Characterization of Two KRAB-Containing Zinc Finger Transcription Factors In Bovine Preimplantation Embryonic Development

Oocyte developmental competence or oocyte intrinsic quality describes the capability of oocytes to resume meiosis, cleave and develop to blastocyst stage after fertilization, implant and develop to term in a good health. A growing number of evidences indicate that the majority of embryonic mortality occurs during early embryonic development in different species, including human, horse and cattle primarily due to poor oocyte quality. Maternal effect genes are key aspects of oocyte quality, which are transcribed during the process of oogenesis and folliculogenesis. The maternal factors are accumulated in oocytes, orchestrating various early developmental events including fertilization, epigenetic reprogramming and zygotic genome activation (ZGA). The key step to acquire development competence is oocyte maturation. The fully matured oocytes, which obtain required maternal factors, are determining factor for fertility. During the process of in vitro maturation, manipulation of synchronization of meiotic maturation and cytoplasmic maturation, which determines the acquisition of maternal factors, increases the oocyte competence. C2H2 (Cys2-His2) zinc finger domain represents one of most common domains of transcription factor in mammals, which dominate around 53% of mammalian transcription factor repertoire. Approximately 2/3 of C2H2 zinc finger transcription factors contain a Küppel associated box (KRAB) domain, which is known to interact with KRAB-associated protein-1 (KAP1) corepressor. KAP1 serves as a scaffold to recruit repressive complexes. Interestingly, even though KRAB domain is present in some C2H2-zinc finger proteins, the interaction with KAP1 is not guaranteed, especially for those that have a SCAN domain. Despite being highly abundant in mammalian genome, the KRAB containing zinc finger proteins are still poorly understood. Our laboratory previously identified a novel member of KRAB-ZFPs family, ZNFO. As a maternal effect gene, ZNFO transcript is highly abundant in germinal vesicle (GV), MII-stage oocytes, and early-stage embryos but barely detectable in morula and blastocyst stage embryos. RNAi experiments demonstrated that ZNFO is indispensable during early embryonic development in cattle. However, the molecular mechanism regulating ZNFO transcription and regulatory mechanism downstream of ZNFO remain elusive. In the present study, we identified the core promoter that controls the ZNFO basal transcription. Using 5’RACE followed by Sanger sequencing, the 5’ untranslated region (UTR) and the transcription start site (TSS) of ZNFO transcript were identified. A 1.7 kb of putative promoter region of ZNFO spanning from -1665 to +36 was cloned into pGL4.14 luciferase reporter vector. A series of 5′ deletion in the ZNFO promoter followed by luciferase reporter assays indicated that the core promoter region has to include the sequence located between 57 bp to 31 bp upstream of the TSS. Sequence analysis revealed that a putative upstream stimulatory factor 1/2 (USF1/USF2) binding site (GGTCACGTGACC) containing an enhancer box (E-box) motif (CACGTG) is located within the essential region. Depletion of USF1/USF2 by RNAi and E-box mutation analysis demonstrated that the USF1/USF2 binding site is required for the ZNFO basal transcription. Furthermore, EMSA and super-shift assays indicated that the observed effects are dependent on the specific interactions between USF proteins and the ZNFO core promoter. From these results, it is concluded that USF1 and USF2 are essential for the basal transcription of the ZNFO gene. Regarding the regulatory mechanism downstream ZNFO, the previous study identified an 18-nucleotide ZNFO binding element (ZNFOBE), ATATCCTGTTTAAACCCC. The present study confirmed the sequence-specific binding of ZNFO to its target element using EMSA in combination with competition assays. Furthermore, it was confirmed that the interaction between ZNFO and ZNFOBE is required for the repressive effect of ZNFO via a luciferase reporter assay. Zinc Finger Imprinted 2 (ZIM2) is isolated from a highly conserved Paternally Expressed 3 (PEG3) imprinted domain. Compared to mouse Zim2, the human and bovine ZIM2 maintain the protein coding ability. Both human and bovine ZIM2 encode a KRAB-containing zinc finger protein. In addition, SCAN domain, a protein interaction domain is also present in the N-terminal of human and bovine ZIM2 protein. It has been reported that ZIM2 is highly abundant in testis. Consistent with human microarray data, analysis of RNA-seq data from our laboratory revealed that ZIM2 is highly abundant in bovine oocytes as well. In the present study, characterization of ZIM2 transcript expression revealed that ZIM2 mRNA is expressed in testis, oocytes, and early embryos. Interestingly, ZIM2 mRNA is not detectable in morula but re-transcribed in blastocyst. In addition, western blot analysis using a customized ZIM2 antibody indicated that ZIM2 protein is present in oocytes and 2-cell, 4-cell, 8-cell, 16-cell embryos, morula, and blastocyst. The RNAi-mediated knockdown indicated that deletion of ZIM2 by microinjecting siRNA targeting ZIM2 reduced the blastocyst rate. In addition, using a GAL4-luciferase reporter assay, ZIM2 was demonstrated to contain an intrinsic repressive effect. Furthermore, ZIM2 interacted with a highly conserved co-repressor KAP-1. Present studies demonstrated that maternally derived ZIM2 is indispensable for early embryonic development, presumably serving as a transcription repressor. Overall, the present projects elucidate the molecular mechanism regulating basal transcription of ZNFO, as well as the downstream regulatory mechanism of ZNFO. In addition, ZIM2 was confirmed to be a transcription repressor, which might be indispensable for bovine early development

Bovine Lhx8, a Germ Cell-SpecificNuclear Factor, Interacts with Figla

LIM homeobox 8 (Lhx8) is a germ cell-specific transcription factor essential for the development of oocytes during early oogenesis. In mice, Lhx8 deficiency causes postnatal oocyte loss and affects the expression of many oocyte-specific genes. The aims of this study were to characterize the bovine Lhx8 gene, determine its mRNA expression during oocyte development and early embryogenesis, and evaluate its interactions with other oocyte-specific transcription factors. The bovine Lhx8 gene encodes a protein of 377 amino acids. A splice variant of Lhx8 (Lhx8_v1) was also identified. The predicted bovine Lhx8 protein contains two LIM domains and one homeobox domain. However, one of the LIM domains in Lhx8_v1 is incomplete due to deletion of 83 amino acids near the N terminus. Both Lhx8 and Lhx8_v1 transcripts were only detected in the gonads but none of the somatic tissues examined. The expression of Lhx8 and Lhx8_v1 appears to be restricted to oocytes as none of the transcripts was detectable in granulosa or theca cells. The maternal Lhx8 transcript is abundant in GV and MII stage oocytes as well as in early embryos but disappear by morula stage. A nuclear localization signal that is required for the import of Lhx8 into nucleus was identified, and Lhx8 is predominantly localized in the nucleus when ectopically expressed in mammalian cells. Finally, a novel interaction between Lhx8 and Figla, another transcription factor essential for oogenesis, was detected. The results provide new information for studying the mechanisms of action for Lhx8 in oocyte development and early embryogenesis

Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image

Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research. However, recent deep learning algorithms have predominantly focused on detecting cloud regions in satellite imagery, with insufficient attention to the specificity required for accurate cloud recognition. This limitation inspired us to develop the novel FY-4A-Himawari-8 (FYH) dataset, which includes nine distinct cloud categories and uses precise domain adaptation methods to align 70,419 image-label pairs in terms of projection, temporal resolution, and spatial resolution, thereby facilitating the training of supervised deep learning networks. Given the complexity and diversity of cloud formations, we have thoroughly analyzed the challenges inherent to cloud recognition tasks, examining the intricate characteristics and distribution of the data. To effectively address these challenges, we designed a Distribution-aware Interactive-Attention Network (DIAnet), which preserves pixel-level details through a high-resolution branch and a parallel multi-resolution cross-branch. We also integrated a distribution-aware loss (DAL) to mitigate the imbalance across cloud categories. An Interactive Attention Module (IAM) further enhances the robustness of feature extraction combined with spatial and channel information. Empirical evaluations on the FYH dataset demonstrate that our method outperforms other cloud recognition networks, achieving superior performance in terms of mean Intersection over Union (mIoU). The code for implementing DIAnet is available at https://github.com/icey-zhang/DIAnet

Signal Transducers and Activators of Transcription-1 (STAT1) Regulates microRNA Transcription in Interferon γ-Stimulated HeLa Cells

Constructing and modeling the gene regulatory network is one of the central themes of systems biology. With the growing understanding of the mechanism of microRNA biogenesis and its biological function, establishing a microRNA-mediated gene regulatory network is not only desirable but also achievable.In this study, we propose a bioinformatics strategy to construct the microRNA-mediated regulatory network using genome-wide binding patterns of transcription factor(s) and RNA polymerase II (RPol II), derived using chromatin immunoprecipitation following next generation sequencing (ChIP-seq) technology. Our strategy includes three key steps, identification of transcription start sites and promoter regions of primary microRNA transcripts using RPol II binding patterns, selection of cooperating transcription factors that collaboratively function with the transcription factors targeted by ChIP-seq assay, and construction of the network that contains regulatory cascades of both transcription factors and microRNAs.Using CAMDA (Critical Assessment of Massive Data Analysis) 2009 data set that includes ChIP-seq data on RPol II and STAT1 (signal transducers and activators of transcription 1) in HeLa S3 cells in control condition and with interferon gamma stimulation, we first identified promoter regions of 83 microRNAs in HeLa cells. We then identified two potential STAT1 collaborating factors, AP-1 and C/EBP (CCAAT enhancer-binding proteins), and further established eight feedback network elements that may regulate cellular response during interferon gamma stimulation.This study offers a bioinformatics strategy to provide testable hypotheses on the mechanisms of microRNA-mediated transcriptional regulation, based upon genome-wide protein-DNA interaction data derived from ChIP-seq experiments

Research on energy sharing between distribution network and multiple systems based on the mixed game strategy and water-electric-gas integrated energy complementation

Introduction: It is significant for energy sharing to study the complementary utilization of multiple energy sources, such as water, electricity and gas, and the interaction among multiple stakeholders.Methods: We propose a research on energy sharing between distribution network and multiple systems based on the mixed game strategy and water-electric-gas integrated energy complementation. Firstly, this paper describes the relationship and functions of all stakeholders under the research framework, and establishes the mathematical model of each unit in the water-electric-gas complementary IES. Secondly, the internal roles are layered based on the relationship between stakeholders in the system. Then a non-cooperative game model for the distribution network operator and multiple subsystems is established according to the theory of Stackelberg game, and a cooperative game model for multiple subsystems is further established based on the theory of Nash bargaining. In the next step, the complexity of the problem is analyzed, followed by the description of the specific algorithm and process of solving the model.Results: Finally, the results of example analysis show that the model proposed in this paper not only balances the interests of stakeholders at the upper and lower layers of the system, but also allocates the interests of multiple subsystems at the lower layer.Discussion: Thus effectively improving the energy utilization of the system

Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution

Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education. Most existing works decoupled this problem into several independent sub-tasks of text spotting (text detection and recognition) and information extraction, which completely ignored the high correlation among them during optimization. In this paper, we propose a robust visual information extraction system (VIES) towards real-world scenarios, which is a unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction by taking a single document image as input and outputting the structured information. Specifically, the information extraction branch collects abundant visual and semantic representations from text spotting for multimodal feature fusion and conversely, provides higher-level semantic clues to contribute to the optimization of text spotting. Moreover, regarding the shortage of public benchmarks, we construct a fully-annotated dataset called EPHOIE (https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for both text spotting and visual information extraction. EPHOIE consists of 1,494 images of examination paper head with complex layouts and background, including a total of 15,771 Chinese handwritten or printed text instances. Compared with the state-of-the-art methods, our VIES shows significant superior performance on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used SROIE dataset under the end-to-end scenario.Comment: 8 pages, 5 figures, to be published in AAAI 202

Prioritization of disease microRNAs through a human phenome-microRNAome network

<p>Abstract</p> <p>Background</p> <p>The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.</p> <p>Results</p> <p>Herein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.</p> <p>Conclusions</p> <p>We presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.</p

Research on joint dispatch of wind, solar, hydro, and thermal power based on pumped storage power stations

In the context of energy conservation and emission reduction, the integration and consumption of large-scale wind and solar resources is an inevitable trend in future energy development. However, with the increase of wind and solar grid-connected capacity, the power system also requires more flexible resources to ensure safe operation. To enhance the economic efficiency of the complementary operation of wind, solar, hydro, and thermal sources, considering the peak regulation characteristics of different types of power sources, the study of the joint dispatch model of complementary utilization of various generation methods like wind, solar, hydro, thermal, and storage is of great significance for the economic dispatch of the power system. Existing studies mainly focus on traditional thermal power units or hydropower units, with few studies investigating the impact of pumped-storage power stations on the absorption of renewable energy. Firstly, this paper introduces the composition and function of each unit under the research framework and establishes a joint dispatch model for wind, solar, hydro, and thermal power. Secondly, the paper elaborates on the objective function within the model, mainly covering the operating costs of thermal power units, hydropower units, pumped storage, wind and solar units, the cost of discarding new energy, and the cost of load shedding. Subsequently, the paper presents the constraints of the system model, mainly the feasible boundaries for the operation of each unit within the system. Finally, The results of the calculations show that the proposed model reduces the total operating cost by 12% and the power abandonment rate by 82% compared to the conventional model. It is shown that the proposed model can not only significantly improve the economic efficiency of the system operation but also reduce the level of energy waste and load shedding, effectively enhancing the degree of energy utilization within the system

Model estimates of China's terrestrial water storage variation due to reservoir operation

Understanding the role of reservoirs in the terrestrial water cycle is critical to support the sustainable management of water resources especially for China where reservoirs have been extensively built nationwide. However, this has been a scientific challenge due to the limited availability of continuous, long-term reservoir operation records at large scales, and a process-based modeling tool to accurately depict reservoirs as part of the terrestrial water cycle is still lacking. Here, we develop a continental-scale land surface-hydrologic model over the mainland China by explicitly representing 3,547 reservoirs in the model with a calibration-free conceptual operation scheme for ungauged reservoirs and a hydrodynamically based two-way coupled scheme. The model is spatially calibrated and then extensively validated against streamflow observations, reservoir storage observations and GRACE-based terrestrial water storage anomalies. A 30-year simulation is then performed to quantify the seasonal dynamics of China’s reservoir water storage (RWS) and its role in China\u27s terrestrial water storage (TWS) over recent decades. We estimate that, over a seasonal cycle, China\u27s RWS variation is 15%, 16%, and 25% of TWS variation during 1981–1990, 1991–2000, and 2001–2010, respectively, and one-fifth of China’s reservoir capacity are effectively used annually. In most regions, reservoirs play a growing role in modulating the water cycle over time. Despite that, an estimated 80 million people have faced increasing water resources challenges in the past decades due to the significantly weakened reservoir regulation of the water cycle. Our approaches and findings could help the government better address the water security challenges under environmental changes

miR2Disease: a manually curated database for microRNA deregulation in human disease

‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org