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Table 42.

Laboratory Diagnosis of Human Bite Wound Infections

There is no utility in collecting a specimen at the time of the bite; collect samples only if infection occurs.

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Table 42.

Laboratory Diagnosis of Human Bite Wound Infections

There is no utility in collecting a specimen at the time of the bite; collect samples only if infection occurs.

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As with human bite wounds, the oral cavity of animals is the primary source of potential pathogens and thus the anticipated etiological agent(s) is highly dependent upon the type of animal that inflicted the bite ( Table 43 ). As dogs and cats account for the majority of animal-inflicted bite wounds, the 2 most prominent groups of microorganisms initially considered in the evaluation of patients are Pasteurella spp, namely P. canis (dogs) and P. multocida subsp multocida and subsp septica (cats) or Capnocytophaga canimorsus . Other common aerobes include streptococci, staphylococci, Moraxella spp, and saprophytic Neisseria spp. Animal bite wounds are often polymicrobial in nature and include a variety of anaerobes. Due to the complexity of the microbial flora in animals, examination of cultures for organisms other than those listed in Table 43 is of little benefit since these organisms are not included in most of the commercial identification systems (conventional and automated) databases [ 229–238 ]. Matrix-assisted laser desorption–ionization mass spectrometry has proven valuable in identifying organisms when conventional phenotypic systems have failed. If rabies or herpes B infection is suspected, contact the local or state public health laboratory for assistance and advice on how to proceed.

Table 43.

Laboratory Diagnosis of Animal Bite Wound Infections

Abbreviation: RT, room temperature.

Additional potential pathogens to consider: , , spp, spp, spp, spp, spp, , , Centers for Disease Control and Prevention (CDC) group EF-4, CDC NO-1, spp, rabies, herpes B, or other viruses (refer to Section XIV).

Anaerobic transport media preserve all other organisms for culture.

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Table 43.

Laboratory Diagnosis of Animal Bite Wound Infections

Abbreviation: RT, room temperature.

Additional potential pathogens to consider: , , spp, spp, spp, spp, spp, , , Centers for Disease Control and Prevention (CDC) group EF-4, CDC NO-1, spp, rabies, herpes B, or other viruses (refer to Section XIV).

Anaerobic transport media preserve all other organisms for culture.

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Infections from trauma are usually caused by exogenous or environmental microbial flora but can be due to the individual’s endogenous (normal) flora ( Table 44 ). It is strongly recommended that specimens not be submitted for culture within the first 48 hours posttrauma as growth from specimens collected within this time frame most likely represents environmental flora acquired at the time of the trauma episode (motor vehicle accident, stabbings, gunshot wounds, etc). The optimal time to acquire cultures is immediately after debridement of the trauma site [ 239–242 ]. It is strongly recommended that initial cultures focus on common pathogens, with additional testing being reserved for uncommon or rare infections associated with special circumstances (eg, detection of Vibrio spp following saltwater exposure) or patients with chronic manifestations of infection or who do not respond to an initial course of therapy.

Solving Forward-Backward Stochastic Differential Equations Explicitly – a Four Step Scheme (1994)


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by Jiongmin Yong


@ARTICLE{Yong94solvingforward-backward,author = {Jiongmin Yong},title = {Solving Forward-Backward Stochastic Differential Equations Explicitly – a Four Step Scheme},journal = {Prob. Th. Rel. Fields},year = {1994},pages = {339--359}}




Abstract. The problem of nding adapted solutions to systems of coupled linear forwardbackward stochastic di erential equations (FBSDEs, for short) is investigated. A necessary condition of solvability leads to a reduction of general linear FBSDEs to a special one. By some ideas from controllability in control theory, using some functional analysis, we obtain a necessary and su cient condition for the solvability of the linear FBSDEs with the processes Z (serves as a correction, see x1) being absent in the drift. Then a Riccati type equation for matrix-valued (not necessarily square) functions is derived using the idea of the Four-Step-Scheme (introduced in [11] for general FBSDEs). The solvability of such a Riccati type equation is studied which leads to a representation of adapted solutions to linear FBSDEs. Keywords. Linear forward-backward stochastic di erential equations, adapted solution, Riccati type equation. AMS Mathematics subject classi cation. 60H10.


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Intuitively, it feels a bit like the two languages have a similar ‘shape’ and that by forcing them to line up at different points, they overlap and other points get pulled into the right positions.

t-SNE visualization of the bilingual word embedding. Green is Chinese, Yellow is English. ( Socher (2013a) )

In bilingual word embeddings, we learn a shared representation for two very similar kinds of data. But we can also learn to embed very different kinds of data in the same space.

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The basic idea is that one classifies images by outputting a vector in a word embedding. Images of dogs are mapped near the “dog” word vector. Images of horses are mapped near the “horse” vector. Images of automobiles near the “automobile” vector. And so on.

The interesting part is what happens when you test the model on new classes of images. For example, if the model wasn’t trained to classify cats – that is, to map them near the “cat” vector – what happens when we try to classify images of cats?

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It turns out that the network is able to handle these new classes of images quite reasonably. Images of cats aren’t mapped to random points in the word embedding space. Instead, they tend to be mapped to the general vicinity of the “dog” vector, and, in fact, close to the “cat” vector. Similarly, the truck images end up relatively close to the “truck” vector, which is near the related “automobile” vector.

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This was done by members of the Stanford group with only 8 known classes (and 2 unknown classes). The results are already quite impressive. But with so few known classes, there are very few points to interpolate the relationship between images and semantic space off of.

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The results are impressive. While they may not get images of unknown classes to the precise vector representing that class, they are able to get to the right neighborhood. So, if you ask it to classify images of unknown classes and the classes are fairly different, it can distinguish between the different classes.

Even though I’ve never seen a Aesculapian snake or an Armadillo before, if you show me a picture of one and a picture of the other, I can tell you which is which because I have a general idea of what sort of animal is associated with each word. These networks can accomplish the same thing.

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