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In recent years, the Internet of Things (IoT) has gradually developed applications such as collecting sensory data and building intelligent services, which has led to an explosion in mobile data traffic. Meanwhile, with the rapid development of artificial intelligence, semantic communication has attracted great attention as a new communication paradigm. However, for IoT devices, however, processing image information efficiently in real time is an essential task for the rapid transmission of semantic information. With the increase of model parameters in deep learning methods, the model inference time in sensor devices continues to increase. In contrast, the Pulse Coupled Neural Network (PCNN) has fewer parameters, making it more suitable for processing real-time scene tasks such as image segmentation, which lays the foundation for real-time, effective, and accurate image transmission. However, the parameters of PCNN are determined by trial and error, which limits its application. To overcome this limitation, an Improved Pulse Coupled Neural Networks (IPCNN) model is proposed in this work. The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons, and all its parameters are set adaptively, which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images. Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets. The IPCNN method achieves a better segmentation result without training, providing a new solution for the real-time transmission of image semantic information.

Lead (Pb) is an environmentally widespread bone toxic pollutant, contributes to the development of osteoporosis. Butyric acid, mainly produced by the fermentation of indigestible dietary fiber by gut microbiota, plays a pivotal role in the maintenance of bone homeostasis. However, the effects of butyric acids on the Pb induced osteoporosis have not yet been elucidated. In this study, our results showed that Pb exposure was negatively related to the abundance of butyric acid, in the Pb-exposed population and Pb-exposed mice. Pb exposure caused gut microbiota disorders, resulting in the decline of butyric acid-producing bacteria, such as Butyrivibrio_crossotus, Clostridium_sp._JN9, and the butyrate-producing enzymes through the acetyl-CoA pathway. Moreover, results from the NHANES data suggested that dietary intake of butyrate was associated with a reduced risk of osteoporosis in lead-burdened populations, particularly among men or participants aged 18–60 years. In addition, butyrate supplementation in mice with chronic Pb exposure improved the bone microarchitectures, repaired intestinal damage, upregulated the proportion of Treg cells. Taken together, these results demonstrated that chronic Pb exposure disturbs the gut-bone axis, which can be restored by butyric acid supplement. Our results suggest that butyrate supplementation is a possible therapeutic strategy for lead-induced bone toxicity.

The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently.

Depression ranks among the most prevalent mood-related psychiatric disorders. Existing clinical diagnostic approaches relying on scale interviews are susceptible to individual and environmental variations. In contrast, the integration of neuroimaging techniques and computer science has provided compelling evidence for the quantitative assessment of major depressive disorder (MDD). However, one of the major challenges in computer-aided diagnosis of MDD is to automatically and effectively mine the complementary cross-modal information from limited datasets. In this study, we proposed a few-shot learning framework that integrates multi-modal MRI data based on contrastive learning. In the upstream task, it is designed to extract knowledge from heterogeneous data. Subsequently, the downstream task is dedicated to transferring the acquired knowledge to the target dataset, where a hierarchical fusion paradigm is also designed to integrate features across inter- and intra-modalities. Lastly, the proposed model was evaluated on a set of multi-modal clinical data, achieving average scores of 73.52% and 73.09% for accuracy and AUC, respectively. Our findings also reveal that the brain regions within the default mode network and cerebellum play a crucial role in the diagnosis, which provides further direction in exploring reproducible biomarkers for MDD diagnosis.

Abstract Shading in combination with extended photoperiods can cause exaggerated stem elongation (ESE) in soybean, leading to lodging and reduced yields when planted at high-density in high-latitude regions. However, the genetic basis of plant height in adaptation to these regions remains unclear. Here, through a genome-wide association study, we identify a plant height regulating gene on chromosome 13 (PH13) encoding a WD40 protein with three main haplotypes in natural populations. We find that an insertion of a Ty1/Copia-like retrotransposon in the haplotype 3 leads to a truncated PH13H3 with reduced interaction with GmCOP1s, resulting in accumulation of STF1/2, and reduced plant height. In addition, PH13 H3 allele has been strongly selected for genetic improvement at high latitudes. Deletion of both PH13 and its paralogue PHP can prevent shade-induced ESE and allow high-density planting. This study provides insights into the mechanism of shade-resistance and offers potential solutions for breeding high-yielding soybean cultivar for high-latitude regions.

IntroductionDeep-learn methods based on convolutional neural networks (CNNs) have demonstrated impressive performance in depression analysis. Nevertheless, some critical challenges need to be resolved in these methods: (1) It is still difficult for CNNs to learn long-range inductive biases in the low-level feature extraction of different facial regions because of the spatial locality. (2) It is difficult for a model with only a single attention head to concentrate on various parts of the face simultaneously, leading to less sensitivity to other important facial regions associated with depression. In the case of facial depression recognition, many of the clues come from a few areas of the face simultaneously, e.g., the mouth and eyes.MethodsTo address these issues, we present an end-to-end integrated framework called Hybrid Multi-head Cross Attention Network (HMHN), which includes two stages. The first stage consists of the Grid-Wise Attention block (GWA) and Deep Feature Fusion block (DFF) for the low-level visual depression feature learning. In the second stage, we obtain the global representation by encoding high-order interactions among local features with Multi-head Cross Attention block (MAB) and Attention Fusion block (AFB).ResultsWe experimented on AVEC2013 and AVEC2014 depression datasets. The results of AVEC 2013 (RMSE = 7.38, MAE = 6.05) and AVEC 2014 (RMSE = 7.60, MAE = 6.01) demonstrated the efficacy of our method and outperformed most of the state-of-the-art video-based depression recognition approaches.DiscussionWe proposed a deep learning hybrid model for depression recognition by capturing the higher-order interactions between the depression features of multiple facial regions, which can effectively reduce the error in depression recognition and gives great potential for clinical experiments.

Heavy metal contamination of soils has been a global environmental issue over the past decades, threatening food security and human health. Understanding the migration and transformation of heavy metals in soils is critical for restoring an impaired environment and developing sustainable agriculture, particularly in the face of global warming. However, little effort has been devoted to investigating the impact of elevated temperatures on the migration and distribution of exogenous heavy metals in soils. This study experimented with a 180-day incubation at 15 °C, 30 °C, and 45 °C with an arable soil (Alfisol) of Huang-Huai-Hai River Basin, China, which was initially spiked with copper (Cu). A comparison of the results revealed that the percentage of soil water-soluble Cu doubled at 45 °C compared with 15 °C. The percentage of protein-like substances in dissolved organic matter (DOM) was the highest at 45 °C, suggesting that proteinaceous components play a more significant role in controlling the dissolution of Cu into DOM. Moreover, by sequential extraction and micro-X-ray fluorescence (μ-XRF), Cu was facilitatively transformed from exchangeable, and specifically adsorbed fractions, to iron (Fe)/manganese (Mn) oxides bound species by 7.75%23.63% with the elevation of temperature from 15 °C to 45 °C. The conversion of Cu speciation is attributed to the significant release of organic carbon from Fe/Mn oxides, especially the Mn oxide components, which are available for Cu binding. The findings of this work will provide an in-depth understanding of the fate of Cu in soils, which is fundamental for the risk assessment and remediation of Cu-polluted soils in the Huang-Huai-Hai River Basin under the context of global warming.

Measurement(s) Human Brainwave • spoken language Technology Type(s) EEG collector • audio recorder Sample Characteristic - Organism Homo Sapiens Sample Characteristic - Location China

Abstract Background Frogeye leaf spot (FLS) is a destructive fungal disease that affects soybean production. The most economical and effective strategy to control FLS is the use of resistant cultivars. However, the use of a limited number of resistant loci in FLS management will be countered by the emergence of new high-virulence Cercospora sojina races. Therefore, we identified quantitative trait loci (QTL) that control resistance to FLS and identified novel resistant genes using a genome-wide association study (GWAS) on 234 Chinese soybean cultivars. Results A total of 30,890 single nucleotide polymorphism (SNP) markers were used to estimate linkage disequilibrium (LD) and population structure. The GWAS results showed four loci (p

Soybean is a source of edible oil for humans and provides a third of the vegetable oil consumed worldwide. Increasing seed oil content in seeds is thus a key objective in soybean breeding. In the present study, a four-way recombinant inbred line (FW-RIL) population comprising 144 lines, planted in 10 environments, and a germplasm panel of 455 accessions, planted in two environments, were used to collect oil-content phenotypes. First, 59 quantitative trait loci (QTL) were detected in the FW-RIL population by inclusive complete interval mapping on a linkage map consisting of 2232 single-nucleotide polymorphism (SNP) markers. Also in the FW-RILs, 44 quantitative trait nucleotides (QTNs) were detected by association analysis using 109,676 SNP markers and five methods of multi-locus genome-wide association study. Second, 77 QTN were detected by association analysis in the germplasm panel using 63,306 markers. Comparison of the QTL and QTN suggested four QTN controlling oil content. Pathway analysis was performed on genes in attenuation regions of these four QTN, and two candidate genes involved in the synthesis or metabolism of soybean oil were identified. These findings provide useful information about the genetics of oil content and may contribute to its genetic improvement by marker-assisted selection.

Soybean is an important global crop for edible protein and oil, and plant height is a main breeding goal which is closely related to its plant shape and yield. In this research, a high-density genetic linkage map was constructed by 1996 SNP-bin markers on the basis of a recombinant inbred line population derived from Dongnong L13 × Henong 60. A total of 33 QTL related to plant height were identified, of which five were repeatedly detected in multiple environments. In addition, a 455-germplasm population with 63,306 SNP markers was used for multi-locus association analysis. A total of 62 plant height QTN were detected, of which 26 were detected repeatedly under multiple methods. Two candidate genes, Glyma.02G133000 and Glyma.05G240600, involving in plant height were predicted by pathway analysis in the regions identified by multiple environments and backgrounds, and validated by qRT-PCR. These results enriched the soybean plant height regulatory network and contributed to molecular selection-assisted breeding.

Backgrounds: Nowadays, risks of Cognitive Impairment (CI) [highly suspected Alzheimer's disease (AD) in this study] threaten the quality of life for more older adults as the population ages. The emergence of Transcranial Magnetic Stimulation-Electroencephalogram (TMS-EEG) enables noninvasive neurophysiological investi-gation of the human cortex, which might be potentially used for CI detection.Objectives: The aim of this study is to explore whether the spatiotemporal features of TMS Evoked Potentials (TEPs) could classify CI from healthy controls (HC).Methods: Twenty-one patients with CI and 22 HC underwent a single-pulse TMS-EEG stimulus in which the pulses were delivered to the left dorsolateral prefrontal cortex (left DLPFC). After preprocessing, seven regions of interest (ROIs) and two most reliable TEPs' components: N100 and P200 were selected. Next, seven simple and interpretable linear features of TEPs were extracted for each region, three common machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used to detect CI. Meanwhile, data augmentation and voting strategy were used for a more robust model. Finally, the performance differences of features in classifiers and their contributions were investigated.Results: 1. In the time domain, the features of N100 had the best performance in the SVM classifier, with an accuracy of 88.37%. 2. In the aspect of spatiality, the features of the right frontal region and left parietal region had the best performance in the SVM classifier, with an accuracy of 83.72%. 3. The Local Mean Field Power (LMFP), Average Value (AVG), Latency and Amplitude contributed most in classification.Conclusions: The TEPs induced by TMS over the left DLPFC has significant differences spatially and temporally between CI and HC. Machine learning based on the spatiotemporal features of TEPs have the ability to separate the CI and HC which suggest that TEPs has potential as non-invasive biomarkers for CI diagnosis.

In the mobile computing-based Internet of Things, the computational complexity of applications is constrained by the capacity of the user equipment. In order to reduce the computational complexity of compressed sensing (CS)-based magnetic resonance image (MRI) reconstruction algorithms, we propose a novel multi-resolution-based parallel MRI reconstruction framework in this paper. We break down CS-based MRI reconstruction problem into four independent low-resolution image reconstruction sub-problems. Compared with the original problem, each sub-problem has a lower computational complexity. Assigned to four cores of the central processing unit (CPU), the sub-problems are solved simultaneously, and therefore the MRI reconstruction is accelerated. The combination of reconstructed low-resolution images achieves high-resolution image reconstruction. The proposed framework is applicable to the state-of-the-art CS-based MRI reconstruction algorithms to compute low-resolution images and involves multi-resolution processing. Compared with conventional serial computing, the proposed MRI reconstruction framework speeds at least four times up. Therefore, the parallel computation framework is especially suitable for widely used mobile devices with lower computational capability than workstations. To validate and evaluate the proposed scheme, when selecting the outstanding MRI reconstructing algorithm fast dictionary learning method on classified patches for numerical simulation, peak-signal-to-noise-ratio values of parallel reconstruction results are at least 0.929 dB higher than that of serial computation reconstruction results calculated by classical MRI reconstruction algorithm.

In this paper, we used electroencephalography (EEG)-eye movement (EM) synchronization acquisition network to simultaneously record both EEG and EM physiological signals of the mild depression and normal controls during free viewing. Then, we consider a multimodal feature fusion method that can best discriminate between mild depression and normal control subjects as a step toward achieving our long-term aim of developing an objective and effective multimodal system that assists doctors during diagnosis and monitoring of mild depression. Based on the multimodal denoising autoencoder, we use two feature fusion strategies (feature fusion and hidden layer fusion) for fusion of the EEG and EM signals to improve the recognition performance of classifiers for mild depression. Our experimental results indicate that the EEG-EM synchronization acquisition network ensures that the recorded EM and EEG data require that both the data streams are synchronized with millisecond precision, and both fusion methods can improve the mild depression recognition accuracy, thus demonstrating the complementary nature of the modalities. Compared with the unimodal classification approach that uses only EEG or EM, the feature fusion method slightly improved the recognition accuracy by 1.88%, while the hidden layer fusion method significantly improved the classification rate by up to 7.36%. In particular, the highest classification accuracy achieved in this paper was 83.42%. These results indicate that the multimodal deep learning approaches with input data using a combination of EEG and EM signals are promising in achieving real-time monitoring and identification of mild depression.

In recent years, an increasing number of university students are found to be at high risk of depression. Through a large scale depression screening, this paper finds that around 6.5% of the university postgraduate students in China experience depression. We then investigate whether the gait patterns of these individuals have already changed as depression is suggested to associate with gait abnormality. Significant differences are found in several spatiotemporal, kinematic and postural gait parameters such as walking speed, stride length, head movement, vertical head posture, arm swing, and body sway, between the depressed and non-depressed groups. Applying these features to classifiers with different machine learning algorithms, we examine whether natural gait analysis may serve as a convenient and objective tool to assist in depression recognition. The results show that when using a random forest classifier, the two groups can be classified automatically with a maximum accuracy of 91.58%. Furthermore, a reasonable accuracy can already be achieved by using parameters from the upper body alone, indicating that upper body postures and movements can effectively contribute to depression analysis.

Classification of Alzheimer's Disease (AD) has been becoming a hot issue along with the rapidly increasing number of patients. This task remains tremendously challenging due to the limited data and the difficulties in detecting mild cognitive impairment (MCI). Existing methods use gait [or EEG (electroencephalogram)] data only to tackle this task. Although the gait data acquisition procedure is cheap and simple, the methods relying on gait data often fail to detect the slight difference between MCI and AD. The methods that use EEG data can detect the difference more precisely, but collecting EEG data from both HC (health controls) and patients is very time-consuming. More critically, these methods often convert EEG records into the frequency domain and thus inevitably lose the spatial and temporal information, which is essential to capture the connectivity and synchronization among different brain regions. This paper proposes a cascade neural network with two steps to achieve a faster and more accurate AD classification by exploiting gait and EEG data simultaneously. In the first step, we propose attention-based spatial temporal graph convolutional networks to extract the features from the skeleton sequences (i.e., gait) captured by Kinect (a commonly used sensor) to distinguish between HC and patients. In the second step, we propose spatial temporal convolutional networks to fully exploit the spatial and temporal information of EEG data and classify the patients into MCI or AD eventually. We collect gait and EEG data from 35 cognitively health controls, 35 MCI, and 17 AD patients to evaluate our proposed method. Experimental results show that our method significantly outperforms other AD diagnosis methods (91.07 vs. 68.18%) in the three-way AD classification task (HC, MCI, and AD). Moreover, we empirically found that the lower body and right upper limb are more important for the early diagnosis of AD than other body parts. We believe this interesting finding can be helpful for clinical researches.

Mobile cyber-physical systems (CPS) that take the advantages and extend the application domains of CPS have become increasingly popular in recent years. For example, mobile CPS could be a kind of foundational techniques to support the development of vehicular networking systems, thereby improving security and privacy of users in the dynamic environments of vehicular networks. In this paper, we first distinguish mobile CPS from traditional CPS. Then, we introduce their three emerging application areas, i.e., vehicular networking systems, healthcare systems, and mobile education. After that, we discuss four main research challenges of mobile CPS regarding security, energy consumption, mobile dynamic environment, and system stability. Also, we consider the corresponding techniques, which may address these challenges, and analyze the inter-relations among them. Finally, we outline the possible research directions and applications of mobile CPS in the future.

Due to the dynamic characteristics of mobile wireless ad hoc networks, a node may join or leave/fail at anytime, and links are usually intermittently unavailable. The destruction of the network connectivity would dramatically degrade the performance. To resolve this challenge, topology control is an efficient way to maintain network connectivity, which focuses on the adjustment of node transmit power in the past few decades. However, the adjustment of node movement has not been adequately addressed to restore the connectivity in mobile wireless ad hoc networks. In this paper, we propose three hybrid recovering algorithms, namely, cascade movement based on nearest nodes, cascade movement based on connected dominating set, and cascade movement based on critical nodes. With the adoption of Kalman filter, all these schemes can predict the movement of nodes so as to identify and maintain connectivity of mobile wireless ad hoc networks. Meanwhile, once a node fails or a link breaks down, they can also recover the connectivity immediately. We conduct extensive simulations on the metrics of the average number of participating nodes, the deviation from original direction, and the ratio of cascade movement for the three proposed algorithms, and the algorithm of cascade movement based on critical nodes achieves the best results.

The mineral-organo composites control the speciation, mobility and bioavailability of heavy metals in soils and sediments by surface adsorption and precipitation. The dynamic changes of soil mineral, organic matter and their associations under redox, aging and microbial activities further complicate the fate of heavy metals. Over the past decades, the wide application of advanced instrumental techniques and modelling has largely extended our understanding on heavy metal behavior within mineral-organo assemblages. In this review, we provide a comprehensive summary of recent progress on heavy metal immobilization by mineral-humic and mineral-microbial composites, with a special focus on the interfacial reaction mechanisms of heavy metal adsorption. The impacts of redox and aging conditions on heavy metal speciations and associations with mineral-organo complexes are discussed. The modelling of heavy metals adsorption and desorption onto synthetic mineral-organo composites and natural soils and sediments are also critically reviewed. Future challenges and prospects in the mineral-organo interface are outlined. More in-depth investigations are warranted, especially on the function and contribution of microorganisms in the immobilization of heavy metals at the complex mineral-organo interface. It has become imperative to use the state-of-the-art methodologies to characterize the interface and develop in situ analytical techniques in future studies. Keywords: Heavy metal, Mineral, Organic matter, Microorganism, Interface, Modelling

In sustainable smart cities, power saving is a severe challenge in the energy-constrained Internet of Things (IoT). Efficient utilization of limited multiple non-overlap channels and time resources is a promising solution to reduce the network interference and save energy consumption. In this paper, we propose a joint channel allocation and time slot optimization solution for IoT. First, we propose a channel ranking algorithm which enables each node to rank its available channels based on the channel properties. Then, we propose a distributed channel allocation algorithm so that each node can choose a proper channel based on the channel ranking and its own residual energy. Finally, the sleeping duration and spectrum sensing duration are jointly optimized to maximize the normalized throughput and satisfy energy consumption constraints simultaneously. Different from the former approaches, our proposed solution requires no central coordination or any global information that each node can operate based on its own local information in a total distributed manner. Also, theoretical analysis and extensive simulations have validated that when applying our solution in the network of IoT: (i) each node can be allocated to a proper channel based on the residual energy to balance the lifetime; (ii) the network can rapidly converge to a collision-free transmission through each node’s learning ability in the process of the distributed channel allocation; and (iii) the network throughput is further improved via the dynamic time slot optimization.