High False Positive Rate Research Articles

FLSec-RPL: a fuzzy logic-based intrusion detection scheme for securing RPL-based IoT networks against DIO neighbor suppression attacks

The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet-connected devices has led to concerns regarding privacy and security. In particular, related to the routing protocol for low-power and lossy networks (RPL), which lacks robust security functions, many IoT devices in RPL networks are resource-constrained, with limited computing power, bandwidth, memory, and battery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifically targets neighboring nodes through DIO messages and poses a significant security threat to RPL-based IoT networks. Recent studies have proposed methods for detecting and mitigating this attack; however, they produce high false-positive and false-negative rates in detection tasks and cannot fully protect RPL networks against this attack type. In this paper, we propose a novel fuzzy logic-based intrusion detection scheme to secure the RPL protocol (FLSec-RPL) to protect against this attack. Our method is built of three key phases consecutively: (1) it tracks attack activity variables to determine potential malicious behaviors; (2) it performs fuzzy logic-based intrusion detection to identify malicious neighbor nodes; and (3) it provides a detection validation and blocking mechanism to ensure that both malicious and suspected malicious nodes are accurately detected and blocked. To evaluate the effectiveness of our method, we conduct comprehensive experiments across diverse scenarios, including Static-RPL and Mobile-RPL networks. We compare the performance of our proposed method with that of the state-of-the-art methods. The results demonstrate that our method outperforms existing methods in terms of the detection accuracy, F1 score, power consumption, end-to-end delay, and packet delivery ratio metrics.

Diagnostic Performance of Thyroid FNA: Experience of Belarusian National Referral Thyroid Cancer Center.

A fine-needle aspiration (FNA) biopsy is a basic procedure to verify the morphology of thyroid nodules. However, significant variations in the diagnostic performance of FNA, as well as fluctuations in the rate of malignancy (ROM) in different categories of the Bethesda System for Reporting Thyroid Cytopathology, have been discovered in many studies. The development and employment of additional molecular tests in daily clinical practice require an understanding of the prevalence and structure of thyroid malignancy in each category of the Bethesda system. Current research is a continuous retrospective cohort study of the results of the examination and treatment of 1652 patients with thyroid tumors who have undergone primary surgery in 2021 at a national referral thyroid cancer center. FNA diagnostic performance was studied by comparing cytological diagnoses with histological outcomes. The authors evaluated ROM in each Bethesda category and identified the tumors representing a diagnostic pitfall. The overall ROM was 72.4%. When considering ROM in each Bethesda category, it was 16.7% in Bethesda I, 10.1% in Bethesda II, 23.0% in Bethesda IV, 74.8% in Bethesda V, 99.4% in Bethesda VI. The false-positive rate was 65.0%, false negative-0.67%. Follicular and oncocytic adenomas, as well as the follicular variant of papillary thyroid cancer, were the most troublesome entities for patients' management. ROM was comparable to the values implied by the Bethesda system. Follicular tumors were the main source of high false-positive rates. This necessitates the refinement of existing tests and the development of new diagnostic tests to overcome the abovementioned problems.

Using autonomous recording units to identify and monitor western yellow‐billed cuckoo habitat

AbstractAutonomous recording units (ARUs) paired with signal classification software can be used to detect species‐specific calls, making them useful for evaluating patterns in avian occurrence and activity. However, classification of target signals is not always reliable and may be especially challenging for species that vocalize infrequently. We assessed the use of ARUs to identify and monitor habitat for a cryptic and federally threatened distinct population segment, the western yellow‐billed cuckoo (Coccyzus americanus) in mountainous xeroriparian drainages. Using Kaleidoscope Pro, we developed a call‐classifier and processed acoustic data collected in sites also surveyed using traditional human‐observer methods, applying the same spatial and temporal detection criteria to estimate breeding territories for each method. The classifier detected a total of 4,061 true positive calls at 4 sites, had an overall precision score of 0.07, recall score of 0.09, and F‐score (beta = 1) of 0.08, indicating high false positive and false negative classification rates. Our results were, however, consistent with other ARU studies of rare and cryptic species and ARUs estimated occupancy as effectively as human surveys with as little as 2 hours of daily recording. Total detections varied among sites, likely due to differences in cuckoo population densities and the interaction between topography and ARU detection space. Our results suggest that despite performance shortcomings of call‐classifiers, ARUs can be effective for monitoring cuckoos, with potential for providing higher resolution temporal and spatial information on activity and habitat use and may be particularly effective in remote locations where cuckoos often occur.

TDBAMLA: Temporal and dynamic behavior analysis in Android malware using LSTM and attention mechanisms

The increasing ubiquity of Android devices has precipitated a concomitant surge in sophisticated malware attacks, posing critical challenges to cybersecurity infrastructures worldwide. Existing models have achieved significant strides in malware detection but often suffer from high false-positive rates, lower recall, and computational delays, thus demanding a more efficient and accurate system. Current techniques primarily rely on static features and simplistic learning models, leading to inadequate handling of temporal aspects and dynamic behaviors exhibited by advanced malware. These limitations compromise the detection of modern, evasive malware, and impede real-time analysis. This paper introduces a novel framework for Android malware detection that incorporates Temporal and Dynamic Behavior Analysis using Long Short-Term Memory (LSTM) networks and Attention Mechanisms. We further propose development of an efficient Grey Wolf Optimized (GWO) Decision Trees to find the most salient API call patterns associated with malwares. An Iterative Fuzzy Logic (IFL) layer is also deployed before classification to assess the "trustworthiness" of app metadata samples. For Ongoing Learning, we propose use of Deep Q-Networks (DQNs), which helps the reinforcement learning model to adapt more quickly to changes in the threat landscapes. By focusing on crucial system calls and behavioral characteristics in real-time, our model captures the nuanced temporal patterns often exhibited by advanced malwares. Empirical evaluations demonstrate remarkable improvements across multiple performance metrics. Compared to existing models, our approach enhances the precision of malware identification by 8.5 %, accuracy by 5.5 %, and recall by 4.9 %, while also achieving an 8.3 % improvement in the Area Under the Receiver Operating Characteristic Curve (AUC), with higher specificity and a 4.5 % reduction in identification delay. In malware pre-emption tasks, our model outperforms by improving precision by 4.3 %, accuracy by 3.9 %, recall by 4.9 %, AUC by 3.5 %, and increasing specificity by 2.9 %. These gains make our framework highly applicable for real-time detection systems, cloud-based security solutions, and threat intelligence services, thereby contributing to a safer Android ecosystem.

A novel methodology for anomaly detection in smart home networks via Fractional Stochastic Gradient Descent

With the rising integration of IoT devices within smart home environments, securing these interconnected systems against unauthorized access and cyber threats has become increasingly critical. This study introduces a novel methodology utilizing an advanced Artificial Neural Network (ANN) frame-work to enhance attack and anomaly detection capabilities within smart home networks. The objective is to develop a robust model that outperforms traditional detection methods and provides high accuracy and low false positive rates in identifying potential security threats. The research employs a pioneering approach to train the ANN by incorporating a Fractional Stochastic Gradient Descent optimizer grounded in Grunwald–Letnikov fractional calculus. This method was chosen for its potential to refine learning processes and improve detection accuracy over standard optimizers. The evaluation of the model was performed using the DS2OS traffic traces dataset, applying precision, sensitivity (recall), and specificity metrics to assess performance. The proposed model demonstrated exceptional performance with an accuracy of 0.9951. It significantly surpassed traditional methods like Logistic Regression and Support Vector Machines. The precision achieved was high, indicating a low rate of false positives, while the sensitivity and specificity values underscore the model’s ability to identify both typical and unconventional behaviours within the network accurately. This study introduces a robust and efficient ANN-based methodology for enhancing security in smart home IoT networks. Using a fractional stochastic gradient descent optimizer has proven effective in improving the model’s accuracy and reliability in detecting anomalies and attacks. The findings suggest significant implications for the future of IoT security, highlighting the potential for broader applications of fractional calculus in machine learning to enhance cybersecurity measures in various domains.

Curvature analysis of CI electrode arrays: a novel approach to categorize perimodiolar positions without anatomical landmarks.

Perimodiolar electrode arrays may be positioned regular, over-inserted or under-inserted into the cochlea depending on the cochlear size and shape. The study aimed to examine whether there are differences between these groups in the local curvature along the intracochlear array. Individual curvature variables were developed to categorize the groups and the relationship between the curvature and the angular insertion depth at the electrode tip was analyzed. The curvature along the intracochlear array was measured in the CBCT image of 85 perimodiolar electrodes of a single type. The mean curvature and the ratio of the mean curvature at contacts E14-16 to the mean curvature at E7-8 (bowing ratio) were calculated across the array, and its true positive rate (TPR) and false positive rate (FPR) were calculated to establish optimal threshold values to categorize the groups. 68.2% of the cases were categorized as regular positioned, 22.4% had an over-insertion and 9.4% had an under-insertion. The mean curvature was significantly weaker with under-insertion (< 342°) than with normal insertion depth (≥ 342°). With an over-insertion, the bowing ratio was < 1 and otherwise > 1. Both the mean curvature and bowing ratio were found to have an optimal threshold value with high TPR (= 1.00) and low FPR (≤ 0.06) for categorizing under-insertion and over-insertion, respectively. Curvature analysis is a useful tool to assess if a perimodiolar electrode array has been inserted deep enough into the cochlea. Independent of critical anatomical landmarks, over-inserted arrays and under-inserted arrays could be well categorized by using individual curvature variables. The results need to be validated using additional data sets.

An evaluation of the racial equity of the actuarial Family risk assessment instrument used in Queensland, Australia

BackgroundRisk assessment instruments used in child protection to guide intervention decisions should be equitable. To be considered equitable, two criteria are necessary: instruments should avoid discriminatory logic and display comparative validity for children of different races. Empirical evidence about the equity of risk assessment instruments is limited. Objective: To investigate the racial equity of a widely used risk assessment instrument, the Structured Decision Making Family Risk Assessment (FRA). Participants and setting: Administrative data were obtained for all children subject to investigation in Queensland, Australia in 2018, for whom an FRA was completed (n = 17,851). MethodsRelationships between FRA items, race, and recurrence (subsequent investigation within 12 months) were explored using crosstabulations and Cox Proportional Hazards. Accuracy of FRA recommendations and practitioner decisions were compared using sensitivity and specificity for Indigenous and non-Indigenous children. ResultsMost FRA items predicted race better than recurrence. Differences in recurrence by race were only partially explained by FRA items. The FRA produced high rates of false positives for Indigenous children (incorrectly classifying 48.9% of children who did not recur compared to 31.7% for non-Indigenous children) and high rates of false negatives for non-Indigenous children (incorrectly classifying 59% of children who did recur compared to 39% for Indigenous children). Practitioner discretion did not mitigate inequitable FRA recommendations. ConclusionsThe FRA failed both criteria of equity, discriminating indirectly by relying heavily on factors correlated with race, and demonstrating less accuracy for Indigenous children. High rates of false positives for Indigenous children exacerbate Indigenous overrepresentation in child protection.

Accurate prenatal diagnosis of coarctation of the aorta by 3-step echocardiographic diagnostic protocol

BackgroundCoarctation of the aorta (CoA) is the most common undiagnosed congenital heart defect during prenatal screening. High false positive and false negative rates seriously affect prenatal consultation and postnatal management. The objective of the study was to assess the utility of various measurements to predict prenatal CoA and to derive a diagnostic algorithm.MethodsOne hundred and fifty-four fetuses with suspected CoA who presented at Fuwai Hospital between December 2017 and August 2021 were enrolled and divided into confirmed CoA cases (n = 47) and false positive cases (n = 107), according to their postnatal outcomes. The transverse aortic arch, isthmus, and descending aorta were measured in the long-axis view of the aortic arch. The angle between the transverse aortic arch (TAO) and the descending aortic arch (DAO) was defined as the TAO-DAO angle and measured in the long axis or sagittal view. Based on the database in GE Voluson E10 and the formula (Z = \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{\ ext{x}-{\\mu }}{\\alpha }$$\\end{document}), the standard score (Z-score) of the dimensions of the aorta were calculated in relation to the gestational age. The main echocardiographic indices were combined to design a 3-step diagnostic protocol. The TAO-DAO angle was used as the first step in the diagnostic model. The diameter of the transverse arch and the Z-score of the isthmus were the second step. The third-step indices included a Z-score of the transverse arch, diameter of the isthmus, distance from the left subclavian artery (LSA) to left common carotid artery (LCCA), the ratio of isthmus diameter and LSA diameter and ratio of the distances (the distance between the LSA and LCCA to the distance between the right innominate artery and LCCA). The receiver operating characteristic (ROC) curve determined the predictive capability of each diagnostic parameter, and the kappa test determined the diagnostic accuracy of the proposed model.ResultsThe cases with confirmed CoA had thinner transverse arches (1.92 ± 0.32 mm vs. 3.06 ± 0.67 mm, P = 0.0001), lower Z-scores of the isthmus (-8.97 ± 1.45 vs. -5.65 ± 1.60, P = 0.0001), smaller TAO-DAO angles (105.54 ± 11.51° vs. 125.29 ± 8.97°, P = 0.0001) and larger distance between the LSA and LCCA (4.45 ± 1.75 mm vs. 2.74 ± 1.07 mm, P = 0.0001) than the false positive cases. The area under the curve (AUC) was 0.947 (95% CI 0.91–0.98) for the TAO-DAO angle ≤ 115.75°, 0.942 (95% CI 0.91–0.98) for the transverse arch diameter ≤ 2.31 mm, 0.937 (95% CI 0.90–0.98) for the Z-score of the isthmus ≤ -7.5, and 0.975 (95% CI 0.95–1.00) for the 3-step diagnostic protocol with 97.8% sensitivity and 97.2% specificity. The kappa test showed that the model’s diagnostic accuracy was consistent with postnatal outcomes (kappa value 0.936, P = 0.0001).ConclusionsThe 3-step diagnostic protocol included the three most useful measurements and the additional indices with appropriate cut-off values. The algorithm is useful for the detection of aortic coarctation in fetuses with a high degree of accuracy.Trial registrationRetrospectively registered.

A Novel Approach for Detection, Segmentation, and Classification of Brain Tumors in MRI Images Using Neural Network and Special C Means Fuzzy Clustering Techniques

Brain tumors present a significant health challenge globally, necessitating advanced techniques for accurate detection, segmentation, and classification. This paper presents a comprehensive study focused on the development and evaluation of innovative methodologies for brain tumor analysis using medical imaging data. The primary objective of this research is to enhance the accuracy and efficiency of brain tumor detection, segmentation, and classification processes. To achieve this goal, a multi-step approach is proposed, integrating various computational techniques and machine learning algorithms. First, the study explores novel methods for preprocessing medical imaging data to enhance image quality and reduce noise artifacts. This preprocessing step plays a crucial role in improving the subsequent analysis stages' accuracy and reliability. Next, a robust tumor detection algorithm is developed, leveraging advanced image processing techniques and deep learning models. The proposed algorithm effectively identifies tumor regions within brain images with high accuracy and minimal false positives. Following tumor detection, a segmentation framework is introduced to precisely delineate tumor boundaries from surrounding healthy tissues. The segmentation algorithm combines traditional image processing methods with state-of-the-art deep learning architectures to achieve accurate and efficient tumor delineation. In this paper, we proposed three novel methods for automatic detection, classification, and segmentation of brain tumors. Comprehensive experiments are conducted on the BRATS dataset and show that the proposed model obtains competitive results. The parameters under study are Accuracy rate, Specificity, and Sensitivity. First approach focuses on classifying cancerous and non-cancerous brain tumors in MRI scans. The system first reads the images and employs a novel fusion method to combine information from various modalities (Flair, T1, T1C, T2) for a more comprehensive picture. This enhances the accuracy of tumor characterization. Following this fusion, the images undergo preprocessing, feature extraction, and classification. The preprocessing stage involves grayscale conversion, binarization, wavelet analysis, and region-of-interest (ROI) calculation. Finally, a robust Neural Network (NN) classification method effectively differentiates cancerous and non-cancerous brain tissue. Performance is evaluated by metrics like accuracy, sensitivity, and specificity. Compared to existing methods, this research system demonstrates superior results, achieving a classification accuracy of 96.61%, sensitivity of 96.66%, and specificity of 96.55%. The Second approach combines Backpropagation Neural Networks (BPNN) with Spatial Fuzzy C-Means (SFCM) clustering for brain tumor analysis. The BPNN classifies brains into normal, benign (non-cancerous abnormality), or malignant (cancerous) categories. SFCM helps pinpoint the exact tumor location and size within the MRI scan through segmentation. A dual-tree complex wavelet transform is employed to extract image features efficiently. This method achieved exceptional results: 99.77% classification accuracy, 99.87% sensitivity (correctly identifying tumors), and 98.69% specificity (correctly identifying healthy tissue). Additionally, the over 90% precision demonstrates the effectiveness of this technique in feature extraction and classification. The experiments demonstrate that BPNN-SFCM successfully segment, and extracts brain tumors from MRI scans. The overall accuracy of BPNN-SFCM is better as compared to other proposed methods and existing methods.

Anomaly detection for multivariate time series in IoT using discrete wavelet decomposition and dual graph attention networks

Effective anomaly detection in multivariate time series data is critical to ensuring the security of Internet of Things (IoT) devices and systems. However, building a high precision and low false positive rate anomaly detection model for the complex and volatile IoT environment is a challenging task. This is often due to issues such as a lack of anomaly labeling, high data volatility, and the complexity of device mechanisms. Traditional machine learning algorithms and sequence models frequently fail to account for feature correlation and temporal dependency in anomaly detection. Although deep learning-based anomaly detection methods have progressed, there is still room for improvement in precision, recall, and generalization ability. In this paper, we propose an anomaly detection model called Meta-MWDG to address these issues. The model is based on a multi-scale discrete wavelet decomposition and a dual graph attention network, which can effectively extract feature correlation and temporal dependency in multivariate time series data. Additionally, model-agnostic meta-learning (MAML) is introduced to improve the model’s generalization performance, enabling it to perform well on new tasks even with a few samples. A gated recurrent unit (GRU) is combined with a multi-head self-attention network to output both prediction and reconstruction results in a joint optimization strategy, improving the precision of anomaly detection. Extensive experimental studies demonstrate that Meta-MWDG outperforms the state-of-the-art methods in anomaly detection.

Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks

PurposeTo classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in Stargardt disease (STGD).MethodsA retrospective study of 170 eyes from 85 consecutive patients with confirmed STGD. Fundus autofluorescence...

HybGBS: A hybrid neural network and grey wolf optimizer for intrusion detection in a cloud computing environment

SummaryThe cloud computing environment is subject to unprecedented cyber‐attacks as its infrastructure and protocols may contain vulnerabilities and bugs. Among these, Distributed Denial of Service (DDoS) is chosen by most cyber extortionists, creating unusual traffic that drains cloud resources, making them inaccessible to customers and end users. Hence, security solutions to combat this attack are in high demand. The existing DDoS detection techniques in literature have many drawbacks, such as overfitting, delay in detection, low detection accuracy for attacks that target multiple victims, and high False Positive Rate (FPR). In this proposed study, an Artificial Neural Network (ANN) based hybrid GBS (Grey Wolf Optimizer (GWO) + Back Propagation Network (BPN) + Self Organizing Map (SOM)) Intrusion Detection System (IDS) is proposed for intrusion detection in the cloud computing environment. The base classifier, BPN, was chosen for our research after evaluating the performance of a comprehensive set of neural network algorithms on the standard benchmark UNSW‐NS 15 dataset. BPN intrusion detection performance is further enhanced by combining it with SOM and GWO. Hybrid Feature Selection (FS) is made using a correlation‐based approach and Stratified 10‐fold cross‐validation (STCV) ranking based on Weight matrix value (W). These selected features are further fine‐tuned using metaheuristic GWO hyperparameter tuning based on a fitness function. The proposed IDS technique is validated using the standard benchmark UNSW‐NS 15 dataset, which consists of 1,75,341 and 82,332 attack cases in the training and testing datasets. This study's findings demonstrate that the proposed ANN‐based hybrid GBS IDS model outperforms other existing IDS models with a higher intrusion detection accuracy of 99.40%, fewer false alarms (0.00389), less error rate (0.001), and faster prediction time (0.29 ns).

Y-switch: a spring-loaded synthetic gene switch for robust DNA/RNA signal amplification and detection.

Nucleic acid tests (NATs) are essential for biomedical diagnostics. Traditional NATs, often complex and expensive, have prompted the exploration of toehold-mediated strand displacement (TMSD) circuits as an economical alternative. However, the wide application of TMSD-based reactions is limited by 'leakage'-the spurious activation of the reaction leading to high background signals and false positives. Here, we introduce the Y-Switch, a new TMSD cascade design that recognizes a custom nucleic acid input and generates an amplified output. The Y-Switch is based on a pair of thermodynamically spring-loaded DNA modules. The binding of a predefined nucleic acid target triggers an intermolecular reaction that activates a T7 promoter, leading to the perpetual transcription of a fluorescent aptamer that can be detected by a smartphone camera. The system is designed to permit the selective depletion of leakage byproducts to achieve high sensitivity and zero-background signal in the absence of the correct trigger. Using Zika virus (ZIKV)- and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-derived nucleic acid sequences, we show that the assay generates a reliable target-specific readout. Y-Switches detect native RNA under isothermal conditions without reverse transcription or pre-amplification, with a detection threshold as low as ∼200 attomole. The modularity of the assay allows easy re-programming for the detection of other targets by exchanging a single sequence domain. This work provides a low-complexity and high-fidelity synthetic biology tool for point-of-care diagnostics and for the construction of more complex biomolecular computations.

CircCNNs, a convolutional neural network framework to better understand the biogenesis of exonic circRNAs

Circular RNAs (circRNAs) as biomarkers for cancer detection have been extensively explored, however, the biogenesis mechanism is still elusive. In contrast to linear splicing (LS) involved in linear transcript formation, the so-called back splicing (BS) process has been proposed to explain circRNA formation. To investigate the potential mechanism of BS via the machine learning approach, we curated a high-quality BS and LS exon pairs dataset with evidence-based stringent filtering. Two convolutional neural networks (CNN) base models with different structures for processing splicing junction sequences including motif extraction were created and compared after extensive hyperparameter tuning. In contrast to the previous study, we are able to identify motifs corresponding to well-established BS-associated genes such as MBNL1, QKI, and ESPR2. Importantly, despite prevalent high false positive rates in existing circRNA detection pipelines and databases, our base models demonstrated a notable high specificity (greater than 90%). To further improve the model performance, a novo fast numerical method was proposed and implemented to calculate the reverse complementary matches (RCMs) crossing two flanking regions and within each flanking region of exon pairs. Our CircCNNs framework that incorporated RCM information into the optimal base models further reduced the false positive rates leading to 88% prediction accuracy.

A rigorous and versatile statistical test for correlations between stationary time series.

In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here, we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between 2 time series. We prove that this test correctly controls the false positive rate as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to datasets from climatology, animal behavior, and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships.

BR-ChromNet: Banding resolution localization of chromosome structural abnormality with conditional random field

Detecting chromosome structural abnormalities in medical genetics is essential for diagnosing genetic disorders and understanding their implications for an individual’s health. However, existing computational methods are formulated as a binary-class classification problem trained only on representations of positive/negative chromosome pairs. This paper introduces an innovative framework for detecting chromosome abnormalities with banding resolution, capable of precisely identifying and masking the specific abnormal regions. We highlight a pixel-level abnormal mapping strategy guided by banding features. This approach integrates data from both the original image and banding characteristics, enhancing the interpretability of prediction results for cytogeneticists. Furthermore, we have implemented an ensemble approach that pairs a discriminator with a conditional random field heatmap generator. This combination significantly reduces the false positive rate in abnormality screening. We benchmarked our proposed framework with state-of-the-art (SOTA) methods in abnormal screening and structural abnormal region segmentation. Our results show cutting-edge effectiveness and greatly reduce the high false positive rate. It also shows superior performance in sensitivity and segmentation accuracy. Being able to identify abnormal regions consistently shows that our model has demonstrated significant clinical utility with high model interpretability. BRChromNet is open-sourced and available at https://github.com/frankchen121212/BR-ChromNet

Cluster effect for SNP–SNP interaction pairs for predicting complex traits

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP–SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP–SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP–SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP–SNP interaction detection accuracy.

Poor performance of regime shift detection methods in marine ecosystems

Abstract Regime shifts have been reported as ubiquitous features across the world’s oceans. Many regime shift detection methods are available, but their performance is rarely evaluated, and the supporting evidence for regime shifts may be thin because of the nature of marine ecological time series that are often short, autocorrelated, and uncertain. In the Norwegian Sea, a regime shift has been reported to have occurred in the mid-2000s, with simultaneous changes in oceanography, plankton, and fish. Here, we evaluate the evidence for this regime shift using four commonly used regime shift detection methods (Strucchange, STARS, EnvCpt, and Chronological Clustering) on 32 annual time series that describe the main components of the Norwegian Sea ecosystem, from hydrography and primary production up to fish population metrics. We quantify the performance of each method by measuring its false-positive rate, i.e. the proportion of times the method detects a regime shift that was not present in simulated control time series. Our results show that all methods have high to very high false-positive rates. This challenges the evidence for a regime shift in the Norwegian Sea and questions earlier reviews of regime shifts across the world’s oceans.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

BackgroundEffective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.ResultsWe present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh’s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl.ConclusionsThe REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces

Objective. Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch. Approach. Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model. Main results. Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s. Significance. This study provides a new approach that could significantly reduce the brain switch’s FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.