Pre-screening Method Research Articles

A machine learning approach to predict radioxenon isotopes concentrations using experimental data

Monitoring atmospheric radioxenon isotope concentration is crucial for verifying compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The most sensitive method for radioxenon isotope detection is beta-gamma coincidence measurement. Quantities of activity concentrations and, more specifically, their ratios are vital for analyzing spectra from atmospheric radioactivity to characterize the emission source. This study aims to develop a novel approach utilizing a machine learning algorithm to estimate the activity concentrations of radioxenon isotopes from real spectra in the format of experimental two-dimensional raw spectra without the need for extensive mathematical calculations for subtracting interferences and backgrounds. This method can be applied as a pre-screening method in a fast way to highlight spectra with concentration anomalies and help analysts in the interactive review step. Traditional methods like region-of-interest (ROI) and simultaneous decomposition analysis tool (SDAT) have been used to estimate net counts for each specific ROIs. However, this study employs deep convolutional neural networks (CNNs) to extract fine structure and patterns from the complex spectra, capturing both local and global features from entire beta-gamma coincidence spectra without relying on predefined ROIs.

Occurrence and screening- flotation separation for the beneficiation of rare earth elements and yttrium (REY) in core sediments from the Pacific Ocean

Rare earth elements (REE) and yttrium (termed as REY), as a significant strategic resource, are recently found to be extremely enriched in deep-sea sediments, which is expected to be an important REY resource in the future. However, the efficient development and utilization of deep-sea REY-rich sediments are greatly limited by the current common beneficiation and metallurgy method. In this study, detailed grain size analysis, mineral identification and geochemical measurement aiming at deep-sea sediments collected from a station in the Central Pacific Basin were employed to determine the occurrence of deep-sea REY-rich sediments. Based on that, the Screening-flotation separation was adopted to achieve efficient enrichment of REY in the sediments. The ΣREY contents in the samples can reach 790 ppm, and sieve analysis reveal that bioapatite (fish teeth and bones) and Fe-Si-Mn oxide (oxyhydr) on the micro-nodule surface are the major REY carriers. About 87.27% REY was scavenged by bioapatite fossils. The REE patterns show distinct negative Ce/Ce* and slightly positive Eu/Eu* anomalies, similar to the Western and central North Pacific seawater, indicating a promising source of REY in the samples may be pore water in sediments, taking into account other studies as well. The grain size analysis suggests that REY enrichment can be achieved by removing the fine-grained particles (<15 μm). Screening-flotation separation, as a newly developed approach for the beneficiation, uses the pre-screening method to pre-detach the clay-size fraction (<15 μm), and combines flotation and magnetic separation, which further upgrades REY concentrate to achieve about 9 times enhancement of REY recovery from marine sediments. Although further research is needed, the study we determined can provide reference for industrial exploitation of rare earth resources in deep-sea REY-rich sediments.

Diagnostic models to predict nuclear DNA and mitochondrial DNA recovery from incinerated teeth.

Teeth are frequently used for human identification from burnt remains, as the structure of a tooth is resilient against heat exposure. The intricate composition of hydroxyapatite (HA) mineral and collagen in teeth favours DNA preservation compared to soft tissues. Regardless of the durability, the integrity of the DNA structure in teeth can still be disrupted when exposed to heat. Poor DNA quality can negatively affect the success of DNA analysis towards human identification. The process of isolating DNA from biological samples is arduous and costly. Thus, an informative pre-screening method that could aid in selecting samples that can potentially yield amplifiable DNA would be of excellent value. A multiple linear regression model to predict the DNA content in incinerated pig teeth was developed based on the colourimetry, HA crystallite size and quantified nuclear and mitochondrial DNA. The chromaticity a* was found to be a significant predictor of the regression model. This study outlines a method to predict the viability of extracting nuclear and mitochondrial DNA from pig teeth that were exposed to a wide range of temperatures (27 to 1000 °C) with high accuracy (99.5-99.7%).

Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks

Novel coronavirus disease 2019 (Corona Virus Disease 2019, COVID-19) is rampant all over the world, threatening human life and health. Currently, the detection of the presence of nucleic acid from SARS-CoV-2 is mainly based on the nucleic acid test as the standard. However, this method not only takes up a lot of medical resources but also takes a long time to achieve detection results. According to medical analysis, the surface protein of the novel coronavirus can invade the respiratory epithelial cells of patients and cause severe inflammation of the respiratory system, making the cough of COVID-19 patients different from that of healthy people. In this study, the cough sound is used as a large-scale pre-screening method before the nucleic acid test. Firstly, the Mel spectrum features, Mel Frequency Cepstral Coefficients, and VGG embeddings features of cough sound are extracted and oversampling technology is used to balance the dataset for classes with a small number of samples. In terms of the model, we designed multi-headed convolutional neural networks to predict audio samples, and adopted an early stop method to avoid the over-fitting problem of the model. The performance of the model is measured by the binary cross-entropy loss function. Our model performs well on the dataset of the AICovidVN 115M challenge that its accuracy rate is 98.1%, and on the dataset of the University of Cambridge that its accuracy rate is 91.36%.

Prevalence of concealed arrhythmogenic substrate for sudden cardiac arrest in the general population

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Heart Lung and Blood institute National Institute of Health. Background Individuals who present with SCA as the first manifestation of heart disease (Concealed Substrate) present the greatest challenge for clinical risk stratification but population-based information in the current era is lacking. We hypothesized that improved awareness and access to health care has resulted in a reduction in prevalence of Concealed Substrate. Purpose We compared the overall prevalence, secular trends and SCA etiologies of Concealed Substrate in two geographically distinct US populations. Methods Consecutive cases of SCA were ascertained prospectively from 2 large ongoing US studies, the Population Cohort A (PopulationCohortA, 2002-2018) and Population Cohort B (PopulationCohortB, 2015-2022), total population 1.85 million residents. All out-of-hospital SCA events attended by emergency medical services were determined following an established multi-source ascertainment and adjudication process, and detailed lifetime clinical record evaluation conducted for each individual. This included the comprehensive electronic health record, evaluation of SCA survivors as well as all available autopsy information. Concealed Substrate cases were defined as SCA with no prior heart disease diagnosis. Results From a total of 5708 SCA cases (age 66.5 ± 18.2, female 35%), n=3390 (age 64.3 ± 18.8, female 34%) were identified in PopulationCohortA and n=2318 (age 71.6 ± 15.7, female 36%) in PopulationCohortB. In PopulationCohortA, prevalence of Concealed Substrate was 39% (n=1282, age 58.6 ± 21.7, 32% female) and decreased modestly over time (Figure; 37% in 2002-06 vs. to 33% in 2014-18; p=0.001). In PopulationCohortB, overall prevalence of Concealed Substrate was 41% (n=949, age 68.3 ± 17.6, 33% female) and did not change significantly over time (Figure; 2015-2022; p=0.06). Etiology of SCA was determined in a subgroup of overall cases (n=3210, 56%) of which n=2727 (85%) were ischemic SCA and n=483 (15%) non-ischemic SCA. Non-ischemic SCA was more commonly identified in Concealed Substrate (29% vs. 11%; p&amp;lt;0001) and ischemic SCA was more common in Revealed Substrate (89% vs. 72%; p&amp;lt;0.0001). Conclusion There was a modest reduction in prevalence over time, 33-41% of individuals continued to present with Concealed Substrate, with higher likelihood of non-ischemic SCA. These findings suggest that widely available and inexpensive tests such as the 12-lead ECG warrant further evaluation as a pre-screening method in the general population.

MP-453090-5 SUDDEN CARDIAC ARREST AS THE FIRST MANIFESTATION OF HEART DISEASE IN THE GENERAL POPULATION

The subgroup of individuals who present with sudden cardiac arrest (SCA) as the first manifestation of heart disease (FM-SCA) present the greatest challenge to effective clinical risk stratification, but population-based data in the current era is lacking. We hypothesized that improved awareness and access to health care has resulted in a reduction in prevalence of FM-SCA. We compared the prevalence secular trends of FM-SCA in two geographically distinct US populations and evaluated distinguishing characteristics. Consecutive cases of SCA were ascertained prospectively from 2 large ongoing US studies, the Oregon Sudden Unexpected Death Study (OreSUDS, Portland OR, USA, 2002-2018) and Prediction of Sudden Death in Multi-ethnic Communities (PRESTO, Ventura CA, USA, 2015-2022), total population 1.85 million residents. All out-of-hospital SCA events attended by emergency medical services were determined following an established multi-source ascertainment and adjudication process, and a detailed review of the comprehensive lifetime clinical records was conducted for each individual. Both SCA survivors and non-survivors were included. FM-SCA cases were defined as SCA with no prior heart disease diagnosis. A multi-variable model using combined data from both cohorts was used to identify distinguishing characteristics of FM-SCA vs. other SCA. A total of 3390 SCA cases (age 64.3 ± 18.8, female 34%) were identified in OreSUDS and n=2318 (age 71.6 ± 15.7, female 36%) in PRESTO. In OreSUDS, prevalence of FM-SCA was 39% (n=1282, age 58.6 ± 21.7, 32% female) and decreased modestly over time (Figure; 37% in 2002-06 to 33% in 2014-18; p=0.001 for trend). In PRESTO, overall prevalence of FM-SCA was 41% (n=949, age 68.3 ± 17.6, 33% female) and did not change significantly over time (Figure; 2015-2022; p=0.06 for trend). In multivariate comparisons of FM-SCA vs. other SCA performed in the subset of individuals with healthcare visits, younger age, male sex and lower rates of diabetes, hypertension, seizure disorder, chronic obstructive pulmonary disease and chronic renal insufficiency were associated with FM-SCA (p <0.0001). There was a modest reduction in prevalence over time, but 33-41% of individuals continued to present with FM-SCA and were distinguished by younger age, male sex and lower rates of co-morbid conditions. These findings suggest the need to develop and investigate user-friendly and cost-effective pre-screening methods to reduce FM-SCA in the general population.

Development of a pre-screening-method for the qualification of environmentally acceptable lubricants for stern tube systems—The evaluation of friction efficiency and scuffing capacity

Lubricants for stern tube systems are subject to increasingly stringent requirements in terms of environmental compatibility, as any lubricant leakage leads to contamination of the sea. As a result, interest in environmentally acceptable lubricants (EALs) is increasing. Biodegradable ester lubricants represent a possible alternative to conventional, mineral oil-based lubricants. In addition to environmental compatibility requirements, these ester lubricants must also meet tribological requirements. In this study, suitable tribometers were therefore combined in a pre-screening method to cost-effectively test lubricants for their tribological requirements (friction efficiency, wear protection and scuffing capacity). For this purpose, the contact parameters are transferred from the real conditions in the stern tube system’s journal bearing and from the standardized component tests (FZG for gears and FE8 for rolling bearings) to tribological model tests on a Mini-Traction-Machine (MTM, PCS Instruments). The result of the study is a method for the pre-screening of EALs under the application-related journal bearing and standardized component test conditions, respectively. The results show an influence of the lubricant composition on the friction efficiency in fluid friction as well as the transition point from fluid to mixed friction. Furthermore, an influence of the lubricant composition on the scuffing capacity could be shown. The results of friction efficiency and scuffing capacity confirm also a high reproducibility for those pre-screening tests.

Towards a simple in vitro surface chemistry pre-screening method for nanoparticles to be used for drug delivery to solid tumours.

An efficient nanoparticulate drug carrier intended for chemotherapy based on intravenous administration must exhibit a long enough blood circulation time, a good penetrability into the tumour volume, as well as an efficient uptake by cancer cells. Limiting factors for the therapeutic outcome in vivo are recognition of the nanoparticles as foreign objects, which triggers nanoparticle uptake by defence organs rich in macrophages, e.g. liver and spleen, on the time-scale of accumulation and uptake in/by the tumour. However, the development of nanomedicine towards efficient nanoparticle-based delivery to solid tumours is hampered by the lack of simple, reproducible, cheap, and predictive means for early identification of promising nanoparticle formulations. The surface chemistry of nanoparticles is known to be the most important determinant for the biological fate of nanoparticles, as it influences the extent of serum protein adsorption, and also the relative composition of the protein corona. Here we preliminarily evaluate an extremely simple screening method for nanoparticle surface chemistry pre-optimization based on nanoparticle uptake in vitro by PC-3 cancer cells and THP-1 macrophages. Only when both selectivity for the cancer cells as well as the extent of nanoparticle uptake are taken into consideration do the in vitro results mirror literature results obtained for small animal models. Furthermore, although not investigated here, the screening method does also lend itself to the study of actively targeted nanoparticles.

Screen the unforeseen: Microbiome-profiling for detection of zoonotic pathogens in wild rats.

Wild rats can host various zoonotic pathogens. Detection of these pathogens is commonly performed using molecular techniques targeting one or a few specific pathogens. However, this specific way of surveillance could lead to (emerging) zoonotic pathogens staying unnoticed. This problem may be overcome by using broader microbiome-profiling techniques, which enable broad screening of a sample's bacterial or viral composition. In this study, we investigated if 16S rRNA gene amplicon sequencing would be a suitable tool for the detection of zoonotic bacteria in wild rats. Moreover, we used virome-enriched (VirCapSeq) sequencing to detect zoonotic viruses. DNA from kidney samples of 147 wild brown rats (Rattus norvegicus) and 42 black rats (Rattus rattus) was used for 16S rRNA gene amplicon sequencing of the V3-V4 hypervariable region. Blocking primers were developed to reduce the amplification of rat host DNA. The kidney bacterial composition was studied using alpha- and beta-diversity metrics and statistically assessed using PERMANOVA and SIMPER analyses. From the sequencing data, 14 potentially zoonotic bacterial genera were identified from which the presence of zoonotic Leptospira spp. and Bartonella tribocorum was confirmed by (q)PCR or Sanger sequencing. In addition, more than 65% of all samples were dominated (>50% reads) by one of three bacterial taxa: Streptococcus (n=59), Mycoplasma (n=39) and Leptospira (n=25). These taxa also showed the highest contribution to the observed differences in beta diversity. VirCapSeq sequencing in rat liver samples detected the potentially zoonotic rat hepatitis E virus in three rats. Although 16S rRNA gene amplicon sequencing was limited in its capacity for species level identifications and can be more difficult to interpret due to the influence of contaminating sequences in these low microbial biomass samples, we believe it has potential to be a suitable pre-screening method in the future to get a better overview of potentially zoonotic bacteria that are circulating in wildlife.

A finite difference-based approach for strain demand prediction of inelastic pipes subjected to permanent ground displacements

Permanent ground displacement triggered by geohazards constitutes a major threat to the integrity of long-distance pipelines. In this study, a novel and simple method is proposed to evaluate inelastic pipes’ behavior under ground movements considering soil-pipe interaction based on the finite difference method. The existing finite difference-based method previously proposed by the authors for strain analysis of buried pipelines subjected to ground movements excludes material nonlinearity in pipes, which limits its applicability in engineering practice. To remedy this situation, the method presented herein maintains the well-established concepts of the existing finite-difference-based method but also introduces the scheme to consider the inelastic material behavior of steel pipes. More specifically, the expressions of internal axial force and bending moment, required in the finite difference equations, are explicitly derived based on the actual stress distribution on the pipe cross-section. The proposed method is validated against the finite element method (one-dimensional beam model) in terms of the strain and deformation demands using two indicative case studies, referred to as symmetric and non-symmetric soil resistance conditions, respectively. The comparison between finite element analysis and the proposed method indicates good agreement. Additionally, the proposed method is compared with four existing analytical methods for pipes subjected to strike-slip fault displacement. The proposed method is a simple but general technique to analyze pipes’ response under a wide range of geohazards, and thus can be potentially used as an alternative method for preliminary design, safety prescreening, and reliability-based assessment of pipes against geohazards.

Effects of dairy processing sludge and derived biochar on greenhouse gas emissions from Danish and Irish soils

Globally, to ensure food security bio-based fertilizers must replace a percentage of chemical fertilizers. Such replacement must be deemed sustainable from agronomic and greenhouse gas (GHG) emission perspectives. For agronomic performance several controlled protocols are in place but not for testing GHG emissions. Herein, a pre-screening tool is presented to examine GHG emissions from bio-waste as fertilizers. The various treatments examined are as follows: soil with added mineral nitrogen (N, 140 kg N ha−1) fertilizer (MF), the same amount of MF combined with dairy processing sludge (DS), sludge-derived biochar produced at 450 °C (BC450) and 700 °C (BC700) and untreated control (CK). These treatments were combined with Danish (sandy loam) or Irish (clay loam) soils, with carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions and soil inorganic-N contents measured on selected days. During the incubation, biochar mitigated N2O emissions by regulating denitrification. BC450 reduced N2O emissions from Danish soil by 95.5% and BC700 by 97.7% compared to emissions with the sludge application, and for Irish soil, the N2O reductions were 93.6% and 32.3%, respectively. For both soils, biochar reduced CO2 emissions by 50% as compared to the sludge. The lower N2O reduction potential of BC700 for Irish soil could be due to the high soil organic carbon and clay content and pyrolysis temperature. For the same reasons emissions of N2O and CO2 from Irish soil were significantly higher than from Danish soil. The temporal variation in N2O emissions was correlated with soil inorganic-N contents. The CH4 emissions across treatments were not significantly different. This study developed a simple and cost-effective pre-screening method to evaluate the GHG emission potential of new bio-waste before its field application and guide the development of national emission inventories, towards achieving the goals of circular economy and the European Green Deal.

Methodology for evaluation of potential sites for large-scale riverbank filtration

Despite being a simple and inexpensive pretreatment technology, the cost-effectiveness of riverbank filtration (RBF) depends on complex hydrogeological and hydrogeochemical variables. One of the most important issues for decision makers regarding RBF is optimal site selection. Therefore, a methodology for multicriteria site evaluation for large-scale RBF schemes is offered. The methodology is primarily designed as a prescreening method, applied over a wide area, but can also serve as a guide for evaluating individual RBF sites. To facilitate further discussion about improvements on the methodology, the reasoning behind each relevant factor and its weight in the evaluation is presented. The methodology is divided into three sequential steps through which a site can be assessed. The first step is to establish the existence of connectivity between the river and aquifer. This is termed the essential criterion, and is a binary determination of site suitability. If the site is determined to be suitable, it is then assessed via a set of quantity criteria, which measure the aquifer capacity and amount of bank filtrate that can be effectively abstracted. Lastly, water quality criteria are assessed by means of surface-water and groundwater quality. The quantity and quality criteria form a result expressed as the site suitability index (SSI), which ranges from 0 to 1, where higher scores represent increased suitability. Finally, the methodology is applied to evaluate existing sites of large-scale RBF application as a demonstration of its applicability. The success of these existing sites is compared to the calculated SSI value and discussed.

The sensitivity of pan-TRK immunohistochemistry in solid tumours: A meta-analysis

IntroductionSince the approval of neurotrophic tropomyosin receptor kinase (NTRK) tyrosine kinase inhibitors for fist-line advanced stage pan-cancer therapy, pathologists and molecular biologists have been facing a complex question: how should the large volume of specimens be screened for NTRK fusions? Immunohistochemistry is fast and cheap, but the sensitivity compared to RNA NGS is unclear. MethodsWe performed RNA-based next-generation sequencing on 1,329 cases and stained 24 NTRK-rearranged cases immunohistochemically with pan-TRK (ERP17341). Additionally, we performed a meta-analysis of the literature. After screening 580 studies, 200 additional NTRK-rearranged cases from 13 studies, analysed with sensitive molecular diagnostics as well as pan-TRK IHC, were included. ResultsIn the included 224 NTRK-rearranged solid tumours, the sensitivity for pan-TRK IHC was 82% and the false-negative rate was 18%. NTRK3 fusions had more false negatives (27%) compared to NTRK1 (6%) and NTRK2 (14%) (p = 0.0006). Membranous, nuclear and peri-nuclear staining patterns strongly correlated with different fusion products, with membranous staining being more prevalent in NTRK1 and NTRK2, nuclear in NTRK3, and perinuclear in NTRK1. ConclusionDespite a reduction in the number of molecular analysis, using pan-TRK immunohistochemistry as a prescreening method to detect NTRK fusions in solid tumours will miss 18% of all NTRK-fused cases (especially involving NTRK3). Therefore, the most comprehensive and optimal option to detect NTRK fusions is to perform molecular testing on all eligible cases. However, in case of financial or logistical limitations, an immunohistochemistry-first approach is defensible in tumours with a low prevalence of NTRK fusions.

Cancer Screening Technology and Attitude of Women Towards Cervical Cancer

Cervical cancer claims over a quarter of a million lives of women annually worldwide. It is believed to be the second most common cancer among women worldwide. Screening is used to detect precancerous changes or early cancers before signs or symptoms of cancer occur. The first case of cervical cancer was founded in the 1970s by Harald Zur Hausen. It is believed to be the second most common cancer among women worldwide. Females becoming sexually active in early age with multiple partners are on high risk. Virtually all cervical cancers are associated with human papilloma viruses (HPV). This study was conducted to understand the levels of knowledge and attitudes of women towards cervical cancer screening in Al Khan Dubai.It assessed the knowledge and attitudes of women about cervical cancer prevention. 70% of the sexually active women really need to go for cancer screening. It shows that 66% of women in al khan are being affected due to lifestyle and it is affecting women’s decision in relation to cervical cancer screening. To improve cervical cancer screening in al khan area, women should be given more information, motivation, awareness, and sensitization, in order to encourage them to go for a cervical cancer screening. Keywords: Cervix, High risk, HPV, Pre-screening methods, Women.

Pre-screening to guide coronary artery calcium scoring for early identification of high-risk individuals in the general population.

To evaluate the ability of Systematic COronary Risk Estimation 2 (SCORE2) and other pre-screening methods to identify individuals with high coronary artery calcium score (CACS) in the general population. Computed tomography-based CACS quantification was performed in 6530 individuals aged 45 years or older from the general population. Various pre-screening methods to guide referral for CACS were evaluated. Miss rates for high CACS (CACS ≥300 and ≥100) were evaluated for various pre-screening methods: moderate (≥5%) and high (≥10%) SCORE2 risk, any traditional coronary artery disease (CAD) risk factor, any Risk Or Benefit IN Screening for CArdiovascular Disease (ROBINSCA) risk factor, and moderately (>3 mg/24 h) increased urine albumin excretion (UAE). Out of 6530 participants, 643 (9.8%) had CACS ≥300 and 1236 (18.9%) had CACS ≥100. For CACS ≥300 and CACS ≥100, miss rate was 32 and 41% for pre-screening by moderate (≥5%) SCORE2 risk and 81 and 87% for high (≥10%) SCORE2 risk, respectively. For CACS ≥300 and CACS ≥100, miss rate was 8 and 11% for pre-screening by at least one CAD risk factor, 24 and 25% for at least one ROBINSCA risk factor, and 67 and 67% for moderately increased UAE, respectively. Many individuals with high CACS in the general population are left unidentified when only performing CACS in case of at least moderate (≥5%) SCORE2, which closely resembles current clinical practice. Less stringent pre-screening by presence of at least one CAD risk factor to guide CACS identifies more individuals with high CACS and could improve CAD prevention.

Lynch syndrome pre-screening and comprehensive characterization in a multi-center large cohort of Chinese patients with colorectal cancer.

Objective:Lynch syndrome (LS) pre-screening methods remain under-investigated in colorectal cancers (CRCs) in Asia. Here, we aimed to systematically investigate LS pre-screening and comprehensively characterize LS CRCs.Methods:Microsatellite instability (MSI) and germline variants of DNA mismatch repair (MMR) genes were examined in 406 deficient MMR (dMMR) and 250 proficient MMR CRCs. The genetic differences between LS and sporadic CRCs were studied with whole exome sequencing analysis.Results:The incidence of dMMR in Chinese patients with CRCs was 13.8%. Consistency analysis between MMR immunohistochemistry (IHC) and MSI testing showed the kappa value was 0.758. With next-generation sequencing (NGS), germline variants were detected in 154 CRCs. Finally, 88 patients with CRC were identified as having LS by Sanger sequencing. Among them, we discovered 21 previously unreported pathogenic germline variants of MMR genes. Chinese patients with LS, compared with sporadic CRCs, tended to be early-onset, right-sided, early-stage and mucinous. Overall, the performance of MMR IHC and MSI testing for LS pre-screening was comparable: the area under the ROC curve for dMMR, MSI-H, and MSI-H/L was 0.725, 0.750, and 0.745, respectively. dMMR_MSI-H LS and sporadic CRCs showed substantial differences in somatic genetic characteristics, including different variant frequencies of APC, CREBBP, and KRAS, as well as different enriched pathways of VEGF, Notch, TGFβR, mTOR, ErbB, and Rac protein signal transduction.Conclusions:MMR IHC and MSI testing were effective methods for LS pre-screening. The revealed clinical and somatic genetic characteristics in LS CRCs may have the potential to improve the performance of LS pre-screening in combination with dMMR/MSI.

A distortion model-based pre-screening method for document image tampering localization under recapturing attack

Document images are vulnerable to tampering by image editing tools. The forgery trace can be concealed by a simple but effective counter-forensic measure, i.e., recapturing the altered document image. It is of practical need to study the tampering localization method under recapturing attack. In this work, we first study spatial and spectral distortion models in the printing and scanning process. The distortion models are then employed in extracting spectral features in both tampered and untampered regions. The proposed forensic scheme can then be established by comparing the spectral features in both the questioned document image and the reference halftone patterns (obtained by exploiting the prior knowledge of the printing device). To evaluate the performance of our approach, we gather a high-quality image database of 528 captured or recaptured documents (about 185K patches) as well as 72 tampered-and-recaptured documents (about 27K patches). The experimental results show that the proposed method can accurately classify recaptured document images with AUC as high as 0.9999 even though the training and testing samples are collected by different devices. In the tampering localization experiment, the proposed method can be combined with some generic CNN models to yield a two-stage scheme with high efficiency and accuracy, i.e., F1-score as high as 0.9. Finally, we also show that the proposed method is a practical solution even without the prior knowledge of the printer model is unavailable. To benefit the academic society, the resource of our work is online available at http://shorturl.at/jxELP.

QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds.

Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user’s home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease.

A food safety prescreening method with domain-specific information using online reviews

A food safety prescreening method with domain-specific information using online reviews

Relations Between Collision Probability, Mahalanobis Distance, and Confidence Intervals for Conjunction Assessment

Some of the most common metrics for collision risk assessment are the probability of collision, miss distance in Mahalanobis space, and confidence intervals. Sometimes they are used in combination; for example, the miss distance is used as a prescreening method to identify potentially hazardous conjunctions; other times they are used as alternative means; that is, covariance ellipse overlapping checks are employed instead of computing the probability of collision. In this work, we show that the three risk indexes are intimately connected once a suitable distance is defined. We argue that Mahalanobis miss distance is a proper metric only when the sigma-normalized hard-body size is negligible; we thus investigate the minimum Mahalanobis distance between the hard-body circle and the combined position covariance as an alternative. Its computation is fully analytic, as the most complex operation is finding the roots of a quartic polynomial. When multiplied by the sigma-normalized hard-body radius, such distance provides an upper bound to the collision probability. When used to scale the covariance matrix, it provides the largest confidence interval supporting a noncollision event. Finally, when adopted as an actionable threshold, analytical bounds on the probability of miss detection and of false alarms can be computed.