Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing.

Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user's participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user's profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user's income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms.

MRPL48 is a novel prognostic and predictive biomarker of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most prevalent forms of cancer and poses a threat to the health and survival of humans. Mitochondrial ribosomal protein L48 (MRPL48) belongs to the mitochondrial ribosomal protein family, which participates in energy production. Studies have shown that MRPL48 can predict osteosarcoma incidence and prognosis, as well as promotes colorectal cancer progression. However, the role of MRPL48 in HCC remains unknown. METHODS: TCGA, GEO, HCCDB, CPTAC, SMART, UALCAN, Kaplan-Meier plotter, cBioPortal, and MethSurv were performed for bioinformatics purposes. Quantitative RT-PCR, immunoblotting, and functional studies were conducted to validate the methodology in vitro. RESULTS: MRPL48 was greatly overexpressed in HCC tissues, compared with healthy tissue, which was subsequently demonstrated in vitro as well. The survival and regression analyses showed that MRPL48 expression is of significant clinical prognostic value in HCC. The ROC curve and nomogram analysis indicated that MRPL48 is a powerful predictor of HCC. MRPL48 methylation was adversely associated with the expression of MRPL48, and patients with a low level of methylation had poorer overall survival than those with a high level of methylation. GSEA showed that the expression of the MRPL48 was correlated with Resolution of Sister Chromatid Cohesion, Mitotic Prometaphase, Retinoblastoma Gene in Cancer, RHO Gtpases Activate Formins, Mitotic Metaphase and Anaphase, and Cell Cycle Checkpoints. An analysis of immune cell infiltration showed a significant association between MRPL48 and immune cell infiltration subsets, which impacted the survival of HCC patients. Additionally, MRPL48 knockdown reduced HCC cell proliferation, migration, and invasion in vitro. CONCLUSIONS: We demonstrated that MRPL48 expression may be associated with HCC development and prognosis. These findings may open up new research directions and opportunities for the development of HCC treatments.

Isolated traumatic gallbladder injury: A case report.

BACKGROUND: Isolated gallbladder injury (GI) (IGI) directly induced by abdominal trauma is rare. Symptoms, indications, and imaging examinations of IGI are frequently non-specific, posing tremendous diagnostic challenges, which are simple to overlook and may have severe implications. Improving doctors' understanding of gallbladder injury (GI) facilitates early detection and decreases the likelihood of severe consequences, including death. CASE SUMMARY: We report a case of IGI caused by blunt violence (after falling from three meters with the umbilicus as the stress point) and performed laparoscopic repair of the gallbladder rupture, which helps clinicians understand IGI and reduce the severe consequences of delayed diagnosis. Through extensive medical history and dynamic abdominal ultrasound evaluation, doctors can identify GI early and begin surgery, thereby decreasing the devastating repercussions of delayed diagnosis. CONCLUSION: This article aims to improve clinicians' understanding of IGI and propose a method for the diagnosis and treatment of GI.

Arithmetic Optimization AOMDV Routing Protocol for FANETs.

Flying ad hoc networks (FANETs), composed of small unmanned aerial vehicles (UAVs), possess characteristics of flexibility, cost-effectiveness, and rapid deployment, rendering them highly attractive for a wide range of civilian and military applications. FANETs are special mobile ad hoc networks (MANETs), FANETs have the characteristics of faster network topology changes and limited energy. Existing reactive routing protocols are unsuitable for the highly dynamic and limited energy of FANETs. For the lithium battery-powered UAV, flight endurance lasts from half an hour to two hours. The fast-moving UAV not only affects the packet delivery rate, average throughput, and end-to-end delay but also shortens the flight endurance. Therefore, research is urgently needed into a high-performance routing protocol with high energy efficiency. In this paper, we propose a novel routing protocol called AO-AOMDV, which utilizes arithmetic optimization (AO) to enhance the ad hoc on-demand multi-path distance vector (AOMDV) routing protocol. The AO-AOMDV utilizes a fitness function to calculate the fitness value of multiple paths and employs arithmetic optimization for selecting the optimal route for routing selection. Our experiments were conducted using NS3 with three evaluation metrics: the packet delivery ratio, network lifetime, and average end-to-end delay. We compare this algorithm to routing protocols including AOMDV and AODV. The results indicate that the proposed AO-AOMDV attained a higher packet delivery ratio, network lifetime, and lower average end-to-end delay.

An Improved SAMP Algorithm for Sparse Channel Estimation in OFDM System.

Channel estimation of an orthogonal frequency division multiplexing (OFDM) system based on compressed sensing can effectively reduce the pilot overhead and improve the utilization rate of spectrum resources. The traditional SAMP algorithm with a fixed step size for sparse channel estimation has the disadvantages of a low estimation efficiency and limited estimation accuracy. An Improved SAMP (ImpSAMP) algorithm is proposed to estimate the channel state information of the OFDM system. In the proposed ImpSAMP algorithm, the received signal is firstly denoised based on the energy-detection method, which can reduce the interferences on channel estimation. Furthermore, the step size is adjusted dynamically according to the l2 norm of difference between two estimated sparse channel coefficients of adjacent phases to estimate the sparse channel coefficients quickly and accurately. In addition, the double threshold judgment is adopted to enhance the estimation efficiency. The simulation results show that the ImpSAMP algorithm outperforms the traditional SAMP algorithm in estimation efficiency and accuracy.

Resource Allocation and Data Offloading Strategy for Edge-Computing-Assisted Intelligent Telemedicine System.

Intelligent telemedicine technology has been widely applied due to the quick development of the Internet of Things (IoT). The edge-computing scheme can be regarded as a feasible solution to reduce energy consumption and enhance the computing capabilities for the Wireless Body Area Network (WBAN). For an edge-computing-assisted intelligent telemedicine system, a two-layer network architecture composed of WBAN and Edge-Computing Network (ECN) was considered in this paper. Moreover, the age of information (AoI) was adopted to describe the time cost for the TDMA transmission mechanism in WBAN. According to the theoretical analysis, the strategy for resource allocation and data offloading in edge-computing-assisted intelligent telemedicine systems can be expressed as a system utility function optimizing problem. To maximize the system utility, an incentive mechanism based on contract theory (CT) was considered to motivate edge servers (ESs) to participate in system cooperation. To minimize the system cost, a cooperative game was developed to address the slot allocation in WBAN, while a bilateral matching game was utilized to optimize the data offloading problem in ECN. Simulation results have verified the effectiveness of the strategy proposed in terms of the system utility.

Development of preoperative and postoperative machine learning models to predict the recurrence of huge hepatocellular carcinoma following surgical resection.

Resection has been commonly utilized for treating huge hepatocellular carcinoma (HCC) with a diameter of ≥10 cm; however, a high rate of mortality is reported due to recurrence. The present study was designed to predict the recurrence following resection based on preoperative and postoperative machine learning models. In total, 1,082 patients with HCC who underwent liver resection in the Eastern Hepatobiliary Surgery Hospital cohort between January 2008 and December 2016 were divided into a training cohort and an internal validation cohort. In addition, 164 patients from Mengchao Hepatobiliary Hospital cohort between January 2014 and December 2016 served as an external validation cohort. The demographic information, and serological, MRI, and pathological data were obtained from each patient prior to and following surgery, followed by evaluating the model performance using the concordance index, time-dependent receiver operating characteristic curves, prediction error cures, and a calibration curve. A preoperative random survival forest (RSF) model and a postoperative RSF model were constructed based on the training set, which outperformed the conventional models, such as the Barcelona Clinic Liver Cancer (BCLC), the 8th edition of the American Joint Committee on Cancer (AJCC 8th) staging systems, and the Chinese stage systems. In addition, the preoperative and postoperative RSF models could also re-stratify patients with BCLC stage A/B/C or AJCC 8th stage IB/II/IIIA/IIIB or Chinese stage IB/IIA/IIB/IIIA into low-risk, intermediate-risk, and high-risk groups in the training and the two validation cohorts. The preoperative and postoperative RSF models were effective for predicting recurrence in patients with huge HCC following hepatectomy.

Collaborative Task Offloading and Service Caching Strategy for Mobile Edge Computing.

Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance.

Multi-Agent Reinforcement Learning for Joint Cooperative Spectrum Sensing and Channel Access in Cognitive UAV Networks.

This paper studies the problem of distributed spectrum/channel access for cognitive radio-enabled unmanned aerial vehicles (CUAVs) that overlay upon primary channels. Under the framework of cooperative spectrum sensing and opportunistic transmission, a one-shot optimization problem for channel allocation, aiming to maximize the expected cumulative weighted reward of multiple CUAVs, is formulated. To handle the uncertainty due to the lack of prior knowledge about the primary user activities as well as the lack of the channel-access coordinator, the original problem is cast into a competition and cooperation hybrid multi-agent reinforcement learning (CCH-MARL) problem in the framework of Markov game (MG). Then, a value-iteration-based RL algorithm, which features upper confidence bound-Hoeffding (UCB-H) strategy searching, is proposed by treating each CUAV as an independent learner (IL). To address the curse of dimensionality, the UCB-H strategy is further extended with a double deep Q-network (DDQN). Numerical simulations show that the proposed algorithms are able to efficiently converge to stable strategies, and significantly improve the network performance when compared with the benchmark algorithms such as the vanilla Q-learning and DDQN algorithms.

UNC5B-AS1 promotes the proliferation, migration and EMT of hepatocellular carcinoma cells via regulating miR-4306/KDM2A axis.

Being one of the most prevalent malignancies, hepatocellular carcinoma (HCC) threatens the health of population all over the world. Numerous researches have confirmed that long noncoding RNAs (lncRNAs) play an important role in tumor progression. Nonetheless, the mechanisms of unc-5 netrin receptor B antisense RNA 1 (UNC5B-AS1) in HCC remain obscure. Thus, this study aims to investigate the regulatory role and mechanism of UNC5B-AS1 in HCC cells. In our research, UNC5B-AS1 was subjected to gene expression analysis by RT-qPCR. Biological functions of UNC5B-AS1 in HCC cells were measured by MTT, colony formation, EdU and transwell assays. The combination between UNC5B-AS1, lysine demethylase 2A (KDM2A) and miR-4306 was validated by mechanism assays. Result showed UNC5B-AS1 was upregulated in HCC tissues and cells, contributing to the development of cancer staging and survival rate of HCC patients. Moreover, UNC5B-AS1 deficiency inhibited the proliferation, migration and epithelial-mesenchymal transition (EMT) of HCC cells. Furthermore, UNC5B-AS1 could interact with miR-4306 in HCC cells. Similarly, KDM2A was proved as the target gene of miR-4306. Finally, miR-4306 downregulation or KDM2A overexpression reversed the prohibitive role of UNC5B-AS1 knockdown in HCC progression. In short, UNC5B-AS1 accelerates the proliferation, migration and EMT of HCC cells via the regulation of miR-4306/KDM2A axis.

Receptor-Interacting Protein Kinase 1 Promotes Cholangiocarcinoma Proliferation And Lymphangiogenesis Through The Activation Protein 1 Pathway.

PURPOSE: Receptor-interacting protein kinase 1 (RIPK1) is an important upstream regulator of multiple cell signaling pathways including inflammatory signals. RIPK1 is reported to be closely associated with the prognostic implications of cancer, especially epithelial tumors. But its role in proliferation and lymphangiogenesis in cholangiocarcinoma (CCA) remains unclear and requires further investigation. PATIENTS AND METHODS: Expression of RIPK1 in human CCA tissues and CCA cell lines (QBC939, HUH28 and CCPL-1) was measured using qPCR, immunoblotting and immunohistochemistry. Silencing of RIPK1 was achieved by transduction of CCA cells via lentiviral plasmids (LV3-H1/GFP&Puro) encapsulating RIPK1 shRNA (LV-shRIPK1) or negative control shRNA (LV-shNC), and puromycin was used to select stable colonies. Proliferation and lymphangiogenesis were assessed in vitro by CCK-8 and matrigel-based tube formation assays, respectively. Activity of the activation protein-1 (AP-1) was evaluated by double-luciferase reporter gene assay. Protein expression of JNK, P38MAPK, ERK1/2, AP-1, P-AP-1, E-cadherin, N-cadherin and vascular endothelial growth factor-C (VEGF-C) was measured by immunoblotting or ELISA. An orthotopic CCA model in null mice was generated by transplanting QBC939 LV-shRIPK1, LV-shNC and control cells to further evaluate the role of RIPK1 on lymphangiogenesis in vivo. Immunohistochemistry was utilized to evaluate the expression of RIPK1 and VEGF-C, and tumor lymphatic vessels in the CCA model mice. RESULTS: Upregulated expression of RIPK1 in CCA tissues was closely related to tumor size, lymph node metastasis and poor prognosis. RIPK1 promoted proliferation and lymphangiogenesis in CCA cells, and regulated the activation of JNK and P38MAPK-mediated AP-1/VEGF-C pathway. Finally, in vivo animal experiments in the orthotopic CCA mouse model further confirmed the function of RIPK1 in lymphangiogenesis. CONCLUSION: This is the first report demonstrating the role of RIPK1 in proliferation and lymphangiogenesis through the MAPK (JNK and P38MAPK)- AP-1 pathway in CCA.

HBx inhibits HMGB1 expression and active oxygen production in LO2 cells through the NF-κB signaling pathway.

Hepatitis B virus (HBV) infection is one of major causes of various kinds of liver diseases. Among the four open reading frames (ORFs) of the HBV genome, the X region of HBV encodes HBx protein, which plays an important regulatory role in HBV infection. NF-κB and high-mobility group protein box1 (HMGB1) are potentially able to enhance the scavenging activity of host cells against foreign microorganisms. The present study focuses on the effect of HBx on the expression of HMGB1 in vitro. First, the lentiviral vector was used to induce the overexpression of HBx protein in LO2 cells (a normal hepatocyte cell line). Then, NF-κB activity, HMGB1 expression and the production of ROS were detected by Western blot and DCFH-DA (ROS detector) staining. Afterward, rotenone and LPS, which are activors of ROS and NF-κB, respectively, were used to stimulate HBx-overexpressing cells. Then, the expression differentiation of HMGB1 and ROS or the activity alternation of NF-κB was detected again. HBx inhibited the activity of NF-κB, inhibited the expression of HMGB1 and inhibited the production of ROS. The stimulation study with retenone or LPS suggested that there were mutual regulations between NF-κB and HMGB1. HBx inhibits the expression of HMGB1 and the generation of ROS via the NF-κB signaling pathway.

A Low Power and Fast Tracking Light-to-Frequency Converter With Adaptive Power Scaling for Blood SpO 2 Sensing.

This paper presents a monolithic low power and fast tracking light-to-frequency converter for blood SpO 2 sensing. Normally, the tracking speed and the power consumption are two contradictory characteristics. However, different gain-bandwidth specifications for various ambient light intensities allow the dynamic optimization of the power consumption according to the light intensity. In this paper, the amplifier power consumption is adaptively scaled by the generated light-intensity-positively-correlated control voltage. Thus, the chip total power consumption at low light intensity is significantly decreased. Moreover, the proposed adaptive power scaling is achieved with a continuous analog domain, which does not introduce extra switching noise. The proposed light-to-frequency sensor chip is fabricated by using 0.35  µm CMOS technology with a die area of 1 × 0.9 mm 2. The measurement results show that the pulse light response for any light intensity is no longer than two new output square-wave cycles. The maximum total current consumption is 1.9 mA from a 3.3 V supply voltage, which can be adaptively scaled down to only 0.7 mA if the output frequency is about 25 KHz or lower. The minimum operational supply voltage of the proposed sensor chip is 2.5 V in the temperature range of -25 to 80  °C with 4 KV ESD level (human-body model).