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Urinary tract infections are primarily caused by uropathogenic Escherichia coli (UPEC). UPEC infects bladder epithelial cells (BECs) via fusiform vesicles and escapes into the cytosol by disrupting fusiform vesicle membrane using outer membrane phospholipase PldA, and establishes biofilm-like intracellular bacterial communities (IBCs) for protection from host immune clearance. Cytosolic UPEC is captured by autophagy to form autophagosomes, then transported to lysosomes, triggering the spontaneous exocytosis of lysosomes. The mechanism by which UPEC evades autophagy to recognize and form IBCs remains unclear. Here, we demonstrate that by inhibiting autophagic flux, UPEC PldA reduces the lysosome exocytosis of BECs. By reducing intracellular phosphatidylinositol 3-phosphate levels, UPEC PldA increases the accumulation of NDP52 granules and decreases the targeting of NDP52 to autophagy, hence stalling preautophagosome structures. Thus, our results uncover a critical role for PldA to inhibit autophagic flux, favoring UPEC escapes from lysosome exocytosis, thereby contributing to acute urinary tract infection. [ABSTRACT FROM AUTHOR]

ABSTRACTEnteric bacteria need to adapt to endure the antibacterial activities of bile salts in the gut. Phospholipase A (PldA) is a key enzyme in the maintenance of bacterial membrane homeostasis. Bacteria respond to stress by modulating their membrane composition. Campylobacter jejuni is the most common cause of human worldwide. However, the mechanism by which C. jejuni adapts and survives in the gut environment is not fully understood. In this study, we investigated the roles of PldA, bile salt sodium deoxycholate (DOC), and oxygen availability in C. jejuni biology, mimicking an in vivo situation. Growth curves were used to determine the adaptation of C. jejuni to bile salts. RNA-seq and functional assays were employed to investigate the PldA-dependent and DOC-induced changes in gene expression that influence bacterial physiology. Survival studies were performed to address oxidative stress defense in C. jejuni. Here, we discovered that PldA of C. jejuni is required for optimal growth in the presence of bile salt DOC. Under high oxygen conditions, DOC is toxic to C. jejuni, but under low oxygen conditions, as is present in the lumen of the gut, C. jejuni benefits from DOC. C. jejuni PldA seems to enable the use of iron needed for optimal growth in the presence of DOC but makes the bacterium more vulnerable to oxidative stress. In conclusion, DOC stimulates C. jejuni growth under low oxygen conditions and alters colony morphology in a PldA-dependent manner. C. jejuni benefits from DOC by upregulating iron metabolism in a PldA-dependent manner.

Objective: Microarray and RNA sequencing (RNASeq) technologies are frequently employed in genetic data analysis for detecting disease-associated genes, identifying cancer subtypes, and enabling molecular diagnosis. While numerous methods have been proposed for classification problems using microarray data, there is a paucity of developed methods for classifying RNA-Seq data. This study aims to compare the performance of novel methods developed for RNA-Seq data on 3 distinct real-life datasets. Material and Methods: Cervical cancer, Alzheimer's disease, and kidney cancer RNA-Seq data were utilized in this study. The data were divided into training and test sets in a %70 and %30 ratio, respectively. Various preprocessing steps, such as normalization, power transformation, and variance filtering, were applied to the data. The Poisson Linear Discriminant Analysis (PLDA) and Negative Binomial Linear Discriminant Analysis (NBLDA) models were used for classification purposes, and the predictive performances of these models were compared. Results: Among the three datasets, the Alzheimer's data exhibited the lowest level of dispersion, while the cervical cancer data had the highest overdispersion. The NBLDA model demonstrated superior classification performance compared to the PLDA model. In cases of mild-to-moderate overdispersion, the predictive performance of the PLDA model improved when power transformation was applied, resulting in performance similar to that of the NBLDA model. Conclusion: PLDA and NBLDA models are two novel and promising techniques used in classifying RNA-Seq data. The performance of these models is influenced by the degree of overdispersion. In cases of high overdispersion, it is recommended to utilize the NBLDA model. [ABSTRACT FROM AUTHOR]

Recently, the speaker recognition techniques have been widely attractive for their extensive use in many fields, such as speech communications, domestic services, security and access control and smart terminals. Today's interactive devices like smart-phone assistants and smart speakers need to deal with short duration speech segments. However, existing speaker recognition applications perform poorly when short utterances are available and require relatively long speech to perform well. Aiming at solving this problem, we introduce in this paper, a novel method to enhance the speaker recognition capability with short utterance speaker recognition applications. For this purpose, we considered new deep neural network architectures based on convolutional neural network (CNN) and recurrent neural network (RNN). The proposed method is evaluated with the standard i-vector based on Probabilistic Linear discriminant analysis (PLDA) approach. The experimental results show that our model could outperform the i-vector -PLDA baseline system and enhance the speaker recognition capability when significant and short utterance duration are used. [ABSTRACT FROM AUTHOR]

Automatic speaker recognition has garnered significant attention in research, displaying impressive performance in matched conditions where training and testing environments are similar. However, the system's efficacy diminishes considerably when confronted with mismatched conditions, such as noise and reverberation. The extraction of features plays a pivotal role in determining the speaker recognition system's overall performance. Gammatone Frequency Cepstral Coefficients (GFCCs) have emerged as a commonly employed method for feature extraction in this domain. GFCCs exhibit robustness in handling environmental variations, encompassing diverse speaking styles and languages. Nevertheless, they prove sensitive to background conditions, including noise and reverberation, leading to a significant decline in system performance. A novel "Entrocy" feature has been proposed in response to this challenge. Entrocy is the Fourier Transform of Entrocy and aims to estimate the variation of information (or entropy) within an audio segment over time. A composite feature vector is formed by combining the Entrocy feature with GFCCs. The performance of this proposed approach was meticulously assessed using the i-vector PLDA baseline speaker recognition systems. Notably, the Entrocy feature consistently outperforms the well-established GFCC features, exhibiting robustness in these challenging conditions. Experiments conducted on speaker verification in controlled environments reveal that the speaker verification system can deliver high performance if the reverberation time does not exceed 1.0 s. Moreover, the speech samples need to be longer than 5 s are. These results highlight the effectiveness of the proposed method in reducing the equal error rate and improving the detection error trade-off, ultimately enhancing the system's overall accuracy. [ABSTRACT FROM AUTHOR]

Tetralogy of Fallot (TOF) is a cyanotic congenital condition contributed by genetic, epigenetic as well as environmental factors. We applied sparse machine learning algorithms to RNAseq and sRNAseq data to select the prospective biomarker candidates. Furthermore, we applied filtering techniques to identify a subset of biomarker pairs in TOF. Differential expression analysis disclosed 2757 genes and 214 miRNAs, which are dysregulated. Weighted gene co-expression network analysis on the differentially expressed genes extracted five significant modules that are enriched in GO terms, extracellular matrix, signaling and calcium ion binding. Also, voomNSC selected two genes and five miRNAs and transformed PLDA-predicted 72 genes and 38 miRNAs as prognostic biomarkers. Out of the selected biomarkers, miRNA target analysis revealed 14 miRNA–gene interactions. Also, 10 out of 14 pairs were oppositely expressed and four out of 10 oppositely expressed biomarker pairs shared common pathways of focal adhesion and P13K–Akt signaling. In conclusion, our study demonstrated the concept of biomarker pairs, which may be considered for clinical validation due to the high literature as well as experimental support. [ABSTRACT FROM AUTHOR]

An array of features and methods are being developed over the past six decades for Speaker Identification (SI) and Speaker Verification (SV), jointly known as Speaker Recognition(SR). Mel Frequency Cepstral Coefficients (MFCC) is generally used as feature vectors in most of the cases because it gives higher accuracy compared to other features. The presented paper focuses on comparative study of state-of-the-art SR techniques along with their design challenges, robustness issues and performance evaluation methods. Rigorous experiments have been performed using Gaussian Mixture Model (GMM) with variations like Universal Background Model (UBM) and/or Vector Quantization (VQ) and/or VQ based UBM-GMM (VQ-UBM-GMM) with detail discussion. Other popular methods have been included, namely, Linear Discriminate Analysis (LDA), Probabilistic LDA (PLDA), Gaussian PLDA (GPLDA), Multi-condition GPLDA (MGPLDA), Identity Vector (i-vector) for comparative study only. Three popular audio data-sets have been used in the experiments, namely, IITG-MV SR, Hyke-2011 and ELSDSR. Hyke-2011 and ELSDSR contain clean speech while IITG-MV SR contains noisy audio data with variations in channel (device), environment, spoken style. We propose a new data mixing approach for SR to make the system independent of recording device, spoken style and environment. The accuracy we obtained for VQ and GMM based methods for databases, Hyke-2011 and ELSDSR are varies from 99.6 % to 100 % whereas accuracy for IITG-MV SR is upto 98 % . Indeed, in some cases the accuracies degrade drastically due to mismatch between training and testing data as well as singularity problem of GMM. The experimental results serve as a benchmark for VQ/GMM/UBM based methods for the IITG-MV SR database. [ABSTRACT FROM AUTHOR]

کاربرپسند بودن، پیچیدگی محاسباتي کم و هزینه پياده‌سازي پایین از مزایای تصدیق هویت مبتنی بر گفتار است. اما کارایی این شيوه در محیط‌های واقعی به دلیل حضور نویزهای متفاوت صوتی و عوارض كانال به شدت افت می‏کند. روش i-vector PLDA شيوه‌ای موفق در بهبود عملکرد سیستم‌های تصدیق هویت گوینده است. استفاده از ویژگی‌های آماری بردارهای ثبت‌نام گویندگان هدف برای وزن‌دهی به بردارهای مدل و تست، دقت امتیازدهی و در نتیجه کل سیستم تصدیق هویت را در شرایط آزمون با گفتار تمیز ‌افزایش می‌دهد. در این مقاله تاثیر استفاده از بردارهای هویت موزون بر عملکرد سیستم در محیط‌های نویزی مورد ارزیابی قرار گرفته است. آزمایش‌ها با استفاده از دادگان TIMIT، بردارهای ویژگی MFCCو PNCCو روش امتیازدهی PLDA انجام شده است. همچنین برای بهبود عملکرد سیستم در شرایط عدم تطبیق نویزی از آموزش چند-شرطی برای LDA و PLDA استفاده شده است. نتایج آزمون‌ها مبین آن است که استفاده از بردارهای موزون دقت سیستم تصدیق هویت گوینده را براي گفتار‌های نویزی نیز افزایش می‌دهد.

Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.

Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA. [ABSTRACT FROM AUTHOR]

Loss of the Escherichia coli inner membrane protein YhcB results in pleomorphic cell morphology and clear growth defects. Prior work suggested that YhcB was directly involved in cell division or peptidoglycan assembly. We found that loss of YhcB is detrimental in genetic backgrounds in which lipopolysaccharide (LPS) or glycerophospholipid (GPL) synthesis is altered. The growth defect of Δ yhcB could be rescued through inactivation of the Mla pathway, a system responsible for the retrograde transport of GPLs that are mislocalized to the outer leaflet of the outer membrane. Interestingly, this rescue was dependent upon the outer membrane phospholipase PldA that cleaves GPLs at the bacterial surface. Since the freed fatty acids resulting from PldA activity serve as a signal to the cell to increase LPS synthesis, this result suggested that outer membrane lipids are imbalanced in Δ yhcB . Mutations that arose in Δ yhcB populations during two independent suppressor screens were in genes encoding subunits of the acetyl coenzyme A carboxylase complex, which initiates fatty acid biosynthesis (FAB). These mutations fully restored cell morphology and reduced GPL levels, which were increased compared to wild-type bacteria. Growth of Δ yhcB with the FAB-targeting antibiotic cerulenin also increased cellular fitness. Furthermore, genetic manipulation of FAB and lipid biosynthesis showed that decreasing FAB rescued Δ yhcB filamentation, whereas increasing LPS alone could not. Altogether, these results suggest that YhcB may play a pivotal role in regulating FAB and, in turn, impact cell envelope assembly and cell division.IMPORTANCESynthesis of the Gram-negative cell envelope is a dynamic and complex process that entails careful coordination of many biosynthetic pathways. The inner and outer membranes are composed of molecules that are energy intensive to synthesize, and, accordingly, these synthetic pathways are under tight regulation. The robust nature of the Gram-negative outer membrane renders it naturally impermeable to many antibiotics and therefore a target of interest for antimicrobial design. Our data indicate that when the inner membrane protein YhcB is absent in Escherichia coli , the pathway for generating fatty acid substrates needed for all membrane lipid synthesis is dysregulated which leads to increased membrane material. These findings suggest a potentially novel regulatory mechanism for controlling the rate of fatty acid biosynthesis., Competing Interests: The authors declare no conflict of interest.

Expressive speech can be synthesized using acoustic feature modeling by mapping the spectral and fundamental frequency parameters between neutral speech and target emotions based on context. Speaker and text-independent emotion conversion are challenging modeling problems in this paradigm. In this paper, spectral mapping using an i-vector-based framework of fixed dimensions is proposed for the speaker-independent emotion conversion, considering the entire problem in the utterance domain, rather than the existing approaches using frame-level processing. The high dimensionality of i-vectors and reduced utterances for i-vector training necessitate the use of Probabilistic Linear Discriminant Analysis (PLDA) to derive the emotion dependent latent vector. The i-vector setup does not require parallel data or alignment procedures at any stage of training. F0 training is conducted on a multilayer feed-forward neural network using limited aligned seed parallel data. The framework is tested on three different languages (datasets) viz. German (EmoDB), Telugu (IITKGP), and English (SAVEE). The proposed approach delivered superior performance compared to the baseline under both clean and noisy data conditions considered for analysis. Under clean data conditions, the proposed model was found to perform better than the baseline with a Mel Cepstral Distortion as low as 3.8 (fear), an F0-RMSE of 26.31 (happiness), and a Perceptual Evaluation of Speech Quality (PESQ) of 3.64 (anger) across datasets. Subjective testing provided a maximum CMOS of 4.10 (anger), 4.44 (fear), and 3.43 (happiness).

Abstract Several factors contribute to the performance of speaker diarization systems. For instance, the appropriate selection of speech features is one of the key aspects that affect speaker diarization systems. The other factors include the techniques employed to perform both segmentation and clustering. While the static mel frequency cepstral coefficients are the most widely used features in speech-related tasks including speaker diarization, several studies have shown the benefits of augmenting regular speech features with the static ones. In this work, we have proposed and assessed the use of voice-quality features (i.e., jitter, shimmer, and Glottal-to-Noise Excitation ratio) within the framework of speaker diarization. These acoustic attributes are employed together with the state-of-the-art short-term cepstral and long-term prosodic features. Additionally, the use of delta dynamic features is also explored separately both for segmentation and bottom-up clustering sub-tasks. The combination of the different feature sets is carried out at several levels. At the feature level, the long-term speech features are stacked in the same feature vector. At the score level, the short- and long-term speech features are independently modeled and fused at the score likelihood level. Various feature combinations have been applied both for Gaussian mixture modeling and i-vector-based speaker diarization systems. The experiments have been carried out on Augmented Multi-party Interaction meeting corpus. The best result, in terms of diarization error rate, is reported by using i-vector-based cosine-distance clustering together with a signal parameterization consisting of a combination of static cepstral coefficients, delta, voice-quality, and prosodic features. The best result shows about 24% relative diarization error rate improvement compared to the baseline system which is based on Gaussian mixture modeling and short-term static cepstral coefficients.

In response to changes in their environment bacteria need to change both their protein and phospholipid repertoire to match environmental requirements, but the dynamics of bacterial phospholipid composition under different growth conditions is still largely unknown. In the present study, we investigated the phospholipidome of the bacterial pathogen Campylobacter jejuni. Transcription profiling on logarithmic and stationary phase grown cells of the microaerophilic human pathogen C. jejuni using RNA-seq revealed differential expression of putative phospholipid biosynthesis genes. By applying high-performance liquid chromatography tandem–mass spectrometry, we identified 203 phospholipid species representing the first determination of the phospholipidome of this pathogen. We identified nine different phospholipid classes carrying between one and three acyl chains. Phospholipidome analysis on bacteria of different ages (0–5 days) showed rapid changes in the ratio of phospholipids containing ethanolamine, or glycerol as phospholipid head group and in the number of cyclopropane bond containing fatty acids. Oxygen concentration influenced the percentage of lysophospholipids, and cyclo-propane bonds containing acyl chains. We show that large amounts of the phospholipids are lysophospholipids (30–45%), which mutant studies reveal are needed for normal C. jejuni motility at low oxygen conditions. C. jejuni possesses an unusual phospholipidome that is highly dynamic in response to environmental changes. Unlabelled Image • First analysis of the complete phospholipidome of the bacterial pathogen C. jejuni • Nine phospholipid classes were discovered carrying between one and three acyl chains • C.jejuni phospholipidome possess an unusually high percentage of lysophospholipids. • Lysophospholipids are needed to allow C.jejuni to be motile under low O 2 conditions. • The C.jejuni phospholipidome is highly dynamic in response to environmental changes. [ABSTRACT FROM AUTHOR]

Speaker recognition systems achieve good performance under controlled conditions. However, in real-world conditions, the performance degrades drastically. The principal cause being when limited data are presented. The presence of background noise is another main factor of performance distortion. In spite of the major advances in speaker recognition field, the effect of noise and the limitation of the amount of available speech data are still open problems, and no optimal solution has been found yet to cope with them. In this paper, we propose a new system using new enhanced and reduced gammatone coefficients in order to improve robustness with limited speech data duration. We demonstrate the usefulness of these coefficients compared to the well-known features with speakers taken from different databases recorded under different conditions. [ABSTRACT FROM AUTHOR]

This paper builds on a multi-task Deep Neural Network (DNN), which provides an utterance-level feature representation called j-vector, to implement a Text-dependent Speaker Verification (TDSV) system. This technique exploits the speaker idiosyncrasies associated with individual pass-phrases. However, speaker information is known to be characteristic of more specific speech units and, thus, it is likely that important speaker identity traits might get averaged out if it is considered as a coarse entity spread uniformly across the whole pass-phrase. This work attempts to overcome this limitation and devises a technique to leverage the finer speaker traits. It proposes to align the training data for Multi-task DNN using Hierarchical Multi-Layer Acoustic Model (HiLAM). HiLAM is an HMM-based text-dependent model that defines refined segments of a pass-phrase using Gaussian Mixture Model (GMM) states. This helps to exploit the speaker idiosyncrasies associated with finer and more specific segments of speech. Also, as HiLAM is built using the particular text in question, this alignment technique automatically takes care of the exact context of the speech units in the concerned pass-phrase. The proposed technique has been found to improve the performance of the system significantly. Integrating Dynamic Time Warping (DTW) with this technique leads to further improvement in the performance of the system. Experiments have been validated on Part 1 of RSR2015, RedDots, and NITS-TD databases. The best-performing proposed system achieves a relative Equal Error Rate (EER) reduction of up to 50.98% with respect to the baseline j-vector-based system for the overall test condition in case of RSR2015 database. [ABSTRACT FROM AUTHOR]

Automatic speaker recognition has emerged as an important technology for voice-based biometric systems. However, text-independent speaker recognition against short utterances remains a challenging task despite of recent advances in the domain of speaker recognition. The presence of background noise presents another critical issue in this field. In this paper, we propose effective features for speaker identification with short utterances, which perform well in both clean and noisy conditions. Speaker identification performance for utterances having very short training and testing durations are presented which provide a clearer description of the proposed system performance. Te proposed features have shown strong robustness in these challenging situations and they consistently perform better than the well known MFCC and GFCC features. The efficiency of the proposed approach was thoroughly tested by comparisons with the most recently successful SVM and i-vector PLDA baseline speaker recognition systems. [ABSTRACT FROM AUTHOR]

Recently several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed. These systems have been proven to be competitive for text-dependent tasks as well as for text-independent tasks with short utterances. However, for text-independent tasks with longer utterances, end-to-end systems are still outperformed by standard i-vector + PLDA systems. In this work, we present an end-to-end speaker verification system that is initialized to mimic an i-vector + PLDA baseline. The system is then further trained in an end-to-end manner but regularized so that it does not deviate too far from the initial system. In this way we mitigate overfitting which normally limits the performance of end-to-end systems. The proposed system outperforms the i-vector + PLDA baseline on both long and short duration utterances. [ABSTRACT FROM AUTHOR]

• We focus on exact hierarchical clustering of large sets of utterances. • Hierarchical clustering is challenging due to memory constraints. • We propose an efficient, exact and parallel implementation of UPGMA clustering. • We extend the Clustering Features concept to speaker recognition scoring functions. • We assess the efficiency of our method on datasets including 4 million utterances. This work focuses on clustering large sets of utterances collected from an unknown number of speakers. Since the number of speakers is unknown, we focus on exact hierarchical agglomerative clustering, followed by automatic selection of the number of clusters. Exact hierarchical clustering of a large number of vectors, however, is a challenging task due to memory constraints, which make it ineffective or unfeasible for large datasets. We propose an exact memory–constrained and parallel implementation of average linkage clustering for large scale datasets, showing that its computational complexity is approximately O (N 2) , but is much faster (up to 40 times in our experiments), than the Reciprocal Nearest Neighbor chain algorithm, which has O (N 2) complexity. We also propose a very fast silhouette computation procedure that, in linear time, determines the set of clusters. The computational efficiency of our approach is demonstrated on datasets including up to 4 million speaker vectors. [ABSTRACT FROM AUTHOR]

Background: Bacterial vaginosis is an important female reproductive tract infection. It push a threat of miscarriage or fertility-related problems if it untreated. Cervico-vaginal E. coli (CVEC) is the common cause agent of microbial vaginosis. It equipped by a set of adhesins, immune-disguise protein, cytolysins, siderophores and metal efflux pump that enable them from establishing and endure the infection. Methodology: One hundred fifty seven (157) vaginal swabs were collected from female clinically diagnosed with bacterial vaginitis. The swab was put in tubes of broth of Brain heart infusion (BHIB) to keep them vital and non-dry until being taken to the laboratory and the inoculated on MacConkey agar Eosin methylene blue agar. The only pinky colony with green-metallic shine, on MacConkey agar Eosin methylene blue agar respectively, were selected for study. Polymerase chain reaction using specific primer pairs was used to investigate the virulence factors at molecular level. Results: The results revealed that, out of (157) clinical samples only 32 (20.38%) isolates were belonged to E. coli. Regarding the adhesins the results revealed that FimH adhesin was the most common virulence factor and was discovered in 25(78.13%) of CVEC strains, followed by FocG 23(71.87%), PapGII 22(68.75%), GafD 12(37.5%) and SfaS 8(21.87%) while PapGI and PapGIII not detected in the current study. Concern the cytolysins the results showed that 25(78.13%), 27(84.38%) of CVEC isolates possess hemolysin gene (hlyA) and phospholipase A gene (pldA). The investigation of both kpsMT-K1 and kpsMT-K5 genes. The results revealed that kpsMT-K1 were predominant and compile 10(31.25%), while kpsMT-K5 (which encodes for K5 capsule) were not detected. Aerobactin gene (iucD) were detected in 24(75%) of CVEC isolates. The presence of cusC gene (as a representative for cusCFBA operon responsible for copper/silver efflux) revealed that all CVEC have cusC 32(100%). Conclusion: The current study conclude possession of CVEC for a sets of virulence effectors that enable them from establish the infection, dissemination, immune system evading and sustainability of infection. [ABSTRACT FROM AUTHOR]

To explore the association between the virulence genes exoU and pldA in isolated mucoid Pseudomonas aeruginosa and the clinical outcomes of patients with non–cystic fibrosis (CF) bronchiectasis. A prospective observational cohort study was performed in the Shanghai Pulmonary Hospital from October 2012 to January 2015. We consecutively enrolled all non-CF bronchiectasis patients with mucoid P. aeruginosa isolates obtained from bronchoalveolar lavage fluid or sputum. The exposure variable was the presence of virulence gene, exoU or pldA, in the strains. The primary outcome was exacerbation of bronchiectasis. Logistic regression analysis was performed to evaluate the association between virulence genes and exacerbation. The final analysis included 147 patients (mean (SD) age, 57.86 (11.43) years, 101 female subjects) with median (interquartile range) follow-up of 18 (13–26) months. The following factors were relative to exacerbations: body mass index ≤18.5 kg/m2 (odds ratio (OR) = 5.05; 95% confidence interval (CI), 1.37–18.57), length of stay ≥8 days (OR = 2.65; 95% CI, 1.14–6.19) and positive for either virulence gene (OR = 6.80; 95% CI, 1.47–31.37). The gene-positive group had more exacerbations per year (mean 2.37, SD 2.10, n = 33 vs. mean 0.79, SD 0.83, n = 114) and a higher proportion of patients with exacerbation (31/33, 93.94% vs. 74/114, 64.91%). The proportion of patients being exoU or pldA positive increased as the exacerbation frequency of bronchiectasis increased. The virulence genes exoU and pldA in mucoid P. aeruginosa are significant risk factors for exacerbations in patients with non-CF bronchiectasis. [ABSTRACT FROM AUTHOR]

Pseudomonas aeruginosa can manipulate eukaryotic host cells using secreted effectors delivered by the type III or the type VI Secretion Systems (T3SS and T6SS). The T3SS allows the injection of bacterial effectors (Exo toxins) into eukaryotic cell. P. aeruginosa, encodes three T6SSs, H1-, H2- and H3-T6SS. The H1-T6SS is mainly involved in delivering toxins to kill bacterial competitors. Recently, two T6SS-secreted phospholipases D, PldA (H2-T6SS) and PldB (H3-T6SS), were identified as trans-kingdom virulence effectors, triggering both killing of bacterial competitors and internalization into non-phagocytic cells. We deciphered the prevalence of T3SS and T6SS effectors encoding genes in 185 clinical isolates responsible for infections (septicaemia, pulmonary infections, urinary tract infections, and chronic infections in CF patients), 47 environmental strains, and on 33 carbapenemase-producers. We included 107 complete genomes of P. aeruginosa available in public databases. The prevalence of pldA is increased in clinical isolates responsible for severe acute infection and particularly in multi-drug resistant strains. In contrast, the pldB prevalence was high (96.8%) in all isolates. Regarding T3SS effectors, exoT and exoY are present in nearly all isolates while exoS and exoU were found to be exclusive with a higher prevalence of exoU+ strains in severe acute infections. The hypervirulent exoU+ isolates are more prone to be pldA+, suggesting a role of PldA in virulence. Finally, we observed that extremely drug resistant isolates producing an IMP-type carbapenemase were all pldA+. Our results suggest that PldA might have a role during pulmonary infections and have been co-selected in multidrug resistant strains particularly IMP-producers.

The duration of speech segments can significantly impact the performance of text-independent speaker verification systems. In real world applications which require high accuracy on short utterances, the performance of i -vector speaker verification framework degrades significantly considering that i -vectors extracted from short utterances are less reliable (i.e., uncertainty is higher) than those extracted from long utterances. Therefore, to handle duration variability properly, a more realistic approach seems to be required. This study is an extension to our recently proposed nearest neighbor probabilistic linear discriminant analysis (NN-PLDA) which estimates the parameters of PLDA in i -vector speaker verification framework using a nonparametric form rather than maximum likelihood estimation (MLE) obtained by an EM algorithm, and has been shown to provide superior performance. In NN-PLDA, the between-speaker covariance matrix that represents global information about the speaker variability is replaced with a local estimation computed on a nearest neighbor basis for each target speaker. Compared to their parametric counterparts, the nonparametric between- and within-speaker scatter matrices can better exploit the discriminant information in training data and are more adapted to sample distributions. In this paper, we provide further analysis on the proposed nonparametrically trained PLDA as well as introduce a duration variability modeling technique in the estimation of the within-speaker scatter matrix as to compensate for the effect of limited speech data. We evaluate our approach using core–10sec and 10sec–10sec telephone trial conditions of NIST 2010 SRE as well as on the truncated test utterances in extended core condition with duration less than 10 s. We also present the results obtained by the successful incorporation of NN-PLDA on the recent NIST 2016 speaker recognition evaluation. In all experiments, considerable performance improvement is obtained with the proposed technique compared to a generatively trained PLDA model. [ABSTRACT FROM AUTHOR]

Abstract Voice is an important human trait in natural human-to-human interaction / communication for identifying a person. So voice can be regarded as a biometric measure for recognizing or identifying the person similar to other biometric measures such as face, iris and fingerprints. Speaker recognition is a class of voice recognition where speaker is identified from the speech rather than the message. Automatic speaker recognition (SR) is an approach to identify people based on features extracted from speech utterances. The major task in any speaker recognition is to extract useful features and allow meaningful patterns of speaker models. This paper compares the performance of two feature extraction techniques Mel Frequency Cepstral Coefficient (MFCC) and Inner Hair Cell Coefficient (IHC) with two different modelling methods Gaussian Mixture Model - Universal background model (GMM - UBM) and i- vector approach. In this experiment speech samples of 600 speakers from TIMIT database with 10 utterances of each speaker are taken for identifying the speaker. A text independent speaker recognition system was implemented and this study resulted in an inference of MFCC feature outperforms IHC feature for both GMM and i vector. The performance of voiced speech IHC feature which simulates the physiological behaviour of human ear is better in terms of accuracy than full speech (voiced and unvoiced) in GMM. [ABSTRACT FROM AUTHOR]

The outer membrane of Gram-negative bacteria is a critical barrier that prevents entry of noxious compounds. Integral to this functionality is the presence of lipopolysaccharide (LPS) or lipooligosaccharide (LOS), a molecule that is located exclusively in the outer leaflet of the outer membrane. Its lipid anchor, lipid A, is a glycolipid whose hydrophobicity and net negative charge are primarily responsible for the robustness of the membrane. Because of this, lipid A is a hallmark of Gram-negative physiology and is generally essential for survival. Rare exceptions have been described, including Acinetobacter baumannii,whichcan survive in the absence of lipid A, albeit with significant growth and membrane permeability defects. Here, we show by an evolution experiment that LOS-deficient A. baumannii can rapidly improve fitness over the course of only 120 generations. We identified two factors which negatively contribute to fitness in the absence of LOS,Mla and PldA. These proteins are involved in glycerophospholipid transport (Mla) and lipid degradation (PldA); both are active only on mislocalized, surface-exposed glycerophospholipids. Elimination of these two mechanisms was sufficient to cause a drastic fitness improvement in LOS-deficient A. baumannii. The LOS-deficient double mutant grows as robustly as LOS-positive wild-type bacteriawhile remaining resistant to the last-resort polymyxin antibiotics. These data provide strong biological evidence for the directionality of Mla-mediated glycerophospholipid transport in Gramnegative bacteria and furthers our knowledge of asymmetrymaintenancemechanisms in the context of the outer membrane barrier. [ABSTRACT FROM AUTHOR]

In practical applications, speaker verification systems have to be developed and trained using data which is outside the domain of the intended application as the collection of significant amount of in-domain data could be difficult. Experimental studies have found that when a GPLDA system is trained using out-domain data, it significantly affects the speaker verification performance due to the mismatch between development data and evaluation data. This paper proposes several unsupervised inter-dataset variability compensation approaches for the purpose of improving the performance of GPLDA systems trained using out-domain data. We show that when GPLDA is trained using out-domain data, we can improve the performance by as much as 39% by using by score normalisation using small amounts of in-domain data. Also in situations where rich out-domain data and only limited in-domain data are available, a pooled-linear-weighted technique to estimate the GPLDA parameters shows 35% relative improvements in equal error rate (EER) on int-int conditions. We also propose a novel inter-dataset covariance normalization (IDCN) approach to overcome in- and out-domain data mismatch problem. Our unsupervised IDCN-compensated GPLDA system shows 14 and 25% improvement respectively in EER over out-domain GPLDA speaker verification on tel-tel and int-int training-testing conditions. We provide intuitive explanations as to why these inter-dataset variability compensation approaches provide improvements to speaker verification accuracy. [ABSTRACT FROM AUTHOR]

Recently, the vulnerability of automatic speaker recognition systems to spoofing attacks has received significant interest among researchers. A robust speaker recognition system demands not only high recognition accuracy but also robustness to spoofing attacks. Several spoofing and countermeasure challenges have been organized to draw attention to this problem among the speaker recognition communities. Low-level descriptors designed to detect artifacts in spoofed speech are found to be the most effective countermeasures against spoofing attacks. In this work, we used Fisher vector encoding of low-level descriptors extracted from speech signals. The idea behind Fisher vector encoding is to determine the amount of change induced by the descriptors of the signal on a background probability model which is typically a Gaussian mixture model. The Fisher vector encodes the amount of change of the model parameters to optimally fit the new- coming data. For performance evaluation of the proposed approach we carried out spoofing detection experiments on the 2015 edition of automatic speaker verification spoofing and countermeasure challenge (ASVspoof2015) and report results on the evaluation set. As baseline systems, we used the standard Gaussian mixture model and i-vector/PLDA paradigms. For a fair comparison, in all systems, Constant Q cepstral coefficient (CQCC) features were used as low-level descriptors. With the Fisher vector-based approach, we achieved an equal error rate (EER) of 0.1145% on the known attacks, 1.223% on the unknown attacks, and 0.668% on the average. Moreover, with a single decision threshold this approach yielded an EER of 1.05% on the evaluation set.

Currently, the majority of the state-of-the-art speaker verification systems are based on i-vector and PLDA; however, PLDA requires a huge volume of development data from multiple different speakers. This makes it difficult to learn PLDA parameters for a domain with scarce data. In this paper, we study and extend an effective transfer learning method based on Bayesian joint probability, in which the Kullback–Leibler (KL) divergence between the source domain and the target domain is added as a regularization factor. This method utilizes the development data from the source domain to help find the optimal PLDA parameters for the target domain. Specifically, speaker verification of short utterances can be viewed as a task in the domain with a limited amount of long utterances. Therefore, transfer learning for PLDA can also be adopted to learn discriminative information from other domains with a great deal of long utterances. Experimental results based on the NIST SRE and Switchboard corpus demonstrate that the proposed method offers a significant performance gain when compared with the traditional PLDA. [ABSTRACT FROM AUTHOR]

Although i-vectors together with probabilistic LDA (PLDA) have achieved a great success in speaker verification, how to suppress the undesirable effects caused by the variability in utterance length and background noise level is still a challenge. This paper aims to improve the robustness of i-vector based speaker verification systems by compensating for the utterance-length variability and noise-level variability. Inspired by the recent findings that noise-level variability can be modeled by a signal-to-noise ratio (SNR) subspace and that duration variability can be modeled as additive noise in the i-vector space, we propose to add an SNR factor and a duration factor to the PLDA model. In this framework, we assume that i-vectors derived from utterances with comparable durations share similar duration-specific information and that i-vectors extracted from utterances within a narrow SNR range have similar SNR-specific information. Based on these assumptions, an i-vector can be represented as a linear combination of four components: speaker, SNR, duration, and channel. A variational Bayes algorithm is developed to infer this latent variable model via a discriminative subspace training procedure. In the testing stage, different variabilities are compensated for when computing the likelihood ratio. Experiments on Common Conditions 1 and 4 in NIST 2012 SRE show that the proposed model outperforms the conventional PLDA and SNR-invariant PLDA. Results also show that the proposed model performs better than the uncertainty-propagation PLDA (UP-PLDA) for long test utterances. [ABSTRACT FROM AUTHOR]

There are many factors affecting the variability of an i -vector extracted from a speech segment such as the acoustic content, segment duration, handset type and background noise. The language being spoken is one of the sources of variation which has received limited focus due to the lack of multilingual resources available. Consequently, the discrimination performance is much lower under multilingual trial condition. Standard session-compensation techniques such as Within-Class Covariance Normalization (WCCN), Linear Discriminant Analysis (LDA) and Probabilistic LDA (PLDA) cannot robustly compensate for language source of variation as the amount of data is limited to represent such variability. Source normalization technique which was developed to compensate for speech-source-variation, offered superior performance in cross-language trials by providing better estimation of within-speaker scatter matrix in WCCN and LDA techniques. However, neither language normalization nor the state-of-the-art PLDA algorithm is capable of modeling language variability on a dataset with insufficient multilingual utterances for each speaker, resulting in a poor performance in cross-language trial condition. This study is an extension to our initial developments of a language-independent PLDA training algorithm which aimed at reducing the effect of language as a source of variability on the performance of speaker recognition. We will provide a thorough analysis of how the proposed approach can utilize multilingual training data from bilingual speakers to robustly compensate for the effect of languages. Evaluated on multilingual trial condition, the proposed solution demonstrated over 10% EER and 13% minimum DCF relative improvement on NIST 2008 speaker recognition evaluation as well as 12.4% EER and 23% minimum DCF on PRISM evaluation set over the baseline system while also providing improvement in other trial conditions. [ABSTRACT FROM AUTHOR]

Voice biometric provides a person’s unique identity and hence is widely employed for security applications. Here, Text Independent Speaker Recognition system is implemented using Gaussian Mixture Models (GMM) and i-Vector method with two features PNCC (Power Normalized Cepstral Coefficients) and RASTA PLP (Relative Spectral Perceptual Linear Prediction) coefficients. It was observed that accuracy for speaker identification is better when pitch and formants are appended to basic features. Also, the accuracy of i-vector method with PLDA (Probabilistic Linear Discriminant Analysis) classifier is better than that with CDS (Cosine Distance Scoring) classifier. Further, the performance improves when longer utterances are used. [ABSTRACT FROM AUTHOR]

Automatic speaker recognition system identifies a person from the information contained in the speech signal. These systems are the most user-friendly means of biometric recognition and are being used in applications like teleconferencing, banking, forensics etc. The accuracy of these depends on the methods used to extract features from the speech signal, modeling methods, classifiers used to identify the speaker and amount of data available for training and testing. In this paper, recognition systems are implemented using both spectro-temporal features and voice-source features. Classification is done with two different classifiers for i-vector method and the accuracy rates are compared. [ABSTRACT FROM AUTHOR]

In this paper, we make use of hidden Markov model (HMM) state alignment information in i‐vector/probabilistic linear discriminant analysis (PLDA) framework to improve the verification performance in a text‐dependent single utterance (TDSU) task. In the TDSU task, speakers repeat a fixed utterance in both enrollment and authentication sessions. Despite Gaussian mixture models (GMMs) have been the dominant modeling technique for text‐independent applications, an HMM based method might be better suited for the TDSU task since it captures the co‐articulation information better. Recently, powerful channel compensation techniques such as joint factor analysis (JFA), i‐vectors and PLDA have been proposed for GMM based text‐independent speaker verification. In this study, we train a separate i‐vector/PLDA model for each sentence HMM state in order to utilize the alignment information of the HMM states in a TDSU task. The proposed method is tested using a multi‐channel speaker verification database. In the experiments, it is observed that HMM state based i‐vector/PLDA (i‐vector/PLDA‐HMM) provides approximately 67% relative reduction in equal error rate (EER) when compared to the i‐vector/PLDA. The proposed method also outperforms the baseline GMM and sentence HMM methods. It yields approximately 51% relative reduction in EER over the best performing sentence HMM method. [ABSTRACT FROM AUTHOR]