Search Results (45)

The primary objective of this paper is to quantify a realistic pathway for Canada to reach net-zero emissions by 2050. This study analyzed greenhouse gas (GHG) emissions from the 10 provinces and 3 territories of Canada based on the emissions from their economic sectors. A time series analysis was performed to understand the trajectory of the emissions profile from 1990 to 2023. Using the 2023 emissions as the baseline, a linear reduction, based on the GHG proportions from each jurisdiction, was performed and projected to 2050 (except for Prince Edward Island (PEI), where net zero was targeted for 2040). Moreover, a machine learning technique (k-means unsupervised algorithm) was used to group all the jurisdictions into homogeneous regions for national strategic climate policy initiatives. The within-cluster sum of squares identified the following clusters: Cluster 1: Manitoba (MB), New Brunswick, Nova Scotia, and Newfoundland and Labrador; Cluster 2: Alberta (AB); Cluster 3: Quebec (QC) and Saskatchewan; Cluster 4: Ontario (ON); and Cluster 5: PEI, Northwest Territories, Nunavut, and Northwest Territories. Considering the maximum GHG reductions needed per cluster (Clusters 1–5), the results show that 0.309 Mt CO₂ eq/year, 5.447 Mt CO₂ eq/year, 1.293 Mt CO₂ eq/year, 2.217 Mt CO₂ eq/year, and 0.04 Mt CO₂ eq/year must be targeted from MB (transportation), AB (stationary combustion), QC (transportation), ON (stationary combustion) and PEI (transportation), respectively. The concept of climate stabilization wedges, which provides a practical framework for addressing the monumental challenge of mitigating climate change, was introduced to each derived region to cut GHG emissions in Canada through tangible, measurable actions that is specific to each sector/cluster. The clustering-based method breaks climate mitigation problems down into manageable pieces by grouping the jurisdictions into efficient regions that can be managed effectively by fostering collaboration across jurisdictions and economic sectors. Actionable and strategic recommendations were made within each province to reach the goal of net-zero. The implications of this study for policy and climate action include the fact that actionable strategies and tailored policies are applied to each cluster’s emission profile and economic sector, ensuring equitable and effective climate mitigation strategies in Canada. Full article

Background: Urinary steroid profiling plays a key role in the diagnosis of inherited and acquired endocrine disorders. Despite the proven diagnostic value of gas chromatography–mass spectrometry (GC-MS), standardized and clinically validated protocols for extended steroid panels remain limited. Methods: We developed and validated a GC-MS method for the quantification of 32 urinary steroid metabolites, including androgens, estrogens, progestins, glucocorticoids, and mineralocorticoids. Sample preparation involved solid-phase extraction, enzymatic hydrolysis, and dual derivatization, followed by chromatographic separation and mass detection under full scan mode. Validation followed ICH M10 guidelines. Results: The method demonstrated high selectivity, accuracy (within ±15%), and precision (CV% < 15%) across three QC levels. Limits of Quantification were estimated using the Hubaux–Vos approach and were suitable for detecting both physiological and pathological steroid concentrations. Robustness and matrix effect tests confirmed the method’s reliability and reproducibility. Conclusions: This GC-MS protocol enables comprehensive urinary steroid profiling and calculation of diagnostic ratios for inborn errors of steroid metabolism and endocrine disorders. The method is suitable for clinical application and future integration into personalized medicine workflows. Full article

To improve the utilization of port resources and reduce the consumption of resources due to vessel waiting and delays, this paper investigates the Berth Allocation and Quay Crane Assignment Problem (BACAP) in container ports, focusing on the Quay Crane (QC) profile. The objective is to assign berths, berthing times, and QC profiles to vessels arriving at the port within a given planning horizon, thereby extending the traditional BACAP framework. To minimize the sum of idle time costs caused by vessel waiting and delay time costs due to late vessel departures, a mixed-integer linear programming (MILP) model is proposed. Additionally, a variable neighborhood search (VNS) algorithm is designed to solve the model, tailored to the specific characteristics of the problem. The proposed MILP model and VNS algorithm are evaluated using two sets of BACAP instances. The numerical results demonstrate the effectiveness of both the model and the algorithm, showing that VNS efficiently and reliably solves instances of various sizes. Furthermore, each neighborhood structure contributes uniquely to the iterative process. This study also analyzes the impact of different idle and delay costs on BACAP, providing valuable managerial insights. The proposed framework contributes to enhancing operational efficiency and supports sustainable port management. Full article

Yinhua Pinggan Granules (YPG) is a patented traditional Chinese medicine (TCM) compound prescription, with wide clinical application against cold, cough, and relevant diseases. However, the chemical profiles of YPG in vivo are still unknown, hindering further pharmacological and quality control (QC) researches. This study presents an ultra-high-performance liquid chromatography coupled with high-resolution orbitrap mass spectrometry (UHPLC-MS)-based method. Using the Compound Discoverer platform and a self-built ‘in-house’ compound database, the metabolic profiles and pharmacokinetics characters of YPG were investigated. Consequently, a total of 230 compounds (including 39 prototype components and 191 metabolites) were tentatively identified, in which the parent compounds were mainly flavonoids, alkaloids, and terpenoids, and the main metabolic pathways of metabolites include hydration, dehydration, and oxidation. The serum concentration of seven major representative compounds, including quinic acid, chlorogenic acid, amygdalin, 3′-methoxypuerarin, puerarin, glycyrrhizic acid, and polydatin, were also measured, to elucidate their pharmacokinetics behaviors in vivo. The pharmacokinetic study showed that the seven representative compounds were quantified in rat plasma within 5 min post-administration, with T_max of less than 2 h, followed by a gradual decline in concentration over a 10 h period. The method demonstrated excellent linearity (R² > 0.998), precision, and recovery (RSD < 15%). As the first systematic characterization of YPG’ s in vivo components and metabolites using UHPLC-MS, this study may contribute to comprehensively elucidate the metabolic profiles of the major components in YPG, and provide a critical foundation for further investigation on the QC and bioactivity research of YPG. Full article

Alternaria brassicicola is a seed-borne pathogen that causes black spot disease in Brassica crops, yet the seed defense mechanisms against this fungus remain poorly understood. Building upon recent reports that highlighted the involvement of indole pathways in seeds infected by Alternaria, this study provides transcriptomic resources to further elucidate the role of these metabolic pathways during the interaction between seeds and fungal pathogens. Using RNA sequencing, we examined the gene expression of glucosinolate-deficient mutant lines (cyp79B2/cyp79B3 and qko) and a camalexin-deficient line (pad3), generating a dataset from 14 samples. These samples were inoculated with Alternaria or water, and collected at 3, 6, and 10 days after sowing to extract total RNA. Sequencing was performed using DNBseq™ technology, followed by bioinformatics analyses with tools such as FastQC (version 0.11.9), multiQC (version 1.13), Venny (version 2.0), Salmon software (version 0.14.1), and R packages DESeq2 (version 1.36.0), ClusterProfiler (version 4.12.6) and ggplot2 (version 3.4.0). By providing this valuable dataset, we aim to contribute to a deeper understanding of seed defense mechanisms against Alternaria, leveraging RNA-seq for various analyses, including differential gene expression and co-expression correlation. This work serves as a foundation for a more comprehensive grasp of the interactions during seed infection and highlights potential targets for enhancing crop protection and management. Full article

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy. Full article

Amyloid β protein (Aβ) deposition has been implicated as the molecular driver of Alzheimer’s disease (AD) progression. The modulation of the formation of abnormal aggregates and their post-translational modification is strongly suggested as the most effective approach to anti-AD. Beta-site APP-cleaving enzyme 1 (BACE1) acts upstream in amyloidogenic processing to generate Aβ, which rapidly aggregates alone or in combination with acetylcholinesterase (AChE) to form fibrils. Accumulated Aβ promotes BACE1 activation via glycogen synthase kinase-3β (GSK-3β) and is post-translationally modified by glutaminyl cyclase (QC), resulting in increased neurotoxicity. A novel multi-target inhibitor as a potential AD agent was identified using an in silico approach and experimental validation. Magnolia officinalis, which showed the best anti-AD activity in our preliminary study, was subjected to analysis, and 82 compounds were studied. Among 23 compounds with drug-likeness, blood–brain barrier penetration, and safety, honokiol emerged as a lead structure for the inhibition of BACE1, AChE, QC, and GSK-3β in docking and molecular dynamics (MD) simulations. Furthermore, honokiol was found to be an excellent multi-target inhibitor of these enzymes with an IC₅₀ of 6–90 μM, even when compared to other natural single-target inhibitors. Taken together, the present study is the first to demonstrate that honokiol acts as a multiple enzyme inhibitor with an excellent pharmacokinetic and safety profile which may provide inhibitory effects in broad-range areas including the overproduction, aggregation, and post-translational modification of Aβ. It also provides insight into novel structural features for the design and discovery of multi-target inhibitors for anti-AD. Full article

This study focuses on how to define an Analytical Target Profile (ATP) which is intended for use in practice and on facilitating the selection of in vitro release test (IVRT) technology for diclofenac sodium topical hydrogel and cream. The implementation involves incorporating the new draft guidelines of the International Council for Harmonisation (ICH Q14) and USP (United States Pharmacopeia) Chapter 1220. Four IVRT apparatuses were compared (USP Apparatus II with immersion cell, USP Apparatus IV with semisolid adapter, static vertical diffusion cell, and a new, in-house-developed flow-through diffusion cell) with the help of the ATP. Performance characteristics such as accuracy, precision, cumulative amount released at the end of the IVRT experiment, and robustness were investigated. We found that the best apparatus for developing IVRT quality control (QC) tests in both cases was USP II with an immersion cell. All four different IVRT apparatuses were compared with each other and with the data found in the literature. Full article

Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface friction metrics such as mean profile depth (MPD) or friction number. These metrics assess chip seal quality by targeting issues such as aggregate loss or excessive bleeding, which may yield low friction numbers or texture depths. However, aggregate loss, particularly due to snowplow operations, does not always result in slippery conditions and may lead to uneven surfaces. The correlation between higher MPD or friction number and superior chip seal quality is not straightforward. This research introduces an innovative machine learning-based approach to enhance chip seal QC. Using a hybrid DBSCAN-Isolation Forest model, anomaly detection was conducted on a dataset comprising 183,794 20 m MPD measurements from actual chip seal projects across six districts in Indiana. This resulted in typical 20 m segment MPD ranges of [0.9 mm, 1.9 mm], [0.6 mm, 2.1 mm], [0.3 mm, 1.3 mm], [1.0 mm, 1.7 mm], [0.6 mm, 1.9 mm], and [1.0 mm, 2.3 mm] for the respective six districts in Indiana. A two-step QC procedure tailored for chip seal evaluation was proposed. The first step calculated outlier percentages across 1-mile segments, with an established limit of 25% outlier segments per wheel track. The second step assessed unqualified rates across projects, setting a threshold of 50% for unqualified 1-mile wheel track segments. Through validation analysis of four chip seal projects, both field inspection and friction measurements closely aligned with the proposed methodology’s results. The methodology presented establishes a foundational QC standard for chip seal projects, enhancing both acceptance efficiency and safety by using a quantitative method and minimizing the extended presence of practitioners on roadways. Full article

In order to promote the efficient and intelligent construction of container ports, we focus on the optimization of berth-and-quay-crane (QC) allocation in tidal terminal operations. This paper investigates the quay-crane-profile-(QC-profile)-based assignment problem, and considers the uncertainty in QC profiles regarding QC efficiency for the first time. A mixed-integer programming (MIP) model is established for a discrete berth allocation with a crane-assignment problem (BACAP), considering the tide time window. We aim to minimize the total time loss caused by anchorage and the delay of vessels. Leveraging the theory of uncertainty optimization, the proposed deterministic model is extended into a stochastic programming (SP) model and a distributionally robust optimization (DRO) model, via the consideration of the random QC efficiency. To solve the proposed models, a column generation (CG) algorithm is employed, utilizing the mathematical method and subproblem-solving approach. The numerical experiments with different instances demonstrate that the DRO model yields a smaller variation in the objective function values, and the effectiveness of the CG method. The experimental results verify the robustness of the constructed models, and the efficiency of the proposed algorithm. Full article

Accurate modeling of small molecules substantially reduces the logistical effort and time consumption to discover and then obtain chemicals with various applications. Molecular stereochemistry is fundamentally involved in the intermolecular interactions that give rise to biological activity. Establishing the configuration of the asymmetric carbon in diastereomers can be decisive in drug design. In the presented analytical technique, on the basis of quantitative structure–fragmentation relationship (QSFR), mass–energy profiles obtained by electron ionization mass spectrometry (EI-MS) for analytes are used, along with some profiles for candidate structures calculated by quantum chemical (QC) methods. Our paper establishes the analytical conditions that lead to the best matching scores of such profiles corresponding to the actual structures for some isomers of acetalized monosaccharides. The optimization was achieved by group validation of five analytes, using four independent variables: the QC method, the descriptor of calculated energy, the impact energy of electrons, and the descriptor of experimental energy. The true structures were obtained using experimental profiles obtained at low electronic impact energies, and profiles were calculated using the DFT (B3LYP/6-31G) and RM1 QC methods. The double quantification of the ionic mass and the energy that generates it, for only a few primary ions of the mass spectrum, even allows the differentiation of acetalized diastereomers. Full article

The atmospheric temperature profiles (ATPs) retrieved through the geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4A satellite (GIIRS/FY-4A) can effectively fill the gap of the scarce conventional sounding data in the Taklimakan Desert (TD), the second largest desert in the world, with an area of 330,000 square kilometers. In this study, we take the experimental radiosonde observations (RAOB) from one RAOB station in the hinterland of TD and seven conventional radiosondes in the oasis region around the desert as the true values and analyze the bias distribution characteristics of GIIRS/FY-4A ATPs with quality control (QC) flags 0 or 1 for this region. In addition, a bias comparison is made with GIIRS/FY-4A ATPs, and the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) ATPs. The results show that (1) Missing measurements in GIIRS/FY-4A ATPs are the most frequent in the near-surface layer, accounting for more than 80% of all the retrieved grid points. The averaged total proportion of GIIRS/FY-4A ATPs with QC marks 0 or 1 is about 33.06%. (2) The root mean square error (RMSE) of GIIRS/FY-4A ATPs is less than 3 K, smaller than that of ERA5 ATPs. The RMSE of ERA5 ATPs can exceed 10 K in the desert hinterland. The absolute mean biases of GIIRS/FY-4A ATPs and ERA5 ATPs are, respectively, smaller than 3 K and 2 K, the former being slightly larger. The correlation coefficients of GIIRS/FY-4A ATPs with ERA5 ATPs and RAOB ATPs are higher than 0.98 and 0.99, respectively, and the correlation between GIIRS/FY-4A ATPs and RAOB ATPs is inferior to the latter. (3) The overall atmospheric temperature retrieved by GIIRS/FY-4A is 0.08 K higher than the temperature of RAOB, on average, while the overall temperature from ERA5 is 0.13 K lower than that of RAOB, indicating that the temperature profile obtained by integrating GIIRS/FY-4A ATPs and ERA5 ATPs may be much closer to RAOB ATPs. (4) The probability density of the GIIRS/FY-4A ATP biases in the TD region generally follows the Gaussian distribution so that it can be effectively assimilated in the 3-D variational assimilation modules. The probability density distribution characteristics of the GIIRS/FY-4A ATP biases in the desert hinterland and oasis are not much different. However, due to the fusion analysis of the relatively rich multi-source conventional observation data from the oasis stations, the probability density of ERA5 ATPs biases at the oasis stations is nearer to Gaussian distribution than that of the GIIRS/FY-4A ATPs. In the desert hinterland, where conventional observation is not enough, the probability density distributions of the ATPs biases from ERA5 and GIIRS/FY-4A are alike. Therefore, the GIIRS FY4A can contribute to a more accurate estimation of ERA5 ATPs in the TD region. Full article

MicroRNA transcriptomes from fresh tumors and the adjacent normal tissues were profiled in 10 Korean patients diagnosed with lung adenocarcinoma using a next-generation sequencing (NGS) technique called miRNA-seq. The sequencing quality was assessed using FastQC, and low-quality or adapter-contaminated portions of the reads were removed using Trim Galore. Quality-assured reads were analyzed using miRDeep2 and Bowtie. The abundance of known miRNAs was estimated using the reads per million (RPM) normalization method. Subsequently, using DESeq2 and Wx, we identified differentially expressed miRNAs and potential miRNA biomarkers for lung adenocarcinoma tissues compared to adjacent normal tissues, respectively. We defined reliable miRNA biomarkers for lung adenocarcinoma as those detected by both methods. The miRNA-seq data are available in the Gene Expression Omnibus (GEO) database under accession number GSE196633, and all processed data can be accessed via the Mendeley data website. Full article

Real-time measurement is important in modern dissolution testing to aid in parallel drug characterisation and quality control (QC). The development of a real-time monitoring platform (microfluidic system, a novel eye movement platform with temperature sensors and accelerometers and a concentration probe setup) in conjunction with an in vitro model of the human eye (PK-Eye™) is reported. The importance of surface membrane permeability when modelling the PK-Eye™ was determined with a “pursing model” (a simplified setup of the hyaloid membrane). Parallel microfluidic control of PK-Eye™ models from a single source of pressure was performed with a ratio of 1:6 (pressure source:models) demonstrating scalability and reproducibility of pressure-flow data. Pore size and exposed surface area helped obtain a physiological range of intraocular pressure (IOP) within the models, demonstrating the need to reproduce in vitro dimensions as closely as possible to the real eye. Variation of aqueous humour flow rate throughout the day was demonstrated with a developed circadian rhythm program. Capabilities of different eye movements were programmed and achieved with an in-house eye movement platform. A concentration probe recorded the real-time concentration monitoring of injected albumin-conjugated Alexa Fluor 488 (Alexa albumin), which displayed constant release profiles. These results demonstrate the possibility of real-time monitoring of a pharmaceutical model for preclinical testing of ocular formulations. Full article

High-throughput metabolomics has enabled the development of large-scale cohort studies. Long-term studies require multiple batch-based measurements, which require sophisticated quality control (QC) to eliminate unexpected bias to obtain biologically meaningful quantified metabolomic profiles. Liquid chromatography–mass spectrometry was used to analyze 10,833 samples in 279 batch measurements. The quantified profile included 147 lipids including acylcarnitine, fatty acids, glucosylceramide, lactosylceramide, lysophosphatidic acid, and progesterone. Each batch included 40 samples, and 5 QC samples were measured for 10 samples of each. The quantified data from the QC samples were used to normalize the quantified profiles of the sample data. The intra- and inter-batch median coefficients of variation (CV) among the 147 lipids were 44.3% and 20.8%, respectively. After normalization, the CV values decreased by 42.0% and 14.7%, respectively. The effect of this normalization on the subsequent analyses was also evaluated. The demonstrated analyses will contribute to obtaining unbiased, quantified data for large-scale metabolomics. Full article