An NGS‐based approach to identify Y‐chromosome variation in non‐obstructive azoospermia

Liu, Yongjie; Wang, Guoping; Zhang, Fan; Dai, Liang

doi:10.1111/and.14201

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…The deletion of azoospermia factor (AZF) confirmed by a large number of studies is a typical example. In fact, there are thousands of genes involved in spermatogenesis, and most of the mechanisms are not clear (3,11,12). It is particularly important to find and clarify the mechanisms of genes related to spermatogenesis, which can aid in many aspects, such as species evolution, spermatogenic arrest, in vitro culture, and clinical intervention (13)(14)(15).…”

Section: Discussionmentioning

confidence: 99%

Analysis of copy number variations of the autosomal genome in 156 patients with azoospermia

Liu¹,

Shi²,

Zhang³

et al. 2022

Transl Androl Urol

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Background:To analyze the relationship between copy number variations (CNVs) and azoospermia by analyzing the chromosome gene copy data of 156 patients with azoospermia.Methods: A total of 156 azoospermia patients who were treated in our hospital from October 2018 to May 2021 were selected. Informed consent was signed, and semen analysis, testicular biopsy, and chromosome gene detection were carried out. CNVs were analyzed by next-generation sequencing (NGS) detection, and the obtained results were statistically analyzed.Results: Among the 156 azoospermia patients, 81 cases had sperm in the testicular puncture and 75 cases had no sperm in the testicular puncture. There was a significant difference in CNV detection between the 2 groups (P<0.05). Detailed analysis of CNVs on chromosomes 2, 3, 5, 10, and 11 yielded the following results: 132 genes were found in autosomal chromosomes with CNVs and the percentage was >5%, including 8 deletions and 124 repetitions; CNVs on chromosome 2 found 11 genes, of which 3 were deleted and 8 were duplicated; 17 genes were found in CNVs on chromosome 3, including 3 deletions and 14 duplications; 12 genes were found in CNVs on chromosome 5, of which 2 were deleted and 10 were repeated; 72 genes were found in CNVs on chromosome 10, all of which were duplicates; CNVs on chromosome 11 found 20 genes, all of which were duplicates. Conclusions:The chromosome changes caused by CNVs in structure or function may affect the component of spermatogenesis, interfere with mitosis and/or meiosis in the process of spermatogenesis, and lead to azoospermia.

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Section: Discussionmentioning

confidence: 99%

Analysis of copy number variations of the autosomal genome in 156 patients with azoospermia

Liu¹,

Shi²,

Zhang³

et al. 2022

Transl Androl Urol

Self Cite

View full text Add to dashboard Cite

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“…The model not only enhances deep cue learning but also provides a hierarchical interpretability system, enabling the development of phishing threat intelligence for detecting phishing websites at different levels. In another study, Lin et al [39] present Phishpedia, a hybrid deep learning system designed to overcome technical challenges in phishing identification. It focuses on accurately recognizing identity logos on webpage screenshots and matching logo variants of the same brand.…”

Section: Machine Learning and Xai In Phishing Studymentioning

confidence: 99%

Towards Personalized Anti-Phishing: Counterfactual Explanation Approach

Fan,

Li,

Laskey

et al. 2024

Preprint

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In today’s digital landscape, phishing attacks persist as a formidable challenge, highlighting the need for robust strategies to mitigate individual risk. While advanced machine learning techniques have excelled in identifying those most susceptible to phishing, existing research has primarily focused on refining prediction accuracy rather than leveraging this understanding to mitigate risk. To bridge this gap, we present a novel counterfactual explanation approach aimed at identifying the specific traits that heighten an individual’s vulnerability to phishing. Our approach integrates uncertainties and causal insights from the data generation process, producing actionable intelligence to effectively lower individual susceptibility. This enables us to tailor personalized recommendations to reduce individual’s vulnerability. Through experimentation, we assess the efficacy of our methodology and demonstrate capacity to reduce susceptibility to phishing. These findings emphasize the importance of personalized interventions, arming individuals with the knowledge necessary to improve their online security protocols.

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“…Moreover, some studies used web page content for similarity analysis, such as calculating the hash or edit distance of the DOM tree or the page structure to judge whether web pages are similar [11,12,17,36,37]. In addition, some previous studies [9,10,[38][39][40][41] used web page visuals for similar page analysis, such as the detection of similar phishing pages. Despite being successful in their respective goals, such approaches still have limitations.…”

Section: Related Workmentioning

confidence: 99%

APIMiner: Identifying Web Application APIs Based on Web Page States Similarity Analysis

Chen,

Lu,

Pan

et al. 2024

Electronics

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Modern web applications offer various APIs for data interaction. However, as the number of these APIs increases, so does the potential for security threats. Essentially, more APIs in an application can lead to more detectable vulnerabilities. Thus, it is crucial to identify APIs as comprehensively as possible in web applications. However, this task faces challenges due to the increasing complexity of web development techniques and the abundance of similar web pages. In this paper, we propose APIMiner, a framework for identifying APIs in web applications by dynamically traversing web pages based on web page state similarity analysis. APIMiner first builds a web page model based on the HTML elements of the current web page. APIMiner then uses this model to represent the state of the page. Then, APIMiner evaluates each element’s similarity in the page model and determines the page state similarity based on these similarity values. From the different states of the page, APIMiner extracts the data interaction APIs on the page. We conduct extensive experiments to evaluate APIMiner’s effectiveness. In the similarity analysis, our method surpasses state-of-the-art methods like NDD and mNDD in accurately distinguishing similar pages. We compare APIMiner with state-of-the-art tools (e.g., Enemy of the State, Crawlergo, and Wapiti3) for API identification. APIMiner excels in the number of identified APIs (average 1136) and code coverage (average 28,470). Relative to these tools, on average, APIMiner identifies 7.96 times more APIs and increases code coverage by 142.72%.

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An NGS‐based approach to identify Y‐chromosome variation in non‐obstructive azoospermia

Cited by 5 publications

References 25 publications

Analysis of copy number variations of the autosomal genome in 156 patients with azoospermia

Analysis of copy number variations of the autosomal genome in 156 patients with azoospermia

Towards Personalized Anti-Phishing: Counterfactual Explanation Approach

APIMiner: Identifying Web Application APIs Based on Web Page States Similarity Analysis

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