Canonical Set Research Articles

A computational model for bacteriophage ϕX174 gene expression.

Bacteriophage ϕX174 has been widely used as a model organism to study fundamental processes in molecular biology. However, several aspects of ϕX174 gene regulation are not fully resolved. Here we construct a computational model for ϕX174 and use the model to study gene regulation during the phage infection cycle. We estimate the relative strengths of transcription regulatory elements (promoters and terminators) by fitting the model to transcriptomics data. We show that the specific arrangement of a promoter followed immediately by a terminator, which occurs naturally in the ϕX174 genome, poses a parameter identifiability problem for the model, since the activity of one element can be partially compensated for by the other. We also simulate ϕX174 gene expression with two additional, putative transcription regulatory elements that have been proposed in prior studies. We find that the activities of these putative elements are estimated to be weak, and that variation in ϕX174 transcript abundances can be adequately explained without them. Overall, our work demonstrates that ϕX174 gene regulation is well described by the canonical set of promoters and terminators widely used in the literature.

EZH2 functional dichotomy in reactive oxygen species-stratified glioblastoma.

EZH2, well-known for its canonical methyltransferase activity in transcriptional repression in many cancers including glioblastoma (GBM), has an understudied non-canonical function critical for sustained tumor growth. Recent GBM consortial efforts reveal complex molecular heterogeneity for which therapeutic vulnerabilities correlated with subtype stratification remain relatively unexplored. Current enzymatic EZH2 inhibitors (EZH2inh) targeting its canonical SET domain show limited efficacy and lack durable response, suggesting that underlying differences in the non-canonical pathway may yield new knowledge. Here, we unveiled dual roles of the EZH2 CXC domain in therapeutically-distinct, reactive oxygen species (ROS)-stratified tumors. We analyzed differentially expressed genes between ROS classes by examining cis-regulatory elements as well as clustering of activities and pathways to identify EZH2 as the key mediator in ROS-stratified cohorts. Pull-down assays and CRISPR knockout of EZH2 domains were used to dissect the distinct functions of EZH2 in ROS-stratified GBM cells. The efficacy of NF-κB-inducing kinase inhibitor (NIKinh) and standard-of-care temozolomide was evaluated using orthotopic patient-derived GBM xenografts. In ROS(+) tumors, CXC-mediated co-interaction with RelB drives constitutive activation of non-canonical NF-κB2 signaling, sustaining the ROS(+) chemoresistant phenotype. In contrast, in ROS(-) subtypes, PRC2 methyltransferase activity represses canonical NF-κB. Addressing the lack of EZH2inh targeting its non-methyltransferase roles, we utilized a brain-penetrant NIKinh that disrupts EZH2-RelB binding, consequently prolonging survival in orthotopic ROS(+)-implanted mice. Our findings highlight the functional dichotomy of the EZH2 CXC domain in governing ROS-stratified therapeutic resistance, thereby advocating for the development of therapeutic approaches targeting its non-canonical activities and underscoring the significance of patient stratification methodologies.

Dual subspace clustering for spectral-spatial hyperspectral image clustering

Subspace clustering supposes that hyperspectral image (HSI) pixels lie in a union vector spaces of multiple sample subspaces without considering their dual space, i.e., spectral space. In this article, we propose a promising dual subspace clustering (DualSC) for improving spectral-spatial HSIs clustering by relaxing subspace clustering. To this end, DualSC simultaneously optimizes row and column subspace-representations of HSI superpixels to capture the intrinsic connection between spectral and spatial information. From the new perspective, the original subspace clustering can be treated as a special case of DualSC that has larger solution space, so tends to finding better sample representation matrix for applying spectral clustering. Besides, we provide theoretical proofs that show the proposed method relaxes the subspace space clustering with dual subspace, and can recover subspace-sparse representation of HSI samples. To the best of our knowledge, this work could be one of the first dual clustering method leveraging sample and spectral subspaces simultaneously. As a result, we conduct several clustering experiments on four canonical data sets, implying that our proposed method with strong interpretability reaches comparable performance and computing efficiency with other state-of-the-art methods.

Endogenous Aggregate Beliefs: Equity Trading under Heterogeneity in Ambiguity Aversion

We build a simple and tractable model of consumer heterogeneity in ambiguity aversion and use it to illustrate how asymmetric beliefs in the asset markets affect the dynamics of asset pricing, portfolio allocation, and the wealth distribution. The model is an otherwise canonical exchange-economy setting with two aggregate states of nature and two assets. The key focus is on how asset prices, when subjected to ambiguity, behave very differently in the short run than in the long run. In the short run, heterogeneity plays an important role. In the long run, belief heterogeneity persists, but the wealth distribution has evolved so that only the least ambiguity-averse investors matter for prices. As part of the short-run dynamics, the model can generate endogenous aggregate movements in non-participation—a drastic form of trading less—in response to ambiguity, with strong depressing effects on asset prices.

Fair division with two-sided preferences

We study a fair division setting in which participants are to be fairly distributed among teams, where not only do the teams have preferences over the participants as in the canonical fair division setting, but the participants also have preferences over the teams. We focus on guaranteeing envy-freeness up to one participant (EF1) for the teams together with a stability condition for both sides. We show that an allocation satisfying EF1, swap stability, and individual stability always exists and can be computed in polynomial time, even when teams may have positive or negative values for participants. When teams have nonnegative values for participants, we prove that an EF1 and Pareto optimal allocation exists and, if the valuations are binary, can be found in polynomial time. We also show that an EF1 and justified envy-free allocation does not necessarily exist, and deciding whether such an allocation exists is computationally difficult.

Mutually unbiased coherence of Bell Diagonal States* *This work is supported by NSFC under No. 11761073.

This study explores the coherence of Bell-diagonal states under two complete sets of mutually unbiased bases (MUBs), focusing on four measures: the l 1 norm, l p norm, Rényi relative α-entropy, and relative entropy. We initially construct a complete set of MUBs in C4 by incorporating two maximally entangled bases unbiased with respect to the autotensor of mutually unbiased bases (AMUBs), subsequently comparing it with the canonical set documented in existing literature. We establish the expressions for Bell-diagonal states under these two complete sets of MUBs and demonstrate that the aggregate of each coherence measure across both sets is equivalent. Additionally, we present the upper bounds for the sums of the l 1 norm, l p norm, and Rényi relative 2-entropy of coherence. Furthermore, we describe the geometric structure associated with the sum of relative entropy coherence, revealing that an increase in the sum of relative entropy of coherence corresponds to an increase in the parameter of the Bell-diagonal states.

A physics-informed machine learning model for the prediction of drop breakup in two-phase flows

Predictive simulations of two-phase flows are highly sought after because of their widespread applications in propulsion, energy, agriculture, and medicine. One crucial goal for many of these simulations is the accurate and efficient prediction of the size distribution and number density of atomized drops. The multi-scale nature of these flows makes it practically impossible to capture all scales within a single simulation. In particular, the breakup processes producing the smallest drops through secondary breakup often necessitate resolutions far below the Kolmogorov scale. Consequently, models must be employed for secondary breakup. Existing physics-based and stochastic breakup models are not universal and fail to account for the local and instantaneous flow field and drop geometry. We present a physics-informed machine learning model for predicting the statistics of daughter drops generated during the breakup of under-resolved drops. By training on high-fidelity simulations, the model can predict breakup outcomes from severely under-resolved input fields. This is made possible by a careful choice of quantities of interest and by taking inspiration from the discrete nature of breakup events to encode the temporal evolution via a mixture of sigmoid functions. We showcase proof-of-concept results from the canonical settings of 3D Taylor–Green vortex flows and homogeneous isotropic turbulence. Compared to results generated by low-resolution simulations (i.e., without a model) and baseline state-of-the-art models, our approach achieves superior accuracy in predicting drop size distribution and critical quantities of interest, such as surface area. This work paves the path towards the development of embedded machine learning models for the prediction of primary and secondary breakup.

Paradigmas canônicos de apoio aos espaços e infraestruturas náuticas e desportivas

Sport, particularly nautical and sporting areas and infrastructures, tends to play a dominant role in the urban landscape they inhabit. The study theme "Canonical paradigms to support nautical and sports spaces and infrastructures" seeks to elicit from students of the Sports Management course at the Autonomous University of Lisbon their awareness of the importance of canonical paradigms in supporting spaces and nautical and sports infrastructures, with a particular emphasis on canons, nautical and sports infrastructures, sustainability vectors, materials, and future challenges. According to sociodemographic studies, pupils varied little in terms of age, domicile, and place of residence. The study employed a quantitative, descriptive methodology. When students were asked if they believed that sustainable construction is founded on a set of practices and canons that address environmental, economic, and social considerations while meeting human needs without harming ecological sustainability or future generations. The results show a concordant opinion above 76.7%, indicating a level of worry about the usage of current resources when it comes to sporting facilities and spaces. In brief, the goal of this study was to wake up present sports managers, designers, space planners, and sports facilities to a fresh perspective and serious thinking about the future of sports structures.

Size-consistency and orbital-invariance issues revealed by VQE-UCCSD calculations with the FMO scheme.

The fragment molecular orbital (FMO) scheme is one of the popular fragmentation-based methods and has the potential advantage of making the circuit shallow for quantum chemical calculations on quantum computers. In this study, we used a GPU-accelerated quantum simulator (cuQuantum) to perform the electron correlation part of the FMO calculation as unitary coupled-cluster singles and doubles (UCCSD) with the variational quantum eigensolver (VQE) for hydrogen-bonded (FH) and (FH) -H O systems with the STO-3G basis set. VQE-UCCSD calculations were performed using both canonical and localized MO sets, and the results were examined from the point of view of size-consistency and orbital-invariance affected by the Trotter error. It was found that the use of localized MO leads to better results, especially for (FH) -H O. The GPU acceleration was substantial for the simulations with larger numbers of qubits, and was about a factor of 6.7-7.7 for 18 qubit systems.

Comprehensive data mining reveals RTK/RAS signaling pathway as a promoter of prostate cancer lineage plasticity through transcription factors and CNV

Prostate cancer lineage plasticity is a key driver in the transition to neuroendocrine prostate cancer (NEPC), and the RTK/RAS signaling pathway is a well-established cancer pathway. Nevertheless, the comprehensive link between the RTK/RAS signaling pathway and lineage plasticity has received limited investigation. In particular, the intricate regulatory network governing the interplay between RTK/RAS and lineage plasticity remains largely unexplored. The multi-omics data were clustered with the coefficient of argument and neighbor joining algorithm. Subsequently, the clustered results were analyzed utilizing the GSEA, gene sets related to stemness, multi-lineage state datasets, and canonical cancer pathway gene sets. Finally, a comprehensive exploration of the data based on the ssGSEA, WGCNA, GSEA, VIPER, prostate cancer scRNA-seq data, and the GPSAdb database was conducted. Among the six modules in the clustering results, there are 300 overlapping genes, including 3 previously unreported prostate cancer genes that were validated to be upregulated in prostate cancer through RT-qPCR. Function Module 6 shows a positive correlation with prostate cancer cell stemness, multi-lineage states, and the RTK/RAS signaling pathway. Additionally, the 19 leading-edge genes of the RTK/RAS signaling pathway promote prostate cancer lineage plasticity through a complex network of transcriptional regulation and copy number variations. In the transcriptional regulation network, TP63 and FOXO1 act as suppressors of prostate cancer lineage plasticity, whereas RORC exerts a promoting effect. This study provides a comprehensive perspective on the role of the RTK/RAS pathway in prostate cancer lineage plasticity and offers new clues for the treatment of NEPC.

Mirror, Mirror on the Wall, Who Is the Whitest of All? Racial Biases in Social Media Beauty Filters

Digital beauty filters are pervasive in social media platforms. Despite their popularity and relevance in the selfies culture, there is little research on their characteristics and potential biases. In this article, we study the existence of racial biases on the set of aesthetic canons embedded in social media beauty filters, which we refer to as the Beautyverse. First, we provide a historic contextualization of racial biases in beauty practices, followed by an extensive empirical study of racial biases in beauty filters through state-of-the-art face processing algorithms. We show that beauty filters embed Eurocentric or white canons of beauty, not only by brightening the skin color, but also by modifying facial features.

Empirical rovibrational energy levels for nitrous oxide

A survey of the huge number of measured rovibrational transitions of the 14N216O isotopologue of nitrous oxide is performed which either confirms the positions, the assignments, and the uncertainties of the measurements or refutes at least one of them. Data from 95 literature sources are analyzed and their assignments adjusted to a uniform set of polyads and associated counting numbers. This is an important result of the present study and this canonical set of vibrational state assignments is recommended for future studies. The adjusted list of 67930 transitions (43246 unique ones) then underwent a thorough Marvel (Measured Active Rotational–Vibrational Energy Levels) analysis, yielding 17561 empirical rovibrational energy levels. Uncertainties for these levels are determined using a newly implemented bootstrap approach. The bootstrap uncertainties indicate that the uncertainties for about 1.5% of the energy levels had to be increased significantly, often by more than 10 times compared to previous level uncertainty estimates. This study yields empirical values for 78 band origins of 14N216O for states with ℓ=0, where ℓ is the vibrational angular momentum quantum number. The measured transitions and the empirical energy levels are compared to the SISAM and the recent NOSL-296 line lists with the result that while the overall agreement is good, there are still a number of issues requiring further careful experimental and modeling studies.

Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs

We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. Our CSs are based on local linear regression, and are bias‐aware, in the sense that they take possible bias explicitly into account. Their construction shares similarities with that of Anderson–Rubin CSs in exactly identified instrumental variable models, and thereby avoids issues with “delta method” approximations that underlie most commonly used existing inference methods for fuzzy regression discontinuity analysis. Our CSs are asymptotically equivalent to existing procedures in canonical settings with strong identification and a continuous running variable. However, they are also valid under a wide range of other empirically relevant conditions, such as setups with discrete running variables, donut designs, and weak identification.

Enhanced Brain Tumor Diagnosis Through Differential and Canonical Quadri –Partitioned Neutrosophic Set Classification Methods:A Comparative Study

An early cancer diagnosis is carried out for adequate management of diseases. Magnetic resonance imaging (MRI) is most commonly preferred method for cancer diagnosis. Due to the uncontrolled and rapid growth of cells, brain tumor is occurred. If not treated at a preliminary phase, it may lead to death. Thus, a noteworthy prerequisite for a successful treatment outcome is an early and precise diagnosis.Many conventional methods are discussed for performing efficient tumor detection. But, conventional classification methods not distinguish MRI as primary and metastases tumors in an accurate manner. Therefore, the performance comparison of deep learning-based classification (i.e., Differential Quadri-Partitioned Neutrosophic Interval-valued Polynomial Attention-based Deep CNN (DQNI-PADCNN) method and Canonical Quadri-Partitioned Neutrosophic Set based Otsuka–Ochiai Deep Recurrent Neural Network (CQNS-ODRNN) method) is introduced to provide exact image classification results. The brain MRI images are considered as an input. MRI image classification is carried out through CNN and RNN to find the brain tumor disease. Before the classification process, input images are de-noised. The noise-removed images are get segmented to identify the region of interested regions. Later, the images are classified into four classes such as glioma, meningioma, no tumor, and pituitary classes to detect the brain tumor. Both classification methods use Quadri-Partitioned Neutrosophic set for categorizing the images. Depending on CNNs and RNNs achievement in handling intricate tasks, an optimal multi-class brain tumor diagnosis is carried out. Experimental evaluation is implemented using MATLAB 2017 for brain tumor detection with the Brain Tumor MRI dataset. To the total number of MRI images, the various performance metrics are calculated in terms of sensitivity, specificity, accuracy, and time for the detection of brain tumors.

Simple Monotonic Readjustment Policies with Applications to Markdown Pricing and Pricing in the Presence of Strategic Customers

A Simple Monotonic Dynamic Pricing Policy We consider a canonical revenue management setting in which a monopolist seeks to maximize expected total revenues by selling a fixed inventory of product to customers who arrive sequentially over time. It is known in the literature that a simple dynamic pricing policy can perform reasonably well in such setting. However, the literature typically assumes that price can move freely over time (i.e., it can either go up or down). In practice, firms often impose certain restrictions on price movement. In this paper, we consider the setting in which price can only move monotonically over time (either only decreasing or only increasing). We devise a simple provably near-optimal dynamic pricing policy. Aside from markdown applications, our policy can also be applied to the setting with strategic customers.

Abstract PO-082: Antigen presentation and inflammatory effects on immune surveillance: Window of opportunity metformin monotherapy trial for squamous cell carcinoma of the head and neck (HNSCC)

Abstract Introduction: The tumor microenvironment frequently displays dysregulated cellular metabolism, which contributes to aggressive behavior of the tumor. Though the mechanism of effect is unclear, clinical data suggest an association between metformin and improved outcomes in head and neck squamous cell carcinoma (HNSCC). The aim of this study is to investigate post-metformin treatment effects on the tumor microenvironment (TME) of HNSCC by using spatial RNA sequencing data from a metformin monotherapy window-of-opportunity clinical trial. Methods: This was a single-center prospective clinical trial of metformin in patients with HNSCC and a plan for definitive surgical resection. Primary lesions were biopsied prior to treatment and compared to matched post-treatment surgical specimens. Following biopsy-proven HNSCC diagnosis, metformin was initiated at 500 mg/day and increased to 1,000 mg twice daily by day 6. Spatial RNA sequencing of 18,000+ genes in CD45, CD163, and PanCK segments was conducted using Nanostring’s GeoMx digital spatial profiling (DSP). Sequencing data was analyzed by using unbiased gene set enrichment analysis (GSEA) and reported as a rank in gene list from 18,000+ total genes for post- vs pre-treatment analyses. Results: In all HPV positive, post-treatment samples in all three segments, GSEA demonstrated enrichment of antigen presenting cell types including NK cells, monocytes, and dendritic cells (q< 0.001). Inflammatory and metabolism signatures (IFN-y, IFN-a, oxidative phosphorylation) were enriched after metformin monotherapy in HPV+ samples within the PanCK positive segment (q< 0.001). Epithelial mesenchymal transition (EMT) was noted to be positively enriched while plasma cell signatures were negatively enriched within in HPV- post-treatment samples in the CD163 segment (q< 0.001). Within the CD45 positive segment, enriched expression of mRNAs for ribosomal-protein subsets were noted in T-cell gene sets and not in B-cell gene sets; in the CD45 segment, cytoplasmic ribosomal proteins is the second-most enriched gene set from C2 canonical pathways gene sets. Conclusion: Antigen presentation, inflammation, and ribosomal protein encoding genes were enriched with metformin monotherapy in the window of opportunity setting for HNSCC. Ribosomal proteins may also play a role in antigen presentation and immune surveillance in the post-treatment setting. Further research is required to describe the TME effects of metformin monotherapy and its effects on treatment response. Citation Format: Derek S. Mann, Sruti Tekumalla, Amiti Jain, Jacob Riordan, Alban Linnenbach, Diana Menezes, Ubaldo Martinez-Outschoorn, Joseph M. Curry. Antigen presentation and inflammatory effects on immune surveillance: Window of opportunity metformin monotherapy trial for squamous cell carcinoma of the head and neck (HNSCC) [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-082.

Large-scale coherent structure and canopy-level turbulence in the convective boundary layer over urban areas

Thermally unstable stratification alters the winds over urban areas and excites organized, large-scale motions (LSMs) in the atmospheric boundary layer (ABL). The current understanding of the interaction between urban turbulence and buoyancy-induced coherent structures is rather limited due to their multiscale, multiphysics nature. Nine sets of large-eddy simulation (LES) are performed to critically examine the influence of atmospheric instability on turbulent boundary layers (TBLs) interacting with idealized urban roughness elements. Varying the stratification from neutral to almost free convective conditions enables the coherent structures to transit from horizontal rolls to open cellular patterns. The aerodynamic roughness parameters tend to decrease in more unstable stratification, potentially a result of buoyancy effects that might enhance organized ejections and suppress energetic sweeps within the roughness sublayers (RSLs). Unexpectedly, roughness-induced sweeps dominate the vertical momentum fluxes within urban canopies even under the most convective condition. However, decoupling the momentum and heat transports in highly convective states results in a subtle influence of urban roughness on the vertical heat fluxes therein. The spatial distribution of canopy-level turbulent momentum fluxes is found to be strongly modulated by the overlying large-scale coherent structures. The modulation is enhanced with increasing instability. In addition, urban-like surfaces elevate the large-scale rolls and assist them in forming under a weaker stratification than in canonical settings.

Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding.

In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10-17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing.

Interpretation, Argumentation, and the Determinacy of Law

AbstractThis article models legal interpretation through argumentation and provides a logical analysis of interpretive arguments, their conflicts, and the resulting indeterminacies. Interpretive arguments are modelled as defeasible inferences, which can be challenged and defeated by counterarguments and be reinstated through further arguments. It is shown what claims are possibly (defensibly) or necessarily (justifiably) supported by the arguments constructible from a given interpretive basis, i.e., a set of interpretive canons coupled with reasons for their application. It is finally established under what conditions such arguments provide single outcomes or rather support alternative interpretive conclusions, thus leading to propositions of law whose truth‐value is undetermined.

LUCID: Exposing Algorithmic Bias through Inverse Design

AI systems can create, propagate, support, and automate bias in decision-making processes. To mitigate biased decisions, we both need to understand the origin of the bias and define what it means for an algorithm to make fair decisions. Most group fairness notions assess a model's equality of outcome by computing statistical metrics on the outputs. We argue that these output metrics encounter intrinsic obstacles and present a complementary approach that aligns with the increasing focus on equality of treatment. By Locating Unfairness through Canonical Inverse Design (LUCID), we generate a canonical set that shows the desired inputs for a model given a preferred output. The canonical set reveals the model's internal logic and exposes potential unethical biases by repeatedly interrogating the decision-making process. We evaluate LUCID on the UCI Adult and COMPAS data sets and find that some biases detected by a canonical set differ from those of output metrics. The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.