Training nu-support vector classifiers: theory and algorithms

Creating an atlas of normal tissue for pruning WSI patching through anomaly detection

Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches.

An ensemble of stacking classifiers for improved prediction of miRNA-mRNA interactions

MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in regulating gene expression at the post-transcriptional level by binding to potential target sites of messenger RNAs (mRNAs), facilitated by the Argonaute family of proteins. Selecting the conservative candidate target sites (CTS) is a challenging step, considering that most of the existing computational algorithms primarily focus on canonical site types, which is a time-consuming and inefficient utilization of miRNA target site interactions. We developed a stacking classifier algorithm that addresses the CTS selection criteria using feature-encoding techniques that generates feature vectors, including k-mer nucleotide composition, dinucleotide composition, pseudo-nucleotide composition, and sequence order coupling. This innovative stacking classifier algorithm surpassed previous state-of-the-art algorithms in predicting functional miRNA targets. We evaluated the performance of the proposed model on 10 independent test datasets and obtained an average accuracy of 79.77%, which is a significant improvement of 7.26 % over previous models. This improvement shows that the proposed method has great potential for distinguishing highly functional miRNA targets and can serve as a valuable tool in biomedical and drug development research.

Multi-study evaluation of neuroimaging-based prediction of medication class in mood disorders

Clinicians often face a dilemma in diagnosing bipolar disorder patients with complex symptoms who spend more time in a depressive state than a manic state. The current gold standard for such diagnosis, the Diagnostic and Statistical Manual (DSM), is not objectively grounded in pathophysiology. In such complex cases, relying solely on the DSM may result in misdiagnosis as major depressive disorder (MDD). A biologically-based classification algorithm that can accurately predict treatment response may help patients suffering from mood disorders. Here we used an algorithm to do so using neuroimaging data. We used the neuromark framework to learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework achieves up to 95.45% accuracy, 0.90 sensitivity, and 0.92 specificity in predicting antidepressant (AD) vs. mood stabilizer (MS) response in patients. We incorporated two additional datasets to evaluate the generalizability of our approach. The trained algorithm achieved up to 89% accuracy, 0.88 sensitivity, and 0.89 specificity in predicting the DSM-based diagnosis on these datasets. We also translated the model to distinguish responders to treatment from nonresponders with up to 70% accuracy. This approach reveals multiple salient biomarkers of medication-class of response within mood disorders.

Brain-gut microbiota multimodal predictive model in patients with bipolar depression

BACKGROUND

The "microbiota-gut-brain axis" which bridges the brain and gut microbiota is involved in the pathological mechanisms of bipolar disorder (BD), but rare is known about the exact association patterns and the potential for clinical diagnosis and treatment outcome prediction.

METHODS

At baseline, fecal samples and resting-state MRI data were collected from 103 BD depression patients and 39 healthy controls (HCs) for metagenomic sequencing and network-based functional connectivity (FC), grey matter volume (GMV) analyses. All patients then received 4-weeks quetiapine treatment and were further classified as responders and non-responders. Based on pre-treatment datasets, the correlation networks were established between gut microbiota and neuroimaging measures and the multimodal kernal combination support vector machine (SVM) classifiers were constructed to distinguish BD patients from HCs, and quetiapine responders from non-responders.

RESULTS

The multi-modal pre-treatment characteristics of quetiapine responders, were closer to the HCs compared to non-responders. And the correlation network analyses found the substantial correlations existed in HC between the Anaerotruncus_ unclassified，Porphyromonas_asaccharolytica，Actinomyces_graevenitzii et al. and the functional connectomes involved default mode network (DMN),somatomotor (SM), visual, limbic and basal ganglia networks were disrupted in BD. Moreover, in terms of the multimodal classifier, it reached optimized area under curve (AUC-ROC) at 0.9517 when classified BD from HC, and also acquired 0.8292 discriminating quetiapine responders from non-responders, which consistently better than even using the best unique modality.

LIMITATIONS

Lack post-treatment and external validation datasets; size of HCs is modest.

CONCLUSIONS

Multi-modalities of combining pre-treatment gut microbiota with neuroimaging endophenotypes might be a superior approach for accurate diagnosis and quetiapine efficacy prediction in BD.

Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods

The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike-pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R ²) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R ² = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection.

Kernel Path for ν-Support Vector Classification

It is well known that the performance of a kernel method is highly dependent on the choice of kernel parameter. However, existing kernel path algorithms are limited to plain support vector machines (SVMs), which has one equality constraint. It is still an open question to provide a kernel path algorithm to ν -support vector classification ( ν -SVC) with more than one equality constraint. Compared with plain SVM, ν -SVC has the advantage of using a regularization parameter ν for controlling the number of support vectors and margin errors. To address this problem, in this article, we propose a kernel path algorithm (KP ν SVC) to trace the solutions of ν -SVC exactly with respect to the kernel parameter. Specifically, we first provide an equivalent formulation of ν -SVC with two equality constraints, which can avoid possible conflicts during tracing the solutions of ν -SVC. Based on this equivalent formulation of ν -SVC, we propose the KP ν SVC algorithm to trace the solutions with respect to the kernel parameter. However, KP ν SVC traces nonlinear solutions of kernel method rather than the errors of loss function, and it is still a challenge to provide the algorithm that guarantees to find the global optimal model. To address this challenging problem, we extend the classical error path algorithm to the nonlinear kernel solution paths and propose a new kernel error path (KEP) algorithm that ensures to find the global optimal kernel parameter by minimizing the cross validation error. We also provide the finite convergence analysis and computational complexity analysis to KP ν SVC and KEP. Extensive experimental results on a variety of benchmark datasets not only verify the effectiveness of KP ν SVC but also show the advantage of applying KEP to select the optimal kernel parameter.

A CEEMD-ARIMA-SVM model with structural breaks to forecast the crude oil prices linked with extreme events

This paper develops an integrated framework to forecast the volatility of crude oil prices by considering the impacts of extreme events (structural breaks). The impacts of extreme events are vital to improving prediction accuracy. Aiming to demonstrate the crude oil price fluctuation and the impacts of external events, this paper employs the complementary ensemble empirical mode decomposition (CEEMD). It decomposes the crude oil price into some constituents at various frequencies to extract a market fluctuation, a shock from extreme events and a long-term trend. The shock from extreme events is found to be the most crucial element in deciding the crude oil prices. Then we combine the iterative cumulative sum of squares (ICSS) test with the Chow test to get the structural breaks and analyze the extreme event impacts. Finally, this paper combines the structural breaks, the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) to make a forecast of the crude oil prices. The empirical process proves that the CEEMD-ARIMA-SVM model with structural breaks performs the best when compared with the other ARIMA-type models and SVM-type models. The framework offers an insightful view to help decision-makers and can be used in many areas.

Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning

Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI1 and WFCI2). Considering the average classification accuracy (ACA) in both years of experiments, WFCI1 manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI1 at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI1 and WFCI2 with independent data produced R2 and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI1 and WFCI2) manifested the maximum accuracy for disease estimation with RMSE of 11.61 and R2 = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.

Filtering Photon Cloud Data in Forested Areas Based on Elliptical Distance Parameters and Machine Learning Approach

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was successfully launched. Due to its small spot size, multibeam configuration, high sampling rate, and strong immunity to terrain slopes, it has been regarded as a powerful tool for forest resources surveying and managing. However, the ICESat-2 photon cloud data contain considerable background photons, which discretely distribute in the background space of signal photons. Therefore, it is necessary to filter these noise photons. In this study, photons are divided into three categories: signal photons, noise photons far away from signal photons, and noise photons adjacent to signal photons. Based on the existing research, forward and backward elliptical distances were used to express the spatial relationship between two photons, and backward local density (BLD) was used to describe the density distribution of the photons. However, the single statistical parameter cannot clearly distinguish three types of photon cloud. Therefore, forward local density (FLD) and neighboring forward local density difference (NFLDD) also were defined to describe the density distribution of the photons. Finally, by combining the support vector machine (SVM), the above three density parameters were used to classify the photons by signal and noise photons. The proposed method was validated with photon cloud data acquired by the Simulated Advanced Terrain Laser Altimeter System (MATLAS), the Multiple Altimeter Beam Experimental Lidar (MABEL), and the ICESat-2 systems over different forested areas. The results demonstrated that the proposed method can well remove the noise photons and retain the signal photons without depending on any statistical assumptions or thresholds. The comprehensive accuracy of the three test sites was 0.99, 0.98, and 0.99, respectively, which was higher than those of the existing method. In addition, the total errors corresponding to the three test sites were about 0.4%, 0.5%, and 1.0% respectively, which were lower than those of the existing method.

Detection of captan residues in apple juice using fluorescence spectroscopy combined with a genetic algorithm and support vector machines

The captan residues in apple juice were detected by fluorescence spectrometry, and the content level of captan was predicted based on a genetic algorithm and support vector machines (GA-SVMs). According to the captan concentration in apple juice, the experimental samples were divided into four levels, including no excess, slight excess, moderate excess, and severe excess. A GA was used to select the characteristic wavelength and optimize SVM parameters, and SVM was applied to train the classification model. 50 characteristic wavelength points were selected from the original fluorescence spectra, which contained 401 wavelength points, and the classification accuracy of the training set and test set is 99.02% and 100%, respectively, which is higher than the traditional PLS method. The results show that a GA can effectively select the feature wavelengths, and an SVM model can accurately predict the content level of captan residues. A fast and non-destructive analysis method, combined with a GA and SVM based on fluorescence spectroscopy, was realized.

MCMS-STM: An Extension of Support Tensor Machine for Multiclass Multiscale Object Recognition in Remote Sensing Images

The support tensor machine (STM) extended from support vector machine (SVM) can maintain the inherent information of remote sensing image (RSI) represented as tensor and obtain effective recognition results using a few training samples. However, the conventional STM is binary and fails to handle multiclass classification directly. In addition, the existing STMs cannot process objects with different sizes represented as multiscale tensors and have to resize object slices to a fixed size, causing excessive background interferences or loss of object’s scale information. Therefore, the multiclass multiscale support tensor machine (MCMS-STM) is proposed to recognize effectively multiclass objects with different sizes in RSIs. To achieve multiclass classification, by embedding one-versus-rest and one-versus-one mechanisms, multiple hyperplanes described by rank-R tensors are built simultaneously instead of single hyperplane described by rank-1 tensor in STM to separate input with different classes. To handle multiscale objects, multiple slices of different sizes are extracted to cover the object with an unknown class and expressed as multiscale tensors. Then, M-dimensional hyperplanes are established to project the input of multiscale tensors into class space. To ensure an efficient training of MCMS-STM, a decomposition algorithm is presented to break the complex dual problem of MCMS-STM into a series of analytic sub-optimizations. Using publicly available RSIs, the experimental results demonstrate that the MCMS-STM achieves 89.5% and 91.4% accuracy for classifying airplanes and ships with different classes and sizes, which outperforms typical SVM and STM methods.

Inferring hidden symmetries of exotic magnets from detecting explicit order parameters

An unconventional magnet may be mapped onto a simple ferromagnet by the existence of a high-symmetry point. Knowledge of conventional ferromagnetic systems may then be carried over to provide insight into more complex orders. Here we demonstrate how an unsupervised and interpretable machine-learning approach can be used to search for potential high-symmetry points in unconventional magnets without any prior knowledge of the system. The method is applied to the classical Heisenberg-Kitaev model on a honeycomb lattice, where our machine learns the transformations that manifest its hidden O(3) symmetry, without using data of these high-symmetry points. Moreover, we clarify that, in contrast to the stripy and zigzag orders, a set of D_{2} and D_{2h} ordering matrices provides a more complete description of the magnetization in the Heisenberg-Kitaev model. In addition, our machine also learns the local constraints at the phase boundaries, which manifest a subdimensional symmetry. This paper highlights the importance of explicit order parameters to many-body spin systems and the property of interpretability for the physical application of machine-learning techniques.

Online Detecting of Inter-Turn Short-Circuit in Generator Rotor Winding Relying on ν-SVR Machine

To probe an accurate diagnosing approach for synchronous generator (SG) with rotor winding inter-turn short-circuit, a novel online monitoring and detecting method relying on the [Formula: see text]-support vector regression ([Formula: see text]-SVR) machine was proposed, and its effectiveness was further verified by the micro-synchronous generator dynamic simulation. Terminal voltage, active and reactive power of SG were selected as input variables for a novel prediction model based on the [Formula: see text]-SVR, and field current was selected as an output variable of the prediction model. The structures and parameters of the field current prediction model were optimized with the particle swarm optimization (PSO) algorithm and training samples, then the prediction model was established and the field current prediction got under way. By comparing the predicted field current with the corresponding online measured field current, inter-turn short-circuit of rotor winding in SG could be detected sensitively once its absolute value of the prediction relative error exceeded a specific threshold. The micro-synchronous generator dynamic simulation indicated that the proposed online detecting approach based on the [Formula: see text]-SVR machine overcame the shortage of the back-propagation (BP) diagnosis method for misdiagnosis, and its accuracy, sensitivity and threshold setting range of the diagnosis method was the most prominent among these diagnosis methods such as the BP diagnosis method, the Bayesian regularization back-propagation (BRBP) diagnosis method and the [Formula: see text]-support vector regression ([Formula: see text]-SVR) diagnosis method.

The view of TK-SVM on the phase hierarchy in the classical kagome Heisenberg antiferromagnet

We illustrate how the tensorial kernel support vector machine (TK-SVM) can probe the hidden multipolar orders and emergent local constraint in the classical kagome Heisenberg antiferromagnet. We show that TK-SVM learns the finite-temperature phase diagram in an unsupervised way. Moreover, in virtue of its strong interpretability, it identifies the tensorial quadrupolar and octupolar orders, which define a biaxialD_3hspin nematic, and the local constraint that underlies the selection of coplanar states. We then discuss the disorder hierarchy of the phases, which can be inferred from both the analytical order parameters and an SVM bias parameter. For completeness we mention that the machine also picks up the leading3×3correlations in the dipolar channel at very low temperature, which are however weak compared to the quadrupolar and octupolar orders. Our work shows how TK-SVM can facilitate and speed up the analysis of classical frustrated magnets.

A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence

Background: The surgical wound is a unique problem requiring continuous postoperative care, and mobile health technology is implemented to bridge the care gap. Our study aim was to design an integrated framework to support the diagnosis of wound infection. Methods: We used a computer-vision approach based on supervised learning techniques and machine learning algorithms, to help detect the wound region of interest (ROI) and classify wound infection features. The intersection-union test (IUT) was used to evaluate the accuracy of the detection of color card and wound ROI. The area under the receiver operating characteristic curve (AUC) of our model was adopted in comparison with different machine learning approaches. Results: 480 wound photographs were taken from 100 patients for analysis. The average value of IUT on the validation set with fivefold stratification to detect wound ROI was 0.775. For prediction of wound infection, our model achieved a significantly higher AUC score (83.3%) than the other three methods (kernel support vector machines, 44.4%; random forest, 67.1%; gradient boosting classifier, 66.9%). Conclusions: Our evaluation of a prospectively collected wound database demonstrates the effectiveness and reliability of the proposed system, which has been developed for automatic detection of wound infections in patients undergoing surgical procedures.

Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data-Part I

The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user’s system being aware, resulting in presented hazardous misleading information and signal integrity deterioration without an alarm being triggered. Among the number of proposed spoofing detection and mitigation techniques applied at different stages of the signal processing, we present a method for the cross-correlation monitoring of multiple and statistically significant GNSS observables and measurements that serve as an input for the supervised machine learning detection of potentially spoofed or meaconed GNSS signals. The results of two experiments are presented, in which laboratory-generated spoofing signals are used for training and verification within itself, while two different real-world spoofing and meaconing datasets were used for the validation of the supervised machine learning algorithms for the detection of the GNSS spoofing and meaconing.

Acute and Chronic Effects of Betel Quid Chewing on Brain Functional Connectivity

BACKGROUND

The active alkaloid in Betel quid is arecoline. Consumption of betel quid is associated with both acute effects and longer-term addictive effects. Despite growing evidence that betel quid use is linked with altered brain function and connectivity, the neurobiology of this psychoactive substance in initial acute chewing, and long-term dependence, is not clear.

METHODS

In this observational study, functional magnetic resonance imaging in a resting-state was performed in 24 male betel quid-dependent chewers and 28 male controls prior to and promptly after betel quid chewing. Network-based statistics were employed to determine significant differences in functional connectivity between brain networks for both acute effects and in long-term betel users versus controls. A support vector machine was employed for pattern classification analysis.

RESULTS

Before chewing betel quid, higher functional connectivity in betel quid-dependent chewers than in controls was found between the temporal, parietal and frontal brain regions (right medial orbitofrontal cortex, right lateral orbital frontal cortex, right angular gyrus, bilateral inferior temporal gyrus, superior parietal gyrus, and right medial superior frontal gyrus). In controls, the effect of betel quid chewing was significantly increased functional connectivity between the subcortical regions (caudate, putamen, pallidum, and thalamus), and the visual cortex (superior occipital gyrus and right middle occipital gyrus).

CONCLUSION

These findings show that individuals who chronically use betel quid have higher functional connectivity than controls of the orbitofrontal cortex, and inferior temporal and angular gyri. Acute effects of betel quid are to increase the functional connectivity of some visual cortical areas (which may relate to the acute symptoms) and the basal ganglia and thalamus.

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow prediction. This paper aims to identify models with higher accuracy, robustness, and generalization ability by inspecting the accuracy of a number of machine learning models. The proposed models for river flow include support vector regression (SVR), a hybrid of SVR with a fruit fly optimization algorithm (FOA) (so-called FOASVR), and an M5 model tree (M5). Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of the proposed models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performance in forecasting river flows at Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Qt−1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both the FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of the FOASVR was moderately better than the M5 and periodicity noticeably increased the performance of the models; consequently, FOASVR can be suggested as the most accurate method for forecasting river flows.

A Patient-Specific Approach for Short-Term Epileptic Seizures Prediction Through the Analysis of EEG Synchronization

Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting about 65 millions individuals worldwide.

OBJECTIVE

This paper proposes a patient-specific approach for short-term prediction (i.e., within few minutes) of epileptic seizures.

METHODS

We use noninvasive EEG data, since the aim is exploring the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. Our approach is based on finding synchronization patterns in the EEG that allow to distinguish in real time preictal from interictal states. In practice, we develop easily computable functions over a graph model to capture the variations in the synchronization, and employ a classifier for identifying the preictal state.

RESULTS

We compare two state-of-the-art classification algorithms and a simple and computationally inexpensive threshold-based classifier developed ad hoc. Results on publicly available scalp EEG database and on scalp data of the patients of the Unit of Neurology and Neurophysiology at University of Siena show that this simple and computationally viable processing is able to highlight the changes in synchronization when a seizure is approaching.

CONCLUSION AND SIGNIFICANCE

The proposed approach, characterized by low computational requirements and by the use of noninvasive techniques, is a step toward the development of portable and wearable devices for real-life use.

iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC

Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called "recombination hotspot", while the latter the "recombination coldspot". Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called "iRSpot-Pse6NC" was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

Dynamic Trial-by-Trial Recoding of Task-Set Representations in the Frontoparietal Cortex Mediates Behavioral Flexibility

Cognitive flexibility forms the core of the extraordinary ability of humans to adapt, but the precise neural mechanisms underlying our ability to nimbly shift between task sets remain poorly understood. Recent functional magnetic resonance imaging (fMRI) studies employing multivoxel pattern analysis (MVPA) have shown that a currently relevant task set can be decoded from activity patterns in the frontoparietal cortex, but whether these regions support the dynamic transformation of task sets from trial to trial is not clear. Here, we combined a cued task-switching protocol with human (both sexes) fMRI, and harnessed representational similarity analysis (RSA) to facilitate a novel assessment of trial-by-trial changes in neural task-set representations. We first used MVPA to define task-sensitive frontoparietal and visual regions and found that neural task-set representations on switch trials are less stably encoded than on repeat trials. We then exploited RSA to show that the neural representational pattern dissimilarity across consecutive trials is greater for switch trials than for repeat trials, and that the degree of this pattern dissimilarity predicts behavior. Moreover, the overall neural pattern of representational dissimilarities followed from the assumption that repeating sets, compared with switching sets, results in stronger neural task representations. Finally, when moving from cue to target phase within a trial, pattern dissimilarities tracked the transformation from previous-trial task representations to the currently relevant set. These results provide neural evidence for the longstanding assumptions of an effortful task-set reconfiguration process hampered by task-set inertia, and they demonstrate that frontoparietal and stimulus processing regions support "dynamic adaptive coding," flexibly representing changing task sets in a trial-by-trial fashion.SIGNIFICANCE STATEMENT Humans can fluently switch between different tasks, reflecting an ability to dynamically configure "task sets," rule representations that link stimuli to appropriate responses. Recent studies show that neural signals in frontal and parietal brain regions can tell us which of two tasks a person is currently performing. However, it is not known whether these regions are also involved in dynamically reconfiguring task-set representations when switching between tasks. Here we measured human brain activity during task switching and tracked the similarity of neural task-set representations from trial to trial. We show that frontal and parietal brain regions flexibly recode changing task sets in a trial-by-trial fashion, and that task-set similarity over consecutive trials predicts behavior.

DC Algorithm for Extended Robust Support Vector Machine

Nonconvex variants of support vector machines (SVMs) have been developed for various purposes. For example, robust SVMs attain robustness to outliers by using a nonconvex loss function, while extended [Formula: see text]-SVM (E[Formula: see text]-SVM) extends the range of the hyperparameter by introducing a nonconvex constraint. Here, we consider an extended robust support vector machine (ER-SVM), a robust variant of E[Formula: see text]-SVM. ER-SVM combines two types of nonconvexity from robust SVMs and E[Formula: see text]-SVM. Because of the two nonconvexities, the existing algorithm we proposed needs to be divided into two parts depending on whether the hyperparameter value is in the extended range or not. The algorithm also heuristically solves the nonconvex problem in the extended range. In this letter, we propose a new, efficient algorithm for ER-SVM. The algorithm deals with two types of nonconvexity while never entailing more computations than either E[Formula: see text]-SVM or robust SVM, and it finds a critical point of ER-SVM. Furthermore, we show that ER-SVM includes the existing robust SVMs as special cases. Numerical experiments confirm the effectiveness of integrating the two nonconvexities.

Probing the Heterogeneity of Protein Kinase Activation in Cells by Super-resolution Microscopy

Heterogeneity of mitogen-activated protein kinase (MAPK) activation in genetically identical cells, which occurs in response to epidermal growth factor receptor (EGFR) signaling, remains poorly understood. MAPK cascades integrate signals emanating from different EGFR spatial locations, including the plasma membrane and endocytic compartment. We previously hypothesized that in EGF-stimulated cells the MAPK phosphorylation (pMAPK) level and activity are largely determined by the spatial organization of the EGFR clusters within the cell. For experimental testing of this hypothesis, we used super-resolution microscopy to define EGFR clusters by receptor numbers (N) and average intracluster distances (d). From these data, we predicted the extent of pMAPK with 85% accuracy on a cell-to-cell basis with control data returning 54% accuracy (P < 0.001). For comparison, the prediction accuracy was only 61% (P = 0.382) when the diffraction-limited averaged fluorescence intensity/cluster was used. Large clusters (N ≥ 3) with d > 50 nm were most predictive for pMAPK level in cells. Electron microscopy revealed that these large clusters were primarily localized to the limiting membrane of multivesicular bodies (MVB). Many tighter packed dimers/multimers (d < 50 nm) were found on intraluminal vesicles within MVBs, where they were unlikely to activate MAPK because of the physical separation. Our results suggest that cell-to-cell differences in N and d contain crucial information to predict EGFR-activated cellular pMAPK levels and explain pMAPK heterogeneity in isogenic cells.

An Approach to Biometric Verification Based on Human Body Communication in Wearable Devices

In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer’s forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency range 0.3 MHz to 1500 MHz, and each group includes 1601 sample data. In addition, to achieve the rapid verification, 30 groups of data for each volunteer are acquired at the chosen frequency, and each group contains only 21 sample data. Furthermore, a threshold-adaptive template matching (TATM) algorithm based on weighted Euclidean distance is proposed for rapid verification in this work. The results indicate that the chosen frequency for biometric verification is from 650 MHz to 750 MHz. The false acceptance rate (FAR) and false rejection rate (FRR) based on TATM are approximately 5.79% and 6.74%, respectively. In contrast, the FAR and FRR were 4.17% and 37.5%, 3.37% and 33.33%, and 3.80% and 34.17% using K-nearest neighbor (KNN) classification, support vector machines (SVM), and naive Bayesian method (NBM) classification, respectively. In addition, the running time of TATM is 0.019 s, whereas the running times of KNN, SVM and NBM are 0.310 s, 0.0385 s, and 0.168 s, respectively. Therefore, TATM is suggested to be appropriate for rapid verification use in wearable devices.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens’ quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.