Ordered Subsets Research Articles

The Evolutionary Biology of Chelicerata.

Chelicerata constitutes an ancient, biodiverse, and ecologically significant group of Arthropoda. The study of chelicerate evolution has undergone a renaissance in the past decade, resulting in major changes to our understanding of the higher-level phylogeny and internal relationships of living orders. Included among these conceptual advances are the discoveries of multiple whole-genome duplication events in a subset of chelicerate orders, such as horseshoe crabs, spiders, and scorpions. As a result, longstanding hypotheses and textbook scenarios of chelicerate evolution, such as the monophyly of Arachnida and a single colonization of land by the common ancestor of arachnids, have come into contention. The retention of ancient, duplicated genes across this lineage also offers fertile ground for investigating the role of gene duplication in chelicerate macroevolution. This new frontier of investigation is paralleled by the timely establishment of the first gene editing protocols for arachnid models, facilitating a new generation of experimental approaches.

Mechanisms underlying aryl hydrocarbon receptor-driven divergent macrophage function.

Macrophages play an essential role in the innate immune system by differentiating into functionally diverse subsets in order to fight infection, repair damaged tissues, and regulate inappropriate immune responses. This functional diversity stems from their ability to adapt and respond to signals in the environment, which is in part mediated through aryl hydrocarbon receptor (AHR)-signaling. AHR, an environmental sensor, can be activated by various ligands, ranging from environmental contaminants to microbially derived tryptophan metabolites. This review discusses what is currently known about how AHR-signaling influences macrophage differentiation, polarization, and function. By discussing studies that are both consistent and divergent, our goal is to highlight the need for future research on the mechanisms by which AHR acts as an immunological switch in macrophages. Ultimately, understanding the contexts in which AHR-signaling promotes and/or inhibits differentiation, proinflammatory functions, and immunoregulatory functions, will help uncover functional predictions of immunotoxicity following exposure to environmental chemicals as well as better design AHR-targeted immunotherapies.

Cytokine and T cell responses in post-chikungunya viral arthritis: A cross-sectional study.

To define the relationship between chronic chikungunya post-viral arthritis disease severity, cytokine response and T cell subsets in order to identify potential targets for therapy. Participants with chikungunya arthritis were recruited from Colombia from 2019-2021. Arthritis disease severity was quantified using the Disease Activity Score-28 and an Arthritis-Flare Questionnaire adapted for chikungunya arthritis. Plasma cytokine concentrations (interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, interferon-γ and tumor necrosis factor (TNF)) were measured using a Meso Scale Diagnostics assay. Peripheral blood T cell subsets were measured using flow cytometry. Among participants with chikungunya arthritis (N = 158), IL-2 levels and frequency of regulatory T cells (Tregs) were low. Increased arthritis disease activity was associated with higher levels of inflammatory cytokines (IL-6, TNF and CRP) and immunoregulatory cytokine IL-10 (p<0.05). Increased arthritis flare activity was associated with higher Treg frequencies (p<0.05) without affecting T effector (Teff) frequencies, Treg/Teff ratios and Treg subsets. Finally, elevated levels of IL-2 were correlated with increased Treg frequency, percent Tregs out of CD4+ T cells, and Treg subsets expressing immunosuppressive markers, while also correlating with an increased percent Teff out of live lymphocytes (p<0.05). Chikungunya arthritis is characterized by increased inflammatory cytokines and deficient IL-2 and Treg responses. Greater levels of IL-2 were associated with improved Treg numbers and immunosuppressive markers. Future research may consider targeting these pathways for therapy.

Applying Machine Learning to Blood Count Data Predicts Sepsis with ICU Admission.

Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature. After exclusion, 1 381 358 laboratory requests (2016 from sepsis cases) were available. The CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857-0.887). External validations show AUROCs of 0.805 (95% CI, 0.787-0.824) for University Medicine Greifswald and 0.845 (95% CI, 0.837-0.852) for MIMIC-IV. The model including PCT revealed a significantly higher AUROC (0.857; 95% CI, 0.836-0.877) than PCT alone (0.790; 95% CI, 0.759-0.821; P < 0.001). Our results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety.

On Some Topological Indices for the Orbit Graph of Dihedral Groups

Let ΓG be the orbit graph of G, with non-central orbits in the subset of order two commuting elements in G, and the vertices of ΓG connected if they are conjugate. The main objective of this study is to compute several topological indices for the orbit graph of a dihedral group, including the Wiener index, the Zagreb index, the Schultz index, and others. We also develop a relationship between the Wiener index and the other indices for the dihedral group’s orbit graph. Furthermore, their polynomial has been computed as well.

An Extensive Analysis of Feature Selection in Machine Learning

Abstract: Researchers and engineers working in the disciplines of data mining and machine learning have difficulties while analysing high-dimensional data. A dimension reduction method called feature selection is used to pick characteristics that are pertinent to machine learning tasks. Improving the efficiency of machine learning algorithms, hastening the learning process, and creating basic models all depend critically on reducing the size of the dataset by removing superfluous and useless information. Numerous feature selection techniques have been put forth in the literature to find the pertinent feature or feature subsets in order to accomplish the goals of clustering and classification. The purpose of this study is to review the state of the art for these methods.

A combinatorial algorithm and its application in computing all minimum toll sets of graphs

Abstract This article formalizes an algorithm that computes the minimum toll sets in an undirected graph. A core process in our algorithm is to check vertex subsets in order of size. We add a new flavor to the implementation of this process; when the k − 1 k-1 -vertex subsets are already constructed, our algorithm produces the k k -vertex subsets building on the k − 1 k-1 -vertex subsets rather than reconstructing the k k -vertex subsets from the ground up as the existing algorithms would do. Our implementation is usable in combinatorial minimization problems that require checking variable-size combinations in order of size.

Randomized iterative spherical‐deconvolution informed tractogram filtering

Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable for judging the compliance of individual streamlines with the acquired data since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo-ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of complying and non-complying streamlines with the acquired data with an accuracy above 80%.

Mathematical immunology: processes, models and data assimilation

The immune system is a complex multiscale multiphysical object. Understanding its functioning in the frame of systemic analysis implies the use of mathematical modelling, formulation of data consistency criterion, estimation of parameters, uncertainty analysis, and optimal model selection. In this work, we present some promising approaches to modelling the multi-physics immune processes, i.e., cell migration in lymph nodes (LN), lymph flow, homeostatic regulation of immune responses in chronic infections.&#x0D; To describe the spatial-temporal dynamics of immune responses in lymph LN, we propose a model of lymphocyte migration, based on the second Newtons law and considering three kinds of forces. The empirical distributions of three lymphocytes motility characteristics were used for model calibration using the KolmogorovSmirnov metric.&#x0D; Prediction of lymph flow in a lymph node requires costly computations, due to diversity of sizes, forms, inner structure of LNs and boundary conditions. We proposed an approach to lymph flow modelling based on replacing the full-fledged computational physics-based model with an artificial neural network (ANN), trained on the set of pre-formed results computed using an initial mechanistic model. The ANN-based model reduces the computational time for some lymph flow characteristics by four orders of magnitude.&#x0D; Calibration of MarchukPetrov model of antiviral immune response for SARS-CoV-2 infection was performed. To this end, we used previously published data on the viral load kinetics in nasopharynx of volunteers, and data on the observed ranges of interferon, antibodies and CTLs in the blood. The parameters, which have the most significant impact at different stages of infection process, were identified.&#x0D; Inhibition of immune mechanisms, e.g., T cell exhaustion, is a distinctive feature of chronic viral infections and malignant diseases. We propose a mathematical model for the studies of regulation parameters of four exhausted T cell subsets in order to examine the balance of their proliferation and differentiation determined by interaction with SIRPa+ PD-L1+ and XCR+1 dendritic cells. The model parameters are evaluated, in order to study the reinvigoration effect of aPD-L1 therapy on the homeostasis of exhausted cells.

The Rest-frame Submillimeter Spectrum of High-redshift, Dusty, Star-forming Galaxies from the SPT-SZ Survey

We present the average rest-frame spectrum of the final catalog of dusty star-forming galaxies (DSFGs) selected from the South Pole Telescope's SPT-SZ survey and measured with Band 3 of the Atacama Large Millimeter/submillimeter Array. This work builds on the previous average rest-frame spectrum, given in Spilker et al. (2014) for the first 22 sources, and is comprised of a total of 78 sources, normalized by their respective apparent dust masses. The spectrum spans 1.9 < z < 6.9 and covers rest-frame frequencies of 240–800 GHz. Combining this data with low-J CO observations from the Australia Telescope Compact Array, we detect multiple bright line features from 12CO, [C i], and H2O, as well as fainter molecular transitions from 13CO, HCN, HCO+, HNC, CN, H2O+, and CH. We use these detections, along with limits from other molecules, to characterize the typical properties of the interstellar medium (ISM) for these high-redshift DSFGs. We are able to divide the large sample into subsets in order to explore how the average spectrum changes with various galaxy properties, such as effective dust temperature. We find that systems with hotter dust temperatures exhibit differences in the bright 12CO emission lines, and contain either warmer and more excited dense gas tracers or larger dense gas reservoirs. These observations will serve as a reference point to studies of the ISM in distant luminous DSFGs (L IR > 1012 L ⊙), and will inform studies of chemical evolution before the peak epoch of star formation at z = 2–3.

Hereditary Perfect Order Subset Groups

A finite group $G$ is said to be a POS-group if, for each $x\in G$, the cardinality of the set $\{y\in G\,\,:\,\, o(y)=o(x)\}$ is a divisor of the order of $G$. A POS-group $G$ is said to be a Hereditary Perfect Order Subset group if all even order subgroups of $G$ are POS-group. In this paper we study the structure of Hereditary perfect order subset groups.

L-fuzzy concept analysis using fuzzy categories

The purpose of this paper is to investigate topics in fuzzy concept analysis using the theory of arrow and fuzzy categories. Our approach deals with the original, fuzzy data using fuzzy subsets in order to obtain a notion of fuzzy concepts, fuzzy concept lattices, and attribute implications between fuzzy sets of attributes. Our approach is general in the sense that we only require a Heyting algebra together with a t-norm like operation as the lattice of truth values instead of the unit interval [0,1] or a substructure thereof. As a consequence, regular concept analysis, i.e., concept analysis based on regular sets and the Boolean truth values is just a special case of our approach. In addition, our approach differs from a lot of approaches in the literature, which usually first apply some kind of defuzzyfication, e.g., α-cuts, and then apply concept analysis in the classical (crisp) sense.

Machine Learning Models Accurately Predict PTHrP Results But Fail To Predict Physician Behavior

Abstract Quantification of circulating parathyroid hormone-related peptide (PTHrP) aids in the diagnosis of humoral hypercalcemia of malignancy. However, this test is often ordered in settings of low pre-test probability or mistakenly ordered when parathyroid hormone (PTH) was desired. To improve utilization, all PTHrP orders at our institutions are reviewed by a laboratory medicine resident. If the order appears to be inappropriate, the ordering physician is contacted to ask for permission to cancel. In this work, we attempted to automate this labor-intensive review process by developing machine learning models to predict the orders the physician is willing to cancel. We collected 2171 PTHrP orders that were subjected to manual review over the past 10 years. We removed any repeats, leaving 1649 first-time orders. For each order, we assigned a class label of ‘canceled’ or ‘completed’ based upon the notes in the resident’s documentation logs. For each order, we aggregated all data for the patient existing within the laboratory information system at the time of the first order (n = 40 million). Various strategies were applied to impute missing data, including leaving missingness as a feature, fill by medians, k-nearest neighbors, bagged trees, and linear regression. Class imbalances were adjusted using synthetic minority upsampling technique (SMOTE) and adaptive synthetic upsampling (ADASYN) for a final ratio of one:one. The dataset was partitioned into a 70:30 split between training and testing sets with five-fold cross-validation. Several machine learning algorithms were trained, including logistic regression, naive Bayes, random forest, and XGBoost. After training and cross-validation, the models were applied to the held-out test set, and performance was evaluated using the area under the receiver operating characteristic curve (AUC). XGBoost was the best performer at predicting the provider’s likelihood to cancel the test, but with an AUC of only 0.63. Surprised by this poor performance, we devised a second classification task of predicting the PTHrP results (normal vs abnormal, threshold = 4.2 pmol/L) for the subset of orders that were completed (n=1371). Using the same machine learning pipeline described above, we again observed that XGBoost was the best performer, but this time with an AUC of 0.89. The striking performance difference between the models trained on the two different targets suggests that the physician’s willingness to assent to our intervention may be unrelated to existing laboratory data or underlying biology. Likely explanations may include that our intervention is reaching a provider who is not the primary medical decision-maker or the correct provider at the wrong time when they are too busy to revisit details of prior work. In either case, a reflex order set that defines specific criteria for performing PHTrP upfront may be a more effective way to improve utilization.

Fusion of Clinical and Lived Experiences of Psychosis: Lessons Learned and Implications for Future Clinical Teaching

Psychosis is a clinical syndrome that can cause significant distress leading to hospitalisation/long term stays in psychiatric services. However, limited academic evidence is available examining the lived experience of psychosis. Additionally, no evidence is available looking to combine both learned and experiential knowledge as it pertains to psychosis. As such this article was created to combine both knowledge subsets in order to provide a more complete interpretation of the syndrome itself. This was achieved through academic input from a psychiatrist’s perspective as well as a reflective, autoethnographic input from a service user who has experienced psychosis. Following this collaboration, several recommendations were made to support health professionals to engage appropriately with service users with psychosis. However, the lived experiences of psychosis itself requires further investigation to identify commonalities in experiences that can support clinicians in the diagnosis and co-production of treatment regimens for these service users.

Prediction and optimization of heating and cooling loads for low energy buildings in Morocco: An application of hybrid machine learning methods

In this paper, a metamodel-based approach involving simulation data collection and data-driven techniques was used to forecast and optimize heating and cooling loads in three different climates in Morocco. The metamodel method is gaining popularity as it offers a better balance between accuracy and calculation time. In addition, a wrapper method was used as a feature selection approach to find the best feature subsets in order to reduce models’ complexity. Therefore, the performances of eleven state-of-the-art algorithms including evolutionary-based algorithms, swarm intelligence-based algorithms and human-based algorithms, coupled with a learner such as Artificial Neural Networks (ANNs) and Support vector machine (SVM), were assessed. Hybrid model based on League championship algorithm (LCA), Discrete symbiotic organisms search (DSOS) algorithm, Particle swarm optimization (PSO) algorithm showed better results in terms of accuracy and reduction of feature input parameters. Indeed, the best desired performances were obtained with LCA-SVM for cooling load, DSOS-SVM for heating load, PSO-ANN for both heating and cooling loads. On the other hand, to optimize the annual thermal load, the NSGA-II algorithm was used. Results showed a reduction of 68% of the total annual load compared to the base case in Meknes city, 73% in Ifrane and 67% in Marrakech. The solution that reflects a compromise between the two objective functions (i.e., cooling load and heating load) gives better results in terms of CO2 emissions reduction in all climates evaluated, except in the cold climate.

Efficient Reduced Order Modeling of Large Data Sets Obtained from CFD Simulations

The ever-increasing computational power has shifted direct numerical simulations towards higher Reynolds numbers and large eddy simulations towards industrially-relevant flow scales. However, this increase in both temporal and spatial resolution has severely increased the computational cost of model order reduction techniques. Reducing the full data set to a smaller subset in order to perform reduced-order modeling (ROM) may be an interesting method to keep the computational effort reasonable. Moreover, non-tomographic particle image velocimetry measurements obtain a 2D data set of a 3D flow field and an interesting research question would be to quantify the difference between this 2D ROM compared to the 3D ROM of the full flow field. To provide an answer to both issues, the aim of this study was to test a new method for obtaining POD basis functions from a small subset of data initially and using them afterwards in the ROM of either the complete data set or the reduced data set. Hence, no new method of ROM is presented, but we demonstrate a procedure to significantly reduce the computational effort required for the ROM of very large data sets and a quantification of the error introduced by reducing the size of those data sets. The method applies eigenvalue decomposition on a small subset of data extracted from a full 3D simulation and the obtained temporal coefficients are projected back on the 3D velocity fields to obtain the 3D spatial modes. To test the method, an annular jet was chosen as a flow topology due to its simple geometry and the rich dynamical content of its flow field. First, a smaller data set is extracted from the 2D cross-sectional planes and ROM is performed on that data set. Secondly, the full 3D spatial structures are reconstructed by projecting the temporal coefficients back on the 3D velocity fields and the 2D spatial structures by projecting the temporal coefficients back on the 2D velocity fields. It is shown that two perpendicular lateral planes are sufficient to capture the relevant large-scale structures. As such, the total processing time can be reduced by a factor of 136 and up to 22 times less RAM is needed to complete the ROM processing.

Reconfigurable Intelligent Surface-Aided Quadrature Reflection Modulation for Simultaneous Passive Beamforming and Information Transfer

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism, which does not require any additional radio frequency chains. However, existing reflection modulation schemes consider manipulating the ON/OFF states of RIS elements, which may result in some power loss. In this paper, we propose a new scheme, called RIS-aided quadrature reflection modulation (RIS-QRM), for better utilizing the reflection power in RIS-aided downlink multiple-input single-output (MISO) wireless systems. To this end, RIS-QRM partitions the RIS elements into two subsets in order to reflected the incident signals towards two orthogonal directions by using passive beamforming and encodes its local data onto the two partitions. A closed-form expression for the unconditional pairwise error probability of RIS-QRM in Rician fading is derived assuming maximum-likelihood detection at the receiver. Moreover, a low-complexity detection method that decouples the joint search of the constellation symbol and the RIS element partition is proposed. Computer simulation results corroborate the effectiveness of RIS-QRM and validate the analytical results. It is shown that RIS-QRM improves the error performance of the additional bits delivered by the RIS without deteriorating the error performance of the bits modulated on the constellation symbol, as compared to ON/OFF-based RIS-aided schemes.

OS-LALM-OGM Algorithm-Based Computed Tomography Image for Characteristics and Comorbidities of Patients before Transcatheter Aortic Valve Implantation.

Based on the ordered subsets (OS), a linear augmentation Lagrangian method (OS-LALM) was constructed, which was then combined with the optimized gradient method (OGM) to construct the OS-LALM-OGM, so as to discuss application of the computed tomography (CT) images based on OS-LALM-OGM in evaluation of clinical manifestations and complications of patients before transcatheter aortic valve implantation (TAVI). The OS-LALM-OGM was compared with the filtered back projection (FBP) and OS-LALM. In addition, it was applied to evaluate the conditions of 128 patients before TAVI. It was found that the peak signal-to-noise ratio (PSNR) of OS-LALM-OGM was greater than that of the FBP and OS-LALM when the number of iterations was 5, 20, and 40, while the root mean square error (RMSD) was the opposite (P < 0.05). The proportion of dyspnea was the highest, 38.28%, followed by angina (19.53%) and fainting (21.09%). The long diameter of the annulus and the average inner diameter of the annulus measured by the CT image based on the OS-LALM-OGM algorithm were greatly larger than the inner diameter of the aortic annulus measured by the CT based on the FBP algorithm (P < 0.05); the evaluation sensitivity (95.24%) and specificity (85.85%) of CT based on the OS-LALM-OGM algorithm were obviously greater than those of X-ray, which were 84.43% and 76.77%, respectively (P < 0.05). In short, the OS-LALM-OGM proposed had a relatively excellent effect on CT image reconstruction. The CT image based on the OS-LALM-OGM algorithm showed a better evaluation performance for patients before TAVI than the traditional FBP algorithm, showing higher sensitivity and specificity.

A Column Generation Algorithm for the Resource-Constrained Order Acceptance and Scheduling on Unrelated Parallel Machines

In this paper, we investigate the resource-constrained order acceptance and scheduling on unrelated parallel machines that arise in make-to-order systems. The objective of this problem is to simultaneously select a subset of orders to be processed and schedule the accepted orders on unrelated machines in such a way that the resources are not overutilized at any time. We first propose two formulations for the problem: mixed integer linear programming formulation and set partitioning. In view of the complexity of the problem, we then develop a column generation approach based on the set partitioning formulation. In the proposed column generation approach, a differential evolution algorithm is designed to solve subproblems efficiently. Extensive numerical experiments on different-sized instances are conducted, and the results demonstrate that the proposed column generation algorithm reports optimal or near-optimal solutions that are evidently better than the solutions obtained by solving the mixed integer linear programming formulation.

Weak⁎ derived sets of convex sets in duals of non-reflexive spaces

We investigate weak⁎ derived sets, that is the sets of weak⁎ limits of bounded nets, of convex subsets of duals of non-reflexive Banach spaces and their possible iterations. We prove that a dual space of any non-reflexive Banach space contains convex subsets of any finite order and a convex subset of order ω+1.