Research Tool Research Articles

Why VR? Exploring the Intersection of Virtual Reality and Information Science

ABSTRACTThis 90‐minute panel is structured to provide a space for open conversation among panelists and the ASIST community to consider the role and impact of virtual reality (VR) in teaching and research in information science. In the panel, we will address two key areas, including the current landscape of VR related research in information science and why VR is a valuable tool for teaching and research. The panel will begin with speakers briefly discussing their areas of expertise. From there, the panelists will facilitate open discussion using guiding questions and open dialogue with the audience. Finally, the panel will conclude with hands‐on sessions where audience members can try out several different types of VR headsets, projects, and games.

Establishment of a genetically amenable fibroblast cell line from Atlantic salmon skin

The Atlantic salmon, Salmo Salar, is a societally important species of fish, both as a food source and as a component of aquatic biosphere. Its sustainable production is hampered by a wide range of infectious diseases, which is difficult to address due to the lack of in vitro tools to study the disease-host interaction. In this paper, we describe the establishment and characterization of a homogenous Atlantic salmon skin fibroblast (ASSF) cell line. This immortalized cell line grows well in standard media formulations and is capable of migration. It is transcriptionally stable over dozens of passages, and its transcriptome is distinct from other publicly available Atlantic salmon cell lines (SHK1 and ASK). Even though ASSF cells show limited cytopathic effects when challenged with Infectious Pancreatic Necrosis Virus (IPNV) molecular evidence reveals that they are infected and support IPNV production, especially compared to other cell lines like ASK or SHK1. The potential of the ASSF cell line as a tool for Atlantic salmon research is highlighted by its permissibility to genetic manipulation with various methods including CRISPR/cas9, transfection and transduction.

A Data-driven Spectral Model of Main-sequence Stars in Gaia DR3

Precise spectroscopic classification of planet hosts is an important tool of exoplanet research at both the population and individual system level. In the era of large-scale surveys, data-driven methods offer an efficient approach to spectroscopic classification that leverages the fact that a subset of stars in any given survey has stellar properties that are known with high fidelity. Here, we use The Cannon, a data-driven framework for modeling stellar spectra, to train a generative model of spectra from the Gaia Data Release 3 Radial Velocity Spectrometer (RVS). Our model derives stellar labels with precisions of 72 K in T eff, 0.09 dex in logg, 0.06 dex in [Fe/H], 0.05 dex in [α/Fe], and 1.9 km s−1 in v broad for main-sequence stars observed by Gaia DR3 by transferring GALAH labels, and is publicly available at https://github.com/isabelangelo/gaiaspec. We validate our model performance on planet hosts with available Gaia RVS spectra at SNR>50 by showing that our model is able to recover stellar parameters at ≥20% improved accuracy over the existing Gaia stellar parameter catalogs, measured by the agreement with high-fidelity labels from the Spectroscopic Observations of Cool Stars survey. We also provide metrics to test for stellar activity, binarity, and reliability of our model outputs and provide instructions for interpreting these metrics. Finally, we publish updated stellar labels and metrics that flag suspected binaries and active stars for Kepler Input Catalog objects with published Gaia RVS spectra.

Low-dose radiation research insights in experimental animals: A gateway to therapeutic implications

In recent years, a significant research effort has been underway to explore the effects of low-dose radiation (LDR). Animal models play a key role in various fields of research, including biomedicine, pharmaceutical, environmental, and behavioral studies. The use of animal models has been an invaluable tool in radiation research for understanding radiation biology, assessing radiation risks, and developing strategies for radiation protection and medical management. In the present review, the initial part focuses on the deleterious effects of high-dose radiation, and in correlation to that, in the later part of the review, the emphasis has been given to experimental approaches to explore the beneficial effects of LDR using animal models. This review could help explore the innovative approach for future research targeting the therapeutic role of LDR in various diseases, including depression, Cancer, Parkinson’s disease, and Alzheimer’s disease. Keywords: animal model, high dose radiation, low dose radiation, mice, rat.

Predicting Survival Among Colorectal Cancer Patients: Development and Validation of Polygenic Survival Score.

Colorectal cancer is the second leading cause of cancer-related death in the United States. A multi-omics approach has contributed in identifying various cancer-specific mutations, epigenetic alterations, and cells response to chemotherapy. This study aimed to determine the factors associated with colorectal cancer survival and develop and validate a polygenic survival scoring system (PSS) using a multi-omics approach. Data were obtained from the Cancer Genome Atlas (TCGA). Colon Adenocarcinoma (TCGA-COAD) data were used to develop a survival prediction model and PSS, whereas rectal adenocarcinoma (TCGA-READ) data were used to validate the PSS. Cox proportional hazards regression analysis was conducted to examine the association between the demographic characteristics, clinical variables, and mRNA gene expression. Overall accuracy of PSS was also evaluated. The median overall survival for TCGA-COAD patients was 7 years and for TCGA-READ patients was 5 years. The multivariate Cox proportional hazards model identified age, cancer stage, and expression of nine genes as predictors of colon cancer survival. Based on the median PSS of 0.38, 48% of TCGA-COAD patients had high mortality risk. Patients in the low risk group had significantly higher 5-year survival rates than those in the high group (p <0.0001). The PSS demonstrated a high overall accuracy in predicting colorectal cancer survival. This study integrated clinical and transcriptome data to identify survival predictors in patients with colorectal cancer. PSS is an accurate and valid measure for estimating colorectal cancer survival. Thus, it can serve as an important tool for future colorectal cancer research.

The industrial technological spillover effect and differentiation characteristics of industrial chains in the ecological efficiency of the yellow river basin

The Yellow River Basin (YRB) stands as a critical ecological barrier and a significant economic hub, bridging the western and eastern regions of China. To investigate the synergy of ecoefficiency within the industrial chain of the YRB, this paper introduces a novel research approach. By establishing an industrial technology similarity matrix, spatial econometric models can be employed to analyze spillover effects, which provides a new tool for related research and overcomes some drawbacks of previous studies. The study reveals that industrial ecoefficiency exhibits distinct interaction characteristics across different spatial dimensions. Specifically, the interaction effect within the province is notably superior to that observed within the YRB, as well as in the broader region encompassing the adjacent provinces of the YRB. The upstream and downstream provinces demonstrate a robust level of interaction within their local scopes. However, the middle reaches of provinces exhibit a weak link that undermines the spillover capacity of industrial ecoefficiency across the entire YRB. This finding suggests that hierarchical policy measures should be devised according to the characteristics of spillover effects across various spatial dimensions.

Snorkel-tag based affinity chromatography for recombinant extracellular vesicle purification.

Extracellular vesicles (EVs) are lipid nanoparticles and play an important role in cell-cell communications, making them potential therapeutic agents and allowing to engineer for targeted drug delivery. The expanding applications of EVs in next generation medicine is still limited by existing tools for scaling standardized EV production, single EV tracing and analytics, and thus provide only a snapshot of tissue-specific EV cargo information. Here, we present the Snorkel-tag, for which we have genetically fused the EV surface marker protein CD81, to a series of tags with an additional transmembrane domain to be displayed on the EV surface, resembling a snorkel. This system enables the affinity purification of EVs from complex matrices in a non-destructive form while maintaining EV characteristics in terms of surface protein profiles, associated miRNA patterns and uptake into a model cell line. Therefore, we consider the Snorkel-tag to be a widely applicable tool in EV research, allowing for efficient preparation of EV standards and reference materials, or dissecting EVs with different surface markers when fusing to other tetraspanins in vitro or in vivo.

DEVELOPMENT OF AN EXPERT SYSTEM BASED ON FUZZY LOGIC FOR EARLY DIAGNOSIS OF GLAUCOMA

Nowadays, decision-making systems that rely on images are becoming increasingly crucial, especially in the medical field. Images have become a fundamental tool for clinical research and diagnosing illnesses. In the case of glaucoma, a disease that can damage the optic nerve head and result in irreversible vision loss, a new Fuzzy Expert System has been developed for early diagnosis. Original ONH images are preprocessed with filters to remove noise, followed by using the Canny detector algorithm to detect contours. Key parameters are then extracted by identifying elliptical forms of the optic disc and excavation using the Randomized Hough Transform. A classification algorithm based on fuzzy logic is used to assess patients' conditions, taking into account both instrumental parameters and risk factors such as age, race, and family history. The system is tested on a dataset of ophthalmologic images, showing a significant improvement in predictions compared to existing methods, with over 96% accuracy in identifying cases suspected to have glaucoma.

From words to senses: A sense-based approach to quantitative polysemy detection across disciplines

Interdisciplinary polysemy refers to the phenomenon in which a word is understood and used differently across disciplines or fields. Nuanced variations in word meanings pose a significant challenge to effective communication and knowledge integration, especially in academic and professional settings. To address these issues, we propose the Interdisciplinary Polysemy Detection (IPD) method, which streamlines the identification of polysemous words by making the process more automatic, objective and efficient. IPD not only identifies polysemous words but also quantifies the extent of semantic variation within and across disciplines, providing deeper insights into the scale and nature of meaning shifts in different contexts. The evaluation results demonstrate that IPD can identify interdisciplinary polysemy effectively, showing heightened sensitivity to subtle differences in word senses. This sensitivity makes IPD a useful tool for both researchers and educators, helping to improve communication across fields and contributing to the development of more tailored pedagogical resources. The broader implications of this method may extend to enhancing the understanding of semantic variation in interdisciplinary research and advancing knowledge transfer between specialised domains.

ANTIPASTI: Interpretable prediction of antibody binding affinity exploiting normal modes and deep learning.

The high binding affinity of antibodies toward their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a convolutional neural network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of normal mode correlation maps derived from elastic network models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the normal mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.

Structured pruning of neural networks for constraints learning

In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity in applications such as cancer treatment, algorithmic configuration, and chemical process optimization. This integration often uses Mixed Integer Programming (MIP) formulations to represent the chosen ML model, that is often an Artificial Neural Networks (ANNs) due to their widespread use. However, ANNs frequently contain a large number of parameters, resulting in MIP formulations impractical to solve. In this paper we showcase the effectiveness of a ANN pruning, when applied to models prior to their integration into MIPs. We discuss why pruning is more suitable in this context than other ML compression techniques, and we highlight the potential of appropriate pruning strategies via experiments on MIPs used to construct adversarial examples to ANNs. Our results demonstrate that pruning offers remarkable reductions in solution times without hindering the quality of the final decision, enabling the resolution of previously unsolvable instances.

EXERCISES FOR DEVELOPING CRITICAL AND DIGITAL THINKING IN TEACHING FOREIGN LANGUAGES

The article explores the interplay between critical thinking and digital literacy in today's technology-driven society. This article explores various exercises designed to enhance critical and digital thinking skills in the context of teaching foreign languages. It emphasizes the integration of language learning with digital literacy, presenting activities for developing critical thinking such as source evaluation, comparative analysis, digital footprint assessment, online discussion evaluation, ethical considerations, digital content analysis, online research and presentation tools to produce their project, reflective blogging, virtual debate. Additionally, it highlights the importance of engaging in digital scavenger hunt, evaluating online language tools, creating digital content, online discussion forums, web-based language projects, analyzing digital media, digital storytelling, online forums for better digital communication and project-based learning for collaboration based on digital thinking.

Enriching central memory T cells using novel bioreactor design for T cell manufacturing

The manufacturing of T cell therapies aims to achieve high yields of product with potent phenotypes. We have developed a novel bioreactor, Bioreactor with an Expandable Culture Area – Dual Chamber (BECA-D), which has previously demonstrated functionality for scaled T cell manufacturing. In this study, incorporation of a stirring mechanism into the double-chamber bioreactor design was tested to homogenize the media components between the two chambers. In addition to the improved media homogenization, the stirring culture was also observed to have higher yield and enrichment of central memory T cells, a T cell subpopulation that has been associated with improved therapeutic efficacy, as compared to a static control. BECA-D with a stirring mechanism (BECA-DS+), was evaluated for its performance in culturing T cells in comparison to a static control, BECA-D, and an industry benchmark, G-Rex10. BECA-DS+ was able to preferentially promote the enrichment of central memory T cells compared to the static cultures, indicative of the effect of the stirring mechanism. By achieving high T cell yields with a favorable subpopulation profile, the mechanical method of incorporating stirring into a double-chamber bioreactor such as BECA-D carries great potential as a useful research and manufacturing tool to support advanced T cell therapy manufacturing.

Multi-gene duplication removal in an engineered human cellular MECP2 duplication syndrome model with an IRAK1-MECP2 duplication

Recent progress in genome editing technologies has catalyzed the generation of sophisticated cell models, however; the precise modeling of copy number variation (CNV) diseases remains a significant challenge, despite their substantial prevalence in the human population. To overcome this barrier, we have explored the utility of HAP1 cells for the accurate modeling of disease genomes with large structural variants. As an example, this study details the strategy to generate a novel cell line that serves as a model for the neurological disorder, MECP2 Duplication Syndrome (MDS), featuring the critical duplication of both the MECP2 and IRAK1 genes. This model faithfully recapitulates MDS genomic rearrangement, allowing for the mechanistic study of gene overexpression and the development of therapeutic interventions. Employing a single-guide RNA (sgRNA) CRISPR-Cas9 strategy, we successfully excised the duplicated genomic segment, notably halving both MECP2 and IRAK1 expression levels. The evidence establishes our model as a crucial tool for research into MDS. Furthermore, the outlined workflow is readily adaptable to model other CNV disorders and subsequently test genomic and pharmacological interventions.

‘Out of the “basement”’: exploring positionality and reflexivity in EU migration law research

ABSTRACT This article questions the absence of clear reflexivity and positionality in EU migration law research and it explores how they could be integrated through the real-life example of its author. While they are not usually part of the methodological tools of researchers in EU migration law, this article intends to show that they might be crucial to better understand its underlying processes. As EU migration law has a major influence on the lives of both migrants and those who research it, reflexivity and positionality could be a way for a dialogue to be acknowledged between them as part of the research itself. This article is presented as an ‘exploration’ rather than a theoretical contribution strictly following regular academic structures. As such, it is mostly articulated as a narrative using extended metaphors to convey the entanglement between the state of mind of the researcher and the evolution of her research process.

Analysis of Cytokine-Inducible SH2-Containing Protein (CISH) in the Study of Immunosenescence.

The aging immune system undergoes significant changes, leading to a state known as immunosenescence. Understanding the molecular mechanisms underlying immunosenescence is crucial for developing targeted interventions to enhance immune functions in older individuals. This bio-protocol review focuses on the application of quantitative reverse transcription-polymerase chain reaction (qRT-PCR) for the mRNA quantification of cytokine-inducible SH2-containing protein (CISH), an immune regulator overexpressed in T-cell responses from older adults. We outline a comprehensive protocol for the quantitative assessment of CISH mRNA expression, providing a valuable tool for researchers investigating immunosenescence.

A 45-Year Updating Wind and Wave Hindcast Over the Oman Sea and the Arabian Sea

This study focuses on developing a comprehensive and reliable wind and wave hindcast for the Oman Sea and Arabian Sea, spanning a significant 45-year period (1979-2024). The objective is to capture the intricate wind and wave climate of the region, characterized by distinct monsoon cycles and occasional tropical cyclones. The availability of extensive wave data over four decades would be an irreplaceable tool for researchers and engineers, enabling improved accuracy in extreme value analysis, sediment transport studies, and wave-induced current simulations. Despite limited field observations, all available measured data within the region were collected, analyzed for configuration of the models and utilized for model calibration and validation. The hindcast data was generated using the Weather Research and Forecasting (WRF) model for winds and WAVEWATCH III (WW3) model for waves, and evaluated against observational and measured wind and wave parameters. A comprehensive statistical analysis of the hindcast model's performance reveals adequate agreement with measured data, as evidenced by root mean square errors (RMSEs) of 1.23m/s for wind speeds and 0.37m for significant wave heights in average. These results underscore the model's reliability for research and engineering applications, particularly in the Arabian Sea, with a focus on the northern coastlines of the Oman Sea. The specific model configuration employed in this study holds significant potential for future investigations in the northern Indian Ocean, offering a valuable tool for understanding and predicting climatical conditions in this region.

SimplySmart_v1, a new tool for the analysis of DNA damage optimized in primary neuronal cultures

BackgroundThe increased interest in research on DNA damage in neurodegeneration has created a need for the development of tools dedicated to the analysis of DNA damage in neurons. Double-stranded breaks (DSBs) are among the most detrimental types of DNA damage and have become a subject of intensive research. DSBs result in DNA damage foci, which are detectable with the marker γH2AX. Manual counting of DNA damage foci is challenging and biased, and there is a lack of open-source programs optimized specifically in neurons. Thus, we developed a new, fully automated application, SimplySmart_v1, for DNA damage quantification and optimized its performance specifically in primary neurons cultured in vitro.ResultsCompared with control neurons, SimplySmart_v1 accurately identifies the induction of DNA damage with etoposide in primary neurons. It also accurately quantifies DNA damage in the desired fraction of cells and processes a batch of images within a few seconds. SimplySmart_v1 was also capable of quantifying DNA damage effectively regardless of the cell type (neuron or NSC-34). The comparative analysis of SimplySmart_v1 with other open-source tools, such as Fiji, CellProfiler and a focinator, revealed that SimplySmart_v1 is the most ‘user-friendly’ and the quickest tool among others and provides highly accurate results free of variability between measurements. In the context of neurodegenerative research, SimplySmart_v1 revealed an increase in DNA damage in primary neurons expressing abnormal TAR DNA/RNA binding protein (TDP-43).ConclusionsThese findings showed that SimplySmart_v1 is a new and effective tool for research on DNA damage and can successfully replace other available software.

Design and Practice of Virtual Experimental Scenes Integrating Computer Vision and Image Processing Technologies

In this work, we introduce a novel framework, GAN-based Image Processing (GAN-IP), to design and manipulate digital experimental settings that effectively combine computer vision and image processing technology. Using the skills of Generative Adversarial Networks (GANs), GAN-IP creates and complements virtual scenes and affords rich, adaptive surroundings for analysing and creating computationally smart and perceptive algorithms. GAN-IP solves the pressing troubles of variability and absence of data through synthesising realistic pictures and scenarios, enabling simulations of the diverse environments in which computer vision structures have to function. Our approach improves the fidelity and sort of digital scenes and introduces a way to mechanically alter and evoke a pleasant image, enabling more accurate and powerful computer vision and prescient models. Through extensive experimentation, GAN-IP demonstrates remarkable improvements in the performance of computer vision tasks, including object detection, segmentation, and recognition in complicated virtual environments. This research lays the foundation for future studies in this field and provides an adaptive tool for researchers and practitioners to simulate and test superior computer imaginative and prescient image processing technologies.

The birthday problem: repeated sampling of animal populations and ethics of experimental design

Researchers who use animals in science must balance statistical power with the need to satisfy the three Rs, whereby researchers are required to reduce numbers of animals, refine what they experience, and use alternatives to (replace) higher animals where possible. In repeated sampling over time-series studies, there is potential loss of power as well as ethics implications posed by repeated sampling of individual animals, where this cannot be managed or avoided. Here, we consider the mathematics of repeated sampling from three perspectives: that of the population at large, from the experience of the individual, and the conditional probability of sampled individuals being sampled again. The calculations are illustrated using four theoretical case studies across veterinary epidemiology with different practical implications and a provided R Shiny tool for researchers. Despite the availability of exact calculations, it is necessary to also consider the biological factors which may affect capture and recapture rates in sampling studies such as animal personality and response to capture. Researchers must also choose their question carefully to avoid inappropriate framing of ethical concerns around repeated sampling.