Least-squares (LS)-based integration computes the function values by solving a set of integration equations (IEs) in LS sense, and is widely used in wavefront reconstruction and other fields where the measured data forms a slope. It is considered that the applications of IEs with smaller truncation errors (TEs) will improve the reconstruction accuracy. This paper proposes a general method based on the Taylor theorem to derive all kinds of IEs, and finds that an IE with a smaller TE has a higher-order TE. Three specific IEs with higher-order TEs in the Southwell geometry are deduced using this method, and three LS-based integration algorithms corresponding to these three IEs are formulated. A series of simulations demonstrate the validity of applying IEs with higher-order TEs in improving reconstruction accuracy. In addition, the IEs with higher-order TEs in the Hudgin and Fried geometries are also deduced using the proposed method, and the performances of these IEs in wavefront reconstruction are presented.

The high degree of absorption and scattering of photons propagating through biological tissues makes fluorescence molecular tomography (FMT) reconstruction a severe ill-posed problem and the reconstructed result is susceptible to noise in the measurements. To obtain a reasonable solution, Tikhonov regularization (TR) is generally employed to solve the inverse problem of FMT. However, with a fixed regularization parameter, the Tikhonov solutions suffer from low resolution. In this work, an adaptive Tikhonov regularization (ATR) method is presented. Considering that large regularization parameters can smoothen the solution with low spatial resolution, while small regularization parameters can sharpen the solution with high level of noise, the ATR method adaptively updates the spatially varying regularization parameters during the iteration process and uses them to penalize the solutions. The ATR method can adequately sharpen the feasible region with fluorescent probes and smoothen the region without fluorescent probes resorting to no complementary priori information. Phantom experiments are performed to verify the feasibility of the proposed method. The results demonstrate that the proposed method can improve the spatial resolution and reduce the noise of FMT reconstruction at the same time.

Due to different writing styles and various kinds of noise, the recognition of handwritten numerals is an extremely complicated problem. Recently, a new trend has emerged to tackle this problem by the use of multiple classifiers. This method combines individual classification decisions to derive the final decisions. This is called "Combination of Multiple Classifiers" (CME). In this paper, a novel approach to CME is developed and discussed in detail. It contains two steps: data transformation and data classification. In data transformation, the output values of each classifier are first transformed into a form of likeness measurement. The larger a likeness measurement is, the more probable the corresponding class has the input. In data classification, neural networks have been found very suitable to aggregate the transformed output to produce the final classification decisions. Some strategies for further improving the performance of neural networks have also been proposed in this paper. Experiments with several data transformation functions and data classification approaches have been performed on a large number of handwritten samples. The best result among them is achieved by using both the proposed data transformation function and the multi-layer perceptron neural net, which increased the recognition rate of three individual classifications considerably.

This Letter studies the non-ideal analogue filters performance, time delay and phase offset in hybrid filter bank (HFB) DACs. After compensating the non-ideal filters performance using a digital signal, a linear phase function containing time delay and phase was deduced based on the auto-power spectrum of the HFB DAC output in the overlapped frequency band. 'Three-point' method was used to simultaneously estimate these two errors by unwrapping the phase affected the principal value interval of the inverse trigonometric function. These two errors were compensated by the digital pre-distortion technique. Finally, simulation results were provided to verify the effectiveness of this proposed method.

Hepatocellular carcinoma (HCC) is a primary liver cancer with high incidence and mortality. miR-185, a microRNA with appriximately 22-28 nucleotides, was reported to be involved in many cancers. The potential mechanism of miR-185 on HCC through cell division cycle 42 (CDC42) was investigated. RT-qPCR was used to measure the RNA level of miR-185 and CDC42 in HCC tissues and cells. The dual luciferase reporter assay was used to verify whether CDC42 was a target gene for miR-185. Transwell assay was employed to detect the ability of migration and invasion to change miR-185. miR-185 expression was low in HCC and negatively correlated with CDC42. miR-185 inhibited HCC migration, invasion and miR-185 low expression predicted poor prognosis. CDC42 was predicted to be a target gene for miR-185, and regulated by miR-185. miR-185 suspressed the ability of cell migration and invasion through CDC42 in HCC. In conclusion, miR-185 suspressed migration and invasion of HCC cells by directly targeting CDC42. It is suggested that miR-185/CDC42 axis may present a novel target for HCC treatment.