Refractive errors in infants with retinopathy of prematurity treated using laser or anti-vascular endothelial growth factor monotherapy

Background: Infants treated for retinopathy of prematurity (ROP) could develop visually significant refractive errors. In this study, we report pre-treatment refractive errors in premature infants with treatment-requiring ROP treated using laser or anti-VEGF monotherapy and compare the components of post-treatment refractive error values between the two treatment groups at different follow-up timepoints. Methods: In this retrospective cohort study, we analyzed 360 eyes of 181 premature infants with treatment-requiring ROP who were referred to Farabi Eye Hospital, Tehran, Iran between March 2020 and April 2021. Of the 360 eyes, 195 received laser monotherapy (laser treatment group) and 165 received an intravitreal anti-VEGF injection (anti-VEGF therapy group). All included eyes underwent pre- and post-treatment cycloplegic refraction. Cycloplegia was induced for each infant by instilling a mixed eye drop containing 1% tropicamide, 2.5% phenylephrine, and 0.5% tetracaine (in equal volumes) in each eye three times at five-minute intervals. Cycloplegic refraction was performed 30 minutes after the third instillation. Results: The mean (standard deviation [SD]) gestational age (GA) and birth weight (BW) of the infants were 29.0 (2.0) weeks and 1241.0 (403.0) g, respectively. The male-to-female ratio in the entire study cohort was 107 (59.1%) / 74 (40.9%), whereas the ratios in the anti-VEGF therapy group and laser treatment group were 47 (56.6%) / 36 (43.4%) and 60 (61.2%) / 38 (38.8%), respectively. The pre-treatment assessment revealed that 218 (60.6%) eyes were hyperopic, 112 (31.1%) were myopic, and 30 (8.3%) were emmetropic. In the anti-VEGF therapy group, 87 (52.7%) eyes were hyperopic, 63 (38.2%) were myopic, and 15 (9.1%) were emmetropic. In the laser treatment group, 131 (67.2%) eyes were hyperopic, 49 (25.1%) were myopic, and 15 (7.7%) were emmetropic. The mean (SD) spherical refractive error and spherical equivalent of refractive error (SEQ) at the 1-week, 1-month, and > 6-month post-treatment follow-up timepoints; the mean cylindrical refractive error at the 3-month post-treatment timepoint; and the mean SEQ at the time of ROP regression were significantly different between the treatment groups (all P < 0.05). The rate of anisometropia increased significantly from 3.4% at baseline to 9.2% at the 6-month post-treatment follow-up timepoint (P < 0.05). Conclusions: In this study, the most common pre-treatment refractive status of all included eyes with treatment-requiring ROP and eyes in each treatment group was hyperopia, followed by myopia and emmetropia. At the more than 6-month post-treatment follow-up, cycloplegic refraction revealed that the laser-treated eyes were significantly more hyperopic than the anti-VEGF-treated eyes, a finding similar to the pre-treatment refraction results. Further studies of same cohort with a longer follow-up period and a control group are needed to determine the real-world effect of each treatment modality on the refractive statuses of children treated for ROP

Personalized Recommendation Using Reinforcement Learning

The massive volume of information available on the web leads to the problem of information overload, which makes it difficult for a decision maker to make right decisions. Recommender systems (RSs) are software tools and algorithms that have been developed with the idea of helping users find their items of interest through predicting their preferences or ratings on items. It has been shown that the problem of recommending items to the user could be considered as a sequential decision problem and be formulated as a Markov decision process, so reinforcement learning (RL) algorithms can be used to solve this problem. The primary aim of this dissertation is to investigate this topic and to propose new recommendation approaches using RL. The first part of this thesis, namely chapters 2 and 3, presents a healthcare use case of intelligent agents and RSs. In particular, chapter 2 presents a high-level design, called ALAN, to play the role of a patient decision aid for prostate cancer patients. ALAN is a multilayered, multi-agent system in which each agent is responsible to provide a specific service in order to facilitate shared decision making for these patients. Moreover, an article RS with learning ability is proposed in chapter 3 to represent the Learning agent in ALAN, which combines multi-armed bandits with knowledge-based RSs for the provision of information for cancer patients. Motivated by the first part, the second part of this thesis (chapters 4 and 5) deeply explores the topic of recommendation using RL algorithms. More precisely, chapter 4 provides a thorough literature review on reinforcement learning based recommender systems (RLRSs). The main goal of this chapter is to provide a deep analysis of almost all important RLRSs proposed and to depict a vista toward the field since the beginning. This chapter illustrates the importance of deep RL (DRL) in reviving the use of RL for RSs. Chapter 5 is an outcome of this chapter, which tries to address an important problem of DRL when applied to real applications like RSs, i.e., sample inefficiency. In this chapter, RL is combined with imitation learning in order to accelerate RL agent’s learning and to make it sample efficient. Finally, chapter 6 proposes a new recommendation approach from a totally new perspective. This chapter borrows ideas from Computer Networks field, clustering in wireless sensor networks in particular, and presents a probabilistic recommendation approach that can balance the similarity-diversity trade-off. The proposed approach is simple, scalable, and completely explainable

Delineation of Water Logging and Salinity for Salvaging Built Environment

Millions of acres of splendidly productive land and valuable infrastructure are deteriorated continuously. The reason for such deterioration in majority of areas is mainly due to water logging and salinity. Rise of water table level and the dearth of drainage and lack of continuous monitoring and timely remedial measures, extended the circle of devastation to historical heritage and precious archeological sites as well. Mohenjo-Daro has been selected for this study, it has global significance but due to water logging and salinity, it is in danger of total destruction. The archeological buildings and other infrastructure and land in its environs are being gradually eroded by the capillary rise of saline ground the intensity of which constitutes a serious threat. For delineating and periodic monitoring of the salinity and waterlogging and to effectively implement the appropriate remedies, use of the latest technologies is essential. In this study the remote sensing technologies are used to address this issue with the help of Soil investigation parameters mainly EC and pH. The aftermaths of this study would provide a methodological framework along with practical application in delineation saline areas using satellite technology. The final value-added products of this research would be useful for all interested stakeholders including conservationists, environmentalists, archeologists, planners and decision-makers at various levels. The international community at large would be the beneficiary of this study since Mohenjo-Daro is the heritage of entire mankind

Delineation of Water Logging and Salinity for Salvaging Built Environment

Millions of acres of splendidly productive land and valuable infrastructure are deteriorated continuously. The reason for such deterioration in majority of areas is mainly due to water logging and salinity. Rise of water table level and the dearth of drainage and lack of continuous monitoring and timely remedial measures, extended the circle of devastation to historical heritage and precious archeological sites as well. Mohenjo-Daro has been selected for this study, it has global significance but due to water logging and salinity, it is in danger of total destruction. The archeological buildings and other infrastructure and land in its environs are being gradually eroded by the capillary rise of saline ground the intensity of which constitutes a serious threat. For delineating and periodic monitoring of the salinity and waterlogging and to effectively implement the appropriate remedies, use of the latest technologies is essential. In this study the remote sensing technologies are used to address this issue with the help of Soil investigation parameters mainly EC and pH. The aftermaths of this study would provide a methodological framework along with practical application in delineation saline areas using satellite technology. The final value-added products of this research would be useful for all interested stakeholders including conservationists, environmentalists, archeologists, planners and decision-makers at various levels. The international community at large would be the beneficiary of this study since Mohenjo-Daro is the heritage of entire mankind

Clustering in sensor networks: A literature survey

Wireless sensor networks (WSNs) have recently gained the attention of researchers in many challenging aspects. The most important challenge in these networks is energy conservation. One of the most popular solutions in making WSNs energy-efficient is to cluster the networks. In clustering, the nodes are divided into some clusters and then some nodes, called cluster-heads, are selected to be the head of each cluster. In a typical clustered WSN, the regular nodes sense the field and send their data to the cluster-head, then, after gathering and aggregating the data, the cluster-head transmits them to the base station. Clustering the nodes in WSNs has many benefits, including scalability, energy-efficiency, and reducing routing delay. In this paper we present a state-of-the-art and comprehensive survey on clustering approaches. We first begin with the objectives of clustering, clustering characteristics, and then present a classification on the clustering algorithms in WSNs. Some of the clustering objectives considered in this paper include scalability, fault-tolerance, data aggregation/fusion, increased connectivity, load balancing, and collision avoidance. Then, we survey the proposed approaches in the past few years in a classified manner and compare them based on different metrics such as mobility, cluster count, cluster size, and algorithm complexity

Reinforcement learning based recommender systems: A survey

Recommender systems (RSs) have become an inseparable part of our everyday lives. They help us find our favorite items to purchase, our friends on social networks, and our favorite movies to watch. Traditionally, the recommendation problem was considered to be a classification or prediction problem, but it is now widely agreed that formulating it as a sequential decision problem can better reflect the user-system interaction. Therefore, it can be formulated as a Markov decision process (MDP) and be solved by reinforcement learning (RL) algorithms. Unlike traditional recommendation methods, including collaborative filtering and content-based filtering, RL is able to handle the sequential, dynamic user-system interaction and to take into account the long-term user engagement. Although the idea of using RL for recommendation is not new and has been around for about two decades, it was not very practical, mainly because of scalability problems of traditional RL algorithms. However, a new trend has emerged in the field since the introduction of deep reinforcement learning (DRL), which made it possible to apply RL to the recommendation problem with large state and action spaces. In this paper, a survey on reinforcement learning based recommender systems (RLRSs) is presented. Our aim is to present an outlook on the field and to provide the reader with a fairly complete knowledge of key concepts of the field. We first recognize and illustrate that RLRSs can be generally classified into RL- and DRL-based methods. Then, we propose an RLRS framework with four components, i.e., state representation, policy optimization, reward formulation, and environment building, and survey RLRS algorithms accordingly. We highlight emerging topics and depict important trends using various graphs and tables. Finally, we discuss important aspects and challenges that can be addressed in the future.Comment: To appear in ACM Computing Survey

Investigation of the relation between the return periods of major drought characteristics using copula functions

Different drought characteristics (e.g. duration, average severity, and average areal extent) often have monotonic relation that increased magnitude of one often follows a similar increase in the magnitude of the other drought characteristic. Hence it is viable to establish a relationship between different drought characteristics with the goal of predicting one using other ones. Copula functions that relate different variables using their joint and conditional cumulative probability distributions are often used to statistically model the drought characteristics. In this study bivariate and trivariate joint probabilities of these characteristics are obtained over Ankara (Turkey) between 1960 and 2013. Copula-based return period estimation of drought characteristics of duration, average severity, and average areal extent show joint probabilities of these characteristics can be satisfactorily achieved. Among different copula families investigated in this study, elliptical family (i.e. including normal and t-student copula functions) resulted in the lowest root mean square error