Ideal stoichiometric technetium nitrides under pressure: a first-principles study

Technetium nitrides with various ideal stoichiometries have been investigated with the first-principle method at the pressures between 0–60 GPa. It have been found that there could be many stable technetium nitrides including Tc₃N, Tc₂N, TcN, Tc₂N₃, TcN₂, TcN₃, and TcN₄, among which Tc₃N and Tc₂N sub-nitrides are synthesizable at zero pressure and could be applied to nuclear waste management, such as separate radioactive 99Tc from nuclear fuel cycle. Moreover, N-rich TcN₃ and TcN₄ exhibit remarkable bulk properties and can be potential ultrastiff and hard materials.Нітриди технеція з різною ідеальної стехіометрією досліджені із застосуванням методу перших принципів при тисках від 0 до 60 ГПа. Встановлено, що може бути багато стабільних нітридів технеція, включаючи Tc₃N, Tc₂N, ТКС, Tc₂N₃, TcN₂, TcN₃ і TcN₄, серед яких субнітриди Tc₃N і Tc₂N синтезуються при нульовому тиску і можуть бути використані для обробки ядерних відходів, таких як виділений при ядерному паливному циклі радіоактивний 99Tc. Більш того, TcN₃ і TcN₄, збагачені N, демонструють чудові об’ємні властивості і можуть бути потенційними ультражорсткими і твердими матеріалами.Нитриды технеция с различной идеальной стехиометрией исследованы с применением метода первых принципов при давлениях от 0 до 60 ГПа. Установлено, что может быть много стабильных нитридов технеция, включая Tc₃N, Tc₂N, ТКС, Tc₂N₃, TcN₂, TcN₃ и TcN₄, среди которых субнитриды Tc₃N и Tc₂N синтезируются при нулевом давлении и могут быть использованы для обработки ядерных отходов, таких как выделенный при ядерном топливном цикле радиоактивный 99Tc. Более того, TcN₃ и TcN₄, обогащенные N, демонстрируют замечательные объемные свойства и могут быть потенциальными ультражесткими и твердыми материалами

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning [version 2; referees: 3 approved]

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% (DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% (DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation

Prevalence of Type 2 Diabetes among Newly Detected Pulmonary Tuberculosis Patients in China: A Community Based Cohort Study

Background: Patients with type 2 diabetes (DM) have a higher risk of developing pulmonary tuberculosis (PTB); moreover, DM co-morbidity in PTB is associated with poor PTB treatment outcomes. Community based prevalence data on DM and prediabetes (pre-DM) among TB patients is lacking, particularly from the developing world. Therefore we conducted a prospective study to investigate the prevalence of DM and pre-DM and evaluated the risk factors for the presence of DM among newly detected PTB patients in rural areas of China. Methods and Findings: In a prospective community based study carried out from 2010 to 2012, a representative sample of 6382 newly detected PTB patients from 7 TB clinics in Linyi were tested for DM. A population of 6674 non-TB controls from the same community was similarly tested as well. The prevalence of DM in TB patients (6.3%) was higher than that in non-TB controls (4.7%, p = 10000 RMB yuan) were positively associated and frequent outdoor activity was negatively associated with DM in PTB patients. Conclusions: The prevalence of DM in PTB patients was higher than in non-TB controls with a 3 fold higher adjusted odds ratio of having DM. Given the increasing DM prevalence and still high burden of TB in China, this association may represent a new public health challenge concerning the prevention and treatment of both diseases

Molecular characterization of different Triticum monococcum ssp. monococcum Glu-A1^mx alleles

High-molecular-weight glutenin subunits (HMW-GSs) are important seed storage proteins associated with bread-making quality in common wheat (Triticum aestivum L., 2n = 6x = 42, AABBDD). Variation in the Glu-A1x locus in common wheat is scare. Diploid Triticum monococcum ssp. monococcum (2n = 2x = 14, AmAm) is the first cultivated wheat. In the present study, allelic variations at the Glu-A1mx locus were systematically investigated in 197 T. monococcum ssp. monococcum accessions. Out of the 8 detected Glu-A1mx alleles, 5 were novel, including Glu-A1m-b, Glu-A1m-c, Glu-A1m-d, Glu-A1m-g, and Glu-A1m-h. This diversity is higher than that of common wheat. Compared with 1Ax1 and 1Ax2*, which are present in common wheat, these alleles contained three deletions/insertions as well as some single nucleotide polymorphism variations that might affect the elastic properties of wheat flour. New variations in T. monococcum probably occurred after the divergence between A and Am and are excluded in common wheat populations. These allelic variations could be used as novel resources to further improve wheat quality

Probing the function of neuronal populations : combining micromirror-based optogenetic photostimulation with voltage-sensitive dye imaging

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Neuroscience Research 75 (2013): 76-81, doi:10.1016/j.neures.2012.11.006.Recent advances in our understanding of brain function have come from using light to either control or image neuronal activity. Here we describe an approach that combines both techniques: a micromirror array is used to photostimulate populations of presynaptic neurons expressing channelrhodopsin-2, while a red-shifted voltage-sensitive dye allows optical detection of resulting postsynaptic activity. Such technology allowed us to control the activity of cerebellar interneurons while simultaneously recording inhibitory responses in multiple Purkinje neurons, their postsynaptic targets. This approach should substantially accelerate our understanding of information processing by populations of neurons within brain circuits.This work was supported by a Grass Foundation fellowship, National Institutes of Health (NIH grant: R01 EB001963), Duke‐NUS Signature Research Program (SRP) block grant, CRP grant from the National Research Foundation (Singapore) and by the World Class Institute (WCI) Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology of Korea (MEST) (NRF Grant Number: WCI 2009-003)

Partial wave analysis of J/\psi \to \gamma \phi \phi

Using $5.8 \times 10^7 J/\psi$ events collected in the BESII detector, the radiative decay $J/\psi \to \gamma \phi \phi \to \gamma K^+ K^- K^0_S K^0_L$ is studied. The $\phi\phi$ invariant mass distribution exhibits a near-threshold enhancement that peaks around 2.24 GeV/$c^{2}$. A partial wave analysis shows that the structure is dominated by a $0^{-+}$ state ($\eta(2225)$) with a mass of $2.24^{+0.03}_{-0.02}{}^{+0.03}_{-0.02}$ GeV/$c^{2}$ and a width of $0.19 \pm 0.03^{+0.06}_{-0.04}$ GeV/$c^{2}$. The product branching fraction is: $Br(J/\psi \to \gamma \eta(2225))\cdot Br(\eta(2225)\to \phi\phi) = (4.4 \pm 0.4 \pm 0.8)\times 10^{-4}$.Comment: 11 pages, 4 figures. corrected proof for journa

A study of charged kappa in J/ψ→K±Ksπ∓π0J/\psi \to K^{\pm} K_s \pi^{\mp} \pi^0

Based on $58 \times 10^6$ $J/\psi$ events collected by BESII, the decay $J/\psi \to K^{\pm} K_s \pi^{\mp} \pi^0$ is studied. In the invariant mass spectrum recoiling against the charged $K^*(892)^{\pm}$, the charged $\kappa$ particle is found as a low mass enhancement. If a Breit-Wigner function of constant width is used to parameterize the kappa, its pole locates at $(849 \pm 77 ^{+18}_{-14}) -i (256 \pm 40 ^{+46}_{-22})$ MeV/$c^2$. Also in this channel, the decay $J/\psi \to K^*(892)^+ K^*(892)^-$ is observed for the first time. Its branching ratio is $(1.00 \pm 0.19 ^{+0.11}_{-0.32}) \times 10^{-3}$.Comment: 14 pages, 4 figure

Measurements of the observed cross sections for e+e−→e^+e^-\to exclusive light hadrons containing π0π0\pi^0\pi^0 at s=3.773\sqrt s= 3.773, 3.650 and 3.6648 GeV

By analyzing the data sets of 17.3, 6.5 and 1.0 pb$^{-1}$ taken, respectively, at $\sqrt s= 3.773$, 3.650 and 3.6648 GeV with the BES-II detector at the BEPC collider, we measure the observed cross sections for $e^+e^-\to \pi^+\pi^-\pi^0\pi^0$, $K^+K^-\pi^0\pi^0$, $2(\pi^+\pi^-\pi^0)$, $K^+K^-\pi^+\pi^-\pi^0\pi^0$ and $3(\pi^+\pi^-)\pi^0\pi^0$ at the three energy points. Based on these cross sections we set the upper limits on the observed cross sections and the branching fractions for $\psi(3770)$ decay into these final states at 90% C.L..Comment: 7 pages, 2 figure