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Two-factor authentication for intellectual property transactions based on improved zero-knowledge proof
Study on the mechanism of decomposition of methane hydrate by the compound inhibitor
Predictors of anemia among HIV-infected children in Ethiopia: systematic review and meta-analysis
Water agriculture nexus a system dynamics approach for the next three decades
The Rhizophagus irregularis permease RiFTR1 functions without a ferroxidase partner for reductive iron transport
Abstract The contribution of arbuscular mycorrhizal fungi (AM fungi) to plant iron (Fe) acquisition has been demonstrated in several studies. A previous investigation revealed that the AM fungus Rhizophagus irregularis utilizes a high-affinity reductive pathway for Fe uptake, mediated by the Fe transporter RiFTR1. In this study, we used a genome-wide approach in R. irregularis to find genes encoding ferroxidases of the multicopper oxidase (MCO) gene family in an attempt to identify the ferroxidase partner of RiFTR1. Nine genes putatively encoding MCOs (RiMCO1-9) were identified. Yeast complementation assays demonstrated that RiMCO1 and RiMCO3 can function as ferroxidases, suggesting their involvement in the reductive Fe uptake pathway. Surprisingly, RiFTR1 was capable of transporting Fe in yeast without a ferroxidase partner, resembling the Fe transport mechanism of plant IRT1-like systems. RiFTR1 exhibited increase expression in arbuscules. Overexpression of RiFTR1 in Medicago truncatula roots led to enhanced mycorrhizal colonization and arbuscule abundance, highlighting the significance of Fe for AM symbiosis.
Correction to Supporting Information for Guo et al., Structural basis for coupling of the WASH subunit FAM21 with the endosomal SNX27–Retromer complex
Metabolic profiling and transcriptome analysis of Sinomenium acutum provide insights into the biosynthesis of structurally diverse benzylisoquinoline alkaloids
Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury
Production of protein-rich fungal biomass from pistachio dehulling waste using edible Neurospora intermedia
Abstract Pistachio dehulling waste, known as Pistachio byproduct mixture (PBM), is a valuable resource that is often overlooked. An effective sustainable approach involves utilizing this agricultural waste through a fermentation process using edible filamentous fungi, demonstrating potential applications in nutrition and animal feed. The focus of this study was on converting PBM extract obtained from a hot water extraction pre-treatment into a protein-rich fungal biomass of Neurospora intermedia. The optimal conditions for growth were achieved at 72 h, pH 5.5, and 30 °C which are achieved by one-factor-at-a-time approach (OFAT), resulting in 6.7 g/L of dried fungal biomass, with a protein content of 20.4%. The conversion efficiency, expressed as grams of fungal biomass per gram of initial Total COD, was 0.37 g/g, highlighting the significant potential of PBM extract with high COD levels and low sugar content for fermentation processes. Additionally, an investigation was carried out to assess the impact of inoculation method, culture adaptation, COD/N ratio, and pH control on fungal biomass growth during cultivation. The results of optimal conditions with response of fungal biomass growth showed production of 0.44, 0.45, and 0.49 g of fungal biomass per gram of initial total COD, with protein contents of 20.2%, 27.1%, and 18.6%, respectively, leading to improved fungal biomass yield. The resulting protein-rich fungal biomass with a focus on the biorefinery platform to complete the value-added cycle, holds promise for applications in various sectors including food, animal feed, biochemical, and biomaterial industries.
A nomogram for predicting T315I-free survival in chronic phase chronic myeloid leukemia patients: a multicenter retrospective study
Spatiotemporal evolution effects of habitat quality with the conservation policies in the Upper Yangtze River, China
Author Correction: The failure mechanism of the Baishi landslide in Beichuan County, Sichuan, China
Fuzzy and crisp computational analysis of certain graphs structures via machine learning techniques
AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra
Understanding the dynamic evolution of protein structures is crucial for uncovering their biological functions. Yet, real-time prediction of these dynamic structures remains a significant challenge. Two-dimensional infrared (2DIR) spectroscopy is a powerful tool for analyzing protein dynamics. However, translating its complex, low-dimensional signals into detailed three-dimensional structures is a daunting task. In this study, we introduce a machine learning-based approach that accurately predicts dynamic three-dimensional protein structures from 2DIR descriptors. Our method establishes a robust “spectrum-structure” relationship, enabling the recovery of three-dimensional structures across a wide variety of proteins. It demonstrates broad applicability in predicting dynamic structures along different protein folding trajectories, spanning timescales from microseconds to milliseconds. This approach also shows promise in identifying the structures of previously uncharacterized proteins based solely on their spectral descriptors. The integration of AI with 2DIR spectroscopy offers insights and represents a significant advancement in the real-time analysis of dynamic protein structures.
Catalytic reduction of SO2 to elemental sulfur with methane over CuOx/γ-Al2O3 catalysts
Applying deep learning and the ecological home range concept to document the spatial distribution of Atlantic salmon parr (Salmo salar L.) in experimental tanks
Abstract Measuring and monitoring fish welfare in aquaculture research relies on the use of outcome- (biotic) and input-based (e.g., abiotic) welfare indicators (WIs). Incorporating behavioural auditing into this toolbox can sometimes be challenging because sourcing quantitative data is often labour intensive and it can be a time-consuming process. Digitalization of this process via the use of computer vision and artificial intelligence can help automate and streamline the procedure, help gather continuous quantitative data and help process optimisation and assist in decision-making. The tool introduced in this study (1) adapts the DeepLabCut framework, based on computer vision and machine learning, to obtain pose estimation of Atlantic salmon parr under replicated experimental conditions, (2) quantifies the spatial distribution of the fish through a toolbox of metrics inspired by the ecological concepts home range and core area, and (3) applies it to inspect behavioural variability in and around feeding. This proof of concept study demonstrates the potential of our methodology for automating the analysis of fish behaviour in relation to home range and core area, including fish detection, spatial distribution and the variations within and between tanks. The impact of feeding on these patterns is also briefly outlined, using 5 days of experimental data as a demonstrative case study. This approach can provide stakeholders with valuable information on how the fish use their rearing environment in small-scale experimental settings and can be used for the further development of technologies for measuring and monitoring the behaviour of fish in research settings in future studies.
Fast Interpretable Greedy-Tree Sums
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure. To overcome this bias, we propose Fast Interpretable Greedy-Tree Sums (FIGS), which generalizes the Classification and Regression Trees (CART) algorithm to simultaneously grow a flexible number of trees in summation. By combining logical rules with addition, FIGS adapts to additive structure while remaining highly interpretable. Experiments on real-world datasets show FIGS achieves state-of-the-art prediction performance. To demonstrate the usefulness of FIGS in high-stakes domains, we adapt FIGS to learn clinical decision instruments (CDIs), which are tools for guiding decision-making. Specifically, we introduce a variant of FIGS known as Group Probability-Weighted Tree Sums (G-FIGS) that accounts for heterogeneity in medical data. G-FIGS derives CDIs that reflect domain knowledge and enjoy improved specificity (by up to 20% over CART) without sacrificing sensitivity or interpretability. Theoretically, we prove that FIGS learns components of additive models, a property we refer to as disentanglement. Further, we show (under oracle conditions) that tree-sum models leverage disentanglement to generalize more efficiently than single tree models when fitted to additive regression functions. Finally, to avoid overfitting with an unconstrained number of splits, we develop Bagging-FIGS, an ensemble version of FIGS that borrows the variance reduction techniques of random forests. Bagging-FIGS performs competitively with random forests and XGBoost on real-world datasets.
Mechanical properties and biodegradability of samples obtained by 3D printing using FDM technology from PLA filament with by-products
Bioinspired energy-free temperature gradient regulator for significant enhancement of thermoelectric conversion efficiency
Enhancing thermoelectric conversion efficiency (TCE) is pivotal for advancing global energy conservation and emission reduction initiatives. Traditional approaches primarily focus on microscopic strategies such as bandgap engineering, chemical potential adjustments, and entropy engineering. However, these methods face substantial limitations in practical applications due to challenging material property requirements. Additionally, the efficiencies achieved remain modest, constrained by the interdependent nature of electrical and thermal conductivities, which typically vary concurrently. Inspired by the thermoregulation mechanisms in biological organisms, we propose here a macroscopic strategy based on an expanded-plane (EP) meta-structure. Such a meta-structure, of the thermal gradient regulator type, exploits the temperature gradient concentrating effect to significantly boost TCE. We prove, both numerically and experimentally, how the proposed device can achieve temperature gradient concentration under diverse conditions. Remarkably, under undistorted temperature background conditions, we measured an striking efficiency enhancement of 59.0 % . This work highlights the EP thermal gradient regulator’s ability to boost thermoelectric efficiency, valuable across domains: aiding efficient chip cooling in microelectronics and powering wearable medical devices.