bluuebunny/crossref_metadata_2025_split huggingface dataset:
Importin α inhibitors act against the differentiated stages of apicomplexan parasitesPlasmodium falciparumandToxoplasma gondii
Authors: Bhambid Manasi , Walunj Sujata , C. A. Anupama , Jain Shilpi , Mehta Diksha , Wagstaff Kylie , Panda Ashutosh , Jans David A. , Mohmmed Asif , Patankar Swati 
Published: July 2024

AbstractProtozoan parasites of the phylum Apicomplexa, includingPlasmodium falciparumandToxoplasma gondii, cause widespread disease in humans. New drugs and protein targets are required for the treatment of these diseases, particularly therapies targeting multiple stages of the parasite life cycles. Nuclear import, carried out by the transporter’s importin (IMP) α and β subunits, is a valid target for the discovery of lead compounds against these protozoan parasites: small molecules were identified that inhibit interactions between IMPα and nuclear localisation signalsin vitroand also inhibit the growth of the rapidly-dividing stages ofP. falciparumandT. gondii(asexual stages and tachyzoites) in culture. In this report, we add another small molecule (Bay 11-7082) to the panel of inhibitors of IMPα and test the ability of these inhibitors to first, inhibit nuclear transport in the rapidly dividing stages and, next, the maturation of differentiated stages of both parasites. We show that GW5074 and CAPE inhibit nuclear transport in theP. falciparumblood stages, while Bay 11-7085 inhibits nuclear transport inT. gondiitachyzoites. Interestingly, CAPE strongly inhibits gametocyte maturation, the sexual stages ofP. falciparum, and Bay 11-7085 weakly inhibits bradyzoite differentiation, the latent stages ofT. gondii. As differentiation of both these stages is dependent on activation of gene expression, triggered by the nuclear translocation of transcription factors, our work provides a “proof of concept” that targeting nuclear import is a viable strategy for the development of therapeutics against multiple stages of apicomplexan parasites, some of them recalcitrant to existing drugs.
Proton-pump inhibitors increaseC. difficileinfection risk by altering pH rather than by affecting the gut microbiome based on a bioreactor model
Authors: Schumacher Julia , Müller Patrick , Sulzer Johannes , Faber Franziska , Molitor Bastian , Maier Lisa 
Published: July 2024

AbstractClostridioides difficileinfections often occur after antibiotic use, but they have also been linked to proton-pump inhibitor (PPI) therapy. The underlying mechanism—whether infection risk is due to a direct effect of PPIs on the gut microbiome or changes in gastrointestinal pH—has remained unclear.To disentangle both possibilities, we studied the impact of the proton-pump inhibitor omeprazole and pH changes on key members of the human gut microbiome and stool-derived microbial communities from different donorsin vitro. We then developed a custom multiple-bioreactor system to grow a model human microbiome community in chemostat mode and tested the effects of omeprazole exposure, pH changes, and their combination onC. difficilegrowth within this community.Our findings show that changes in pH significantly affect the gut microbial community’s biomass and the abundances of different strains, leading to increasedC. difficilegrowth within the community. However, omeprazole treatment alone did not result in such effects. These findings imply that the higher risk ofC. difficileinfection following proton-pump inhibitor therapy is probably because of alterations in gastrointestinal pH rather than a direct interaction between the drug and the microbiome. This understanding paves the way for reducing infection risks in proton-pump inhibitor therapy.
Pleiotropic regulation of bacterial toxin production and Allee effect govern microbial predator–prey interactions
Authors: Suma Harikumar R. , Stallforth Pierre 
Published: July 2024

AbstractBacteria are social organisms, which are constantly exposed to predation by nematodes or amoebae. To counteract these predation pressures, bacteria have evolved a variety of potent antipredator strategies. Bacteria of the genusPseudomonas, for instance, evade amoebal predation by the secretion of amoebicidal natural products. The soil bacteriumPseudomonas fluorescensHKI0770 produces pyreudione alkaloids that can kill amoebae. Even though the mode of action of the pyreudiones has been elucidated, the spatiotemporal dynamics underlying this predator–prey interaction remain unknown. Using a combination of microscopic and analytical techniques, we elucidated the intricate relationship of this predator−prey association. We used the chromatic bacteria toolbox for intraspecific differentiation of the amoebicide-producing wildtype and the non-producing mutant within microcosms. These allow for variations in nutrient availability and the emergence of predation-evasion strategies of interacting microorganisms. Imaging of the co-cultures revealed that the amoebae initially ingest both the non-producer as well as the toxin-producer cells. The outcomes of predator–prey interactions are governed by the population size and fitness of the interacting partners. We identified that changes in the cell density coupled with alterations in nutrient availability led to a strong Allee effect resulting in the diminished production of pyreudione A. The loss of defence capabilities rendersP. fluorescensHKI0770 palatable to amoebae. Such a multifaceted regulation provides the basis for a model by which predator–prey populations are being regulated in specific niches. Our results demonstrate how the spatiotemporal regulation of bacterial toxin production alters the feeding behaviour of amoebae.
Structural constraints and drivers of molecular evolution in a macromolecular complex; the kinetochore
Authors: Pare Hannah K. , Nguyen Alexandra L. , Pankey M. Sabrina , Cheeseman Iain M. , Plachetzki David C. 
Published: July 2024

ABSTRACTEvolutionary theory suggests that critical cellular structures should be subject to strong purifying selection as protein changes would result in inviability. However, how this evolutionary principle relates to multi-subunit complexes remains incompletely explored. For example, the macromolecular kinetochore complex, which mediates the faithful segregation of DNA during cell division, violates the expectation of purifying selection as subsets of kinetochore proteins exhibit rapid evolution despite its critical role. Here, we developed a multi-level approach to investigate the evolutionary dynamics of the kinetochore as a model for understanding how an essential multi-protein structure can experience high rates of diversifying selection while maintaining function. Our comprehensive approach analyzed 57 kinetochore genes for signatures of purifying and diversifying selection across 70 mammalian species. Intraspecies comparisons of kinetochore gene evolution showed that members of the order Afrotheria experience higher rates of diversifying selection than other mammalian orders. Among individual loci, genes that serve regulatory functions, such as the mitotic checkpoint genes, are conserved under strong purifying selection. In contrast, the proteins that serve as the structural base of the kinetochore, including the inner and outer kinetochore, evolve rapidly across species. We also demonstrated that diversifying selection is targeted to protein regions that lack clear structural predictions. Finally, we identified sites that exhibit corresponding trends in evolution across different genes, potentially providing evidence of compensatory evolution in this complex. Together, our study of the kinetochore reveals a potential avenue by which selection can alter the genes that comprise an essential cellular complex without compromising its function.
2D Transfer Learning for ECG Classification using Continuous Wavelet Transform
Authors: Zhang Wei 
Published: July 2024

AbstractAdvanced deep neural networks, when trained on extensive datasets, can outperform cardiologists in diagnosing cardiac arrhythmias. However, the availability of large-scale training data is often impractical. This study explores the use of transfer learning to identify and classify three ECG patterns. It applies knowledge gained from 2D image classification tasks to the domain of 1D time-series ECG signal classification. The research leverages various deep learning models to classify continuous wavelet transform (2D representations) of ECG signals. The effectiveness of these transferred deep learning models in classifying ECG time-series data is then evaluated.
Enhancing the accuracy of stock return movement prediction in Indonesia through recent fundamental value incorporation in multilayer perceptron
Authors: Agusta Stiven , Rakhman Fuad , Mustakini Jogiyanto Hartono , Wijayana Singgih 
Published: July 2024

PurposeThe study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.Design/methodology/approachThe study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.FindingsIncorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.Research limitations/implicationsThe study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.Practical implicationsGovernments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.Originality/valueStudies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.
Recognizing the importance of collaborative competencies and facilitative leadership in local government in the United States
Authors: Bukalova Dominika 
Published: July 2024

PurposeThis article describes what department directors and chiefs of staff reported when asked about the competencies they need to be effective in addressing on-the-job challenges. The study analyzed the generated data in two different ways to both understand what participants said in their own terms and to determine whether there is a fit between participants’ responses and facilitative leadership theory.Design/methodology/approachTwenty-four semi-structured interviews were conducted with five department directors and seven chiefs of staff in a US city that had a mayor-council form of government. Responses were analyzed in two ways. Initially, coding categories were developed inductively in an effort to employ what anthropologists characterize as an emic or insider perspective. Then the data were recoded from an etic perspective using the theory of facilitative leadership as a conceptual framework.FindingsAlthough participants identified a wide variety of competencies, all participants emphasized the importance of working collaboratively with others, including the members of their teams, the elected official they worked with (and for) and constituents.Originality/valueWhile most studies of facilitative leadership have focused on mayors and city managers, i.e. those at the top of the city administration hierarchy, this study’s focus is on middle managers who are not necessarily thought of as leaders but who must in fact, exercise leadership at least at times. Another relatively unique feature of this paper is its focus on a city that employs a mayor-council form of governance, a type of governance structure that has been underdiscussed in the literature to date.
What does it mean to be engaged? The engagement of student engineers with sustainability: a literature review
Authors: Guerra Aida , Jiang Dan , Du Xiangyun 
Published: July 2024

Purpose
Student engagement has become increasingly significant in sustainability education for engineers because it enables future engineers to develop competencies, knowledge and values relevant to acting for sustainability. Therefore, this paper aims to examine characteristics of student engineer engagement with sustainability and to discuss the meanings of this concept.

Design/methodology/approach
To build a more holistic picture of student engineer engagement with sustainability, this study followed a literature review approach to search, screen and appraise relevant journal articles on this topic. As a result of this research, 30 articles were identified as eligible.

Findings
Based on the theoretical framework for student engagement with sustainability, newly synthesized here, and the content analysis of the 30 papers included in this study, four patterns of engagement were identified: intrapersonal engagement, inter-relational engagement, engagement as connection and disconnection and situated engagement.

Practical implications
This review provides practical recommendations about how to support the engagement of student engineers with sustainability at the levels of the individual, staff, educational programmes and associated curricula. Future research directions are also discussed.

Originality/value
This study contributes a theoretical framework synthesizing student engagement theory with sustainability education. It also describes current characteristics of student engineers’ engagement with sustainability.
Visual servoing method of high voltage capacitor tower maintenance robot in bolt tightening
Authors: Wu Yuze , Liao Jianbin , Liu Liangyu , Yan Yu , Ai Yunfei , Li Yunxiang , Wei Wang 
Published: July 2024

Purpose
This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning due to tilted angles and anti-bird cover occlusion and issues with fast and accurate docking of bolts while the base is moving.

Design/methodology/approach
This paper proposes a visual servoing method for the capacitor tower maintenance robot, including bolt pose estimation and visual servoing control. Bolt pose estimation includes four components: constructing a keypoint detection network to identify the approximate position, precise positioning, rapid prediction and calculation of bolt pose. In visual servoing, an improved position-based visual servoing (PBVS) is proposed, which eliminate steady-state error and enhance response speed during dynamic tracking by incorporating integral and differential components.

Findings
The bolt detection method exhibits high robustness against varying lighting conditions, partial occlusions, shooting distances and angles. The maximum positioning error at a distance of 250 mm is 2.8 mm. The convergence speed of the improved PBVS is 10% higher than that of the traditional PBVS when the base and target remain relatively stationary. When the base moves at a constant speed, the improved method eliminates steady-state error in dynamic tracking. When the base moves rapidly and intermittently, the maximum error of the improved method in the tracking process is 30% smaller than that of traditional PBVS.

Originality/value
This method enables real-time detection and positioning of bolts in an unstructured environment with tilt angles, variable lighting conditions and occlusion by anti-bird covers. An improved PBVS is proposed to enhance its capability in tracking dynamic targets.
Post-fire shear strengths of drilling screws and post-fire transitions of failure modes for screwed connections
Authors: Liu Ying , Ozaki Fuminobu 
Published: July 2024

PurposeThis study was aimed at clarifying the post-fire shear strength of self-drilling screws and the load-bearing capacity of single overlapped screwed connections using steel sheets and self-drilling screws. The self-drilling screws for shear tests were made of high-strength, martensitic-stainless and austenitic stainless-steel bars.Design/methodology/approachShear loading tests were conducted on self-drilling screws to obtain basic information on post-fire shear strength. Tensile tests were conducted on the screwed connections to examine the transition of failure modes depending on the test temperature after experiencing the heating and cooling procedures.FindingsThe post-fire shear strengths and reduction factors of self-drilling screws of each steel grade were quantified. Furthermore, heated temperature-dependent sheet bearing failure, net sheet failure and screw shear failure modes were observed for the screwed connections.Originality/valueThe transition of the failure modes of the screwed connection could be explained using the equations of the post-fire shear strength proposed in this study. The basic experimental data required to evaluate the post-fire shear strength of screws were obtained.
ARIMA-SVR-based risk aggregation modeling in the financial behavior
Authors: Huang Zhangong , Li Huwei 
Published: July 2024

PurposeOnce regional financial risks erupt, they not only affect the stability and security of the financial system in the region, but also trigger a comprehensive financial crisis, damage the national economy, and affect social stability. Therefore, it is necessary to regulate regional financial risks through artificial intelligence methods.Design/methodology/approachIn this manuscript, we scrutinize the loan data pertaining to aggregated regional financial risks and proffer an ARIMA-SVR loan data regression model, amalgamating traditional statistical regression methods with a machine learning framework. This model initially employs the ARIMA model to accomplish historical data fitting and subsequently utilizes the resultant error as input for SVR to refine the non-linear error. Building upon this, it integrates with the original data to derive optimized prediction results.FindingsThe experimental findings reveal that the ARIMA-SVR (Autoregress Integrated Moving Average Model-Support Vector Regression) method advanced in this discourse surpasses individual methods in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) indices, exhibiting superiority to the deep learning LSTM method.Originality/valueAn ARIMA-SVR framework for the financial risk recognition is proposed. This presentation furnishes a benchmark for future financial risk prediction and the forecasting of associated time series data.
Examining trade flow dynamics in the face of deglobalization and decoupling: a comparative analysis of developing and developed nations
Authors: Imran-ur-Rahman Imran-ur-Rahman , Shafi Mohsin , Fauzi Muhammad Ashraf , Fetuu Enitilina 
Published: July 2024

PurposeThis article examines the concepts of “deglobalization” and “decoupling” from the perspectives of developing and developed nations. It also assesses the short-term impacts of globalization, particularly in the context of the COVID-19 pandemic and predicts the long-term effects on global trade and cooperation between nations.Design/methodology/approachPanel data from 85 countries (2000–2022) were utilized. Poisson Pseudo-Maximum Likelihood (PPML) regression analysis was conducted to analyze pre- and post-COVID-19 globalization levels. The analysis focuses on trade patterns and trends, specifically comparing the effects on developing and developed nations.FindingsFirst, there was a slight decline in global trade in 2020 due to COVID-19, followed by recovery in 2021–2022. Second, developing nations experienced more significant trade declines than did developed nations. Third, while US? China trade decreased slightly, China-India and US-India trade increased during the pandemic. These findings suggest that while there may be short-term disruptions, long-term trends indicate resilience in global trade patterns, with shifts in output and new partnerships emerging.Originality/valueThis study contributes to the understanding of deglobalization and decoupling by providing empirical evidence on pre- and post-COVID-19 trade patterns. The findings suggest that while globalization may have short-term effects, it is likely to lead to post-pandemic recovery and strengthened cooperation between developing and developed nations. This research also highlights the importance of developing strategies to manage uncertainty and external shocks in global trade, emphasizing the role of lockdown measures, national security considerations, and trade policies in shaping the future of globalization and decoupling.
Authenticity matters: a nonmarket perspective on the competitive strategy-firm performance nexus
Authors: Parnell John A. 
Published: July 2024

PurposeScholars have underscored the importance of organizational authenticity, but it is unclear how it influences the links among market strategy, and nonmarket strategy (NMS) and firm performance. This study addresses this gap in the literature.Design/methodology/approachA survey of 294 managers in firms based in the United States investigates configurations among competitive strategy (e.g. cost leadership or differentiation), political and social nonmarket strategy (NMS), authenticity, and firm performance.FindingsCost leaders tend to engage in political nonmarket strategy (PNMS), but the interaction does not necessarily improve firm performance. Differentiators are more likely to pursue social nonmarket strategy (SNMS) and perform better, but neither market-nonmarket strategy configuration is inherently optimal.Research limitations/implicationsThe results support market-nonmarket strategy configurations but do not prescribe optimal combinations. However, the sample is cross-sector and employs self-reports for firm performance.Practical implicationsPolitical and social authenticity can enhance firm performance, but nonmarket activity can compromise a firm’s ability to be politically and socially authentic. Authenticity can drive performance, but a firm’s nonmarket activity can compromise its ability to be politically and socially authentic. Firms should view a prospective loss in authenticity as a potential cost of nonmarket activity.Originality/valueThis paper investigates how a firm’s emphasis on market (competitive) strategies, political and social nonmarket strategies, and political and social authenticity impact financial and non-financial performance. It also tests the veracity of two market-nonmarket configurations, cost leadership with political NMS and differentiation with social NMS.