Critical factors driving spatiotemporal variability in the phytoplankton community structure of the coral habitat in Dongshan Bay, China.

This study investigated the spatiotemporal distribution of the phytoplankton in the coral habitat of Dongshan Bay (China), along with critical factors affecting the distribution, during June, August, and December 2022. Phytoplankton abundance in Dongshan Bay exhibited considerably temporal variation, peaking in June 2022, gradually decreasing thereafter, and reaching its lowest point in December 2022. The abundance of bottom-layer phytoplankton consistently exceeded that of the surface layer throughout all seasons. The average phytoplankton abundance in the coral habitat of Dongshan Bay was lower than that in non-coral habitat areas. Fluctuations in the Zhangjiang River and coastal upwelling influenced the diversity and community structure of the phytoplankton. Critical factors causing spatiotemporal variability in phytoplankton community structure included nutrient concentrations and seawater temperature. Nutrients played key roles in influencing various phytoplankton groups. Dominant diatom species, such as Thalassionema nitzschioides and Thalassiosira diporocyclus, were positively correlated with ammonia nitrogen, seawater salinity, coral cover, and the number of coral species present. In winter, Calanus sinicus exhibited a negative correlation with harmful algal bloom species. Additionally, it was found that both in the coral habitat and surrounding open sea, currents, nutrients, and zooplankton may play crucial roles in determining the spatiotemporal variability in the phytoplankton community structure.

Construction of Gene Expression Patterns to Identify Critical Genes under SARS-CoV-2 Infection Conditions.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-stranded single-stranded RNA virus with an envelope frequently altered by unstable genetic material, making it extremely difficult for vaccines, drugs, and diagnostics to work. Understanding SARS-CoV-2 infection mechanisms requires studying gene expression changes. Deep learning methods are often considered for large-scale gene expression profiling data. Data feature-oriented analysis, however, neglects the biological process nature of gene expression, making it difficult to describe gene expression behaviors accurately. In this paper, we propose a novel scheme for modeling gene expression during SARS-CoV-2 infection as networks (gene expression modes, GEM), to characterize their expression behaviors. On this basis, we investigated the relationships among GEMs to determine SARS-CoV-2's core radiation mode. Our final experiments identified key COVID-19 genes by gene function enrichment, protein interaction, and module mining. Experimental results show that ATG10, ATG14, MAP1LC3B, OPTN, WDR45, and WIPI1 genes contribute to SARS-CoV-2 virus spread by affecting autophagy.

Federated Active Learning for Multicenter Collaborative Disease Diagnosis.

Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.

Graph Motif Entropy for Understanding Time-Evolving Networks.

The structure of networks can be efficiently represented using motifs, which are those subgraphs that recur most frequently. One route to understanding the motif structure of a network is to study the distribution of subgraphs using statistical mechanics. In this article, we address the use of motifs as network primitives using the cluster expansion from statistical physics. By mapping the network motifs to clusters in the gas model, we derive the partition function for a network, and this allows us to calculate global thermodynamic quantities, such as energy and entropy. We present analytical expressions for the number of certain types of motifs, and compute their associated entropy. We conduct numerical experiments for synthetic and real-world data sets and evaluate the qualitative and quantitative characterizations of the motif entropy derived from the partition function. We find that the motif entropy for real-world networks, such as financial stock market networks, is sensitive to the variance in network structure. This is in line with recent evidence that network motifs can be regarded as basic elements with well-defined information-processing functions.

Multi-source detection based on neighborhood entropy in social networks.

The rapid development of social networking platforms has accelerated the spread of false information. Effective source location methods are essential to control the spread of false information. Most existing methods fail to make full use of the infection of neighborhood information in nodes, resulting in a poor source localization effect. In addition, most existing methods ignore the existence of multiple source nodes in the infected cluster and hard to identify the source nodes comprehensively. To solve these problems, we propose a new method about the multiple sources location with the neighborhood entropy. The method first defines the two kinds of entropy, i.e. infection adjacency entropy and infection intensity entropy, depending on whether neighbor nodes are infected or not. Then, the possibility of a node is evaluated by the neighborhood entropy. To locate the source nodes comprehensively, we propose a source location algorithm with the infected clusters. Other unrecognized source nodes in the infection cluster are identified by the cohesion of nodes, which can deal with the situation in the multiple source nodes in an infected cluster. We conduct experiments on various network topologies. Experimental results show that the two proposed algorithms outperform the existing methods.

Complete mitochondrial genome of the Turbinaria bifrons (Scleractinia, Dendrophylliidae).

In this study, the complete mitogenome sequence of the stony coral, Turbinaria bifrons Brüggemann, 1877, has been decoded for the first time by next-generation sequencing (NGS) and genome assembly. The assembled mitogenome was 18,880 bp in length, contained 13 protein coding genes, 2 transfer RNAs, and 2 ribosomal RNAs. The complete mitogenome of T. bifrons showing 97.09% identities to Tubastraea tagusensis. The complete mitogenome provides essential and important DNA molecular data for further phylogenetic and evolutionary analysis for coral phylogeny.

A small collection of Endeis juveniles (Arthropoda: Pycnogonida: Endeidae) sorted from the fouling organisms on the R/V Dayangyihao.

BACKGROUND: Endeis straughani Clark, 1970 was originally described from Queensland, Australia. Its range was extended to Ghana, West Africa when E. picta Bamber, 1979 was synonymised with E. straughani by Staples (1982). The current paper extends this range further, to include Port Louis, Mauritius. NEW INFORMATION: Five immature individuals gathered amongst hydroids during the 5th leg of the DY125-34 expedition were tentatively identified as E. straughani juveniles. Since these were collected from the fouling community on the ship's hull, they probably originated in Port Louis (Mauritius) when the ship was docked there.

Erratum: Wang, J., et al. Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer's Disease. Entropy 2021, 23, 216.

We sincerely apologize for the inconvenience of updating the authorship [...].

Complete mitochondrial DNA sequence of the Psammocora profundacella (Scleractinia, Psammocoridae): mitogenome characterisation and phylogenetic implications.

Complete mitochondrial DNA sequence data have played a significant role in phylogenetic and evolutionary studies of scleractinian corals. In this study, the complete mitogenome of Psammocora profundacella Gardiner, 1898, collected from Guangdong Province, China, was sequenced by next-generation sequencing for the first time. Psammocora profundacella is the first species for which a mitogenome has been sequenced in the family Psammocoridae. The length of its assembled mitogenome sequence was 16,274 bp, including 13 protein-coding genes, two tRNAs and two rRNAs. Its gene content and gene order were consistent with the other Scleractinia species. All genes were encoded on the H strand and the GC content of the mitochondrial genome was 30.49%. Gene content and order were consistent with the other Scleractinia species. Based on 13 protein-coding genes, Maximum Likelihood phylogenetic analysis showed that P. profundacella belongs to the "Robust" clade. Mitochondrial genome data provide important molecular information for understanding the phylogeny of stony corals. More variable markers and additional species should be sequenced to confirm the evolutionary relationships of Scleractinia in the future.

Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer's Disease.

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer's disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer's patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

COVID-AL: The diagnosis of COVID-19 with deep active learning.

The efficient diagnosis of COVID-19 plays a key role in preventing the spread of this disease. The computer-aided diagnosis with deep learning methods can perform automatic detection of COVID-19 using CT scans. However, large scale annotation of CT scans is impossible because of limited time and heavy burden on the healthcare system. To meet the challenge, we propose a weakly-supervised deep active learning framework called COVID-AL to diagnose COVID-19 with CT scans and patient-level labels. The COVID-AL consists of the lung region segmentation with a 2D U-Net and the diagnosis of COVID-19 with a novel hybrid active learning strategy, which simultaneously considers sample diversity and predicted loss. With a tailor-designed 3D residual network, the proposed COVID-AL can diagnose COVID-19 efficiently and it is validated on a large CT scan dataset collected from the CC-CCII. The experimental results demonstrate that the proposed COVID-AL outperforms the state-of-the-art active learning approaches in the diagnosis of COVID-19. With only 30% of the labeled data, the COVID-AL achieves over 95% accuracy of the deep learning method using the whole dataset. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed COVID-AL framework.

Recent collections of sea spiders (Arthropoda: Pycnogonida) from China seas, with some new records and a checklist of the area.

Though research about sea spiders limited in China seas, a complete checklist has not yet exist so far. After checking recent collections obtained from Shandong, Zhejiang, Fujian and the East China Sea, all these sixty-eight specimens were identified as six species belonged to four families and five genera, including three ones new to China. We described and illustrated them in this study, and also revised all previous records about Chinese pycnogonids and provided a checklist for the presently known forty-one species.

VNF Chain Placement for Large Scale IoT of Intelligent Transportation.

With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.

FedMed: A Federated Learning Framework for Language Modeling.

Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next word or phrase and facilitating the human-machine interaction in a virtual keyboard of the smartphone or laptop. Mobile keyboard prediction with FL hopes to satisfy the growing demand that high-level data privacy be preserved in artificial intelligence applications even with the distributed models training. However, there are two major problems in the federated optimization for the prediction: (1) aggregating model parameters on the server-side and (2) reducing communication costs caused by model weights collection. To address the above issues, traditional FL methods simply use averaging aggregation or ignore communication costs. We propose a novel Federated Mediation (FedMed) framework with the adaptive aggregation, mediation incentive scheme, and topK strategy to address the model aggregation and communication costs. The performance is evaluated in terms of perplexity and communication rounds. Experiments are conducted on three datasets (i.e., Penn Treebank, WikiText-2, and Yelp) and the results demonstrate that our FedMed framework achieves robust performance and outperforms baseline approaches.

A new species of Cilunculus Loman, 1908 (Arthropoda: Pycnogonida: Ammotheidae) from the South-western Indian Ocean.

BACKGROUND: A new species of Cilunculus was determined by the unique characteristic of the three distal processes present on the almost horizontal proboscis and the other differences, including a shorter and blind ocular tubercle, fewer setae on the legs and a glabrate trunk. NEW INFORMATION: New Cilunculus species from the South-western Indian Ocean.

Characterization of the complete mitochondrial genome sequences of three Merulinidae corals and novel insights into the phylogenetics.

Over the past few decades, modern coral taxonomy, combining morphology and molecular sequence data, has resolved many long-standing questions about scleractinian corals. In this study, we sequenced the complete mitochondrial genomes of three Merulinidae corals (Dipsastraea rotumana, Favites pentagona, and Hydnophora exesa) for the first time using next-generation sequencing. The obtained mitogenome sequences ranged from 16,466 bp (D. rotumana) to 18,006 bp (F. pentagona) in length, and included 13 unique protein-coding genes (PCGs), two transfer RNA genes, and two ribosomal RNA genes . Gene arrangement, nucleotide composition, and nucleotide bias of the three Merulinidae corals were canonically identical to each other and consistent with other scleractinian corals. We performed a Bayesian phylogenetic reconstruction based on 13 protein-coding sequences of 86 Scleractinia species. The results showed that the family Merulinidae was conventionally nested within the robust branch, with H. exesa clustered closely with F. pentagona and D. rotumana clustered closely with Favites abdita. This study provides novel insight into the phylogenetics of species within the family Merulinidae and the evolutionary relationships among different Scleractinia genera.

Comparative study of radioactivity levels and radionuclide fingerprints in typical marine ecosystems of coral reefs, mangroves, and hydrothermal vents.

As a key environmental parameter to induce radiation dose and effect on non-human species, radioactivity level is rarely evaluated in typical ecosystems of coral reefs, mangroves, and hydrothermal vents. In this study, naturally occurring radionuclides (238U, 226Ra, 228Ra, and 40K) in carbonate, silicate, and sulfide sediments collected from coral reefs, mangroves, and hydrothermal vents were simultaneously measured using high purity germanium (HPGe) γ spectrometry. Radioactivity levels and radionuclide fingerprints (226Ra/238U and 228Ra/226Ra) were interpreted and explored for tracking sources and formation processes of marine sediments in distinct marine ecosystems. Additionally, ionizing radiation dose rate on representative marine biotas (mollusc-bivalve, crustacean, polychaete worm, benthic fish, and pelagic fish) was evaluated using the ERICA tool with an increasing rank in coral reefs < mangroves < hydrothermal vents. Polychaete worm received the highest radiation dose relative to other marine biotas. We also emphasized the dominant contribution of 210Po to total radiation dose rate on marine biotas.

First record and a new species of Sericosura Fry Hedgpeth, 1969 (Arthropoda: Pycnogonida: Ammotheidae) from a hydrothermal vent of Southwestern Indian Ridge.

This is the first record of the genus Sericosura from the "Duanqiao" hydrothermal field on the Southwestern Indian Ridge. Collected material included two juvenile specimens temporarily assigned to S. bamberi and S. heteroscela respectively, and one male specimen with dimorphic legs here proposed as a new species, S. duanqiaoensis sp. nov. The discovery suggests potential connectivity among the hydrothermal vents of the Atlantic, Indian and Southern Oceans, expecting obtaining more specimens for further evolutionary studies to clarify the relationships of these hydrothermal vents.

The complete mitogenome of Pentacheles validus (Decapoda: Polychelidae).

In this study, the complete mitogenome sequence of Pentacheles validus has been decoded for the first time. The overall base composition is 33.4% for A, 25.0% for C, 10.8% for G, and 30.9% for T and has low GC content of 35.8%. The assembled mitogenome, 16,079 bp in length, has unique 13 protein-coding genes (PCGs), 22 transfer RNAs and 2 ribosomal RNAs. The mitogenome shares 80% identity to Polycheles typhlops. The complete mitogenome of P. validus provides essential DNA molecular data for further phylogenetic and evolutionary analysis for Polychelidae and deep-sea faunas.

Characterization of the Complete Mitochondrial Genome Sequence of the Globose Head Whiptail Cetonurus globiceps (Gadiformes: Macrouridae) and Its Phylogenetic Analysis.

The particular environmental characteristics of deep water such as its immense scale and high pressure systems, presents technological problems that have prevented research to broaden our knowledge of deep-sea fish. Here, we described the mitogenome sequence of a deep-sea fish, Cetonurus globiceps. The genome is 17,137 bp in length, with a standard set of 22 transfer RNA genes (tRNAs), two ribosomal RNA genes, 13 protein-coding genes, and two typical non-coding control regions. Additionally, a 70 bp tRNA(Thr)-tRNA(Pro) intergenic spacer is present. The C. globiceps mitogenome exhibited strand-specific asymmetry in nucleotide composition. The AT-skew and GC-skew values in the whole genome of C. globiceps were 0 and -0.2877, respectively, revealing that the H-strand had equal amounts of A and T and that the overall nucleotide composition was C skewed. All of the tRNA genes could be folded into cloverleaf secondary structures, while the secondary structure of tRNA(Ser(AGY)) lacked a discernible dihydrouridine stem. By comparing this genome sequence with the recognition sites in teleost species, several conserved sequence blocks were identified in the control region. However, the GTGGG-box, the typical characteristic of conserved sequence block E (CSB-E), was absent. Notably, tandem repeats were identified in the 3' portion of the control region. No similar repetitive motifs are present in most of other gadiform species. Phylogenetic analysis based on 12 protein coding genes provided strong support that C. globiceps was the most derived in the clade. Some relationships however, are in contrast with those presented in previous studies. This study enriches our knowledge of mitogenomes of the genus Cetonurus and provides valuable information on the evolution of Macrouridae mtDNA and deep-sea fish.