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Selective patients with multiple myeloma (MM) receiving immunomodulatory drugs (IMiD) are at high risk for venous thromboembolism (VTE). The SAVED score is a VTE risk prediction model recently incorporated into the National Comprehensive Cancer Network (NCCN) guidelines. Using retrospective data from 501 MM patients with new IMiD initiation between 2010 and 2019, we performed the first independent external validation of this model. The cumulative incidence of VTE after IMiD initiation at 6 and 12 months was 32% and 42% in the high-risk group, versus 6% and 9% in the low-risk group respectively. The C-statistic of the SAVED score to predict VTE within 12 months of IMiD-based treatment start was 0.74 [95% confidence interval (CI): 0.69-0.78], which outperformed several other VTE risk models in MM patients. Our findings suggest that the SAVED score is an accurate risk assessment tool for VTE stratification in patients initiating IMiD-containing regimens.

Gemcitabine has shown clinical activity against angiosarcoma in small series, alone, or combined with taxanes. We aimed to evaluate its activity as a single-agent in a larger series of patients with advanced angiosarcoma. We retrospectively reviewed the electronic medical records of consecutive adult patients with advanced angiosarcoma treated with single-agent gemcitabine at our institutions from January 2010 to January 2021. Response was evaluated according to RECIST 1.1, and toxicity was graded according to NCI-CTC v5.0. 42 patients were identified. 38 patients (90%) had received prior anthracyclines and weekly paclitaxel, and 9 (21%) had received pazopanib. The best tumor response was partial response (PR) in 16 patients (38%), or stable disease (10 patients, 24%). All 8 patients with cardiac angiosarcoma experienced a PR. Median PFS was 5.4 months (95%CI: 3.1-6.5), and median OS was 9.9 months (95%CI: 6.6-13.4). Single-agent gemcitabine has clinically meaningful activity in advanced, heavily pre-treated angiosarcoma.

Key points Diffuse correlation spectroscopy (DCS) is emerging as a powerful tool to assess skeletal muscle perfusion. Near-infrared spectroscopy (NIRS) is an established technique for characterizing the transport and utilization of oxygen through the microcirculation. Here we compared a combined NIRS-DCS system with conventional measures of oxygen delivery and utilization during handgrip exercise. The data show good concurrent validity between convective oxygen delivery and DCS-derived blood flow index, as well as between oxygen extraction at the conduit and microvascular level. We then manipulated forearm arterial perfusion pressure by adjusting the position of the exercising arm relative to the position of the heart. The data show that microvascular perfusion can be uncoupled from convective oxygen delivery, and that tissue saturation seemingly compensates to maintain skeletal muscle oxygen consumption. Taken together, these data support a novel role for NIRS-DCS in understanding the determinants of muscle oxygen consumption at the microvascular level. Abstract Diffuse correlation spectroscopy (DCS) is emerging as a powerful tool to assess skeletal muscle perfusion. Combining DCS with near-infrared spectroscopy (NIRS) introduces exciting possibilities for understanding the determinants of muscle oxygen consumption; however, no investigation has directly compared NIRS-DCS to conventional measures of oxygen delivery and utilization in an exercising limb. To address this knowledge gap, nine healthy males performed rhythmic handgrip exercise with simultaneous measurements by NIRS-DCS, Doppler blood flow and venous oxygen content. The two approaches showed good concurrent validity, with directionally similar responses between: (a) Doppler-derived forearm blood flow and DCS-derived blood flow index (BFI), and (b) venous oxygen saturation and NIRS-derived tissue saturation. To explore the utility of combined NIRS-DCS across the physiological spectrum, we manipulated forearm arterial perfusion pressure by altering the arm position above or below the level of the heart. As expected, Doppler-derived skeletal muscle blood flow increased with exercise in both arm positions, but with markedly different magnitudes (below: +424.3 ± 41.4 ml/min, above: +306 ± 12.0 ml/min, P = 0.002). In contrast, DCS-derived microvascular BFI increased to a similar extent with exercise, regardless of arm position (P = 0.65). Importantly, however, the time to reach BFI steady state was markedly slower with the arm above the heart, supporting the experimental design. Notably, we observed faster tissue desaturation at the onset of exercise with the arm above the heart, resulting in similar muscle oxygen consumption profiles throughout exercise. Taken together, these data support a novel role for NIRS-DCS in understanding the determinants of skeletal muscle oxygen utilization non-invasively and throughout exercise.

Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments. This article is characterized under:Analytical and Computational Methods > Computational MethodsTranslational, Genomic, and Systems Medicine > Therapeutic MethodsModels of Systems Properties and Processes > Organ, Tissue, and Physiological Models, Hybrid modeling framework of cancer combines three classes of mathematical models: data‐driven, physics‐based, and optimization for development of a clinically relevant and quantitative decision‐making system for personalized medicine.

Anomaly-based detection techniques have been widely studied in recent years. Most of these efforts have focused to improve the accuracy of these techniques. The poor accuracy performance is caused by two factors: i the data used for the analysis is insufficient and/or unrepresentative of the application behavior, or ii inappropriate algorithms are used to model the behavior of the application. In this paper, we attempt to improve anomaly-based detection techniques by examining these two factors. First, we use system call filtering and abstraction process. This process refines the system call traces. The refined traces are compact and should be more representative of the application main behavior. Second, we use machine learning classifiers to characterize the benign behavior. Generally, there are two main categories of machine learning classifiers: generative classifiers and discriminative classifiers. In their initial training phases, the classifiers build models characterizing the benign behavior. Later on, these models are used to distinguish between different classes of data. They are simply defined by their parameters. The k-means classifier is considered as a representative of the generative classifier category, and the support vector machine classifier as a representative of the discriminative classifier category. The efficiency of these classifiers are reviewed and compared, as well as the impact of the filtering and abstraction process on their performances is evaluated. The experimental results show that the support vector machine model outperforms the k-means model, and the filtering and abstraction process has positive impacts on the performance of both models. Copyright © 2016 John Wiley & Sons, Ltd.

A novel, small-volume vertically arranged spin bath was successfully developed for an air gap lyocell-type spinning process. A maximum regeneration bath length with a minimum free volume characterizes the concept of the new spin bath. Using the ionic liquid (IL) 1,5-diazabicyclo[4.3.0]non-5-enium acetate [DBNH][OAc], the spin bath showed very good spinning performances of IL-cellulose dopes at high draw ratios and spinning duration for single filament spinning experiments. Using this new device, it was possible to get a step further in the optimization of the Ioncell® process and simulate a process closed loop operation by performing single filament spinning in IL/H2O mixtures. Good dope spinnability and preserved fibers mechanical properties were achieved in a coagulation bath containing up to 30 wt% IL. It is only at 45 wt% of IL in the bath that the spinnability and fibers mechanical properties started to deteriorate. The fibers fibrillar structure was less pronounced in IL-containing spinning bath in comparison to a pure water bath. However, their crystallinity after washing was preserved regardless of the spinning bath composition. The results presented in this work have a high relevance to the upscaling of emerging IL-based cellulose dissolution and spinning processes.

Improving anomaly-based malware detection techniques has been widely studied in recent years. Most of these efforts have focused on the dataset available for analysis and/or the algorithms used to distinguish between normal or abnormal behavior. These factors have major impacts on the accuracy performance of the detection techniques as well as on their time and space complexities. In this paper, we revisit a classical anomaly-based malware detection approach (i.e., database of normal behavior) analyzing Android system calls with two conflicting objectives: reducing the time and space complexities of the selected approach without decreasing its accuracy performance. To achieve this goal, we introduce a filtering and abstraction process, which (i) removes irrelevant system calls to describe the main behavior of an Android application and (ii) unifies system calls having the same functionality but different names. This process is used to build a database describing a canonical normal behavior model of Android applications. This model is based on the 200 most popular free Android applications available in the Android market. It represents the last line of defense of an in-depth protection strategy for smartphone systems. The results of our experimentations show that our filtering and abstraction process has positive impacts on the performance and the accuracy of the selected malware detection approach. Copyright © 2014 John Wiley & Sons, Ltd.

During the past 30 years, various fault-tolerant control (FTC) methods have been developed to address actuator or component faults for various systems with or without tracking control objectives. However, very few FTC strategies establish a relation between the post-fault reference trajectory to track and the remaining resources in the system after fault occurrence. This is an open problem that is not well considered in the literature. The main contribution of this paper is in the design of a reconfigurable FTC and trajectory planning scheme with emphasis on online decision making using differential flatness. In the fault-free case and on the basis of the available actuator resources, the reference trajectories are synthesized so as to drive the system as fast as possible to its desired setpoint without violating system constraints. In the fault case, the proposed active FTC system (AFTCS) consists in synthesizing a reconfigurable feedback control along with a modified reference trajectories once an actuator fault has been diagnosed by a fault detection and diagnosis scheme, which uses a parameter-estimation-based unscented Kalman filter. Benefited with the integration of trajectory re-planning using the flatness concept and the compensation-based reconfigurable controller, both faults and saturation in actuators can be handled effectively with the proposed AFTCS design. Advantages and limitations of the proposed AFTCS are illustrated using an experimental quadrotor unmanned aerial vehicle testbed

SUMMARY This paper proposes a strategy to design a minimal and fault-tolerant sensor network for observability of complex systems. Complex systems are large-scale dynamic systems composed of interconnected subsystems. The objective is to determine the sensors to be used so that the system is observable while minimizing the number of sensors. The strategy is based on breaking down complex systems into interconnected subsystems. System breakdown helps in treating each subsystem separately and allows using reduced-order observers rather than a large-scale observer for the overall system. An academic example is given to illustrate the proposed strategy.Copyright © 2011 John Wiley & Sons, Ltd.

An efficient and simple DMAP-catalyzed reaction of 2-aminobenzophenone with aromatic aldehydes and NH4OAc is developed to give 1,2-dihydroquinazoline derivatives under mild conditions.

Type II superstring theory with mixed Dirichlet and Neumann boundary conditions admit antisymmetric tensors with varying degrees in the spectrum. We show that there exists a family of dual supergravity lagrangians to the $N=2$ type IIA action in ten dimensions. The duality transformations and the resulting actions are constructed explicitely., Comment: Talk presented at the Cern workshop on ``STU-Dualities and Non-perturbative phenomena in Superstrings and Supergravity'' December 1995. Tex file

The independent effect of consanguinity on the prevalence of congenital heart defects (CHDs), all and specific types, was investigated in newborns admitted to nine hospitals located in Beirut, Lebanon and members of the National Collaborative Perinatal Neonatal Network (NCPNN). Cases were 173 newborns admitted to the Neonatal Intensive Care Units (NICU) of participating hospitals during the 3-year period from January 1, 2000 to December 31, 2002 and diagnosed during their hospital stay as having one or more CHD. Cases with chromosomal abnormalities were excluded. Cases with more than one CHD were assigned one principal malformation. Controls consisted of a random sample of 865 newborns without a CHD admitted to the NICU during the same period. After controlling for confounders, first cousin consanguinity remained significantly associated with an increased risk of CHD: infants born to first cousin marriages had a 1.8 times higher risk of having a CHD diagnosed at birth compared to those born to unrelated parents (95% CI: 1.1-3.1). In particular, first-cousin marriage was a significant risk factor for ventricular septal defect (VSD), atrial septal defect (ASD), hypoplastic left heart (HLH), and single ventricle (SV). No association was found with d-transposition of the great arteries, coarctation, pulmonary atresia (PA), atrioventricular septal defect (AVSD), and tetralogy of Fallot (TOF). The results of this study suggest a familial factor in the multifactorial etiology of CHDs. Additional epidemiologic and family-based genetic studies are needed to understand the complex cause of CHDs.