Tricky and terrible T-cell tumors: these are thrilling times for testing: molecular pathology of peripheral T-cell lymphomas.

Peripheral T-cell lymphomas (PTCLs) encompass a group of rare and usually clinically aggressive diseases. The classification and diagnosis of these diseases are compounded by their marked pathological heterogeneity and complex clinical features. With the exception of ALK-positive anaplastic large cell lymphoma (ALCL), which is defined on the basis of ALK rearrangements, genetic features play little role in the definition of other disease entities. In recent years, hitherto unrecognized chromosomal translocations have been reported in small subsets of PTCLs, and genome-wide array-based profiling investigations have provided novel insights into their molecular characteristics. This article summarizes the current knowledge on the best-characterized genetic and molecular alterations underlying the pathogenesis of PTCLs, with a focus on recent discoveries, their relevance to disease classification, and their management implications from a diagnostical and therapeutical perspective.

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline.

Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer.

BACKGROUND: A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients. METHODS: Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from five European centers were divided into high- and low-risk groups based on the gene signature classification and on clinical risk classifications. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis-free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER]-positive patients) or 92% (for ER-negative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups. RESULTS: The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confidence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low- and high-clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively. CONCLUSIONS: The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer.

Comparison between detailed digital and conventional soil maps of an area with complex geology

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

Arsenic toxicity in Acacia mangium willd. and mimosa Caesalpiniaefolia benth. seedlings

Acacia mangium and Mimosa caesalpiniaefolia are fast-growing woody fabaceous species that might be suitable for phytoremediation of arsenic (As)-contaminated sites. To date, few studies on their tolerance to As toxicity have been published. Therefore, this study assessed As toxicity symptoms in A. mangium and M. caesalpiniaefolia seedlings under As stress in a greenhouse. Seedlings of Acacia mangium and M. caesalpiniaefolia were grown for 120 d in an Oxisol-sand mixture with 0, 50, 100, 200, and 400 mg kg-1 As, in four replications in four randomized blocks. The plants were assessed for visible toxicity symptoms, dry matter production, shoot/root ratio, root anatomy and As uptake. Analyses of variance and regression showed that the growth of A. mangium and M. caesalpiniaefolia was severely hindered by As, with a reduction in dry matter production of more than 80 % at the highest As rate. The root/shoot ratio increased with increasing As rates. At a rate of 400 mg kg-1 As, whitish chlorosis appeared on Mimosa caesalpiniaefolia seedlings. The root anatomy of both species was altered, resulting in cell collapse, death of root buds and accumulation of phenolic compounds. Arsenic concentration was several times greater in roots than in shoots, with more than 150 and 350 mg kg-1 in M. caesalpiniaefolia and A. mangium roots, respectively. These species could be suitable for phytostabilization of As-contaminated sites, but growth-stimulating measures should be used.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

The systematic profiling of false identity documents: method validation and performance evaluation using seizures known to originate from common and different sources

False identity documents constitute a potential powerful source of forensic intelligence because they are essential elements of transnational crime and provide cover for organized crime. In previous work, a systematic profiling method using false documents' visual features has been built within a forensic intelligence model. In the current study, the comparison process and metrics lying at the heart of this profiling method are described and evaluated. This evaluation takes advantage of 347 false identity documents of four different types seized in two countries whose sources were known to be common or different (following police investigations and dismantling of counterfeit factories). Intra-source and inter-sources variations were evaluated through the computation of more than 7500 similarity scores. The profiling method could thus be validated and its performance assessed using two complementary approaches to measuring type I and type II error rates: a binary classification and the computation of likelihood ratios. Very low error rates were measured across the four document types, demonstrating the validity and robustness of the method to link documents to a common source or to differentiate them. These results pave the way for an operational implementation of a systematic profiling process integrated in a developed forensic intelligence model.

Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Apport du CT-3D dans le diagnostic et le traitement des fractures des plateaux tibiaux [Value of 3D CT in diagnosis and treatment of fractures of the tibial plateau]

A precise classification and an optimal understanding of tibial plateau fractures are the basis of a conservative treatment or adequate surgery. The aim of this prospective study is to determine the contribution of 3D CT to the classification of fractures (comparison with standard X-rays) and as an aid to the surgeon in preoperative planning and surgical reconstruction. Between November 1994 and July 1996, 20 patients presenting 22 tibial plateau fractures were considered in this study. They all underwent surgical treatment. The fractures were classified according to the Müller AO classification. They were all investigated by means of standard X-rays (AP, profile, oblique) and the 3D CT. Analysis of the results has shown the superiority of 3D CT in the planning (easier and more acute), in the classification (more precise), and in the exact assessment of the lesions (quantity of fragments); thereby proving to be of undeniable value of the surgeon.

Sweet orange trees grafted on selected rootstocks fertilized with nitrogen, phosphorus and potassium

The majority of citrus trees in Brazil are grafted on 'Rangpur lime' (Citrus limonia Osb.) rootstock. Despite its good horticultural performance, search for disease tolerant rootstock varieties to improve yield and longevity of citrus groves has increased. The objective of this work was to evaluate yield efficiency of sweet oranges on different rootstocks fertilized with N, P, and potassium. Tree growth was affected by rootstock varieties; trees on 'Swingle' citrumelo [Poncirus trifoliata (L.) Raf. × C. paradisi Macf.] presented the smallest canopy (13.3 m³ in the fifth year after tree planting) compared to those on 'Rangpur lime' and 'Cleopatra' mandarin [C. reshni (Hayata) hort. ex Tanaka] grown on the same grove. Although it was observed an overall positive relationship between canopy volume and fruit yield (R² = 0.95**), yield efficiency (kg m-3) was affected by rootstocks, which demonstrated 'Rangpur lime' superiority in relation to Cleopatra. Growth of citrus trees younger than 5-yr-old might be improved by K fertilization rates greater than currently recommended in Brazil, in soils with low K and subjected to nutrient leaching losses.

Differential impact of lacunes and microvascular lesions on poststroke depression.

BACKGROUND AND PURPOSE: Previous studies have postulated that poststroke depression (PSD) might be related to cumulative vascular brain pathology rather than to the location and severity of a single macroinfarct. We performed a detailed analysis of all types of microvascular lesions and lacunes in 41 prospectively documented and consecutively autopsied stroke cases. METHODS: Only cases with first-onset depression <2 years after stroke were considered as PSD in the present series. Diagnosis of depression was established prospectively using DSM-IV criteria for major depression. Neuropathological evaluation included bilateral semiquantitative assessment of microvascular ischemic pathology and lacunes; statistical analysis included Fisher exact test, Mann-Whitney U test, and regression models. RESULTS: Macroinfarct site was not related to the occurrence of PSD for any of the locations studied. Thalamic and basal ganglia lacunes occurred significantly more often in PSD cases. Higher lacune scores in basal ganglia, thalamus, and deep white matter were associated with an increased PSD risk. In contrast, microinfarct and diffuse or periventricular demyelination scores were not increased in PSD. The combined lacune score (thalamic plus basal ganglia plus deep white matter) explained 25% of the variability of PSD occurrence. CONCLUSIONS: The cumulative vascular burden resulting from chronic accumulation of lacunar infarcts within the thalamus, basal ganglia, and deep white matter may be more important than single infarcts in the prediction of PSD.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Total Cost of Transportation Analysis of Road and Highway Issues, HR-388, 2002

In the administration, planning, design, and maintenance of road systems, transportation professionals often need to choose between alternatives, justify decisions, evaluate tradeoffs, determine how much to spend, set priorities, assess how well the network meets traveler needs, and communicate the basis for their actions to others. A variety of technical guidelines, tools, and methods have been developed to help with these activities. Such work aids include design criteria guidelines, design exception analysis methods, needs studies, revenue allocation schemes, regional planning guides, designation of minimum standards, sufficiency ratings, management systems, point based systems to determine eligibility for paving, functional classification, and bridge ratings. While such tools play valuable roles, they also manifest a number of deficiencies and are poorly integrated. Design guides tell what solutions MAY be used, they aren't oriented towards helping find which one SHOULD be used. Design exception methods help justify deviation from design guide requirements but omit consideration of important factors. Resource distribution is too often based on dividing up what's available rather than helping determine how much should be spent. Point systems serve well as procedural tools but are employed primarily to justify decisions that have already been made. In addition, the tools aren't very scalable: a system level method of analysis seldom works at the project level and vice versa. In conjunction with the issues cited above, the operation and financing of the road and highway system is often the subject of criticisms that raise fundamental questions: What is the best way to determine how much money should be spent on a city or a county's road network? Is the size and quality of the rural road system appropriate? Is too much or too little money spent on road work? What parts of the system should be upgraded and in what sequence? Do truckers receive a hidden subsidy from other motorists? Do transportation professions evaluate road situations from too narrow of a perspective? In considering the issues and questions the author concluded that it would be of value if one could identify and develop a new method that would overcome the shortcomings of existing methods, be scalable, be capable of being understood by the general public, and utilize a broad viewpoint. After trying out a number of concepts, it appeared that a good approach would be to view the road network as a sub-component of a much larger system that also includes vehicles, people, goods-in-transit, and all the ancillary items needed to make the system function. Highway investment decisions could then be made on the basis of how they affect the total cost of operating the total system. A concept, named the "Total Cost of Transportation" method, was then developed and tested. The concept rests on four key principles: 1) that roads are but one sub-system of a much larger 'Road Based Transportation System', 2) that the size and activity level of the overall system are determined by market forces, 3) that the sum of everything expended, consumed, given up, or permanently reserved in building the system and generating the activity that results from the market forces represents the total cost of transportation, and 4) that the economic purpose of making road improvements is to minimize that total cost. To test the practical value of the theory, a special database and spreadsheet model of Iowa's county road network was developed. This involved creating a physical model to represent the size, characteristics, activity levels, and the rates at which the activities take place, developing a companion economic cost model, then using the two in tandem to explore a variety of issues. Ultimately, the theory and model proved capable of being used in full system, partial system, single segment, project, and general design guide levels of analysis. The method appeared to be capable of remedying many of the existing work method defects and to answer society's transportation questions from a new perspective.

Maize yield and rainfall on different spatial and temporal scales in Southern Brazil

This study aimed to establish relationships between maize yield and rainfall on different temporal and spatial scales, in order to provide a basis for crop monitoring and modelling. A 16-year series of maize yield and daily rainfall from 11 municipalities and micro-regions of Rio Grande do Sul State was used. Correlation and regression analyses were used to determine associations between crop yield and rainfall for the entire crop cycle, from tasseling to 30 days after, and from 5 days before tasseling to 40 days after. Close relationships between maize yield and rainfall were found, particularly during the reproductive period (45-day period comprising the flowering and grain filling). Relationships were closer on a regional scale than at smaller scales. Implications of the crop-rainfall relationships for crop modelling are discussed.

Antibacterial activity of essential oils on Xanthomonas vesicatoria and control of bacterial spot in tomato

The objective of this work was to evaluate the effects of plant essential oils (EOs) on the growth of Xanthomonas vesicatoria, on bacterial morphology and ultrastructure, and on the severity of tomato bacterial spot. EOs from citronella, clove, cinnamon, lemongrass, eucalyptus, thyme, and tea tree were evaluated in vitro at concentrations of 0.1, 1.0, 10, and 100% in 1.0% powdered milk. The effect of EOs, at 0.1%, on the severity of tomato bacterial spot was evaluated in tomato seedlings under greenhouse conditions. The effects of citronella, lemongrass, clove, and tea tree EOs, at 0.1%, on X. vesicatoria cells were evaluated by transmission electron microscopy. All EOs showed direct toxic effect on the bacteria at a 10%-concentration in vitro. Under greenhouse conditions, the EOs of clove, citronella, tea tree, and lemongrass reduced disease severity. EOs of clove and tea tree, and streptomycin sulfate promoted loss of electron-dense material and alterations in the cytoplasm, whereas EO of tea tree promoted cytoplasm vacuolation, and those of citronella, lemongrass, clove, and tea tree caused damage to the bacterial cell wall. The EOs at a concentration of 0.1% reduce the severity of the disease.