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In routine procedures, pathogenic Entamoeba histolytica cannot be differentiated from nonpathogenic Entamoeba dispar using morphologic criteria, so the laboratory report may indicate E. histolytica/dispar [ 156 ]. Only an immunoassay or NAAT can differentiate these organisms.

Viral causes of gastroenteritis are often of short duration and self-limited. Viral shedding may persist after resolution of symptoms. Although included as part of some multiplex NAAT, testing is not routinely performed except in immunocompromised patients, infection control purposes, or outbreak investigations. In immunocompromised hosts, laboratory testing for CMV should be considered, using a quantitative NAAT performed on plasma. Of note, a negative NAAT does not rule out the possibility of CMV disease, and repeat testing may be required.

Proctitis is most commonly due to sexually transmitted agents, a result of anal–genital contact, although abscesses or perirectal wound infections may present with similar symptoms. One sample is usually sufficient for diagnosis ( Table 28 ).

Table 28.

Laboratory Diagnosis of Proctitis

Abbreviations: IgG, immunoglobulin G; NAAT, nucleic acid amplification test; RPR, rapid plasma reagin; RT, room temperature; VDRL, Venereal Disease Research Laboratory.

This is not yet a US Food and Drug Administration–approved specimen source. Availability of testing on this sample type is laboratory specific based on individual laboratory validation. Provider needs to check with the laboratory for optimal specimen and turnaround time.

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Table 28.

Laboratory Diagnosis of Proctitis

Abbreviations: IgG, immunoglobulin G; NAAT, nucleic acid amplification test; RPR, rapid plasma reagin; RT, room temperature; VDRL, Venereal Disease Research Laboratory.

This is not yet a US Food and Drug Administration–approved specimen source. Availability of testing on this sample type is laboratory specific based on individual laboratory validation. Provider needs to check with the laboratory for optimal specimen and turnaround time.

View Large

This section is designed to optimize the activities of the microbiology laboratory to achieve the best approach for the identification of microorganisms associated with peritonitis and intraperitoneal abscesses, hepatic and splenic abscesses, pancreatitis, and biliary tract infection. As molecular analyses begin to be used to define the microbiome of the gastrointestinal and genitourinary tract, contemporary culture protocols will surely evolve to accommodate new, emerging information. The future use of gene amplification and sequencing for identification of microorganisms in these infections will likely show that for every organism currently identified by culture, there will be several times that number that cannot be cultivated using current technologies. To remain focused on contemporary methods currently available in the diagnostic microbiology laboratory, the tables outline the most likely agents of each entity ( Table 29 ) and how best to evaluate the situation with existing techniques ( Table 30 ).

We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.

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Parameter noise helps algorithms more efficiently explore the range of actions available to solve an environment. After 216 episodes of training without parameter noise will frequently develop inefficient running behaviors, whereas policies trained with parameter noise often develop a high-scoring gallop.

Parameter noise lets us teach agents tasks much more rapidly than with other approaches. After learning for 20 episodes on the HalfCheetah Gym environment (shown above), the policy achieves a score of around 3,000, whereas a policy trained with traditional action noise only achieves around 1,500.

Parameter noise adds adaptive noise to the parameters of the neural network policy, rather than to its action space. Traditional RL uses action space noise to change the likelihoods associated with each action the agent might take from one moment to the next. Parameter space noise injects randomness directly into the parameters of the agent, altering the types of decisions it makes such that they always fully depend on what the agent currently senses. The technique is a middle ground between Free Shipping Clearance Buy Cheap Clearance checked straight leg trousers Blue Incotex Fast Delivery Online S9zslpY
(where you manipulate the parameters of your policy but don’t influence the actions a policy takes as it explores the environment during each rollout) and deep reinforcement learning approaches like , , and DDPG (where you don’t touch the parameters, but add noise to the action space of the policy).

Action space noise (left), compared to parameter space noise (right)

Parameter noise helps algorithms explore their environments more effectively, leading to higher scores and more elegant behaviors. We think this is because adding noise in a deliberate manner to the parameters of the policy makes an agent’s exploration consistent across different timesteps, whereas adding noise to the action space leads to more unpredictable exploration which isn’t correlated to anything unique to the agent’s parameters.

People have previously tried applying parameter noise to policy gradients. We’ve extended this by showing that the technique works on policies based on deep neural networks and that it can be applied to both on- and off-policy algorithms.

When conducting this research we ran into three problems:

We use layer normalization to deal with the first problem, which ensures that the output of a perturbed layer (which will be the input to the next one) is still within a similar distribution.

We tackle the second and third problem by introducing an adaptive scheme to adjust the size of the parameter space perturbations. This adjustment works by measuring the effect of the perturbation on action space and whether the action space noise level is larger or smaller than a defined target. This trick allows us to push the problem of choosing noise scale into action space, which is more interpretable than parameter space.