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| 2. |
- Calcagno, A., et al.
(författare)
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Alzheimer Dementia in People Living With HIV
- 2021
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Ingår i: Neurology-Clinical Practice. - : Ovid Technologies (Wolters Kluwer Health). - 2163-0402 .- 2163-0933. ; 11:5
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Tidskriftsartikel (refereegranskat)abstract
- Objective Given the aging of people living with HIV (PLWH) and the high prevalence of HIV-associated neurocognitive disorders, we aimed at describing the clinical, instrumental, and CSF features of PLWH diagnosed with Alzheimer dementia (AD). Methods The databases of 3 large Italian outpatient clinics taking care of more than 9,000 PLWH were searched for the diagnosis of AD. After obtaining patients' or their next of kin's consent for publication, anonymous data were collected in an excel spreadsheet and described. Routinely collected CSF biomarkers and radiologic imaging results were recorded whether available. Results Four patients were included in this case series who were diagnosed with AD aged between 60 and 74 years. All participants were on highly active antiretroviral therapy and showed nondetectable serum HIV RNA. Memory impairment was the most prominent cognitive feature. The diagnosis was obtained considering the exclusion of other potential causes, MRI and fluorodeoxyglucose-PET features, and, in (in 2/4), CSF AD biomarkers levels. In 1 patient, longitudinal CSF tau/p-tau increased, and beta-amyloid(1-42) decreased over time despite antiretroviral therapy containing nucleotide reverse transcriptase inhibitors. Conclusions In older PLWH cognitive symptoms may represent the onset of AD: a multidisciplinary team may be needed for reaching a likely in vivo diagnosis.
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| 3. |
- Colabrese, S., et al.
(författare)
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Flow Navigation by Smart Microswimmers via Reinforcement Learning
- 2017
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Ingår i: Physical Review Letters. - 0031-9007. ; 118:15
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Tidskriftsartikel (refereegranskat)abstract
- Smart active particles can acquire some limited knowledge of the fluid environment from simple mechanical cues and exert a control on their preferred steering direction. Their goal is to learn the best way to navigate by exploiting the underlying flow whenever possible. As an example, we focus our attention on smart gravitactic swimmers. These are active particles whose task is to reach the highest altitude within some time horizon, given the constraints enforced by fluid mechanics. By means of numerical experiments, we show that swimmers indeed learn nearly optimal strategies just by experience. A reinforcement learning algorithm allows particles to learn effective strategies even in difficult situations when, in the absence of control, they would end up being trapped by flow structures. These strategies are highly nontrivial and cannot be easily guessed in advance. This Letter illustrates the potential of reinforcement learning algorithms to model adaptive behavior in complex flows and paves the way towards the engineering of smart microswimmers that solve difficult navigation problems.
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| 4. |
- Colabrese, S., et al.
(författare)
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Smart inertial particles
- 2018
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Ingår i: Physical Review Fluids. - 2469-990X. ; 3:8
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Tidskriftsartikel (refereegranskat)abstract
- We performed a numerical study to train smart inertial particles to target specific flow regions with high vorticity through the use of reinforcement learning algorithms. The particles are able to actively change their size to modify their inertia and density. In short, using local measurements of the flow vorticity, the smart particle explores the interplay between its choices of size and its dynamical behavior in the flow environment. This allows it to accumulate experience and learn approximately optimal strategies of how to modulate its size in order to reach the target high-vorticity regions. We consider flows with different complexities: a two-dimensional stationary Taylor-Green-like configuration, a two-dimensional time-dependent flow, and finally a three-dimensional flow given by the stationary Arnold-Beltrami-Childress (ABC) helical flow. We show that smart particles are able to learn how to reach extremely intense vortical structures in all the tackled cases.
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| 5. |
- Gustavsson, Kristian, 1980, et al.
(författare)
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Finding efficient swimming strategies in a three-dimensional chaotic flow by reinforcement learning
- 2017
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Ingår i: European Physical Journal E. - : Springer Science and Business Media LLC. - 1292-8941 .- 1292-895X. ; 40:12
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Tidskriftsartikel (refereegranskat)abstract
- We apply a reinforcement learning algorithm to show how smart particles can learn approximately optimal strategies to navigate in complex flows. In this paper we consider microswimmers in a paradigmatic three-dimensional case given by a stationary superposition of two Arnold-Beltrami-Childress flows with chaotic advection along streamlines. In such a flow, we study the evolution of point-like particles which can decide in which direction to swim, while keeping the velocity amplitude constant. We show that it is sufficient to endow the swimmers with a very restricted set of actions (six fixed swimming directions in our case) to have enough freedom to find efficient strategies to move upward and escape local fluid traps. The key ingredient is the learning-from-experience structure of the algorithm, which assigns positive or negative rewards depending on whether the taken action is, or is not, profitable for the predetermined goal in the long-term horizon. This is another example supporting the efficiency of the reinforcement learning approach to learn how to accomplish difficult tasks in complex fluid environments.
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