Search Results (51)

Causal discovery involves searching intractably large spaces. Decomposing the search space into classes of observationally equivalent causal models is a well-studied avenue to making discovery tractable. This paper studies the topological structure underlying causal equivalence to develop a categorical formulation of Chickering’s transformational characterization of Bayesian networks. A homotopic generalization of the Meek–Chickering theorem on the connectivity structure within causal equivalence classes and a topological representation of Greedy Equivalence Search (GES) that moves from one equivalence class of models to the next are described. Specifically, this work defines causal models as propable symmetric monoidal categories (cPROPs), which define a functor category ${\mathcal{C}}^P$ from a coalgebraic PROP P to a symmetric monoidal category $\mathcal{C}$. Such functor categories were first studied by Fox, who showed that they define the right adjoint of the inclusion of Cartesian categories in the larger category of all symmetric monoidal categories. cPROPs are an algebraic theory in the sense of Lawvere. cPROPs are related to previous categorical causal models, such as Markov categories and affine CDU categories, which can be viewed as defined by cPROP maps specifying the semantics of comonoidal structures corresponding to the “copy-delete” mechanisms. This work characterizes Pearl’s structural causal models (SCMs) in terms of Cartesian cPROPs, where the morphisms that define the endogenous variables are purely deterministic. A higher algebraic K-theory of causality is developed by studying the classifying spaces of observationally equivalent causal cPROP models by constructing their simplicial realization through the nerve functor. It is shown that Meek–Chickering causal DAG equivalence generalizes to induce a homotopic equivalence across observationally equivalent cPROP functors. A homotopic generalization of the Meek–Chickering theorem is presented, where covered edge reversals connecting equivalent DAGs induce natural transformations between homotopically equivalent cPROP functors and correspond to an equivalence structure on the corresponding string diagrams. The Grothendieck group completion of cPROP causal models is defined using the Grayson–Quillen construction and relate the classifying space of cPROP causal equivalence classes to classifying spaces of an induced groupoid. A real-world domain modeling genetic mutations in cancer is used to illustrate the framework in this paper. Full article

Accurately predicting protein–ligand binding affinity is an important step in the drug discovery process. Deep learning (DL) methods have improved binding affinity prediction by using diverse categories of molecular data. However, many models rely heavily on interaction and sequence data, which impedes proper learning and limits performance in de novo applications. To address these limitations, we developed a novel graph neural network model, called StructureNet (structure-based graph neural network), to predict protein–ligand binding affinity. StructureNet improves existing DL methods by focusing entirely on structural descriptors to mitigate data memorization issues introduced by sequence and interaction data. StructureNet represents the protein and ligand structures as graphs, which are processed using a GNN-based ensemble deep learning model. StructureNet achieved a PCC of 0.68 and an AUC of 0.75 on the PDBBind v.2020 Refined Set, outperforming similar structure-based models. External validation on the DUDE-Z dataset showed that StructureNet can effectively distinguish between active and decoy ligands. Further testing on a small subset of well-known drugs indicates that StructureNet has high potential for rapid virtual screening applications. We also hybridized StructureNet with interaction- and sequence-based models to investigate their impact on testing accuracy and found minimal difference (0.01 PCC) between merged models and StructureNet as a standalone model. An ablation study found that geometric descriptors were the key drivers of model performance, with their removal leading to a PCC decrease of over 15.7%. Lastly, we tested StructureNet on ensembles of binding complex conformers generated using molecular dynamics (MD) simulations and found that incorporating multiple conformations of the same complex often improves model accuracy by capturing binding site flexibility. Overall, the results show that structural data alone are sufficient for binding affinity predictions and can address pattern recognition challenges introduced by sequence and interaction features. Additionally, structural representations of protein–ligand complexes can be considerably improved using geometric and topological descriptors. We made StructureNet GUI interface freely available online. Full article

The goal of this paper is to shed light on the nature of mathematical practice, i.e., on “doing mathematics”. It explores Gödel’s perspective, which offers an approach to understanding mathematics centered on concepts, objects, and structures. The paper has two parts. In the first part, we situate Gödel’s reflections against the backdrop of formalism and Platonism. In the second part, we present the view shaped by Gödel’s ideas that resonates with contemporary discussions in the philosophy of mathematical practice, particularly in its attention to abstraction, generalization, and conceptual discovery, as essential components of mathematical reasoning. We illustrate this view through concrete examples from category theory and geometry. This approach reveals that mathematical practice, far from being merely formal, is a dynamic interplay of intuition, abstraction, structural, and conceptual reasoning. Such a focus underscores the need for developing the theory of concepts along the lines proposed by Gödel to provide a more natural framework for thinking about mathematics. Full article

Introduction: Ultra-processed foods (UPFs) are widely consumed despite their established associations with obesity, cardiovascular diseases, and other metabolic disorders. One potential factor contributing to high UPF consumption is the decline in cooking skills, particularly among younger generations. This study aimed to describe the cooking skill perceptions of a sample of Spanish adolescents to examine the relationship between perceived cooking skills and UPF consumption, and to identify the specific UPF subcategories most associated with perceived cooking skills. Methods: This study is a secondary cross-sectional analysis using data from the Eating Healthy and Daily Life Activities (EHDLA) study, which was conducted among 847 Spanish adolescents (12–17 years) from three secondary schools in Valle de Ricote (Region of Murcia, Spain). The participants’ perceptions of their cooking skills were assessed through the following question: “How would you rate your cooking skills?”. The response options included (a) very adequate, (b) adequate, (c) inadequate, and (d) very inadequate. UPF consumption was evaluated via a self-administered food frequency questionnaire (FFQ) previously validated for the Spanish population. UPFs were classified according to the NOVA system, which distinguishes four groups: (1) unprocessed or minimally processed foods; (2) processed culinary ingredients, such as salt, sugar, and oils, used to enhance the preparation of fresh foods; (3) processed foods; and (4) UPF and drink products. To examine the associations between perceived cooking skills and UPF consumption, marginal means and 95% confidence intervals for servings of individual UPF groups were calculated via generalized linear models. These models were adjusted for age, sex, socioeconomic status, physical activity, sedentary behavior, sleep duration, and body mass index to control for potential confounders. Post hoc comparisons between cooking skill categories were conducted via false discovery rate correction following the Benjamini–Hochberg procedure, with significance set at p < 0.05. Results: Most participants (47%) rated their cooking skills as adequate (47%) or very adequate (18%). Overall UPF intake showed a decreasing trend across skill levels, with the “very adequate” group consuming significantly fewer servings than the “very inadequate” group did (p = 0.015). Among the specific UPF categories, adolescents in the “very adequate” category consumed significantly fewer sweets than those in the “very inadequate” and “inadequate” categories did (p < 0.05 for all). Conclusions: This study revealed evidence of a relationship between cooking skills and overall UPF intake. These results support the importance of interventions that promote cooking competencies among adolescents. School-based culinary programs and community initiatives that teach adolescents simple, time-efficient, and cost-effective cooking techniques could help reduce the overall intake of UPFs. Full article

Understanding tourist profiles and behaviors during health pandemics is key to better preparedness for unforeseen future outbreaks, particularly for tourism and hospitality businesses. This study develops and applies a novel data analytics methodology to gain insights into the health risk reduction behavior of restaurant diners/patrons during their dining out experiences in a pandemic. The methodology builds on data relating to four constructs (question categories) and measurements (questions and attributes), with the constructs being worry, health risk prevention behavior, health risk reduction behavior, and demographic characteristics. As a unique contribution, the methodology generates a behavioral typology by identifying risk profiles, which are expressed as one- and two-level decision rules. For example, the results highlighted the significance of restaurants’ adherence to cautionary measures and diners’ perception of seclusion. These and other factors enable a multifaceted analysis, typology, and understanding of diners’ risk profiles, offering valuable guidance for developing managerial strategies and skill development programs to promote safer dining experiences during pandemics. Besides yielding novel types of insights through rules, another practical contribution of the research is the development of a public web-based analytics dashboard for interactive insight discovery and decision support. Full article

Oraclebone characters (OBCs) are crucial for understanding ancient Chinese history, but existing recognition methods only recognize known categories in labeled data, neglecting novel categories in unlabeled data. This work introduces a novel approach to discovering new OBC categories in unlabeled data through generalized category discovery. We address the challenges posed by OBCs’ instinctive characteristics, such as misleading contrastive views from random cropping, sub-optimal learned representation, and insufficient supervision for unlabeled data. Our method features a symmetrical structure enhanced by character component distillation and self-merged pseudo-label. We utilize random geometric transforms to create symmetrical contrastive views to avoid misleading views. Then, the proposed character component distillation procedure optimizes symmetrical shared character components for better transferable representation. Finally, we construct a self-merged pseudo-label from the model and a symmetrical teacher model to provide stable and robust supervision for unlabeled data. Extensive experiments validate the superiority of our method in recognizing ’All’ and ’Novel’ OBC categories, providing an effective tool to aid OBC researchers. Full article

Examining the composition of the typical urinary peptidome and identifying the enzymes responsible for its formation holds significant importance, as it mirrors the normal physiological state of the human body. Any deviation from this normal profile could serve as an indicator of pathological processes occurring in vivo. Consequently, this study focuses on characterizing the normal urinary peptidome and investigating the various catalytic enzymes that are involved in generating these native peptides in urine. Our findings reveal that 1503 endogenous peptides, corresponding to 436 precursor proteins, were consistently identified robustly in at least 10 samples out of a total of 19 samples. Notably, the liver and kidneys exhibited the highest number of tissue-enriched or enhanced genes in the analyzed urinary peptidome. Furthermore, among the catalytic types, CTSD (cathepsin D) and MMP2 (matrix metalloproteinase-2) emerged as the most prominent peptidases in the aspartic and metallopeptidases categories, respectively. A comparison of our dataset with two of the most comprehensive urine peptidome datasets to date indicates a consistent relative abundance of core endogenous peptides for different proteins across all three datasets. These findings can serve as a foundational reference for the discovery of biomarkers in various human diseases. Full article

PURPOSE: Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER–Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women. We considered two categories of cancer care providers and settings: practitioners providing care in the outpatient units of hospitals and institutions and private practitioners providing care in their offices. PATIENTS AN METHODS: We considered women enrolled in the Medicare fee-for-service option at age 65 who qualified by age and not disability, who were diagnosed with breast cancer with attributed patient factors of age and race, marital status, comorbidities, prior malignancies, prior therapy, disease factors of stage, grade, and ER/PR and Her2 status and laterality. We included 141 HCPCS drug J codes for chemotherapy, biotherapy, and hormone therapy drugs, which we consolidated into 46 mechanistic categories and generated AE data. We consolidated AEs from ICD9 codes into 18 categories associated with breast cancer therapy. We applied TAR mining to determine associations between the 46 TR and 18 AE categories in the context of the patient categories outlined. We applied the spark.mllib implementation of the FPGrowth algorithm, a parallel version called PFP. We considered differences of at least one unit of lift as significant between groups. The model’s results demonstrated a high overlap between the model’s identified TR-AEs associated set and the actual set. RESULTS: Our results demonstrate that specific TR/AE associations are highly dependent on race, stage, and venue of care administration. CONCLUSIONS: Our data demonstrate the usefulness of this approach in identifying differences in the associations between TRs and AEs in different populations and serve as a reference for predicting the likelihood of AEs in different patient populations treated for breast cancer. Our novel approach using unsupervised learning enables the discovery of association rules while paying special attention to temporal information, resulting in greater predictive and descriptive power as a patient’s health and life status change over time. Full article

Aerial images record the dynamic Earth terrain, reflecting changes in land cover patterns caused by natural processes and human activities. Nonetheless, prevailing aerial image classification methodologies predominantly function within a closed-set framework, thereby encountering challenges when confronted with the identification of newly emerging scenes. To address this, this paper explores an aerial image recognition scenario in which a dataset comprises both labeled and unlabeled aerial images, intending to classify all images within the unlabeled subset, termed Generalized Category Discovery (GCD). It is noteworthy that the unlabeled images may pertain to labeled classes or represent novel classes. Specifically, we first develop a contrastive learning framework drawing upon the cutting-edge algorithms in GCD. Based on the multi-object characteristics of aerial images, we then propose a slot attention-based GCD training process (Slot-GCD) that contrasts learning at both the object and image levels. It decouples multiple local object features from feature maps using slots and then reconstructs the overall semantic feature of the image based on slot confidence scores and the feature map. Finally, these object-level and image-level features are input into the contrastive learning module to enable the model to learn more precise image semantic features. Comprehensive evaluations across three public aerial image datasets highlight the superiority of our approach over state-of-the-art methods. Particularly, Slot-GCD achieves a recognition accuracy of 91.5% for known old classes and 81.9% for unknown novel class data on the AID dataset. Full article

DNA-targeted drugs constitute a specialized category of pharmaceuticals developed for cancer treatment, directly influencing various cellular processes involving DNA. These drugs aim to enhance treatment efficacy and minimize side effects by specifically targeting molecules or pathways crucial to cancer growth. Unlike conventional chemotherapeutic drugs, recent discoveries have yielded DNA-targeted agents with improved effectiveness, and a new generation is anticipated to be even more specific and potent. The sequencing of the human genome in 2001 marked a transformative milestone, contributing significantly to the advancement of targeted therapy and precision medicine. Anticipated progress in precision medicine is closely tied to the continuous development in the exploration of synthetic lethality, DNA repair, and expression regulatory mechanisms, including epigenetic modifications. The integration of technologies like circulating tumor DNA (ctDNA) analysis further enhances our ability to elucidate crucial regulatory factors, promising a more effective era of precision medicine. The combination of genomic knowledge and technological progress has led to a surge in clinical trials focusing on precision medicine. These trials utilize biomarkers for identifying genetic alterations, molecular profiling for potential therapeutic targets, and tailored cancer treatments addressing multiple genetic changes. The evolving landscape of genomics has prompted a paradigm shift from tumor-centric to individualized, genome-directed treatments based on biomarker analysis for each patient. The current treatment strategy involves identifying target genes or pathways, exploring drugs affecting these targets, and predicting adverse events. This review highlights strategies incorporating DNA-targeted drugs, such as PARP inhibitors, SLFN11, methylguanine methyltransferase (MGMT), and ATR kinase. Full article

Gout results from elevated serum urate (SU) levels, or hyperuricemia, and is a globally widespread and increasingly burdensome disease. Recent studies have illuminated the pathophysiology of gout/hyperuricemia and its epidemiology, diagnosis, treatment, and complications. The genetic involvement of urate transporters and enzymes is also proven. URAT1, a molecular therapeutic target for gout/hyperuricemia, was initially derived from research into hereditary renal hypouricemia (RHUC). RHUC is often accompanied by complications such as exercise-induced acute kidney injury, which indicates the key physiological role of uric acid. Several studies have also revealed its physiological role as both an anti-oxidant and a pro-oxidant, acting as both a scavenger and a generator of reactive oxygen species (ROSs). These discoveries have prompted research interest in SU and xanthine oxidoreductase (XOR), an enzyme that produces both urate and ROSs, as status or progression biomarkers of chronic kidney disease and cardiovascular disease. The notion of “the lower, the better” is therefore incorrect; a better understanding of uric acid handling and metabolism/transport comes from an awareness that excessively high and low levels both cause problems. We summarize here the current body of evidence, demonstrate that uric acid is much more than a metabolic waste product, and finally propose the novel disease concept of “dysuricemia” on the path toward “normouricemia”, or optimal SU level, to take advantage of the dual roles of uric acid. Our proposal should help to interpret the spectrum from hypouricemia to hyperuricemia/gout as a single disease category. Full article

Charles Scott Sherrington (1857–1952) is widely acclaimed as the most important neurophysiologist in history. He became a legend in his own time, coined the term “synapse”, and in 1932 received the Nobel Prize in medicine for his discoveries on the function of neurons. By the time he presented the Gifford Lectures 1937–38, he represented the best that science had to offer on behalf of the relationship of the mind to the natural world. The lectures, including one never publicly presented, were published as Man on His Nature (1941). Here neurology meets theology at the busy and often treacherous intersection of science and religion. Examining Sherrington’s views in some detail, the standard rendering of Sherrington as a theist cannot be sustained by their contents; he ends up as at least a humanist and perhaps an atheist. Views by neurologists and philosophers of mind some seventy to eighty years later are compared and contrasted with Sherrington’s. Although expectations of a materialist/reductionist answer to the mind/body problem have not been realized, neuroscientist Raymond Tallis appears as a parallel figure to Sherrington: both are clearly naturalistic humanists. A theistic response is presented addressing the mind/body problem from a hylomorphic process theology perspective, along with some comments regarding natural theology in general. In the end, this essay has two complementary aims: (1) to relocate Sherrington’s neurotheology—if it can be called that—in a more appropriate historiographical category; and (2) to offer a viable answer to the mind/body problem. Full article

Topic discovery involves identifying the main ideas within large volumes of textual data. It indicates recurring topics in documents, providing an overview of the text. Current topic discovery models receive the text, with or without pre-processing, including stop word removal, text cleaning, and normalization (lowercase conversion). A topic discovery process that receives general domain text with or without processing generates general topics. General topics do not offer detailed overviews of the input text, and manual text categorization is tedious and time-consuming. Extracting topics from text with an automatic classification task is necessary to generate specific topics enriched with top words that maintain semantic relationships among them. Therefore, this paper presents an approach that integrates text classification for topic discovery from large amounts of English textual data, such as 20-Newsgroups and Reuters Corpora. We rely on integrating automatic text classification before the topic discovery process to obtain specific topics for each class with relevant semantic relationships between top words. Text classification performs a word analysis that makes up a document to decide what class or category to identify; then, the proposed integration provides latent and specific topics depicted by top words with high coherence from each obtained class. Text classification accomplishes this with a convolutional neural network (CNN), incorporating an embedding model based on semantic relationships. Topic discovery over categorized text is realized with latent Dirichlet analysis (LDA), probabilistic latent semantic analysis (PLSA), and latent semantic analysis (LSA) algorithms. An evaluation process for topic discovery over categorized text was performed based on the normalized topic coherence metric. The 20-Newsgroups corpus was classified, and twenty topics with the ten top words were identified for each class. The normalized topic coherence obtained was 0.1723 with LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters Corpus was also classified, and twenty and fifty topics were identified. A normalized topic coherence of 0.1441 was achieved when applying the LDA algorithm, obtaining 20 topics for each class; with LSA, the coherence was 0.1360, and with PLSA, it was 0.1436. Full article

Candida spp. are common opportunistic microorganisms in the human body and can cause mucosal, cutaneous, and systemic infections, mainly in individuals with weakened immune systems. Candida albicans is the most isolated and pathogenic species; however, multi-drug-resistant yeasts like Candida auris have recently been found in many different regions of the world. The increasing development of resistance to common antifungals by Candida species limits the therapeutic options. In light of this, the present review attempts to discuss the significance of marine natural products in controlling the proliferation and metabolism of C. albicans and non-albicans species. Natural compounds produced by sponges, algae, sea cucumber, bacteria, fungi, and other marine organisms have been the subject of numerous studies since the 1980s, with the discovery of several products with different chemical frameworks that can inhibit Candida spp., including antifungal drug-resistant strains. Sponges fall under the topmost category when compared to all other organisms investigated. Terpenoids, sterols, and alkaloids from this group exhibit a wide array of inhibitory activity against different Candida species. Especially, hippolide J, a pair of enantiomeric sesterterpenoids isolated from the marine sponge Hippospongia lachne, exhibited strong activity against Candida albicans, Candida parapsilosis, and Candida glabrata. In addition, a comprehensive analysis was performed to unveil the mechanisms of action and synergistic activity of marine products with conventional antifungals. In general, the results of this review show that the majority of chemicals derived from the marine environment are able to control particular functions of microorganisms belonging to the Candida genus, which can provide insights into designing new anti-candidal therapies. Full article

The aim of this review article is to collate recent contributions of proteomic studies to cystic fibrosis transmembrane conductance regulator (CFTR) biology. We summarize advances from these studies and create an accessible resource for future CFTR proteomic efforts. We focus our attention on the CFTR interaction network at the cell surface, thus generating a CFTR ‘surfaceome’. We review the main findings about CFTR interactions and highlight several functional categories amongst these that could lead to the discovery of potential biomarkers and drug targets for CF. Full article