Phase to intensity modulation and fading compensation using a single ring resonator for radio-over-fiber links.

In this study, we present a simulation-based analysis of radio-over-fiber (ROF) transmission links incorporating both phase modulation (PM) and a single ring resonator (RR) as the modulation transformer (MT). This configuration offers cost-effectiveness, enhanced operational stability, facile reconfiguration, and heightened robustness. The optimization of the RR involves a comprehensive adjustment of the power coupler coupling coefficient (k) and the roundtrip optical phase shift (φ) to attain superior characteristics in terms of power output, bandwidth, dispersion, and nonlinearity, individually. The simulation encompasses the transmission of diverse data formats, including QPSK, 16QAM, and 16QAM-based OFDM, modulated by the PM-RR system. The results reveal a 0.25 dB improvement in nonlinearity tolerance, increased power, and superior fading mitigation compared to the conventional intensity modulation (IM) approach. Furthermore, through careful tuning of the phase response, the Q factor of the PM-RR system exhibits an enhancement exceeding 40% over a 100 km fiber length when compared to the Mach-Zehnder modulator (MZM) system.

Scheme to eliminate the time shift of code edges based on the optimal transmission point of a DP-MZM.

To eliminate the time shift of code edges on a single-sideband (SSB) modulation signal transmission in a radio-over-fiber (RoF) system, a new, to the best of our knowledge, SSB modulation scheme based on an optimal transmission point for a double-parallel Mach-Zehnder modulator (DP-MZM) is proposed. The scheme is based on DP-MZM to realize the separation of the carrier and the +1st-order sideband at the optimal transmission point, and the baseband signal modulates the 2.5 Gb/s data signal to the +1st-order sideband of the SSB signal; then, the carrier and the +1st-order sideband are transmitted with a carrier-to-sideband ratio of 0 dB. Theoretical analysis shows that compared to the traditional SSB-modulated optical millimeter-wave signal generation scheme this scheme completely solves the problem of the time shift of code edges caused by dispersion. The simulation results show that the improved SSB modulation scheme has a Q factor of 23.362, the minimum bit error rate is 4.207×10-127 at 73.453 km, and the eye diagram is still very clear. Under the premise of meeting the basic requirements of communications, the maximum communications distance can reach 135 km, which is 270% of the transmission distance of a traditional SSB modulation model. Thus, the system performance has been greatly improved.

Neutralization of the new coronavirus by extracting their spikes using engineered liposomes.

The devastating COVID-19 pandemic motivates the development of safe and effective antivirals to reduce morbidity and mortality associated with infection. We developed nanoscale liposomes that are coated with the cell receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. Lentiviral particles pseudotyped with the spike protein of SARS-CoV-2 were constructed and used to test the virus neutralization potential of the engineered liposomes. Under TEM, we observed for the first time a dissociation of spike proteins from the pseudovirus surface when the pseudovirus was purified. The liposomes potently inhibit viral entry into host cells by extracting the spike proteins from the pseudovirus surface. As the receptor on the liposome surface can be readily changed to target other viruses, the receptor-coated liposome represents a promising strategy for broad spectrum antiviral development.

Translocator Protein 18 kDa (TSPO) as a Novel Therapeutic Target for Chronic Pain.

Chronic pain is an enormous modern public health problem, with significant numbers of people debilitated by chronic pain from a variety of etiologies. Translocator protein 18 kDa (TSPO) was discovered in 1977 as a peripheral benzodiazepine receptor. It is a five transmembrane domain protein, mainly localized in the outer mitochondrial membrane. Recent and increasing studies have found changes in TSPO and its ligands in various chronic pain models. Reversing their expressions has been shown to alleviate chronic pain in these models, illustrating the effects of TSPO and its ligands. Herein, we review recent evidence and the mechanisms of TSPO in the development of chronic pain associated with peripheral nerve injury, spinal cord injury, cancer, and inflammatory responses. The cumulative evidence indicates that TSPO-based therapy may become an alternative strategy for treating chronic pain.

A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs.

In data clustering, the measured data are usually regarded as uncertain data. As a probability-based clustering technique, possible world can easily cluster the uncertain data. However, the method of possible world needs to satisfy two conditions: determine the data of different possible worlds and determine the corresponding probability of occurrence. The existing methods mostly make multiple measurements and treat each measurement as deterministic data of a possible world. In this paper, a possible world-based fusion estimation model is proposed, which changes the deterministic data into probability distribution according to the estimation algorithm, and the corresponding probability can be confirmed naturally. Further, in the clustering stage, the Kullback-Leibler divergence is introduced to describe the relationships of probability distributions among different possible worlds. Then, an application in wearable body networks (WBNs) is given, and some interesting conclusions are shown. Finally, simulations show better performance when the relationships between features in measured data are more complex.

Fabrication and Characterization of Tumor Nano-Lysate as a Preventative Vaccine for Breast Cancer.

Breast cancer is the most common cancer among women in the United States, with late stages associated with the lowest survival rates. The latest stage, defined as metastasis, accounts for 90% of all cancer-related deaths. There is a strong need to develop antimetastatic therapies. TRAIL, or TNF-related apoptosis inducing ligand, has been used as an antimetastatic therapy in the past, and conjugating TRAIL to nanoscale liposomes has been shown to enhance its targeting efficacy. When circulating tumor cells (CTCs) released during metastasis are exposed to TRAIL-conjugated liposomes and physiologically relevant fluid shear stress, this results in rapid cancer cell destruction into cell fragments. We sought to artificially recreate this phenomenon using probe sonication to mechanically disrupt cancer cells and characterized the resulting cell fragments, termed "tumor nano-lysate", with respect to size, charge, morphology, and composition. Furthermore, an in vivo pilot study was performed to investigate the efficacy of tumor nano-lysate as a preventative vaccine for breast cancer in an immunocompetent mouse model.

A high-performance functional phthalonitrile resin with a low melting point and a low dielectric constant.

A monomer of fluorinated phthalonitrile, namely 4,4'-bis(p-perfluoro-phenol-(bis(p-phenol)propane-2,2-diyl)-p-oxy-diphthalonitrile) (PBDP), was synthesized by the nucleophilic substitution reaction of bisphenol A, decafluorobiphenyl and 4-nitrophthalonitrile. The structure of the monomer was characterized by nuclear magnetic resonance (NMR) spectroscopy and Fourier transform infrared (FTIR) spectroscopy. The results indicated that the PBDP monomer was synthesized successfully. The monomer was cured in the presence of 4-(aminophenoxy)phthalonitrile (APPH) and the curing behaviour was investigated by differential scanning calorimetry (DSC), suggesting a low melting point of 96 °C and an excellent processing window (96-262 °C). Thermogravimetric analysis (TGA) and dynamic mechanical analysis (DMA) showed that the fluorinated phthalonitrile resin possessed outstanding thermal and thermo-oxidative stabilities as well as good mechanical properties. The glass transition temperature was >400 °C and the 5% thermal degradation temperature was 501 °C. When the frequency was 50 MHz, the dielectric constant and dielectric loss of the polymer were 2.84 and 0.007, respectively. The PBDP resin has ultra-low water absorption of 0.77% and 1.4%, when exposed to an aqueous environment for 50 days at 24 °C and for 24 h at 100 °C, respectively. The prepared PBDP resin with outstanding thermal stability and low dielectric constant is an ideal candidate for aerospace industries, and microelectronic and other electronic packaging materials.

A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory.

As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, which are continuously sampled by wearable sensors, and a fine-grained evidence reasoning approach has been proposed to produce a timely and reliable result. First, the basic time unit of input data is selected by finding a tradeoff between accuracy and time cost. Then, the approach uses Long Short Term Memory to extract features and project raw multidimensional data into probability assignments, followed by trainable evidence combination and inference network that reduce uncertainly to improve the classification accuracy. Experiments validate the effectiveness of fine granularity and evidence reasoning while the final results indicate that the recognition accuracy of this approach can reach 96.4% with no additional complexity in training.

Micelle-in-Liposomes for Sustained Delivery of Anticancer Agents That Promote Potent TRAIL-Induced Cancer Cell Apoptosis.

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) induces cancer cell-specific apoptosis and has garnered intense interest as a promising agent for cancer treatment. However, the development of TRAIL has been hampered in part because most human cancer cells are resistant to TRAIL. A few small molecules including natural compounds such as piperlongumine (PL) have been reported to sensitize cancer cells to TRAIL. We prepared a novel type of nanomaterial, micelle-in-liposomes (MILs) for solubilization and delivery of PL. PL-loaded MILs were used to sensitize cancer cells to TRAIL. As visualized by cryo-TEM, micelles were successfully loaded inside the aqueous core of liposomes. The MILs increased the water solubility of PL by ~20 fold. A sustained PL release from MILs in physiologically relevant buffer over 7 days was achieved, indicating that the liposomes prevented premature drug release from the micelles in the MILs. Also demonstrated is a potent synergistic apoptotic effect in cancer cells by PL MILs in conjunction with liposomal TRAIL. MILs provide a new formulation and delivery vehicle for hydrophobic anticancer agents, which can be used alone or in combination with TRAIL to promote cancer cell death.

Complete genome sequence analysis of PS2, a novel T4-like bacteriophage that infects Serratia marcescens clinical isolates.

In this study, we isolated and characterized a lytic phage, named vB_SmaM_PS2 (abbreviated as PS2), which can infect Serratia marcescens clinical isolates. Morphologically, phage PS2 can be classified within the Myoviridae family. The 167,276 bp double-stranded DNA genome of PS2 possesses 41.7% GC content. A total of 276 protein-coding genes and 4 tRNA genes were predicted in the PS2 genome. Of the 276 genes, 131 (47%) encoded T4-like genes, most of which are DNA replication and virion structural genes. Therefore, phage PS2 should be a new member of the T4-like Serratia phage.

GWS-A Collaborative Load-Balancing Algorithm for Internet-of-Things.

This paper firstly replaces the first-come-first-service (FCFS) mechanism with the time-sharing (TS) mechanism in fog computing nodes (FCNs). Then a collaborative load-balancing algorithm for the TS mechanism is proposed for FCNs. The algorithm is a variant of a work-stealing scheduling algorithm, and is based on the Nash bargaining solution (NBS) for a cooperative game between FCNs. Pareto optimality is achieved through the collaborative working of FCNs to improve the performance of every FCN. Lastly the simulation results demonstrate that the game-theory based work-stealing algorithm (GWS) outperforms the classical work-stealing algorithm (CWS).

Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating.

With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks.

In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values. In many cases, the misclassification is unacceptable considering that it probably brings catastrophic damages to the data users. In this paper, a novel classification approach of incomplete data is proposed to reduce the misclassification errors. This method uses the regularized extreme learning machine to estimate the potential values of missing data at first, and then it converts the estimations into multiple classification results on the basis of the distance between interval numbers. Finally, an evidential reasoning rule is adopted to fuse these classification results. The final decision is made according to the combined basic belief assignment. The experimental results show that this method has better performance than other traditional classification methods of incomplete data.

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks.

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance.

Solution structure of a 2:1 complex of anticancer drug XR5944 with TFF1 estrogen response element: insights into DNA recognition by a bis-intercalator.

XR5944, a deoxyribonucleic acid (DNA) bis-intercalator with potent anticancer activity, can bind the estrogen response element (ERE) sequence to inhibit estrogen receptor-α activities. This novel mechanism of action may be useful for overcoming drug resistance to currently available antiestrogen treatments, all of which target the hormone-receptor complex. Here we report the nuclear magnetic resonance solution structure of the 2:1 complex of XR5944 with the naturally occurring TFF1-ERE, which exhibits important and unexpected features. In both drug-DNA complexes, XR5944 binds strongly at one intercalation site but weakly at the second site. The sites of intercalation within a native promoter sequence appear to be context and sequence dependent. The binding of one drug molecule influences the binding site of the second. Our structures underscore the fact that the DNA binding of a bis-intercalator is directional and different from the simple addition of two single intercalation sites. Our study suggests that improved XR5944 bis-intercalators targeting ERE may be designed through optimization of aminoalkyl linker and intercalation moieties at the weak binding sites.

[Collaborative Work Based on Heterogeneous Systems：a New Mode of Hospital Information System].

Hospital information technology has introduced a new mode of colaborative work based on heterogeneous systems and it wil become the new norm. The article ilustrates some successful typical cases of information exchange in the heterogeneous systems for colaborative work in Chinese PLA general hospital and puts forward some principles in the hospital's design of heterogeneous information systems.

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks.

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier's training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster's combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule.

Data Division Scheme Based on Homomorphic Encryption in WSNs for Health Care.

The use of wireless sensor networks for wearable computing in health care is growing quickly. Numerous applications are already in use, such as blood pressure monitors and heart rate monitors. As such, it is very important for system designers to consider how to protect patient privacy, especially in wireless sensor networks. After studying and analyzing the features of wireless sensor networks in medical systems, a data division scheme was proposed in this paper, provided the advantages of homomorphic encryption. In the proposed scheme, even if a forwarding node is compromised, the attacker will not be able to eavesdrop on the data, resulting in much stronger privacy than existing schemes. Experimental results shows that the scheme provides a good trade off in resources consumed and system security, and is efficient for encrypting or decrypting sensitive medical data.

[Study on the Effects of Different Ferron System, Ferron Solubility, Experimental Temperature and Other Factors for the Accurate Determination of Mononuclear Aluminum Concentration in Aqueous Solution].

Ferron colorimetric method has been widely used in aluminum speciation analysis. However, some important factors that may affect the accurate determination of mononuclear Al concentration have not been studied yet. Using molar absorption coefficient ε as a key indicator, the critical issues in Al-Ferron coloring reaction system have been investigated experimentally in this paper, including different Ferron system, Ferron solubility, experimental temperature, ionic strength and the reference system. The results show that three vital factors (different Ferron colorimetric system, Ferron solubility and experimental temperature) would significantly affect the accurate measurement of mononuclear Al concentration; whereas, other two factors like ionic strength and the reference have litter influence. Base on our experimental results and statistic analysis of relevant important literatures, we suggest that optimizing colorimetric conditions and standardizing operational procedures should be paid much more attention in Ferron colorimetric assay in order to obtain the accurate measurement results.

Near infrared laser-induced targeted cancer therapy using thermoresponsive polymer encapsulated gold nanorods.

External stimuli, such as ultrasound, magnetic field, and light, can be applied to activate in vivo tumor targeting. Herein, we fabricated polymer encapsulated gold nanorods to couple the photothermal properties of gold nanorods and the thermo- and pH-responsive properties of polymers in a single nanocomposite. The activation mechamism was thus transformed from heat to near-infrared (NIR) laser, which can be more easily controlled. Doxorubicin, a clinical anticancer drug, can be loaded into the nanocomposite through electrostatic interactions with high loading content up to 24%. The nanocomposite's accumulation in tumor post systematic administration can be significantly enhanced by NIR laser irradiation, providing a prerequisite for their therapeutic application which almost completely inhibited tumor growth and lung metastasis. Since laser can be manipulated very precisely and flexibly, the nanocomposite provides an ideally versatile platform to simultaneously deliver heat and anticancer drugs in a laser-activation mechanism with facile control of the area, time, and dosage. The NIR laser-induced targeted cancer thermo-chemotherapy without using targeting ligands represents a novel targeted anticancer strategy with facile control and practical efficacy.