Well-Differentiated Extraskeletal Osteosarcoma Arising from the Retroperitoneum That Recurred as Anaplastic Spindle Cell Sarcoma

Extraskeletal osteosarcoma is an uncommon high-grade malignant soft tissue sarcoma. Well-differentiated extraskeletal osteosarcoma is thought to have a better prognosis than classical extraskeletal osteosarcoma, but dedifferentiation after recurrence has also been reported. We present a case of a primary retroperitoneal extraskeletal osteosarcoma in a 62-year-old Japanese woman. Abdominal CT revealed a large mass with diffuse calcification in the right retroperitoneal space and tumor resection was performed. The histopathological diagnosis was well-differentiated retroperitoneal extraskeletal osteosarcoma. She was followed up by CT every 6 months without adjuvant radiotherapy and chemotherapy for 31 months until anaplastic high-grade spindle cell sarcoma recurred in the retroperitoneum. Our case is the seventh reported description of well-differentiated extraskeletal sarcoma, and the first to arise in the retroperitoneum and recur as an entirely dedifferentiated spindle cell sarcoma

Improving Students’ Communicative Competence in “Speaking (Presentation)” in Primary School Foreign Language Activities and Foreign Language Studies-Based on Explicit and Implicit Teaching of Communicative Competence, Peer Assessment by Pairs and Groups, and Cooperative Learning-

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Optimal Column Subset Selection by A-Star Search

Approximating a matrix by a small subset of its columns is a known problem in numerical linear algebra. Algorithms that address this problem have been used in areas which include, among others, sparse approximation, unsupervised feature selection, data mining, and knowledge representation. Such algorithms were investigated since the 1960's, with recent results that use randomization. The problem is believed to be NP-Hard, and to the best of our knowledge there are no previously published algorithms aimed at computing optimal solutions. We show how to model the problem as a graph search, and propose a heuristic based on eigenvalues of related matrices. Applying the A* search strategy with this heuristic is guaranteed to find the optimal solution. Experimental results on common datasets show that the proposed algorithm can effectively select columns from moderate size matrices, typically improving by orders of magnitude the run time of exhaustive search. We also show how to combine the proposed algorithm with other non-optimal (but much faster) algorithms in a ``two stage'' framework, which is guaranteed to improve the accuracy of the other algorithms

Unsupervised Feature Selection by Heuristic Search with Provable Bounds on Suboptimality

Identifying a small number of features that can represent the data is a known problem that comes up in areas such as machine learning, knowledge representation, data mining, and numerical linear algebra. Computing an optimal solution is believed to be NP-hard, and there is extensive work on approximation algorithms. Classic approaches exploit the algebraic structure of the underlying matrix, while more recent approaches use randomization. An entirely different approach that uses the A* heuristic search algorithm to find an optimal solution was recently proposed. Not surprisingly it is limited to effectively selecting only a small number of features. We propose a similar approach related to the Weighted A* algorithm. This gives algorithms that are not guaranteed to find an optimal solution but run much faster than the A* approach, enabling effective selection of many features from large datasets. We demonstrate experimentally that these new algorithms are more accurate than the current state-of-the-art while still being practical. Furthermore, they come with an adjustable guarantee on how different their error may be from the smallest possible (optimal) error. Their accuracy can always be increased at the expense of a longer running time

Weighted A* Algorithms for Unsupervised Feature Selection with Provable Bounds on Suboptimality

Identifying a small number of features that can represent the data is believed to be NP-hard. Previous approaches exploit algebraic structure and use randomization. We propose an algorithm based on ideas similar to the Weighted A* algorithm in heuristic search. Our experiments show this new algorithm to be more accurate than the current state of the art

Proposal for a screening questionnaire for detecting habitual mouth breathing, based on a mouth-breathing habit score

Abstract Background When mouth breathing becomes habitual, it can cause sleep disorders and abnormal maxillofacial growth, thus early detection of habitual mouth breathing is important. We created a questionnaire for early detection of habitual mouth breathing using a score based on a spectrum of factors found to be characteristic of mouth breathers. Methods First, a draft 50-question questionnaire was given to 101 random dental clinic patients, classified by dental professionals into habitual mouth breathers (n = 28) and nose breathers (n = 73). The 10 questions that significantly differentiated mouth and nose breathers (p < 0.05) were identified from this questionnaire. These questions, regarding nasal obstruction, open mouth at rest, awareness of mouth breathing, gum swelling and dental staining of the front teeth, bad breath, maxillary protrusion, nasal obstruction in childhood, bottle-feeding, and history of asthma, formed the basis for a second questionnaire. This second survey was completed by another 242 participants, separately classified into mouth breathing (n = 26), suspected mouth breathing (n = 40), and nose breathing groups (n = 176). Results Receiver operating characteristic curve analysis of the resulting mouth breathing habit scores, representing the responses to the 10-question survey, showed moderate checklist diagnosability. Sensitivity of cut-off values was 61.5% (specificity 92.0%) for the mouth-breathing group, and 77.5% (specificity 56.3%) for the suspected mouth-breathing group. Information was also obtained from visual assessment of maxillofacial characteristics. We found that the mouth-breathing and suspected mouth-breathing groups showed significantly high odds ratios for 7 items: discomfort while breathing and increased chin muscle tonus with lip closure, maxillary protrusion, tongue thrust, open mouth at rest, open bite, and childhood asthma. For 94.6% of the nose breathing group, ≥1 of these items applied. Conclusions These findings were then used together to create a sample screening form. We believe that screening of this kind can facilitate more accurate diagnosis of habitual mouth breathing and contribute to its early detection

A case of facial paralysis with swallowing disorder in the pharyngeal phase

　Facial nerve paralysis leads to swallowing disorder in the oral and oropharyngeal phases. However, swallowing disorders in the pharyngeal phase have also been reported. We report a case of a male adult patient who was diagnosed with herpetic pharyngolaryngitis and right auricular shingles and prescribed an anti-herpes drug. Fourteen days later, right facial nerve paralysis was observed. He was diagnosed with Hunt syndrome, and steroid pulse therapy was started on the same day. A fluoroscopy swallow study revealed that the hyoid bone was leaning to the right when moving forward (lateral view) and that the shadow of the liquid in the pharyngeal cavity was lower on the right (anteroposterior view). The patient was instructed to perform facial massage and swallowing exercises. Approximately 3 months after the onset of facial nerve paralysis, the inclination of the hyoid bone and the shadow of the liquid in the pharyngeal cavity disappeared, and the facial nerve paralysis was cured. We believe that the elevation of the hyoid bone was impaired on the paralyzed side because of posterior abdominal digastric and stylohyoid muscle paralysis. When evaluating the swallowing function in patients with facial nerve paralysis, both the oral and pharyngeal phases should be evaluated