Cosmogenic neutron production at the Sudbury Neutrino Observatory

Neutrons produced in nuclear interactions initiated by cosmic-ray muons present an irreducible background to many rare-event searches, even in detectors located deep underground. Models for the production of these neutrons have been tested against previous experimental data, but the extrapolation to deeper sites is not well understood. Here we report results from an analysis of cosmogenically produced neutrons at the Sudbury Neutrino Observatory. A specific set of observables are presented, which can be used to benchmark the validity of geant4 physics models. In addition, the cosmogenic neutron yield, in units of 10-4 cm2/(g·μ), is measured to be 7.28±0.09(stat)-1.12+1.59(syst) in pure heavy water and 7.30±0.07(stat)-1.02+1.40(syst) in NaCl-loaded heavy water. These results provide unique insights into this potential background source for experiments at SNOLAB

Learning a peptide-protein binding affinity predictor with kernel ridge regression

We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of accurately predicting the binding affinity of any peptide to any protein. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. On all benchmarks, our method significantly (p-value < 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. The method should be of value to a large segment of the research community with the potential to accelerate peptide-based drug and vaccine development.Comment: 22 pages, 4 figures, 5 table

The HITRAN2020 molecular spectroscopic database

The HITRAN database is a compilation of molecular spectroscopic parameters. It was established in the early 1970s and is used by various computer codes to predict and simulate the transmission and emission of light in gaseous media (with an emphasis on terrestrial and planetary atmospheres). The HITRAN compilation is composed of five major components: the line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, experimental infrared absorption cross-sections (for molecules where it is not yet feasible for representation in a line-by-line form), collision-induced absorption data, aerosol indices of refraction, and general tables (including partition sums) that apply globally to the data. This paper describes the contents of the 2020 quadrennial edition of HITRAN. The HITRAN2020 edition takes advantage of recent experimental and theoretical data that were meticulously validated, in particular, against laboratory and atmospheric spectra. The new edition replaces the previous HITRAN edition of 2016 (including its updates during the intervening years). All five components of HITRAN have undergone major updates. In particular, the extent of the updates in the HITRAN2020 edition range from updating a few lines of specific molecules to complete replacements of the lists, and also the introduction of additional isotopologues and new (to HITRAN) molecules: SO, CH3F, GeH4, CS2, CH3I and NF3. Many new vibrational bands were added, extending the spectral coverage and completeness of the line lists. Also, the accuracy of the parameters for major atmospheric absorbers has been increased substantially, often featuring sub-percent uncertainties. Broadening parameters associated with the ambient pressure of water vapor were introduced to HITRAN for the first time and are now available for several molecules. The HITRAN2020 edition continues to take advantage of the relational structure and efficient interface available at www.hitran.org and the HITRAN Application Programming Interface (HAPI). The functionality of both tools has been extended for the new edition

Ligand engagement of Toll-like receptors regulates their expression in cortical microglia and astrocytes

BACKGROUND: Toll-like receptor (TLR) activation on microglia and astrocytes are key elements in neuroinflammation which accompanies a number of neurological disorders. While TLR activation on glia is well-established to up-regulate pro-inflammatory mediator expression, much less is known about how ligand engagement of one TLR may affect expression of other TLRs on microglia and astrocytes. METHODS: In the present study, we evaluated the effects of agonists for TLR2 (zymosan), TLR3 (polyinosinic-polycytidylic acid (poly(I:C)), a synthetic analogue of double-stranded RNA) and TLR4 (lipopolysaccaride (LPS)) in influencing expression of their cognate receptor as well as that of the other TLRs in cultures of rat cortical purified microglia (>99.5 %) and nominally microglia-free astrocytes. Elimination of residual microglia (a common contaminant of astrocyte cultures) was achieved by incubation with the lysosomotropic agent L-leucyl-L-leucine methyl ester (L-LME). RESULTS: Flow cytometric analysis confirmed the purity (essentially 100 %) of the obtained microglia, and up to 5 % microglia contamination of astrocytes. L-LME treatment effectively removed microglia from the latter (real-time polymerase chain reaction). The three TLR ligands robustly up-regulated gene expression for pro-inflammatory markers (interleukin-1 and interleukin-6, tumor necrosis factor) in microglia and enriched, but not purified, astrocytes, confirming cellular functionality. LPS, zymosan and poly(I:C) all down-regulated TLR4 messenger RNA (mRNA) and up-regulated TLR2 mRNA at 6 and 24 h. In spite of their inability to elaborate pro-inflammatory mediator output, the nominally microglia-free astrocytes (>99 % purity) also showed similar behaviours to those of microglia, as well as changes in TLR3 gene expression. LPS interaction with TLR4 activates downstream mitogen-activated protein kinase and nuclear factor-κB signalling pathways and subsequently causes inflammatory mediator production. The effects of LPS on TLR2 mRNA in both cell populations were antagonized by a nuclear factor-κB inhibitor. CONCLUSIONS: TLR2 and TLR4 activation in particular, in concert with microglia and astrocytes, comprise key elements in the initiation and maintenance of neuropathic pain. The finding that both homologous (zymosan) and heterologous (LPS, poly(I:C)) TLR ligands are capable of regulating TLR2 gene expression, in particular, may have important implications in understanding the relative contributions of different TLRs in neurological disorders associated with neuroinflammation

Combined Analysis of all Three Phases of Solar Neutrino Data from the Sudbury Neutrino Observatory

We report results from a combined analysis of solar neutrino data from all phases of the Sudbury Neutrino Observatory. By exploiting particle identification information obtained from the proportional counters installed during the third phase, this analysis improved background rejection in that phase of the experiment. The combined analysis resulted in a total flux of active neutrino flavors from 8B decays in the Sun of (5.25 \pm 0.16(stat.)+0.11-0.13(syst.))\times10^6 cm^{-2}s^{-1}. A two-flavor neutrino oscillation analysis yielded \Deltam^2_{21} = (5.6^{+1.9}_{-1.4})\times10^{-5} eV^2 and tan^2{\theta}_{12}= 0.427^{+0.033}_{-0.029}. A three-flavor neutrino oscillation analysis combining this result with results of all other solar neutrino experiments and the KamLAND experiment yielded \Deltam^2_{21} = (7.41^{+0.21}_{-0.19})\times10^{-5} eV^2, tan^2{\theta}_{12} = 0.446^{+0.030}_{-0.029}, and sin^2{\theta}_{13} = (2.5^{+1.8}_{-1.5})\times10^{-2}. This implied an upper bound of sin^2{\theta}_{13} < 0.053 at the 95% confidence level (C.L.)