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Context Sharks are frequently caught as bycatch in trawl fisheries in the North Aegean Sea. However, very little information exists on their biology and ecology in the area. Aims Biological parameters and spatial segregation of sharks in relation to size class, sex, depth and geographic location were examined. Methods Morphological characteristics and generalised additive models were used to explore biological traits and aggregating behaviour respectively, by using data from the Mediterranean International Trawl Survey between 2014 and 2021. Key results Overall, 7761 specimens were measured, with Scyliorhinus canicula, Galeus melastomus and Squalus blainville being the most common species. Scyliorhinus canicula was found in shallow coastal waters, whereas the remaining species were detected in deeper waters. Conclusions Females were larger and heavier than males and the weight–length relationships corroborated size differences between sexes in all species, whereas allometric growth did not share the same patterns as those from the Mediterranean Sea. Generalised additive models showed that the distribution of dominant species is bathymetric and geographically dependent. Additionally, the depth and geographic segregation patterns are highly associated with ontogeny and not sex. Implications These findings highlighted the importance of incorporating elasmobranch behavioural characteristics in the poorly developed management regulations in the North Aegean Sea.

Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.

Glioblastoma, the most common and aggressive adult brain tumor, is characterized by extreme phenotypic diversity and treatment failure. Through fluorescence-guided resection, we identified fluorescent tissue in the sub-ependymal zone (SEZ) of patients with glioblastoma. Histologic analysis and genomic characterization revealed that the SEZ harbors malignant cells with tumor-initiating capacity, analogous to cells isolated from the fluorescent tumor mass (T). We observed resistance to supramaximal chemotherapy doses along with differential patterns of drug response between T and SEZ in the same tumor. Our results reveal novel insights into glioblastoma growth dynamics, with implications for understanding and limiting treatment resistance. Cancer Res; 75(1); 194–202. ©2014 AACR.

Supplementary experimental procedures: this file contains an expanded materials and methods section on 5-ALA administration and sample collection, cell lines propagation, immunofluorescence and in vivo assays, genomic and molecular clock analysis, fluorescence in situ hybridization, drug concentration used in the proliferation assay and MGMT promoter methylation analysis. Supplementary Figure S1: this figure illustrates the identification of the sub-ependymal zone (SEZ) in two GB patients included in this study. Supplementary Figure S2: this figure shows the sampling and morphology of the SEZ tissue. Supplementary Figure S3: this figure summarizes the results of real time analysis for markers of glial, precursor, stem cells and proliferation in paired T and SEZ from three GB patients. Supplementary Figure S4: this figure shows an example of common copy number aberration breakpoints between the SEZ and the corresponding T in each of the GB patients included in this study. Supplementary Figure S5: this figure summarizes the fluorescence in situ hybridization (FISH) results for EGFR, MET and PTEN in three GB patients. Supplementary Figure S6: this figure shows the copy number aberration breakpoints between T and SEZ and the corresponding cell lines in patient sp14. Supplementary Figure S7: this figure shows the results of growth curve analysis of tumor-initiating cells (TICs) derived from matched T and SEZ of five GBs. Supplementary Figure S8: this figure summarizes the results of clonogenic index between three paired T and SEZ cells and multipotency of SEZ cells. Supplementary Figure S9: this figure shows the immunofluorescence results for stem cell markers on T and SEZ cells in two GBs. Supplementary Figure S10: this figure summarizes the results of cumulative Kaplan-Meier survival analysis from five additional GBs. Supplementary Figure S11: this figure shows an analysis of the in vivo properties of TICs isolated from the SEZ of three GBs. Supplementary Figure S12: this figure shows the results of the cell proliferation assay of 7 paired TICs using 50μM of Temozolomide (TMZ). Supplementary Figure S13: this figure summarizes the cell proliferation data of additional 20 TICs treated with TMZ for dose escalation analysis. Supplementary Figure S14: this figure shows the results of cell proliferation assay of additional 4 paired TICs treated with TMZ, Cisplatin and Cediranib. Supplementary Figure S15: this figure shows a gel image of MGMT promoter status analysis for T and SEZ of the analyzed 7 paired TICs. Supplementary Figure S16: this figure summarizes the immunofluorescence results for vascular endothelial growth factor receptor 2 (Vegfr2) on T and SEZ cells from three GBs. Supplementary Figure S17: this figure summarizes the cell proliferation data of three control lines. Supplementary Figure S18: this figure illustrates a model of residual disease in human GB and summarizes the cardinal features of T and SEZ. Supplementary Figure S19: this figure shows the results of growth curve analysis of two SEZ in absence of fluorescence. Supplementary Table S1: this table summarizes the results of the histological features of T and SEZ tissues. Supplementary Table S2: this table provides a summary of the samples used for the genomic analysis and of the clinical information available for the eleven GB patients included in this study. Supplementary Table S3: this table shows the gene ontology (GO) terms whose genes are differentially expressed between SEZ and T. Supplementary Table S4: this table provides a summary of the results of MGMT methylation analysis by pyrosequencing in sixteen GBs. Supplementary Table S5: this table summarizes the overall survival of the eight GB patients whose TICs were used for the cell proliferation assay and includes the MGMT methylation by pyrosequencing. Supplementary Table S6: this table shows the gene expression data of PDGF and VEGF receptors in T and SEZ.

This paper is the first to establish the impact of colour on users’ ability to interpret the informational content of concept diagrams, a logic designed for ontology engineering. Motivation comes from results for Euler diagrams, which form a fragment of concept diagrams: manipulating curve colours affects user performance. In particular, using distinct curve colours yields significant performance benefits in Euler diagrams. Naturally, one would expect to obtain similar empirical results for concept diagrams, since colour is a graphical feature to which we are perceptually sensitive. Thus, this paper sets out to test this expectation by conducting a crowdsourced empirical study involving 261 participants. Our study suggests that manipulating curve colours no longer yields significant performance differences in this syntactically richer logic. Consequently, when using colour to visually group syntactic elements with common semantic properties, we ask how different do the elements’ shapes need to be in order for there to be significant performance benefits arising from using colours?

Increasing energy demand plays a significant part in greenhouse gas emissions and environmental pollution, contributing to climate change. Energy Efficiency (EE) is one of the most cost-effective ways to reduce energy consumption while maintaining an equivalent level of economic activity. To this end, private investments are considered necessary in increasing EE by updating building stock and industry processes. The Triple-A Tools facilitate energy efficiency stakeholders by evaluating the risks and maturity of their investment ideas, benchmarking them and identifying the most bankable ones while matching them with state-of-the-art green financing schemes. The Triple-A Web-Based Database incorporates data on the fundamental aspects of EE financing, contributing significantly to upscaling EE investments, making them more attractive to investors while providing a better understanding of their framework to project developers. The Triple-A Tools and Database establish a one-stop-shop approaAch orientated towards energy efficiency stakeholders, facilitating project developers to benchmark their projects in a standardised way while also providing a hub to investors to finance bankable green projects.