Distributed Semi-Supervised Sparse Statistical Inference

This paper is devoted to studying the semi-supervised sparse statistical inference in a distributed setup. An efficient multi-round distributed debiased estimator, which integrates both labeled and unlabelled data, is developed. We will show that the additional unlabeled data helps to improve the statistical rate of each round of iteration. Our approach offers tailored debiasing methods for $M$-estimation and generalized linear model according to the specific form of the loss function. Our method also applies to a non-smooth loss like absolute deviation loss. Furthermore, our algorithm is computationally efficient since it requires only one estimation of a high-dimensional inverse covariance matrix. We demonstrate the effectiveness of our method by presenting simulation studies and real data applications that highlight the benefits of incorporating unlabeled data.Comment: 41 pages, 4 figure

Development of SCAR markers related to heat tolerance in Kentucky bluegrass

As a high-quality cool-season grass, Kentucky bluegrass (Poa pratensis) is facing increasing threat of high temperature, so improving its heat tolerance (HT) has become an important breeding target. In this study, the HT of 84 materials was identified in the artificial climate chamber, and 15 most heat-tolerant and 15 most heat-sensitive materials were selected respectively to construct two DNA pools. There was a significant difference in high-temperature tolerance time between the plants in the two pools, which was more than 22 days. A total of 304 sequence-related amplified polymorphism (SRAP) and 88 simple sequence repeat (SSR) markers were used to screen the polymorphic bands between the two pools. Then, these bands were transformed into sequence characterized amplified region (SCAR) markers, and finally 12 SCAR dominant markers related to HT were obtained, which could detect the heat-sensitive materials efficiently. Among them, S-me8×em2 and S-me52×em5 had the best identification effect, and the consistency between the absence of these two markers and the heat-sensitive phenotype was 87%. According to the comparison with NCBI database, the sequences of 12 SCAR markers had no homology with known HT related genes. Next, we would further verify the HT identification efficiency of these SCAR markers in single plants within materials, and try to use them in molecular marker-assisted breeding. &nbsp

Analysis of Genetic Diversity in 73 Kentucky Bluegrass Materials by SSR and SRAP Markers

Kentucky bluegrass (Poa pratensisL.) (KBG) is a commonly used grass that possesses excellent quality, as well as a complex genetic background and reproductive patterns. In this study, a total of 73 KBG germplasms were collected, of which 49 were imported varieties, 5 were Chinese breeding varieties, and 19 were wild materials. A total of 70 simple sequence repeat (SSR) and 75 sequence-related amplification polymorphism (SRAP) markers were selected to use for genetic diversity analysis. From these studies, high levels of polymorphisms were observed in SRAPs (91.8%) and SSRs (94.5%), respectively. Three dendrograms that were generated from SRAP, SSR, and SRAP+SSR combined data revealed a general similarity for the positioning of the majority of materials. However, certain materials, including Z65, Z25, and Z27, were found to be located in diverse clusters among different dendrograms. Further analysis demonstrated no significant association between geographical origin and molecular marker clusters in the wild materials. Combined with the seedling phenotype identification carried out in our prior study, it seems as though there is no significant relationship between agronomic characterization and marker-based clustering in these materials, except for in the case of leaf color. These studies provided an increased understanding of genetic diversity among KBG materials, which will be beneficial for genetic improvement and germplasm conservation in the future

Transcriptome Sequencing of Two Kentucky Bluegrass (Poa pratensis L.) Genotypes in Response to Heat Stress

Kentucky bluegrass (Poa pratensis L.) (KBG) is a major cool-season turfgrass. However, as its complex genetic background and production modes, limited genomic and transcriptomic information of KBG was known so far. In this study, a transcriptome study between wild type material Ninglan (summer stress sensitive) and cultivar material KBG03 (summer stress tolerant) was conducted, under optimal (25 °C) and high (40 °C) temperatures. A total of 81.42 Gb clean reads were generated and de novo assembled into 110,784 unigenes with an average length of 1,105 bp. About 50% KBG unigenes were similar to the non-redundant (NR) database. Among the NR BLASTx top hits, 27.47% unigenes were matched to Brachypodium distachyon. After heat stress, a massive amount of unigenes showed significantly differential expression in both genotypes. After 2h heat stress, more specially up-regulated differentially expressed unigenes (DEGs) and less down-regulated DEGs were detected in Ninglan than in KBG03. At 24h, the expression of 1710 and 730 unigenes were increased and decreased uniquely in Ninglan, and 1361 up-regulated DEGs and 757 down-regulated DEGs were just found in KBG03. More heat shock proteins (HSPs) and heat shock transcription factors (HSFs) DEGs were also identified at 2h than 24h in both genotypes. In addition, by Gene Ontology (GO) enrichment analysis, three core terms (“protein folding”, “response to heat”, and “response to hydrogen peroxide”) of biological process (BP) ontology were found in both genotypes under different heat stress time. The DEGs shared in both genotypes might be related to the basic mechanism of thermal response in KBG

Unraveling the Regulatory Mechanism of Color Diversity in Camellia japonica Petals by Integrative Transcriptome and Metabolome Analysis

Camellia japonica petals are colorful, rich in anthocyanins, and possess important ornamental, edible, and medicinal value. However, the regulatory mechanism of anthocyanin accumulation in C. japonica is still unclear. In this study, an integrative analysis of the metabolome and transcriptome was conducted in five C. japonica cultivars with different petal colors. Overall, a total of 187 flavonoids were identified (including 25 anthocyanins), and 11 anthocyanins were markedly differentially accumulated among these petals, contributing to the different petal colors in C. japonica. Moreover, cyanidin-3-O-(6″-O-malonyl) glucoside was confirmed as the main contributor to the red petal phenotype, while cyanidin-3-O-rutinoside, peonidin-3-O-glucoside, cyanidin-3-O-glucoside, and pelargonidin-3-O-glucoside were responsible for the deep coloration of the C. japonica petals. Furthermore, a total of 12,531 differentially expressed genes (DEGs) and overlapping DEGs (634 DEGs) were identified by RNA sequencing, and the correlation between the expression level of the DEGs and the anthocyanin content was explored. The candidate genes regulating anthocyanin accumulation in the C. japonica petals were identified and included 37 structural genes (especially CjANS and Cj4CL), 18 keys differentially expressed transcription factors (such as GATA, MYB, bHLH, WRKY, and NAC), and 16 other regulators (mainly including transporter proteins, zinc-finger proteins, and others). Our results provide new insights for elucidating the function of anthocyanins in C. japonica petal color expression

A Metamorphic Testing Approach for Assessing Question Answering Systems

Question Answering (QA) enables the machine to understand and answer questions posed in natural language, which has emerged as a powerful tool in various domains. However, QA is a challenging task and there is an increasing concern about its quality. In this paper, we propose to apply the technique of metamorphic testing (MT) to evaluate QA systems from the users’ perspectives, in order to help the users to better understand the capabilities of these systems and then to select appropriate QA systems for their specific needs. Two typical categories of QA systems, namely, the textual QA (TQA) and visual QA (VQA), are studied, and a total number of 17 metamorphic relations (MRs) are identified for them. These MRs respectively focus on some characteristics of different aspects of QA. We further apply MT to four QA systems (including two APIs from the AllenNLP platform, one API from the Transformers platform, and one API from CloudCV) by using all of the MRs. Our experimental results demonstrate the capabilities of the four subject QA systems from various aspects, revealing their strengths and weaknesses. These results further suggest that MT can be an effective method for assessing QA systems

Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation

The fast motion of low Earth orbit (LEO) satellites provides rapid geometric changes in a short time, which can accelerate the initialization of precise point positioning (PPP). The rapid convergence of ambiguity parameters is conducive to the rapid success of ambiguity fixing. This paper presents the performance of single- and four-system combined PPP Ambiguity Resolution (AR), enhanced with an ambiguity-float solution LEO. Two LEO constellations were designed: L was a typical polar orbit constellation, with a higher number of visible satellites at high latitudes than at low and middle latitudes; and M was designed to compensate for the lack of visible satellites at low and middle latitudes. The ground observation data of the LEO satellites at the MGEX stations were simulated. Because the global navigation satellite systems (GNSSs) were fully operational, the GNSS data were real observation data from the MGEX stations. Based on the daily observation datasets collected at 258 stations in the global MGEX observation network over three days (from 1 January to 3 January 2022), in addition to the LEO simulation data, we evaluated the positioning performance of LEO ambiguity-float solution-enhanced PPP ambiguity resolution and compared it with LEO-enhanced PPP. The L+M mixed constellation was able to reduce the time to first fix (TTFF) of the four-system combined PPP-AR to 5 min, and four LEO satellites were sufficient to achieve this. L+M mixed constellation was able to reduce the convergence time of the four-system combined PPP to 2 min. Unlike PPP-AR, PPP required more LEO satellites for augmentation to saturate

Inter-Satellite Single-Difference Ionospheric Delay Interpolation Model for PPP-RTK and Its Positioning Performance Verification

In PPP-RTK, obtaining accurate atmospheric delay information for the user through interpolation is one of the keys to achieving high-precision real-time positioning. The ionospheric delay that is extracted by a reference network based on uncalibrated phase delay (UPD) products is often difficult to separate from errors such as receiver code hardware delay and UPD reference error. Inter-satellite single-difference (SD) ionospheric delay information is typically provided to the user. This paper proposes an interpolation model that uses the atmospheric delay coefficient to represent the SD ionospheric delay, based on the mean position of the ionospheric pierce point (IPP) of each satellite pair and the center position of the network, which is called the differenced surface model (DSM). We chose four scenarios to compare the interpolation accuracy of the proposed model with the inverse distance-based linear interpolation method (DIM) and USM based on the difference between the longitude and latitude of the reference and ionospheric pierce point (IPP) of every satellite (here, we call it USM for short). The four scenarios involve a medium-scale reference network with an average distance to the reference station of 41 km, a large-scale reference network with an average distance to the reference station of 98 km, and out-of-network users, and a network with a common minimum of three reference stations. The results show that the root mean square (RMS) of the SD residuals of ionospheric delay for DSM were 1.4, 3.2, 2.2, and 1.4 cm, respectively, for the four scenarios that were considered, which are slightly better delay values than those that were achieved using DIM and USM. For the scenario with three reference stations, the interpolation accuracies of DIM and DSM were no different from those for four reference stations, indicating that the server can still try to provide ionospheric correction service under the condition of fewer reference stations. In contrast, USM could not provide service because it lacked the sufficient number of reference stations. DSM was used as the ionospheric delay interpolation model to analyze GPS and Galileo dual-system PPP-RTK positioning performance. In addition, the atmospheric parameter constraint method of users was used in PPP-RTK in reference networks of different scales. For the 41-km and 98-km reference networks, the time to first fix (TTFF) were 14.5 s and 33.1 s, respectively, and the mean RMS values for the east (E), north (N), and up (U) directions were 0.80, 0.93, and 2.72 cm, respectively, and 1.0, 1.1, and 4.0 cm, respectively, for a period of 5 min after convergence. The fixing rate and positioning accuracy of DSM during the 5-min period were better than those of DIM when the same empirical model was used to determine the mean square error of atmospheric delay

Copper-iron supported bimodal pore catalyst and its application for higher alcohols synthesis

A Cu-Fe supported bimodal pore catalyst prepared by incipient-wetness impregnation exhibited two kinds of nano-pores. Increasing amount of copper and iron species decreased BET surface area and the size of both the small pores and large pores, while promoted the aggregation of bi-metal particles inside the large pores. Higher bimetal species content in the bimodal derived catalyst facilitated the reduction of metal oxides and the formation of metallic Cu and iron carbides during higher alcohols synthesis (HAS) reaction. The increasing of active bimetal sites and spatial effect of pore structures enhanced probably the synergistic effect of Cu-Fe, promoting the catalytic activity for HAS. Furthermore, the bimodal derived catalyst with higher Cu and Fe species loading resulted in a larger metallic particle size and lower surface area, which facilitated the product distribution of alcohols shifting towards C-2 +OH. (C) 2014 Elsevier B.V. All rights reserved