Observation of superabsorption by correlated atoms

Emission and absorption of light lie at the heart of light-matter interaction. Although the emission and absorption rates are regarded as intrinsic properties of atoms and molecules, various ways to modify these rates have been sought in critical applications such as quantum information processing, metrology and light-energy harvesting. One of the promising approaches is to utilize collective behavior of emitters as in superradiance. Although superradiance has been observed in diverse systems, its conceptual counterpart in absorption has never been realized. Here, we demonstrate superabsorption, enhanced cooperative absorption, by correlated atoms of phase-matched superposition state. By implementing an opposite-phase-interference idea on a superradiant state or equivalently a time-reversal process of superradiance, we realized the superabsorption with its absorption rate much faster than that of the ordinary ground-state absorption. The number of photons completely absorbed for a given time interval was measured to be proportional to the square of the number of atoms. Our approach, breaking the limitation of the conventional absorption, can help weak-signal sensing and advance efficient light-energy harvesting as well as light-matter quantum interfaces.Comment: 7 pages, 5 figure

Multiphasic analysis of whole exome sequencing data identifies a novel mutation of ACTG1 in a nonsyndromic hearing loss family

BACKGROUND: The genetic heterogeneity of sensorineural hearing loss is a major hurdle to the efficient discovery of disease-causing genes. We designed a multiphasic analysis of copy number variation (CNV), linkage, and single nucleotide variation (SNV) of whole exome sequencing (WES) data for the efficient discovery of mutations causing nonsyndromic hearing loss (NSHL). RESULTS: From WES data, we identified five distinct CNV loci from a NSHL family, but they were not co-segregated among patients. Linkage analysis based on SNVs identified six candidate loci (logarithm of odds [LOD] >1.5). We selected 15 SNVs that co-segregated with NSHL in the family, which were located in six linkage candidate loci. Finally, the novel variant p.M305T in ACTG1 (DFNA20/26) was selected as a disease-causing variant. CONCLUSIONS: Here, we present a multiphasic CNV, linkage, and SNV analysis of WES data for the identification of a candidate mutation causing NSHL. Our stepwise, multiphasic approach enabled us to expedite the discovery of disease-causing variants from a large number of patient variants

Anti-inflammatory activity of hydrosols from Tetragonia tetragonoides in LPS-induced RAW 264.7 cells

The present study was performed to investigate the anti-inflammatory activity of Tetragonia tetragonoides hydro- sols (TTH) and its underlying mechanism in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Gas chromatog- raphy (GC) coupled with mass spectrometry and retention index calculations showed that TTH were mainly com- posed of tetratetracontane (29.5 %), nonacosane (27.6 %), and oleamide (17.1 %). TTH significantly decreased the production of nitric oxide (NO), prostaglandin E2 (PGE2), interleukin (IL)-6, and IL-1β in LPS-stimulated RAW 264.7 cells. Consistent with these observations, TTH treatment decreased the protein expression levels of inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2). The molecular mechanism of its anti-inflamma- tory activity was found to be associated with inhibition of nuclear factor-kappa B (NF-κB) phosphorylation and nuclear translocation of NF-κB 65. Furthermore, TTH markedly suppressed the LPS-induced phosphorylation of mitogen-activated protein kinases (MAPKs). Taken together, these data indicate that TTH exerts an anti-inflam- matory activity by inhibiting the NF-κB and MAPK signaling pathways in LPS-stimulated RAW 264.7 cells

PHF7 Modulates BRDT Stability and Histone-to-Protamine Exchange during Spermiogenesis

Chang Rok Kim, Taichi Noda, Hyunkyung Kim, Gibeom Kim, Seongwan Park, Yongwoo Na, Seiya Oura, Keisuke Shimada, Injin Bang, Jun-Yeong Ahn, Yong Ryoul Kim, Se Kyu Oh, Hee-Jung Choi, Jong-Seo Kim, Inkyung Jung, Ho Lee, Yuki Okada, Masahito Ikawa, Sung Hee Baek, PHF7 Modulates BRDT Stability and Histone-to-Protamine Exchange during Spermiogenesis, Cell Reports, Volume 32, Issue 4, 2020, 107950, ISSN 2211-1247, https://doi.org/10.1016/j.celrep.2020.107950

Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea

Several semi-analytical algorithms have been developed to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in inland waters. This study aimed at identifying the influence of algorithm parameters on the output variables and searching optimal parameter values. The optimal parameters of seven semi-analytical algorithms were applied to estimate the Chl-a and PC concentrations. The absorption coefficient measurements were coupled with pigment measurements to calibrate the algorithm parameters. For sensitivity analysis, the elementary effect test was conducted to analyze the influence of the algorithm parameters. The sensitivity analysis results showed that the parameters in the Y function and specific absorption coefficient were the most sensitive parameters. Then, the parameters were optimized via a single-objective optimization that involved one objective function being minimized and a multi-objective optimization that contained more than one objective function. The single-objective optimization led to substantial errors in absorption coefficients. In contrast, the multi-objective optimization improved the algorithm performance with respect to both the absorption coefficient estimates and pigment concentration estimates. The optimized parameters of the absorption coefficient reflected the high-particulate content in waters of the Baekje reservoir using an infrared backscattering wavelength and relatively high value of Y. Moreover, the results indicate the value of measuring the site-specific absorption if site-specific optimization of semi-analyical algorithm parameters was envisioned

Identification of a novel locus C2 controlling canary yellow flesh color in watermelons

The flesh color of watermelon is an important trait that is determined by carotenoid composition and affects consumers’ fruit desirability. Although a complete dominant control by C locus (Cllcyb) for canary yellow flesh (CY) over red flesh has been reported, red and CY colors frequently appear as a mixed pattern in the same flesh (incomplete canary yellow, ICY) in F1 and inbred lines carrying dominant C alleles. Therefore, we examined the genetic control of the mixed color pattern in ICY using whole-genome resequencing of three ICY (ICY group) and three CY inbred lines (CY group), as well as genetic linkage mapping of an F2 population. The segregation pattern in 135 F2 plants indicated that CY is controlled by a single locus (named C2) dominant over ICY. The whole-genome resequencing of ICY and CY inbred lines revealed an ICY/CY-specific region of approximately 27.60–27.88 Mb on Chr. 2 that was polymorphic between the ICY and CY groups. Our genetic map, using nine cleaved amplified polymorphic sequence markers developed based on the single-nucleotide polymorphisms from the ICY/CY-specific region, confirmed that C2 is located on Chr. 2 and cosegregated with the marker (M7) derived from a non-synonymous single-nucleotide polymorphism of the pentatricopeptide repeat (PPR) gene (ClPPR, Cla97C02G039880). Additionally, 27 watermelon inbred lines of ICY, CY, and red flesh were evaluated using previously reported Cllcyb (C locus)-based markers and our C2 locus-linked ClPPR-based marker (M7). As a result, dominant alleles at the C2 locus were required to produce CY, in addition to dominant alleles at the C locus, while a recessive homozygous genotype at the C locus gave the red flesh irrespective of the genotype at the C2 locus. Using a ClPPR-based cleaved amplified polymorphic sequence developed in this study and Cllcyb-based markers, watermelon cultivars with CY, ICY, and red flesh could be successfully discerned, implying that the combined use of these markers will be efficient for marker-assisted selection of flesh color in watermelon breeding

Solving partial differential equation for atmospheric dispersion of radioactive material using physics-informed neural network

The governing equations of atmospheric dispersion most often taking the form of a second-order partial differential equation (PDE). Currently, typical computational codes for predicting atmospheric dispersion use the Gaussian plume model that is an analytic solution. A Gaussian model is simple and enables rapid simulations, but it can be difficult to apply to situations with complex model parameters. Recently, a method of solving PDEs using artificial neural networks called physics-informed neural network (PINN) has been proposed. The PINN assumes the latent (hidden) solution of a PDE as an arbitrary neural network model and approximates the solution by optimizing the model. Unlike a Gaussian model, the PINN is intuitive in that it does not require special assumptions and uses the original equation without modifications. In this paper, we describe an approach to atmospheric dispersion modeling using the PINN and show its applicability through simple case studies. The results are compared with analytic and fundamental numerical methods to assess the accuracy and other features. The proposed PINN approximates the solution with reasonable accuracy. Considering that its procedure is divided into training and prediction steps, the PINN also offers the advantage of rapid simulations once the training is over

Application of particle filtering for prognostics with measurement uncertainty in nuclear power plants

For nuclear power plants (NPPs) to have long lifetimes, ageing is a major issue. Currently, ageing management for NPP systems is based on correlations built from generic experimental data. However, each system has its own characteristics, operational history, and environment. To account for this, it is possible to resort to prognostics that predicts the future state and time to failure (TTF) of the target system by updating the generic correlation with specific information of the target system. In this paper, we present an application of particle filtering for the prediction of degradation in steam generator tubes. With a case study, we also show how the prediction results vary depending on the uncertainty of the measurement data. Keywords: Prognostics, Particle filtering, Model-based method, Steam generator tube rupture, Nuclear power plan

High Energy Efficiency Millimeter-Wave Frequency Quadrupler Based on Phase Controlled Push-Push Topology in 40nm CMOS

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