История возникновения католической общины города Севастополя и строительство римско-католического костёла

Цель статьи – проследить этапы формирования римско-католической общины города Севастополя и историю строительства римско-католического костёла во имя Священномученника Климента Римского

Livestock-associated Methicillin-Resistant Staphylococcus aureus Sequence Type 398 in Humans, Canada

Recent emergence of infections resulting from this strain is of public health concern

Increased coronary perfusion augments cardiac contractility in the rat through stretch-activated ion channels

The role of stretch-activated ion channels (SACs) in coronary perfusion-induced increase in cardiac contractility was investigated in isolated isometrically contracting perfused papillary muscles from Wistar rats. A brief increase in perfusion pressure (3–4 s, perfusion pulse, n = 7), 10 repetitive perfusion pulses ( n = 4), or a sustained increase in perfusion pressure (150–200 s, perfusion step, n = 7) increase developed force by 2.7 ± 1.1, 7.7 ± 2.2, and 8.3 ± 2.5 mN/mm2(means ± SE, P &lt; 0.05), respectively. The increase in developed force after a perfusion pulse is transient, whereas developed force during a perfusion step remains increased by 5.1 ± 2.5 mN/mm2( P &lt; 0.05) in the steady state. Inhibition of SACs by addition of gadolinium (10 μmol/l) or streptomycin (40 and 100 μmol/l) blunts the perfusion-induced increase in developed force. Incubation with 100 μmol/l Nω-nitro-l-arginine [nitric oxide (NO) synthase inhibition], 10 μmol/l sodium nitroprusside (NO donation) and 0.1 μmol/l verapamil (L-type Ca2+channel blockade) are without effect on the perfusion-induced increase of developed force. We conclude that brief, repetitive, or sustained increases in coronary perfusion augment cardiac contractility through activation of stretch-activated ion channels, whereas endothelial NO release and L-type Ca2+channels are not involved.</jats:p

Data-driven approach for measuring the severity of the signs of depression using reddit posts ::women and men in the orchestra

In response to the CLEF eRisk 2019 shared task on measuring the severity of the signs of depression from threads of user submissions on social media, our team has developed a data-driven, ensemble model approach. Our system leverages word polarities, token extraction via mutual information, keyword expansion and semantic similarities for classifying Reddit posts according to the Beck’s Depression Inventory (BDI). Individual models were combined at the post level by majority voting. The approach achieved a baseline performance for the assessed metrics, including Average Hit Rate and Depression Category Hit Rate, being equivalent to the median system in the limit of one standard deviation

SIB text mining at TREC 2018 precision medicine track

The TREC 2018 Precision Medicine Track largely repeats the structure and evaluation of the 2017 track. The collection remains identical. Again, our team participated in the both tasks of the track: 1) retrieving scientific abstracts addressing relevant treatments for a given case and 2) retrieving clinical trials for which a patient is eligible. Regarding the retrieval of scientific abstracts, we queried all abstracts concerning one of the entities of the topic (i.e. the disease, the gene or the genetic variant) using various strategies (e.g. search in annotations of the collection, free text search using or not using synonyms, search in the MeSH terms, etc.). Then, for a given topic, the complete set of abstracts was based on the generation of different queries with decreasing levels of specificity. The idea was to start with a very specific query containing gene, disease and variant, from which less specific queries would be inferred. Abstracts were then re-ranked based on different strategies to favor abstracts that we considered more relevant to the given task. In 2017 we tested the use of drug densities to identify abstracts related to treatment. For this year we refined this strategy by giving more weight to drugs related to cancer treatment. Secondly, we used demographic information to favor abstracts concerning patients of the specified age-group and gender, and disfavoring abstracts targeting other age-group or gender patients. For the third strategy we utilized a word-level convolutional neural network to increase the rank of abstracts related to precision medicine. The fourth strategy consisted to expand the query to parent and children diseases. Finally, we tested an exact run which only retrieved abstracts respecting all information given in the topic. Results showed that all strategies but the last one resulted in some improvement of the retrieval power of the engine. As expected, our final run, focusing of precision, resulted in our best results regarding precision at rank 10, while other measures were negatively impacted. Regarding the retrieval of scientific abstracts, we boosted our last year’s approach – which achieved competitive results – with supplementary strategies issued from other participants. Regarding the retrieval of clinical trials, we investigated filtering strategies for managing the condition (disease), and standard information retrieval for managing the gene and genetic variant. The results show that, despite the presence of a structured condition tag in the document, better performances are obtained when relaxing constraints: using synonyms and detecting the diseases in various fields, such as the summary

A Data-Driven Approach for Measuring the Severity of the Signs of Depression using Reddit Posts

In response to the CLEF eRisk 2019 shared task on measuring the severity of the signs of depression from threads of user submissions on social media, our team has developed a data-driven, ensemble model approach. Our system leverages word polarities, token extraction via mutual information, keyword expansion and semantic similarities for classifying Reddit posts according to the Beck’s Depression Inventory (BDI). Individual models were combined at the post level by majority voting. The approach achieved a baseline performance for the assessed metrics, including Average Hit Rate and Depression Category Hit Rate, being equivalent to the median system in the limit of one standard deviation