Conjugate Polymer Anchor Enhancing Matrix Vacuum Stability and Improving MALDI MSI via Ion Bond.

Poor vacuum stability limits the application of many matrices in matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) that requires long-term measurement duration in high vacuum. In this study, a new approach using conjugate polymer anchor to protect unstable matrix from volatilizing in MALDI source based on ion bond is provided. Unlike strong covalent bonds which often introduce unnecessary groups, the weaker ion bonds are more conducive to breaking under laser radiation while effectively preventing matrix volatilization in a vacuum environment. The results confirm that conjugate polymer anchor will neither introduce additional ion peaks nor affect signal intensity, yet maintains comparable quantification properties. Vacuum stability of three kinds of typical matrices is enhanced using polymer anchors, and the in situ MALDI MS imaging of mouse brain and liver cancer is improved significantly.

Biofluid Metabolic Profiling for Lung Cancer Screening via Reactive Matrix-Assisted Laser Desorption Ionization Mass Spectrometry.

Lung cancer (LC) has the highest mortality rate among various cancer diseases. Developing an early screening method for LC with high classification accuracy is essential. Herein, 2-hydrazinoquinoline (2-HQ) is utilized as a dual-mode reactive matrix for metabolic fingerprint analysis and LC screening via matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). Metabolites in both positive mode and negative mode can be detected using 2-HQ as the matrix, and derivative analysis of aldehyde and ketone compounds can be achieved simultaneously. Hundreds of serum and urine samples from LC patients and healthy volunteers were analyzed. Combined with machine learning, LC patients and healthy volunteers were successfully distinguished with a high area under the curve value (0.996 for blind serum samples and 0.938 for urine). The MS signal was identified for metabolic profiling, and dysregulated metabolites of the LC group were analyzed. The above results showed that this method has great potential for rapid screening of LC.

Point-of-Care Test Paper for Exhaled Breath Aldehyde Analysis via Mass Spectrometry.

Volatile organic compounds (VOCs) from exhaled breath (EB) are considered to be promising biomarkers for lung diseases. A convenient and sensitive point-of-care (POC) testing method for EB VOCs is essential. Here, we developed a POC test paper for the analysis of EB aldehydes, which are potential biomarkers for lung cancer. A probe molecule, 4-aminothiophenol (4-ATP), was anchored on a paper substrate to specifically capture gas-phase aldehydes through the Schiff base reaction. Meanwhile, thin-film reaction acceleration was utilized to increase capture efficiency. By directly coupling the test paper to a mass spectrometer through paper spray, high sensitivity (0.1 ppt) and a wide quantification linear range (from 10 ppt to 1 ppm) were obtained. Analysis of EB from lung cancer patients with the test paper showed a significant increase in several reported aldehyde markers compared to EB from healthy volunteers, indicating the potential of this method for sensitive, low-cost, and convenient lung cancer screening and diagnosis.

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation.

Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

A Novel, Hybrid-Integrated, High-Precision, Vacuum Microelectronic Accelerometer with Nano-Field Emission Tips.

In this paper, a novel, hybrid-integrated, high-precision, vacuum microelectronic accelerometer is put forward, based on the theory of field emission; the accelerometer consists of a sensitive structure and an ASIC interface (application-specific integrated circuit). The sensitive structure has a cathode cone tip array, a folded beam, an emitter electrode, and a feedback electrode. The sensor is fabricated on a double-sided polished (1 0 0) N-type silicon wafer; the tip array of the cathode is shaped by wet etching with HNA (HNO3, HF, and CH3COOH) and metalized by TiW/Au thin film. The structure of the sensor is finally released by the ICP (inductively coupled plasma) process. The ASIC interface was designed and fabricated based on the P-JFET (Positive-Junction Field Effect Transistor) high-voltage bipolar process. The accelerometer was tested through a static field rollover test, and the test results show that the hybrid-integrated vacuum microelectronic accelerometer has good performance, with a sensitivity of 3.081 V/g, the non-linearity is 0.84% in the measuring range of -1 g~1 g, the average noise spectrum density value is 36.7 µV/ Hz in the frequency range of 0⁻200 Hz, the resolution of the vacuum microelectronic accelerometer can reach 1.1 × 10-5 g, and the zero stability reaches 0.18 mg in 24 h.

Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.