CAPTDURE: Captioned Sound Dataset of Single Sources

In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model training. However, when we collect the captions for multiple-source sound, it is not easy to collect detailed captions for each sound source, such as the number of sound occurrences and timbre. Therefore, it is difficult to extract only the single-source target sound by the model-training method using a conventional captioned sound dataset. In this work, we constructed a dataset with captions for a single-source sound named CAPTDURE, which can be used in various tasks such as environmental sound separation and synthesis. Our dataset consists of 1,044 sounds and 4,902 captions. We evaluated the performance of environmental sound extraction using our dataset. The experimental results show that the captions for single-source sounds are effective in extracting only the single-source target sound from the mixture sound.Comment: Accepted to INTERSPEECH202

A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data

Many diseases, including cancer and chronic conditions, require extended treatment periods and long-term strategies. Machine learning and AI research focusing on electronic health records (EHRs) have emerged to address this need. Effective treatment strategies involve more than capturing sequential changes in patient test values. It requires an explainable and clinically interpretable model by capturing the patient's internal state over time. In this study, we propose the "deep state-space analysis framework," using time-series unsupervised learning of EHRs with a deep state-space model. This framework enables learning, visualizing, and clustering of temporal changes in patient latent states related to disease progression. We evaluated our framework using time-series laboratory data from 12,695 cancer patients. By estimating latent states, we successfully discover latent states related to prognosis. By visualization and cluster analysis, the temporal transition of patient status and test items during state transitions characteristic of each anticancer drug were identified. Our framework surpasses existing methods in capturing interpretable latent space. It can be expected to enhance our comprehension of disease progression from EHRs, aiding treatment adjustments and prognostic determinations.Comment: 21 pages, 6 figure

Stromal micropapillary component as a novel unfavorable prognostic factor of lung adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>Pulmonary adenocarcinomas with a micropapillary component having small papillary tufts and lacking a central fibrovascular core are thought to result in poor prognosis. However, the component consists of tumor cells often floating within alveolar spaces (aerogenous micropapillary component [AMPC]) rather than invading fibrotic stroma observed in other organs like breast (stromal invasive micropapillary component [SMPC]). We previously observed cases of lung adenocarcinoma with predominant SMPC that was associated with micropapillary growth of tumors in fibrotic stroma observed in other organs. We evaluated the incidence and clinicopathological characteristics of SMPC in lung adenocarcinoma cases.</p> <p>Patients and Methods</p> <p>We investigated the clinicopathological characteristics and prognostic significance of SMPC in lung adenocarcinoma cases by reviewing 559 patients who had undergone surgical resection. We examined the SMPC by performing immunohistochemical analysis with 17 antibodies and by genetic analysis with epidermal growth factor receptor (<it>EGFR</it>) and <it>KRAS </it>mutations.</p> <p>Results</p> <p>SMPC-positive (SMPC(+)) tumors were observed in 19 cases (3.4%). The presence of SMPC was significantly associated with tumor size, advanced-stage disease, lymph node metastasis, pleural invasion, lymphatic invasion, and vascular invasion. Patients with SMPC(+) tumors had significantly poorer outcomes than those with SMPC-negative tumors. Multivariate analysis revealed that SMPC was a significant independent prognostic factor of lung adenocarcinoma, especially for disease-free survival of pathological stage I patients (<it>p </it>= 0.035). SMPC showed significantly higher expression of E-cadherin and lower expression of CD44 than the corresponding expression levels shown by AMPC and showed lower surfactant apoprotein A and phospho-c-Met expression level than corresponding expression levels shown by tumor cell components without a micropapillary component. Fourteen cases with SMPC(+) tumors (74%) showed <it>EGFR </it>mutations, and none of them showed <it>KRAS </it>mutations.</p> <p>Conclusions</p> <p>SMPC(+) tumors are rare, but they may be associated with a poor prognosis and have different phenotypic and genotypic characteristics from those of AMPC(+) tumors.</p> <p>Virtual Slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/9433341526290040</url>.</p

Plasma Free Amino Acid Profiling of Five Types of Cancer Patients and Its Application for Early Detection

BACKGROUND: Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection. METHODS AND FINDINGS: Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)-electrospray ionization (ESI)-mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling. CONCLUSIONS: These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods