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Granular cell tumors (GCTs) are rare, indolent neoplasms classically characterized by eosinophilic granular cytoplasm, infiltrations of polygonal cells in the collagenous stroma, and pustulo‐ovoid bodies of Milian. We describe a case of a 10‐year‐old female presenting with a GCT of the upper arm, remarkable for positive Melan‐A expression without additional melanocytic features. The differentiation between granular cells versus melanocytic neoplasms carries significant implications for clinical management, and such diagnoses should be considered carefully in the setting of unusual immunophenotypes. [ABSTRACT FROM AUTHOR]

When children act to involve mothers in positive interaction, they influence the amount, timing, and content of parent-child exchanges. By assessing children's smiling and positive initiation, we examined child behaviors that function to create positive interaction. In a non-clinical North American sample of 103 mothers and their 14- to 27-month-olds, we observed that children attempted to connect positively with mothers (1) more with age, (2) more frequently and quickly immediately after mothers were responsive, (3) more with mothers who were generally supportive, and (4) more with mothers low in depressive symptoms. When mothers were high in depressive symptoms, age-related increases in smiling and positive initiation were absent. Findings demonstrate the importance of maternal depression and responsiveness to child behaviors that involve mothers in ongoing activity. They suggest that both immediate cues and children's stored knowledge related to how mothers will respond may regulate children's positive connecting behaviors.

An undergraduate experiment in redox titrimetry involving determination of free lime in clinker and cement using iodometry can teach students several skills, extraction, filtration, redox and acid-base titrations, and statistical data analysis.Free lime is first extracted with nonaqueous ethylene glycol, the suspension filtered and allowed to react with a known excess of hydrochloric acid and the excess acid determined by iodometry. In speed, sensitivity and precision, the method is better than the classical British Standard (BS) method, which is discussed. It shows students how traditional wet chemistry can be used in industry and lets students learn about cement processing.

Examines the use of iodometry in the determination of free lime clinker and cement. Extraction of free lime with nonaqueous ethylene glycol; Development of the method by an undergraduate chemistry majors; Precision of the iodometry method; Advantages of the method to students.

We propose a novel recurrent variational network, SegMorph, to perform concurrent segmentation and motion estimation on cardiac cine magnetic resonance image (CMR) sequences. Our model establishes a recurrent latent space that captures spatiotemporal features from cine-MRI sequences for multitask inference and synthesis. The proposed model follows a recurrent variational auto-encoder framework and adopts a learnt prior from the temporal inputs. We utilise a multi-branch decoder to handle bi-ventricular segmentation and motion estimation simultaneously. In addition to the spatiotemporal features from the latent space, motion estimation enriches the supervision of sequential segmentation tasks by providing pseudo-ground truth. On the other hand, the segmentation branch helps with motion estimation by predicting deformation vector fields (DVFs) based on anatomical information. Experimental results demonstrate that the proposed method performs better than state-of-the-art approaches qualitatively and quantitatively for both segmentation and motion estimation tasks. We achieved an 81% average Dice Similarity Coefficient (DSC) and a less than 3.5 mm average Hausdorff distance on segmentation. Meanwhile, we achieved a motion estimation Dice Similarity Coefficient of over 79%, with approximately 0.14% of pixels displaying a negative Jacobian determinant in the estimated DVFs.

Melanoma is one of the most deadly skin lesion, which often uses the skin dermoscopy to detect it. However, the low interclass variations between melanoma images make manual dermoscopic detection time-consuming and laborious. Therefore, an automatic recognition algorithm of skin image is highly desirable. However, the traditional methods still have the limitations (e.g., weak robustness and generalization ability). To meet the challenge, we propose an effective architecture based on residual - squeeze - and - excitation -Inception-v4 network (MelanomaNet) to detect melanoma. Specifically, Inception-v4 structure is utilized to get the rich spatial features and increase feature diversity. We also consider the relationship between feature channels by adding residual-squeeze-and-excitation (RSE) blocks in Inception- v4 network using the feature recalibration strategies. Finally, we use the support vector machine (SVM) as the classifier for the skin lesion classification. We evaluate our proposed method on the public available ISIC skin lesion challenge datasets in 2018 for training and evaluation. The experimental results show that the proposed method has achieved better performance over the state-of-the-arts methods.

Detection of mild cognitive impairment (MCI) is important, and appropriate interventions can be taken to delay or prevent its progression to Alzheimer's disease (AD). The construction of brain networks based on brain image data to depict the interaction of brain functions or structures at the level of brain connections has been widely used to identify individuals with MCI/AD from the normal control (NC). Exploring the structural and functional connections and interactions between brain regions is beneficial to detect MCI. For this reason, we propose a new model for automatic MCI diagnosis based on this information. Firstly, a new functional brain network estimation method is proposed. Self-calibration is introduced using quality indicators, and functional brain network estimation is performed at the same time. Then we integrate the functional and structural connected neuroimaging patterns into our multitask learning model to select informative feature. By identifying synergies and differences between different tasks, the most discriminative features are determined. Finally, the most relevant features are sent to the support vector machine classifier for diagnosis and identification of MCI. The experimental results based on the public Alzheimer's disease neuroimaging (ADNI) show that our method can effectively diagnose different stages of MCI and assist the physician to improve the MCI diagnostic accuracy. At the same time, compared with the existing classification methods, the proposed method achieves relatively high classification accuracy. In addition, it can identify the most discriminative brain regions. These findings suggest that our approach not only improves classification performance, but also successfully identifies important biomarkers associated with disease.

Objective: Osteoarthritis (OA) is characterized by synovial tissue inflammation and underlying bone degeneration in the joints. Aging and obesity are among the major risk factors. This study evaluated the effects of aging and body mass index (BMI) on hip joint inflammation and bone degeneration using fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) and fluorine-18 sodium fluoride ( 18 F-NaF) PET/CT imaging, respectively., Subjects and Methods: In this retrospective study, a total of 116 subjects (58 males and 58 females) who had undergone both 18 F-FDG and 18 F-NaF PET/CT imaging were analyzed. The mean age of these subjects was 48.6±14.5 with an age range of 21-75 years. Fluorine-18-FDG and 18 F-NaF PET/CT imaging was conducted 180min and 90min (respectively) after intravenous administration of the appropriate tracer. The hip joint was segmented on fused PET/CT images using OsiriX MD v.9.5 (DICOM viewer and image-analysis program, Pixmeo SARL; Bernex, Switzerland). The region of interest (ROI) for the hip joint was indicated by using a 3D-growing region algorithm with upper/lower Hounsfield Units (HU) followed by a morphological closing algorithm. The metabolic activity for the left and right side of the joint was measured and correlated with age and BMI., Results: Fluorine-18-FDG uptake in the hip was 0.83±0.22 (right side: 0.83±0.23, left side: 0.83±0.22, P=0.82). Fluorine-18-NaF uptake in the hip was 3.20±1.07 (right side: 3.25±1.14, left side: 3.15±1.04, P=0.02). Body mass index positively correlated with both 18 F-FDG (r=0.29, P=0.001) and NaF (r=0.26, P=0.005) uptake. No significant correlation was seen between age and either 18 F-FDG (r=0.12, P=0.19) or 18 F-NaF (r=0.03, P=0.78) uptake., Conclusion: Body mass index had a significant impact on 18 F-FDG and 18 F-NaF uptake, whereas age had no correlation with either tracer uptake. Obesity increases the mechanical forces applied on weight-bearing joints such as the hip. Body mass index was related to increased joint inflammation and bone degeneration. These findings further support the studies explaining the role of adipose tissue in promoting OA.