Research Track Research Articles

Hedging in Medical Articles from Two Pandemics

As academic and scientific disciplines continue to evolve, it remains essential for scholars to present their claims with caution. Hedging, a vital linguistic tool, is pervasive in academic writing, although its boundaries are not strictly defined. This research tracks the changes in the use of hedging within medical literature, underpinning the idea that linguistic patterns reflect societal changes. Our study focuses on the analysis of hedging devices within a corpus of 30 medical articles, spanning two distinct pandemic periods: the 1918-1919 influenza pandemic and the 2020-2021 COVID-19 pandemic. A comprehensive review, involving contextual analysis, was conducted for each article to identify hedging instances. Types of hedges were documented and their frequency was calculated, while ambiguous cases were clarified through in-depth discussions and consistency checks. Our analysis confirms that contextual conditions influence both the frequency and types of hedges used. The results show a significant decrease in overall hedging frequency between the two pandemic periods, with approximators declining sharply while shields remained stable. This shift, along with a reduction in the variety of hedging devices used, suggests an evolution towards more precise quantification and a more formulaic style in scientific writing, while maintaining caution in knowledge claims. The conclusions drawn in this paper contribute to our understanding of scientific discourse and its evolution.

Hedging in Medical Articles from Two Pandemics

As academic and scientific disciplines continue to evolve, it remains essential for scholars to present their claims with caution. Hedging, a vital linguistic tool, is pervasive in academic writing, although its boundaries are not strictly defined. This research tracks the changes in the use of hedging within medical literature, underpinning the idea that linguistic patterns reflect societal changes. Our study focuses on the analysis of hedging devices within a corpus of 30 medical articles, spanning two distinct pandemic periods: the 1918-1919 influenza pandemic and the 2020-2021 COVID-19 pandemic. A comprehensive review, involving contextual analysis, was conducted for each article to identify hedging instances. Types of hedges were documented and their frequency was calculated, while ambiguous cases were clarified through in-depth discussions and consistency checks. Our analysis confirms that contextual conditions influence both the frequency and types of hedges used. The results show a significant decrease in overall hedging frequency between the two pandemic periods, with approximators declining sharply while shields remained stable. This shift, along with a reduction in the variety of hedging devices used, suggests an evolution towards more precise quantification and a more formulaic style in scientific writing, while maintaining caution in knowledge claims. The conclusions drawn in this paper contribute to our understanding of scientific discourse and its evolution.

Evolution of Green Finance: Mapping Its Role as a Catalyst for Economic Growth and Innovation

This scientometric study analyzes the evolving landscape and outlook of green finance as a driver of economic innovation and growth, highlighting key trends and influential research within this critical field. A dataset of 371 publications was compiled from the Scopus and Web of Science databases and analyzed using VOSviewer, Bibliometrix, and Voyant tools to map the research landscape. By systematically reviewing the scientific literature, this research tracks the development of green finance’s role as a catalyst for economic innovation and growth, identifying trending topics, key studies, and major contributors through bibliometric and scientometric methods. The analysis reveals a growing interdisciplinary approach, integrating environmental, social, and political dimensions into green finance research. Keyword analysis identified three primary thematic clusters: (1) green finance and innovation, (2) economic growth, carbon neutrality, and fintech, and (3) renewable energy and urbanization. This study provides a comprehensive overview of the field and aims to guide future research while contributing to ongoing debates on the role of green finance in fostering economic innovation and sustainable growth.

Faculty perspectives on the changing research and teaching career tracks of academics at private universities in Kazakhstan

ABSTRACT Since gaining academic and financial autonomy in 2018, a number of leading private universities in Kazakhstan have separated teaching and research into two distinct career tracks and implemented outcome-based faculty performance evaluation. This initiative aimed to increase the quality of both teaching and research, boost faculty’s research productivity, and improve the competitiveness of these universities in the local higher education market. However, despite being central to the development of universities, the restructuring of academic careers in Kazakhstan has received little scholarly attention. This study employs the framework of managerialism and collegiality to explore how faculty in research-track and teaching-track positions perceive and respond to the new career structures. Drawing upon the thematic analysis of 25 semi-structured interviews with faculty members working in the country’s top private universities, the study provides nuanced insights into the changing career structures in Kazakhstan by examining faculty experiences related to research and teaching, faculty evaluation and promotion, development of research culture, and work environment. By interpreting the study findings in light of the concepts of managerialism and collegiality, the study offers a fresh perspective on the neoliberal trends in university governance in the context where universities historically lacked autonomy from the state. The paper concludes by discussing the potential implications of the findings for improving the conditions of research-track and teaching-track faculty. The study may be of interest to policymakers, university administrators, and researchers concerned with changes in academic careers in transitional economies.

From the Lab to Librarianship: Advancing Open Science

Three library professionals share their perspectives on transitioning from STEM research to librarianship. A STEM PhD or postdoctoral researcher can bring technical expertise and a deep understanding of researcher culture to library services and at the same time put their skill sets to use in unexpected and interesting ways. A postdoctoral position focused on an emerging area such as open science can provide the perfect opportunity for libraries to leverage the unique skill sets of doctoral students who are increasingly exploring careers beyond research track faculty.

2024: A "nucleoid space" odyssey featuring H-NS.

The three-dimensional architecture of the bacterial chromosome is intertwined with genome processes such as transcription and replication. Conspicuously so, that the structure of the chromosome permits accurate prediction of active genome processes. Although appreciation of this interplay has developed rapidly in the past two decades, our understanding of this subject is still in its infancy, with research primarily focusing on how the process of transcription regulates and is regulated by chromosome structure. Here, we summarize the latest developments in the field with a focus on the interplay between chromosome structure and transcription in Escherichia coli (E. coli) as mediated by H-NS-a model nucleoid structuring protein. We describe how the organization of chromosomes at the global and local scales is dependent on transcription, and how transcription is regulated by chromosome structure. Finally, we take note of studies that highlight our limited knowledge of structure-function relationships in the chromosome, and we point out research tracks that will improve our insight in the topic.

An Analysis on the Reform and Innovation Path of Research-based Teaching of Philosophy Major Courses—Taking Jaeggi's Critical Theory of Life Form as an Example

With the advent of the era of artificial intelligence, the traditional humanities are facing unprecedented challenges and impacts, especially philosophy as a strong foundation, without the characteristics of practical function, facing a particularly severe disciplinary development dilemma. This topic aims at the problems faced by the construction of philosophy professional courses in local colleges and universities, and focuses on the topic of Marxism for graduate professional courses Taking Jaeggi 's critical theory of life form as an example, this paper attempts to explore new teaching methods from daily professional teaching practice, fundamentally changing the traditional single teaching mode of basic philosophy theory courses, overcoming the mechanical rigidity of the binary teaching and research between teachers and students, so as to correct the poor interaction between teachers and students In particular, it has a good effect on stimulating students' initiative and originality of thought, and promotes students to enter the research track as soon as possible. In order to fundamentally form a philosophy academic community with clear boundaries, it provides research exploration experiments in philosophy teaching.

PACMMOD Volume 2 Issue 4: Editorial

We are pleased to present the 4th issue of Volume 2 of PACMMOD. This issue contains papers that were submitted to the SIGMOD research track in January 2024. Papers accepted in this issue have been invited for presentation in the research track of the ACM SIGMOD Conference on Management of Data 2025, to be held in Berlin, Germany. We look forward to seeing you in Berlin from the 22nd to the 27th June, 2025.

Bringing Artificial Intelligence to Research Analytics: Research Highlighter-MatchMaker

It is more significant to identify accurate grant opportunities given the increasing challenges in securing grants these days. Combining Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques, we have developed Research Highlighter-MatchMaker to facilitate the grant application process. The tool aims to provide an overview of research strength and to recommend matched funding opportunities as well as potential collaborators for researchers based on their research track records. It has the potential to expand to provide matching for on-campus researchers and industrial partners. It will also include a chatbot to facilitate the research administration infrastructure and process on campus.

Juvenile responses to immune challenges are not carried through to subsequent life stages in an insect

Environmental variability can significantly impact individual survival and reproduction. Meanwhile, high population densities can lead to resource scarcity and increased exposure to parasites and pathogens. Studies with insects can offer valuable insights into eco-immunology, allowing us to explore the connections between these variables. Here we use the moth Anticarsia gemmatalis to examine how increases in population density and immunological challenge during the larval stage shape its investment in immune defence and reproduction. Larvae reared at a high population density exhibited greater lytic activity against bacteria compared to those reared at low density, whilst bacterial challenge (i.e. bacteria-immersed needles) also increased lytic activity. There was no interaction between the variables population density and bacterial challenge, indicating that these are independent. Surprisingly, neither increase in lytic activity carried through to activity in prepupal haemolymph. Rearing of larvae at a high density delayed pupation and decreased pupal weight. The immunological stimulus did not significantly influence pupal development. Lower population density as a larva resulted in greater adult weight, but did not significantly influence lytic activity in the eggs or the number of eggs laid. Negative correlations were found between lytic activity in the eggs and the number of eggs, as well as between adult weight and the number of eggs. Overall, this study demonstrates that high population density and immune challenge trigger increased lytic activity in caterpillars, but this effect is transient, not persisting into later stages. The trade-offs observed, such as delayed pupation and reduced prepupal weights under high density, suggest a balancing act between immune investment and developmental aspects. The findings hint at a short-term adaptive response rather than a sustained strategy. The implications of delayed pupation and smaller adult moths could influence the moth's life history strategy, impacting its role in the ecosystem. Further research tracking larval immune investment and subsequent reproductive success will unveil the evolutionary dynamics of this relationship in changing environments.

Implementing mobile eye tracking in psychological research: A practical guide

Eye tracking provides direct, temporally and spatially sensitive measures of eye gaze. It can capture visual attention patterns from infancy through adulthood. However, commonly used screen-based eye tracking (SET) paradigms are limited in their depiction of how individuals process information as they interact with the environment in “real life”. Mobile eye tracking (MET) records participant-perspective gaze in the context of active behavior. Recent technological developments in MET hardware enable researchers to capture egocentric vision as early as infancy and across the lifespan. However, challenges remain in MET data collection, processing, and analysis. The present paper aims to provide an introduction and practical guide to starting researchers in the field to facilitate the use of MET in psychological research with a wide range of age groups. First, we provide a general introduction to MET. Next, we briefly review MET studies in adults and children that provide new insights into attention and its roles in cognitive and socioemotional functioning. We then discuss technical issues relating to MET data collection and provide guidelines for data quality inspection, gaze annotations, data visualization, and statistical analyses. Lastly, we conclude by discussing the future directions of MET implementation. Open-source programs for MET data quality inspection, data visualization, and analysis are shared publicly.

Efficient cytosolic delivery of proteins enabled by modular fusion protein

The development of protein-based biotechnology and drugs holds tremendous potential for various applications. However, the effective intracellular delivery of natural proteins remains a challenge due to their inability to spontaneously penetrate the cell membrane. In this study, we have developed a modular fusion protein sequence that overcomes this limitation and enables efficient cytoplasmic protein delivery . This modular fusion protein we designed has demonstrated efficient lysosomal escape ability, allowing for successful delivery of various protein cargoes into the cytoplasm of different cell types. Importantly, the fusion with the mCherry tag does not affect the biological activity of the protein. The fusion protein can still exert its catalytic activity when delivered into cells. We also designed a fluorescent protein probe capable of precisely targeting lysosomes, providing powerful fluorescent probe tools for intracellular localization and tracking in biomedical research. This approach contributes to the advancement of protein drug development and opens up new possibilities for therapeutic interventions.

Unpacking the role of AI ethics online education for science and engineering students

BackgroundAs artificial intelligence (AI) technology rapidly advances, it becomes imperative to equip students with tools to navigate through the many intricate ethical considerations surrounding its development and use. Despite growing recognition of this necessity, the integration of AI ethics into higher education curricula remains limited. This paucity highlights an urgent need for comprehensive ethics education initiatives in AI, particularly for science and engineering students who are at the forefront of these innovations. Hence, this research investigates the role of an online explicit-reflective learning module in fostering science and engineering graduate students' ethical knowledge, awareness, and problem-solving skills. The study’s participants included 90 graduate students specializing in diverse science and engineering research tracks. Employing the embedded mixed-methods approach, data were collected from pre- and post-intervention questionnaires with closed-ended and open-ended questions.ResultsThe study's results indicate that the online explicit-reflective learning module significantly enhanced students' knowledge of AI ethics. Initially, students exhibited a medium–high level of perceived ethical awareness, which saw a modest but statistically significant enhancement following the participation. Notably, a more distinct increase was observed in students' actual awareness of ethical issues in AI, before and after the intervention. Content analysis of students’ responses to the open-ended questions revealed an increase in their ability to identify and articulate concerns relating to privacy breaches, the utilization of flawed datasets, and issues of biased social representation. Moreover, while students initially displayed limited problem-solving abilities in AI ethics, a considerable enhancement in these competencies was evident post-intervention.ConclusionsThe study results highlight the important role of explicit-reflective learning in preparing future professionals in science and engineering with the skills necessary for ethical decision-making. The study highlights the need for placing more emphasis not only on students’ ability to identify AI-related ethical issues but also on their capacity to resolve and perhaps mitigate the impact of such ethical dilemmas.

KineWheel-DeepLabCut Automated Paw Annotation Using Alternating Stroboscopic UV and White Light Illumination.

Uncovering the relationships between neural circuits, behavior, and neural dysfunction may require rodent pose tracking. While open-source toolkits such as DeepLabCut have revolutionized markerless pose estimation using deep neural networks, the training process still requires human intervention for annotating key points of interest in video data. To further reduce human labor for neural network training, we developed a method that automatically generates annotated image datasets of rodent paw placement in a laboratory setting. It uses invisible but fluorescent markers that become temporarily visible under UV light. Through stroboscopic alternating illumination, adjacent video frames taken at 720 Hz are either UV or white light illuminated. After color filtering the UV-exposed video frames, the UV markings are identified and the paw locations are deterministically mapped. This paw information is then transferred to automatically annotate paw positions in the next white light-exposed frame that is later used for training the neural network. We demonstrate the effectiveness of our method using a KineWheel-DeepLabCut setup for the markerless tracking of the four paws of a harness-fixed mouse running on top of the transparent wheel with mirror. Our automated approach, made available open-source, achieves high-quality position annotations and significantly reduces the need for human involvement in the neural network training process, paving the way for more efficient and streamlined rodent pose tracking in neuroscience research.

The Constitution in the Public Mind

Scholars have long held that the Constitution occupies a sacred and reverent place in the public mind. Recent research tracking respondents across a 45-year period reports that almost all of these persons either continued to hold a mythical view of the Constitution or switched to this viewpoint later in life. These results are important and tell us how a group of mature individuals (their average age was 66) currently view the Constitution and how these views changed across time, but they don't address what the Constitution might mean to young adults today who have grown up in a much different political environment. The current study, using the same Q sample as that in the longitudinal studies referred to, provides answers to that question, and examines the beliefs about the Constitution of a cohort of younger adults. The results indicate a good deal of similarity to the results of previous research, but some noteworthy differences.

The ACM Web Conference 2024 Report

The 33rd edition of The Web Conference was successfully held from May 13 to 17, 2024 at Resorts World Convention Centre, Singapore. It was held as in-person conference that also featured rich virtual elements. The first two days of the five-day event featured workshops, tutorials, special days, and symposia. The last three days were the main conference days dedicated to keynote speeches, parallel presentation sessions of research, industry and special tracks, as well as multiple poster and demo sessions. This brief report provides an overview with organization and program details and statistics from Conference Chairs and Program Committee Chairs. Further details can be found at https://www2024.thewebconf.org/. At the conclusion of this report, we express our deep gratitude to authors, volunteers, SIGWEB, steering committee, sponsors and supporters.

Global thermospheric mass density monitoring using LEO constellations: Simulation and analysis

Neutral thermospheric density is a crucial parameter for orbital tracking and upper atmosphere research. Here, we present a method to monitor global neutral thermospheric density testing simulations in various space environments with various satellite configurations and altitudes. The simulation results at 1° × 1° pixel resolution show that (1) within the range of 300–600 km orbital altitudes, the monitoring accuracy improves with the increase of the number of satellites, but this gain is significantly attenuated when the number of satellites increases to approximately 1,000; (2) the monitoring accuracy decreases with the increase of the altitude; and (3) the method presented in this study exhibits robustness under different space weather conditions. Furthermore, a differentiated satellite usage strategy (High Tightness and Low Looseness, HTLL) is developed to reduce the number of satellites used. Preliminary results indicate that the use of the HTLL method can reduce the number of satellites used from 2,592 to 1,044, whereas the maximum average relative deviation (ARD) does not exceed 1 %. The methods presented in this study have potential for future applications in the development of thermosphere research and satellite tracking.

Video Coding for Machines: Compact Visual Representation Compression for Intelligent Collaborative Analytics.

As an emerging research practice leveraging recent advanced AI techniques, e.g. deep models based prediction and generation, Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. With the rapid advances of deep feature representation and visual data compression in mind, in this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established including feature-assisted coding, scalable coding, intermediate feature compression/optimization, and machine vision targeted codec, from broader perspectives of vision tasks, analytics resources, etc. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks.

Solid-State Electromechanical Smart Material Actuators for Pumps—A Review

Solid-state electromechanical smart material actuators are versatile as they permit diverse shapes and designs and can exhibit different actuation modes. An important advantage of these actuators compared to conventional ones is that they can be easily miniaturized to a sub-millimeter scale. In recent years, there has been a great surge in novel liquid pumps operated by these smart material actuators. These devices create opportunities for applications in fields ranging from aerospace and robotics to the biomedical and drug delivery industries. Although these have mainly been prototypes, a few products have already entered the market. To assist in the further development of this research track, we provide a taxonomy of the electromechanical smart material actuators available, and subsequently focus on the ones that have been utilized for operating pumps. The latter includes unidirectional shape memory alloy-, piezoelectric ceramic-, ferroelectric polymer-, dielectric elastomer-, ionic polymer metal composite- and conducting polymer-based actuators. Their properties are reviewed in the context of engineering pumps and summarized in comprehensive tables. Given the diverse requirements of pumps, these varied smart materials and their actuators offer exciting possibilities for designing and constructing devices for a wide array of applications.

Generative AI unlocks PET insights: brain amyloid dynamics and quantification.

Studying the spatiotemporal patterns of amyloid accumulation in the brain over time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography (PET) imaging plays a pivotal role because it allows for the visualization and quantification of abnormal amyloid beta (Aβ) load in the living brain, providing a powerful tool for tracking disease progression and evaluating the efficacy of anti-amyloid therapies. Generative artificial intelligence (AI) can learn complex data distributions and generate realistic synthetic images. In this study, we demonstrate for the first time the potential of Generative Adversarial Networks (GANs) to build a low-dimensional representation space that effectively describes brain amyloid load and its dynamics. Using a cohort of 1,259 subjects with AV45 PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we develop a 3D GAN model to project images into a latent representation space and generate back synthetic images. Then, we build a progression model on the representation space based on non-parametric ordinary differential equations to study brain amyloid evolution. We found that global SUVR can be accurately predicted with a linear regression model only from the latent representation space (RMSE = 0.08 ± 0.01). We generated synthetic PET trajectories and illustrated predicted Aβ change in four years compared with actual progression. Generative AI can generate rich representations for statistical prediction and progression modeling and simulate evolution in synthetic patients, providing an invaluable tool for understanding AD, assisting in diagnosis, and designing clinical trials. The aim of this study was to illustrate the huge potential that generative AI has in brain amyloid imaging and to encourage its advancement by providing use cases and ideas for future research tracks.