Chemical and aerosol characterisation of the troposphere over West Africa during the monsoon period as part of AMMA

During June, July and August 2006 five aircraft took part in a campaign over West Africa to observe the aerosol content and chemical composition of the troposphere and lower stratosphere as part of the African Monsoon Multidisciplinary Analysis (AMMA) project. These are the first such measurements in this region during the monsoon period. In addition to providing an overview of the tropospheric composition, this paper provides a description of the measurement strategy (flights performed, instrumental payloads, wing-tip to wing-tip comparisons) and points to some of the important findings discussed in more detail in other papers in this special issue. The ozone data exhibits an "S" shaped vertical profile which appears to result from significant losses in the lower troposphere due to rapid deposition to forested areas and photochemical destruction in the moist monsoon air, and convective uplift of ozone-poor air to the upper troposphere. This profile is disturbed, particularly in the south of the region, by the intrusions in the lower and middle troposphere of air from the southern hemisphere impacted by biomass burning. Comparisons with longer term data sets suggest the impact of these intrusions on West Africa in 2006 was greater than in other recent wet seasons. There is evidence for net photochemical production of ozone in these biomass burning plumes as well as in urban plumes, in particular that from Lagos, convective outflow in the upper troposphere and in boundary layer air affected by nitrogen oxide emissions from recently wetted soils. This latter effect, along with enhanced deposition to the forested areas, contributes to a latitudinal gradient of ozone in the lower troposphere. Biogenic volatile organic compounds are also important in defining the composition both for the boundary layer and upper tropospheric convective outflow. Mineral dust was found to be the most abundant and ubiquitous aerosol type in the atmosphere over Western Africa. Data collected within AMMA indicate that injection of dust to altitudes favourable for long-range transport (i.e. in the upper Sahelian planetary boundary layer) can occur behind the leading edge of mesoscale convective system (MCS) cold-pools. Research within AMMA also provides the first estimates of secondary organic aerosols across the West African Sahel and have shown that organic mass loadings vary between 0 and 2 μg m−3 with a median concentration of 1.07 μg m−3. The vertical distribution of nucleation mode particle concentrations reveals that significant and fairly strong particle formation events did occur for a considerable fraction of measurement time above 8 km (and only there). Very low concentrations were observed in general in the fresh outflow of active MCSs, likely as the result of efficient wet removal of aerosol particles due to heavy precipitation inside the convective cells of the MCSs. This wet removal initially affects all particle size ranges as clearly shown by all measurements in the vicinity of MCSs

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

C. PRESL) at the transcriptional level.

This paper investigates differences in gene expression among the two Thlaspi caerulescens ecotypes La Calamine (LC) and Lellingen (LE) that have been shown to differ in metal tolerance and metal uptake. LC originates from a metalliferous soil and tolerates higher metal concentrations than LE which originates from a non-metalliferous soil. The two ecotypes were treated with different levels of zinc in solution culture, and differences in gene expression were assessed through application of a cDNA microarray consisting of 1,700 root and 2,700 shoot cDNAs. Hybridisation of root and shoot cDNA from the two ecotypes revealed a total of 257 differentially expressed genes. The regulation of selected genes was verified by quantitative reverse transcriptase polymerase chain reaction. Comparison of the expression profiles of the two ecotypes suggests that LC has a higher capacity to cope with reactive oxygen species and to avoid the formation of peroxynitrite. Furthermore, increased transcripts for the genes encoding for water channel proteins could explain the higher Zn tolerance of LC compared to LE. The higher Zn tolerance of LC was reflected by a lower expression of the genes involved in disease and defence mechanisms. The results of this study provide a valuable set of data that may help to improve our understanding of the mechanisms employed by plants to tolerate toxic concentrations of metal in the soil

Frog Information

File Information The Frog.csv data set includes information on each of the 68,359 individual amphibians surveyed for this effort. Information on data in each column is in the ReadMe file

Temperature Data

Contains temperature data from deep and shallow loggers programmed to record hourly during the summer months. When loggers were out of water or data were otherwise suspect, data were deleted. Please see TemperatureAnomalies.csv file for issues with loggers and their resolution when dates are missing from the Temperature.csv file

Temperature Data

Contains temperature data from deep and shallow loggers programmed to record hourly during the summer months. When loggers were out of water or data were otherwise suspect, data were deleted. Please see TemperatureAnomalies.csv file for issues with loggers and their resolution when dates are missing from the Temperature.csv file

Using Camera Traps and AI to Improve Efficacy and Reduce Bycatch at Goodnature A24 Rodent Traps in Hawaii

Camera traps provide an unobtrusive means to monitor wildlife presence and behavior. Yet there is a steep learning curve associated with their deployment. Camera model, settings and position, target behavior, and technicians’ skill greatly influence the success of camera trapping. Furthermore, data storage and management are complex, as copious photos occupy considerable storage space. Finally, evaluating large numbers of digital images is time-consuming for low frequency events; in each of our trials we amassed 10,000-50,000 photos, of which 6-20% were target animals. The application of artificial intelligence (AI) to digital image datasets can greatly increase efficiency, but few existing algorithms have been trained on small animals. We embarked on a camera trapping project to assess interactions of target (rodent) and non-target (bird) species with 125 GoodNature A24 rat traps deployed in rainforest sites on Kauai, Hawaii, following several observations of non-target mortality. While our long-term goal was to use camera trap data to suggest modifications to traps that would maintain target kills while minimizing bycatch, the short-term goal presented in this manuscript focused on perfecting our camera trapping program and AI to classify photos of small animals. Specifically, we described lessons learned regarding 1) the performance of several camera models, 2) camera placement, 3) data management, and 4) artificial network training and development. First, we report on field studies assessing Bushnell TrophyCam HD, Bushnell HD, Reconyx HyperFire, and Reconyx HyperFire2 models on a variety of settings, distances, and angles with respect to the traps. Camera model and placement at traps are critical to capturing images amenable to AI development, as is variation in the training dataset. Second, we outline our data management and sharing protocols. Third, we describe the development of preliminary AI models to review and sort camera trap data. Early models reduced the workload of reviewing camera trap data by correctly classifying photos of rats, birds, humans, pigs, and empty frames. We expect these results to further improve with more training data. These results will greatly enhance the efficacy of several camera trapping studies that we have recently undertaken and help us modify traps to avoid bycatch