Onset of Odorant Receptor Gene Expression during Olfactory Sensory Neuron Regeneration

AbstractIndividual olfactory sensory neurons are thought to express only one odorant receptor gene from a repertoire of hundreds to thousands of genes. How do these sensory neurons choose just one specific odorant receptor to express during their differentiation? As an initial attempt toward understanding the process of odorant receptor gene regulation, we studied when odorant receptor expression is activated during sensory neuron regeneration. We find that receptor gene expression is activated in postmitotic neurons and can occur in the absence of the olfactory bulb. These results suggest that receptor expression is restricted to the terminal stages of olfactory neuron differentiation, and sensory neurons do not simply inherit the odorant receptor that is already expressed in mitotic precursor cells. Our results also support a model in which odorant receptor gene expression occurs independent of the olfactory bulb

Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference

This paper presents a general stochastic model developed for a class of cooperative wireless relay networks, in which imperfect knowledge of the channel state information at the destination node is assumed. The framework incorporates multiple relay nodes operating under general known non-linear processing functions. When a non-linear relay function is considered, the likelihood function is generally intractable resulting in the maximum likelihood and the maximum a posteriori detectors not admitting closed form solutions. We illustrate our methodology to overcome this intractability under the example of a popular optimal non-linear relay function choice and demonstrate how our algorithms are capable of solving the previously intractable detection problem. Overcoming this intractability involves development of specialised Bayesian models. We develop three novel algorithms to perform detection for this Bayesian model, these include a Markov chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC) approach; an Auxiliary Variable MCMC (MCMC-AV) approach; and a Suboptimal Exhaustive Search Zero Forcing (SES-ZF) approach. Finally, numerical examples comparing the symbol error rate (SER) performance versus signal to noise ratio (SNR) of the three detection algorithms are studied in simulated examples

Radar-assisted Predictive Beamforming for Vehicle-to-Infrastructure Links

In this paper, we propose a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by relying on the joint sensing and communication functionalities at road side units (RSUs). We present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of the vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. Numerical results have demonstrated that the proposed radar-assisted approach significantly outperforms the communication-only feedback based technique in both the angle tracking and the downlink communication.Comment: 6 pages, 3 figures, accepted by IEEE ICC 2020. arXiv admin note: substantial text overlap with arXiv:2001.0930

Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks

In this paper, we develop a predictive beamforming scheme based on the dual-functional radar-communication (DFRC) technique, where the road-side units estimates the motion parameters of vehicles exploiting the echoes of the DFRC signals. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the tracking performance. A novel message passing algorithm is proposed, which yields a near optimal performance achieved by the maximum a posteriori estimation. Simulation results have shown the effectiveness of the proposed DFRC based scheme.Comment: IEEE RadarConf 202

Physics-Informed Supervised Residual Learning for Electromagnetic Modeling

In this study, physics-informed supervised residual learning (PhiSRL) is proposed to enable an effective, robust, and general deep learning framework for 2D electromagnetic (EM) modeling. Based on the mathematical connection between the fixed-point iteration method and the residual neural network (ResNet), PhiSRL aims to solve a system of linear matrix equations. It applies convolutional neural networks (CNNs) to learn updates of the solution with respect to the residuals. Inspired by the stationary and non-stationary iterative scheme of the fixed-point iteration method, stationary and non-stationary iterative physics-informed ResNets (SiPhiResNet and NiPhiResNet) are designed to solve the volume integral equation (VIE) of EM scattering. The effectiveness and universality of PhiSRL are validated by solving VIE of lossless and lossy scatterers with the mean squared errors (MSEs) converging to $\sim 10^{-4}$ (SiPhiResNet) and $\sim 10^{-7}$ (NiPhiResNet). Numerical results further verify the generalization ability of PhiSRL.Comment: This preprint has been published in IEEE Transactions on Antennas and Propagation on 01 March 2023. Please cite the final published version as [T. Shan et al., "Physics-Informed Supervised Residual Learning for Electromagnetic Modeling," in IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3393-3407, April 2023, doi: 10.1109/TAP.2023.3245281

Global, regional, and national burden of chronic kidney disease attributable to high fasting plasma glucose from 1990 to 2019: a systematic analysis from the global burden of disease study 2019

PurposeGiven the rising prevalence of high fasting plasma glucose (HFPG) over the past three decades, it is crucial to assess its global, national, and regional impact on chronic kidney disease (CKD). This study aims to investigate the burden of CKD attributed to HFPG and its distribution across various levels.Methods and materialsThe data for this research was sourced from the Global Burden of Diseases Study 2019. To estimate the burden of CKD attributed to HFPG, we utilized DisMod-MR 2.1, a Bayesian meta-regression tool. The burden was measured using age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALYs) rate. Correlation analysis was performed using the Spearman rank order correlation method. Temporal trends were analyzed by estimating the estimated annual percentage change (EAPC).ResultsGlobally in 2019, there were a total of 487.97 thousand deaths and 13,093.42 thousand DALYs attributed to CKD attributed to HFPG, which represent a substantial increase of 153.8% and 120%, respectively, compared to 1990. Over the period from 1990 to 2019, the burden of CKD attributable to HFPG increased across all regions, with the highest increases observed in regions with high socio-demographic index (SDI) and middle SDI. Regions with lower SDI exhibited higher ASMR and age-standardized DALYs (ASDR) compared to developed nations at the regional level. Additionally, the EAPC values, which indicate the rate of increase, were significantly higher in these regions compared to developed nations. Notably, high-income North America, belonging to the high SDI regions, experienced the greatest increase in both ASMR and ASDR over the past three decades. Furthermore, throughout the years from 1990 to 2019, males bore a greater burden of CKD attributable to HFPG.ConclusionWith an increasing population and changing dietary patterns, the burden of CKD attributed to HFPG is expected to worsen. From 1990 to 2019, males and developing regions have experienced a more significant burden. Notably, the EAPC values for both ASMR and ASDR were higher in males and regions with lower SDI (excluding high-income North America). This emphasizes the pressing requirement for effective interventions to reduce the burden of CKD attributable to HFPG

Association between platelet distribution width and serum uric acid in Chinese population

© 2019 International Union of Biochemistry and Molecular Biology Platelet distribution width (PDW) is a simple and inexpensive parameter, which could predict activation of coagulation efficiently. And it has been confirmed to have a significant role in many diseases. We aimed to explore the association between PDW and hyperuricemia in a large Chinese cohort. This cross-sectional study recruited 61,091 ostensible healthy participants (29,259 males and 31,832 females) after implementing exclusion criteria. Clinical data of the enrolled population included anthropometric measurements and serum parameters. Database was sorted by gender, and the association between PDW and hyperuricemia was analyzed after dividing PDW into quartiles. Crude and adjusted odds ratios of PDW for hyperuricemia with 95% confidence intervals were analyzed using binary logistic regression models. We found no significant difference in PDW values between the genders. Males showed significantly higher incidence of hyperuricemia than females. From binary logistic regression models, significant hyperuricemia risks only were demonstrated in PDW quartiles 2 and 3 in males (P < 0.05). This study displayed close association between PDW and hyperuricemia as a risk factor. It is meaningful to use PDW as a clinical risk predictor for hyperuricemia in males. © 2019 BioFactors, 45(3):326–334, 2019

Genome-wide Association Study (GWAS) of mesocotyl elongation based on re-sequencing approach in rice

Annotation of candidate genes anchored by associated SNPs. (XLSX 34 kb

QTL Mapping and Heterosis Analysis for Fiber Quality Traits Across Multiple Genetic Populations and Environments in Upland Cotton

An “immortalized F2” (IF2) population and two reciprocal backcross (HSBCF1 and MARBCF1) populations were constructed to investigate the genetic bases of fiber quality traits in upland cotton across four different environments. A relatively high level of heterosis for micronaire (MIC) in IF2 population as well as fiber length (FL) and MIC in MARBCF1 population was observed. A total of 167 quantitative trait loci (QTLs) were detected in the three related experimental populations and their corresponding midparental heterosis (MPH) datasets using the composite interval mapping (CIM) approach. An analysis of genetic effects of QTLs detected in different populations and their MPH datasets showed 16 (24.24%) QTLs of partial dominance, and 46 (69.70%) QTLs of overdominance were identified in an IF2 population; 89 (62.68%) additive QTLs, three (2.11%) partial dominant QTLs, and 49 (34.51%) over-dominant QTLs were detected in two BCF1 populations. Multi-environment analysis showed 48 and 56 main-QTLs (m-QTLs) and 132 and 182 epistasis-QTLs (e-QTLs), by inclusive composite interval mapping (ICIM) in IF2 and two BCF1 populations, respectively. Phenotypic variance explained by e-QTLs, except for MARBCF1 population, was higher than that by m-QTLs. Thus, the overdominant, partial dominant, and epistasis effects were the main causes of heterosis in the IF2 population, whereas the additive, overdominant, and epistasis effects were the primary genetic basis of heterosis in the two BCF1 populations. Altogether, additive effect, partial dominance, overdominance, and epistasis contributed to fiber quality heterosis in upland cotton, but overdominance and epistasis were the most important factors