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Abstract Background The 2018 BNMS Glomerular Filtration Rate (GFR) guidelines recommend a single-sample technique with the sampling time dictated by the expected renal function, but this is not known with any accuracy before the test. We aimed to assess whether the sampling regime suggested in the guidelines is optimal and determine the error in GFR result if the sample time is chosen incorrectly. We can then infer the degree of flexibility in the sampling regime. Methods Data from 6328 patients referred for GFR assessment at 6 different hospitals for a variety of indications were reviewed. The difference between the single-sample (Fleming) GFR result at each sample time and the slope–intercept GFR result at each hospital was calculated. A second dataset of 777 studies from one hospital with nine samples collected from 5 min to 8 h post-injection was analysed to provide a reference GFR to which the single-sample results were compared. Results Recommended single-sample times have been revised: for an expected GFR above 90 ml/min/1.73m2 a 2-h sample is recommended; between 50 and 90 ml/min/1.73m2 a 3-h sample is recommended; and between 30 and 50 ml/min/1.73m2 a 4-h sample is recommended. Root mean square error in single-sample GFR result compared with slope–intercept can be kept less than or equal to 3.30 ml/min/1.73m2 by following these recommendations. Conclusion The results of this multisite study demonstrate a reassuringly wide range of sample times for an acceptably accurate single-sample GFR result. Modified recommended single-sample times have been proposed in line with the results, and a lookup table has been produced of rms errors across the full range of GFR results for the three sample times which can be used for error reporting of a mistimed sample.

Due to the semantic gap between the insufficient facial features and facial identifying information, the single sample per person (SSPP) problem has always been a significant challenge in the field of facial recognition. To address this problem, this paper proposes a Self-Organizing Map (SOM)-based binary coding (SOM-BC) method, which extracts the middle-level semantic features by merging the SOM network with the Bag-of-Features (BoF) model. First, we extract the local features of the facial images using the SIFT descriptor. Next, inspired by human visual perception, we utilize a SOM neural network to obtain a visual words dictionary capable of reflecting the intrinsic structure of facial features in semantic space. Subsequently, a binary coding method is further proposed to map the local features into semantic space. Finally, we propose a simple but effective similarity measure method for classification. Experimental results on three public databases not only demonstrate the effectiveness of the proposed method, but also its high computational efficiency. [ABSTRACT FROM AUTHOR]

Abstract Background During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect. Results Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt. Conclusions These results all indicate that the static model in pathways could simplify the detection of the early-warning signals.

At present, using single-sample training to generate adversarial networks has become the focus of researchers. However, the problems that the model is not easy to converge, the generated image structure collapses, and the training speed is slow still need to be solved urgently. Researchers propose to use a self-attention model in the generative adversarial network to obtain a larger range of samples and improve the quality of the generated images. It is found that using the traditional convolutional self-atention modelcausesawasteofcomputingresourcesduetotheredundancyofinformationinthe attention map. A novel linear attention model is proposed, in which a double normalization method is usedtoaleviatetheproblem of the atent on modelbeingsenstivetoinputfeatures, andanewsingle-samplegenerativeadversarialnetworkmodelisbuiltusingthismodel. In addition, themodelusesresidualnetworkandspectralnormalization methods for stable training, reducing the risk of col apse. A largenumberofexperimentsshowthat, comparedwiththeexistingtraining model, this model has the characteristicsoffasttrainingspeed, highresolutionofgeneratedimages, andobviousimprovementof evaluationindicators. [ABSTRACT FROM AUTHOR]

Background: The 2018 BNMS Glomerular Filtration Rate (GFR) guidelines recommend a single-sample technique with the sampling time dictated by the expected renal function, but this is not known with any accuracy before the test. We aimed to assess whether the sampling regime suggested in the guidelines is optimal and determine the error in GFR result if the sample time is chosen incorrectly. We can then infer the degree of flexibility in the sampling regime. Methods: Data from 6328 patients referred for GFR assessment at 6 different hospitals for a variety of indications were reviewed. The difference between the single-sample (Fleming) GFR result at each sample time and the slope–intercept GFR result at each hospital was calculated. A second dataset of 777 studies from one hospital with nine samples collected from 5 min to 8 h post-injection was analysed to provide a reference GFR to which the single-sample results were compared. Results: Recommended single-sample times have been revised: for an expected GFR above 90 ml/min/1.73m2 a 2-h sample is recommended; between 50 and 90 ml/min/1.73m2 a 3-h sample is recommended; and between 30 and 50 ml/min/1.73m2 a 4-h sample is recommended. Root mean square error in single-sample GFR result compared with slope–intercept can be kept less than or equal to 3.30 ml/min/1.73m2 by following these recommendations. Conclusion: The results of this multisite study demonstrate a reassuringly wide range of sample times for an acceptably accurate single-sample GFR result. Modified recommended single-sample times have been proposed in line with the results, and a lookup table has been produced of rms errors across the full range of GFR results for the three sample times which can be used for error reporting of a mistimed sample. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect. Results: Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt. Conclusions: These results all indicate that the static model in pathways could simplify the detection of the early-warning signals. [ABSTRACT FROM AUTHOR]

Application of near-infrared spectroscopy to the prediction of sample content is strongly limited by signal peak overlap. To analyze the spectral information directly related to the target components and to make the chemometric model more explanatory, an independent characteristic projection algorithm is proposed. The algorithm was applied to the independent spectral analysis of a single sample using corn as a representative example. Moisture, oil, protein, and starch, which are the four main components of corn, were the target components. The pure component spectra were used the projection directions to decompose the near-infrared spectrum of a single corn sample; then four decomposed spectra corresponding to the four pure component spectra were obtained. Their corresponding relationship was determined using their correlation coefficients and by comparing their characteristic peaks, and the molecular absorption patterns corresponding to the characteristic absorption peaks of each decomposed spectrum were analyzed in detail. The theoretical analysis and experimental results indicate that the independent characteristic projection algorithm can be applied to single-sample spectral analysis to extract more complete physicochemical information about the target components and provide a theoretical basis for establishing a robust near-infrared spectral chemometric model with great extrapolation capability and stability. [ABSTRACT FROM AUTHOR]

Face recognition (FR) with a single sample per person (SSPP) is one of the most challenging problems in computer vision. In this scenario, it is difficult to predict facial variation such as pose, illumination, and disguise due to the lack of enough training samples. Therefore, the development of the FR system with only a small number of training samples is hindered. To address this problem, this paper proposes a scheme combined transfer learning and sample expansion in feature space. First, it uses transfer learning by training a deep convolutional neural network on a common multi-sample face dataset and then applies the well-trained model to a target data set. Second, it proposes a sample expansion method in feature space called k class feature transfer (KCFT) to enrich intra-class variation information for a single-sample face feature. Compared with other expanding sample methods in the image domain, this method of expanding the samples in the feature domain is novel and easy to implement. Third, it trains a softmax classifier with expanded face features. The experimental results on ORL, FERET, and LFW face databases demonstrate the effectiveness and robustness of the proposed method for various facial variations.

Since similar complex diseases are much alike in clinical symptoms, patients are easily misdiagnosed and mistreated. It is crucial to accurately predict the disease status and identify markers with high sensitivity and specificity for classifying similar complex diseases. Many approaches incorporating network information have been put forward to predict outcomes, but they are not robust because of their low reproducibility. Several pathway-based methods are robust and functionally interpretable. However, few methods characterize the disease-specific states of single samples from the perspective of pathways. In this study, we propose a novel framework, Pathway Activation for Single Sample (PASS), which utilizes the pathway information in a single sample way to better recognize the differences between two similar complex diseases. PASS can mainly be divided into two parts: for each pathway, the extent of perturbation of edges and the statistic difference of genes caused by a single disease sample are quantified; then, a novel method, named as an AUCpath, is applied to evaluate the pathway activation for single samples from the perspective of genes and their interactions. We have applied PASS to two main types of inflammatory bowel disease (IBD) and widely verified the characteristics of PASS. For a new patient, PASS features can be used as the indicators or potential pathway biomarkers to precisely diagnose complex diseases, discover significant features with interpretability and explore changes in the biological mechanisms of diseases.

Abstract Background Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore (https://bioconductor.org/packages/singscore). Results We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (N S

Abstract Background Current guidelines regarding plasma-sampling techniques for glomerular filtration rate (GFR) determination are inconsistent. Single-sample methods are commonly believed not to be precise enough to meet clinical demands. The present study compared the agreement between single- and dual- plasma sampling methods with a three-point plasma clearance of iohexol. Methods A total of 46 healthy volunteers and 124 chronic kidney disease (CKD) patients with varying degrees of renal dysfunction received 5 ml iohexol (300 mgI/ml) i.v. and plasma samples were drawn at 2-, 3- and 4-h post-injection. Plasma-iodine concentrations were detected by high-performance liquid chromatography (HPLC). Results Bias was similar among single-plasma sampling methods (SPSM) and dual-plasma sampling methods (DPSM). The best correlation was obtained from the 2- and 4-h DPSM (concordance correlation coefficient [CCC]: 0.9988) with none of the estimates differed by more than 30% from the reference GFR and only one (0.06%) estimate differed by more than 10% (P30, 100%; P10, 99.4%). SPSM using samples around 3- or 4-h demonstrated acceptable accuracy at a GFR level of ≥60 ml/min/1.73m2 (P30 = 100% and P10 > 75% for both measurements). Conclusion 4-h SPSM is advantageous in clinical practice in subjects with GFR ≥ 60 ml/min/1.73m2. For patients with an expected GFR

Face recognition for a single sample per person is challenging due to the lack of sufficient sample information. However, using generic training set to learn an auxiliary dictionary is an effective way to alleviate this problem. Considering generic training sample of diversity, we proposed an algorithm of auxiliary dictionary of diversity learning (ADDL). We first produced virtual face images by mirror images, square block occlusion and grey transform, and then learned an auxiliary dictionary of diversity using a designed objective function. Considering patch-based method can reduce the influence of variations, we seek extended sparse representation with l2-minimization for each probe patch. Experimental results in the CMUPIE, Extended Yale B and LFW datasets demonstrate that ADDL performs better than other related algorithms. [ABSTRACT FROM AUTHOR]

Since similar complex diseases are much alike in clinical symptoms, patients are easily misdiagnosed and mistreated. It is crucial to accurately predict the disease status and identify markers with high sensitivity and specificity for classifying similar complex diseases. Many approaches incorporating network information have been put forward to predict outcomes, but they are not robust because of their low reproducibility. Several pathway-based methods are robust and functionally interpretable. However, few methods characterize the disease-specific states of single samples from the perspective of pathways. In this study, we propose a novel framework, Pathway Activation for Single Sample (PASS), which utilizes the pathway information in a single sample way to better recognize the differences between two similar complex diseases. PASS can mainly be divided into two parts: for each pathway, the extent of perturbation of edges and the statistic difference of genes caused by a single disease sample are quantified; then, a novel method, named as an AUCpath, is applied to evaluate the pathway activation for single samples from the perspective of genes and their interactions. We have applied PASS to two main types of inflammatory bowel disease (IBD) and widely verified the characteristics of PASS. For a new patient, PASS features can be used as the indicators or potential pathway biomarkers to precisely diagnose complex diseases, discover significant features with interpretability and explore changes in the biological mechanisms of diseases. [ABSTRACT FROM AUTHOR]

Biometrics are being increasingly used across the world, but it also raises privacy and security concerns of the enrolled identities. The main reason is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. Cancelable biometric template is a solution to this problem which can be reissued if compromised. In this paper, we suggest a simple and powerful method called Random Permutation Locality Preserving Projection (RP-LPP) for Cancelable Biometric Recognition. Here, we exploit the mathematical relationship between the eigenvalues and eigenvectors of the original biometric image and its randomly permuted version is exploited for carrying out cancelable biometric recognition. The proposed technique work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The effectiveness of the proposed techniques is demonstrated on three freely available face (ORL), iris (UBIRIS) and ear (IITD) datasets against state-of-the-art methods. The advantages of proposed technique are (i) the classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation, (ii) security and quality of generated templates and (iii) robustness across different biometrics. In addition, no image registration is required for performing recognition. [ABSTRACT FROM AUTHOR]

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Under the existing single sample condition, the fatigue detection method of an automobile driver has some problems, such as an improper camera calibration method, image denoising beyond the controllable range, low fatigue detection accuracy, and unsatisfactory effect. A fatigue detection method for drivers based on face recognition under a single sample condition is proposed. Firstly, the camera is calibrated by Zhang Zhengyou’s calibration method. The optimal camera parameters were calculated by linear simulation analysis, and the image was nonlinear refined by the maximum likelihood method. Then, the corrected image effect is enhanced, and the scale parameter gap in the MSRCR image enhancement method is adjusted to the minimum. The detection efficiency is improved by a symmetric algorithm. Finally, the texture mapping technology is used to enhance the authenticity of the enhanced image, and the face image recognition is carried out. The constraint conditions of fatigue detection are established, and the fatigue detection of car drivers under the condition of a single sample is completed. Experimental results show that the proposed method has a good overall detection effect: the fatigue detection accuracy is 20% higher than that of the traditional method, and the average detection time is over 30%. Compared with the traditional fatigue detection methods, this method has obvious advantages, can effectively extract more useful information from the image, and has strong applicability.

Face recognition for a single sample per person (SSPP) is a challenging task due to the lack of sufficient sample information. In this paper, in order to raise the performance of face recognition for SSPP, we propose an algorithm of cropped and extended patch collaborative representation for a single sample per person (CEPCRC). Considering the fact that patch-based method can availably avoid the effect of variations, and the fact that intra-class variations learned from a generic training set can sparsely represent the possible facial variations, thus, we extend patch collaborative representation based classification into the SSPP scenarios by using the intra-class variant dictionary and learn patch weight by exploiting regularized margin distribution optimization. For more complementary information, we construct multiple training samples by the means of cropping. Experimental results in the CMU PIE, Extended Yale B, AR, and LFW datasets demonstrate that CEPCRC performs better compared to the related algorithms. [ABSTRACT FROM AUTHOR]

Linear regression classification (LRC) has attracted a great amount of attention owning to its promising performance in face recognition. However, its performance will dramatically decline in the scenario of limited training samples per class, particularly when only single training sample is available for a specific person. In this paper, a novel LRC based method is proposed to solve this problem. Specifically, we first perform LRC on the training set to obtain a kind of residual for each class. Next, a reverse representation residual is derived for each training sample of a class by exploiting the linear combination of the training samples of its nearest classes and the test sample. Then, we combine the reverse representation residuals of the class by an adaptive weighted-average approach to produce the other kind of residual. Finally, two kinds of residuals are fused to classify the test sample. Experimental results on the ORL, FERET, Libor94 and CMU-PIE face databases demonstrate that the proposed method obtains a higher recognition rate than some state-of-the-art face recognition methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore (https://bioconductor.org/packages/singscore). Results: We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. Conclusions: The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification. [ABSTRACT FROM AUTHOR]

Sparse representation (SR) has successfully addressed the face recognition problem with sufficient training images of each gallery subject, however, its performance will deteriorate much for single sample face recognition (SSFR). To improve the generalization ability of SR for SSFR, this paper proposed a robust sparse representation (RSR) method. By creating a set of position images for each training picture to expand the training samples of each gallery subject and used L2,1 -norm to prompt RSR selecting the correct position images. Finally, the proposed method was evaluated on AR and extended Yale B face databases. The experimental results demonstrate the effectiveness and robustness of the approach. [ABSTRACT FROM AUTHOR]