Yolov8n-FADS: A Study for Enhancing Miners' Helmet Detection Accuracy in Complex Underground Environments.

A new algorithm, Yolov8n-FADS, has been proposed with the aim of improving the accuracy of miners' helmet detection algorithms in complex underground environments. By replacing the head part with Attentional Sequence Fusion (ASF) and introducing the P2 detection layer, the ASF-P2 structure is able to comprehensively extract the global and local feature information of the image, and the improvement in the backbone part is able to capture the spatially sparsely distributed features more efficiently, which improves the model's ability to perceive complex patterns. The improved detection head, SEAMHead by the SEAM module, can handle occlusion more effectively. The Focal Loss module can improve the model's ability to detect rare target categories by adjusting the weights of positive and negative samples. This study shows that compared with the original model, the improved model has 29% memory compression, a 36.7% reduction in the amount of parameters, and a 4.9% improvement in the detection accuracy, which can effectively improve the detection accuracy of underground helmet wearers, reduce the workload of underground video surveillance personnel, and improve the monitoring efficiency.

Deciphering and investigating fragment mechanism of quinolones using multi-collision energy mass spectrometry and computational chemistry strategy.

RATIONALE: Quinolones show characteristic fragments in mass spectrometry (MS) analysis due to their common core structures, and energy-dependent differences among these fragments are generated through the same fragmentation pathway of different molecules. Computational chemistry, which provides quantitative results of molecule parameters, is helpful for investigating the mechanisms of chemistry. METHODS: MS/MS spectra of five quinolones, namely norfloxacin (NOR), enoxacin (ENO), enrofloxacin (ENR), gatifloxacin (GAT), and lomefloxacin (LOM), were acquired for deciphering fragmentation pathways under multi-collision energy (CE). Computational methods were used for excluding little possibility pathways from the point of view of energy and stable conformations, whereas optimized collision energy (OCE) and maximum relative intensity (MRI) of major competitive fragments were investigated and confirmed using computational results. RESULTS: Fragmentation results of NOR, ENO, ENR, and GAT were deciphered using experimental and computational data, of which fragmentation regularities were summarized. Fragmentation pathways of LOM were deciphered under the guidance of foregoing regularities. Meanwhile, the whole process was validated by comparing OCE and MRI and computational energy results, which showed good agreement. CONCLUSIONS: A strategy for explaining quinolone fragmentation results of multi-CE values and deciphering fragment mechanism using computational methods was developed. Relevant data and strategy may provide ideas for how to design and decipher new drug molecules with similar structures.

A pre-column derivatization high-performance liquid chromatography method for simultaneous determination of short-chain and medium-chain fatty acids in a fecal sample.

Short-chain and medium-chain fatty acids have plentiful biological functions, which play a crucial role in the diagnosis and therapy of many diseases. Herein, a new method for simultaneous quantifying 17 short-chain and medium-chain fatty acids with high-performance liquid chromatography coupled with an ultraviolet detector was developed and the pre-column derivatization by indole-3-acetic acid hydrazide was performed to improve the separation and detection. The conditions of the derivatization reaction were systematically investigated. Subsequently, the method was validated and the results showed a satisfactory linearity (linear regression coefficients > 0.9969), the limit of detection (4.0×10-3 -1.9×10-2 µmol/L), precision (0.9%-7.3% for intra-day and 2.0%-9.8% for inter-day), recovery (90.0%-109.1% with relative standard deviation <7.7%) and stability (0.1%-3.3% for standard solution and 0.2%-3.9% for fecal sample). Finally, the established method was successfully applied to quantify short-chain and medium-chain fatty acids in the feces of healthy control and diabetic rats. Eleven kinds of short-chain and medium-chain fatty acids were detected and six of them showed a significant difference between the control group and the model group.

Simultaneous quantification of fatty acids in serum with dual derivatization by liquid chromatography-tandem mass spectrometry.

Fatty acids have multitudinous biological functions and play a crucial role in many biological processes, but due to poor ionization efficiency and lack of appropriate internal standards, the comprehensive quantification of fatty acids by liquid chromatography-tandem mass spectrometry is still challenging. In this study, a new, accurate, and reliable method for quantifying 30 fatty acids in serum using dual derivatization was proposed. Indole-3-acetic acid hydrazide derivants of fatty acids were used as the internal standard and indole-3-carboxylic acid hydrazide derivants of them were used to quantify. The derivatization conditions were systematically optimized and the method validation results showed good linearity with R2  > 0.9942, low detection limit (0.03-0.6 nM), precision (1.6%-9.8% for intra-day and 4.6%-14.1% for inter-day), recovery (88.2%-107.2% with relative standard deviation < 10.5%), matrix effect (88.3%-105.2% with the relative standard deviation < 9.9%) and stability (3.4%-13.8% for fatty acids derivants in 24 h at 4°C and 4.2%-13.8% for three freeze-thaw cycles). Finally, this method was successfully applied to quantify fatty acids in serum samples of Alzheimer's patients. In contrast to the healthy control group, nine fatty acids showed a significant increase in the Alzheimer's disease group.

A colorimetric aptasensor based on a hemin/EpCAM aptamer DNAzyme for sensitive exosome detection.

Exosomes are considered as potential biomarkers that can reflect information from their parent cell-associated cancer microenvironment. Recently, aptasensors have been widely used for cancer and tumor exosome detection. Aptamers related to exosome surface proteins are usually used to introduce a sequence; the aptamer is used for exosome recognition, and the introduced sequence is used to form G-quadruplexes and for signal amplification. In this paper, we found that the EpCAM aptamer is rich in guanine and unimolecular G-quadruplex with a two-layer G-tetrad under acidic conditions, and we investigated its topology, thermal stability and dissociation constant with hemin. Based on this, our proposed colorimetric aptamer sensor combines the unmodified EpCAM aptamer with hemin to construct a hemin/G-quadruplex DNAzyme and catalyze the TMB-H2O2 system to generate a strong colorimetric signal. Therefore, colorimetric signal changes were negatively correlated with the exosome concentration. The linear range of the 1 h assay was 106-108 particles per mL, and the detection limit was 3.94 × 105 particles per mL. In addition, this method can detect exosomes in complex fetal bovine serum samples with good specificity and high sensitivity toward exosomes from breast, liver, and lung cancers with abnormal EpCAM protein expression.

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm.

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.

Research on improved gangue target detection algorithm based on Yolov8s.

An improved algorithm based on Yolov8s is proposed to address the slower speed, higher number of parameters, and larger computational cost of deep learning in coal gangue target detection. A lightweight network, Fasternet, is used as the backbone to increase the speed of object detection and reduce the model complexity. By replacing Slimneck with the C2F part in the HEAD module, the aim is to reduce model complexity and improve detection accuracy. The detection accuracy is effectively improved by replacing the Detect layer with Detect-DyHead. The introduction of DIoU loss function instead of CIoU loss function and the combination of BAM block attention mechanism makes the model pay more attention to critical features, which further improves the detection performance. The results show that the improved model compresses the storage size of the model by 28%, reduces the number of parameters by 28.8%, reduces the computational effort by 34.8%, and improves the detection accuracy by 2.5% compared to the original model. The Yolov8s-change model provides a fast, real-time and efficient detection solution for gangue sorting. This provides a strong support for the intelligent sorting of coal gangue.

Research on lightweight algorithm for gangue detection based on improved Yolov5.

In order to solve the problems of slow detection speed, large number of parameters and large computational volume of deep learning based gangue target detection method, we propose an improved algorithm for gangue target detection based on Yolov5s. First, the lightweight network EfficientVIT is used as the backbone network to increase the target detection speed. Second, C3_Faster replaces the C3 part in the HEAD module, which reduces the model complexity. once again, the 20 × 20 feature map branch in the Neck region is deleted, which reduces the model complexity; thirdly, the CIOU loss function is replaced by the Mpdiou loss function. The introduction of the SE attention mechanism makes the model pay more attention to critical features to improve detection performance. Experimental results show that the improved model size of the coal gang detection algorithm reduces the compression by 77.8%, the number of parameters by 78.3% the computational cost is reduced by 77.8% and the number of frames is reduced by 30.6%, which can be used as a reference for intelligent coal gangue classification.

Deciphering mechanism of Zhishi-Xiebai-Guizhi Decoction against hypoxia/reoxygenation injury in cardiomyocytes by cell metabolomics: Regulation of oxidative stress and energy acquisition.

Myocardial ischemia/reperfusion (MI/R) injury is a life-threatening syndrome with high morbidity and mortality. Zhishi-Xiebai-Guizhi Decoction (ZSXBGZD) is a classic traditional Chinese medicine formula, used to treat cardiovascular diseases for centuries. However, its underlying medicinal mechanism has not been clearly elucidated, which hinders its widespread application. Here, the curative effects and therapeutic mechanism of ZSXBGZD against MI/R were addressed based on an integration of pharmaceutical evaluation and cellular metabolomics. First, a hypoxia/reoxygenation (H/R) model in H9c2 cells was employed to resemble MI/R and multiple pharmacological indicators were performed to assess the efficacy of ZSXBGZD. The results showed that ZSXBGZD possessed exceptional ability in attenuating cardiomyocyte injury, concerning oxidative stress, mitochondrial dysfunction, energy acquisition and cell apoptosis. Furthermore, a cell metabolomics approach based on HILIC and UPLC-Q-TOF-MS coupled with multivariate analysis was conducted to explore the metabolic regulation of ZSXBGZD. 38 differential polar metabolites related to H/R were uncovered, and 34 of them were reversed to normal state after the treatment of ZSXBGZD, revealing the perturbations of energy metabolism and amino acid metabolism. Moreover, formula decomposition justified the combination of single herbs to form ZSXBZGD and confirmed the pivotal status of Allii Macrostemonis Bulbus and Trichosanthis Fructus.

Simultaneous quantification of eleven short-chain fatty acids by derivatization and solid phase microextraction - Gas chromatography tandem mass spectrometry.

As metabolites of the gut microbiome, short-chain fatty acids (SCFAs) played an important role in the diagnosis of the metabolic diseases. Because of the high polarity, high volatility and complex matrix of biological samples, the highly sensitive, selective and accurate method to determine SCFAs remains a major challenge. Herein, a new method for simultaneous quantification of eleven SCFAs by derivatization combined with solid phase microextraction (SPME) and gas chromatography tandem mass spectrometry (GC-MS/MS) was developed. Isobutyl chloroformate coupled with isobutanol was used as the reaction reagent to derivatize SCFAs. The method validation data showed a satisfactory linearity with the linear regression coefficients (R) ranging from 0.9964 to 0.9996. The limit of detection (LOD) of all SCFAs ranges from 0.01 ng·mL-1 to 0.72 ng·mL-1 and the limit of quantification (LOQ) ranges from 0.04 ng·mL-1 to 2.41 ng·mL-1. The intra-day and inter-day precision (RSDs) ranged from 0.65% to 8.92% and 1.62% to 15.61%, respectively. The recovery ranged from 88.10% to 108.71%. Finally, the developed method was successfully used to determine SCFAs in mice fecal sample, and ten of the SCFAs were found in feces of mice, including formic acid.

Application of ß-Cyclodextrin metal-organic framework/titanium dioxide hybrid nanocomposite as dispersive solid-phase extraction adsorbent to organochlorine pesticide residues in honey samples.

A simple and efficient dispersive solid-phase extraction (D-SPE) method combined with gas chromatography tandem mass spectrometry (GC-MS/MS) was developed to determine organochlorine pesticides (OCP) in honey. Firstly, a type of hybrid nanocomposite (CD-MOF/TiO2) was prepared by grafting a metal-organic framework material synthesized with cyclodextrin as an organic ligand onto titanium dioxide. Then, the CD-MOF/TiO2 was used as a D-SPE adsorbent to extract the OCP, and the effects of the amount of adsorbent, ultrasonic time, vortex time, pH, and salinity on the extraction were investigated using Plackett-Burman design and Box-Behnken Design. Under the optimized adsorption and desorption conditions, an analysis method that combined D-SPE with GC-MS/MS was established. The linear ranges of 14 OCP are 1-500 µg kg-1 and the correlation coefficients are between 0.9991 and 1.000. The limits of detection and quantification vary from 0.01 to 0.04 µg kg-1 and 0.04 to 0.12 µg kg-1, respectively. The intra-day and inter-day precision of this method are suitable (RSDs% less than 11.3%). The established CD-MOF/TiO2 / D-SPE method was used for the extraction of OCP in honey samples with recovery in the range of 76.4 to 114.3%. The results demonstrate that the CD-MOF/TiO2 has a good selective enrichment ability for OCP and is suitable for the D-SPE pretreat of honey sample analysis.

An investigation of in-ear sensing for motor task classification.

Objective.Our study aims to investigate the feasibility of in-ear sensing for human-computer interface.Approach.We first measured the agreement between in-ear biopotential and scalp-electroencephalogram (EEG) signals by channel correlation and power spectral density analysis. Then we applied EEG compact network (EEGNet) for the classification of a two-class motor task using in-ear electrophysiological signals.Main results.The best performance using in-ear biopotential with global reference reached an average accuracy of 70.22% (cf 92.61% accuracy using scalp-EEG signals), but the performance in-ear biopotential with near-ear reference was poor.Significance.Our results suggest in-ear sensing would be a viable human-computer interface for movement prediction, but careful consideration should be given to the position of the reference electrode.