Gadjah Mada University

About: Gadjah Mada University is a education organization based out in Yogyakarta, Indonesia. It is known for research contribution in the topics: Population & Adsorption. The organization has 17307 authors who have published 21389 publications receiving 116561 citations. The organization is also known as: University of Gajah Mada & Universitas Gadjah Mada.

Human Neonatal Rotavirus Vaccine (RV3-BB) to Target Rotavirus from Birth

Abstract: Background A strategy of administering a neonatal rotavirus vaccine at birth to target early prevention of rotavirus gastroenteritis may address some of the barriers to global implementation of a rotavirus vaccine. Methods We conducted a randomized, double-blind, placebo-controlled trial in Indonesia to evaluate the efficacy of an oral human neonatal rotavirus vaccine (RV3-BB) in preventing rotavirus gastroenteritis. Healthy newborns received three doses of RV3-BB, administered according to a neonatal schedule (0 to 5 days, 8 weeks, and 14 weeks of age) or an infant schedule (8 weeks, 14 weeks, and 18 weeks of age), or placebo. The primary analysis was conducted in the per-protocol population, which included only participants who received all four doses of vaccine or placebo within the visit windows, with secondary analyses performed in the intention-to-treat population, which included all participants who underwent randomization. Results Among the 1513 participants in the per-protocol population...

The Sambungmacan 3 Homo erectus calvaria: A comparative morphometric and morphological analysis

Abstract: The Sambungmacan (Sm) 3 calvaria, discovered on Java in 1977, was illegally removed from Indonesia in 1998 and appeared in New York City in early 1999 at the Maxilla & Mandible, Ltd natural history shop Here we undertake an analysis of its phylogenetic and systematic position using geometric morphometrics and comparative morphology The coordinates of points in the sagittal plane from glabella to opisthion were resampled to yield "lines" of 50 semi-landmarks Coordinates of glabella, bregma, lambda, inion, and opisthion were also collected and analyzed separately Casts of Homo erectus fossils from Indonesia, China, and Kenya and of "archaic H sapiens" from Kabwe and Petralona, as well as 10 modern human crania, were used as the primary comparative sample The modern humans were well separated from the fossils in a graphical superimposition of Procrustes-aligned semi-landmarks as well as in principal component and canonical discriminant analyses In all of these, Sm 3 falls intermediate between the fossil and modern groups Morphological comparisons of Sm 3 with a selection of Homo erectus fossils revealed its greatest similarity to specimens from Ngandong and the Sm 1 calvaria Compared to all other H erectus, Sm 3 was distinctive in its more vertical supratoral plane, less anteriorly projecting glabella and less sharply angled occiput In these features it was somewhat similar to modern humans It is not yet possible to determine if this similarity implies an evolutionary relationship or (more likely) individual or local populational variation Several features of Sm 3 (small size, gracile supraorbital torus and lack of angular torus, and position in principal component analysis) suggest that it was a female The use of geometric morphometrics provides a means to statistically test the shapes of such fossils in a manner not easily duplicated by other methods The intermediate position of Sm 3 between fossil and modern samples in several different subanalyses exemplifies the value of this approach

Wavelet Augmented Cough Analysis for Rapid Childhood Pneumonia Diagnosis

Abstract: Pneumonia is the cause of death for over a million children each year around the world, largely in resource poor regions such as sub-Saharan Africa and remote Asia. One of the biggest challenges faced by pneumonia endemic countries is the absence of a field deployable diagnostic tool that is rapid, low-cost and accurate. In this paper, we address this issue and propose a method to screen pneumonia based on the mathematical analysis of cough sounds. In particular, we propose a novel cough feature inspired by wavelet-based crackle detection work in lung sound analysis. These features are then combined with other mathematical features to develop an automated machine classifier, which can separate pneumonia from a range of other respiratory diseases. Both cough and crackles are symptoms of pneumonia, but their existence alone is not a specific enough marker of the disease. In this paper, we hypothesize that the mathematical analysis of cough sounds allows us to diagnose pneumonia with sufficient sensitivity and specificity. Using a bedside microphone, we collected 815 cough sounds from 91 patients with respiratory illnesses such as pneumonia, asthma, and bronchitis. We extracted wavelet features from cough sounds and combined them with other features such as Mel Cepstral coefficients and non-Gaussianity index. We then trained a logistic regression classifier to separate pneumonia from other diseases. As the reference standard, we used the diagnosis by physicians aided with laboratory and radiological results as deemed necessary for a clinical decision. The methods proposed in this paper achieved a sensitivity and specificity of 94% and 63%, respectively, in separating pneumonia patients from non-pneumonia patients based on wavelet features alone. Combining the wavelets with features from our previous work improves the performance further to 94% and 88% sensitivity and specificity. The performance far surpasses that of the WHO criteria currently in common use in resource-limited settings.