Wildcat population density in NE Portugal: A regional stronghold for a nationally threatened felid

Population density data on depleted and endangered wildlife species are essential to assure their effective management and, ultimately, conservation. The European wildcat is an elusive and threatened species inhabiting the Iberian Peninsula, with fragmented populations and living in low densities. We fitted spatial capture–recapture models on camera-trap data, to provide the first estimate of wildcat density for Portugal and assess the most influential drivers determining it. The study was implemented in Montesinho Natural Park (NE Portugal), where we identified nine individuals, over a total effort of 3,477 trap-nights. The mean density estimate was 0.032 ± 0.012 wildcat/km², and density tended to increase with distance to humanized areas, often linked to lower human disturbance and domestic cat presence, with forest and herbaceous vegetation cover and with European rabbit abundance. Although, this density estimate is within the range of values estimated for protected areas elsewhere in the Iberian Peninsula, our estimates are low at the European level. When put in context, our results highlight that European wildcats may be living in low population densities across the Iberian Mediterranean biogeographic region. No phenotypic domestic or hybrid cats were detected, suggesting potentially low admixture rates between the two species, although genetic sampling would be required to corroborate this assertion. We provide evidence that Montesinho Natural Park may be a suitable area to host a healthy wildcat population, and thus be an important protected area in this species' conservation context.     

LncRNA TP73-AS1 enhances the malignant properties of pancreatic ductal adenocarcinoma by increasing MMP14 expression through miRNA -200a sponging

Pancreatic ductal adenocarcinoma (PDAC) is an invasive and aggressive cancer that remains a major threat to human health across the globe. Despite advances in cancer treatments and diagnosis, the prognosis of PDAC patients remains poor. New and more effective PDAC therapies are therefore urgently required. In this study, we identified a novel host factor, namely the LncRNA TP73-AS1, as overexpressed in PDAC tissues compared to adjacent healthy tissue samples. The overexpression of TP-73-AS1 was found to correlate with both PDAC stage and lymph node metastasis. To reveal its role in PDCA, we targeted TP73-AS1 using LnRNA inhibitors in a range of pancreatic cancer (PC) cell lines. We found that the inhibition of TP73-AS1 led to a loss of MMP14 expression in PC cells and significantly inhibited their migratory and invasive capacity. No effects of TP73-AS1 on cell survival or proliferation were observed. Mechanistically, we found that TP73-AS1 suppressed the expression of the known oncogenic miR-200a. Taken together, these data highlight the prognostic potential of TP73-AS1 for PC patients and highlight it as a potential anti-PDAC therapeutic target.     

Phrasal Paraphrase Based Question Reformulation for Archived Question Retrieval

Lexical gap in cQA search, resulted by the variability of languages, has been recognized as an important and widespread phenomenon. To address the problem, this paper presents a question reformulation scheme to enhance the question retrieval model by fully exploring the intelligence of paraphrase in phrase-level. It compensates for the existing paraphrasing research in a suitable granularity, which either falls into fine-grained lexical-level or coarse-grained sentence-level. Given a question in natural language, our scheme first detects the involved key-phrases by jointly integrating the corpus-dependent knowledge and question-aware cues. Next, it automatically extracts the paraphrases for each identified key-phrase utilizing multiple online translation engines, and then selects the most relevant reformulations from a large group of question rewrites, which is formed by full permutation and combination of the generated paraphrases. Extensive evaluations on a real world data set demonstrate that our model is able to characterize the complex questions and achieves promising performance as compared to the state-of-the-art methods.