Intravitreal bevacizumab in diabetic retinopathy. Recommendations from the Pan-American Collaborative Retina Study Group (PACORES): The 2016 knobloch lecture

The advent of intravitreal anti-vascular endothelial growth factor (anti-VEGF) medications has revolutionized the treatment of diabetic eye diseases. Herein, we report the outcomes of clinical studies carried out by the Pan-American Collaborative Retina Study Group (PACORES), with a specific focus on the efficacy of intravitreal bevacizumab in the management of diabetic macular edema and proliferative diabetic retinopathy. We will also discuss the use of intravitreal bevaci-zumab as a preoperative, adjuvant therapy before vitrectomy for prolif-erative diabetic retinopathy. Copyright © 2017 by Asia Pacific Academy of Ophthalmology

Long-term safety and efficacy of Eculizumab in Aquaporin-4 IgG-positive NMOSD

Objective During PREVENT (NCT01892345), eculizumab significantly reduced relapse risk versus placebo in patients with aquaporin-4 immunoglobulin G-positive neuromyelitis optica spectrum disorder (AQP4-IgG+ NMOSD). We report an interim analysis of PREVENT's ongoing open-label extension (OLE; NCT02003144) evaluating eculizumab's long-term safety and efficacy. Methods Patients who completed PREVENT could enroll in the OLE to receive eculizumab (maintenance dose = 1,200 mg/2 weeks, after a blinded induction phase). Safety and efficacy data from PREVENT and its OLE (interim data cut, July 31, 2019) were combined for this analysis. Results Across PREVENT and the OLE, 137 patients received eculizumab and were monitored for a median (range) of 133.3 weeks (5.1–276.9 weeks), for a combined total of 362.3 patient-years (PY). Treatment-related adverse event (AE) and serious adverse event (SAE) rates were 183.5 in 100 PY and 8.6 in 100 PY, respectively. Serious infection rates were 10.2 in 100 PY in eculizumab-treated patients versus 15.1 in 100 PY in the PREVENT placebo group. No patient developed a meningococcal infection. At 192 weeks (3.7 years), 94.4% (95% confidence interval [CI], 88.6–97.3) of patients remained adjudicated relapse-free. The adjudicated annualized relapse rate was 0.025 (95% CI = 0.013–0.048) in all eculizumab-treated patients versus 0.350 (95% CI = 0.199–0.616) in the PREVENT placebo group. During the OLE, 37% of patients (44 of 119 patients) stopped or decreased background immunosuppressive therapy use. Interpretation This analysis demonstrates that eculizumab's long-term safety profile in NMOSD is consistent with its established profile across other indications. This analysis also demonstrated the sustained ability of long-term eculizumab treatment to reduce relapse risk in patients with AQP4-IgG+ NMOSD. ANN NEUROL 2021;89:1088–109

Domain-dependent distributed models for railway scheduling

Many combinatorial problems can be modelled as Constraint Satisfaction Problems (CSPs). Solving a general CSP is known to be NP-complete, so closure and heuristic search are usually used. However, many problems are inherently distributed and the problem complexity can be reduced by dividing the problem into a set of subproblems. Nevertheless, general distributed techniques are not always appropriate to distribute real-life problems. In this work, we model the railway scheduling problem by means of domain-dependent distributed constraint models, and we show that these models maintained better behaviors than general distributed models based on graph partitioning. The evaluation is focused on the railway scheduling problem, where domain-dependent models carry out a problem distribution by means of trains and contiguous sets of stations

An Assessment of Railway Capacity

In this paper, the authors review the main concepts and methods to perform capacity analyses, and present an automated tool that is able to perform several capacity analyses. Capacity is extremely dependent on infrastructure, traffic, and operating parameters. Therefore, an in-depth study of the main factors that influence railway capacity is performed on several Spanish railway infrastructures. The results show how the capacity varies according to factors such as train speed, commercial stops, train heterogeneity, distance between railway signals, and timetable robustness

An Interactive Train Scheduling Tool for Solving and Plotting Running Maps

We present a tool for solving and plotting train schedules which has been developed in collaboration with the National Network of Spanish Railways (RENFE). This tool transforms railway problems into formal mathematical models that can be solved and then plots the best possible solution available. Due to the complexity of problems of this kind, the use of preprocessing steps and heuristics become necessary

An Efficient Method to Schedule New Trains on a Heavily Loaded Railway Network

With the aim of supporting the process of adapting railway infrastructure to present and future traffic needs, we have developed a method to build train timetables efficiently. In this work, we describe the problem in terms of constraints derived from railway infrastructure, user requirements and traffic constraints, and we propose a method to solve it efficiently. This method carries out the search by assigning values to variables in a given order and verifying the satisfaction of constraints where these are involved. When a constraint is not satisfied, a guided backtracking is done. The technique reduces the search space allowing us to solve real and complex problems efficiently

An assessment of railway capacity

In this paper, we review the main concepts and methods to perform capacity analyses, and we present an automated tool that is able to perform several capacity analyses. Capacity is extremely dependent on infrastructure, traffic, and operating parameters. Therefore, an in-depth study of the main factors that influence railway capacity is performed on several Spanish railway infrastructures. The results show how the capacity varies according to factors such as train speed, commercial stops, train heterogeneity, distance between railway signals, and timetable robustness.Capacity Railway Timetable Train Scheduling

A Decision Support System (DSS) for the Railway Scheduling Problem

The recent deregulation occurred in the public railway sector in many parts of the world has increased the awareness of this sector of the need for quality service that must be oered to its customers. In this paper, we present a software system for solving and plotting the Single-Track Railway Scheduling Problem eciently and quickly. The problem is formulated as a Constraint Satisfaction Problem (CSP), which must be optimized. The solving process uses dierent stages to translate the problem into mathematical models, which are solved to optimality by means of mixed integer programming tools. The Decision Support System (DSS) we present allows the user to interactively specify the parameters of the problem, guarantees that constraints are satised and plots the optimized timetable obtained

Intelligent Train Scheduling on a High-Loaded Railway Network

We present an interactive application to assist planners in adding new trains on a complex railway network. It includes many trains with di#erent characteristics, whose timetables cannot be modified because they are already in circulation. The application builds the timetable for new trains linking the available time slots to trains to be scheduled. A very flexible interface allows the user to specify the parameters of the problem. The resulting problem is formulated as a CSP and e#ciently solved. The solving method carries out the search assigning values to variables in a given order verifying the satisfaction of constraints where these are involved. When a constraint is not satisfied, a guided backtracking is done. Finally, the resulting timetable is delivered to the user who can interact with it, guaranteeing the tra#c constraint satisfaction.

New Heuristics to Solve the ”CSOP ” Railway Timetabling Problem

Abstract. The efficient use of infrastructures is a hard requirement for railway companies. Thus, the scheduling of trains should aim toward optimality, which is an NP-hard problem. The paper presents a friendly and flexible computer-based decision support system for railway timetabling. It implements an efficient method, based on meta-heuristic techniques, which provides railway timetables that satisfy a realistic set of constraints and, that optimize a multi-criteria objective function