The cornea is a self-renewing, multilayered tissue maintained with remarkable precision. Its outermost layer, the corneal epithelium, consists of five to seven stratified cell layers, sustained by two coordinated processes: the centripetal migration of transit amplifying cells (TACs) from peripheral limbal epithelial stem cells (LESCs), and delamination (vertical movement) of cells between layers. Despite this well-organized renewal, the mechanisms governing epithelial stratification remain largely unknown. In this study, we present a two-dimensional Voronoi cell-based model that captures the dynamics of epithelial stratification. Our model incorporates two distinct epithelial layers-the basal and the suprabasal layers-and accounts for key cellular processes. These processes are mediated by mechanical interactions such as cell-substrate adhesion, as well as horizontal and vertical intercellular forces. Our simulations show that cell delamination, which drives stratification, is strongly linked to TAC proliferation. In contrast, LESC division remains largely unchanged, suggesting that TACs buffer LESC activity, consistent with the slow-cycling nature of stem cells. This reveals that processes weakening the cell-to-substrate interaction will enhance the turnover of epithelial cells without the need for external growth factor induction, which is a notable finding. Interestingly, while increased shedding promotes division and delamination, excessive shedding leads to mechanical compensation through cell stretching in the upper layers. This mechanical response provides a simple, plausible explanation for the presence of enlarged cells in the superficial epithelial layers, while not excluding the potential contributions of other mechanisms. Our model reveals a direct link between the shedding rate and the centripetal velocity of clonal growth, predicting that increased surface cell loss accelerates cell movement-a response similar to wound healing, where cells rapidly migrate to restore the damaged area. These results highlight how cell size, migration, and turnover are tightly coupled, and offer deeper insights into how physical forces work together to maintain and rapidly restore epithelial integrity. Although the real cornea contains five to seven layers, this two-layer framework focuses on the key mechanical principles of stratification and can be viewed as a foundational step toward more comprehensive multilayer modelling.

MOTIVATION: Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cα-Cβ (HSEβ) vector or neighboring Cα-Cα vectors (HSEα). HSEα calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSEα and only one method to predict HSEβ. RESULTS: This study developed a novel method for predicting both HSEα and HSEβ (SPIDER-HSE) that achieved a consistent performance for 10-fold cross validation and two independent tests. The correlation coefficients between predicted and measured HSEβ (0.73 for upper sphere, 0.69 for down sphere and 0.76 for contact numbers) for the independent test set of 1199 proteins are significantly higher than existing methods. Moreover, predicted HSEα has a higher correlation coefficient (0.46) to the stability change by residue mutants than predicted HSEβ (0.37) and ASA (0.43). The results, together with its easy Cα-atom-based calculation, highlight the potential usefulness of predicted HSEα for protein structure prediction and refinement as well as function prediction. AVAILABILITY AND IMPLEMENTATION: The method is available at http://sparks-lab.org CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The functioning of a protein relies on its location in the cell. Therefore, predicting protein subcellular localization is an important step towards protein function prediction. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve the prediction performance. However, for newly sequenced proteins, the GO is not available. Therefore, for these cases, the prediction performance of GO based methods degrade significantly. RESULTS: In this study, we develop a method to effectively employ physicochemical and evolutionary-based information in the protein sequence. To do this, we propose segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids to tackle Gram-positive and Gram-negative subcellular localization. We explore our proposed feature extraction techniques using 10 attributes that have been experimentally selected among a wide range of physicochemical attributes. Finally by applying the Rotation Forest classification technique to our extracted features, we enhance Gram-positive and Gram-negative subcellular localization accuracies up to 3.4% better than previous studies which used GO for feature extraction. CONCLUSION: By proposing segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids as well as using Rotation Forest classification technique, we are able to enhance the Gram-positive and Gram-negative subcellular localization prediction accuracies, significantly.

Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature.

Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. Despite all the efforts that have been made so far, finding an accurate and fast computational approach to solve the PFR still remains a challenging problem for bioinformatics and computational biology. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in position specific scoring matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of occurrence feature to extract global discriminatory information from PSSM and SPINE-X. By applying a support vector machine (SVM) to our extracted features, we enhance the protein fold prediction accuracy for 7.4 percent over the best results reported in the literature. We also report 73.8 percent prediction accuracy for a data set consisting of proteins with less than 25 percent sequence similarity rates and 80.7 percent prediction accuracy for a data set with proteins belonging to 110 folds with less than 40 percent sequence similarity rates. We also investigate the relation between the number of folds and the number of features being used and show that the number of features should be increased to get better protein fold prediction results when the number of folds is relatively large.

BACKGROUND: Assigning a protein into one of its folds is a transitional step for discovering three dimensional protein structure, which is a challenging task in bimolecular (biological) science. The present research focuses on: 1) the development of classifiers, and 2) the development of feature extraction techniques based on syntactic and/or physicochemical properties. RESULTS: Apart from the above two main categories of research, we have shown that the selection of physicochemical attributes of the amino acids is an important step in protein fold recognition and has not been explored adequately. We have presented a multi-dimensional successive feature selection (MD-SFS) approach to systematically select attributes. The proposed method is applied on protein sequence data and an improvement of around 24% in fold recognition has been noted when selecting attributes appropriately. CONCLUSION: The MD-SFS has been applied successfully in selecting physicochemical attributes of the amino acids. The selected attributes show improved protein fold recognition performance.

In this paper, we propose to combine the Kalman filter with a recent speech enhancement technique, called the phase spectrum compensation procedure, or PSC. More specifically, we apply the PSC technique to initialise the Kalman filter, whereby PSC is used to clean the noisy speech prior to LPC estimation for the Kalman recursion. We refer to the combined technique as the Kalman-PSC filter. Using an objective speech quality measure, formal subjective listening tests and spectrogram analysis, we show that the proposed method results in improved speech quality.

The speech signal is used for transmission of linguistic information. High energy portions of the speech spectrum have higher signal-to-noise ratios than the low energy portions. As a result, these regions are more robust to noise. Since the speech signal is known to be very robust to noise, it is expected that the high energy regions of the speech spectrum carry the majority of the linguistic information. This letter tries to derive a frequency warping function directly from the speech signal by sampling the frequency axis nonuniformly with the high energy regions sampled more densely than the low energy regions. To achieve this, an ensemble average short-time power spectrum is computed from a large speech corpus. The speech-signal-based frequency warping is obtained by considering equal area portions of the log spectrum. The proposed frequency warping is shown to be similar to the frequency scales obtained through psycho-acoustic experiments, namely the mel and bark scales. The warping is then used in filterbank design for automatic speech recognition experiments. The results of these experiments show that cepstral features based on the proposed warping achieve performance under clean conditions comparable to that of mel-frequency cepstral coefficients, while outperforming them under noisy conditions.

Griffith Sciences, Griffith School of Engineering

Typical speech enhancement algorithms operate on the shorttime magnitude spectrum, while keeping the short-time phase spectrum unchanged for synthesis. Recently, a novel approach to speech enhancement has been proposed where the noisy magnitude spectrum is recombined with a changed phase spectrum to produce a modified complex spectrum. During synthesis the low energy components of the modified complex spectrum cancel out more than the high energy components, thus reducing background noise. In the present work, a procedure that employs noise estimates to compensate the phase spectrum for additive noise distortion is formulated. The proposed approach is objectively evaluated against several popular speech enhancement methods under various noise conditions and is shown to compare favourably. Index Terms: speech enhancement, magnitude spectrum, phase spectrum, phase spectrum compensation

Typical speech enhancement algorithms operate on the short-time magnitude spectrum, while keeping the short-time phase spectrum unchanged for synthesis. We propose a novel approach where the noisy magnitude spectrum is recombined with a changed phase spectrum to produce a modified complex spectrum. During synthesis, the low energy components of the modified complex spectrum cancel out more than the high energy components, thus reducing background noise. Using objective speech quality measures, informal subjective listening tests and spectrogram analysis, we show that the proposed method results in improved speech quality.