Plastic Paradox in Blue Carbon Ecosystems.

Plastics are rapidly accumulating in blue carbon ecosystems, i.e., mangrove forests, tidal marshes, and seagrass meadows. Accumulated plastic is diverted from the ocean, but the extent and nature of impacts on blue carbon ecosystem processes, including carbon sequestration, are poorly known. Here, we explore the potential positive and negative consequences of plastic accumulation in blue carbon ecosystems. We highlight the effects of plastic accumulation on organic carbon stocks and sediment biogeochemistry through microbial metabolism. The notion of beneficial plastic accumulation in blue carbon ecosystems is controversial, yet considering the alternative impacts of plastics on oceanic and aboveground environments, this may be the "lesser of evils". Using environmental life cycle impact assessment, we propose a research framework to address the potential positive and negative impacts of plastic accumulation in blue carbon ecosystems. Considering the multifaceted benefits, we prioritize expanding and managing blue carbon ecosystems, which may help with ecosystem conservation, as well as mitigating the negative effects of plastic.

Eukaryotes contribute more than bacteria to the recovery of freshwater ecosystem functions under different drought durations.

Global climate change mostly impacts river ecosystems by affecting microbial biodiversity and ecological functions. Considering the high functional redundancy of microorganisms, the unknown relationship between biodiversity and ecosystem functions obstructs river ecological research, especially under the influence of increasing weather extremes, such as in intermittent rivers and ephemeral streams (IRES). Herein, dry-wet alternation experiments were conducted in artificial stream channels for 25 and 90 days of drought, both followed by 20 days of rewetting. The dynamic recovery of microbial biodiversity and ecosystem functions (represented by ecosystem metabolism and denitrification rate) were determined to analyse biodiversity-ecosystem-function (BEF) relationships after different drought durations. There was a significant difference between bacterial and eukaryotic biodiversity recovery after drought. Eukaryotic biodiversity was more sensitive to drought duration than bacterial, and the eukaryotic network was more stable under dry-wet alternations. Based on the establishment of partial least squares path models, we found that eukaryotic biodiversity has a stronger effect on ecosystem functions than bacteria after long-term drought. Indeed, this work represents a significant step forward for further research on the ecosystem functions of IRES, especially emphasizing the importance of eukaryotic biodiversity in the BEF relationship.

Transformation of Biofilm to Carbon Sinks after Prolonged Droughts Linked with Algal Biodiversity Change.

Global climate change significantly increased the duration of droughts in intermittent rivers, impacting benthic microbial-mediated biogeochemical processes. However, the impact of prolonged droughts on the carbon contribution of intermittent rivers remains poorly understood. In this study, we investigated the potential effects of varying drought gradients (ranging from 20 to 130 days) on benthic biofilms community structure (algae, bacteria, and fungi) and their carbon metabolism functions (ecosystem metabolism and carbon dioxide (CO2) emission fluxes) using mesocosm experiments. Our findings indicate that longer drought durations lead to reduced alpha diversity and community heterogeneity, tighter interdomain networks, and an increased role of stochastic processes in community assembly, with a discernible threshold at around 60 days. Concurrently, the biofilm transforms into a carbon sink following a drought period of 60 days, as evidenced by the transformation of CO2 emission fluxes from 633.25 ± 194.69 to -349.61 ± 277.79 mg m-2 h-1. Additionally, the partial least-squares path model revealed that the resilience of algal communities and network stability may drive biofilm's transformation into a carbon sink, primarily through the heightened resilience of autotrophic metabolism. This study underscores the significance of the carbon contribution from intermittent rivers, as the shift in carbon metabolism functions with increasing droughts could lead to skewed estimations of current riverine carbon fluxes.

Effects of the Desiccation Duration on the Dynamic Responses of Biofilm Metabolic Activities to Rewetting.

Global climate changes have increased the duration and frequency of river flow interruption, affecting the physical and community structure of benthic biofilms. However, the dynamic responses of biofilm metabolism during the dry-wet transition remain poorly understood. Herein, the dynamic changes in biofilm metabolic activities were investigated through mesocosm experiments under short-term (25 day) and long-term drought (90 day), followed by a 20 day rewetting. The biofilm ecosystem metabolism, as measured by gross primary production and community respiration, was significantly inhibited and turned heterotrophic during the desiccation phase and then recovered, becoming autotrophic during the rewetting period regardless of the desiccation periods due to the high resilience of the autotrophic community. However, long-term drought decreased the recovery rate of the ecosystem metabolism and also caused irreparable damage to the biofilm carbon metabolism, measured using Biolog Eco Plates. Specifically, the recovery of the total carbon metabolic activity is related to the specific carbon source utilized by biofilm microorganisms, such as polymers, carbohydrates, and carboxylic acids. However, the divergent changes of amino acids caused the failure of the total carbon metabolism in long-term drought treatments to recover to the control level even after 20 days of rewetting. This research provides direct evidence that the increased duration of non-flow periods affects biofilm-mediated carbon biogeochemical processes.

Identification of chimera using machine learning.

Chimera state refers to the coexistence of coherent and non-coherent phases in identically coupled dynamical units found in various complex dynamical systems. Identification of chimera, on one hand, is essential due to its applicability in various areas including neuroscience and, on the other hand, is challenging due to its widely varied appearance in different systems and the peculiar nature of its profile. Therefore, a simple yet universal method for its identification remains an open problem. Here, we present a very distinctive approach using machine learning techniques to characterize different dynamical phases and identify the chimera state from given spatial profiles generated using various different models. The experimental results show that the performance of the classification algorithms varies for different dynamical models. The machine learning algorithms, namely, random forest, oblique random forest based on Tikhonov, axis-parallel split, and null space regularization achieved more than 96% accuracy for the Kuramoto model. For the logistic maps, random forest and Tikhonov regularization based oblique random forest showed more than 90% accuracy, and for the Hénon map model, random forest, null space, and axis-parallel split regularization based oblique random forest achieved more than 80% accuracy. The oblique random forest with null space regularization achieved consistent performance (more than 83% accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance. This work provides a direction for employing machine learning techniques to identify dynamical patterns arising in coupled non-linear units on large-scale and for characterizing complex spatiotemporal patterns in real-world systems for various applications.

Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm.

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the "STrutred Analysis of the REtina (STARE) Database" accessed on Oct. 2020 (https://cecas.clemson.edu/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm.

A survey on cancer detection via convolutional neural networks: Current challenges and future directions.

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however, manual interpretation of these images by radiologists is observer-dependent, time-consuming, and tedious. An automatic decision-making process is thus an essential need for cancer detection and diagnosis. This paper presents a comprehensive survey on automated cancer detection in various human body organs, namely, the breast, lung, liver, prostate, brain, skin, and colon, using convolutional neural networks (CNN) and medical imaging techniques. It also includes a brief discussion about deep learning based on state-of-the-art cancer detection methods, their outcomes, and the possible medical imaging data used. Eventually, the description of the dataset used for cancer detection, the limitations of the existing solutions, future trends, and challenges in this domain are discussed. The utmost goal of this paper is to provide a piece of comprehensive and insightful information to researchers who have a keen interest in developing CNN-based models for cancer detection.

Deep learning-based auditory attention decoding in listeners with hearing impairment.

Objective.This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population.Approach.Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set had not seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with HI, listening to competing talkers amidst background noise.Main results.Using 1 s classification windows, DCNN models achieve accuracy (ACC) of 69.8%, 73.3% and 82.9% and area-under-curve (AUC) of 77.2%, 80.6% and 92.1% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9%, 80.1% and 97.5%, along with AUC of 94.6%, 89.1%, and 99.8%. Our DCNN models show good performance on short 1 s EEG samples, making them suitable for real-world applications. Conclusion: Our DCNN models successfully addressed three tasks with short 1 s EEG windows from participants with HI, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks.Significance.Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative DL architectures and their potential constraints.

Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning.

The domain of machine learning is confronted with a crucial research area known as class imbalance (CI) learning, which presents considerable hurdles in the precise classification of minority classes. This issue can result in biased models where the majority class takes precedence in the training process, leading to the underrepresentation of the minority class. The random vector functional link (RVFL) network is a widely used and effective learning model for classification due to its good generalization performance and efficiency. However, it suffers when dealing with imbalanced datasets. To overcome this limitation, we propose a novel graph-embedded intuitionistic fuzzy RVFL for CI learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets. The proposed GE-IFRVFL-CIL model offers a plethora of benefits: 1) leveraging graph embedding (GE) to preserve the inherent topological structure of the datasets; 2) employing intuitionistic fuzzy (IF) theory to handle uncertainty and imprecision in the data; and 3) the most important, it tackles CI learning. The amalgamation of a weighting scheme, GE, and IF sets leads to the superior performance of the proposed models on KEEL benchmark imbalanced datasets with and without Gaussian noise. Furthermore, we implemented the proposed GE-IFRVFL-CIL on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and achieved promising results, demonstrating the model's effectiveness in real-world applications. The proposed GE-IFRVFL-CIL model offers a promising solution to address the CI issue, mitigates the detrimental effect of noise and outliers, and preserves the inherent geometrical structures of the dataset.

GRA: Graph Representation Alignment for Semi-Supervised Action Recognition.

Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependency on huge labeled datasets. Acquiring such datasets is often prohibitive, and the frequent occurrence of incomplete skeleton data, typified by absent joints and frames, complicates the testing phase. To tackle these issues, we present graph representation alignment (GRA), a novel approach with two main contributions: 1) a self-training (ST) paradigm that substantially reduces the need for labeled data by generating high-quality pseudo-labels, ensuring model stability even with minimal labeled inputs and 2) a representation alignment (RA) technique that utilizes consistency regularization to effectively reduce the impact of missing data components. Our extensive evaluations on the NTU RGB+D and Northwestern-UCLA (N-UCLA) benchmarks demonstrate that GRA not only improves GCN performance in data-constrained environments but also retains impressive performance in the face of data incompleteness.

Prioritising plastic pollution research in blue carbon ecosystems: A scientometric overview.

The Blue Carbon Ecosystems (BCEs), comprising mangroves, saltmarshes, and seagrasses, located at the land-ocean interface provide crucial ecosystem services. These ecosystems serve as a natural barrier against the transportation of plastic waste from land to the ocean, effectively intercepting and mitigating plastic pollution in the ocean. To gain insights into the current state of research, and uncover key research gaps related to plastic pollution in BCEs, this study conveyed a comprehensive overview using bibliometric, altmetric, and literature synthesis approaches. The bibliometric analysis revealed a significant increase in publications addressing plastic pollution in BCEs, particularly since 2018. Geographically, Chinese institutions have made substantial contributions to this research field compared to countries and regions with extensive BCEs and established blue carbon science programs. Furthermore, many studies have focused on mangrove ecosystems, while limited attention was given to exploring plastic pollution in saltmarsh, seagrass, and multiple ecosystems simultaneously. Through a systematic analysis, this study identified four major research themes in BCE-plastics research: a) plastic trapping by vegetated coastal ecosystems, b) microbial plastic degradation, c) ingestion of plastic by benthic organisms, and d) effects of plastic on blue carbon biogeochemistry. Upon synthesising the current knowledge in each theme, we employed a perspective lens to outline future research frameworks, specifically emphasising habitat characteristics and blue carbon biogeochemistry. Emphasising the importance of synergistic research between plastic pollution and blue carbon science, we underscore the opportunities to progress our understanding of plastic reservoirs across BCEs and their subsequent effects on blue carbon sequestration and mineralisation. Together, the outcomes of this review have overarching implications for managing plastic pollution and optimising climate mitigation outcomes through the blue carbon strategies.

Effects of microplastics on soil microorganisms and microbial functions in nutrients and carbon cycling - A review.

The harmful effects of microplastics (MPs) pollution in the soil ecosystem have drawn global attention in recent years. This paper critically reviews the effects of MPs on soil microbial diversity and functions in relation to nutrients and carbon cycling. Reports suggested that both plastisphere (MP-microbe consortium) and MP-contaminated soils had distinct and lower microbial diversity than that of non-contaminated soils. Alteration in soil physicochemical properties and microbial interactions within the plastisphere facilitated the enrichment of plastic-degrading microorganisms, including those involved in carbon (C) and nutrient cycling. MPs conferred a significant increase in the relative abundance of soil nitrogen (N)-fixing and phosphorus (P)-solubilizing bacteria, while decreased the abundance of soil nitrifiers and ammonia oxidisers. Depending on soil types, MPs increased bioavailable N and P contents and nitrous oxide emission in some instances. Furthermore, MPs regulated soil microbial functional activities owing to the combined toxicity of organic and inorganic contaminants derived from MPs and contaminants frequently encountered in the soil environment. However, a thorough understanding of the interactions among soil microorganisms, MPs and other contaminants still needs to develop. Since currently available reports are mostly based on short-term laboratory experiments, field investigations are needed to assess the long-term impact of MPs (at environmentally relevant concentration) on soil microorganisms and their functions under different soil types and agro-climatic conditions.

Brain Age Prediction With Improved Least Squares Twin SVR.

Alzheimer's disease (AD) is the prevalent form of dementia and shares many aspects with the aging pattern of the abnormal brain. Machine learning models like support vector regression (SVR) based models have been successfully employed in the estimation of brain age. However, SVR is computationally inefficient than twin support vector machine based models. Hence, different twin support vector machine based models like twin SVR (TSVR), ε-TSVR, and Lagrangian TSVR (LTSVR) models have been used for the regression problems. ε-TSVR and LTSVR models seek a pair of ε-insensitive proximal planes for generation of end regressor. However, SVR and TSVR based models have several drawbacks- i) SVR model is computationally inefficient compared to the TSVR based models. ii) Twin SVM based models involve the computation of matrix inverse which is intractable in real world scenario's. iii) Both TSVR and LTSVR models are based on empirical risk minimization principle and hence may be prone to overfitting. iv) TSVR and LTSVR assume that the matrices appearing in their formulation are positive definite which may not be satisfied in real world scenario's. To overcome these issues, we formulate improved least squares twin support vector regression (ILSTSVR). The proposed ILSTSVR modifies the TSVR by replacing the inequality constraints with the equality constraints and minimizes the slack variables using squares of L2 norm instead of L1. Also, we introduce a different Lagrangian function to avoid the computation of matrix inverses. We evaluated the proposed ILSTSVR model on the subjects including cognitively healthy, mild cognitive impairment and Alzheimer's disease for brain-age estimation. Experimental evaluation and statistical tests demonstrate the efficiency of the proposed ILSTSVR model for brain-age prediction.

Reducing rip current drowning: An improved residual based lightweight deep architecture for rip detection.

Rip Currents are contributing around 25 fatal drownings each year in Australia. Previous research has indicated that most of beachgoers cannot correctly identify a rip current, leaving them at risk of experiencing a drowning incident. Automated detection of rip currents could help to reduce drownings and assist lifeguards in supervision planning; however, varying beach conditions have made this challenging. This work presents the effectiveness of an improved lightweight framework for detecting rip currents: RipDet+1, aided with residual mapping to boost the generalization performance. We have used Yolo-V3 architecture to build RipDet+ framework and utilize pretrained weight by fully exploiting the detection training set from some base classes which in result quickly adapt the detection prediction to the available rip data. Extensive experiments are reported which show the effectiveness of RipDet+ architecture in achieving a detection accuracy of 98.55%, which is significantly greater compared to other state-of-the-art methods for Rip currents detection.

Investigating White Matter Abnormalities Associated with Schizophrenia Using Deep Learning Model and Voxel-Based Morphometry.

Schizophrenia (SCZ) is a devastating mental condition with significant negative consequences for patients, making correct and prompt diagnosis crucial. The purpose of this study is to use structural magnetic resonance image (MRI) to better classify individuals with SCZ from control normals (CN) and to locate a region of the brain that represents abnormalities associated with SCZ. Deep learning (DL), which is based on the nervous system, could be a very useful tool for doctors to accurately predict, diagnose, and treat SCZ. Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) brain regions are extracted from 99 MRI images obtained from the open-source OpenNeuro database to demonstrate SCZ's regional relationship. In this paper, we use a pretrained ResNet-50 deep network to extract features from MRI images and an ensemble deep random vector functional link (edRVFL) network to classify those features. By examining the results obtained, the edRVFL deep model provides the highest classification accuracy of 96.5% with WM and is identified as the best-performing algorithm compared to the traditional algorithms. Furthermore, we examined the GM, WM, and CSF tissue volumes in CN subjects and SCZ patients using voxel-based morphometry (VBM), and the results show 1363 significant voxels, 6.90 T-value, and 6.21 Z-value in the WM region of SCZ patients. In SCZ patients, WM is most closely linked to structural alterations, as evidenced by VBM analysis and the DL model.

Inverse free reduced universum twin support vector machine for imbalanced data classification.

Imbalanced datasets are prominent in real-world problems. In such problems, the data samples in one class are significantly higher than in the other classes, even though the other classes might be more important. The standard classification algorithms may classify all the data into the majority class, and this is a significant drawback of most standard learning algorithms, so imbalanced datasets need to be handled carefully. One of the traditional algorithms, twin support vector machines (TSVM), performed well on balanced data classification but poorly on imbalanced datasets classification. In order to improve the TSVM algorithm's classification ability for imbalanced datasets, recently, driven by the universum twin support vector machine (UTSVM), a reduced universum twin support vector machine for class imbalance learning (RUTSVM) was proposed. The dual problem and finding classifiers involve matrix inverse computation, which is one of RUTSVM's key drawbacks. In this paper, we improve the RUTSVM and propose an improved reduced universum twin support vector machine for class imbalance learning (IRUTSVM). We offer alternative Lagrangian functions to tackle the primal problems of RUTSVM in the suggested IRUTSVM approach by inserting one of the terms in the objective function into the constraints. As a result, we obtain new dual formulation for each optimization problem so that we need not compute inverse matrices neither in the training process nor in finding the classifiers. Moreover, the smaller size of the rectangular kernel matrices is used to reduce the computational time. Extensive testing is carried out on a variety of synthetic and real-world imbalanced datasets, and the findings show that the IRUTSVM algorithm outperforms the TSVM, UTSVM, and RUTSVM algorithms in terms of generalization performance.

Intuitionistic Fuzzy Weighted Least Squares Twin SVMs.

Fuzzy membership is an effective approach used in twin support vector machines (SVMs) to reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs (TWSVMs) assign membership weights to reduce the effect of outliers, however, it ignores the positioning of the input data samples and hence fails to distinguish between support vectors and noise. To overcome this issue, intuitionistic fuzzy TWSVM combined the concept of intuitionistic fuzzy number with TWSVMs to reduce the effect of outliers and distinguish support vectors from noise. Despite these benefits, TWSVMs and intuitionistic fuzzy TWSVMs still suffer from some drawbacks as: 1) the local neighborhood information is ignored among the data points and 2) they solve quadratic programming problems (QPPs), which is computationally inefficient. To overcome these issues, we propose a novel intuitionistic fuzzy weighted least squares TWSVMs for classification problems. The proposed approach uses local neighborhood information among the data points and also uses both membership and nonmembership weights to reduce the effect of noise and outliers. The proposed approach solves a system of linear equations instead of solving the QPPs which makes the model more efficient. We evaluated the proposed intuitionistic fuzzy weighted least squares TWSVMs on several benchmark datasets to show the efficiency of the proposed model. Statistical analysis is done to quantify the results statistically. As an application, we used the proposed model for the diagnosis of Schizophrenia disease.

High-Density Electroencephalography and Speech Signal Based Deep Framework for Clinical Depression Diagnosis.

Depression is a mental disorder characterized by persistent depressed mood or loss of interest in performing activities, causing significant impairment in daily routine. Possible causes include psychological, biological, and social sources of distress. Clinical depression is the more-severe form of depression, also known as major depression or major depressive disorder. Recently, electroencephalography and speech signals have been used for early diagnosis of depression; however, they focus on moderate or severe depression. We have combined audio spectrogram and multiple frequencies of EEG signals to improve diagnostic performance. To do so, we have fused different levels of speech and EEG features to generate descriptive features and applied vision transformers and various pre-trained networks on the speech and EEG spectrum. We have conducted extensive experiments on Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset, which showed significant improvement in performance in depression diagnosis (0.972, 0.973 and 0.973 precision, recall and F1 score respectively) for patients at the mild stage. Besides, we provided a web-based framework using Flask and provided the source code publicly.1.