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Some new Steiner designs S(2, 6, 91)
No full textThe Kramer–Mesner method for constructing designs with a prescribed automorphism group G has proven effective many times. In the special case of Steiner designs, the task reduces to solving an exact cover problem, with the advantage that fast backtracking solvers like Donald Knuth’s dancing links and dancing cells can be used. We find ways to encode the inherent symmetry of the problem space, induced by the action of the normalizer of G, into a single instance of the exact cover problem. This eliminates redundant computations of certain isomorphic search branches, while preventing the overhead caused by repeatedly restarting the solver. Our improved approach is applied to the parameters S(2, 6, 91). Previously, only four such Steiner designs were known, all of which had been constructed as cyclic designs over four decades ago. We find 23 new designs, each with full automorphism group of order 84
Parameter-expanded data augmentation for analyzing multinomial probit models
No full textThe multinomial probit model has been a prominent tool to analyze nominal categorical data, but the computational complexity of maximum likelihood functions presents challenges in the usage of this model. Furthermore, the model identification is extremely tenuous and usually necessitates the covariance matrix of the latent multivariate normal variables to be a restricted covariance matrix, which brings a rigorous task for both likelihood-based estimation and Markov chain Monte Carlo (MCMC) sampling. We tackle this issue by constructing a non-identifiable model and developing parameter-expanded data augmentation. Our proposed methods circumvent sampling a restricted covariance matrix commonly implemented by a painstaking Metropolis-Hastings (MH) algorithm and enable to sample a covariance matrix without restriction through a Gibbs sampler. Therefore, our proposed methods advance the convergence and mixing of the MCMC components considerably. We investigate our proposed methods along with the method based on the identifiable model through simulation studies and further illustrate their performance by an application to consumer choice on liquid laundry detergents data
Fungal Bioreactor System: Heavy Metal Removal from Mining-Influenced Waters
No full textWith thousands of abandoned mine sites across the United States, the effects of mining waste on natural water bodies can be seen in both ecological and human health. Mycoremediation, the use of fungi for contaminant removal, is a potential new technology that can be utilized to combat the presence of heavy metals in a variety of water bodies. The implementation of a low impact system containing the fungi species Rhizopus oryzae and Pleurotus ostreatus has had 95% removal rates for Copper, Zinc, and Manganese
FractionalNet: a symmetric neural network to compute fractional-order derivatives
No full textFractional calculus extends classical differentiation to non-integer orders, providing a more flexible mathematical framework for modeling systems with memory effects and nonlocal behavior. However, the use of fractional calculus requires quite a bit of mathematical expertise and familiarity with some mathematical concepts that are not in everyday use across the broad spectrum of engineering disciplines. In this work, we present FractionalNet, a computational tool to approximate fractional derivatives. The tool design uses a symmetric neural network that is trained exclusively on integer-order data but can predict fractional-order derivatives. We demonstrate training a FractionalNet to compute half-order derivatives by using first-order derivative data. A Genetic Algorithm is employed to optimize key hyperparameters in the training process to improve the model performance. These parameters are evaluated across models with varying depths, defined by the number of identical hidden layers symmetrically placed around a central output layer. We further investigate how weight initialization techniques can improve prediction accuracy and training stability. Experimental results show that a FractionalNet with three symmetric hidden layers, particularly when paired with He-Uniform initialization and ReLU activation, consistently achieves high accuracy and consistency when predicting half-order derivatives. The results also demonstrate that combining evolutionary optimization with structured weight initialization enables FractionalNet to serve as an effective and less-complex tool for fractional derivative computation, highlighting the potential of using FractionalNet in broad engineering applications
The State and Democracy: Revitalizing America’s Government
No full textCorporate domination of public policy during the Reagan years resulted not only in increasing inequality and deteriorating living standards for millions of Americans, but in a diminution in the capacity of government to solve basic problems that are not amenable to market-oriented solutions. The authors of The State and Democracy (originally published in 1988) propose a new public philosophy for America: one which comprises communitarian values; governments at all levels which actively pursue the public interest; a participatory political culture; and a democratic, accountable process of public choice. Because of the authors’ extensive experience both inside and outside government, they offer a fresh, interdisciplinary perspective based not only on extensive research and study, but also on first-hand experience
A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification
No full textCurrent machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associated cost and time for data acquisition. The purpose of this study is to create a multi-stage ML model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This model exploits uncertainty quantification to recommend additional collection of single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) variables if baseline clinical variables and features from electrocardiogram (ECG) are not sufficient. Two hundred eighteen patients who underwent rest-gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) \u3e 5% at a 6 ± 1 month follow-up. A multi-stage ML model was created by combining two ensemble models: Ensemble 1 was trained with clinical variables and ECG; Ensemble 2 included Ensemble 1 plus SPECT MPI features. Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary. The performance of the multi-stage model was compared with that of Ensemble models 1 and 2. The response rate for CRT was 55.5% (n = 121) with overall male gender 61.0% (n = 133), an average age of 62.0 ± 11.8, and LVEF of 27.7 ± 11.0. The multi-stage model performed similarly to Ensemble 2 (which utilized the additional SPECT data) with AUC of 0.75 vs. 0.77, accuracy of 0.71 vs. 0.69, sensitivity of 0.70 vs. 0.72, and specificity 0.72 vs. 0.65, respectively. However, the multi-stage model only required SPECT MPI data for 52.7% of the patients across all folds. By using rule-based logic stemming from uncertainty quantification, the multi-stage model was able to reduce the need for additional SPECT MPI data acquisition without significantly sacrificing performance
DiazoTIME: a metabolically-resolved reference database of nitrogen-fixing microbial genomes
No full textMicrobial nitrogen fixation (diazotrophy) is a critical ecological process. We curated DiazoTIME (Diazotroph Taxonomic Identity and MEtabolism), a comprehensive database of diazotroph genomes including taxonomic annotation and metabolic prediction. DiazoTIME is unique among databases for classifying diazotrophs because it resolves both taxonomy and metabolic functionality
Methylquinolinium-enhanced near-infrared hemicyanine dye for ratiometric NAD(P)H sensing in live cells via carbon-carbon bond conjugation
No full textWe introduce a ratiometric sensor designed for sensitive and specific detection of NAD(P)H in living cells, tissues, and whole organisms. The sensor incorporates a methylquinolinium acceptor linked to a near-infrared hemicyanine dye, enabling a dual-emission ratiometric mechanism. Upon binding to NAD(P)H, the near-infrared emission at 684 nm decreases, while visible emission at 517 nm increases. This change results from the reduction of the methylquinolinium acceptor to an electron-giving 1-methyl-1,4-dihydroquinoline donor, which quenches the near-infrared emission through photon-induced electron transfer (PET). This ratiometric behavior ensures precise, live tracking of NAD(P)H levels while overcoming the systematic errors associated with intensity-based measurements. We validate the sensor\u27s performance in several experimental settings. In HeLa cells, treatment with oxaliplatin, fludarabine, and glucose all induced a dose-dependent increase in NAD(P)H, as indicated by the rising visible emission and decreasing near-infrared emission. These treatments reflect changes in cellular NADH levels, demonstrating the sensor\u27s ability to track metabolic shifts in response to pharmacological and nutritional stimuli. Additionally, larvae of the fruit fly Drosophila melanogaster treated with increasing concentrations of NADH showed similar dose-dependent emission responses, confirming the utility of this sensor in live organisms. Finally, we applied the sensor to human and mouse kidney tissue samples, including normal and diseased (autosomal dominant polycystic kidney disease, ADPKD) tissues. Diseased tissues exhibited higher NADH activity and viscosity, as evidenced by stronger visible emission and enhanced near-infrared emission, providing insights into the metabolic alterations in kidney diseases. The ratiometric nature of this near-infrared sensor allows for accurate, spatially resolved measurement of NAD(P)H activity in living systems, offering in-depth understanding of cellular metabolism, oxidative stress, and the pathophysiology of diseases such as cancer, metabolic disorders, and polycystic kidney disease. This innovative tool has great potential for advancing research in cellular bioenergetics, redox regulation, and disease mechanisms
Slope Instability Predictor-Kerala (SLIP-K): A mobile/web Application for Landslide Hazard Prediction in Idukki, India
No full textThe Western Ghats region of Idukki district in southern India is highly susceptible to rainfall-induced landslides due to steep topography, intense monsoons, and increasing land-use pressures. To address the need for localized landslide early warning system (LEWS), we developed the Slope Instability Predictor–Kerala (SLIP-K), a real-time system that integrates a physics-based landslide susceptibility model (Geographic Information System-Tool for Infinite Slope Stability Analysis (GIS-TISSA)) with empirical rainfall thresholds (RTs) quantified through data from eight automated weather stations (AWS). SLIP-K operates through an interactive web/mobile application, delivering 15-minute interval risk updates and user-friendly alerts to communities using Google Earth Engine-driven geospatial mapping. Beyond inventory-based or statistical models, SLIP-K offers physically interpretable outputs, community participatory reporting, and multilingual support. A unique aspect of this study is the multi-year, quantitative validation of SLIP-K using AWS data and fatal landslide inventories (2021–2024). Confusion matrix analysis across all AWS sites demonstrated high sensitivity (recall = 1.00), robust accuracy (0.91), and successful identification of all recorded fatal events. Additional assessments yielded a red alert success rate of 33.3% and an Area Under the Curve (AUC) of 0.82–0.88, comparable to national and international benchmarks. These findings establish SLIP-K as a transparent, statistically robust, and scalable landslide early warning framework, supporting risk reduction strategies across data-limited and topographically complex mountain environments
Pan-modification Profiling Facilitates a Cross-evolutionary Dissection of the Thermoregulated Ribosomal Epitranscriptome
No full textRibosomal RNA (rRNA) constitutes the core of ribosomes and is extensively chemically modified. Technical challenges have precluded systematically dissecting rRNA modifications and their dynamics. We develop Pan-Mod-seq, permitting inference of 16 distinct modifications across dozens of samples in parallel. We applied Pan-Mod-seq to RNA from 14 species spanning all domains of life, cultured under highly diverse conditions. While dynamic modifications are rare in mesophiles, in extreme hyperthermophiles, ∼50% of modifications are dynamic. We dissect the biogenesis and function of a conserved module of tandem m5C-ac4C modifications, co-induced at high temperatures, via enzymes intrinsically regulated by temperature and required for growth at higher temperatures. Cryo-electron microscopy (cryo-EM) structures of ribosomes from wild-type (WT) and enzyme-deficient archaea reveal recurrent molecular interactions through which they confer structural stability, and biophysical studies demonstrate their synergistic thermostabilizing role. Our findings systematically dissect rRNA modification plasticity and pave the way for surveying the rRNA epitranscriptome in health and disease