Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition.

G.K.Villena is supported by doctoral scholarships from INCAGRO and CONCYTEC.

This work was supported by INCAGRO (Ministry of Agriculture, Peru) and CONCYTEC (Ministry of Education, Peru). The authors wish to thank CERTINTEX (Lima, Peru) for the use of its SEM facilities, and Mr. Gianangelo Nava (CERTINTEX) for his SEM technical assistance.

We evaluate a set of ultraviolet solar radiation (UVR) measurements collected in six locations in Peru: Ica, Tacna, Moquegua, Arequipa, Cajamarca and Marcapomacocha, whose altitudes vary from sea level to 4479 m. The analysis involved the validation and analysis of the ultraviolet index (UVI) and the daily erythemal doses (D-Er). Clear-sky filtered data were compared with numerical simulations provided by the Tropospheric Ultraviolet and Visible (TUV) radiation model in order to estimate numerical regression equations. These equations showed high determination coefficients (R 2 > 0.91) statistical significance (p < 0.0001). Results show that the maximum UVI observed in Peru was 23.5 in Marcapomacocha (4479 m above sea level). Regarding D-Er, the highest means were observed in summer at the city of Arequipa with daily averages closer than 7.9 kJm -2 (± 1.3 kJm -2 ). However, the highest ...

This work was supported by INCAGRO (Ministry of Agriculture, Perú), CONCYTEC (Ministry of Education, Perú), and the Institute of Molecular Biology and Biotechnology (Shizuoka Univesity, Japan).

Here, we report the complete genome sequence of a high alkaline cellulase producing Aspergillus fumigatus strain LMB-35Aa isolated from soil of Peruvian Amazon rainforest. The genome is ∼27.5 mb in size, comprises of 228 scaffolds with an average GC content of 50%, and is predicted to contain a total of 8660 protein-coding genes. Of which, 6156 are with known function; it codes for 607 putative CAZymes families potentially involved in carbohydrate metabolism. Several important cellulose degrading genes, such as endoglucanase A, endoglucanase B, endoglucanase D and beta-glucosidase, are also identified. The genome of A. fumigatus strain LMB-35Aa represents the first whole sequenced genome of non-clinical, high cellulase producing A. fumigatus strain isolated from forest soil.

Pneumonia is one of the major causes of child mortality. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia.

Pneumonia is one of the major causes of child mortality, but it is curable if one can achieves early diagnostics. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. The use of ultrasound to detect pneumonia is limited by the image analysis for interpretation, which is carried by human experts. Pattern recognition and image analysis is a potential tool to facilitate recognition of pneumonia consolidates in absence of medical experts for automatic diagnostics. To perform an automatic analysis of lung ultrasound images for pneumonia detection, the noise introduced by the image portion of the skin, notably complicates the processing and interpretation. This paper presents a...