Cephalopoda as prey of juvenile Southern elephant seals at Isla 25 de Mayo/King George, South Shetland Islands

The aim of the present study was to enhance the knowledge of the feeding habits of the juvenile component of the population of Southern elephant seals [Mirounga leonina (Linnaeus, 1758)] from Isla 25 de Mayo, South Shetland Islands, age class whose diet information is scarce. A total of 60 individuals were stomach lavaged in the spring - summer seasons of three consecutive years (2003, 2004 and 2005) of which 53.3 % (n = 32) presented food remnants. The Antarctic glacial squid Psychroteuthis glacialis Thiele, 1921 was the dominant prey taxon in terms of frequency of occurrence (68.7%), numerical abundance (60.1%) and biomass (51.5%), contributing 84.1% to the total relative importance index. Other squid prey species of importance were Slosarczykovia circumantartica Lipinski, 2001 in terms of occurrence (37.5%) and numerical abundance (14%) and Moroteuthis knipovitchi Filippova, 1972 in terms of biomass (16%). All identified cephalopod prey taxa are distributed south of the Antarctic Polar Front, except for the squid Martialia hyadesi Rochebrune & Mabille, 1889 which has a circumpolar distribution associated to the Polar Frontal Zone. No significant differences in the sizes of P. glacialis preyed upon by elephant seals were found between sexes and years. However, significant interannual differences were found in the taxonomical composition of their diet. This would be associated with temporal changes in food availability at the foraging areas of seals, which in turn may have been influenced by changes in oceanographic conditions as a result of the El Niño Southern Oscillation (ENSO) phenomenon that occurred during part of the study period. Furthermore, a differential response of males and females to this temporal variation was observed, with the former being also associated to a predation on octopods. This would suggest a sexual segregation in foraging habits of this species from the early stages of its life cycle.

Colonization by mites of glacier-free areas in King George Island, Antarctica

This work aimed to investigate the ratio of colonization by terrestrial mites on ice-free areas created by the ongoing climate-induced melting of Antarctic glaciers. Glacier retreat opens new ice-free areas for the colonization by vegetation and animals. The study was undertaken on the Antarctic Specially Protected Area no. 128 (West Coast of the Admiralty Bay, King George Island, South Shetlands Islands). Transects marked between the Ecology, Baranowski and Windy Glaciers, and a sea shore were used to collect soil samples. Oribatid mites were found only on near-shore areas, on patches of vegetation of more than 30 years of age. The colonization by mite communities is strongly determined by the presence of plants.

O médico George Thomson e os primeiros desenvolvimentos do conceito de gás

The word gas was coined by the "chemical philosopher" Joan Baptista Van Helmont (1579 -- 1644) to name a very broad concept in his chemico-medical system. Eventually, some physicians who followed Helmontian ideas adopted the concept. The present paper aims to analyze the reception of the original idea of gas by an English Helmontian physician, George Thomson (1619 -- 1677). Thomson wrote that the "material cause" of the plague was a gas, and compared it to the "Gas of sulphur". He also related the human archeus to a gas, and explained some observations in the laboratory in terms of production of gases. We observe, however, that Thomson was not as interested as Van Helmont in discussing details about the structure of the matter. Thus, gas did not have the same relevance in Thomson's work as it had in Van Helmont's.

Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.