Fabrication of Nano-Sized Hybrid Janus Particles from Strawberry-Like Hierarchical Composites

Janus nanoparticles possess amphiphilic properties and thus exhibit vast applications in the field of catalysis, drug delivery, displays, and surface/interface stabilizers. Despite several successful approaches, that were developed for micro-sized Janus particle fabrication, the achievement of nano-sized Janus particles is still facing challenges due to the difficulty with nanoscale processing. Here, new options for the preparation of Janus nanoparticles are demonstrated from strawberry-like hierarchical composites with designed surface functional groups for both "satellites" and spherical "core." The "satellites" of the hierarchical composites can be freely varied, from iron oxide to silica nanoparticles. Results from transmission electron microscopy, fourier transform infrared spectroscopy, and thermal gravimetric analysis measurements clearly prove the successful production of hybrid Janus silica nanoparticles coated by polystyrene and poly(acrylic acid). This technique demonstrates the vast flexibility of the abovementioned technique in terms of size, type, and surface chemistry design of Janus nanoparticles, which thus offers an additional approach to the current synthesis library of hybrid Janus nanoparticles

A Source Localization Method Using Complex Variational Mode Decomposition

Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources’ direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time

A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend

Complex variational mode decomposition (CVMD) has been proposed to extend the original variational mode decomposition (VMD) algorithm to analyze complex-valued data. Conventionally, CVMD divides complex-valued data into positive and negative frequency components using bandpass filters, which leads to difficulties in decomposing signals with the low-frequency trend. Moreover, both decomposition number parameters of positive and negative frequency components are required as prior knowledge in CVMD, which is difficult to satisfy in practice. This paper proposes a modified complex variational mode decomposition (MCVMD) method. First, the complex-valued data are upsampled through zero padding in the frequency domain. Second, the negative frequency component of upsampled data are shifted to be positive. Properties of analytical signals are used to get the real-valued data for standard variational mode decomposition and the complex-valued decomposition results after frequency shifting back. Compared with the conventional method, the MCVMD method gives a better decomposition of the low-frequency signal and requires less prior knowledge about the decomposition number. The equivalent filter bank structure is illustrated to analyze the behavior of MCVMD, and the MCVMD bi-directional Hilbert spectrum is provided to give the time–frequency representation. The effectiveness of the proposed algorithm is verified by both synthetic and real-world complex-valued signals

Exact Outage Probability Caused by Multiple Nakagami Interferers with Arbitrary Parameters

In this paper, outage probability caused by multiple interferers in Nakagami-m fading channels is studied. A novel method is proposed to derive the exact and closed form expressions of outage probability in the presence of multiple Nakagami independent cochannel interference. Unlike some previous conclusions, the method proposed in this paper is not only usable with various integer Nakagami fading parameters or average powers but also presents exact derivation of outage probability. To circumvent the difficulties, proper iteration functions are adopted by studying and integrating the definitions without numerical integration and residue calculation. The method generally deals with the cases that with or without minimum level constraint at the receiver for satisfactory reception. Finally, the exact expressions are compared with previous proposed approximated expressions and provide the understanding of the nature of interference

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than −27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing

Evaluación del quimerismo celular en un modelo de enfermedad aguda de injerto contra huésped establecido en ratones knockout para TLR4

Antecedentes: La enfermedad de injerto contra huésped (EICH) es una complicación importante después del trasplante alogénico de células madre hematopoyéticas. Objetivos: Para dilucidar el papel de TLR4, el principal receptor de LPS bacteriano, en el desarrollo de GVHD, construimos un modelo de GVHD en ratones knockout para TLR4 (TLR4-/-) y monitoreamos el quimerismo celular. Métodos: En este estudio, usamos PCR para identificar si se establecieron ratones knockout para TLR4 (TLR4-/-). Antes del trasplante, pretratamos a los ratones con irradiación para obtener la dosis de irradiación adecuada. Se aplicó citometría de flujo para medir el estado de quimerismo, las distribuciones de APC y células T en ratones receptores TLR4+/+ y TLR4-/-. Resultados: El estado general de los receptores de TLR4-/- fue mejor que el de los receptores de TLR4+/+, y los ratones receptores de TLR4-/- mostraron manifestaciones de GVHD menos graves que los ratones receptores de TLR4+/+. La mayoría de las APC y las células T en el bazo del ratón huésped se derivaron de las células del donante, y las células T CD4+, incluidas las células T de memoria, se encontraban en su mayoría en los ratones huéspedes. Conclusión: En general, nuestros datos muestran que la eliminación de TLR4 atenuó el desarrollo de GVHD, lo que sugiere que TLR4 podría usarse como un nuevo objetivo y paradigma terapéutico en las terapias de GVHD

Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

Abstract Background Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning. Methods An endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy-proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts. Results All images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM-DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM-DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM-DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively. Conclusions The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images

6G Near-field Technologies White Paper

No abstract available