Eliminate Background Interference Research Articles

Recent Advances in Wash-Free Detection Methods of Extracellular Vesicles: A Mini Review.

Extracellular vesicles (EVs) are emerging biomarkers in liquid biopsy that have gained increasing attention in disease diagnosis and prognosis monitoring. Most reported detection methods require the isolation of EVs from complex body liquids, often involving multiple washing steps to remove excess reagents and eliminate background interference. Nonetheless, these methods not only cause the loss of EVs but also result in poor repeatability and prolonged detection duration. The focus on wash-free detection methods is increasing due to the specific ability to avoid the removal of surplus reagents and, in some cases, even the isolation and purification of EVs. Viewing from different methodological perspectives, this review summarizes the recent advances in wash-free detection of EVs, containing aggregation induction, proximity sensing, allosteric probes, phase separation, Roman spectroscopy, field-effect transistor and microcantilever. The pros and cons of each detection strategy are impartially evaluated and this review concludes the prospects for future developments in this field.

Covalent organic framework-based ratiometric electrochemical sensing platform for ultrasensitive determination of amyloid-β 42 oligomer

Accurate and sensitive detection of amyloid-β 42 oligomer (Aβ42O) is of great significance for early diagnosis of Alzheimer's disease (AD). Herein, a signal on-off ratiometric electrochemical immunosensor was developed for highly selective and quantitative determination of Aβ42O by using novel covalent organic frameworks (COFs) composites as the sensing platform. This immunosensor produced two independent electrochemical signals from the [Fe(CN)6]3−/4− and methylene blue (MB) probes at different potentials based on the electrocatalytic activity of gold nanoparticle−functionalized porphyrinyl COFs nanocomposites toward [Fe(CN)6]3−/4− and the signal probe of MB encapsulated in the aptamer-modified alkynyl COFs. Because the two signals of [Fe(CN)6]3−/4− and MB changed in opposite directions, a signal on−off mode was generated which can correct the results by introducing a reference signal and effectively eliminate background interference. Under optimal experimental conditions, the current ratio (IMB/I[Fe(CN)6]3−/4−) was well linearly related to the logarithmic value of Aβ42O concentrations in the range of 10 pM to 1 μM, and the detection limit was 5.1 pM (S/N = 3). Additionally, the immunosensor exhibited satisfactory performance in case of real cerebrospinal fluid samples. The designed ratiometric electrochemical immunosensor provides a valuable route for early diagnosis of AD and our results also pave the way for designing of sensing platforms using COF-based nanomaterials and extending their functions and applications to bioanalysis.

Improved method for drone sound event detection system aiming at the impact of background noise and angle deviation

Drones pose a hidden threat to public safety, and effective and accurate detection technology for the drone that exist in the environment is imminent, in which how to weaken the influence of target sound source localization deviation and strong background interference noise on the detection task is the key to improve the accuracy of drone sound event detection. In this paper, a method has been proposed to improve the accuracy of drone acoustic event detection, named as the linear shrinkage-subspace projection-power spectral density filter method (LSP). This method mainly including covariance matrix reconstruction, steering vector recalibration, and filter coefficient redesign method. Firstly, based on Minimum Variance Distortionless Response, the linear shrinkage method is used to suppress the interference and noise components in the signal plus interference covariance matrix, and the sample covariance matrix is reconstructed to eliminate background interference noise. Then, the correlation between the steering vector and the eigenvector is used to eliminate the angle correlation term, and the subspace projection method is combined to recalibrate the steering vector, so as to improve the ability of the beamforming method to resist the angle deviation and realize the correction of the target source positioning deviation. Next, a redesign method for wiener filter coefficients based on the estimated power spectral density is used to further weaken background interference noise. In order to verify the accuracy of the proposed method, a complete drone sound event detection system is constructed by combining the deep learning drone sound event detection classifier, and the evaluation is carried out according to different angular deviations and interference sound distances. In addition, a new evaluation criterion is proposed, named as the Machine-Human Extreme Hearing Distance Rate (MHDR), which analogizes the system's detection ability with the ear's auditory detection ability. The research results of this article indicate that the detection accuracy of the detection system shows satisfactory accuracy when the proposed method is applied to circular microphone array, that improved by 15.12 % compared to existing methods. The proposed method improves the detection accuracy of the drone acoustic event detection task under the influence of sound source position deviation and strong background speech interference, and provides a reference for the development of anti-drone technology.

Classification and quantification of adulterants in Alaska pollock based on fourier transform near-infrared spectroscopy with chemometrics

Alaska pollock is one of the most economically valuable and consumed cod species, but the adulterants in the market affect its quality and value. This study used a combination of FT-NIR spectroscopy and chemometrics to construct a qualitative and quantitative analysis model. Before the establishment of detection models, different pre-processing techniques were used to eliminate background interference. Principal component analysis (PCA) and analysis of variance (ANOVA) showed the feasibility of spectral methods for the detection of Alaska pollock adulteration. The modeling results show that the RF classification model had the most suitable prediction results (accuracy: 92.18%, precision: 98.04%, recall: 92.59%, and F1-score: 95.29%), and SVR was the optimal prediction model for quantitative analysis, with an RC2 of 0.941, an RMSEC of 0.060, an RP2 of 0.946, and an RMSEP of 0.062. These results indicate that, based on the developed detection model, adulterants in Alaska pollock could be rapidly detected and accurately quantified. In addition, it provides a reliable basis for rapid and non-destructive detection of and has great potential for determining other seafood species.

Fused-Ring Pyrrole-Based Near-Infrared Emissive Organic RTP Material for Persistent Afterglow Bioimaging.

Organic near-infrared room temperature phosphorescence (RTP) materials offer remarkable advantages in bioimaging due to their characteristic time scales and background noise elimination. However, developing near-infrared RTP materials for deep tissue imaging still faces challenges since the small band gap may increase the non-radiative decay, resulting in weak emission and short phosphorescence lifetime. In this study, fused-ring pyrrole-based structures were employed as the guest molecules for the construction of long wavelength emissive RTP materials. Compared to the decrease of the singlet energy level, the triplet energy level showed a more effectively decrease with the increase of the conjugation of the substituent groups. Moreover, the sufficient conjugation of fused ring structures in the guest molecule suppresses the non-radiative decay of triplet excitons. Therefore, a near-infrared RTP material (764 nm) was achieved for deep penetration bioimaging. Tumor cell membrane is used to coat RTP nanoparticles (NPs) to avoid decreasing the RTP performance compared to traditional coating by amphiphilic surfactants. RTP NPs with tumor-targeting properties show favorable phosphorescent properties, superior stability, and excellent biocompatibility. These NPs are applied for time-resolved luminescence imaging to eliminate background interference with excellent tissue penetration. This study provides a practical solution to prepare long-wavelength and long-lifetime organic RTP materials and their applications in bioimaging.

Fused‐Ring Pyrrole‐Based Near‐Infrared Emissive Organic RTP Material for Persistent Afterglow Bioimaging

AbstractOrganic near‐infrared room temperature phosphorescence (RTP) materials offer remarkable advantages in bioimaging due to their characteristic time scales and background noise elimination. However, developing near‐infrared RTP materials for deep tissue imaging still faces challenges since the small band gap may increase the non‐radiative decay, resulting in weak emission and short phosphorescence lifetime. In this study, fused‐ring pyrrole‐based structures were employed as the guest molecules for the construction of long wavelength emissive RTP materials. Compared to the decrease of the singlet energy level, the triplet energy level showed a more effectively decrease with the increase of the conjugation of the substituent groups. Moreover, the sufficient conjugation of fused ring structures in the guest molecule suppresses the non‐radiative decay of triplet excitons. Therefore, a near‐infrared RTP material (764 nm) was achieved for deep penetration bioimaging. Tumor cell membrane is used to coat RTP nanoparticles (NPs) to avoid decreasing the RTP performance compared to traditional coating by amphiphilic surfactants. RTP NPs with tumor‐targeting properties show favorable phosphorescent properties, superior stability, and excellent biocompatibility. These NPs are applied for time‐resolved luminescence imaging to eliminate background interference with excellent tissue penetration. This study provides a practical solution to prepare long‐wavelength and long‐lifetime organic RTP materials and their applications in bioimaging.

Reading recognition of pointer meters based on an improved UNet++ network

Pointer meters are widely used in modern industries, such as petrochemical applications, substations, and nuclear power plants. To overcome the reading errors and inaccurate measurements due to uneven or fluctuating illumination in practical applications, this paper proposes an improved UNet++ network for recognizing pointer meter readings. First, the scale invariant feature transform feature-matching algorithm is used to adjust the captured tilted meter images to a symmetrical and upright shape. Then, the UNet++ network is used to segment the scale and pointer regions in the dashboard to eliminate background interference. Furthermore, part of the convolution in the UNet++ network is replaced with dilated convolution with different expansion rates to expand the perceptual field during network training. In the UNet++ network jump connection, the attention mechanism module is also introduced in the path to enhance the region’s features to be segmented and suppress the parts of the non-segmented area. A hybrid loss function is used for the network model training to prevent the imbalance of the segmented region share. Finally, the distance method is used to read the gauge representation. Experiments were conducted to compare the performance of the proposed method with that of the original UNet++ network in terms of feasibility and precision. The experimental results showed that the recognition reading accuracy was significantly improved by the enhanced network, with the accuracy, sensitivity, and specificity reaching 98.65%, 84.33%, and 99.38%, respectively. Furthermore, when using the improved UNet++ network for numerical reading, the average relative error was only 0.122%, indicating its robustness in a natural environment.

Recognition of pear leaf disease under complex background based on DBPNet and modified mobilenetV2

AbstractGiven the challenge of pear leaf disease recognition caused by uneven illumination, overlapping leaves, and other green plants in the background, a two‐stage strategy‐based framework for pear leaf segmentation and disease classification is proposed. Initially, a double branch polymerization net fusing features of the low‐level feature branch and semantic branch is constructed to extract the target diseased pear leaf and eliminate background interference. Then an improved lightweight neural network (Inverted‐Inception Efficient‐Excitation‐and‐Filtering‐Bottleneck MobileNet‐v2, I2EMv2) is used to capture multi‐scale lesion information and ainhibit invalid feature channels and filter redundant features for the final classification. The experimental results show that the proposed framework can accurately extract the pear leaf region with complete boundary from the complex background, maximize the retention of lesion information, and achieve high‐precision pear leaf disease identification. The mean absolute error, F‐measure, and intersection over union of leaf segmentation are 0.027, 0.947, and 0.880, respectively, and the average recognition accuracy of leaf disease is 92.05%. Compared with others, the method proposed in this paper has superior performance on segmentation and classification, which provides a reference for pear leaf disease classification under complex background.

Multi‐emitting fluorescent system–assisted lab‐in‐a‐syringe device for on‐site and background‐free detection of 2,4‐dichlorophenoxyacetic acid

AbstractHerbicide residuals have posed severe threat to food safety and public health; thus, the development of smart, portable, efficient, and accurate on‐site detection system is in urgent demand. Here, a multi‐emitting fluorescent system‐assisted lab‐in‐a‐syringe (LIS) device was developed for the on‐site and background‐free determination of 2,4‐dichlorophenoxyacetic acid (2,4‐D). 2,4‐D‐responsive cascade reactions were constructed: 2,4‐D could specifically suppress alkaline phosphatase (ALP) activity and restrain the generation of ascorbic acid (AA), MnO2 nanosheets would oxidize AA into dehydroascorbic acid that subsequently reacts with o‐phenylenediamine (OPD) to generate blue‐emission product (λem, 435 nm), whereas remained MnO2 nanosheets and OPD further produced yellow‐emission product (λem, 570 nm). Red‐emission carbon dots (RCDs, λem, 680 nm), synthesized via the solvothermal treatment of cilantro, were introduced to construct a multi‐emitting fluorescent system. Then, four paper pads, respectively, dropped with ALP, sodium l‐ascorbyl‐2‐phosphate, MnO2 nanosheets + RCDs, and OPD were held into reusable plastic filters and assembled paper‐based LIS device to trigger cascade reactions (total analysis time, 65 min) and eliminate background interference. As a result, with the support of color recognizer application in smartphone, fluorescent color on detection pads from blue–purple to red to yellow was achieved for the sensitive and visual detection of 2,4‐D with LOD of 5.0 μg L−1, recoveries of 94.6%–106.8%, and relative standard deviations of 2.3%–6.8%. Obviously, this strategy provides a robust, visual, and background‐free platform for 2,4‐D detection, which expands application prospects in the field of herbicide analysis.

A fast tracking method for magnetic abnormalities using distributed Overhauser magnetometer system based on genetic algorithm.

Magnetic anomaly detection technologies have been widely used for tracking moving targets. In this paper, we present a fast-tracking method for magnetic abnormalities using a distributed Overhauser magnetometer system based on the genetic algorithm. Our proposed framework of the Overhauser magnetometer system employs multiple sensors to eliminate background interference, and the genetic algorithm efficiently solves magnetic anomaly data without requiring the derivation of the objective function. Test platforms were built to evaluate the distributed Overhauser magnetometer system and the genetic algorithm. Results from the natural outdoor magnetism laboratories showed that the noise of our presented magnetometers was below 0.134 nT. The optimal factors for solution precision and effectiveness in the genetic algorithm were obtained from the simulation. Moreover, the outdoor tracking experiments indicated that the proposed method could accurately and quickly detect the moving ferromagnetic object within 6.9% maximum positioning error in 0.55m, and the tracking precision of the object velocity can get 5.88% maximum error in 4.33 km/h.

Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images

ABSTRACT As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects.

Fluorescent aptasensor based on the MNPs-CRISPR/Cas12a-TdT for the determination of nasopharyngeal carcinoma-derived exosomes.

A fluorescence aptasensor based on taking the advantage of the combination of magnetic nanoparticles (MNPs), terminal deoxynucleotidyl transferase (TdT), and CRISPR/Cas12a was developed for the determination of nasopharyngeal carcinoma (NPC)-derived exosomes. The MNPs can eliminate background interference due to their magnetic separation capability. TdT can form an ultra-long polynucleotide tail which can bind with multiple crRNA, generating a signal amplification effect. The trans-cleavage activity of CRISPR/Cas12a can be specifically triggered via the crRNA binding with DNA, resulting in the bi-labeled DNA reporter with fluorophore and quencher being cleaved. The excitation wavelength of the fluorescence spectra was 490nm. Fluorescence spectra with emission wavelengths ranging from 511 to 600nm were collected. Under the optimization condition, the fabricated fluorescence aptasensor for NPC-derived exosome determination exhibited excellent sensitivity and specificity, with the linear range between 500 to 5 × 104 particles mL-1 and the limit of detection of 100 particles mL-1. It can be used for the determination of NPC-derived exosomes in clinical samples, which has a considerable clinical potential and prospect.

Active and passive mixed millimeter wave imaging target recognition method based on multi‐feature fusion

Due to millimeter-wave (MMW) has a strong ability to penetrate clothing, MMW holographic imaging technology can conduct a non-contact inspection of the human body's surface. Therefore, it is of great significance to study the security inspection equipment and target recognition technology based on millimeter wave imaging. In this paper, an active and passive hybrid millimeter wave imaging target recognition method based on multi-feature fusion is proposed, which improves the ability of the imaging system to identify different dangerous targets. First, active and passive millimeter wave imaging techniques are discussed, the reconstruction algorithm of active millimeter wave holographic imaging is derived in detail, and the measured optical photos and data of active and passive imaging are obtained. Secondly, the image preprocessing technology applied to active and passive millimeter wave imaging is studied, which can effectively highlight the target, eliminate background interference, and clearly describe the target contour. Then the methods of image feature extraction, data feature extraction and multi-feature fusion are studied. On this basis, a multi-feature fusion method based on weighted series fusion is proposed to obtain the fusion feature vector form of active and passive MMW imaging. Finally, this paper proposed the target recognition method applied to millimeter wave imaging, and concludes that the fusion feature vector is better than the original feature vector, which provides an idea for the fusion of active and passive millimeter wave imaging at the feature level. It also provides a theoretical basis for the application of security equipment.

Visual inspection of surface defects of extreme size based on an advanced FCOS

ABSTRACT Surface defects of industrial products are generally detected through anchor-based object detection methods during manufacturing. However, these methods are prone to missed and false detection for ultra-elongated and ultra-fine defects. An advanced fully convolutional one-stage object detector (FCOS) is proposed. This method is based on an anchor-free FCOS network model. First, a novel type of center-ness is proposed to reduce the suppression of off-centered positions of defects of extreme size. In addition, to eliminate background interference, a self-adaptive center sampling method is proposed as a replacement for the conventional center sampling method. The regularization method and the loss function are also improved according to the defect characteristics. Experimental results show that this advanced-FCOS-based method outperforms anchor-based methodson the surface defect dataset. The proposed method effectively detects defects of extreme size without affecting the detection of normal defects. The performance of the proposed method meets the requirements of real industrial applications.

Mitochondria-Targeted Fluorescence/Photoacoustic Dual-Modality Imaging Probe Tailored for Visual Precise Diagnosis of Drug-Induced Liver Injury.

The multispectral optoacoustic tomography (MSOT) technique can be used to perform high-resolution molecular imaging under deep tissues, which gives the technology significant prospective for clinical application. Here, we developed a superoxide anion (O2•-)-activated MSOT and fluorescence dual-modality imaging probe (APSA) for early diagnosis of drug-induced liver injury (DILI). APSA can respond quickly to O2•-, resulting in an absorption peak blueshift from 845 to 690 nm, which also leads to the photoacoustic (PA) signal at 690 nm and the fluorescence signal at 748 nm increases linearly with increasing O2•- concentration, which can be utilized to assess the extent of liver damage. The developed MSOT imaging method can eliminate background interference from hematopoietic tissue by collecting the PA signals excited at 680, 690, 740, 760, 800, 845, and 900 nm wavelengths to achieve noninvasive in situ visual diagnosis of DILI. The developed fluorescence imaging method can be used for the imaging of endogenous O2•- in living cells and anatomic diagnosis of liver injury. The developed probe has broad application prospects in the early diagnosis of DILI.

Dual-Signal-Encoded Barcodes with Low Background Signal for High-Sensitivity Analysis of Multiple Tumor Markers

The suspension array technology (SAT) is promising for high-sensitivity multiplexed analysis of tumor markers. Barcodes as the core elements of SAT, can generate encoding fluorescence signals (EFS) and detection fluorescence signals (DFS) in the corresponding flow cytometer channel. However, the bleed-through effect of EFS in the DFS channel and the reagent-driven non-specific binding (NSB) lead to background interference for ultrasensitive assay of multiple targets. Here, we report an ingenious method to eliminate background interference between barcode and reporter using low-background dual-signal-encoded barcodes (DSBs) based on microbeads (MBs) and quantum dots (QDs). The low-background DSBs were prepared via combination strategy of two signals containing scatter signals and fluorescence signals. Three types of MBs were distinguished by the scattering channel of flow cytometer (FSC vs. SSC) to obtain the scattered signals. Green quantum dots (GQDs) or red quantum dots (RQDs) were coupled to the surface of MBs by sandwich immune structure to obtain the distinguishable fluorescent signals. Furthermore, the amount of conjugated capture antibody on the MB’s surface was optimized by comparing the change of detection sensitivity with the addition of capture antibody. The combination measurements of specificity and NSB in SAT platform were performed by incubating the capture antibody-conjugated MBs (cAb-MBs) with individual QD-conjugated detection antibody (QDs-dAb). Finally, an SAT platform based on DSBs was successfully established for highly sensitive multiplexed analysis of six tumor markers in one test, which suggests the promising tool for highly sensitive multiplexed bioassay applications.

Static Light Scattering Method for Measuring Particle Sizes of Suspended Particles in Water Body to Eliminate Background Interference

Static Light Scattering Method for Measuring Particle Sizes of Suspended Particles in Water Body to Eliminate Background Interference

Recognition and sorting of coal and gangue based on image process and multilayer perceptron

ABSTRACT The recognition and sorting of coal and gangue are an important part of the process of reducing costs and improving production efficiency. In this paper, under the influence of long running pollution and similar background grayscale, a new method was proposed based on image process and multilayer perceptron. The robust image extraction method was used for the reverse selection edge extraction method (RS-EEM) to eliminate background interference. Moreover, a deep learning model called multilayer perceptron (MLP) was proposed to recognize coal and gangue with simple structure, which is easy to transplant with high precision and great timeliness. In order to evaluate the correctness and efficiency of the algorithm, a sorting robot of coal and gangue was built based on MLP and image processing. The experiment results showed that the images of coal and gangue can be precisely recognized based on RS-EEM under the influence of long running pollution and similar background grayscale, and the recognition accuracy can reach to 96.15% as well as grasping accuracy to 85% at 0.4 m/s conveyor belt speed.

Pose estimation at night in infrared images using a lightweight multi-stage attention network

Human Keypoints Detection is a relatively basic task in computer vision; it is the pre-task of human action recognition, behavior analysis and human–computer interaction. Since most abnormal actions occur at night, how to effectively extract skeleton sequence data in a low-light or completely dark environment poses a huge challenge for its identification. This paper proposes to use far infrared images to detection key points of the human body, which can solve the problem of human pose estimation under challenging weather conditions such as total darkness, smoke, inclement weather and glare. However, far-infrared images have some shortcomings, such as low resolution, noise and thermal characteristics; the skeleton data need to be provided in real time for the next stage of task. Based on the above reasons, this paper proposes a lightweight multi-stage attention network (LMANet) to detect the key points of human at night. This new network structure adds context information through the large receptive field, which helps to assist the detection of neighboring key points through this information, but for the sake of lightweight consideration, this article only extends the network to two stages. In addition, this article uses the attention module to effectively select channels with a large amount of information and highlight the features of key points, while eliminating background interference. In order to detect key points of the human in various complex environments, we use techniques such as difficult sample mining which improves the accuracy of key points with low confidence. Our network has been verified on two visible light datasets, fully demonstrating excellent performance. This paper successfully introduces far-infrared images into the field of pose estimation, because there is no public dataset for far-infrared pose estimation. In this paper, 700 images are selected for annotation from multiple public far-infrared object detection, segmentation and action recognition datasets; our algorithm is verified on this dataset; the effect is very good. After the paper is published, we will publish our key points of the human body annotated documents.

Detection of Single Nucleotide Polymorphisms by Fluorescence Embedded Dye SYBR Green I Based on Graphene Oxide.

The detection of single nucleotide polymorphisms (SNPs) is of great significance in the early diagnosis of diseases and the rational use of drugs. Thus, a novel biosensor based on the quenching effect of fluorescence-embedded SYBR Green I (SG) dye and graphene oxide (GO) was introduced in this study. The probe DNA forms a double helix structure with perfectly complementary DNA (pcDNA) and 15 single-base mismatch DNA (smDNA) respectively. SG is highly intercalated with perfectly complementary dsDNA (pc-dsDNA) and exhibits strong fluorescence emission. Single-base mismatch dsDNA (SNPs) has a loose double-stranded structure and exhibits poor SG intercalation and low fluorescence sensing. At this time, the sensor still showed poor SNP discrimination. GO has a strong effect on single-stranded DNA (ssDNA), which can reduce the fluorescence response of probe DNA and eliminate background interference. And competitively combined with ssDNA in SNPs, quenching the fluorescence of SG/SNP, while the fluorescence value of pc-dsDNA was retained, increasing the signal-to-noise ratio. At this time, the sensor has obtained excellent SNP resolution. Different SNPs detect different intensities of fluorescence in the near-infrared region to evaluate the sensor's identification of SNPs. The experimental parameters such as incubation time, incubation temperature and salt concentration were optimized. Under optimal conditions, 1 nM DNA with 0–10 nM linear range and differentiate 5% SNP were achieved. The detection method does not require labeling, is low cost, simple in operation, exhibits high SNP discrimination and can be distinguished by SNP at room temperature.