Visual pattern discovery using web images

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Despite the superior classification performance of support vector machines (SVMs), training SVMs on large datasets is still a challenging problem. Sample reduction methods have been proposed and shown to reduce the training complexity significantly, but more or less trade off the generalization performance. This paper presents an efficient sample reduction method for multi-class classification using one-vs-rest SVMs, called Multi-class Sample Selection (MUSS). For each binary one-vs-rest classification problem, positive samples and negative samples are selected based on the distances from the cluster centers of positive class, assuming that positive samples with large distances from the positive centers and negative samples with small distances from the positive centers are near the classification boundary. The intention of clustering is to improve the computation efficiency of sample selection, other than to select from cluster centers as previous methods did. Experiments on a wide variety of datasets demonstrate the superiority of the proposed MUSS over other competitive algorithms in respect of the tradeoff between reduced sample size and classification performance. The experimental results show that MUSS also works well for binary classification problems.

The Covid-19 pandemic has not ended yet. Therefore, laboratory tests are very important for determining the diagnosis of Covid-19. At this case, PCR is the gold standard in laboratory tests. Therefore, the accuracy of the PCR assay should be a major concern. This study aims to evaluate the validation results of several Covid-19 PCR reagent kits. A total of 10 positive and negative clinical samples were used as test samples. DNA extraction was performed using a commercial reagent kit. PCR examination was carried out using four different blinded commercial reagent kits. The results of the examination were compared in each sample. The results showed that Reagents kit A, C, and D gave the same results for each sample, namely five positive and five negative samples. Reagent kit B showed different results, namely eight positive samples and two negative samples. A total of 3 (33.3%) of the 10 sample examinations indicated inaccurate results for Reagent kit B. The Reagent kit B test results using two different boxes showed different results with seven positive results by box 1, while eight positive results by another box. In conclusion, 1 out of 4 tested Covid-19 PCR kit reagents gave inaccurate results. Therefore, in covid-19 testing laboratories, it is recommended to choose a PCR reagent kit that has been approved by a trusted organization and perform reagent validation tests.

Live HIV rapid test kits (RTKs) are usually packaged with only a single test buffer vial. A major challenge with HIV testing is the problem of lack of the RTK buffer vial to complete the number of tests in an RTK pack due to misuse, misplacement or contamination of buffer with clients' blood during HIV testing. This has led to the use of sterile normal saline and tap water as substitute liquids in place of the buffer vial. This study compared the use of the commercially provided buffer vial and these substitute liquids in HIV rapid testing while using the commercially provided buffer as gold standard. One hundred (100) HIV positive samples from patients attending antiretroviral therapy (ART) clinic and 100 HIV negative blood samples from blood donors placed in EDTA were tested with Determine RTK using the commercially provided buffer and substitute liquids (Sterile normal saline and tap water). The Determine RTK was also tested using the commercially provided buffer, normal saline and tap water without addition of blood samples. Results of the tests with substitute liquids were compared with those obtained with that of RTK's commercially provided buffer. With HIV positive and negative blood samples, the expected test line and control line or only control line (for negative samples) were observed with the commercially provided buffer and sterile normal saline. When tested without blood samples using the RTK's buffer and normal saline, the control line was observed but no test line for all 100 tests done. Invalid test results were recorded in 48% of the negative samples and 40% of test with no blood added when with tap water was used. This study also shows varied colour intensity for the buffer vial and substitute liquids used. For samples tested with sterile normal saline, 52% of the tested samples showed stronger colour intensity in test line that control line, 37% had equal colour intensity while 11% had higher colour intensity in control than test line. Seventy seven (77%) of Samples tested with tap water had the colour intensity greater in test line than control line while 23% had equal colour intensity in both test and control line. With sterile normal saline used with HIV positive and negative blood samples, a 100% clear background like that of the gold standard was obtained. However, tap water when used as a substitute buffer to run all 100 HIV positive samples had an unclear background. An unclear background was also observed in 100% and 28% of the test ran with tap water only (without blood samples) and with HIV Negative samples respectively.

This paper presents a focused Web crawling system iSurfer for information retrieval from the Web. Different from other focused crawlers, iSurfer uses an incremental method to learn a page classification model and a link prediction model. It employs an online sample detector to incrementally distill new samples from crawled Web pages for online updating of the model learned. Other focused crawling systems use classifiers that are built from initial positive and negative samples and can not learn incrementally. The performances of these classifiers depend on the topical coverage of the initial positive and negative samples. However, the initial samples, particularly the negative ones, with a good coverage of target topics are difficult to find. Therefore, the iSurfer’s incremental learning strategy has an advantage. It starts from a few positive samples and gains more integrated knowledge about the target topics over time. Our experiments on various topics have demonstrated that the incremental learning method can improve the harvest rate with a few initial samples.KeywordsFocused CrawlerIncremental LearningPositive Sample Based LearningLink PredictionWeb Page Classification

Advanced digital image segmentation framework implemented by using service oriented architecture is presented. The intelligence is not incorporated just in a segmentation method, which is controlled by 11 parameters, but mostly in a routine for easier parameters’ values determination. Three different approaches are implemented: 1) manual parameter value selection, 2) interactive step-by-step parameter value selection based on visual image content, and 3) fast and intelligent parameter value determination based on machine learning. Intelligence of second and third approach is introduced by end-users in the repeated interaction with our prototype in attempts to correctly segment out the structures from image. Fast and intelligent parameter determination predicts a new set of parameters’ values for current image being processed based on knowledge models constructed from previous successful (positive samples) and unsuccessful (negative samples) parameter selections. Such approach pointed out to be very efficient and fast, especially if we have many positive and negative samples in the learning set.

Reliable and rapid detection of severe acute respiratory syndrome coronavirus 2 in laboratory setting is critical to control the pandemic. We aimed to an evaluated polymerase chain reaction (PCR) efficiency of nasopharyngeal swabs stored in viral transport medium (VTM) in different temperatures. Ninety swabs taken into VTM were analyzed at the first hour, then divided into two groups with similar numbers of positive and negative samples. Positive samples of each group were also subgrouped according to Fam CT values as low CT (<25), medium CT (25-32), and high CT (32-38) groups. One group was stored at 4°C, while the other was stored at room temperature, PCR analyses were repeated every 24 h for 5 days and on Day 12. There was a total of 30 positive samples (12 low CT, 11 medium CT, and 7 high CT). The CT values of both groups remained unchanged in first 3 days while the CT values of the room temperature group increased after the third day. All of the positive samples remained positive in both groups for the first 5 days. On the 12th day, the total number of positives decreased to 8 in the room temperature group and 11 in the 4°C groups. All the low CT samples remained positive in both groups. In conclusion, it is safe to store positive samples in room temperature for up to 5 days. Only samples with high viral loads remain positive for 12 days, regardless of whether stored at room temperature or 4°C. Negative samples don't turn to invalid if stored in VTM.

Graph contrastive learning has emerged as a powerful unsupervised graph representation learning tool. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs to learn the underlying structural semantics of the input graph. Recent works usually sample negative samples from the same training batch with the positive samples or from an external irrelevant graph. However, a significant limitation lies in such strategies: the unavoidable problem of sampling false negative samples. In this paper, we propose a novel method to utilize Counterfactual mechanism to generate artificial hard negative samples for Graph Contrastive learning, namely CGC. We utilize a counterfactual mechanism to produce hard negative samples, ensuring that the generated samples are similar but have labels that differ from the positive sample. The proposed method achieves satisfying results on several datasets. It outperforms some traditional unsupervised graph learning methods and some SOTA graph contrastive learning methods. We also conducted some supplementary experiments to illustrate the proposed method, including the performances of CGC with different hard negative samples and evaluations for hard negative samples generated with different similarity measurements. The implementation code is available online to ease reproducibility1.

Contrastive learning is an important technique in the field of machine learning for learning data representations. In the field of self-supervised visual representation learning, the strategy of positive and negative sample selection is key to improving the efficiency and effectiveness of model learning. Traditional self-supervised learning methods often employ random sampling strategies to select positive and negative samples, but this approach may result in uneven sample quality when dealing with complex datasets, thereby affecting the learning outcomes. To alleviate this problem, this study is dedicated to exploring more effective strategies for positive and negative sample selection and processing to optimize the self-supervised learning process. To this end, we propose an improved method of self-supervised learning called contrastive learning with enhanced diversity. On the one hand, this method utilizes the weight parameters of the DINO pre-trained model to initialize the feature extraction network of SimCLR, providing more accurate calculations of feature similarity. On the other hand, by setting a threshold on the feature similarity matrix and penalizing (subtracting 0.5 from) similarity scores that do not exceed this threshold, we reduce the excessive impact of high similarity scores on model training, thereby helping the model better distinguish between positive and negative samples. In downstream image classification tasks, we conducted detailed evaluations of the improved model, specifically including fine-tuning and linear evaluation aspects. Experiments show that the proposed approach improves the performance of the loss functions and improves the accuracy of the proposed SimCLR model.

Timely and effective diagnosis plays a pivotal role in schistosomiasis control efforts. This study aims to assess the utility of combined Schistosoma haematobium soluble egg antigen (Sh SEA) and S. mansoni worm antigen (Sm SWA) in serological diagnosis of urogenital schistosomiasis. Admixtures containing 10 µg/mL of both Sm SEA and Sh SEA, as well as Sm SWA and Sh SWA, were employed to detect S. haematobium infection via an indirect enzyme-linked immunosorbent assay (ELISA) using sera and urine from microscopically confirmed positive samples from an endemic population, along with confirmed negative samples from both endemic (NE) and non-endemic (NNE) populations. The diagnostic performance of Schistosoma eggs and worm antigen mixtures varied depending on sample type and negative endemicity. The Sm SEA and Sh SEA mixtures performed poorly with sera and urine from the pair of positive vs negative endemic samples, as well as positive vs non-endemic samples, but excellently with positive vs negative endemic urine samples pair (sensitivity 91.67%; specificity 66.67%). Conversely, SWA mixtures showed superior performance, particularly with the positive vs negative non-endemic sera samples pair (sensitivity 93.75%; specificity 72.92%). Other SWA-based mixtures, except SWA admixture using urine in positive vs NE samples, exhibited acceptable performance. Antibody titers varied significantly, with higher titers generally observed in negative endemic samples for SWA mixtures and in negative non-endemic urine samples for SEA mixtures (p < 0.05). Combined antigens improve Schistosoma diagnostics: SEA admixtures suit endemic urine samples, while SWA admixtures aid non-endemic sera detection.

Deep one-class probability learning for end-to-end image classification.

Many learning techniques have been applied to identify disease-associated genes. At the early, they usually approached this problem as a binary classification, where training set is comprised of positive and negative samples. In which, positive samples are constructed from known disease genes, whereas negative samples are the remaining which are not known to be associated with diseases. This is the limitation of the binary classification-based solutions, since the negative training set should be actual non-disease genes; however, construction of this set is nearly impossible in biomedical researches. Therefore, to reduce this uncertainty, more realistic classification-based methods have been proposed. For instance, unary classification technique based on one-class SVM method was proposed by learning from only positive samples. In addition, the remaining set may contain unknown disease genes; therefore, semi-supervised methods such as binary semi-supervised and positive and unlabeled (PU) learning classifications have been proposed. In particular, PU learning methods, which learn from both known disease genes and the remaining genes, were shown to outperform others. In these studies, data sources are usually represented by vectorial form for binary classifiers, while they are in kernel matrices for unary and PU learning ones. The kernel-based data fusion is only suitable for data with different types and it seems unfair for the comparison based on different data representations. Therefore, in this study, we compared different classification techniques for the disease gene prediction based on vectorial representation of samples. The simulation result showed that the unary classification technique, which combines both density and class probability estimation strategies, achieved the best performance, whereas it is worst for the one-class SVM-based method. Interestingly, performance of the best binary classification technique is comparable with that of biased SVM-based PU learning and binary semi-supervised classification methods. And, they are all better than the multi-level SVM-based one.

Background Carisoprodol is a muscle relaxant, that metabolizes to meprobamate, a compound with sedative and anxiolytic characteristics. Sensitive and specific detection of these drugs in urine is important for monitoring medication adherence. This investigation aimed to validate the Immunalysis Carisoprodol Metabolite / Meprobamate Urine HEIA (331UR) on the Beckman AU 5800, by assessing specificity, sensitivity, precision, stability, and comparison to the validated Immunalysis Carisoprodol Urine Enzyme Immunoassay (331). This validation was required due to a manufacturer reagent reformulation that included switching the antibody source from rabbit to sheep. The new formulation had a change in cutoff for meprobamate cross-reactivity from 70 ng/mL to 280 ng/mL while maintaining the carisoprodol cross-reactivity at 100 ng/mL. Methods A total of 73 urine samples (34 negative and 39 positive) were tested using both Immunalysis Carisoprodol assays (331UR and 331) on the Beckman AU. Of these 73 samples, 59 were created using blank urine pools spiked with concentrations of carisoprodol above and below the assay cutoff of 100 ng/mL. These 59 contrived samples were compared to their assumed spiked concentrations. All other samples (n=14) were residual, de-identified samples previously tested for carisoprodol and meprobamate by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Specificity and sensitivity were measured with a minimum threshold of 90% qualitative agreement. Precision was evaluated for the Carisoprodol 331UR by analyzing 25 replicates of both positive and negative control samples over five days. Reagent stability was evaluated up to 15 days uncapped and onboard the analyzer. Sample carryover was assessed by analyzing a sequence of high positive and negative patient pools. Results The Carisoprodol 331UR demonstrated 100% specificity, correctly identifying all 34 negative urine samples, and 100% sensitivity, accurately detecting all 39 positive urine samples. This was an improvement over the existing assay that showed a 100% specificity but 97.4% sensitivity, falsely identifying 1 positive sample as negative. Precision testing performed using Carisoprodol 331UR positive control resulted in total %CVs ranging from 1.4% to 2.5% and precision testing performed using the low control resulted in total %CVs ranging from 3.0% to 3.5%. The Carisoprodol 331UR assay demonstrated acceptable reagent stability (percent change between -3.98% to 4.86%) when the reagent cartridge was uncapped and onboard the analyzer up to 14 days. Sample carryover studies conducted by testing repeated sequences of a high concentration (2494 ng/mL) carisoprodol sample followed by multiple replicates of a negative sample demonstrated no significant sample carryover. Conclusion Our evaluation of the Immunalysis Carisoprodol Metabolite / Meprobamate Urine HEIA on the Beckman AU 5800 demonstrates satisfactory specificity, sensitivity, and precision for detecting carisoprodol and its metabolite meprobamate in urine. The new formulation performs comparably to the current assay and can seamlessly replace the Carisoprodol 331 assay without significantly impacting testing performance.

The World Wide Web today comprises of billions of Web documents with information on varied topics presented by different types of media such as text, images, audio, and video. Therefore along with textual information, the number of images over WWW is exponentially growing. As compared to text, the annotation of images by its semantics is more complicated as there is a lack of correlation between user’s semantics and computer system’s low-level features. Moreover, the Web pages are generally composed of contents containing multiple topics and the context relevant to the image on the Web page makes only a small portion of the full text, leading to the challenge for image search engines to annotate and index Web images. Existing image annotation systems use contextual information from page title, image src tag, alt tag, meta tag, image surrounding text for annotating Web image. Nowadays, some intelligent approaches perform a page segmentation as a preprocessing step. This paper proposes a novel approach for annotating Web images. In this work, Web pages are divided into Web content blocks based on the visual structure of page and thereafter the textual data of Web content blocks which are semantically closer to the blocks containing Web images are extracted. The relevant keywords from textual information along with contextual information of images are used for annotation.

Deep learning has shown strong performance in time series forecasting tasks. However, issues such as missing values and anomalies in sequential data hinder its further development in prediction tasks. Previous research has primarily focused on extracting feature information from sequence data or addressing these suboptimal data as positive samples for knowledge transfer. A more effective approach would be to leverage these non-ideal negative samples to enhance event prediction. In response, this study highlights the advantages of non-ideal negative samples and proposes the IdealTSF framework, which integrates both ideal positive and negative samples for time series forecasting. IdealTSF consists of three progressive steps: pretraining, training, and optimization. It first pretrains the model by extracting knowledge from negative sample data, then transforms the sequence data into ideal positive samples during training. Additionally, a negative optimization mechanism with adversarial disturbances is applied. Extensive experiments demonstrate that negative sample data unlocks significant potential within the basic attention architecture for time series forecasting. Therefore, IdealTSF is particularly well-suited for applications with noisy samples or low-quality data.