PolarLight: a CubeSat X-ray Polarimeter based on the Gas Pixel Detector

The gas pixel detector (GPD) is designed and developed for high-sensitivity astronomical X-ray polarimetry, which is a new window about to open in a few years. Due to the small mass, low power, and compact geometry of the GPD, we propose a CubeSat mission Polarimeter Light (PolarLight) to demonstrate and test the technology directly in space. There is no optics but a collimator to constrain the field of view to 2.3 degrees. Filled with pure dimethyl ether (DME) at 0.8 atm and sealed by a beryllium window of 100 micron thick, with a sensitive area of about 1.4 mm by 1.4 mm, PolarLight allows us to observe the brightest X-ray sources on the sky, with a count rate of, e.g., ~0.2 counts/s from the Crab nebula. The PolarLight is 1U in size and mounted in a 6U CubeSat, which was launched into a low Earth Sun-synchronous orbit on October 29, 2018, and is currently under test. More launches with improved designs are planned in 2019. These tests will help increase the technology readiness for future missions such as the enhanced X-ray Timing and Polarimetry (eXTP), better understand the orbital background, and may help constrain the physics with observations of the brightest objects.Comment: Accepted for publication in Experimental Astronom

STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better foot-prints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs

Determination of glufosinate ammonium and three metabolites in urine by ultra performance liquid chromatography-tandem mass spectrometry

BackgroudAt present, there is no unified standard for the detection of glufosinate ammonium and three metabolites in urine, which affects the accurate assessment of occupational exposure risk to a certain extent. It is of great significance to establish a rapid and effective inspection method to ensure occupational safety and public health.ObjectiveTo establish an ultra performance liquid chromatography-tandem mass spectrometry for simultaneous determination of glufosinate ammonium and three metabolites in urine.MethodsThe effects of dilution solvents and dilution ratios on the response values of glufosinate ammonium and three metabolites were compared, and the retention capacities of solid phase extraction columns for targets as well as the effects of chromatographic columns and mobile phase systems on chromatographic peaks were analyzed. Samples were quantified by matrix effect matching external standard method. Accuracy of the method was evaluated by recovery rate of standard addition, and precision of the method was evaluated by relative standard deviation of intra-day and inter-day measurements. Urine samples of 30 health individuals were collected to evaluate the application of the method.ResultsThe urine samples were diluted with 0.2 mL water and 0.6 mL acetonitrile, purified by HLB solid phase extraction columns, and separated by Dikma Polyamino HILIC columns, and gradient elution was carried out with 0.5 mmol·L−1 ammonium acetate and 0.1% ammonia water as mobile phase, which achieved a good peak shape and mass spectrum response. The linearities of the four target compounds were good in the range of 0.5-50 ng·mL−1, and the correlation coefficients (r) were all greater than 0.998. The detection limits were 0.56-2.86 μg·L−1, the quantification limits were 1.87-29.54 μg·L−1, and the recovery rates of standard addition ranged from 75.0% to 103.6%, The relative standard deviations of intra-batch and inter-batch were from 2.5% to 8.1% and from 4.3% to 9.3% respectively. The method was applied to detect 30 urine samples of subjects, and no target was detected.ConclusionThe method is simple, rapid, sensitive, and accurate. It is suitable for the determination of glufosinate ammonium and its metabolites in human urine without derivatization

Development of a High-Density Genetic Map Based on Specific Length Amplified Fragment Sequencing and Its Application in Quantitative Trait Loci Analysis for Yield-Related Traits in Cultivated Peanut

High-density genetic maps (HDGMs) are very useful for genomic studies and quantitative trait loci (QTL) mapping. However, the low frequency of DNA polymorphisms in peanut has limited the quantity of available markers and hindered the construction of a HDGM. This study generated a peanut genetic map with the highest number of high-quality SNPs based on specific locus amplified fragment sequencing (SLAF-seq) technology and a newly constructed RIL population (“ZH16” × “sd-H1”). The constructed HDGM included 3,630 SNP markers belonging to 2,636 bins on 20 linkage groups (LGs), and it covers 2,098.14 cM in length, with an average marker distance of 0.58 cM. This HDGM was applied for the following collinear comparison, scaffold anchoring and analysis of genomic characterization including recombination rates and segregation distortion in peanut. For QTL mapping of investigated 14 yield-related traits, a total of 62 QTLs were detected on 12 chromosomes across 3 environments, and the co-localization of QTLs was observed for these traits which were significantly correlated on phenotype. Two stable co-located QTLs for seed- and pod-related traits were significantly identified in the chromosomal end of B06 and B07, respectively. The construction of HDGM and QTL analysis for yield-related traits in this study provide useful information for fine mapping and functional analysis of genes as well as molecular marker-assisted breeding

Human Hepatocytes with Drug Metabolic Function Induced from Fibroblasts by Lineage Reprogramming

SummaryObtaining fully functional cell types is a major challenge for drug discovery and regenerative medicine. Currently, a fundamental solution to this key problem is still lacking. Here, we show that functional human induced hepatocytes (hiHeps) can be generated from fibroblasts by overexpressing the hepatic fate conversion factors HNF1A, HNF4A, and HNF6 along with the maturation factors ATF5, PROX1, and CEBPA. hiHeps express a spectrum of phase I and II drug-metabolizing enzymes and phase III drug transporters. Importantly, the metabolic activities of CYP3A4, CYP1A2, CYP2B6, CYP2C9, and CYP2C19 are comparable between hiHeps and freshly isolated primary human hepatocytes. Transplanted hiHeps repopulate up to 30% of the livers of Tet-uPA/Rag2−/−/γc−/− mice and secrete more than 300 μg/ml human ALBUMIN in vivo. Our data demonstrate that human hepatocytes with drug metabolic function can be generated by lineage reprogramming, thus providing a cell resource for pharmaceutical applications

Genome-wide association study on serum alkaline phosphatase levels in a Chinese population

Background: Serum alkaline phosphatase (ALP) is a complex phenotype influenced by both genetic and environmental factors. Recent Genome-Wide Association Studies (GWAS) have identified several loci affecting ALP levels; however, such studies in Chinese populations are limited. We performed a GWAS analyzing the association between 658,288 autosomal SNPs and serum ALP in 1,461 subjects, and replicated the top SNPs in an additional 8,830 healthy Chinese Han individuals. The interactions between significant locus and environmental factors on serum ALP levels were further investigated. Results: The association between ABO locus and serum ALP levels was replicated (P = 2.50 × 10-21, 1.12 × 10-56 and 2.82 × 10-27 for SNP rs8176720, rs651007 and rs7025162 on ABO locus, respectively). SNP rs651007 accounted for 2.15% of the total variance of serum ALP levels independently of the other 2 SNPs. When comparing our findings with previously published studies, ethnic differences were observed across populations. A significant interaction between ABO rs651007 and overweight and obesity was observed (FDR for interaction was 0.036); for individuals with GG genotype, those with normal weight and those who were overweight or obese have similar serum ALP concentrations; minor allele A of rs651007 remarkably reduced serum ALP levels, but this effect was attenuated in overweight and obese individuals. Conclusions: Our findings indicate that ABO locus is a major determinant for serum ALP levels in Chinese Han population. Overweight and obesity modifies the effect of ABO locus on serum ALP concentrations

Association between occlusal support and cognitive impairment in older Chinese adults: a community-based study

IntroductionThe loss of occlusal support due to tooth loss is associated with systemic diseases. However, there was little about the association between occlusal support and cognitive impairment. The cross-sectional study aimed to investigate their association.MethodsCognitive function was assessed and diagnosed in 1,225 community-dwelling adults aged 60 years or older in Jing’an District, Shanghai. Participants were diagnosed with mild cognitive impairment (MCI) by Peterson’s criteria, or dementia, according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. We determined the number of functional occlusal supporting areas according to Eichner classifications. We used multivariate logistic regression models to analyze the relationship between occlusal support and cognitive impairment and mediation effect models to analyze the mediation effect of age.ResultsSix hundred sixty participants were diagnosed with cognitive impairment, averaging 79.92 years old. After adjusting age, sex, education level, cigarette smoking, alcohol drinking, cardiovascular disease, and diabetes, individuals with poor occlusal support had an OR of 3.674 (95%CI 1.141–11.829) for cognitive impairment compared to those with good occlusal support. Age mediated 66.53% of the association between the number of functional occlusal supporting areas and cognitive impairment.DiscussionIn this study, cognitive impairment was significantly associated with the number of missing teeth, functional occlusal areas, and Eichner classifications with older community residents. Occlusal support should be a significant concern for people with cognitive impairment

Genetic mapping of AhVt1, a novel genetic locus that confers the variegated testa color in cultivated peanut (Arachis hypogaea L.) and its utilization for marker-assisted selection

IntroductionPeanut (Arachis hypogaea L.) is an important cash crop worldwide. Compared with the ordinary peanut with pure pink testa, peanut with variegated testa color has attractive appearance and a higher market value. In addition, the variegated testa represents a distinct regulation pattern of anthocyanin accumulation in integument cells.MethodsIn order to identify the genetic locus underlying variegated testa color in peanut, two populations were constructed from the crosses between Fuhua 8 (pure-pink testa) and Wucai (red on white variegated testa), Quanhonghua 1 (pure-red testa) and Wucai, respectively. Genetic analysis and bulked sergeant analysis sequencing were applied to detect and identify the genetic locus for variegated testa color. Marker-assisted selection was used to develop new variegated testa peanut lines.ResultsAs a result, all the seeds harvested from the F1 individuals of both populations showed the variegated testa type with white trace. Genetic analysis revealed that the pigmentation of colored region in red on white variegated testa was controlled by a previous reported gene AhRt1, while the formation of white region (un-pigmented region) in variegated testa was controlled by another single genetic locus. This locus, named as AhVt1 (Arachis hypogaea Variegated Testa 1), was preliminary mapped on chromosome 08 through bulked sergeant analysis sequencing. Using a secondary mapping population derived from the cross between Fuhua 8 and Wucai, AhVt1 was further mapped to a 1.89-Mb genomic interval by linkage analysis, and several potential genes associated with the uneven distribution of anthocyanin, such as MADS-box, MYB, and Chalcone synthase-like protein, were harbored in the region. Moreover, the molecular markers closely linked to the AhVt1 were developed, and the new variegated testa peanut lines were obtained with the help of marker-assisted selection.ConclusionOur findings will accelerate the breeding program for developing new peanut varieties with “colorful” testa colors and laid a foundation for map-based cloning of gene responsible for variegated testa