New, low estimate for carbon stock in global forest vegetation based on inventory data

Abstract

Contributions of pitch and bandwidth to sound-induced enhancement of visual cortex excitability in humans.

Multisensory interactions have been documented within low-level, even primary, cortices and at early post-stimulus latencies. These effects are in turn linked to behavioral and perceptual modulations. In humans, visual cortex excitability, as measured by transcranial magnetic stimulation (TMS) induced phosphenes, can be reliably enhanced by the co-presentation of sounds. This enhancement occurs at pre-perceptual stages and is selective for different types of complex sounds. However, the source(s) of auditory inputs effectuating these excitability changes in primary visual cortex remain disputed. The present study sought to determine if direct connections between low-level auditory cortices and primary visual cortex are mediating these kinds of effects by varying the pitch and bandwidth of the sounds co-presented with single-pulse TMS over the occipital pole. Our results from 10 healthy young adults indicate that both the central frequency and bandwidth of a sound independently affect the excitability of visual cortex during processing stages as early as 30 msec post-sound onset. Such findings are consistent with direct connections mediating early-latency, low-level multisensory interactions within visual cortices.

On the dual contrast enhancement mechanism in frequency-selective inversion-recovery magnetic resonance angiography (IRON-MRA)

The susceptibility of blood changes after administration of a paramagnetic contrast agent that shortens T(1). Concomitantly, the resonance frequency of the blood vessels shifts in a geometry-dependent way. This frequency change may be exploited for incremental contrast generation by applying a frequency-selective saturation prepulse prior to the imaging sequence. The dual origin of vascular enhancement depending first on off-resonance and second on T(1) lowering was investigated in vitro, together with the geometry dependence of the signal at 3T. First results obtained in an in vivo rabbit model are presented.

Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data

Abstract

A recent inventory of the bats of Mozambique with new documentation of 7 new species for the country

The bat fauna of Mozambique is poorly documented. We conducted a series of inventories across the country between 2005 and 2009, resulting in the identification of 50 species from 41 sites. Of these, seven species represent new national records that increase the country total to 67 species. These data include results from the first detailed surveys across northern Mozambique, over an area representing almost 50% of the country. We detail information on new distribution records and measurements of these specimens. Special attention is paid to the Rhinolophidae, because these include several taxa that are currently in a state of taxonomic confusion. Furthermore, we also present some notes on taxonomy, ecology and echolocation calls. Finally, we combine modelled distributions to present predicted species richness across the country. Species richness was lowest across the coastal plain, to the east and far north, and is predicted to increase in association with rising altitude and higher topographic unevenness of the landscape.

Temozolomide-mediated radiation enhancement in glioblastoma: a report on underlying mechanisms.

PURPOSE: In this study, we investigated the mechanisms by which temozolomide enhances radiation response in glioblastoma cells. EXPERIMENTAL DESIGN: Using a panel of four primary human glioblastoma cell lines with heterogeneous O(6)-methylguanine-DNA methyltransferase (MGMT) protein expression, normal human astrocytes, and U87 xenografts, we investigated (a) the relationship of MGMT status with efficacy of temozolomide-based chemoradiation using a panel of in vitro and in vivo assays; (b) underlying mechanisms by which temozolomide enhances radiation effect in glioblastoma cells; and (c) strategies to overcome resistance to radiation + temozolomide. RESULTS: Temozolomide enhances radiation response most effectively in glioblastomas without detectable MGMT expression. On concurrent radiation + temozolomide administration in MGMT-negative glioblastomas, there seems to be decreased double-strand DNA (dsDNA) repair capacity and enhanced dsDNA damage compared either with radiation alone or with sequentially administered temozolomide. Our data suggest that O(6)-benzylguanine can enhance the antitumor effects of concurrent radiation + temozolomide in MGMT-positive cells by enhancing apoptosis and the degree of dsDNA damage. O(6)-Benzylguanine was most effective when administered concurrently with radiation + temozolomide and had less of an effect when administered with temozolomide in the absence of radiation or when administered sequentially with radiation. Our in vivo data using U87 xenografts confirmed our in vitro findings. CONCLUSIONS: The present study shows that temozolomide enhances radiation response most effectively in MGMT-negative glioblastomas by increasing the degree of radiation-induced double-strand DNA damage. In MGMT-positive glioblastomas, depletion of MGMT by the addition of O(6)-benzylguanine significantly enhances the antitumor effect of concurrent radiation + temozolomide. These are among the first data showing mechanisms of synergy between radiation and temozolomide and the effect of MGMT.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.