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Introduction: Speaker diarization is an essential preprocessing step for diagnosing cognitive impairments from speech-based Montreal cognitive assessments (MoCA)., Methods: This paper proposes three enhancements to the conventional speaker diarization methods for such assessments. The enhancements tackle the challenges of diarizing MoCA recordings on two fronts. First, multi-scale channel interdependence speaker embedding is used as the front-end speaker representation for overcoming the acoustic mismatch caused by far-field microphones. Specifically, a squeeze-and-excitation (SE) unit and channel-dependent attention are added to Res2Net blocks for multi-scale feature aggregation. Second, a sequence comparison approach with a holistic view of the whole conversation is applied to measure the similarity of short speech segments in the conversation, which results in a speaker-turn aware scoring matrix for the subsequent clustering step. Third, to further enhance the diarization performance, we propose incorporating a pairwise similarity measure so that the speaker-turn aware scoring matrix contains both local and global information across the segments., Results: Evaluations on an interactive MoCA dataset show that the proposed enhancements lead to a diarization system that outperforms the conventional x-vector/PLDA systems under language-, age-, and microphone-mismatch scenarios., Discussion: The results also show that the proposed enhancements can help hypothesize the speaker-turn timestamps, making the diarization method amendable to datasets without timestamp information., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xu, Ke, Mak, Wong, Meng, Kwok, Gu, Zhang, Tao and Chang.)

This paper analyzes diverse features extracted from spoken language to select the most discriminative ones for dementia detection. We present a two-step feature selection (FS) approach: Step 1 utilizes filter methods to pre-screen features, and Step 2 uses a novel feature ranking (FR) method, referred to as dual dropout ranking (DDR), to rank the screened features and select spoken language biomarkers. The proposed DDR is based on a dual-net architecture that separates FS and dementia detection into two neural networks (namely, the operator and selector). The operator is trained on features obtained from the selector to reduce classification or regression loss. The selector is optimized to predict the operator's performance based on automatic regularization. Results show that the approach significantly reduces feature dimensionality while identifying small feature subsets that achieve comparable or superior performance compared with the full, default feature set. The Python codes are available at https://github.com/kexquan/dual-dropout-ranking., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiaoquan KE reports financial support was provided by The Research Grant Council of Hong Kong SAR., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Domain adaptation aims to reduce the mismatch between the source and target domains. A domain adversarial network (DAN) has been recently proposed to incorporate adversarial learning into deep neural networks to create a domain-invariant space. However, DAN's major drawback is that it is difficult to find the domain-invariant space by using a single feature extractor. In this article, we propose to split the feature extractor into two contrastive branches, with one branch delegating for the class-dependence in the latent space and another branch focusing on domain-invariance. The feature extractor achieves these contrastive goals by sharing the first and last hidden layers but possessing decoupled branches in the middle hidden layers. For encouraging the feature extractor to produce class-discriminative embedded features, the label predictor is adversarially trained to produce equal posterior probabilities across all of the outputs instead of producing one-hot outputs. We refer to the resulting domain adaptation network as "contrastive adversarial domain adaptation network (CADAN)." We evaluated the embedded features' domain-invariance via a series of speaker identification experiments under both clean and noisy conditions. Results demonstrate that the embedded features produced by CADAN lead to a 33% improvement in speaker identification accuracy compared with the conventional DAN.

When training data are scarce, it is challenging to train a deep neural network without causing the overfitting problem. For overcoming this challenge, this article proposes a new data augmentation network-namely adversarial data augmentation network (ADAN)- based on generative adversarial networks (GANs). The ADAN consists of a GAN, an autoencoder, and an auxiliary classifier. These networks are trained adversarially to synthesize class-dependent feature vectors in both the latent space and the original feature space, which can be augmented to the real training data for training classifiers. Instead of using the conventional cross-entropy loss for adversarial training, the Wasserstein divergence is used in an attempt to produce high-quality synthetic samples. The proposed networks were applied to speech emotion recognition using EmoDB and IEMOCAP as the evaluation data sets. It was found that by forcing the synthetic latent vectors and the real latent vectors to share a common representation, the gradient vanishing problem can be largely alleviated. Also, results show that the augmented data generated by the proposed networks are rich in emotion information. Thus, the resulting emotion classifiers are competitive with state-of-the-art speech emotion recognition systems.

Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classification is the variation in the waveforms and characteristics of ECG signals among different patients. To address this issue, this paper proposes adapting a patient-independent deep neural network (DNN) using the information in the patient-dependent identity vectors (i-vectors). The adapted networks, namely i-vector adapted patient-specific DNNs (iAP-DNNs), are tuned toward the ECG characteristics of individual patients. For each patient, his/her ECG waveforms are compressed into an i-vector using a factor analysis model. Then, this i-vector is injected into the middle hidden layer of the patient-independent DNN. Stochastic gradient descent is then applied to fine-tune the whole network to form a patient-specific classifier. As a result, the adaptation makes use of not only the raw ECG waveforms from the specific patient but also the compact representation of his/her ECG characteristics through the i-vector. Analysis on the hidden-layer activations shows that by leveraging the information in the i-vectors, the iAP-DNNs are more capable of discriminating normal heartbeats against arrhythmic heartbeats than the networks that use the patient-specific ECG only for the adaptation. Experimental results based on the MIT-BIH database suggest that the iAP-DNNs perform better than existing patient-specific classifiers in terms of various performance measures. In particular, the sensitivity and specificity of the existing methods are all under the receiver operating characteristic curves of the iAP-DNNs.

This paper proposes deep learning methods with signal alignment that facilitate the end-to-end classification of raw electrocardiogram (ECG) signals into heartbeat types, i.e., normal beat or different types of arrhythmias. Time-domain sample points are extracted from raw ECG signals, and consecutive vectors are extracted from a sliding time-window covering these sample points. Each of these vectors comprises the consecutive sample points of a complete heartbeat cycle, which includes not only the QRS complex but also the P and T waves. Unlike existing heartbeat classification methods in which medical doctors extract handcrafted features from raw ECG signals, the proposed end-to-end method leverages a deep neural network for both feature extraction and classification based on aligned heartbeats. This strategy not only obviates the need to handcraft the features but also produces optimized ECG representation for heartbeat classification. Evaluations on the MIT-BIH arrhythmia database show that at the same specificity, the proposed patient-independent classifier can detect supraventricular- and ventricular-ectopic beats at a sensitivity that is at least 10% higher than current state-of-the-art methods. More importantly, there is a wide range of operating points in which both the sensitivity and specificity of the proposed classifier are higher than those achieved by state-of-the-art classifiers. The proposed classifier can also perform comparable to patient-specific classifiers, but at the same time enjoys the advantage of patient independence.

Although many web-servers for predicting protein subcellular localization have been developed, they often have the following drawbacks: (i) lack of interpretability or interpreting results with heterogenous information which may confuse users; (ii) ignoring multi-location proteins and (iii) only focusing on specific organism. To tackle these problems, we present an interpretable and efficient web-server, namely FUEL-mLoc, using eature- nified prediction and xplanation of m ulti- oc alization of cellular proteins in multiple organisms. Compared to conventional localization predictors, FUEL-mLoc has the following advantages: (i) using unified features (i.e. essential GO terms) to interpret why a prediction is made; (ii) being capable of predicting both single- and multi-location proteins and (iii) being able to handle proteins of multiple organisms, including Eukaryota, Homo sapiens, Viridiplantae, Gram-positive Bacteria, Gram-negative Bacteria and Virus . Experimental results demonstrate that FUEL-mLoc outperforms state-of-the-art subcellular-localization predictors., Availability and Implementation: http://bioinfo.eie.polyu.edu.hk/FUEL-mLoc/., Contacts: shibiao.wan@princeton.edu or enmwmak@polyu.edu.hk., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Predicting the localization of chloroplast proteins at the sub-subcellular level is an essential yet challenging step to elucidate their functions. Most of the existing subchloroplast localization predictors are limited to predicting single-location proteins and ignore the multi-location chloroplast proteins. While recent studies have led to some multi-location chloroplast predictors, they usually perform poorly. This paper proposes an ensemble transductive learning method to tackle this multi-label classification problem. Specifically, given a protein in a dataset, its composition-based sequence information and profile-based evolutionary information are respectively extracted. These two kinds of features are respectively compared with those of other proteins in the dataset. The comparisons lead to two similarity vectors which are weighted-combined to constitute an ensemble feature vector. A transductive learning model based on the least squares and nearest neighbor algorithms is proposed to process the ensemble features. We refer to the resulting predictor to as EnTrans-Chlo. Experimental results on a stringent benchmark dataset and a novel dataset demonstrate that EnTrans-Chlo significantly outperforms state-of-the-art predictors and particularly gains more than 4% (absolute) improvement on the overall actual accuracy. For readers' convenience, EnTrans-Chlo is freely available online at http://bioinfo.eie.polyu.edu.hk/EnTransChloServer/.

In the postgenomic era, the number of unreviewed protein sequences is remarkably larger and grows tremendously faster than that of reviewed ones. However, existing methods for protein subchloroplast localization often ignore the information from these unlabeled proteins. This paper proposes a multi-label predictor based on ensemble linear neighborhood propagation (LNP), namely, LNP-Chlo, which leverages hybrid sequence-based feature information from both labeled and unlabeled proteins for predicting localization of both single- and multi-label chloroplast proteins. Experimental results on a stringent benchmark dataset and a novel independent dataset suggest that LNP-Chlo performs at least 6% (absolute) better than state-of-the-art predictors. This paper also demonstrates that ensemble LNP significantly outperforms LNP based on individual features. For readers' convenience, the online Web server LNP-Chlo is freely available at http://bioinfo.eie.polyu.edu.hk/LNPChloServer/ .

Membrane proteins play important roles in various biological processes within organisms. Predicting the functional types of membrane proteins is indispensable to the characterization of membrane proteins. Recent studies have extended to predicting single- and multi-type membrane proteins. However, existing predictors perform poorly and more importantly, they are often lack of interpretability. To address these problems, this paper proposes an efficient predictor, namely Mem-mEN, which can produce sparse and interpretable solutions for predicting membrane proteins with single- and multi-label functional types. Given a query membrane protein, its associated gene ontology (GO) information is retrieved by searching a compact GO-term database with its homologous accession number, which is subsequently classified by a multi-label elastic net (EN) classifier. Experimental results show that Mem-mEN significantly outperforms existing state-of-the-art membrane-protein predictors. Moreover, by using Mem-mEN, 338 out of more than 7,900 GO terms are found to play more essential roles in determining the functional types. Based on these 338 essential GO terms, Mem-mEN can not only predict the functional type of a membrane protein, but also explain why it belongs to that type. For the reader's convenience, the Mem-mEN server is available online at http://bioinfo.eie.polyu.edu.hk/MemmENServer/.

Identifying membrane proteins and their multi-functional types is an indispensable yet challenging topic in proteomics and bioinformatics. However, most of the existing membrane-protein predictors have the following problems: (1) they do not predict whether a given protein is a membrane protein or not; (2) they are limited to predicting membrane proteins with single-label functional types but ignore those with multi-functional types; and (3) there is still much room for improvement for their performance. To address these problems, this paper proposes a two-layer multi-label predictor, namely Mem-ADSVM, which can identify membrane proteins (Layer I) and their multi-functional types (Layer II). Specifically, given a query protein, its associated gene ontology (GO) information is retrieved by searching a compact GO-term database with its homologous accession number. Subsequently, the GO information is classified by a binary support vector machine (SVM) classifier to determine whether it is a membrane protein or not. If yes, it will be further classified by a multi-label multi-class SVM classifier equipped with an adaptive-decision (AD) scheme to determine to which functional type(s) it belongs. Experimental results show that Mem-ADSVM significantly outperforms state-of-the-art predictors in terms of identifying both membrane proteins and their multi-functional types. This paper also suggests that the two-layer prediction architecture is better than the one-layer for prediction performance. For reader׳s convenience, the Mem-ADSVM server is available online at http://bioinfo.eie.polyu.edu.hk/MemADSVMServer/., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Identifying membrane proteins and their multi-functional types is an indispensable yet challenging topic in proteomics and bioinformatics. In this article, we provide data that are used for training and testing Mem-ADSVM (Wan et al., 2016. "Mem-ADSVM: a two-layer multi-label predictor for identifying multi-functional types of membrane proteins" [1]), a two-layer multi-label predictor for predicting multi-functional types of membrane proteins.

Thioamides antithyroid-drugs (ATDs) are important in hyperthyroid disease management. Identification of the susceptibility locus of ATD-induced agranulocytosis is important for clinical management. We performed a genome-wide association study (GWAS) involving 20 patients with ATD-induced agranulocytosis and 775 healthy controls. The top finding was further replicated. A single-nucleotide polymorphism (SNP), rs185386680, showed the strongest association with ATD-induced agranulocytosis in GWAS (odds ratio (OR) = 36.4; 95% confidence interval (CI) = 12.8-103.7; P = 1.3 × 10(-24)) and replication (OR = 37; 95% CI = 3.7-367.4; P = 9.6 × 10(-7)). HLA-B*38:02:01 was in complete linkage disequilibrium with rs185386680. High-resolution HLA typing confirmed that HLA-B*38:02:01 was associated with carbimazole (CMZ)/methimazole (MMI)-induced agranulocytosis (OR = 265.5; 95% CI = 27.9-2528.0; P = 2.5 × 10(-14)), but not associated with propylthiouracil (PTU). The positive and negative predictive values of HLA-B*38:02:01 in predicting CMZ/MMI-induced agranulocytosis were 0.07 and 0.999. Approximately 211 cases need to be screened to prevent one case. Screening for the risk allele will be useful in preventing agranulocytosis in populations in which the frequency of the risk allele is high., (© 2015, The American Society for Clinical Pharmacology and Therapeutics.)

Background: Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved., Results: This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed., Conclusions: Experimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers' convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.

Knowing the subcellular compartments of human proteins is essential to shed light on the mechanisms of a broad range of human diseases. In computational methods for protein subcellular localization, knowledge-based methods (especially gene ontology (GO) based methods) are known to perform better than sequence-based methods. However, existing GO-based predictors often lack interpretability and suffer from overfitting due to the high dimensionality of feature vectors. To address these problems, this paper proposes an interpretable multi-label predictor, namely mLASSO-Hum, which can yield sparse and interpretable solutions for large-scale prediction of human protein subcellular localization. By using the one-vs-rest LASSO-based classifiers, 87 out of more than 8000 GO terms are found to play more significant roles in determining the subcellular localization. Based on these 87 essential GO terms, we can decide not only where a protein resides within a cell, but also why it is located there. To further exploit information from the remaining GO terms, a method based on the GO hierarchical information derived from the depth distance of GO terms is proposed. Experimental results show that mLASSO-Hum performs significantly better than state-of-the-art predictors. We also found that in addition to the GO terms from the cellular component category, GO terms from the other two categories also play important roles in the final classification decisions. For readers' convenience, the mLASSO-Hum server is available online at http://bioinfo.eie.polyu.edu.hk/mLASSOHumServer/., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Proteins located in appropriate cellular compartments are of paramount importance to exert their biological functions. Prediction of protein subcellular localization by computational methods is required in the post-genomic era. Recent studies have been focusing on predicting not only single-location proteins but also multi-location proteins. However, most of the existing predictors are far from effective for tackling the challenges of multi-label proteins. This article proposes an efficient multi-label predictor, namely mPLR-Loc, based on penalized logistic regression and adaptive decisions for predicting both single- and multi-location proteins. Specifically, for each query protein, mPLR-Loc exploits the information from the Gene Ontology (GO) database by using its accession number (AC) or the ACs of its homologs obtained via BLAST. The frequencies of GO occurrences are used to construct feature vectors, which are then classified by an adaptive decision-based multi-label penalized logistic regression classifier. Experimental results based on two recent stringent benchmark datasets (virus and plant) show that mPLR-Loc remarkably outperforms existing state-of-the-art multi-label predictors. In addition to being able to rapidly and accurately predict subcellular localization of single- and multi-label proteins, mPLR-Loc can also provide probabilistic confidence scores for the prediction decisions. For readers' convenience, the mPLR-Loc server is available online (http://bioinfo.eie.polyu.edu.hk/mPLRLocServer)., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Locating proteins within cellular contexts is of paramount significance in elucidating their biological functions. Computational methods based on knowledge databases (such as gene ontology annotation (GOA) database) are known to be more efficient than sequence-based methods. However, the predominant scenarios of knowledge-based methods are that (1) knowledge databases typically have enormous size and are growing exponentially, (2) knowledge databases contain redundant information, and (3) the number of extracted features from knowledge databases is much larger than the number of data samples with ground-truth labels. These properties render the extracted features liable to redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address these problems, this paper proposes an efficient multi-label predictor, namely R3P-Loc, which uses two compact databases for feature extraction and applies random projection (RP) to reduce the feature dimensions of an ensemble ridge regression (RR) classifier. Two new compact databases are created from Swiss-Prot and GOA databases. These databases possess almost the same amount of information as their full-size counterparts but with much smaller size. Experimental results on two recent datasets (eukaryote and plant) suggest that R3P-Loc can reduce the dimensions by seven-folds and significantly outperforms state-of-the-art predictors. This paper also demonstrates that the compact databases reduce the memory consumption by 39 times without causing degradation in prediction accuracy. For readers׳ convenience, the R3P-Loc server is available online at url:http://bioinfo.eie.polyu.edu.hk/R3PLocServer/., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Proteolysis within the lipid bilayer is poorly understood, in particular the regulation of substrate cleavage. Rhomboids are a family of ubiquitous intramembrane serine proteases that harbour a buried active site and are known to cleave transmembrane substrates with broad specificity. In vitro gel and Förster resonance energy transfer (FRET)-based kinetic assays were developed to analyse cleavage of the transmembrane substrate psTatA (TatA from Providencia stuartii). We demonstrate significant differences in catalytic efficiency (kcat/K0.5) values for transmembrane substrate psTatA (TatA from Providencia stuartii) cleavage for three rhomboids: AarA from P. stuartii, ecGlpG from Escherichia coli and hiGlpG from Haemophilus influenzae demonstrating that rhomboids specifically recognize this substrate. Furthermore, binding of psTatA occurs with positive cooperativity. Competitive binding studies reveal an exosite-mediated mode of substrate binding, indicating allostery plays a role in substrate catalysis. We reveal that exosite formation is dependent on the oligomeric state of rhomboids, and when dimers are dissociated, allosteric substrate activation is not observed. We present a novel mechanism for specific substrate cleavage involving several dynamic processes including positive cooperativity and homotropic allostery for this interesting class of intramembrane proteases., (© 2014 The Authors.)

Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

Classical congenital adrenal hyperplasia due to 21-hydroxylase deficiency is a well-known disorder of sexual development (previously known as ambiguous genitalia) in genotypic female neonates. We report on a 66-year-old Chinese, brought up as male, with a simple virilising form of congenital adrenal hyperplasia associated with Turner's syndrome (karyotype 45,X/47,XXX/46,XX). His late presentation was recognised due to his exceptionally short stature and persistent sexual ambiguity. His condition was only brought to medical attention as he developed a huge abdominal mass, which later turned out to be a benign ovarian mucinous cyst. It is therefore important to look out for co-existing congenital adrenal hyperplasia in patients with Turner's syndrome and virilisation, after the presence of Y chromosome material has been excluded.

Prediction of protein subcellular localization is an important yet challenging problem. Recently, several computational methods based on Gene Ontology (GO) have been proposed to tackle this problem and have demonstrated superiority over methods based on other features. Existing GO-based methods, however, do not fully use the GO information. This paper proposes an efficient GO method called GOASVM that exploits the information from the GO term frequencies and distant homologs to represent a protein in the general form of Chou's pseudo-amino acid composition. The method first selects a subset of relevant GO terms to form a GO vector space. Then for each protein, the method uses the accession number (AC) of the protein or the ACs of its homologs to find the number of occurrences of the selected GO terms in the Gene Ontology annotation (GOA) database as a means to construct GO vectors for support vector machines (SVMs) classification. With the advantages of GO term frequencies and a new strategy to incorporate useful homologous information, GOASVM can achieve a prediction accuracy of 72.2% on a new independent test set comprising novel proteins that were added to Swiss-Prot six years later than the creation date of the training set. GOASVM and Supplementary materials are available online at http://bioinfo.eie.polyu.edu.hk/mGoaSvmServer/GOASVM.html., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Rhomboid peptidases (proteases) play key roles in signaling events at the membrane bilayer. Understanding the regulation of rhomboid function is crucial for insight into its mechanism of action. Here we examine the oligomeric state of three different rhomboid proteases. We subjected Haemophilus influenzae, (hiGlpG), Escherichia coli GlpG (ecGlpG) and Bacillus subtilis (YqgP) to sedimentation equilibrium analysis in detergent-solubilized dodecylmaltoside (DDM) solution. For hiGlpG and ecGlpG, rhomboids consisting of the core 6 transmembrane domains without and with soluble domains respectively, and YqgP, predicted to have 7 transmembrane domains with larger soluble domains at the termini, the predominant species was dimeric with low amounts of monomer and tetramers observed. To examine the effect of the membrane domain alone on oligomeric state of rhomboid, hiGlpG, the simplest form from the rhomboid class of intramembrane proteases representing the canonical rhomboid core of six transmembrane domains, was studied further. Using gel filtration and crosslinking we demonstrate that hiGlpG is dimeric and functional in DDM detergent solution. More importantly co-immunoprecipitation studies demonstrate that the dimer is present in the lipid bilayer suggesting a physiological dimer. Overall these results indicate that rhomboids form oligomers which are facilitated by the membrane domain. For hiGlpG we have shown that these oligomers exist in the lipid bilayer. This is the first detailed oligomeric state characterization of the rhomboid family of peptidases., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Background: Although many computational methods have been developed to predict protein subcellular localization, most of the methods are limited to the prediction of single-location proteins. Multi-location proteins are either not considered or assumed not existing. However, proteins with multiple locations are particularly interesting because they may have special biological functions, which are essential to both basic research and drug discovery., Results: This paper proposes an efficient multi-label predictor, namely mGOASVM, for predicting the subcellular localization of multi-location proteins. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the original accession number and the homologous accession numbers of the protein are used as keys to search against the Gene Ontology (GO) annotation database to obtain a set of GO terms. Given a set of training proteins, a set of T relevant GO terms is obtained by finding all of the GO terms in the GO annotation database that are relevant to the training proteins. These relevant GO terms then form the basis of a T-dimensional Euclidean space on which the GO vectors lie. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO vectors. The mGOASVM predictor has the following advantages: (1) it uses the frequency of occurrences of GO terms for feature representation; (2) it selects the relevant GO subspace which can substantially speed up the prediction without compromising performance; and (3) it adopts an efficient multi-label SVM classifier which significantly outperforms other predictors. Briefly, on two recently published virus and plant datasets, mGOASVM achieves an actual accuracy of 88.9% and 87.4%, respectively, which are significantly higher than those achieved by the state-of-the-art predictors such as iLoc-Virus (74.8%) and iLoc-Plant (68.1%)., Conclusions: mGOASVM can efficiently predict the subcellular locations of multi-label proteins. The mGOASVM predictor is available online at http://bioinfo.eie.polyu.edu.hk/mGoaSvmServer/mGOASVM.html.

Lactoferrin (Lf) is a bi-lobed, iron-binding protein found on mucosal surfaces and at sites of inflammation. Gram-negative pathogens from the Neisseriaceae and Moraxellaceae families are capable of using Lf as a source of iron for growth through a process mediated by a bacterial surface receptor that directly binds host Lf. This receptor consists of an integral outer membrane protein, lactoferrin binding protein A (LbpA), and a surface lipoprotein, lactoferrin binding protein B (LbpB). The N-lobe of the homologous transferrin binding protein B, TbpB, has been shown to facilitate transferrin binding in the process of iron acquisition. Currently there is little known about the role of LbpB in iron acquisition or how Lf interacts with the bacterial receptor proteins. No structural information on any LbpB or domain is available. In this study, we express and purify from Escherichia coli the full-length LbpB and the N-lobe of LbpB from the bovine pathogen Moraxella bovis for crystallization trials. We demonstrate that M. bovis LbpB binds to bovine but not human Lf. We also report the crystal structure of the N-terminal lobe of LbpB from M. bovis and compare it with the published structures of TbpB to speculate on the process of Lf mediated iron acquisition.

Background: The functions of proteins are closely related to their subcellular locations. In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means., Results: This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment. Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals. Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles. Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM) classifier. To further reduce the training and recognition time of the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA)., Conclusions: Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences. Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy. It was found that PDA enjoys a short training time as compared to the conventional SVM. We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments.

Objectives: To estimate the cumulative incidence for the development of diabetes mellitus in Chinese women with persistently impaired glucose tolerance after gestational diabetes, and evaluate putative risk factors., Design: Historical cohort study., Setting: A regional hospital in Hong Kong., Patients: Women with postpartum impaired glucose tolerance (as confirmed by a 75-gram oral glucose tolerance test 6 weeks after delivery) seen between January 2000 and December 2006., Results: After a mean follow-up period of 52 (standard deviation, 22; range, 12-106) months, 47 (20%) of the 238 women converted to diabetes mellitus. Concomitant postpartum impaired fasting plasma glucose levels increased the risk of future diabetes mellitus by 3.5-fold (95% confidence interval, 1.7-7.0; P=0.001) when compared to those with postpartum impaired glucose tolerance only. Based on multivariate analysis, only antepartum and postpartum fasting plasma glucose levels predicted future development of diabetes mellitus. At 1 year after delivery in 95/159 (60%) of the women, glucose tolerance regressed to normal, while in only 9/159 (6%) it progressed to diabetes mellitus. At this stage, 29% of those with impaired glucose regulation (impaired glucose tolerance, impaired fasting glucose or both) compared to 2% of those whose glucose tolerance reverted to normal developed diabetes mellitus upon subsequent follow-up (P<0.001). In all, 24/159 (15%) fulfilled the definition of metabolic syndrome and its presence was associated with 4.7-fold increased risk of future diabetes mellitus (95% confidence interval, 1.7-13.4; P=0.004)., Conclusions: Women with persistent postpartum impaired glucose tolerance after gestational diabetes have a high risk of developing diabetes mellitus. However, a significant proportion of these women regress to normal glucose tolerance 1 year after delivery, and their risk of progression to diabetes mellitus is lower than those with persistent impaired glucose regulation. Therefore, women with a history of gestational diabetes, particularly those with persistent glucose intolerance 6 weeks and 1 year after delivery, should have regular surveillance for the development of diabetes mellitus.

Rhomboids are a remarkable class of serine proteases that are embedded in lipid membranes. These membrane-bound enzymes play key roles in cellular signaling events, and disruptions in these events can result in numerous disease pathologies, including hereditary blindness, type 2 diabetes, Parkinson's disease, and epithelial cancers. Recent crystal structures of rhomboids from Escherichia coli have focused on how membrane-bound substrates gain access to a buried active site. In E. coli, it has been shown that movements of loop 5, with smaller movements in helix 5 and loop 4, act as substrate gate, facilitating inhibitor access to rhomboid catalytic residues. Herein we present a new structure of the Haemophilus influenzae rhomboid hiGlpG, which reveals disorder in loop 5, helix 5, and loop 4, indicating that, together, they represent mobile elements of the substrate gate. Substrate cleavage assays by hiGlpG with amino acid substitutions in these mobile regions demonstrate that the flexibilities of both loop 5 and helix 5 are important for access of the substrates to the catalytic residues. Mutagenesis indicates that less mobility by loop 4 is required for substrate cleavage. A reexamination of the reaction mechanism of rhomboid substrates, whereby cleavage of the scissile bond occurs on the si-face of the peptide bond, is discussed., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

The decision-making process of many binary classification systems is based on the likelihood ratio (LR) scores of test patterns. This paper shows that LR scores can be expressed in terms of the similarity between the supervectors (SVs) formed by stacking the mean vectors of Gaussian mixture models corresponding to the test patterns, the target model, and the background model. By interpreting the support vector machine (SVM) kernels as a specific similarity (or discriminant) function between SVs, this paper shows that LR scoring is a special case of SVM scoring and that most sequence kernels can be obtained by assuming a specific form for the similarity function of SVs. This paper further shows that this assumption can be relaxed to derive a new general kernel. The kernel function is general in that it is a linear combination of any kernels belonging to the reproducing kernel Hilbert space. The combination weights are obtained by optimizing the ability of a discriminant function to separate the positive and negative classes using either regression analysis or SVM training. The idea was applied to both high-and low-level speaker verification. In both cases, results show that the proposed kernels achieve better performance than several state-of-the-art sequence kernels. Further performance enhancement was also observed when the high-level scores were combined with acoustic scores.

The subcellular locations of proteins are important functional annotations. An effective and reliable subcellular localization method is necessary for proteomics research. This paper introduces a new method---PairProSVM---to automatically predict the subcellular locations of proteins. The profiles of all protein sequences in the training set are constructed by PSI-BLAST and the pairwise profile-alignment scores are used to form feature vectors for training a support vector machine (SVM) classifier. It was found that PairProSVM outperforms the methods that are based on sequence alignment and amino-acid compositions even if most of the homologous sequences have been removed. This paper also demonstrates that the performance of PairProSVM is sensitive (and somewhat proportional) to the degree of its kernel matrix meeting the Mercer's condition. PairProSVM was evaluated on Reinhardt and Hubbard's, Huang and Li's, and Gardy et al.'s protein datasets. The overall accuracies on these three datasets reach 99.3\\%, 76.5\\%, and 91.9\\%, respectively, which are higher than or comparable to those obtained by sequence alignment and by the methods compared in this paper.

Silver nanodendrites are synthesized by a simple surfactant-free method using a suspension of zinc microparticles as a heterogeneous reducing agent. Structural characterizations suggest the preferential growth along 100 and 111 directions by oriented attachment of silver nanocrystals in the diffusion limit, leading to the formation of silver nanodendrites 20-30 nm in stem and branch diameter and 5-50 microm in length. Surface-enhanced Raman scattering studies show that the silver nanodentrites give an intensive and enhanced Raman scattering when pyridine was used as a probing molecule. We have also demonstrated that the silver nanodendrites increase the sensitivity of an electrochemical glucose biosensor by as much as 1-2 orders of magnitude.

Machine learning techniques offer a viable approach to cluster discovery from microarray data, which involves identifying and classifying biologically relevant groups in genes and conditions. It has been recognized that genes (whether or not they belong to the same gene group) may be co-expressed via a variety of pathways. Therefore, they can be adequately described by a diversity of coherence models. In fact, it is known that a gene may participate in multiple pathways that may or may not be co-active under all conditions. It is therefore biologically meaningful to simultaneously divide genes into functional groups and conditions into co-active categories--leading to the so-called biclustering analysis. For this, we have proposed a comprehensive set of coherence models to cope with various plausible regulation processes. Furthermore, a multivariate biclustering analysis based on fusion of different coherence models appears to be promising because the expression level of genes from the same group may follow more than one coherence models. The simulation studies further confirm that the proposed framework enjoys the advantage of high prediction performance.

A good number of biclustering algorithms have been proposed for grouping gene expression data. Many of them have adopted matrix norms to define the similarity score of a bicluster. We shall show that almost all matrix metrics can be converted into vector norms while preserving the rank equivalence. Vector norms provide a much more efficient vehicle for biclustering analysis and computation. The advantages are two folds: ease of analysis and saving of computation. Most existing biclustering algorithms have also implicitly assumed the use of univariate (i.e., single metric) evaluation for identifying biclusters. Such an approach however overlooks the fundamental principle that genes (even though they may belong to the same gene group) (1) may be subdivided into different substructures; and (2) they may be co-expressed via a diversity of coherence models (a gene may participate in multiple pathways that may or may not be co-active under all conditions). The former leads to the adoption of a multi-substurcture analysis, while the latter to the multivariate analysis. This paper will show that the proposed multivariate and multi-subscluster analysis is very effective in identifying and classifying biologically relevant groups in genes and conditions. For example, it has successfully yielded highly discriminant and accurate classification based on known ribosomal gene groups.

A "universal non-metazoan" polymerase chain reaction (UNonMet-PCR) that selectively amplifies a segment of nonmetazoan Small Subunit (SSU) rDNA gene was validated. The primers used were: 18S-EUK581-F (5'-GTGCCAGCAGCCGCG-3') and 18S-EUK1134-R (5'-TTTAAGTTTCAGCCTTGCG-3') with specificity provided by the 19-base reverse primer. Its target site is highly conserved across the Archaea, Bacteria, and eukaryotes (including fungi), but not most Metazoa (except Porifera, Ctenophora, and Myxozoa) which have mismatches at bases 14 and 19 resulting in poor or failed amplification. During validation, UNonMet-PCR amplified SSU rDNA gene fragments from all assayed protists (n = 16 from 7 higher taxa, including two species of marine phytoplankton) and Fungi (n = 3) but amplified very poorly or not at all most assayed Metazoa (n = 13 from 8 higher taxa). When a nonmetazoan parasite was present in a metazoan host, the parasite DNA was preferentially amplified. For example, DNA from the parasite Trypanosoma danilewskyi was preferentially amplified in mixtures containing up to 1,000 x more goldfish Carassius auratus (host) DNA. Also, the weak amplification of uninfected host (Chionoecetes tanneri) SSU rDNA did not occur in the presence of a natural infection with a parasite (Hematodinium sp.). Only Hematodinium sp. SSU rDNA was amplified in samples from infected C. tanneri. This UNonMet-PCR is a powerful tool for amplifying SSU rDNA from non-metazoan pathogens or symbionts that have not been isolated from metazoan hosts.

This paper compares kernel-based probabilistic neural networks for speaker verification based on 138 speakers of the YOHO corpus. Experimental evaluations using probabilistic decision-based neural networks (PDBNNs), Gaussian mixture models (GMMs) and elliptical basis function networks (EBFNs) as speaker models were conducted. The original training algorithm of PDBNNs was also modified to make PDBNNs appropriate for speaker verification. Results show that the equal error rate obtained by PDBNNs and GMMs is less than that of EBFNs (0.33% vs. 0.48%), suggesting that GMM- and PDBNN-based speaker models outperform the EBFN ones. This work also finds that the globally supervised learning of PDBNNs is able to find decision thresholds that not only maintain the false acceptance rates to a low level but also reduce their variation, whereas the ad-hoc threshold-determination approach used by the EBFNs and GMMs causes a large variation in the error rates. This property makes the performance of PDBNN-based systems more predictable.

Background: There is a recent trend to switching medications from prescription to nonprescription status. Often, such switches are accompanied by dramatic changes in utilization due to increased availability or decreased insurance coverage. The histamine(2)-receptor antagonists (H(2)RAs) underwent such status change in the UK in 1994, the US in 1995, and Canada in 1996., Objective: To examine the impact of the status change for H(2)RAs on the market for gastrointestinal (GI) agents in the US, UK, and Canada., Methods: IMS market sales data from 1992 to 1997 were procured. All costs were converted to 1997 US dollars using the consumer price index. Per capita sales figures were determined using population data from the US Census Bureau's International Database., Results: Overall spending on GI remedies increased in all 3 markets between 1992 and 1997; however, the contribution of prescription sales and number of prescriptions varied across the 3 countries. An increased market share for nonprescription H(2)RAs occurred in the US, correlating with a decline in prescription numbers for GI remedies. The opposing trend occurred in the UK, where market share of nonprescription H(2)RAs was minimal and use of prescription H(2)RAs increased. Prescription and nonprescription H(2)RA sales could not be differentiated for Canada., Conclusions: The impact of the H(2)RA status change varied across countries. Differences in utilization may be attributed to many factors such as differing healthcare systems, patient convenience, and physician prescribing practices. Further research is required to identify the reasons for differences in utilization and to quantify the potential clinical impact.

This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the expectation-maximization (EM) algorithm to estimate the basis function parameters. The resulting networks, referred to as elliptical basis function (EBF) networks, are evaluated through a series of text-independent speaker verification experiments involving 258 speakers from a phonetically balanced, continuous speech corpus (TIMIT).We propose a verification procedure using RBF and EBF networks as speaker models and show that the networks are readily applicable to verifying speakers using LP-derived cepstral coefficients as features. Experimental results show that small EBF networks with basis function parameters estimated by the EM algorithm outperform the large RBF networks trained in the conventional approach. The results also show that the equal error rate achieved by the EBF networks is about two-third of that achieved by the vetor quantization (VQ)-based speaker models.

Purpose: Three different polymeric delivery systems, composed of either poly(ethylene-co-vinyl acetate) (EVAc) or poly(lactide-co-glycolide) (PLGA), were used to administer recombinant human nerve growth factor (rhNGF) intracranially in rats., Methods: The delivery systems were characterized with respect to release kinetics, both in the brain and in well-stirred buffer solutions., Results: During incubation in buffered saline, the delivery systems released rhNGF in distinct patterns: sustained (EVAc), immediate (PLGA1) and delayed (PLGA2). One 10-mg delivery system was implanted in each rat and an ELISA technique was used to determine the amount of rhNGF in 1-mm coronal brain slices produced immediately after removal of the delivery system. High levels of rhNGF (as high as 60,000 ng in a brain slice of approximately 50 microliters) were recovered from the brain tissue at 1, 2, and 4 weeks after implantation. With all three delivery systems, the amount of rhNGF in each brain slice decreased exponentially with distance from the implant site: the distance over which concentration decreased by 10-fold was 2-3 mm for all delivery systems. When rhNGF release was moderate (10 to 200 ng rhNGF/day), the total amount of rhNGF in the brain increased linearly with release rate, suggesting an overall rate of rhNGF elimination of 0.4 hr-1 or a half-life of 1.7 hr. With higher release rates (500 to 50,000 ng rhNGF/day), total amounts of rhNGF in the brain were considerably higher than anticipated based on this rate of elimination., Conclusions: Polymeric controlled release can provide high, localized doses of rhNGF in the brain. All of the experimental data were consistent with penetration of rhNGF through the brain tissue with a diffusion coefficient approximately 8 x 10(-7) cm2/s, which is approximately 50% of the diffusion coefficient in water.

This paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GA's) for training recurrent neural networks (RNN's). Each weight of an RNN is encoded as a floating point number, and a concatenation of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point representing a chromosome. Two approaches, Lamarckian and Baldwinian mechanisms, for combining cellular GA's and learning have been compared. Different hill-climbing algorithms are incorporated into the cellular GA's as learning methods. These include the real-time recurrent learning (RTRL) and its simplified versions, and the delta rule. The RTRL algorithm has been successively simplified by freezing some of the weights to form simplified versions. The delta rule, which is the simplest form of learning, has been implemented by considering the RNN's as feedforward networks during learning. The hybrid algorithms are used to train the RNN's to solve a long-term dependency problem. The results show that Baldwinian learning is inefficient in assisting the cellular GA. It is conjectured that the more difficult it is for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning, the poorer is the convergence of Baldwinian learning. Most of the combinations using the Lamarckian mechanism show an improvement in reducing the number of generations required for an optimum network; however, only a few can reduce the actual time taken. Embedding the delta rule in the cellular GA's has been found to be the fastest method. It is also concluded that learning should not be too extensive if the hybrid algorithm is to be benefit from learning.