Anatomical characteristics and potential gene mutation sites of a familial recurrent patellar dislocation.

BACKGROUND: Recurrent patellar dislocation is the result of anatomical alignment and imbalance of restraint of bone and soft tissue. We investigate the anatomical characteristics of the knee joint in a family of patients with recurrent patella dislocation, and to screen the possible pathogenic genes in this family by whole exome sequencing in 4 patients and 4 healthy subjects, so as to provide theoretical basis for the pathogenesis of this disease. METHODS: The data related to patella dislocation were measured by imaging data. The peripheral blood DNA of related family members was extracted for the whole exome sequencing, and then the sequencing results were compared with the human database. By filtering out synonymous variants and high-frequency variants in population databases, and then integrating single nucleotide non-synonymous variants of family members, disease-causing genes were found. RESULTS: All patients in this family have different degrees of abnormal knee anatomy, which is closely related to patella dislocation. The sequencing results of patients and normal persons in this patella dislocation family were compared and analyzed, and the data were filtered through multiple biological databases. Find HOXB9 (NM_024017.4:c.404A>G:p.Glu135Gly),COL1A1(NM_000088.3:c.3766G>A:p.Ala1256Thr),GNPAT(NM_014236.3:c1556A>G:p.Asp519Gly),NANS(NM_018946.3:c.204G>C:p.Glu68Asp),SLC26A2(NM_000112.3:c.2065A>T:p.Thr689Ser) are nonsynonymous variants (MISSENSE). Through Sanger sequencing, the identified mutations in HOXB9 and SLC26A2 genes were only present in samples from patients with recurrent patellar dislocation. CONCLUSIONS: The patients with recurrent patellar dislocation had markedly abnormal knee anatomy in this family. HOXB9 gene and SLC26A2 gene were found to be the possible pathogenic genes or related genes for patella dislocation.

Pixel dislocation correction method for a laser confocal scanning microscope through the nonlinear triangular wave driving mode and square wave index reconstruction.

The reciprocate scanning scheme of two-dimensional galvanometers is widely used in laser confocal scanning microscopes with high speed. However, the equal interval acquisition of an analog digital acquisition card (AD) and the unequal change of the galvanometer's scanning speed will cause the dislocation of pixels and distortion of the reconstructed image. Meanwhile, the movement properties of the galvanometers in the edge of the scanning area are complex, which will increase the difficulty of segmenting the collected one-dimensional data stream the AD collected into row data of a two-dimensional reconstructed image. Therefore, how to timely and accurately segment the one-dimensional data stream the AD collected into the row data of two-dimensional reconstructed image is not only the key to solve image distortion of a laser confocal scanning microscope with high speed but also the prerequisite to improve the accuracy of row data dislocation correction. A driving mode based on the nonlinear triangular wave and a dislocation-correcting method using a square wave index are proposed. Namely, on the basis of the galvanometer's scanning analysis, the equation of a nonlinear triangular wave driving voltage is established, and the switching frequency of the Y-galvanometer's driving voltage is obtained by calculating the collected switching frequency of the X-galvanometer; thus, the uniformity of the galvanometer's scanning trajectories is secured. Finally, the row segmentation flag pulse is first introduced into the one-dimensional data stream the AD collected, and the square wave index is used to segment the collected data, which means the one-dimensional data stream can be segmented timely and accurately via hardware method. Meanwhile, the pixel dislocation can be corrected. The experimental result shows that, compared with the Nikon A1R+ confocal microscope, the proposed method can effectively correct the pixel dislocation, and the position coincidence error is less than 0.7%. The proposed method will be helpful to improve the image quality of a laser confocal scanning microscope with high speed.

[Fast Discrimination of Drugs by Improved Projection Algorithm Based on Raman Spectroscopy].

The projection algorithm used in mixture analysis to determine whether there is unknown disturbance existing in grey system can not accurately identify different samples and similar samples at the same time when it is used in the identification of drugs, because of the insufficient criteria. In the present study, one of its criteria for whether the size of measurement error of testing sample is at a limited level is improved for whether the size and distribution of measurement error is equal and similar between testing sample and standard sample. By testing 6 kinds of normal drugs (including BAYER Aspirin Enteric-coated Tablets, TYLENOL Acetaminophen Sustained Release Tablets, BAYER Compound Paracetamol Tablets(II), HUAZHONG Compound Vitamin C, HUAZHONG Vitamin B and MADINGLIN Demperidone Tablets) and 3 kinds of similar drugs of aspirin (including BAYER Aspirin Enteric-coated Tablets, Shanghai SINE Aspirin Enteric-coated Tablets and Bamyl Aspirin Effervescent Tablets), it was found that the un-improved projection algorithm directly used in discrimination of drugs shows poor performance with many problems existing, however, the improved projection algorithm can discriminate different drugs and similar drugs with accuracy up to 100%. The improved projection algorithm can be a universal, accurate and reliable automated pharmaceutical identification algorithm and can provide a reference for the study on identification of substance.

[Locally Dynamically Moving Average Algorithm for the Fully Automated Baseline Correction of Raman Spectrum].

The baseline correction is an, extremely important spectral preprocessing step and can significantly improve the accuracy of the subsequent spectral analysis algorithm. At present most of the baseline correction algorithms are manual and semi-automated. The manual baseline correction depends on the user experience and its accuracy is greatly affected by the subjective factor. The semi-automated baseline correction needs to set different optimizing parameters for different Raman spectra, which will be inconvenient to users. In this paper, a locally.dynamically moving average algorithm (LDMA) for the fully automated baseline correction is presented and its basic ideas.and steps are demonstrated in detail. In the LDMA algorithm the modified moving averaging algorithm (MMA) is used to strip the Raman peaks. By automatically finding the baseline subintervals of the raw Raman spectrum to divide the total spectrum range into multi Raman peak subintervals, the LDMA algorithm succeed in dynamically changing the window half width of the MA algorithm and controlling the numbers of the smoothing iterations in each Raman peak subinterval. Hence, the phenomena of overcorrection and under-correction are avoided to the most degree. The LDMA algorithm has achieved great effect not only to the synthetic Raman spectra with the convex, exponential, or sigmoidal baseline but also to the real Raman spectra.

[Spectral Smoothing with Adaptive Multiscale Window Average].

In order to smooth the spectra automatically and reliably, a spectral smoothing algorithm with adaptive multiscale window average (AWMA) is demonstrated. In this method, different positions of the spectra are smoothed by windows of different width, and the width of the windows will directly affect smoothing. The window with inappropriate width may cause excessive denoising (peak distortion or loss) or inadequate denoising (the flat region of the spectra still contains a lot of noise). So, how to get the right width of the window is the key of spectral smoothing. The algorithm optimized the width of windows by an iterative method, and verified whether the width is the best according to statistical Z-test. In order to increase the reliability of the algorithm, a comprehensive comparison of the thresholds of hypothesis according to simulation data of different SNR was performed. When the threshold is set to 1. 1, the denoising effect can be the best. In this work, the AMWA algorithm was tested by simulated spectra and real syectra, and it can automatically adapt to different spectral shape and different noise intensity. A comprehensive comparison of AMWA smoothing, Savitzky-Golay smoothing and moving average smoothing was performed in this paper, and the AMWA algorithm is better than the other two algorithms. Results show that the AMWA algorithm not only has better denoising effect, but also has higher accuracy and fidelity. This method has achieved great effect not only to simulated spectra but also to real spectra.