the passenger strand miR-29b-1-5p in culture-expanded CD271+ BMSCs significantly down-regulated the expression of stromal cell-derived factor 1 (CXCL12)/ C-X-C chemokine receptor type 4 (SDF-1(CXCL12)/CXCR4) axis and other osteogenic genes including bone morphogenetic protein-2 (BMP-2) and runt-related transcription factor 2 (RUNX2). In contrast, blocking of miR-29b-1-5p function using an antagomir inhibitor up-regulated expression of BMP-2 and RUNX2 genes. Functional assays confirmed that miR-29b-1-5p negatively regulates BMSC osteogenesis in vitro. These novel findings provide evidence of a pathogenic anti-osteogenic role for miR-29b-1-5p and other miRNAs in age-related defects in osteogenesis and bone regeneration

Auditory hedonic phenotypes in dementia: A behavioural and neuroanatomical analysis

Patients with dementia may exhibit abnormally altered liking for environmental sounds and music but such altered auditory hedonic responses have not been studied systematically. Here we addressed this issue in a cohort of 73 patients representing major canonical dementia syndromes (behavioural variant frontotemporal dementia (bvFTD), semantic dementia (SD), progressive nonfluent aphasia (PNFA) amnestic Alzheimer's disease (AD)) using a semi-structured caregiver behavioural questionnaire and voxel-based morphometry (VBM) of patients' brain MR images. Behavioural responses signalling abnormal aversion to environmental sounds, aversion to music or heightened pleasure in music (‘musicophilia’) occurred in around half of the cohort but showed clear syndromic and genetic segregation, occurring in most patients with bvFTD but infrequently in PNFA and more commonly in association with MAPT than C9orf72 mutations. Aversion to sounds was the exclusive auditory phenotype in AD whereas more complex phenotypes including musicophilia were common in bvFTD and SD. Auditory hedonic alterations correlated with grey matter loss in a common, distributed, right-lateralised network including antero-mesial temporal lobe, insula, anterior cingulate and nucleus accumbens. Our findings suggest that abnormalities of auditory hedonic processing are a significant issue in common dementias. Sounds may constitute a novel probe of brain mechanisms for emotional salience coding that are targeted by neurodegenerative disease

Hydrodynamic slip can align thin nanoplatelets in shear flow

The large-scale processing of nanomaterials such as graphene and MoS2 relies on understanding the flow behaviour of nanometrically-thin platelets suspended in liquids. Here we show, by combining non-equilibrium molecular dynamics and continuum simulations, that rigid nanoplatelets can attain a stable orientation for sufficiently strong flows. Such a stable orientation is in contradiction with the rotational motion predicted by classical colloidal hydrodynamics. This surprising effect is due to hydrodynamic slip at the liquid-solid interface and occurs when the slip length is larger than the platelet thickness; a slip length of a few nanometers may be sufficient to observe alignment. The predictions we developed by examining pure and surface-modified graphene is applicable to different solvent/2D material combinations. The emergence of a fixed orientation in a direction nearly parallel to the flow implies a slip-dependent change in several macroscopic transport properties, with potential impact on applications ranging from functional inks to nanocomposites.Energy Technolog