False Positive Detection Research Articles

AI-Based Malware Detection and Classification for Wireless Networks

Abstract: This study highlights the shortcomings of conventional detection strategies like signature-based and anomaly-based approaches by examining the application of artificial intelligence (AI) techniques for malware detection and classification in wireless networks. Using machine learning and deep learning algorithms, the study shows notable gains in false positive rates, detection accuracy, and adaptation to changing threats. The acquisition of data from many sources, the extraction of features from malware samples, and the thorough training and assessment of models are important techniques. The results demonstrate how successful AI-based systems are in detecting and reducing malware threats, even in the face of issues like dataset variety and interpretability. The potential of AI technology to improve cybersecurity measures in wireless environments is highlighted by this research.

Detecting and sizing the Earth with PLATO: A feasibility study based on solar data

The PLAnetary Transits and Oscillations of stars (PLATO) mission will observe the same area of the sky continuously for at least two years in an effort to detect transit signals of an Earth-like planet orbiting a solar-like star. We aim to study how short-term solar-like variability caused by oscillations and granulation would affect PLATO's ability to detect and size Earth if PLATO were to observe the Solar System itself. We also compare different approaches to mitigate noise caused by short-term solar-like variability and perform realistic transit fitting of transit signals in PLATO-like light curves. We injected Earth-like transit signals onto real solar data taken by the Helioseismic and Magnetic Imager (HMI) instrument on board the Solar Dynamics Observatory (SDO). We isolated short-term stellar variability in the HMI observations by removing any variability with characteristic timescales longer than five hours using a smooth Savitzky-Golay filter. We then added a noise model for a variety of different stellar magnitudes computed by assuming an observation by all 24 normal cameras. We first compared four different commonly used treatments of correlated noise in the time domain by employing them in a transit fitting scheme. We then tried to recover pairs of transit signals using an algorithm similar to the transit least squares algorithm. Finally, we performed transit fits using realistic priors on planetary and stellar parameters and assessed how accurately the pair of two injected transits was recovered. We find that short-term solar-like variability affects the correct retrieval of Earth-like transit signals in PLATO data. Variability models accounting for variations with typical timescales at the order of one hour are sufficient to mitigate these effects. We find that when the limb-darkening coefficients of the host star are properly constrained, the impact parameter does not negatively affect the detectability of a transit signal or the retrieved transit parameters, except for high values ($b > 0.8$). For bright targets (8.5 - 10.5 mag), the transit signal of an Earth analogue can reliably be detected in PLATO data. For faint targets a detection is still likely, though the results of transit search algorithms have to be verified by transit-fitting algorithms to avoid false positive detections being flagged. For bright targets (V-mag leq 9.5), the radius of an Earth-like planet orbiting a solar-like star can be correctly determined at a precision of 3<!PCT!> or less, assuming that at least two transit events are observed and the characteristics of the host star are well understood.

Detection and Quantification of Soil-Borne Wheat Mosaic Virus, Soil-Borne Cereal Mosaic Virus and Japanese Soil-Borne Wheat Mosaic Virus by ELISA and One-Step SYBR Green Real-Time Quantitative RT-PCR.

Furoviruses are bipartite viruses causing mosaic symptoms and stunting in cereals. Infection with these viruses can lead to severe crop losses. The virus species Furovirus tritici with soil-borne wheat mosaic virus (SBWMV), Furovirus cerealis with soil-borne cereal mosaic virus (SBCMV) and Furovirus japonicum with Japanese soil-borne wheat mosaic virus (JSBWMV) and French barley mosaic virus (FBMV) as members are biologically and genetically closely related. Here, we develop SYBR green-based real-time quantitative RT-PCR assays to detect and quantify the RNA1 and RNA2 of the three virus species. Using experimental data in combination with Tm-value prediction and analysis of primer and amplicon sequences, we determine the capacity of our method to discriminate between the different viruses and evaluate its genericity to detect different isolates within the same virus species. We demonstrate that our method is suitable for discriminating between the different virus species and allows for the detection of different virus isolates. However, JSBWMV RNA1 primers may amplify SBWMV samples, bearing a risk for false positive detection with this primer. We also test the efficiency of polyclonal antibodies to detect the different viruses by ELISA and suggest that ELISA may be applied as a first screening to identify the virus. The real-time qRT-PCR assays developed provide the possibility to screen for quantitative disease resistance against SBCMV, SBWMV and JSBWMV. Moreover, with our method, we hope to promote research to unravel yet unresolved questions with respect to furovirus-host interaction concerning host range and resistance as well as regarding the role of multipartite genomes.

B-232 One Year Post-Implementation Experience Using a Custom Designed DNA Contamination Detection Module for a Clinical Myeloid Neoplasm Next Generation Sequencing Assay

Abstract Background In complex automated clinical laboratory Next Generation Sequencing (NGS) workflows, numerous opportunities exist for contamination events to occur. For oncology testing applications, detection of these events is crucial for accurate results reporting. A contamination detection module (MICon) using microhaplotype (MH) regions and an in-house designed analysis model was developed for use in a NGS assay for myeloid neoplasms (NGSHM) [1]. Variant detection for NGSHM has a validated analytical sensitivity of 2% variant allele frequency (VAF). Gross contamination events can potentially cause erroneous false positive variant detection whereas true low VAF mutations may be masked and evade detection. The following study was performed to evaluate MICon module performance on samples processed over the course of a year. Methods MH regions contain highly conserved tandem single nucleotide polymorphisms (SNPs) with high global heterogeneity occurring within a 300-nucleotide span. From the work of Kidd et al [2], 27 MH regions covering 92 SNPs and spanning 16 chromosomes were selected for inclusion in the 47 gene NGSHM target-capture panel. Sequencing was performed on a NovaSeq 6000 and analyzed through an internal bioinformatics pipeline, which incorporates the MICon module. MICon uses a binary classification model incorporating VAFs from MH regions, number of MH genotypes, and the contamination estimation score from verifyBamID to compute a single value on a 0-100 scale. Scores <50 are classified as non-contaminated and scores ≥50 require further investigation. Results The dataset was comprised of 10,990 samples. A total of 10,232 patient cases (93.1%) had non-contaminated MICon scores <50. Control replicates accounted for 261 samples (2.4%). There were 497 patient cases (4.5%) with MICon scores ≥50. Of the 497 patient cases, 137 (27.6%) had undergone hematopoietic stem cell transplantation (HSCT) and 114 (22.9%) contained gross chromosomal abnormalities, representing iatrogenic or tumor biological factors. An additional 135 (27.2%) of cases were contaminated from laboratory processing errors, 45 (9.1%) occurred due to chemistry failures during run processing, and 66 (13.2%) had an undetermined cause for a high score, representing laboratory-related and unknown factors. Overall, the 135 laboratory processing errors detected accounted for 1.3% of patient cases analyzed by the laboratory. These cases were repeated, and the non-contaminated results were accurately reported. Conclusions Herein it was shown the implementation of MICon has improved patient safety over the course of a year. The 1.3% of patient cases identified as arising from laboratory processing errors were successfully reprocessed. While the overall rate of laboratory processing errors appears low, the absolute number can be significant in a high-volume test setting, despite reliance on highly automated processes. Value is added by MICon through identifying errors that could result in the release of inaccurate patient results. By detecting previously unrealized errors, MICon has become an invaluable asset to the NGSHM assay by enhancing patient laboratory testing safety.

A-356 A hand-held microfluidic platform for rapid and sensitive identification of respiratory viruses from nasal swab samples

Abstract Background Identifying the infected individual with a fast and accurate test is essential to constrain the spread of viruses and provide the proper treatment. Antigen rapid test (ART) provides quick screening of infection for patients; however, it shows less promising in the detection of early infection and patent’s full recovery as its inferior sensitivity to the nucleic acid detection method. On the other hand, bulky and costly instrument, tedious and prolonged procedures in traditional nucleic acid testing is also unfavorable for the screening. Delta is developing a fully automated hand-held POC using molecular technology to simultaneously detect 4 targets in 20 mins. The POC can be stand-alone or linked to mobile or PC app for different application scenarios. The proprietary recipe of the lysis buffer could neutralize the inhibition in the sample with effective pathogen lysis in seconds. The microfluidic cartridge containing lyophilized reagent can be stored at room temperature. The amplification signal is detected via the optical system from the colorimetric LAMP. This study is to evaluate the performance of the POC system in detecting SARS-CoV2 (SC2), FluA and B viruses in nasal swab. In addition, the assay performance was further compared with other commercial products available. Methods The attenuated SC2 virus, FluA and B viruses were diluted with 1xTE into different concentrations. The diluted viruses were spiked into the lysis buffer in 5% v/v ratio. Nasal samples collected from donors were soaked and swirled inside the cartridges containing lysis buffer according to the manufacturers’ instructions. For commercial products without lysis buffer, direct swabs were used. The cartridges were then put into the respective devices for further testing. Results The lyophilized SC2 assay consistently detected the viruses at 1000 copies/ml (20 out of 20) in real nasal samples in the presence of Delta Lysis Buffer (containing lyophilized neutralizer). However, without Lysis buffer, there was no signal detected even in the presence of 240x LoD viruses. The whole workflow sensitivity was tested with SC2 positive samples at 1x, 4x, 10x LoD and negative samples in the nasal sample matrix on Delta hand-held system. The results were processed via embedded algorithm and displayed on device as well as on PC and mobile app respectively. Out of 79 positive samples tested, the detection rate was 100%. There was no false positive detection for 31 negative samples tested. Positive internal control indicated the valid runs. 5 major SC2 subtypes (99.8% in silico analysis of 366,707 strains), 10 FluA and 9 FluB subtypes were successfully detected. Further, Delta hand-held platform outperformed in the LoD of SC2 with 500 copies/swab comparing to the ≥50,000 copies/swab of the benchmarked commercial platforms. Conclusions The performance of the hand-held molecular detection system we developed demonstrated a rapid, sensitive, and competitive analytical performance in the respiratory panel detection. The capability of device linking to apps and clouds for data management and the extendable docking system for multiple devices to run samples simultaneously and independently, make it applicable for the infectious diseases screening in both clinical settings and for home-use.

Mosquitoes on a chip-environmental DNA-based detection of invasive mosquito species using high-throughput real-time PCR.

The monitoring of mosquitoes is of great importance due to their vector competence for a variety of pathogens, which have the potential to imperil human and animal health. Until now mosquito occurrence data is mainly obtained with conventional monitoring methods including active and passive approaches, which can be time- and cost-consuming. New monitoring methods based on environmental DNA (eDNA) could serve as a fast and robust complementary detection system for mosquitoes. In this pilot study already existing marker systems targeting the three invasive mosquito species Aedes (Ae.) albopictus, Ae. japonicus and Ae. koreicus were used to detect these species from water samples via microfluidic array technology. We compared the performance of the high-throughput real-time PCR (HT-qPCR) system Biomark HD with real-time PCR (qPCR) and also tested the effect of different filter media (Sterivex® 0.45 µm, Nylon 0.22 µm, PES 1.2 µm) on eDNA detectability. By using a universal qPCR protocol and only 6-FAM-MGB probes we successfully transferred these marker systems on the HT-qPCR platform. All tested marker systems detected the target species at most sites, where their presence was previously confirmed. Filter media properties, the final filtration volume and observed qPCR inhibition did not affect measured Ct values via qPCR or HT-qPCR. The Ct values obtained from HT-qPCR were significantly lower as Ct values measured by qPCR due to the previous preamplification step, still these values were highly correlated. Observed incongruities in eDNA detection probability, as manifested by non-reproducible results and false positive detections, could be the result of methodological aspects, such as sensitivity and specificity issues of the used assays, or ecological factors such as varying eDNA release patterns. In this study, we show the suitability of eDNA-based detection of mosquito species from water samples using a microfluidic HT-qPCR platform. HT-qPCR platforms such as Biomark HD allow for massive upscaling of tested species-specific assays and sampling sites with low time- and cost-effort, thus this methodology could serve as basis for large-scale mosquito monitoring attempts. The main goal in the future is to develop a robust (semi)-quantitative microfluidic-based eDNA mosquito chip targeting all haematophagous culicid species occurring in Western Europe. This chip would enable large-scale eDNA-based screenings to assess mosquito diversity, to monitor species with confirmed or suspected vector competence, to assess the invasion progress of invasive mosquito species and could be used in pathogen surveillance, when disease agents are incorporated.

Synthesis of trisaccharide antigens featuring colitose, abequose and fucose residues and assessment of antibody binding on antigen arrays

Deoxy-hexose sugars, such as rhamnose and quinovose, and the dideoxy-hexoses colitose, abequose, and tyvelose are highly antigenic given that they are absent from animal glycoconjugates. To investigate the specificity of antibodies towards structurally similar carbohydrate epitopes found in bacteria, we synthesized trisaccharides containing colitose, abequose, and fucose motifs. Each trisaccharide was designed with a spacer ending with a primary amino group. These trisaccharide constructs were immobilized on O-succinimide coated glass slides alongside bacterial lipopolysaccharides (LPS) containing colitose, abequose, and fucose residues. We compared the recognition of the synthetic trisaccharides and natural LPS including structurally related epitopes by monoclonal and polyclonal antibodies targeting bacterial LPS. Additionally, we used arrays displaying the synthetic trisaccharides and natural LPS to assess the variability of IgA reactivity from breast milk samples towards the carbohydrate antigens. The results obtained underlined the cross-reactivity of polyclonal antibodies towards structurally related carbohydrate antigens and revealed a broad reactivity of breast milk-derived IgA towards the carbohydrate antigens tested. The significant cross-reactivity of antibodies towards structurally related LPS antigens may lead to false-positive detection of bacterial serotypes when used for diagnostic purposes.

Generative Haplotype Prediction Outperforms Statistical Methods for Small Variant Detection in NGS Data

Abstract Motivation Detection of germline variants in next-generation sequencing data is an essential component of modern genomics analysis. Variant detection tools typically rely on statistical algorithms such as de Bruijn graphs or Hidden Markov Models, and are often coupled with heuristic techniques and thresholds to maximize accuracy. Despite significant progress in recent years, current methods still generate thousands of false positive detections in a typical human whole genome, creating a significant manual review burden. Results We introduce a new approach that replaces the handcrafted statistical techniques of previous methods with a single deep generative model. Using a standard transformer-based encoder and double-decoder architecture, our model learns to construct diploid germline haplotypes in a generative fashion identical to modern Large Language Models (LLMs). We train our model on 37 Whole Genome Sequences (WGS) from Genome-in-a-Bottle samples, and demonstrate that our method learns to produce accurate haplotypes with correct phase and genotype for all classes of small variants. We compare our method, called Jenever, to FreeBayes, GATK HaplotypeCaller, Clair3 and DeepVariant, and demonstrate that our method has superior overall accuracy compared to other methods. At F1-maximizing quality thresholds, our model delivers the highest sensitivity, precision, and the fewest genotyping errors for insertion and deletion variants. For single nucleotide variants our model demonstrates the highest sensitivity but at somewhat lower precision, and achieves the highest overall F1 score among all callers we tested. Availability and Implementation Jenever is implemented as a python-based command line tool. Source code is available at https://github.com/ARUP-NGS/jenever/

Long-term use of an implantable loop recorder for ECG recording in horses.

Potential arrhythmias recorded by cardiac monitors using the built-in human algorithm are as follows: total number of episodes, ventricular tachycardia, asystole, bradycardia, atrial tachycardia, atrial fibrillation and the percentage of time spent in atrial tachycardia or atrial fibrillation. Long-term use of an insertable cardiac monitor (loop recorder) has been reported in one horse earlier, but its accurate role as a diagnostic tool is still unclear. The aim of the present study was to investigate the long-term applicability of the Reveal XT cardiac monitor for recording cardiac arrhythmias in adult horses. The Reveal XT cardiac monitor was implanted in 12 horses under sedation in standing position. Median duration of data recording calculated for the population was 1,169 days. The number of false positive detections of asystole and bradycardia was extremely high in all horses. For atrial fibrillation, false positive detection occurred in five and false negative detection occurred in one horse. The present study showed that the long-term use of the Reveal XT cardiac monitor is feasible, well-tolerated and the devices work reliably, without complications. The human algorithm could not be used for automatic detection of arrhythmic episodes in the study population. The device could detect atrial fibrillation in horses, but the recorded AF burden was inaccurate and the stored ECGs had to be manually interpreted. Because the human analyser algorithm of this cardiac monitor fails to identify bradyarrhythmias in horses based on our results, this instrument is not capable for accurately determining the aetiology of episodic collapse in this species.

Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.

Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

"Real-world" performance of the Confirm Rx™ SharpSense AF detection algorithm: UK Confirm Rx study.

The novel Confirm Rx™ implantable cardiac monitor (ICM) with SharpSense™ technology incorporates a new P-wave discriminator designed to improve AF detection. This study aimed to evaluate the diagnostic performance of the Confirm Rx™ ICM in detecting AF episodes of varying durations. We conducted a multicenter retrospective analysis of consecutive patients implanted with a Confirm Rx™ ICM (v1.2) across nine UK hospitals, all with documented AF lasting at least 6 min. Electrocardiograms (ECGs) were manually adjudicated by cardiologists. To account for intra- and inter-reviewer variability, a random sample of 10% of ECGs underwent additional review. Disagreements were resolved by a third reviewer. Diagnostic performance was determined by calculating the gross and patient-averaged positive predictive value (PPV) for AF episodes of different duration. The source of false positive (FP) detection was also categorized. Overall, 16,230 individual ECGs from 232 patients were included. The median AF episode duration was 14 min. R-wave amplitude remained stable during follow-up (0.52 ± 0.27 mV [initial] vs. 0.54 ± 0.29 mV [end of follow-up], p = .10). The gross and patient-averaged PPV were 75.0% and 67.0%, respectively. Diagnostic performance (gross) increased with progressively longer AF episodes: 88.0% for ≥1 h, 97.3% for 6 h, and 100% for 24 h. The main source of FP during tachycardia was T-wave oversensing (54.2%), while in non-tachycardic episodes it was predominantly ectopy (71.2%). The AF burden precision was excellent (93.3%). The Confirm Rx™ ICM diagnostic performance was modest for all AF episodes (75%), with accuracy increasing for longer AF episodes.

Exploring AI Progress in GNSS Remote Sensing: A Deep Learning Based Framework for Real‐Time Detection of Earthquake and Tsunami Induced Ionospheric Perturbations

AbstractGlobal Navigation Satellite System Ionospheric Seismology investigates the ionospheric response to earthquakes and tsunamis. These events are known to generate Traveling Ionospheric Disturbances (TIDs) that can be detected through GNSS‐derived Total Electron Content (TEC) observations. Real‐time TID identification provides a method for tsunami detection, improving tsunami early warning systems (TEWS) by extending coverage to open‐ocean regions where buoy‐based warning systems are impractical. Scalable and automated TID detection is, hence, essential for TEWS augmentation. In this work, we present an innovative approach to perform automatic real‐time TID monitoring and detection, using deep learning insights. We utilize Gramian Angular Difference Fields (GADFs), a technique that transforms time‐series into images, in combination with Convolutional Neural Networks (CNNs), starting from VARION (Variometric Approach for Real‐time Ionosphere Observation) real‐time TEC estimates. We select four tsunamigenic earthquakes that occurred in the Pacific Ocean: the 2010 Maule earthquake, the 2011 Tohoku earthquake, the 2012 Haida‐Gwaii, the 2015 Illapel earthquake. The first three events are used for model training, whereas the out‐of‐sample validation is performed on the last one. The presented framework, being perfectly suitable for real‐time applications, achieves 91.7% of F1 score and 84.6% of recall, highlighting its potential. Our approach to improve false positive detection, based on the likelihood of a TID at each time step, ensures robust and high performance as the system scales up, integrating more data for model training. This research lays the foundation for incorporating deep learning into real‐time GNSS‐TEC analysis, offering a joint and substantial contribution to TEWS progression.

Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist training.

Accurate interpretation of mammograms presents challenges. Tailoring mammography training to reader profiles holds the promise of an effective strategy to reduce these errors. This proof-of-concept study investigated the feasibility of employing convolutional neural networks (CNNs) with transfer learning to categorize regions associated with false-positive (FP) errors within screening mammograms into categories of "low" or "high" likelihood of being a false-positive detection for radiologists sharing similar geographic characteristics. Mammography test sets assessed by two geographically distant cohorts of radiologists (cohorts A and B) were collected. FP patches within these mammograms were segmented and categorized as "difficult" or "easy" based on the number of readers committing FP errors. Patches outside 1.5 times the interquartile range above the upper quartile were labeled as difficult, whereas the remaining patches were labeled as easy. Using transfer learning, a patch-wise CNN model for binary patch classification was developed utilizing ResNet as the feature extractor, with modified fully connected layers for the target task. Model performance was assessed using 10-fold cross-validation. Compared with other architectures, the transferred ResNet-50 achieved the highest performance, obtaining receiver operating characteristics area under the curve values of 0.933 ( ) and 0.975 ( ) on the validation sets for cohorts A and B, respectively. The findings highlight the feasibility of employing CNN-based transfer learning to predict the difficulty levels of local FP patches in screening mammograms for specific radiologist cohort with similar geographic characteristics.

Industrial image anomaly detection based on multi Gaussian discriminant model and robust core set

Abstract To address the issue of false positive (FP) detections in image anomaly detection caused by the loss of low-frequency features when dealing with high-dimensional feature distributions, we propose the multi-layer Gaussian discriminant anomaly detection model (MGAD). This model utilizes distance metrics based on multiple normal distributions to perform anomaly detection. By mining multi-layer feature combinations from normal samples and incorporating a Gaussian mixture model strategy for pixel-by-pixel probability density estimation, a weighting mechanism is designed to emphasize the role of low-frequency features in Gaussian space. This approach effectively models data collections that do not follow a single normal distribution as a mixture of several Gaussian distributions, thereby reducing false detections. Additionally, we propose a method for calculating the minimum Mahalanobis distance based on the estimation of the minimum covariance determinant. By identifying a subset with the smallest covariance matrix determinant, this method enhances the robust estimation of the data’s central position and spread, thereby reducing the impact of outliers. On the MVTec-AD dataset, MGAD demonstrates outstanding performance with an anomaly detection area under the receiver operating characteristic curve (AUROC) of 98.8%, the anomaly localization AUROC of 98.2%, and the per-class true negative rate for normal samples of 93.1%. Compared with the state-of-the-art models, MGAD improves the detection accuracy for normal samples by 3.6%, demonstrating the best performance among all models. These results highlight the model’s excellent capability in anomaly recognition and reduction of FPs.

Histoplasma capsulatum urinary antigen detection in a kidney transplant recipient with acute paracoccidioidomycosis: Case study and literature review.

Paracoccidioidomycosis (PCM) and histoplasmosis are endemic fungal diseases in South America. Both can lead to lung involvement with fungal dissemination progressing to systemic and severe clinical manifestations, especially in immunosuppressed hosts. As the population of immunosuppressed individuals has been rising, a higher occurrence of fungal infections is predicted in this setting. This poses challenges regarding the differential diagnosis due to overlapping clinical and laboratorial findings, hampering the management of cases. In this study, the authors discuss the occurrence of a false-positive Histoplasma urinary antigen detection in a kidney transplant recipient with acute PCM. Given the scarce information about this subject, a review on literature data is provided. A comprehensive literature search was conducted to investigate previous studies that found cross-reactivity between Histoplasma urinary antigen assays in human patients with confirmed diagnosis of PCM. Additionally, an update of PCM in transplant recipients is provided. The included studies reported 120 samples from patients with PCM tested for Histoplasma antigen, presenting an overall cross-reactivity of 51.67% and 17 cases of PCM in transplant recipients. CONCLUSIONS: The galactomannan urinary antigen developed to diagnose histoplasmosis can cross react with PCM, which may represent a concern in countries where both mycoses overlap.

Upaya Pencegahan Hasil Positif Palsu melalui Edukasi Pemeriksaan Narkotika pada Siswa Sekolah Mengenah Kejuruan

Vocational High School (SMK) is a place of education that prepares its students to be ready to face the world of work. Drug tests are often carried out targeting prospective employees as a requirement to enter the workplace in an agency. Knowing about drug tests is very important for Vocational High School (SMK) students who will enter the world of work. Education about false positive results in drug tests is very important for SMK students who will enter the world of work. The right education program can increase efforts to prevent false positives in drug tests when entering the world of work for SMK graduates. To increase knowledge related to drug tests and false positive detection, health education is carried out and also a demonstration of drug tests on urine. Before and after education, each student takes a pre-test and post-test through a questionnaire to see the increase in their knowledge. After community service was carried out, with education about drug tests, there was an increase in understanding about drug tests, the drug test process with immunochromatography (ICT) and the detection of false positive results in drug tests.

Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging.

Artificial intelligence (AI) with white light imaging (WLI) is not enough for detecting non-polypoid colorectal polyps and it still has high false positive rate (FPR). We developed AIs using blue laser imaging (BLI) and linked color imaging (LCI) to detect them with specific learning sets (LS). The contents of LS were as follows, LS (WLI): 1991 WLI images of lesion of 2-10 mm, LS (IEE): 5920 WLI, BLI, and LCI images of non-polypoid and small lesions of 2-20 mm. LS (IEE) was extracted from videos and included both in-focus and out-of-focus images. We designed three AIs as follows: AI (WLI) finetuned by LS (WLI), AI (IEE) finetuned by LS (WLI)+LS (IEE), and AI (HQ) finetuned by LS (WLI)+LS (IEE) only with images in focus. Polyp detection using a test set of WLI, BLI, and LCI videos of 100 non-polypoid or non-reddish lesions of 2-20 mm and FPR using movies of 15 total colonoscopy were analyzed, compared to 2 experts and 2 trainees. The sensitivity for LCI in AI (IEE) (83%) was compared to that for WLI in AI (IEE) (76%: p=0.02), WLI in AI (WLI) (57%: p<0.01), BLI in AI (IEE) (78%: p=0.14), and LCI in trainees (74%: p<0.01). The sensitivity for LCI in AI (IEE) (83%) was significantly higher than that in AI (HQ) (78%: p<0.01). The FPR for LCI (6.5%) in AI (IEE) were significantly lower than that in AI (HQ) (17.3%: p<0.01). AI finetuned by appropriate LS detected non-reddish and non-polypoid polyps under LCI.

A novel dual-recognition fluorescent biosensor for sialyl-Lewisx sensitive detection.

Sialyl-Lewisx (SLex) is a tetrasugar, which plays an important role in initial inflammation and cancer cell metastasis, and can be used as a marker for cancer diagnosis and prognosis or a therapeutic target. Detecting SLex from complex biological media remains a significant challenge. Herein, a single-stranded DNA aptamer of SLex was screened based on the double-stranded DNA library-modified magnetic bead (MB)-SELEX technology. After 14 rounds of screening, 12,639 sequences were obtained and divided into nine families. Three representative sequences were selected based on the number of sequence repeats and Gibbs binding free energy, and the aptamer SLex-Apt2 with 80 nt length (Kd = 23.01nM) had the best affinity and relatively high specificity for targeting SLex. Then, a novel dual-recognition fluorescent biosensor for SLex-sensitive detection based on aptamer SLex-Apt2 bio-dots and 3-aminobenzoboric acid-modified MB was developed. This method can detect SLex as low as 32 μM and has a good linear response in the range 100 μM to 2 mM. It has the advantages of low preparation cost, good targeting, and avoiding the occurrence of false-positive and false-negative detection results, which makes the biosensor more valuable in biological detection and clinical diagnosis.

Linearity assessment: deviation from linearity and residual of linear regression approaches.

In this computer simulation study, we examine four different statistical approaches of linearity assessment, including two variants of deviation from linearity (individual (IDL) and averaged (AD)), along with detection capabilities of residuals of linear regression (individual and averaged). From the results of the simulation, the following broad suggestions are provided to laboratory practitioners when performing linearity assessment. A high imprecision can challenge linearity investigations by producing a high false positive rate or low power of detection. Therefore, the imprecision of the measurement procedure should be considered when interpreting linearity assessment results. In the presence of high imprecision, the results of linearity assessment should be interpreted with caution. Different linearity assessment approaches examined in this study performed well under different analytical scenarios. For optimal outcomes, a considered and tailored study design should be implemented. With the exception of specific scenarios, both ADL and IDL methods were suboptimal for the assessment of linearity compared. When imprecision is low (3 %), averaged residual of linear regression with triplicate measurements and a non-linearity acceptance limit of 5 % produces<5 % false positive rates and a high power for detection of non-linearity of>70 % across different types and degrees of non-linearity. Detection of departures from linearity are difficult to identify in practice and enhanced methods of detection need development.

Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network

Passive Acoustic Monitoring (PAM), which involves using autonomous record units for studying wildlife behaviour and distribution, often requires handling big acoustic datasets collected over extended periods. While these data offer invaluable insights about wildlife, their analysis can present challenges in dealing with geophonic sources. A major issue in the process of detection of target sounds is represented by wind-induced noise. This can lead to false positive detections, i.e., energy peaks due to wind gusts misclassified as biological sounds, or false negative, i.e., the wind noise masks the presence of biological sounds. Acoustic data dominated by wind noise makes the analysis of vocal activity unreliable, thus compromising the detection of target sounds and, subsequently, the interpretation of the results. Our work introduces a straightforward approach for detecting recordings affected by windy events using a pre-trained convolutional neural network. This process facilitates identifying wind-compromised data. We consider this dataset pre-processing crucial for ensuring the reliable use of PAM data. We implemented this preprocessing by leveraging YAMNet, a deep learning model for sound classification tasks. We evaluated YAMNet as-is ability to detect wind-induced noise and tested its performance in a Transfer Learning scenario by using our annotated data from the Stony Point Penguin Colony in South Africa. While the classification of YAMNet as-is achieved a precision of 0.71, and recall of 0.66, those metrics strongly improved after the training on our annotated dataset, reaching a precision of 0.91, and recall of 0.92, corresponding to a relative increment of >28 %.Our study demonstrates the promising application of YAMNet in the bioacoustics and ecoacoustics fields, addressing the need for wind-noise-free acoustic data. We released an open-access code that, combined with the efficiency and peak performance of YAMNet, can be used on standard laptops for a broad user base.