Constraints on the mantle sources of the Deccan traps from the petrology and geochemistry of the basalts of Gujarat state (Western India)

The late Cretaceous-early Tertiary flood basalts in the Gujarat area of the northwestern Deccan Traps (Kathiawar peninsula, Pavagadh hills and Rajpipla) exhibit a wide range of compositions, from picrite basalts to rhyolites; moreover, the basaltic rocks have clearly distinct TiO2 contents at any given degree of differentiation and strongly resemble the low-titanium and hightitanium basalts found in most of the Gondwana continental flood basalt (CFB) suites. Four magma groups are petrologically and geochemically distinguished: (1) A low-Ti group, characterized by rocks with varying SiO2 saturation, and with TiO2 <1.8 wt%, extremely low incompatible trace element abundances, low Zr/γ (av- 3.8), Ti/ V (av. 27), and a very slight large ion lithophile element (LJLE) enrichment over high field strength elements (HFSE). These rocks share some features with the Bushe Formation of the Western Ghats farther south, but have distinct geochemical characters, in particular the strong depletion in most incompatible trace elements. (2) A high-Ti group, characterized by a more K-rich character than the low-Ti rocks, and with a strong enrichment in incompatible elements, similar to average ocean island basalt (OIB), e.g. high TiO2 (>1.8 wt% in picrites), Nb (>19 p.p.m.) Zr/γ (av. 6.5) and Tt/V (av. 47). (3) An intermediate-Ti group, with TiO2 contents slightly lower than the high-Ti rocks at the same degree of evolution, and with correspondingly lower incompatible trace element contents and ratios, in particular K2O, Nb, Ba and Zr/Y (av. 5.2). (4) A potassium-rich group (KT), broadly similar in geochemical character to the high-Ti group but showing more extreme K, Rb and Ba enrichment (av. K20/Na20~l; Ba/Y~20). The most primitive low-Ti and high-Ti picrites, when corrected for low-pressure olivine fractionation, show distinct major (and trace) element geochemistry, in particular for CaO/AI2O3, CaO/TiO2 and Al2O3/TiO2, and moderate but significant variations in their SiO2 and Fe2Ost contents; these characteristics strongly suggest the involvement of different mantle sources, more depleted for the low-Ti picrites, and richer in cpxfor the high-Ti picrites, but with broadly the same pressures of equilibration (27-14 kbar). This, in turn, suggests a strong lateral heterogeneity in the Gujarat Trap mantle. Low-Ti picrites and related differentiates in Kathiawar are reported systematically for the first time here, and suggest the existence of HFSE-depleted mantle in the northwestern Deccan Traps, with extension at least to the Seychelles Islands and to the area of the Bushe Formation near Bombay in the pre-drift position, before the development of the Carlsberg Ridge. The absence of correlations between LILE/HFSE ratios and SiO2 argues against crustal contamination processes acting on the low-Ti picrites, possibly owing to their probably rapid uprise to the surface. Consequently, the mantle region of this rock group was probably re-enriched by small amounts of ULE-rich materials. The substantially higher, trace element enrichment of the least differentiated high-Ti picrites, relative to the basalts of the Ambe-noli and Mahableshwar Formations of the Western Ghats, testifies also to the presence of more incompatible element rich, OIB4ike mantle sources in northern and northwestern Gujarat. These sources were geochemicaily similar to the present-day Reunion mantle sources

Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles

Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. Results: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets

Crystal chemistry of igneous rock biotites. Granites and their surroundings

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Dependence of chemistry on genesis in biotites assessed by multivariate statistical analysis

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Crystal Chemistry of Igneous Rock Biotites

A study of 304 selected biotite analyses, with 17 chemical variables (Al IV , Fe IV , Al VI Fe VI , Mg, Mn, Ti, Li, Na, K, Rb, Ca, Ba, OH, F, Cl,fH2O and fO2 , can cause more limited variations