Exploration Tool Research Articles

Instagram Engagement Helps Increase Residency Applicant Interest in Orthopaedic Surgery.

This study aims to evaluate whether Instagram engagement data affect residency application volumes for orthopaedic surgery residency programs and rank the top 50 Instagram accounts associated with programs based on engagement. Data from January 1, 2020 to June 30, 2023 were collected in August 2023 for Instagram metrics through Popsters social media analytic tool for business accounts and manually for nonbusiness accounts, as well as applicant numbers through the Association of American Medical College (AAMC) Residency Explorer Tool. Top 50 rankings were created from 2020 to 2022 based on engagement score, number of applicants, and growth in application numbers. Correlational analysis was done for programs with active Instagram accounts and full AAMC data from 2020 to 2022. A list of the top 50 most influential programs' Instagram accounts was compiled from January 1, 2020, to June 30, 2023, based on engagement scores including follower count. Of 210 programs, 163 (78%) had a total of 184 Instagram accounts. The average number of likes and comments per post for each account increased from 60 to 98 and 1.4 to 2.3 from 2020 to June 2023 (r = 0.95 to 0.9). For the 153 programs with active accounts and corresponding AAMC applicant data from 2020 to 2022, an increase in average engagement score (2,938 to 3,483) and the number of applicants (636 to 824) were observed with a weak positive correlation (2020: r = 0.15, P = 0.06; 2021: r = 0.18, P = 0.02; 2022: r = 0.20, P = 0.01). Instagram has become a useful tool that orthopaedic surgery residency programs are leveraging to highlight aspects of their programs to prospective applicants. The findings of this study demonstrate a positive correlation between social media engagement and applicant numbers from 2020 to 2022, with the strength of correlation increasing each subsequent year. Several influential orthopaedic surgery residency programs, based on high engagement scores, received the most applications in the previous three cycles, reinforcing the value of social media, particularly Instagram, in promoting residency programs.

Thematic Issue on Magmatic PGE-(Ni-Cu-Cr-V) deposits: petrology, geochemistry and exploration tools

Thematic Issue on Magmatic PGE-(Ni-Cu-Cr-V) deposits: petrology, geochemistry and exploration tools

Generative Literature: The Role of Artificial Intelligence in the Creative Writing Process

This study explores the emerging phenomenon of AI-generated literature and its implications for creative writing, focusing on the characteristics of AI-generated texts, the impact of AI-human collaboration on the creative process, and the challenges posed by these technologies for traditional concepts of authorship, originality, and creativity. Through a comparative analysis of selected AI-generated literary works and a case study of the "Pharmako-AI" project, this research reveals the distinct stylistic, thematic, and structural features of AI-generated literature, as well as the complex dynamics of AI-human collaboration in the creative process. The findings suggest that while AI can serve as a powerful tool for creative exploration and experimentation, it also has limitations in terms of consistency, coherence, and emotional depth, and requires significant human input and judgment to shape the final literary output. The study contributes to the understanding of AI in creative writing by providing concrete insights into the capabilities and limitations of these technologies, and by highlighting the need for new frameworks and models to understand the nature of creative agency in the age of AI. The implications of AI-generated literature for the field of literature and future literary practices are discussed, including the potential for new forms of literary expression, new modes of authorship and collaboration, and new challenges to traditional concepts of originality and creativity. The study concludes with recommendations for future research, emphasizing the need for interdisciplinary collaboration and the development of new theoretical and methodological approaches to analyze and evaluate AI-generated literature.

MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays

Multiplexed assays of variant effect (MAVEs) are a critical tool for researchers and clinicians to understand genetic variants. Here we describe the 2024 update to MaveDB (https://www.mavedb.org/) with four key improvements to the MAVE community’s database of record: more available data including over 7 million variant effect measurements, an improved data model supporting assays such as saturation genome editing, new built-in exploration and visualization tools, and powerful APIs for data federation and streamlined submission and access. Together these changes support MaveDB’s role as a hub for the analysis and dissemination of MAVEs now and into the future.

Deep-TEMNet: A Hybrid U-Net–2D LSTM Network for Efficient and Accurate 2.5D Transient Electromagnetic Forward Modeling

The transient electromagnetic (TEM) method is a crucial tool for subsurface exploration, providing essential insights into the electrical resistivity structures beneath the Earth’s surface. Traditional forward modeling approaches, such as the finite-difference time-domain (FDTD) method and the finite-element method (FEM), are computationally intensive, limiting their practicality for real-time, high-resolution, or large-scale investigations. To address these challenges, we present Deep-TEMNet, an advanced deep learning framework specifically designed for two-dimensional TEM forward modeling. Deep-TEMNet integrates the U-Net architecture with a tailored two-dimensional long short-term memory (2D LSTM) module, allowing it to effectively capture complex spatial-temporal relationships in TEM data. The U-Net component enables high-resolution spatial feature extraction, while the 2D LSTM module enhances temporal modeling by processing spatial sequences in two dimensions, thereby optimizing the representation of electromagnetic field dynamics over time. Trained on high-fidelity FEM-generated datasets, Deep-TEMNet achieves exceptional accuracy in reproducing electromagnetic field distributions across diverse geological scenarios, with a mean squared error of 0.00000134 and a root mean square percentage error of 0.002373019. The framework offers over 150 times the computational speed of traditional FEMs, with an average inference time of just 3.26 s. Extensive validation across varied geological conditions highlights Deep-TEMNet’s robustness and adaptability, establishing its potential for efficient, large-scale subsurface mapping and real-time data processing. By combining U-Net’s spatial resolution capabilities with the sequential processing strength of the 2D LSTM module, Deep-TEMNet significantly advances computational efficiency and accuracy, positioning it as a valuable tool for geophysical exploration, environmental monitoring, and other applications requiring scalable, real-time TEM analyses that are easily integrated into remote sensing workflows.

Discovery of WDR5-MLL1 and HDAC Dual-Target Inhibitors for the Treatment of Acute Myeloid Leukemia.

Targeting the WDR5-MLL1 protein-protein interaction (PPI) is considered to be an effective approach for the treatment of MLL-rearranged leukemia. However, interfering with WDR5-MLL1 PPI reduces methylated H3K4 levels and induces a decline in acetylated H3 levels, which may contribute to the suboptimal cellular efficacy of WDR5 inhibitors. We observed that cotreatment with WDR5-MLL1 PPI and HDAC inhibitors augmented the antiproliferative effect in MV-4-11 cells. Thus, a series of dual-target inhibitors was developed by merging the pharmacophores of the WDR5 and HDAC inhibitors. Among the developed inhibitors, compound 32d displayed an 89-fold increase in antiproliferative efficacy and induced potent cell apoptosis by impeding the DNA damage repair signaling pathway. Furthermore, the administration of 30 mg/kg of compound 32d was well tolerated, inhibiting MV-4-11 xenograft growth by 87.1%. Our investigation established the therapeutic effectiveness of the developed WDR5-MLL1/HDAC dual-target inhibitor against acute myeloid leukemia, providing a valuable tool for further exploration of crosstalk between the two targets.

Synergizing CRISPR-Cas9 with Advanced Artificial Intelligence and Machine Learning for Precision Drug Delivery: Technological Nexus and Regulatory Insights.

The evolution of genetic exploration tools, from laborious methods like radiationinduced mutations to the transformative CRISPR-Cas9 system, has fundamentally reshaped genetic research and gene editing capabilities. This journey, initiated by foundational techniques such as ZFNs and TALENs and culminating in the groundbreaking work of Doudna and Charpentier in 2012, has ushered in an era of precise DNA alteration and profound insights into gene functions. The CRISPR/Cas9 system uses the Cas9 enzyme and guides RNA (gRNA) to precisely target and cleave DNA, with subsequent repair via error-prone NHEJ or precise HDR, enabling versatile gene editing. Complementary computational tools like E-CRISP and Azimuth 2.0, alongside advanced deep learning models like DeepCRISPR, have significantly contributed to refining CRISPR experiments, optimizing gRNA efficiency, and predicting outcomes with greater precision. In clinical applications, CRISPR-Cas9 shows great promise for treating complex genetic disorders like sickle cell disease and β-thalassemia, but faces challenges such as off-target effects, immune responses to viral vectors, and ethical issues in germline editing. Overcoming these challenges requires meticulous experimentation and robust regulatory frameworks to ensure responsible and beneficial utilization of the CRISPR-Cas9 technology across diverse fields, including cancer treatment, genetic disease therapies, agriculture, and synthetic biology, while continually addressing ethical, safety, and legal considerations for its advancement and widespread adoption.

Optimized biochemical method for human Polyphosphate quantification.

Polyphosphate (polyP), a biopolymer composed of phosphates, impacts a wide range of biological functions and pathological conditions in all organisms. However, polyP's intricate physiology and structure in human cells have remained elusive, largely due to the lack of a reliable quantification method including its extraction. In this study, we assess critical points in the whole process: extraction, purification, and quantification polyP from human cell lines. We developed a highly efficient method that extracts between 3 and 100 times more polyP than previously achieved. Supported by Nuclear Magnetic Resonance (NMR), our approach confirms that mammalian polyP is primarily a linear unbranched polymer. We applied the optimized method to commonly used human cell lines, uncovering important variations of intracellular polyP that correlate with the expression levels of specific polyP converting enzymes. This study underscores the importance of employing several techniques for polyP characterization in parallel and provides a valuable and standardized tool for further exploration in this field.

Geochemical exploration of rare earth element resources in highland karstic bauxite deposits in the Sierra de Bahoruco, Pedernales Province, Southwestern Dominican Republic.

This study investigates the geochemical characteristics of rare earth elements (REEs) in highland karstic bauxite deposits located in the Sierra de Bahoruco, Pedernales Province, Dominican Republic. These deposits, formed through intense weathering of volcanic material, represent a potentially valuable REE resource for the nation. Surface and subsurface soil samples were analyzed using portable X-ray fluorescence (pXRF) and a NixPro 2 color sensor validated with inductively coupled plasma optical emission spectrometry (ICP-OES). We employed compositional data analysis (CoDA) and machine learning models to estimate total REE concentrations, demonstrating that pXRF and the color sensor, when properly calibrated, are effective tools for remote geochemical exploration. The results reveal that REE concentrations increase with depth and elevation, with light REEs (LREEs) dominating the profiles. The correlation of REE concentrations with morphological soil development suggests that higher-altitude areas are enriched in REEs due to progressive weathering processes. The study also shows a strong relationship between REE concentrations and environmental factors such as latitude and elevation. While pXRF provided reliable estimates of total REE concentrations, to our surprise, the NixPro2 color sensor proved similarly accurate. The research emphasizes the practical value of the x-ray and color sensors for remote exploration, provided that a well-explored, robust calibration is performed to account for site-specific variability. These findings contribute to understanding the geochemical distribution of REEs in karstic bauxite deposits and highlight the potential for further exploration in remote, high-altitude regions. Future research should explore using these and other portable sensors, singly or combined, to predict REE speciation, for expediting information related to the environmentally sustainable extractability and potential economic feasibility of resources in expeditionary locations.

Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks.

Genomics experts rely on visualization to extract and share insights from complex and large-scale datasets. Beyond off-the-shelf tools for data exploration, there is an increasing need for platforms that aid experts in authoring customized visualizations for both exploration and communication of insights. A variety of interactive techniques have been proposed for authoring data visualizations, such as template editing, shelf configuration, natural language input, and code editors. However, it remains unclear how genomics experts create visualizations and which techniques best support their visualization tasks and needs. To address this gap, we conducted two user studies with genomics researchers: (1) semi-structured interviews (n = 20) to identify the tasks, user contexts, and current visualization authoring techniques and (2) an exploratory study (n = 13) using visual probes to elicit users' intents and desired techniques when creating visualizations. Our contributions include (1) a characterization of how visualization authoring is currently utilized in genomics visualization, identifying limitations and benefits in light of common criteria for authoring tools, and (2) generalizable design implications for genomics visualization authoring tools based on our findings on task- and user-specific usefulness of authoring techniques. All supplemental materials are available at https://osf.io/bdj4v/.

Continuum of non-targeted data for long term study of complex samples generated by direct infusion ultra-high resolution mass spectrometry.

Non-targeted chemical analysis is a powerful tool for exploration of the unknown chemistry of complex matrices such as food, biological, geochemical, environmental and even extra-terrestrial samples. It allows researchers to ask open, unbiased questions about their system chemistry. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) offers these options and has been widely used to study complex mixtures, with its unmatched mass resolution enabling direct infusion methods and eliminating the challenges of chromatographic alignment in large-scale longitudinal projects. In this article, we demonstrate the use of FT-ICR-MS for generating a dataset for hundreds of complex samples with diverse chemistries over a period of 6 years and 32 batches, allowing confident comparison of data between samples from different batches. The resulting chemical database will be continuously expanded in future and retrospectively interrogated to test new hypotheses utilizing data of past projects and new knowledge of coming projects. We discuss the experimental setup and how other researchers could apply the same approaches, which is relevant for wide ranging applications and research fields.

The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling.

We apply an approach from cognitive linguistics by mapping Conceptual Metaphor Theory (CMT) to the visualization domain to address patterns of visual conceptual metaphors that are often used in science infographics. Metaphors play an essential part in visual communication and are frequently employed to explain complex concepts. However, their use is often based on intuition, rather than following a formal process. At present, we lack tools and language for understanding and describing metaphor use in visualization to the extent where taxonomy and grammar could guide the creation of visual components, e.g., infographics. Our classification of the visual conceptual mappings within scientific representations is based on the breakdown of visual components in existing scientific infographics. We demonstrate the development of this mapping through a detailed analysis of data collected from four domains (biomedicine, climate, space, and anthropology) that represent a diverse range of visual conceptual metaphors used in the visual communication of science. This work allows us to identify patterns of visual conceptual metaphor use within the domains, resolve ambiguities about why specific conceptual metaphors are used, and develop a better overall understanding of visual metaphor use in scientific infographics. Our analysis shows that ontological and orientational conceptual metaphors are the most widely applied to translate complex scientific concepts. To support our findings we developed a visual exploratory tool based on the collected database that places the individual infographics on a spatio-temporal scale and illustrates the breakdown of visual conceptual metaphors.

MAXIMIZING REGIONAL SENSITIVITY ANALYSIS INDICES TO FIND SENSITIVE MODEL BEHAVIORS

We address the question of sensitivity analysis for model outputs of any dimension using regional sensitivity analysis (RSA). Classical RSA computes sensitivity indices related to the impact of model input variations on the occurrence of a target region of the model output space. In this work, we take this perspective one step further by proposing to find, for a given model input, the region whose occurrence is best explained by the variations of this input. When it exists, this region can be seen as a model behavior whose occurrence is particularly sensitive to the variations of the model input under study. We name this method mRSA (for maximized RSA). mRSA is formalized as an optimization problem using region-based sensitivity indices. Two formulations are studied, one theoretically and one numerically, using a dedicated algorithm. Using a 2D test model and an environmental model producing time series, we show that mRSA, as a new model exploration tool, can provide interpretable insights on the sensitivity of model outputs of various dimensions.

The next generation of dashboards: a spatial online analytical processing (SOLAP) platform for COVID-19

ABSTRACT The health and societal impacts of COVID-19 have created tremendous interest in the scientific community, resulting in interdisciplinary research teams that combine their expertise to provide new insights into the epidemic. However, spatial computation, exploratory data analysis, and spatial data exploration tools have yet to be integrated into these dashboards. We present a Spatial Online Analytical Platform that integrates spatial analysis tools that enable users to explore and learn more about spatial patterns of COVID-19. We present three interaction classes to support users needs. Our first class allows users to apply user-defined data classifications and custom map color choices. The second class applies a risk index across the time series, informing users of the recent temporal trends. The third class allows users to hypothesize about the presence of spatial clusters and receive results on demand. Our SOLAP platform supports the data analysis and exploration needs of big spatial-temporal data.

Cine-MRI of Deglutition: A Systematic Review.

Videofluoroscopy, recognized as the gold standard for dysphagia exploration, has inherent limitations, including poor soft tissue discrimination, radiation exposure, and aspiration risk. In response to these challenges, cine-MRI of swallowing has evolved over the past three decades, yielding diverse methodologies and results across various studies.This systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, meticulously outlines cine-MRI protocols, applications, advantages, and limitations. Our qualitative analysis encompasses 36 studies involving 715 subjects from 2001 to 2023. Cine-MRI, a promising alternative, provides safe application for dysphagic patients, offering insights into the intricacies of deglutition mechanisms and eliminating the necessity for oral contrast material. Notably, this approach is adaptable to both 1.5 and 3T MRI scanners with specific image acceleration protocols.Despite these merits, a crucial gap remains, as no non-inferiority prospective study has yet been conducted to compare cine-MRI with the gold standard. Additionally, the lack of uniform swallowing assessment criteria and the uncertain impact of the supine position underscore the need for further investigation. In conclusion, while cine-MRI of deglutition emerges as a compelling tool for dysphagia exploration, its precise indications and role in the diagnostic approach to swallowing disorders necessitate comprehensive evaluation in future studies.

Automatic Target Detection Using Ground Penetrating Radar (GPR) Data Based on A Machine Learning Approach: A Survey

Ground Penetrating Radar (GPR) is a valuable tool for subsurface exploration across diverse fields such as archaeology, defence, and civil engineering. Traditionally, GPR data has been interpreted manually, requiring extensive expertise and being time-consuming. Recently, machine- learning approaches have emerged as effective solutions for automating target detection, significantly reducing time and human dependency while enhancing accuracy. This review provides a comprehensive overview of the current advancements in automatic target detection using GPR data, focusing on machine learning-based methods. We summarize feature extraction techniques, popular machine learning algorithms, and challenges within this domain. This paper also identifies future directions and potential improvements for robust, accurate, and efficient GPR-based automatic target detection systems. Key Words: Machine Learning, Ground Penetrating Radar, Object Detection, Signal Processing.

Financial Communication in Crisis Situations

Financial communication plays a key role in shaping stakeholders' decision-making processes, acquiring even greater importance in today's turbulent global landscape marked by various overlapping crises. Thus, the aim of the research revolves around a theoretical exploration, supported by a bibliometric analysis, to emphasize the intrinsic importance of financial communication. The research methodology is based on a bibliometric analysis of 3,000 scientific papers indexed in the Web of Science database, from 1975 to 2023, focusing on financial communication. To facilitate this analysis, specialized software tools for data processing and bibliometric exploration, namely VOSviewer and Bibliometrix, were used. The research findings underline the complexity and interdependence of the financial communication process in organizations, highlighting its evolution from transparency and reduction of information asymmetry in periods of stability, to the need for rapid adoption of digital technologies and integration of ESG dimensions during recent crises, all of which are essential to meet stakeholders' demands and maintain investors' trust.

Using coupled bulk-rock geochemistry and short-wave infrared (SWIR) spectral reflectance data as rapid exploration tools in metamorphosed VHMS deposits: insights from the King Zn deposit, Yilgarn Craton, Western Australia

AbstractBulk rock geochemistry and SWIR reflectance spectroscopy are widely used by companies for rapid and cost-effective exploration of volcanic-hosted massive sulfide (VHMS) deposits. However, few studies have integrated bulk-rock geochemistry with hyperspectral reflectance spectroscopy in greenstone belts that have undergone high-grade metamorphism. Here we present an extensive dataset combining bulk-rock geochemistry with chlorite and white mica SWIR spectral reflectance from the amphibolite-grade King VHMS deposit of the Yilgarn Craton, Western Australia. At King, the footwall stratigraphy is dominated by tholeiitic mafic rocks overlain by a sequence of calc-alkaline intermediate-felsic metavolcanic rocks. The hanging-wall stratigraphy is characterized by a thin metaexhalite layer, overlain by thick succession of interbedded metasedimentary and metavolcanic rocks. Chlorite spectral signatures are more Fe-rich in mafic lithologies and Mg-rich in felsic rocks, particularly where intense Mg-metasomatism occurred before metamorphism. In all units, Fe/Mg ratios of chlorite are strongly tied to bulk rock Fe/Mg ratios. White mica in the footwall is primarily muscovitic, with minor amounts of phengite in deep Fe-rich mafic rocks. By contrast, the hanging-wall sequence is dominated by phengitic signatures in both the Fe-rich metaexhalite, and weakly Ca-Mg altered volcanic rocks. This study concludes that chlorite SWIR reflectance is largely influenced by the bulk Fe/Mg composition of the host rock, whereas white mica reflectance correlates with the type and intensity of hydrothermal alteration prior to metamorphism. These findings underscore the potential of using chlorite and white mica spectral signatures to understand hydrothermal alteration patterns and detect new orebodies in metamorphosed VHMS systems.

Influence of single observations on the choice of the penalty parameter in ridge regression

Penalized regression methods such as ridge regression heavily rely on the choice of the penalty parameter, which is often computed via cross-validation. Discrepancies in the value of the penalty parameter may lead to substantial differences in regression coefficient estimates and predictions. This paper investigates the effect of single observations on the optimal choice of the penalty parameter, showing how the presence of influential points can change it dramatically. The points are distinguished as ‘expanders’ or ‘shrinkers’ based on their effect on the model complexity. The approach supplies a visual exploratory tool to identify influential points, naturally implementable for high-dimensional data where traditional approaches usually fail. Applications to simulated and real data examples, both low- and high-dimensional, are presented. The visual tool is implemented in the R package influridge.

Exploring Thiazolidinedione-Naphthalene Analogues as Potential Antidiabetic Agents: Design, Synthesis, Molecular Docking and In-vitro Evaluation.

Thiazolidinedione-naphthalene analogues were synthesized and evaluated for antidiabetic activity as Pancreatic α-Amylase (PAA) and intestinal α-glucosidase (IAG) inhibitors. The activity of the compounds (14a-g,17a-k) is compared with acarbose as the standard drug and all the compounds shows good to moderate antidiabetic activity. In-vitro PAA and IAG inhibition assay is performed for the all compounds, the compounds 17e shows superior PAA and IAG inhibitory activity with respective to standard (IC50 = 12.455 ± 0.04 μM and 9.145 ± 0. 01 μM). The molecular interaction with PAA and IAG protein was also studied with the help of molecular docking studies using AutoDock software. while SwissADME and Osiris property explorer tools computed in-silico drug likeliness and toxicity properties. The in-silico results confirmed the 17e molecule as a superior drug with high binding affinity and good drug likeness against PAA and IAG, confirming in-vitro results. We also studied antioxidant activity (AOA) of all synthesized compounds and results confined that the compound 14g and 17e has good antioxidant potential IC50 = 8.04 ± 0.02 μM and 6.36 ± 0.03 μM respectively among all compounds. In conclusion, in-vitro, in-silico antidiabetic and antioxidant studies revealed 17e compound was found to be potential compound.