A conformal multi-port MIMO patch antenna for 5G wireless devices.

This paper presents a multiple-input-multiple-output (MIMO) antenna array with low-profile and flexible characteristics. Multiple microstrip patches are arranged in the E-plane configuration and decoupled by shorted quarter-wavelength stubs. The antenna has a small element spacing of 0.032 λ, where λ is a free-space wavelength at the center frequency. To demonstrate the feasibility of the proposed concept, a 1 × 4 MIMO array prototype is fabricated. The measured results on the fabricated prototype demonstrate that the MIMO antenna has good operation features at 4.8 GHz with a reflection coefficient of less than -10 dB and an isolation of better than 20 dB. Besides, good radiation patterns and broadside gain of around 4.5 dBi are also attained. The antenna also works in the bending mode and has the capability of extending to large-scale MIMO arrays. Such attractive features prove the utility of the proposed antenna in various modern electronic devices.

Circularly polarized MIMO patch antenna with high isolation and wideband characteristics for WLAN applications.

This paper presents a two-element multiple-input multiple-output (MIMO) antenna with circular polarization diversity. The primary radiating elements are two conventional truncated corner microstrip patches. To enhance the antenna's bandwidth, gain, and port isolation, multiple parasitic elements are employed and they are positioned in different layer with the radiating patches. For further isolation improvement, a method of using defected ground structure is employed. The measured results demonstrate that the proposed antenna possesses wideband operation of 9.3% (5.1-5.6 GHz) with the gain of better than 8.0 dBi. Regarding the MIMO performances, the antenna obtains high isolation, which is from 30 to 63 dB. Besides, the MIMO parameters such as envelope correlation coefficient and diversity gain are found to be good in the scale of diversity standards. Compared to other related works, the proposed design has the best operating BW with 40-dB isolation and stable gain while keeping small element spacing.

Host Genetic Risk Factors Associated with COVID-19 Susceptibility and Severity in Vietnamese.

Since the emergence and rapid transmission of SARS-CoV-2, numerous scientific reports have searched for the association of host genetic variants with COVID-19, but the data are mostly acquired from Europe. In the current work, we explored the link between host genes (SARS-CoV-2 entry and immune system related to COVID-19 sensitivity/severity) and ABO blood types with COVID-19 from whole-exome data of 200 COVID-19 patients and 100 controls in Vietnam. The O blood type was found to be a protective factor that weakens the worst outcomes of infected individuals. For SARS-CoV-2 susceptibility, rs2229207 (TC genotype, allele C) and rs17860118 (allele T) of IFNAR2 increased the risk of infection, but rs139940581 (CT genotype, allele T) of SLC6A20 reduced virus sensitivity. For COVID-19 progress, the frequencies of rs4622692 (TG genotype) and rs1048610 (TC genotype) of ADAM17 were significantly higher in the moderate group than in the severe/fatal group. The variant rs12329760 (AA genotype) of TMPRSS2 was significantly associated with asymptomatic/mild symptoms. Additionally, rs2304255 (CT genotype, allele T) of TYK2 and rs2277735 (AG genotype) of DPP9 were associated with severe/fatal outcomes. Studies on different populations will give better insights into the pathogenesis, which is ethnic-dependent, and thus decipher the genetic factor's contribution to mechanisms that predispose people to being more vulnerable to COVID-19.

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms.

Assessments of genomic prediction accuracies using artificial intelligent (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a nonlinear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP, and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 single nucleotide polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP, and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1-15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3-19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3-5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318-400 SNPs for survival status and 1,362-1,589 SNPs for survival time) were somewhat lower (0.3-15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to E. ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease-resistant traits studied in this population of striped catfish P. hypophthalmus.