Salla disease

Salla disease (SD), also called sialic acid storage disease or Finnish type sialuria, is an autosomal recessive lysosomal storage disease characterized by early physical impairment and mental retardation. It was first described in 1979, after Salla, a municipality in Finnish Lapland. Salla disease is one of 40 Finnish heritage diseases and affects approximately 130 individuals, mainly from Finland and Sweden.

Individuals with Salla disease may present with nystagmus in the first months of life as well as hypotonia, reduced muscle tone and strength, and cognitive impairment. The most severely impaired children do not walk or acquire language, but the typical patient learns to walk and speak and has normal life expectancy. The MRI shows arrested or delayed myelination.
SD is caused by a mutation in the SLC17A5 gene, located at human chromosome 6q14-15

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. This gene codes for sialin, a lysosomal membrane protein that transports the charged sugar, N-acetylneuraminic acid (sialic acid), out of lysosomes. The mutation causes sialic acid to build up in the cells.
The disease is inherited in an autosomal recessive manner. This means the defective gene responsible for the disorder is located on an autosome (chromosome 6 is an autosome), and two copies of the defective gene (one inherited from each parent) are required in order to be born with the disorder. The parents of an individual with an autosomal recessive disorder both carry one copy of the defective gene, but usually do not experience any signs or symptoms of the disorder.
The life expectancy for individuals with Salla disease is between the ages of 50 and 60.
A diagnosis of this disorder can be made by measuring urine to look for elevated levels of free sialic acid. Prenatal testing is also available for known carriers of this disorder.
There is no cure for Salla Disease. Treatment is limited to controlling the symptoms of this disorder. Anti-convulsant medication may control seizure episodes. Physical therapists can assist an affected individual to build muscle strength and coordination, and speech therapists may assist the affected individual in improving his or her speech.

Friends of Israel in the Parliament of Norway

Friends of Israel in the Parliament of Norway (Norwegian: Israels Venner på Stortinget) is a pro-Israel caucus group consisting of members of the Parliament of Norway.
In 1974 the group constituted a majority in the Parliament of Norway for the first time, with 86 members among the 150 parliamentary representatives. All political parties except for the Socialist Electoral League were represented. In 1981 the group had 100 members, but this number declined following the Israeli invasion of Lebanon in 1982.
In the 2005 the group had 28 members from three political parties; 16 from the Progress Party (FrP), 10 from the Christian Democratic Party (KrF) and 2 from the Conservative Party (H). One member, KrF’s Jon Lilletun, died during the term. In comparison, the number of seats in parliament for each party was 38 from the Progress Party, 11 from the Christian Democratic Party and 23 from the Conservative Party. The group was chaired by Ingebrigt S. Sørfonn of the Christian Democratic Party.
In late 2007, an initiative was taken to form an opposing parliamentary group, Friends of Palestine in the Parliament of Norway, in support of Palestinians.
After the 2009 election, the groups supporting Israel and Palestine reconvened. Torbjørn Røe Isaksen (H) joined both groups. In late November 2009 the group had 26 members; 13 from the Progress Party (FrP), 10 from the Christian Democratic Party (KrF) and 3 from the Conservative Party (H). In comparison, the number of seats in parliament for each party was 41 from the Progress Party, 10 from the Christian Democratic Party and 30 from the Conservative Party. The members elected Hans Olav Syversen as leader from 2009 to 2011, and Jørund Rytman as leader from 2011 to 2013

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.
As of June 2014, the Friends of Israel have a total of 37 members, 20 (of 29) from the Progress Party (FrP), 10 (of 10) from the Christian Democratic Party (KrF), 4 (of 48) from the Conservative Party (H), and 3 (of 55) from the Labour Party (Ap). Hans Fredrik Grøvan (KrF) would chair the caucus the first two years, and Jørund Rytman (FrP) the final two years.
Two members, Kari Henriksen and Sverre Myrli (both Ap) were also members of Friends of Palestine.

Little lorikeet

The little lorikeet (Glossopsitta pusilla) is a species of parrot in the Psittaculidae family. It is endemic to Australia. It is a small parrot, predominantly green in plumage with a red face bogner ski jacket. Its natural habitats are subtropical or tropical dry forests and subtropical or tropical moist lowland forests.

The little lorikeet was first described by ornithologist George Shaw in 1790 as Psittacus pusillus. Its specific epithet is the Latin pusilla “small”. Other common names include tiny lorikeet, red-faced lorikeet, gizzie

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, slit, and formerly a local indigenous term gerryang.
Measuring 15 cm (5.9 in) in length, the male and female are similarly coloured, although the latter is a little duller. The crown, lores and throat are red, the nape and shoulder bronze-coloured and the remainder of the plumage green. The underparts yellow-tinged. The bill is black and the iris golden in colour.
The little lorikeet is found in eastern and southern Australia, from the vicinity of Cairns southwards through Queensland and New South Wales from the western slopes of the Great Dividing Range eastwards to the coast, though most of Victoria and southeastern South Australia. It also occurs in Tasmania although is uncommon there. They are found in forest, especially in the vicinity of flowering or fruit-bearing vegetation.
Fruit and flowers form the bulk of their diet, including native grasstrees (Xanthorrhoea spp.), and tea-tree (Melaleuca spp.), Loranthus, and the introduced loquat (Eriobotrya japonica). They will occasionally visit orchards.
Breeding season is from May in the north, or August in the south, to December. The nest is a hollow in a tree and a clutch of 3-5 matte white roundish eggs, measuring 20 x 16 mm, is laid. The incubation period is around three weeks.
Although first exported to Europe in 1877, the little lorikeet is only very rarely seen outside Australia. Even in its native country, it is uncommon in captivity. It has a reputation of being difficult to keep.

Pigeonit

Pigeonit ist ein selten vorkommendes Mineral aus der Mineralklasse der „Silikate und Germanate“. Es kristallisiert im monoklinen Kristallsystem mit der chemischen Zusammensetzung (Mg,Fe,Ca)2[Si2O6] und entwickelt prismatische, bis zu einem Zentimeter große, durchscheinende Kristalle von grau-brauner, grünlicher oder schwarzer Farbe.

Erstmals entdeckt wurde Pigeonit am Pigeon Point im US-Bundesstaat Montana und beschrieben 1900 von Alexander Newton Winchell, der das Mineral nach seiner Typlokalität benannte Wellensteyn Damenjacken.
In der mittlerweile veralteten, aber noch gebräuchlichen 8. Auflage der Mineralsystematik nach Strunz gehörte der Pigeonit zur Mineralklasse der „Silikate und Germanate“ und dort zur Abteilung der „Ketten- und Bandsilikate (Inosilikate)“, wo er zusammen mit Aegirin, Augit, Diopsid, Esseneit MCM Rucksack, Hedenbergit, Jadeit, Jervisit, Johannsenit, Kanoit, Klinoenstatit, Klinoferrosilit, Kosmochlor, Namansilit, Natalyit, Omphacit, Petedunnit und Spodumen die Untergruppe der „Klinopyroxene“ mit der System-Nr. VIII/F.01 innerhalb der Pyroxengruppe bildete.
Die seit 2001 gültige und von der International Mineralogical Association (IMA) verwendete 9. Auflage der Strunz’schen Mineralsystematik ordnet den Pigeonit ebenfalls in die Klasse der „Silikate und Germanate“ und dort in die Abteilung der „Ketten- und Bandsilikate (Inosilikate)“ ein. Diese Abteilung ist allerdings weiter unterteilt nach der Art der Kettenbildung und der Zugehörigkeit zu größeren Mineralfamilien, so dass das Mineral entsprechend in der Unterabteilung „Ketten- und Bandsilikate mit 2-periodischen Einfachketten Si2O6; Pyroxen-Familie“ zu finden ist, wo es zusammen mit Klinoenstatit, Klinoferrosilit, Halagurit und Kanoit die „Mg,Fe,Mn-Klinopyroxene, Klinoenstatitgruppe“ mit der System-Nr. 9.DA.10 bildet.
Auch die vorwiegend im englischen Sprachraum gebräuchliche Systematik der Minerale nach Dana ordnet den Pigeonit in die Klasse der „Silikate und Germanate“ und dort in die Abteilung der „Kettensilikatminerale“ ein. Hier ist er zusammen mit Klinoenstatit, Klinoferrosilit und Kanoit in der Gruppe der „P2/c Klinopyroxene“ mit der System-Nr. 65.01.01 innerhalb der Unterabteilung der „Kettensilikate: Einfache unverzweigte Ketten, W=1 mit Ketten P=2“ zu finden.
Pigeonit bildet sich in hocherhitzten mafischen Gesteinen wie Basalt, die schnell abgekühlt werden. Gleichzeitig darf für die Bildung nur geringe Mengen Calcium anwesend sein, da sonst das ähnliche Mineral Augit entsteht. Typischerweise ist dies in manchen Vulkanen der Fall. Neben diesen findet man es auch in Meteoriten, die auf die Erde gestürzt sind.
Ein typisches Beispiel für einen Vulkan, bei dessen Ausbrüchen Pigeonit entsteht, ist der Soufrière Hills auf der Karibikinsel Montserrat und ein Beispiel für einen Meteoritenfund ist der Cassigny-Meteorit in Frankreich.
Insgesamt konnte Pigeonit bisher (Stand: 2011) an rund 120 Fundorten nachgewiesen werden. Neben seiner Typlokalität Pigeon Point trat das Mineral in den Vereinigten Staaten noch an mehreren Orten der Bundesstaaten Alabama, Arizona, Maine, Massachusetts, Michigan, Nevada, New Mexico, Pennsylvania und Virginia sowie im San-Juan-Gebirge in Colorado, bei Red Oak im Fulton County (Georgia), Lafayette (Indiana), im Gray County (Kansas), bei Beaver Bay in Minnesota, im Stillwater County (Montana), im Moore County (North Carolina), bei Shrewsbury im Rutland County (Vermont), bei Washougal in Washington und am Potato River im Ashland County (Wisconsin).
In Deutschland findet man Pigeonit unter anderem bei Röhrnbach im bayerischen Wald, bei Bad Harzburg im niedersächsischen Harz und im Rockeskyll Vulkankomplex in der rheinland-pfälzischen Eifel.
Weitere Fundorte sind Algerien, die Antarktis, Australien, Brasilien, China, Grönland, Indien, Cogne in Italien, Japan, der Jemen, Libyen, Marokko, Whangarei in Neuseeland, der Oman, Papua-Neuguinea, Rumänien, Russland, Schweden die Slowakei, Spanien, St. Lucia, Südafrika, Südkorea, Stonařov in Tschechien, Ungarn, Usbekistan und das Vereinigte Königreich (Großbritannien).
Auch in Gesteinsproben vom Mond, genauer in der Nähe der Landestelle der Luna 16-Mission im Mare Fecunditatis sowie im Mondmeteorit NWA 773 aus Dchira (Westsahara) konnte Pigeonit nachgewiesen werden.
Pigeonit kristallisiert im monoklinen Kristallsystem in der Raumgruppe P21/c (Raumgruppen-Nr. 14), den Gitterparametern a = 9,71 Å, b = 8,95 Å, c = 5,25 Å und β = 108,6° sowie vier Formeleinheiten pro Elementarzelle.
Oberhalb von 950 °C geht die Struktur durch einen Phasenübergang in eine ebenfalls monokline Struktur mit der Raumgruppe C2/c über.

Awtomobilist Petersburg

Awtomobilist Petersburg (ros. Футбольный клуб «Автомобилист» Санкт-Петербург, Futbolnyj Kłub “Awtomobilist” Sankt-Pietierburg) – rosyjski klub piłkarski z siedzibą w Petersburgu.

Chronologia nazw:
Założony w 1931 jako Promkoopieracyja Leningrad. W latach 1931-1935 uczestniczył w miejskich rozgrywkach piłkarskich Leningradu.
Wiosną 1936 zespół pod nazwą Spartak Leningrad debiutował w Grupie B Mistrzostw ZSRR.
W 1937 zajął pierwsze miejsce w Klasie B i awansował do Klasy A, jednak nie utrzymał się w niej.
W 1940 zespół zajął 2 miejsce i ponownie awansował do Klasy A kurtki bogner. Jednak wojna w 1941 przeszkodziła ukończyć rozgrywki.
Po zakończeniu wojny w 1945 klub startuje w Drugiej Grupie, w której występuje do 1949.
W 1957 klub ponownie startuje w Klasie B, grupie 1 i spada z niej.
Od 1959 klub już na dłużej zostaje w Klasie B, grupie 1.
Po reformie systemu lig ZSRR w 1963 okazał się w niższej Klasie B, grupie 1, w której występował do 1966.
Potem zespół pod nazwą Awtomobilist Leningrad występował w rozgrywkach lokalnych.
Dopiero w 1992 klub został odrodzony i występował w Amatorskiej Lidze w Mistrzostwach Rosji. Kolejne podejście było w 1997 roku.

Latent Dirichlet allocation

In natural language processing, Latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word’s creation is attributable to one of the document’s topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael I. Jordan in 2003. Essentially the same model was also proposed independently by J. K. Pritchard, M. Stephens, and P. Donnelly in the study of population genetics. Both papers have been highly influential, with 13320 and 15857 citations respectively in January 2016.

In LDA, each document may be viewed as a mixture of various topics. This is similar to probabilistic latent semantic analysis (pLSA), except that in LDA the topic distribution is assumed to have a Dirichlet prior. In practice, this results in more reasonable mixtures of topics in a document. It has been noted, however, that the pLSA model is equivalent to the LDA model under a uniform Dirichlet prior distribution.
For example, an LDA model might have topics that can be classified as CAT_related and DOG_related. A topic has probabilities of generating various words, such as milk, meow, and kitten, which can be classified and interpreted by the viewer as “CAT_related”. Naturally, the word cat itself will have high probability given this topic. The DOG_related topic likewise has probabilities of generating each word: puppy, bark, and bone might have high probability. Words without special relevance, such as the (see function word), will have roughly even probability between classes (or can be placed into a separate category). A topic is not strongly defined, neither semantically nor epistemologically. It is identified on the basis of supervised labeling and (manual) pruning on the basis of their likelihood of co-occurrence. A lexical word may occur in several topics with a different probability, however, with a different typical set of neighboring words in each topic.
Each document is assumed to be characterized by a particular set of topics. This is akin to the standard bag of words model assumption, and makes the individual words exchangeable.
With plate notation, the dependencies among the many variables can be captured concisely. The boxes are “plates” representing replicates. The outer plate represents documents, while the inner plate represents the repeated choice of topics and words within a document. M denotes the number of documents, N the number of words in a document. Thus:
The are the only observable variables, and the other variables are latent variables. Mostly, the basic LDA model will be extended to a smoothed version to gain better results .[citation needed] The plate notation is shown on the right, where K denotes the number of topics considered in the model and:
The generative process is as follows. Documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words. LDA assumes the following generative process for a corpus consisting of documents each of length :
1. Choose , where and is the Dirichlet distribution for parameter
2. Choose , where
3. For each of the word positions , where , and
(Note that the Multinomial distribution here refers to the Multinomial with only one trial. It is formally equivalent to the categorical distribution.)
The lengths are treated as independent of all the other data generating variables ( and ). The subscript is often dropped gucci outlet, as in the plate diagrams shown here.
A formal description of smoothed LDA is as follows:
We can then mathematically describe the random variables as follows:
Learning the various distributions (the set of topics, their associated word probabilities, the topic of each word, and the particular topic mixture of each document) is a problem of Bayesian inference. The original paper used a variational Bayes approximation of the posterior distribution; alternative inference techniques use Gibbs sampling and expectation propagation.
Following is the derivation of the equations for collapsed Gibbs sampling, which means s and s will be integrated out. For simplicity, in this derivation the documents are all assumed to have the same length . The derivation is equally valid if the document lengths vary.
According to the model, the total probability of the model is:
where the bold-font variables denote the vector version of the variables. First of all, and need to be integrated out.
All the s are independent to each other and the same to all the s. So we can treat each and each separately. We now focus only on the part.
We can further focus on only one as the following:
Actually, it is the hidden part of the model for the document. Now we replace the probabilities in the above equation by the true distribution expression to write out the explicit equation.
Let be the number of word tokens in the document with the same word symbol (the word in the vocabulary) assigned to the topic. So, is three dimensional. If any of the three dimensions is not limited to a specific value, we use a parenthesized point to denote. For example, denotes the number of word tokens in the document assigned to the topic. Thus, the right most part of the above equation can be rewritten as:
So the integration formula can be changed to:
Clearly, the equation inside the integration has the same form as the Dirichlet distribution. According to the Dirichlet distribution,
Thus,
Now we turn our attentions to the part. Actually, the derivation of the part is very similar to the part. Here we only list the steps of the derivation:
For clarity, here we write down the final equation with both and integrated out:
The goal of Gibbs Sampling here is to approximate the distribution of . Since is invariable for any of Z, Gibbs Sampling equations can be derived from directly. The key point is to derive the following conditional probability:
where denotes the hidden variable of the word token in the document. And further we assume that the word symbol of it is the word in the vocabulary. denotes all the s but . Note that Gibbs Sampling needs only to sample a value for , according to the above probability, we do not need the exact value of but the ratios among the probabilities that can take value. So, the above equation can be simplified as:
Finally, let be the same meaning as but with the excluded. The above equation can be further simplified leveraging the property of gamma function. We first split the summation and then merge it back to obtain a -independent summation, which could be dropped:
Note that the same formula is derived in the article on the Dirichlet-multinomial distribution, as part of a more general discussion of integrating Dirichlet distribution priors out of a Bayesian network.
Recent research has been focused on speeding up the inference of Latent Dirichlet Allocation to support capture of a massive number of topics in large number of documents. The update equation of the Collapsed Gibbs Sampler mentioned in the earlier section has a natural sparsity within it that can be taken advantage of. Intuitively, since each document only contains a subset of topics , and a word also only appears in a subset of topics , the above update equation could be rewritten to take advantage of this sparsity.

In this equation, we have three terms, out of which two of them are sparse, and the other is small. We call these terms and respectively. Now, if we normalize each term by summing over all the topics, we get:

Here, we can see that is a summation of the topics that appear in document , and is also a sparse summation of the topics that a word has been observed by in the whole corpus. on the other hand, is dense but because of the small values of & , the value is very small compared to the two other terms.
Now, while sampling a topic, if we sample a random variable uniformly from , we can check which bucket our sample lands in. Since is small, we are very unlikely to fall into this bucket; however, if we do fall into this bucket, sampling a topic takes O(K) time (same as the original Collapsed Gibbs Sampler). However, if we fall into the other two buckets, we only need to check a subset of topics if we keep record of the sparse topics. A topic can be sampled from the bucket in time, and a topic can be sampled in time.
Notice that after sampling each topic, updating these buckets are all basic arithmetic operations.
Topic modeling is a classic problem in information retrieval gucci outlet. Related models and techniques are, among others, latent semantic indexing, independent component analysis, probabilistic latent semantic indexing, non-negative matrix factorization, and Gamma-Poisson distribution.
The LDA model is highly modular and can therefore be easily extended. The main field of interest is modeling relations between topics. This is achieved by using another distribution on the simplex instead of the Dirichlet. The Correlated Topic Model follows this approach, inducing a correlation structure between topics by using the logistic normal distribution instead of the Dirichlet. Another extension is the hierarchical LDA (hLDA), where topics are joined together in a hierarchy by using the nested Chinese restaurant process. LDA can also be extended to a corpus in which a document includes two types of information (e.g., words and names), as in the LDA-dual model. Nonparametric extensions of LDA include the Hierarchical Dirichlet process mixture model, which allows the number of topics to be unbounded and learnt from data and the nested Chinese Restaurant Process which allows topics to be arranged in a hierarchy whose structure is learnt from data.
As noted earlier, PLSA is similar to LDA. The LDA model is essentially the Bayesian version of PLSA model. The Bayesian formulation tends to perform better on small datasets because Bayesian methods can avoid overfitting the data. For very large datasets, the results of the two models tend to converge. One difference is that PLSA uses a variable to represent a document in the training set. So in PLSA, when presented with a document the model hasn’t seen before, we fix —the probability of words under topics—to be that learned from the training set and use the same EM algorithm to infer —the topic distribution under . Blei argues that this step is cheating because you are essentially refitting the model to the new data.
Variations on LDA have been used to automatically put natural images into categories, such as “bedroom” or “forest”, by treating an image as a document, and small patches of the image as words; one of the variations is called Spatial Latent Dirichlet Allocation.
Recently, LDA has been also applied to bioinformatics context.