PU.1 controls the expression of long noncoding RNA HOTAIRM1 during granulocytic differentiation

Abstract Background Long noncoding RNA HOX antisense intergenic RNA myeloid 1 (HOTAIRM1) has been characterized as a critical factor in all-trans retinoic acid (ATRA)-induced differentiation of acute promyelocytic leukemia (APL) cells. However, the essential transcription factor for gene expression of HOTAIRM1 is still unknown. Findings Chromatin immunoprecipitation (ChIP) assays revealed that PU.1 constitutively bound to the regulatory region of HOTAIRM1. Co-expression of PU.1 led to the transactivation of the regulatory region of HOTAIRM1 in a reporter assay. Detailed analysis showed that two PU.1 motifs, which were located around +1100 bp downstream of the transcriptional start site of the HOTAIRM1 promoter, were responsible for the PU.1-dependent transactivation. The induction of HOTAIRM1 by ATRA was dependent on PU.1, and ectopic expression of PU.1 significantly up-regulated HOTAIRM1. Furthermore, low HOTAIRM1 expression was observed in APL cells, which was attributed to the reduced PU.1 expression rather than the repression by PML-RARα via the direct binding. Conclusion PU.1 directly activates the expression of HOTAIRM1 through binding to the regulatory region of HOTAIRM1 during granulocytic differentiation. The reduced PU.1 expression, rather than PML-RARα itself, results in the low expression of HOTAIRM1 in APL cells. Our findings enrich the knowledge on the regulation of lncRNAs and the underlying mechanisms of the abnormal expression of lncRNAs involved in APL

A Comprehensive Survey on Database Management System Fuzzing: Techniques, Taxonomy and Experimental Comparison

Database Management System (DBMS) fuzzing is an automated testing technique aimed at detecting errors and vulnerabilities in DBMSs by generating, mutating, and executing test cases. It not only reduces the time and cost of manual testing but also enhances detection coverage, providing valuable assistance in developing commercial DBMSs. Existing fuzzing surveys mainly focus on general-purpose software. However, DBMSs are different from them in terms of internal structure, input/output, and test objectives, requiring specialized fuzzing strategies. Therefore, this paper focuses on DBMS fuzzing and provides a comprehensive review and comparison of the methods in this field. We first introduce the fundamental concepts. Then, we systematically define a general fuzzing procedure and decompose and categorize existing methods. Furthermore, we classify existing methods from the testing objective perspective, covering various components in DBMSs. For representative works, more detailed descriptions are provided to analyze their strengths and limitations. To objectively evaluate the performance of each method, we present an open-source DBMS fuzzing toolkit, OpenDBFuzz. Based on this toolkit, we conduct a detailed experimental comparative analysis of existing methods and finally discuss future research directions.Comment: 34 pages, 22 figure

Identification of ubiquitination-related gene classification and a novel ubiquitination-related gene signature for patients with triple-negative breast cancer

Background: Ubiquitination-related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in triple-negative breast cancer (TNBC) before. Our study aimed to explore the roles of URGs in TNBC.Methods: The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the Gene Expression Omnibus (GEO) databases were used to identify URG expression patterns in TNBC. Non-negative matrix factorization (NMF) analysis was used to cluster TNBC patients. The least absolute shrinkage and selection operator (LASSO) analysis was used to construct the multi-URG signature in the training set (METABRIC). Next, we evaluated and validated the signature in the test set (GSE58812). Finally, we evaluated the immune-related characteristics to explore the mechanism.Results: We identified four clusters with significantly different immune signatures in TNBC based on URGs. Then, we developed an 11-URG signature with good performance for patients with TNBC. According to the 11-URG signature, TNBC patients can be classified into a high-risk group and a low-risk group with significantly different overall survival. The predictive ability of this 11-URG signature was favorable in the test set. Moreover, we constructed a nomogram comprising the risk score and clinicopathological characteristics with favorable predictive ability. All of the immune cells and immune-related pathways were higher in the low-risk group than in the high-risk group.Conclusion: Our study indicated URGs might interact with the immune phenotype to influence the development of TNBC, which contributes to a further understanding of molecular mechanisms and the development of novel therapeutic targets for TNBC

One Size Cannot Fit All: a Self-Adaptive Dispatcher for Skewed Hash Join in Shared-nothing RDBMSs

Shared-nothing architecture has been widely adopted in various commercial distributed RDBMSs. Thanks to the architecture, query can be processed in parallel and accelerated by scaling up the cluster horizontally on demand. In spite of that, load balancing has been a challenging issue in all distributed RDBMSs, including shared-nothing ones, which suffers much from skewed data distribution. In this work, we focus on one of the representative operator, namely Hash Join, and investigate how skewness among the nodes of a cluster will affect the load balance and eventual efficiency of an arbitrary query in shared-nothing RDBMSs. We found that existing Distributed Hash Join (Dist-HJ) solutions may not provide satisfactory performance when a value is skewed in both the probe and build tables. To address that, we propose a novel Dist-HJ solution, namely Partition and Replication (PnR). Although PnR provide the best efficiency in some skewness scenario, our exhaustive experiments over a group of shared-nothing RDBMSs show that there is not a single Dist-HJ solution that wins in all (data skew) scenarios. To this end, we further propose a self-adaptive Dist-HJ solution with a builtin sub-operator cost model that dynamically select the best Dist-HJ implementation strategy at runtime according to the data skew of the target query. We implement the solution in our commercial shared-nothing RDBMSs, namely KaiwuDB (former name ZNBase) and empirical study justifies that the self-adaptive model achieves the best performance comparing to a series of solution adopted in many existing RDBMSs

Rapid detection of porcine circovirus type 4 via multienzyme isothermal rapid amplification

Porcine circovirus type 4 (PCV4) is a newly emerging pathogen that was first detected in 2019 and is associated with diverse clinical signs, including respiratory and gastrointestinal distress, dermatitis and various systemic inflammations. It was necessary to develop a sensitive and specific diagnostic method to detect PCV4 in clinical samples, so in this study, a multienzyme isothermal rapid amplification (MIRA) assay was developed for the rapid detection of PCV4 and evaluated for sensitivity, specificity and applicability. It was used to detect the conserved Cap gene of PCV4, operated at 41°C and completed in 20 min. With the screening of MIRA primer-probe combination, it could detect as low as 101 copies of PCV4 DNA per reaction and was highly specific, with no cross-reaction with other pathogens. Further assessment with clinical samples showed that the developed MIRA assay had good correlation with real-time polymerase chain reaction assay for the detection of PCV4. The developed MIRA assay will be a valuable tool for the detection of the novel PCV4 in clinical samples due to its high sensitivity and specificity, simplicity of operation and short testing time

3D face recognition based on a modified iterative closest point method

Face recognition has gained extensive attention recently, with many applications in a broad range of domains such as access control in security systems and picture tagging in social network web sites. This project builds a 3D face database and recognizes the unknown 3D face images in comparison with the 3D face database. In 3D face images used in this thesis are acquired by a 3D data acquisition system based on Digital Fringe Projection Profilometry (DFPP). DFPP is an efficient 3D data acquisition system to capture 3D data, with its simple system structure, high resolution and low cost. The 3D database consists of thirty group images In each group, there are three images corresponding with three views with (i.e. left-side view, right-side view, and frontal view) at the same scale of the same subject. The scale is different from group to group. To achieve 3D face recognition, there are two parts devised: image alignment and comparison. In order to implement efficient and accurate image alignment, two steps which are coarse alignment and fine alignment are implemented. In the coarse alignment step, two 3D images are roughly aligned into a same coordinates system and roughly aligned. After the coarse alignment step, the two face images will be aligned closer and an initial estimated value will be given for the fine alignment. A modified partial Iterative Closest Point (ICP) method is proposed in the fine alignment step. The partial ICP method is an efficient alignment method for 3D data reconstruction and 3D face recognition. It iteratively aligns the two point sets based on repetitive calculation of the closest points as the corresponding points in each iteration. However, if two 3D face images with different scales are from the same person, the partial ICP method does not work. In this thesis, the scaling effect problem of 3D face recognition has been solved. A 3×3 diagonal matrix as the scale matrix in each iteration of the partial ICP has been well designed. The probing face image which is multiplied by the scale matrix will keep the similar scale with the reference face image. Therefore even if the scales of the probing image and the reference image are different, the corresponding points can be accurately determined. The mean square distance between the two face images are compared to recognize that whether the two face images are from the same person or not. Based on the experiment results, the 3D face recognition can be achieved via the method proposed in this thesis. The mean square distance between two face images from the same person can reach to less than 0.05 while the two face images from the different persons can only keep 0.10 to 0.30

Environments

3D face recognition technique has gained much more attention recently, and it is widely used in security system, identification system, and access control system, etc. The core technique in 3D face recognition is to find out the corresponding points in different 3D face images. The classic partial Iterative Closest Point (ICP) method is iteratively align the two point sets based on repetitively calculate the closest points as the corresponding points in each iteration. After several iterations, the corresponding points can be obtained accurately. However, if two 3D face images with different scale are from the same person, the classic partial ICP does not work. In this paper we propose a modified partial Iterative Closest Point (ICP) method in which the scaling effect is considered to achieve 3D face recognition. We design a 3x3 diagonal matrix as the scale matrix in each iteration of the classic partial ICP. The probing face image which is multiplied by the scale matrix will keep the similar scale with the reference face image. Therefore, we can accurately determine the corresponding points even the scales of probing image and reference image are different. 3D face images in our experiments are acquired by a 3D data acquisition system based on Digital Fringe Projection Profilometry (DFPP). A 3D database consists of 30 group images, three images with the same scale, which are from the same person with different views, are included in each group. And in different groups, the scale of the 3 images may be different from other groups. The experiment results show that our proposed method can achieve 3D face recognition, especially in the case that the scales of probing image and referent image are different

Image1_Identification of ubiquitination-related gene classification and a novel ubiquitination-related gene signature for patients with triple-negative breast cancer.TIF

Background: Ubiquitination-related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in triple-negative breast cancer (TNBC) before. Our study aimed to explore the roles of URGs in TNBC.Methods: The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the Gene Expression Omnibus (GEO) databases were used to identify URG expression patterns in TNBC. Non-negative matrix factorization (NMF) analysis was used to cluster TNBC patients. The least absolute shrinkage and selection operator (LASSO) analysis was used to construct the multi-URG signature in the training set (METABRIC). Next, we evaluated and validated the signature in the test set (GSE58812). Finally, we evaluated the immune-related characteristics to explore the mechanism.Results: We identified four clusters with significantly different immune signatures in TNBC based on URGs. Then, we developed an 11-URG signature with good performance for patients with TNBC. According to the 11-URG signature, TNBC patients can be classified into a high-risk group and a low-risk group with significantly different overall survival. The predictive ability of this 11-URG signature was favorable in the test set. Moreover, we constructed a nomogram comprising the risk score and clinicopathological characteristics with favorable predictive ability. All of the immune cells and immune-related pathways were higher in the low-risk group than in the high-risk group.Conclusion: Our study indicated URGs might interact with the immune phenotype to influence the development of TNBC, which contributes to a further understanding of molecular mechanisms and the development of novel therapeutic targets for TNBC.</p

The significance of low PU.1 expression in patients with acute promyelocytic leukemia

Abstract Background Although the importance of the hematopoietic transcription factor PU.1 in acute myeloid leukemia (AML) has been demonstrated, the expression of PU.1 in acute promyelocytic leukemia (APL) patient samples awaits further investigation. The current study used APL patient samples to assess the expression pattern of PU.1 in the initiation and progression of APL. Findings We used real-time RT-PCR to compare PU.1 expression between de novo APL patient samples and normal blood specimens, and the results indicated that PU.1 expression was significantly lower in newly diagnosed APL patient samples as compared to normal hematopoietic cells. Further evidence showed a significant inverse correlation between the expression level of PML-RARα and that of PU.1. In addition, we analyzed the correlation between PML-RARα and PU.1 expression in a large population of AML patients retrieved from the expression profiles. The results showed that PU.1 expression was lower in patients with APL than other AML subtypes and there was also a trend towards increasing PU.1 expression from AML-M0 to AML-M5, with the exception of AML-M3 (APL). These observations suggested that PU.1 expression was reduced by PML-RARα in APL patients. Furthermore, we measured PU.1 expression in APL-initiating cells isolated from de novo APL patients by side population cell analysis and found that suppression of PU.1 expression occurred concurrently with PML-RARα expression, indicating the pivotal role of PU.1 in APL initiation. Conclusion Our findings provide evidence that low PU.1 expression in APL patients is required for disease initiation and progression.</p