Biomarkers for therapy selection in metastatic urothelial cancer

The treatment of metastatic urothelial cancer (mUC) has been transformed by recent progress in clinical trials and drug development. There are now three therapeutic classes with proven benefits in mUC: chemotherapy, immunotherapy, and targeted therapy. The optimal sequence and combination of these classes remain to be defined. Biomarker development is essential to guide treatment selection at each therapeutic juncture. Two biomarkers, programmed death-ligand 1 expression and fibroblast growth factor receptor alterations, have been incorporated into the mUC treatment paradigm thus far. This review discusses predictive biomarkers in development and their potential to influence mUC treatment selection moving forward.

Triangular intuitionistic fuzzy sequencing problem

In this paper, we discuss different types of fuzzy sequencing problem with Triangular Intuitionistic Fuzzy Number. Algorithm is given for different types of fuzzy sequencing problem to obtain an optimal sequence, minimum total elapsed time and idle time for machines. To illustrate this, numerical examples are provided.

Diffusion Approximations for a Class of Sequential Experimentation Problems

A decision maker (DM) must choose an action in order to maximize a reward function that depends on the DM’s action as well as on an unknown parameter Θ. The DM can delay taking the action in order to experiment and gather additional information on Θ. We model the problem using a Bayesian sequential experimentation framework and use dynamic programming and diffusion-asymptotic analysis to solve it. For that, we consider environments in which the average number of experiments that is conducted per unit of time is large and the informativeness of each individual experiment is low. Under such regimes, we derive a diffusion approximation for the sequential experimentation problem, which provides a number of important insights about the nature of the problem and its solution. First, it reveals that the problems of (i) selecting the optimal sequence of experiments to use and (ii) deciding the optimal time when to stop experimenting decouple and can be solved independently. Second, it shows that an optimal experimentation policy is one that chooses the experiment that maximizes the instantaneous volatility of the belief process. Third, the diffusion approximation provides a more mathematically malleable formulation that we can solve in closed form and suggests efficient heuristics for the nonasympototic regime. Our solution method also shows that the complexity of the problem grows only quadratically with the cardinality of the set of actions from which the decision maker can choose. We illustrate our methodology and results using a concrete application in the context of assortment selection and new product introduction. Specifically, we study the problem of a seller who wants to select an optimal assortment of products to launch into the marketplace and is uncertain about consumers’ preferences. Motivated by emerging practices in e-commerce, we assume that the seller is able to use a crowd voting system to learn these preferences before a final assortment decision is made. In this context, we undertake an extensive numerical analysis to assess the value of learning and demonstrate the effectiveness and robustness of the heuristics derived from the diffusion approximation. This paper was accepted by Omar Besbes, revenue management and market analytics.

Force control in girder-cable systems

The object of the study is the forces in the beam-cable systems. The introduction of these systems in construction is associated with the task of creating a pre-stress in order to regulate the stress-strain state of the beam-cable system as a whole. Prestressing will make it possible to rationally use high-strength materials in the structure, and to design the structure economically. When designing girder-cable-stayed structures of bridge spans, it is necessary to determine the sequence of stresses of the structural elements-shrouds in order to regulate the forces in the beam element of the structure. This problem is considered in this article. The dynamic programming method is used to regulate the stress-strain state of the system by pulling the shrouds in the optimal sequence. To solve the problems, formulas for the output value and the optimality criterion, as well as the matrix, are given. As a result, the values of the output values of interest at all stages of the tension of the shrouds are found.

A Short Inroad to Optimized Compactification of Composite Neutronic Shields

Compact neutronic shields for mobile nuclear reactors or accelerator-based neutron beams are known to be optimized multilayered composites. This paper is a simplified short inroad to the complex problem of optimizing the design of such shields when they attenuate a neutron beam to extremise certain quality criteria, in plane geometry, subject to equality and inequality constraints. In the equality constraints, the interfacial polychromatic neutron fluxes are solutions to course-mesh finite-difference holonomic state equations. The set of these interfacial fluxes act as state variables,while the set of layer thicknesses, or their poisoning (by added neutron absorbers) concentrations are decision variables. The entire procedure is then demonstrated to be reducible to standard Kuhn-Tucker semi-linear programming that may also lead robustly to an optimal sequence for these layers.

PP180 Optimal Treatment Sequence For Targeted Immune Modulators For The Treatment Of Moderate To Severe Ulcerative Colitis

IntroductionSeveral targeted immune modulators (TIMs) have demonstrated effectiveness in moderate-to-severe ulcerative colitis, including adalimumab, golimumab, infliximab, infliximab biosimilars, tofacitinib, ustekinumab, and vedolizumab. In addition to assessing individual TIMs, evaluating TIM sequences can inform clinical care as well as coverage and reimbursement policies. Our objective was to identify optimal treatment sequences based on maximum net health benefit (NHB), lowest total cost (cost minimizing), quality-adjusted life-year (QALY) maximization, or convenience (avoidance of intravenous treatments), and to evaluate their cost effectiveness compared with conventional treatment from the health sector perspective.MethodsWe developed a Markov model with eight-week cycles and a lifetime time horizon. The health states were active, clinical response without remission, remission, and death. TIM efficacy was informed by a network meta-analysis conducted by the Institute for Clinical and Economic Review. Sequences were generated by ranking TIMs and then conventional treatment according to NHB, cost minimization, QALY maximization, or convenience and combining top ranked TIMs in the biologic naïve and biologic experienced populations. NHB was calculated at USD 150,000 per QALY. Probabilistic sensitivity analysis (PSA) was undertaken to estimate the probability of each sequence having the highest NHB rank, QALY maximizing rank, and cost-minimizing rank.ResultsTwenty-one sequences were evaluated. The sequence with the highest NHB was infliximab followed by tofacitinib (-0.12 QALYs), which also had the lowest incremental costs (USD37,266). For orally and subcutaneously administered TIMs, the sequence of golimumab-tofacitinib had the highest NHB (-0.34 QALYs). Ustekinumab-vedolizumab was not only the top ranked sequence as measured by QALY maximization (0.172 incremental QALYs), but it also had the highest total incremental cost (USD166,094). Results of the PSA were consistent with deterministic rankings for the top-ranking sequences and showed that the top two or three regimens were close in magnitude.ConclusionsThe optimal sequence with regard to NHB and cost minimization was infliximab or biosimilars, followed by tofacitinib, adalimumab, or vedolizumab. Sequences that generated the most QALYs began with ustekinumab, followed by vedolizumab, tofacitinib, and adalimumab.

The Due Date Assignment Scheduling Problem with Delivery Times and Truncated Sum-of-Processing-Times-Based Learning Effect

This paper considers a single-machine scheduling problem with past-sequence-dependent delivery times and the truncated sum-of-processing-times-based learning effect. The goal is to minimize the total costs that comprise the number of early jobs, the number of tardy jobs and due date. The due date is a decision variable. There will be corresponding penalties for jobs that are not completed on time. Under the common due date, slack due date and different due date, we prove that these problems are polynomial time solvable. Three polynomial time algorithms are proposed to obtain the optimal sequence.

SCHEDULING OF THE OPERATIONS OPTIMAL SEQUENCE

The presented material considers the possibility of management planning the optimization of the load of the production equipment of the enterprise by step-by-step sorting-through of the ana-lyzed options based on branch-and-bound procedure.

Fast and Optimal Sequence-to-Graph Alignment Guided by Seeds

We present a novel A* seed heuristic enabling fast and optimal sequence-to-graph alignment, guaranteed to minimize the edit distance of the alignment assuming non-negative edit costs. We phrase optimal alignment as a shortest path problem and solve it by instantiating the A* algorithm with our novel seed heuristic. The key idea of the seed heuristic is to extract seeds from the read, locate them in the reference, mark preceding reference positions by crumbs, and use the crumbs to direct the A* search. We prove admissibility of the seed heuristic, thus guaranteeing alignment optimality. Our implementation extends the free and open source AStarix aligner and demonstrates that the seed heuristic outperforms all state-of-the-art optimal aligners including GraphAligner, Vargas, PaSGAL, and the prefix heuristic previously employed by AStarix. Specifically, we achieve a consistent speedup of >60x on both short Illumina reads and long HiFi reads (up to 25kbp), on both the E. coli linear reference genome (1Mbp) and the MHC variant graph (5Mbp). Our speedup is enabled by the seed heuristic consistently skipping >99.99% of the table cells that optimal aligners based on dynamic programming compute.

Optimal Sequence and Second-Line Systemic Treatment of Patients with RAS Wild-Type Metastatic Colorectal Cancer: A Meta-Analysis

Although several sequential therapy options are available for treating patients with RAS wild-type (WT) metastatic colorectal cancer (mCRC), the optimal sequence of these therapies is not well established. A systematic review and meta-analysis of 13 randomized controlled trials and 4 observational studies were performed, resulting from a search of the Cochrane Library, PubMed, and Embase databases. Overall survival (OS) did not differ significantly in patients with RAS-WT failure who were administered a second-line regimen of changed chemotherapy (CT) plus anti-epidermal growth factor receptor (EGFR) versus only changed CT, changed CT plus bevacizumab versus changed CT plus anti-EGFR, or changed CT versus maintaining CT plus anti-EGFR after first-line therapy with CT, plus bevacizumab. However, OS was significantly different with a second-line regimen that included changed CT plus bevacizumab, versus only changing CT. Analysis of first-line therapy with CT plus anti-EGFR for treatment of RAS-WT mCRC indicated that second-line therapy of changed CT plus an anti-EGFR agent resulted in better outcomes than changing CT without targeted agents. The pooled data study demonstrated that the optimal choice of second-line treatment for improved OS was an altered CT regimen with retention of bevacizumab after first-line bevacizumab failure. The best sequence for first-to-second-line therapy of patients with RAS-WT mCRC was cetuximab-based therapy, followed by a bevacizumab-based regimen.