Phosphocholine-induced energy source shift alleviates mitochondrial dysfunction in lung cells caused by geospecific PM2.5 components.

Fine particulate matter (PM2.5) is globally recognized for its adverse implications on human health. Yet, remain limited the individual contribution of particular PM2.5 components to its toxicity, especially considering regional disparities. Moreover, prevention solutions for PM2.5-associated health effects are scarce. In the present study, we comprehensively characterized and compared the primary PM2.5 constituents and their altered metabolites from two locations: Taiyuan and Guangzhou. Analysis of year-long PM2.5 samples revealed 84 major components, encompassing organic carbon, elemental carbon, ions, metals, and organic chemicals. PM2.5 from Taiyuan exhibited higher contamination, associated health risks, dithiothreitol activity, and cytotoxicities than Guangzhou's counterpart. Applying metabolomics, BEAS-2B lung cells exposed to PM2.5 from both cities were screened for significant alterations. A correlation analysis revealed the metabolites altered by PM2.5 and the critical toxic PM2.5 components in both regions. Among the PM2.5-down-regulated metabolites, phosphocholine emerged as a promising intervention for PM2.5 cytotoxicities. Its supplementation effectively attenuated PM2.5-induced energy metabolism disorder and cell death via activating fatty acid oxidation and inhibiting Phospho1 expression. The highlighted toxic chemicals displayed combined toxicities, potentially counteracted by phosphocholine. Our study offered a promising functional metabolite to alleviate PM2.5-induced cellular disorder and provided insights into the geo-based variability in toxic PM2.5 components.

TC-SfM: Robust Track-Community-Based Structure-From-Motion.

Structure-from-Motion (SfM) aims to recover 3D scene structures and camera poses based on the correspondences between input images, and thus the ambiguity caused by duplicate structures (i.e., different structures with strong visual resemblance) always results in incorrect camera poses and 3D structures. To deal with the ambiguity, most existing studies resort to additional constraint information or implicit inference by analyzing two-view geometries or feature points. In this paper, we propose to exploit high-level information in the scene, i.e., the spatial contextual information of local regions, to guide the reconstruction. Specifically, a novel structure is proposed, namely, track-community, in which each community consists of a group of tracks and represents a local segment in the scene. A community detection algorithm is performed on the track-graph to partition the scene into segments. Then, the potential ambiguous segments are detected by analyzing the neighborhood of tracks and corrected by checking the pose consistency. Finally, we perform partial reconstruction on each segment and align them with a novel bidirectional consistency cost function which considers both 3D-3D correspondences and pairwise relative camera poses. Experimental results demonstrate that our approach can robustly alleviate reconstruction failure resulting from visually indistinguishable structures and accurately merge the partial reconstructions.

Inline phase transition trapping-selective supercritical fluid extraction-supercritical fluid chromatography: A green and efficient integrated method for determining prohibited substances in cosmetics.

BACKGROUND: Developing an environmentally friendly and efficient integrated analytical approach is a cutting-edge topic in current analytical science. Due to the unique properties of supercritical carbon dioxide (sc-CO2), online supercritical fluid extraction-supercritical fluid chromatography (SFE-SFC) is developing rapidly and has been widely applied in many fields. However, it still faces several challenges such as peak broadening and matrix interference. In order to solve the problems, we developed an inline phase transition trapping-selective supercritical fluid extraction-supercritical fluid chromatography (PTT-SSFE-SFC)-tandem mass spectrometry (MS/MS) method in this study. RESULTS: This method integrated extraction, purification, separation, and detection, which was applied to determine 114 prohibited substances in cosmetics within 33 min, covering ten categories. The PTT strategy trapped the extracts on the head of the column by transforming CO2 from a supercritical state to a gaseous state, preventing peak spreading and improving sensitivity. Several adsorbents were tested when analyzing aqueous samples to reduce matrix interference and absorb water. Compared with conventional online SFE-SFC, this method improved the matrix effects of 93 and 87 target substances in the toner and mask matrix, respectively. Because the integrated method reduced sample loss, it achieved high sensitivity with LODs ranging from 0.00104 µg L-1 to 3.09 µg L-1. Furthermore, compared with other reported green methods, the inline method showed advantages in automation, efficiency, sample amount, and waste volume. SIGNIFICANCE AND NOVELTY: With the introduction of the PTT strategy and the adsorbent, the system obtained good peak shapes, high sensitivity, low matrix effect, and good recovery. Based on the results, inline PTT-SSFE-SFC-MS/MS as a green and efficient integrated method has great potential for analyzing low abundance and multiple categories of targets in complex samples.

Online Pressure Change Focusing-Supercritical Fluid Selective Extraction Chromatography for Analyzing Chiral Drugs in Microliter-Scale Plasma Samples.

The online coupling technique of sample preparation with chromatography is a frontier topic in analytical chemistry since it minimizes errors caused by sample loss, shortens analysis time, and reduces solvent consumption. An online pressure change focusing-supercritical fluid selective extraction chromatography (PCF-SFSEC) technique was developed in this study, realizing extraction, purification, separation, and detection in a single run with only microliter-scale samples. The pressure change focusing strategy achieved column-head stacking by decreasing the dissolving capacity of the supercritical fluid, enabling the large volume introduction of extractants into supercritical fluid chromatography without causing peak broadening or distortion. All the extracts could be directly loaded into the chromatography system without split flow. Based on the supercritical fluid selective extraction (SFSE) strategy, the sorbents removed interferences and water from samples, effectively alleviating matrix effects and realizing the direct aqueous sample analysis. The efficiency of online PCF-SFSEC was demonstrated by the enantioselective analysis of 22 chiral drugs in rat plasma, covering eight categories with different pharmacological effects. The entire analysis took 25 min, consuming only 5 µL samples. All analytes in PCF-SFSEC obtained sharp and symmetrical peaks with resolutions higher than 1.0, and 86% had resolutions higher than 1.5. Limits of quantification (LOQs) ranged from 0.0600 to 32.1 µg/L. Recoveries were in the range of 75.8-117.2%. In addition, the developed approach obtained more satisfactory repeatability and significantly reduced matrix effects than conventional methods. The newly established online PCF-SFSEC technique is believed to be a green and powerful tool for the chiral analysis of complex samples.

Simultaneous Analysis of the Metabolome and Lipidome Using Polarity Partition Two-Dimensional Liquid Chromatography-Mass Spectrometry.

Comprehensive metabolic profiling is a considerable challenge for systems biology since the metabolites in biological samples have significant polarity differences. A heart-cutting two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method-based polarity partition was established to analyze both the metabolome and lipidome in a single run. Based on the polarity partition strategy, metabolites with high polarity were retained and separated by one-dimensional hydrophilic chromatography, while low- and medium-polarity lipids were collected into a sample loop and injected into two-dimensional reversed-phase chromatography for separation. A simple online dilution strategy realized the online coupling of the 2D-LC-MS, which effectively solved band broadening and peak distortion caused by solvent incompatibility. Moreover, a dual gradient elution procedure was introduced to further broaden the coverage of low-polarity lipids. The metabolites' log P values, which this 2D-LC-MS method could analyze, ranged from -8.79 to 26.86. The feasibility of the 2D-LC-MS system was demonstrated by simultaneous analysis of the metabolome and lipidome in rat plasma related to depression. A total of 319 metabolites were determined within 40 min, including organic acids, nucleosides, carbohydrate derivatives, amino acids, lipids, and other organic compounds. Finally, 44 depression-related differential metabolites were screened. Compared with conventional LC-MS-based methods, the 2D-LC method covered over 99% of features obtained by two conventional methods. In addition, the selectivity and resolution of the hydrophilic metabolites were improved, and the matrix effects of the hydrophobic metabolites were reduced in the developed method. The results indicated that the established 2D-LC system is a powerful tool for comprehensive metabolomics studies.

Scribble-Based 3D Shape Segmentation via Weakly-Supervised Learning.

Shape segmentation is a fundamental problem in shape analysis. Previous research shows that prior knowledge helps to improve the segmentation accuracy and quality. However, completely labeling each 3D shape in a large training data set requires a heavy manual workload. In this paper, we propose a novel weakly-supervised algorithm for segmenting 3D shapes using deep learning. Our method jointly propagates information from scribbles to unlabeled faces and learns deep neural network parameters. Therefore, it does not rely on completely labeled training shapes and only needs a really simple and convenient scribble-based partially labeling process, instead of the extremely time-consuming and tedious fully labeling processes. Various experimental results demonstrate the proposed method's superior segmentation performance over the previous unsupervised approaches and comparable segmentation performance to the state-of-the-art fully supervised methods.