Two-point calibration method for a zoom camera with an approximate focal-invariant radial distortion model.

A zoom camera can change its focal length and track moving objects with an adjustable resolution. To extract precise geometric information for the tracked objects, a zoom camera requires an accurate calibration method. High-precision camera calibration methods, however, usually require a number of control points that are not guaranteed in some practical situations. Most zoom cameras suffer radial distortion. Athough a traditional method can recover an undistorted image with known intrinsic parameters, it fails to work for a zoom camera with an unknown focal length. Motivated by these problems, we propose a two-point calibration method (TPCM). In this scheme, we first propose an approximate focal-invariant radial distortion (AFRD) model. With the AFRD model, an RGB image can be undistorted with an unknown focal length. After that, the TPCM method is presented to estimate the focal length and rotation matrix with only two control points for one image. Synthetic experiments demonstrate that the AFRD model is efficient. In the real data experiment, the mean reprojection error of the TPCM method is less than one pixel, which is smaller than current state-of-the-art methods, and we believe meets the demand for high-precision calibration.

Large-scale localization of touching somas from 3D images using density-peak clustering.

BACKGROUND: Soma localization is an important step in computational neuroscience to map neuronal circuits. However, locating somas from large-scale and complicated datasets is challenging. The challenges primarily originate from the dense distribution of somas, the diversity of soma sizes and the inhomogeneity of image contrast. RESULTS: We proposed a novel localization method based on density-peak clustering. In this method, we introduced two quantities (the local density ρ of each voxel and its minimum distance δ from voxels of higher density) to describe the soma imaging signal, and developed an automatic algorithm to identify the soma positions from the feature space (ρ, δ). Compared with other methods focused on high local density, our method allowed the soma center to be characterized by high local density and large minimum distance. The simulation results indicated that our method had a strong ability to locate the densely positioned somas and strong robustness of the key parameter for the localization. From the analysis of the experimental datasets, we demonstrated that our method was effective at locating somas from large-scale and complicated datasets, and was superior to current state-of-the-art methods for the localization of densely positioned somas. CONCLUSIONS: Our method effectively located somas from large-scale and complicated datasets. Furthermore, we demonstrated the strong robustness of the key parameter for the localization and its effectiveness at a low signal-to-noise ratio (SNR) level. Thus, the method provides an effective tool for the neuroscience community to quantify the spatial distribution of neurons and the morphologies of somas.

Wide-scope screening of pesticides in fruits and vegetables using information-dependent acquisition employing UHPLC-QTOF-MS and automated MS/MS library searching.

This paper presents an application of ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) for simultaneous screening and identification of 427 pesticides in fresh fruit and vegetable samples. Both full MS scan mode for quantification, and an artificial-intelligence-based product ion scan mode information-dependent acquisition (IDA) providing automatic MS to MS/MS switching of product ion spectra for identification, were conducted by one injection. A home-in collision-induced-dissociation all product ions accurate mass spectra library containing more than 1700 spectra was developed prior to actual application. Both qualitative and quantitative validations of the method were carried out. The result showed that 97.4 % of the pesticides had the screening detection limit (SDL) less than 50 µg kg-1 and more than 86.7 % could be confirmed by accurate MS/MS spectra embodied in the home-made library. Meanwhile, calibration curves covering two orders of magnitude were performed, and they were linear over the concentration range studied for the selected matrices (from 5 to 500 µg kg-1 for most of the pesticides). Recoveries between 80 and 110 % in four matrices (apple, orange, tomato, and spinach) at two spiked levels, 10 and 100 µg kg-1, was 88.7 or 86.8 %. Furthermore, the overall relative standard deviation (RSD, n = 12) for 94.3 % of the pesticides in 10 µg kg-1 and 98.1 % of the pesticides in 100 µg kg-1 spiked levels was less than 20 %. In order to validate the suitability for routine analysis, the method was applied to 448 fruit and vegetable samples purchased in different local markets. The results show 83.3 % of the analyzed samples have positive findings (higher than the limits of identification and quantification), and 412 commodity-pesticide combinations are identified in our scope. The approach proved to be a cost-effective, time-saving and powerful strategy for routine large-scope screening of pesticides.

Multi-class, multi-residue analysis of trace veterinary drugs in milk by rapid screening and quantification using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry.

A simple and rapid multi-class multi-residue analytical method was developed for the screening and quantification of veterinary drugs in milk by ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). A total of 90 veterinary drugs investigated belonged to almost 20 classes including lincomycins, macrolides, sulfonamides, quinolones, tetracyclines, ß-agonists, ß-lactams, sedatives, ß-receptor antagonists, sex hormones, glucocorticoids, nitroimidazoles, benzimidazoles, nitrofurans, and some others. A modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) procedure was developed for the sample preparation without the solid-phase extraction step. The linearity, sensitivity, accuracy, repeatability, and reproducibility of the method were fully validated. The response of the detector was linear for each target compound in a wide concentration range with a correlation coefficient (R(2)) of 0.9973 to 0.9999 (among them R(2)>0.999 for 73 of 90 analytes). The range of the limit of quantification for these compounds in the milk ranged from 0.10 to 17.30µg/kg. The repeatability and reproducibility were in the range of 2.11 to 9.62% and 2.76 to 13.9%, respectively. The average recoveries ranged from 72.62 to 122.2% with the RSD (n=6) of 1.30 to 9.61% at 3 concentration levels. For the screening method, the data of the precursor and product ions of the target analytes were simultaneously acquired under the all ions MS/MS mode in a single run. An accurate mass database for the confirmation and identification of the target compounds was established. The applicability of the screening method was verified by applying to real milk samples. The proposed analytical method allows the identification and confirmation of the target veterinary drugs at trace levels employing quick analysis time. Certain veterinary drugs were detected in some cases.

High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion.

Localization-based super-resolution microscopy (or called localization microscopy) rely on repeated imaging and localization of active molecules, and the spatial resolution enhancement of localization microscopy is built upon the sacrifice of its temporal resolution. Developing algorithms for high-density localization of active molecules is a promising approach to increase the speed of localization microscopy. Here we present a new algorithm called SSM_BIC for such purpose. The SSM_BIC combines the advantages of the Structured Sparse Model (SSM) and the Bayesian Information Criterion (BIC). Through simulation and experimental studies, we evaluate systematically the performance between the SSM_BIC and the conventional Sparse algorithm in high-density localization of active molecules. We show that the SSM_BIC is superior in processing single molecule images with weak signal embedded in strong background.

[Influence of level-Ib lymphadenopathy on the prognosis of nasopharyngeal carcinoma].

BACKGROUND AND OBJECTIVE: The level-Ib lymph node metastasis is rare in nasopharyngeal carcinoma (NPC). When and how this level should be irradiated with precise radiotherapy remains controversial. This study evaluated the prevalence and prognostic significance of level-Ib lymphadenopathy on the prognosis of NPC patients. METHODS: From January 1990 and December 1999, 933 newly diagnosed patients with NPC treated at Sun Yat-sen University Cancer Center were randomly selected, examined with computed tomography (CT) imagining for evidence of level-Ib lymphadenopathy before treatment. All patients received radical radiotherapy with or without chemotherapy. The relationship between level-Ib lymphadenopathy and post-treatment outcomes including overall survival (OS), locoregional recurrence-free survival (LRFS), and distant metastasis-free survival (DMFS) were analyzed using Kaplan-Meier methods. The Cox proportional hazards regression model was used to adjust for other prognostic factors. RESULTS: Of the 933 patients, 55 (5.9%) were found to have level-Ib lymphadenopathy, which was associated with carotid sheath involvement, oropharynx involvement and levels, and lateral cervical lymph node involvement. In the subgroup with carotid sheath involvement, with multivariate analysis accounting for all previously known prognostic factors, level-Ib lymphadenopathy was still associated with a risk of decreased OS (RR, 2.124; P<0.001), DMFS (RR, 2.168; P<0.001), and LRFS (RR, 1.989; P=0.001). CONCLUSION: Level-Ib lymphadenopathy in the patients with carotid sheath involvement is an independent prognostic factor.

DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.

Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology. Here, we developed an automated method for recognizing single-neuron axonal boutons in whole-brain fluorescence microscopy datasets. The method is based on deep convolutional neural networks and density-peak clustering. High-dimensional feature representations of bouton morphology can be learned adaptively through convolutional networks and used for bouton recognition and subtype classification. We demonstrate that the approach is effective for detecting single-neuron boutons at the brain-wide scale for both long-range pyramidal projection neurons and local interneurons.

Significant vector learning to construct sparse kernel regression models.

A novel significant vector (SV) regression algorithm is proposed in this paper based on an analysis of Chen's orthogonal least squares (OLS) regression algorithm. The proposed regularized SV algorithm finds the significant vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing the orthogonalization needed in the OLS algorithm.