Small cell lung cancer (SCLC) cells express a variety of neuropeptides which act as autocrine growth factors. Although several neuropeptide analogs have been reported to antagonize SCLC proliferation, the development of these compounds has been limited by their low potency and the cytostatic nature of their effects. In the present study we evaluated the cytotoxic activity of four short-chain substance P analogs (NY3460, NY3238[-pHOPA], NY3238[Phe1], NY3238[Lys5]) against a panel of five SCLC cell lines. NY3460 was the most potent compound in all five SCLC cell lines (IC50 = 2.8-3.7 microM) as assessed by a MTT growth inhibitory assay. NY3238[Phe1] was also relatively active in all cell lines (IC50 = 3.5-11.2 microM), while NY3238[Lys5] and NY3238[-pHOPA] were substantially less active. NY3460 was the only agent to induce an increase in the percentage of cells with subdiploid DNA content suggestive of apoptosis by flow cytometric DNA content analysis. The induction of apoptosis was confirmed by fluorescent microscopy in NCI-H69, NCI-H82, NCI-H446, and NCI-H510 cells after exposure to 5.0 microM NY3460 for 48 h. These findings suggest that NY3460 is a relatively potent cytotoxic inhibitor of SCLC growth, and that short-chain neuropeptide analogs deserve further evaluation as anti-SCLC agents. 相似文献
Temperature attained during machining has significant effects on the properties of tool, chip and workpiece. It governs the parameters like shear angle, cutting force, tool wear, surface finish etc. Review of literature reveals that hardly any information is available about the analytical determination of the tool-chip interface temperature and the temperature distribution during the accelerated cutting.
This paper presents the temperature analysis of accelerated cutting (i.e. taper turning and facing) as well as longitudinal turning, using the finite element technique. It has been concluded that the temperature distribution within the tool-chip-work system and the average tool-chip interface temperature for the two classes of machining (viz longitudinal turning and accelerated cutting) are not the same, even though the conditions of machining are identical. Further, the average tool-chip interface temperature is lowest in case of facing and highest in case of longitudinal turning. 相似文献
Emotion detection from the text is a challenging problem in the text analytics.
The opinion mining experts are focusing on the development of emotion detection
applications as they have received considerable attention of online community including
users and business organization for collecting and interpreting public emotions. However,
most of the existing works on emotion detection used less efficient machine learning
classifiers with limited datasets, resulting in performance degradation. To overcome this
issue, this work aims at the evaluation of the performance of different machine learning
classifiers on a benchmark emotion dataset. The experimental results show the
performance of different machine learning classifiers in terms of different evaluation
metrics like precision, recall ad f-measure. Finally, a classifier with the best performance
is recommended for the emotion classification. 相似文献
In research evaluation of single researchers, the assessment of paper and journal impact is of interest. High journal impact reflects the ability of researchers to convince strict reviewers, and high paper impact reflects the usefulness of papers for future research. In many bibliometric studies, metrics for journal and paper impact are separately presented. In this paper, we introduce two graph types, which combine both metrics in a single graph. The graphs can be used in research evaluation to visualize the performance of single researchers comprehensively. 相似文献
There are many dynamic events like new order arrivals, machine breakdowns, changes in due dates, order cancellations, arrival of urgent orders etc. that makes static scheduling approaches very difficult. A dynamic scheduling strategy should be adopted under such production circumstances. In the present study an event driven dynamic job shop scheduling mechanism under machine capacity constraints is proposed. The proposed method makes use of the greedy randomised adaptive search procedure (GRASP) by also taking into account orders due dates and sequence-dependent set-up times. Moreover, order acceptance/rejection decision and Order Review Release mechanism are integrated with scheduling decision in order to meet customer due date requirements while attempting to execute capacity adjustments. We employed a goal programming-based logic which is used to evaluate four objectives: mean tardiness, schedule unstability, makespan and mean flow time. Benchmark problems including number of orders, number of machines and different dynamic events are generated. In addition to event-driven rescheduling strategy, a periodic rescheduling strategy is also devised and both strategies are compared for different problems. Experimental studies are performed to evaluate effectiveness of the proposed method. Obtained results have proved that the proposed method is a feasible approach for rescheduling problems under dynamic environments. 相似文献
Transformation from conventional business management systems to smart digital systems is a recurrent trend in the current era. This has led to digital revolution, and in this context, the hardwired technologies in the software industry play a significant role However, from the beginning, software security remains a serious issue for all levels of stakeholders. Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data, compromising the organizations’ reputation that translates into, financial losses as well. Most of the data breaches are financially motivated, especially in the healthcare sector. The cyber invaders continuously penetrate the E-Health data because of the high cost of the data on the dark web. Therefore, security assessment of healthcare web-based applications demands immediate intervention mechanisms to weed out the threats of cyber-attacks. The aim of this work is to provide efficient and effective healthcare web application security assessment. The study has worked with the hybrid computational model of Multi-Criteria Decision Making (MCDM) based on Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal-Solutions (TOPSIS) under the Hesitant Fuzzy (HF) environment. Hesitant fuzzy sets provide effective solutions to address decision making problems where experts counter hesitation to make a decision. The proposed research endeavor will support designers and developers in identifying, selecting and prioritizing the best security attributes for web applications’ development. The empirical analysis concludes that Robustness got highest priority amongst the assessed security attributes set followed by Encryption, Authentication, Limit Access, Revoke Access, Data Validation, and Maintain Audit Trail. The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the web application security. The study also establishes guidelines which the developers can refer for the identification and prioritization of security attributes to build more secure and trustworthy web-based applications. 相似文献
Journal of Materials Science: Materials in Electronics - In this work, sol–gel-processed Ho-doped PbTiO3 powder samples with the compositions Pb1?XHoxTiO3 (x?=?0; 1; 3; 6;... 相似文献
Extensive research has been carried out in the past on face recognition, face detection, and age estimation. However, age-invariant face recognition (AIFR) has not been explored that thoroughly. The facial appearance of a person changes considerably over time that results in introducing significant intraclass variations, which makes AIFR a very challenging task. Most of the face recognition studies that have addressed the ageing problem in the past have employed complex models and handcrafted features with strong parametric assumptions. In this work, we propose a novel deep learning framework that extracts age-invariant and generalized features from facial images of the subjects. The proposed model trained on facial images from a minor part (20–30%) of lifespan of subjects correctly identifies them throughout their lifespan. A variety of pretrained 2D convolutional neural networks are compared in terms of accuracy, time, and computational complexity to select the most suitable network for AIFR. Extensive experimental results are carried out on the popular and challenging face and gesture recognition network ageing dataset. The proposed method achieves promising results and outperforms the state-of-the-art AIFR models by achieving an accuracy of 99%, which proves the effectiveness of deep learning in facial ageing research. 相似文献
The image semantic segmentation has been extensively studying. The modern methods rely on the deep convolutional neural networks, which can be trained to address this problem. A few years ago networks require the huge dataset to be trained. However, the recent advances in deep learning allow training networks on the small datasets, which is a critical issue for medical images, since the hospitals and research organizations usually do not provide the huge amount of data. In this paper, we address medical image semantic segmentation problem by applying the modern CNN model. Moreover, the recent achievements in deep learning allow processing the whole image per time by applying concepts of the fully convolutional neural network. Our qualitative and quantitate experiment results demonstrated that modern CNN can successfully tackle the medical image semantic segmentation problem.