Structures and organizations of PSI-AcpPCI supercomplexes from red tidal and coral symbiotic photosynthetic dinoflagellates.

Marine photosynthetic dinoflagellates are a group of successful phytoplankton that can form red tides in the ocean and also symbiosis with corals. These features are closely related to the photosynthetic properties of dinoflagellates. We report here three structures of photosystem I (PSI)-chlorophylls (Chls) a/c-peridinin protein complex (PSI-AcpPCI) from two species of dinoflagellates by single-particle cryoelectron microscopy. The crucial PsaA/B subunits of a red tidal dinoflagellate Amphidinium carterae are remarkably smaller and hence losing over 20 pigment-binding sites, whereas its PsaD/F/I/J/L/M/R subunits are larger and coordinate some additional pigment sites compared to other eukaryotic photosynthetic organisms, which may compensate for the smaller PsaA/B subunits. Similar modifications are observed in a coral symbiotic dinoflagellate Symbiodinium species, where two additional core proteins and fewer AcpPCIs are identified in the PSI-AcpPCI supercomplex. The antenna proteins AcpPCIs in dinoflagellates developed some loops and pigment sites as a result to accommodate the changed PSI core, therefore the structures of PSI-AcpPCI supercomplex of dinoflagellates reveal an unusual protein assembly pattern. A huge pigment network comprising Chls a and c and various carotenoids is revealed from the structural analysis, which provides the basis for our deeper understanding of the energy transfer and dissipation within the PSI-AcpPCI supercomplex, as well as the evolution of photosynthetic organisms.

Sequence-Guided Localization of DNA Hybridization Enables Highly Selective and Robust Genotyping.

Genetic variations are always related to human diseases or susceptibility to therapies. Nucleic acid probes that precisely distinguish closely related sequences become an indispensable requisite both in research and clinical applications. Here, a Sequence-guided DNA LOCalization for leaKless DNA detection (SeqLOCK) is introduced as a technique for DNA hybridization, where the intended targets carrying distinct "guiding sequences" act selectively on the probes. In silicon modeling, experimental results reveal considerable agreement (R2  = 0.9228) that SeqLOCK is capable of preserving high discrimination capacity at an extraordinarily wide range of target concentrations. Furthermore, SeqLOCK reveals high robustness to various solution conditions and can be directly adapted to nucleic acid amplification techniques (e.g., polymerase chain reaction) without the need for laborious pre-treatments. Benefiting from the low hybridization leakage of SeqLOCK, three distinct variations with a clinically relevant mutation frequency under the background of genomic DNA can be discriminated simultaneously. This work establishes a reliable nucleic acid hybridization strategy that offers great potential for constructing robust and programmable systems for molecular sensing and computing.

How to better select SARS-CoV-2 preservation solution of virus nucleic acid testing.

BACKGROUND: Sampling and testing for SARS-CoV-2 is a widely recognized method for identifying patients with COVID-19. However, there is limited research available on the stability of nucleic acids in viral storage solutions. METHODS: This paper investigates the components that provide better protection for virus and nucleic acid detection. The study utilized real-time quantitative fluorescent PCR to detect SARS-CoV-2 and evaluate the preservation effect and stability of SARS-CoV-2 viral storage solution under various conditions, including different guanidinium salts, brands, and storage conditions. RESULTS: All brands of inactivated virus preservation solutions demonstrated effective preservation and stability. However, 0.5 mol/L guanidine hydrochloride and guanidine isothiocyanate solutions exhibited poor antiseptic effects. Additionally, refrigerated storage showed better preservation compared to room temperature storage. CONCLUSIONS: We recommend using inactivated virus collection solution to preserve and transport samples and testing preferably within 6 hours to reduce false negatives of NAT results.

Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes.

Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in seriously mutual occlusions captured by moving cameras, the accuracy and speed of petrochemical equipment tracking would be limited because of the false and missed tracking of equipment with extreme sizes and severe occlusion, due to image quality, equipment scale, light, and other factors. In this paper, a new multiple petrochemical equipment tracking method is proposed by combining an improved Yolov7 network with attention mechanism and small target perceive layer and a hybrid matching that incorporates deep feature and traditional texture and location feature. The model incorporates the advantages of channel and spatial attention module into the improved Yolov7 detector and Siamese neural network for similarity matching. The proposed model is validated on the self-built petrochemical equipment video data set and the experimental results show it achieves a competitive performance in comparison with the related state-of-the-art tracking algorithms.

Iterated Residual Graph Convolutional Neural Network for Personalized Three-Dimensional Reconstruction of Left Myocardium from Cardiac MR Images.

Three-dimensional reconstruction of the left myocardium is of great significance for the diagnosis and treatment of cardiac diseases. This paper proposes a personalized 3D reconstruction algorithm for the left myocardium using cardiac MR images by incorporating a residual graph convolutional neural network. The accuracy of the mesh, reconstructed using the model-based algorithm, is largely affected by the similarity between the target object and the average model. The initial triangular mesh is obtained directly from the segmentation result of the left myocardium. The mesh is then deformed using an iterated residual graph convolutional neural network. A vertex feature learning module is also built to assist the mesh deformation by adopting an encoder-decoder neural network to represent the skeleton of the left myocardium at different receptive fields. In this way, the shape and local relationships of the left myocardium are used to guide the mesh deformation. Qualitative and quantitative comparative experiments were conducted on cardiac MR images, and the results verified the rationale and competitiveness of the proposed method compared to related state-of-the-art approaches.

Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification.

Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great value in computer-aided cardiovascular diagnosis. However, it is challenging due to the complex distribution of various tissues and organs in cardiac MRI (magnetic resonance imaging) slices, low discriminative and large spanning organs. To handle these problems, a two-stage CNN (convolutional neural network) segmentation method based on the combination of Log-Gabor filter attention mechanism and metric classification is proposed. The Log-Gabor filterbank is applied to selectively enhance the texture information and contour information of each tissue and organ, and the spatial and channel attention mechanism jointly with the varying kernel size of Log-Gabor filterbank is incorporated into the codec structure to adaptively extract target features of different sizes and focus on the discriminative features in the network. To solve the problem of insufficient segmentation on subtle and adherent edges involving different tissues, a metric classification network is incorporated to finely optimize the hard-to-be-segmented boundaries. The proposed method was tested on cardiac MRI data set to segment 7 cardiac tissues, and the rationality and effectiveness of the method were verified. In comparison to a series of deep learning-based segmentation models, the proposed method achieves competitive performance.

High concentrations of hypochlorous acid-based disinfectant in the environment reduced the load of SARS-CoV-2 in nucleic acid amplification testing.

During the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic, chlorine-containing disinfectants have been widely used in nucleic acid amplification testing laboratories. Whether the use of disinfectants affect the results of viral nucleic acid amplification is unknown. We examined the impact of different hypochlorous acid (HOCl) concentrations on the quantitative results of SARS-CoV-2 by real-time reverse-transcription polymerase chain reaction (RT-PCR). We also explored the mechanisms and models of action of chlorine-containing disinfectants that affected the detection of SARS-CoV-2. The results showed that different HOCl concentrations and different action times had an impact on the SARS-CoV-2 results. High concentrations of ambient HOCl have a greater impact than low concentrations, and this effect will increase with the extension of the action time and with the increase in ambient humidity. Compared with the enzymes or the extracted RNA required for RT-PCR, the impact of HOCl on the SARS-CoV-2 detection is more likely to be caused by damage to primers and probes in the PCR system. The false negative result still existed after changing the ambient disinfectant to ethanol but not peracetic acid. The use of HOCl in the environment will have an unpredictable impact on the nucleic acid test results of SARS-CoV-2. In order to reduce the possibility of false negative of SARS-CoV-2 nucleic acid test and prevent the spread of epidemic disease, environmental disinfectants should be used at the beginning and end of the experiment rather than during the experimental operation.

Serum Ceruloplasmin Depletion is Associated With Magnetic Resonance Evidence of Widespread Accumulation of Brain Iron in Parkinson's Disease.

BACKGROUND: Excessive iron accumulation is one of the main pathogeneses of Parkinson's disease (PD). Ceruloplasmin plays an important role in keeping the iron homoeostasis. PURPOSE: To explore the association between serum ceruloplasmin depletion and subcortical iron distribution in PD. STUDY TYPE: Prospective. POPULATION: One hundred and twenty-one normal controls, 34 PD patients with low serum ceruloplasmin (PD-LC), and 28 patients with normal serum ceruloplasmin (PD-NC). SEQUENCE: Enhanced susceptibility-weighted angiography (ESWAN) on a 3 T scanner. ASSESSMENT: Quantitative susceptibility mapping was employed to quantify the regional iron content by using a semi-automatic method. Serum ceruloplasmin concentration was measured from peripheral blood sample. Clinical assessments were conducted by a neurologist. STATISTICAL TESTS: General linear model was used to compare the intergroup difference of region iron distribution among groups, and the statistics was adjusted by Bonferroni method (P < 0.01). Partial correlation analysis was used to detect the association between regional iron distribution and serum ceruloplasmin concentration (P < 0.05). RESULTS: Compared with normal controls, significant iron accumulation in substantia nigra, putamen, and red nucleus was observed in PD-LC, while the only region showing significant iron accumulation was SN in PD-NC. Between PD-NC and PD-LC, the iron accumulation in putamen remained significantly different, which had a negative correlation with serum ceruloplasmin in whole PD patients (r = -0.338, P = 0.008). DATA CONCLUSION: Nigral iron accumulation characterizes PD patients without significant association with serum ceruloplasmin. Differentially, when PD patients appear with reduced serum ceruloplasmin, more widespread iron accumulation would be expected with additionally involving putamen and red nucleus. All these findings provide insightful evidence for the abnormal iron metabolism behind the ceruloplasmin depletion in PD. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: 2.

A quantitative detection of Cardiotrophin-1 in chronic heart failure by chemiluminescence immunoassay.

BACKGROUND: Cardiotrophin-1 (CT-1) is a cytokine that could induce cardiomyocytes hypertrophy and dysfunction. Plasma CT-1 might serve as a cardiac biomarker both in diagnosis, staging, and prognostic assessment of heart failure. METHODS: In this study, a one-step paramagnetic particles-based chemiluminescence immunoassay (MPs-CILA) for rapid and sensitive detection of plasma CT-1 was established. Plasma samples were directly incubated with biotin-labeled anti-CT-1 antibody (bio-Ab) and acridine ester labeled anti-CT-1 antibody (AE-Ab) to form sandwiched complex. The sandwiched CT-1 was then captured by streptavidin modified paramagnetic particles (MPs-SA) for rapid separation and signal generation. RESULTS: The proposed MPs-CLIA presents a laudable linear relationship ranging from 7.8 pg/mL to 200 ng/mL with a detection limit of 1.0 pg/mL. The recoveries of spiked human plasma samples at low (10pg/mL), medium (100 pg/mL), and high (800 pg/mL) levels of CT-1 were 96%, 104%, and 110% respectively. The intra-analysis coefficient variation (CVs) of the 3 samples was 8.92%, 6.69%, and 3.54%, respectively. And the inter-analysis coefficient variation (CVs) was 9.25%, 10.9%, and 4.3%, respectively. These results strongly indicate high sensitivity, wide linear range, acceptable precision, and applicable reproducibility of the proposed method to detect plasma level of CT-1. Finally, Plasma CT-1 from 140 subjects with or without chronic heart failure was analyzed to assess the clinical application of MPs-CILA. CONCLUSIONS: Noteworthily, the MPs-CLIA method is highly automated such that it is suitable for high-throughput detection of CT-1 in clinical inspection.

Determination of plasma ß-amyloids by rolling circle amplification chemiluminescent immunoassay for noninvasive diagnosis of Alzheimer's disease.

A rolling circle amplification chemiluminescence immunoassay (RCA-CLIA) was developed for precise quantitation of Aß in plasma. Capture antibodies conjugated with magnetic beads and detection antibodies with collateral single-stranded DNA (ssDNA) were bound to Aß42/Aß40 antigens to form a typical double-antibody sandwich structure. The RCA reaction was triggered by the addition of ssDNA, which generated products with a large number of sites for the binding of acridinium ester (AE)-labeled detection probes, thereby realizing the purpose of the amplification. The RCA-CLIA method had higher sensitivity than conventional CLIA without loss of specificity. Under optimum conditions, the linear range of Aß42 and Aß40 detection was 3.9-140 pg/mL and 3.9-180 pg/mL, respectively, with corresponding low detection limits of 1.99 pg/mL and 3.14 pg/mL, respectively. Plasma Aß42 and Aß40 were detected in the blood of 21 AD patients and 22 healthy people, wherein this ratio could significantly distinguish AD patients from healthy individuals with a sensitivity of 90.48% and specificity of 63.64% for a cutoff value of 154. The Aß42/Aß40 ratio of plasma acts as an accurate indicator for AD diagnosis; therefore, detection of plasma Aß using the RCA-CLIA exhibits great potential in noninvasive diagnosis and progressive assessment of AD.

A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection.

Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

ARMS TaqMan real-time PCR for genotyping factor V Leiden mutation in Han Chinese.

Factor V Leiden (FVLeiden ) is a missense mutation of 1691 position (G1691A) in exon 10 of FV gene, and being a genetic risk for venous thrombosis. Currently, there are several PCR-based methods for detecting FVLeiden mutation; however, these methods have disadvantages such as time-consuming, cumbersome steps and potentially hazardous gels. The aims of present study were to develop a simple, time-saving, accurate, and gel-free method, called amplification refractory mutation system (ARMS) TaqMan real-time PCR, for detecting FVLeiden mutation. We severally designed two specific reverse primers for mutant and wild-type through intentional introduction of mismatched nucleotide at the penultimate 3' position. Although target amplicon amplification efficiency is reduced, but another corresponding amplicon is almost completely inhibited. Then, specific TaqMan-probe was designed to detect target amplicon. Established method was used to detect 500 unselected samples in Han Chinese, the results showed 499 cases of wild-type and one heterozygote. Afterward, 50 randomly picked wild-type cases and one heterozygote were reexamined by bidirectional DNA sequencing, which is considered as "Gold standard method." Exhilaratingly, the results detected by the two methods were completely consistent. At last, allelic frequency of FVLeiden was calculated the in Han Chinese. Given the above results, A FVLeiden heterozygote has been found in 500 random samples in Han Chinese, and the allelic frequency was 0.1%. In conclusion, the ARMS TaqMan real-time PCR is an ideal detecting system for genotyping FVLeiden mutation in clinical application, and FVLeiden mutation exists in Han Chinese despite extremely low prevalence.

Red Blood Cell Distribution Width is Associated with Glomerulonephritis in Diabetic Patients with Albuminuria.

BACKGROUND The aim of this study was to explore predictive factors to inform accurate diagnosis of glomerulonephritis (GNs) in patients with diabetes. MATERIAL AND METHODS Clinical characteristics and laboratory data were retrospectively analyzed from 200 patients with diabetes including 115 patients who had undergone a renal biopsy. Eligible patients were categorized into three groups: pure type 2 diabetes mellitus (T2DM), isolated diabetic nephropathy (DN), and GN. Odds ratios (ORs) were calculated to evaluate the contributions of predictive factors for GN. A receiver operating characteristic curve (ROC) was created to obtain cut-off values for predictive factors for GNs and investigate their corresponding predictive accuracy. RESULTS Red cell distribution width (RDW) was significantly higher in the GN group than in the DN group. Multivariate regression analysis revealed that baseline RDW level (OR=1.988, 95% CI=1.237~3.194, P=0.005) was an independent predictive factor for development of GNs. CONCLUSIONS Increased RDW levels are independently associated with a greater risk of GN in patients with diabetes who have albuminuria, and may be an additional valuable and noninvasive predictive tool for differentiating GNs and DN.

Identification and Characterization of Glycoproteins and Their Responsive Patterns upon Ethylene Stimulation in the Rubber Latex.

Natural rubber is an important industrial material, which is obtained from the only commercially cultivated rubber tree, Hevea brasiliensis. In rubber latex production, ethylene has been extensively used as a stimulant. Recent research showed that post-translational modifications (PTMs) of latex proteins, such as phosphorylation, glycosylation and ubiquitination, are crucial in natural rubber biosynthesis. In this study, comparative proteomics was performed to identify the glycosylated proteins in rubber latex treated with ethylene for different days. Combined with Pro-Q Glycoprotein gel staining and mass spectrometry techniques, we provided the first visual profiling of glycoproteomics of rubber latex and finally identified 144 glycosylated protein species, including 65 differentially accumulated proteins (DAPs) after treating with ethylene for three and/or five days. Gene Ontology (GO) functional annotation showed that these ethylene-responsive glycoproteins are mainly involved in cell parts, membrane components and metabolism. Pathway analysis demonstrated that these glycosylated rubber latex proteins are mainly involved in carbohydrate metabolism, energy metabolism, degradation function and cellular processes in rubber latex metabolism. Protein-protein interaction analysis revealed that these DAPs are mainly centered on acetyl-CoA acetyltransferase and hydroxymethylglutaryl-CoA synthase (HMGS) in the mevalonate pathway for natural rubber biosynthesis. In our glycoproteomics, three protein isoforms of HMGS2 were identified from rubber latex, and only one HMGS2 isoform was sharply increased in rubber latex by ethylene treatment for five days. Furthermore, the HbHMGS2 gene was over-expressed in a model rubber-producing grass Taraxacum Kok-saghyz and rubber content in the roots of transgenic rubber grass was significantly increased over that in the wild type plant, indicating HMGS2 is the key component for natural rubber production.

An improved protein extraction method applied to cotton leaves is compatible with 2-DE and LC-MS.

BACKGROUND: Two-dimensional electrophoresis (2-DE) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) are widely used in plant proteomics research. However, these two techniques cannot be simultaneously satisfied by traditional protein extraction methods when investigate cotton leaf proteome. RESULTS: Here, we evaluated the efficiency of three different protein extraction methods for 2-DE and LC-MS/MS analyses of total proteins obtained from cotton leaves. The protein yield of the borax/PVPP/phenol (BPP) method (0.14%) was significantly lower than the yields of the trichloroacetic acid/acetone (TCA) precipitation method (1.42%) and optimized TCA combined with BPP (TCA-B) method (0.47%). The BPP method was failed to get a clear 2-DE electrophoretogram. Fifty pairs of protein spots were randomly selected from the 2-DE gels of TCA- and TCA-B-extracted proteins for identification by MALDI TOF/TOF, and the results of 42 pairs were consistent. High-throughput proteomic analysis showed that 6339, 9282 and 9697 unique proteins were identified from the total cotton leaf proteins extracted by the TCA, BPP and TCA-B methods, respectively. Gene Ontology (GO) analysis revealed that the proteins specifically identified by TCA method were primarily distributed in the plasma membrane, while BPP and TCA-B methods specific proteins distributed in the cytosol, indicating the sub-cellular preference of different protein extraction methods. Further, ATP-dependent zinc metalloprotease FTSH 8 could be observed in the 2-DE gels of TCA and TCA-B methods, and could only be detected in the LC-MS/MS results of the BPP and TCA-B methods, showing that TCA-B method might be the optimized choice for both 2-DE and LC-MS/MS. CONCLUSION: Our data provided an improved TCA-B method for protein extraction that is compatible with 2-DE and LC-MS/MS for cotton leaves and similar plant tissues which is rich in polysaccharides and polyphenols.

Potential reservoirs and routes of Cronobacter transmission during cereal growing, processing and consumption.

Cronobacter are opportunistic bacterial pathogens of both infants and adults. We investigated the incidence and distribution of Cronobacter in 1245 samples of cereal and related environments. 39.1% (101/258) rice-related and 46.9% (98/209) wheat-related samples tested positive for Cronobacter, and the positive rate differed notably according to processing method. Cronobacter was found in rice and wheat plants at the tillering, filling and mature stages. Soil, water and swab samples from nearby milling plants were assayed, and results revealed that 6.3% (7/122) of water from paddy fields, 49.1% (28/57) and 62.1% (41/67) of swab samples from rice and wheat flour milling plants were Cronobacter positive. Pulsed-field gel electrophoresis (PFGE) subtyping indicated that some strains had a common profile, which suggested their persistence in the environment, potential transmission routes and cross-contamination in processing. Finally, we surveyed 18 families to evaluate potential risks. None of the families who primarily ate rice cooked with water tested positive for Cronobacter, though of 66.7% families (6/9) whose food staples were produced from wheat flour tested positive. Taken together, our results are important for understanding Cronobacter transmission and will aid in the development of additional control measures to reduce the risk of infection by these opportunistic pathogenic bacteria.

Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor.

Automatic detection of left ventricle myocardium is essential to subsequent cardiac image registration and tissue segmentation. However, it is considered challenging mainly because of the complex and varying shape of the myocardium and surrounding tissues across slices and phases. In this study, a hybrid model is proposed to detect myocardium in cardiac magnetic resonance (MR) images combining region proposal and deep feature classification and regression. The model firstly generates candidate regions using new structural similarity-enhanced supervoxel over-segmentation plus hierarchical clustering. Then it adopts a deep stacked sparse autoencoder (SSAE) network to learn the discriminative deep feature to represent the regions. Finally, the features are fed to train a novel nonlinear within-class neighborhood preserved soft margin support vector (C-SVC) classifier and multiple-output support vector ( ε -SVR) regressor for refining the location of myocardium. To improve the stability and generalization, the model also takes hard negative sample mining strategy to fine-tune the SSAE and the classifier. The proposed model with impacts of different components were extensively evaluated and compared to related methods on public cardiac data set. Experimental results verified the effectiveness of proposed integrated components, and demonstrated that it was robust in myocardium localization and outperformed the state-of-the-art methods in terms of typical metrics. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

Automatic Vertebrae Localization and Identification by Combining Deep SSAE Contextual Features and Structured Regression Forest.

Automatic vertebrae localization and identification in medical computed tomography (CT) scans is of great value for computer-aided spine diseases diagnosis. In order to overcome the disadvantages of the approaches employing hand-crafted, low-level features and based on field-of-view priori assumption of spine structure, an automatic method is proposed to localize and identify vertebrae by combining deep stacked sparse autoencoder (SSAE) contextual features and structured regression forest (SRF). The method employs SSAE to learn image deep contextual features instead of hand-crafted ones by building larger-range input samples to improve their contextual discrimination ability. In the localization and identification stage, it incorporates the SRF model to achieve whole spine localization, then screens those vertebrae within the image, thus relieves the assumption that the part of spine in the field of image is visible. In the end, the output distribution of SRF and spine CT scans properties are assembled to develop a two-stage progressive refining strategy, where the mean-shift kernel density estimation and Otsu method instead of Markov random field (MRF) are adopted to reduce model complexity and refine vertebrae localization results. Extensive evaluation was performed on a challenging data set of 98 spine CT scans. Compared with the hidden Markov model and the method based on convolutional neural network (CNN), the proposed approach could effectively and automatically locate and identify spinal targets in CT scans, and achieve higher localization accuracy, low model complexity, and no need for any assumptions about visual field in CT scans.

Protein and Proteome Atlas for Plants under Stresses: New Highlights and Ways for Integrated Omics in Post-Genomics Era.

In the post-genomics era, integrative omics studies for biochemical, physiological, and molecular changes of plants in response to stress conditions play more crucial roles. Among them, atlas analysis of plants under different abiotic stresses, including salinity, drought, and toxic conditions, has become more important for uncovering the potential key genes and proteins in different plant tissues. High-quality genomic data and integrated analyses of transcriptomic, proteomic, metabolomics, and phenomic patterns provide a deeper understanding of how plants grow and survive under environmental stresses. This editorial mini-review aims to synthesize the 27 papers including two timely reviews that have contributed to this Special Issue, which focuses on concluding the recent progress in the Protein and Proteome Atlas in plants under different stresses. It covers various aspects of plant proteins ranging from agricultural proteomics, structure and function of proteins, novel techniques and approaches for gene and protein identification, protein quantification, proteomics for post-translational modifications (PTMs), and new insights into proteomics. The proteomics-based results in this issue will help the readers to gain novel insights for the understanding of complicated physiological processes in crops and other important plants in response to stressed conditions. Furthermore, these target genes and proteins that are important candidates for further functional validation in economic plants and crops can be studied.

Proteomic Landscape Has Revealed Small Rubber Particles Are Crucial Rubber Biosynthetic Machines for Ethylene-Stimulation in Natural Rubber Production.

Rubber particles are a specific organelle for natural rubber biosynthesis (NRB) and storage. Ethylene can significantly improve rubber latex production by increasing the generation of small rubber particles (SRPs), regulating protein accumulation, and activating many enzyme activities. We conducted a quantitative proteomics study of different SRPs upon ethylene stimulation by differential in-gel electrophoresis (DIGE) and using isobaric tags for relative and absolute quantification (iTRAQ) methods. In DIGE, 79 differentially accumulated proteins (DAPs) were determined as ethylene responsive proteins. Our results show that the abundance of many NRB-related proteins has been sharply induced upon ethylene stimulation. Among them, 23 proteins were identified as rubber elongation factor (REF) and small rubber particle protein (SRPP) family members, including 16 REF and 7 SRPP isoforms. Then, 138 unique phosphorylated peptides, containing 129 phosphorylated amino acids from the 64 REF/SRPP family members, were identified, and most serine and threonine were phosphorylated. Furthermore, we identified 226 DAPs from more than 2000 SRP proteins by iTRAQ. Integrative analysis revealed that almost all NRB-related proteins can be detected in SRPs, and many proteins are positively responsive to ethylene stimulation. These results indicate that ethylene may stimulate latex production by regulating the accumulation of some key proteins. The phosphorylation modification of REF and SRPP isoforms might be crucial for NRB, and SRP may act as a complex natural rubber biosynthetic machine.