Semantic Characteristics Prediction of Pulmonary Nodule Using Artificial Neural Networks

Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately.The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u2713), July 3-7, 2013, Osaka, Japa

Semantic Characteristics Prediction of Pulmonary Nodule Using Artificial Neural Networks

The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13), July 3-7, 2013, Osaka, JapanSince it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately

Pilot study of the optimal protocol of low dose step‐up follicle stimulating hormone therapy for infertile women

Purpose: To evaluate the optimized protocol of low dose follicle‐stimulating hormone (FSH) therapy that has a starting dose of 50 IU/62.5 IU with a small increment dose (12.5 IU) for women with World Health Organization (WHO) II ovulatory disorder and unexplained infertility. Methods: Anovulatory women with WHO group II ovulatory disorder (ovulation induction [OI] patients, n = 29), and with an unexplained infertility (ovarian stimulation [OS] patients, n = 21) were enrolled. The protocol of low dose step‐up FSH therapy was optimized for the starting dose as 50 IU (body mass index [BMI] < 20 group) and 62.5 IU (BMI ≥ 20 group) with the increment dose of 12.5 IU. Study outcomes were ovulation, monofollicular development and other variables. Results: In the OIpatients, the ovulation rate was 100% (BMI < 20 group) and 90.9% (BMI ≥ 20 group). Monofollicular development was 80.0% (BMI < 20) and 77.3% (BMI ≥ 20). The pregnancy rate was 60% (3/5 BMI < 20) and 18.2% (4/22 BMI ≥ 20). There was no multiple pregnancy. In the OSpatients, the ovulation rate was 100%. Monofollicular development was 85.7% (BMI < 20) and 76.6% (BMI ≥ 20). No pregnancy was achieved in the OSpatients. Conclusion: Optimized protocol of low dose FSH therapy setting a starting dose 50 IU/62.5 IU by BMI with an increment dose of 12.5 IU was safe and highly effective in WHO group II anovulatory patients. However, this protocol seemed uneffective for patients with unexplained infertility

Biotin levels in blood and follicular fluid

It has been shown that biotin, a water-soluble vitamin (B7), plays roles in reproductive functions, such as oocyte maturation and embryo development, in experimental animals. On the other hand, little is known about the clinical effects of biotin on human reproduction. In this study, serum and follicular fluid biotin levels were measured in patients who underwent in vitro fertilization / intracytoplasmic sperm injection (IVF / ICSI), and their associations with reproductive outcomes were evaluated. As a result, biotin was detected in follicular fluid, as well as serum, and the biotin levels of follicular fluid were found to be positively correlated with those of serum. The biotin levels of serum were higher than those of follicular fluid, suggesting that biotin may be taken up into the follicular fluid from the blood. Although serum and follicular fluid biotin levels tended to be higher in pregnant patients than in non-pregnant patients, these data did not show the significant statistical difference. These findings indicate that biotin does not contribute to the maintenance of oocyte quality, and hence, it does not increase fertilization and pregnancy rates

A dehydrated space-weathered skin cloaking the hydrated interior of Ryugu

Without a protective atmosphere, space-exposed surfaces of airless Solar System bodies gradually experience an alteration in composition, structure and optical properties through a collective process called space weathering. The return of samples from near-Earth asteroid (162173) Ryugu by Hayabusa2 provides the first opportunity for laboratory study of space-weathering signatures on the most abundant type of inner solar system body: a C-type asteroid, composed of materials largely unchanged since the formation of the Solar System. Weathered Ryugu grains show areas of surface amorphization and partial melting of phyllosilicates, in which reduction from Fe3+ to Fe2+ and dehydration developed. Space weathering probably contributed to dehydration by dehydroxylation of Ryugu surface phyllosilicates that had already lost interlayer water molecules and to weakening of the 2.7 µm hydroxyl (–OH) band in reflectance spectra. For C-type asteroids in general, this indicates that a weak 2.7 µm band can signify space-weathering-induced surface dehydration, rather than bulk volatile loss

Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study

Existing electronic cleansing (EC) methods for computed tomographic colonography (CTC) are generally based on image segmentation, which limits their accuracy to that of the underlying voxels. Because of the limitations of the available CTC datasets for training, traditional deep learning is of limited use in EC. The purpose of this study was to evaluate the technical feasibility of using a novel self-supervised adversarial learning scheme to perform EC with a limited training dataset with subvoxel accuracy. A three-dimensional (3D) generative adversarial network (3D GAN) was pre-trained to perform EC on CTC datasets of an anthropomorphic phantom. The 3D GAN was then fine-tuned to each input case by use of the self-supervised scheme. The architecture of the 3D GAN was optimized by use of a phantom study. The visually perceived quality of the virtual cleansing by the resulting 3D GAN compared favorably to that of commercial EC software on the virtual 3D fly-through examinations of 18 clinical CTC cases. Thus, the proposed self-supervised 3D GAN, which can be trained to perform EC on a small dataset without image annotations with subvoxel accuracy, is a potentially effective approach for addressing the remaining technical problems of EC in CTC

A Sparse Representation Based Method to Classify Pulmonary Patterns of Diffuse Lung Diseases

We applied and optimized the sparse representation (SR) approaches in the computer-aided diagnosis (CAD) to classify normal tissues and five kinds of diffuse lung disease (DLD) patterns: consolidation, ground-glass opacity, honeycombing, emphysema, and nodule. By using the K-SVD which is based on the singular value decomposition (SVD) and orthogonal matching pursuit (OMP), it can achieve a satisfied recognition rate, but too much time was spent in the experiment. To reduce the runtime of the method, the K-Means algorithm was substituted for the K-SVD, and the OMP was simplified by searching the desired atoms at one time (OMP1). We proposed three SR based methods for evaluation: SR1 (K-SVD+OMP), SR2 (K-Means+OMP), and SR3 (K-Means+OMP1). 1161 volumes of interest (VOIs) were used to optimize the parameters and train each method, and 1049 VOIs were adopted to evaluate the performances of the methods. The SR based methods were powerful to recognize the DLD patterns (SR1: 96.1%, SR2: 95.6%, SR3: 96.4%) and significantly better than the baseline methods. Furthermore, when the K-Means and OMP1 were applied, the runtime of the SR based methods can be reduced by 98.2% and 55.2%, respectively. Therefore, we thought that the method using the K-Means and OMP1 (SR3) was efficient for the CAD of the DLDs

Capturing the Effects of Domestication on Vocal Learning Complexity

Domesticated and vocal learning species can serve as informative model organisms for the reduction of reactive aggression and emergence of speech in our lineage. Amidst mounting evidence that domestication modifies vocal repertoires across different species, we focus on the domesticated Bengalese finch, which has a more complex song than the wild-type white-rumped munia. Our explanation for this effect revolves around the glutamate neurotransmitter system. Glutamate signaling (i) is implicated in birdsong learning, (ii) controls dopamine activity in neural circuits crucial for vocal learning, (iii) is disproportionately targeted in the evolution of domesticates, and (iv) regulates stress responses and aggressive behaviors attenuated under domestication. We propose that attenuated excitation of stress-related neural circuits potentiates vocal learning via altered dopaminergic signaling