Minimal Requirements Research Articles

Enhanced selective detection of Sudan III dye in cosmetics utilizing nitrogen-doped carbon quantum dots (NCQDs) combined with Molecularly Imprinted Polymer (MIP)

Sudan III, a commonly used dye in cosmetics, poses health risks due to its potential carcinogenicity and allergenic properties, necessitating rigorous monitoring of its illegal presence in consumer goods. In this study, a novel fluorometric assay employing Nitrogen-doped Carbon Quantum Dots coated with Molecularly Imprinted Polymers (NCQDs/SiO2@MIP) is presented to accurately and sensitively detect Sudan III. Unlike other majority analytes, Sudan III shows fluorescence enhancement upon binding with our NCQDs-MIPs. Under optimal conditions—pH 7.0, histidine buffer, 15 min incubation, and 500 mg/L NCQDs/SiO2@MIP—the method achieves a low 1.4 nM (0.49 ppb) detection limit and a broad linear range (5–800 nM). With precision (RSD < 5 %) and selectivity surpassing Sudan dyes and common cosmetic ingredients, the method’s reliability is evident. Cross-validation against LC-MS/MS confirms the accuracy of Sudan III quantification in real cosmetic samples. This NCQDs/SiO2@MIP fluorometric sensor holds promise for rapid on-site detection of Sudan III and other contaminants in cosmetics, offering superior sensitivity, selectivity, and minimal sample preparation requirements.

Heat Treatment Optimization and Nitriding Effects on Mechanical Properties of 32CrMoV12-10 Steel

32CrMoV12-10 steel is widely utilized in industries where high strength and toughness are crucial. This alloy is commonly used in manufacturing components like gun barrels, gears, and bearings, which demand exceptional mechanical properties. These parts typically undergo heat treatments, including hardening and surface enhancement techniques such as nitriding, to extend their fatigue life significantly. It is crucial to identify optimal heat treatment conditions to achieve desired material performance. In this study, commercial 32CrMoV12-10 steel was selected to investigate the impact of heat treatment parameters (temperature, duration time, and quenching media) on its mechanical properties. To analyse the effect of these parameters, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Taguchi Method were employed, facilitating a systematic examination of the process stages and subsequent mechanical testing outcomes, including Charpy V-notch impact, hardness, and tensile strength assessments. An optimal heat treatment protocol was established based on these analyses, and the nitriding depth of the optimally treated sample was examined using a micro-Vickers hardness tester. For comparative analysis, another sample with closely related outcomes was also evaluated. Results indicated that the surface hardness of the optimally treated Sample 10 reached 870 HV, with a core hardness of 417 HV, compared to non-nitrided Sample 3, which showed a surface hardness of 860 HV and a core hardness of 415 HV. Despite the proximity in their values, Sample 10 exhibited slightly higher micro-Vickers hardness than Sample 3. However, while Sample 3 fails to meet the specified tensile stress and hardness criteria, Sample 10, produced through optimization, meets criteria, ranging from 970 to 1040 N/mm², underscoring the efficacy of the optimization process facilitated by TOPSIS. This optimization was notable for its minimal experimental requirements, proving effective in conserving time, energy, and resources while investigating the processed material.

B-036 Systematic Evaluation of Antibody-Antigen Interactions for Suitability in Immunoassays Using Parallel Microarray ELISA

Abstract Background A critical step in immunoassay development is the selection of the appropriate capture and detection antibodies. The use of suboptimal antibodies can drastically affect the quality of the final assay. Traditional selection methods involve a direct testing of potential antibodies in the final assay setting; a time-consuming and material-intensive exercise that is often too complex to test all possibilities. Alternatives, such as abstract K-on/K-off determinations via SPR or BLI can be costly and often show limited translatability to the ultimate assay format. Here, we describe a novel systematic approach to rapidly screen antibody-antigen interactions for key assay specifications and test for translatability of the findings. Methods Nanoliter volumes of potential capture antibodies were spotted onto the bottom of ELISA wells, generating a microarray of antibodies in each well (&amp;gt;11,000 spots per plate). For the analysis of capture antibody-antigen interactions, a titration series of biotinylated analyte in negative sample matrix was applied. Binding was visualized using strep-polyHRP, precipitating TMB and high resolution scanning of each ELISA well. Antibody pair performance was investigated using unlabeled analyte and biotinylated detection antibody options. Sensitivity, specificity, dynamic range, binding speed, binding strength and matrix compatibility were investigated using time courses, concentration ranges, dilutions, interferent spikes and pH/salt/detergent challenges. Signals were quantified to calculate and statistically compare each possible interaction. Results Antibody-antigen interactions were investigated via parallel microarray ELISA for two example immunoassays: the HBsAg and cTnI test. For both analytes, a panel of antibodies was screened revealing antibodies with diverse characteristics. Striking differences were observed depending on the sample matrix composition. Without optimization, antibodies could be identified showing dynamic assay ranges of 3-4 log orders. Time course experiments revealed high-affinity antibodies suitable for rapid diagnostic tests. For HBsAg, levels down to 0.01 IU/mL (∼40 pg/mL) could be reproducibly detected, which is well below the minimal requirement of 0.13 IU/mL. Reactivity against multiple virus serotypes could be confirmed. Interestingly, for the cTnI test, a strong synergistic effect was observed when specific monoclonal antibodies were combined in the capture mix, resulting in a 6-fold sensitivity enhancement compared to their separate performances. Finally, we show that the results obtained with the parallel microarray ELISA approach are highly translatable to the final assay format, ranging from bead-based dry-chemistry assays to chemiluminescent immunoassays. Conclusions The parallel microarray ELISA evaluation presents a robust and reliable method for identifying the best antibodies for an immunoassay. It allows for a systematic and quantitative screen of numerous antibody options within 1-2 weeks under real immunoassay conditions with minimal antibody amounts. This method has shown to be highly translatable and is customizable to accommodate required assay specifications.

Isolation-protocol, characterization, and in-vitro performance of equine umbilical vein endothelial cells.

Angiogenesis plays a crucial role in various physiological and pathological conditions. However, research in equine angiogenesis is relative limited, necessitating the development of suitable in-vitro models. To effectively analyze angiogenesis in-vitro, it is essential to target the specific cells responsible for this process, namely endothelial cells. Human umbilical vein endothelial cells (HUVECs) are one of the most used in vitro models for studying angiogenesis in humans. Serving as an equivalent to HUVECs, we present a comprehensive isolation protocol for equine umbilical vein endothelial cells (EqUVECs) with relatively minimal requirements, thereby enhancing accessibility for researchers. Umbilical cords obtained from five foals were used to isolate endothelial cells, followed by morphological and immunohistochemical identification. Performance of the cells in various assays commonly used in angiogenesis research was studied. Additionally, EqUVEC expression of vascular endothelial growth factor (VEGF) was assessed using ELISA. EqUVECs exhibited endothelial characteristics, forming a homogeneous monolayer with distinctive morphology. Immunohistochemical staining confirmed positive expression of key endothelial markers including von Willebrand factor (vWF), CD31, and vascular endothelial growth factor receptor-2 (VEGFR-2). Furthermore, performance assessments in in-vitro assays demonstrated the viability, proliferation, migration, tube formation and VEGF-expression capabilities of EqUVECs. The findings suggest that EqUVECs are a promising in-vitro model for studying equine angiogenesis, offering a foundation for further investigations into equine-specific vascular processes and therapeutic interventions.

Recent Advances in the Preparation of Protein/peptide Microspheres by Solvent Evaporation Method.

Protein/peptide drugs are extensively used to treat various chronic and serious diseases. The short half-life in vivo of protein and peptide as therapeutics drug limit the realization of complete effects. Encapsulating drugs in microspheres can slow the speed of drug release and prolong the efficacy of drugs. The solvent evaporation method is widely used to prepare protein/ peptide microspheres because of its facile operation and minimal equipment requirements. This method has several challenges in the lower encapsulation efficiency, fluctuant release profiles and the stabilization of protein/peptides, which researchers believe may be solved by adjusting the preparation parameter or formulation of microspheres. The article discusses the formulation parameters that govern the preparation of protein/peptide-loaded microspheres by the solvent evaporation method, which provides an overview of the current promising strategies for solvent evaporation for protein/peptide microspheres. The article takes parameter evaluation as the framework, facilitating subsequent researchers to quickly find possible solutions when encountering problems.

The Universal Module for Integration of an Intelligent Assistant into iOS Applications

Investigated current implementations of the integration of intelligent assistants into mobile applications. Identified key disadvantages of existing implementations and formed the criteria for a universal intelligent assistant. Developed a proprietary software module for integrating an intelligent assistant into iOS application, which provides autonomy, minimal resource requirements, and simplifies the development process. Created a photo editor application to test the operation of the software module. The test results were presented and further development prospects were described.

The application of heating film to hands reduces the decline in manual dexterity performance associated with cold exposure.

Exposure to cold temperatures decreases finger temperature (Tfing) and dexterity. Decreased manual function and dexterity can be serious safety risks, especially in tasks that require fine motor movements that must be performed outdoors. The aim of this study was to determine whether hand heating with a minimal power requirement (14.8W) results in a smaller reduction in Tfing and manual dexterity performance during mild cold exposure compared to a non-heated control condition. In a randomized crossover design, twenty-two healthy participants were exposed to a moderately cold environment (5 ºC) for 90min. One condition had no intervention (CON), while the other had the palmar and dorsal hands heated (HEAT) by using electric heating films. Tfing and cutaneous vascular conductance (CVC) were continuously monitored using laser Doppler flowmetry. Manual dexterity performance and cognitive function were assessed by the Grooved Pegboard Test (GPT) and Stroop Color-Word (SCW) test, respectively, during the baseline period and every 30min during the cold exposure. After the cold exposure, Tfing was higher in HEAT relative to CON (CON 9.8 vs. HEAT 13.7ºC, p < 0.0001). GPT placing time, as an index of dexterity performance, was also shorter in HEAT by 14.5% (CON 69.10 ± 13.08 vs. HEAT 59.06 ± 7.99s, p < 0.0001). There was no difference in CVC between the two conditions during the cold exposure (p > 0.05 for all). Cognitive function was similar between two conditions (p > 0.05 for all). The proposed hand heating method offers a practical means of heating fingers to maintain dexterity throughout prolonged cold exposure.

UPLC-MS/MS analysis of axitinib and pharmacokinetic application in beagle dogs

ObjectiveTo develop a method for determining the concentration of axitinib in beagle dog plasma and utilize this method to investigate the pharmacokinetics of orally administered axitinib in beagle dogs. MethodsPlasma samples were processed using acetonitrile precipitation and analyzed by UPLC-MS/MS with sunitinib as an internal standard (IS). Chromatographic separation was achieved on a Waters Acquisition UPLC BEH C18 column (50 mm × 2.1 mm, 1.7 μm) with a gradient elution of acetonitrile and 0.1 % formic acid. Mass spectrometry uses an electrospray ion source for positive ion detection in a multiple reaction monitoring mode. The monitored ion transitions for axitinib and sunitinib were m/z 387 → 355.96 and m/z 399.3 → 282.96, respectively. Six beagle dogs were administered 0.33 mg/kg of axitinib orally, and venous blood samples were collected at 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, and 24 h post-dose for pharmacokinetic analysis. ResultsThe assay demonstrated a linear range of 0.5–100 ng/mL (r2 = 0.9992), and the lower limit of quantification was up to 0.5 ng/mL. Precision, as assessed by relative standard deviation (RSD), was within 8.64 % for both intraday and interday variability. The relative error (RE) for precision from −2.77 %–1.20 %. The recovery rate of the analytes exceeded 85.28 % and the matrix effect was approximately 100 %. Plasma samples maintained stability under various conditions, including room temperature storage for 12 h, processed on an automatic sampler at 4 °C for 6 h, three freeze-thaw cycles, and long-term storage at −80 °C for 60 days. Pharmacokinetic parameters were determined using DAS 2.0 software, revealing a half-life (T1/2) of 6.05 h and an area under the curve (AUC (0 →∞)) of 97.13 ng h/mL for axitinib. ConclusionsThe UPLC-MS/MS method developed in this study offers high specificity, rapid analysis, high recovery, excellent linearity, and minimal plasma volume requirements, making it well-suited for pharmacokinetic and drug interaction studies in beagles dogs.

Thermostat standardization, technology trends, future considerations: Expert interviews

Thermostats dictate the majority of energy consumption in households across North America. Despite this, there are minimal requirements for interface configuration or usability. Furthermore, thermostats have developed into highly sophisticated, multi-modal consumer devices, integrating smart technology and complex automated features. A comprehensive understanding of the benefits and challenges for new, complex thermostat features, as well advisement for the way forward, is unclear. This paper addresses this gap and establishes a harmonized expert opinion through a series of semi-structured interviews. Twenty-five interviewees from private/public industries and academia, with an average of 21 years of experience related to heating/cooling engineering, share their expert opinions for thermostat standardization, technology trends, and future considerations. Overall, experts agree that some degree of standardization is required to improve thermostat usability and put forward critical elements for a standard. Experts offer suggestions for the adoption of smart technology, including demand response programs, automated setbacks, and integration with smartphones/smart homes. Results are supplemented by an AI-generated affinity diagramming exercise and contextualized with a characterization of sixty EnergyStar certified smart thermostats.

Near‐Infrared Spectroscopy‐Based Chilled Fresh Lamb Quality Detection Using Machine Learning Algorithms

ABSTRACTTraditionally methods for assessing mutton quality rely on physical and chemical examination analyses that necessitate precise experimental environment conditions and specialized knowledge, often resulting in the compromise of the sample's structural integrity. To address these challenges, this study explores the application of near‐infrared spectroscopy (NIR) as a non‐destructive alternative for mutton quality evaluation, leveraging its operational simplicity, rapid analysis capabilities, and minimal requirement for technical expertise. Among various spectral data preprocessing techniques evaluated, multiple scattering correction (MSC) was found to significantly enhance model detection performance. Furthermore, principal component analysis (PCA) combined with the Mahalanobis Distance method was utilized for outlier identification. Finally, a mutton freshness detection model is constructed based on stacking ensemble learning, yielding an impressive accuracy rate of 0.976, outperforming other advanced approaches. In conclusion, our findings establish a robust theoretical framework for the rapid and non‐destructive assessment of meat freshness, contributing to advancements in meat quality detection.

Interlimb Coordination and Auditory-Motor Synchronization in Children with Developmental Coordination Disorder: Examining Antiphase Knee Movements with Auditory Metronomes While Seated.

Background: Children with developmental coordination disorder (DCD) exhibit reduced interlimb coordination compared to typically developing children (TDC) during complex tasks like running, which requires dynamic postural control. However, the extent of interlimb coordination difficulties in DCD during tasks that demand minimal dynamic balance, such as self-paced and externally auditory-paced tasks, remains unclear. This study aimed to compare interlimb coordination and auditory-motor synchronization between children with DCD and TDC during a seated antiphase coordination task of the lower limbs, which has minimal postural control requirements. Methods: Twenty-one children with DCD and 22 TDC performed an antiphase knee flexion and extension task while seated, in three conditions (baseline silence, metronome discrete, and metronome continuous), for three minutes. The interlimb coordination, synchronization, and spatiotemporal movement parameters were analyzed using a mixed model analysis; Results: Children with DCD displayed less coordinated interlimb movements compared to TDC (p = 0.0140), which was the result of the greater variability in coordinating antiphase knee flexion-extension movements (p < 0.0001). No group differences in spatiotemporal movement parameters were observed. Children with DCD, compared to TDC, had a lower synchronization consistency to metronomes (p = 0.0155). Discrete metronomes enhanced interlimb coordination compared to the baseline silence condition (p = 0.0046); Conclusions: The study highlights an inferior interlimb coordination and auditory-motor synchronization in children with DCD compared to TDC. Implementing metronomes with a discrete temporal structure improved the interlimb coordination of both groups during the used fundamental seated interlimb coordination task, supporting theorical frameworks of event-based timing.

Plasmonic-Enhanced Colorimetric Lateral Flow Immunoassays Using Bimetallic Silver-Coated Gold Nanostars.

The colorimetric lateral flow immunoassay (cLFIA) has gained widespread attention as a point-of-care testing (POCT) technique due to its low cost, short analysis time, portability, and capability of being performed by unskilled operators with minimal requirement of reagents. However, the low analytical sensitivity of conventional LFIA based on colloidal gold nanospheres limits their applications for sensitive detection of trace amounts of target analytes. In this study, we introduced a novel plasmonic-enhanced colorimetric LFIA (PE-cLFIA) platform featuring bimetallic silver-coated gold nanostars (BGNS) with exceptional optical properties, leading to ultrahigh visual color brightness. The BGNS-based PE-cLFIA was successfully applied to detect a model analyte, low-calcium response V (LcrV), a virulence protein factor found in Yersinia pestis, the causative agent of bubonic plague. The PE-cLFIA sensing using BGNS-3 composed of 45 nm silver thickness showed a high visual colorimetric sensitivity with a detection limit as low as 13.7 pg/mL, which was around 50 times more sensitive than that of a traditional gold nanoparticle-based LFIA. In addition, the antibody-conjugated BGNS-3 showed excellent stability over 6 months. To illustrate the potential for clinical applications, we demonstrated that our LFIA platform for detecting LcrV spiked in human serum without any sample preprocessing exhibited a detection limit of 22.8 pg/mL. These results open up new opportunities for developing hybrid nanoparticle systems for sensitive POCT PE-cLFIA screening for infectious disease detection.

Novel 3D FRP system used for more effectively and readily strengthening of precast concrete elements

The burgeoning precast concrete market necessitates innovative strengthening solutions to ensure the durability and safety of precast elements. This study presents a 3D Fiber Reinforced Polymer (FRP) system designed to reinforce precast concrete elements, providing a more effective and efficient method compared to traditional Externally Bonded (EB) and Near Surface Mounted (NSM) FRP techniques. The system utilizes extended FRP strips that function as bent anchorages, U-wraps for anchorage protection, and additional FRP patches to prevent anchorage cutoff at bent corners. A comprehensive experimental campaign involving 33 specimens assessed the system's performance, demonstrating significant improvements in flexural and shear capacities with minimal surface preparation requirements. The experiments included varying strip widths and anchorage configurations to optimize the system's effectiveness. Numerical models were developed to simulate the flexural behavior of the 3D FRP reinforced elements, accurately predicting load-deflection responses and failure modes. Both experimental and numerical results suggest that the 3D FRP system not only enhances the load capacity and structural integrity of precast concrete elements but also simplifies the installation process, offering a promising solution for the construction industry. The findings also offer practical guidelines for the design and fabrication of 3D FRP reinforced concrete elements, contributing to a more sustainable and economical construction approach.

Modelling and validating soil carbon dynamics at the long-term plot scale using the rCTOOL R package

We introduce rCTOOL, an open-source R package for carbon (C) turnover modelling, featuring comprehensive documentation and a user-friendly interface. As an enhanced version of the widely used Danish C-TOOL model, rCTOOL maintains minimal input data requirements and reliable performance, while addressing the original model's limitations in openness and documentation. To validate rCTOOL, we analysed topsoil Soil Organic Carbon (SOC) dynamics using data from the long-term Askov straw disposal experiment (1981–2019), which quantifies the impact of varying annual straw C inputs on SOC storage. Our validation shows an unbiased global prediction error of less than 10%, with a mean error of 1.1 Mg C/ha (CI -0.7–3.0 Mg C/ha). The discrepancies between observed and predicted values were primarily due to spatiotemporal variability represented by the block and year in the long-term field experiment. We demonstrate how rCTOOL is a reliable asset for diverse applications in SOC management and research.

Performance evaluation of solar-assisted humidification dehumidification system for seawater desalination: An experimental approach

Humidification/dehumidification desalination presents a promising method for small-scale water production due to its ability to utilize solar energy and minimal technological requirements. This research examines the performance of solar-assisted humidification/dehumidification (SA-HDH) desalination system, which is designed to treat seawater from Dumas Beach in Surat city of India. The system integrates a solar air heater, a packed humidifier, and a dehumidifier with an indirect evaporative cooler. Through comprehensive energy, exergy, sustainability, and economic assessments, the study aims to assess system efficiency and viability. Results demonstrate that increasing airflow rates significantly enhances heat transfer efficiency within the humidifier and dehumidifier, boosting system performance and increasing yield by 5.4–7.7 %. The average energy efficiency of 35.5 % is observed at an airflow rate of 125 kg/h. The system effectively removed 99.7 % of total dissolved solids, total hardness, and chloride from the seawater, producing high-quality freshwater. The cost of water production ranging from 0.025 to 0.028 $/L, with a sustainability index ranging from 1.052 to 1.064. These findings underscore the SA-HDH system’s potential as an efficient, sustainable, and cost-effective solution for mitigating water scarcity.

Ionic liquids based azeotropic separation of refrigerants R410A (R32+R125) and R508B (R23+R116) through hybrid extractive distillation

ABSTRACT The increment in GreenHouse Gases (GHGs) promotes high Global Warming Potential (GWP) due to the production and consumption of hydrofluorocarbons (HFCs) that diverts the aim toward the recycling and reclaiming of refrigerant gases. However, the presence of azeotrope in refrigerant mixtures restricts the recycling process. Therefore, in this work, the refrigerant R410A and R508B have been separated through the hybrid extractive distillation method by using [HMIM][TF2N] and [BMIM][BF4] as a solvent in ASPEN Plus. UNIFAC model has been used for the correlation of ionic liquid in ASPEN Plus. For thermodynamic properties, Cosmotherm has been used with the collaboration of TurbromoleX. The Vapor-liquid Equilibrium (VLE) data has been predicted by using the UNIFAC group contribution method. The Total Annual Cost (TAC) analysis with optimization has been performed. Besides that, the energy analysis has also been performed. In the overall separation process and (TAC) analysis, [BMIM][BF4] proves to be more efficient with high separation efficiency and minimal TAC requirements. Whereas, [HMIM][TF2N]/R508B provides less CO2 emission.

RNA language models predict mutations that improve RNA function.

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. While advances in deep learning enable the prediction of accurate protein structural models, RNA structure prediction is not possible at present due to a lack of abundant high-quality reference data 1 . Furthermore, available sequence data are generally not associated with organismal phenotypes that could inform RNA function 2-4 . We created GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB) 5 . GARNET links RNA sequences derived from GTDB genomes to experimental and predicted optimal growth temperatures of GTDB reference organisms. This enables construction of deep and diverse RNA sequence alignments to be used for machine learning. Using GARNET, we define the minimal requirements for a sequence- and structure-aware RNA generative model. We also develop a GPT-like language model for RNA in which overlapping triplet tokenization provides optimal encoding. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identified mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB- derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

Can rheometer be used for determination of baking stability of cocoa based filling creams

Bake stable filling creams are important for some products such as biscuits, cakes, cookies, etc. In the present study, a rheometer-based method for determining the baking stability of the cream fillings was developed considering the consistency and fluidity of the fillings after baking. For this aim, the heating and cooling process was simulated depending on the temperature of the filling reached during the baking process of cookies. Three bake-stable and three bake-unstable filling creams were obtained from the market. Rheological, textural, and baking stability analysis were performed on the samples. Change in the apparent viscosity values of the samples was observed at temperature values from 20 °C to 105 °C and from 105 °C to 30 °C using rheometer. The percentage of viscosity change at 30 °C before and after the heating process was calculated. This value for stable samples was 67%, 73% and 9%. Fluctuation observation during the measurement is also important for stability. Unstable fillings have many fluctuations in the temperature versus apparent viscosity curve as their viscosity change value was lower. The absence of fluctuations on the temperature versus viscosity graph indicates the stability of the filling creams. The findings of the study indicated that a rheometer can be used as an effective tool for determining the baking stability of the creams with minimal effort, labor, sample requirement, and time.

Genome-Wide Identification and Characterization of Alternative Oxidase (AOX) Genes in Foxtail Millet (Setaria italica): Insights into Their Abiotic Stress Response.

Alternative oxidase (AOX) serves as a critical terminal oxidase within the plant respiratory pathway, playing a significant role in cellular responses to various stresses. Foxtail millet (Setaria italica), a crop extensively cultivated across Asia, is renowned for its remarkable tolerance to abiotic stresses and minimal requirement for fertilizer. In this study, we conducted a comprehensive genome-wide identification of AOX genes in foxtail millet genome, discovering a total of five SiAOX genes. Phylogenetic analysis categorized these SiAOX members into two subgroups. Prediction of cis-elements within the promoter regions, coupled with co-expression network analysis, intimated that SiAOX proteins are likely involved in the plant's adaptive response to abiotic stresses. Employing RNA sequencing (RNA-seq) and real-time quantitative PCR (RT-qPCR), we scrutinized the expression patterns of the SiAOX genes across a variety of tissues and under multiple abiotic stress conditions. Specifically, our analysis uncovered that SiAOX1, SiAOX2, SiAOX4, and SiAOX5 display distinct tissue-specific expression profiles. Furthermore, SiAOX2, SiAOX3, SiAOX4, and SiAOX5 exhibit responsive expression patterns under abiotic stress conditions, with significant differences in expression levels observed between the shoot and root tissues of foxtail millet seedlings. Haplotype analysis of SiAOX4 and SiAOX5 revealed that these genes are in linkage disequilibrium, with Hap_2 being the superior haplotype for both, potentially conferring enhanced cold stress tolerance in the cultivar group. These findings suggest that both SiAOX4 and SiAOX5 may be targeted for selection in future breeding programs aimed at improving foxtail millet's resilience to cold stress.

Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility

This paper investigates the use of deep multi-agent reinforcement learning (MARL) for the coordination of residential energy flexibility. Particularly, we focus on achieving cooperation between homes in a way that is fully privacy-preserving, scalable, and that allows for the management of distribution network voltage constraints. Previous work demonstrated that MARL-based distributed control can be achieved with no sharing of personal data required during execution. However, previous cooperative MARL-based approaches impose an ever greater training computational burden as the size of the system increases, limiting scalability. Moreover, they do not manage their impact on distribution network constraints. We therefore adopt a deep multi-agent actor–critic method that uses a centralised but factored critic to rehearse coordination ahead of execution, such that homes can successfully cooperate at scale, with only first-order growth in computational time as the system size increases. Training times are thus 34 times shorter than with a previous state-of-the-art reinforcement learning approach without the factored critic for 30 homes. Moreover, experiments show that the cooperation of agents allows for a decrease of 47.2% in the likelihood of under-voltages. The results indicate that there is significant potential value for management of energy user bills, battery depreciation, and distribution network voltage management, with minimal information and communication infrastructure requirements, no interference with daily activities, and no sharing of personal data.