Response to Dabrafenib Plus Trametinib in a Patient With an Uncommon Activating BRAF Mutation: A First in Non-Small Cell Lung Cancer.

Mutations in BRAF are present in 4% of non-small cell lung cancer (NSCLC), of which half are well-characterized activating variants affecting codon 600 (classified as class I). These mutations, most commonly BRAF V600E, have been associated with response to BRAF/MEK-directed small molecule kinase inhibitors. NSCLC with kinase-activating BRAF mutations occurring at other codons (class II variants) represent a substantial portion of BRAF-mutated NSCLC, but use of targeted therapy in these tumors is still under investigation. Class II mutations have been described in other tumor types and have been associated with response to BRAF/MEK-targeted agents, although optimal treatment strategies for these patients are lacking. This report presents a case of a woman with metastatic NSCLC harboring a class II BRAF p.N486_P490del variant who had a sustained clinical response to combination therapy with dabrafenib and trametinib. This first report of the use of BRAF/MEK-targeted therapy for this variant in NSCLC supports consideration of such treatment for tumors with class II BRAF variants.

A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition.

Support vector machines (SVMs) are powerful statistical learning tools, but their application to large datasets can cause time-consuming training complexity. To address this issue, various instance selection (IS) approaches have been proposed, which choose a small fraction of critical instances and screen out others before training. However, existing methods have not been able to balance accuracy and efficiency well. Some methods miss critical instances, while others use complicated selection schemes that require even more execution time than training with all original instances, thus violating the initial intention of IS. In this work, we present a newly developed IS method called Valid Border Recognition (VBR). VBR selects the closest heterogeneous neighbors as valid border instances and incorporates this process into the creation of a reduced Gaussian kernel matrix, thus minimizing the execution time. To improve reliability, we propose a strengthened version of VBR (SVBR). Based on VBR, SVBR gradually adds farther heterogeneous neighbors as complements until the Lagrange multipliers of already selected instances become stable. In numerical experiments, the effectiveness of our proposed methods is verified on benchmark and synthetic datasets in terms of accuracy, execution time and inference time.

Diffusion capacity of single and interconnected networks.

Understanding diffusive processes in networks is a significant challenge in complexity science. Networks possess a diffusive potential that depends on their topological configuration, but diffusion also relies on the process and initial conditions. This article presents Diffusion Capacity, a concept that measures a node's potential to diffuse information based on a distance distribution that considers both geodesic and weighted shortest paths and dynamical features of the diffusion process. Diffusion Capacity thoroughly describes the role of individual nodes during a diffusion process and can identify structural modifications that may improve diffusion mechanisms. The article defines Diffusion Capacity for interconnected networks and introduces Relative Gain, which compares the performance of a node in a single structure versus an interconnected one. The method applies to a global climate network constructed from surface air temperature data, revealing a significant change in diffusion capacity around the year 2000, suggesting a loss of the planet's diffusion capacity that could contribute to the emergence of more frequent climatic events.

Physician scheduling problem in Mobile Cabin Hospitals of China during Covid-19 outbreak.

In this paper, we investigate a novel physician scheduling problem in the Mobile Cabin Hospitals (MCH) which are constructed in Wuhan, China during the outbreak of the Covid-19 pandemic. The shortage of physicians and the surge of patients brought great challenges for physicians scheduling in MCH. The purpose of the studied problem is to get an approximately optimal schedule that reaches the minimum workload for physicians on the premise of satisfying the service requirements of patients as much as possible. We propose a novel hybrid algorithm integrating particle swarm optimization (PSO) and variable neighborhood descent (VND) (named as PSO-VND) to find the approximate global optimal solution. A self-adaptive mechanism is developed to choose the updating operators dynamically during the procedures. Based on the special features of the problem, three neighborhood structures are designed and searched in VND to improve the solution. The experimental comparisons show that the proposed PSO-VND has a significant performance increase than the other competitors.

Traffic-Driven Epidemic Spreading in Networks: Considering the Transition of Infection From Being Mild to Severe.

Realistic epidemic spreading is usually driven by traffic flow in networks, which is not captured in classic diffusion models. Moreover, the progress of a node's infection from mild to severe phase has not been particularly addressed in previous epidemic modeling. To address these issues, we propose a novel traffic-driven epidemic spreading model by introducing a new epidemic state, that is, the severe state, which characterizes the serious infection of a node different from the initial mild infection. We derive the dynamic equations of our model with the tools of individual-based mean-field approximation and continuous-time Markov chain. We find that, besides infection and recovery rates, the epidemic threshold of our model is determined by the largest real eigenvalue of a communication frequency matrix we construct. Finally, we study how the epidemic spreading is influenced by representative distributions of infection control resources. In particular, we observe that the uniform and Weibull distributions of control resources, which have very close performance, are much better than the Pareto distribution in suppressing the epidemic spreading.

Optimization methods for large-scale vaccine supply chains: a rapid review.

Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal allocation of resources, vaccination strategy, inventory management, among others, can be improved through optimization approaches. This work aims to understand how optimization has been applied to vaccine supply chain and logistics. To achieve this, we conducted a rapid review and searched for peer-reviewed journal articles, published between 2009 and March 2020, in four scientific databases. The search resulted in 345 articles, of which 25 unique studies met our inclusion criteria. Our analysis focused on the identification of article characteristics such as research objectives, vaccine supply chain stage addressed, the optimization method used, whether outbreak scenarios were considered, among others. Approximately 64% of the studies dealt with vaccination strategy, and the remainder dealt with logistics and inventory management. Only one addressed market competition (4%). There were 14 different types of optimization methods used, but control theory, linear programming, mathematical model and mixed integer programming were the most common (12% each). Uncertainties were considered in the models of 44% of the studies. One resulting observation was the lack of studies using optimization for vaccine inventory management and logistics. The results provide an understanding of how optimization models have been used to address challenges in large-scale vaccine supply chains.

Differential Human Learning Optimization Algorithm.

Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.

Interdependent Networks: A Data Science Perspective.

Traditionally, networks have been studied in an independent fashion. With the emergence of novel smart city technologies, coupling among networks has been strengthened. To capture the ever-increasing coupling, we explain the notion of interdependent networks, i.e., multi-layered networks with shared decision-making entities, and shared sensing infrastructures with interdisciplinary applications. The main challenge is how to develop data analytics solutions that are capable of enabling interdependent decision making. One of the emerging solutions is agent-based distributed decision making among heterogeneous agents and entities when their decisions are affected by multiple networks. We first provide a big picture of real-world interdependent networks in the context of smart city infrastructures. We then provide an outline of potential challenges and solutions from a data science perspective. We discuss potential hindrances to ensure reliable communication among intelligent agents from different networks. We explore future research directions at the intersection of network science and data science.

Noise-Tolerant Techniques for Decomposition-Based Multiobjective Evolutionary Algorithms.

Over the last few decades, the decomposition-based multiobjective evolutionary algorithms (DMOEAs) have became one of the mainstreams for multiobjective optimization. However, there is not too much research on applying DMOEAs to uncertain problems until now. Usually, the uncertainty is modeled as additive noise in the objective space, which is the case this paper concentrates on. This paper first carries out experiments to examine the impact of noisy environments on DMOEAs. Then, four noise-handling techniques based upon the analyses of empirical results are proposed. First, a Pareto-based nadir point estimation strategy is put forward to provide a good normalization of each objective. Next, we introduce two adaptive sampling strategies that vary the number of samples used per solution based on the differences among neighboring solutions and their variance to control the tradeoff between exploration and exploitation. Finally, a mixed objective evaluation strategy and a mixed repair mechanism are proposed to alleviate the effects of noise and remedy the loss of diversity in the decision space, respectively. These features are embedded in two popular DMOEAs (i.e., MOEA/D and DMOEA- [Formula: see text]), and DMOEAs with these features are called noise-tolerant DMOEAs (NT-DMOEAs). NT-DMOEAs are compared with their various variants and four noise-tolerant multiobjective algorithms, including the improved NSGA-II, the classical algorithm Bayesian (1+1)-ES (BES), and the state-of-the-art algorithms MOP-EA and rolling tide evolutionary algorithm to show the superiority of proposed features on 17 benchmark problems with different strength levels of noise. Experimental studies demonstrate that two NT-DMOEAs, especially NT-DMOEA- [Formula: see text], show remarkable advantages over competitors in the majority of test instances.

Assessing diversity in multiplex networks.

Diversity, understood as the variety of different elements or configurations that an extensive system has, is a crucial property that allows maintaining the system's functionality in a changing environment, where failures, random events or malicious attacks are often unavoidable. Despite the relevance of preserving diversity in the context of ecology, biology, transport, finances, etc., the elements or configurations that more contribute to the diversity are often unknown, and thus, they can not be protected against failures or environmental crises. This is due to the fact that there is no generic framework that allows identifying which elements or configurations have crucial roles in preserving the diversity of the system. Existing methods treat the level of heterogeneity of a system as a measure of its diversity, being unsuitable when systems are composed of a large number of elements with different attributes and types of interactions. Besides, with limited resources, one needs to find the best preservation policy, i.e., one needs to solve an optimization problem. Here we aim to bridge this gap by developing a metric between labeled graphs to compute the diversity of the system, which allows identifying the most relevant components, based on their contribution to a global diversity value. The proposed framework is suitable for large multiplex structures, which are constituted by a set of elements represented as nodes, which have different types of interactions, represented as layers. The proposed method allows us to find, in a genetic network (HIV-1), the elements with the highest diversity values, while in a European airline network, we systematically identify the companies that maximize (and those that less compromise) the variety of options for routes connecting different airports.

Extended vertical lists for temporal pattern mining from multivariate time series.

In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level-wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.

Effects of government regulations on Manufacturer's behaviors under carbon emission reduction.

This paper shifts the discussion of low-carbon technology from science to the economy, especially the reactions of a manufacturer to government regulations. One major concern in this paper is uncertainty about the effects of government regulation on the manufacturing industry. On the trust side, will manufacturers trust the government's commitment to strictly supervise carbon emission reduction? Will a manufacturer that is involved in traditional industry consciously follow a low-carbon policy? On the profit side, does equilibrium between a manufacturer and a government exist on deciding which strategy to undertake to meet a profit maximization objective under carbon emission reduction? To identify the best solutions to these problems, this paper estimates the economic benefits of manufacturers associated with policy regulations in a low-carbon technology market. The problem of an interest conflict between the government and the manufacturer is formalized as a game theoretic model, and a mixed strategy Nash equilibrium is derived and analyzed. The experiment results indicate that when the punishment levied on the manufacturer or the loss to the government is sizable, the manufacturer will be prone to developing innovative technology and the government will be unlikely to supervise the manufacturer.

Traffic congestion and the lifetime of networks with moving nodes.

For many power-limited networks, such as wireless sensor networks and mobile ad hoc networks, maximizing the network lifetime is the first concern in the related designing and maintaining activities. We study the network lifetime from the perspective of network science. In our model, nodes are initially assigned a fixed amount of energy moving in a square area and consume the energy when delivering packets. We obtain four different traffic regimes: no, slow, fast, and absolute congestion regimes, which are basically dependent on the packet generation rate. We derive the network lifetime by considering the specific regime of the traffic flow. We find that traffic congestion inversely affects network lifetime in the sense that high traffic congestion results in short network lifetime. We also discuss the impacts of factors such as communication radius, node moving speed, routing strategy, etc., on network lifetime and traffic congestion.

Quantification of network structural dissimilarities.

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

Neoadjuvant irinotecan, cisplatin, and concurrent radiation therapy with celecoxib for patients with locally advanced esophageal cancer.

BACKGROUND: Patients with locally advanced esophageal cancer who are treated with trimodality therapy have a high recurrence rate. Preclinical evidence suggests that inhibition of cyclooxygenase 2 (COX2) increases the effectiveness of chemoradiation, and observational studies in humans suggest that COX-2 inhibition may reduce esophageal cancer risk. This trial tested the safety and efficacy of combining a COX2 inhibitor, celecoxib, with neoadjuvant irinotecan/cisplatin chemoradiation. METHODS: This single arm phase 2 trial combined irinotecan, cisplatin, and celecoxib with concurrent radiation therapy. Patients with stage IIA-IVA esophageal cancer received weekly cisplatin 30 mg/m(2) plus irinotecan 65 mg/m(2) on weeks 1, 2, 4, and 5 concurrently with 5040 cGy of radiation therapy. Celecoxib 400 mg was taken orally twice daily during chemoradiation, up to 1 week before surgery, and for 6 months following surgery. RESULTS: Forty patients were enrolled with stage IIa (30 %), stage IIb (20 %), stage III (22.5 %), and stage IVA (27.5 %) esophageal or gastroesophageal junction cancer (AJCC, 5th Edition). During chemoradiation, grade 3-4 treatment-related toxicity included dysphagia (20 %), anorexia (17.5 %), dehydration (17.5 %), nausea (15 %), neutropenia (12.5 %), diarrhea (10 %), fatigue (7.5 %), and febrile neutropenia (7.5 %). The pathological complete response rate was 32.5 %. The median progression free survival was 15.7 months and the median overall survival was 34.7 months. 15 % (n = 6) of patients treated on this study developed brain metastases. CONCLUSIONS: The addition of celecoxib to neoadjuvant cisplatin-irinotecan chemoradiation was tolerable; however, overall survival appeared comparable to prior studies using neoadjuvant cisplatin-irinotecan chemoradiation alone. Further studies adding celecoxib to neoadjuvant chemoradiation in esophageal cancer are not warranted. TRIAL REGISTRATION: Clinicaltrials.gov: NCT00137852 , registered August 29, 2005.

Optimización de la hemoglobina preoperatoria en cirugía ortopédica mayor utilizando hierro intravenoso con o sin eritropoyetina. Estudio epidemiológico / Optimizing preoperative haemoglobin in major orthopaedic surgery using intravenous iron with or without erythropoietin. An epidemiologic study

Objetivo: Evaluar la efectividad del tratamiento con hierro intravenoso, solo o asociado a eritropoyetina (rHuEPO), mediante el incremento de hemoglobina (Hb). Como objetivo secundario se analizó la relación del incremento de Hb con los parámetros utilizados para evaluar la anemia. Material y método: Estudio observacional retrospectivo realizado en 2 centros entre enero de 2005 y diciembre de 2009. Se incluyeron pacientes sometidos a cirugía ortopédica electiva con déficit de hierro y que fueron tratados con hierro sacarosa intravenosos solo o asociado a rHuEPO. Se analizó la efectividad del tratamiento mediante la diferencia entre la Hb basal y la Hb previa a la cirugía y se valoró la relación entre los parámetros hematimétricos y del metabolismo del hierro con la efectividad del tratamiento. Resultados: Se incluyeron 412 pacientes que recibieron una mediana de 800 mg de hierro sacarosa. A 125 pacientes (30,4%) se les asoció 2,4 viales de rHuEPO. El incremento de Hb fue de 0,8 (1,1) g/dL en los pacientes tratados con hierro intravenoso y de 1,5 (1,3) g/dL en los que se asoció rHuEPO (p < 0,01). El incremento de Hb en los pacientes tratados con hierro se correlacionó con el porcentaje de hematíes hipocromos (r = 0,52) y el valor del receptor soluble de la transferrina (r = 0,59). Conclusiones: La efectividad del tratamiento con hierro sacarosa en pacientes con déficit de hierro para la optimización de la Hb preoperatoria fue moderada, siendo mayor con la administración adyuvante de eritropoyetina. La determinación de los parámetros funcionales del estado del hierro nos podría guiar para mejorar la efectividad del tratamiento (AU)

Aim: To evaluate the effectiveness of intravenous iron treatment, with or without associated erythropoietin (rHuEPO), measured as haemoglobin (Hb) increase. The relationships between the Hb increase and parameters used to evaluate anaemia were analysed. Material and method: Retrospective observational study carried out in two third-level hospitals between January 2005 and December 2009. The study included patients with iron deficiency anaemia scheduled for elective orthopaedic surgery and treated with intravenous iron sucrose alone or associated with rHuEPO. Treatment efficacy was analysed based on the Hb increase from baseline to just before surgery. Results: A total of 412 patients who received a median of 800 mg of iron sucrose were included; 125 of them (30.4%) additionally received 2.4 vials of rHuEPO. The Hb increase was 0.8 (1.1) g/dL in patients treated with intravenous iron and 1.5 (1.3) g/dL in those additionally given rHuEPO (P < .01). The percentage of hypochromic red blood cells (r = 0.52) and soluble transferrin receptor (r = 0.59) value were significantly correlated to the Hb increase in patients receiving iron. Conclusions: In patients with iron deficiency anaemia, the effectiveness of iron sucrose treatment to optimize Hb before surgery was moderate; adjuvant administration of erythropoietin improved the results. Determination of functional iron status parameters may improve the treatment effectiveness (AU)