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This is a retrospective single-center study of patients with neovascular age-related macular degeneration whose follow-up was delayed due to COVID-19 pandemic with at least three months between visits in Madrid, Spain. The purpose of the study was to evaluate best corrected visual acuity (BCVA) changes and try to identify features in optical coherence tomography (OCT) that could be related to more profound visual loss. It included 270 eyes. The two last visits before lockdown were used for comparison with the visit after lockdown. BCVA changed from 60.2 ± 18.2 to 55.9 ± 20.5 ETDRS letters. 29% of the eyes lost more than 5 letters. OCT was active in 67% of eyes before lockdown and in 80.4% after lockdown. Multiple lineal analysis showed that patients whose OCT before lockdown presented with a combination of intra and subretinal fluid were more likely to suffer a greater visual loss (p = 0.002). These patients should be encouraged to not miss any visits in case a new lockdown is imposed.

To investigate long-term effect (96 months) of intravitreal ranibizumab administered for exudative age-related macular degeneration (AMD) on retinal nerve fiber layer (RNFL) thickness when used following a pro re nata regimen. In this prospective study, 20 eyes of 20 patients diagnosed with exudative AMD were included. Contralateral non-exudative AMD eyes of nine of these patients were included as controls. Data on intraocular pressure (IOP) and number of injections were recorded. Spectralis optic coherence tomography (OCT) of the circumpapillary RNFL was performed under dilation when diagnosis was made and before the three loading injections. “Follow-up” software was selected to accurately compare baseline with subsequent images through the 8 years of the study. Baseline IOP was 14.1 mmHg both in study (standard deviation, SD: 0.8) and control eyes (SD: 0.9) and remained unchanged during the study. Mean number of injections was 21 (SD: 2.8) at the end of the study. Mean average thickness of RNFL in the study eye group at the end of the study was 96.5 μm (SD: 2.1). Mean loss for the study period was 5.3 μm (SD: 0.7; p

In this article, we present a learning mechanism that allows a multiagent system to cooperate to achieve a gathering task efficiently in unknown and changing environments. The multiagent system is a team of autonomous behavior-based agents with limited communication capabilities. Cooperation is based on the acquisition of signaling behaviors and on the specialization of the agents into different types. Every agent has the same collection of built-in reactive behaviors. Some of the built-in behaviors are fixed, whereas others can be modified through reinforcement learning. The reinforcement signal is delayed until a trial is completed and assesses the collective performance of the team. Each agent uses this common signal to learn what individual behaviors are more suitable for the team. Simulation results, and the corresponding statistical analysis, show that the multiagent system always achieves near-optimal performances.

The quality of the sludge in Wastewater Treatment Plants (WWTPs) depends on the suitable colonization of the flocs by microorganisms. Due to the functional importance of these biological constituents, several biological or biological-related parameters have been commonly used for the control of depuration efficiency. According to national and international water regulation recommendations, interlaboratory studies have a great relevance to determine which parameters are more reliable for their extensive application in routine control. However, these studies are also very useful to demonstrate consistency in results from multiple laboratories and to develop reliable and reproducible methodologies which might be necessary for protocol validation and also for accreditation issues to meet regulatory environmental requirements. The main purpose of this work was to assess the results obtained in consecutive interlaboratory assays in order to determine the concordance degree in the application of biological parameters by participating laboratories. Following the international recommendations about these studies, a common working protocol was proposed. Statistical tests indicated that Sludge Index and several routine physical–chemical analyses [V30, Mixed Liquor Suspended Solids (MLSS), Mixed Liquor Volatile Solids (MLVS) and Sludge Volumetric Index (SVI)] show low variability and therefore are suitable tools for laboratory control. Shannon Index and Sludge Biotic Index also presented low variability although a more precise protocol would be necessary, in particular the methodology to count small flagellates. The abundance and identification of protist species showed low concordance among laboratories and three factors were responsible for the low reliability of data: population density, size and morphological distinguishable characters of the specimens.

Background: The resurgence of home hemodialysis (HHD) underlines the importance of educational pro- grams. Brainstorming is a powerful tool for innovation, widely employed in industry but seldom used in medi- cine. The aim of this study was to define an e-learning Web platform for HHD patients via a brainstorming ap- proach. Methods: Four brainstorming sessions were held 2-6 weeks apart. Twelve people were involved: 2 dialysis physicians, 2 nurses, 2 HHD patients, 2 caregivers, a filmmaker, 2 computer experts (1 with a psychology de- gree) and a senior engineer. Each session was summa- rized as the starting point for the following one. The top- ics discussed were the platform structure and its logo. Results: For the platform, the following requirements were defined: teaching should be extensive and tai- lored to different levels of knowledge; all available teaching tools (tutorials, demonstrations, recorded and written materials) should be used; films enhance emotional participation and can be used to reduce fears; the contents should include general information on chronic kidney disease, details of all types of renal replacement therapy (RRT) (how and why), dialysis ac- cidents, blood and imaging tests, laws and reimburse- ments, direct experiences and history of RRT. Remote monitoring and visual interactions are important for reassurance about HHD and should be provided. The requirements for the logo were that it be innovative, related to daily life, representative of a holistic ap- proach and convey happiness. The logo "Hom-e-hem" was created, playing on the assonance between the religious term Om and the word home, with the e of electronic linking it with hem, short for hemodialysis. Conclusion: Brainstorming sessions can be used to design patient-tailored educational interventions. The key message, "self-care is a bridge from illness back to life," may apply to a wider context

Specialization is a common feature in animal societies that leads to an improvement in the fitness of the team members and to an increase in the resources obtained by the team. In this paper we propose a simple reinforcement learning approach to specialization in an artificial multi-agent system. The system is composed of homogeneous and non-communicating agents. Because there is no communication, the number of agents in the team can easily scale up. Agents have the same initial functionalities, but they learn to specialize and so cooperate to achieve a complex gathering task efficiently. Simulation experiments show how the multi-agent system specializes appropriately so as to reach optimal (or near-to-optimal) performance in unknown and changing environments.

Optimization of performance in collective systems often requires altruism. The emergence and stabilization of altruistic behaviors are difficult to achieve because the agents incur a cost when behaving altruistically. In this paper, we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. This strategy in conjunction with learning capabilities make altruistic agents cooperate only between themselves, thus preventing their exploitation by selfish agents, if future benefits are greater than the current cost of altruistic acts. Our multi-agent system is made up of agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so achieving optimal (or near-to-optimal) performances in unknown and changing environments.

Optimization of performance in collective systems often requires altruism. Emergence and stabilization of altruistic behaviors are difficult because the agents incur a cost when behaving altruistically. In this paper we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. Our multi-agent system is made up of autonomous agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so reaching optimal (or near-to-optimal) performances in unknown and changing environments.