Fluorogenic Detection of Human Serum Albumin Using Curcumin-Capped Mesoporous Silica Nanoparticles

Mesoporous silica nanoparticles loaded with rhodamine B and capped with curcumin are used for the selective and sensitive fluorogenic detection of human serum albumin (HSA). The sensing mesoporous silica nanoparticles are loaded with rhodamine B, decorated with aminopropyl moieties and capped with curcumin. The nanoparticles selectively release the rhodamine B cargo in the presence of HSA. A limit of detection for HSA of 0.1 mg/mL in PBS (pH 7.4)-acetonitrile 95:5 v/v was found, and the sensing nanoparticles were used to detect HSA in spiked synthetic urine samples.     

Domain-Specific On-Device Object Detection Method

Object detection is a significant activity in computer vision, and various approaches have been proposed to detect varied objects using deep neural networks (DNNs). However, because DNNs are computation-intensive, it is difficult to apply them to resource-constrained devices. Here, we propose an on-device object detection method using domain-specific models. In the proposed method, we define object of interest (OOI) groups that contain objects with a high frequency of appearance in specific domains. Compared with the existing DNN model, the layers of the domain-specific models are shallower and narrower, reducing the number of trainable parameters; thus, speeding up the object detection. To ensure a lightweight network design, we combine various network structures to obtain the best-performing lightweight detection model. The experimental results reveal that the size of the proposed lightweight model is 21.7 MB, which is 91.35% and 36.98% smaller than those of YOLOv3-SPP and Tiny-YOLO, respectively. The f-measure achieved on the MS COCO 2017 dataset were 18.3%, 11.9% and 20.3% higher than those of YOLOv3-SPP, Tiny-YOLO and YOLO-Nano, respectively. The results demonstrated that the lightweight model achieved higher efficiency and better performance on non-GPU devices, such as mobile devices and embedded boards, than conventional models.     

Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data

As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.     

HMD-Net: A Vehicle Hazmat Marker Detection Benchmark

Vehicles carrying hazardous material (hazmat) are severe threats to the safety of highway transportation, and a model that can automatically recognize hazmat markers installed or attached on vehicles is essential for intelligent management systems. However, there is still no public dataset for benchmarking the task of hazmat marker detection. To this end, this paper releases a large-scale vehicle hazmat marker dataset named VisInt-VHM, which includes 10,000 images with a total of 20,023 hazmat markers captured under different environmental conditions from a real-world highway. Meanwhile, we provide an compact hazmat marker detection network named HMD-Net, which utilizes a revised lightweight backbone and is further compressed by channel pruning. As a consequence, the trained-model can be efficiently deployed on a resource-restricted edge device. Experimental results demonstrate that compared with some established methods such as YOLOv3, YOLOv4, their lightweight versions and popular lightweight models, HMD-Net can achieve a better trade-off between the detection accuracy and the inference speed.     

A new liquid-phase microextraction method based on solidification of floating organic drop

In the present study, a new and versatile liquid-phase microextraction method is described. This method requires very simple and cheap apparatus and also a small amount of organic solvent. Eight microliters of 1-undecanol was delivered to the surface of solution containing analytes and solution was stirred for a desired time. Then sample vial was cooled by inserting it into an ice bath for 5 min. The solidified 1-undecanol was transferred into a suitable vial and immediately melted; then, 2 μL of it was injected into a gas chromatograph for analysis.Some polycyclic aromatic hydrocarbons (PAHs) were used as model compounds for developing and evaluating of the method performance. Analysis was carried out by gas chromatography/flame ionization detection (GC/FID).Several factors influencing the microextraction efficiency, such as the nature and volume of organic solvent, the temperature and volume of sample solution, stirring rate and extraction time were investigated and optimized. The applicability of the technique was evaluated by determination of trace amounts of PAHs in environmental samples. Under the optimized conditions, the detection limits (LOD) of the method were in the range of 0.07-1.67 μg L⁻¹ and relative standard deviations (R.S.D.) for 10 μg L⁻¹ PAHs were <7%. A good linearity (r² > 0.995) in a calibration range of 0.25-300.00 μg L⁻¹ was obtained. After 30 min extraction duration, enrichment factors were in the range of 594-1940. Finally, the proposed method was applied to the determination of trace amounts of PAHs in several real water samples, and satisfactory results were resulted. Since very simple devices were used, this new technique is affordable, efficient, and convenient for extraction and determination of low concentrations of PAHs in water samples.     

Direct determination of glyphosate using hydrophilic interaction chromatography with coulometric detection at copper microelectrode

A simple, rapid, and low-cost coulometric method for direct detection of glyphosate using hydrophilic interaction chromatography is presented. The principle of detection is based on the enhancement of the anodic current of copper microelectrode in the presence of complexing agents, such as glyphosate, with the formation of a soluble Cu(II) complex. Under optimized conditions, the limit of detection (S/R = 3) for glyphosate was 0.1 mg L⁻¹ (0.59 μM) without any preconcentration method. The calibration curve has been found linear in all concentration range tested (from limit of detection to 34 mg L⁻¹) with an excellent correlation coefficient (0.9999). The present method was successfully applied for the determination of glyphosate in fruit juices without any kind of extraction, clean-up, or preconcentration step, with recoveries of 92 and 90% for apple and grape juice, respectively.     

基于纳米金探针和基因芯片的DNA检测新方法

运用荧光纳米金探针和基因芯片杂交建立一种新的DNA检测方法. 荧光纳米金探针表面标记有两种DNA探针: 一种为带有Cy5荧光分子的信号探针BP1, 起信号放大作用; 另一种为与靶DNA一部分互补的检测探针P532, 两种探针比例为5∶1. 当靶DNA存在时, 芯片上捕捉探针(与靶DNA的另一部分互补)通过碱基互补配对结合靶DNA, 将靶DNA固定于芯片上; 荧光纳米金探针通过检测探针与靶DNA及芯片结合, 在芯片上形成“三明治”复合结构, 最后通过检测信号探针上荧光分子的信号强度来确定靶DNA的量. 新方法检测灵敏度高, 可以检测浓度为1 pmol/L的靶DNA, 操作简单, 检测时间短. 通过改进纳米金探针的标记和优化杂交条件, 可进一步提高核酸检测的灵敏度, 这将在核酸检测方面具有重要的应用价值.     

Applications of Raman-based techniques to on-site and in-vivo analysis

Raman-based technologies have proved to be excellent tools for on-site and in-vivo analysis, due to the non-invasive nature of their detection, their capability of providing structure information, their high tolerance to aqueous samples, the ultra-sensitivity of surface-enhanced Raman scattering (SERS) and resonance Raman scattering (RRS), the high spatial resolution of tip-enhanced Raman scattering (TERS), and the ultrashort spectra-acquisition time for coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS).In this review, we highlight the recent success of Raman-based technologies in various applications, including:

• (1) 

  on-site surface analysis and chemical-reaction monitoring;

• (2) 

  on-site identification of cultural objects, archeological studies and planetary science;

• (3) 

  in-vivo analysis of cells and microorganisms;

• (4) 

  in-vivo diagnosis inside human and animal bodies;

• (5) 

  in-vivo fast Raman imaging and mapping;

• (6) 

  the study of SERS processes; and,

• (7) 

  assessment of nanomaterial safety.

     

Rapid determination of trace polycyclic aromatic hydrocarbons in particulate matter using accelerated solvent extraction followed by ultra high performance liquid chromatography with fluorescence detection

A method has been developed for the trace analysis of polycyclic aromatic hydrocarbons, which are known as persistent organic pollutants and ubiquitous constituents of fine particulate matter that causes growing airborne pollution. The method, which was especially for samples of airborne particulate matter less than 2.5 μm in diameter, utilized accelerated solvent extraction and ultra high performance liquid chromatography with fluorescence detector. Four principal parameters of accelerated solvent extraction were optimized to obtain maximum extraction efficiency. Using the established synergetic programs of gradient elution and fluorescence wavelength switching, a rapid separation was achieved in 6.56 min with good linearity for 15 polycyclic aromatic hydrocarbons (coefficient of determination above 0.999). The limits of detection ranged from 0.833 to 10.0 pg/m³. The precision of the method expressed as inter‐day relative standard deviation ranged from 0.2 to 1%, which was calculated from nine repetitive measurements of 8.00 μg/L analytes. Average spiked recoveries ranged from 71.6 to 97.7%, with the exception of naphthalene. The rapid, sensitive, and accurate method can meet the pressing needs of health risk assessment and increasingly heavy daily tasks of air quality monitoring.     

Toll-like receptors for pathogen detection in water: challenges and benefits

The need for effective and efficient methods for pathogen detection in water is as serious as ever due to the health risk posed to human population by the consumption of pathogen-contaminated water. One of the important research streams which have been focused on by researchers for development of novel techniques for this purpose is biosensor technologies. Using different bio-recognition elements and transduction methodologies, biosensors have the potential to detect their analyte of interest in a fast and highly specific manner. Different pathogenic agents can be recognised by toll-like receptors (TLRs). The innate immune system of higher organisms employs TLRs for triggering intracellular signalling and induction of the expression of immune response genes. In this report, we explore the challenges associated with employing TLRs for pathogen detection in water samples. Although methods using TLR expressing cells also have been discussed, the focus of this review is on using TLR proteins as the bio-recognition elements in biosensors.