Fast Tool Research Articles

Precise and fast spillage estimation for a central receiver tower based solar plant

An accurate and computationally cheap method to estimate the optical efficiency of a heliostat field associated with a central receiver tower (CRT) system is essential for an optimized design. In this article, an improved method is developed to estimate the intercept efficiency of a field with flat heliostats. Based on the slant distance, a judicious combination of two different schemes, i.e. Elliptical Gaussian Distribution (EGD) and Pseudo Ray Tracing (PRT), is employed to estimate flux in an image plane. The flux from the image plane is translated to the receiver to estimate spillage. The proposed method is rigorously validated against the state of the art tool, SolTRACE, under various conditions which demonstrates robustness and accuracy of the method. The deviation of intercept efficiency from SolTRACE is small (within 1.5 %). However, such a high degree of accuracy is achieved at a meagre computational cost (below 15 %) compared to SolTRACE even without parallel computing. Thus, the intercept efficiency or spillage estimation of a given heliostat field is rendered ten times faster by the method presented in the current article without a loss of accuracy. An earlier study by the same authors showed the accurate and fast estimation of shading and blocking efficiency. The present study adds to the previous effort and proceeds towards the objective of achieving a fast and precise tool for the design and analysis of heliostat fields. The proposed tool is developed in Python environment which exploits the parallel computing features based on NumPy, Pandas and multiprocessing.

Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning.

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the past few decades. Predicting IDPs from the primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize their advantages of precision and efficiency. In this article, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disordered flexible linkers and disordered protein binding. This method utilizes high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test data sets from CAID (critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew's correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.

Determination of bilingualism subtypes and their relationship with linguistic abilities in Lebanese bilinguals

Aims and objectives: Profiles of bilinguals vary among studies due to the diversity of intrinsic and extrinsic factors, bilingualism classification and to the discrepancies between measures. Thus, a generalizable index with a defined threshold is needed to capture the linguistic dominance of bilinguals and facilitate comparisons between studies. This study’s objective was to define and examine the validity of a new bilingualism index. Methodology: This index was derived from the Language Experience and Proficiency Questionnaire (LEAP-Q) conducted on 100 bilingual Lebanese participants (age = 68.2 ± 9.4 years; education = 12.8 ± 5.2 years). Data and analysis: The bilingualism index was based on selected items of the LEAP-Q, optimizing the explained variance on a linear regression using the differential Arabic-French score on the Boston Naming Test as the dependent variable. The validity of the classification was examined using other linguistic (articulatory rate, shortened Token test, Stroop reading subtest) and cognitive screening tests (mini-mental state examination [MMSE]). Findings and conclusion: LEAP speaking and oral comprehension scores provided a parsimonious index that accounted for naming variance ( R2 = .435, p = .0001) and subdivide our population into three bilingualism subgroups (prominent Arabic, balanced, prominent French). A prominent language advantage was found in the expected direction on linguistic (articulatory rate: p = .03; shortened Token test: p = .026; Stroop reading subtest: p = .0001) and cognitive screening tests (MMSE: p = .08). Originality and implications: These results show that a simple index can accurately characterize adult bilingualism subtypes and offers clinicians an easy and fast tool compared with the usual procedure used to determine individuals and patient’s bilingualism subtype. More broadly, the index’s validity in other bilingual populations is warranted for generalizability of the present findings.

Photogrammetric survey and spatial recording of settlement features at al-Jumayil (Jordan). 3D terrain model, DEM, orthographic map, and ground plan

This paper discusses the modality of the digital surface documentation of the archaeological site of al-Jumayil during the 2021 excavation campaign organised by the Institute of Classical Archaeology of the University of Vienna. A digital terrain model encompassing an area of 23,783.4 m2 was created using the photogrammetric range imaging technique SfM (Structure from Motion). It is based on 12,983 orthographic photographs. The resulting high-resolution 3D model – a snapshot in time displaying the current condition of the visible structures related to historic al-Jumayil – laid the foundation for further off-site data processing. In addition to the three-dimensional model, a digital elevation model (DEM), a georeferenced orthographic map, and a ground plan containing all visible wall structures of the settlement was produced from the collected data. This case study aims to present the in-field application of a systematic photogrammetric approach used as a fast and accurate tool for mapping an archaeological site step-by-step in two and three dimensions within a topographic survey.

Advancements in Rapid and Affordable Diagnostic Testing for Respiratory Infectious Diseases: Evaluation of Aptamer Beacon Technology for Rapid and Sensitive Detection of SAR-CoV-2 in Breath Condensate.

The demand for rapid and efficient diagnostic point-of-care tests for respiratory infectious diseases has become increasingly critical in the current landscape. The emphasis on accessibility has been underscored over the past year, making it crucial to have biological components that exhibit fast and accurate kinetics. The foundation for precise, swift, and effective testing relies on the availability of highly responsive biological agents. Two published aptamer DNA sequences designated Song and MSA52 and their truncated internal stem-loop structures were studied for their potential to serve as aptamer beacons for rapid COVID detection. The candidate beacons were covalently labeled with Atto 633 dye attached to their 5' ends and Iowa Black quencher attached to their 3' ends. The whole aptamer structures exhibited the greatest fluorescence signal intensities and higher fluorescence background than their truncated internal stem-loop beacon structures suggesting that the distance between fluorophores and quenchers was greater for the whole aptamer beacon candidates versus the isolated stem-loop structures. Beacon candidates were tested against two heat- or gamma radiation-killed SARS-CoV-2 Washington 1/2020 virus samples and three different COVID spike (S) proteins to test their effectiveness. Despite the higher background fluorescence, the whole aptamer beacons showed better signal-to-noise ratios and were selected for further investigation. Limit of detection (LOD) studies revealed that both the whole Song and whole MSA52 aptamer beacon candidates had a LOD of 9.61 × 103 genome equivalents in phosphate-buffered saline using the red channel of a Promega Quantus™ fluorometer which correlated well with confirmatory spectrofluorometry. Cross-reactivity studies using numerous COVID variants, related coronaviruses, and other common respiratory pathogens suggested greater COVID selectivity for the whole MSA52 versus the whole Song aptamer beacon candidate, indicating promise for specific COVID detection. Importantly, both whole aptamer beacon candidates exhibited very rapid "bind and detect" fluorescence increases within the first 1-2min of mixing the beacons with killed SARS-CoV-2 viruses in 100µl samples. Overall, this work illustrates the strong potential for aptamer beacons for rapid, on-site detection and presumptive diagnosis of COVID in breath condensates or other small liquid samples. This research highlights the strong potential of aptamer beacons for addressing the need for fast and convenient diagnostic tools in global health contexts, especially in resource-limited settings.

Determination of disease risk factors using binary data envelopment analysis and logistic regression analysis (case study: a stroke risk factors)

PurposeA stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.Design/methodology/approachIn this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.FindingsThe study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.Originality/valueThe main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

Printed Capacitive Immunoassay for Detecting SARS-CoV-2 Viral Particles

The key to fight against a global pandemic such as COVID-19 is to have low-cost, reliable and fast response diagnostic tools. Electronic biosensors are preferred because of their ease of integration into current centralized health care networks and integration with modern point-of-care testing (POCT) devices. Printed electronic sensors provide a sensitive and reliable diagnostic platform to aid in controlling transmissible diseases. In this work, we demonstrate a fully printed capacitive biosensor. The sensor uses coplanar electrodes, coupled with capture antibodies immobilized on microporous Polyvinylidene-fluoride (PVDF) film to detect the SARS-CoV-2 spike protein in spiked buffer solutions. Antibody immobilization on PVDF surface is confirmed with confocal fluorescent imaging microscopy. Gold nanoparticle (GNP) tagged detection antibodies are also introduced to provide increased sensitivity. The gold nanoparticles provide a reflectance layer which leads to increased capacitance. This increased capacitance can be measured directly and has demonstrated the ability to screen for spiked samples with statistical significance. This fully printed capacitive immunoassay has the potential to be used as a transmissible disease screening and vaccine efficacy assessment tool for resource-limited areas.

Forecasting Covid-19 Cases in Türkiye with the Help of LSTM

Even though, it is thought that the pandemic has come to an end, the humanity is still under the danger of upcoming pandemics. In that sense, every effort to understand or predict the nature of an infectious disease is very precious since those efforts will provide experience for upcoming infectious disease epidemic/pandemic. Mathematical models provide a common way to analyze the nature of the pandemic. Apart from those mathematical models that mostly determine which variables should be used in the model to predict the nature of the epidemic and at which rate the disease will spread, deep learning models can also provide a fast and practical tool. Moreover, they can shed a light on which variables should be taken into account in the construction of a mathematical model. And also, deep learning methods give rapid results in the robust forecasting trends of the number of new patients that a country will deal with. In this work, a deep learning model that forecasts time series data using a long short-term memory (LSTM) network is used. The time series data used in this project is COVID-19 data taken from the Health Ministry of Republic of Türkiye. The weekend isolation and vaccination are not considered in the deep learning model. It is seen that even though the graph is consistent and similar to the graph of real number of patients, and LSTM is an effective tool to forecast new cases, those parameters, isolation and vaccination, must be taken into account in the construction of mathematical models and also in deep learning models as well.

Electrochemical Analysis of Clinical Samples – A Path to Translational Research

Precise diagnostics of cancer or other diseases is crucial when selecting proper treatment. Personalized medicine puts high demands on the accuracy of nucleic acid biomarkers analysis, where subtle differences at the nucleotide level are often involved. Isothermal amplification techniques offer new possibilities of DNA and RNA amplification without using PCR, and their combination with electrochemistry provide a promising fast and cost-effective alternative diagnostic tool. Although electrochemical biosensors are still insufficiently applied to clinical material, thus hindering their development, recent advancements show great promise in translational research. Banks of biological material (biobanks) are specialized workplaces focused on the long-term preservation and processing of clinical material and offer a wide range of expert services, primarily for research purposes, in particular the provision of biological samples and associated pseudonymized data. Their involvement in the field of electrochemical biosensors can facilitate application of electrochemical methods into clinical laboratories and expand the portfolio of currently used diagnostic methods.

Marine propeller optimisation through user interaction and machine learning for advanced blade design scenarios

ABSTRACT The complexity of the marine propeller design process is well recognised and is related to contradicting requirements of the stakeholders, complex physical phenomena, and fast analysis tools, where the latter are preferred due to the strict time limitations under which the entire process is carried out. With all this in mind, an optimisation methodology has been proposed and presented earlier that combines user interactivity with machine learning and proved to be useful for a simple blade design scenario. More specifically, the blade designer manually evaluates the cavitation of the designs during the optimisation and this information is systematically returned into the optimisation algorithm, a process called interactive optimisation. As part of the optimisation, a machine learning pipeline has been implemented in this study, which is used for cavitation evaluation prediction in order to solve the user fatigue problem that is connected to interactive optimisation processes. The proposed methodology is investigated for two case studies of advanced design scenarios, relevant for a real commercial situation, that regard controllable-pitch propellers for ROPAX vessels, and the aim is to obtain a set of optimal, competent blade designs. Both cases represent scenarios with several design variables, objectives and constraints and with conditions that have either suction side or pressure side cavitation. The results show that the proposed methodology can be used as a support tool for the blade designers to, under strict time constraints, find a suitable set of propeller designs, some of which can be considered equal or even superior to the delivered design.

Machine learning-based cardiac activity non-linear analysis for discriminating COVID-19 patients with different degrees of severity

Objective:This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. Methods:In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). Results:The classification report results were: (1) Low vs. Moderate - 100% of Accuracy and 100% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67% and an F1–Score of 94.92%; (3) Moderate vs. Severe - Accuracy of 94.12% and an F1–Score of 96.43%; and (4) All vs All - 78.57% of Accuracy and 84.75% of F1–Score. Conclusion:The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. Significance:The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57% improvement in diagnostic accuracy for the All vs All comparison.

Development of an intelligent field investigation system for Liriomyza using SeResNet-Liriomyza for accurate identification

Liriomyza (Diptera: Agromyzidae) is genus of insects found worldwide. Among the species in this genus, L. sativae, L. huidobrensis, L. trifolii, and L. bryoniae are the four most globally invasive, with L. chinensis being native to China. Real-time and accurate monitoring is a prerequisite for reducing the spread and crop damage caused by this pest. Owing to the small size of Liriomyza, specialized technicians using a microscope are needed to identify them. We developed an intelligent field investigation system to conveniently and accurately identify these five species. The system includes a portable image acquisition device based on a smartphone, a deep learning-based image recognition model for Liriomyza, and a mobile platform-based investigation application. To capture magnified images of Liriomyza, we designed a magnifying lens and a rotatable carrier assembled outside the smartphone camera. To address the problem of local blurring in images captured by smartphones, all images were sharpened to enhance their features. To accurately distinguish these five species from similar species, we developed a SeResNet-Liriomyza model, which embeds spatial feature pyramid networks (SFPN) in the SeResNet model to prevent the false positives caused by different sizes of input images and generate new feature maps favorable for classification by combining global and local image features. Compared with the excellent classification models, DenseNet-201, ResNeXt-50, MobileNet-v3, EfficientNet-B4, and ResNeSt-101, SeResNet-Liriomyza showed excellent recognition performance, with an average recognition accuracy of 99.88%. We designed and developed a mobile application (app) for the intelligent investigation of Liriomyza. The app includes functions such as intelligent investigation, intelligent recognition, a biological dictionary, and investigation result retrieval. The proposed intelligent field investigation system provides investigators with a fast, convenient, and accurate tool for investigating Liriomyza, a pest that can cause serious damage to agriculture in the field.

Integrated urban heat sinks for low-carbon neighbourhoods: dissipating heat to the ground and sky through building structures

In a global context of simultaneous urbanization and rising ambient temperatures, it is imperative to design heat-resilient and material-efficient neighbourhoods that respond to the pressing demand for housing with minimal environmental impact. With this goal in mind, the work presented here focuses on the integration of heat dissipation systems within structural building components, introducing a novel framework for their systems-level simulation and design. Two well-studied, low-cost systems (shallow geothermal and night-sky cooling) are modelled within a parametric design workflow that combines bottom-up structural embodied carbon calculations with annual building energy simulations that account for heat sink availability. The proposed method results in a fast and reliable early-stage design tool that allows urban planners, policymakers, and designers to evaluate the suitability of available heat dissipation technologies across climates and urban morphologies. This paper analyzes specifically the multi-domain performance of a hypothetical urban geometry within three different cooling-dominated locations (Algiers, Cairo, and Bangkok).

Fast tool servo-based ultra-precision diamond sculpturing for fabricating micro-structured surfaces

To eliminate differences in surface quality and improve the machining flexibility of micro-structured surfaces, this paper proposes a novel ultra-precision diamond sculpturing method based on a triaxial fast tool servo (FTS). In this method, the diamond tool is directly driven by the FTS rather than the ultra-precision lathe to fabricate the micro-structure surfaces. The corresponding toolpath generation algorithm is established to take full advantage of the triaxial FTS. The toolpath is first generated through traditional calculating the shortest tool edge projection on the desired surface and the curve interpolation. The overshoot around the path corner is predicted by simulating the tracking path. Then an extended path is added to the original toolpath to stabilize the overshoot before removing the material. The simulation indicates that the hexagonal lens array can be favorably fabricated by the proposed ultra-precision diamond sculpturing method, and the developed toolpath reduces the form error from 20 to 5nm. The sculpturing experiments of sinusoidal micro-structure array and micro-lens array surfaces suggest that their actual peak-to-valley (PV) form errors are 70nm and 50nm, respectively. The single lens with the surface roughness Sa 3.44nm can be precisely fabricated in 0.1s, demonstrating the high machining efficiency and high machining quality of the proposed ultra-precision diamond sculpturing method.

Predicting Loss Analysis from Luminescence Images in Si Solar Cells with Convolutional Neural Networks

In this work, deep convolutional neural networks (CNNs) are used to speed up the calculation of spatial nonuniform device parameters, voltage and fill factor (FF) losses in crystalline Si solar cells. Luminescence images on finished solar cells, specifically as‐measured electroluminescence (EL) and photoluminescence (PL) images, are used as input features to train the CNN models and losses analysis as output is from a finite‐element modeling program called Griddler. From the analysis of a small dataset of 250 commercial grade solar cells, the CNN models are able to predict the spatially nonuniform distribution of contact resistance and defect parameters of the devices under test and exhibit good performance in the inference of voltage and FF losses. As EL and PL images are widely collected in‐line production data, in the result, the possibility of deploying 2D spatial power loss analysis is implied as a fast, in‐line, and nondestructive analytic tool for solar cells manufacturing.

SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra.

Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making. The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.

HoCoRT: host contamination removal tool

BackgroundShotgun metagenome sequencing data obtained from a host environment will usually be contaminated with sequences from the host organism. Host sequences should be removed before further analysis to avoid biases, reduce downstream computational load, or ensure privacy in the case of a human host. The tools that we identified, as designed specifically to perform host contamination sequence removal, were either outdated, not maintained, or complicated to use. Consequently, we have developed HoCoRT, a fast and user-friendly tool that implements several methods for optimised host sequence removal. We have evaluated the speed and accuracy of these methods.ResultsHoCoRT is an open-source command-line tool for host contamination removal. It is designed to be easy to install and use, offering a one-step option for genome indexing. HoCoRT employs a variety of well-known mapping, classification, and alignment methods to classify reads. The user can select the underlying classification method and its parameters, allowing adaptation to different scenarios. Based on our investigation of various methods and parameters using synthetic human gut and oral microbiomes, and on assessment of publicly available data, we provide recommendations for typical datasets with short and long reads.ConclusionsTo decontaminate a human gut microbiome with short reads using HoCoRT, we found the optimal combination of speed and accuracy with BioBloom, Bowtie2 in end-to-end mode, and HISAT2. Kraken2 consistently demonstrated the highest speed, albeit with a trade-off in accuracy. The same applies to an oral microbiome, but here Bowtie2 was notably slower than the other tools. For long reads, the detection of human host reads is more difficult. In this case, a combination of Kraken2 and Minimap2 achieved the highest accuracy and detected 59% of human reads. In comparison to the dedicated DeconSeq tool, HoCoRT using Bowtie2 in end-to-end mode proved considerably faster and slightly more accurate. HoCoRT is available as a Bioconda package, and the source code can be accessed at https://github.com/ignasrum/hocort along with the documentation. It is released under the MIT licence and is compatible with Linux and macOS (except for the BioBloom module).

A novel well-logging data generation model integrated with random forests and adaptive domain clustering algorithms

With geological evolution, reservoirs have hierarchical properties, leading to distinct domain characteristics in their logging curves, which can contribute to the reconstruction of unknown well logging curves. In recent years, many studies have used machine learning (ML) algorithms to reconstruct logging curves without incorporating domain features, which can cause their poor performance. Since decomposing raw data into domain data can optimize an expected variance and thus improves the accuracy and robustness of ML prediction models, we propose a random-forest-adaptive-domain-clustering (RF-ADC) model to generate unknown well logging curves. First, we present an adaptive domain clustering algorithm based on gap statistics (GS) and balanced iterative reducing and clustering using hierarchies (BIRCH). With the adaptive domain clustering process, well logging curves are automatically divided into microscope domains, and their corresponding domain models are constructed. Then we utilize the random forest (RF) algorithm to generate unknown well logging curves based on domain data. Taking logging curves of a shale gas reservoir as an example, our experiments show that the proposed RF-ADC model achieves promising results. Compared with the model without clustering operation, the RF-ADC model provides superior performance with its mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) values reducing by 15.55%, 39.30%, 18.95%, and 11.43%, respectively. In addition, the predictive performance of the proposed RF-ADC model is better than that of baseline models of long short-term memory network and convolutional neural network. Furthermore, after analyzing, the GS and BIRCH methods are applicable to obtaining an optimal number of domains and decomposing well logging data for horizontal wells. Our delineation results also provide insights into detailed reservoir recognition, which is important for further identification of reservoirs and stratification work. Moreover, the proposed RF-ADC model can also achieve effective prediction with a small dataset (MAE = 8.205, RMSE = 10.922, and MAPE = 0.039). In summary, this RF-ADC model provides a fast, economical, and effective tool for the generation of unknown well logging curves for horizontal wells in shale gas reservoirs. It will further facilitate the exploration and development of unconventional reservoirs.

A-378 Clinical Evaluation and Implementation of Rapid COVID-19 Antigen Test in Qatar

Abstract Background In March, the World Health Organization (WHO) declared COVID-19 as a global public health emergency. To curb the spread of virus, timely detection of infected patients and screening of suspected cases in massive population posed a challenge for the healthcare system. Rapid antigen tests (RAT) were high in demand as a fast, accurate and cost-effective screening tool to increasing the testing capacity. In this crucial timing, clinical evaluation and procurement of available COVID-19 RAT following the World Health Organization (WHO) guidelines was a huge challenge for Point of Care testing (POCT) department. The aim of this study is to highlight the challenges overcame in establishing a Point-of-care COVID-19 testing system including personnel training and building a compatible Laboratory Information System (LIS) upholding Hamad Medical Corporation (HMC) cyber security regulation. Methods Three regulatory approved RATs were evaluated against real-time polymerase chain reaction (RT-PCR) and analytical performance were verified. Maintaining the Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologist (CAP) standards for COVID-19 waived testing, POCT team initiated to conduct practical and educational training vastly through online platforms and face to face sessions. We utilized the extendable LIS functionality for fast medical decisions in hospitals and enabled “reason of testing” feature in Qatar's COVID-19 tracking application. Results Our performance verification showed 98.0% sensitivity and 100% specificity for PanbioTM COVID-19 RAT, 98.0% sensitivity and 100% specificity for Quidel SOFIA2 COVID-19 RAT and Roche COVID-19 RAT yielded a sensitivity of 97.0% and specificity of 100%. Total of 291 training sessions were conducted for 9710 clinical staffs across 17 HMC facilities and 185 private clinics. Using LIS and POC middleware features, obvious increment in RAT of more than 509 tests per day has been reported in a single emergency unit. Overall, 5.5% decrement in incorrect patient identification data using QUIDEL SOFIA2 automated device and LIS functions was recorded in laboratory quality indicator data. COVID-19 tracking application was linked to antigen results for contact tracing, isolating, and breaking the chain of transmission. Conclusion The methods established for the COVID-19 implementation showed acceptable performance fulfilling the elements of HMC product procurement specification and CAP Accreditation standards. Synergic actions of HMC-POCT with supportive laboratory management aided prompt decision making to optimize patient management and added efficiency to the public health control measures in Qatar.

B-389 Validation of One-step In-house RT-qPCR Method for Detecting MLL::AF4 Fusion

Abstract Background Rearrangements of mixed lineage leukemia gene (MLL) with 1 of &amp;gt;130 alternative partner genes is a recurrent cytogenetic finding in both acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) and is generally associated with a poor prognosis. Among the most common MLL rearrangement is t(4;11)(q21;q23), forming the MLL::AF4 (also known as KMT2A::AFF1) fusion gene. Unique among MLL rearrangements, MLL::AF4 is almost exclusively associated with pro-B cell ALL and is prototypical of infant ALL, where it carries a very poor prognosis. At least 10 different MLL-AF4 fusion transcripts have been found, due to break-points in different introns, some transcripts being more frequent in either adult or infant ALL. Fast and reliable diagnostic tests represent an important tool in clinical laboratories to detect these rearrangements and . In this context, this work aimed to validate a qualitative method to detect the main MLL::AF4 fusion isoforms. Methods We evaluated the performance of the Hs03043614_ft (e10e4, e9e4 and e9e5) and Hs03024412_ft (e11e4) inventoried TaqMan assays (Thermo Fisher Scientific). Total mRNA from negative and positive samples for each fusion was extracted by the TRIzol reagent according to the manufacturer’s instructions. All assays were performed using TaqMan RNA-to-Ct 1-Step kit (Thermo Fisher Scientific). The inventoried ABL1 assay (Thermo Fisher Scientific) was used as endogenous control. Using positive samples, negative samples and controls of each fusion were performed: sequence verification tests, standardization of RNA input, evaluation of reaction efficiency and determination of the detection limit (LOD). The performance of the assays was evaluated with ABL1 control separately from the fusions. Results Our results showed that both assays worked well for the detection of specific fusions. The best RNA input for the reactions was 200 ng. The efficiencies reactions were: a) with ABL1 in separate reactions: 92.6% and 110.0% for Hs03043614_ft and Hs03024412_ft assays, respectively; The literature recommends the limit of efficiency was between 90% and 110%. The LODs with ABL1 in separate reactions were 10−3 for both assays, respectively. There was no unspecific amplification in negative samples. Conclusion The TaqMan assays with RT one-step protocol evaluated are highly sensitive, fast, and reliable tools for detecting the most frequent MLL::AF4 fusion transcripts.