Marine environments harbour a vast diversity of micro-eukaryotic organisms (protists and other small eukaryotes) that play important roles in structuring marine ecosystems. However, micro-eukaryote diversity is not well understood. Likewise, knowledge is limited regarding micro-eukaryote spatial and seasonal distribution, especially over long temporal scales. Given the importance of this group for mobilizing energy from lower trophic levels near the base of the food chain to larger organisms, assessing community stability, diversity and resilience is important to understand ecosystem health. Herein, we use a metabarcoding approach to examine pelagic micro-eukaryote communities over a 2.5-year time series. Bimonthly surface sampling (July 2009 to December 2011) was conducted at four locations within Mobile Bay (Bay) and along the Alabama continental shelf (Shelf). Alpha-diversity only showed significant differences in Shelf sites, with the greatest differences observed between summer and winter. Beta-diversity showed significant differences in community composition in relation to season and the Bay was dominated by diatoms, while the Shelf was characterized by dinoflagellates and copepods. The northern Gulf of Mexico is heavily influenced by the Mobile River Basin, which brings low-salinity nutrient-rich water mostly during winter and spring. Community composition was correlated with salinity, temperature and dissolved silicate. However, species interactions (e.g. predation and parasitism) may also contribute to the observed variation, especially on the Shelf, which warrants further exploration. Metabarcoding revealed clear patterns in surface pelagic micro-eukaryote communities that were consistent over multiple years, demonstrating how these techniques could be greatly beneficial to ecological monitoring and management over temporal scales.
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Sensors (Basel)
December 2024
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN).
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December 2024
Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
Generating accurate and contextually rich captions for images and videos is essential for various applications, from assistive technology to content recommendation. However, challenges such as maintaining temporal coherence in videos, reducing noise in large-scale datasets, and enabling real-time captioning remain significant. We introduce MIRA-CAP (Memory-Integrated Retrieval-Augmented Captioning), a novel framework designed to address these issues through three core innovations: a cross-modal memory bank, adaptive dataset pruning, and a streaming decoder.
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December 2024
Department of Computer Science, University of Bristol, Bristol BS8 1QU, UK.
Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions.
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December 2024
School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300132, China.
With the escalating threat posed by network intrusions, the development of efficient intrusion detection systems (IDSs) has become imperative. This study focuses on improving detection performance in programmable logic controller (PLC) network security while addressing challenges related to data imbalance and long-tail distributions. A dataset containing five types of attacks targeting programmable logic controllers (PLCs) in industrial control systems (ICS) was first constructed.
View Article and Find Full Text PDFPlants (Basel)
December 2024
Institute of Botany and Botanical Garden, Faculty of Biology, University of Belgrade, Takovska 43, 11000 Belgrade, Serbia.
The Balkan Peninsula represents an important center of plant diversity, exhibiting remarkable ecological heterogeneity that renders it an optimal region for studying the diversification patterns of complex taxa such as . In the Balkan Peninsula, is a highly plastic and morphologically variable species with unresolved taxonomic status. To ascertain the patterns of genetic and morphological diversification, a comparative genetic and morphological analysis was conducted.
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