The relationships between vertical variations of shallow gas and pore water geochemical characteristics in boreholes from the inner shelf of the East China Sea

Shallow gas was widely recognized in the coastal region, especially in the estuarine delta areas with high organic matter (OM) burial flux. In this study, the vertical variations of shallow gas and the related geochemical indicators from two boreholes in the coastal region of the East China Sea (ECS) were investigated. Two gas-bearing layers were identified in the sediments from the Holocene and late Pleistocene series. Both boreholes exhibit a “typical” and an “inverse” Sulfate Methane Transition Zone (SMTZ). The “typical” SMTZs (SMTZ1 and SMTZ3) were in the upper part of the gas-bearing layers, where sulfate levels decrease and methane levels increase with depth. Conversely, the “inverse” SMTZs (SMTZ2 and SMTZ4) were in the lower part of the gas-bearing layers, exhibiting an increase in sulfate levels and a decrease in methane levels with depth, a phenomenon rarely documented in previous research. The downward variations of pore water geochemical characteristics indicates that these ions were related to Anaerobic Oxidation of Methane (AOM) processes. The increase in Ca2+ and Ba2+ concentrations and the gradual decrease in sulfate at the SMTZ reflect a series of biogeochemical processes resulting from the dissolution of carbonate and other minerals by AOM. The research indicates that sulfate in AOM may originate from multiple sources. Through analyzing the vertical distribution of shallow gas and the geochemical properties of pore water, this study elucidates the shallow gas formation mechanism and the features of the SMTZ, laying the groundwork for further investigations

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A Dynamic Cache Allocation Mechanism (DCAM) for Reliable Multicast in Information-Centric Networking

As a new network architecture, information-centric networking (ICN) decouples the identifiers and locators of network entities and makes full use of in-network cache technology to improve the content distribution efficiency. For reliable multicast, ICN in-network cache can help reduce the loss recovery delay. However, with the development of applications and services, a multicast tree node often serves multiple reliable multicast groups. How to reasonably allocate cache resources for each multicast group will greatly affect the performance of reliable multicast. In order to improve the overall loss recovery performance of reliable multicast, this paper designs a dynamic cache allocation mechanism (DCAM). DCAM considers the packet loss probability, the node depth of the multicast tree, and the multicast transmission rate of multicast group, and then allocates cache space for multicast group based on the normalized cache quota weight. We also explore the performance of three cache allocation mechanisms (DCAM, AARM, and Equal) combined with four cache strategies (LCE, CAPC, Prob, and ProbCache), respectively. Experimental results show that DCAM can adjust cache allocation results in time according to network changes, and its combinations with various cache strategies outperform other combinations. Moreover, the combination of DCAM and CAPC can achieve optimal performance in loss recovery delay, cache hit ratio, transmission completion time, and overhead

Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification

Hyperspectral image classification has received a lot of attention in the remote sensing field. However, most classification methods require a large number of training samples to obtain satisfactory performance. In real applications, it is difficult for users to label sufficient samples. To overcome this problem, in this work, a novel multi-scale superpixel-guided structural profile method is proposed for the classification of hyperspectral images. First, the spectral number (of the original image) is reduced with an averaging fusion method. Then, multi-scale structural profiles are extracted with the help of the superpixel segmentation method. Finally, the extracted multi-scale structural profiles are fused with an unsupervised feature selection method followed by a spectral classifier to obtain classification results. Experiments on several hyperspectral datasets verify that the proposed method can produce outstanding classification effects in the case of limited samples compared to other advanced classification methods. The classification accuracies obtained by the proposed method on the Salinas dataset are increased by 43.25%, 31.34%, and 46.82% in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient compared to recently proposed deep learning methods

Extraction and Identification of Phlorotannins from the Brown Alga, Sargassum fusiforme (Harvey) Setchell

Phlorotannins are a group of complex polymers of phloroglucinol (1,3,5-trihydroxybenzene), which are unique compounds from marine brown algae. In our present study, a procedure for extraction and enrichment of phlorotannins from S. fusiforme with highly antioxidant potentials was established. After comparison of different extraction methods, the optimal extraction conditions were established as follows. The freeze-dried seaweed powder was extracted with 30% ethanol-water solvent with a solid/liquid ratio of 1:5 at temperature of 25 °C for 30 min. After extraction, the phlorotannins were fractioned by different solvents, among which the ethyl acetate fraction exhibited both the highest total phlorotannin content (88.48 ± 0.30 mg PGE/100 mg extract) and the highest antioxidant activities. The extracts obtained from these locations were further purified and characterized using a modified UHPLC-QQQ-MS method. Compounds with 42 different molecular weights were detected and tentatively identified, among which the fuhalol-type phlorotannins were the dominant compounds, followed by phlorethols and fucophlorethols with diverse degree of polymerization. Eckol-type phlorotannins including some newly discovered carmalol derivatives were detected in Sargassum species for the first time. Our study not only described the complex phlorotannins composition in S. fusiforme, but also highlighted the challenges involved in structural elucidation of these compounds

A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method

Abstract Advances in data assimilation (DA) methods and the increasing amount of observations have continuously improved the accuracy of initial fields in numerical weather prediction during the last decades. Meanwhile, in order to effectively utilize the rapidly increasing data, Earth scientists must further improve DA methods. Recent studies have introduced machine learning (ML) methods to assist the DA process. In this paper, we explore the potential of a four‐dimensional variational (4DVar) constrained neural network (NN) method for accurate DA. Our NN is trained to approximate the solution of the variational problem, thereby avoiding the need for expensive online optimization when generating the initial fields. In the context that the full‐field system truths are unavailable, our approach embeds the system's kinetic features described by a series of analysis fields into the NN through a 4DVar‐form loss function. Numerical experiments on the Lorenz96 physical model demonstrate that our method can generate better initial fields than most traditional DA methods at a low computational cost, and is robust when assimilating observations with higher error outside of the distributions where it is trained. Furthermore, our NN‐based DA model is effective against Lorenz96 physical models with larger variable numbers. Our approach exemplifies how ML methods can be leveraged to improve both the efficiency and accuracy of DA techniques

Transcriptomics Integrated with Metabolomics Reveals 2-Methoxy-1, 4-Naphthoquinone-Based Carbon Dots Induced Molecular Shifts in Penicillium italicum

Penicillium italicum (P. italicum), a citrus blue mold, is a pathogenic fungus that greatly affects the postharvest quality of citrus fruits with significant economic loss. Our previous research showed that 2-methoxy-1, 4-naphthoquinone (MNQ) inhibited the growth of Penicillium italicum. However, the water dispersibility of MNQ will limit its further application. Herein, we synthesized MNQ-based carbon dots (2−CDs) with better water dispersibility, which showed a potential inhibitory effect on P. italicum (MIC = 2.8 μg/mL) better than that of MNQ (MIC = 5.0 μg/mL). Transcriptomics integrated with metabolomics reveals a total of 601 differentially enriched genes and 270 differentially accumulated metabolites that are co-mapped as disruptive activity on the cell cytoskeleton, glycolysis, and histone methylation. Furthermore, transmission electron microscopy analysis showed normal appearances and intracellular septum of P. italicum after treatment. These findings contribute tofurther understanding of the possible molecular action of 2−CDs