bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-center evaluation of the Alinity m HR HPV assay with liquid-based cytology cervical specimens in the United States
Authors: Goldstein D. Yitzchak , Yang Tong , Lucic Danijela , Zhang Yan , Cullum Richard , Kostera Joshua , Patel Anami
Published: January 2025

Incorporating molecular testing for human papillomavirus (HPV) into the screening of cervical specimens can improve risk stratification and, in turn, patient management. Infection with a high-risk (HR) HPV genotype is associated with greater risk for persistent infection, viral integration, and progression of cervical neoplasia. Current guidelines consider HPV 16 or HPV 18 clinically actionable with referral to colposcopy; however, 12 Other HR HPV genotypes have been associated with cervical cancer risk, suggesting a benefit of extended genotyping. In this multi-center study, we evaluated the performance of the Alinity m HR HPV assay, which reports HPV 16, 18, and 45 individually and aggregates of HPV 31/33/52/58 and HPV 35/39/51/56/59/66, compared with cobas HPV and Aptima HPV assays, across a variety of cytology result categories. A total of 746 de-identified residual cervical specimens, collected as part of routine cervical cancer screening programs, were tested using Alinity m HR HPV and at least one comparator assay. The overall percent agreement was ≥90.7% for results from the Alinity m HR HPV assay and cobas HPV assays and 90.5% for results from the Alinity m HR HPV and Aptima HPV assay. In patients with any abnormal cytology result, Alinity m identified 78 specimens with non-HPV 16/18 results, underscoring the benefit of detecting additional HR HPV genotypes to guide patient management more accurately. Among specimens with normal cytology, Alinity m detected 14 additional specimens with non-HPV 16/18 genotypes. Extended HR HPV testing can provide additional information to triage patients for appropriate testing and follow-up.

IMPORTANCE

Extended genotyping for high-risk human papillomavirus (HPV) types enhances diagnostic precision by identifying additional oncogenic HPV types beyond 16 and 18 therefore offering a more nuanced risk profile. This more comprehensive detection may aid in identifying persistent infections that are more likely to progress, thereby supporting future risk-based patient management strategies.
Post-transcriptional regulation of aromatic amino acid metabolism by GcvB small RNA in
Escherichia coli
Authors: Kanda Takeshi , Sekijima Toshiko , Miyakoshi Masatoshi
Published: January 2025

Escherichia coli
synthesizes aromatic amino acids (AAAs) through the common pathway to produce the precursor, chorismate, and the three terminal pathways to convert chorismate into Phe, Tyr, and Trp.
E. coli
also imports exogenous AAAs through five transporters. GcvB small RNA post-transcriptionally regulates more than 50 genes involved in amino acid uptake and biosynthesis in
E. coli
, but the full extent of GcvB regulon is still underestimated. This study examined all genes involved in AAA biosynthesis and transport using translation reporter assay and qRT-PCR analysis. In addition to previously verified targets,
aroC
,
aroP
, and
trpE
, we identified new target genes that were significantly repressed by GcvB primarily via the R1 seed region. Exceptionally, GcvB strongly inhibits the expression of
aroG
, which encodes the major isozyme of the first reaction in the common pathway, through direct base pairing between the
aroG
translation initiation region and the GcvB R3 seed sequence. RNase E mediates the degradation of target mRNAs except
aroC
and
aroP
via its C-terminal domain. GcvB overexpression prolongs the lag phase and reduces the growth rate in minimal media supplemented with AAAs and confers resistance to an antibiotic compound, azaserine, by repressing AAA transporters.

IMPORTANCE

E. coli
strains have been genetically modified in relevant transcription factors and biosynthetic enzymes for industrial use in the fermentative production of aromatic amino acids (AAAs) and their derivative compounds. This study focuses on GcvB small RNA, a global regulator of amino acid metabolism in
E. coli
, and identifies new GcvB targets involved in AAA biosynthesis and uptake. GcvB represses the expression of the first and last enzymes of the common pathway and the first enzymes of Trp and Phe terminal pathways. GcvB also limits import of AAAs. This paper documents the impact of RNA-mediated regulation on AAA metabolism in
E. coli
.
Synthesis of Functionalized Spiro[1-benzofuran-2,5'-pyrimidine] Derivatives of 5-Arylidenebarbituric Acids
Authors: Andin A. N. , Engelgardt M. A.
Published: January 2025

Adducts of dimedone and 5-arylidenebarbituric acids react with N-bromosuccinimide in aqueous ethanol to give functionalized spiro[1-benzofuran-2,5'-pyrimidine] derivatives with moderate yields. The reaction of 5-benzylidenebarbituric acid with ethyl acetoacetate in the presence of N-bromosuccinimide provides spiro[pyrimidine-5,6'-furo[2,3-d]pyrimidine].
The filamins of Drosophila
Authors: Mulder Tiara , Johnson Jennifer , González-Morales Nicanor
Published: January 2025

The actin cytoskeleton is a dynamic mesh of filaments that provide structural support for cells and respond to external deformation forces. Active sensing of these forces is crucial for the function of the actin cytoskeleton, and some actin crosslinkers accomplish it. One such crosslinker is filamin, a highly conserved actin crosslinker dimeric protein with an elastic region capable of responding to mechanical changes in the actin cytoskeleton. Filamins are required across various cells and tissues. In Drosophila early and recent studies have provided many details about filamin functions. This review centers on the two Drosophila filamins encoded by the cheerio and jitterbu g genes. We examine the structural and evolutionary aspects of filamin genes in flies, contrasting them with those of other model organisms. Then, we synthesize phenotypic data across diverse cell types. Additionally, we outline the genetic tools available for both genes. We also propose to divide filamins into typical and atypical based on the number of actin-binding domains and their relationship with other filamins.
Vortical waves in a quantum fluid with vector, axial and helical charges. II. Dissipative effects
Authors: Morales-Tejera Sergio , Ambruş Victor E. , Chernodub Maxim N.
Published: January 2025

In this paper, we consider the effect of interactions on the local, average polarization of a quantum plasma of massless fermion particles characterized by vector, axial, and helical quantum numbers. Due to the helical and axial vortical effects, perturbations in the vector charge in a rotating plasma can lead to chiral and helical charge transfer along the direction of the vorticity vector. At the same time, interactions between the plasma constituents lead to the dissipation of the helical charge through helicity-violating pair annihilation (HVPA) processes and of the axial charge through the axial anomaly. We will discuss separately a QED-like plasma, in which we ignore background electromagnetic fields and thus the axial charge is approximately conserved, as well as a QCD-like plasma, where instanton effects lead to the violation of the axial charge conservation, even in the absence of background chromomagnetic fields. The non-conservation of helicity and chirality leads to a gapping of the Helical, Axial, and mixed Axial-Helical vortical waves that prevents their infrared modes from propagating. On the other hand, usual dissipative effects, such as charge diffusion, lead to significant damping of ultraviolet modes. We end this paper with a discussion of the regimes where these vortical waves may propagate.
Metric-affine cosmological models and the inverse problem of the calculus of variations. Part II: Variational bootstrapping of the $$\Lambda $$CDM model
Authors: Ducobu Ludovic , Voicu Nicoleta
Published: January 2025

The method of variational bootstrapping, based on canonical variational completion, allows one to construct a Lagrangian for a physical theory depending on two sets of field variables, starting from a guess of the field equations for only one such set. This setup is particularly appealing for the construction of modified theories of gravity, since one can take lessons from GR for an “educated guess” of the metric field equations; the field equations for the other fields are then fixed by the obtained Lagrangian (up to terms that are completely independent from the metric tensor). In the present paper, we apply variational bootstrapping to determine metric-affine models which are, in a variational sense, closest to the

$$\Lambda $$

Λ

CDM model of cosmology. Starting from an “educated guess” that formally resembles the Einstein field equations with a cosmological “constant” (actually, a scalar function built from the metric and the connection) and a dark matter term, the method then allows to find “corrected” metric equations and to “bootstrap” the connection field equations. Lagrangians obtained via this method, though imposing some restricting criteria, still encompass a wide variety of metric-affine models. In particular, they allow for a subclass of quadratic metric-affine theories restricted to linear terms in the curvature tensor.
QScratch: introduction to quantum mechanics concepts through block-based programming
Authors: Escanez-Exposito Daniel , Rodriguez-Vega Marcos , Rosa-Remedios Carlos , Caballero-Gil Pino
Published: January 2025

This article introduces QScratch, a novel educational tool designed to introduce fundamental quantum concepts and principles. It is an extension of the high-level block-based visual programming language Scratch, developed by the MIT Media Lab. The quantum concepts taught are presented in a simple and illustrative, yet rigorous way. The selection of topics and their adaptation for this project has been made taking into account the huge complexity of the subject, developing specific intuitive blocks to model the quantum behaviours of superposition, entanglement and measurement. A pilot study carried out with a group of 68 students has demonstrated the validity of the software developed as a tool for introducing complex quantum physics concepts. Thus, the proposed tool complements the original Scratch tool, advancing in the construction of Science, Technology, Engineering and Mathematics (STEM) tools that facilitate the introduction of quantum concepts to everyone.
Construction and Evaluation of English Immersive Learning Environment Integrating Virtual Reality and Wearable Devices
Authors: Bian Yuzhu , Li Yanfang
Published: January 2025

A major focus among educators is how to enhance the immersion experience for English language learners. Individual subjectivity commonly affects conventional measurements, such as scales and questionnaires, when evaluating learning immersion. With this study, we aim to remove subjectivity by investigating the relationship between learning immersion and physiological signs in English language learners. 165 college students participated in Virtual Reality (VR) enhanced English learning sessions, during which we captured their self-report on immersion and electroencephalogram (EEG) and photoplethysmography (PPG) data. VR was integrated to create an immersive English-speaking environment, and wearable technology recorded physiological reactions were collected. We suggested a hybrid model that combined the Owl Search Algorithm fused Adaptive k-Nearest Neighbor (OSA-AkNN) with remarkable accuracy to assess students’ learning immersion and performance. This method provided superior learning immersion prediction than conventional machine learning models. Our findings provide an innovative approach to improving language immersion through technology by demonstrating the effectiveness of utilizing physiological data to predict students’ English learning immersion experience.
Application of Algorithm-Driven Visual Communication Strategy in Advertising Design
Authors: Liu Zhenyan
Published: January 2025

In today’s fast-paced digital era, advertising design heavily depends on advanced visual communication techniques to capture and maintain consumer attention. By leveraging dynamic visuals, brands can effectively engage diverse audiences and create long-lasting connections with consumers. Traditional advertising design often lacks effectiveness due to subjective judgments. The challenge lies in combining aesthetics with audience engagement using algorithmic techniques for improved visual communication. The objective of this study is to explore the application of algorithm-driven visual communication strategies in advertising design, focusing on enhancing the effectiveness of visual content by aligning with audience preferences and behaviors. The collected data undergo preprocessing using the normalization technique. Feature extraction is performed using convolutional neural networks (CNNs) to analyze advertising background images, allowing for the selection of suitable visuals that resonate with the target audience. This study proposed an intelligent moth flame-optimized malleable-gated recurrent units (IMF-MGRU) method that synthesizes textual information to generate effective product taglines, enhancing the expressiveness of the advertising image. Malleable-gated recurrent units (MGRU) are utilized to create relevant taglines that align with the selected visuals, while the intelligent moth flame (IMF) optimizes the layout of elements within the image, minimizing overlap among key components. Experimental findings show that the suggested method enhances the appeal of advertising content, particularly highlighting considerable advantages in performance during the evaluation experiment. The proposed IMF-MGRU method represents a significant advancement in synthesizing visual and textual elements, resulting in cohesive and compelling advertising designs.
New Energy Access Distribution Network Line Loss Management Based on Deep Learning and Fuzzy Algorithm
Authors: Lijun Wang , Jingbian Wang , Qi Ren , Dongwei Li
Published: January 2025

This paper introduces a novel method for managing line losses in energy access distribution networks, utilizing deep learning and fuzzy algorithms. As new energy sources become more integrated into distribution networks, traditional line loss management techniques struggle to address the complexities and uncertainties that arise. To tackle this issue, a comprehensive model for line loss management is developed, leveraging the predictive strengths of deep learning alongside the fuzzy algorithm’s capability to handle uncertainty. The deep learning model forecasts the operational conditions of the power grid following the integration of new energy. At the same time, the fuzzy algorithm assesses and adjusts potential line losses, facilitating effective management of distribution network line losses. The model is trained and validated using a comprehensive dataset from the IEEE 39-Bus System, with performance metrics like Accuracy, F1-score, and recall rate evaluated against traditional models like CNN and GAT. The results show that the FGAN model achieves an accuracy of 96.32%, an F1-score of 95.17%, and a recall rate of 97.05%, significantly outperforming baseline models. Experimental findings indicate that this proposed method substantially reduces line losses and improves the efficiency and reliability of the distribution network. This research offers a fresh perspective on line loss management for intelligent distribution networks in the context of new energy integration, demonstrating considerable practical value.
Generalized manev potential in the perturbed circular restricted four interacting bodies hill problem with asteroids belt
Authors: Prasad Sada Nand
Published: January 2025

This paper starts with the determination of equations of motion and Jacobian integral of the fourth smallest body in the perturbed circular restricted four interacting bodies configuration. Then by the use of proper scale and transformations in the obtained equations of motion, we develop the equations of motion and Jacobian integral for the perturbed hill four-body configuration. Further, we analyze numerically the motion properties such as potential surfaces, equilibrium points, zero-velocity curves, Poincaré surfaces of sections with trajectories allocation, basins of attracting domain and stability of the equilibrium points of the fourth smallest body in both the above said cases.
Comparative study of degree and neighborhood degree sum-based topological indices for predicting physicochemical properties of skin cancer drug structures
Authors: Arockiaraj Micheal , Godlin J. J. Jeni , Radha S.
Published: January 2025

Quantitative structure property relationship (QSPR) is essential in rational drug design by facilitating the prediction, optimization, and prioritization of potential drug candidates based on their chemical structures and properties, optimizing the utilization of computational resources and minimizing costs. Topological indices play a fundamental role in QSPR studies by providing a quantitative representation of molecular structures and facilitating the prediction of various chemical properties and activities. Metastatic skin cancer can be aggressive and potentially fatal, and thus developing effective drugs can improve patient outcomes, including overall survival rates and progression-free survival. This research work focuses on the structural behaviors of medications used in the treatment of skin cancer, including binimetinib, fisetin, encorafenib, picato, fluorouracil, trametinib, vemurafenib, imiquimod, odomzo, vismodegib, dacarbazine, cobimetinib, dabrafenib, sesamol, curcumin, doxorubicin, temozolomide, paclitaxel, itraconazole, and hyaluronan. We have developed QSPR models involving degree and neighborhood degree sum-based indices and conducted a comparative analysis to highlight the efficacy of the models.
Diversity of soliton dynamics, positive multi-complexiton solutions and modulation instability for (3+1)-dimensional extended Kairat-X equation
Authors: Rafiq Muhammad Naveed , Rafiq Muhammad Hamza , Alsaud Huda
Published: January 2025

This study explores the dynamics of a [Formula: see text]-dimensional extended Kairat-X equation, revealing its connections to differential geometry of curves and equivalence principles. Using the Hirota bilinear method and linear superposition principle (LSP), we derived lump-periodic solutions, higher-order solitons with bifurcations, resonant multi-soliton waves, and positive complexiton solutions. To highlight the significance of these results, we present 3D, density and contour plots for specific parameter values. Also, we analyzed the modulation instability (MI) by determining the stability region and gain spectrum. This novel work offers valuable insights into the integrable model introduced by Wazwaz, and provides algorithms for investigating newly developed systems related to plasma physics, optical communications, marine dynamics, and the geometry of curves.
Design and Development of Schema for Schemaless Databases
Authors: Mandale Ashwini , Sharma Neeraj
Published: January 2025

A schema database functions as a repository for interconnected data points, facilitating comprehension of data structures by organizing information into tables with rows and columns. These databases utilize established connections to arrange data, with attribute values linking related tuples. This integrated approach to data management and distributed processing enables schema databases to maintain models even when the working set size surpasses available RAM. However, challenges such as data quality, storage, scarcity of data science professionals, data validation, and sourcing from diverse origins persist. Notably, while schema databases excel at reviewing transactions, they often fall short in updating them effectively. To address these issues, a Chimp-based radial basis neural model (CbRBNM) is employed. Initially, the Schemaless database was considered and integrated into the Python system. Subsequently, compression functions were applied to both schema and schema-less databases to optimize relational data size by eliminating redundant files. Performance validation involved calculating compression parameters, with the proposed method achieving memory usage of 383.37[Formula: see text]Mb, a computation time of 0.455[Formula: see text]s, a training time of 167.5[Formula: see text]ms, and a compression rate of 5.60%. Extensive testing demonstrates that CbRBNM yields a favorable compression ratio and enables direct searching on compressed data, thereby enhancing query performance.
A Systematic Review on Website Phishing Attack Detection for Online Users
Authors: Deshpande Kirti Vasantrao , Singh Jaibir
Published: January 2025

Phishing is the criminal effort to steal delicate information such as account details, passwords, usernames, credit, and debit card details for malicious use. Phishing fraud might be the most popular cybercrime used today. The online and webmail payment sectors are highly affected by phishing attacks. Nowadays, attackers create many techniques that pave the way for them to steal all personal information from the selected victims easily. However, numerous anti-phishing techniques are used to detect the phishing attack, such as blacklist, visual similarity, heuristic detection, Deep Learning (DL), and Machine Learning (ML) techniques. ML techniques are more efficient at detecting phishing attacks, and these techniques also rectify the drawbacks of existing approaches. This paper provides a detailed review of various phishing techniques, encompassing phishing mediums, phishing vectors, and numerous technical approaches. Also, new research works are analyzed to detect phishing websites using heuristic, visual similarity, DL, and ML models. Neural Networks (NNs), Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machine (SVM), fuzzy logic methods, Long Short-Term Memory (LSTM) techniques, Random Forest (RF), Decision Tree (DT), Adaboost –Extra Tree (AET) classifiers based on ML models are examined in this review paper.
Copy-Move Image Multiple Forgery Detection Based on Transit Flow Regime Algorithm-Enabled ShuffleNet
Authors: Chaitra B. , Bhaskar Reddy P. V.
Published: January 2025

The copy-move forgery is considered one among difficult kinds of image forgeries that must be detected. This occurs by duplicating image parts or portions and thereafter adding up again in an image by itself but in other locations. The forgery detection techniques are utilized in image protection while an actual image is not obtainable. The current forgery detection methods detect tampered areas with lesser effectiveness owing to larger size as well as low contrast of images. Here, transit flow regime algorithm-based ShuffleNet (TFRA-ShuffleNet) is presented for multiple forgery detection. In this work, the input image considered is given to multiple object detection. The multiple object detection is carried out in an input image utilizing YOLO V3. After that, features are extracted from object detected image that include local vector pattern (LVP), local optimal-oriented pattern (LOOP), pyramid histogram of oriented gradients (PHoG), local Gabor XOR patterns (LGXP), local directional pattern (LDP), local directional ternary pattern (LDTP) and local binary pattern (LBP). Lastly, multiple forgery detection is conducted employing ShuffleNet. The ShuffleNet is trained to employ TFRA, which is an integration of transit search (TS) and flow regime algorithm (FRA). Furthermore, TFRA-ShuffleNet achieved maximal accuracy, TPR and TNR values of about 96.5%, 96.5% and 97.5%.
Ataxia telangiectasia in a Bahraini child treated with intensive physiotherapy: A case report
Authors: Razzaqi Fatima , Albastaki Aysha , Sinan Israa
Published: January 2025

Ataxia telangiectasia (AT) is a rare neurodegenerative condition with a prevalence of 1 in 40,000 to 1 in 300,000 worldwide. It involves a genetic mutation of chromosome 11q.26. The condition is inherited in an autosomal recessive manner causing atrophy of the cerebellum due to loss of Purkinje fibres. AT presents early in childhood and the clinical features depend on the type of mutation. The study is a case report of a rare genetic disorder of a 9-year-old female who came to the physiotherapy clinic with a diagnosis of AT. The patient was presented with progressively worsening gait problems with frequent falls, with complete dependence on assistance and impaired balance and coordination. The treatment program was 12 months divided into an intense physiotherapy program for two months followed by 10 months of two times per week of physiotherapy sessions. The program was divided into four elements which are: (1) Lifestyle changes, (2) Strengthening exercises, (3) Coordination exercises, and (4) Balance training exercises. The result showed a positive outcome in increasing the patient’s independence, increased muscle strength, reduced ataxia symptoms intensity, and the patient can carry out complex activities with the help of accessory orthosis devices.
Distinguishing LLM-generated from Human-written Code by Contrastive Learning
Authors: Xu Xiaodan , Ni Chao , Guo Xinrong , Liu Shaoxuan , Wang Xiaoya , Liu Kui , Yang Xiaohu
Published: January 2025

Large language models (LLMs), such as ChatGPT released by OpenAI, have attracted significant attention from both industry and academia due to their demonstrated ability to generate high-quality content for various tasks. Despite the impressive capabilities of LLMs, there are growing concerns regarding their potential risks in various fields, such as news, education, and software engineering. Recently, several commercial and open-source LLM-generated content detectors have been proposed, which, however, are primarily designed for detecting natural language content without considering the specific characteristics of program code. This paper aims to fill this gap by proposing a novel ChatGPT-generated code detector, CodeGPTSensor, based on a contrastive learning framework and a semantic encoder built with UniXcoder. To assess the effectiveness of CodeGPTSensor on differentiating ChatGPT-generated code from human-written code, we first curate a large-scale Human and Machine comparison Corpus (HMCorp), which includes 550K pairs of human-written and ChatGPT-generated code (i.e., 288K Python code pairs and 222K Java code pairs). Based on the HMCorp dataset, our qualitative and quantitative analysis of the characteristics of ChatGPT-generated code reveals the challenge and opportunity of distinguishing ChatGPT-generated code from human-written code with their representative features. Our experimental results indicate that CodeGPTSensor can effectively identify ChatGPT-generated code, outperforming all selected baselines.
Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation
Authors: Wang Chong , Zhang Jian , Feng Yebo , Li Tianlin , Sun Weisong , Liu Yang , Peng Xin
Published: January 2025

Recent code large language models (LLMs) have shown promising performance in generating standalone functions. However, they face limitations in repository-level code generation due to their lack of awareness of
repository-level dependencies
(
e.g.,
user-defined attributes), resulting in
dependency errors
such as undefined-variable and no-member errors. In this work, we introduce
ToolGen
, an approach that integrates autocompletion tools into the code LLM generation process to address these dependencies.
ToolGen
comprises two main phases: Trigger Insertion and Model Fine-tuning (Offline), and Tool-integrated Code Generation (Online). During the offline phase,
ToolGen
augments functions within a given code corpus with a special mark token, indicating positions to trigger autocompletion tools. These augmented functions, along with their corresponding descriptions, are then used to fine-tune a selected code LLM. In the online phase,
ToolGen
iteratively generates functions by predicting tokens step-by-step using the fine-tuned LLM. Whenever a mark token is encountered,
ToolGen
invokes the autocompletion tool to suggest code completions and selects the most appropriate one through constrained greedy search.

We conduct comprehensive experiments to evaluate
ToolGen
’s effectiveness in repository-level code generation across three distinct code LLMs: CodeGPT, CodeT5, and CodeLlama. To facilitate this evaluation, we create a benchmark comprising 671 real-world code repositories and introduce two new dependency-based metrics:
Dependency Coverage
and
Static Validity Rate
. The results demonstrate that
ToolGen
significantly improves
Dependency Coverage
by 31.4% to 39.1% and
Static Validity Rate
by 44.9% to 57.7% across the three LLMs, while maintaining competitive or improved performance in widely recognized similarity metrics such as BLEU-4, CodeBLEU, Edit Similarity, and Exact Match. On the CoderEval dataset,
ToolGen
achieves improvements of 40.0% and 25.0% in test pass rate (Pass@1) for CodeT5 and CodeLlama, respectively, while maintaining the same pass rate for CodeGPT.
ToolGen
also demonstrates high efficiency in repository-level code generation, with latency ranging from 0.63 to 2.34 seconds for generating each function. Furthermore, our generalizability evaluation confirms
ToolGen
’s consistent performance when applied to diverse code LLMs, encompassing various model architectures and scales.
Automated Recommendation of Extracting Local Variable Refactorings
Authors: Jiang Yanjie , Chi Xiaye , Zhang Yuxia , Ji Weixing , Li Guangjie , Wang Weixiao , Xia Yunni , Zhang Lu , Liu Hui
Published: January 2025

Extracting local variable refactoring is frequently employed to replace one or more occurrences of a complex expression with simple accesses to a newly introduced variable. To facilitate the refactoring, most IDEs can automate the extract local variable refactorings when the to-be-extracted expressions are selected by developers. However, refactoring tools usually replace all expressions that are lexically identical to the selected one without a comprehensive analysis of the safety of the refactoring. The automatically conducted refactorings may lead to serious software defects. Besides that, existing refactoring tools rely heavily on software developers to spot to-be-extracted expressions although it is often challenging for inexperienced developers and maintainers to make the selection. To this end, in this paper, we propose an automated approach, called
ValExtractor+
, to recommending extract local variable refactoring opportunities and to automatically and safely conduct the refactorings.
ValExtractor+
is composed of two parts, i.e., solutionAdvisor and opportunityAdvisor. Given a to-be-extracted expression, solutionAdvisor leverages lightweight static source code analysis to validate potential side effects of the expression, and to identify expressions that could be extracted together with the selected expression as a single variable without changing the semantics of the program or introducing any new exceptions. The static code analysis significantly improves the safety in automated extraction of local variables. To free programmers from manually selecting to-be-extracted expressions, opportunityAdvisor leverages solutionAdvisor to automatically retrieve all expressions that could be extracted safely as well as their refactoring solutions. It then leverages a learning-based classifier to predict which of the retrieved expressions should be extracted. Evaluations on open-source applications suggest that
solutionAdvisor
successfully avoided all defects (more than two hundred) caused by extracting local variable refactorings conducted by
Eclipse
(243 defects) or
IntelliJ IDEA
(263 defects). Additionally,
opportunityAdvisor
was able to effectively recommend expressions for extraction, achieving 307 true positives (TP) and 21,121 true negatives (TN). Four pull requests from our work (PR IDs:  
66
,
333
,
439
, and
360
) were successfully merged into the Eclipse community repository, showcasing the practical impact and robustness of our approach as recognized by the wider developer community.
Impact of Race/Ethnicity on Clinical and Genomic Characteristics, Trial Participation, and Genotype-Matched Therapy among Patients with Metastatic Breast Cancer
Authors: Sood Rupali , Niemierko Andrzej , Ryan Lianne , Spring Laura , Moy Beverly , Bardia Aditya , Vidula Neelima
Published: January 2025

Background: Race/ethnicity may affect outcomes in metastatic breast cancer (MBC) due to biological and social determinants. We evaluated the impact of race/ethnicity on clinical, socioeconomic, and genomic characteristics, clinical trial participation, and receipt of genotype-matched therapy among patients with MBC. Methods: A retrospective study of patients with MBC who underwent cell-free DNA testing (cfDNA, Guardant360â, 74 gene panel) between 11/2016 and 11/2020 was conducted. Receipt of genotype-matched therapy targeted at a cfDNA actionable mutation was determined. Pearson's chi-squared and Wilcoxon rank-sum tests were used to compare categorical and continuous variables between groups. Multivariable logistic regression was used to assess the association of race and receiving matched therapy. Results: 425 patients with MBC and cfDNA results were identified (White: 369, Black: 27, Hispanic 15, and Asian 14). White patients traveled further for cancer care than other groups (p<0.001). White patients had the highest rates of commercial insurance, Black patients had the highest rates of state-supported insurance, and Asian patients had the highest uninsured rates (p<0.001). Clinical trial enrollment did not differ by race/ethnicity (p=0.34). The proportion of patients with ≥1 actionable mutation in cfDNA did not vary by race/ethnicity (p=0.18). The highest rates of matched therapy were observed in White patients (p<0.001). After multivariable logistic regression adjusting for subtype, commercial vs. other insurance, Charlson comorbidity index, and distance to center, White patients remained more likely to receive matched therapy (p=0.024). Conclusions: Racial/ethnic minority patients were less likely to receive matched therapy. Further research is needed to identify barriers to precision medicine.
Figure 2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
Authors: Kurian Nikhil Cherian , Gann Peter H. , Kumar Neeraj , McGregor Stephanie M. , Verma Ruchika , Sethi Amit
Published: January 2025

Schematic overview of the method for quantifying subtype heterogeneity in held-out whole-slide H&E images from TCGA-BRCA cohort. Conv-layer, convolutional layer; SSGCE, sample-specific generalized cross-entropy.
Figure 1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
Authors: Kurian Nikhil Cherian , Gann Peter H. , Kumar Neeraj , McGregor Stephanie M. , Verma Ruchika , Sethi Amit
Published: January 2025

Diagram depicting the allocation of breast cancer WSIs for analysis. Phase A: filtering of TCGA images. Phase B: 680 cases were divided by assigned subtype, and then subsets with transcriptomically pure subtype adherence were randomly split into training, validation, and test subgroups. Phase C: Pure cases for each subtype were held out for initial testing. The final test set used all LumA cases that were held out, representing the full range of LumA transcriptomic purity. IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma.
Reproductive outcomes in infertile women with endometriosis undergoing ART
Authors: Benlioglu Can , Telek Savci Bekir , Ata Baris
Published: January 2025

Background
Endometriosis-related infertility and its treatment with assisted reproductive technologies (ART) have been broadly researched. Yet, underlying mechanisms of infertility, particularly in the absence of tubal dysfunction, remain unclear. While the impact of inflammatory milieu on the ovary and/or endometrium has been indicated as a contributing factor, recent evidence from euploid transfers and donor cycles questions the extent of these effects. Moreover, the frequent coexistence of other confounders, such as adenomyosis, further complicates the clinical picture, making it difficult to isolate the specific impact of endometriosis on ART outcomes.
Objectives
To evaluate the influence of endometriosis on various aspects of ART, including oocyte competence, ART success and whether surgical or medical treatments improve these.
Methods
We primarily focused on recent high-quality sources, including systematic reviews, large-scale observational studies, and meta-analyses, to ensure a robust and reliable synthesis of the available evidence.
Outcome
While oocyte yield can decrease in the presence of an endometrioma or history of endometrioma excision, oocyte quality, early embryo development indicators, aneuploidy rates, and clinical outcomes of endometriosis patients do not differ from other infertility diagnoses in ART settings. Surgical treatments and hormonal suppression before ART do not seem to improve outcomes. Ovarian stimulation for ART does not seem to exacerbate endometriosis.
Conclusions and Outlook
Endometriosis, despite its high prevalence among infertile patients, does not inherently impair ART success, except in cases where ovarian reserve is compromised due to ovarian disease or its surgical treatment. The causal link between endometriosis and infertility remains an enigma, and future studies should continue to explore this association with other confounding factors.
Neural Associations between Inhibitory Control and Counterintuitive Reasoning in Science and Maths in Primary School Children
Authors: Palmer Lucy R. J. , Sumanapala Dilini K. , Mareschal Denis , Dumontheil Iroise ,   the UnLocke Team
Published: January 2025

Emerging evidence suggests that inhibitory control (IC) plays a pivotal role in science and maths counterintuitive reasoning by suppressing incorrect intuitive concepts, allowing correct counterintuitive concepts to come to mind. Neuroimaging studies have shown greater activation in the ventrolateral and dorsolateral pFCs when adults and adolescents reason about counterintuitive concepts, which has been interpreted as reflecting IC recruitment. However, the extent to which neural systems underlying IC support science and maths reasoning remains unexplored in children. This developmental stage is of particular importance, as many crucial counterintuitive concepts are learned in formal education in middle childhood. To address this gap, fMRI data were collected while fifty-six 7- to 10-year-olds completed counterintuitive science and math problems, plus IC tasks of interference control (Animal Size Stroop) and response inhibition (go/no-go). Univariate analysis showed large regional overlap in activation between counterintuitive reasoning and interference control, with more limited activation observed in the response inhibition task. Multivariate similarity analysis, which explores fine-scale patterns of activation across voxels, revealed neural activation similarities between (i) science and maths counterintuitive reasoning and interference control tasks in frontal, parietal, and temporal regions, and (ii) maths reasoning and response inhibition tasks in the precuneus/superior parietal lobule. Extending previous research in adults and adolescents, this evidence is consistent with the proposal that IC, specifically interference control, supports children's science and maths counterintuitive reasoning, although further research will be needed to demonstrate the similarities observed do not reflect more general multidemand processes.
Bilingualism Is Associated with Significant Structural and Connectivity Alterations in the Thalamus in Adulthood
Authors: Ayyildiz Behcet , Sayman Dila , Ayyildiz Sevilay , Oktem Ece Ozdemir , Arslan Ruhat , Colak Tuncay , Bamac Belgin , Yulug Burak
Published: January 2025

Language is a sophisticated cognitive skill that relies on the coordinated activity of cerebral cortex. Acquiring a second language creates intricate modifications in brain connectivity. Although considerable studies have evaluated the impact of second language acquisition on brain networks in adulthood, the results regarding the ultimate form of adaptive plasticity remain inconsistent within the adult population. Furthermore, due to the assumption that subcortical regions are not significantly involved in language-related tasks, the thalamus has rarely been analyzed in relation to other language-relevant cortical regions. Given these limitations, we aimed to evaluate the functional connectivity and volume modifications of thalamic subfields using magnetic resonance imaging (MRI) modalities following the acquisition of a second language. Structural MRI and fMRI data from 51 participants were collected from the OpenNeuro database. The participants were divided into three groups: monolingual (ML), early bilingual (EB), and late bilingual (LB). The EB group consisted of individuals proficient in both English and Spanish, with exposure to these languages before the age of 10 years. The LB group consisted of individuals proficient in both English and Spanish, but with exposure to these languages after the age of 14 years. The ML group included participants proficient only in English. Our results revealed that the ML group exhibited increased functional connectivity in all thalamic subfields (anterior, intralaminar-medial, lateral, ventral, and pulvinar) compared with the EB and LB groups. In addition, a significantly decreased volume of the left suprageniculate nucleus was found in the bilingual groups compared with the ML group. This study provides valuable evidence suggesting that acquiring a second language may be protective against dementia, due to its high plasticity potential, which acts synergistically with cognitive functions to slow the degenerative process.
School structure explains age group differences in friend selection similarity
Authors: Selover Molly , Leggett-James Mary Page , Kaniušonytė Goda , Laursen Brett
Published: January 2025

The present study tests the hypothesis that friend selection similarity increases after the transition into middle school but only for those attending schools with an expanded pool of similar others who could potentially serve as friends. Participants included 456 (247 girls, 209 boys) students (ages 10–15) attending public primary and middle schools in Florida (USA) and Lithuania. In Florida, 40.2% were European American, 26.5% were Hispanic-American, 22.6% were African American; the remainder had other backgrounds. Most Lithuania students were ethnic Lithuanian. Florida and Lithuania primary school students attended classes with the same 22–24 classmates. Florida middle school students attended classes with a rotating group of students, drawn from 78 to 89 potential classmates. Lithuania middle school students attended classes with the same 21–30 classmates. Participants completed peer nomination surveys twice during a single academic year, from which 344 (Florida n = 168, Lithuania n = 176) future-friend dyads [i.e., partners who reciprocally nominated one another as friends in January (Time 2) but not in September/November (Time 1)] were identified. Within-dyad intraclass partial correlations indicated that Florida middle school future friends were more similar than primary school future friends on peer-reported academic achievement, acceptance, athleticism, and popularity (but not on disruptiveness or rejection). In Lithuania, there were no differences between primary school and middle school future-friend similarity. The findings suggest that previously identified developmental increases in friend similarity can be traced, in part, to structural changes in schools, such that middle schools with rotating class rosters provide students with more options to identify compatible others than primary schools.
Gendered punishment? How the corruption of female politicians affects public opinion of female political leadership
Authors: Woo Byung-Deuk , Kim Mi-son , Osborn Tracy
Published: January 2025

Does the corruption of a woman politician decrease public support for other women political leaders? To answer this question, this article examines how the impeachment of Park Geun-hye, the first female president of South Korea, affects people’s perceptions of women’s political leadership in South Korea. We argue that in South Korea, the high-level political corruption scandal of the first woman president led to lower evaluations of women’s political leadership because of the intense and gendered media coverage of Park’s failure. Using the two waves of the World Values Survey conducted in South Korea before and after Park Geun-hye’s impeachment in 2017, we show that Park’s impeachment negatively affects people’s views of female political leaders. This negative effect is conditioned by party identification. Our work suggests that the effects of gendered evaluations of leaders’ corruption can extend beyond the evaluation of the leaders themselves, thus affecting women’s representation more generally.
Traffic Flow Prediction Based on Dynamic Time Slot Graph Convolution
Authors: Chen Hongwei , Wang Han , Chen Zexi
Published: January 2025

Flow prediction, a critical component of intelligent transportation systems, is essential for travel planning and traffic control. However, existing methods often struggle with inflexible sharing patterns and difficulty capturing dynamic global temporal dependencies. To address these issues, this paper proposes a traffic flow prediction model based on dynamic time-gap graph convolution (DTGCN). The DTGCN model achieves parameter sharing and cross-layer independence through independent and shared modules, enabling the utilization of distinct patterns between layers while capturing stable patterns across layers. Additionally, the paper introduces a novel method for constructing a dynamic time slot graph by viewing historical time slots as nodes, effectively modeling the ever-changing temporal interactions. Lastly, a new temporal convolution module is designed to capture flexible global temporal dependencies. Experimental results on two widely used traffic network datasets, METR-LA and PEMS-BAY, demonstrate the effectiveness of the proposed model.
Assessing pre-service teachers’ willingness to receive formal training in integrating Indigenous knowledge into science education
Authors: Anor Charity Esenam
Published: January 2025

The study focused on assessing pre-service teachers’ willingness to receive formal training specifically related to integrating Indigenous knowledge into the teaching of science to promote culturally responsive science education. The study was conducted at the University of Education, Winneba, Ghana. Participants were 248 purposely sampled level 400 undergraduate pre-service science teachers in the Department of Integrated Science Education. Quantitative and qualitative data were gathered through an online survey questionnaire. The quantitative responses were analysed using descriptive and inferential statistics, while qualitative data were thematised using both inductive and deductive strategies. The pre-service teachers expressed openness to receiving formal training indicating a positive response. The study recommends that the pre-service science curriculum should include formal training on integrating Indigenous knowledge by adopting a comprehensive approach that involves resource development, cultural sensitivity training, addressing biases, designing of courses and targeted modules that include pedagogical training.