CD34/QBEND10 immunostaining in bone marrow biopsies: an additional parameter for the diagnosis and classification of myelodysplastic syndromes.

CD34/QBEND10 immunostaining has been assessed in 150 bone marrow biopsies (BMB) including 91 myelodysplastic syndromes (MDS), 16 MDS-related AML, 25 reactive BMB, and 18 cases where RA could neither be established nor ruled out. All cases were reviewed and classified according to the clinical and morphological FAB criteria. The percentage of CD34-positive (CD34 +) hematopoietic cells and the number of clusters of CD34+ cells in 10 HPF were determined. In most cases the CD34+ cell count was similar to the blast percentage determined morphologically. In RA, however, not only typical blasts but also less immature hemopoietic cells lying morphologically between blasts and promyelocytes were stained with CD34. The CD34+ cell count and cluster values were significantly higher in RA than in BMB with reactive changes (p<0.0001 for both), in RAEB than in RA (p=0.0006 and p=0.0189, respectively), in RAEBt than in RAEB (p=0.0001 and p=0.0038), and in MDS-AML than in RAEBt (p<0.0001 and p=0.0007). Presence of CD34+ cell clusters in RA correlated with increased risk of progression of the disease. We conclude that CD34 immunostaining in BMB is a useful tool for distinguishing RA from other anemias, assessing blast percentage in MDS cases, classifying them according to FAB, and following their evolution.

A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature.

BACKGROUND: Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis. RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients. CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.

Semi-supervised remote sensing image classification with cluster kernels

A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.

The use of total luminescence spectroscopy in the investigation of the effects of different rice management practices on humic substances of a planosol

In the Earth's carbon cycle, C stocks in the soil are higher than in vegetation and atmosphere. Maintaining and conserving organic C concentrations in the soil by specific management practices can improve soil fertility and productivity. The aim of this study was to evaluate the impact of agricultural management techniques and influence of water regime (flooded or drained) on the structure of humic substances by excitation/emission matrix fluorescence. Six samples of a Planosol (Planossolo by the Brazilian System of Soil Classification) were collected from a rice field. Humic substances (HS) were extracted from flooded and drained soil under different agricultural management techniques: conventional tillage, reduced tillage and grassland. Two peaks at a long emission wavelength were observed in the EEM spectra of HA whereas those of the corresponding FA contained a unique fluorophore at an intermediate excitation/emission wavelength pair (EEWP) value. The fluorescence intensity measured by total luminescence (FI TL) of HA was lower than that of the corresponding FA. A comparison of all samples (i.e., the HA values compared to each other) revealed only slight differences in the EEWP position, but the FI TL values were significantly different. In this soil, anoxic conditions and reduced tillage (little plowing) seem to favor a higher degree of humification of the soil organic matter compared with aerated conditions and conventional tillage.

Characteristic of Dystrustepts in the Venezuelan Andes

The majority (60 %) of the soils in the Venezuelan Andes are Inceptisols, a large percentage of which are classified as Dystrustepts by the US Soil Taxonomy, Second Edition of 1999. Some of these soils were classified as Humitropepts (high organic - C-OC-soils) and Dystropepts by the Soil Taxonomy prior to 1999, but no equivalent large group was created for high-OC soils in the new Ustepts suborder. Dystrusepts developed on different materials, relief and vegetation. Their properties are closely related with the parent material. Soils developed on transported deposits or sediments have darker and thicker A horizons, a slightly acid reaction, greater CEC and OC contents than upland slope soils. Based on the previous classification into large groups (Humitropepts and Dystropepts) we found that: Humitropepts have a slightly less acid and higher values of CEC than Dystropepts. These properties or characteristics seem to be related to the fact that Humitropepts have a higher clay and OC content than the Dystropepts. Canonical discrimination analysis showed that the variables that discriminate the two great soil groups from each other are OC and silt. Data for Humitropepts are grouped around the OC vector (defining axis 3, principal component analysis), while Dystropepts are associated with the clay and sand vectors, with significant correlation. Given the importance of OC for soil properties, we propose the creation of a new large group named Humustepts for the order Inceptisol, suborder Ustepts.

Genesis and classification of Oxisols in a highland toposequence of the upper Jequitinhonha Valley (MG)

The Brazilian System of Soil Classification (SiBCS) is a taxonomic system, open and in permanent construction, as new knowledge on Brazilian soils is obtained. The objective of this study was to characterize the chemical, physical, morphological, micro-morphological and mineralogical properties of four pedons of Oxisols in a highland toposequence in the upper Jequitinhonha Valley, emphasizing aspects of their genesis, classification and landscape development. The pedons occupy the following slope positions: summit - Red Oxisol (LV), mid slope (upper third) - Yellow-Red Oxisol (LVA), lower slope (middle third)- Yellow Oxisol (LA) and bottom of the valley (lowest third) - "Gray Oxisol" ("LAC"). These pedons were described and sampled for characterization in chemical and physical routine analyses. The total Fe, Al and Mn contents were determined by sulfuric attack and the Fe, Al and Mn oxides in dithionite-citrate-bicarbonate and oxalate extraction. The mineralogy of silicate clays was identified by X ray diffraction and the Fe oxides were detected by differential X ray diffraction. Total Ti, Ga and Zr contents were determined by X ray fluorescence spectrometry. The "LAC" is gray-colored and contains significant fragments of structure units in the form of a dense paste, characteristic of a gleysoil, in the horizons A and BA. All pedons are very clayey, dystrophic and have low contents of available P and a pH of around 5. The soil color was related to the Fe oxide content, which decreased along the slope. The decrease of crystalline and low- crystalline Fe along the slope confirmed the loss of Fe from the "LAC". Total Si increased along the slope and total Al remained constant. The clay fraction in all pedons was dominated by kaolinite and gibbsite. Hematite and goethite were identified in LV, low-intensity hematite and goethite in LVA, goethite in LA. In the "LAC", no hematite peaks and goethite were detected by differential X ray diffraction. The micro-morphology indicated prevalence of granular microstructure and porosity with complex stacking patterns.. The soil properties in the toposequence converged to a single soil class, the Oxisols, derived from the same source material. The landscape evolution and genesis of Oxisols of the highlands in the upper Jequitinhonha Valley are related to the evolution of the drainage system and the activity of excavating fauna.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Soil spectral library and its use in soil classification

Soil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a Fieldspec sensor, 350-2,500 nm) for the horizons of 223 soil profiles from the regions of Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracajú, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarênicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litólicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350-400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.

A Research Report on Roadside Vegetation Management, December 1975

According to prevailing ecological theory one would expect the most stable vegetation on sites which are least disturbed (Odum 1971). According to theory one would also expect the most diversity of species on undisturbed sites (Odum 1971). This stable and diverse community would be produced over a period of many years through a process of plant succession where annual herbs are replaced by perennial herbs and finally woody plants would come to dominate and perpetuate the community. Another ecological theory holds that the complexity (structure and species diversity) of a plant community is dependent upon the amount of disturbance to which it is subjected (Woodwell, 1970). According to this theory the normal succession of a plant community through its various stages may be arrested at some point depending upon the nature and severity of the disturbance. In applying these theories to roadside vegetation it becomes apparent that mass herbicide spraying and extensive mowing of roadsides has produced a relatively simple and unstable vegetation. It follows that if disturbances were reduced not only would the roadside plant community increase in stability but maintenance costs and energy usage would be reduced. In this study we have investigated several aspects of reduced disturbances on roadside vegetation. Research has centered on the effectiveness of spot spraying techniques on noxious weed control, establishment of native grass cover where ditch cleaning and other disturbance has left the bare soil exposed and the response of roadside vegetation when released from annual mass spraying.

New vineyard cultivation practices create patchy ground vegetation, favouring Woodlarks

Intensive agriculture, in which detrimental farming practices lessen food abundance and/or reduce food accessibility for many animal species, has led to a widespread collapse of farmland biodiversity. Vineyards in central and southern Europe are intensively cultivated; though they may still harbour several rare plant and animal species, they remain little studied. Over the past decades, there has been a considerable reduction in the application of insecticides in wine production, with a progressive shift to biological control (integrated production) and, to a lesser extent, organic production. Spraying of herbicides has also diminished, which has led to more vegetation cover on the ground, although most vineyards remain bare, especially in southern Europe. The effects of these potentially positive environmental trends upon biodiversity remain mostly unknown as regards vertebrates. The Woodlark (Lullula arborea) is an endangered, short-distance migratory bird that forages and breeds on the ground. In southern Switzerland (Valais), it occurs mostly in vineyards. We used radiotracking and mixed effects logistic regression models to assess Woodlark response to modern vineyard farming practices, study factors driving foraging micro-habitat selection, and determine optimal habitat profile to inform management. The presence of ground vegetation cover was the main factor dictating the selection of foraging locations, with an optimum around 55% at the foraging patch scale. These conditions are met in integrated production vineyards, but only when grass is tolerated on part of the ground surface, which is the case on ca. 5% of the total Valais vineyard area. In contrast, conventionally managed vineyards covering a parts per thousand yen95% of the vineyard area are too bare because of systematic application of herbicides all over the ground, whilst the rare organic vineyards usually have a too-dense sward. The optimal mosaic with ca. 50% ground vegetation cover is currently achieved in integrated production vineyards where herbicide is applied every second row. In organic production, ca. 50% ground vegetation cover should be promoted, which requires regular mechanical removal of ground vegetation. These measures are likely to benefit general biodiversity in vineyards.

Understorey vegetation in fresh and Herb-Rich upland forests in South-west Lapland

Summary

Generalized Schmidt decomposition and classification of three-quantum-bit states

We prove for any pure three-quantum-bit state the existence of local bases which allow one to build a set of five orthogonal product states in terms of which the state can be written in a unique form. This leads to a canonical form which generalizes the two-quantum-bit Schmidt decomposition. It is uniquely characterized by the five entanglement parameters. It leads to a complete classification of the three-quantum-bit states. It shows that the right outcome of an adequate local measurement always erases all entanglement between the other two parties.

Characterization and classification of soils in the Taquari river basin - Pantanal region, state of Mato Grosso do Sul, Brazil

Among the soils in the Mato Grosso do Sul, stand out in the Pantanal biome, the Spodosols. Despite being recorded in considerable extensions, few studies aiming to characterize and classify these soils were performed. The purpose of this study was to characterize and classify soils in three areas of two physiographic types in the Taquari river basin: bay and flooded fields. Two trenches were opened in the bay area (P1 and P2) and two in the flooded field (P3 and P4). The third area (saline) with high sodium levels was sampled for further studies. In the soils in both areas the sand fraction was predominant and the texture from sand to sandy loam, with the main constituent quartz. In the bay area, the soil organic carbon in the surface layer (P1) was (OC) > 80 g kg-1, being diagnosed as Histic epipedon. In the other profiles the surface horizons had low OC levels which, associated with other properties, classified them as Ochric epipedons. In the soils of the bay area (P1 and P2), the pH ranged from 5.0 to 7.5, associated with dominance of Ca2+ and Mg2+, with base saturation above 50 % in some horizons. In the flooded fields (P3 and P4) the soil pH ranged from 4.9 to 5.9, H+ contents were high in the surface horizons (0.8-10.5 cmol c kg-1 ), Ca2+ and Mg² contents ranged from 0.4 to 0.8 cmol c kg-1 and base saturation was < 50 %. In the soils of the bay area (P1 and P2) iron was accumulated (extracted by dithionite - Fed) and OC in the spodic horizon; in the P3 and P4 soils only Fed was accumulated (in the subsurface layers). According to the criteria adopted by the Brazilian System of Soil Classification (SiBCS) at the subgroup level, the soils were classified as: P1: Organic Hydromorphic Ferrohumiluvic Spodosol. P2: Typical Orthic Ferrohumiluvic Spodosol. P3: Typical Hydromorphic Ferroluvic Spodosol. P4: Arenic Orthic Ferroluvic Spodosol.