Opportunities For Discovery Research Articles

Phase separation in cGAS-STING signaling.

Biomolecular condensates formed by phase separation are widespread and play critical roles in many physiological and pathological processes. cGAS-STING signaling functions to detect aberrant DNA signals to initiate anti-infection defense and antitumor immunity. At the same time, cGAS-STING signaling must be carefully regulated to maintain immune homeostasis. Interestingly, exciting recent studies have reported that biomolecular phase separation exists and plays important roles in different steps of cGAS-STING signaling, including cGAS condensates, STING condensates, and IRF3 condensates. In addition, several intracellular and extracellular factors have been proposed to modulate the condensates in cGAS-STING signaling. These studies reveal novel activation and regulation mechanisms of cGAS-STING signaling and provide new opportunities for drug discovery. Here, we summarize recent advances in the phase separation of cGAS-STING signaling and the development of potential drugs targeting these innate immune condensates.

The enduring importance of comparative models for understanding hearing loss

Comparative hearing research has historically focused on diverse organisms’ solutions to sensory problems such as localizing sounds in space, responding to conspecific vocalizations, and using sound for navigation and prey capture. These studies have advanced the bioacoustics and auditory neuroscience fields for many decades, despite decreasing funding for comparative auditory research whose primary intent is not to model human communication disorders. However, animals from across many taxa can experience hearing loss, and to varying degrees, recover their hearing abilities after exposure to damaging events. The genetic diversity of these animals, species-specific variations in evolved protective mechanisms, the rich repertoire of acoustic behaviors, and differences in the capacity for self-repair present important opportunities for discovery of the fundamental mechanisms of normal and disordered hearing. No one species best models humans. It is critical that we support and promote analysis of the physiological, anatomical, and behavioral manifestations of hearing loss using a comparative approach. This presentation will highlight some of the opportunities and challenges in working with diverse species as models of hearing dysfunction.

Identification of Rare Genetic Variants in Familial Spontaneous Coronary Artery Dissection and Evidence for Shared Biological Pathways.

Rare familial spontaneous coronary artery dissection (SCAD) kindreds implicate genetic disease predisposition and provide a unique opportunity for candidate gene discovery. Whole-genome sequencing was performed in fifteen probands with non-syndromic SCAD who had a relative with SCAD, eight of whom had a second relative with extra-coronary arteriopathy. Co-segregating variants and associated genes were prioritized by quantitative variant, gene, and disease-level metrics. Curated public databases were queried for functional relationships among encoded proteins. Fifty-four heterozygous coding variants in thirteen families co-segregated with disease and fulfilled primary filters of rarity, gene variation constraint, and predicted-deleterious protein effect. Secondary filters yielded 11 prioritized candidate genes in 12 families, with high arterial tissue expression (n = 7), high-confidence protein-level interactions with genes associated with SCAD previously (n = 10), and/or previous associations with connective tissue disorders and aortopathies (n = 3) or other vascular phenotypes in mice or humans (n = 11). High-confidence associations were identified among 10 familial SCAD candidate-gene-encoded proteins. A collagen-encoding gene was identified in five families, two with distinct variants in COL4A2. Familial SCAD is genetically heterogeneous, yet perturbations of extracellular matrix, cytoskeletal, and cell-cell adhesion proteins implicate common disease-susceptibility pathways. Incomplete penetrance and variable expression suggest genetic or environmental modifiers.

A Bischler-Napieralski and homo-Mannich sequence enables diversified syntheses of sarpagine alkaloids and analogues

Sarpagine alkaloids offer signicant opportunities in drug discovery, yet the efficient total syntheses and diverse structural modifications of these natural products remain highly challenging due to the architectural complexity. Here we show a homo-Mannich reaction of cyclopropanol with imines generated via a Bischler-Napieralski reaction enables a protecting-group-free, redox economic, four-step access to the tetracyclic sarpagine core from L-tryptophan esters. Based on this advancement, diversified syntheses of sarpagine alkaloids and analogues are achieved in a short synthetic route. The systematic anticancer evaluation indicates that natural products vellosimine and Na-methyl vellosimine possess modest anticancer activity. Intensive structural optimization of these lead molecules and exploration of the structure−activity relationship lead to the identification of analogue 15ai with an allene unit showing a tenfold improvement in anticancer activities. Further mechanism studies indicate compound 15ai exertes antiproliferation effects by inducing ferroptosis, which is an appealing non-apoptotic cell death form that may provide new solutions in future cancer therapies.

Knowledge in the discovery of market opportunities

An opportunity is a relationship between a company's objectives, the resources at its disposal, and the situation in the environment, such that it favours the achievement of the objectives with the available resources. Opportunities arise in a changing environment. The dynamics of change causes the life cycle of an opportunity to shorten and companies that discover the opportunity and can exploit it gain a competitive advantage. Opportunity discovery means that opportunities exist but the company does not know about them. Opportunity discovery is the subject of several scientific disciplines. They are dealt with by strategic management using the methods of strategic analysis. They are also of interest to agility theory, which focuses on reacting quickly to market opportunities. In addition, entrepreneurship theory deals with them, which addresses the issue of cognitive traits in their discovery. New possibilities in discovering opportunities are created by artificial intelligence. The article aims to present the opportunity discovery model understood as the relationship between three vectors: the company's goals, its resources, and the external environment in which it operates. The discovery of the opportunity consists in finding such a value of the environment vector at which the desired value of the vector of goals is achieved at a given value of the resource vector. The article answers the question of how the knowledge necessary to find such a relationship between these vectors must be structured so that this relationship reflects the opportunity. It was hypothesized that this structure can be modelled using a network of cause-and-effect relationships leading to the determination of the relationship between demand and supply. This hypothesis was verified using artificial intelligence with the Monte Carlo algorithm, Markov chain, and the Metropolis-Hastings algorithm. The results obtained confirmed the validity of the model structuring the knowledge. The article presents the result of a pilot study. The results concerning knowledge structuring are useful for opportunity discovery both in the case of traditional approaches i.e. using the achievements of strategic management theory, corporate agility and entrepreneurship theory, and in the case of using artificial intelligence. In the first case, the target audience for these results is business executives while in the second case, the research community working on this problem and IT companies.

Strategic knowledge bases in the context of discovering and creating business opportunities

Due to the level of interdisciplinarity and complexity, knowledge management processes in organizations need to be considered from different points of view. The key area of interest of the article is systemic processes of creating knowledge bases that take into account strategic events in organizations in the form of market opportunities. Thus, the article’s main purpose is to create two models of knowledge bases, the structure and types of which are directly related to the processes of opportunity management: their discovery and creation. For the main objective thus formulated, the researchers defined the following research questions: 1) What are the information needs for creating a strategic knowledge base model in the opportunity discovery process? 2) what is the information needed for creating a strategic knowledge base model in the opportunity creation process? 3) What are the benefits and utilitarian goals for the organization from the creation of strategic knowledge bases in the context of discovering and creating business opportunities?. The detailed objectives derive from analyzing the organization's interior models and environment. The methodology used in the article is a critical review of the literature, the method of deduction in the area of generating assumptions for discovery processes and creating opportunities in the context of creating knowledge models. The results will enable extracting the cognitive knowledge of strategic knowledge bases in the context of the organization's opportunities. Furthermore, this will result in research models detailing the organization's strategic activities in opportunities and knowledge management processes, along with the specification of the potential benefits of the processes carried out (utilitarian purpose). The research results will expand the understanding of the utilitarian nature of knowledge management processes and strategic theory.

Geochemistry and Petrology: Collaborative Roles in Resource Exploration and Environmental Research

Geochemistry and petrology, as distinct yet interrelated fields within geology, play pivotal roles in understanding Earth’s composition, processes, and history. This paper explores the collaborative synergy between these disciplines and their significance in resource exploration and environmental research. It delves into their fundamental principles, applications, and emerging trends, highlighting successful interdisciplinary projects. Despite communication challenges and funding limitations, the future promises exciting opportunities for innovation and discovery through continued collaboration. As we address pressing global challenges, the partnership between geochemistry and petrology remains vital for a sustainable and resilient future.

Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52.

Within over 800 members of G-protein-coupled receptors, there are numerous orphan receptors whose endogenous ligands are largely unknown, providing many opportunities for novel drug discovery. However, the lack of an in-depth understanding of the intrinsic working mechanism for orphan receptors severely limits the related rational drug design. The G-protein-coupled receptor 52 (GPR52) is a unique orphan receptor that constitutively increases cellular 5'-cyclic adenosine monophosphate (cAMP) levels without binding any exogenous agonists and has been identified as a promising therapeutic target for central nervous system disorders. Although recent structural biology studies have provided snapshots of both active and inactive states of GPR52, the mechanism of the conformational transition between these states remains unclear. Here, an acceptable self-activation pathway for GPR52 was proposed through 6 μs Gaussian accelerated molecular dynamics (GaMD) simulations, in which the receptor spontaneously transitions from the active state to that matching the inactive crystal structure. According to the three intermediate states of the receptor obtained by constructing a reweighted potential of mean force, how the allosteric regulation occurs between the extracellular orthosteric binding pocket and the intracellular G-protein-binding site is revealed. Combined with the independent gradient model, several important microswitch residues and the allosteric communication pathway that directly links the two regions are both identified. Transfer entropy calculations not only reveal the complex allosteric signaling within GPR52 but also confirm the unique role of ECL2 in allosteric regulation, which is mutually validated with the results of GaMD simulations. Overall, this work elucidates the allosteric mechanism of GPR52 at the atomic level, providing the most detailed information to date on the self-activation of the orphan receptor.

Coronavirus pandemic as a universal catalyst of identification political processes in the states of The Associated Trio

The authors analyze possible consequences of COVID-19 which triggered political processes both in the states of the European Union and the states of the Associated Trio. It is noted that in current conditions, European nations will have to affirm their European status and their own self-reliance. Besides, in a new world order, during new political fragmentation, there is the probability of individual states’ enhanced roles and the discovery of new opportunities for the states of the Associated Trio. The article states that the pandemic objectively provided a convenient opportunity for the legitimization of the new world order philosophy, based on self-interest and protectionism. Because many states adopted a self-centered strategy of behavior, the issue of identifying opportunities and related political proceedings had been brought to the forefront. The authors of the article defined the main negative and positive consequences of the pandemic. Among the negative ones, the democratic regime’s weakening of competitive abilities is highlighted, which can lead to the volatility of the identification of political processes in the Associated Trio states. The possible revision of the balance of power between nations is noted as positive consequence for the Associated Trio states, which is associated with the effectiveness of response to the spread of the virus in each individual nation and the societal unity based on identification priorities.

Opportunities and Challenges of Metal-Organic Framework Micro/Nano Reactors for Cascade Reactions.

Building bridges among different types of catalysts to construct cascades is a highly worthwhile pursuit, such as chemo-, bio-, and chemo-bio cascade reactions. Cascade reactions can improve the reaction efficiency and selectivity while reducing steps of separation and purification, thereby promoting the development of "green chemistry". However, compatibility issues in cascade reactions pose significant constraints on the development of this field, particularly concerning the compatibility of diverse catalyst types, reaction conditions, and reaction rates. Metal-organic framework micro/nano reactors (MOF-MNRs) are porous crystalline materials formed by the self-assembly coordination of metal sites and organic ligands, possessing a periodic network structure. Due to the uniform pore size with the capability of controlling selective transfer of substances as well as protecting active substances and the organic-inorganic parts providing reactive microenvironment, MOF-MNRs have attracted significant attention in cascade reactions in recent years. In this Perspective, we first discuss how to address compatibility issues in cascade reactions using MOF-MNRs, including structural design and synthetic strategies. Then we summarize the research progress on MOF-MNRs in various cascade reactions. Finally, we analyze the challenges facing MOF-MNRs and potential breakthrough directions and opportunities for the future.

An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI

In this work, we present a dataset that combines functional magnetic imaging (fMRI) and electroencephalography (EEG) to use as a resource for understanding human brain function in these two imaging modalities. The dataset can also be used for optimizing preprocessing methods for simultaneously collected imaging data. The dataset includes simultaneously collected recordings from 22 individuals (ages: 23–51) across various visual and naturalistic stimuli. In addition, physiological, eye tracking, electrocardiography, and cognitive and behavioral data were collected along with this neuroimaging data. Visual tasks include a flickering checkerboard collected outside and inside the MRI scanner (EEG-only) and simultaneous EEG-fMRI recordings. Simultaneous recordings include rest, the visual paradigm Inscapes, and several short video movies representing naturalistic stimuli. Raw and preprocessed data are openly available to download. We present this dataset as part of an effort to provide open-access data to increase the opportunity for discoveries and understanding of the human brain and evaluate the correlation between electrical brain activity and blood oxygen level-dependent (BOLD) signals.

High-content screening of drug combinations of an mPGES-1 inhibitor in multicellular tumor spheroids leads to mechanistic insights into neuroblastoma chemoresistance.

High-throughput drug screening enables the discovery of new anticancer drugs. Although monolayer cell cultures are commonly used for screening, their limited complexity and translational efficiency require alternative models. Three-dimensional cell cultures, such as multicellular tumor spheroids (MCTS), mimic tumor architecture and offer promising opportunities for drug discovery. In this study, we developed a neuroblastoma MCTS model for high-content drug screening. We also aimed to decipher the mechanisms underlying synergistic drug combinations in this disease model. Several agents from different therapeutic categories and with different mechanisms of action were tested alone or in combination with selective inhibition of prostaglandin E2 by pharmacological inhibition of microsomal prostaglandin E synthase-1 (mPGES-1). After a systematic investigation of the sensitivity of individual agents and the effects of pairwise combinations, GFP-transfected MCTS were used in a confirmatory screen to validate the hits. Finally, inhibitory effects on multidrug resistance proteins were examined. In summary, we demonstrate how MCTS-based high-throughput drug screening has the potential to uncover effective drug combinations and provide insights into the mechanism of synergy between an mPGES-1 inhibitor and chemotherapeutic agents.

A versatile approach to high-density microcrystals in lipidic cubic phase for room-temperature serial crystallography.

Serial crystallography has emerged as an important tool for structural studies of integral membrane proteins. The ability to collect data from micrometre-sized weakly diffracting crystals at room temperature with minimal radiation damage has opened many new opportunities in time-resolved studies and drug discovery. However, the production of integral membrane protein microcrystals in lipidic cubic phase at the desired crystal density and quantity is challenging. This paper introduces VIALS (versatile approach to high-density microcrystals in lipidic cubic phase for serial crystallography), a simple, fast and efficient method for preparing hundreds of microlitres of high-density microcrystals suitable for serial X-ray diffraction experiments at both synchrotron and free-electron laser sources. The method is also of great benefit for rational structure-based drug design as it facilitates in situ crystal soaking and rapid determination of many co-crystal structures. Using the VIALS approach, room-temperature structures are reported of (i) the archaerhodopsin-3 protein in its dark-adapted state and 110 ns photocycle intermediate, determined to 2.2 and 1.7 Å, respectively, and (ii) the human A2A adenosine receptor in complex with two different ligands determined to a resolution of 3.5 Å.

Personal initiative, risk-taking, creativity and opportunity discovery among students

PurposeDiscovering opportunities is a key entrepreneurship competence for those who want to start their own business and who choose to enter the workforce. In this study, the authors focus on the antecedents of the ability to discover entrepreneurial opportunities by uncovering how and when students' personal initiative (Frese and Fay, 2001) leads to an increase in this key competency. The purpose of this study was to examine the role of risk-taking and creativity in the interplay between personal initiative and opportunity discovery competencies among university students.Design/methodology/approachData were collected with a self-assessment tool in two moments in time, using a sample of 103 university students from Portugal enrolled in an entrepreneurship course. The authors measured personal initiative and entrepreneurial risk-taking at the beginning of the entrepreneurship course (Time 1). Two months later (Time 2), by the end of an entrepreneurship course, the authors measured creativity and opportunity discovery abilities.FindingsThe results of this study showed that risk-taking mediates the effect of personal initiative on opportunity discovery and that creativity interacts with risk-taking and opportunity discovery. Specifically, the authors found that the relationship between entrepreneurial risk-taking and opportunity discovery is positive and statistically significant when students display average or above-average creativity. The indirect effect of the personal initiative on opportunity discovery through entrepreneurial risk-taking seems to increase when the student's creativity increases, as the index of moderated mediation is positive.Research limitations/implicationsAs with all studies, there are limitations to work of this study. First, data of this study is restricted to a sample of students from Portugal. As such, the authors should be careful about generalizations concerning students from other cultural settings; entrepreneurship competencies can differ across countries. Second, the findings of the present study are based on students’ self-reports regarding their own entrepreneurship competencies.Originality/valueThis work can inspire entrepreneurship educators to look at the entrepreneurship competencies models holistically and inspire future work to explore the relationship patterns between entrepreneurial competencies.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Obtaining advantages from knowledge base: mapping the potential to develop new technologies

ABSTRACT It is necessary for the company to find suitable new growth points when growth hits the ceiling. Technology opportunity discovery is one kind of knowledge based risk mitigation strategy (TOD). Computer-based TOD has the advantage of fast and repeatable analysis, which can be used as a complement and reference to expert decision methods. This research shows an approach to help firms to discover technology opportunities based on their knowledge foundation to obtain a competitive advantage. The proposed method involves (1) identifying the critical technologies by text analysis, measuring the importance of technology in the network and the time of bursts, (2) evaluating the relative technological advantage (RTA) of the firms as knowledge base, (3) Calculating the relatedness between technologies, assessing the development potential of non-RTA technologies from the firm’s current knowledge base and the relatedness between technologies, (4) proposing a technology opportunity analysis framework that integrates the potential and importance for technological development. This article chooses Self-Driving Vehicles (SDV) as a case study to further explain and validate the approach. This approach helps firms understand their current state and growth potential. Such recommendations are based on the knowledge foundation of different firms and therefore are individualised.

Virtual screening indicates potential inhibitors of the P2X7 receptor

Anti-inflammatory agents can be synthetic or natural compounds and are often used to attenuate different levels of inflammation. Inflammatory diseases, due to the involvement of multiple systems, are becoming difficult to treat, involve long durations of therapy where applicable, have a high cost of management and have a deleterious impact on public health. The search for natural and synthetic compounds with anti-inflammatory activity is an important strategy in drug design. Bioactive synthetic drugs may be repurposed for other pharmacological applications, and natural product chemical structures offer unlimited opportunities for new drug discoveries due to the unparalleled availability of chemical diversity. Virtual screening of 2774 molecules on the mouse P2X7 protein showed that potential ligands are composed of five flavonoids (narirutin, diosmin, complanatuside, hesperidin, and oroxin B) and other drugs such as velpatasvir, itacitinib and lifitegrast. In vitro studies in mouse cells confirmed the inhibitory activity of the indicated ligands on the P2X7 receptor by applying virtual screening. The behavior of protein bonded to the ligands was verified by analysis of the molecular dynamic simulation trajectories for four of the most potent inhibitor compounds, indicating that the ligands velpatasvir, itacitinib, lithospermic acid and narirutin remained in the binding site indicated by molecular docking.

Real-time, multiplexed rendering of lymphoma diagnostic panels from unstained tissue sections using virtual staining.

115 Background: Lymphoproliferative disorders are complex, heterogeneous diseases with an ever expanding array of subtypes and variants requiring stepwise assay approach to arrive at a precise diagnosis. While extensive characterization of lesional cells and the microenvironment (TME) via immunophenotyping and genotyping remains the gold standard, a full workup may be unachievable due to limited input tissue, turnaround time pressure, or unavailability of particular markers in-house. Even when available, the interpretation of large numbers of markers by pathologists is limited by the ability to mentally spatially align markers by recall-superimposition of single-plex markers, limiting the discovery of patterns relevant for novel treatments strategies. Here we present a deep-learning based virtual staining method which transforms a single unstained tissue section into a panel of perfectly aligned virtual immunohistochemical (IHC) stained images. Methods: 4um unstained sections were cut from 30 formalin-fixed paraffin-embedded blocks. Using a standard commercial slide scanner (Axio Scan.Z1, Zeiss), autofluorescence images were captured from unstained sections and paired with brightfield images captured from the same sections after chemical staining with H&E or various IHC stains of interest. The virtual staining was performed by a deep neural network trained as a conditional GAN using accurately co-registered patches of paired images . The training dataset comprised seven types of lymphoma plus benign lymphoid tissues. Results: We successfully created models for virtual CD3, CD8, CD20 and CD30 IHC stains as well as H&E. Analysis by board certified pathologists confirmed consistent and accurate results between virtual and IHC stained slides in a range of lymphomas and reactive lymphoid tissue. We quantitatively evaluated the co-expression of CD3/CD8 and the spatial distribution of all four markers in eight holdout slides. We demonstrated that our virtual multiplex IHC staining achieves comparable performance for WSI scoring and cellular localization with actual staining across lymphoma types. Conclusions: We present the feasibility of spatially aligned, rapid virtual H&E and IHC staining of four different markers from single, label-free autofluorescence images using deep-learning techniques. To the best of our knowledge, this is the first report of a panel of virtual IHC stains, perfectly aligned and with overlaid H&E structural context, for analysis of neoplastic and reactive lymphoid tissues. Our technique provides an opportunity for a rapid, comprehensive histopathology and immunophenotyping workflow. Multiplex virtual staining greatly expands diagnostic and discovery opportunities: high accuracy TME profiling, seamless downstream computational image analysis, and screening of large biomarker pools in retrospective analysis of clinical trials.

Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics

The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody–drug conjugates and immunotherapeutic agents.

QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)