Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework.

The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. It improves the energy efficiency of the underwater sensors personalized according to the various water depth layers of sensor locations. In particular, we integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. Mathematically, the optimal mechanism is formulated as a complex nonlinear integer programming (NIP) problem. A new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is thus further proposed to tackle this sophisticated NIP problem. The systematic simulation results demonstrate the effectiveness of our mechanism in improving the energy efficiency of UWSNs. Moreover, our presented E-DDTMD algorithm achieves significantly superior performance to the baseline schemes.

All-Organic Hyper-Crosslinked Polymer/Polyimide Composite Films with Ultralow High-Frequency Dielectric Constant.

The ever increasing demand for high-speed communication at high frequency promotes the rapid development of low-dielectric polymer films. Aromatic polyimide (PI) has been widely used as the main dielectrics in the flexible circuit board due to its excellent dielectric, mechanical, and thermal properties. Nevertheless, the dielectric constant of PI films at a high frequency range (several GHz) is relatively high and cannot satisfy the requirement of high-frequency communication. On this basis, a hyper-crosslinked polymer (HCP) and fabricated all-organic HCP/PI composite films through a physical blending method is synthesized. The porous structure of HCP is helpful to reduce the dielectric constant of PI matrix. The effects of HCP loadings on the dielectric, mechanical, and thermal properties of HCP/PI composite films are systematically investigated. The dielectric constants of the composite films can be reduced to 1.6-1.8 in the frequency range of 8.2-9.6 GHz when the HCP content reached 10 wt.%. The proposed method in this work is simple and effective to reduce the dielectric constant of PI and can be easily extended to other organic component-filled PI systems.

Study on Preparation of Chitosan/Polyvinyl Alcohol Aerogel with Graphene-Intercalated Attapulgite(GO-ATP@CS-PVA) and Adsorption Properties of Crystal Violet Dye.

Adsorption is one of the effective methods of treating dye wastewater. However, the selection of suitable adsorbent materials is the key to treating dye wastewater. In this paper, GO-ATP was prepared by an intercalation method by inserting graphene oxide (GO) into the interlayer of alabaster attapulgite (ATP), and GO-ATP@CS-PVA aerogel was prepared by co-blending-crosslinking with chitosan (CS) and polyvinyl alcohol (PVA) for the adsorption and removal of crystalline violet dye from the solution. The physicochemical properties of the materials are characterized by various methods. The results showed that the layer spacing of the GO-ATP increased from 1.063 nm to 1.185 nm for the ATP, and the specific surface area was 187.65 m2·g-1, which was 45.7% greater than that of the ATP. The FTIR results further confirmed the success of the GO-ATP intercalation modification. The thermogravimetric analysis (TGA) results show that the aerogel has good thermal stability properties. The results of static adsorption experiments show that at 302 K and pH 9.0, the adsorption capacity of the GO-ATP@CS-PVA aerogel is 136.06 mg·g-1. The mass of the aerogel after adsorption-solution equilibrium is 11.4 times that of the initial mass, with excellent adsorption capacity. The quasi-secondary kinetic, Freundlich, and Temkin isotherm models can better describe the adsorption process of the aerogel. The biobased composite aerogel GO-ATP@CS-PVA has good swelling properties, a large specific surface area, easy collection and a low preparation cost. The good network structure gives it unique resilience. The incorporation of clay as a nano-filler can also improve the mechanical properties of the composite aerogel.

Intravoxel incoherent motion imaging combined with diffusion kurtosis imaging to assess the response to radiotherapy in a rabbit VX2 malignant bone tumor model.

PURPOSE: To combine intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) parameters for the evaluation of radiotherapy response in rabbit VX2 malignant bone tumor model. MATERIAL AND METHODS: Forty-seven rabbits with bone tumor were prospectively enrolled and divided into pre-treatment, considerable effect and slight effect group. Treatment response was evaluated using IVIM-DKI. IVIM-based parameters (tissue diffusion [Dt], pseudo-diffusion [Dp], perfusion fraction [fp]), and DKI-based parameters (mean diffusion coefficient [MD] and mean kurtosis [MK]) were calculated for each animal. Corresponding changes in MRI parameters before and after radiotherapy in each group were studied with one-way ANOVA. Correlations of diffusion parameters of IVIM and DKI model were computed using Pearson's correlation test. A diagnostic model combining different diffusion parameters was established using binary logistic regression, and its ROC curve was used to evaluate its diagnostic performance for determining considerable and slight effect to malignant bone tumor. RESULTS: After radiotherapy, Dt and MD increased, whereas fp and MK decreased (p <  0.05). The differences in Dt, fp, MD, and MK between considerable effect and slight effect groups were statistically significant (p <  0.05). A combination of Dt, fp, and MK had the best diagnostic performance for differentiating considerable effect from slight effect (AUC = 0.913, p <  0.001). CONCLUSIONS: A combination of IVIM- and DKI-based parameters allowed the non-invasive assessment of cellular, vascular, and microstructural changes in malignant bone tumors after radiotherapy, and holds great potential for monitoring the efficacy of tumor radiotherapy.

Exosomal microRNA-506 inhibits biological activity of lung adenocarcinoma cells and increases sensitivity to cisplatin-based hyperthermia.

Exosomal microRNAs (miRNAs) play a vital role in the occurrence and development of lung adenocarcinoma (LUAD). Based on the bioinformatics analyses, the current study sought to explore the effects of exosomal miR-506 on LUAD cell biology and the efficacy of cisplatin (CDDP)-based hyperthermia (HT). After sample preparation, we identified decreased miR-506 and elevated ATAD2. LUAD cells were subsequently transfected with miR-506 mimic, oe-ATAD2 and PI3K/AKT signaling pathway inhibitor LY294002 to analyze effects of the miR-506/ATAD2/PI3K/AKT axis on cell biological processes and chemoresistance. Effects of exosomal miR-506 on sensitivity of LUAD cells to CDDP-based HT were further assessed in a co-culture system of BMSC-derived exosomes and LUAD cells, which was also validated in tumor-bearing nude mice. miR-506 down-regulated ATAD2 to inhibit the PI3K/AKT signaling pathway, thereby inhibiting the malignant phenotypes of LUAD cells and augmenting LUAD cell sensitivity to CDDP-based HT. Further, BMSCs-derived exosomes harboring miR-506 sensitized LUAD cells to DDP/HT both in vitro and in vivo. Collectively, our findings revealed that exosomal miR-506 sensitized LUAD cells to CDDP-based HT by inhibiting ATAD2/PI3K/AKT signaling pathway, offering a potential therapeutic target for LUAD treatment.

Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.

OBJECTIVES: To evaluate the performance of a deep learning radiomic nomogram (DLRN) model at predicting tumor relapse in patients with soft tissue sarcomas (STS) who underwent surgical resection. METHODS: In total, 282 patients who underwent MRI and resection for STS at three independent centers were retrospectively enrolled. In addition, 113 of the 282 patients received additional contrast-enhanced MRI scans. We separated the participants into a development cohort and an external test cohort. The development cohort consisted of patients from one center and the external test cohort consisted of patients from two other centers. Two MRI-based DLRNs for prediction of tumor relapse after resection of STS were established. We universally tested the DLRNs and compared them with other prediction models constructed by using widespread adopted predictors (i.e., staging systems and Ki67) instead of radiomics features. RESULTS: The DLRN1 model incorporated plain MRI-based radiomics signature into the clinical data, and the DLRN2 model integrated radiomics signature extracted from plain and contrast-enhanced MRI with the clinical predictors. Across both study sets, the two MRI-based DLRNs had relatively better prognostic capability (C index ≥ 0.721 and median AUC ≥ 0.746; p < 0.05 compared with most other models and predictors) and less opportunity for prediction error (integrated Brier score ≤ 0.159). The decision curve analysis indicates that the DLRNs have greater benefits than staging systems, Ki67, and other models. We selected appropriate cutoff values for the DLRNs to divide STS recurrence into three risk strata (low, medium, and high) and calculated those groups' cumulative risk rates. CONCLUSION: The DLRNs were shown to be a reliable and externally validated tool for predicting STS recurrence by comparing with other prediction models. KEY POINTS: • The prediction of a high recurrence rate of STS before emergence of local recurrence can help to determine whether more active treatment should be implemented. • Two MRI-based DLRNs for prediction of tumor relapse were shown to be a reliable and externally validated tool for predicting STS recurrence. • We used the DLRNs to divide STS recurrence into three risk strata (low, medium, and high) to facilitate more targeted postoperative management in the clinic.

Diffusion kurtosis imaging assessment of the response to radiotherapy in a VX2 bone tumor model: an animal study.

BACKGROUND: Neoadjuvant radiotherapy plays a vital role in the treatment of malignant bone tumors, and non-invasive imaging methods are needed to evaluate the response to treatment. PURPOSE: To assess the value of diffusion kurtosis imaging (DKI) for monitoring early response to radiotherapy in malignant bone tumors. MATERIAL AND METHODS: Treatment response was evaluated in a rabbit VX2 bone tumor model (n = 35) using magnetic resonance imaging (MRI), DKI, and histopathologic examinations. Subjects were divided into three groups: pre-treatment, post-treatment, and control groups. The post-treatment group was subclassified into good response and poor response groups according to the results of histopathologic examination. Apparent diffusion coefficient (ADC) and DKI parameters (mean diffusion coefficient [MD] and mean kurtosis [MK]) were recorded. The relationship between ADC, DKI parameters, and histopathologic changes after radiotherapy was determined using Pearson's correlation coefficient. The diagnostic performance of these parameters was assessed using receiver operating characteristic analysis. RESULTS: MD in the good response group was higher after treatment than before treatment (P < 0.001) and higher than that in the poor response group (P = 0.009). MD was highly correlated with tumor cell density and apoptosis rate (r = -0.771, P < 0.001 and r = 0.625, P < 0.001, respectively). MD was superior to other parameters for determining the curative effect of radiotherapy, with a sensitivity of 75.0%, specificity of 100.0%, and area under the curve of 0.917 (P < 0.001). CONCLUSION: The correlations between MD, tumor cell density, and apoptosis suggest that MD could be useful for assessing the early response to radiotherapy in rabbit VX2 malignant bone tumors.

A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours.

BACKGROUND: We sought to evaluate the performance of a computed tomography (CT)-based radiomics nomogram we devised in distinguishing benign from malignant bone tumours. METHODS: Two hundred and six patients with bone tumours were spilt into two groups: a training set (n = 155) and a validation set (n = 51). A feature extraction process based on 3D Slicer software was used to extract the radiomics features from unenhanced CT images, and least absolute shrinkage and selection operator logistic regression was used to calculate the radiomic score to generate a radiomics signature. A clinical model comprised demographics and CT features. A radiomics nomogram combined with the clinical model and the radiomics signature was constructed. The performance of the three models was comprehensively evaluated from three aspects: identification ability, accuracy, and clinical value, allowing for generation of an optimal prediction model. RESULTS: The radiomics nomogram comprised clinical and radiomics signature features. The nomogram model displayed good performance in training and validation sets with areas under the curve of 0.917 and 0.823, respectively. The areas under the curve, decision curve analysis, and net reclassification improvement showed that the radiomics nomogram model could obtain better diagnostic performance than the clinical model and achieve greater clinical net benefits than the clinical and radiomics signature models alone. CONCLUSIONS: We constructed a combined nomogram comprising a clinical model and radiomics signature as a noninvasive preoperative prediction method to distinguish between benign and malignant bone tumours and assist treatment planning.