Welcome VMware Communities. Microsoft Hyper-V 3.0: Hyper-V 3.0 is the virtualization feature created for the client version of Windows 8 and Windows Server 8. It is offered as a stand-alone product. 0.1.2 Feb 21, 2019 0.1.1 Aug 27, 2018 0.1 Nov 23, 2016 0.0.2 Aug 7, 2013 0.0.1 Jan 9, 2013 Download files. Download the file for your platform. If you're not sure which to choose, learn more about installing packages. Files for hyperopt, version 0.2.5.
Hypertriglyceridemia | |
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Triglyceride, which cause hypertriglyceridemia at high level | |
Specialty | Endocrinology |
Hypertriglyceridemia denotes high (hyper-) blood levels (-emia) of triglycerides, the most abundant fatty molecule in most organisms. Elevated levels of triglycerides are associated with atherosclerosis, even in the absence of hypercholesterolemia (high cholesterol levels), and predispose to cardiovascular disease. Very high triglyceride levels also increase the risk of acute pancreatitis. Hypertriglyceridemia itself is usually symptomless, although high levels may be associated with skin lesions known as xanthomas.[1]
Signs and symptoms[edit]
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Blood samples of a young patient with extreme hypertriglyceridemia
Most people with elevated triglycerides experience no symptoms. Some forms of primary hypertriglyceridemia can lead to specific symptoms: both familial chylomicronemia and primary mixed hyperlipidemia include skin symptoms (eruptive xanthoma), eye abnormalities (lipemia retinalis), hepatosplenomegaly (enlargement of the liver and spleen), and neurological symptoms. Some experience attacks of abdominal pain that may be mild episodes of pancreatitis. Eruptive xanthomas are 2–5 mm papules, often with a red ring around them, that occur in clusters on the skin of the trunk, buttocks and extremities.[2]Familial dysbetalipoproteinemia causes larger, tuberous xanthomas; these are red or orange and occur on the elbows and knees. Palmar crease xanthomas may also occur.[1][2]
The diagnosis is made on blood tests, often performed as part of screening. Once diagnosed, other blood tests are usually required to determine whether the raised triglyceride level is caused by other underlying disorders ('secondary hypertriglyceridemia') or whether no such underlying cause exists ('primary hypertriglyceridaemia'). There is a hereditary predisposition to both primary and secondary hypertriglyceridemia.[1]
Acute pancreatitis may occur in people whose triglyceride levels are above 1000 mg/dL (11.3 mmol/L).[1][2][3] Hypertriglyceridemia is associated with 1–4% of all cases of pancreatitis. The symptoms are similar to pancreatitis secondary to other causes, although the presence of xanthomas or risk factors for hypertriglyceridemia may offer clues.[3]
Causes[edit]
- Overeating[4][5]
- Diabetes mellitus and insulin resistance - it is one of the defined components of metabolic syndrome (along with central obesity, hypertension, and hyperglycemia)
- Excess alcohol consumption
- Kidney failure, nephrotic syndrome
- Genetic predisposition; some forms of familial hyperlipidemia such as familial combined hyperlipidemia i.e. Type II hyperlipidemia
- Lipoprotein lipase deficiency - Deficiency of this water-soluble enzyme, that hydrolyzes triglycerides in lipoproteins, leads to elevated levels of triglycerides in the blood.
- Lysosomal acid lipase deficiency or Cholesteryl ester storage disease
- Certain medications e.g. isotretinoin, hydrochlorothiazide diuretics, beta blockers, protease inhibitors
- Hypothyroidism (underactive thyroid)
- Lupus and associated autoimmune responses [6]
- Glycogen storage disease type 1.
- HIV medications
Diagnosis[edit]
The diagnosis is made on blood tests, often performed as part of screening. The normal triglyceride level is less than 150 mg/dL (1.7 mmol/L).[1][5] Once diagnosed, other blood tests are usually required to determine whether the raised triglyceride level is caused by other underlying disorders ('secondary hypertriglyceridemia') or whether no such underlying cause exists ('primary hypertriglyceridaemia'). There is a hereditary predisposition to both primary and secondary hypertriglyceridemia.[1]
Screening[edit]
In 2016 the United States Preventive Services Task Force concluded that testing the general population under the age of 40 without symptoms is of unclear benefit.[7][8]
Treatment[edit]
Lifestyle changes including weight loss, exercise and dietary modification may improve hypertriglyceridemia.[1][9][10] This may include restriction of carbohydrates (specifically fructose)[9] and fat in the diet and the consumption of omega-3 fatty acids from algae, nuts, and seeds.[11][12]
The decision to treat hypertriglyceridemia with medication depends on the levels and on the presence of other risk factors for cardiovascular disease. Very high levels that would increase the risk of pancreatitis is treated with a drug from the fibrate class. Niacin and omega-3 fatty acids as well as drugs from the statin class may be used in conjunction, with statins being the main drug treatment for moderate hypertriglyceridemia where reduction of cardiovascular risk is required.[1] Medications are recommended in those with high levels of triglycerides that are not corrected with lifestyle modifications, with fibrates being recommended first.[1][13][14]Epanova (omega-3-carboxylic acids) is another prescription drug used to treat very high levels of blood triglycerides.[15]
Epidemiology[edit]
As of 2006, the prevalence of hypertriglyceridemia in the United States was 30%.[5]
See also[edit]
References[edit]
- ^ abcdefghiBerglund L, Brunzell JD, Goldberg AC, et al. (September 2012). 'Evaluation and treatment of hypertriglyceridemia: an endocrine society clinical practice guideline'. J. Clin. Endocrinol. Metab. 97 (9): 2969–89. doi:10.1210/jc.2011-3213. PMC3431581. PMID22962670.
- ^ abcYuan G, Al-Shali KZ, Hegele RA (April 2007). 'Hypertriglyceridemia: its etiology, effects and treatment'. CMAJ. 176 (8): 1113–20. doi:10.1503/cmaj.060963. PMC1839776. PMID17420495.
- ^ abTsuang W, Navaneethan U, Ruiz L, Palascak JB, Gelrud A (April 2009). 'Hypertriglyceridemic pancreatitis: presentation and management'. Am. J. Gastroenterol. 104 (4): 984–91. doi:10.1038/ajg.2009.27. PMID19293788.
- ^Garg, A; Grundy, SM; Unger, RH (Oct 1992). 'Comparison of effects of high and low carbohydrate diets on plasma lipoproteins and insulin sensitivity in patients with mild NIDDM'. Diabetes. 41 (10): 1278–85. doi:10.2337/diabetes.41.10.1278. PMID1397701.
- ^ abcPejic RN, Lee DT (May–Jun 2006). 'Hypertriglyceridemia'. J Am Board Fam Med. 19 (3): 310–6. doi:10.3122/jabfm.19.3.310. PMID16672684.
- ^Beigneux, Anne P.; Miyashita, Kazuya; Ploug, Michael; Blom, Dirk J.; Ai, Masumi; Linton, Macrae F.; Khovidhunkit, Weerapan; Dufour, Robert; Garg, Abhimanyu; McMahon, Maureen A.; Pullinger, Clive R.; Sandoval, Norma P.; Hu, Xuchen; Allan, Christopher M.; Larsson, Mikael; Machida, Tetsuo; Murakami, Masami; Reue, Karen; Tontonoz, Peter; Goldberg, Ira J.; Moulin, Philippe; Charrière, Sybil; Fong, Loren G.; Nakajima, Katsuyuki; Young, Stephen G. (August 27, 2017). 'Autoantibodies against GPIHBP1 as a Cause of Hypertriglyceridemia'. NEJM. 376 (17): 1647–1658. doi:10.1056/NEJMoa1611930. PMC5555413. PMID28402248.
- ^Chou, Roger; Dana, Tracy; Blazina, Ian; Daeges, Monica; Bougatsos, Christina; Jeanne, Thomas L. (9 August 2016). 'Screening for Dyslipidemia in Younger Adults: A Systematic Review for the U.S. Preventive Services Task Force'. Annals of Internal Medicine. 165 (8): 560–564. doi:10.7326/M16-0946. PMID27538032.
- ^Bibbins-Domingo, Kirsten; Grossman, David C.; Curry, Susan J.; Davidson, Karina W.; Epling, John W.; García, Francisco A. R.; Gillman, Matthew W.; Kemper, Alex R.; Krist, Alex H.; Kurth, Ann E.; Landefeld, C. Seth; Lefevre, Michael; Mangione, Carol M.; Owens, Douglas K.; Phillips, William R.; Phipps, Maureen G.; Pignone, Michael P.; Siu, Albert L. (August 9, 2016). 'Screening for Lipid Disorders in Children and Adolescents'. JAMA. 316 (6): 625–33. doi:10.1001/jama.2016.9852. PMID27532917.
- ^ abNordestgaard, BG; Varbo, A (August 2014). 'Triglycerides and cardiovascular disease'. The Lancet. 384 (9943): 626–635. doi:10.1016/S0140-6736(14)61177-6. PMID25131982.
- ^GILL, Jason; Sara HERD; Natassa TSETSONIS; Adrianne HARDMAN (Feb 2002). 'Are the reductions in triacylglycerol and insulin levels after exercise related?'. Clinical Science. 102 (2): 223–231. doi:10.1042/cs20010204. PMID11834142.
- ^Davidson, MH (28 January 2008). 'Pharmacological Therapy for Cardiovascular Disease'. In Davidson, Michael H; Toth, Peter P; Maki, Kevin C (eds.). Therapeutic Lipidology. Contemporary Cardiology. Cannon, Christopher P.; Armani, Annemarie M. Totowa, New Jersey: Humana Press, Inc. pp. 141–142. ISBN978-1-58829-551-4.
- ^Anagnostis, P; Paschou, SA; Goulis, DG; Athyros, VG; Karagiannis, A (February 2018). 'Dietary management of dyslipidaemias. Is there any evidence for cardiovascular benefit?'. Maturitas. 108: 45–52. doi:10.1016/j.maturitas.2017.11.011. PMID29290214.
- ^Abourbih S, Filion KB, Joseph L, Schiffrin EL, Rinfret S, Poirier P, Pilote L, Genest J, Eisenberg MJ (2009). 'Effect of fibrates on lipid profiles and cardiovascular outcomes: a systematic review'. Am J Med. 122 (10): 962.e1–962.e8. doi:10.1016/j.amjmed.2009.03.030. PMID19698935.
- ^Jun M, Foote C, Lv J (2010). 'Effects of fibrates on cardiovascular outcomes: a systematic review and meta-analysis'. Lancet. 375 (9729): 1875–1884. doi:10.1016/S0140-6736(10)60656-3. PMID20462635.
- ^Blair HA, Dhillon S (2014). 'Omega-3 carboxylic acids (Epanova): a review of its use in patients with severe hypertriglyceridemia'. Am J Cardiovasc Drugs. 14: 393–400. doi:10.1007/s40256-014-0090-3. PMID25234378.
Classification |
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External resources |
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Retrieved from 'https://en.wikipedia.org/w/index.php?title=Hypertriglyceridemia&oldid=983939812'
Starting Python in Windows¶
If you used the bundle installation you should be able to use the context menusto get started. Right-click on the folder containing the data you wish toanalyse and select “Jupyter notebook here” or “Jupyter qtconsole here”. Werecommend the former, since notebooks have many advantages over conventionalconsoles, as will be illustrated in later sections. The examples in some latersections assume Notebook operation. A new tab should appear in your defaultbrowser listing the files in the selected folder. To start a python notebookchoose “Python 3” in the “New” drop-down menu at the top right of the page.Another new tab will open which is your Notebook.
Starting Python in Linux and MacOS¶
You can start IPython by opening a system terminal and executing
ipython
,(optionally followed by the “frontend”: “qtconsole” for example). However, inmost cases, the most agreeable way to work with HyperSpy interactivelyis using the Jupyter Notebook (previously known asthe IPython Notebook), which can be started as follows:Linux users may find it more convenient to start Jupyter/IPython from thefile manager context menu.In either OS you can also start by double-clicking a notebook file if one already exists.
Starting HyperSpy in the notebook (or terminal)¶
Typically you will need to set up IPython for interactive plotting withmatplotlib using
%matplotlib
(which is known as a ‘Jupyter magic’)before executing any plotting command. So, typically, after startingIPython, you can import HyperSpy and set up interactive matplotlib plotting byexecuting the following two lines in the IPython terminal (In these docs wenormally use the general Python prompt symbol >>>
but you will probablysee In[1]:
etc.):Note that to execute lines of code in the notebook you must press
Shift+Return
. (For details about notebooks and their functionality trythe help menu in the notebook). Next, import two useful modules: numpy andmatplotlib.pyplot, as follows:The rest of the documentation will assume you have done this. It also assumesthat you have installed at least one of HyperSpy’s GUI packages:jupyter widgets GUIand thetraitsui GUI.
By default, HyperSpy warns the user if one of the GUI packages is not installed.These warnings can be turned off using the
Preferences
GUI(see here for more information) orprogrammatically as follows:Now you are ready to loadyour data (see below).
Changed in version v1.3: HyperSpy works with all matplotlib backends, including the nbagg backendthat enables interactive plotting embedded in the jupyter notebook.
Warning
When using the qt4 backend in Python 2 the matplotlib magic must beexecuted after importing HyperSpy and qt must be the default HyperSpybackend.
Note
When running in a headless system it is necessary to set the matplotlibbackend appropiately to avoid a cannot connect to X server error, forexample as follows:
Getting help¶
When using IPython, the documentation (docstring in Python jargon) can beaccessed by adding a question mark to the name of a function. e.g.:
This syntax is a shortcut to the standard way one of displaying the helpassociated to a given functions (docstring in Python jargon) and it is one ofthe many features of IPython, which is theinteractive python shell that HyperSpy uses under the hood.
Please note that the documentation of the code is a work in progress, so notall the objects are documented yet.
Up-to-date documentation is always available in the HyperSpy website.
Autocompletion¶
Another useful IPython feature is theautocompletion of commands and filenames using the tab and arrow keys. Nxpowerlite portable. It ishighly recommended to read the Ipython documentation (specially their Gettingstartedsection) for many more useful features that will boost your efficiency whenworking with HyperSpy/Python interactively.
Loading data¶
Once HyperSpy is running, to load from a supported file format (seeSupported formats) simply type:
Hint
The load function returns an object that contains data read from the file.We assign this object to the variable
s
but you can choose any (valid)variable name you like. for the filename, don’t forget to include thequotation marks and the file extension.If no argument is passed to the load function, a window will be raised thatallows to select a single file through your OS file manager, e.g.:
It is also possible to load multiple files at once or even stack multiplefiles. For more details read Loading files: the load function
“Loading” data from a numpy array¶
HyperSpy can operate on any numpy array by assigning it to a BaseSignal class.This is useful e.g. for loading data stored in a format that is not yetsupported by HyperSpy—supposing that they can be read with another Pythonlibrary—or to explore numpy arrays generated by other Pythonlibraries. Simply select the most appropriate signal from the
signals
module and create a new instance by passing a numpy arrayto the constructor e.g.The numpy array is stored in the
data
attributeof the signal class.Loading example data and data from online databases¶
HyperSpy is distributed with some example data that can be found inhs.datasets.example_signals. The following example plots one of the examplesignals:
New in version 1.0:
eelsdb()
function.The
eelsdb()
function in hs.datasets candirectly load spectra from The EELS Database. Forexample, the following loads all the boron trioxide spectra currentlyavailable in the database:The navigation and signal dimensions¶
In HyperSpy the data is interpreted as a signal array and, therefore, the dataaxes are not equivalent. HyperSpy distinguishes between signal andnavigation axes and most functions operate on the signal axes anditerate on the navigation axes. For example, an EELS spectrum image (i.e.a 2D array of spectra) has three dimensions X, Y and energy-loss. InHyperSpy, X and Y are the navigation dimensions and the energy-loss is thesignal dimension. To make this distinction more explicit therepresentation of the object includes a separator
|
between thenavigation and signal dimensions e.g.In HyperSpy a spectrum image has signal dimension 1 and navigation dimension 2and is stored in the Signal1D subclass.
An image stack has signal dimension 2 and navigation dimension 1 and is storedin the Signal2D subclass.
Note that HyperSpy rearranges the axes when compared to the array order. Thefollowing few paragraphs explain how and why it does it.
Depending how the array is arranged, some axes are faster to iterate thanothers. Consider an example of a book as the dataset in question. It istrivially simple to look at letters in a line, and then lines down the page,and finally pages in the whole book. However if your words are writtenvertically, it can be inconvenient to read top-down (the lines are stillhorizontal, it’s just the meaning that’s vertical!). It’s very time-consumingif every letter is on a different page, and for every word you have to turn 5-6pages. Exactly the same idea applies here - in order to iterate through thedata (most often for plotting, but applies for any other operation too), youwant to keep it ordered for “fast access”.
In Python (more explicitly numpy) the “fast axes order” is C order (alsocalled row-major order). This means that the last axis of a numpy array isfastest to iterate over (i.e. the lines in the book). An alternative orderingconvention is F order (column-major), where it is the reverse - the first axisof an array is the fastest to iterate over. In both cases, the further an axisis from the fast axis the slower it is to iterate over it. In the bookanalogy you could think, for example, think about reading the first lines ofall pages, then the second and so on.
When data is acquired sequentially it is usually stored in acquisition order.When a dataset is loaded, HyperSpy generally stores it in memory in the sameorder, which is good for the computer. However, HyperSpy will reorder andclassify the axes to make it easier for humans. Let’s imagine a single numpyarray that contains pictures of a scene acquired with different exposure timeson different days. In numpy the array dimensions are
(D,E,Y,X)
. Thisorder makes it fast to iterate over the images in the order in which they wereacquired. From a human point of view, this dataset is just a collection ofimages, so HyperSpy first classifies the image axes (X
and Y
) assignal axes and the remaining axes the navigation axes. Then it reversesthe order of each sets of axes because many humans are used to get the X
axis first and, more generally the axes in acquisition order from left toright. So, the same axes in HyperSpy are displayed like this: (E,D|X,Y)
.Extending this to arbitrary dimensions, by default, we reverse the numpy axes,chop it into two chunks (signal and navigation), and then swap those chunks, atleast when printing. As an example:
In the background, HyperSpy also takes care of storing the data in memory ina “machine-friendly” way, so that iterating over the navigation axes is alwaysfast.
Setting axis properties¶
The axes are managed and stored by the
AxesManager
classthat is stored in the axes_manager
attribute ofthe signal class. The individual axes can be accessed by indexing theAxesManager. e.g.The axis properties can be set by setting the
DataAxis
attributes e.g.Once the name of an axis has been defined it is possible to request it by itsname e.g.:
It is also possible to set the axes properties using a GUI by calling the
gui()
method of the AxesManager
AxesManager ipywidgets GUI.
or the
DataAxis
, e.g:DataAxis ipywidgets GUI.
To simply change the “current position” (i.e. the indices of the navigationaxes) you could use the navigation sliders:
Navigation sliders ipywidgets GUI.
Saving Files¶
The data can be saved to several file formats. The format is specified bythe extension of the filename.
Some file formats are much better at maintaining the information abouthow you processed your data. The preferred format in HyperSpy is hspy,which is based on the HDF5 format. This format keeps the most informationpossible.
There are optional flags that may be passed to the save function. SeeSaving data to files for more details.
Accessing and setting the metadata¶
When loading a file HyperSpy stores all metadata in the BaseSignal
original_metadata
Formz 8 6. attribute. In addition,some of those metadata and any new metadata generated by HyperSpy are stored inmetadata
attribute.Configuring HyperSpy¶
The behaviour of HyperSpy can be customised using the
Preferences
class. The easiest way to do it is bycalling the gui()
method:This command should raise the Preferences user interface if one of thehyperspy gui packages are installed and enabled:
Preferences user interface.
New in version 1.3: Possibility to enable/disable GUIs in the
It is also possible to set the preferences programmatically. For example,to disable the traitsui GUI elements and save the changes to disk:
Changed in version 1.3: The following items were removed from preferences:
General.default_export_format
, General.lazy
,Model.default_fitter
, Machine_learning.multiple_files
,Machine_learning.same_window
, Plot.default_style_to_compare_spectra
,Plot.plot_on_load
, Plot.pylab_inline
, EELS.fine_structure_width
,EELS.fine_structure_active
, EELS.fine_structure_smoothing
,EELS.synchronize_cl_with_ll
, EELS.preedge_safe_window_width
,EELS.min_distance_between_edges_for_fine_structure
.Messages log¶
Hyperpdf 1 1 3 Sezon
HyperSpy writes messages to the Python logger. Thedefault log level is “WARNING”, meaning that only warnings and more severeevent messages will be displayed. The default can be set in thepreferences. Alternatively, it can be setusing
set_log_level()
e.g.: