Effects of Rock Fragments on the Soil Physicochemical Properties and Vegetation on the Northeastern Tibetan Plateau

Stony soils are very widely distributed and contain abundant rock fragments (>2 mm), which impose major effects on soil properties and plant growth. However, the role of rock fragments is still often neglected, which can lead to an inadequate understanding of the interaction between plants and soil. Undisturbed soil columns were collected from three alpine grasslands on the Qilian Mountain, and the X-ray computed tomography method was applied to investigate the characteristics of rock fragments. The results showed there was significant difference in number density, volumetric content and surface area density of rock fragment among the three grasslands, and followed the order of alpine meadow > alpine steppe > alpine desert steppe. In addition, the soil organic carbon, total nitrogen, total phosphorus, available phosphorus, N-NH4+, and N-NO3− contents in fine earth all increased with increasing number density, volumetric content and surface area density but to different degrees. Furthermore, positive correlations were observed between the rock shape factor and belowground biomass (R2 = 0.531, p < 0.05), between the rock volumetric content and aboveground biomass (R2 = 0.527, p < 0.05), and between number density and Simpson’s index (R2 = 0.875, p < 0.05). Our findings suggest that within a certain range, the increase in rock fragment content is conducive to soil nutrient accumulation and soil water storage and circulation and changes plant features, which contributes to the growth of plants. In addition, rock fragments should be given more consideration when investigating the relationships between soil and vegetation and their response to climate change in future studies

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework

The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-regulatory motif prediction using deep neural networks and the binomial distribution model. DESSO outperformed existing tools, including DeepBind, in predicting motifs in 690 human ENCODE ChIP-sequencing datasets. Furthermore, the deep-learning framework of DESSO expanded motif discovery beyond the state-of-the-art by allowing the identification of known and new protein-protein-DNA tethering interactions in human transcription factors (TFs). Specifically, 61 putative tethering interactions were identified among the 100 TFs expressed in the K562 cell line. In this work, the power of DESSO was further expanded by integrating the detection of DNA shape features. We found that shape information has strong predictive power for TF-DNA binding and provides new putative shape motif information for human TFs. Thus, DESSO improves in the identification and structural analysis of TF binding sites, by integrating the complexities of DNA binding into a deep-learning framework

Unequal Perylene Diimide Twins in a Quadruple Assembly

Natural light-harvesting (LH) systems can divide identical dyes into unequal aggregate states, thereby achieving intelligent "allocation of labor". From a synthetic point of view, the construction of such kinds of unequal and integrated systems without the help of proteinaceous scaffolding is challenging. Here, we show that four octatetrayne-bridged ortho-perylene diimide (PDI) dyads (POPs) self-assemble into a quadruple assembly (POP)4 both in solution and in the solid state. The two identical PDI units in each POP are compartmentalized into weakly coupled PDIs (P520) and closely stacked PDIs (P550) in (POP)4 . The two extreme pools of PDI chromophores were unambiguously confirmed by single-crystal X-ray crystallography and NMR spectroscopy. To interpret the formation of the discrete quadruple assembly, we also developed a two-step cooperative model. Quantum-chemical calculations indicate the existence of multiple couplings within and across P520 and P550, which can satisfactorily describe the photophysical properties of the unequal quadruple assembly. This finding is expected to help advance the rational design of dye stacks to emulate functions of natural LH systems.</p

Unraveling immunotherapeutic targets for endometriosis: a transcriptomic and single-cell analysis

BackgroundEndometriosis (EMs), a common gynecological disorder, adversely affects the quality of life of females. The pathogenesis of EMs has not been elucidated and the diagnostic methods for EMs have limitations. This study aimed to identify potential molecular biomarkers for the diagnosis and treatment of EMs.MethodsDifferential gene expression (DEG) and functional enrichment analyses were performed using the R language. WGCNA, Random Forest, SVM-REF and LASSO methods were used to identify core immune genes. The CIBERSORT algorithm was then used to analyse the differences in immune cell infiltration and to explore the correlation between immune cells and core genes. In addition, the extent of immune cell infiltration and the expression of immune core genes were investigated using single-cell RNA (scRNA) sequencing data. Finally, we performed molecular docking of three core genes with dienogest and goserelin to screen for potential drug targets.ResultsDEGs enriched in immune response, angiogenesis and estrogen processes. CXCL12, ROBO3 and SCG2 were identified as core immune genes. RT-PCR confirmed that the expression of CXCL12 and SCG2 was significantly upregulated in 12Z cells compared to hESCs cells. ROC curves showed high diagnostic value for these genes. Abnormal immune cell distribution, particularly increased macrophages, was observed in endometriosis. CXCL12, ROBO3 and SCG2 correlated with immune cell levels. Molecular docking suggested their potential as drug targets.ConclusionThis study investigated the correlation between EMs and the immune system and identified potential immune-related biomarkers. These findings provided valuable insights for developing clinically relevant diagnostic and therapeutic strategies for EMs

IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq

group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development

Drought Enhances the Role of Competition in Mediating the Relationship between Tree Growth and Climate in Semi-Arid Areas of Northwest China

Climate variability can exert a powerful impact on biotic competition, but past studies have focused largely on short-lived species, with a lack of attention to long-lived species such as trees. Therefore, there is a need to evaluate how competition regulates the climate-growth relationship in mature trees. We sampled the dominant tree species, Picea wilsonii Mast., on Xinglong Mountain, China, and studied the above issues by analyzing the relationship between tree radial growth, precipitation, and competition. In relatively wet years (precipitation &gt; average), there was no significant difference in climate sensitivity between different competition classes. However, trees suffering from highly competitive stress were more sensitive to climate variability in all years, and particularly in the subset of years that was relatively drought (precipitation &lt; average). These results suggest that competition enhances its ability to regulate tree growth response to climate variability in adverse weather conditions. Competition for resources between trees was asymmetrical, and an increase in height could give trees a disproportionate benefit. Thus, at trunk-level, both basal area incremental growth and intrinsic water-use efficiency of trees subjected to low competitive stress were significantly higher than trees that are subjected to highly competitive stress. Although the intrinsic water-use efficiency of trees under highly competitive stress increased more rapidly as the drought level increases, this did not change the fact that the radial growth of them declined more. Our research is valuable for the development of individual-tree growth models and advances our understanding for forest management under global climate change

High-Efficiency Microsatellite-Using Super-Resolution Algorithm Based on the Multi-Modality Super-CMOS Sensor

To realize the application of super-resolution technology from theory to practice, and to improve microsatellite spatial resolution, we propose a special super-resolution algorithm based on the multi-modality super-CMOS sensor which can adapt to the limited operation capacity of microsatellite computers. First, we designed an oblique sampling mode with the sensor rotated at an angle of 26.56 ∘ ( arctan 1 2 ) to obtain high overlap ratio images with sub-pixel displacement. Secondly, the proposed super-resolution algorithm was applied to reconstruct the final high-resolution image. Because the satellite equipped with this sensor is scheduled to be launched this year, we also designed the simulation mode of conventional sampling and the oblique sampling of the sensor to obtain the comparison and experimental data. Lastly, we evaluated the super-resolution quality of images, the effectiveness, the practicality, and the efficiency of the algorithm. The results of the experiments showed that the satellite-using super-resolution algorithm combined with multi-modality super-CMOS sensor oblique-mode sampling can increase the spatial resolution of an image by about 2 times. The algorithm is simple and highly efficient, and can realize the super-resolution reconstruction of two remote-sensing images within 0.713 s, which has good performance on the microsatellite

Geometric Quality Improvement Method of Optical Remote Sensing Satellite Images Based on Rational Function Model

The high-precision geometric positioning of optical remote sensing satellites is the prerequisite to determine the application capability of satellite image products. Its positioning accuracy is related to the observation accuracy of each link in the imaging process, including satellite attitude, orbit measurement accuracy, time synchronization accuracy, camera measurement accuracy, and so on. Untimely and inaccurate on-orbit calibration will lead to great geometric positioning errors. To optimize the positioning accuracy of satellite images with the rational function model (RFM) under low positioning accuracy, our paper proposes an improved geometric quality model based on the reorientation of internal and external orientation elements in the RFM model of remote sensing images. By establishing the rational function positioning model, the external orientation model, and the internal orientation model, the original image can be reorientated. Then, we use the improved model to generate uniformly distributed virtual ground control points. By analyzing and verifying the relationship between each rational polynomial coefficient (RPC) and its influence on geometric positioning accuracy, we propose an RPC coefficients optimization method based on image offset correction and positioning dominant coefficients. Finally, we use the small satellite &ldquo;MN200Sar-1&rdquo; with low geometric accuracy for experimental verification. The results show that the model can effectively eliminate the errors of internal and external elements in the on-orbit calibration, and the positioning accuracy is improved from one hundred pixels to one pixel. At the same time, the rational polynomial dominant coefficient optimization method can improve geometric positioning accuracy without introducing additional compensation parameters

Geometric Quality Improvement Method of Optical Remote Sensing Satellite Images Based on Rational Function Model

The high-precision geometric positioning of optical remote sensing satellites is the prerequisite to determine the application capability of satellite image products. Its positioning accuracy is related to the observation accuracy of each link in the imaging process, including satellite attitude, orbit measurement accuracy, time synchronization accuracy, camera measurement accuracy, and so on. Untimely and inaccurate on-orbit calibration will lead to great geometric positioning errors. To optimize the positioning accuracy of satellite images with the rational function model (RFM) under low positioning accuracy, our paper proposes an improved geometric quality model based on the reorientation of internal and external orientation elements in the RFM model of remote sensing images. By establishing the rational function positioning model, the external orientation model, and the internal orientation model, the original image can be reorientated. Then, we use the improved model to generate uniformly distributed virtual ground control points. By analyzing and verifying the relationship between each rational polynomial coefficient (RPC) and its influence on geometric positioning accuracy, we propose an RPC coefficients optimization method based on image offset correction and positioning dominant coefficients. Finally, we use the small satellite “MN200Sar-1” with low geometric accuracy for experimental verification. The results show that the model can effectively eliminate the errors of internal and external elements in the on-orbit calibration, and the positioning accuracy is improved from one hundred pixels to one pixel. At the same time, the rational polynomial dominant coefficient optimization method can improve geometric positioning accuracy without introducing additional compensation parameters