Simple Summary Endocrine therapy is a common treatment for estrogen-responsive breast cancer, which is used to block the tumor growth-promoting effect of this hormone. The resistance of breast cancer patients to endocrine therapy is a major clinical problem which could be solved by a deeper understanding of its mechanisms. Analysis of the human genome has revealed that messenger RNA (mRNA) coding for proteins represents only a small portion of human RNA. The vast majority of RNAs do not code for proteins and thus are called non-coding RNAs (ncRNAs), which have important functions in health and disease. Here, we summarize current insights into the effects of ncRNAs on estrogen actions in breast cancer cells and their role in endocrine resistance. NcRNAs have been identified which affect the estrogen response and reduce the efficacy of this therapy. Thus, these could be used to predict therapy response and might represent novel therapy targets to overcome endocrine resistance. Abstract The largest part of human DNA is transcribed into RNA that does not code for proteins. These non-coding RNAs (ncRNAs) are key regulators of protein-coding gene expression and have been shown to play important roles in health, disease and therapy response. Today, endocrine therapy of ERα-positive breast cancer (BC) is a successful treatment approach, but resistance to this therapy is a major clinical problem. Therefore, a deeper understanding of resistance mechanisms is important to overcome this resistance. An increasing amount of evidence demonstrate that ncRNAs affect the response to endocrine therapy. Thus, ncRNAs are considered versatile biomarkers to predict or monitor therapy response. In this review article, we intend to give a summary and update on the effects of microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) on estrogen signaling in BC cells, this pathway being the target of endocrine therapy, and their role in therapy resistance. For this purpose, we reviewed articles on these topics listed in the PubMed database. Finally, we provide an assessment regarding the clinical use of these ncRNA types, particularly their circulating forms, as predictive BC biomarkers and their potential role as therapy targets to overcome endocrine resistance.

/pdf/non-coding-rnas-modulating-estrogen-signaling-and-response-134l8y3t.pdf

Non-Coding RNAs Modulating Estrogen Signaling and Response to Endocrine Therapy in Breast Cancer

Competitive programming has become a popular way for programmers to test their skills. Competition-level programming problems are challenging in nature, and participants often fail to solve the problem on their first attempt. Some online platforms for competitive programming allow programmers to practice on competition-level problems, and the standard feedback for an incorrect practice submission is the first test case that the submission fails. Often, the failed test case does not provide programmers with enough information to resolve the errors in their code, and they abandon the problem after making several more unsuccessful attempts. We present Clef, the first data-driven tool that can generate feedback on competition-level code automatically by repairing programmers’ incorrect submissions. The key development is that Clef can learn how to generate repairs for incorrect submissions by examining the repairs that other programmers made to their own submissions over time. Since the differences between an incorrect program and a correct program for the same task may be significant, we introduce a new data structure, merge trees, to capture the changes between submissions. Merge trees are versatile: they can encode both large algorithm-level redesigns and small statement-level alterations. We evaluated Clef on six real-world problems from Codeforces, the world’s largest platform for competitive programming. Clef achieves accuracy in repairing programmers’ incorrect submissions. When given incorrect submissions from programmers who never found the solution to a problem on their own, Clef repairs the users’ programs of the time.

pdf/automated-feedback-generation-for-competition-level-code-3ujf6p0x.pdf

Automated Feedback Generation for Competition-Level Code

Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic vulnerability identification is important because it can evaluate large codebases more efficiently than manual code auditing. Many Machine Learning (ML) and Deep Learning (DL) based models for detecting vulnerabilities in source code have been presented in recent years. However, a survey that summarises, classifies, and analyses the application of ML/DL models for vulnerability detection is missing. It may be difficult to discover gaps in existing research and potential for future improvement without a comprehensive survey. This could result in essential areas of research being overlooked or under-represented, leading to a skewed understanding of the state of the art in vulnerability detection. This work address that gap by presenting a systematic survey to characterize various features of ML/DL-based source code level software vulnerability detection approaches via five primary research questions (RQs). Specifically, our RQ1 examines the trend of publications that leverage ML/DL for vulnerability detection, including the evolution of research and the distribution of publication venues. RQ2 describes vulnerability datasets used by existing ML/DL-based models, including their sources, types, and representations, as well as analyses of the embedding techniques used by these approaches. RQ3 explores the model architectures and design assumptions of ML/DL-based vulnerability detection approaches. RQ4 summarises the type and frequency of vulnerabilities that are covered by existing studies. Lastly, RQ5 presents a list of current challenges to be researched and an outline of a potential research roadmap that highlights crucial opportunities for future work.

A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning

TFAP2C Activates CST1 Transcription to Facilitate Breast Cancer Progression and Suppress Ferroptosis.

Immediate feedback has been shown to improve student learning. In programming courses, immediate, automated feedback is typically provided in the form of pre-defined test cases run by a submission platform. While these are excellent for highlighting the presence of logical errors, they do not provide novice programmers enough scaffolding to help them identify where an error is or how to fix it. To address this, several tools have been developed that provide richer feedback in the form of program repairs. Studies of such tools, however, tend to focus more on whether correct repairs can be generated, rather than how novices are using them. In this paper, we describe our experience of using CLARA, an automated repair tool, to provide feedback to novices. First, we extended CLARA to support a larger subset of the Python language, before integrating it with the Jupyter Notebooks used for our programming exercises. Second, we devised a preliminary study in which students tackled programming problems with and without support of the tool using the 'think aloud' protocol. We found that novices often struggled to understand the proposed repairs, echoing the well-known challenge to understand compiler/interpreter messages. Furthermore, we found that students valued being told where a fix was needed - without necessarily the fix itself - suggesting that 'less may be more' from a pedagogical perspective.

pdf/how-helpful-do-novice-programmers-find-the-feedback-of-an-l6xxpknpi0.pdf

How Helpful do Novice Programmers Find the Feedback of an Automated Repair Tool?

The endocrine therapy resistance of breast cancer is the difficulty and challenge to be urgently solved in the current treatment. In this study, we examined the effects of noncoding RNA LINC00094 and miR-19a-3p on breast cancer in vivo and in vitro by RT-QPCR, Western Blot, luciferase assay, immunofluorescence and drug sensitivity tests. The plasma level of CYP19A1 in patients with breast cancer resistance was lower than that in drug sensitive patients. Compared with normal subjects, miR-19a-3p was highly expressed in plasma of patients with breast cancer. miR-19a-3p is highly expressed in estrogen receptor positive breast cancer cells. The expression of miR-19a-3p promoted the migration and EMT of breast cancer cells and reduced the sensitivity of breast cancer to Letrozole. LINC00094 sponge adsorbed miR-19a-3p. LINC00094 promotes the expression of CYP19A1, the target gene of miR-19a-3p, and inhibits the EMT process of breast cancer, ultimately promoting the sensitivity of ER-positive breast cancer cells to Letrozole. This study found a new mechanism of Letrozole sensitivity in ER positive breast cancer.

/pdf/linc00094-mir-19a-3p-cyp19a1-axis-affects-the-sensitivity-of-3k27zeif.pdf

LINC00094/miR-19a-3p/CYP19A1 axis affects the sensitivity of ER positive breast cancer cells to Letrozole through EMT pathway

In programming courses, providing students with concise and constructive feedback on faulty submissions (programs) is highly desirable. However, providing feedback manually is often time-consuming and tedious. To release tutors from the manual construction of concise feedback, researchers have proposed approaches such as CLARA and Refactory to construct feedback automatically. The key to such approaches is to fix a faulty program by making it equivalent to one of its correct reference programs whose overall structure is identical to that of the faulty submission. However, for a newly released assignment, it is likely that there are no correct reference programs at all, let alone correct reference programs sharing identical structure with the faulty submission. Therefore, in this paper, we propose AssignmentMender generating concise patches for newly released assignments. The key insight of AssignmentMender is that a faulty submission can be repaired by reusing fine-grained code snippets from submissions (even when they are faulty) for the same assignment. It automatically locates suspicious code in the faulty program and leverages static analysis to retrieve reference code from existing submissions with a graph-based matching algorithm. Finally, it generates candidate patches by modifying the suspicious code based on the reference code. Different from existing approaches, AssignmentMender exploits faulty submissions in addition to bug-free submissions to generate patches. Another advantage of AssignmentMender is that it can leverage submissions whose overall structures are different from those of the to-be-fixed submission. Evaluation results on 128 faulty submissions from 10 assignments show that AssignmentMender improves the state-of-the-art in feedback generation for newly released assignments. A case study involving 40 students and 80 submissions further provides initial evidence showing that the proposed approach is useful in practice.

Generating Concise Patches for Newly Released Programming Assignments

lncRNA IGF2-AS regulates miR-500a-3p/PPP4R1/p-VEGFR2 signalling pathway to promote thyroid carcinoma progression and tubulogenesis.

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LINC00094/miR-19a-3p/CYP19A1 axis affects the sensitivity of ER positive breast cancer cells to Letrozole through EMT pathway

Generating Concise Patches for Newly Released Programming Assignments

lncRNA IGF2-AS regulates miR-500a-3p/PPP4R1/p-VEGFR2 signalling pathway to promote thyroid carcinoma progression and tubulogenesis.