Evaluations of 5-fluorourcil treated lung cancer cells by atomic force microscopy

Atomic force microscopy (AFM) can be used to obtain the physical information of single live cancer cells; however, the physical changes in live cells with time based on AFM remain to be studied, which play a key role in the evaluation of the efficacy and side effects of drugs. Herein, the treatment of the A549 cell line with the anticarcinogen 5-fluorouracil has been discussed based on the AFM analysis of their continuous physical changes, including their surface morphology, height, adhesion and Young's modulus, with time. In comparison, the African green monkey kidney (Vero) cell line was tested as normal cells to determine the side effects of 5-fluorouracil. The results show that the optimal concentration of 5-fluorouracil is about 500 μM, which presents the best anticancer effect and mild side effects

Fast Fourier transport analysis of surface structures fabricated by laser interference lithography

This paper presents an FFT (fast Fourier transform) analytical method for the study of surface structures fabricated by laser interference lithography (LIL). In the work, the FFT analytical method combined with Gaussian fitting is used to determine the periods and pattern distributions of surface structures from frequency spectra. For LIL, the processing parameters of incident and azimuth angles can be obtained corresponding to the period and pattern distribution. This work facilitates the detection of micro- and nano-structures, the analysis of pattern distribution in engineering, and the processing error analysis of LIL

Effect of extract from ginseng rust rot on the inhibition of human hepatocellular carcinoma cells in vitro

Hepatocellular carcinoma (HCC) is one of major leading causes of cancer death worldwide. As a traditional medicine, the anti-cancer function of ginseng is being growingly recognized and investigated. However, the effect of ginseng rust rot on human HCC is unknown yet. In this study, the HCC cells were treated with different parts of mountain cultivated ginseng rust rot and compared with human normal liver cells. The morphology, survival rate and β-actin expression of the cells were changed by introducing the ginseng epidermis during the incubation process. Notably, the results reveal that the ginseng epidermis can induce apoptosis by altering the morphologies of cells, indicating the practical implication for the HCC treatment and drug development

The Preliminary Investigation of the Effect of Caries on the Extension of Dentin Cracks

Dentin is part of the structural composition of the teeth and consists of intertubular dentin (ITD), peritubular dentin (PTD) and the dentinal tubules. The interaction of the three components provides significant strength and durability to the dentin. Caries is a dental disease caused by bacteria, which can damage the microstructure of teeth and lead to teeth damage or even fracture. It is necessary to investigate the mechanism of teeth damage from the perspective of fracture mechanics. In order to study the effect of caries on crack extension, this study uses finite element simulation (FEM) to establish a monophasic dentin model, a bidirectional dentin model, and a dentin model with different caries degrees to compare and analyze the crack extension under the same magnitude of displacement load. The experimental results reveal the influence of different caries degrees on crack extension, which is important for exploring the damage and fracture mechanism of teeth and the design of bionic teeth

Infrared Object Detection Method Based on DBD-YOLOv8

An innovative and improved method for infrared object detection, namely DBD-YOLOv8 (DCN-BiRA-DyHeads-YOLOv8), is presented. The inherent limitations of the YOLOv8 model in scenarios with a low signal-to-noise ratio and complex tasks are addressed, with a focus on improving the multi-scale feature representation within the YOLOv8 framework and effectively filtering out irrelevant regions. To achieve this, two crucial modules, D_C2f and D_SPPF, are integrated. Deformable convolutions (DCN) are utilized by these modules to dynamically adjust the visual receptive fields of the network. Furthermore, a Bi-level Routing Attention mechanism (BRA) and Dynamic Heads (DyHeads) are adapted within the feature fusion network, refining feature maps and enhancing semantic representation through attention mechanisms. Significant improvements are demonstrated by DBD-YOLOv8 when compared to the YOLOv8- $\text{n}\backslash \text{s}\backslash \text{m}\backslash \text{l}\backslash \text{x}$ series models. Notably, improved average [email protected] values on benchmark datasets, including FLIR, OTCBVS (Dataset 01), OTCBVS (Dataset 03), and VEDAI, are achieved by DBD-YOLOv8. The corresponding values are 84.8%, 96.3%, 99.7%, and 76.0%, respectively. These results represent increases of 7.9%, 1.5%, 0.1%, and 3.5%, respectively. Importantly, real-time requirements are met by the model’s inference times, which measure 10.9ms, 32.0ms, 37.3ms, and 28.4ms accordingly for the previous datasets

DS-YOLOv8-Based Object Detection Method for Remote Sensing Images

The improved YOLOv8 model (DCN_C2f+SC_SA+YOLOv8, hereinafter referred to as DS-YOLOv8) is proposed to address object detection challenges in complex remote sensing image tasks. It aims to overcome limitations such as the restricted receptive field caused by fixed convolutional kernels in the YOLO backbone network and the inadequate multi-scale feature learning capabilities resulting from the spatial and channel attention fusion mechanism’s inability to adapt to the input data’s feature distribution. The DS-YOLOv8 model introduces the Deformable Convolution C2f (DCN_C2f) module in the backbone network to enable adaptive adjustment of the network’s receptive field. Additionally, a lightweight Self-Calibrating Shuffle Attention (SC_SA) module is designed for spatial and channel attention mechanisms. This design choice allows for adaptive encoding of contextual information, preventing the loss of feature details caused by convolution iterations and improving the representation capability of multi-scale, occluded, and small object features. Moreover, the DS-YOLOv8 model incorporates the dynamic non-monotonic focus mechanism of Wise-IoU and employs a position regression loss function to further enhance its performance. Experimental results demonstrate the excellent performance of the DS-YOLOv8 model on various public datasets, including RSOD, NWPU VHR-10, DIOR, and VEDAI. The average mAP@0.5 values achieved are 97.7%, 92.9%, 89.7%, and 78.9%, respectively. Similarly, the average mAP@0.5:0.95 values are observed to be 74.0%, 64.3%, 70.7%, and 51.1%. Importantly, the model maintains real-time inference capabilities. In comparison to the YOLOv8 series models, the DS-YOLOv8 model demonstrates significant performance improvements and outperforms other mainstream models in terms of detection accuracy

Localization in wireless sensor networks using a mobile anchor node

In wireless sensor networks (WSN), sensor location plays a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes, called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper describes MACL, a mobile anchor centroid localization method, which uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. The proposed method is radiofrequency based, so no extra hardware or data communication is needed between the sensor nodes. We use simulations and tests from an indoor deployment using the Cricket location system to investigate the localization accuracy of MACL, and find that the localization method is principle simple, less computing and communication overhead, low costly, and flexible accuracy. © 2008 IEEE

Effect of liquid on the magnetic force microscope imaging

It is known that when the probe vibrating in liquid, the oscillation of the cantilever is significantly damped by the interaction forces between the water molecule and the probe surface, which is known as the hydration force. Thus, the parameters of a tapping magnetic probe are affected. In this work, the resonant frequency, Q-factor and spring constant of the magnetic probe in air and liquid environments were analyzed. The MFM images of a hard disk acquired in ambient and liquid conditions were compared. It was found that the hydration force affected the parameters of the magnetic probe and then the quality of the MFM images was decreased. To improve the quality of the magnetic images, the drive amplitude and the lift height were adjusted. The results showed that the magnetic features were recognized with the increases of the drive amplitude and the appropriate lift height

Controlled manipulation of TRAIL into single human colon cancer cells using atomic force microscope

In this study, an AFM tip was used to penetrate the human colon cancer cells (SW480) in the culture medium containing pEGFP-N1-TRAIL plasmids. The trail plasmids encoded with the enhanced green fluorescent protein (EGFP) were moved into the SW480 cells through membrane holes created by the AFM probe. Following the penetration, the culture medium was changed into the RPMI1640 medium supplemented with 10% of fetal bovine serum and incubated for 24h. The expression of PEGFP-N1-TRAIL in SW480 cells was then observed by inverted fluorescence microscope. The experiment results indicate that the AFM tip can be used to penetrate the membranes of targeted cells individually