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Obtaining frozen sections of bone tissue for intraoperative examination is challenging. To identify the bony edge of resection, orthopaedic oncologists therefore rely on pre-operative X-ray computed tomography or magnetic resonance imaging. However, these techniques do not allow for accurate diagnosis or for intraoperative confirmation of the tumour margins, and in bony sarcomas, they can lead to bone margins up to 10-fold wider (1,000-fold volumetrically) than necessary. Here, we show that real-time three-dimensional contour-scanning of tissue via ultraviolet photoacoustic microscopy in reflection mode can be used to intraoperatively evaluate undecalcified and decalcified thick bone specimens, without the need for tissue sectioning. We validate the technique with gold-standard haematoxylin-and-eosin histology images acquired via a traditional optical microscope, and also show that an unsupervised generative adversarial network can virtually stain the ultraviolet-photoacoustic-microscopy images, allowing pathologists to readily identify cancerous features. Label-free and slide-free histology via ultraviolet photoacoustic microscopy may allow for rapid diagnoses of bone-tissue pathologies and aid the intraoperative determination of tumour margins.

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The free LIFETIME RESIDENT INLAND FISHING LICENSE-AGE 65+ and LIFETIME RESIDENT FIREARM LICENSE-AGE 65+ became ANNUAL licenses effective October 1, 2009. All age 65+ licenses will need to be renewed at no cost each year.

A Seafood Dealer License is required to purchase, for resale, fish, lobsters, blue crabs, squid, and sea scallops from Connecticut Licensed Commercial Fishermen. Dealer License Application (Fillable PDF). For further information contact DEEP Fisheries Division at 860-434-6043 or write to Marine Fisheries Office, P.O. Box 719, Old Lyme, CT 06371 or e-mail at

A Bait Dealer License is required to sell Bait Species. Dealer License Application (Fillable PDF). For further information contact DEEP Inland Fisheries Division at 860-424-3474 or write to Inland Fisheries Office, 79 Elm Street, Hartford, CT 06106 or e-mail at

Youth Fishing Passport - The Youth Fishing Passport is a free printable certificate available through the DEEP Online Sportsmen Licensing System or by phone (860-424-3474) to any person who is under the age of 16. For more information, please visit the Youth Fishing Passport page. Hunting and Fishing Guide Registration - Effective 1/1/2015, individuals providing fishing and hunting guide services in Connecticut must register annually with the DEEP. The registration is valid for the calendar year. Please note: any hunting or fishing guide carrying paying passengers on a vessel on marine waters, or on the Thames River, Connecticut River south of the Bulkeley Bridge (Route 84) in Hartford, or on the Housatonic River below the Derby Dam is required to hold a current passenger-for-hire license issued by the United States Coast Guard. Operators and crew of vessels registered in Connecticut as a charter, party or head boat are exempt from the guide registration when providing guide services on the registered vessel. Please refer to the Marine Fisheries Information Circular if you are considering purchasing a Fishing Guide Registration to take marine species. Registration fee is $100 and can be purchased through the Online Outdoor Licensing System or in person at DEEP offices and other vendors where hunting and fishing licenses are available.

The Deep Learning textbook is a resource intended to help studentsand practitioners enter the field of machine learning in generaland deep learning in particular.The online version of the book is now complete and will remainavailable online for free.

If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to thewebsite, do not hesitate to contact the authors directly by e-mailat:

Neural Networks and Deep Learning is a free online book. Thebook will teach you about:Neural networks, a beautiful biologically-inspired programmingparadigm which enables a computer to learn from observational dataDeep learning, a powerful set of techniques for learning in neuralnetworksNeural networks and deep learning currently provide the best solutionsto many problems in image recognition, speech recognition, and naturallanguage processing. This book will teach you many of the coreconcepts behind neural networks and deep learning.

  • Imports

  • Check Supported Languages

  • Language Detection

  • Google Translate

  • Mymemory Translator

  • DeeplTranslator

  • QcriTranslator

  • Linguee Translator

  • PONS Translator

  • Yandex Translator

  • Microsoft Translator

  • Papago Translator

  • Libre Translator

  • Proxy usage

  • File Translation

  • Usage from Terminal

  • Tests

  • Links

  • Help

  • Next Steps

  • Credits

  • License

  • Swagger UI

  • The Translator++ mobile app

  • Website & Desktop app

MotivationI needed to translate a text using python. It was hard to find a simple way to do it.There are other libraries that can be used for this task, but most of themare buggy, not free, limited, not supported anymore or complex to use.

You can also detect language automatically. Notice that this package is free and my goal is to keep it free.Therefore, you will need to get your own api_key if you want to use the language detection function.I figured out you can get one for free here:

In order to use the DeeplTranslator translator, you need to generate an api key. Deepl offers a Pro and a free API.deep-translator supports both Pro and free APIs. Just check the examples below.Visit -api/ for more information on how to generate your Deepl api key

You need to require an api key if you want to use the microsoft translator.Visit the official website for more information about how to get one.Microsoft offers a free tier 0 subscription (2 million characters per month).

  • You can pass languages by name or by abbreviation:translated = LibreTranslator(source='german', target='english').translate(text=text)# Alternatively, you can pass languages by their abbreviation:translated = LibreTranslator(source='de', target='en').translate(text=text)Translate batch of texts

  • texts = ["hallo welt", "guten morgen"]translated = LibreTranslator(source='auto', target='en').translate_batch(texts)Translate from a file:

translated = LibreTranslator(source='auto', target='en').translate_file('path/to/file')Proxy usagedeep-translator provides out of the box usage of proxies. Just define your proxies config as a dictionaryand pass it to the corresponding translator. Below is an example using the GoogleTranslator, but this featurecan be used with all supported translators.

Long story short, I started working on the app. I decided to use the kivy framework sinceI wanted to code in python and to develop a cross platform app.I open sourced the Translator++ app on my github too.Feel free to take a look at the code or make a pull request ;)

  • PhoneTablet:

Website & Desktop appCurrently, there are propositions for a website and/or desktop app based on deep-translator.You can follow the issue here: -translator/issues/144

This is the ULTIMATE deep cleaning checklist for your entire home plus a downloadable, printable PDF at the end of the post. It goes room by room and covers all of the areas that need to be deep cleaned regularly.

Question What is the discriminative accuracy of deep learning algorithms compared with the diagnoses of pathologists in detecting lymph node metastases in tissue sections of women with breast cancer?

Finding In cross-sectional analyses that evaluated 32 algorithms submitted as part of a challenge competition, 7 deep learning algorithms showed greater discrimination than a panel of 11 pathologists in a simulated time-constrained diagnostic setting, with an area under the curve of 0.994 (best algorithm) vs 0.884 (best pathologist).

Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

Cell-free DNA (cfDNA) found in the bloodstream is primarily a byproduct of cell death in both normal and cancer cells [4]. Circulating DNA fragments are mainly short molecules with an average length of mononucleosome size that tend to be more fragmented in internucleosomal linkers and open chromatin regions. This leads to a biased, non-random fragmentation pattern [5]. Moreover, tumor-derived DNA fragments (ctDNA) tend to be shorter than the non-tumor cell-derived fraction, and constantly accumulating evidence suggests that cfDNA fragmentation may serve as a cancer biomarker at the whole-genome level [6, 7]. Some studies argue the presence of specific genomic regions with preferential tissue-specific or tumor-specific cfDNA fragmentation [8]. Recently, several groups have thoughtfully characterized open chromatin landscapes in human cancer [9, 10], allowing further extrapolations to the cfDNA fragmentation footprints [11]. Here, we focus on targeted high-resolution profiling of cancer-specific open-chromatin regions in cfDNA from the blood of healthy individuals and patients with colorectal and renal cancers. We demonstrate that the proposed approach can facilitate cancer detection.


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