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Chemotherapy Related Cognitive Impairment (CRCI), also called chemobrain, diminishes cancer patient���s life quality. Breast cancer (BC) patients have been described to be importantly affected, however, the mechanism leading to CRCI has not been fully elucidated. Recent research proposes microglia as the main architect of CRCI, thus dysregulations in these cells could trigger CRCI. The aim of this research was to evaluate the effects of two drugs commonly used against breast cancer, cyclophosphamide (CTX) and epirubicin (EPI), on the microglia cell line SIM-A9, using the BC cell line, 4T1, as a control. Our results show that CTX and EPI decrease microglia-cell viability and increase cell death on a concentration-dependent manner, being 5 and 2 times more cytotoxic to microglia cell line than to breast cancer 4T1cells, respectively. Both chemotherapies induce cell cycle arrest and a significant increase in p53, p16 and ��-H2AX in breast cancer and microglia cells. Furthermore, mitochondrial membrane potential (����m) diminishes as cell death increases, and both chemotherapies induce reactive oxygen species (ROS) production on SIM-A9 and 4T1. Moreover, caspase activation increases with treatments and its pharmacological blockade inhibits CTX and EPI induced-cell death. Finally, low concentrations of CTX and EPI induce ��-H2AX, and EPI induces cytokine release, NO production and Iba-1 overexpression. These findings indicate that microglia cells are more sensitive to CTX and EPI than BC cells and undergo DNA damage and cell cycle arrest at very low concentrations, moreover EPI induces microglia activation and a pro-inflammatory profile., EXCLI Journal; 21:Doc197; ISSN 1611-2156

Due to the stochastic nature of traffic conditions and demand fluctuations, it is a challenging task for operators to maintain reliable services, and passengers often suffer from longer travel time...

This research presents an arrival notification system for paratransit passengers with disabilities. Almost all curb-to-curb paratransit services have a significantly large pickup time window, ranging from 20 to 40 min from the scheduled time and producing substantial passenger waiting times. The arrival notification system presented in this study delivers an automated voice call to a registered user once the paratransit vehicle is in proximity to the pickup location. The system utilizes the Google Traffic application programming interface (API) for the vehicle arrival estimation. Unlike other vehicle arrival notification systems in the state of the practice, the proposed system is compact and does not require additional equipment such as radio transmitting and positioning devices. The proposed system, which uses consumer mobile devices with the Android or iOS platform, is designed to exploit commercial cellular network service (i.e., 3G and 4G-LTE). In addition to the passenger notification, the proposed system provides paratransit drivers with real-time route guidance information developed through the Google Maps API. Field evaluation conducted in Essex County, New Jersey, revealed significant reduction in passenger waiting time. The passenger waiting time was reduced by 15 to 20 min. In addition, the accuracy of the notification system was tested. During the test, in almost all cases, the vehicle arrived 1 min earlier than the proposed arrival time.

Highway lane closures due to road reconstruction and the resulting work zones have been a major source of nonrecurring congestion on freeways. It is extremely important to calculate the safety and cost impacts of work zones: the use of new technologies that track drivers and vehicles make that possible. A multilayer feed-forward artificial neural network (ANN) model is developed in this paper to estimate work zone delay by using the probe-vehicle data. The probe data include the travel speeds under normal and work zone conditions. Unlike previous models, the proposed model estimates temporal and spatial delays, which are applied to a real world case study in New Jersey. The work zone data (i.e., starting time, duration, length, and number of closed lanes) were collected on New Jersey freeways in 2014 together with actual probe-vehicle speeds. A comparative analysis was conducted; the results indicate that the ANN model outperforms the traditional deterministic queuing model in terms of the accuracy in estimating travel delays. The ANN model can be used to calculate contractor penalty in terms of cost overruns as well as incentivize a reward schedule in case of early work competition. The model can assist work zone planners in designing optimal start and end time of work zone as function of time of day. In assessing the performance of work zones, the model can assist transportation engineers to better develop and evaluate traffic mitigation and management plans.

Mössbauer reflectivity spectra and nuclear resonance reflectivity (NRR) curves have been measured using the Synchrotron Mössbauer Source (SMS) for a [

This research evaluates the impact of In-vehicle Signal Advisory System (ITSAS) on signalized arterial. ITSAS provides individual drivers equipped with a mobile communication device with advisory speed information enabling to minimize the time delay and fuel consumption when crossing intersection. Given the instantaneous vehicular driving information, such as position, speed, and acceleration rate, ITSAS produces advisory speed information by taking into consideration the traffic signal changes at a downstream intersection. The advisory speed information includes not only an optimal speed range updated every 300-ft for individual drivers but also a descriptive message to warn drivers stop to ensure safety at the downstream intersection. Unlike other similar Connected Vehicles applications for intersection management, ITSAS does not require Roadside Equipment (RSE) to disseminate the advisory speed information as it is designed to exploit commercial cellular network service (i.e., 3G and 4G-LTE). Thus, ITSAS can be easily plugged into existing traffic control management system to rapidly conduct its implementation without significant additional cost. This research presents the field evaluations of ITSAS on a signalized corridor in New Jersey, which discovered significant travel time savings for the equipped vehicle.

Current incident detection and traffic monitoring method using closed-circuit television (CCTV) cameras meets with limitations as the coverage of CCTV cameras rapidly expands. In general, traffic operators at Traffic Operation Center (TOC) have to manage and monitor numerous CCTV cameras deployed on roadways. Thus, many transportation agencies consider the use of video analytics system to reduce incident detection time and minimize traffic impacts, but they also want to validate the performance of the video analytics system whether it can work with their existing video surveillance infrastructure before procuring the system. To that end, a pilot study was designed and conducted to evaluate the accuracy of a video analytics product by integrating with CCTV cameras deployed on highways. The pilot study was designed to evaluate the accuracy of video analytics in detecting incidents and collecting traffic counts. The test results show that the performance of video analytics is significantly impacted by video quality and other environmental factors such as lighting and weather conditions.

Effective learning and recognition methods play an important role in intelligent image search and video retrieval. This chapter therefore reviews some popular learning and recognition methods that are broadly applied for image search and video retrieval . First some popular deep learning methods are discussed, such as the feedforward deep neural networks , the deep autoencoders , the convolutional neural networks, and the Deep Boltzmann Machine (DBM) . Second, Support Vector Machine (SVM), which is one of the popular machine learning methods, is reviewed. In particular, the linear support vector machine, the soft-margin support vector machine, the non-linear support vector machine , the simplified support vector machine , the efficient Support Vector Machine (eSVM) , and the applications of SVM to image search and video retrieval are discussed. Finally, other popular kernel methods and new similarity measures are briefly reviewed.

The ever-increasing popularity of intelligent image search and video retrieval warrants a comprehensive study of the major feature representation and extraction methods often applied in image search and video retrieval. Towards that end, this chapter reviews some representative feature representation and extraction approaches, such as the Spatial Pyramid Matching (SPM) , the soft assignment coding, the Fisher vector coding , the sparse coding and its variants, the Local Binary Pattern (LBP) , the Feature Local Binary Patterns (FLBP) , the Local Quaternary Patterns (LQP), the Feature Local Quaternary Patterns (FLQP) , the Scale-invariant feature transform (SIFT) , and the SIFT variants, which are broadly applied in intelligent image search and video retrieval .

A hybrid machine-learning model, integrating an artificial neural network (ANN) and a support vector machine (SVM) model, is developed to predict spatiotemporal delays, subject to road geometry, number of lane closures, and work zone duration in different periods of a day and in the days of a week. The model is very user friendly, allowing the least inputs from the users. With that the delays caused by a work zone on any location of a New Jersey freeway can be predicted. To this end, tremendous amounts of data from different sources were collected to establish the relationship between the model inputs and outputs. A comparative analysis was conducted, and results indicate that the proposed model outperforms others in terms of the least root mean square error (RMSE). The proposed hybrid model can be used to calculate contractor penalty in terms of cost overruns as well as incentive reward schedule in case of early work competition. Additionally, it can assist work zone planners in determining the best start and end times of a work zone for developing and evaluating traffic mitigation and management plans.