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Having fast, accurate, and broad spectrum methods for the identification of microorganisms is of paramount importance to public health, research, and safety. Bottom-up mass spectrometer-based proteomics has emerged as an effective tool for the accurate identification of microorganisms from microbial isolates. However, one major hurdle that limits the deployment of this tool for routine clinical diagnosis, and other areas of research such as culturomics, is the instrument time required for the mass spectrometer to analyze a single sample, which can take ∼1 h per sample, when using mass spectrometers that are presently used in most institutes. To address this issue, in this study, we employed, for the first time, tandem mass tags (TMTs) in multiplex identifications of microorganisms from multiple TMT-labeled samples in one MS/MS experiment. A difficulty encountered when using TMT labeling is the presence of interference in the measured intensities of TMT reporter ions. To correct for interference, we employed in the proposed method a modified version of the expectation maximization (EM) algorithm that redistributes the signal from ion interference back to the correct TMT-labeled samples. We have evaluated the sensitivity and specificity of the proposed method using 94 MS/MS experiments (covering a broad range of protein concentration ratios across TMT-labeled channels and experimental parameters), containing a total of 1931 true positive TMT-labeled channels and 317 true negative TMT-labeled channels. The results of the evaluation show that the proposed method has an identification sensitivity of 93-97% and a specificity of 100% at the species level. Furthermore, as a proof of concept, using an in-house-generated data set composed of some of the most common urinary tract pathogens, we demonstrated that by using the proposed method the mass spectrometer time required per sample, using a 1 h LC-MS/MS run, can be reduced to 10 and 6 min when samples are labeled with TMT-6 and TMT-10, respectively. The proposed method can also be used along with Orbitrap mass spectrometers that have faster MS/MS acquisition rates, like the recently released Orbitrap Astral mass spectrometer, to further reduce the mass spectrometer time required per sample.

DNA 4 mC plays a crucial role in the genetic expression process of organisms. However, existing deep learning algorithms have shortcomings in the ability to represent DNA sequence features. In this paper, we propose a 4 mC site identification algorithm, DNABert-4mC, based on a fusion of the pruned pre-training DNABert-Pruning model and artificial feature encoding to identify 4 mC sites. The algorithm prunes and compresses the DNABert model, resulting in the pruned pre-training model DNABert-Pruning. This model reduces the number of parameters and removes redundancy from output features, yielding more precise feature representations while upholding accuracy.Simultaneously, the algorithm constructs an artificial feature encoding module to assist the DNABert-Pruning model in feature representation, effectively supplementing the information that is missing from the pre-trained features. The algorithm also introduces the AFF-4mC fusion strategy, which combines artificial feature encoding with the DNABert-Pruning model, to improve the feature representation capability of DNA sequences in multi-semantic spaces and better extract 4 mC sites and the distribution of nucleotide importance within the sequence. In experiments on six independent test sets, the DNABert-4mC algorithm achieved an average AUC value of 93.81%, outperforming seven other advanced algorithms with improvements of 2.05%, 5.02%, 11.32%, 5.90%, 12.02%, 2.42% and 2.34%, respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Motion rehabilitation is increasingly required owing to an aging population and suffering of stroke, which means human motion analysis must be valued. Based on the concept mentioned above, a deep-learning-based system is proposed to track human motion based on three-dimensional (3D) images in this work; meanwhile, the features of traditional red green blue (RGB) images, known as two-dimensional (2D) images, were used as a comparison. The results indicate that 3D images have an advantage over 2D images due to the information of spatial relationships, which implies that the proposed system can be a potential technology for human motion analysis applications.

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.

Motivation: There is a growing trend for biomedical researchers to extract evidence and draw conclusions from mass spectrometry based proteomics experiments, the cornerstone of which is peptide identification. Inaccurate assignments of peptide identification confidence thus may have far-reaching and adverse consequences. Although some peptide identification methods report accurate statistics, they have been limited to certain types of scoring function. The extreme value statistics based method, while more general in the scoring functions it allows, demands accurate parameter estimates and requires, at least in its original design, excessive computational resources. Improving the parameter estimate accuracy and reducing the computational cost for this method has two advantages: it provides another feasible route to accurate significance assessment, and it could provide reliable statistics for scoring functions yet to be developed., Results: We have formulated and implemented an efficient algorithm for calculating the extreme value statistics for peptide identification applicable to various scoring functions, bypassing the need for searching large random databases., Availability and Implementation: The source code, implemented in C ++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit, Contact: yyu@ncbi.nlm.nih.gov, Supplementary Information: Supplementary data are available at Bioinformatics online., (Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.)

Background: Disease-disease similarities can be investigated from multiple perspectives. Identifying similar diseases based on the underlying biomolecular interactions can be especially useful, because it may shed light on the common causes of the diseases and therefore may provide clues for possible treatments. Here we introduce DeCoaD, a web-based program that uses a novel method to assign pair-wise similarity scores, called correlations, to genetic diseases., Findings: DeCoaD uses a random walk to model the flow of information in a network within which nodes are either diseases or proteins and links signify either protein-protein interactions or disease-protein associations. For each protein node, the total number of visits by the random walker is called the weight of that node. Using a disease as both the starting and the terminating points of the random walks, a corresponding vector, whose elements are the weights associated with the proteins, can be constructed. The similarity between two diseases is defined as the cosine of the angle between their associated vectors. For a user-specified disease, DeCoaD outputs a list of similar diseases (with their corresponding correlations), and a graphical representation of the disease families that they belong to. Based on a probabilistic clustering algorithm, DeCoaD also outputs the clusters that the disease of interest is a member of, and the corresponding probabilities. The program also provides an interface to run enrichment analysis for the given disease or for any of the clusters that contains it., Conclusions: DeCoaD uses a novel algorithm to suggest non-trivial similarities between diseases with known gene associations, and also clusters the diseases based on their similarity scores. DeCoaD is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/mn/DeCoaD/.

Motivation: Assigning statistical significance accurately has become increasingly important as metadata of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of metadata at any level may propagate to downstream analyses, undermining the validity of scientific conclusions thus drawn. From the perspective of mass spectrometry-based proteomics, even though accurate statistics for peptide identification can now be achieved, accurate protein level statistics remain challenging., Results: We have constructed a protein ID method that combines peptide evidences of a candidate protein based on a rigorous formula derived earlier; in this formula the database P-value of every peptide is weighted, prior to the final combination, according to the number of proteins it maps to. We have also shown that this protein ID method provides accurate protein level E-value, eliminating the need of using empirical post-processing methods for type-I error control. Using a known protein mixture, we find that this protein ID method, when combined with the Sorić formula, yields accurate values for the proportion of false discoveries. In terms of retrieval efficacy, the results from our method are comparable with other methods tested., Availability and Implementation: The source code, implemented in C++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit., (Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.)

The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.

Organizing experimentally determined protein associations as a hierarchy can be a good approach to elucidating the content of protein complexes and the modularity of subcomplexes. Several challenges exist. First, intrinsically sticky proteins, such as chaperones, are often falsely assigned to many functionally unrelated complexes. Second, the reported collections of proteins may not be true "complexes" in the sense that they bind together and perform a joint cellular function. Third, due to imperfect sensitivity of protein detection methods, both false positive and false negative assignments of a protein to complexes may occur. We mitigate the first issue by down-weighting sticky proteins by their occurrence frequencies. We approach the other two problems by merging nearly identical complexes and by constructing a directed acyclic graph (DAG) based on the relationship of partial inclusion. The constructed DAG, within which smaller complexes form parts of the larger, can reveal how different complexes are joined. By merging almost identical complexes one can deemphasize the influence of false positives, while allowing false negatives to be rescued by other nearly identical association data. We investigate several protein weighting schemes and compare their corresponding DAGs using yeast and human complexes. We find that the scheme incorporating weights based on information flow in the network of direct protein-protein interactions produces biologically most meaningful DAGs. In either yeast or human, isolated nodes form a large proportion of the final hierarchy. While most connected components encompass very few nodes, the largest one for each species contains a sizable portion of all nodes. By considering examples of subgraphs composed of nodes containing a specified protein, we illustrate that the graphs' topological features can correctly suggest the biological roles of protein complexes. The input data, final results and the source code are available at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/ProteinComplexDAG/.

Objective: The ABC/2 equation is commonly applied to measure the volume of intracranial hematoma. However, the precision of ABC/2 equation in estimating the tumor volume of acoustic neuromas is less addressed. The study is to evaluate the accuracy of the ABC/2 formula by comparing with planimetry method for estimating the tumor volumes., Methods: Thirty-two patients diagnosed with acoustic neuroma received contrast-enhanced magnetic resonance imaging of brain were recruited. The volume was calculated by the ABC/2 equation and planimetry method (defined as exact volume) at the same time. The 32 patients were divided into three groups by tumor volume to avoid volume-dependent overestimation (<3 ml, 3-6 ml and >6 ml)., Results: The tumor volume by ABC/2 method was highly correlated to that calculated by planimetry method using linear regression analysis (R2=0.985). Pearson correlation coefficient (r=0.993, p<0.001) demonstrates nearly perfect association between two methods., Conclusions: The ABC/2 formula is an easy method in estimating the tumor volume of acoustic neuromas that is not inferior to planimetry method., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Because of its high specificity, trypsin is the enzyme of choice in shotgun proteomics. Nonetheless, several publications do report the identification of semitryptic and nontryptic peptides. Many of these peptides are thought to be signaling peptides or to have formed during sample preparation. It is known that only a small fraction of tandem mass spectra from a trypsin-digested protein mixture can be confidently matched to tryptic peptides. If other possibilities such as post-translational modifications and single-amino acid polymorphisms are ignored, this suggests that many unidentified spectra originate from semitryptic and nontryptic peptides. To include them in database searches, however, may not improve overall peptide identification because of the possible sensitivity reduction from search space expansion. To circumvent this issue for E-value-based search methods, we have designed a scheme that categorizes qualified peptides (i.e., peptides whose differences in molecular weight from the parent ion are within a specified error tolerance) into three tiers: tryptic, semitryptic, and nontryptic. This classification allows peptides that belong to different tiers to have different Bonferroni correction factors. Our results show that this scheme can significantly improve retrieval performance compared to those of search strategies that assign equal Bonferroni correction factors to all qualified peptides.

Importance: Risk score models predicting the progression of age-related macular degeneration (AMD) to its advanced forms may be useful for targeting high-risk individuals for lifestyle changes that reduce risk for AMD progression, helping with differential diagnosis of AMD and its subtypes, identifying high-risk subjects for participation in clinical trials, and selecting appropriate therapies., Objective: To develop and validate a predictive model for progression to advanced stages of AMD in 2 independent cohorts. DESIGN Participants in a validation cohort and an independent derivation population were classified into 5 stages of AMD based on ocular examination and fundus photographs at baseline. Progression was defined as either eye progressing from stage 1, 2, or 3 to either stage 4 or stage 5 at any follow-up visit to the end of the study. Cox proportional hazards models were used for progression analyses. Covariates included demographic and environmental factors, 6 variants in 5 genes, and baseline AMD grades in both eyes. The algorithm developed with the derivation sample was assessed for calibration and discrimination in the validation data set., Setting: Clinic populations and referrals., Participants: The derivation population comprised 2914 subjects with 809 progressors. The independent validation cohort comprised 980 individuals with no, early, or intermediate AMD in at least one eye at baseline, of whom 294 progressed to advanced stages of geographic atrophy or neovascular disease., Main Outcome Measure: Progression to advanced AMD. RESULTS For the model with all nongenetic and genetic factors, the respective C statistics for progression to advanced AMD in the derivation and validation samples were 0.858 and 0.750 at 5 years and 0.884 and 0.809 at 10 years, and models also discriminated risk for progression to geographic atrophy and neovascular disease separately. For unilateral or bilateral intermediate AMD, 5-year cumulative incidence rates of progression to advanced AMD were 10% with the low-risk score and 50% with the high-risk score; for unilateral advanced disease, the progression rates were 22% and 80% for the fellow eye., Conclusions and Relevance: The risk prediction model was validated in an independent study of progression from no, early, or intermediate stages to advanced subtypes of AMD and will be useful for research, clinical trials, and personalized medicine.

In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by constructing a potential function facilitating a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework.

Dirichlet mixture priors provide a Bayesian formalism for scoring alignments of protein profiles to individual sequences, which can be generalized to constructing scores for multiple-alignment columns. A Dirichlet mixture is a probability distribution over multinomial space, each of whose components can be thought of as modeling a type of protein position. Applied to the simplest case of pairwise sequence alignment, a Dirichlet mixture is equivalent to an implied symmetric substitution matrix. For alphabets of even size L, Dirichlet mixtures with L/2 components and symmetric substitution matrices have an identical number of free parameters. Although this suggests the possibility of a one-to-one mapping between the two formalisms, we show that there are some symmetric matrices no Dirichlet mixture can imply, and others implied by many distinct Dirichlet mixtures. Dirichlet mixtures are derived empirically from curated sets of multiple alignments. They imply "background" amino acid frequencies characteristic of these sets, and should thus be non-optimal for comparing proteins with non-standard composition. Given a mixture Θ, we seek an adjusted Θ' that implies the desired composition, but that minimizes an appropriate relative-entropy-based distance function. To render the problem tractable, we fix the mixture parameter as well as the sum of the Dirichlet parameters for each component, allowing only its center of mass to vary. This linearizes the constraints on the remaining parameters. An approach to finding Θ' may be based on small consecutive parameter adjustments. The relative entropy of two Dirichlet distributions separated by a small change in their parameter values implies a quadratic cost function for such changes. For a small change in implied background frequencies, this function can be minimized using the Lagrange-Newton method. We have implemented this method, and can compositionally adjust to good precision a 20-component Dirichlet mixture prior for proteins in under half a second on a standard workstation.

Statistically meaningful comparison/combination of peptide identification results from various search methods is impeded by the lack of a universal statistical standard. Providing an E-value calibration protocol, we demonstrated earlier the feasibility of translating either the score or heuristic E-value reported by any method into the textbook-defined E-value, which may serve as the universal statistical standard. This protocol, although robust, may lose spectrum-specific statistics and might require a new calibration when changes in experimental setup occur. To mitigate these issues, we developed a new MS/MS search tool, RAId&#95;aPS, that is able to provide spectrum-specific-values for additive scoring functions. Given a selection of scoring functions out of RAId score, K-score, Hyperscore and XCorr, RAId&#95;aPS generates the corresponding score histograms of all possible peptides using dynamic programming. Using these score histograms to assign E-values enables a calibration-free protocol for accurate significance assignment for each scoring function. RAId&#95;aPS features four different modes: (i) compute the total number of possible peptides for a given molecular mass range, (ii) generate the score histogram given a MS/MS spectrum and a scoring function, (iii) reassign E-values for a list of candidate peptides given a MS/MS spectrum and the scoring functions chosen, and (iv) perform database searches using selected scoring functions. In modes (iii) and (iv), RAId&#95;aPS is also capable of combining results from different scoring functions using spectrum-specific statistics. The web link is http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/raid&#95;aps/index.html. Relevant binaries for Linux, Windows, and Mac OS X are available from the same page.

Motivation: The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments., Results: We found that position-specific gap penalties have an advantage over uniform gap costs. We did not explore optimizing distinct uniform gap costs for each query. For Pfam, PSSMs iteratively constructed from seeds based on HMM consensus sequences perform equivalently to HMMs that were adjusted to have constant gap transition probabilities, albeit with much greater variance. We observed no effect of composition-specific gap costs on retrieval performance. These results suggest possible improvements to the PSI-BLAST protein database search program., Availability: The scripts for performing evaluations are available upon request from the authors.

Sequence alignment is one of the most important bioinformatics tools for modern molecular biology. The statistical characterization of gapped alignment scores has been a long-standing problem in sequence alignment research. Using a variant of the directed path in random media model, we investigate the score statistics of global sequence alignment taking into account, in particular, the compositional bias of the sequences compared. Such statistics are used to distinguish accidental similarity due to compositional similarity from biologically significant similarity. To accommodate the compositional bias, we introduce an extra parameter p indicating the probability for positive matching scores to occur. When p is small, a high scoring alignment obviously cannot come from compositional similarity. When p is large, the highest scoring point within a global alignment tends to be close to the end of both sequences, in which case we say the system percolates. By applying finite-size scaling theory on percolating probability functions of various sizes (sequence lengths), the critical p at infinite size is obtained. For alignment of length t, the fact that the score fluctuation grows as chi(t)1/3 is confirmed upon investigating the scaling form of the alignment score. Using the Kolmogorov-Smirnov statistics test, we show that the random variable , if properly scaled, follows the Tracy-Widom distributions: Gaussian orthogonal ensemble for p slightly larger than pc and Gaussian unitary ensemble for larger p. Although these results deepen our understanding of the distribution of alignment scores, the use of these results in practical applications remains somewhat heuristic and needs to be further developed. Nevertheless, the possibility of characterizing score statistics for modest system size (sequence lengths), via proper reparametrization of alignment scores, is illustrated.

The reaction mechanism between cefoperazone and human serum albumin (HSA) and the affinity between cefoperazone and beta-lactamase were investigated by spectrometry and spectrofluorimetry. The interaction dissociation constants of human serum albumin and cefoperazone have been determined from a double reciprocal Lineweaver-Burk plot. The binding distance and the transfer efficiency between cefoperazone and HSA were also obtained according to the theory of Förster' non-radiation energy transfer. The result suggested that the main binding force between cefoperazone and HSA is electrostatic force interaction. The high beta-lactamase stability of cefoperazone may be correlative with its molecular structure. The antibiotic activity and valency connect with transfer efficiency and dissociation constant. The effect of cefoperazone on the conformation of HSA was also analyzed using synchronous fluorescence spectrometry.

Motivation: Amino acid substitution matrices play a central role in protein alignment methods. Standard log-odds matrices, such as those of the PAM and BLOSUM series, are constructed from large sets of protein alignments having implicit background amino acid frequencies. However, these matrices frequently are used to compare proteins with markedly different amino acid compositions, such as transmembrane proteins or proteins from organisms with strongly biased nucleotide compositions. It has been argued elsewhere that standard matrices are not ideal for such comparisons and, furthermore, a rationale has been presented for transforming a standard matrix for use in a non-standard compositional context., Results: This paper presents the mathematical details underlying the compositional adjustment of amino acid or DNA substitution matrices.

Sequence alignment is one of the most important bioinformatics tools for modern molecular biology. The statistical characterization of gapped alignment scores has been a long-standing problem in sequence alignment research. In this paper, we provide a self-contained exposition of sequence alignment, a short review about how this problem is related to the directed polymer problem in statistical physics, and some analytical results that can be used for predicting alignment score statistics. Basically, we present two classes of solutions for the gapped alignment statistics by explicitly calculating the evolution of the few-replica partition function in 1+1 dimensions. We have obtained the conditions under which the more important extremal parameter lambda, characterizing the alignment score statistics, becomes predictable.

The main thrust of this paper is application of a novel data mining approach on the log of user's feedback to improve web multimedia information retrieval performance. A user space model was constructed based on data mining, and then integrated into the original information space model to improve the accuracy of the new information space model. It can remove clutter and irrelevant text information and help to eliminate mismatch between the page author's expression and the user's understanding and expectation. User space model was also utilized to discover the relationship between high-level and low-level features for assigning weight. The authors proposed improved Bayesian algorithm for data mining. Experiment proved that the authors' proposed algorithm was efficient.

The normalized subband adaptive filtering algorithm provides fast convergence rate for colored input signals as compared to the normalized least mean square algorithm, but it suffers from a poor convergence issue in the α -stable noise. In light of this, the normalized subband p-norm (NSPN) algorithm, which is based on the least mean p-power error (MPE) criterion, is proposed in this study. This technique is not only robust against impulsive noise samples, but it also maintains a fast convergence rate when colored input signals are used. In addition to this, we develop both the steady-state and the transient models of the NSPN algorithm and provide some insights. Then, in order to solve the problem of making a choice regarding the order p in the NSPN algorithm, we design an autonomous system and come up with the NSPN algorithm with a variable p-norm (VP-NSPN). In addition, we offer the TFC-based VP-NSPN algorithm with a fast convergence rate and low steady-state misadjustment simultaneously by making use of the tap-weights feedback-based convex combination (TFC) scheme. In conclusion, simulation results on system identification and acoustic echo cancellation are undertaken in order to validate the superiority of the proposed algorithms and validate the usefulness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

The normalized least mean M-estimate (NLMM) algorithm exhibits good robustness in impulsive noises, but it suffers from the trade-off between convergence rate and steady-state misadjustment. In response to this problem, this paper proposes a switching step-sizes NLMM (SSS-NLMM) algorithm, which selects the optimal step-size at each iteration by comparing the mean square deviation (MSD) values of the algorithm with different step-sizes. Furthermore, to improve the convergence of the algorithm in the transition stage, we embed a feedback mechanism on the minimum MSD. Simulations in various environments have verified that the proposed SSS-NLMM algorithm has better performance than its counterparts in terms of convergence rate and steady-state misadjustment. [ABSTRACT FROM AUTHOR]

Edge computing is a key technology in computational imaging, where the algorithms determine reconstruction quality and reconstruction speed. Recovering spectral information from ill-conditioned data of coded aperture snapshots is a novel but challenging solution in spectral imaging for dynamic scenes. Further study is necessary to enhance the edge computing power of computational spectral programs. Deep unfolding networks have made considerable headway in this direction, but the existing methods still have two drawbacks: (1) The 3D spectral cube exhibits long-range dependency and non-local self-similarity in both spatial and spectral dimensions, yet learn the global features of spatial and spectral is difficult, which remains to be investigated. (2) The global features degenerate as the number of iterations increases. To solve the above problems, this paper proposes a Momentum Accelerated Unfolding Network with Spectral–Spatial prior (MAUNSS). Specifically, a denoising module based on a spectral–spatial transformer is designed, which uses both sparse spectral features to recover global information and dense spatial features to enhance detail texture. Within such a framework, cross-stage feature transmission channels transmitting features across spectral and spatial transformers at different stages are built to avoid feature degeneration. In order to increase the training speed and save the time and cost associated with the iterative approach, we improve it by means of the momentum acceleration module. Supplemented by a re-projection loss, a technique introduced from 3D measurement, the accuracy is further improved. To the best of our knowledge, the proposed method is an attempt to use momentum to expedite the convergence of unfolding networks. Extensive experiments demonstrate that our proposed method outperforms previous state-of-the-art methods by more than 1.94 dB. • MAUN with learnable hyper-parameters and Momentum methods ensures better accuracy. • A reprojection loss is proposed to improve the reconstruction accuracy of HIS. • MAUNSS exceeds the previous SOTA methods while requiring less memory costs. [ABSTRACT FROM AUTHOR]

Auxiliary stopping area (ASA) is the key guarantee for high-speed maglev train operation safety. Aiming at the layout of the ASA along the track, this article constructs an optimized model of ASA layout and proposes a solving algorithm based on the single target genetic algorithm by analyzing the influence factors of ASA layout. The result of the numerical experiment shows that the proposed method could fulfill the requirements of the maglev train operation safety and efficiency and optimize the cost of the ASA layout while taking the complex line situation, train tracking operation, and bidirectional operation on a single line into account. [ABSTRACT FROM AUTHOR]

In this paper, two arctangent-based normalized subband adaptive filter (Arc-NSAF) algorithms are proposed, which are derived from two different forms of arctangent cost function and their difference is the order of the arctangent computation and the summation operation of the normalized subband error signal. Benefiting from the excellent property of the arctangent function for suppressing impulsive interferences, the proposed Arc-NSAF algorithms not only possess good robustness against impulsive interferences, but also obtain faster convergence rate for the colored input signals. To further improve the convergence performance of the Arc-NSAFs when identifying sparse system, the proportionate versions of the Arc-NSAF algorithms are also proposed. Simulation results have demonstrated the superiority of the proposed algorithms in impulsive interferences environments as compared to their respective competitors. [ABSTRACT FROM AUTHOR]

To improve the performance of the recently presented individual weighting factors sign subband adaptive filter (IWF-SSAF) algorithm, its 2 combination algorithms using different step sizes are proposed. The first algorithm is to convexly combine the weight vectors of a large step-size IWF-SSAF filter and a small step-size one; and the second algorithm is to obtain a time-varying step size for the IWF-SSAF by combining a large step size and a small one. The minimization of the sum of the l1-norm of subband errors is used to indirectly update the mixing parameters in these 2 algorithms through a modified sigmoidal function. Moreover, in the first algorithm, to implement a smooth transition from the large step-size IWF-SSAF filter to the small step-size one, the component filters receive a cyclic feedback of the combined weight vector. Both proposed algorithms have almost the same convergence performance, but the second algorithm saves computational cost. Simulation results in impulsive noise scenarios demonstrate the superiority of our proposed algorithms. [ABSTRACT FROM AUTHOR]

A novel normalized subband adaptive filter algorithm with combined step size is proposed for acoustic echo cancellation, which is derived by utilizing a variable mixing parameter to combine a large step size and a small one, thus providing fast convergence rate and small steady-state error. The mixing parameter is indirectly updated by utilizing the stochastic gradient method which minimizes the sum of squared subband errors. Simulation results demonstrate the superiority of the proposed algorithm in terms of the convergence rate and steady-state error as compared to other algorithms mentioned in this paper. [ABSTRACT FROM AUTHOR]

In acoustic echo cancellation (AEC), the sparseness of impulse responses can vary over time or/and context. For such scenario, the proportionate normalized subband adaptive filter (PNSAF) and μ -law (MPNSAF) algorithms suffer from performance deterioration. To this end, we propose their sparseness-measured versions by incorporating the estimated sparseness into the PNSAF and MPNSAF algorithms, respectively, which can adapt to the sparseness variation of impulse responses. In addition, based on the energy conservation argument, we provide a unified formula to predict the steady-state mean-square performance of any PNSAF algorithm, which is also supported by simulations. Simulation results in AEC have shown that the proposed algorithms not only exhibit faster convergence rate than their competitors in sparse, quasi-sparse and dispersive environments, but also are robust to the variation in the sparseness of impulse responses. [ABSTRACT FROM AUTHOR]

Background: Isokinetic muscle strength training is presently the most advanced method of muscle strength training. However, the existing control schemes of the training equipment are usually limited to the structure of the brake.Objective: In order to solve this problem, this paper presents a solution to an isokinetic system based on the force control of a DC servomotor.Methods: A new fuzzy impedance nonlinear controller is designed by analyzing the relevant requirements of isokinetic motion. A series of force tracking comparison experiments between a fuzzy PI controller and a classical PI controller are studied. In addition, some strength training experiments employing different driving forces and target speeds are also conducted.Results: The results demonstrate the effectiveness of this fuzzy impedance force tracking control strategy.Conclusion: Using the aforementioned methods, a comprehensive motion algorithm was designed. [ABSTRACT FROM AUTHOR]

This paper presents a new proportionate normalized subband adaptive filter (PNSAF) algorithm, called the IAF-PNSAF, which assigns an individual activation factor for each filter coefficient instead of a common one for all filter coefficients as in the standard PNSAF algorithm. This algorithm achieves an improved performance in terms of the convergence rate and tracking capability for identifying a highly sparse system. Moreover, to overcome the trade-off of the fixed-step-size IAF-PNSAF algorithm between the fast convergence rate and small steady-state misalignment, its adaptive combination version is proposed. Simulation results for identifying the impulse response with high sparseness have demonstrated that the proposed algorithms outperform their counterparts. [ABSTRACT FROM AUTHOR]

To obtain fast convergence rate and low steady-state error in acoustic echo cancellation, a convex combination scheme of the improved proportionate normalized subband adaptive filter algorithm is proposed. Instead of the gradient method in the conventional combination theory, the mixing parameter is adapted by using the normalized gradient method which makes it more robust to the variations of subband error signals. Also, to implement a smooth transition from the fast filter to the accurate filter, a cyclic feedback of the overall tap-weights giving to all component filters is applied. [ABSTRACT FROM AUTHOR]

Proportionate-type normalized subband adaptive filter (PNSAF-type) algorithms are very attractive choices for echo cancellation. To further obtain both fast convergence rate and low steady-state error, in this paper, a variable step size (VSS) version of the presented improved PNSAF (IPNSAF) algorithm is proposed by minimizing the square of the noise-free a posterior subband error signals. A noniterative shrinkage method is used to recover the noise-free a priori subband error signals from the noisy subband error signals. Significantly, the proposed VSS strategy can be applied to any other PNSAF-type algorithm, since it is independent of the proportionate principles. Simulation results in the context of acoustic echo cancellation have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• To analyze the performance of three proportionate algorithms in impulsive noises. • To reveal the ranges and relations of the step-sizes in three proportionate algorithms. • To develop a decorrelation technique based variable step-size scheme to deal with the trade-off between convergence rate and steady-state error. In the literature, the proportionate least mean M-estimate (PLMM) algorithm exhibits good performance when dealing with sparse systems in the presence of impulsive noises. In this paper, the mean and mean-square behaviors of three recursion types of the PLMM algorithm are studied in depth. We derive analytically the stability, transient and steady-state results of these PLMM recursions, and find that they can achieve the same performance when properly choosing the step-size and the proportionate matrix. To improve the filter performance in both convergence rate and steady-state error, we derive a variable step-size (VSS) scheme and then present the VSS-based PLMM algorithms. In addition, based on the adaptive decorrelation strategy aiming at the colored input signals, the VSS adaptive decorrelation PLMM algorithms are developed to further speed up the convergence. Computer simulations have verified our theoretical analyses and the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new sign subband adaptive filter (SSAF) algorithm with an individual-weighting-factor (IWF) for each subband, instead of a common weighting factor in the original SSAF algorithm, called the IWF-SSAF. Each individual weighting factor only depends on the corresponding subband input signal power. Compared with the SSAF algorithm, the proposed approach fully utilizes the inherent decorrelating property of subband adaptive filter for colored inputs, leading to a better convergence performance. After that, to further enhance the performance of the IWF-SSAF in a sparse system, an improved proportionate IWF-SSAF (IWF-IPSSAF) algorithm is proposed. The proposed algorithms not only inherit the good robustness of sign algorithm against impulsive interferences, but also obtain a significant improvement in the performance as compared to their counterparts (i.e., SSAF and IPSSAF), in terms of the convergence rate and tracking capability. Besides, the IWF-IPSSAF algorithm has faster convergence rate than the IWF-SSAF algorithm for sparse impulse responses. Finally, the performances of two proposed algorithms are demonstrated in the system identification and the acoustic echo cancellation with double-talk. [ABSTRACT FROM AUTHOR]

Proposed is a new subband adaptive filter (SAF) algorithm by minimizing Huber’s cost function that is robust to impulsive noises. Generally, this algorithm works in the mode of the normalized SAF (NSAF) algorithm, while it behaves like the sign SAF (SSAF) algorithm only when the impulsive noises appear. To further improve the robustness of this algorithm against impulsive noises, the subband cutoff parameters are updated in a recursive way. Moreover, the proposed algorithm can be interpreted as a variable step size NSAF algorithm, thus it exhibits faster convergence rate and lower steady-state error than the NSAF. Simulation results, using different colored input signals in both impulsive and free-impulsive noise environments, show that the proposed algorithm works better than some existing algorithms. [ABSTRACT FROM AUTHOR]

In order to reduce the steady-state misalignment of real-coefficient proportionate affine projection sign algorithm (RP-APSA) for sparse impulse responses, a memory RP-APSA is proposed in this paper by exploiting the historical values of proportionate factors, called MP-APSA. Further, to improve the robustness of MP-APSA and the recently proposed memory-improved RP-APSA (MIP-APSA) for impulse responses with mutative sparseness, two sparseness-controlled algorithms (SC-MP-APSA and SC-MIP-APSA) are developed by estimating the sparseness of the impulse response at each iteration. Simulation results in the context of acoustic echo cancellation show that the proposed algorithms retain good robustness against impulsive interferences. More importantly, the proposed sparseness-controlled algorithms can not only obtain low steady-state misalignment in both the sparse and dispersive situations, but also adapt to the variations in the sparseness of the impulse response. [ABSTRACT FROM AUTHOR]

Base on the integration of urban transportation, the paper studied the dispatching problem of bus vehicles associating with urban rail transit to transport passengers during the large-scale events. For the operation characteristics of dedicated-line and over-line, optimizing models of independent dispatching and centralized dispatching were developed separately while whose algorithms were provided. An application of the models to the case of Expo 2010 Shanghai shows that the optimizing models are practical and useful for reducing the cost of bus transit operation and improving the efficiency of the intermodal transportation system composed of urban rail transit and bus transit. [ABSTRACT FROM AUTHOR]

Dirichlet mixtures provide an elegant formalism for constructing and evaluating protein multiple sequence alignments. Their use requires the inference of Dirichlet mixture priors from curated sets of accurately aligned sequences. This article addresses two questions relevant to such inference: of how many components should a Dirichlet mixture consist, and how may a maximum-likelihood mixture be derived from a given data set. To apply the Minimum Description Length principle to the first question, we extend an analytic formula for the complexity of a Dirichlet model to Dirichlet mixtures by informal argument. We apply a Gibbs-sampling based approach to the second question. Using artificial data generated by a Dirichlet mixture, we demonstrate that our methods are able to approximate well the true theory, when it exists. We apply our methods as well to real data, and infer Dirichlet mixtures that describe the data better than does a mixture derived using previous approaches. [ABSTRACT FROM AUTHOR]

In this paper, a receiver algorithm for uplink multiuser orthogonal frequency-division multiplexing (OFDM) with a receiver-diversity system is proposed. The receiver uses a simple multiuser generalization of the maximum a posteriori probability (MAP) selection diversity combining followed by parallel interference cancellation with least-squares projection (PIC-LSP) for multiuser detection. Compared with the near-maximal-ratio-combining (near-MRC) scheme, simulation results show that the proposed approach achieves very good performance and has much lower complexity. [ABSTRACT FROM AUTHOR]

Abstract: This paper demonstrates an approach to predict the chaotic time series with outliers using annealing robust fuzzy neural networks (ARFNNs). A combination model that merges support vector regression (SVR), radial basis function networks (RBFNs) and simplified fuzzy inference system is used. The SVR has the good performances to determine the number of rules in the simplified fuzzy inference system and initial weights for the fuzzy neural networks (FNNs). Based on these initial structures, and then annealing robust learning algorithm (ARLA) can be used effectively to overcome outliers and adjust the parameters of structures. Simulation results show the superiority of the proposed method with different SVR for training and prediction of chaotic time series with outliers. [Copyright &y& Elsevier]

This letter proposes a receiver algorithm for orthogonal frequency division multiplexing (OFDM) with receiver diversity that uses a new multiuser generalization of the maximum a posteriori probability (MAP) selection diversity combining followed by successive interference cancellation with least-square projection for multiuser detection. Simulation results show that the proposed approach achieves near-maximal-ratio-combining performance at a much lower complexity. [ABSTRACT FROM AUTHOR]

The power harnessed by wave energy converters (WECs) in oceans is highly variable and, therefore, has a high peak-to-average power (PTAP) ratio. To minimize the cost of a WEC power take off (PTO) system, it is desirable to reduce the PTAP ratio while maximizing the mean power extracted by WECs. The important issue of how PTAP ratio reduction measures (such as adding an inertia element) can affect the mean power extracted in a reference model has not been thoroughly addressed in the literature. To investigate this correlation, this study focuses on the integration of the U.S. Department of Energy's Reference Model 3, a two-body point absorber, with a slider-crank WEC for linear-to-rotational conversion. In the first phase of this study, a full-scale numerical model was developed that predicts how PTO system parameters, along with an advanced control algorithm, can potentially affect the proposed WEC's PTAP ratio as well as the mean power extracted. In the second phase, an appropriate scaled-down model was developed, and extracted power results were successfully validated against the full-scale model. Finally, numerical and hardware-in-the-loop (HIL) simulations based on the scaled-down model were designed and conducted to optimize or make trade-offs between the operational performance and PTAP ratio. The initial results with numerical and HIL simulations reveal that gear ratio, crank radius, and generator parameters substantially impact the PTAP ratio and mean power extracted. [ABSTRACT FROM AUTHOR]

A computational method combining the genetic algorithm (GA) and shape mutation is reported for electromagnetic imaging of a homogeneous cylinder of arbitrary shape. By measuring the scattered field, the shape location, size, and permittivity of the object are retrieved quite successfully. The forward problem is solved based on the equivalent source current and the method of moments (MoM), while the inverse problem is reformulated into an optimization one, and is solved by the proposed scheme. Numerical simulation shows that, even with a bad initial guess, good reconstruction can be obtained for a hollow object or multiple-cylinder object as long as the noise level is ≤-20 dB. © 1998 John Wiley & Sons, Inc. Microwave Opt Technol Lett 19: 188–192, 1998. [ABSTRACT FROM AUTHOR]

A novel method combining the genetic algorithm (GA) and regular shape expansion technique is reported for electromagnetic imaging of a multilayer dielectric object of arbitrary shape. By measuring the scattered field, the shape, location, size, and permittivity of each layer of the object are retrieved quite successfully. The forward problem is solved based on the equivalent source current and the method of moments (MoM), while the inverse problem is reformulated as an optimization problem. The optimization problem is solved by the proposed method. Numerical simulation shows that good image reconstruction can be obtained for various multilayer dielectric objects as long as the noise level is ≤-20 dB. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 347–354, 1999 [ABSTRACT FROM AUTHOR]

• By incorporating the sparsity-aware technique, we propose the S-IWF-SSAF algorithm, and analyze its performance in-depth in impulsive noise. • The proposed analysis covers the behaviors of the IWF-SSAF algorithm in impulsive noise, with better accuracy than the analysis in [15]. • We devise a joint optimization scheme to automatically choose the step-size and the sparsity penalty parameter for the S-IWF-SSAF algorithm. • To make the proposed algorithms suitable for AEC, we develop delayless implementation of the proposed algorithms. In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A relatively simple method to improve the image resolution of light field based on a liquid crystal (LC) microlens doped with multi-walled carbon nanotubes (MWCNTs) was developed and evaluated. As the nanoparticles were doped in LC, its electro-optical features could enhance, leading to a short response time compared to the pure LC microlens. With the maximum use of the proposed LC microlens, a method combining aperiodicity extraction and weighted average algorithm was adopted to realize the high-resolution light field imaging. The aperiodicity extraction method was proposed, which could effectively improve resolution of view angle image. For synthesizing the full resolution image at 0 Vrms and the extracted view angle image of light field imaging at 2.0 Vrms, the final high-resolution light field imaging could be obtained in a short time by weighted average algorithm. In this way, the common problem of low resolution in light field imaging could be solved. This proposed method was in good agreement with our experimental results. And it was also in line with the development of the trend of the smart imaging sensor combining algorithm with hardware. [ABSTRACT FROM AUTHOR]

Singing voice detection or vocal detection is a classification task that determines whether a given audio segment contains singing voices. This task plays a very important role in vocal-related music information retrieval tasks, such as singer identification. Although humans can easily distinguish between singing and nonsinging parts, it is still very difficult for machines to do so. Most existing methods focus on audio feature engineering with classifiers, which rely on the experience of the algorithm designer. In recent years, deep learning has been widely used in computer hearing. To extract essential features that reflect the audio content and characterize the vocal context in the time domain, this study adopted a long-term recurrent convolutional network (LRCN) to realize vocal detection. The convolutional layer in LRCN functions in feature extraction, and the long short-term memory (LSTM) layer can learn the time sequence relationship. The preprocessing of singing voices and accompaniment separation and the postprocessing of time-domain smoothing were combined to form a complete system. Experiments on five public datasets investigated the impacts of the different features for the fusion, frame size, and block size on LRCN temporal relationship learning, and the effects of preprocessing and postprocessing on performance, and the results confirm that the proposed singing voice detection algorithm reached the state-of-the-art level on public datasets. [ABSTRACT FROM AUTHOR]

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at .[Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Abstract: A practical brain–machine interface (BMI) requires real-time decoding algorithms to be realised in a portable device rather than a personal computer. In this article, a field-programmable gate array (FPGA) implementation of a probabilistic neural network (PNN) is proposed and developed to decode motor cortical ensemble recordings in rats performing a lever-pressing task for water rewards. A chronic 16-channel microelectrode array was implanted into the primary motor cortex of the rat to record neural activity, and the pressure signal of the lever were recorded simultaneously. To decode the pressure value from neural activity, both Matlab-based and FPGA-based mapping algorithms using a PNN were implemented and evaluated. In the FPGA architecture, training data of the network were stored in random access memory (RAM) blocks and multiply–add operations were realised by on-chip DSP48E slices. In the approximation of the activation function, a Taylor series and a look-up table (LUT) are used to achieve an accurate approximation. The results of FPGA implementation are as accurate as the realisation of Matlab, but the running speed is 37.9 times faster. This novel and feasible method indicates that the performance of current FPGAs is competent for portable BMI applications. [Copyright &y& Elsevier]