Quality Control Information Research Articles

HPLC-based metabolomics of Dendrobium officinale revealing its antioxidant ability.

Dendrobium officinale is an orchid with medicinal and nutritional properties that has received increasing attention because of its health benefits; however, there is limited information about the metabolic basis of these properties. In this report, secondary metabolites and the antioxidant activity of D. officinale stem samples from three provenances were analyzed, using a UHPLC-QqQ-MS/MS-based metabolomics approach. In total, 411 metabolites were identified including 8 categories such as flavonoids and phenolic acids, 136 of which were differential metabolites. These differentially accumulated metabolites (DAMs) were mainly enriched in secondary metabolic pathways such as flavone, flavonol, tropane, piperidine, pyridine, isoquinoline alkaloid biosynthesis and tyrosine metabolism. The metabolomic profiling suggested that the quantity and content of flavonoid compounds accounted for the highest proportion of total metabolites. Hierarchical cluster analysis (HCA) showed that the marker metabolites of D. officinale from the three provenances were mainly flavonoids, alkaloids and phenolic acids. Correlation analysis identified that 48 differential metabolites showed a significant positive correlation with antioxidant capacity (r ³ 0.8 and p< 0.0092), and flavonoids were the main factors affecting the different antioxidant activities. It is worth noting that quercetin-3-O-sophoroside-7-O-rhamnoside and dihydropinosylvin methyl ether might be the main compounds causing the differences in antioxidant capacity of Yunnan provenance (YN), Zhejiang provenance (ZJ), and Guizhou provenance (GZ). These finding provides valuable information for screening varieties, quality control and product development of D. officinale.

The AntAWS dataset: a compilation of Antarctic automatic weather station observations

Abstract. A new meteorological dataset derived from records of Antarctic automatic weather stations (here called the AntAWS dataset) at 3 h, daily and monthly resolutions including quality control information is presented here. This dataset integrates the measurements of air temperature, air pressure, relative humidity, and wind speed and direction from 267 Antarctic AWSs obtained from 1980 to 2021. The AWS spatial distribution remains heterogeneous, with the majority of instruments located in near-coastal areas and only a few inland on the East Antarctic Plateau. Among these 267 AWSs, 63 have been operating for more than 20 years and 27 of them in excess of more than 30 years. Of the five meteorological parameters, the measurements of air temperature have the best continuity and the highest data integrity. The overarching aim of this comprehensive compilation of AWS observations is to make these data easily and widely accessible for efficient use in local, regional and continental studies; it may be accessed at https://doi.org/10.48567/key7-ch19 (Wang et al., 2022). This dataset is invaluable for improved characterization of the surface climatology across the Antarctic continent, to improve our understanding of Antarctic surface snow–atmosphere interactions including precipitation events associated with atmospheric rivers and to evaluate regional climate models or meteorological reanalysis products.

Building Information Modeling (BIM) Process and Assessment methods

The construction sector has seen the potential of Building Information Modeling (BIM) to assist a transformation in the design and construction processes. The use of BIM throughout a building project for quality control and effective information use has received very little investigation, even though it can improve design quality by reducing dispute and rework. The promise of BIM application in quality management rests in its capacity to offer multi-dimensional data including design data and time sequence due to the compatibility of design data with quality and construction process with quality control. An evolutionary method of transforming the entire building process is the 5D BIM approach. This paper introduces BIM as a strong instrument for "model analysis, detection of clashes, simulation and evaluation." In addition, BIM offers a digital machine-readable representation of building data and various other processes to enhance design, development, operating processes and boost life-cycle building functions.

Challenges and possibilities in consumers’ views of organic foods in Rajshahi

This study attempts to investigate how customers view organic food. This essay also tries to investigate the issues with promoting organic farming. Lack of knowledge about organic technology and consumer distrust of goods and advertising strategies are among the important problems. It is discovered that a variety of factors affected people's decisions to buy organic food. Health and environmental awareness were positively correlated with educational attainment. The majority of consumers expressed interest in learning more about the production of organic foods, and it is evident that organic agriculture might lessen the harmful effects of chemical agriculture. The majority of customers expressed a lack of trust in organic food products by being unwilling to pay more for quality assurance and organic certification. Furthermore, there is a significant opportunity to meet the local demand for organic food, notably on the vast agricultural acreage in Rajshahi. Rajshahi has the chance to increase organic food production thanks to the lower wage, which will afterwards lead to the creation of good jobs. The barriers to the growth of the organic markets were recognized as ignorance, ecological consciousness, consumer trust, accreditation, quality control mechanisms, current technology, information literacy, storage capabilities, and crop insurance. The agricultural market chain, which is made up of many intermediary organizations, is also vulnerable. A big benefit of producing organic products that require meticulous care is the accessibility of cheaper labor forces. &#x0D;

Multimodal deep learning for predicting the choice of cut parameters in the milling process

• Information for quality control with various multimodal sources in machining sector • Multimodal machine learning for processing & relating information from modalities. • Integrating information from two modalities for prediction of a rugosity measure. • Multimodal architecture comparison between three neural networks (CNN, & LSTM). • Classification of cutting parameters & machining tools for reduced processing time. In this paper, we use multimodal deep learning to predict the choice of optimal cutting parameters (cutting speed, depth of cut, and feed rate per tooth) and the appropriate cutting tool for reproducing an existent piece of the same surface state, considering the footprints left by the cutting tool. We use the image of the aluminum plate's surface states considering the tool's footprints, the cutting parameters, and the roughness average (Ra) obtained with a roughness meter to drive our model. We built a late multimodal fusion model with two networks, a convolutional neural network (CNN) and a recurrent neural network with long short-term memory layers (LSTM). The first network consists of the first branch with a convolutional network that receives the input images. In the second network, modeling is performed by the LSTM network to receive the digital input data. This provides a framework to integrate information from two modalities to ensure surface quality in machining processes. This approach aims to assist in selecting the appropriate cutting tool and cutting parameters to automatically reproduce a machined piece using the image and roughness of an already existing piece. It is observed that the performance of the multimodal model is better than that of the unimodal model on image data. The accuracy continues to improve on both sets (training and validation), and the multimodal model finally reaches good accuracy results. Contrary to the unimodal model, which fails to generalize the training on a validation dataset. The results estimated by the multimodal fusion model are encouraging when applied to the milling activity in industrial production processes.

Application of heart-cutting two-dimensional liquid chromatography-mass spectrometry to the characterization of highly polar impurities in calcium gluconate injection

The separation and characterization of small polar impurities in polar drugs such as calcium gluconate products are always challenging, due to their poor retention on traditional reversed phase (RP) columns. Although ion-pair reversed-phase liquid chromatography (IPRP-LC) and hydrophilic interaction liquid chromatography (HILIC) are commonly used methods for polar compound analysis, both methods have some drawbacks. For example, IPRP-LC is incompatible with mass spectrometry (MS) due to the presence of non-volatile salts in its mobile phase and HILIC has limited sensitivity due to the poor solubility of polar drugs in the organic-rich sample diluents used in HILIC separations. In order to characterize the highly polar impurities in calcium gluconate injections, a heart-cutting two-dimensional liquid chromatography (2D-LC) method coupled with quadrupole time-of-flight mass spectrometry (Q-TOF/MS) was developed in this study. An IPRP-LC method in the first dimension (1D) provided the selectivity for the separation of polar analytes, using a 100% aqueous mobile phase containing phosphate buffer and ion-pair reagent. Heart cuts of target peaks were collected with sample loops and transferred to the second dimension (2D) HILIC column using an organic-rich mobile phase. In order to solve the mobile phase mismatch problem between the two dimensions, a make-up flow module was introduced in the 2D-LC system to dilute the 1D-water-rich fractions with acetonitrile before entering the sample loops. By optimizing the loop size and dilution factor, good retention and peak shape of the highly polar impurities were obtained on the 2D-HILIC column, and the ion suppression effect for MS detection from the ion-pair reagent and non-volatile salt in the 1D-effluent was minimized. A total of five impurities were identified through fragmentation studies by Q-TOF/MS analysis and their fragmentation pathways were proposed. Four of them were further confirmed by reference substances. This study not only provided useful information for quality control of calcium gluconate injections, but also provided an alternative method for polar impurity characterization in pharmaceuticals.

Ultra-high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry coupled with three-step data post-processing techniques for comprehensive profiling of the multiple components in Fufang Xianzhuli Ye.

Fufang Xianzhuli (FXZL) Ye, a classical formula of traditional Chinese medicine, is composed of Succus Bambusae, Houttuyniae herba, Pinelliae Rhizoma, Zingiberis Rhizoma Recens, Eriobotryae Folium, Platycodonis Radix, and peppermint oil. For many years, FXZL has been primarily utilised in China to treat cough and phlegm. The chemical composition of FXZL has not been reported, which seriously affects the safety of the clinical application. To establish a systematic method for rapidly classifying and recognising the chemical constituents in the FXZL for the safety of the clinical application. An ultra-high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry coupled with a three-step data post-processing strategy was developed to screen the chemical constituents of FXZL. In this experiment, the diagnostic ions in FXZL were classified into six main compounds. A total of 106 compounds were unambiguously identified in FXZL based on their retention times, accurate masses, and tandem mass spectrometry data. These include 11 chlorogenic acids, three flavonoids, eight sesquiterpenoids, six organic acids, 65 triterpenoid saponins, and 13 other compounds. The chemical composition of FXZL was identified and summarised, providing useful information for quality control and a basis for further exploration of its active ingredients in vivo.

Potential Health Risk of Aluminum in Four Camellia sinensis Cultivars and Its Content as a Function of Leaf Position.

Tea plants can accumulate aluminum (Al) in their leaves to a greater extent than most other edible plants. Few studies, however, address the Al concentration in leaves at different positions, which is important information for tea quality control. Leaves from four different cultivars of Camellia sinensis L. grown in Hawaii were analyzed for Al concentrations at 10 different leaf positions. Each cultivar was harvested in the winter and summer to determine seasonal variations of Al concentrations in the leaves. The results showed that Al concentrations in the winter leaves were an average of 1.2-fold higher than those in the summer leaves, although the seasonal variations were not statistically significant. The total Al concentration of successively lower leaves showed an exponential increase (R2 ≥ 0.900) for all four cultivars in the summer season, whereas those of the winter leaves fit a bi-phase linear regression (R2 ≥ 0.968). The regression of the Al concentrations against the top-5 leaf positions in the winter season fit one linear regression, while that against leaf positions 6–11 fit another linear regression. The average Al concentrations between the third leaf and the shoot plus first two leaves increased approximately 2.7-fold and 1.9-fold for all cultivars in the winter and summer months, respectively. The Al concentrations in the rest of the leaves increased approximately 1.5-fold in a sequential order. The target hazard quotient being between 1.69 × 10−2 and 5.06 × 10−1 in the tea leaf samples of the four cultivars in Hawaii were all less than 1, suggesting negligible health risks for consumers. The results of this study may be useful for directing harvest practices and estimating tea quality.

Study on Quality Efficiency for Bim-Based Construction Projects

The Fourth Industrial Revolution (IR 4.0) has recently compelled the construction sector to employ Building Information Modelling (BIM) as a central repository for digital project information. BIM has thus become a major influence in the Architecture, Engineering, and Construction industries (AEC). Although BIM improves design quality by minimising disputes and decreasing rework, there is limited study on applying it throughout a project for quality control and effective information use. Thus, this paper explores and discusses the quality efficiency of BIM-based construction projects. There were 206 questionnaires distributed to government agencies/clients, consultants, and contractors. Quantitative data analysis was conducted using Statistical Package for Social Science (SPSS). Consequently, twenty-seven (27) attributes of quality efficiency of BIM-based construction projects were deemed the determining factor by the three research groups (Government, consultant, and contractor). The research result was to help BIM-based construction project players, especially government agencies/clients, contractors, and consultants, by giving a roadmap to be BIM-compliant and seeing the adoption process to attain BIM level 3 (full integration) for the Malaysian construction sector.

Vehicle based system for continuous non-contact surface wave testing of pavements in 50 km/h

Non-destructive seismic surface wave measurements can be used for quality control of civil engineering infrastructures such as pavements and bridges. The seismic wave speed is directly linked to the dynamic E-modulus of asphalt concrete providing valuable information for quality control of both old and new structures. Non-destructive pavement testing is here demonstrated using a vehicle based system for continuous non-contact surface wave testing of pavements in about 50 km/h. A 48-channel air-coupled microphone array is mounted on the tow bar of a normal car along with a surface temperature IR sensor and GPS. Two metal heads on plastic strips are bouncing on the surface while driving producing high frequency (5-40 kHz) seismic waves in the asphalt concrete. Results demonstrate that the system can record continuous repeatable surface wave measurements with about two measurement points per meter while driving in a speed of 50 km/h. This measurement system opens up the possibility to perform faster and more economically efficient automated measurements with minimal disruption of traffic.

Quality Control for Offsite Construction: Review and Future Directions

The quality of building products is vital for the safety of construction and building occupants. The application of off-site construction (OSC) has been increasing in the industry, one of the reasons being its potential for improving project quality. Yet, various quality problems have been reported, such as cracks and air pockets of prefabricated components, sleeve grouting noncompactness in key nodes, which hinders the development of the OSC sector. It is urgent to carry out quality control (QC) for OSC projects. Substantial research efforts have thus been devoted to these topics in recent decades, resulting in a certain amount of literature regarding QC for OSC. The objective of this study is to conduct a state-of-the-art review of QC for OSC projects to deeply understand its knowledge gaps and future research directions. An integrated review method was used to carry out bibliometric analysis and in-depth qualitative content analysis based on selecting targeted articles. Results show that the identified knowledge gaps include: (1) OSC lacks suitable QC rules, (2) QC means for OSC is out of date, (3) QC information technology for OSC is immature, and (4) existing limitation connotation of QC for OSC. Finally, based on the identified knowledge gaps, corresponding future research directions were suggested, including quality risk transmission control for OSC, supply chain quality management for OSC projects, BIM-based quality data management for OSC, blockchain technology and quality traceability for OSC, and performance and assessment model of QC for OSC. This research contributes to an in-depth and comprehensive understanding of QC for OSC, which offers valuable implications for the quality improvement of the OSC industry.

The Global Revival of Interest in the Herbal Medicine

Herbal medicines and nutraceuticals are becoming increasingly popular and more and more individuals are turning to these products to tackle a wide range of health-related issues. In the last 10 years alone, the use of herbal medicine has spiked significantly in the world, where herbal and natural drug preparations have made their way into drug stores, food stores, and supermarkets. About 70% of the global population which make up about 4 billion people see herbal medicines as the primary source of health care. Orthodox medicinal practice and the use of herbs is an essential part of the culture of these communities The United Kingdom, North America, Europe, and Australia are also beginning to recognize the benefits of herbal remedies and are working to bring Complementary and Alternative Medicine (CAM) into mainstream therapeutics. UK and Europe, interestingly, have a well-reported history when it comes to the use of traditional herbal medicines. The primary school of thought behind the uplift in the use of herbal medicines is that it will promote a healthier lifestyle among the population of rich countries where obesity and sedentary lifestyle are bigger problems. Considered a moderate approach to healing, people in developed countries spend billions on herbal medicines as traditional home remedies and over-the-counter drugs. Henceforth, it is reasonable to conclude that the sales of traditional medicines will rise and this boom will allow herbal products to make a substantial chunk of the international drug market With the advent of herbal medicines and their use becoming increasingly common, questions with regard to their safety are also being raised. Herbal medicines, even today, remain untested and most of their use goes unregulated despite the fact that they bear significant therapeutic potential. Henceforth, there is little information about the potentially harmful effects of these products and how their use can be rationalized. The safety of these traditional therapies is further questioned due to the absence of quality control, poor labeling, and insufficient patient information. It is vital that healthcare professionals and the general public both be made aware of the traditional medicines in order to ensure maximum health benefits for them as well as enhance the quality of these herbal products. However, challenges remain to tackle matters pertaining to the toxicity of herbal medicines as well as the factors that are associated with increasing/decreasing them. It is time to ensure the safety of these traditional herbal products by drawing the attention of the regulatory bodies to the need of making sure appropriate safety of public health.

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning.

Objective The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent.

Advanced Fuel Property Data Platform: Overview and Potential Applications

This report outlines the establishment of distributed databases for management and integration of current and future aviation fuels. Aviation fuel property and performance data has been gathered for many years in public and company specific fuel surveys. These surveys are suitable for use as overall quality control information and for monitoring changes and trends in the fuels in used for flight. In recent years, significant data has been generated for alternative fuels as part of the due diligence of their approval for use through ASTM D4054, including those outside of the specification. Recently, this data, along with fundamental chemistry data has led to the creation of the Fast Track route for fuels approval when the fuel is constrained to a necessarily narrowly defined composition. The data behind these developments are often stored in a disparate, unindexed way, resulting in their underutilisation for a range of research, engineering design, specification, and in service quality control applications. To make the best use of this data, we present a scalable, Json based format for the storing of fuels data. This concept has been proposed by the Horizon 2020 Jet Fuel SCREENing and Optimization (JETSCREEN) project in conjunction with the Center of Excellence for Alternative Jet Fuels and Environment (ASCENT) programme. We have worked collaboratively to develop a joint database which currently contains data from around 30,000 conventional and 400 alternative fuels/fuel blends from a range of European and United States of America (U.S.) lead research programmes and data sources. This database can be used for a variety of purposes, both in conjunction with, or in isolation of commercially sensitive data with a greater degree of restriction. We present a number of test cases for how we see this model for data storage could be used for the benefit of all. We invite further suggestions as to how this approach could be used and welcome opportunities to work with the wider fuels community to develop this idea further.

Evaluation of microenvironment cleanliness for computer assisted sperm analysis system based on fusion of neutrosophic feasures

Background and ObjectiveComputer assisted sperm analysis (CASA) provides a quantitative assessment of the quality of male fertility. But sometimes there are difficulties in comparing among diagnoses of different laboratories due to the deficiency of quality control indicators, especially for microenvironment cleanliness. An unclean microenvironment may seriously degrade the quality of microscopic images, resulting in diagnosis inaccuracy. The primary reason for the scarcity of microenvironment cleanliness indicators is non-sperm objects can't be recognized from microscopic images accurately. In this paper, to overcome the issues mentioned above and lay a foundation for CASA's quality control from the perspective of digital image processing, an algorithm for non-sperm objects extraction was designed and then an approach for evaluating of microenvironment cleanliness proposed. MethodFirst, two features neutrosophic grayscale and neutrosophic gradient were designed. Then based on them, a neutrosophic similarity measurement was developed. Combined with Otsu method, the measurement was applied to extract non-sperm objects by adjusting the value of noise factor. Finally, the indicator ratio of non-sperm objects (RNSO) was formed for evaluating the degree of microenvironment cleanliness. ConclusionThe experiments demonstrated non-sperm targets can be extracted efficiently and effectively by adjusting the noise factor of the proposed algorithm. The RNSO indicator can reflect microenvironment cleanliness degree properly and indicate whether the operations of sample production or video acquiring meet standards. So, it can provide convictive and quantitative information for CASA's quality control.

Numerical and experimental investigations on cutting force of broaching internal spline holes

Cutting force in broaching process is essential information for quality control, troubleshooting and tool life prediction, yet existing technical bottlenecks in prediction and acquirement in internal spline holes broaching process. Based on the mechanics of orthogonal metal cutting, a numerical model of the cutting force in internal spline broaching is constructed by Johnson–Cook material constitutive law and failure model taking friction on both rake face and flank face into consideration and is used to numerically estimate the cutting forces. A measurement apparatus is developed, and real-time cutting force is monitored, which contains the effects of structural vibration and can be divided into static cutting force and dynamic cutting force. Wavelet transform filtering method is therefore employed to separate the static component from dynamic component. Calculated value of the cutting force by numerical model is in good agreement with the static cutting force by experimental measurement. The model and measurement method are feasible in intelligent manufacturing where internal broaching process is used.

Interpretation of Rehabilitation Medicine Specialty Indicators in National Report on the Services， Quality and Safety in Medical Care System

Since 2016, Peking University Third hospital has been entrusted by the National Health Commission to undertake the work of National Center for Healthcare Quality Management in Rehabilitation Medicine. For five consecutive years, it has compiled the National Report on the Services, Quality and Safety in Medical Care System (the section of rehabilitation medicine) (hereinafter referred to as the report), making up the gap in relevant data of rehabilitation medicine in China. This paper aims to interpret the indicators extracted and analyzed in the report, in order to continuously improve the integrity rate and accuracy rate of rehabilitation medical service and quality and safety investigation, and promote the continuous improvement of the quality of rehabilitation medical care in China. The index is composed of two kinds of indexes: quality control index and resource consumption index. The quality control index includes structure index, process quality index and result quality index, and consists of 16 major items and 149 sub-items. The report is carried out by the National Clinical Improvement System (NCIS), and the quality, accuracy and completeness of the data report will be used as the reference for hospital evaluation and key specialty setting. In NCIS indicators, data collection can be divided into the following four forms: automatic extraction of home page, extraction of medical records, manual reading of medical records and provision of relevant departments. This paper focuses on the early rehabilitation intervention, ADL improvement rate, complications and other key indicators, structural indicators and other process quality indicators. In recent years, National Center for Healthcare Quality Management in Rehabilitation Medicine has found that whether the rehabilitation medicine department has established an information system is an important factor determining the accuracy and convenience of quality control data extraction. Rehabilitation medical treatment and quality control information system is changing from information to intelligent, information management will combine with artificial intelligence natural language analysis and other emerging technologies, it can simplify and accurately the extraction process of quality control data, constantly improve the efficiency of rehabilitation medical quality control.

Research and Practice on the Quality Control of Medical Equipment Combined with the Overall Maintenance Service of Medical Equipment

<i>Purpose </i>In the face of the problems in medical equipment quality control, such as manpower shortage, lack of standardization, insufficient quality control equipment and weak information means, the implementation methods of medical equipment quality control are improved effectively by reorganizing the key points of quality control and introducing the overall maintenance service of medical equipment. <i>Methods</i> On the basis of researching and determining the quality control objects of medical equipment, formulating the work content of medical equipment quality control and supporting special quality control record sheets, formulating the work plan of medical equipment quality control, and adjusting the quality control content in time according to the actual clinical needs, our hospital has passed and the overall maintenance company of medical equipment cooperates closely and obtains the support of manpower, quality control equipment and information management tools to ensure that the quality control of medical equipment is in place. <i>Result </i>Through the standardization of medical equipment quality control, the efficiency of medical equipment quality control has been significantly improved, and the operation quality failure rate of quality control objects has decreased year by year, and we can find the hidden trouble of medical equipment operation quality and solve it accordingly. In addition, compared with hospital engineers, there is a certain gap in the medical equipment quality control ability of maintenance engineers in medical equipment overall maintenance service companies. At the same time, there are still some shortcomings in the coverage of quality control work. <i>Conclusion</i> Overall medical equipment maintenance service is conducive to the implementation of medical equipment quality control, and quality control is effective in improving the operation quality of medical equipment, and can guarantee the safety of medical service, but there is still a large space for improvement.

ECTyper: in silico Escherichia coli serotype and species prediction from raw and assembled whole-genome sequence data.

Escherichia coli is a priority foodborne pathogen of public health concern and phenotypic serotyping provides critical information for surveillance and outbreak detection activities. Public health and food safety laboratories are increasingly adopting whole-genome sequencing (WGS) for characterizing pathogens, but it is imperative to maintain serotype designations in order to minimize disruptions to existing public health workflows. Multiple in silico tools have been developed for predicting serotypes from WGS data, including SRST2, SerotypeFinder and EToKi EBEis, but these tools were not designed with the specific requirements of diagnostic laboratories, which include: speciation, input data flexibility (fasta/fastq), quality control information and easily interpretable results. To address these specific requirements, we developed ECTyper (https://github.com/phac-nml/ecoli_serotyping) for performing both speciation within Escherichia and Shigella , and in silico serotype prediction. We compared the serotype prediction performance of each tool on a newly sequenced panel of 185 isolates with confirmed phenotypic serotype information. We found that all tools were highly concordant, with 92–97 % for O-antigens and 98–100 % for H-antigens, and ECTyper having the highest rate of concordance. We extended the benchmarking to a large panel of 6954 publicly available E. coli genomes to assess the performance of the tools on a more diverse dataset. On the public data, there was a considerable drop in concordance, with 75–91 % for O-antigens and 62–90 % for H-antigens, and ECTyper and SerotypeFinder being the most concordant. This study highlights that in silico predictions show high concordance with phenotypic serotyping results, but there are notable differences in tool performance. ECTyper provides highly accurate and sensitive in silico serotype predictions, in addition to speciation, and is designed to be easily incorporated into bioinformatic workflows.

Reconstructing Precipitation Events Using Collocated Soil Moisture Information

Abstract Complete and accurate precipitation records are important for developing reliable flood warning systems, streamflow forecasts, rainfall–runoff estimates, and numerical land surface predictions. Existing methods for flagging missing precipitation events and filling gaps in the precipitation record typically rely on precipitation from neighboring stations. In this study, we investigated an alternative method for back-calculating precipitation events using changes in root-zone soil water storage. Our hypothesis was that using a different variable (i.e., soil moisture) from the same monitoring station will be more accurate in estimating hourly precipitation than using the same variable (i.e., precipitation) from the nearest neighboring station. Precipitation events were estimated from soil moisture as the sum of hourly changes in profile soil water storage. Hourly precipitation and soil moisture observations were obtained from a mesoscale network in the central U.S. Great Plains from May 2017 to December 2020. The proposed method based on soil moisture had a minimum detectable limit of 7.6 mm (95th percentile of undetected precipitation events) due to canopy and soil interception. The method was outperformed by the nearest neighbor (NN) interpolation method when neighboring stations were at distances of &lt;10 km. However, the proposed method outperformed the NN method in 22 out of 27 stations when nearest stations were at distances &gt; 10 km. Using changes in soil water storage was an effective approach for flagging and estimating actual missing precipitation events caused by pluviometer malfunction, highlighting new opportunities for using readily available in situ soil moisture information for operational quality control of precipitation observations in mesoscale environmental monitoring networks. Significance Statement This study investigated a new method for reconstructing precipitation events using changes in root-zone soil water storage. The method consists of a new option for improving the quality control of precipitation observations collected at in situ environmental monitoring networks. Using the sum of hourly changes in soil water storage proved effective as a qualitative method for flagging missing precipitation events caused by pluviometer failure and as a quantitative method for reconstructing precipitation events. This study presents a promising application of in situ soil moisture information as an alternative method for quality control of precipitation and as a method for filling gaps in the historical precipitation record of catchment-scale hydrological networks and mesoscale environmental monitoring networks.