bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
Authors: Yang Zhen , Liu Fang , Yu Zhongxing , Keung Jacky Wai , Li Jia , Liu Shuo , Hong Yifan , Ma Xiaoxue , Jin Zhi , Li Ge
Published: July 2024

Code translation tools, namely transpilers, are developed for automatic source-to-source translation. Latest learning-based transpilers have shown impressive enhancement against rule-based counterparts in both translation accuracy and readability, owing to their task-specific pre-training on extensive monolingual corpora. Nevertheless, their current performance still remains unsatisfactory for practical deployment, and the associated training resources are also prohibitively expensive. Large Language Models (LLMs), pre-trained on huge amounts of human-written code/text, have shown remarkable performance in many code intelligence tasks due to their powerful generality, even without task-specific re-training/fine-tuning. Thus, LLMs can potentially circumvent the above limitations, but they have not been exhaustively explored yet. This paper investigates diverse LLMs and learning-based transpilers for automated code translation tasks, finding that: although certain LLMs have outperformed current transpilers, they still have some accuracy issues, where most of the failures are induced by a lack of comprehension of source programs (38.51%), missing clear instructions on I/O types in translation (14.94%), and ignoring discrepancies between source and target programs (41.38%). Enlightened by the above findings, we further propose
UniTrans
, a
Uni
fied code
Trans
lation framework, applicable to various LLMs, for unleashing their power in this field. Specifically,
UniTrans
first crafts a series of test cases for target programs with the assistance of source programs. Next, it harnesses the above auto-generated test cases to augment the code translation and then evaluate their correctness via execution. Afterward,
UniTrans
further (iteratively) repairs incorrectly translated programs prompted by test case execution results. Extensive experiments are conducted on six settings of translation datasets between Python, Java, and C++. Three recent LLMs of diverse sizes, including GPT-3.5 and LLaMA-13B/7B, are tested with
UniTrans
, and all achieve substantial improvements in terms of computational accuracy and exact match accuracy among almost all translation settings, showing the universal effectiveness of
UniTrans
in practice.
Finding and Understanding Defects in Static Analyzers by Constructing Automated Oracles
Authors: He Weigang , Di Peng , Ming Mengli , Zhang Chengyu , Su Ting , Li Shijie , Sui Yulei
Published: July 2024

Static analyzers are playing crucial roles in helping find programming mistakes and security vulnerabilities. The correctness of their analysis results is crucial for the usability in practice. Otherwise, the potential defects in these analyzers (, implementation errors, improper design choices) could affect the soundness (leading to false negatives) and precision (leading to false positives). However, finding the defects in off-the-shelf static analyzers is challenging because these analyzers usually lack clear and complete specifications, and the results of different analyzers may differ. To this end, this paper designs two novel types of automated oracles to find defects in static analyzers with randomly generated programs. The first oracle is constructed by using dynamic program executions and the second one leverages the inferred static analysis results. We applied these two oracles on three state-of-the-art static analyzers: Clang Static Analyzer (CSA), GCC Static Analyzer (GSA), and Pinpoint. We found 38 unique defects in these analyzers, 28 of which have been confirmed or fixed by the developers. We conducted a case study on these found defects followed by several insights and lessons learned for improving and better understanding static analyzers. We have made all the artifacts publicly available at https://github.com/Geoffrey1014/SA_Bugs for replication and benefit the community.
Towards Better Graph Neural Network-Based Fault Localization through Enhanced Code Representation
Authors: Rafi Md Nakhla , Kim Dong Jae , Chen An Ran , Chen Tse-Hsun (Peter) , Wang Shaowei
Published: July 2024

Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization is a commonly used technique, which applies statistics on coverage spectra to rank faulty code based on suspiciousness scores. However, statistics- based approaches based on formulae are often rigid, which calls for learning-based techniques. Amongst all, Grace, a graph-neural network (GNN) based technique has achieved state-of-the-art due to its capacity to preserve coverage spectra, i.e., test-to-source coverage relationships, as precise abstract syntax-enhanced graph representation, mitigating the limitation of other learning-based technique which compresses the feature representation. However, such representation is not scalable due to the increasing complexity of software, correlating with increasing coverage spectra and AST graph, making it challenging to extend, let alone train the graph neural network in practice. In this work, we proposed a new graph representation, DepGraph, that reduces the complexity of the graph representation by 70% in nodes and edges by integrating the interprocedural call graph in the graph representation of the code. Moreover, we integrate additional features—code change information—into the graph as attributes so the model can leverage rich historical project data. We evaluate DepGraph using Defects4j 2.0.0, and it outperforms Grace by locating 20% more faults in Top-1 and improving the Mean First Rank (MFR) and the Mean Average Rank (MAR) by over 50% while decreasing GPU memory usage by 44% and training/inference time by 85%. Additionally, in cross-project settings, DepGraph surpasses the state-of-the-art baseline with a 42% higher Top-1 accuracy, and 68% and 65% improvement in MFR and MAR, respectively. Our study demonstrates DepGraph’s robustness, achieving state-of-the-art accuracy and scalability for future extension and adoption.
Learning to Detect and Localize Multilingual Bugs
Authors: Yang Haoran , Nong Yu , Zhang Tao , Luo Xiapu , Cai Haipeng
Published: July 2024

Increasing studies have shown bugs in multi-language software as a critical loophole in modern software quality assurance, especially those induced by language interactions (i.e., multilingual bugs). Yet existing tool support for bug detection/localization remains largely limited to single-language software, despite the long-standing prevalence of multi-language systems in various real-world software domains. Extant static/dynamic analysis and deep learning (DL) based approaches all face major challenges in addressing multilingual bugs. In this paper, we present xLoc, a DL-based technique/tool for detecting and localizing multilingual bugs. Motivated by results of our bug-characteristics study on top locations of multilingual bugs, xLoc first learns the general knowledge relevant to differentiating various multilingual control-flow structures. This is achieved by pre-training a Transformer model with customized position encoding against novel objectives. Then, xLoc learns task-specific knowledge for the task of multilingual bug detection/localization, through another new position encoding scheme (based on cross-language API vicinity) that allows for the model to attend particularly to control-flow constructs that bear most multilingual bugs during fine-tuning. We have implemented xLoc for Python-C software and curated a dataset of 3,770 buggy and 15,884 non-buggy Python-C samples, which enabled our extensive evaluation of xLoc against two state-of-the-art baselines: fine-tuned CodeT5 and zero-shot ChatGPT. Our results show that xLoc achieved 94.98% F1 and 87.24%@Top-1 accuracy, which are significantly (up to 162.88% and 511.75%) higher than the baselines. Ablation studies further confirmed significant contributions of each of the novel design elements in xLoc. With respective bug-location characteristics and labeled bug datasets for fine-tuning, our design may be applied to other language combinations beyond Python-C.
Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?
Authors: Kou Bonan , Chen Shengmai , Wang Zhijie , Ma Lei , Zhang Tianyi
Published: July 2024

Large Language Models (LLMs) have recently been widely used for code generation. Due to the complexity and opacity of LLMs, little is known about how these models generate code. We made the first attempt to bridge this knowledge gap by investigating whether LLMs attend to the same parts of a task description as human programmers during code generation. An analysis of six LLMs, including GPT-4, on two popular code generation benchmarks revealed a consistent misalignment between LLMs' and programmers' attention. We manually analyzed 211 incorrect code snippets and found five attention patterns that can be used to explain many code generation errors. Finally, a user study showed that model attention computed by a perturbation-based method is often favored by human programmers. Our findings highlight the need for human-aligned LLMs for better interpretability and programmer trust.
A Weak Supervision-Based Approach to Improve Chatbots for Code Repositories
Authors: Farhour Farbod , Abdellatif Ahmad , Mansour Essam , Shihab Emad
Published: July 2024

Software chatbots are growing in popularity and have been increasingly used in software projects due to their benefits in saving time, cost, and effort. At the core of every chatbot is a Natural Language Understanding (NLU) component that enables chatbots to comprehend the users' queries. Prior work shows that chatbot practitioners face challenges in training the NLUs because the labeled training data is scarce. Consequently, practitioners resort to user queries to enhance chatbot performance. They annotate these queries and use them for NLU training. However, such training is done manually and prohibitively expensive.

Therefore, we propose AlphaBot to automate the query annotation process for SE chatbots. Specifically, we leverage weak supervision to label users' queries posted to a software repository-based chatbot. To evaluate the impact of using AlphaBot on the NLU's performance, we conducted a case study using a dataset that comprises 749 queries and 52 intents. The results show that using AlphaBot improves the NLU's performance in terms of F1-score, with improvements ranging from 0.96% to 35%. Furthermore, our results show that applying more labeling functions improves the NLU's classification of users' queries. Our work enables practitioners to focus on their chatbots' core functionalities rather than annotating users' queries.
Partial Solution Based Constraint Solving Cache in Symbolic Execution
Authors: Shuai Ziqi , Chen Zhenbang , Ma Kelin , Liu Kunlin , Zhang Yufeng , Sun Jun , Wang Ji
Published: July 2024

Constraint solving is one of the main challenges for symbolic execution. Caching is an effective mechanism to reduce the number of the solver invocations in symbolic execution and is adopted by many mainstream symbolic execution engines. However, caching can not perform well on all programs. How to improve caching’s effectiveness is challenging in general. In this work, we propose a partial solution-based caching method for improving caching’s effectiveness. Our key idea is to utilize the partial solutions inside the constraint solving to generate more cache entries. A partial solution may satisfy other constraints of symbolic execution. Hence, our partial solution-based caching method naturally improves the rate of cache hits. We have implemented our method on two mainstream symbolic executors (KLEE and Symbolic PathFinder) and two SMT solvers (STP and Z3). The results of extensive experiments on real-world benchmarks demonstrate that our method effectively increases the number of the explored paths in symbolic execution. Our caching method achieves 1.07x to 2.3x speedups for exploring the same amount of paths on different benchmarks.
Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-Coded Credentials
Authors: Huang Yizhan , Li Yichen , Wu Weibin , Zhang Jianping , Lyu Michael R.
Published: July 2024

Neural Code Completion Tools (NCCTs) have reshaped the field of software engineering, which are built upon the language modeling technique and can accurately suggest contextually relevant code snippets. However, language models may emit the training data verbatim during inference with appropriate prompts. This memorization property raises privacy concerns of NCCTs about hard-coded credential leakage, leading to unauthorized access to applications, systems, or networks. Therefore, to answer whether NCCTs will emit the hard-coded credential, we propose an evaluation tool called Hard-coded Credential Revealer (HCR). HCR constructs test prompts based on GitHub code files with credentials to reveal the memorization phenomenon of NCCTs. Then, HCR designs four filters to filter out ill-formatted credentials. Finally, HCR directly checks the validity of a set of non-sensitive credentials. We apply HCR to evaluate three representative types of NCCTs: Commercial NCCTs, open-source models, and chatbots with code completion capability. Our experimental results show that NCCTs can not only return the precise piece of their training data but also inadvertently leak additional secret strings. Notably, two valid credentials were identified during our experiments. Therefore, HCR raises a severe privacy concern about the potential leakage of hard-coded credentials in the training data of commercial NCCTs. All artifacts and data are released for future research purposes in https://github.com/HCR-Repo/HCR.
PyRadar: Towards Automatically Retrieving and Validating Source Code Repository Information for PyPI Packages
Authors: Gao Kai , Xu Weiwei , Yang Wenhao , Zhou Minghui
Published: July 2024

A package's source code repository records the package's development history, which is critical for the use and risk monitoring of the package. However, a package release often misses its source code repository due to the separation of the package's development platform from its distribution platform. To establish the link, existing tools retrieve the release's repository information from its metadata, which suffers from two limitations: the metadata may not contain or contain wrong information. Our analysis shows that existing tools can only retrieve repository information for up to 70.5% of PyPI releases. To address the limitations, this paper proposes PyRadar, a novel framework that utilizes the metadata and source distribution to retrieve and validate the repository information for PyPI releases. We start with an empirical study to compare four existing tools on 4,227,425 PyPI releases and analyze phantom files (files appearing in the release's distribution but not in the release's repository) in 14,375 correct and 2,064 incorrect package-repository links. Based on the findings, we design PyRadar with three components, i.e., Metadata-based Retriever, Source Code Repository Validator, and Source Code-based Retriever, that progressively retrieves correct source code repository information for PyPI releases. In particular, the Metadata-based Retriever combines best practices of existing tools and successfully retrieves repository information from the metadata for 72.1% of PyPI releases. The Source Code Repository Validator applies common machine learning algorithms on six crafted features and achieves an AUC of up to 0.995. The Source Code-based Retriever queries World of Code with the SHA-1 hashes of all Python files in the release's source distribution and retrieves repository information for 90.2% of packages in our dataset with an accuracy of 0.970. Both practitioners and researchers can employ the PyRadar to better use PyPI packages.
Mobile Bug Report Reproduction via Global Search on the App UI Model
Authors: Zhang Zhaoxu , Tawsif Fazle Mohammed , Ryu Komei , Yu Tingting , Halfond William G. J.
Published: July 2024

Bug report reproduction is an important, but time-consuming task carried out during mobile app maintenance. To accelerate this task, current research has proposed automated reproduction techniques that rely on a guided dynamic exploration of the app to match bug report steps with UI events in a mobile app. However, these techniques struggle to find the correct match when the bug reports have missing or inaccurately described steps. To address these limitations, we propose a new bug report reproduction technique that uses an app’s UI model to perform a global search across all possible matches between steps and UI actions and identify the most likely match while accounting for the possibility of missing or inaccurate steps. To do this, our approach redefines the bug report reproduction process as a Markov model and finds the best paths through the model using a dynamic programming based technique. We conducted an empirical evaluation on 72 real-world bug reports. Our approach achieved a 94% reproduction rate on the total bug reports and a 93% reproduction rate on bug reports with missing steps, significantly outperforming the state-of-the-art approaches. Our approach was also more effective in finding the matches from the steps to UI events than the state-of-the-art approaches.
Natural Symbolic Execution-Based Testing for Big Data Analytics
Authors: Wu Yaoxuan , Humayun Ahmad , Gulzar Muhammad Ali , Kim Miryung
Published: July 2024

Symbolic execution is an automated test input generation technique that models individual program paths as logical constraints. However, the realism of concrete test inputs generated by SMT solvers often comes into question. Existing symbolic execution tools only seek arbitrary solutions for given path constraints. These constraints do not incorporate the naturalness of inputs that observe statistical distributions, range constraints, or preferred string constants. This results in unnatural-looking inputs that fail to emulate real-world data. In this paper, we extend symbolic execution with consideration for incorporating naturalness. Our key insight is that users typically understand the semantics of program inputs, such as the distribution of height or possible values of zipcode, which can be leveraged to advance the ability of symbolic execution to produce natural test inputs. We instantiate this idea in NaturalSym, a symbolic execution-based test generation tool for data-intensive scalable computing (DISC) applications. NaturalSym generates natural-looking data that mimics real-world distributions by utilizing user-provided input semantics to drastically enhance the naturalness of inputs, while preserving strong bug-finding potential. On DISC applications and commercial big data test benchmarks, NaturalSym achieves a higher degree of realism —as evidenced by a perplexity score 35.1 points lower on median, and detects 1.29× injected faults compared to the state-of-the-art symbolic executor for DISC, BigTest. This is because BigTest draws inputs purely based on the satisfiability of path constraints constructed from branch predicates, while NaturalSym is able to draw natural concrete values based on user-specified semantics and prioritize using these values in input generation. Our empirical results demonstrate that NaturalSym finds injected faults 47.8× more than NaturalFuzz (a coverage-guided fuzzer) and 19.1× more than ChatGPT. Meanwhile, TestMiner (a mining-based approach) fails to detect any injected faults. NaturalSym is the first symbolic executor that combines the notion of input naturalness in symbolic path constraints during SMT-based input generation. We make our code available at https://github.com/UCLA-SEAL/NaturalSym.
Investigating Documented Privacy Changes in Android OS
Authors: Yan Chuan , Meng Mark Huasong , Xie Fuman , Bai Guangdong
Published: July 2024

Android has empowered third-party apps to access data and services on mobile devices since its genesis.This involves a wide spectrum of user privacy-sensitive data, such as the device ID and location. In recent years, Android has taken proactive measures to adapt its access control policies for such data, in response to the increasingly strict privacy protection regulations around the world. When each new Android version is released, its privacy changes induced by the version evolution are transparently disclosed, and we refer to them as documented privacy changes (DPCs). Implementing DPCs in Android OS is a non-trivial task, due to not only the dispersed nature of those access control points within the OS, but also the challenges posed by backward compatibility. As a result, whether the actual access control enforcement in the OS implementations aligns with the disclosed DPCs becomes a critical concern.

In this work, we conduct the first systematic study on the consistency between the operational behaviors of the OS at runtime and the officially disclosed DPCs. We propose DopCheck, an automatic DPC-driven testing framework equipped with a large language model (LLM) pipeline. It features a serial of analysis to extract the ontology from the privacy change documents written in natural language, and then harnesses the few-shot capability of LLMs to construct test cases for the detection of DPC-compliance issues in OS implementations. We apply DopCheck with the latest versions (10 to 13) of Android Open Source Project (AOSP). Our evaluation involving 79 privacy-sensitive APIs demonstrates that DopCheck can effectively recognize DPCs from Android documentation and generate rigorous test cases. Our study reveals that the status quo of the DPC-compliance issues is concerning, evidenced by 19 bugs identified by DopCheck. Notably, 12 of them are discovered in Android 13 and 6 in Android 10 for the first time, posing more than 35% Android users to the risk of privacy leakage. Our findings should raise an alert to Android users and app developers on the DPC compliance issues when using or developing an app, and would also underscore the necessity for Google to comprehensively validate the actual implementation against its privacy documentation prior to the OS release.
SPARTA: High-Level Synthesis of Parallel Multi-Threaded Accelerators
Authors: Gozzi Giovanni , Fiorito Michele , Curzel Serena , Barone Claudio , Castellana Vito Giovanni , Minutoli Marco , Tumeo Antonino , Ferrandi Fabrizio
Published: July 2024

This article presents a methodology for the Synthesis of PARallel multi-Threaded Accelerators (SPARTA) from OpenMP annotated C/C++ specifications. SPARTA extends an open-source HLS tool, enabling the generation of accelerators that provide latency tolerance for irregular memory accesses through multithreading, support fine-grained memory-level parallelism through a hot-potato deflection-based network-on-chip (NoC), support synchronization constructs, and can instantiate memory-side caches. Our approach is based on a custom runtime OpenMP library, providing flexibility and extensibility. Experimental results show high scalability when synthesizing irregular graph kernels. The accelerators generated with our approach are, on average, 2.29

\(\times\)

faster than state-of-the-art HLS methodologies.
COBIRAS: Offering a Continuous Bit Rate Slide to Maximize DASH Streaming Bandwidth Utilization
Authors: Seufert Michael , Spangenberger Marius , Poignée Fabian , Wamser Florian , Robitza Werner , Timmerer Christian , Hoßfeld Tobias
Published: July 2024

Reaching close-to-optimal bandwidth utilization in dynamic adaptive streaming over HTTP (DASH) systems can, in theory, be achieved with a small discrete set of bit rate representations. This includes typical bit rate ladders used in state-of-the-art DASH systems. In practice, however, we demonstrate that bandwidth utilization, and consequently the quality of experience (QoE), can be improved by offering a continuous set of bit rate representations, i.e., a continuous bit rate slide (COBIRAS). Moreover, we find that the buffer fill behavior of different standard adaptive bit rate (ABR) algorithms is sub-optimal in terms of bandwidth utilization. To overcome this issue, we leverage COBIRAS’ flexibility to request segments with any arbitrary bit rate and propose a novel ABR algorithm
MinOff
, which helps maximizing bandwidth utilization by minimizing download off-phases during streaming. To avoid extensive storage requirements with COBIRAS and to demonstrate the feasibility of our approach, we design and implement a proof-of-concept DASH system for video streaming that relies on just-in-time encoding (
JITE
), which reduces storage consumption on the DASH server. Finally, we conduct a performance evaluation on our testbed and compare a state-of-the-art DASH system with few bit rate representations and our
JITE
DASH system, which can offer a COBIRAS, in terms of bandwidth utilization and video QoE for different ABR algorithms.
CBAs: Character-level Backdoor Attacks against Chinese Pre-trained Language Models
Authors: He Xinyu , Hao Fengrui , Gu Tianlong , Chang Liang
Published: July 2024

Pre-trained language models (PLMs) aim to assist computers in various domains to provide natural and efficient language interaction and text processing capabilities. However, recent studies have shown that PLMs are highly vulnerable to malicious backdoor attacks, where triggers could be injected into the models to guide them to exhibit the expected behavior of the attackers. Unfortunately, existing research on backdoor attacks has mainly focused on English PLMs and paid less attention to Chinese PLMs. Moreover, these extant backdoor attacks do not work well against Chinese PLMs. In this article, we disclose the limitations of English backdoor attacks against Chinese PLMs, and propose the character-level backdoor attacks (CBAs) against the Chinese PLMs. Specifically, we first design three Chinese trigger generation strategies to ensure that the backdoor is effectively triggered while improving the effectiveness of the backdoor attacks. Then, based on the attacker’s capabilities of accessing the training dataset, we develop trigger injection mechanisms with either the target label similarity or the masked language model, which select the most influential position and insert the trigger to maximize the stealth of backdoor attacks. Extensive experiments on three major natural language processing tasks in various Chinese PLMs and English PLMs demonstrate the effectiveness and stealthiness of our method. In addition, CBAs have very strong resistance against three state-of-the-art backdoor defense methods.

1
All-Solid-State Iron-Air Batteries: A Promising High-Temperature Battery Technology for Large-Scale Energy Storage
Authors: Wang Hao , Sun Bingqian , Peng Cheng
Published: July 2024

All-solid-state iron-air batteries (ASSIABs) offer a promising high-temperature battery technology for sustainable large-scale energy storage. However, current ASSIAB performance is insufficient to meet the application requirements, primarily due to the sluggish nature of solid-state electrochemical redox reactions. Here, we briefly describe the development of high-temperature iron-air batteries and conduct an in-depth analysis of ASSIABs, including key materials and the battery reaction mechanisms. We also discuss the current challenges of ASSIABs, suggesting possible strategies to enhance their performance. We hope that this perspective can offer valuable insights into the development of high-performance ASSIABs for large-scale energy storage applications.
Improved Stability of Oxysulfide Solid-State Electrolytes in Li(G3)TFSI Solvate Ionic Liquid Electrolyte
Authors: Yersak Thomas A. , Zhang Yubin , Hafiz Hasnain , Pieczonka Nicholas P. W. , Gonzalez Malabet Hernando J. , Cunningham Hayden , Cai Mei
Published: July 2024

The performance of all solid-state batteries is limited by poor interfacial contact between active material and solid-state electrolyte (SSE) particles. Semi-solid batteries utilize a secondary electrolyte phase to wet the SSE/AM interface to improve cell performance. Solvate ionic liquids (SILs) are one class of liquid electrolytes under consideration for use in semi-solid batteries. This paper focuses on the Li(G3)TFSI SIL consisting of the bis(trifluoromethanesulfonyl)imide (TFSI−) anion coupled to a [Li(G3)]+ solvate cation. Sulfide SSEs are normally subject to nucleophilic attack by trigylme (G3), however, strong coordination of Li+ to G3 in the [Li(G3)]+ solvate cation prevents this reaction from taking place. Consequently, the stability of sulfide SSE depends on the ideal 1:1 molar ratio of G3 to TFSI, which may be difficult to maintain. We studied the chemical stability of 70Li2S·(30-x)P2S5·xP2O5 (x = 0, 2, 5, 10) (oxy)sulfide solid-state electrolyte in Li(G3)TFSI SIL. By physical measurement, UV–vis spectroscopy, electrochemical evaluation, X-ray photoelectron spectroscopy, and first principles calculation it is shown that increased oxygen content improves the stability of SSE in various Li(G3)xTFSI (x = 1, 2, 3, 4) liquid electrolytes. The results suggest that an oxysulfide SSE + SIL semi-solid electrolyte is a good choice for future semi-solid battery designs.
Psychosocial–Spiritual Experiences and Outcomes in Parents of Children with Type 1 Diabetes Mellitus from the Middle East and North Africa Region: A Systematic Review
Authors: Asaad Mariam , Abu Ghazaleh Haya , Tzouvara Vasiliki , Zhao Xiaoyan , Sturt Jackie
Published: July 2024

Background. Type 1 diabetes mellitus (T1DM) is prevalent in the Middle East and North Africa (MENA). Parents of children or young people (CYP) with T1D experience shock, devastation, guilt, and societal blame, which impact both physical and psychosocial–spiritual aspects of their lives. However, our knowledge of the breadth of these psychosocial–spiritual experiences and how they are assessed is limited.Aim. (1) To examine the diabetes‐specific psychosocial experiences of parents of CYP with T1D in the MENA region; (2) to assess the person‐reported outcome measures (PROMs) that measure the psychosocial–spiritual outcomes in this population; and (3) to assess their reliability and validity.Materials and Methods. A systematic review methodology was implemented using the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) guidelines. Ovid MEDLINE, Embase, APA PsycINFO, CINAHL, and Global Health databases were searched for relevant articles. A narrative synthesis approach was used for data analysis.Results. Twenty‐three studies were included. We identified four categories: (1) spiritual functioning, parents’ ability to accept and cope with their CYP’s condition, (2) psychological functioning, parents’ emotional distress due to insufficient diabetes‐related knowledge and skills, (3) social functioning, describing financial challenges, social support experiences, and cultural concerns faced by parents, and (4) physical functioning, parents’ struggle with sleep deprivation. Our results revealed methodological and conceptual limitations of the current tools measuring these experiences. Some of the limitations of this review are (1) heterogeneity in the tools captured perhaps some but not all domains of the parents’ psychosocial experiences, (2) only English studies were included, as no Arabic studies were found.Conclusion. Our studied population experiences psychosocial–spiritual distress by managing the condition of their CYP and needs culturally specific psychosocial–spiritual support. Further studies are needed to develop a new measure to specifically assess the psychosocial–spiritual outcomes of this population.
HER2DX Genomic Assay in HER2-Positive Early Breast Cancer Treated with Trastuzumab and Pertuzumab: A Correlative Analysis from the PHERGain Phase II Trial
Authors: Llombart-Cussac Antonio , Pérez-García Jose , Brasó-Maristany Fara , Paré Laia , Villacampa Guillermo , Gion Maria , Schmid Peter , Colleoni Marco , Borrego Manuel R. , Galván Patricia , Parker Joel S. , Buckingham Wesley , Perou Charles M. , Villagrasa Patricia , Guerrero Jose A. , Sampayo-Cordero Miguel , Mancino Mario , Prat Aleix , Cortés Javier
Published: July 2024

Purpose:

The purpose of this study was to assess the predictive capability of HER2DX assay following (neo)adjuvant trastuzumab–pertuzumab (HP)–based therapy in HER2-positive (HER2+) early breast cancer.

Experimental Design:

HER2DX was analyzed in baseline pretreatment tumors from the PHERGain trial. Patients with stage I–IIIA HER2+ early breast cancer were randomized to group A [docetaxel, carboplatin, and HP (TCHP)] and group B (HP ± endocrine therapy). PET response was evaluated after two cycles. Group A received TCHP for six cycles regardless of PET response. Group B continued with HP ± endocrine therapy for six cycles (PET responders) or with TCHP for six cycles (PET nonresponders). The primary objective of this retrospective study was to associate the HER2DX pathologic complete response (pCR) score with pCR. The secondary objective was the association of the HER2DX risk score with 3-year invasive disease-free survival (iDFS).

Results:

HER2DX was performed on 292 (82.0%) tumors. The overall pCR rate was 38.0%, with pCR rates of 56.4% in group A and 33.8% in group B. In multivariable analysis including treatment and clinicopathologic factors, the HER2DX pCR score (continuous variable) significantly correlated with pCR [OR, 1.29; 95% confidence interval (CI), 1.10–1.54; P < 0.001]. HER2DX-defined pCR-high, -med, and -low groups exhibited pCR rates of 50.4%, 35.8%, and 23.2%, respectively (pCR-high vs. pCR-low OR, 3.27; 95% CI, 1.54–7.09; P < 0.001). In patients with residual disease, the HER2DX high-risk group demonstrated numerically worse 3-year iDFS than the low-risk group (89.8% vs. 100%; HR, 2.70; 95% CI, 0.60–12.18; P = 0.197).

Conclusions:

HER2DX predicts pCR in the context of neoadjuvant HP-based therapy, regardless of chemotherapy addition, and might identify patients at higher risk of recurrence among patients with residual disease.
Table 2 from Race-Related Differences in Sipuleucel-T Response among Men with Metastatic Castrate–Resistant Prostate Cancer
Authors: Heath Elisabeth I. , Thakur Archana , Chen Wei , Hwang Clara , Paller Channing J. , Cackowski Frank C. , Boerner Julie L. , Heilbrun Lance , Smith Melanie P. , Schalk Dana L. , Schienschang Amy , Whitaker Sarah A. , Polend Amanda , Smith Daryn , Vaishampayan Ulka N. , Dickow Brenda , Lum Lawrence G.
Published: July 2024

Spearman correlation between marker difference (diff; week 10-baseline) and PSA baseline or PSA fold change (PSA.FC; week 10/baseline; rho, P value).
Table 1 from Race-Related Differences in Sipuleucel-T Response among Men with Metastatic Castrate–Resistant Prostate Cancer
Authors: Heath Elisabeth I. , Thakur Archana , Chen Wei , Hwang Clara , Paller Channing J. , Cackowski Frank C. , Boerner Julie L. , Heilbrun Lance , Smith Melanie P. , Schalk Dana L. , Schienschang Amy , Whitaker Sarah A. , Polend Amanda , Smith Daryn , Vaishampayan Ulka N. , Dickow Brenda , Lum Lawrence G.
Published: July 2024

Baseline clinicopathologic characteristics of patients with mCRPC and their sipuleucel-T cumulative product parameters
Figure 5 from Race-Related Differences in Sipuleucel-T Response among Men with Metastatic Castrate–Resistant Prostate Cancer
Authors: Heath Elisabeth I. , Thakur Archana , Chen Wei , Hwang Clara , Paller Channing J. , Cackowski Frank C. , Boerner Julie L. , Heilbrun Lance , Smith Melanie P. , Schalk Dana L. , Schienschang Amy , Whitaker Sarah A. , Polend Amanda , Smith Daryn , Vaishampayan Ulka N. , Dickow Brenda , Lum Lawrence G.
Published: July 2024

A, Longitudinal PSA levels by race groups. Curves were fitted with LOESS smoothing method. Dot and triangle symbols indicate mean log10(PSA) at the given time point. Vertical error bars indicate standard errors. B, Waterfall plot: PSA change from baseline to nadir.
Figure 2 from Race-Related Differences in Sipuleucel-T Response among Men with Metastatic Castrate–Resistant Prostate Cancer
Authors: Heath Elisabeth I. , Thakur Archana , Chen Wei , Hwang Clara , Paller Channing J. , Cackowski Frank C. , Boerner Julie L. , Heilbrun Lance , Smith Melanie P. , Schalk Dana L. , Schienschang Amy , Whitaker Sarah A. , Polend Amanda , Smith Daryn , Vaishampayan Ulka N. , Dickow Brenda , Lum Lawrence G.
Published: July 2024

Peripheral antibody and IFNγ ELISpot immune responses in AA men (n = 29) and non-AA men (n = 28). A, Significantly higher serum IgM antibody levels against PA2024 (P < 0.0001) and PAP (PAP; P < 0.0001) were noted at 10 weeks post-treatment above the baseline in each group. No significant differences were observed in either group for anti-PSMA antibody levels at 10 weeks post-treatment vs. baseline. There were significantly higher anti-PSA antibody levels in non-AA group (P = 0.0018) at 10 weeks post-treatment above the baseline. B, IFNγ ELISpot responses to PA2024 increased significantly (P = 0.0008) in non-AA men at 10 weeks post-treatment vs. baseline. In AA men, IFNγ ELISpot responses to PA2024 also increased (P = 0.04) at 10 weeks post-treatment. No significant differences in IFNγ ELISpot responses were seen at 10 weeks against PAP, PSMA, or PSA in either group. For (A and B), there were no significant racial differences in either antibody or IFNγ ELISpot responses against PA2024, PAP, PSMA, or PSA at baseline or at 10 weeks post-treatment. All responses and racial differences were analyzed by the Mann–Whitney test and P values < 0.0032 were considered as significant in the multiple comparisons sense. The horizontal line in each dot plot marks the median value. Base, baseline; wks, weeks.
Spatial Landscape of Malignant Pleural and Peritoneal Mesothelioma Tumor Immune Microenvironments
Authors: Ma Xiaojun , Lembersky David , Kim Elena S. , Becich Michael J. , Testa Joseph R. , Bruno Tullia C. , Osmanbeyoglu Hatice U.
Published: July 2024

Immunotherapies have demonstrated limited clinical efficacy in malignant mesothelioma treatment. We conducted multiplex immunofluorescence analyses on tissue microarrays (n = 3) from patients with malignant pleural mesothelioma (MPM, n = 88) and malignant peritoneal mesothelioma (MPeM, n = 25). Our study aimed to elucidate spatial distributions of key immune cell populations and their association with lymphocyte activation gene 3 (LAG3), BRCA1-associated protein 1 (BAP1), neurofibromatosis type 2 (NF2), and methylthioadenosine phosphorylase (MTAP), with MTAP serving as a cyclin-dependent kinase inhibitor 2A/2B (CDKN2A/B) surrogate marker. Additionally, we examined the relationship between the spatial distribution of major immune cell types and prognosis and clinical characteristics of patients with malignant mesothelioma. We observed a higher degree of interaction between immune cells and tumor cells in MPM compared with MPeM. Notably, within MPM tumors, we detected a significantly increased interaction between tumor cells and CD8+ T cells in tumors with low BAP1 expression compared with those with high BAP1 expression. To support the broader research community, we have developed The Human Spatial Atlas of Malignant Mesothelioma, containing hematoxylin and eosin and multiplex immunofluorescence images with corresponding metadata.

Significance:

Considering the limited therapeutic options available to patients with malignant mesothelioma, there is substantial translational potential in understanding the correlation between the spatial architecture of the malignant mesothelioma tumor immune microenvironment and tumor biology. Our investigation reveals critical cell–cell interactions that may influence the immune response against malignant mesothelioma tumors, potentially contributing to the differential behaviors observed in MPM and MPeM. These findings represent a valuable resource for the malignant mesothelioma cancer research community.
p300 KAT Regulates SOX10 Stability and Function in Human Melanoma
Authors: Waddell Aaron , Grbic Nicole , Leibowitz Kassidy , Wyant William Austin , Choudhury Sabah , Park Kihyun , Collard Marianne , Cole Philip A. , Alani Rhoda M.
Published: July 2024

SOX10 is a lineage-specific transcription factor critical for melanoma tumor growth; on the other hand, SOX10 loss-of-function drives the emergence of therapy-resistant, invasive melanoma phenotypes. A major challenge has been developing therapeutic strategies targeting SOX10’s role in melanoma proliferation while preventing a concomitant increase in tumor cell invasion. In this study, we report that the lysine acetyltransferase (KAT) EP300 and SOX10 gene loci on chromosome 22 are frequently co-amplified in melanomas, including UV-associated and acral tumors. We further show that p300 KAT activity mediates SOX10 protein stability and that the p300 inhibitor A-485 downregulates SOX10 protein levels in melanoma cells via proteasome-mediated degradation. Additionally, A-485 potently inhibits proliferation of SOX10+ melanoma cells while decreasing invasion in AXLhigh/MITFlow melanoma cells through downregulation of metastasis-related genes. We conclude that the SOX10/p300 axis is critical to melanoma growth and invasion and that inhibition of p300 KAT activity through A-485 may be a worthwhile therapeutic approach for SOX10-reliant tumors.

Significance:

The p300 KAT inhibitor A-485 blocks SOX10-dependent proliferation and SOX10-independent invasion in hard-to-treat melanoma cells.
Composite Pulmonary Embolism Shock Score and Risk of Adverse Outcomes in Patients With Pulmonary Embolism
Authors: Zhang Robert S. , Yuriditsky Eugene , Zhang Peter , Maqsood Muhammad H. , Amoroso Nancy E. , Maldonado Thomas S. , Xia Yuhe , Horowitz James M. , Bangalore Sripal
Published: July 2024

BACKGROUND:

In hemodynamically stable patients with acute pulmonary embolism (PE), the Composite Pulmonary Embolism Shock (CPES) score predicts normotensive shock. However, it is unknown if CPES predicts adverse clinical outcomes. The objective of this study was to determine whether the CPES score predicts in-hospital mortality, resuscitated cardiac arrest, or hemodynamic deterioration.

METHODS:

Patients with acute intermediate-risk PE admitted from October 2016 to July 2019 were included. CPES was calculated for each patient. The primary outcome was a composite of in-hospital mortality, resuscitated cardiac arrest, or hemodynamic decompensation. Secondary outcomes included individual components of the primary outcome. The association of CPES with primary and secondary outcomes was evaluated.

RESULTS:

Among the 207 patients with intermediate-risk PE (64.7% with intermediate-high risk PE), 29 (14%) patients had a primary outcome event. In a multivariable model, a higher CPES score was associated with a worse primary composite outcome (adjusted hazard ratio [aHR], 1.81 [95% CI, 1.29–2.54];
P
=0.001). Moreover, a higher CPES score predicted death (aHR, 1.76 [95% CI, 1.04–2.96];
P
=0.033), resuscitated cardiac arrest (aHR, 1.99 [95% CI, 1.17–3.38];
P
=0.011), and hemodynamic decompensation (aHR, 1.96 [95% CI, 1.34–2.89];
P
=0.001). A high CPES score (≥3) was associated with the worse primary outcome when compared with patients with a low CPES score (22% versus 2.4%;
P
=0.003; aHR, 6.48 [95% CI, 1.49–28.04];
P
=0.012). CPES score provided incremental prognostic value for the prediction of primary outcome over baseline demographics and European Society of Cardiology intermediate-risk subcategories (global Χ
2
value increased from 0.63 to 1.39 to 13.69;
P
=0.005)

CONCLUSIONS:

In patients with acute intermediate-risk PE, the CPES score effectively risk stratifies and prognosticates patients for the prediction of clinical events and provides incremental value over baseline demographics and European Society of Cardiology intermediate-risk subcategories.
Alone Together, Together Alone: The Effects of Social Context on Nonverbal Behavior in Virtual Reality
Authors: Han Eugy , DeVeaux Cyan , Miller Mark Roman , Harari Gabriella M. , Hancock Jeffrey T. , Ram Nilam , Bailenson Jeremy N.
Published: July 2024

Social virtual reality (VR), by definition, focuses on people using networked VR systems to bring avatars together. Previous studies have examined how different factors affect social interaction, in small groups such as dyads or triads. However, in a typical social VR scene there tends to be dozens of avatars, even those not directly interacting with a given user. Furthermore, beyond the virtual environment, VR users are also situated in various immediate physical social contexts. In two field experiments, we investigate how the presence of virtual and physical people contextualize and influence nonverbal behaviors. Study 1 examines virtual context and asks how interacting with others in a private or public virtual environment influences nonverbal outcomes during interactions in a social VR platform. Across two sessions, participants (n = 104) met either in a private virtual environment with their group members alone or in a public environment surrounded by four other groups. Results showed that participants moved their avatars slower and stood closer to group members in public versus private environments. Study 2 examines physical context and asks how interacting with virtual others while physically together or alone influences nonverbal behaviors. Participants (n = 61) met in virtual environments while they were in either a shared physical environment or separated physical environments. Results showed that, compared to remote participants, participants who were physically together moved their bodies more slowly, but their avatars faster. Moreover, there was more mutual gaze among remote participants. We discuss implications to theories of social influence in VR.
Risk prediction for clonal cytopenia: multicenter real-world evidence
Authors: Xie Zhuoer , Komrokji Rami , Al Ali Najla , Regelson Alexandra , Geyer Susan , Patel Anand , Saygin Caner , Zeidan Amer M. , Bewersdorf Jan Philipp , Mendez Lourdes , Kishtagari Ashwin , Zeidner Joshua F. , Coombs Catherine C. , Madanat Yazan F. , Chung Stephen , Badar Talha , Foran James , Desai Pinkal , Tsai Charlton , Griffiths Elizabeth A. , Al Malki Monzr M. , Amanam Idoroenyi , Lai Catherine , Deeg H. Joachim , Ades Lionel , Arana Yi Cecilia , Osman Afaf E. G. , Dinner Shira , Abaza Yasmin , Taylor Justin , Chandhok Namrata , Soong Deborah , Brunner Andrew M. , Carraway Hetty E. , Singh Abhay , Elena Chiara , Ferrari Jacqueline , Gallì Anna , Pozzi Sara , Padron Eric , Patnaik Mrinal M. , Malcovati Luca , Savona Michael R. , Al-Kali Aref
Published: July 2024

Clonal cytopenia of undetermined significance (CCUS) represents a distinct disease entity characterized by myeloid-related somatic mutations with a variant allele fraction of ≥2% in individuals with unexplained cytopenia(s) but without a myeloid neoplasm (MN). Notably, CCUS carries a risk of progressing to MN, particularly in cases featuring high-risk mutations. Understanding CCUS requires dedicated studies to elucidate its risk factors and natural history. Our analysis of 357 patients with CCUS investigated the interplay between clonality, cytopenia, and prognosis. Multivariate analysis identified 3 key adverse prognostic factors: the presence of splicing mutation(s) (score = 2 points), platelet count of <100 × 109/L (score = 2.5), and ≥2 mutations (score = 3). Variable scores were based on the coefficients from the Cox proportional hazards model. This led to the development of the clonal cytopenia risk score (CCRS), which stratified patients into low- (score of <2.5 points), intermediate- (score of 2.5 to <5), and high-risk (score of ≥5) groups. The CCRS effectively predicted 2-year cumulative incidence of MN for low- (6.4%), intermediate- (14.1%), and high-risk (37.2%) groups, respectively, by the Gray test (P < .0001). We further validated the CCRS by applying it to an independent CCUS cohort of 104 patients, demonstrating a c-index of 0.64 (P = .005) in stratifying the cumulative incidence of MN. Our study underscores the importance of integrating clinical and molecular data to assess the risk of CCUS progression, making the CCRS a valuable tool that is practical and easily calculable. These findings are clinically relevant, shaping the management strategies for CCUS and informing future clinical trial designs.