Objective To evaluate the diagnostic utility of conventional magnetic resonance imaging (MRI)-based characteristics and a texture analysis (TA) for discriminating between ovarian thecoma-fibroma groups (OTFGs) and ovarian granulosa cell tumors (OGCTs). Methods This retrospective multicenter study enrolled 52 patients with 32 OGCTs and 21 OTFGs, which were dissected and pathologically diagnosed between January 2008 and December 2019. MRI-based features (MBFs) and texture features (TFs) were evaluated and compared between OTFGs and OGCTs. A least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select features and construct the discriminating model. ROC analyses were conducted on MBFs, TFs, and their combination to discriminate between the two diseases. Results We selected 3 features with the highest absolute value of the LASSO regression coefficient for each model: the apparent diffusion coefficient (ADC), peripheral cystic area, and contrast enhancement in the venous phase (VCE) for the MRI-based model; the 10th percentile, difference variance, and maximal correlation coefficient for the TA-based model; and ADC, VCE, and the difference variance for the combination model. The areas under the curves of the constructed models were 0.938, 0.817, and 0.941, respectively. The diagnostic performance of the MRI-based and combination models was similar (p = 0.38), but significantly better than that of the TA-based model (p < 0.05). Conclusions The conventional MRI-based analysis has potential as a method to differentiate OTFGs from OGCTs. TA did not appear to be of any additional benefit. Further studies are needed on the use of these methods for a preoperative differential diagnosis of these two diseases.

This paper proposes cooking support using ubiquitous sensors. We developed a machine learning algorithm that recognizes cooking procedures by taking into account widely varying sensor information and user behavior. To provide appropriate instructions to users, we developed a Markov-model-based behavior prediction algorithm. Using these algorithms, we developed cooking support automatically displaying cooking instruction videos based on user progress. Experiments and experimental results confirmed the feasibility of our proposed cooking support.

NANOS3 is an evolutionarily conserved gene expressed in primordial germ cells that is important for germ cell development. Germ cell deletion by NANOS3 knockout has been reported in several mammalian species, but its function in pigs is unclear. In the present study, we investigated the germline effects of NANOS3 knockout in pigs using CRISPR/Cas9. Embryo transfer of CRISPR/Cas9modified embryos produced ten offspring, of which one showed wild-type NANOS3 alleles, eight had two mutant NANOS3 alleles, and the other exhibited mosaicism (four mutant alleles). Histological analysis revealed no germ cells in the testes or ovaries of any of the nine mutant pigs. These results demonstrated that NANOS3 is crucial for porcine germ cell production.

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