The regional-scale surface mass balance of Pine Island Glacier, West Antarctica, over the period 2005--2014, derived from airborne radar soundings and neutron probe measurements

We derive recent surface mass balance (SMB) estimates from airborne radar observations along the iSTAR traverse (2013, 2014) at Pine Island Glacier (PIG), West Antarctica. Ground-based neutron probe measurements provide information of snow and firn density with depth at 22 locations and were used to date internal annual reflection layers. The 2005 layer was traced for a total distance of 2367 km to determine annual mean SMB for the period 2005–2014. Using complementary SMB estimates from two regional climate models, RACMO2.3p2 and MAR, and a geostatistical kriging scheme, we determine a regional-scale SMB distribution with similar main characteristics to that determined for the period 1985–2009 in previous studies. Local departures exist for the northern PIG slopes, where the orographic precipitation shadow effect appears to be more pronounced in our observations, and the southward interior, where the SMB gradient is more pronounced in previous studies. We derive total mass inputs of 79.9 +/- 19.2 and 82.1 +/- 19.2 Gt yr-1 to the PIG basin based on complementary ASIRAS–RACMO and ASIRAS–MAR SMB estimates, respectively. These are not significantly different to the value of 78.3 +/- 6.8 Gt yr-1 for the period 1985–2009. Thus, there is no evidence of a secular trend at decadal scales in total mass input to the PIG basin. We note, however, that our estimated uncertainty is more than twice the uncertainty for the 1985–2009 estimate on total mass input. Our error analysis indicates that uncertainty estimates on total mass input are highly sensitive to the selected krige methodology and assumptions made on the interpolation error, which we identify as the main cause for the increased uncertainty range compared to the 1985–2009 estimates

The assessment of science: the relative merits of post- publication review, the impact factor, and the number of citations

The assessment of scientific publications is an integral part of the scientific process. Here we investigate three methods of assessing the merit of a scientific paper: subjective post-publication peer review, the number of citations gained by a paper, and the impact factor of the journal in which the article was published. We investigate these methods using two datasets in which subjective post-publication assessments of scientific publications have been made by experts. We find that there are moderate, but statistically significant, correlations between assessor scores, when two assessors have rated the same paper, and between assessor score and the number of citations a paper accrues. However, we show that assessor score depends strongly on the journal in which the paper is published, and that assessors tend to over-rate papers published in journals with high impact factors. If we control for this bias, we find that the correlation between assessor scores and between assessor score and the number of citations is weak, suggesting that scientists have little ability to judge either the intrinsic merit of a paper or its likely impact. We also show that the number of citations a paper receives is an extremely error-prone measure of scientific merit. Finally, we argue that the impact factor is likely to be a poor measure of merit, since it depends on subjective assessment. We conclude that the three measures of scientific merit considered here are poor; in particular subjective assessments are an error-prone, biased, and expensive method by which to assess merit. We argue that the impact factor may be the most satisfactory of the methods we have considered, since it is a form of pre-publication review. However, we emphasise that it is likely to be a very error-prone measure of merit that is qualitative, not quantitative

First-line treatment of metastatic clear cell renal cell carcinoma: a decision-making analysis among experts

Background: The treatment landscape of metastatic clear cell renal cell carcinoma (mccRCC) has been transformed by targeted therapies with tyrosine kinase inhibitors (TKI) and more recently by the incorporation of immune checkpoint inhibitors (ICI). Today, a spectrum of single agent TKI to TKI/ICI and ICI/ICI combinations can be considered and the choice of the best regimen is complex. Materials and methods: We performed an updated decision-making analysis among 11 international kidney cancer experts. Each expert provided their treatment strategy and relevant decision criteria in the first line treatment of mccRCC. After the collection of all input a list of unified decision criteria was determined and compatible decision trees were created. We used a methodology based on diagnostic nodes, which allows for an automated cross-comparison of decision trees, to determine the most common treatment recommendations as well as deviations. Results: Diverse parameters were considered relevant for treatment selection, various drugs and drug combinations were recommended by the experts. The parameters, chosen by the experts, were performance status, International Metastatic renal cell carcinoma Database Consortium (IMDC) risk group, PD-L1 status, zugzwang and contraindication to immunotherapy. The systemic therapies selected for first line treatment were sunitinib, pazopanib, tivozanib, cabozantinib, ipilimumab/nivolumab or pembrolizumab/axitinib. Conclusion: A wide spectrum of treatment recommendations based on multiple decision criteria was demonstrated. Significant inter-expert variations were observed. This demonstrates how data from randomized trials are implemented differently when transferred into daily practice

Transcriptome pathways unique to dehydration tolerant relatives of modern wheat

Among abiotic stressors, drought is a major factor responsible for dramatic yield loss in agriculture. In order to reveal differences in global expression profiles of drought tolerant and sensitive wild emmer wheat genotypes, a previously deployed shock-like dehydration process was utilized to compare transcriptomes at two time points in root and leaf tissues using the Affymetrix GeneChip(R) Wheat Genome Array hybridization. The comparison of transcriptomes reveal several unique genes or expression patterns such as differential usage of IP(3)-dependent signal transduction pathways, ethylene- and abscisic acid (ABA)-dependent signaling, and preferential or faster induction of ABA-dependent transcription factors by the tolerant genotype that distinguish contrasting genotypes indicative of distinctive stress response pathways. The data also show that wild emmer wheat is capable of engaging known drought stress responsive mechanisms. The global comparison of transcriptomes in the absence of and after dehydration underlined the gene networks especially in root tissues that may have been lost in the selection processes generating modern bread wheats

ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems

ToppCluster is a web server application that leverages a powerful enrichment analysis and underlying data environment for comparative analyses of multiple gene lists. It generates heatmaps or connectivity networks that reveal functional features shared or specific to multiple gene lists. ToppCluster uses hypergeometric tests to obtain list-specific feature enrichment P-values for currently 17 categories of annotations of human-ortholog genes, and provides user-selectable cutoffs and multiple testing correction methods to control false discovery. Each nameable gene list represents a column input to a resulting matrix whose rows are overrepresented features, and individual cells per-list P-values and corresponding genes per feature. ToppCluster provides users with choices of tabular outputs, hierarchical clustering and heatmap generation, or the ability to interactively select features from the functional enrichment matrix to be transformed into XGMML or GEXF network format documents for use in Cytoscape or Gephi applications, respectively. Here, as example, we demonstrate the ability of ToppCluster to enable identification of list-specific phenotypic and regulatory element features (both cis-elements and 3′UTR microRNA binding sites) among tissue-specific gene lists. ToppCluster’s functionalities enable the identification of specialized biological functions and regulatory networks and systems biology-based dissection of biological states. ToppCluster can be accessed freely at http://toppcluster.cchmc.org

Variable strength of forest stand attributes and weather conditions on the questing activity of Ixodes ricinus ticks over years in managed forests

Given the ever-increasing human impact through land use and climate change on the environment, we crucially need to achieve a better understanding of those factors that influence the questing activity of ixodid ticks, a major disease-transmitting vector in temperate forests. We investigated variation in the relative questing nymph densities of Ixodes ricinus in differently managed forest types for three years (2008–2010) in SW Germany by drag sampling. We used a hierarchical Bayesian modeling approach to examine the relative effects of habitat and weather and to consider possible nested structures of habitat and climate forces. The questing activity of nymphs was considerably larger in young forest successional stages of thicket compared with pole wood and timber stages. Questing nymph density increased markedly with milder winter temperatures. Generally, the relative strength of the various environmental forces on questing nymph density differed across years. In particular, winter temperature had a negative effect on tick activity across sites in 2008 in contrast to the overall effect of temperature across years. Our results suggest that forest management practices have important impacts on questing nymph density. Variable weather conditions, however, might override the effects of forest management practices on the fluctuations and dynamics of tick populations and activity over years, in particular, the preceding winter temperatures. Therefore, robust predictions and the detection of possible interactions and nested structures of habitat and climate forces can only be quantified through the collection of long-term data. Such data are particularly important with regard to future scenarios of forest management and climate warming

Gene expression patterns associated with p53 status in breast cancer

BACKGROUND: Breast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53's independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function). METHODS: The p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors. RESULTS: Each cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data. CONCLUSION: In the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes

Design of a combinatorial DNA microarray for protein-DNA interaction studies

BACKGROUND: Discovery of precise specificity of transcription factors is an important step on the way to understanding the complex mechanisms of gene regulation in eukaryotes. Recently, double-stranded protein-binding microarrays were developed as a potentially scalable approach to tackle transcription factor binding site identification. RESULTS: Here we present an algorithmic approach to experimental design of a microarray that allows for testing full specificity of a transcription factor binding to all possible DNA binding sites of a given length, with optimally efficient use of the array. This design is universal, works for any factor that binds a sequence motif and is not species-specific. Furthermore, simulation results show that data produced with the designed arrays is easier to analyze and would result in more precise identification of binding sites. CONCLUSION: In this study, we present a design of a double stranded DNA microarray for protein-DNA interaction studies and show that our algorithm allows optimally efficient use of the arrays for this purpose. We believe such a design will prove useful for transcription factor binding site identification and other biological problems

An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs

Background: Transcription factors (TFs) control transcription by binding to specific regions of DNA called transcription factor binding sites (TFBSs). The identification of TFBSs is a crucial problem in computational biology and includes the subtask of predicting the location of known TFBS motifs in a given DNA sequence. It has previously been shown that, when scoring matches to known TFBS motifs, interdependencies between positions within a motif should be taken into account. However, this remains a challenging task owing to the fact that sequences similar to those of known TFBSs can occur by chance with a relatively high frequency. Here we present a new method for matching sequences to TFBS motifs based on intuitionistic fuzzy sets (IFS) theory, an approach that has been shown to be particularly appropriate for tackling problems that embody a high degree of uncertainty. Results: We propose SCintuit, a new scoring method for measuring sequence-motif affinity based on IFS theory. Unlike existing methods that consider dependencies between positions, SCintuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SCintuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position. We used SCintuit to identify known TFBSs in DNA sequences. Our method provides excellent results when dealing with both synthetic and real data, outperforming the sensitivity and the specificity of two existing methods in all the experiments we performed. Conclusions: The results show that SCintuit improves the prediction quality for TFs of the existing approaches without compromising sensitivity. In addition, we show how SCintuit can be successfully applied to real research problems. In this study the reliability of the IFS theory for motif discovery tasks is proven