data format Dotaz Zobrazit nápovědu
PURPOSE: Multiple data formats in the MRS community currently hinder data sharing and integration. NIfTI-MRS is proposed as a standard spectroscopy data format, implemented as an extension to the Neuroimaging informatics technology initiative (NIfTI) format. This standardized format can facilitate data sharing and algorithm development as well as ease integration of MRS analysis alongside other imaging modalities. METHODS: A file format using the NIfTI header extension framework incorporates essential spectroscopic metadata and additional encoding dimensions. A detailed description of the specification is provided. An open-source command-line conversion program is implemented to convert single-voxel and spectroscopic imaging data to NIfTI-MRS. Visualization of data in NIfTI-MRS is provided by development of a dedicated plugin for FSLeyes, the FMRIB Software Library (FSL) image viewer. RESULTS: Online documentation and 10 example datasets in the proposed format are provided. Code examples of NIfTI-MRS readers are implemented in common programming languages. Conversion software, spec2nii, currently converts 14 formats where data is stored in image-space to NIfTI-MRS, including Digital Imaging and Communications in Medicine (DICOM) and vendor proprietary formats. CONCLUSION: NIfTI-MRS aims to solve issues arising from multiple data formats being used in the MRS community. Through a single conversion point, processing and analysis of MRS data are simplified, thereby lowering the barrier to use of MRS. Furthermore, it can serve as the basis for open data sharing, collaboration, and interoperability of analysis programs. Greater standardization and harmonization become possible. By aligning with the dominant format in neuroimaging, NIfTI-MRS enables the use of mature tools present in the imaging community, demonstrated in this work by using a dedicated imaging tool, FSLeyes, for visualization.
1. české vyd. sv. ; 30 cm
- MeSH
- katalogizace MeSH
- knihovní věda MeSH
- Publikační typ
- příručky MeSH
- Konspekt
- Katalogizace. Selekční jazyky
- NLK Obory
- knihovnictví, informační věda a muzeologie
3D imaging approaches based on X-ray microcomputed tomography (microCT) have become increasingly accessible with advancements in methods, instruments and expertise. The synergy of material and life sciences has impacted biomedical research by proposing new tools for investigation. However, data sharing remains challenging as microCT files are usually in the range of gigabytes and require specific and expensive software for rendering and interpretation. Here, we provide an advanced method for visualisation and interpretation of microCT data with small file formats, readable on all operating systems, using freely available Portable Document Format (PDF) software. Our method is based on the conversion of volumetric data into interactive 3D PDF, allowing rotation, movement, magnification and setting modifications of objects, thus providing an intuitive approach to analyse structures in a 3D context. We describe the complete pipeline from data acquisition, data processing and compression, to 3D PDF formatting on an example of craniofacial anatomical morphology in the mouse embryo. Our procedure is widely applicable in biological research and can be used as a framework to analyse volumetric data from any research field relying on 3D rendering and CT-biomedical imaging.
- MeSH
- automatizované zpracování dat MeSH
- komprese dat statistika a číselné údaje MeSH
- lebka anatomie a histologie embryologie MeSH
- modely anatomické MeSH
- myši MeSH
- obličejové kosti anatomie a histologie embryologie MeSH
- rentgenová mikrotomografie statistika a číselné údaje MeSH
- rentgenový obraz - interpretace počítačová MeSH
- šíření informací metody MeSH
- software * MeSH
- zobrazování trojrozměrné statistika a číselné údaje MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called "indirect" damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.
Východiska: Monoklonální gamapatie nejasného významu (monoclonal gammopathy of undetermined significance – MGUS) a doutnající mnohočetný myelom (smouldering multiple myeloma – SMM) jsou prekancerózními stadii mnohočetného myelomu (MM). MM je malignita plazmatických buněk s mediánem přežití od 5 do 7 let. MM tvoří zhruba 10 % diagnóz v oblasti hematoonkologie. Pacienti a metody: Na datech z 19 českých center zadaných v Registru monoklonálních gamapatií (Registry of Monoclonal Gammopathies – RMG) byla provedena popisná analýza. Výsledky: Za posledních 10 let sběru dat, spolu s retrospektivně zadanými daty pacientů diagnostikovaných před založením registru, registr disponuje daty o 7 467 pacientech se asymptomatickou nebo symptomatickou formou MM. Validační kritéria pro analýzu splňovalo 2 506 pacientů s MGUS, 400 pacientů s SMM a 4 378 pacientů s MM. Medián délky sledování pacientů byl 4,3 roku u MGUS a 2,4 roku u SMM. Celkové roční riziko progrese z MGUS do maligního onemocnění bylo 1,7 %. Riziko progrese z SMM do MM bylo nejvyšší první roky po diagnóze; za celou dobu sledování bylo riziko progrese 16,6 % každý rok. Medián délky sledování od diagnózy MM byl 2,8 roku. Medián celkového přežití (overall survival – OS) od diagnózy byl 5,7 roku. Medián OS od zahájení léčby/doby bez progrese klesl z 60,5/21,0 měsíce u 1. linie léčby na 34,3/12,4 měsíce u 2. linie, 22,6/8,9 měsíce u 3. linie a 13,8/5,8 měsíce u 4. nebo vyšší linie léčby. Díky dostupnosti nových léků pro léčbu MM v České republice došlo v posledním desetiletí k dramatickým změnám v léčebných postupech. Závěr: RMG je registr určený pro sběr klinických dat týkajících se diagnózy, léčby, jejích výsledků a přežití pacientů s monoklonálními gamapatiemi. RMG je cenným zdrojem dat z reálné klinické praxe.
Background: Monoclonal gammopathy of undetermined significance (MGUS) and smouldering multiple myeloma (SMM) are premalignant stages of multiple myeloma (MM). MM is a malignancy of plasma cells, which is associated with a median overall survival of 5 to 7 years. MM accounts for approximately 10% of hematological malignancies. Patients and Methods: Descriptive analysis of data from 19 Czech centres collected in the Registry of Monoclonal Gammopathies (RMG) was performed. Results: Over the last 10 years of prospective collection of data, together with retrospectively recorded data on patients diagnosed before the registry establishment, data on 7,467 patients with either asymptomatic or symptomatic form of MM have been gathered. Validation criteria for the analysis were met by 2,506 MGUS patients, 400 SMM patients and 4,738 MM patients. The median duration of follow-up was 4.3 years in MGUS patients and 2.4 years in SMM patients. The overall risk of progression from MGUS to malignancy was 1.7% per year. The risk of progression from SMM to MM was highest in the 1st years after diagnosis: overall, this risk was 16.6% per year. The median duration of follow-up was 2.8 years in MM patients. The median overall survival from the diagnosis was 5.7 years. The median OS from treatment initiation/progression-free survival decreased from 60.5/21.0 months in the 1st line therapy to 34.3/12.4 months in the 2nd line therapy, 22.6/8.9 months in the 3rd line therapy and 13.8/5.8 months in the 4th or higher line therapies. Thanks to the availability of novel drugs for MM treatment in the Czech Republic, treatment strategies have changed dramatically over the last decade. Conclusion: RMG is a registry designated for the collection of data on diagnosis, treatment, treatment results and survival of patients with monoclonal gammopathies in the long-term follow-up. RMG is a valuable source of data from real clinical practice.
- MeSH
- adresáře jako téma MeSH
- interpretace statistických dat MeSH
- lidé MeSH
- mnohočetný myelom * diagnóza terapie MeSH
- monoklonální gamapatie nejasného významu * diagnóza MeSH
- přežití MeSH
- progrese nemoci MeSH
- sběr dat MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
- Geografické názvy
- Česká republika MeSH
- Slovenská republika MeSH
The Neurodata Without Borders (abbreviation NWB) format is a current technology for storing neurophysiology data along with the associated metadata. Data stored in the format is organized into separate HDF5 files, each file usually storing the data associated with a single recording session. While the NWB format provides a structured method for storing data, so far there have not been tools which enable searching a collection of NWB files in order to find data of interest for a particular purpose. We describe here three tools to enable searching NWB files. The tools have different features making each of them most useful for a particular task. The first tool, called the NWB Query Engine, is written in Java. It allows searching the complete content of NWB files. It was designed for the first version of NWB (NWB 1) and supports most (but not all) features of the most recent version (NWB 2). For some searches, it is the fastest tool. The second tool, called "search_nwb" is written in Python and also allow searching the complete contents of NWB files. It works with both NWB 1 and NWB 2, as does the third tool. The third tool, called "nwbindexer" enables searching a collection of NWB files using a two-step process. In the first step, a utility is run which creates an SQLite database containing the metadata in a collection of NWB files. This database is then searched in the second step, using another utility. Once the index is built, this two-step processes allows faster searches than are done by the other tools, but does not enable as complete of searches. All three tools use a simple query language which was developed for this project. Software integrating the three tools into a web-interface is provided which enables searching NWB files by submitting a web form.
- Publikační typ
- časopisecké články MeSH
Práce s big daty vyžaduje použití prostředků umělé inteligence. Přináší to možnost transformace laboratorních výsledků do formy strojového učení-machine learning (ML). Od něho se očekává aktivace dat, přinášející zlepšení diagnostických možností laboratorních vyšetření. Jde o posuv od použití počítačů, sloužících z části jako skladiště mrtvých dat, k aktivnějšímu využití jejich potenciálu pro diagnostiku, management, edukaci, výzkum a další. Zejména pak k predikci stavu chorob a k precizní medicíně v onkologii i jinde. Důsledkem by měl být integrovaný mezioborový přístup k diagnostice a reálné dosažení efektivní personalizace při diagnostice a terapii pacientů. Sdělení je pokusem o pomoc při zavádění práce s big daty a umělou inteligencí v klinických laboratořích. Vychází z faktu obrovské akcelerace tohoto přístupu, zdaleka nejen pouze v laboratorní medicíně.
Working the big data needs using of artificial intelligence tools. This approach introduced currently into practice by large velocity leads to machine learning. Machine learning should be a strong way namely for the prediction of patient's state, for precision medicine in oncology and many more cases. For example for aiming the real personalisation of patients in dese of their diagnosis and therapy. This work can be a helpful tool for the introduction of artificial intelligence in routine clinical laboratories.
1. vyd. 256 s. ; 23 cm
MEMO01Dvojice autorů ukáže moderní přístup ke zpracování dat, který nepracuje se vzorkem, ale celou množinou. Takto lze získat cenné informace mnohem rychleji než v případě konvenčních postupů a v řadě případů lze objevit i úplně nové spojitosti. S novými možnostmi zpracování dat se seznámíte poutavou čtivou formou, autoři je popisují na příkladech z běžného života, na známých společnostech, zcela bez komplikovaných, těžko srozumitelných technických popisů. Nakladatelská anotace. Kráceno
Seznamte se s novým fenoménem, díky kterému získáte zcela nový pohled nejen na proces získávání důležitých informací z dostupných dat, ale na fungování světa jako celku v technologiemi protkaném 21. století
- Konspekt
- Sociální procesy
- NLK Publikační typ
- monografie
