ABM Simulation Model of a Pandemic for Optimizing Vaccination Strategy

This study presents a process-oriented hybrid model for individuals\u27 immune responses and interactions involving vaccination to describe the trend of contagious disease and estimate the future societal cost. The model considers recovery as a non-absorbing state and incorporates various infection stage states including two symptomatic states. To model contagiousness to be consistent with the current pandemic and include that the spread of a disease depends on the mobility of people, we developed an Agent-Based Simulator that fitted to the particular model used in this study and can test various what-if scenarios. We improved the simulator considerably by appying data structures tuned to the specific model used in this monograph. We believe that the simulator\u27s performance exceeds existing packages for our particular model. For example, the simulator can deal with a population of one hundred (100) individuals over more than 17,000 iterations in less than seven (7) seconds in CPU time

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

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

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

Importance of Ag–Cu Biphasic Boundaries for Selective Electrochemical Reduction of CO₂ to Ethanol

In recent years, electrochemical reduction of carbon dioxide (CO₂) has received a great deal of attention due to the potential that this process can mitigate the atmospheric CO₂ concentration and produce valuable organic compounds. In particular, Cu and Cu-based catalysts have exhibited the capability of converting CO₂ into multicarbon fuels and chemicals in significant quantities. Here, we report a facile and cheap fabrication method for the development of an Ag-incorporated cuprous oxide (Ag-Cu₂O) electrode enabling selective synthesis of ethanol via electrochemical CO₂ reduction and reveal the key factor improving the ethanol (C₂H₅OH) selectivity. The incorporation of Ag into Cu₂O leads to the suppression of hydrogen (H₂) evolution, and furthermore, by varying the elemental arrangement (phase-separated and phase-blended) of Ag and Cu, we observe that C₂H₅OH selectivity can be controlled. Consequently, the Faradaic efficiency for C₂H₅OH on phase-blended Ag-Cu₂O (Ag-Cu₂O_PB) is 3 times higher than that of the Cu₂O without Ag dopant. We propose that the electrochemical reaction behavior is not solely associated with a role of Ag dopant, carbon monoxide (CO) leading to an ethanol formation pathway over ethylene, but also the doping pattern related population of Ag-Cu biphasic boundaries relatively suppresses the H₂ evolution reaction and encourages the reaction of mobile CO generated on Ag to a residual intermediate on a Cu site

Identification of Candidate Genes for Rind Color and Bloom Formation in Watermelon Fruits Based on a Quantitative Trait Locus-Seq

Watermelon fruit rind color (RC) and bloom formation (BF) affect product value and consumer preference. However, information on the candidate gene(s) for additional loci involved in dark green (DG) RC and the genetic control of BF and its major chemical components is lacking. Therefore, this study aimed to identify loci controlling RC and BF using QTL-seq of the F2 population derived by crossing ‘FD061129’ with light-green rind and bloom and ‘SIT55616RN’ with DG rind and bloomless. Phenotypic evaluation of the F1 and 219 F2 plants indicated the genetic control of two complementary dominant loci, G1 and G2, for DG and a dominant locus, Bf, for BF. QTL-seq identified a genomic region on Chr.6 for G1, Chr.8 for G2, and Chr.1 for Bf. G1 and G2 helped determine RC with possible environmental effects. Chlorophyll a-b binding protein gene-based CAPS (RC-m5) at G1 matched the highest with the RC phenotype. In the 1.4 cM Bf map interval, two additional gene-based CAPS markers were designed, and the CAPS for a nonsynonymous SNP in Cla97C01G020050, encoding a CSC1-like protein, cosegregated with the BF trait in 219 F2 plants. Bloom powder showed a high Ca2+ concentration (16,358 mg·kg−1), indicating that the CSC1-like protein gene is possibly responsible for BF. Our findings provide valuable information for marker-assisted selection for RC and BF and insights into the functional characterization of genes governing these watermelon-fruit-related traits

Enhancing the Contact between a-IGZO and Metal by Hydrogen Plasma Treatment for a High-Speed Varactor (> 30 GHz)

We achieved the lowest contact resistance between a-IGZO and a metal electrode for >30 GHz operation of an oxide semiconductor device. For high-resolution display and high-speed electronic devices, both bulk and contact resistances need to be reduced. In this study, hydrogen plasma was used to lower the contact resistance significantly by modifying the surface of the a-IGZO thin film. The potential barrier width at the interface was decreased by increasing the carrier concentration, and weak M-OH bonds were sufficiently diffused out with optimized plasma process. The minimum contact resistance was measured to be 1.33 x 10(-6) Omega.cm(2) by the transfer line method, which is the lowest reported value to the best of our knowledge. Utilizing this enhanced contact property between a-IGZO and metal, the metal-insulator-semiconductor varactor was fabricated, and its operating frequency was measured to be higher than 30 GHz.11Nsci

Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage

Massive cyanobacterial blooms in river water causes adverse impacts on aquatic ecosystems and water quality. Complex and diverse data sources are available to investigate the cyanobacteria phenomena, including in situ data, synthetic measurements, and remote sensing images. Deep learning attention models can process these intricate sources to forecast cyanobacteria by identifying important variables in the data sources. However, deep learning attention models for predicting cyanobacteria have rarely been studied using an assemblage of various datasets. Thus, in this study, a convolutional neural network (CNN) model with a convolutional block attention module (CNNan) was developed to predict cyanobacterial cell concentrations by using the observed cell data from field monitoring, chlorophyll-a distribution map from hyperspectral image sensing, and simulated water quality outputs from a hydrodynamic model. Then, the prediction performance of the CNNan model was compared to an environmental fluid dynamics code (EFDC) simulation and a CNN model without an attention network. The seasonal variations of the predicted cyanobacteria that was obtained from CNNan showed the best agreement with the observed variations with Nash-Sutcliffe efficiency values higher than 0.76 when compared to the EFDC and CNN predictions. The daily hydrodynamic outputs allowed the prediction of cyanobacteria cells, while the rich information of the chlorophyll-a map contributed to the improvement of the prediction performance at certain periods. Moreover, the attention network visualized the importance of the additional chlorophyll-a map and improved the CNNan model prediction performance by refining the input features. Therefore, this study demonstrated that a deep learning model with data assemblage is practically feasible for predicting the presence of harmful algae in inland water