bluuebunny/crossref_metadata_2025 huggingface dataset:
AI-Assisted Code Authoring at Scale: Fine-Tuning, Deploying, and Mixed Methods Evaluation
Authors: Murali Vijayaraghavan , Maddila Chandra , Ahmad Imad , Bolin Michael , Cheng Daniel , Ghorbani Negar , Fernandez Renuka , Nagappan Nachiappan , Rigby Peter C.
Published: July 2024

Generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and deployed at Meta internally. CodeCompose is based on the InCoder LLM that merges generative capabilities with bi-directionality. We have scaled up CodeCompose to serve tens of thousands of developers at Meta, across 9 programming languages and several coding surfaces. We present our experience in making design decisions about the model and system architecture for CodeCompose that addresses these challenges.

To release a LLM model at this scale, we needed to first ensure that it is sufficiently accurate. In a random sample of 20K source code files, depending on the language, we are able to reproduce hidden lines between 40% and 58% of the time, an improvement of 1.4× and 4.1× over a model trained only on public data.

We gradually rolled CodeCompose out to developers. At the time of this writing, 16K developers have used it with 8% of their code coming directly from CodeCompose.

To triangulate our numerical findings, we conduct a thematic analysis on the feedback from 70 developers. We find that 91.5% of the feedback is positive, with the most common themes being discovering APIs, dealing with boilerplate code, and accelerating coding. Meta continues to integrate this feedback into CodeCompose.
Refactoring to Pythonic Idioms: A Hybrid Knowledge-Driven Approach Leveraging Large Language Models
Authors: Zhang Zejun , Xing Zhenchang , Ren Xiaoxue , Lu Qinghua , Xu Xiwei
Published: July 2024

Pythonic idioms are highly valued and widely used in the Python programming community. However, many
Python users find it challenging to use Pythonic idioms. Adopting rule-based approach or LLM-only approach is not sufficient to overcome three persistent challenges of code idiomatization including code miss, wrong detection and wrong refactoring. Motivated by the determinism of rules and adaptability of LLMs, we propose a hybrid approach consisting of three modules. We not only write prompts to instruct LLMs to complete tasks, but we also invoke Analytic Rule Interfaces (ARIs) to accomplish tasks. The ARIs are Python code generated by prompting LLMs to generate code. We first construct a knowledge module with three elements including ASTscenario, ASTcomponent and Condition, and prompt LLMs to generate Python code for incorporation into an ARI library for subsequent use. After that, for any syntax-error-free Python code, we invoke ARIs from the ARI library to extract ASTcomponent from the ASTscenario, and then filter out ASTcomponent that does not meet the condition. Finally, we design prompts to instruct LLMs to abstract and idiomatize code, and then invoke ARIs from the ARI library to rewrite non-idiomatic code into the idiomatic code. Next, we conduct a comprehensive evaluation of our approach, RIdiom, and Prompt-LLM on nine established Pythonic idioms in RIdiom. Our approach exhibits superior accuracy, F1 score, and recall, while maintaining precision levels comparable to RIdiom, all of which consistently exceed or come close to 90% for each metric of each idiom. Lastly, we extend our evaluation to encompass four new Pythonic idioms. Our approach consistently outperforms Prompt-LLM, achieving metrics with values consistently exceeding 90% for accuracy, F1-score, precision, and recall.
Can GPT-4 Replicate Empirical Software Engineering Research?
Authors: Liang Jenny T. , Badea Carmen , Bird Christian , DeLine Robert , Ford Denae , Forsgren Nicole , Zimmermann Thomas
Published: July 2024

Empirical software engineering research on production systems has brought forth a better understanding of the software engineering process for practitioners and researchers alike. However, only a small subset of production systems is studied, limiting the impact of this research. While software engineering practitioners could benefit from replicating research on their own data, this poses its own set of challenges, since performing replications requires a deep understanding of research methodologies and subtle nuances in software engineering data. Given that large language models (LLMs), such as GPT-4, show promise in tackling both software engineering- and science-related tasks, these models could help replicate and thus democratize empirical software engineering research. In this paper, we examine GPT-4’s abilities to perform replications of empirical software engineering research on new data. We specifically study their ability to surface assumptions made in empirical software engineering research methodologies, as well as their ability to plan and generate code for analysis pipelines on seven empirical software engineering papers. We perform a user study with 14 participants with software engineering research expertise, who evaluate GPT-4-generated assumptions and analysis plans (i.e., a list of module specifications) from the papers. We find that GPT-4 is able to surface correct assumptions, but struggles to generate ones that apply common knowledge about software engineering data. In a manual analysis of the generated code, we find that the GPT-4-generated code contains correct high-level logic, given a subset of the methodology. However, the code contains many small implementation-level errors, reflecting a lack of software engineering knowledge. Our findings have implications for leveraging LLMs for software engineering research as well as practitioner data scientists in software teams.
A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization
Authors: Kang Sungmin , An Gabin , Yoo Shin
Published: July 2024

Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce developer burden, many automated FL techniques have been proposed. However, prior work has noted that existing techniques fail to provide rationales for the suggested locations, hindering developer adoption of these techniques. With this in mind, we propose AutoFL, a Large Language Model (LLM)-based FL technique that generates an explanation of the bug along with a suggested fault location. AutoFL prompts an LLM to use function calls to navigate a repository, so that it can effectively localize faults over a large software repository and overcome the limit of the LLM context length. Extensive experiments on 798 real-world bugs in Java and Python reveal AutoFL improves method-level acc@1 by up to 233.3% over baselines. Furthermore, developers were interviewed on their impression of AutoFL-generated explanations, showing that developers generally liked the natural language explanations of AutoFL, and that they preferred reading a few, high-quality explanations instead of many.
Do Words Have Power? Understanding and Fostering Civility in Code Review Discussion
Authors: Rahman Md Shamimur , Codabux Zadia , Roy Chanchal K.
Published: July 2024

Modern Code Review (MCR) is an integral part of the software development process where developers improve product quality through collaborative discussions. Unfortunately, these discussions can sometimes become heated by the presence of inappropriate behaviors such as personal attacks, insults, disrespectful comments, and derogatory conduct, often referred to as incivility. While researchers have extensively explored such incivility in various public domains, our understanding of its causes, consequences, and courses of action remains limited within the professional context of software development, specifically within code review discussions. To bridge this gap, our study draws upon the experience of 171 professional software developers representing diverse development practices across different geographical regions. Our findings reveal that more than half of these developers (56.72%) have encountered instances of workplace incivility, and a substantial portion of that group (83.70%) reported experiencing such incidents at least once a month. We also identified

various causes, positive and negative consequences, and potential courses of action for uncivil communication. Moreover, to address the negative aspects of incivility, we propose a model for promoting civility that detects uncivil comments during communication and provides alternative civil suggestions while preserving the original comments’ semantics, enabling developers to engage in respectful and constructive discussions. An in-depth analysis of 2K uncivil review comments using eight different evaluation metrics and a manual evaluation suggested that our proposed approach could generate civil alternatives significantly compared to the state-of-the-art politeness and detoxification models. Moreover, a survey involving 36 developers who used our civility model reported its effectiveness in enhancing online development interactions, fostering better relationships, increasing contributor involvement, and expediting development processes. Our research is a pioneer in generating civil alternatives for uncivil discussions in software development, opening new avenues for research in collaboration and communication within the software engineering context.
Misconfiguration Software Testing for Failure Emergence in Autonomous Driving Systems
Authors: Chen Yuntianyi , Huai Yuqi , Li Shilong , Hong Changnam , Garcia Joshua
Published: July 2024

The optimization of a system’s configuration options is crucial for determining its performance and functionality, particularly in the case of autonomous driving software (ADS) systems because they possess a multitude of such options. Research efforts in the domain of ADS have prioritized the development of automated testing methods to enhance the safety and security of self-driving cars. Presently, search-based approaches are utilized to test ADS systems in a virtual environment, thereby simulating real-world scenarios. However, such approaches rely on optimizing the waypoints of ego cars and obstacles to generate diverse scenarios that trigger violations, and no prior techniques focus on optimizing the ADS from the perspective of configuration. To address this challenge, we present a framework called ConfVE, which is the first automated configuration testing framework for ADSes. ConfVE’s design focuses on the emergence of violations through rerunning scenarios generated by different ADS testing approaches under different configurations, leveraging 9 test oracles to enable previous ADS testing approaches to find more types of violations without modifying their designs or implementations and employing a novel technique to identify bug-revealing violations and eliminate duplicate violations. Our evaluation results demonstrate that ConfVE can discover 1,818 unique violations and reduce 74.19% of duplicate violations.
Evolutionary Multi-objective Optimization for Contextual Adversarial Example Generation
Authors: Zhou Shasha , Huang Mingyu , Sun Yanan , Li Ke
Published: July 2024

The emergence of the 'code naturalness' concept, which suggests that software code shares statistical properties with natural language, paves the way for deep neural networks (DNNs) in software engineering (SE). However, DNNs can be vulnerable to certain human imperceptible variations in the input, known as adversarial examples (AEs), which could lead to adverse model performance. Numerous attack strategies have been proposed to generate AEs in the context of computer vision and natural language processing, but the same is less true for source code of programming languages in SE. One of the challenges is derived from various constraints including syntactic, semantics and minimal modification ratio. These constraints, however, are subjective and can be conflicting with the purpose of fooling DNNs. This paper develops a multi-objective adversarial attack method (dubbed MOAA), a tailored NSGA-II, a powerful evolutionary multi-objective (EMO) algorithm, integrated with CodeT5 to generate high-quality AEs based on contextual information of the original code snippet. Experiments on 5 source code tasks with 10 datasets of 6 different programming languages show that our approach can generate a diverse set of high-quality AEs with promising transferability. In addition, using our AEs, for the first time, we provide insights into the internal behavior of pre-trained models.
ClarifyGPT: A Framework for Enhancing LLM-Based Code Generation via Requirements Clarification
Authors: Mu Fangwen , Shi Lin , Wang Song , Yu Zhuohao , Zhang Binquan , Wang ChenXue , Liu Shichao , Wang Qing
Published: July 2024

Large Language Models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in automatically generating code from provided natural language requirements. However, in real-world practice, it is inevitable that the requirements written by users might be ambiguous or insufficient. Current LLMs will directly generate programs according to those unclear requirements, regardless of interactive clarification, which will likely deviate from the original user intents. To bridge that gap, we introduce a novel framework named ClarifyGPT, which aims to enhance code generation by empowering LLMs with the ability to identify ambiguous requirements and ask targeted clarifying questions. Specifically, ClarifyGPT first detects whether a given requirement is ambiguous by performing a code consistency check. If it is ambiguous, ClarifyGPT prompts an LLM to generate targeted clarifying questions. After receiving question responses, ClarifyGPT refines the ambiguous requirement and inputs it into the same LLM to generate a final code solution. To evaluate our ClarifyGPT, we invite ten participants to use ClarifyGPT for code generation on two benchmarks: MBPP-sanitized and MBPP-ET. The results show that ClarifyGPT elevates the performance (Pass@1) of GPT-4 from 70.96% to 80.80% on MBPP-sanitized. Furthermore, to conduct large-scale automated evaluations of ClarifyGPT across different LLMs and benchmarks without requiring user participation, we introduce a high-fidelity simulation method to simulate user responses. The results demonstrate that ClarifyGPT can significantly enhance code generation performance compared to the baselines. In particular, ClarifyGPT improves the average performance of GPT-4 and ChatGPT across five benchmarks from 62.43% to 69.60% and from 54.32% to 62.37%, respectively. A human evaluation also confirms the effectiveness of ClarifyGPT in detecting ambiguous requirements and generating high-quality clarifying questions. We believe that ClarifyGPT can effectively facilitate the practical application of LLMs in real-world development environments.
VRVul-Discovery: BiLSTM-based Vulnerability Discovery for Virtual Reality Devices in Metaverse
Authors: Sha Letian , Chen Xiao , Xiao Fu , Wang Zhong , Long Zhangbo , Fan Qianyu , Dong Jiankuo
Published: July 2024

The rapid development of the metaverse has brought about numerous security challenges.
Virtual Reality (VR)
, as one of the core technologies, plays a crucial role in the metaverse. The security of VR devices directly impacts user authentication and privacy. Currently, no attention has been paid to the vulnerabilities and security risks of VR devices. This article employs a bi-layer BiLSTM neural network to conduct a root cause analysis for user authentication and scene interaction when users enter metaverse environment using VR devices. By establishing the mapping between vulnerable VR firmware file attributes and metaverse interaction scenarios, we implement a vulnerability discovery and verification prototype called VRVul-Discovery, based on the concept of vulnerability discovery. Experiment results demonstrate that VRVul-Discovery provides high-accuracy determinations of firmware vulnerability attributes and scenarios susceptible to hijacking. In the end, the prototype system discovers seven unknown vulnerabilities, all of which are authenticated.
Time-Aware Spectrum-Based Bug Localization for Hardware Design Code with Data Purification
Authors: Wu Jiang , Zhang Zhuo , Yang Deheng , Xu Jianjun , He Jiayu , Mao Xiaoguang
Published: July 2024

The verification of hardware design code is a critical aspect in ensuring the quality and reliability of hardware products. Finding bugs in hardware design code is important for hardware development and is frequently considered as a notoriously challenging and time-consuming activity while being an essential aspect of verification. Thus, bug localization techniques that could assist manual debugging have attracted much attention in the hardware community. However, there exists an unpredictable time span between the precise origin of a bug and its detected manifestation in prior work without costly formal verification. Locating the bug responsible for the exposed discrepancy between expected and exhibited design behavior remains a major challenge. In this work, we propose Tartan, a
T
ime-
a
ware spect
r
um-based bug localiza
t
ion with d
a
ta purificatio
n
for hardware design code to address these limitations. Tartan integrates hardware-specific timing information with the spectrum and captures the changes of executed statements when the state of the circuit changes to effectively locate bugs. Further, Tartan purifies the spectrum data from the simulation and evaluates the suspiciousness of the statements in the design to indicate the likelihood of being buggy. To evaluate the effectiveness of Tartan, we conduct large-scale experiments on 69 versions of 15 hardware projects by the state-of-the-art bug localization techniques. The experimental results clearly show that Tartan is statistically more effective than the baselines. It provides a new perspective on hardware design code bug localization and brings fresh insights to the community.
Prevalence Increases as Treatments Improve: An Evolutionary Perspective on the Treatment–Prevalence Paradox in Depression
Authors: Ezawa Iony D. , Robinson Noah , Hollon Steven D.
Published: July 2024

Depression is an eminently treatable disorder that responds to psychotherapy or medications; the efficacy of each has been established in hundreds of controlled trials. Nonetheless, the prevalence of depression has increased in recent years despite the existence of efficacious treatments—a phenomenon known as the treatment–prevalence paradox. We consider several possible explanations for this paradox, which range from a misunderstanding of the very nature of depression, inflated efficacy of the established treatments, and a lack of access to efficacious delivery of treatments. We find support for each of these possible explanations but especially the notion that large segments of the population lack access to efficacious treatments that are implemented as intended. We conclude by describing the potential of using lay therapists and digital technologies to overcome this lack of access and to reach historically underserved populations and simultaneously guarantee the quality of the interventions delivered.
Recent Progress of Regulation Factors on the Deposition of Sodium Anodes
Authors: Tang Conggu , Cai Chuyi , Zhang Jindan , Gao Feng , Hu Tao , Pu Zhu , Weng Jingzheng , Zhu Mengqi
Published: July 2024

As the demand for portable electronic and electric vehicles increases, it is necessary to pursue batteries with longer cycle life, higher energy density, and overall better performance. Because lithium sources are limited and lithium metal is expensive, it is necessary to find alternatives. Rechargeable sodium (Na) batteries have attracted great research interest because of their high natural abundance, low cost of sodium resources, and electrochemical similarity with lithium batteries. However, despite the potential to become the next generation of energy storage, the application of sodium metal batteries is mainly hindered by sodium dendrites and “dead” sodium, which reduce battery coulombic efficiency, shorten battery life, and even cause safety problems. The formation of Na dendrites is mainly due to the uncontrolled Na deposition behavior of sodium ions in the absence of nucleation site regulation. Therefore, sodium deposition is crucial to the final status of Na anodes. This paper first analyzes the growth mechanism of sodium dendrites, then reviews the research progress of nucleation sites on inhibiting the formation of sodium dendrites, and finally discusses the practical application of sodium metal batteries and the future challenges of metallic sodium anodes, hoping to stimulate more research interests of researchers.
Interplay of race and neighborhood deprivation on resting and ambulatory blood pressure in young adults
Authors: Jeong Soolim , Linder Braxton A. , Barnett Alex M. , Tharpe McKenna A. , Hutchison Zach J. , Culver Meral N. , Sanchez Sofia O. , Nichols Olivia I. , Grosicki Gregory J. , Bunsawat Kanokwan , Nasci Victoria L. , Gohar Eman Y. , Fuller-Rowell Thomas E. , Robinson Austin T.
Published: July 2024

We demonstrate that young Black adults exhibit higher resting blood pressure, nighttime blood pressure, and attenuated systolic blood pressure dipping compared with young White adults. Black adults were exposed to greater neighborhood deprivation, which demonstrated some associations with resting and ambulatory blood pressure. Our findings add to a growing body of literature indicating that neighborhood deprivation may contribute to increased blood pressure.
Healthcare Professionals’ Attitude to Using Mobile Health Technology and Its Associated Factors in a Resource‐Limited Country—An Implication for Digital Health Implementers: A Cross Sectional Study
Authors: Walle Agmasie Damtew , Butta Fikadu Wake , Kassie Sisay Yitayih , Chereka Alex Ayenew , Kanfe Shuma Gosha , Dubale Abiy Tasew , Enyew Ermias Bekele , Dube Geleta Nenko , Shibabaw Adamu Ambachew , Hunde Mekonnen Kenate , Kitil Gemeda Wakgari , Ferede Tigist Andargie , Wubante Sisay Maru , Baykemagn Nebebe Demis , Demsash Addisalem Workie
Published: July 2024

Background: Mobile health has become widely used within the healthcare system, and there is an increasing worldwide trend toward employing this innovation for behavior management, disease monitoring, the control and prevention of various health issues, and rising enrollment in healthcare services. Although mHealth is becoming more widely available, there is no evidence about the attitude of healthcare professionals toward mHealth in southwest Ethiopia. Therefore, this study is aimed at assessing the attitude of healthcare professionals to using mHealth technology and associated factors in Ethiopia.

Methods: An institutional cross‐sectional study was conducted among 422 healthcare professionals. Data were collected using a pretested interviewer‐administered questionnaire, and the study was conducted from January 08 to February 10, 2023. EpiData Version 4.6 for entering the data and STATA Version 14 for analyzing the data were used. A multivariable logistic regression analysis was carried out to identify factors associated with healthcare professionals’ attitudes to using mobile health technology.

Results: A total of 415 study participants were included in the study. About 180 (43.4%) respondents had a favorable attitude toward mHealth technology in southwest public hospitals. Master’s degree and above (adjusted odds ratio [AOR]: 3.67; 95% CI: 1.22, 4.10), good knowledge of mobile health technology (AOR: 4.08; 95% CI: 1.35, 5.31), more than 5 years of work experience (AOR: 3.09; 95% CI: 1.76, 5.60), had ICT infrastructure (AOR: 2.70; 95% CI: 1.38, 5.31), had own smart mobile (AOR: 3.67; 95% CI: 3.20, 4.31), and had taken computer‐related training (AOR: 1.96; 95% CI: 1.03, 3.73) were positively associated with healthcare professionals’ attitude to using mobile health technologies in southwest Ethiopia.

Conclusions: Overall, healthcare professionals’ attitude to using mobile health technologies in southwest Ethiopia was relatively low. Education level, good knowledge, years of work experience, ICT infrastructure, having a smart mobile, and having taken computer‐related training were significant factors of attitude to using mobile health technologies. Considering these factors could provide insight into developing and adopting mobile health technologies in Ethiopia.
Tripotent Divisor Graph of a Commutative Ring
Authors: Khaleel Luma A. , Mohammad Husam Q. , Shuker Nazar H.
Published: July 2024

In this work, we introduce a new concept called the tripotent divisor graph of a commutative ring. It is defined with vertices set in a ring R, where distinct vertices r1 and r2 are connected by an edge if their product belongs to the set of all nonunite tripotent in R. We denote this graph as 3I Γ(R). We utilize this graph to examine the role of tripotent elements in the structure of rings. Additionally, we provide various findings regarding graph‐theoretic characteristics of this graph, including its diameter, vertex degrees, and girth. Furthermore, we investigate the size, central vertices, and distances between vertices for the tripotent divisor graph formed by the direct product of two fields.
Adaptive Attention Module for Image Recognition Systems in Autonomous Driving
Authors: Ma Xianghua , Hu Kaitao , Sun Xiangyu , Chen Shining
Published: July 2024

Lightweight, high‐performance networks are important in vision perception systems. Recent research on convolutional neural networks has shown that attention mechanisms can significantly improve the network performance. However, existing approaches either ignore the significance of using both types of attention mechanisms (channel and space) simultaneously or increase the model complexity. In this study, we propose the adaptive attention module (AAM), which is a truly lightweight yet effective module that comprises channel and spatial submodules to balance model performance and complexity. The AAM initially utilizes the channel submodule to generate intermediate channel‐refined features. In this module, an adaptive mechanism enables the model to autonomously learn the weights between features extracted by global max pooling and global average pooling to adapt to different stages of the model, thus enhancing performance. The spatial submodule employs a group‐interact‐aggregate strategy to enhance the expression of important features. It groups the intermediate channel‐refined features along the channel dimension into multiple subfeatures for parallel processing and generates spatial attention feature descriptors and channelwise refined subfeatures for each subfeature; subsequently, it aggregates all the refined subfeatures and employs a “channel shuffle” operator to transfer information between different subfeatures, thereby generating the final refined features and adaptively emphasizing important regions. Additionally, AAM is a plug‐and‐play architectural unit that can be directly used to replace standard convolutions in various convolutional neural networks. Extensive tests on CIFAR‐100, ImageNet‐1k, BDD100K, and MS COCO demonstrate that AAM improves the baseline network performance under various models and tasks, thereby validating its versatility.
Assessment of Histopathological Alterations and Oxidative Stress in the Liver and Kidney of Male Rats following Exposure to Aluminum Chloride
Authors: Kadhim Anfal , Ben Slima Ahlem , Alneamah Ghusoon , Makni Mohamed
Published: July 2024

The study aims to investigate the residual and histopathological effects of chronic aluminum chloride (AlCl3) toxicity in the kidney and liver of male rats. After 30‐, 60‐, and 90‐day exposure period, analyses were conducted to assess the toxicity in the kidney and liver. The results showed that the concentration of AlCl3 in the kidney and liver increased significantly in 30‐, 60‐, and 90‐day periods. The effects of oxidative stress on the kidneys and liver were dose‐ and time‐dependent. Levels of malondialdehyde (MDA) significantly increased when exposed to AlCl3 groups. Conversely, the activity of antioxidant parameters, including reduced glutathione (GSH), catalase (CAT), and superoxide dismutase (SOD), significantly decreased in the AlCl3 exposed groups, indicating compromised oxidant mechanisms. Both the kidney and liver exhibited severe tissue damage, including necrosis, fibrosis, and inflammatory cell infiltration, in rats exposed to AlCl3. Kidney sections showed hyperplasia of the epithelial cells lining the renal tubules, resembling finger‐like structures. Liver sections displayed severe lobular hyperplasia and an increase in mitotic figures. Our study suggests that AlCl3 has a detrimental impact on these vital organs and emphasizes the importance of monitoring and mitigating aluminum exposure, particularly where it is present in high concentration.
Circulating Tumor DNA Dynamics Reveal KRAS G12C Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors
Authors: Choi Yoonha , Dharia Neekesh V. , Jun Tomi , Chang Julie , Royer-Joo Stephanie , Yau Kenneth K. , Assaf Zoe J. , Aimi Junko , Sivakumar Smruthy , Montesion Meagan , Sacher Adrian , LoRusso Patricia , Desai Jayesh , Schutzman Jennifer L. , Shi Zhen ,   and the GO42144 study group
Published: July 2024

Purpose:

To inform prognosis, treatment response, disease biology, and KRAS G12C mutation heterogeneity, we conducted exploratory circulating tumor DNA (ctDNA) profiling on 134 patients with solid tumors harboring a KRAS G12C mutation treated with single-agent divarasib (GDC-6036) in a phase 1 study.

Experimental Design:

Plasma samples were collected for serial ctDNA profiling at baseline (cycle 1 day 1 prior to treatment) and multiple on-treatment time points (cycle 1 day 15 and cycle 3 day 1).

Results:

KRAS G12C ctDNA was detectable from plasma samples in 72.9% (43/59) and 92.6% (50/54) of patients with non–small cell lung cancer and colorectal cancer, respectively, the majority of whom were eligible for study participation based on a local test detecting the KRAS G12C mutation in tumor tissue. Baseline ctDNA tumor fraction was associated with tumor type, disease burden, and metastatic sites. A decline in ctDNA level was observed as early as cycle 1 day 15. Serial assessment showed a decline in ctDNA tumor fraction associated with response and progression-free survival. Except for a few cases of KRAS G12C sub-clonality, on-treatment changes in KRAS G12C variant allele frequency mirrored changes in the overall ctDNA tumor fraction.

Conclusions:

Across tumor types, the KRAS G12C mutation likely represents a truncal mutation in the majority of patients. Rapid and deep decline in ctDNA tumor fraction was observed in patients responding to divarasib treatment. Early on-treatment dynamics of ctDNA were associated with patient outcomes and tumor response to divarasib treatment.
A Phase II Study Assessing Long-term Response to Ibrutinib Monotherapy in Recurrent or Refractory CNS Lymphoma
Authors: Grommes Christian , Nandakumar Subhiksha , Schaff Lauren R. , Gavrilovic Igor , Kaley Thomas J. , Nolan Craig P. , Stone Jacqueline , Thomas Alissa A. , Tang Sarah S. , Wolfe Julia , Bozza Alexis , Wongchai Venissala , Hyde Alisson , Barrett Emma , Lynch Elizabeth A. , Madzsar Juli T. , Lin Andrew , Piotrowski Anna F. , Pentsova Elena , Francis Jasmine H. , Hatzoglou Vaios , Schultz Nikolaus , Reiner Anne S. , Panageas Katherine S. , DeAngelis Lisa M. , Mellinghoff Ingo K.
Published: July 2024

Purpose:

Ibrutinib is a first-in-class inhibitor of Bruton tyrosine kinase. We previously reported the safety and short-term antitumor activity of ibrutinib in 20 patients with relapsed or refractory (r/r) primary central nervous system (CNS) lymphoma (PCNSL) or secondary CNS lymphoma (SCNSL).

Patients and Methods:

We enrolled 26 additional patients with r/r PCNSL/SCNSL into the dose-expansion cohort of the trial into a combined cohort of 46 patients (31 with PCNSL and 15 with SCNSL). Patients received ibrutinib at 560 or 840 mg daily in the dose-escalation cohort and ibrutinib at 840 mg daily in the expansion cohort. The median follow-up was 49.9 and 62.1 months for patients with PCNSL and SCNSL, respectively. We sequenced DNA from available tumor biopsies and cerebrospinal fluid collected before and during ibrutinib therapy.

Results:

Tumor responses were observed in 23/31 (74%) patients with PCNSL and 9/15 (60%) patients with SCNSL, including 12 complete responses in PCNSL and 7 in SCNSL. The median progression-free survival (PFS) for PCNSL was 4.5 months [95% confidence interval (CI), 2.8–9.2] with 1-year PFS at 23.7% (95% CI, 12.4%–45.1%). The median duration of response in the 23 PCNSL responders was 5.5 months. The median PFS in SCNSL was 5.3 months (95% CI, 1.3–14.5) with a median duration of response of 8.7 months for the 9 responders. Exploratory biomarker analysis suggests that mutations in TBL1XR1 may be associated with a long-term response to ibrutinib in PCNSL (P = 0.0075). Clearance of ctDNA from cerebrospinal fluid was associated with complete and long-term ibrutinib responses.

Conclusions:

Our study confirms single-agent activity of ibrutinib in r/r CNS lymphoma and identifies molecular determinants of response based on long-term follow-up.
Failure to Rescue After Percutaneous Coronary Intervention: Insights From the National Cardiovascular Data Registry
Authors: Doll Jacob A. , Kataruka Akash , Manandhar Pratik , Wojdyla Daniel M. , Yeh Robert W. , Wang Tracy Y. , Hira Ravi S.
Published: July 2024

BACKGROUND:

Failure to rescue (FTR) describes in-hospital mortality following a procedural complication and has been adopted as a quality metric in multiple specialties. However, FTR has not been studied for percutaneous coronary intervention (PCI) complications.

METHODS:

This is a retrospective study of patients undergoing PCI from the American College of Cardiology National Cardiovascular Data Registry’s CathPCI Registry between April 1, 2018, and June 30, 2021. PCI complications evaluated were significant coronary dissection, coronary artery perforation, vascular complication, significant bleeding within 48 hours, new cardiogenic shock, and tamponade. Secular trends for FTR were evaluated with descriptive analysis, and hospital-level variation and clinical predictors were analyzed with logistic regression.

RESULTS:

Among 2 196 661 patients undergoing PCI at 1483 hospitals, 3.5% had at least 1 PCI complication. In-hospital mortality occurred more frequently following a complication compared with cases without a complication (19.7% versus 1.3%). FTR increased during the study period from 17.1% to 20.1% (
P
<0.001). The median odds ratio for FTR was 1.48 (95% CI, 1.44–1.53) indicating significant hospital-level variation. Spearman rank correlation demonstrated the modest correlation between FTR and in-hospital mortality, 0.525 (
P
<0.001).

CONCLUSIONS:

Major procedural complications during PCI are infrequent, but FTR occurs in roughly 1 in 5 patients following a PCI procedural complication with significant hospital-level variation. Improved understanding of practices associated with low FTR could meaningfully improve patient outcomes following a PCI complication.
Stroke Risk After Emergency Department Treat-and-Release Visit for a Fall
Authors: Kaiser Jed H. , Zhang Cenai , Kamel Hooman , Navi Babak B. , Razzak Junaid , Liberman Ava L.
Published: July 2024

BACKGROUND:

Previous cohort studies of hospitalized patients with a delayed diagnosis of ischemic stroke found that these patients often had an initial emergency department (ED) diagnosis of a fall. We sought to evaluate whether ED visits for a fall resulting in discharge to home (ie, treat-and-release visits) were associated with increased short-term ischemic stroke risk.

METHODS:

A case-crossover design was used to compare ED visits for falls during case periods (0–15, 16–30, 31–90, and 91–180 days before stroke) and control periods (equivalent time periods exactly 1 year before stroke) using administrative data from the Healthcare Cost and Utilization Project on all hospital admissions and ED visits across 10 states from 2016 to 2020. To identify ED treat-and-release visits for a fall and patients hospitalized for acute ischemic stroke, we used previously validated
International Classification of Diseases, Tenth Revision, Clinical Modification
codes. Odds ratios and 95% CIs were calculated using conditional logistic regression.

RESULTS:

Among 90 592 hospitalized patients with ischemic stroke, 5230 (5.8%) had an ED treat-and-release visit for a fall within 180 days before their stroke. Patients with an ED treat-and-release visit for a fall were older (mean age, 74.7 [SD, 14.6] versus 70.8 [SD, 15.1] years), more often female (61.9% versus 53.4%), and had higher rates of vascular comorbidities than other patients with stroke. ED treat-and-release visits for a fall were significantly more common in the 15 days before stroke compared with the 15-day control period 1 year earlier (odds ratio, 2.7 [95% CI, 2.4–3.1]). The association between stroke and a preceding ED treat-and-release visit for a fall decreased in magnitude with increasing temporal distance from stroke.

CONCLUSIONS:

ED treat-and-release visits for a fall are associated with significantly increased short-term ischemic stroke risk. These visits may be opportunities to improve stroke diagnostic accuracy and treatment in the ED.
Ten Years of Operating a Center with Large-Scale Virtual-Reality Installations: Developments and Learnings
Authors: Mayer Elisabeth , Hernández Rubén Jesús García , Kolb Daniel , Dreer Jutta , Müller Simone , Odaker Thomas , Kranzlmüller Dieter
Published: July 2024

Virtual reality (VR) can be found in many fields including art history, education, research, and industry 4.0. Next to lightweight head-mounted displays there are also VR installations, such as immersive 3D screens, large-scale displays, and CAVE systems, that are used in research, scientific visualization, and also the automotive industry. These systems offer high visual quality and collaborative VR experiences for researchers and have long been used in research. We present learnings and insights from ten years of operating and maintaining a visualization center with large-scale immersive displays and installations. Our report aims to answer questions on the benefits and challenges of such a location-based VR center. We broke down the ten years into three phases and discuss the installations themselves, software, and other various developments of the center over time, illustrated with exemplary use cases. Finally, we cover our experiences at the beginning of phase four with the installation of a (novel) LED CAVE and the future (forward-looking) developments we expect for location-based VR centers.
TAMU TRACER: Targeted Mobile Measurements to Isolate the Impacts of Aerosols and Meteorology on Deep Convection
Authors: Rapp Anita D. , Brooks Sarah D. , Nowotarski Christopher J. , Sharma Milind , Thompson Seth A. , Chen Bo , Matthews Brianna H. , Etten-Bohm Montana , Nielsen Erik R. , Li Ron
Published: July 2024

Difficulty in using observations to isolate the impacts of aerosols from meteorology on deep convection often stems from the inability to resolve the spatiotemporal variations in the environment serving as the storm’s inflow region. During the U.S. Department of Energy (DOE) Tracking Aerosol Convection interactions Experiment (TRACER) in June–September 2022, a Texas A&M University (TAMU) team conducted a mobile field campaign to characterize the meteorological and aerosol variability in air masses that serve as inflow to convection across the ubiquitous mesoscale boundaries associated with the sea and bay breezes in the Houston, Texas, region. These boundaries propagate inland over the fixed DOE Atmospheric Radiation Measurement (ARM) sites. However, convection occurs on either or both the continental or maritime sides or along the boundary. The maritime and continental air masses serving as convection inflow may be quite distinct, with different meteorological and aerosol characteristics that fixed-site measurements cannot simultaneously sample. Thus, a primary objective of TAMU TRACER was to provide mobile measurements similar to those at the fixed sites, but in the opposite air mass across these moving mesoscale boundaries. TAMU TRACER collected radiosonde, lidar, aerosol, cloud condensation nuclei (CCN), and ice nucleating particle (INP) measurements on 29 enhanced operations days covering a variety of maritime, continental, outflow, and prefrontal air masses. This paper summarizes the TAMU TRACER deployment and measurement strategy, instruments, and available datasets and provides sample cases highlighting differences between these mobile measurements and those made at the ARM sites. We also highlight the exceptional TAMU TRACER undergraduate student participation in high-impact learning activities through forecasting and field deployment opportunities.
Glucagon-like peptide-1 (GLP-1) receptor agonists for headache and pain disorders: a systematic review
Authors: Halloum Wael , Dughem Yousef Al , Beier Dagmar , Pellesi Lanfranco
Published: July 2024

Background

Glucagon-like peptide-1 (GLP-1) plays a crucial role in metabolic disorders by enhancing insulin secretion, inhibiting glucagon release, and slowing gastric emptying, thereby improving glycemic control. In recent years, GLP-1 role in neuronal pathways has expanded its therapeutic potential. We aim to comprehensively evaluate the relevance of GLP-1 in headache and pain disorders.

Methods

A systematic literature search was conducted on PubMed and Embase (Ovid) databases using the search terms “GLP-1” and “pain”. Animal and human studies published in English language were included. Abstracts, reviews, and articles on other disorders than “pain” were excluded.

Results

The search strategy identified 833 hits, of which 42 studies were included in the final review. The studies were categorized into four groups: inflammatory pain and osteoarthritis, headaches, neuropathic pain and diabetic neuropathy, and visceral pain and irritable bowel syndrome. GLP-1 receptor (GLP-1R) agonists, like liraglutide, have shown analgesic effects by modulating pain hypersensitivity in animal models of inflammatory and neuropathic pain. GLP-1 is involved in migraine mechanisms and GLP-1R agonists are beneficial in individuals with idiopathic intracranial hypertension. Additionally, GLP-1R agonists reduce visceral hypersensitivity and ameliorate symptoms in patients with irritable bowel syndrome.

Conclusions

The therapeutic scope of GLP-1R agonists is expanding beyond traditional metabolic targets, highlighting its potential for headache and pain disorders. Engineering bimodal molecules that integrate GLP-1R agonism with specific pain-related mechanisms may offer innovative therapeutic options.
Multimodal training protocols on unstable rather than stable surfaces better improve dynamic balance ability in older adults
Authors: Rizzato Alex , Bozzato Matteo , Rotundo Luca , Zullo Giuseppe , De Vito Giuseppe , Paoli Antonio , Marcolin Giuseppe
Published: July 2024

Background

There has been growing interest in using unstable devices in training protocols. This study aimed to assess the effectiveness of two multimodal exercise interventions (i.e., on stable and unstable surfaces) on dynamic balance control and lower limb strength in older adults.

Methods

Sixty-two older adults were randomly assigned to two intervention groups (N = 20, stable group; N = 19, unstable group), and to a control group (N = 18). In this single-blinded randomized controlled study, the two intervention groups underwent a 12-week training program twice a week for 45 min, consisting of strength and balance exercises. The stable (ST) group performed the training program over stable surfaces, while the unstable (UNST) group over unstable surfaces. Dynamic balance was assessed by computing the center of pressure (CoP) trajectory while a driven movable platform induced an unexpected perturbation of the base of support. Specifically, we considered the following CoP-related parameters within a 2.5-s temporal window from the beginning of the perturbation: displacement (Area95), mean velocity (Unit Path), anterior–posterior first peak (FP), post perturbation variability (PPV), and maximal oscillations (ΔCoPMax). The dominant quadriceps strength was measured through an isometric maximal voluntary contraction on an instrumented chair.

Results

Four out of five CoP-related parameters (i.e., Area95, Unit Path, ΔCoPMax, and PPV) significantly improved in the UNST group from a minimum of 14.28% (d = 0.44) to a maximum of 52.82% (d = 0.58). The ST group significantly improved only in two (i.e., ΔCoPMax, and PPV) out of five CoP-related parameters with an enhancement of 12.48% (d = 0.68) and 19.10% (d = 1.06). Both intervention groups increased the maximal isometric quadriceps strength (UNST:17.27%, d = 0.69; ST:22.29%, d = 0.98). The control group did not show changes in any of the parameters considered.

Conclusions

Stable surfaces promoted faster increments of muscular strength. Unstable surfaces were more effective in enhancing dynamic balance efficiency. These findings suggested the employment of multimodal training on unstable rather than stable surfaces to potentially lower the incidence of falls in older adults.

Trial registration

NCT 05769361, retrospectively registered 13 March 2023, https://clinicaltrials.gov/study/NCT05769361?lat=45.3661864&lng=11.8209139&locStr=Padova,%20Italy&distance=50&page=11&rank=107.
expHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer
Authors: Lee Jae Jun , Kang Hyun Ju , Kim Donghyo , Lim Si On , Kim Stephanie S. , Kim Gahyun , Kim Sanguk , Lee Jin-Ku , Kim Jinho
Published: July 2024

Background

Homologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data. Although several RNA-seq-based HRD prediction algorithms have been developed, they mainly support cohort-wise classification, thereby yielding HRD status without furnishing an analogous quantitative metric akin to scarHRD. This study introduces the expHRD method, which operates as a novel transcriptome-based framework tailored to n-of-1-style HRD scoring.

Results

The prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example.

Conclusions

Our novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.