Revitalizing Endangered Languages: AI-powered language learning as a catalyst for language appreciation

According to UNESCO, there are nearly 7,000 languages spoken worldwide, of which around 3,000 languages are in danger of disappearing before the end of the century. With roughly 230 languages having already become extinct between the years 1950-2010, collectively this represents a significant loss of linguistic and cultural diversity. This position paper aims to explore the potential of AI-based language learning approaches that promote early exposure and appreciation of languages to ultimately contribute to the preservation of endangered languages, thereby addressing the urgent need to protect linguistic and cultural diversity.Comment: 3 page

A model of ganglion axon pathways accounts for percepts elicited by retinal implants.

Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts ('phosphenes'). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes. We characterized these distortions by asking users of the Argus retinal prosthesis system (Second Sight Medical Products Inc.) to draw electrically elicited percepts on a touchscreen. Using ophthalmic fundus imaging and computational modeling, we show that elicited percepts can be accurately predicted by the topographic organization of optic nerve fiber bundles in each subject's retina, successfully replicating visual percepts ranging from 'blobs' to oriented 'streaks' and 'wedges' depending on the retinal location of the stimulating electrode. This provides the first evidence that activation of passing axon fibers accounts for the rich repertoire of phosphene shape commonly reported in psychophysical experiments, which can severely distort the quality of the generated visual experience. Overall our findings argue for more detailed modeling of biological detail across neural engineering applications

Role of yoga prana vidya healing techniques in successful and speedy recovery of orthopaedic cases of bone injuries and fractures: a multiple case study

Bones form a vital part of the skeletal system providing mechanical support, strength, structure and protection to the human body. Inability of the bone to resist any kind of stress caused accidently can result in a bone injury or a fracture. This article provides a summary of eleven cases of bone injury and fracture treated successfully by yoga prana vidya (YPV) techniques as a complementary medicine for faster recovery. The study was carried out by two healers who independently healed eleven cases of bone injury and fracture using the bone regeneration techniques of YPV. Further, the data was collected and the results were analysed. By application of YPV healing techniques complementarily, it is observed that full recovery took place within 10 days to 45 days for the 3 hospitalised cases, and within 3 to 8 days for the two patients who had bandage/dressing done at a medical facility. In case of the remaining 6 patients who sought YPV healing help in preference to seeking medical help the recovery took place within 5 to 20 days. helping the patients to lead a normal life thereafter. It is observed that YPV techniques can be used for faster recovery of patients with injured and fractured bones. This paper shows the successful results when the techniques were applied on eleven participants. It is recommended to conduct further studies on a larger scale for the healing of bone related cases such as injury and fractures

Transcriptomic Analysis of Peritoneal Cells in a Mouse Model of Sepsis: Confirmatory and Novel Results in Early and Late Sepsis.

Background The events leading to sepsis start with an invasive infection of a primary organ of the body followed by an overwhelming systemic response. Intra-abdominal infections are the second most common cause of sepsis. Peritoneal fluid is the primary site of infection in these cases. A microarray-based approach was used to study the temporal changes in cells from the peritoneal cavity of septic mice and to identify potential biomarkers and therapeutic targets for this subset of sepsis patients. Results We conducted microarray analysis of the peritoneal cells of mice infected with a non-pathogenic strain of Escherichia coli. Differentially expressed genes were identified at two early (1 h, 2 h) and one late time point (18 h). A multiplexed bead array analysis was used to confirm protein expression for several cytokines which showed differential expression at different time points based on the microarray data. Gene Ontology based hypothesis testing identified a positive bias of differentially expressed genes associated with cellular development and cell death at 2 h and 18 h respectively. Most differentially expressed genes common to all 3 time points had an immune response related function, consistent with the observation that a few bacteria are still present at 18 h. Conclusions Transcriptional regulators like PLAGL2, EBF1, TCF7, KLF10 and SBNO2, previously not described in sepsis, are differentially expressed at early and late time points. Expression pattern for key biomarkers in this study is similar to that reported in human sepsis, indicating the suitability of this model for future studies of sepsis, and the observed differences in gene expression suggest species differences or differences in the response of blood leukocytes and peritoneal leukocytes

Comprehensive proteomic analysis of bovine spermatozoa of varying fertility rates and identification of biomarkers associated with fertility

<p>Abstract</p> <p>Background</p> <p>Male infertility is a major problem for mammalian reproduction. However, molecular details including the underlying mechanisms of male fertility are still not known. A thorough understanding of these mechanisms is essential for obtaining consistently high reproductive efficiency and to ensure lower cost and time-loss by breeder.</p> <p>Results</p> <p>Using high and low fertility bull spermatozoa, here we employed differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT) and identified 125 putative biomarkers of fertility. We next used quantitative Systems Biology modeling and canonical protein interaction pathways and networks to show that high fertility spermatozoa differ from low fertility spermatozoa in four main ways. Compared to sperm from low fertility bulls, sperm from high fertility bulls have higher expression of proteins involved in: energy metabolism, cell communication, spermatogenesis, and cell motility. Our data also suggests a hypothesis that low fertility sperm DNA integrity may be compromised because cell cycle: G₂/M DNA damage checkpoint regulation was most significant signaling pathway identified in low fertility spermatozoa.</p> <p>Conclusion</p> <p>This is the first comprehensive description of the bovine spermatozoa proteome. Comparative proteomic analysis of high fertility and low fertility bulls, in the context of protein interaction networks identified putative molecular markers associated with high fertility phenotype.</p

Gene Profiling of Aortic Valve Interstitial Cells under Elevated Pressure Conditions: Modulation of Inflammatory Gene Networks

The study aimed to identify mechanosensitive pathways and gene networks that are stimulated by elevated cyclic pressure in aortic valve interstitial cells (VICs) and lead to detrimental tissue remodeling and/or pathogenesis. Porcine aortic valve leaflets were exposed to cyclic pressures of 80 or 120 mmHg, corresponding to diastolic transvalvular pressure in normal and hypertensive conditions, respectively. Linear, two-cycle amplification of total RNA, followed by microarray was performed for transcriptome analysis (with qRT-PCR validation). A combination of systems biology modeling and pathway analysis identified novel genes and molecular mechanisms underlying the biological response of VICs to elevated pressure. 56 gene transcripts related to inflammatory response mechanisms were differentially expressed. TNF-α, IL-1α, and IL-1β were key cytokines identified from the gene network model. Also of interest was the discovery that pentraxin 3 (PTX3) was significantly upregulated under elevated pressure conditions (41-fold change). In conclusion, a gene network model showing differentially expressed inflammatory genes and their interactions in VICs exposed to elevated pressure has been developed. This system overview has detected key molecules that could be targeted for pharmacotherapy of aortic stenosis in hypertensive patients

ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

<p>Abstract</p> <p>Background</p> <p>Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.</p> <p>Results</p> <p><it>ProtQuant </it>was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. <it>ProtQuant </it>has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,<it>ProtQuant </it>implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.</p> <p>Conclusion</p> <p><it>ProtQuant </it>is a tool for protein quantification built for multi-platform use with an intuitive user interface. <it>ProtQuant </it>efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, <it>ProtQuant </it>is available as a self-installing executable for the Windows environment used by many bench scientists.</p

The Proteogenomic Mapping Tool

<p>Abstract</p> <p>Background</p> <p>High-throughput mass spectrometry (MS) proteomics data is increasingly being used to complement traditional structural genome annotation methods. To keep pace with the high speed of experimental data generation and to aid in structural genome annotation, experimentally observed peptides need to be mapped back to their source genome location quickly and exactly. Previously, the tools to do this have been limited to custom scripts designed by individual research groups to analyze their own data, are generally not widely available, and do not scale well with large eukaryotic genomes.</p> <p>Results</p> <p>The Proteogenomic Mapping Tool includes a Java implementation of the Aho-Corasick string searching algorithm which takes as input standardized file types and rapidly searches experimentally observed peptides against a given genome translated in all 6 reading frames for exact matches. The Java implementation allows the application to scale well with larger eukaryotic genomes while providing cross-platform functionality.</p> <p>Conclusions</p> <p>The Proteogenomic Mapping Tool provides a standalone application for mapping peptides back to their source genome on a number of operating system platforms with standard desktop computer hardware and executes very rapidly for a variety of datasets. Allowing the selection of different genetic codes for different organisms allows researchers to easily customize the tool to their own research interests and is recommended for anyone working to structurally annotate genomes using MS derived proteomics data.</p