The immunoregulation effect of tumor microenvironment in pancreatic ductal adenocarcinoma

Pancreatic cancer has the seventh highest death rate of all cancers. The absence of any serious symptoms, coupled with a lack of early prognostic and diagnostic markers, makes the disease untreatable in most cases. This leads to a delay in diagnosis and the disease progresses so there is no cure. Only about 20% of cases are diagnosed early. Surgical removal is the preferred treatment for cancer, but chemotherapy is standard for advanced cancer, although patients can eventually develop drug resistance and serious side effects. Chemoresistance is multifactorial because of the interaction among pancreatic cancer cells, cancer stem cells, and the tumor microenvironment (TME). Nevertheless, more pancreatic cancer patients will benefit from precision treatment and targeted drugs. This review focuses on the immune-related components of TME and the interactions between tumor cells and TME during the development and progression of pancreatic cancer, including immunosuppression, tumor dormancy and escape. Finally, we discussed a variety of immune components-oriented immunotargeting drugs in TME from a clinical perspective

Vernier Ring Based Pre-bond Through Silicon Vias Test in 3D ICs

Defects in TSV will lead to variations in the propagation delay of the net connected to the faulty TSV. A non-invasive Vernier Ring based method for TSV pre-bond testing is proposed to detect resistive open and leakage faults. TSVs are used as capacitive loads of their driving gates, then time interval compared with the fault-free TSVs will be detected. The time interval can be detected with picosecond level resolution, and digitized into a digital code to compare with an expected value of fault-free. Experiments on fault detection are presented through HSPICE simulations using realistic models for a 45 nm CMOS technology. The results show the effectiveness in the detection of time interval 10 ps, resistive open defects 0.2 kΩ above and equivalent leakage resistance less than 18 MΩ. Compared with existing methods, detection precision, area overhead, and test time are effectively improved, furthermore, the fault degree can be digitalized into digital code

Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems

One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS) systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM), by addressing the neighborhood preserving embedding (NPE) algorithm of manifold learning on the wavelet packet node energy (WPNE) of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC) is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity

Stability of the Stochastic Reaction-Diffusion Neural Network with Time-Varying Delays and p-Laplacian

The main aim of this paper is to discuss moment exponential stability for a stochastic reaction-diffusion neural network with time-varying delays and p-Laplacian. Using the Itô formula, a delay differential inequality and the characteristics of the neural network, the algebraic conditions for the moment exponential stability of the nonconstant equilibrium solution are derived. An example is also given for illustration

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method

Design of Small MEMS Microphone Array Systems for Direction Finding of Outdoors Moving Vehicles

In this paper, a MEMS microphone array system scheme is proposed which implements real-time direction of arrival (DOA) estimation for moving vehicles. Wind noise is the primary source of unwanted noise on microphones outdoors. A multiple signal classification (MUSIC) algorithm is used in this paper for direction finding associated with spatial coherence to discriminate between the wind noise and the acoustic signals of a vehicle. The method is implemented in a SHARC DSP processor and the real-time estimated DOA is uploaded through Bluetooth or a UART module. Experimental results in different places show the validity of the system and the deviation is no bigger than 6° in the presence of wind noise