A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models. Our technique can obtain multiple parameters in about 1 s. The experiment involved setting up samples from various drinking water backgrounds and mixed groups, totaling 9360 injections. A raw visible light source ranging from 380 to 780 nm was utilized, uniformly dispersing light into the sample cell through a filter. The residual beam was captured by a high-definition camera, forming a distinctive spectrum. Three deep learning models-ResNet-50, SqueezeNet V1.1, and GoogLeNet Inception V1-were employed. Datasets were divided into training, validation, and test sets in a 6:2:2 ratio, and prediction performance across different datasets was assessed using the coefficient of determination and root mean square error. The experimental results show that a well-trained machine learning model can extract a lot of feature image information and quickly predict multi-dimensional drinking water indicators with almost no preprocessing. The model's prediction performance is stable under different background drinking water systems. The method is accurate, efficient, and real-time and can be widely used in actual water supply systems. This study can improve the efficiency of water quality monitoring and treatment in water supply systems, and the method's potential for environmental monitoring, food safety, industrial testing, and other fields can be further explored in the future.

Weather-Domain Transfer-Based Attention YOLO for Multi-Domain Insulator Defect Detection and Classification in UAV Images

Insulator defect detection of transmission line insulators is an important task for unmanned aerial vehicle (UAV) inspection, which is of immense importance in ensuring the stable operation of transmission lines. Transmission line insulators exist in complex weather scenarios, with small and inconsistent shapes. These insulators under various weather conditions could result in low-quality images captured, limited data numbers, and imbalanced sample problems. Traditional detection methods often struggle to accurately identify defect information, resulting in missed or false detections in real-world scenarios. In this paper, we propose a weather domain synthesis network for extracting cross-modality discriminative information on multi-domain insulator defect detection and classification tasks. Firstly, we design a novel weather domain synthesis (WDSt) module to convert various weather-conditioned insulator images to the uniform weather domain to decrease the existing domain gap. To further improve the detection performance, we leverage the attention mechanism to construct the Cross-modality Information Attention YOLO (CIA-YOLO) model to improve the detection capability for insulator defects. Here, we fuse both shallow and deep feature maps by adding the extra object detection layer, increasing the accuracy for detecting small targets. The experimental results prove the proposed Cross-modality Information Attention YOLO with the weather domain synthesis algorithm can achieve superior performance in multi-domain insulator datasets (MD-Insulator). Moreover, the proposed algorithm also gives a new perspective for decreasing the multi-domain insulator modality gap with weather-domain transfer, which can inspire more researchers to focus on the field.

An Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) Method for Qualifying DAPB in Rat Plasma and Application to Pharmacokinetic Studies

DAPB, a new molecule including danshensu, borneol, and a mother nucleus of ACEI (Angiotensin-converting enzyme inhibitors), is being developed as an antihypertensive candidate compound. A rapid, accurate, and sensitive ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was established and validated for the determination of DAPB in rat plasma. Chromatographic separation was performed on an Agilent SB-C18 column after protein precipitation by acetonitrile with a mobile phase consisting of acetonitrile and deionized water with 0.02% formic acid and 5 mM NH4F (v/v) at a flow rate of 0.2 mL/min. Quantification was performed using electrospray positive ionization mass spectrometry in the multiple reaction monitoring (MRM) mode. The method was linear over the range of 2-1000 ng/mL. The intra- and inter-day precision was within 12%, with accuracies less than 7%. Stability was within the acceptable limits under various storage and processing conditions. No apparent matrix effect was detected. The validated method was applied to the pre-clinical pharmacokinetic study of DAPB after oral administration of 30 mg/kg and intravenous administration of 6 mg/kg in rats.

Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images

Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in real applications. In this paper, we propose a novel efficient cross-modality insulator augmentation algorithm for multi-domain insulator defect detection to mimic real complex scenarios. It also alleviates the overfitting problem without adding the inference resources. The high-resolution insulator cross-modality translation (HICT) module is designed to generate multi-modality insulator images with rich texture information to eliminate the adverse effects of existing modality discrepancy. We propose the multi-domain insulator multi-scale spatial augmentation (MMA) module to simultaneously augment multi-domain insulator images with different spatial scales and leverage these fused images and location information to help the target model locate defects with various scales more accurately. Experimental results prove that the proposed cross-modality insulator augmentation algorithm can achieve superior performance in public UPID and SFID insulator defect datasets. Moreover, the proposed algorithm also gives a new perspective for improving insulator defect detection precision without adding inference resources, which is of great significance for advancing the detection of transmission lines.

Robot Navigation System Assisted PFNA Fixation of Femoral Intertrochanteric Fractures in the Elderly: A Retrospective Clinical Study

Objective

The incidence of hip fracture in the elderly is increasing. Robot navigation technology has the advantages of minimally invasive and accurate. To explore the difference between the clinical effects of proximal femoral anti-rotation intramedullary nail (PFNA) assisted by robot navigation in the treatment of femoral intertrochanteric fracture and traditional PFNA in the treatment of femoral intertrochanteric fracture in the elderly; analyze the advantages and feasibility of PFNA assisted by robot navigation in the treatment of femoral intertrochanteric fracture in the elderly.

Patients and Methods

From February 2021 to October 2022, the elderly (>65 years old) with femoral intertrochanteric fracture underwent surgery in our center. Divided the patients included in the study into 2 groups based on the surgical method. The surgical method of robot group was PFNA fixation assisted by robot navigation, while the surgical method of traditional group was classic PFNA fixation, Baseline data (general condition, Evans classification, time from injury to operation, preoperative hemoglobin) and observation indicators (intraoperative bleeding, operation time, the length of incision for mail nail insertion, postoperative hemoglobin drop, blood transfusion rate and the Harris score of hip joint 1 year after operation) of the two groups were collected to compare whether there were differences between the two groups.

Results

There was no statistical difference in baseline data between the two groups (P>0.05). The intraoperative bleeding in the robot group was 68.17±10.66 mL, the intraoperative bleeding in the traditional group was 174±8.11mL (P<0.001). The operation time in the robot group was 68.81 ± 6.89 min, in the traditional group, the operation time was 76.94 ± 8.18 min (P<0.001). The length of incision for mail nail insertion in the robot group was 3.53 ± 0.63 cm, the length of the incision for mail nail insertion in the traditional group was 4.23 ± 0.71 cm (P<0.001). 5 patients (13.9%) in the robot group received blood transfusion treatment, and 13 patients (36.1%) in the traditional group received blood transfusion treatment (P=0.029). The hemoglobin in the robot group decreased by 14.81 ± 3.27 g/l after operation compared with that before operation, while that in the traditional group decreased by 16.69 ± 3.32 g/l (P=0.018). The Harris score of the hip joint of the affected limb in the robot group was excellent in 25 cases, good in 8 cases and poor in 3 cases one year after the operation; In the traditional group, Harris scores were excellent in 18 cases, good in 11 cases and poor in 7 cases (P=0.021).

Conclusion

PFNA fixation of femoral intertrochanteric fracture with robot navigation assistance has the advantages of minimally invasive and accurate, shorter operation time, less bleeding and lower blood transfusion rate than traditional surgical methods, and has certain advantages in reducing postoperative complications of elderly patients.

Dissecting the Structural Dynamics of Authentic Cholesteryl Ester Transfer Protein for the Discovery of Potential Lead Compounds: A Theoretical Study

Current structural and functional investigations of cholesteryl ester transfer protein (CETP) inhibitor design are nearly entirely based on a fully active mutation (CETPMutant) constructed for protein crystallization, limiting the study of the dynamic structural features of authentic CETP involved in lipid transport under physiological conditions. In this study, we conducted comprehensive molecular dynamics (MD) simulations of both authentic CETP (CETPAuthentic) and CETPMutant. Considering the structural differences between the N- and C-terminal domains of CETPAuthentic and CETPMutant, and their crucial roles in lipid transfer, we identified the two domains as binding pockets of the ligands for virtual screening to discover potential lead compounds targeting CETP. Our results revealed that CETPAuthentic displays greater flexibility and pronounced curvature compared to CETPMutant. Employing virtual screening and MD simulation strategies, we found that ZINC000006242926 has a higher binding affinity for the N- and C-termini, leading to reduced N- and C-opening sizes, disruption of the continuous tunnel, and increased curvature of CETP. In conclusion, CETPAuthentic facilitates the formation of a continuous tunnel in the "neck" region, while CETPMutant does not exhibit such characteristics. The ligand ZINC000006242926 screened for binding to the N- and C-termini induces structural changes in the CETP unfavorable to lipid transport. This study sheds new light on the relationship between the structural and functional mechanisms of CETP. Furthermore, it provides novel ideas for the precise regulation of CETP functions.

The Effect of Transcription Factor MYB14 on Defense Mechanisms in Vitis quinquangularis-Pingyi

In the current study, we identified a transcription factor, MYB14, from Chinese wild grape, Vitis quinquangularis-Pingyi (V. quinquangularis-PY), which could enhance the main stilbene contents and expression of stilbene biosynthesis genes (StSy/RS) by overexpression of VqMYB14. The promoter of VqMYB14 (pVqMYB14) was shown to be induced as part of both basal immunity (also called pathogen-associated molecular pattern (PAMP)-triggered immunity, PTI) and effector-triggered immunity (ETI), triggered by the elicitors flg22 and harpin, respectively. This was demonstrated by expression of pVqMYB14 in Nicotiana benthamiana and Vitis. We identified sequence differences, notably an 11 bp segment in pVqMYB14 that is important for the PTI/ETI, and particularly for the harpin-induced ETI response. In addition, we showed that activation of the MYB14 promoter correlates with differences in the expression of MYB14 and stilbene pattern induced by flg22 and harpin. An experimental model of upstream signaling in V. quinquangularis-PY is presented, where early defense responses triggered by flg22 and harpin partially overlap, but where the timing and levels differ. This translates into a qualitative difference with respect to patterns of stilbene accumulation.