Low-background interference detection of glyphosate, glufosinate, and AMPA in foods using UPLC-MS/MS without derivatization.

The abuse of herbicides has emerged as a great threat to food security. Herein, a low-background interference detection method based on UPLC-MS was developed for the simultaneous determination of glufosinate, glyphosate, and its metabolite aminomethylphosphonic acid (AMPA) in foods. Initially, this study proposed a simple and rapid pretreatment method, utilizing water extraction and PRiME HLB purification to isolate glyphosate, glufosinate, and AMPA from food samples. After the optimization of pretreatment conditions, the processed samples are then analyzed directly by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) without pre-column derivatization. The method can effectively reduce interference from by-products of pre-column derivatization and background substrates of food sample, showing low matrix effects (ME) ranging from - 24.83 to 32.10%. Subsequently, the method has been validated by 13 kinds of food samples. The recoveries of the three herbicides in the food samples range from 84.2 to 115.6%. The limit of detection (LOD) is lower to 0.073 mg/kg, 0.017 mg/kg, and 0.037 mg/kg, respectively. Moreover, the method shows an excellent reproducibility with relative standard deviations (RSD) within 16.9%. Thus, the method can provide high trueness, reproducibility, sensitivity, and interference-free detection to ensure human health safety.

DaylilyNet: A Multi-Task Learning Method for Daylily Leaf Disease Detection.

Timely detection and management of daylily diseases are crucial to prevent yield reduction. However, detection models often struggle with handling the interference of complex backgrounds, leading to low accuracy, especially in detecting small targets. To address this problem, we propose DaylilyNet, an object detection algorithm that uses multi-task learning to optimize the detection process. By incorporating a semantic segmentation loss function, the model focuses its attention on diseased leaf regions, while a spatial global feature extractor enhances interactions between leaf and background areas. Additionally, a feature alignment module improves localization accuracy by mitigating feature misalignment. To investigate the impact of information loss on model detection performance, we created two datasets. One dataset, referred to as the 'sliding window dataset', was obtained by splitting the original-resolution images using a sliding window. The other dataset, known as the 'non-sliding window dataset', was obtained by downsampling the images. Experimental results in the 'sliding window dataset' and the 'non-sliding window dataset' demonstrate that DaylilyNet outperforms YOLOv5-L in mAP@0.5 by 5.2% and 4.0%, while reducing parameters and time cost. Compared to other models, our model maintains an advantage even in scenarios where there is missing information in the training dataset.

Vehicle Logo Recognition Using Spatial Structure Correlation and YOLO-T.

The vehicle logo contains the vehicle's identity information, so vehicle logo detection (VLD) technology has extremely important significance. Although the VLD field has been studied for many years, the detection task is still difficult due to the small size of the vehicle logo and the background interference problem. To solve these problems, this paper proposes a method of VLD based on the YOLO-T model and the correlation of the vehicle space structure. Aiming at the small size of the vehicle logo, we propose a vehicle logo detection network called YOLO-T. It integrates multiple receptive fields and establishes a multi-scale detection structure suitable for VLD tasks. In addition, we design an effective pre-training strategy to improve the detection accuracy of YOLO-T. Aiming at the background interference, we use the position correlation between the vehicle lights and the vehicle logo to extract the region of interest of the vehicle logo. This measure not only reduces the search area but also weakens the background interference. We have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results show that our proposed method achieves high detection accuracy and outperforms the existing vehicle logo detection methods.

Molecular Planarization of Raman Probes to Avoid Background Interference for High-Precision Intraoperative Imaging of Tumor Micrometastases and Lymph Nodes.

The intraoperative imaging applications of a large number of Raman probes are hampered by the overlap of their signals with the background Raman signals generated by biological tissues. Here, we describe a molecular planarization strategy for adjusting the Raman shift of these Raman probes to avoid interference. Using this strategy, we modify the backbone of thiophene polymer-poly(3-hexylthiophene) (P3HT), and obtain the adjacent thiophene units planarized polycyclopenta[2,1-b;3,4-b']dithiophene (PCPDT). Compared with P3HT whose signal is disturbed by the Raman signal of lipids in tissues, PCPDT exhibits a 60 cm-1 blueshift in its characteristic signal. Therefore, the PCPDT probe successfully avoids the signal of lipids, and achieves intraoperative imaging of lymph nodes and tumor micrometastasis as small as 0.30 × 0.36 mm. In summary, our study presents a concise molecular planarization strategy for regulating the signal shift of Raman probes, and brings a tunable thiophene polymer probe for high-precision intraoperative Raman imaging.

Suppression of Strong Background Interference on E-Nose Sensors in an Open Country Environment.

The feature extraction technique for an electronic nose (e-nose) applied in tobacco smell detection in an open country/outdoor environment with periodic background strong interference is studied in this paper. Principal component analysis (PCA), Independent component analysis (ICA), re-filtering and a priori knowledge are combined to separate and suppress background interference on the e-nose. By the coefficient of multiple correlation (CMC), it can be verified that a better separation of environmental temperature, humidity, and atmospheric pressure variation related background interference factors can be obtained with ICA. By re-filtering according to the on-site interference characteristics a composite smell curve was obtained which is more related to true smell information based on the tobacco curer's experience.

Interference of the gas chromatography- mass spectrometry instrumental background on the determination of trace cyclic volatile methylsiloxanes and exclusion of it by delayed injection.

Cyclic volatile methylsiloxanes (cVMS) have been widely found in various types of environmental media and attracted increasing attention as new pollutants. However, there is still a great challenge in the accurate quantification of trace cVMS, due to their volatility, and the high background originating from GC/MS accessories and surroundings. In this work, the main sources of the high background were investigated in detail for octamethylcyclotetrasiloxane (D4), decmethylcyclopentasiloxane (D5) and dodecmethylcyclohexosiloxane (D6). Several effective measures were employed to minimize these backgrounds, including the delayed injection method to minimize the interference from the injection septum. Then, a GC-MS method was developed for the accurate determination of D4, D5 and D6, with a linear range of 2 - 200 µg/L. The coefficient of determination was 0.9982-0.9986, the limit of detection (LOD) was 0.40-0.52 µg/L, and the quantitative range was 1.88-190 µg/L. Good reproducibility and recovery were obtained, indicating the reliability of the established analytical method.

A Near-Infrared Fluorogenic Probe for Rapid, Specific, and Ultrasensitive Detection of Sphingosine in Living Cells and In Vivo.

Sphingosine (Sph) plays important roles in various complex biological processes. Abnormalities in Sph metabolism can result in various diseases, including neurodegenerative disorders. However, due to the lack of rapid and accurate detection methods, understanding sph metabolic in related diseases is limited. Herein, a series of near-infrared fluorogenic probes DMS-X (X = 2F, F, Cl, Br, and I) are designed and synthesized. The fast oxazolidinone ring formation enables the DMS-2F to detect Sph selectively and ultrasensitively, and the detection limit reaches 9.33 ± 0.41 nm. Moreover, it is demonstrated that DMS-2F exhibited a dose- and time-dependent response to Sph and can detect sph in living cells. Importantly, for the first time, the changes in Sph levels induced by Aß42 oligomers and H2 O2 are assessed through a fluorescent imaging approach, and further validated the physiological processes by which Aß42 oligomers and reactive oxygen species (ROS)-induce changes in intracellular Sph levels. Additionally, the distribution of Sph in living zebrafish is successfully mapped by in vivo imaging of a zebrafish model. This work provides a simple and efficient method for probing Sph in living cells and in vivo, which will facilitate investigation into the metabolic process of Sph and the connection between Sph and disease pathologies.

On-line measurement of COD and nitrate in water against stochastic background interference based on ultraviolet-visible spectroscopy and physics-informed multi-task learning.

Traditional ultraviolet-visible spectroscopic quantitative analytical methods face challenges in simultaneous and long-term accurate measurement of chemical oxygen demand (COD) and nitrate due to spectral overlap and the interference from stochastic background caused by turbidity and chromaticity in water. Addressing these limitations, a compact dual optical path spectrum detection sensor is introduced, and a novel ultraviolet-visible spectroscopic quantitative analysis model based on physics-informed multi-task learning (PI-MTL) is designed. Incorporating a physics-informed block, the PI-MTL model integrates pre-existing physical knowledge for enhanced feature extraction specific to each task. A multi-task loss wrapper strategy is also employed, facilitating comprehensive loss evaluation and adaptation to stochastic backgrounds. This novel approach significantly outperforms conventional models in COD and nitrate measurement under stochastic background interference, achieving impressive prediction R2 values of 0.941 for COD and 0.9575 for nitrate, while reducing root mean squared error (RMSE) by 60.89 % for COD and 77.3 % for nitrate in comparison to the conventional chemometric model partial least squares regression (PLSR), and by 30.59 % and 65.96 %, respectively, in comparison to a benchmark convolutional neural network (CNN) model. The promising results emphasize its potential as a spectroscopic instrument designed for online multi-parameter water quality monitoring against stochastic background interference, enabling long-term accurate measurement of COD and nitrate levels.

Dual-Emissive Mn-Doped Lead Halide Perovskite Nanocrystals as Background-Suppressed Latent Fingerprint Detection Probes.

Diverse strategies have been developed to visualize latent fingerprints (LFPs) that are undetectable by the naked eye. Among them, fluorescence-based approaches have emerged as an attractive method for enabling high-resolution LFP imaging. However, the use of fluorescent probes for LFP detection remains challenging due to cumbersome processing, low selectivity, and high background interference. Here, we demonstrate highly efficient, sensitive, and background-free LFP detection with dual-color emission arising from manganese (Mn)-doped lead halide perovskite (CsPb(Cl1-yBry)3) nanocrystals (NCs). The resulting bright, fluorescent, solid-state nanopowder (NP) permits the visualization of LFP ridge structures and the resolution of level 1-3 LFP features. The dual-color emission of the Mn-doped perovskite NP provides a simple, robust, and effective route to overcome background interference, thereby increasing the resolution and sensitivity of the LFP detection. The combination of the high quantum efficiency and dual emission of Mn-doped perovskite NP offers great potential for forensic science.

Delayed gadolinium contrast-enhanced 3D-T1 SPACE STIR sequence can better visualize abnormal cranial nerves: a case report.

With the advancement of magnetic resonance imaging (MRI) techniques, general radiographic methods are no longer sufficient for accurately displaying the structure and pathway of cranial nerves. Various sequences, including 3-dimensional sampling perfection with application-optimized contrast using different flip angle evolution (SPACE), have been developed through MRI technology to effectively display the location and severity of damaged cranial nerves. This current case report describes a 36-year-old male patient with multiple cranial nerve injuries resulting from an invasive Mucor infection. While performing MRI scanning on this patient, a 1-h delayed enhanced MRI 3D-T1 SPACE short tau inversion recovery (STIR) sequence proved more effective in eliminating background interference and assessing neurological damage with greater clarity than conventional enhancement methods. This approach may prove beneficial in accurately evaluating the extent of cranial neuropathy, thus facilitating clinical applications.

A persistent luminescent nanobeacon for practical detection of lead ions via avoiding background interference.

Heavy metal ions are considered to be the most serious sources for water pollution. Accurate detection of metal ions is important for pollution control and ecological protection. Background interference is an inevitable obstacle in the fluorescent analysis of complex samples. Herein, a persistent luminescent nanobeacon is communicated to detect metal ions in real samples via avoiding background interference. The nanobeacon is constituted by persistent luminescence nanomaterials and metal-specific DNAzymes. As a proof of concept, Zn2GeO4: Mn persistent luminescence nanorods (PLNRs) was synthesized and functionalized with the 17E DNAzyme for lead ion (Pb2+) detection. As a result, in the luminescent manner, the nanobeacon could recognize Pb2+ selectively and detect it with high signal-to-background ratios (SBR) both in buffer and real samples, but the fluorescent SBR declined significantly when used in real samples. Thus, this persistent luminescent nanobeacon can achieve practical detection of metal ions via avoiding background interference. Compared to previous methods of improving signal-to-background ratio, this persistent luminescent nanobeacon is more accessible, and all DNAzyme-specific ions can be directly adapted.

Low background interference SERS aptasensor for highly sensitive multiplex mycotoxin detection based on polystyrene microspheres-mediated controlled release of Raman reporters.

Mycotoxin contamination is a serious global issue in food safety. The accurate detection of mycotoxins in complex samples, particularly via a portable detection system that realizes multi-analyte detection, remains a great challenge. Here, a polystyrene (PS)-mediated surface-enhanced Raman scattering (SERS) aptasensor was designed for ultrasensitive and simultaneous detection of multiple mycotoxins in food samples. First, the novel Raman tags were prepared using PS as nanocontainer to encapsulate abundant Raman reporters. The PS nanocontainer with Raman reporters was then functionalized by aptamer as an effective target-recognition platform. Second, the single-strand binding protein modified magnetic nanoparticles (MNPs@SSB) were used as a magnetic capture substrate. When the target mycotoxin was present and recognized by its aptamer, the combination of PS-Apt and mycotoxins can hinder the binding between PS-Apt and MNPs@SSB. Consequently, after the magnetic separation and treatment by THF solution, fewer biological-silent reporter probes were released from PS, causing the reduction of SERS signal as a function of target mycotoxins concentration. Due to the high loading capacity of PS and the excellent signal conversion and amplification of aptamer-assisted SERS assay, the proposed strategy is reliable and ultrasensitive for the determination of multiple mycotoxins. The limit of detection was as low as 0.159 fg L-1, 2.015 fg L-1, and 1.561 fg L-1 for ZEN, OTA and AFB1, respectively. This strategy not only raises a new idea for designing novel Raman tag, but also broadens the application of SERS for multiple detection of trace target.

Analysis of 58 poly-/perfluoroalkyl substances and their occurrence in surface water in a high-technology industrial park.

Poly/perfluoroalkyl substances (PFASs) have some water solubility so that they are more liable to enter surface water. A comprehensive analysis method was established to analyze 17 classes of 58 PFASs in surface water by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) after automated solid phase extraction (SPE). The method showed acceptable recoveries and precision with recoveries of 60%-130% and RSD less than 30% with a few exceptions, and method detection limits (MDLs) of 0.004 ng/L-2.0 ng/L. It was investigated the surface water around Zhangjiang High-Technology Park in Shanghai. The concentration of ∑PFASs ranged from 115 ng/L to 600 ng/L. The main pollutant was perfluoro-3-methylheptanoic acid (P3MHpA), which is isopropyl isomer of perfluorooctanoic acid (PFOA), accounting 41.6% of ∑PFASs. It was detected in all samples and its level was far higher than that of PFOA with the maximum of 432 ng/L determined surrounding integrated circuits company. Short-chain perfluorocarboxylic acids (PFCAs) and PFOA were also major constituents accounting for 39.9% of ∑PFASs. Perfluorosulfonic acid (PFSAs (C4, 6, 8)) were detected in all samples, while most concentrations were below 10 ng/L. In addition, 1H,1H,2H,2H-perfluorooctane sulfonate (6:2) (6:2 FTS), 2,3,3,3-tetrafluoro-2-(1,1,2,2,3,3,3-heptafluoropropoxy)-propanoic acid (HFPO-DA) and 9-chlorohexadecafluoro-3-oxanonane-sulfonate (9Cl-PF3ONS) were also detected in all samples at low level. Long chain PFCAs and PFSAs and other classes of PFASs were also detected with low levels and/or low frequencies. The level of PFASs shows an increasing trend in surface water in Shanghai. The whole risk is low for residents from water exposure, while it should cautious to the emission of P3MHpA from integrated circuits and its potential risk.

Covalent Organic Polymers for Rapid Fluorescence Imaging of Latent Fingerprints.

Rapid, simple and highly sensitive identification of latent fingerprints (LFPs) is an important issue related to national security and recognition of potential crimes. Here, we synthesize a series of covalent organic polymers (COPs) with colorful fluorescence (from blue to green, pale yellow, bright yellow, and red) and further investigate their performance for fluorescence imaging of LFPs. Results indicate that the COP materials can be used as fluorescence probes to rapidly visualize the precision substructure of LFPs within 5 s by simply spraying method, and tunable fluorescent color makes the COP probes have a high contrast and low interference for fluorescence imaging of LFPs on different substrates (including glass slides, paper, aluminum foil, plastic, ironware) in different backgrounds. We also further reveal the mechanism of COP probes for fluorescence imaging of LFPs. Importantly, the COP probes show high stability and could successfully achieve the fluorescence imaging for LFPs after aged for 45 days or washed by water. In short, this is the first report on the porous polymers for fluorescence imaging of LFPs and expected that it can be also applied to the fluorescence imaging of other fields.

A practical background correction method for an immediately repeated first-pass radionuclide angiography.

BACKGROUND: A satisfactory bolus injection is essential for a successful first-pass radionuclide angiography (FPRNA). Rescheduling the FPRNA study is usually needed due to high background interference caused by an unsatisfactory bolus injection. We developed a protocol to correct the pre-existing background activity subsequent to immediately repeating the study. METHODS: Seventy-four consecutive patients who had their bone scan and FPRNA scheduled on the same day were included for analysis. The initial 51 cases constituted the "validation-only" group. In the other 23 cases, the "validation plus clearance constants" group, a 5-min dynamic acquisition was performed during the 5-min equilibrium to obtain the background clearance curve and the clearance constants. For all included 74 cases ejection fraction (EF) analysis was proceeded using the images from the first injection, second injection, and second injection with the corrected background to yield EF1, EF2, and EF2', respectively. EF2 and EF2' were then compared to the ejection fraction without background interference, the EF1. RESULTS: For the LV, the mean difference between the EF1 and the uncorrected EF2 (|LVEF1-LVEF2| in mean ± SD) was 3.1 ± 2.0% and the difference between the EF1 and the corrected EF2' (|LVEF1-LVEF2'|) was 1.6 ± 2.1%, while the mean differences for RV are 2.2 ± 1.9% and 1.8 ± 1.8%, respectively. A significant difference (p < 0.05) was observed between the uncorrected and the corrected data for both the LV and RV. CONCLUSION: In FPRNA, when a bolus injection is immediately readministered, both LVEF and RVEF can be underestimated. With our correction method, the results are superior to those without correction.

Electrospray ionization in concentrated acetonitrile vapor improves the performance of mass spectrometry for proteomic analyses.

Suppressing the background interferences and enhancing the analytes signals are long-term goals in high performance electrospray ionization mass spectrometry (ESI-MS) analyses. We observed that performing electrospray in the presence of a concentrated acetonitrile atmosphere suppresses background interferences and enhances peptide signals. An enclosed nanoESI source was utilized to provide a stable atmosphere of concentrated acetonitrile vapor for high performance ESI-MS analyses. The median MS signal intensity increased by 5 times for a set of 23 BSA tryptic peptides in direct ESI-MS analysis. Further, the number of reproducibly and precisely quantified peptides could be improved 67% in six replicate label-free quantitative proteome analyses by this strategy.