Identifying the Vertical Stratification of Sediment Samples by Visible and Near-Infrared Spectroscopy.

Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0-50 cm (the upper layer), 50-100 cm (the middle layer), and 100-160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR.

Unprecedented nor-seco-diterpene lactones inhibited osteogenic differentiation of valve interstitial cells.

The first examples of ent-atisane and ent-isopimarane diterpene lactones with an unusual 2,3-seco-2-nor-tetrahydro-2H-pyran-2-one nucleus, eufislactones A (1) and B (2), were isolated from the roots of Euphorbia fischeriana, together with a new (3) and fifteen known biosynthetic congeners (4-18). Their structures incorporating absolute configurations were elucidated via the comprehensive spectroscopic analyses, electronic circular dichroism (ECD) calculation, and single-crystal X-ray diffraction analyses. Biogenetically, compounds 1 and 2 were constructed by the plausible monomeric precursors, ent-atis-16-ene-3,14-dione (6) and ent-isopimara-8(14),15-dien-3-one (17), respectively, via key Baeyer-Villiger oxidation, decarboxylation, and semi-acetalization reactions to create a unique 2,3-seco-2-nor-tetrahydro-2H-pyran-2-one core. Our bioassays have revealed that eufislactone A (EFA, 1) displayed significant inhibitory effect on the osteogenic differentiation of human valvular interstitial cells (VICs), highlighting its potential as a preventive agent against the progression of human calcific aortic valve disease (CAVD).

Excitation power-dependent multicolor upconversion in NaLnF4:Er3+ under 1532†nm irradiation for anti-counterfeiting application.

Upconversion (UC) materials are renowned for their ability to convert low-energy photons into high-energy ones. The manipulation of parameters allows for the observation of multicolored UC luminescence (UCL) within a single material system. While modulation of multicolored UCL commonly relies on excitation at approximately 980 nm, investigation into multicolored UC materials activated by a 1532 nm excitation source remains comparatively scarce. In this work, we introduce NaLnF4:Er3+ as a novel class of smart luminescent materials. When the power density of a 1532 nm laser increases from 0.5 to 20.0 W/cm2, the emission peak positions remain unchanged, but the red-to-green (R/G) ratio decreases significantly from 18.82 to 1.48, inducing a color shift from red to yellow and ultimately to green. In contrast, no color variation is observed when NaLnF4:Er3+ is excited with a 980 nm laser at different power densities. This power-dependent multicolored UCL of NaLnF4:Er3+ excited at 1532 nm can be attributed to the competitive processes of upward pumping and downward relaxation of electrons on the 4I9/2 level of Er3+. By utilizing the unique UC characteristics of NaLnF4:Er3+, its potential utility in anti-counterfeiting applications is demonstrated. Our research highlights the distinctive optical properties of NaLnF4:Er3+ and provides novel insights into the use of luminescent materials in optical anti-counterfeiting technologies.

Analysis of four heavy metal concentrations in sediments fromthe Jiaozhou Bay, China by visible and near infrared spectroscopy (225-975 nm).

As an important component ofbiogeochemical cyclein coastal ecosystems, sediments are the sink of heavy metals. Therefore, distribution and dynamics of heavy metals in sediments could assess ecological quality and predict ecological risks. In the new era, rapid and green technology are highly needed, especially that could determine multi-parameters simultaneously. Here, we explored a new method to rapidly determine concentrations of heavy metals in sediments by visible and near infrared reflectance spectroscopy (VIRS).We sampled sediments in the Jiaozhou Bay, China, collected their reflectance spectra, and measured concentrations of four heavy metals (As, Cr, Cu, and Zn). Heavy metal models were established and evaluated using substances highly correlated with heavy metals. This study provides an effective reference for rapid analysis of As, Cr, Cu, and Zn simultaneously in sediments, at least in the Jiaozhou Bay, and for ecological environment protection and resource development of the Jiaozhou Bay.

Nanopore analysis of salvianolic acids in herbal medicines.

Natural herbs, which contain pharmacologically active compounds, have been used historically as medicines. Conventionally, the analysis of chemical components in herbal medicines requires time-consuming sample separation and state-of-the-art analytical instruments. Nanopore, a versatile single molecule sensor, might be suitable to identify bioactive compounds in natural herbs. Here, a phenylboronic acid appended Mycobacterium smegmatis porin A (MspA) nanopore is used as a sensor for herbal medicines. A variety of bioactive compounds based on salvianolic acids, including caffeic acid, protocatechuic acid, protocatechualdehyde, salvianic acid A, rosmarinic acid, lithospermic acid, salvianolic acid A and salvianolic acid B are identified. Using a custom machine learning algorithm, analyte identification is performed with an accuracy of 99.0%. This sensing principle is further used with natural herbs such as Salvia miltiorrhiza, Rosemary and Prunella vulgaris. No complex sample separation or purification is required and the sensing device is highly portable.

Nanopore analysis of cis-diols in fruits.

Natural fruits contain a large variety of cis-diols. However, due to the lack of a high-resolution sensor that can simultaneously identify all cis-diols without a need of complex sample pretreatment, direct and rapid analysis of fruits in a hand-held device has never been previously reported. Nanopore, a versatile single molecule sensor, can be specially engineered to perform this task. A hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore modified with a sole phenylboronic acid (PBA) adapter is prepared. This engineered MspA accurately recognizes 1,2-diphenols, alditols, α-hydroxy acids and saccharides in prune, grape, lemon, different varieties of kiwifruits and commercial juice products. Assisted with a custom machine learning program, an accuracy of 99.3% is reported and the sample pretreatment is significantly simplified. Enantiomers such as DL-malic acids can also be directly identified, enabling sensing of synthetic food additives. Though demonstrated with fruits, these results suggest wide applications of nanopore in food and drug administration uses.

Unambiguous discrimination of all 20 proteinogenic amino acids and their modifications by nanopore.

Natural proteins are composed of 20 proteinogenic amino acids and their post-translational modifications (PTMs). However, due to the lack of a suitable nanopore sensor that can simultaneously discriminate between all 20 amino acids and their PTMs, direct sequencing of protein with nanopores has not yet been realized. Here, we present an engineered hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore containing a sole Ni2+ modification. It enables full discrimination of all 20 proteinogenic amino acids and 4 representative modified amino acids, Nω,N'ω-dimethyl-arginine (Me-R), O-acetyl-threonine (Ac-T), N4-(ß-N-acetyl-D-glucosaminyl)-asparagine (GlcNAc-N) and O-phosphoserine (P-S). Assisted by machine learning, an accuracy of 98.6% was achieved. Amino acid supplement tablets and peptidase-digested amino acids from peptides were also analyzed using this strategy. This capacity for simultaneous discrimination of all 20 proteinogenic amino acids and their PTMs suggests the potential to achieve protein sequencing using this nanopore-based strategy.

Evaluating Middle Cerebral Artery Plaque Characteristics and Lenticulostriate Artery Morphology Associated With Subcortical Infarctions at 7T MRI.

BACKGROUND: Lenticulostriate artery (LSA) obstruction is a potential cause of subcortical infarcts. However, MRI LSA evaluation at 3T is challenging. PURPOSE: To investigate middle cerebral artery (MCA) plaque characteristics and LSA morphology associated with subcortical infarctions in LSA territories using 7-T vessel wall MRI (VW-MRI) and time-of-flight MR angiography (TOF-MRA). STUDY TYPE: Prospective. POPULATION: Sixty patients with 80 MCA atherosclerotic plaques (37 culprit and 43 non-culprit). FIELD STRENGTH/SEQUENCE: 7-T with 3D TOF-MRA and T1-weighted 3D sampling perfection with application-optimized contrast using different flip angle evolutions (SPACE) sequences. ASSESSMENT: Plaque distribution (superior, inferior, ventral, or dorsal walls), LSA origin involvement, LSA morphology (numbers of stems, branches, and length), and plaque characteristics (normalized wall index, maximal wall thickness, plaque length, remodeling index, intraplaque hemorrhage, and plaque surface morphology (regular or irregular)) were assessed. STATISTICAL TESTS: Least absolute shrinkage and selection operator regression, generalized estimating equations regression, receiver operating characteristic curve, independent t-test, Mann-Whitney U test, Chi-square test, Fisher's exact test, and intra-class coefficient. A P value <0.05 was considered statistically significant. RESULTS: Plaque irregular surface, superior wall plaque, longer plaque length, LSA origin involvement, fewer LSA stems, and shorter total and average lengths of LSAs were significantly associated with culprit plaques. Multivariable logistic analysis confirmed that LSA origin involvement (OR, 28.51; 95% CI, 6.34-181.02) and plaque irregular surface (OR, 8.32; 95% CI, 1.41-64.73) were independent predictors in differentiating culprit from non-culprit plaques. A combination of LSA origin involvement and plaque irregular surface (area under curve = 0.92; [95% CI, 0.86-0.98]) showed good performance in identifying culprit plaques, with sensitivity and specificity of 86.5% and 86.0%, respectively. DATA CONCLUSION: 7-T VW-MRI and TOF-MRA can demonstrate plaque involvement with LSA origins. MCA plaque characteristics derived from 7-T VW-MRI showed good diagnostic accuracy in determining the occurrence of subcortical infarctions. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Discrimination of Disaccharide Isomers of Different Glycosidic Linkages Using a Modified MspA Nanopore.

Disaccharides are composed of two monosaccharide subunits joined by a glycosidic linkage in an α or ß configuration. Different combinations of isomeric monosaccharide subunits and different glycosidic linkages result in different isomeric disaccharide products. Thus, direct discrimination of these disaccharide isomers from a mixture is extremely difficult. In this paper, a hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore conjugated with a phenylboronic acid (PBA) adapter was applied for disaccharide sensing, with which three most widely known disaccharides in nature, including sucrose, lactose and maltose, were clearly discriminated. Besides, all six isomeric α-D-glucopyranosyl-D-fructoses, differing only in their glycosidic linkages, were also well resolved. Assisted by a custom machine learning algorithm, a 0.99 discrimination accuracy is achieved. Nanopore discrimination of disaccharide isomers with different glycosidic linkages, which has never been previously demonstrated, is inspiring for nanopore saccharide sequencing. This sensing capacity was also applied in direct identification of isomaltulose additives in a commercial sucrose-free yogurt, from which isomaltulose, lactose and L-lactic acid were simultaneously detected.

Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images.

Visible near-infrared reflectance spectroscopy (VNIR) and hyperspectral images (HSI) have their respective advantages in soil carbon content prediction, and the effective fusion of VNIR and HSI is of great significance for improving the prediction accuracy. But the contribution difference analysis of multiple features in the multi-source data is inadequate, and there is a lack of in-depth research on the contribution difference analysis of artificial feature and deep learning feature. In order to solve the problem, soil carbon content prediction methods based on VNIR and HSI multi-source data feature fusion are proposed. The multi-source data fusion network under the attention mechanism and the multi-source data fusion network with artificial feature are designed. For the multi-source data fusion network based on the attention mechanism, the information are fused through the attention mechanism according to the contribution difference of each feature. For the other network, artificial feature are introduced to fuse multi-source data. The results show that multi-source data fusion network based on the attention mechanism can improve the prediction accuracy of soil carbon content, and multi-source data fusion network combined with artificial feature has better prediction effect. Compared with two single-source data from the VNIR and HSI, the relative percent deviation of Neilu, Aoshan Bay and Jiaozhou Bay based on multi-source data fusion network combined with artificial feature are increased by 56.81% and 149.18%, 24.28% and 43.96%, 31.16% and 28.73% respectively. This study can effectively solve the problem of the deep fusion of multiple features in the soil carbon content prediction by VNIR and HSI, so as to improve the accuracy and stability of soil carbon content prediction, promote the application and development of soil carbon content prediction in spectral and hyperspectral image, and provide technical support for the study of carbon cycle and the carbon sink.

Single-Molecule Interconversion between Chiral Configurations of Boronate Esters Observed in a Nanoreactor.

Isomers of some chemical compounds may be dynamically interconvertible. Due to a lack of sensing methods with a sufficient resolution, however, direct monitoring of such processes can be difficult. Engineered Mycobacterium smegmatis porin A (MspA) nanopores can be applied as nanoreactors so that chemical reactions can be directly monitored. Here, an MspA modified with a phenylboronic acid (PBA) adapter was prepared and was used to observe dynamic interconversion between chiral configurations of boronate esters, which appears as telegraphic switching on top of nanopore events. The mechanism of this behavior was further confirmed by trials with different halogenated catechols, dopamine, adenosine, 1,2-propanediol, and (2R,3R)-2,3-butanediol, and its generality has been demonstrated. These results suggest that an engineered MspA possesses an exceptional resolution in its monitoring of chemical reaction processes and may inspire the future design of nanopore small-molecule sensors.

Discrimination of Ribonucleoside Mono-, Di-, and Triphosphates Using an Engineered Nanopore.

Ribonucleotides, which widely exist in all living organisms and are essential to both physiological and pathological processes, can naturally appear as ribonucleoside mono-, di-, and triphosphates. Natural ribonucleotides can also dynamically switch between different phosphorylated forms, posing a great challenge for sensing. A specially engineered nanopore sensor is promising for full discrimination of all canonical ribonucleoside mono-, di-, and triphosphates. However, such a demonstration has never been reported, due to the lack of a suitable nanopore sensor that has a sufficient resolution. In this work, we utilized a phenylboronic acid (PBA) modified Mycobacterium smegmatis porin A (MspA) hetero-octamer for ribonucleotide sensing. Twelve types of ribonucleotides, including mono-, di-, and triphosphates of cytidine (CMP, CDP, CTP), uridine (UMP, UDP, UTP), adenosine (AMP, ADP, ATP), and guanosine (GMP, GDP, GTP) were simultaneously discriminated. A machine-learning algorithm was also developed, which achieved a general accuracy of 99.9% for ribonucleotide sensing. This strategy was also further applied to identify ribonucleotide components in ATP tablets and injections. This sensing strategy provides a direct, accurate, easy, and rapid solution to characterize ribonucleotide components in different phosphorylated forms.

A Method to Evaluate Spectral Analysis by Spectroscopy.

Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range (max and min), resolution and error for SOC spectral analysis. Here, we proposed a method to evaluate the spectral analysis of SOC. Using 96 sediments sampled in the Yellow Sea and Bohai Sea, China, we established three spectral models of SOC after collecting their spectral reflectance by Agilent Cary 5000, ASD FieldSpec 4 and Ocean Optics QEPro, respectively. For both the calibration set and validation set in each spectrometer, the predicted SOC concentrations followed a distribution curve (function), in which the x-axis was the SOC concentrations. Using these curves, we developed these four technical parameters. The detection ranges were the SOC concentrations where the curve was near to or crossing with the lateral axis, while the detection resolution was the average difference between the two neighboring SOC concentrations. The detection errors were the differences between the predicted SOC and the measured SOC. Results showed that these technical parameters were better in the bench-top spectrometer (Cary 5000) than those in the portable spectrometers when analyzing the same samples. For the portable spectrometers, QEPro had a broader detection range and more consistent detection error than FieldSpec 4, suggesting that the low-cost QEPro performed as well as the high-cost FieldSpec 4. This study provides a good example for evaluating spectral analysis by spectroscopy, which can support the development of the spectral method.

Nanopore Identification of Alditol Epimers and Their Application in Rapid Analysis of Alditol-Containing Drinks and Healthcare Products.

Alditols, which have a sweet taste but produce much lower calories than natural sugars, are widely used as artificial sweeteners. Alditols are the reduced forms of monosaccharide aldoses, and different alditols are diastereomers or epimers of each other and direct and rapid identification by conventional methods is difficult. Nanopores, which are emerging single-molecule sensors with exceptional resolution when engineered appropriately, are useful for the recognition of diastereomers and epimers. In this work, direct distinguishing of alditols corresponding to all 15 monosaccharide aldoses was achieved by a boronic acid-appended hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore (MspA-PBA). Thirteen alditols including glycerol, erythritol, threitol, adonitol, arabitol, xylitol, mannitol, sorbitol, allitol, dulcitol, iditol, talitol, and gulitol (l-sorbitol) could be fully distinguished, and their sensing features constitute a complete nanopore alditol database. To automate event classification, a custom machine-learning algorithm was developed and delivered a 99.9% validation accuracy. This strategy was also used to identify alditol components in commercially available "zero-sugar" drinks and healthcare products, suggesting their use in rapid and sensitive quality control for the food and medical industry.

Identification of nucleoside monophosphates and their epigenetic modifications using an engineered nanopore.

RNA modifications play critical roles in the regulation of various biological processes and are associated with many human diseases. Direct identification of RNA modifications by sequencing remains challenging, however. Nanopore sequencing is promising, but the current strategy is complicated by sequence decoding. Sequential nanopore identification of enzymatically cleaved nucleoside monophosphates may simultaneously provide accurate sequence and modification information. Here we show a phenylboronic acid-modified hetero-octameric Mycobacterium smegmatis porin A nanopore, with which direct distinguishing between monophosphates of canonical nucleosides, 5-methylcytidine, N6-methyladenosine, N7-methylguanosine, N1-methyladenosine, inosine, pseudouridine and dihydrouridine was achieved. A custom machine learning algorithm, which reports an accuracy of 0.996, was also applied to the quantitative analysis of modifications in microRNA and natural transfer RNA. It is generally suitable for sensing of a variety of other nucleoside or nucleotide derivatives and may bring new insights to epigenetic RNA sequencing.

A Nanopore-Based Saccharide Sensor.

Saccharides play critical roles in many forms of cellular activities. Saccharide structures are however complicated and similar, setting a technical hurdle for direct identification. Nanopores, which are emerging single molecule tools sensitive to minor structural differences between analytes, can be engineered to identity saccharides. A hetero-octameric Mycobacterium smegmatis porin A nanopore containing a phenylboronic acid was prepared, and was able to clearly identify nine monosaccharide types, including D-fructose, D-galactose, D-mannose, D-glucose, L-sorbose, D-ribose, D-xylose, L-rhamnose and N-acetyl-D-galactosamine. Minor structural differences between saccharide epimers can also be distinguished. To assist automatic event classification, a machine learning algorithm was developed, with which a general accuracy score of 0.96 was achieved. This sensing strategy is generally suitable for other saccharide types and may bring new insights to nanopore saccharide sequencing.

Cerebral Microbleed Automatic Detection System Based on the "Deep Learning".

Objective: To validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs). Method: This is a retrospective study based on Magnetic Resonance Imaging-Susceptibility Weighted Imaging (MRI-SWI) datasets from 1,615 patients (median age, 56 years; 1,115 males, 500 females) obtained between September 2018 and September 2019. All patients had been diagnosed with cerebral small vessel disease (CSVD) with clear cerebral microbleeds (CMBs) on MRI-SWI. The patients were divided into training and validation cohorts of 1,285 and 330 patients, respectively, and another 30 patients were used for internal testing. The model training and validation data were labeled layer by layer and rechecked by two neuroradiologists with 15 years of work experience. Afterward, a three-dimensional convolutional neural network (CNN) was applied to the MRI data from the training and validation cohorts to construct a deep learning system (DLS) that was tested with the 72 patients, independent of the aforementioned MRI cohort. The DLS tool was used as a segmentation program for these 72 patients. These results were evaluated and revised by five neuroradiologists and subjected to an output analysis divided into the missed label, incorrect label, and correct label. The interneuroradiologists DLS agreement rate, which was assessed using the interrater agreement kappas test, was used for the quality analysis. Results: In the detection and segmentation of the CMBs, the DLS achieved a Dice coefficient of 0.72. In the evaluation of the independent clinical data, the neuroradiologists reported that more than 90% of the lesions were directly detected and less than 10% of lesions were incorrectly labeled or the label was missed by our DLS. The kappa value for interneuroradiologist DLS agreement reached 0.79 on average. Conclusion: Based on the results, the automatic detection and segmentation of CMBs are feasible. The proposed well-trained DLS system might represent a trusted tool for the segmentation and detection of CMB lesions.

Non-binary Encoded Nucleic Acid Barcodes Directly Readable by a Nanopore.

A large collection of unique molecular barcodes is useful in the simultaneous sensing or screening of molecular analytes. Though the sequence of DNA has been widely applied to encode for molecular barcodes, decoding of these barcodes is normally assisted by sequencing. We here demonstrate a barcode system based solely on self-assembly of synthetic nucleic acids and direct nanopore decoding. Each molecular barcode is composed of "n" distinct information nodes in a non-binary manner and can be sequentially scanned and decoded by a Mycobacterium smegmatis porin A (MspA) nanopore. Nanopore events containing step-shaped features were consistently reported. 14 unique information nodes were developed which in principle could encode for 14n unique molecular barcodes in a barcode containing "n" information nodes. These barcode probes were adapted to detect different antibody proteins or cancer-related microRNAs, suggesting their immediate application in a wide variety of sensing applications.

Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition.

Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode decomposition were proposed for lateral continuity consideration. Due to large input data, mini-batch multivariate variational mode decomposition is proposed in this paper. The proposed method takes advantages both of variational mode decomposition and multivariate variational mode decomposition. This proposed method firstly segments the input data into a series of smaller ones with no overlapping and then applies multivariate variational mode decomposition to these smaller ones. High frequency-domain noise is filtered through sifting. Finally, the denoised smaller ones are concatenated to form components (or intrinsic mode functions) of the input signal. Synthetic and field data experiments validate the proposed method with different batch sizes and achieve higher signal-to-noise ratio than the variational mode decomposition method.