Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances.

Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.

Domain Adaptation for Underwater Image Enhancement.

Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and obtain outstanding performance. However, these deep methods ignore the significant domain gap between the synthetic and real data (i.e., inter-domain gap), and thus the models trained on synthetic data often fail to generalize well to real-world underwater scenarios. Moreover, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions on various real images. Motivated by these observations, we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to simultaneously minimize the inter-domain and intra-domain gap. Concretely, in the first phase, a new triple-alignment network is designed, including a translation part for enhancing realism of input images, followed by a task-oriented enhancement part. With performing image-level, feature-level and output-level adaptation in these two parts through jointly adversarial learning, the network can better build invariance across domains and thus bridging the inter-domain gap. In the second phase, an easy-hard classification of real data according to the assessed quality of enhanced images is performed, in which a new rank-based underwater quality assessment method is embedded. By leveraging implicit quality information learned from rankings, this method can more accurately assess the perceptual quality of enhanced images. Using pseudo labels from the easy part, an easy-hard adaptation technique is then conducted to effectively decrease the intra-domain gap between easy and hard samples. Extensive experimental results demonstrate that the proposed TUDA is significantly superior to existing works in terms of both visual quality and quantitative metrics.

Gut-associated Lymphoid Tissue/Dome-Type Carcinoma of the Colon: A Rare Case Report With Review of the Literature.

Gut-associated lymphoid tissue (GALT) carcinoma, also termed dome-type carcinoma, is an infrequent distinctive subtype of colorectal adenocarcinoma and only 18 cases have been reported in the English medical literature. These tumors have unique clinicopathological features and are considered to have a low malignant potential with favorable prognosis. Herein, we described a case of a 49-year-old male with intermittent hematochezia for 2 years. Colonoscopy revealed a sessile broad-based polyp of approximately 20 mm × 17 mm in the sigmoid colon 260 mm away from the anus, with a slightly hyperemic surface. Histologically, this lesion showed typical GALT carcinoma. The patient was followed up for one and a half year and he did not experience any discomfort, such as abdominal pain or hematochezia, and no tumor recurrence occurred. Moreover, we reviewed the literature, summarized the clinicopathological features of GALT carcinoma, and highlighted its pathological differential diagnosis to further explore this infrequent type of colorectal adenocarcinoma.

Long non-coding FAM201A accelerates the tumorigenesis and progression of colorectal cancer through miR-3163/MACC1 axis.

Colorectal cancer (CRC) is the leading contributor to cancer-relevant deaths worldwide with severe incidence and mortality. An extensive body of evidence has demonstrated that lncRNA plays a critical role in the oncogenicity of CRC. Despite the oncogenic function of FAM201A in esophageal squamous cell cancer and non-small-cell lung cancer, the potential of FAM201A in CRC progression remains unknown. FAM201A expression level was significantly enhanced in CRC cells compared with normal cells. Further, functional experiments illustrated that knockdown of FAM201A restrained cell growth, stemness and promoted chemoresistance of CRC cells. By exploring molecular mechanism of FAM201A, we found that FAM201A acted as a sponge of miR-3163. More importantly, oncogene MACC1 was confirmed to be a direct target of miR-3163 and FAM201A modulated MACC1 expression level via competing for miR-3163. Subsequently, we testified that FAM201A exerted its role in the tumorigenesis and development of CRC through targeting miR-3163/MACC1. Animal assay certified that FAM201A expedited CRC cell growth in vivo. In conclusion, our study was the first to unveil that FAM201A promoted cell proliferation and CSC characteristics in CRC via regulation of the miR-3163/MACC1 axis, which provided clues for the clinical treatment of patients with this disease.

Multi-Level Transformer-Based Social Relation Recognition.

Social relationships refer to the connections that exist between people and indicate how people interact in society. The effective recognition of social relationships is conducive to further understanding human behavioral patterns and thus can be vital for more complex social intelligent systems, such as interactive robots and health self-management systems. The existing works about social relation recognition (SRR) focus on extracting features on different scales but lack a comprehensive mechanism to orchestrate various features which show different degrees of importance. In this paper, we propose a new SRR framework, namely Multi-level Transformer-Based Social Relation Recognition (MT-SRR), for better orchestrating features on different scales. Specifically, a vision transformer (ViT) is firstly employed as a feature extraction module for its advantage in exploiting global features. An intra-relation transformer (Intra-TRM) is then introduced to dynamically fuse the extracted features to generate more rational social relation representations. Next, an inter-relation transformer (Inter-TRM) is adopted to further enhance the social relation representations by attentionally utilizing the logical constraints among relationships. In addition, a new margin related to inter-class similarity and a sample number are added to alleviate the challenges of a data imbalance. Extensive experiments demonstrate that MT-SRR can better fuse features on different scales as well as ameliorate the bad effect caused by a data imbalance. The results on the benchmark datasets show that our proposed model outperforms the state-of-the-art methods with significant improvement.

Application of Machine Vision in Classifying Gait Frailty Among Older Adults.

Background: Frail older adults have an increased risk of adverse health outcomes and premature death. They also exhibit altered gait characteristics in comparison with healthy individuals. Methods: In this study, we created a Fried's frailty phenotype (FFP) labelled casual walking video set of older adults based on the West China Health and Aging Trend study. A series of hyperparameters in machine vision models were evaluated for body key point extraction (AlphaPose), silhouette segmentation (Pose2Seg, DPose2Seg, and Mask R-CNN), gait feature extraction (Gaitset, LGaitset, and DGaitset), and feature classification (AlexNet and VGG16), and were highly optimised during analysis of gait sequences of the current dataset. Results: The area under the curve (AUC) of the receiver operating characteristic (ROC) at the physical frailty state identification task for AlexNet was 0.851 (0.827-0.8747) and 0.901 (0.878-0.920) in macro and micro, respectively, and was 0.855 (0.834-0.877) and 0.905 (0.886-0.925) for VGG16 in macro and micro, respectively. Furthermore, this study presents the machine vision method equipped with better predictive performance globally than age and grip strength, as well as than 4-m-walking-time in healthy and pre-frailty classifying. Conclusion: The gait analysis method in this article is unreported and provides promising original tool for frailty and pre-frailty screening with the characteristics of convenience, objectivity, rapidity, and non-contact. These methods can be extended to any gait-related disease identification processes, as well as in-home health monitoring.

Distribution of hydrogen and oxygen stable isotopes and pollution indicators in water during a monsoon transitional period in Min River Basin.

Based on 197 monthly river water and groundwater samples and 30 event-scale precipitation samples, our study reports the distribution of hydrogen and oxygen isotopes and pollution indicators in Min River Basin. The variation of δ18O and d-excess indicate that the water source in the upper main course water is more variable and that in the middle-lower part is relatively stable. Comparison between plots of δ2H versus δ18O in the river water and precipitation reflect the dominant water source is different between river water in the upper and middle-lower parts. The electrical conductivity (EC) shows a similar spatial variation trend for main course water collected in four campaigns. The pollutant concentration change at the confluences of main tributaries shows that the inflow of Heishui River and Dadu River leads to decreased NO3- and Cl-, while that of Xi River, Pu River and Fuhe River leads to a leap in NO3- and Cl-. A significant positive correlation is observed between EC and δ18O, indicating the consistent control of water sources on isotope distribution and water quality. The relationship between elevation and δ18O in the main course river water suggests that the factors affecting isotope distribution vary spatially. "Altitude effect" can only be observed in October and November for the upper steepest plateau zone due to the spatial variation in the precipitation stored during the wet season. The "inverse altitude effect" is observed for the upper part during the wet season and for the middle-lower part during the whole study period, which can be explained by the contribution of tributaries with different discharge regimes. Our findings show that water source with different discharge regimes can serve as the leading factor controlling the stream component in multi-tributary river basins with large spatial span and may mask the influence of spatial distribution of precipitation.

Ultrasonic extraction and nebulization in real-time coupled with carbon fiber ionization mass spectrometry for rapid screening of the synthetic drugs adulterated into herbal products.

Ultrasonic extraction and nebulization in real-time/carbon fiber ionization mass spectrometry (UEN/CFI-MS) was developed to screen the synthetic drugs adulterated into herbal products such as antidiabetic drug, antihypertensive drug, and hypolipidemic drug. Recently, ambient ionization MS techniques have achieved great advance for rapid analysis of sample surface. However, direct analysis of the analytes inside samples remains a challenge due to a lack of effective online sample extraction procedures. Owing to disappointing desorption efficiency, analytes inside the sample suffer from low detecting sensitivity when applying ambient ionization MS techniques. In this study, online ultrasonic extraction combined with carbon fiber ionization was used for real-time extraction, nebulization and detection of the analytes inside samples. The ultrasonic atomizer could produce a high-frequency vibration to realize online extraction and nebulization of sample. Then, the produced sample droplets could be immediately ionized by the carbon fiber ionization mass spectrometry. UEN/CFI-MS has shown great compatibility to solvents and compounds with a wide range of polarity and has few limitations for the shape of sample. UEN/CFI-MS was successfully applied for the rapid screening of synthetic drugs adulterated into herbal products. Among 37 batches of herbal products, 1 batch of Chinese patent medicine and 6 batches of dietary supplements were detected to be adulterated with the synthetic chemicals without labeling.

Single Remote Sensing Image Super-Resolution with an Adaptive Joint Constraint Model.

Remote sensing images have been widely used in many applications. However, the resolution of the obtained remote sensing images may not meet the increasing demands for some applications. In general, the sparse representation-based super-resolution (SR) method is one of the most popular methods to solve this issue. However, traditional sparse representation SR methods do not fully exploit the complementary constraints of images. Therefore, they cannot accurately reconstruct the unknown HR images. To address this issue, we propose a novel adaptive joint constraint (AJC) based on sparse representation for the single remote sensing image SR. First, we construct a nonlocal constraint by using the nonlocal self-similarity. Second, we propose a local structure filter according to the local gradient of the image and then construct a local constraint. Next, the nonlocal and local constraints are introduced into the sparse representation-based SR framework. Finally, the parameters of the joint constraint model are selected adaptively according to the level of image noise. We utilize the alternate iteration algorithm to tackle the minimization problem in AJC. Experimental results show that the proposed method achieves good SR performance in preserving image details and significantly improves the objective evaluation indices.

Improved multipoint statistics method for reconstructing three-dimensional porous media from a two-dimensional image via porosity matching.

Reconstructing a three-dimensional (3D) structure from a single two-dimensional training image (TI) is a challenging issue. Multiple-point statistics (MPS) is an effective method to solve this problem. However, in the traditional MPS method, errors occur while statistical features of reconstruction, such as porosity, connectivity, and structural properties, deviate from those of TI. Due to the MPS reconstruction mechanism that the voxel being reconstructed is dependent on the reconstructed voxel, it may cause error accumulation during simulations, which can easily lead to a significant difference between the real 3D structure and the reconstructed result. To reduce error accumulation and improve morphological similarity, an improved MPS method based on porosity matching is proposed. In the reconstruction, we search the matching pattern in the TI directly. Meanwhile, a multigrid approach is also applied to capture the large-scale structures of the TI. To demonstrate its superiority over the traditional MPS method, our method is tested on different sandstone samples from many aspects, including accuracy, stability, generalization, and flow characteristics. Experimental results show that the reconstruction results by the improved MPS method effectively match the CT sandstone samples in correlation functions, local porosity distribution, morphological parameters, and permeability.

Reverse strand-displacement amplification strategy for rapid detection of p53 gene.

The development of rapid approaches to detect prognostic markers is significant in reducing the morbidity and mortality of cancer. In this paper, we describe a rapid and specific biosensing platform for target DNA (p53 gene as a model) detection based on reverse strand displacement amplification (R-SDA). When the p53 gene is added, multifuctional molecular beacon (MMB) is unfolded via the hybridization with p53 gene. With the assist of Klenow fragment (KF) and Nt.BbvCI (the nicking endonuclease), p53 gene recycling could be initiated and considerable amount of complementary sequences for the MMBs (Nicked fragments, NFs) could be formed, generating enhanced fluorescence signal. Using this amplification strategy, the proposed biosensor displays the detection limit of 1 nM and a wide linear range from 1 to 100 nM, even if only one type of probe is involved. Notably, remarkable detection specificity for single-base mismatched target p53 gene is achieved. Moreover, the described biosensor also exhibited the stability in real biological samples (human serum). The rapid detection strategy can be performed less than 30 min without harsh reaction conditions or expensive nanoparticles. This biosensor shows great potential for application in clinic assay, especially, for early cancer diagnosis.

Single palindromic molecular beacon-based amplification for genetic analysis of cancers.

The detection of biomarkers is of crucial importance in reducing the morbidity and mortality of complex diseases. Thus, there is a great desire to develop highly efficient and simple sensing methods to fulfill the different diagnostic and therapeutic purposes. Herein, using tumor suppressor p53 gene as model target DNA, we developed a novel palindromic fragment-incorporated molecular beacon (P-MB) that can perform multiple functions, including recognition element, signal reporter, polymerization template and primer. Upon specific hybridization with target DNA, P-MBs can interact with each other and are extended by polymerase without any additional probes. As a result, hybridized targets are peeled off from P-MBs and initiate the next round of reactions, leading to the unique strand displacement amplification (SDA). The newly-proposed enzymatic amplification displays the detection limit as low as 100pM and excellent selectivity in distinguishing single-base mutation with the linear response range from 100pM to 75nM. This is the simplest SDA sensing system so far because of only involving one type of DNA probe. This impressive sensing paradigm is expected to provide new insight into developing new-type of DNA probes that hold tremendous potential with important applications in molecular biology research and clinical diagnosis.

Highly sensitive detection of cancer-related genes based on complete fluorescence restoration of a molecular beacon with a functional overhang.

The accurate detection of cancer-related genes is of great significance for early diagnosis and targeted therapy of cancer. In this contribution, an automatically cycling operation of a functional overhang-containing molecular beacon (OMB)-based sensing system was proposed to perform amplification detection of the p53 gene. Contrary to the common molecular beacon (MB), a target DNA is designated to hybridize with a label-free recognition probe (RP) with a hairpin structure rather than OMB. In the presence of a target DNA of interest, the locked primer in RP opens and triggers the subsequent amplification procedures. The newly-developed OMB is not only capable of accomplishing cyclical nucleic acid strand-displacement polymerization (CNDP) with the help of polymerase and nicking endonuclease, but is also cleaved by restriction endonucleases, removing the quencher away from the fluorophore. Thus, the target DNA at an extremely low concentration is expected to generate a considerable amount of double-stranded and cleaved OMBs, and the quenched fluorescence is completely restored, leading to a dramatic increase in fluorescence intensity. Utilizing this sensing platform, the target gene can be detected down to 8.2 pM in a homogeneous way, and a linear response range of 0.01 to 150 nM could be obtained. More strikingly, the mutant genes can be easily distinguished from the wild-type ones. The proof-of-concept demonstrations reported herein are expected to promote the development of DNA biosensing systems, showing great potential in basic research and clinical diagnosis.

Resonantly pumped 1.645 µm single longitudinal mode Er:YAG laser with intracavity etalons.

We report on a 1.645 µm single longitudinal mode Er:YAG laser that was resonantly pumped by a 1.532 µm fiber laser, using intracavity etalons to generate single longitudinal mode operation. We obtained 0.749 W single longitudinal mode output power at 1.645 µm from an Er:YAG laser with two intracavity etalons. The M2-factors of the Er:YAG laser were 1.041 and 1.068 in the x and y directions, respectively.

Inactivation and mechanisms of chlorine dioxide on Nosema bombycis.

Biological tests demonstrated that the inactivation of Nosema bombycis (N. bombycis) spores by chlorine dioxide (ClO(2)) occurs very fast and is highly sensitive. The lowest effective inactivation dosage and time was 15mg/mL for 30min. The inactivation of spores was additionally verified by using double color fluorescence stain and spore germination testing. A series of biological changes, including a large number of substrates that were leaked out from the spores included proteins, DNA, polysaccharide, K(+), and Ca(2+), occurred a short time after N. bombycis spores were treated with ClO(2). In addition, the lipid of spores was disrupted and ATPase activity was inhibited, which resulted in the destruction of the inner structure of the spores.