Plasmodium pre-erythrocytic stages: what’s new?

The pre-erythrocytic (PE) phase of malaria infection, which extends from injection of sporozoites into the skin to the release of the first generation of merozoites, has traditionally been the 'black box' of the Plasmodium life cycle. However, since the advent of parasite transfection technology 13 years ago, our understanding of the PE phase in cellular and molecular terms has dramatically improved. Here, we review and comment on the major developments in the field in the past five years. Progress has been made in many diverse areas, including identifying and characterizing new proteins of interest, imaging parasites in vivo, understanding better the cell biology of hepatocyte infection and developing new vaccines against PE stages of the parasite.     

LIVES: a new habitat modelling technique for predicting the distribution of species’ occurrences using presence-only data based on limiting factor theory

Predictive modelling techniques using presence-only data have attracted increasing attention because they can provide information on species distributions and their potential habitat for conservation and ecosystem management. However, the existing predictive modelling techniques have several limitations. Here, we propose a novel predictive modelling technique, Limiting Variable and Environmental Suitability (LIVES), for predicting the distributions and potential habitats of species using presence-only data. It is based on limiting factor theory, which postulates that the occurrence of a species is only determined by the factor that most limits its distribution. LIVES predicts the suitability of a candidate grid cell for a species in terms of limiting environmental factor. It also predicts the most limiting factor or the potential limiting factor at the grid cell. The environmental factors can be climatic, geological, biological and any other relevant environmental factors, whether quantitative or qualitative. The predicted habitats consist of the current distribution of the species and the potentially suitable areas for the species where there is currently no record of occurrence. We also compare several properties of LIVES and other predictive modelling techniques. On the basis of 1,000 simulations, the average predictions of LIVES are more accurate than the two other commonly used modelling techniques (BIOCLIM and DOMAIN) for presence-only data.     

A principal component regression model to forecast airborne concentration of Cupressaceae pollen in the city of Granada (SE Spain), during 1995–2006

The problem of developing a 2-week-on ahead forecast of atmospheric cypress pollen levels is tackled in this paper by developing a principal component multiple regression model involving several climatic variables. The efficacy of the proposed model is validated by means of an application to real data of Cupressaceae pollen concentration in the city of Granada (southeast of Spain). The model was applied to data from 11 consecutive years (1995–2005), with 2006 being used to validate the forecasts. Based on the work of different authors, factors as temperature, humidity, hours of sun and wind speed were incorporated in the model. This methodology explains approximately 75–80% of the variability in the airborne Cupressaceae pollen concentration.     

Airborne pollen concentration in Seville (Spain), 1993–1996. First results obtained with Hirst’s method

The data presented constitute the longest historical series of results obtained in Seville with a Hirst-type sampler, and are a further contribution to earlier aerobiological studies carried out in the city with a Cour trap. This work supplies an updated pollen calendar of the city, together with pollen counts and other aerobiological parameters for the 14 most important types in the 4-year period of sampling. These werePlatanus hispanica, Olea europaea, Quercus, Cupressaceae, Poaceae, Urticaceae, Moraceae, Chenopodiaceae/ Amaranthaceae,Plantago, Pinaceae,Rumex, Myrtaceae, Compositae, andCasuarina.     

A new ‘bio-comfort’ perspective for Melbourne based on heat stress,air pollution and pollen

Humans are at risk from exposure to extremes in their environment, yet there is no consistent way to fully quantify and understand the risk when considering more than just meteorological variables. An outdoor ‘bio-comfort’ threshold is defined for Melbourne, Australia using a combination of heat stress, air particulate concentration and grass pollen count, where comfortable conditions imply an ideal range of temperature, humidity and wind speed, acceptable levels of air particulates and a low pollen count. This is a new approach to defining the comfort of human populations. While other works have looked into the separate impacts of different variables, this is the first time that a unified bio-comfort threshold is suggested. Composite maps of surface pressure are used to illustrate the genesis and evolution of the atmospheric structures conducive to an uncomfortable day. When there is an uncomfortable day due to heat stress conditions in Melbourne, there is a high pressure anomaly to the east bringing warm air from the northern interior of Australia. This anomaly is part of a slow moving blocking high originating over the Indian Ocean. Uncomfortable days due to high particulate levels have an approaching cold front. However, for air particulate cases during the cold season there are stable atmospheric conditions enhanced by a blocking high emanating from Australia and linking with the Antarctic continent. Finally, when grass pollen levels are high, there are northerly winds carrying the pollen from rural grass lands to Melbourne, due to a stationary trough of low pressure inland. Analysis into days with multiple types of stress revealed that the atmospheric signals associated with each type of discomfort are present regardless of whether the day is uncomfortable due to one or multiple variables. Therefore, these bio-comfort results are significant because they offer a degree of predictability for future uncomfortable days in Melbourne.     

A new method to detect event-related potentials based on Pearson’s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.  Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise.  The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP’s waveform, these waveforms being time- and phase-locked.  In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson’s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase—in consonance with the stimuli—in EEG signal correlation over all channels.  This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs.  These hidden components seem to be caused by variations (between each successive stimulus) of the ERP’s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology.  The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language.     

A specialist’s audit of aggregated occurrence records: An ‘aggregator’s’ perspective

A recent ZooKeys’ paper (Mesibov, 2013: http://www.pensoft.net/journal_home_page.php?journal_id=1&page=article&SESID=df7bcb35b02603283dcb83ee0e0af0c9&type=show&article_id=5111) has highlighted data quality issues in aggregated data sets, but did not provide a realistic way to address these issues. This paper provides an aggregator’s perspective including ways that the whole community can help to address data quality issues. The establishment of GBIF and national nodes (national aggregators) such as the Atlas of Living Australia (ALA) have integrated and exposed a huge diversity of biological observations along with many associated issues. Much of the admirable work by Mesibov (2013) was enabled by having the data exposed.Data quality, one of the highest priorities for GBIF, the national nodes and other aggregators, depends on both automatic methods and community experts to detect and correct data issues. Not all issues can however be automatically detected or corrected, so community assistance is needed to help improve the quality of exposed biological data. We do need to improve the infrastructure and associated processes to more easily identify data issues and document all changes to ensure a full record is permanently and publicly available.     

Darwin’s legacy to rove beetles (Coleoptera,Staphylinidae): A new genus and a new species,including materials collected on the Beagle’s voyage

A species of xanthopygine rove beetles is described and figured here as Darwinilus sedarisigen. n. and sp. n. The holotype was collected by Charles Darwin in Bahía Blanca, Argentina on the Beagle’s voyage. The contributions of Charles Darwin to rove beetle systematics are summarized briefly.