Discovery System Research Articles

Optimal scale combination selection based on genetic algorithm in generalized multi-scale decision systems for classification

Optimal scale combination (OSC) selection plays a crucial role in multi-scale decision systems for data mining and knowledge discovery, and its aim is to select an appropriate subsystem for classification or decision-making while keeping a certain consistency criterion. Selecting the OSC with existing methods requires judging the consistency of all multi-scale attributes; however, judging consistency and selecting scales for unimportant multi-scale attributes increases the selection cost in vain. Moreover, the existing definitions of OSC are only applicable to rough set classifiers (RSCs), which makes the selected OSC perform poorly on other machine learning classifiers. To this end, the main objective of this paper is to investigate multi-scale attribute subset selection and OSC selection applicable to any classifier in generalized multi-scale decision systems. First, a novel consistency criterion based on the multi-scale attribute subset is proposed, which is called p-consistency criterion. Second, the relevance and redundancy among multi-scale attributes are measured based on the information entropy, and an algorithm for selecting the multi-scale attribute subset is given based on this. Third, an extended definition of OSC, called the accuracy OSC, is proposed, which can be widely applied to classification tasks using any classifier. On this basis, an OSC selection algorithm based on genetic algorithm is proposed. Finally, the results of many experiments show that the proposed method can significantly improve the classification accuracy and selection efficiency.

Repurposing endogenous type I-E CRISPR-Cas systems for natural product discovery in Streptomyces

The multifunctional proteins of class 2 CRISPR systems such as Cas9, have been employed to activate cryptic biosynthetic gene clusters (BGCs) in Streptomyces, which represent a large and hidden reservoir of natural products. However, such approaches are not applicable to most Streptomyces strains with reasons to be comprehended. Inspired by the prevalence of the class 1 subtype especially the type I-E CRISPR system in Streptomyces, here we report the development of the type I-E CRISPR system into a series of transcriptional regulation tools. We further demonstrate the effectiveness of such activators in nine phylogenetically distant Streptomyces strains. Using these tools, we successfully activate 13 out of 21 BGCs and lead to the identification and characterization of one polyketide, one Ripp and three alkaloid products. Our work is expected to have a profound impact and to facilitate the discovery of numerous structurally diverse compounds from Streptomyces.

The Distinction Between Search and Discovery Systems : A Scholarly Analysis

This comprehensive analysis explores the fundamental distinctions between search and discovery systems in the context of information retrieval and user interaction. The article examines the underlying principles, methodologies, and user experiences associated with each system, elucidating their unique roles in the modern digital landscape. By comparing key aspects such as user intent, interaction models, algorithmic foundations, and evaluation metrics, the article highlights the complementary strengths of these paradigms. The research also investigates emerging hybrid approaches that combine elements of both search and discovery, addressing evolving user expectations and the challenges of information overload. Through this in-depth exploration, the article contributes to the ongoing discourse on the future of information access and informs the development of more effective, user-centric systems capable of meeting diverse information needs in an increasingly complex digital ecosystem.

Functional expression of the chimera proteins of Nav1.7 and NavAb in Escherichiacoli

Nav1.7 is a eukaryotic voltage-dependent Na channel (Nav) family membrane protein and has four channel domains and four voltage sensor domains (VSD-I–IV). It is involved in pain perception, and VSDs that differ significantly by Nav subtype are targeted in the development of Nav1.7-specific inhibitors. This is expected to result in neuropathic pain treatments with fewer side effects. We previously reported on intra-periplasm secretion and selection (PERISS), a peptide drug discovery system that targets membrane proteins by co-expressing a peptide library and a target membrane protein. For PERISS screening of VSD-specific new Nav1.7 inhibitors, the chimera protein (NavAb/1.7VSD) of Nav from prokaryotic Arcobacter butzleri (NavAb), in which extracellular loops of VSD were replaced with homologous loops from Nav1.7, serves as an effective model. This is because NavAb harbors only one VSD and the biological activity of NavAb/1.7VSD was previously confirmed. To date, NavAb/1.7VSD has only been found to be expressed in insect cells. In this study, we report on the expression and channel activity of NavAb/1.7VSD-II in Escherichia coli (E. coli). The expression of this protein in the inner membrane of E. coli was confirmed by western blotting. Channel activity was assessed by measuring the channel currents of the purified recombinant proteins and inhibition using a Nav1.7-specific peptide inhibitor. The results indicate that NavAb/1.7VSD-II was functionally expressed in E. coli, providing empirical support for the discovery of new VSD-specific Nav1.7 inhibitors using the PERISS screening method.

Machine learning methods for automated interstellar object classification with LSST

The Legacy Survey of Space and Time (LSST), to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. Our results demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables (right ascension, declination, and magnitude) in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.

Structure-guided design and photochemical synthesis of new carbamo(dithioperoxo)thioates with improved potencies to SARS-CoV-2 3CLpro

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered a protracted global pandemic from 2019 to 2022, and posed a significant threat to human health. One of the non-structural proteins 3CLpro of SARS-CoV-2 is considered as a validated target for the development of inhibitors against the virus. Disulfiram has been reported as a covalent inhibitor of 3CLpro; however, its structure lacks bonding site with active pockets of 3CLpro and its highly symmetric structure doesn’t match well with the irregular cavity of the active center, limiting its therapeutic applications. To enhance their affinity for the 3CLpro target, in this study, two kinds of disulfiram derivatives, designed based on the reevaluation and optimization of disulfiram, have been synthesized through photoredox chemistry, and the novel carbamo(dithioperoxo)thioates 4g-m were found to display 5–17 folds potency against SARS-CoV-2 3CLpro compared to the parent disulfiram, with resulting half-maximal inhibitory concentration (IC50) values ranging from 0.14–0.47 μM. Carbamo(dithioperoxo)thioates 4i containing a 4-hydroxy piperidine and a 4-trifluoromethyl phenyl ring, was identified as the most potent inhibitor to both 3CLpro (IC50 = 0.14 μM) and PLpro (IC50 = 0.04 μM). Furthermore, molecular dynamics simulations, binding free energy analysis and mass analysis were performed and provided insights on the stability, conformational behavior, and interactions of 4g with 3CLpro. The green synthetic methodology, the privileged carbamo(dithioperoxo)thioate scaffold, and the molecular mechanisms presented might serve as a useful system for the further discovery of highly potent inhibitors targeting SARS-CoV-2 3CLpro.

MRNA-Based Cancer Vaccines: Advancements and Prospects.

The success of mRNA COVID-19 vaccines has reinvigorated research and interest in mRNA-based cancer vaccines. Despite promising results in clinical trials, therapeutic mRNA-based cancer vaccines have not yet been approved for human use. These vaccines are designed to trigger tumor regression, establish enduring antitumor memory, and mitigate adverse reactions. However, challenges such as tumor-induced immunosuppression and immunoresistance significantly hinder their application. Here, we provide an overview of the recent advances of neoantigen discovery and delivery systems for mRNA vaccines, focusing on improving clinical efficacy. Additionally, we summarize the recent clinical advances involving mRNA cancer vaccines and discuss prospective strategies for overcoming immuneresistance.

Primary Sources as Linked Data: Exploring Motives Across the Sciences and Social Sciences

ABSTRACTWhile long recognized in the humanities, there is growing recognition in the sciences and social sciences that primary sources—as diverse as manuscripts, photographs, cultural belongings, and specimens—hold vast data about scientific and human knowledge for use in scholarship, community research, and global knowledge. Yet, data embedded in these sources are largely disconnected from the systems of discovery, access, and structured data that support reuse and insights across globally dispersed repositories. In this paper, we share select findings of a systematic review to explore the use of primary sources, and the data embedded in them, via linked data across the sciences and social sciences. Our results confirm the use of a variety of primary source data across diverse disciplines, particularly those requiring longitudinal studies and data integration from diverse repositories and contexts. We highlight how linked data are understood to: connect collections to communities; support highly granular credit, attribution, and assessment of impact; and interrelate diverse sources of knowledge. While these results suggest the value of linked data for the specific research needs of anthropology, the effectiveness of linked data in achieving these objectives and the suitability of this approach for a diversity of institutions and communities need further study.

Responsible Practices for Crowdsourced Transcription and Accessibility for Web‐based Library, Archive, and Museum Content

ABSTRACTHundreds of Libraries, Archives, and Museums (LAMs) around the world run crowdsourced transcription projects in order to engage users with their collections. Some LAMs explicitly use crowdsourcing projects to make non‐machine‐readable images of documents, such as manuscripts, discoverable to people who are blind or have low vision. We present findings from Crowdsourced Data: Accuracy, Accessibility, Authority (CDAAA), a 3‐year Institute of Museum and Library Services (IMLS) grant project that investigates whether and how LAMs integrate crowdsourced transcriptions into their discovery systems, and whether these efforts result in accessible web‐content for blind people and those with low vision who use assistive technology to navigate the web. We share research findings as well as practical suggestions for those in charge of crowdsourcing projects, the resulting transcription data, or similar web‐based textual content such as scholarly editions. These research and practice‐oriented findings are relevant to any national or local context where inaccessible images are transcribed, and are especially timely in the US context given recent Federal rule‐making to ensure that all web and app‐based content provided by US State and local governments is accessible, including tools, resources, and content created in‐house, through contracts or by license (2024).

Emphasizing the Social in Sociotechnical Approaches to the Digital Curation of Visual Information

ABSTRACTThis panel explores the unique challenges and opportunities presented by the expanded sociotechnical practices associated with the preservation, curation, and use of visual information and ongoing perceptions of the affordances and constraints associated with new and emerging visual information objects. The panelists and the respondent work with the curation and preservation of visual information across a variety of research areas and utilize both qualitative and quantitative methods to understand the parallel and divergent sociotechnical challenges of curating visual information across analog, digitized, and born‐digital contexts. Sites of analysis range from format‐specific identity‐representation issues to the cultural practices of media archivists, translational challenges when moving complex scientific data across various digital and analog formats, and the role of accessibility in the design and deployment of discovery systems within cultural heritage institutions. Responding to the unique and intersecting challenges produced by working with visual information across various archival and digital curation contexts, the panelist will reflect on the practical and theoretical outcomes from ongoing research projects and identify persistent and emergent issues within the digital curation of visual information.

High-Throughput Preosteoblastic Spheroids Elevate Fibroblast Growth Factor 23 via Parathyroid Hormone Signaling Pathway.

Fibroblast growth factor 23 (FGF23) plays a crucial role in managing renal phosphate and the synthesis of 1,25(OH)2-vitamin D3, which is essential for bone homeostasis. Developing robust in vitro systems to study FGF23-regulating mechanisms is crucial for advancing our knowledge and identifying potential therapeutic targets. The traditional in vitro 2D culture system results in relatively low expression of FGF23, complicating further exploration of its regulatory mechanisms and potential therapeutic targets. Herein, we reported a high-throughput approach to generate preosteoblastic cell spheroids with enhanced FGF23 production. For this purpose, murine preosteoblast cell line (MC3T3-E1) was cultured in our previously reported nonadherent microwells (200 µm in diameter, 148 µm in depth, and 100 µm space in between) and self-assembled into spheroids with a diameter of 92.3 ± 15.0 µm after 24 h. Compared with monolayer culture, the MC3T3-E1 spheroids showed a significant upregulation of FGF23 in both gene and protein levels after 24 h of serum-free induction. RNA sequencing and western blotting analysis further suggested that the enhanced FGF23 production in MC3T3-E1 spheroids was attributed to the activation of the parathyroid hormone (PTH)/PTH1R signaling pathway. Impressively, inhibition of PTH signaling through small molecular inhibitors or short hairpin RNA targeting PTH1R effectively reduced FGF23 production. In summary, the current study revealed the efficacy of the high-throughput formation of preosteoblast cell spheroid in stimulating FGF23 expression for mechanistic studies. Importantly, our findings highlight the potential of the current 3D spheroid system for target identification and drug discovery.

Real-Time Analysis of Neuronal Cell Cultures for CNS Drug Discovery.

The ability to screen for agents that can promote the development and/or maintenance of neuronal networks creates opportunities for the discovery of novel agents for the treatment of central nervous system (CNS) disorders. Over the past 10 years, advances in robotics, artificial intelligence, and machine learning have paved the way for the improved implementation of live-cell imaging systems for drug discovery. These instruments have revolutionized our ability to quickly and accurately acquire large standardized datasets when studying complex cellular phenomena in real-time. This is particularly useful in the field of neuroscience because real-time analysis can allow efficient monitoring of the development, maturation, and conservation of neuronal networks by measuring neurite length. Unfortunately, due to the relative infancy of this type of analysis, standard practices for data acquisition and processing are lacking, and there is no standardized format for reporting the vast quantities of data generated by live-cell imaging systems. This paper reviews the current state of live-cell imaging instruments, with a focus on the most commonly used equipment (IncuCyte systems). We provide an in-depth analysis of the experimental conditions reported in publications utilizing these systems, particularly with regard to studying neurite outgrowth. This analysis sheds light on trends and patterns that will enhance the use of live-cell imaging instruments in CNS drug discovery.

Winning the US–China Technology Race

The United States’ current rivalry with China should be seen as a technology race in which the ability to imagine, discover and apply innovations will decide who wins. While the United States spends more than China on research and development (R&D), China’s state-centred system enables it to transform funding and talent into deployed capabilities faster than the United States’ private-enterprise-centred system can. Accordingly, the US must avoid complacency about the inherent advantage of the democratic-capitalist system in scientific discovery and devise more effective mechanisms for stimulating free enterprise to develop new military technology. This requires the US Department of Defense to devote more resources to R&D by asking allies to contribute more for military operations, and to better incentivise military innovation by investing funds in private enterprise to defray start-up costs before production can yield steady revenue.

Implementing Linked Art in a Multi-Modal Database for Cross-Collection Discovery

Yale University has implemented a knowledge graph based discovery system that brings together the various art, natural history, archival, conservation and bibliographic collections using Linked Open Usable Data standards such as Linked Art and IIIF. This system comprises more than 41 million records, which would expand to more than 2 billion RDF triples, and is thus at a similar scale to Europeana. This paper presents the lessons learned from the five year effort around the usability of linked data structures across the organization, the technologies needed to make use of the knowledge in a performant way, and the appropriate design paradigms for front end applications which make the graph easily and intuitively accessible to researchers and the public, including the necessity of consistency in data modeling, that records are an essential concept to maintain through multi-modal systems, and the use of hypertext and web caches to maintain the separation between systems.

Framework for Measuring Relevancy in Discovery Environments

Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process witha fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments.

Structural perspectives on chemokine receptors.

Chemokine receptors are integral to the immune system and prime targets in drug discovery that have undergone extensive structural elucidation in recent years. We outline a timeline of these structural achievements, discuss the intracellular negative allosteric modulation of chemokine receptors, analyze the mechanisms of orthosteric receptor activation, and report on the emerging concept of biased signaling. Additionally, we highlight differences of G-protein binding among chemokine receptors. Intracellular allosteric modulators in chemokine receptors interact with a conserved motif within transmembrane helix 7 and helix 8 and exhibit a two-fold inactivation mechanism that can be harnessed for drug-discovery efforts. Chemokine recognition is a multi-step process traditionally explained by a two-site model within chemokine recognition site 1 (CRS1) and CRS2. Recent structural studies have extended our understanding of this complex mechanism with the identification of CRS1.5 and CRS3. CRS3 is implicated in determining ligand specificity and surrounds the chemokine by almost 180°. Within CRS3 we identified the extracellular loop 2 residue 45.51 as a key interaction mediator for chemokine binding. Y2917.43 on the other hand was shown in CCR1 to be a key determinant of signaling bias which, along with specific chemokine-dependent phosphorylation ensembles at the G-protein coupled receptors (GPCR's) C-terminus, seems to play a pivotal role in determining the direction of signal bias in GPCRs.

An Examination of Faceted Searching in Discovery Systems and the Impact on Information Discovery

An Examination of Faceted Searching in Discovery Systems and the Impact on Information Discovery

Myeloid Targeted Human MLL-ENL and MLL-AF9 Induces cdk9 and bcl2 Expression in Zebrafish Embryos.

Acute myeloid leukemia (AML) accounts for greater than twenty thousand new cases of leukemia annually in the United States. The average five-year survival rate is approximately 30%, pointing to the need for developing novel model systems for drug discovery. In particular, patients with chromosomal rearrangements in the mixed lineage leukemia (MLL) gene have higher relapse rates with poor outcomes. In this study we investigated the expression of human MLL-ENL and MLL-AF9 in the myeloid lineage of zebrafish embryos. We observed an expansion of MLL positive cells and determined these cells colocalized with the myeloid markers spi1b, mpx, and mpeg. In addition, expression of MLL-ENL and MLL-AF9 induced the expression of endogenous bcl2 and cdk9, genes that are often dysregulated in MLL-r-AML. Co-treatment of lyz: MLL-ENL or lyz:MLL-AF9 expressing embryos with the BCL2 inhibitor, Venetoclax, and the CDK9 inhibitor, Flavopiridol, significantly reduced the number of MLL positive cells compared to embryos treated with vehicle or either drug alone. In addition, cotreatment with Venetoclax and Flavopiridol significantly reduced the expression of endogenous mcl1a compared to vehicle, consistent with AML. This new model of MLL-r-AML provides a novel tool to understand the molecular mechanisms underlying disease progression and a platform for drug discovery.

Applications of Biomolecular Nanostructures for Anti-Angiogenic Theranostics.

Angiogenesis is a physiological process of forming new blood vessels that has pathological importance in seemingly unrelated illnesses like cancer, diabetes, and various inflammatory diseases. Treatment targeting angiogenesis has shown promise for these types of diseases, but current anti-angiogenic agents have critical limitations in delivery and side-effects. This necessitates exploration of alternative approaches like biomolecule-based drugs. Proteins, lipids, and oligonucleotides have recently become popular in biomedicine, specifically as biocompatible components of therapeutic drugs. Their excellent bioavailability and potential bioactive and immunogenic properties make them prime candidates for drug discovery or drug delivery systems. Lipid-based liposomes have become standard vehicles for targeted nanoparticle (NP) delivery, while protein and nucleotide NPs show promise for environment-sensitive delivery as smart NPs. Their therapeutic applications have initially been hampered by short circulation times and difficulty of fabrication but recent developments in nanofabrication and NP engineering have found ways to circumvent these disadvantages, vastly improving the practicality of biomolecular NPs. In this review, we are going to briefly discuss how biomolecule-based NPs have improved anti-angiogenesis-based therapy.

AngioMT: A MATLAB based 2D image-to-physics tool to predict oxygen transport in vascularized microphysiological systems.

Microphysiological models (MPS) are increasingly getting recognized as in vitro preclinical systems of pathophysiology and drug discovery. However, there is also a growing need to adapt and advance MPS to include the physiological contributions of the capillary vascular dynamics, because they undergo angiogenesis or vasculogenesis to deliver soluble oxygen and nutrients to its organs. Currently, the process of formation of microvessels in MPS is measured arbitrarily, and vascularized MPS do not include oxygen measurements in their analysis. Sensing and measuring tissue oxygen delivery is extremely difficult because it requires access to opaque and deep tissue, and/or requires extensive integration of biosensors that makes such systems impractical to use in the real world. Here, a finite element method-based oxygen transport program, called AngioMT, is built in MATLAB. AngioMT processes the routinely acquired 2D confocal images of microvascular networks in vitro and solves physical equations of diffusion-reaction dominated oxygen transport phenomena. This user-friendly image-to-physics transition in AngioMT is an enabling tool of MPS analysis because unlike the averaged morphological measures of vessels, it provides information of the spatial transport of oxygen both within the microvessels and the surrounding tissue regions. Further, it solves the more complex higher order reaction mechanisms which also improve the physiological relevance of this tool when compared directly against in vivo measurements. Finally, the program is applied in a multicellular vascularized MPS by including the ability to define additional organ/tissue subtypes in complex co-cultured systems. Therefore, AngioMT serves as an analytical tool to enhance the predictive power and performance of MPS that incorporate microcirculation.