Heteroatom-Induced Molecular Asymmetry Tunes Quantum Interference in Charge Transport through Single-Molecule Junctions

We studied the interplay between quantum interference (QI) and molecular asymmetry in charge transport through a single molecule. Eight compounds with five-membered core rings were synthesized, and their single-molecule conductances were characterized using the mechanically controllable break junction technique. It is found that the symmetric molecules are more conductive than their asymmetric isomers and that there is no statistically significant dependence on the aromaticity of the core. In contrast, we find experimental evidence of destructive QI in five-membered rings, which can be tuned by implanting different heteroatoms into the core ring. Our findings are rationalized by the presence of antiresonance features in the transmission curves calculated using nonequilibrium Green’s functions. This novel mechanism for modulating QI effects in charge transport via tuning of molecular asymmetry will lead to promising applications in the design of single-molecule devices

Electric-Field-Induced Connectivity Switching in Single-Molecule Junctions

Summary(#br)The manipulation of molecule-electrode interaction is essential for the fabrication of molecular devices and determines the connectivity from electrodes to molecular components. Although the connectivity of molecular devices could be controlled by molecular design to place anchor groups in different positions of molecule backbones, the reversible switching of such connectivities remains challenging. Here, we develop an electric-field-induced strategy to switch the connectivity of single-molecule junctions reversibly, leading to the manipulation of different connectivities in the same molecular backbone. Our results offer a new concept of single-molecule manipulation and provide a feasible strategy to regulate molecule-electrode interaction

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

Interferometric SAR Observation of Permafrost Status in the Northern Qinghai-Tibet Plateau by ALOS, ALOS-2 and Sentinel-1 between 2007 and 2021

With global warming, permafrost is undergoing degradation, which may cause thawing subsidence, collapse, and emission of greenhouse gases preserved in previously frozen permafrost, change the local hydrology and ecology system, and threaten infrastructure and indigenous communities. The Qinghai-Tibet Plateau (QTP) is the world’s largest permafrost region in the middle and low latitudes. Permafrost status monitoring in the QTP is of great significance to global change and local economic development. In this study, we used 66 scenes of ALOS data (2007–2009), 73 scenes of ALOS-2 data (2015–2020) and 284 scenes of Sentinel-1 data (2017–2021) to evaluate the spatial and temporal permafrost deformation over the 83,000 km2 in the northern QTP, passing through the Tuotuohe, Beiluhe, Wudaoliang and Xidatan regions. We use the SBAS-InSAR method and present a coherence weighted least squares estimator without any hypothetical model to calculate long-term deformation velocity (LTDV) and maximum seasonal deformation (MSD) without any prior knowledge. Analysis of the ALOS results shows that the LTDV ranged from −20 to +20 mm/year during 2007–2009. For the ALOS-2 and Sentinel-1 results, the LTDV ranged from −30 to 30 mm/year during 2015–2021. Further study shows that the expansion areas of permafrost subsidence are concentrated on braided stream plains and thermokarst lakes. In these areas, due to glacial erosion, surface runoff and river alluvium, the contents of water and ground ice are sufficient, which could accelerate permafrost subsidence. In addition, by analyzing LTDV and MSD for the different periods, we found that the L-band ALOS-2 is more sensitive to the thermal collapse of permafrost than the C-band sensor and the detected collapse areas (LTDV < −10 mm/year) are consistent with the GF-1/2 thermal collapse dataset. This research indicates that the InSAR technique could be crucial for monitoring the evolution of permafrost and freeze-thaw disasters

Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis

As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments

Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis

As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments

Effect of Saturation Degree on Mechanical Behaviors of Shallow Unsaturated Expansive Soils

In the southwest of China, there are widely distributed expansive soils. However, to save costs and manage the speed of construction, these shallow expansive soils are often filled with subgrade materials. Therefore, it is necessary to clearly understand the mechanical behaviors of unmodified shallow expansive soils. Current research on the mechanical behaviors of shallow expansive soils is mainly focused on shear and compressive strengths but rarely on the tensile strength since general tests are costly, time consuming, and difficult to conduct. Therefore, uniaxial tensile, unconfined compression and direct shear tests were carried out to study the mechanical behavior of shallow unsaturated expansive soils under different saturation degrees, and the tests analyzed the change mechanism of its mechanical behavior. The following were found: (1) with an increase in saturation degree, the uniaxial tensile strength, unconfined compressive strength, shear strength, cohesive force, and internal friction angle first increased and then decreased; (2) when the saturation degree increased from 18.7% to the saturation degree corresponding to the peak, the uniaxial tensile strength, unconfined compressive strength, cohesive force, and internal friction angle increased by about 11 times, 3.24 times, 2.34 times, and 0.52 times, respectively; (3) when the saturation degree increased from the saturation degree corresponding to the peak to 80.3%, they decreases by about 42%, 51.4%, 36%, and 50%, respectively; (4) with the increase in dry density, the saturation degree corresponding to the peak of uniaxial tensile strength gradually increased, while the saturation degree corresponding to the peak of unconfined compressive and shear strength did not significantly change

Integration of Proteomics and Metabolomics Revealed Metabolite-Protein Networks in ACTH-Secreting Pituitary Adenoma

An effective treatment for the management of adrenocorticotropic hormone-secreting pituitary adenomas (ACTH-PA) is currently lacking, although surgery is a treatment option. We have integrated information obtained at the metabolomic and proteomic levels to identify critical networks and signaling pathways that may play important roles in the metabolic regulation of ACTH-PA and therefore hopefully represent potential therapeutic targets. Six ACTH-PAs and seven normal pituitary glands were investigated via gas chromatography-mass spectrometry (GC-MS) analysis for metabolomics. Five ACTH-PAs and five normal pituitary glands were subjected to proteomics analysis via nano liquid chromatography tandem-mass spectrometry (nanoLC-MS/MS). The joint pathway analysis and network analysis was performed using MetaboAnalyst 3.0. software. There were significant differences of metabolites and protein expression levels between the ACTH-PAs and normal pituitary glands. A proteomic analysis identified 417 differentially expressed proteins that were significantly enriched in the Myc signaling pathway. The protein-metabolite joint pathway analysis showed that differentially expressed proteins and metabolites were significantly enriched in glycolysis/gluconeogenesis, pyruvate metabolism, citrate cycle (ICA cycle), and the fatty acid metabolism pathway in ACTH-PA. The protein-metabolite molecular interaction network identified from the metabolomics and proteomics investigation resulted in four subnetworks. Ten nodes in subnetwork 1 were the most significantly enriched in cell amino acid metabolism and pyrimidine nucleotide metabolism. Additionally, the metabolite-gene-disease interaction network established nine subnetworks. Ninety-two nodes in subnetwork 1 were the most significantly enriched in carboxylic acid metabolism and organic acid metabolism. The present study clarified the pathway networks that function in ACTH-PA. Our results demonstrated the presence of downregulated glycolysis and fatty acid synthesis in this tumor type. We also revealed that the Myc signaling pathway significantly participated in the metabolic changes and tumorigenesis of ACTH-PA. This data may provide biomarkers for ACTH-PA diagnosis and monitoring, and could also lead to the development of novel strategies for treating pituitary adenomas