In the Pyrenees, brown bear population abundance is estimated from non-invasive genetic analyses of scat and hair samples. Although such analyses are highly beneficial for population monitoring and research, it can be especially difficult for humans to locate bear scats in the field. To address this, we have incorporated a dog (trained from an early age to detect bear scats) into these efforts since 2014. Here, we compared the effectiveness of the scat-detection dog/handler and human-only teams to locate bear scats using our work in the Pyrenees as a case study. A species validation was systematically carried out, either genetically or visually using a microscope, based on the presence of bear hair, for all scats collected from 2010 to 2019. From 2014 to 2019, the use of the dog/handler team in addition to human-only teams increased the average number of bear scats collected annually by four times in comparison with the 2010-2013 period when only humans were searching for scats. This temporal augmentation could not be explained by the increase in bear population size. From 2014 to 2019, the annual percentage of outings during which at least one bear scat was found was 17 times higher for the dog than for humans. The use of the dog also resulted indirectly in a better genotyping success and genetic identification of more individuals due to a larger choice of viable samples that could be sent to the molecular laboratory, as well as a larger number of cub scats detected by the dog. We found that even the use of a single scat-detection dog can greatly improve the efficiency of detecting target scats in challenging monitoring conditions.
Cíl: Přezkoumání benefitů metody klokánkování pro nedonošené novorozence, to znamená najít dostatek důkazů pro opodstatnění používání metody klokánkování na Jednotce intenzivní péče pro novorozence. Metodika: Vyhledávání validních indikátorů pozitivního vlivu metody klokánkování prostřednictvím metody praxe založené na důkazech. Pro získání relevantních zdrojů byly využity licencované databáze a volně přístupné databáze - Medline, Bibliomedica, Nursing: Best Evidence for Nursing care, Google Scholar. Výsledky: Analýza výsledků prokázala jednoznačně pozitivní vliv metody klokánkování na nedonošené novorozence umístěné na Jednotce intenzivní péče, a to zejména v oblasti vnímání bolesti u invazivních výkonů, efektivního kojení, zvýšení váhového přírůstku, delšího a klidnějšího spánku, pozitivnější interakce matka – dítě, udržení nebo zvýšení tělesné teploty, zkrácení doby hospitalizace, růstu do délky, růstu obvodu hlavičky a aktivace centrální nervové soustavy. Závěry: Výzkumné studie (n = 34) a jeden systematický přehled ukazují pozitivní vliv metody klokánkování na nedonošeného novorozence umístěného na Jednotce intenzivní péče v oblastech biopsychosociálních. V podmínkách českého zdravotnictví chybí vypracované platné standardy pro využívání metody v praxi., Objective: To review benefits of kangaroo care method for premature babies, it means finding enough evidence to justify using the kangaroo care method in the Intensive care unit for newborns. Methods: Searching for valid indicators of a positive influence method of kangaroo care through method evidence-based practice. To obtain relevant sources were used licensed databases and freely available databases - Medline, Bibliomedica, Nursing: Best Evidence for Nursing care, Google Scholar. Results: Analysis of results showed a clearly positive impact of the kangaroo care methods on premature infants placed in intensive care, especially in the perception of pain in invasive procedures, effective breastfeeding, increased weight gain, longer and quieter sleep, more positive interactions of mother - child, maintaining or increasing body temperature, reduced hospitalization, growth in length, head circumference growth and activation of the CNS. Conclusions: The research studies (n = 34) and one systematic review have shown a positive influence of the kangaroo care methods on premature newborns placed in intensive care in the areas of bio-psycho-social. In terms of the Czech healthcare system lacks established standards for the use of valid methods in practice., Lucie Sikorová, Monika Suszková, and Literatura
Data
-------
Bengali Visual Genome (BVG for short) 1.0 has similar goals as Hindi Visual Genome (HVG) 1.1: to support the Bengali language. Bengali Visual Genome 1.0 is the multi-modal dataset in Bengali for machine translation and image
captioning. Bengali Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Bengali multimodal machine translation tasks and multimodal research. We follow the same selection of short English segments (captions) and the associated images from Visual Genome as HGV 1.1 has. For BVG, we manually translated these captions from English to Bengali taking the associated images into account. The manual translation is performed by the native Bengali speakers without referring to any machine translation system.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome. A third test set is
called the ``challenge test set'' and consists of 1.4K segments. The challenge test set was created for the WAT2019 multi-modal task by searching for (particularly) ambiguous English words based on the embedding similarity and
manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word.
Dataset Formats
---------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Bengali Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
---------------
The statistics of the current release are given below.
Parallel Corpus Statistics
--------------------------
Dataset Segments English Words Bengali Words
---------- -------- ------------- -------------
Train 28930 143115 113978
Dev 998 4922 3936
Test 1595 7853 6408
Challenge Test 1400 8186 6657
---------- -------- ------------- -------------
Total 32923 164076 130979
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@inproceedings{hindi-visual-genome:2022,
title= "{Bengali Visual Genome: A Multimodal Dataset for Machine Translation and Image Captioning}",
author={Sen, Arghyadeep
and Parida, Shantipriya
and Kotwal, Ketan
and Panda, Subhadarshi
and Bojar, Ond{\v{r}}ej
and Dash, Satya Ranjan},
editor={Satapathy, Suresh Chandra
and Peer, Peter
and Tang, Jinshan
and Bhateja, Vikrant
and Ghosh, Anumoy},
booktitle= {Intelligent Data Engineering and Analytics},
publisher= {Springer Nature Singapore},
address= {Singapore},
pages = {63--70},
isbn = {978-981-16-6624-7},
doi = {10.1007/978-981-16-6624-7_7},
}