bluuebunny/crossref_metadata_2025_split huggingface dataset:
The efficacy and safety of selective RET inhibitors in RET fusion-positive non-small cell lung cancer: a meta-analysis
Authors: Ke Jun-yi , Huang Shu , Jing Zhi-tao , Duan Min-chao 
Published: August 2023

Abstract
Background
Rearranged during transfection (RET) fusion-positive occurs in approximately 2% of non-small cell lung cancer (NSCLC). This mutation often predicts metastasis risk and poor prognosis, and current mainstream therapies provide limited patient benefit. Selective RET inhibitors Pralsetinib and Selpercatinib are targeted drugs approved by the US Food and Drug Administration for treating RET-mutated tumors. The phase I/II clinical trial results of their treatment of NSCLC have been published. However, the clinical effect of selective RET inhibitors on RET fusion-positive NSCLC remains controversial. Purpose Meta-analysis was performed to investigate the efficacy and safety of selective RET inhibitors in treating RET fusion-positive NSCLC. Methods Qualified literature was searched in Pubmed, Cochrane Library, Embase, and Web of Science. Outcomes included objective response rate (ORR), median progression-free survival (mPFS), disease control rate (DCR), intracranial ORR, and adverse events. Stata 15.1 software was used to analyze the data. Results A total of 8 studies were included in this meta-analysis. The combined results showed that the ORR of patients treated with selective RET inhibitors was 67% (95% confidence interval:0.64 to 0.70, P < 0.01), DCR was 92% (95%CI: 0.91–0.94, P < 0.01), the mPFS was 16.09 months (95%CI: 11.66–20.52, P < 0.01). In treated patients with RET mutation, the intracranial ORR was 86% (95%CI:0.74 ~ 0.96, P < 0.01). ORR in untreated patients was more effective than untreated patients [HR = 0.44 (95%CI: 0.35–0.56, P < 0.01)]. The major adverse events (grade 3–4) are neutropenia (13%) and anaemia (13%). Conclusions Selective RET inhibitors Pralsetinib and Selpercatinib have shown a good effect on RET fusion-positive NSCLC, with a low incidence of adverse events.
Heterogeneous Impacts in Voluntary Agreements: A Changes-in-Changes Approach to the UK Climate Change Agreements
Authors: Mayr Kentaro Florian , Agnolucci Paolo 
Published: August 2023

AbstractThe limited microeconometric evidence on the efficacy of environmental Negotiated Agreements (NAs) is an obstacle to both their introduction and effective design. We help fill this gap by providing evidence on the impact of the second Climate Change Agreements (CCAs) on business electricity consumption and employment. The CCAs are NAs offering a reduction on the Climate Change Levy (CCL), an energy consumption tax, in exchange for commitments to improve energy efficiency. We use the novel changes-in-changes method to account for heterogeneity in treatment effects. Our results indicate that the second CCAs yielded improved outcomes compared to the counterfactual of full CCL with an average reduction of − 4.81% in electricity consumption. They also reveal the importance of allowing for heterogeneity, as the impact on electricity consumption at the identified deciles varied between − 9.33 and 12.54%. This is a marked difference from the first CCAs which were found to increase consumption. The heterogeneity in treatment response is corroborated when extending the study to two large industrial sectors in the sample and when studying firms selecting differing target reporting methods. Confirming the findings from earlier studies of the first scheme, our results indicate a non-statistically significant reduction in employment, about − 4.6% on average, for the second CCAs.
Integrating Biobehavioral and Environmental Components of Developmental Psychopathology via Interpersonal Dynamics: An RDoC-Advancing Model
Authors: Somers Jennifer A. , Ho Tiffany C. , Roubinov Danielle , Lee Steve S. 
Published: August 2023

AbstractAlthough the Research Diagnostic Criteria (RDoC) framework proposes biological and environmental mechanisms intersect in the etiology of psychopathology, there is no guidance on how to define or measure experiences in the environment within the RDoC matrix. Interpersonal dynamics during caregiver–child interactions involve temporal coordination of interacting partners’ biobehavioral functioning; repeated experiences of signaling to caregivers and responding to caregivers’ signals shape children’s subsequent socioemotional and brain development. We begin with a review of the extant literature on caregiver-child dynamics, which reveals that RDoC’s units of analysis (brain circuits, physiology, behavior, and self-report) are inextricably linked with moment-to-moment changes in the caregiving environment. We then offer a proof-of-concept for integrating biobehavioral RDoC units and environmental components via caregiver-child dynamics. Our approach uses dynamic structural equation models to estimate within-dyad dynamics involving arousal, social, cognitive, and negative or positive affective processes based on second-by-second changes in parasympathetic activity (RSA) during a conflict discussion and a positive event-planning task. Our results illustrate variation in parent–child RSA synchrony, suggesting differences depending on the driver (i.e., child- or parent-led) and on the unique and intersecting domains involved (e.g., positive or negative affect valence systems). We conclude with recommendations for conducting robust, methodologically rigorous studies of interpersonal dynamics that advance the RDoC framework and provide a summary of the clinical implications of this research. Examining caregiver-child dynamics during and across multiple dyadic interaction paradigms that differentially elicit key domains of functioning can deepen understanding of how caregiver- and child-led interpersonal dynamics contribute to child psychopathology risk.
Grasping the supremacy of microplastic in the environment to understand its implications and eradication: a review
Authors: Borah Shikha Jyoti , Gupta Abhijeet Kumar , Gupta Akanksha , Bhawna  , Kumar Sanjeev , Sharma Ritika , Kumar Ravinder , Kumar Pramod , Dubey Kashyap Kumar , Kaushik Sandeep , Mishra Ajay Kumar , Kumar Vinod 
Published: August 2023

AbstractOver the last century, accumulation of microplastic has emerged as a greater threat to the environment, plants, microorganisms and even human beings. Microplastics can be intentionally produced for industries such as cosmetics, or they may be unintentionally generated from degradation of bulk plastic debris. Furthermore, mismanagement of plastic waste is a major source of microplastics. When ingested, microplastics can alter several physical, chemical and biological processes in living organisms. Thus, their toxicity silently spreads its roots into the biosphere. Unfortunately, current strategies for the elimination of microplastics are not sufficient for their complete removal and degradation. Therefore, the adoption of green innovative technologies is the first step toward a microplastic-free environment. However, advances for its effective degradation and elimination are hindered by our limited understanding. This literature study investigates microplastic comprehensively, covering their sources, fate, ecological impacts and their effects on biological processes. It includes an analysis of microplastics in Indian rivers, explores methods for its eradication and degradation, emphasizes plastic recycling and offers future recommendations to pave way toward achieving a microplastic-free environment.
Graphical Abstract
Effectiveness of project-based mathematics in first-year high school in terms of learning environment and student outcomes
Authors: Rijken Paul E. , Fraser Barry J. 
Published: August 2023

AbstractThis study of first year high-school students in South Australia focussed on the effectiveness of using an innovative project-based mathematics strategy to improve the classroom learning environment and the student outcomes of enjoyment, academic efficacy and achievement. As well, we investigated the differential effectiveness of project-based mathematics for male and female students. A sample of 284 students from a co-educational private secondary school provided quantitative data about classroom learning environment as assessed with seven scales from the What Is Happening In this Class? (WIHIC), together with data about the three student outcomes assessed with one scale each. Our study also included a mixed-method design involving qualitative information collected through classroom observations and interviews with 20 students and 3 teachers to explain patterns emerging from the analysis of quantitative data.
Transferability prediction among classification and regression tasks using optimal transport
Authors: Hatakeyama Tomoyuki , Wang Xueting , Yamasaki Toshihiko 
Published: August 2023

AbstractTransfer learning is a method for improving generalization performance by training a model for a different task first and then additionally training the pre-learned weights for the target task. However, transferability—the ease with which source task can be effectively transferred to which target task—is often unknown. Existing works proposed methods of measuring the transferability between classification tasks using images and discrete labels, but it cannot be applied to regression tasks. In this work, we investigate transferability among classification and regression tasks, and propose a method for predicting transferability by extending the optimal transport theory. Our transferability prediction model also can be applied to subjective tasks (e.g., aesthetics and memorability), which are usually regression tasks. We show that the appropriate source (pre-training) tasks can be predicted for the chosen target task without conducting actual pre-training and transferring trials. Experimental results demonstrated high prediction accuracy (correlation coefficient of $$\rho =0.791$$

ρ
=
0.791

) and a speed improvement of approximately 300 times compared with the above-mentioned greedy approach.
The evidential future in Italian
Authors: Frana Ilaria , Menéndez-Benito Paula 
Published: August 2023

AbstractThis paper provides a systematic description and analysis of the non-predictive use of the Italian future. Several authors claim that, on this use, the Italian future is an evidential (Squartini 2001, Mari 2010, Eckardt and Beltrama 2019, Frana and Menéndez-Benito 2019). Others argue that the non-predictive future does not directly contribute an evidential signal (e.g., Giannakidou and Mari 2018, Farkas and Ippolito 2022). We side with the evidential camp. From an empirical standpoint, we present the results of a battery of tests that show that the non-predictive future patterns with evidentials cross-linguistically. From a theoretical standpoint, we put forward an analysis that combines a slightly modified version of the proposal for evidentials in Davis et al. (2007) with Schlenker’s (2007) view of expressive content. On this account, the Italian evidential future (i) lowers the quality threshold required to perform a successful speech act (Davis et al. 2007) and (ii) triggers an evidential presupposition relativized to the speaker’s beliefs (modeled after Schlenker’s analysis of expressives). Our treatment of the evidential component as an expressive presupposition opens up a new perspective on the study of evidentiality and highlights the need for further detailed empirical studies exploring the extent to which this perspective is applicable cross-linguistically.
Modulation of Multidrug Resistance Protein 1-mediated Transport Processes by the Antiviral Drug Ritonavir in Cultured Primary Astrocytes
Authors: Arend Christian , Grothaus Isabell L. , Waespy Mario , Ciacchi Lucio Colombi , Dringen Ralf 
Published: August 2023

AbstractThe Multidrug Resistance Protein 1 (Mrp1) is an ATP-dependent efflux transporter and a major facilitator of drug resistance in mammalian cells during cancer and HIV therapy. In brain, Mrp1-mediated GSH export from astrocytes is the first step in the supply of GSH precursors to neurons. To reveal potential mechanisms underlying the drug-induced modulation of Mrp1-mediated transport processes, we investigated the effects of the antiviral drug ritonavir on cultured rat primary astrocytes. Ritonavir strongly stimulated the Mrp1-mediated export of glutathione (GSH) by decreasing the Km value from 200 nmol/mg to 28 nmol/mg. In contrast, ritonavir decreased the export of the other Mrp1 substrates glutathione disulfide (GSSG) and bimane-glutathione. To give explanation for these apparently contradictory observations, we performed in silico docking analysis and molecular dynamics simulations using a homology model of rat Mrp1 to predict the binding modes of ritonavir, GSH and GSSG to Mrp1. The results suggest that ritonavir binds to the hydrophilic part of the bipartite binding site of Mrp1 and thereby differently affects the binding and transport of the Mrp1 substrates. These new insights into the modulation of Mrp1-mediated export processes by ritonavir provide a new model to better understand GSH-dependent detoxification processes in brain cells.
Machine Learning-Based Exploration of the Impact of Move Method Refactoring on Object-Oriented Software Quality Attributes
Authors: Al Dallal Jehad , Abdulsalam Hanady , AlMarzouq Mohammad , Selamat Ali 
Published: August 2023

AbstractRefactoring is a maintenance task that aims at enhancing the quality of a software’s source code by restructuring it without affecting the external behavior. Move method refactoring (MMR) involves reallocating a method by moving it from one class to the class in which the method is used most. Several studies have been performed to explore the impact of MMR on several quality attributes. However, these studies have several limitations related to the applied approaches, considered quality attributes, and size of the selected datasets. This paper reports an empirical study that applies statistical and machine learning (ML) approaches to explore the impact of MMR on code quality. The study overcame the limitations of the existing studies, and this improvement is expected to make the results of this study more reliable and trustworthy. We considered eight quality attributes and thirty quality measures, and a total of approximately 4 K classes from seven Java open-source systems were involved in the study. The results provide evidence that most of the quality attributes were significantly improved by MMR in most cases. In addition, the results show that a limited number of measures, when considered individually, have a significant ability to predict MMR, whereas most of the considered measures, when considered together, significantly contribute to the MMR prediction model. The constructed ML-based prediction model has an area under curve (AUC) value of 96.6%.
Influence of the application of ultrasound on the microstructure of cemented carbide/steel joints brazed with Ag49ZnCuMnNi
Authors: Wojarski L. , Tillmann W. , Ulitzka H. , Ulitzka T. 
Published: August 2023

AbstractThe poor wettability of cemented carbides by molten metals as well as the different material properties compared to steel makes thermal joining process challenging. In this regard, innovative ultrasonic-assisted induction brazing is capable of joining the materials quickly, cost-effectively and without using environmentally hazardous fluxes. Instead, the oxide scales are broken up within the joining process due to ultrasonic-induced cavitation in the molten filler alloy and hence promote the wetting. However, since the use of ultrasound in brazing steel/cemented carbide joints is still insufficiently investigated, there is still a need to explore the fundamental influences of ultrasound on the brazing process. In this work, the effect of ultrasound on the microstructure of the brazed joints and the diffusion depth of different elements were analysed. The two ultrasonic parameters activation time and amplitude were varied during the experiments. Acicular or dendritic structures that have formed in the braze metal without ultrasonic stimulation were transformed into globular structures, when ultrasound was applied. In addition, a non-eutectic silver-rich phase formed, which was not observed in joints manufactured without the use of ultrasound. Furthermore, the use of ultrasound led to increased diffusion between the base material and the filler material as well as the dissolution and distribution of oxides in the edge regions of the brazing joints.
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning Towards Improved Problem Solving
Authors: Shabrina Preya , Mostafavi Behrooz , Abdelshiheed Mark , Chi Min , Barnes Tiffany 
Published: August 2023

AbstractLearning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal-labeled instructional materials in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. The backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic-proof construction. The training session involved backward worked examples (BWE) and problem solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students’ 1) experience with and engagement in learning backward strategy, 2) performance and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in posttest. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.
A multi-layer composite identification scheme of cryptographic algorithm based on hybrid random forest and logistic regression model
Authors: Yuan Ke , Huang Yabing , Du Zhanfei , Li Jiabao , Jia Chunfu 
Published: August 2023

AbstractCryptographic technology can effectively defend against malicious attackers to attack sensitive and private information. The core of cryptographic technology is cryptographic algorithm, and the cryptographic algorithm identification is the premise of in-depth analysis of cryptography. In the cryptanalysis of unknown cryptographic algorithm, the primary task is to identify the cryptographic algorithm used in the encryption and then carry out targeted analysis. With the rapid growth of Internet data, the increasing complexity of communication environment, and the increasing number of cryptographic algorithms, the single-layer identification scheme of cryptographic algorithm faces great challenges in terms of identification ability and stability. To solve these problems, on the basis of existing identification schemes, this paper proposes a new cluster division scheme CMSSBAM-cluster, and then proposes a multi-layer composite identification scheme of cryptographic algorithm using a composite structure. The scheme adopts the method of cluster division and single division to identify various cryptographic algorithms. Based on the idea of ensemble, the scheme uses the hybrid random forest and logistic regression (HRFLR) model for training, and conducts research on a data set consisting of 1700 ciphertext files encrypted by 17 cryptographic algorithms. In addition, two ensemble learning models, hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model and hybrid k-neighbors and random forest (HKNNRF) model are used as controls to conduct controlled experiments in this paper. The experimental results show that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has an accuracy rate close to 100% in the cluster division stage, and the identification results are higher than those of the other two models in both the cluster division and single division stages. In the last layer of cluster division, the identification accuracy of ECB and CBC encryption modes in block cryptosystem is significantly higher than that of the other two classification models by 35.2% and 36.1%. In single division, the identification accuracy is higher than HGBDTLR with a maximum of 9.8%, and higher than HKNNRF with a maximum of 7.5%. At the same time, the scheme proposed in this paper has significantly improved the identification effect compared with the single division identification accuracy of 17 cryptosystem directly and the 17 classification accuracy of 5.9% compared with random classification, which indicates that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has significant advantages in the accuracy of identifying multiple cryptographic algorithms.
Bayesian fused lasso modeling via horseshoe prior
Authors: Kakikawa Yuko , Shimamura Kaito , Kawano Shuichi 
Published: August 2023

AbstractBayesian fused lasso is one of the sparse Bayesian methods, which shrinks both regression coefficients and their successive differences simultaneously. In this paper, we propose a Bayesian fused lasso modeling via horseshoe prior. By assuming a horseshoe prior on the difference of successive regression coefficients, the proposed method enables us to prevent over-shrinkage of those differences. We also propose a Bayesian nearly hexagonal operator for regression with shrinkage and equality selection with horseshoe prior, which imposes priors on all combinations of differences of regression coefficients. Simulation studies and an application to real data show that the proposed method gives better performance than existing methods.
Ball milled Mg/Al hydroxides modified nitrogen-rich biochar for arsenic removal: performance and governing mechanism
Authors: Wang Ming , Yan Jinlong , Diao Yusen , Zhou Xiangqian , Luo Ting , Wang Hui , Quan Guixiang , Sun Xinyu , Wang Jun 
Published: August 2023

AbstractLayered double hydroxides (LDHs) are widely used as effective adsorbents for wastewater treatment due to their simple synthesis, controllable structure, strong stability, large surface area, and large interfacial spacing. In this study, modified-biochar (BMBC) and Mg/Al modified-biochar composite (Mg/Al-BC) were directly prepared using ball milling technology to effectively adsorb As(V), and nitrogen-rich biochar was obtained through pyrolysis using shrimp shells as precursors. Compared to pristine biochar, the oxygen-containing functional groups of Mg/Al-BC increased by 71.9%, and the particle average diameter decreased from 14.26 nm to 12.56 nm. The kinetics and isothermal models of arsenic adsorption were examined in batch experiments to investigate the impacts of pH, temperature, and co-existing anions. The adsorption capacities for As(V) followed the order: Mg/Al-BC > BMBC > BC, with their respective maximum adsorption capacities measured at 22.65, 6.73, and 0.48 mg/g. The arsenic adsorbed onto Mg/Al-BC was dependent on pH and coexisting anions. Precipitation, ion exchange, surface complexation, and electrostatic interaction were the possible governing adsorption mechanisms. Protonation of pyridinic-N/quinone groups in biochar contributed to the electrostatic attraction between arsenic anion and quaternary ammonium cation. Stable reusability indicates that the ball milled Mg/Al-BC composite could be a promising adsorbent for arsenate removal from polluted water.
Graphical Abstract