Evaluation of Acoustic Parameters for Angulimala Sutta using Voiced to Unvoiced Ration and Vowel Distribution

Pirith is believed as a protective doctrine preached by the Load Buddha in Pali language. The aim of this study is to analyze acoustic properties of Pirith using computer-aided methods and identify special characteristics and patterns. In this study, two methods were used to identify special characteristics of Angulimala Sutta. First method calculates voiced to unvoiced ratio using zero crossing rate and energy content associated with the acoustic signal while second method recognizes vowel distribution using first and second formant frequencies. Results of the first method indicates approximately 96% of frames are voiced while the second method suggests approximately 72% of vowels concentrate in the square region of F1,0~1500 Hz and demonstrating when chanting the Angulimala sutta most of the time the tongue height is low positioned in back levels while lips shaped unrounded. KEYWORDS: Formant frequencies, Voiced to unvoiced ratio, Zero-Crossing rate, Vowel distributio

Exploring the interplay between Buddhism and career development : a study of highly skilled women workers in Sri Lanka

This article adopts a socio cultural lens to examine the role of Buddhism in highly skilled women workers’ careers in Sri Lanka. While Buddhism enabled women’s career development by giving them strength to cope with difficult situations in work, it also seemed to restrict their agency and constrain their career advancement. Based on our findings, we argue that being perceived as a good Buddhist woman worked as a powerful form of career capital for the respondents in our sample, who used their faith to combat gender disadvantage in their work settings

Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc

Mapping interactions with the chaperone network reveals factors that protect against tau aggregation.

A network of molecular chaperones is known to bind proteins ('clients') and balance their folding, function and turnover. However, it is often unclear which chaperones are critical for selective recognition of individual clients. It is also not clear why these key chaperones might fail in protein-aggregation diseases. Here, we utilized human microtubule-associated protein tau (MAPT or tau) as a model client to survey interactions between ~30 purified chaperones and ~20 disease-associated tau variants (~600 combinations). From this large-scale analysis, we identified human DnaJA2 as an unexpected, but potent, inhibitor of tau aggregation. DnaJA2 levels were correlated with tau pathology in human brains, supporting the idea that it is an important regulator of tau homeostasis. Of note, we found that some disease-associated tau variants were relatively immune to interactions with chaperones, suggesting a model in which avoiding physical recognition by chaperone networks may contribute to disease

Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery

<p>Abstract</p> <p>Background</p> <p>There is an important need for the identification of novel serological biomarkers for the early detection of cancer. Current biomarkers suffer from a lack of tissue specificity, rendering them vulnerable to non-disease-specific increases. The present study details a strategy to rapidly identify tissue-specific proteins using bioinformatics.</p> <p>Methods</p> <p>Previous studies have focused on either gene or protein expression databases for the identification of candidates. We developed a strategy that mines six publicly available gene and protein databases for tissue-specific proteins, selects proteins likely to enter the circulation, and integrates proteomic datasets enriched for the cancer secretome to prioritize candidates for further verification and validation studies.</p> <p>Results</p> <p>Using colon, lung, pancreatic and prostate cancer as case examples, we identified 48 candidate tissue-specific biomarkers, of which 14 have been previously studied as biomarkers of cancer or benign disease. Twenty-six candidate biomarkers for these four cancer types are proposed.</p> <p>Conclusions</p> <p>We present a novel strategy using bioinformatics to identify tissue-specific proteins that are potential cancer serum biomarkers. Investigation of the 26 candidates in disease states of the organs is warranted.</p

A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines

Proteome Serological Determination of Tumor-Associated Antigens in Melanoma

Proteome serology may complement expression library-based approaches as strategy utilizing the patients' immune responses for the identification pathogenesis factors and potential targets for therapy and markers for diagnosis. Melanoma is a relatively immunogenic tumor and antigens recognized by melanoma-specific T cells have been extensively studied. The specificities of antibody responses to this malignancy have been analyzed to some extent by molecular genetic but not proteomics approaches. We screened sera of 94 melanoma patients for anti-melanoma reactivity and detected seropositivity in two-thirds of the patients with 2–6 antigens per case detected by 1D and an average of 2.3 per case by 2D Western blot analysis. For identification, antigen spots in Western blots were aligned with proteins in 2-DE and analyzed by mass spectrometry. 18 antigens were identified, 17 of which for the first time for melanoma. One of these antigens, galectin-3, has been related to various oncogenic processes including metastasis formation and invasiveness. Similarly, enolase has been found deregulated in different cancers. With at least 2 of 18 identified proteins implicated in oncogenic processes, the work confirms the potential of proteome-based antigen discovery to identify pathologically relevant proteins

A secretome profile indicative of oleate-induced proliferation of HepG2 hepatocellular carcinoma cells

Increased fatty acid (FA) is often observed in highly proliferative tumors. FAs have been shown to modulate the secretion of proteins from tumor cells, contributing to tumor survival. However, the secreted factors affected by FA have not been systematically explored. Here, we found that treatment of oleate, a monounsaturated omega-9 FA, promoted the proliferation of HepG2 cells. To examine the secreted factors associated with oleate-induced cell proliferation, we performed a comprehensive secretome profiling of oleate-treated and untreated HepG2 cells. A comparison of the secretomes identified 349 differentially secreted proteins (DSPs; 145 upregulated and 192 downregulated) in oleate-treated samples, compared to untreated samples. The functional enrichment and network analyses of the DSPs revealed that the 145 upregulated secreted proteins by oleate treatment were mainly associated with cell proliferation-related processes, such as lipid metabolism, inflammatory response, and ER stress. Based on the network models of the DSPs, we selected six DSPs (MIF, THBS1, PDIA3, APOA1, FASN, and EEF2) that can represent such processes related to cell proliferation. Thus, our results provided a secretome profile indicative of an oleate-induced proliferation of HepG2 cell