Genome-Wide Identification of Alternatively Spliced mRNA Targets of Specific RNA-Binding Proteins

BACKGROUND: Alternative splicing plays an important role in generating molecular and functional diversity in multi-cellular organisms. RNA binding proteins play crucial roles in modulating splice site choice. The majority of known binding sites for regulatory proteins are short, degenerate consensus sequences that occur frequently throughout the genome. This poses an important challenge to distinguish between functionally relevant sequences and a vast array of those occurring by chance. METHODOLOGY/PRINCIPAL FINDINGS: Here we have used a computational approach that combines a series of biological constraints to identify uridine-rich sequence motifs that are present within relevant biological contexts and thus are potential targets of the Drosophila master sex-switch protein Sex-lethal (SXL). This strategy led to the identification of one novel target. Moreover, our systematic analysis provides a starting point for the molecular and functional characterization of an additional target, which is dependent on SXL activity, either directly or indirectly, for regulation in a germline-specific manner. CONCLUSIONS/SIGNIFICANCE: This approach has successfully identified previously known, new, and potential SXL targets. Our analysis suggests that only a subset of potential SXL sites are regulated by SXL. Finally, this approach should be directly relevant to the large majority of splicing regulatory proteins for which bonafide targets are unknown

X chromosomal regulation in flies: when less is more

In Drosophila, dosage compensation of the single male X chromosome involves upregulation of expression of X linked genes. Dosage compensation complex or the male specific lethal (MSL) complex is intimately involved in this regulation. The MSL complex members decorate the male X chromosome by binding on hundreds of sites along the X chromosome. Recent genome wide analysis has brought new light into X chromosomal regulation. It is becoming increasingly clear that although the X chromosome achieves male specific regulation via the MSL complex members, a number of general factors also impinge on this regulation. Future studies integrating these aspects promise to shed more light into this epigenetic phenomenon

Strongyloides stercoralis : global distribution and risk factors

The soil-transmitted threadworm, Strongyloides stercoralis, is one of the most neglected among the so-called neglected tropical diseases (NTDs). We reviewed studies of the last 20 years on S. stercoralis's global prevalence in general populations and risk groups.; A literature search was performed in PubMed for articles published between January 1989 and October 2011. Articles presenting information on infection prevalence were included. A Bayesian meta-analysis was carried out to obtain country-specific prevalence estimates and to compare disease odds ratios in different risk groups taking into account the sensitivities of the diagnostic methods applied. A total of 354 studies from 78 countries were included for the prevalence calculations, 194 (62.4%) were community-based studies, 121 (34.2%) were hospital-based studies and 39 (11.0%) were studies on refugees and immigrants. World maps with country data are provided. In numerous African, Asian and South-American resource-poor countries, information on S. stercoralis is lacking. The meta-analysis showed an association between HIV-infection/alcoholism and S. stercoralis infection (OR: 2.17 BCI: 1.18-4.01; OR: 6.69; BCI: 1.47-33.8), respectively.; Our findings show high infection prevalence rates in the general population in selected countries and geographical regions. S. stercoralis infection is prominent in several risk groups. Adequate information on the prevalence is still lacking from many countries. However, current information underscore that S. stercoralis must not be neglected. Further assessments in socio-economic and ecological settings are needed and integration into global helminth control is warranted

Cough sound analysis can rapidly diagnose childhood pneumonia

Pneumonia annually kills over 1,800,000 children throughout the world. The vast majority of these deaths occur in resource poor regions such as the sub-Saharan Africa and remote Asia. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths. The reliable diagnosis of childhood pneumonia in remote regions is fraught with difficulties arising from the lack of field-deployable imaging and laboratory facilities as well as the scarcity of trained community healthcare workers. In this paper, we present a pioneering class of technology addressing both of these problems. Our approach is centred on the automated analysis of cough and respiratory sounds, collected via microphones that do not require physical contact with subjects. Cough is a cardinal symptom of pneumonia but the current clinical routines used in remote settings do not make use of coughs beyond noting its existence as a screening-in criterion. We hypothesized that cough carries vital information to diagnose pneumonia, and developed mathematical features and a pattern classifier system suited for the task. We collected cough sounds from 91 patients suspected of acute respiratory illness such as pneumonia, bronchiolitis and asthma. Non-contact microphones kept by the patient's bedside were used for data acquisition. We extracted features such as non-Gaussianity and Mel Cepstra from cough sounds and used them to train a Logistic Regression classifier. We used the clinical diagnosis provided by the paediatric respiratory clinician as the gold standard to train and validate our classifier. The methods proposed in this paper could separate pneumonia from other diseases at a sensitivity and specificity of 94 and 75% respectively, based on parameters extracted from cough sounds alone. The inclusion of other simple measurements such as the presence of fever further increased the performance. These results show that cough sounds indeed carry critical information on the lower respiratory tract, and can be used to diagnose pneumonia. The performance of our method is far superior to those of existing WHO clinical algorithms for resource-poor regions. To the best of our knowledge, this is the first attempt in the world to diagnose pneumonia in humans using cough sound analysis. Our method has the potential to revolutionize the management of childhood pneumonia in remote regions of the world

Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction

International audienceCough is a symptom in over a hundred respiratory diseases. The audio features in cough signals contain erudition about the predicament of the respiratory system. Using deep learning or signal processing, these features can be used to build an effective disease prediction system. However, cough analysis remains an area that has received scant attention from machine learning researchers. This can be attributed to several factors such as inefficient ancillary systems, high expenses in obtaining datasets, or difficulty in building classifiers. This paper categorized and reviewed the current progress on cough audio analysis for the classification of pulmonary diseases. It also explored potential future issues in research. Additionally, it proposed a model for the classification of ten serious pulmonary ailments commonly seen in Indian adolescents. The proposed model is evaluated against four existing state of the art techniques in the literature