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However, 12 active compounds selected did not necessarily induce apoptosis and mitotic arrest. This compound can be used as a lead compound to manufacture more active compound.The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of adjacent vehicles and distances to them by means of several electronic sensors, whose data can be used for more sophisticated traffic density estimation techniques. Traffic density has a highly nonlinear nature during on-congestion and queue-clearing conditions. Closed-mathematical forms of the traditional density estimation techniques are incapable of dealing with complex nonlinearities, which opens the door for data-driven approaches such as machine learning techniques. Deep learning algorithms excel in data-rich contexts, which recognize nonlinear and highly situation-dependent patterns. Our research is based on an LSTM (Long short-term memory) neural network for the nonlinearity associated with time dynamics of traffic flow. The proposed method is designed to learn the input-output relation of Edie’s definition. At the same time, the method recognizes a temporally nonlinear pattern of traffic. We evaluate our algorithm by using a microscopic simulation program (PARAMICS) and demonstrate that our model accurately estimates traffic density in Free-flow, Transition, and Congested conditions.In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.In order to determine the effect of different gelation temperatures (80 °C and 90 °C) on the structural arrangements in 1,3-β-d-glucan (curdlan) matrices, spectroscopic and microscopic approaches were chosen. Attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) and Raman spectroscopy are well-established techniques that enable the identification of functional groups in organic molecules based on their vibration modes. X-ray photoelectron spectroscopy (XPS) is a quantitative analytical method utilized in the surface study, which provided information about the elemental and chemical composition with high surface sensitivity. Contact angle goniometer was applied to evaluate surface wettability and surface free energy of the matrices. In turn, the surface topography characterization was obtained with the use of atomic force microscopy (AFM) and scanning electron microscopy (SEM). Described techniques may facilitate the optimization, modification, and design of manufacturing processes (such as the temperature of gelation in the case of the studied 1,3-β-d-glucan) of the organic polysaccharide matrices so as to obtain biomaterials with desired characteristics and wide range of biomedical applications, e.g., entrapment of drugs or production of biomaterials for tissue regeneration. This study shows that the 1,3-β-d-glucan polymer sample gelled at 80 °C has a distinctly different structure than the matrix gelled at 90 °C.Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.We report whole-genome and intra-host variability of SARS-Cov-2 assessed by next generation sequencing (NGS) in upper (URT) and lower respiratory tract (LRT) from COVID-19 patients. The aim was to identify possible tissue-specific patterns and signatures of variant selection for each respiratory compartment. Six patients, admitted to the Intensive Care Unit, were included in the study. Thirteen URT and LRT were analyzed by NGS amplicon-based approach on Ion Torrent Platform. Bioinformatic analysis was performed using both realized in-house and supplied by ThermoFisher programs. Phylogenesis showed clade V clustering of the first patients diagnosed in Italy, and clade G for later strains. The presence of quasispecies was observed, with variants uniformly distributed along the genome and frequency of minority variants spanning from 1% to ~30%. For each patient, the patterns of variants in URT and LRT were profoundly different, indicating compartmentalized virus replication. No clear variant signature and no significant difference in nucleotide diversity between LRT and URT were observed. SARS-CoV-2 presents genetic heterogeneity and quasispecies compartmentalization in URT and LRT. Intra-patient diversity was low. The pattern of minority variants was highly heterogeneous and no specific district signature could be identified, nevertheless, analysis of samples, longitudinally collected in patients, supported quasispecies evolution.Spinal cord stimulation may enable recovery of volitional motor control in people with chronic Spinal Cord Injury (SCI). In this study we explored the effects of adding SCS, applied transcutaneously (tSCS) at vertebral levels T10/11, to a sit-to-stand training intervention in people with motor complete and incomplete SCI. Nine people with chronic SCI (six motor complete; three motor incomplete) participated in an 8-week intervention, incorporating three training sessions per week. Participants received either tSCS combined with sit-to-stand training (STIM) or sit-to-stand training alone (NON-STIM). Tanshinone I solubility dmso Outcome measures were carried out before and after the intervention. Seven participants completed the intervention (STIM N = 5; NON-STIM N = 2). Post training, improvements in International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) motor scores were noted in three STIM participants (range 1.0-7.0), with no change in NON-STIM participants. Recovery of volitional lower limb muscle activity and/or movement (with tSCS off) was noted in three STIM participants. Unassisted standing was not achieved in any participant, although standing with minimal assistance was achieved in one STIM participant. This pilot study has shown that the recruitment of participants, intervention and outcome measures were all feasible in this study design. However, some modifications are recommended for a larger trial.This work intends to study the effect of the curing parameters on the mechanical properties of a polyester resin without a complete curing reaction process. For this purpose, cures at room temperature, 40 °C, and 60 °C, and post-cures at 40 °C and 60 °C, with different exposure times, were considered. Three-point bending tests were performed to assess the bending properties and both stress relaxation and creep behavior. The degree of crosslinking was estimated by evaluating the C = C ester bond, by Fourier infrared spectroscopy and complemented with the thermal characterization made by differential scanning calorimetry. The results showed that higher curing temperatures are preferable to methods involving curing and post-curing, which can be confirmed by the higher degree of conversion of unsaturated ester bonds at 60 °C. Compared to the resin cured at room temperature, the bending strength increased by 36.5% at 40 °C and 88.6% at 60 °C. A similar effect was observed for bending stiffness. In terms of stress relaxation and creep strain, the lowest values were obtained for samples cured at 60 °C.