对刺激朝向改变的自动加工:事件相关电位的证据

利用事件相关电位（ＥＲＰ）技术,探讨非注意状态的刺激朝向改变是否引起自动加工。刺激为具有一定朝向（垂直和水平各５０％）和一定空间频率（低频９０％,高频１０％）的光栅。要求被试忽略光栅朝向,对高频光栅作反应。刺激呈现时间为５０ｍｓ,刺激间隔在２５０至４５０ｍｓ之间随机变化。低频光栅刺激被分为两类,“匹配”（与前一刺激朝向相同）和“失匹配”（与前一刺激朝向不同）。结果发现,失匹配刺激比匹配刺激诱发出更大的枕区Ｐ１、更大的前额－中央区Ｎ１以及更大的前部与顶区Ｐ２,但前部与顶区的Ｎ２却更小。这些ＥＲＰｓ变化提示,视觉对非注意的刺激朝向变化进行了一定程度的自动加工;视觉通道可能存在类似听觉失匹配负波（ＭＭＮ）的、然而机制不同的自动加工成分     

A cloud-fog software architecture for dental CBCT dose monitoring using the DICOM structured report: Automated establishment of DRL

PurposeThe diagnostic reference level (DRL) has been established to optimize the diagnostic methods and reduce radiation dose during radiographic examinations. The aim of this study was to present a completely new solution based on Cloud-Fog software architecture for automatic establishment of the DRL values during dental cone-beam computed tomography (CBCT) according to digital imaging and communications in medicine (DICOM) structured reports.Methods and MaterialsA Cloud-Fog software architecture was used for automatic data handling. This architecture used the DICOM structured reports as a source for extracting the required information by fog devices in the imaging center. These devices transferred the derived information to the cloud server. The cloud server calculated the value of indication-based DRL in dental CBCT imaging based upon the parameters and adequate quantities of the absorbed dose. The feedback of DRL value was continuously announced to the imaging centers in 6 phases. In each phase, the level of the dose was optimized in imaging centers.ResultsThe DRL value was established for 5-specific indications, including third molar teeth (511 mGy.cm²), implant (719 mGy.cm²), form and position anomalies of the tooth (408 mGy.cm²), dentoalveolar pathologies (612 mGy.cm²), and endodontics (632 mGy.cm²). The determination of the DRL value in each phase revealed a downward trend until stabilization.ConclusionThe new solution presented in this study makes it possible to calculate and update the DRL value nationally and automatically among all centers. Also, the results showed that this approach is successful in establishing stabilized DRL values.     

Radiomics classifier to quantify automatic segmentation quality of cardiac sub-structures for radiotherapy treatment planning

PurposeA radiomics features classifier was implemented to evaluate segmentation quality of heart structures. A robust feature set sensitive to incorrect contouring would provide an ideal quantitative index to drive autocontouring optimization.MethodsTwenty-five cardiac sub-structures were contoured as regions of interest in 36 CTs. Radiomic features were extracted from manually-contoured (MC) and Hierarchical-Clustering automatic-contouring (AC) structures. A robust feature-set was identified from correctly contoured CT datasets. Features variation was analyzed over a MC/AC dataset. A supervised-learning approach was used to train an Artificial-Intelligence (AI) classifier; incorrect contouring cases were generated from the gold-standard MC datasets with translations, expansions and contractions. ROC curves and confusion matrices were used to evaluate the AI-classifier performance.ResultsTwenty radiomics features, were found to be robust across structures, showing a good/excellent intra-class correlation coefficient (ICC) index comparing MC/AC. A significant correlation was obtained with quantitative indexes (Dice-Index, Hausdorff-distance). The trained AI-classifier detected correct contours (CC) and not correct contours (NCC) with an accuracy of 82.6% and AUC of 0.91. True positive rate (TPR) was 85.1% and 81.3% for CC and NCC. Detection of NCC at this point of the development still depended strongly on degree of contouring imperfection.ConclusionsA set of radiomics features, robust on “gold-standard” contour and sensitive to incorrect contouring was identified and implemented in an AI-workflow to quantify segmentation accuracy. This workflow permits an automatic assessment of segmentation quality and may accelerate expansion of an existing autocontouring atlas database as well as improve dosimetric analyses of large treatment plan databases.     

Comparative study of the free amino acid compositions and contents in three different botanical origins of Coptis herb

Free amino acids are important bioactive ingredients in herbs and herbal products. Coptis herbs contain a variety of free amino acids; however, studies have not yet analyzed the relationship between free amino acids and species of Coptis herbs. In the current study, the contents of 20 free amino acids in Coptis chinensis Franch., Coptis teeta Wall., and Coptis deltoidea C.Y. Cheng et Hsiao were determined using an automatic amino acid analyzer to evaluate the differences between the three species. We found that the major amino acids (Asn, Arg, and γ-aminobutyric acid (GABA)) were significantly more in C. chinensis than in the other two species. In addition, Asp content was significantly more in C. deltoidea. The study concluded that Asn can be used to identify C. deltoidea, C. chinensis, and C. teeta, and Gln and Arg can be used to distinguish C. teeta from C. chinensis and C. deltoidea. These findings suggested that free amino acids as active ingredients can be used to identify Coptis herb species.     

Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning

PurposeTo train and evaluate a very deep dilated residual network (DD-ResNet) for fast and consistent auto-segmentation of the clinical target volume (CTV) for breast cancer (BC) radiotherapy with big data.MethodsDD-ResNet was an end-to-end model enabling fast training and testing. We used big data comprising 800 patients who underwent breast-conserving therapy for evaluation. The CTV were validated by experienced radiation oncologists. We performed a fivefold cross-validation to test the performance of the model. The segmentation accuracy was quantified by the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). The performance of the proposed model was evaluated against two different deep learning models: deep dilated convolutional neural network (DDCNN) and deep deconvolutional neural network (DDNN).ResultsMean DSC values of DD-ResNet (0.91 and 0.91) were higher than the other two networks (DDCNN: 0.85 and 0.85; DDNN: 0.88 and 0.87) for both right-sided and left-sided BC. It also has smaller mean HD values of 10.5 mm and 10.7 mm compared with DDCNN (15.1 mm and 15.6 mm) and DDNN (13.5 mm and 14.1 mm). Mean segmentation time was 4 s, 21 s and 15 s per patient with DDCNN, DDNN and DD-ResNet, respectively. The DD-ResNet was also superior with regard to results in the literature.ConclusionsThe proposed method could segment the CTV accurately with acceptable time consumption. It was invariant to the body size and shape of patients and could improve the consistency of target delineation and streamline radiotherapy workflows.     

Analysis of forearm rotational motion using biplane fluoroscopic intensity-based 2D–3D matching

Measuring three-dimensional (3D) forearm rotational motion is difficult. We aimed to develop and validate a new method for analyzing 3D forearm rotational motion. We proposed biplane fluoroscopic intensity-based 2D–3D matching, which employs automatic registration processing using the evolutionary optimization strategy. Biplane fluoroscopy was conducted for forearm rotation at 12.5 frames per second along with computed tomography (CT) at one static position. An arm phantom was embedded with eight stainless steel spheres (diameter, 1.5 mm), and forearm rotational motion measurements using the proposed method were compared with those using radiostereometric analysis, which is considered the ground truth. As for the time resolution analysis, we measured radiohumeral joint motion in a patient with posterolateral rotatory instability and compared the 2D–3D matching method with the simulated multiple CT method, which uses CTs at multiple positions and interpolates between the positions. Rotation errors of the radius and ulna between these two methods were 0.31 ± 0.35° and 0.32 ± 0.33°, respectively, translation errors were 0.43 ± 0.35 mm and 0.29 ± 0.25 mm, respectively. Although the 2D–3D method could detect joint dislocation, the multiple CT method could not detect quick motion during joint dislocation. The proposed method enabled high temporal- and spatial-resolution motion analyses with low radiation exposure. Moreover, it enabled the detection of a sudden motion, such as joint dislocation, and may contribute to 3D motion analysis, including joint dislocation, which currently cannot be analyzed using conventional methods.     

Automatic VMAT planning for post-operative prostate cancer cases using particle swarm optimization: A proof of concept study

ObjectiveTo investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning.Material and MethodsIn PSO a solution space of planning constraints is searched for the best possible RT plan in an iterative, statistical method, optimizing a population of candidate solutions. To identify the best candidate solution and for final evaluation a plan quality score (PQS), based on dose volume histogram (DVH) parameters, was introduced.Automatic PSO-based RT planning was used for N = 10 postoperative prostate cancer cases, retrospectively taken from our clinical database, with a prescribed dose of EUD = 66 Gy in addition to two constraints for rectum and one for bladder. Resulting PSO-based plans were compared dosimetrically to manually generated VMAT plans.ResultsPSO successfully proposed treatment plans comparable to manually optimized ones in 9/10 cases. The median (range) PTV EUD was 65.4 Gy (64.7–66.0) for manual and 65.3 Gy (62.5–65.5) for PSO plans, respectively. However PSO plans achieved significantly lower doses in rectum D_2% 67.0 Gy (66.5–67.5) vs. 66.1 Gy (64.7–66.5, p = 0.016). All other evaluated parameters (PTV D_98% and D_2%, rectum V_40Gy and V_60Gy, bladder D_2% and V_60Gy) were comparable in both plans. Manual plans had lower PQS compared to PSO plans with −0.82 (−16.43–1.08) vs. 0.91 (−5.98–6.25).ConclusionPSO allows for fully automatic generation of VMAT plans with plan quality comparable to manually optimized plans. However, before clinical implementation further research is needed concerning further adaptation of PSO-specific parameters and the refinement of the PQS.     

A genome-wide association study of facial morphology identifies novel genetic loci in Han Chinese

The human face is a heritable surface with many complex sensory organs. In recent years, many genetic loci associated with facial features have been reported in different populations, yet there is a lack of studies on the Han Chinese population. Here, we report a genome-wide association study of 3 D normal human faces of 2,659 Han Chinese with autosegment phenotypes of facial morphology. We identify singlenucleotide polymorphisms(SNPs) encompassing four genomic regions showing significant associations with different facial regions, including SNPs in DENND1 B associated with the chin, SNPs among PISRT1 associated with eyes, SNPs between DCHS2 and SFRP2 associated with the nose, and SNPs in VPS13 B associated with the nose. We replicate 24 SNPs from previously reported genetic loci in different populations, whose candidate genes are DCHS2, SUPT3 H, HOXD1, SOX9, PAX3, and EDAR. These results provide a more comprehensive understanding of the genetic basis of variation in human facial morphology.     

基于自动录音技术研究三种雉类 鸣叫特征和节律

自动录音机是一种低成本、高效、可以在较长时间和较大空间范围内对物种的活动进行有效监测的设备。2019年4至6月,在北京小龙门地区（40°00′ N,115°26′ E）褐马鸡（Crossoptilon mantchuricum）、勺鸡（Pucrasia macrolopha）和环颈雉（Phasianus colchicus）的栖息地内布设了songmeter自动录音机,对三种雉类的鸣声进行为期2个月的连续收集。利用Kaleidoscope软件（Wildlife Acoustics公司,美国）,对不同雉类鸣声进行了人工辅助的机器学习和自动识别,从录制的22 536 h录音数据中提取了褐马鸡、勺鸡和环颈雉的鸣声。优化鸣声提取的时频参数后,勺鸡鸣声提取的正确率为73.32%,探测率为52.91%;环颈雉鸣声提取的正确率为89.32%,探测率为67.36%;褐马鸡鸣声提取的正确率较低,仅为8.69%,探测率为58.54%。结合三种雉类的繁殖资料,揭示了三种雉类的鸣声节律：褐马鸡和环颈雉有早晚两个鸣叫高峰期,但高峰时段不尽相同,勺鸡只存在鸣叫早高峰;褐马鸡、勺鸡和环颈雉在交配期、孵卵前期具有一个鸣叫高峰。     

Paint4Net: COBRA Toolbox extension for visualization of stoichiometric models of metabolism

A visual analysis of reconstructions and large stoichiometric models with elastic change of the visualization scope and representation methods becomes increasingly important due to the rapidly growing size and number of available reconstructions. The Paint4Net is a novel COBRA Toolbox extension for automatic generation of a hypergraph layout of defined scope with the steady state rates of reaction fluxes of stoichiometric models. Directionalities and fluxes of reactions are constantly represented in the visualization while detailed information about reaction (ID, name and synonyms, and formula) and metabolite (ID, name and synonyms, and charged formula) appears placing the cursor on the item of interest. Additionally Paint4Net functionality can be used to: (1) get lists of involved metabolites and dead end metabolites of the visualized part of the network, (2) exclude (filter) particular metabolites from representation, (3) find isolated parts of a network and (4) find running cycles when all the substrates are cut down. Layout pictures can be saved in various formats and easily distributed. The Paint4Net is open source software under the GPL v3 license. Relevant documentation and sample data is available at http://www.biosystems.lv/paint4net. The Paint4Net works on MATLAB starting from version of 2009.