1H NMR-based metabolomics of paired tissue, serum and urine samples reveals an optimized panel of biofluids metabolic biomarkers for esophageal cancer

IntroductionThe goal of this study was to establish an optimized metabolic panel by combining serum and urine biomarkers that could reflect the malignancy of cancer tissues to improve the non-invasive diagnosis of esophageal squamous cell cancer (ESCC).MethodsUrine and serum specimens representing the healthy and ESCC individuals, together with the paralleled ESCC cancer tissues and corresponding distant non-cancerous tissues were investigated in this study using the high-resolution 600 MHz 1H-NMR technique.ResultsWe identified distinct 1H NMR-based serum and urine metabolic signatures respectively, which were linked to the metabolic profiles of esophageal-cancerous tissues. Creatine and glycine in both serum and urine were selected as the optimal biofluids biomarker panel for ESCC detection, as they were the overlapping discriminative metabolites across serum, urine and cancer tissues in ESCC patients. Also, the were the major metabolites involved in the perturbation of “glycine, serine, and threonine metabolism”, the significant pathway alteration associated with ESCC progression. Then a visual predictive nomogram was constructed by combining creatine and glycine in both serum and urine, which exhibited superior diagnostic efficiency (with an AUC of 0.930) than any diagnostic model constructed by a single urine or serum metabolic biomarkers.DiscussionOverall, this study highlighted that NMR-based biofluids metabolomics fingerprinting, as a non-invasive predictor, has the potential utility for ESCC detection. Further studies based on a lager number size and in combination with other omics or molecular biological approaches are needed to validate the metabolic pathway disturbances in ESCC patients

Transcriptome Analysis of H2O2-Treated Wheat Seedlings Reveals a H2O2-Responsive Fatty Acid Desaturase Gene Participating in Powdery Mildew Resistance

Hydrogen peroxide (H2O2) plays important roles in plant biotic and abiotic stress responses. However, the effect of H2O2 stress on the bread wheat transcriptome is still lacking. To investigate the cellular and metabolic responses triggered by H2O2, we performed an mRNA tag analysis of wheat seedlings under 10 mM H2O2 treatment for 6 hour in one powdery mildew (PM) resistant (PmA) and two susceptible (Cha and Han) lines. In total, 6,156, 6,875 and 3,276 transcripts were found to be differentially expressed in PmA, Han and Cha respectively. Among them, 260 genes exhibited consistent expression patterns in all three wheat lines and may represent a subset of basal H2O2 responsive genes that were associated with cell defense, signal transduction, photosynthesis, carbohydrate metabolism, lipid metabolism, redox homeostasis, and transport. Among genes specific to PmA, ‘transport’ activity was significantly enriched in Gene Ontology analysis. MapMan classification showed that, while both up- and down- regulations were observed for auxin, abscisic acid, and brassinolides signaling genes, the jasmonic acid and ethylene signaling pathway genes were all up-regulated, suggesting H2O2-enhanced JA/Et functions in PmA. To further study whether any of these genes were involved in wheat PM response, 19 H2O2-responsive putative defense related genes were assayed in wheat seedlings infected with Blumeria graminis f. sp. tritici (Bgt). Eight of these genes were found to be co-regulated by H2O2 and Bgt, among which a fatty acid desaturase gene TaFAD was then confirmed by virus induced gene silencing (VIGS) to be required for the PM resistance. Together, our data presents the first global picture of the wheat transcriptome under H2O2 stress and uncovers potential links between H2O2 and Bgt responses, hence providing important candidate genes for the PM resistance in wheat

In Situ Study the Grooving Effect Induced by Ag Particles on Rapid Growth of Cu₆Sn₅ Grain at Sn-xAg/Cu Soldering Interface during the Heat Preservation Stage

Synchrotron radiation X-ray imaging technique was applied for in situ observation of Cu6Sn5 intermetallic compounds (IMC) growth in Sn/Cu and Sn-3.5Ag/Cu joints under isothermal temperature conditions of 250/300/350 °C and time duration of 1.5 h. The IMC in Sn-Ag solder was characterized by the formation of grooves during the interfacial reaction, and this can be attributed to the Ag content. Kinetically, the growth rate constants for the height of Cu6Sn5 were observed to increase with temperatures and the presence of Ag in solder. As compared to pure Sn solders, the Sn-3.5Ag solders were observed with interfacial IMC of greater height, smaller base width, and lowered aspect ratio

Non-Cooperative Target Attitude Estimation Method Based on Deep Learning of Ground and Space Access Scene Radar Images

Determining the attitude of a non-cooperative target in space is an important frontier issue in the aerospace field, and has important application value in the fields of malfunctioning satellite state assessment and non-cooperative target detection in space. This paper proposes a non-cooperative target attitude estimation method based on the deep learning of ground and space access (GSA) scene radar images to solve this problem. In GSA scenes, the observed target satellite can be imaged not only by inverse synthetic-aperture radar (ISAR), but also by space-based optical satellites, with space-based optical images providing more accurate attitude estimates for the target. The spatial orientation of the intersection of the orbital planes of the target and observation satellites can be changed by fine tuning the orbit of the observation satellite. The intersection of the orbital planes is controlled to ensure that it is collinear with the position vector of the target satellite when it is accessible to the radar. Thus, a series of GSA scenes are generated. In these GSA scenes, the high-precision attitude values of the target satellite can be estimated from the space-based optical images obtained by the observation satellite. Thus, the corresponding relationship between a series of ISAR images and the attitude estimation of the target at this moment can be obtained. Because the target attitude can be accurately estimated from the GSA scenes obtained by a space-based optical telescope, these attitude estimation values can be used as training datasets of ISAR images, and deep learning training can be performed on ISAR images of GSA scenes. This paper proposes an instantaneous attitude estimation method based on a deep network, which can achieve robust attitude estimation under different signal-to-noise ratio conditions. First, ISAR observation and imaging models were created, and the theoretical projection relationship from the three-dimensional point cloud to the ISAR imaging plane was constructed based on the radar line of sight. Under the premise that the ISAR imaging plane was fixed, the ISAR imaging results, theoretical projection map, and target attitude were in a one-to-one correspondence, which meant that the mapping relationship could be learned using a deep network. Specifically, in order to suppress noise interference, a UNet++ network with strong feature extraction ability was used to learn the mapping relationship between the ISAR imaging results and the theoretical projection map to achieve ISAR image enhancement. The shifted window (swin) transformer was then used to learn the mapping relationship between the enhanced ISAR images and target attitude to achieve instantaneous attitude estimation. Finally, the effectiveness of the proposed method was verified using electromagnetic simulation data, and it was found that the average attitude estimation error of the proposed method was less than 1°

Non-Cooperative Target Attitude Estimation Method Based on Deep Learning of Ground and Space Access Scene Radar Images

Determining the attitude of a non-cooperative target in space is an important frontier issue in the aerospace field, and has important application value in the fields of malfunctioning satellite state assessment and non-cooperative target detection in space. This paper proposes a non-cooperative target attitude estimation method based on the deep learning of ground and space access (GSA) scene radar images to solve this problem. In GSA scenes, the observed target satellite can be imaged not only by inverse synthetic-aperture radar (ISAR), but also by space-based optical satellites, with space-based optical images providing more accurate attitude estimates for the target. The spatial orientation of the intersection of the orbital planes of the target and observation satellites can be changed by fine tuning the orbit of the observation satellite. The intersection of the orbital planes is controlled to ensure that it is collinear with the position vector of the target satellite when it is accessible to the radar. Thus, a series of GSA scenes are generated. In these GSA scenes, the high-precision attitude values of the target satellite can be estimated from the space-based optical images obtained by the observation satellite. Thus, the corresponding relationship between a series of ISAR images and the attitude estimation of the target at this moment can be obtained. Because the target attitude can be accurately estimated from the GSA scenes obtained by a space-based optical telescope, these attitude estimation values can be used as training datasets of ISAR images, and deep learning training can be performed on ISAR images of GSA scenes. This paper proposes an instantaneous attitude estimation method based on a deep network, which can achieve robust attitude estimation under different signal-to-noise ratio conditions. First, ISAR observation and imaging models were created, and the theoretical projection relationship from the three-dimensional point cloud to the ISAR imaging plane was constructed based on the radar line of sight. Under the premise that the ISAR imaging plane was fixed, the ISAR imaging results, theoretical projection map, and target attitude were in a one-to-one correspondence, which meant that the mapping relationship could be learned using a deep network. Specifically, in order to suppress noise interference, a UNet++ network with strong feature extraction ability was used to learn the mapping relationship between the ISAR imaging results and the theoretical projection map to achieve ISAR image enhancement. The shifted window (swin) transformer was then used to learn the mapping relationship between the enhanced ISAR images and target attitude to achieve instantaneous attitude estimation. Finally, the effectiveness of the proposed method was verified using electromagnetic simulation data, and it was found that the average attitude estimation error of the proposed method was less than 1°

Overexpression of OsRRK1 Changes Leaf Morphology and Defense to Insect in Rice

It has been reported that the receptor-like cytoplasmic kinases (RLCKs) regulate many biological processes in plants, but only a few members have been functionally characterized. Here, we isolated a rice gene encoding AtRRK1 homology protein kinase, OsRRK1, which belongs to the RLCK VI subfamily. OsRRK1 transcript accumulated in many tissues at low to moderate levels and at high levels in leaves. Overexpression of OsRRK1 (OE-OsRRK1) caused adaxial rolling and erect morphology of rice leaves. In the rolled leaves of OE-OsRRK1 plants, both the number and the size of the bulliform cells are decreased compared to the wild-type (WT) plants. Moreover, the height, tiller number, and seed setting rate were reduced in OE-OsRRK1 plants. In addition, the brown planthopper (BPH), a devastating pest of rice, preferred to settle on WT plants than on the OE-OsRRK1 plants in a two-host choice test, indicating that OE-OsRRK1 conferred an antixenosis resistance to BPH. The analysis of transcriptome sequencing demonstrated that several receptor kinases and transcription factors were differentially expressed in OE-OsRRK1 plants and WT plants. These results indicated that OsRRK1 may play multiple roles in the development and defense of rice, which may facilitate the breeding of novel rice varieties

Enhancement of DNA vaccine potency by sandwiching antigen-coding gene between secondary lymphoid tissue chemokine (SLC) and IgG Fc fragment genes.

DNA vaccine has become an attractive approach for generating antigen-specific immunity. Targeting antigens to FcRs for IgG (FcgammaRs) on dendritic cells (DCs) has been demonstrated to enhance antigen presentation. Secondary lymphoid tissue chemokine (SLC) has been shown to increase immune responses not only by promoting coclustering of T cells and DCs in the lymph nodes and spleen but also by regulating their immunogenic potential for the induction of T cell responses. In this study, using HPV 16 E7 as a model antigen, we constructed a chemotactic-antigen plasmid DNA vaccine (pSLC-E7-Fc) by linking SLC and Fc gene sequences to each end of E7 and evaluated its potency of eliciting specific immune response. We found that immunization with pSLC-E7-Fc generated much stronger E7-specific lymphocyte proliferative and cytotoxic T lymphocyte (CTL) responses than control DNA. All the mice receiving pSLC-E7-Fc prophylactic vaccination remained tumor free upon subcutaneous inoculation of TC-1 cells, while those given control DNA all developed tumors. These tumor-free mice were also protected against TC-1 rechallenge. Complete tumor regression with long-term survival occurred in 72% of mice given pSLC-E7-Fc as therapeutic vaccination. In experimental lung metastasis model wherein TC-1 cells were intravenously injected, therapeutic vaccination with pSLC-E7-Fc significantly reduced the number of tumor nodules in the lung. In vivo depletion with antibodies against CD4+or CD8+ T cells both resulted in complete abrogation of the pSLC-E7-Fc-induced immunotherapeutic effect. Our data indicate that the DNA vaccine constructed by the fusion of SLC and IgG Fc fragment genes to antigen-coding gene is an effective approach to induce potent anti-tumor immune response via both CD4+ and CD8+ T cells dependent pathways