Nonparametric Approach for Non-Gaussian Vector Stationary Processes

AbstractSuppose that {z(t)} is a non-Gaussian vector stationary process with spectral density matrixf(λ). In this paper we consider the testing problemH:∫π−πK{f(λ)}dλ=cagainstA:∫π−πK{f(λ)}dλ≠c, whereK{·} is an appropriate function andcis a given constant. For this problem we propose a testTnbased on ∫π−πK{f(λ)}dλ=c, wheref(λ) is a nonparametric spectral estimator off(λ), and we define an efficacy ofTnunder a sequence of nonparametric contiguous alternatives. The efficacy usually depnds on the fourth-order cumulant spectraf4Zofz(t). If it does not depend onf4Z, we say thatTnis non-Gaussian robust. We will give sufficient conditions forTnto be non-Gaussian robust. Since our test setting is very wide we can apply the result to many problems in time series. We discuss interrelation analysis of the components of {z(t)} and eigenvalue analysis off(λ). The essential point of our approach is that we do not assume the parametric form off(λ). Also some numerical studies are given and they confirm the theoretical results

『学生』の聖堂

「ペテロの否み」のエピソードを含むチェーホフの『大学生』の先行研究には、物語の最大の特徴である主人公イワンの極端な内面の変化について、情景（「荒野」「焚き火」「川」「山」等）も含めた精緻な宗教的解釈がすでに多数ある。小論は、三つの根拠： 1) イワンが西から東に移動すること、2) 西から東への移動が、従来個別に解釈されてきた情景（点景）を教会建物イメージに統合すること、3) ゲッセマネの園から続く「鎖」は「二端の鎖」（迷える子羊とイエスの背負った十字架の象徴）であること、に基づき、渡し舟（教会のアナロジー）によるイワンの渡河とその後の登頂は、イコノスタスの王門から至聖所の高所への移行、つまり迷いの克服と修行再開の表明と結論し、従来の宗教的解釈を支持するひとつの論拠を示した。This research was supported by Kansai University\u27s Overseas Research Program for the year of 2014

Expression and intracellular localization of FKHRL1 in mammary gland neoplasms.

FKHRL1 (FOXO3a), a member of the Forkhead family of genes, has been considered to be involved in the development of breast tumors; however, the in vivo expression and activation status of FKHRL1 in breast tumors still remains unclear. We immunohistochemically demonstrated the expression and intracellular localization of FKHRL1 in human breast tumors by the novel anti-FKHRL1 antibody which is available for formalin-fixed paraffin-embedded specimens. In a total of 51 cases of benign tumors, FKHRL1 was diffusely expressed in all cases, and its intracellular localization was revealed to be cytoplasmic (inactive form) in 94% of cases of intraductal papillomas (16/17) and 91% cases of fibroadenomas (31/34), with a similar pattern to normal glandular epithelium. In invasive ductal carcinomas, 83% of the cases (93/112) diffusely expressed FKHRL1; however, unlike benign tumors, 71% of the cases (66/93) showed the nuclear-targeted, active form of FKHRL1. Moreover, activated FKHRL1 was predominantly observed in scirrhous (29/36, 81% of the cases) and papillotubular (30/38, 79% of the cases) subtypes, compared to the solid-tubular subtype (7/19, 37% of the cases). Furthermore, the cases with nuclear-targeted FKHRL1 showed a tendency to have lymph nodal metastasis with statistical significance (P < 0.0001). Thus, the activation of FKHRL1 seems to be recognized as one of the specific features of invasive ductal carcinoma of the breast.</p

Can computed tomography differentiate adenocarcinoma in situ from minimally invasive adenocarcinoma?

Background: Given the subtle pathological signs of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), effective differentiation between the two entities is crucial. However, it is difficult to predict these conditions using preoperative computed tomography (CT) imaging. In this study, we investigated whether histological diagnosis of AIS and MIA using quantitative three-dimensional CT imaging analysis could be predicted. Methods: We retrospectively analyzed the images and histopathological findings of patients with lung cancer who were diagnosed with AIS or MIA between January 2017 and June 2018. We used Synapse Vincent (v. 4.3) (Fujifilm) software to analyze the CT attenuation values and performed a histogram analysis. Results: There were 22 patients with AIS and 22 with MIA. The ground-glass nodule (GGN) rate was significantly higher in patients with AIS (p < 0.001), whereas the solid volume (p < 0.001) and solid rate (p = 0.001) were significantly higher in those with MIA. The mean (p = 0.002) and maximum (p = 0.025) CT values were significantly higher in patients with MIA. The 25th, 50th, 75th, and 97.5th percentiles (all p < 0.05) for the CT values were significantly higher in patients with MIA. Conclusions: We demonstrated that quantitative analysis of 3D-CT imaging data using software can help distinguish AIS from MIA. These analyses are useful for guiding decision-making in the surgical management of early lung cancer, as well as subsequent follow-up