A spatiotemporal graph transformer approach for Alzheimer's disease diagnosis with rs-fMRI.

Alzheimer's disease (AD) is a neurodegenerative disease accompanied by cognitive impairment. Early diagnosis is crucial for the timely treatment and intervention of AD. Resting-state functional magnetic resonance imaging (rs-fMRI) records the temporal dynamics and spatial dependency in the brain, which have been utilized for automatically diagnosis of AD in the community. Existing approaches of AD diagnosis using rs-fMRI only assess functional connectivity, ignoring the spatiotemporal dependency mining of rs-fMRI. In addition, it is difficult to increase diagnosis accuracy due to the shortage of rs-fMRI sample and the poor anti-noise ability of model. To deal with these problems, this paper proposes a novel approach for the automatic diagnosis of AD, namely spatiotemporal graph transformer network (STGTN). The proposed STGTN can effectively extract spatiotemporal features of rs-fMRI. Furthermore, to solve the sample-limited problem and to improve the anti-noise ability of the proposed model, an adversarial training strategy is adopted for the proposed STGTN to generate adversarial examples (AEs) and augment training samples with AEs. Experimental results indicate that the proposed model achieves the classification accuracy of 92.58%, and 85.27% with the adversarial training strategy for AD vs. normal control (NC), early mild cognitive impairment (eMCI) vs. late mild cognitive impairment (lMCI) respectively, outperforming the state-of-the-art methods. Besides, the spatial attention coefficients reflected from the designed model reveal the importance of brain connections under different classification tasks.

First Report of the guava root-knot nematode (Meloidogyne enterolobii) on Selenicereus costaricensis in Guangxi, China.

Pitaya (Selenicereus costaricensis), a tropical and subtropical fruit of Cactaceae family, become very popular in the fruit consumer market in recent years. In June 2022, plant stunting, reduced yields and galled root symptoms were observed on S. costaricensis plants sampled from a commercial production base in Wuming County (23°10'36.67″ N; 108°40'43.24″ E), Guangxi autonomous region, China. The area of S. costaricensis field we investigated was about 19.9 ha. The incidence of root-knot nematode disease was almost 60%. Roots of twenty S. costaricensis plants were dug up, and many root knots and egg masses were observed. The roots with galls were collected, nematodes at different stages were collected and morphologically identified. Females were annulated, pearly white and globular to pear-shaped. The perineal pattern was oval shaped with the dorsal arch being moderately high to high. Average length of adult females (n = 20): body = 614.4 ± 57.3 µm, stylet lengths = 15.1 ± 0.9 µm, dorsal esophageal gland orifice (DGO) = 4.7 ± 0.6 µm. The tail of the second stage juvenile (J2) was very thin with a bluntly pointed tip. The hyaline tail terminus was clearly defined. Average length of J2 (n = 20): body = 469.5 ± 36.7 µm, stylet lengths = 14.7 ± 0.5 µm, DGO = 3.5 ± 0.4 µm, tail lengths averaged = 43.6 ± 9.7 µm. The males were vermiform, annulated, slightly tapering anteriorly, bluntly rounded posteriorly. Typical characteristics of Meloidogyne enterolobii observed were consistent with those previously described by Yang & Eisenback (1983) and Bulletin (2016). J2s hatched from an individual egg mass were collected for DNA extraction and used for molecular biological identification. The specific primers of M. enterolobii, Me-F/Me-R (AACTTTTGTGAAAGTGCCGCTG/TCAGTTCAGGCAGGATCAACC), was used to validate the pathogen (Long et al. 2006). Approximately 236 bp of the target product was amplified, whereas no product was obtained from M. incognita. Further, the rDNA gene sequences (ITS; ITS1_5.8S_ITS2) and large subunit rDNA gene were amplified by the primers V5367/26S (TTGATTACGTCCCTGCCCTTT/TTTCACTCGCCGTTACTAAGG) (Vrain et al. 1992) and D2A/D3B (ACAAGTACCGTGAGGGAAAGT/TCGGAAGGAACCAGCTACTA), respectively (Subbotin et al. 2006). The target sequences of 765 bp (GenBank accession no. OQ512155) and 759 bp (OQ512743) were recorded in the NCBI with GeneBank. The sequences showed 100% identity with M. enterolobii in ITSs (KJ146863, JQ082448) and D2/D3 (MF467276, OL681885). To verify reproduction on S. costaricensis (Jindu 1), twelve ten-week-old seedlings (12 pots) cultured on a sterile substrate soil were inoculated with 5,000 J2s from the original population in a greenhouse at 26 ˚C. Noninoculated control were set up at the same time. After 8 weeks, the noninoculated plants (n = 12) did not present galls in the roots. All inoculated plants had galled roots and showed dwarf plant. The average reproductive factor obtained was 11.6 and the mean root gall rating of the samples was 5.3 (rating scale of 0 to 10), confirming the pathogenicity of M. enterolobii to S. costaricensis. The red dragon fruits (Hylocereus polyrhizus) in Hainan Island (China) were reported infected by M. enterolobii in previous report (Long et al. 2022). To our knowledge, this is the first report of M. enterolobii parasitizing S. costaricensis in Guangxi, China. This finding has important implications for the control of M. enterolobii at the place of discovery, which is the major fruit production area.

Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT.

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.

Compression and Encryption of Heterogeneous Signals for Internet of Medical Things.

Psychophysiological computing can be utilized to analyze heterogeneous physiological signals with psychological behaviors in the Internet of Medical Things (IoMT). Since IoMT devices are generally limited by power, storage, and computing resources, it's very challenging to process the physiological signal securely and efficiently. In this work, we design a novel scheme named Heterogeneous Compression and Encryption Neural Network (HCEN), which aims to protect signal security and reduce the required resources in processing heterogeneous physiological signals. The proposed HCEN is designed as an integrated structure that introduces the adversarial properties of Generative Adversarial Networks (GAN) and the feature extraction functionality of Autoencoder (AE). Moreover, we conduct simulations to validate the performance of HCEN using the MIMIC-III waveform dataset. Electrocardiogram (ECG) and Photoplethysmography (PPG) signals are extracted in the simulation. The results reveal that the proposed HCEN can effectively encrypt floating-point signals. Meanwhile, the compression performance outperforms baseline compression methods.

Ligation to Scavenging Strategy Enables On-Demand Termination of Targeted Protein Degradation.

Event-driven bifunctional molecules, typified by proteolysis targeting chimera (PROTAC) technology, have been successfully applied in degrading many proteins of interest (POI). Due to the unique catalytic mechanism, PROTACs will induce multiple cycles of degradation until the elimination of the target protein. Here, we propose a versatile "Ligation to scavenging" approach to terminate event-driven degradation for the first time. Ligation to the scavenging system consists of a TCO-modified dendrimer (PAMAM-G5-TCO) and tetrazine-modified PROTACs (Tz-PROTACs). PAMAM-G5-TCO can rapidly scavenge intracellular free PROTACs via an inverse electron demand Diels-Alder reaction and terminate the degradation of certain proteins in living cells. Thus, this work proposes a flexible chemical knockdown approach to adjust the levels of POI on-demand in living cells, which paves the way for controlled target protein degradation.

Epidemic-Like Calcium Signaling in Mobile Molecular Communication Networks.

Molecular Communication is an emerging technology enabling communications in nano-networks. Ca2+ signal is one promising option of MC due to the important role in bio-metabolisms and the available characteristics in communication engineering. So far, scientists analyze Ca2+ signaling via bio-experiments and simulations. Current researches lack a mathematical model for quantitative analysis of Ca2+ signal propagation on the network scale. In this work, we investigate the propagation patterns of Ca2+ signals in bio-cellular network. Firstly, we propose an improved Ca2+ dynamics model to describe Ca2+ signals considering movements of cells and attenuation of Ca2+ concentration. Then, we perform multi-modal analysis through the waveform characteristics, and classify cells according to their states. Moreover, a mathematical model is put forward to analyze the propagation of calcium signals based on typical epidemic model. The proposed model fully considers the similarity between: 1) epidemic disease propagates among mobile individuals; 2) Ca2+ signal propagates among mobile cells. The proposed model is amended to fit the case considering unique characters of Ca2+ signal. Finally, simulation results show that the proposed Ca2+ propagation model is coincident with Monte Carlo simulation results, indicating that the model is helpful for understanding how far and how fast Ca2+ signal can propagate.

Erratum: Telocinobufagin inhibits the epithelial-to-mesenchymal transition of breast cancer cells through the PI3K/Akt/Snail signaling pathway.

[This corrects the article DOI: 10.3892/ol.2018.8349.].

Preamble Design and Collision Resolution in a Massive Access IoT System.

How to support massive access efficiently is one of the challenges in the future Internet of Things (IoT) systems. To address such challenge, this paper proposes an effective preamble collision resolution scheme to sustain massive random access (RA) for an IoT system. Specifically, a new sub-preamble structure is first proposed to reduce the preamble collision probability. To identify different devices that send the same preamble to the gNB on the same physical random access channel (PRACH), a multiple timing advance (TA) capturing scheme is then proposed. Thereafter, an RA scheme is designed to sustain massive access and the performance of the scheme is studied analytically. Finally, the effectiveness of the proposed RA scheme is validated by extensive simulation experiments in terms of preamble detection probability, preamble collision probability, RA success probability, resource efficiency and TA capturing.

Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks.

Visual sensor networks (VSNs) can be widely used in multimedia, security monitoring, network camera, industrial detection, and other fields. However, with the development of new communication technology and the increase of the number of camera nodes in VSN, transmitting and compressing the huge amounts of video and image data generated by video and image sensors has become a major challenge. The next-generation video coding standard-versatile video coding (VVC), can effectively compress the visual data, but the higher compression rate is at the cost of heavy computational complexity. Therefore, it is vital to reduce the coding complexity for the VVC encoder to be used in VSNs. In this paper, we propose a sample adaptive offset (SAO) acceleration method by jointly considering the histogram of oriented gradient (HOG) features and the depth information for VVC, which reduces the computational complexity in VSNs. Specifically, first, the offset mode selection (select band offset (BO) mode or edge offset (EO) mode) is simplified by utilizing the partition depth of coding tree unit (CTU). Then, for EO mode, the directional pattern selection is simplified by using HOG features and support vector machine (SVM). Finally, experimental results show that the proposed method averagely saves 67.79% of SAO encoding time only with 0.52% BD-rate degradation compared to the state-of-the-art method in VVC reference software (VTM 5.0) for VSNs.

Stochastic Latency Guarantee in Wireless Powered Virtualized Sensor Networks.

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.

A QoE-Aware Energy Supply Scheme over a FiWi Access Network in the 5G Era.

Integrated fiber-wireless (FiWi) should be regarded as a promising access network architecture in future 5G networks, and beyond; this due to its seamless combination of flexibility, ubiquity, mobility of the wireless mesh network (WMN) frontend with a large capacity, high bandwidth, strong robustness in time, and a wavelength-division multiplexed passive optical network (TWDM-PON) backhaul. However, the key issue in both traditional human-to-human (H2H) traffic and emerging Tactile Internet is the energy conservation network operation. Therefore, a power-saving method should be instrumental in the wireless retransmission-enabled architecture design. Toward this end, this paper firstly proposes a novel energy-supply paradigm of the FiWi converged network infrastructure, i.e., the emerging power over fiber (PoF) technology instead of an external power supply. Then, the existing time-division multiplexing access (TDMA) scheme and PoF technology are leveraged to carry out joint dynamic bandwidth allocation (DBA) and provide enough power for the sleep schedule in each integrated optical network unit mesh portal point (ONU-MPP) branch. Additionally, the correlation between the transmitted optical power of the optical line terminal (OLT) and the quality of experience (QoE) guarantee caused by multiple hops in the wireless frontend is taken into consideration in detail. The research results prove that the envisioned paradigm can significantly reduce the energy consumption of the whole FiWi system while satisfying the average delay constraints, thus providing enough survivability for multimode optical fiber.

Edge and Cloud Collaborative Entity Recommendation Method towards the IoT Search.

There are massive entities with strong denaturation of state in the physical world, and users have urgent needs for real-time and intelligent acquisition of entity information, thus recommendation technologies that can actively provide instant and precise entity state information come into being. Existing IoT data recommendation methods ignore the characteristics of IoT data and user search behavior; thus the recommendation performances are relatively limited. Considering the time-varying characteristics of the IoT entity state and the characteristics of user search behavior, an edge-cloud collaborative entity recommendation method is proposed via combining the advantages of edge computing and cloud computing. First, an entity recommendation system architecture based on the collaboration between edge and cloud is designed. Then, an entity identification method suitable for edge is presented, which takes into account the feature information of entities and carries out effective entity identification based on the deep clustering model, so as to improve the real-time and accuracy of entity state information search. Furthermore, an interest group division method applied in cloud is devised, which fully considers user's potential search needs and divides user interest groups based on clustering model for enhancing the quality of recommendation system. Simulation results demonstrate that the proposed recommendation method can effectively improve the real-time and accuracy performance of entity recommendation in comparison with traditional methods.

Joint Optical and Wireless Resource Allocation for Cooperative Transmission in C-RAN.

Cooperative multipoint transmission (CoMP) is one of the most promising paradigms for mitigating interference in cloud radio access networks (C-RAN). It allows multiple remote radio units (RRUs) to transmit the same data flow to a user to further improve the signal quality. However, CoMP may incur redundant data transmission over fronthaul network in the C-RAN. In a C-RAN employing CoMP, a key problem is how to coordinate heterogeneous resource allocation to maximize the cooperation gain while reducing the fronthaul load. In this paper, the cooperation transmission based on a multi-dimensional resource schedule (MRSCT) scheme, jointly considering user association, spectrum resource allocation, and wavelength resource allocation, is firstly envisioned in the underlying C-RAN integrating time and wavelength division multiplexing passive optical network (TWDM-PON) to maximize fronthaul efficiency. Then a two-timescale resource allocation framework including two sub-approaches is established. More specially, the first sub-approach mainly focuses on exploiting reinforcement learning to obtain a wavelength resource allocation strategy to relieve fronthaul traffic load. Moreover, the second sub-approach adopts the overlapping coalition formation game to establish a user-centric cooperative set, where spectrum resources are dynamically allocated to further alleviate the interference issue. The theoretical analysis and simulation results validate the performance of MRSCT scheme on the fronthaul efficiency, user experience, and system service capability.

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks.

With the widespread application of wireless sensor networks (WSNs), WSN virtualization technology has received extensive attention. A key challenge in WSN virtualization is the survivable virtual network embedding (SVNE) problem which efficiently maps a virtual network on a WSN accounting for possible substrate failures. Aiming at the lack of survivability research towards physical sensor node failure in the virtualized sensor network, the SVNE problem is mathematically modeled as a mixed integer programming problem considering resource constraints. A heuristic algorithm-node reliability-aware backup survivable embedding algorithm (NCS)-is further put forward to solve this problem. Firstly, a node reliability-aware embedding method is presented for initial embedding. The resource reliability of underlying physical sensor nodes is evaluated and the nodes with higher reliability are selected as mapping nodes. Secondly, a fault recovery mechanism based on resource reservation is proposed. The critical virtual sensor nodes are recognized and their embedded physical sensor nodes are further backed up. When the virtual sensor network (VSN) fails caused by the failure physical node, the operation of the VSN is restored by backup switching. Finally, the experimental results show that the strategy put forward in this paper can effectively guarantee the survivability of the VSN, reduce the failure penalty caused by the physical sensor nodes failure, and improve the long-term operating income of infrastructure provider.

Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing.

Mobile crowd sensing (MCS) systems usually attract numerous participants with widely varying sensing costs and interest preferences to perform tasks, where accurate task assignment plays an indispensable role and also faces many challenges (e.g., how to simplify the complicated task assignment process and improve matching accuracy between tasks and participants, while guaranteeing submitted data credibility). To overcome these challenges, we propose a service benefit aware multi-task assignment (SBAMA) strategy in this paper. Firstly, service benefits of participants are modeled based on their task difficulty, task history, sensing capacity, and sensing positivity to meet differentiated requirements of various task types. Subsequently, users are then clustered by enhanced fuzzy clustering method. Finally, a gradient descent algorithm is designed to match task types to participants achieving the maximum service benefit. Simulation results verify that the proposed task assignment strategy not only effectively reduces matching complexity but also improves task completion rate.

Nanogold hybrid silica gel and its 1-octadecanethiol self-assembled modified composite as a stationary phase for liquid chromatography.

Au nanoparticle-hybridized silica (Au@sil) spheres were synthesized in one step as a liquid chromatographic stationary phase for the first time. The hybridized stationary phase showed good separation performances in reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography even without bonding with any organic groups. Compared with the bare silica stationary phase, the Au@sil stationary phase showed better separation performance under the same conditions in RPLC and HILIC modes. The effects of acetonitrile content, buffer concentration, and the pH of the mobile phase on analyte retention were further investigated. The results showed that the Au@sil stationary phase had a complex retention mechanism of electrostatic and partitioning interactions. By comparing Au solution (solAu) with different proportion volumes in silica sol, the optimum hybridized stationary phase was found to comprise 33 vol% solAu. Au@sil was further modified with 1-octadecanethiol by self-assembly and used to separate alkylbenzenes and polycyclic aromatic hydrocarbons by RPLC. The separation efficiency of the 1-octadecanethiol self-assembled modified Au@sil (C18-Au@sil) column was much better than that of Au@sil. Overall, the successful hybridization of Au nanoparticles provided a new method to prepare a stationary phase in a simple and environmentally friendly way.

User Characteristic Aware Participant Selection for Mobile Crowdsensing.

Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages diverse embedded sensors in massive mobile devices. One of its main challenges is to effectively select participants to perform multiple sensing tasks, so that sufficient and reliable data is collected to implement various MCS services. Participant selection should consider the limited budget, the different tasks locations, and deadlines. This selection becomes even more challenging when the MCS tries to efficiently accomplish tasks under different heat regions and collect high-credibility data. In this paper, we propose a user characteristics aware participant selection (UCPS) mechanism to improve the credibility of task data in the sparse user region acquired by the platform and to reduce the task failure rate. First, we estimate the regional heat according to the number of active users, average residence time of users and history of regional sensing tasks, and then we divide urban space into high-heat and low-heat regions. Second, the user state information and sensing task records are combined to calculate the willingness, reputation and activity of users. Finally, the above four factors are comprehensively considered to reasonably select the task participants for different heat regions. We also propose task queuing strategies and community assistance strategies to ensure task allocation rates and task completion rates. The evaluation results show that our mechanism can significantly improve the overall data quality and complete sensing tasks of low-heat regions in a timely and reliable manner.

Telocinobufagin inhibits the epithelial-mesenchymal transition of breast cancer cells through the phosphoinositide 3-kinase/protein kinase B/extracellular signal-regulated kinase/Snail signaling pathway.

Telocinobufagin (TBG), an active ingredient of Venenumbufonis, exhibits an immunomodulatory activity. However, its antimetastatic activity in breast cancer remains unknown. The present study investigated whether TBG prevents breast cancer metastasis and evaluated its regulatory mechanism. TBG inhibited the migration and invasion of 4T1 breast cancer cells. Furthermore, TBG triggered the collapse of F-actin filaments in breast cancer. The epithelial-mesenchymal transition (EMT) markers, vimentin and fibronectin, were downregulated following TBG treatment. However, E-cadherin was upregulated following TBG treatment. Snail, a crucial transcriptional factor of EMT, was downregulated following TBG treatment. Signaling pathway markers, including phosphorylated protein kinase B (P-Akt), p-mechanistic target of rapamycin (mTOR) and p-extracellular signal-regulated kinase (ERK), were decreased following TBG treatment. The same results were obtained from in vivo experiments. In conclusion, in vitro and in vivo experiments reveal that TBG inhibited migration, invasion and EMT via the phosphoinositide 3-kinase (PI3K)/Akt/ERK/Snail signaling pathway in breast cancer.

Epidemiological and genetic characteristics of the fifth avian influenza A(H7N9) wave in Suzhou, China, from October 2016 to April 2017.

Human infections with H7N9 viruses can cause severe pneumonia and even death. To characterize the epidemiology and genetics of the H7N9 viruses circulating during from October 2016 to April 2017 in Suzhou, China, all pharyngeal swab samples were collected from severe acute respiratory infections (SARI) cases during this fifth wave of infection, and we amplified the H7N9 H7 and N9 genes using a real-time polymerase chain reaction (PCR). Positive samples were subjected to virus isolation and gene sequencing to analyze the evolution and variation of the H7N9 strains. The epidemiological features of H7N9 patients have not changed and there were no significant mutations in the key sites of the hemagglutinin (HA) gene sequence, but we identified the K526R and E627 K substitutions in the PB2 protein. In the neuraminidase (NA) protein, drug-resistant mutations (R294 K and H276Y) occurred in a few strains. Most of the H7N9 viruses isolated from Suzhou had no drug resistance mutations, but it is necessary to closely monitor and analyze the probable emergence of mutations and the spread of resistant strains. The reduction of the N-glycosylation site at position 42 of NA was observed in some strains.

Service Demand Discovery Mechanism for Mobile Social Networks.

In the last few years, the service demand for wireless data over mobile networks has continually been soaring at a rapid pace. Thereinto, in Mobile Social Networks (MSNs), users can discover adjacent users for establishing temporary local connection and thus sharing already downloaded contents with each other to offload the service demand. Due to the partitioned topology, intermittent connection and social feature in such a network, the service demand discovery is challenging. In particular, the service demand discovery is exploited to identify the best relay user through the service registration, service selection and service activation. In order to maximize the utilization of limited network resources, a hybrid service demand discovery architecture, such as a Virtual Dictionary User (VDU) is proposed in this paper. Based on the historical data of movement, users can discover their relationships with others. Subsequently, according to the users activity, VDU is selected to facilitate the service registration procedure. Further, the service information outside of a home community can be obtained through the Global Active User (GAU) to support the service selection. To provide the Quality of Service (QoS), the Service Providing User (SPU) is chosen among multiple candidates. Numerical results show that, when compared with other classical service algorithms, the proposed scheme can improve the successful service demand discovery ratio by 25% under reduced overheads.