Three–Dimensional Seismic Diffraction Imaging for Detecting Near-Surface Inhomogeneities

One of the problems encountered in a variety of near-surface investigations is detecting and mapping localized inhomogeneities. Typical examples of such inhomogeneous sources are cavities, caves and tunnels. Different methods for detecting shallow subsurface sources utilizing seismic waves diffracted by these sources were proposed by many researchers in the last three decades. Most of these methods suggest that every subsurface point is a possible location of a point diffractor. Imaging of the diffractors is based on a spatial summation of the diffracted wavefield along diffraction time surfaces (defined by source-receiver geometry) in 2D or 3D space. The summation is performed with a fixed velocity value estimated from velocity analysis of the diffraction data. In this study, we present a path integral summation approach, where for every subsurface point the wavefield is stacked together along all possible diffraction time surfaces having a common apex at a given time. The result of the imaging is a 3D volume in which prominent diffraction anomalies appear at spatial locations close to the imaged sources. This path integral summation approach has been successfully tested on synthetic data and further applied at several sites with known subsurface sources

Multifocusing imaging over an irregular topography

If seismic data are acquired over an irregular topography, standard elevation statics methods may be inaccurate because the assumption of vertical raypaths will no longer be valid. An effective solution to the problem of irregular topography can be found through the use of the multifocusing method, in which large supergathers of seismic traces are stacked, each of which can span many common midpoint (CMP) gathers. This can be done by extending the multifocusing moveout formula to explicitly account for nonzero elevations of the source and receiver, as well as their horizontal coordinates.Implementation of this formula into the multifocusing algorithm is straightforward because estimating the necessary raypath information (i.e., emergence angles) is an integral part of the algorithm. The extended multifocusing moveout correction can be applied directly to the data acquired in areas of irregular topography without the need for prior elevation static corrections. Synthetic tests on such data show that the proposed technique results in a better alignment of reflection events

Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling

The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured question-and-answer sections, we apply topic modeling to identify key themes. We experiment with BERTopic, which leverages recent advances in language modeling technology. We align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. We introduce a novel method to identify testimonies within groups that exhibit atypical topic distributions resembling those of other groups. This study offers unique insights into the complex narratives of Holocaust survivors, demonstrating the power of NLP to illuminate historical discourse and identify potential deviations in survivor experiences.Comment: 9 pages, 7 figures, LREC-COLING 202

MUC1 gene overexpressed in breast cancer: structure and transcriptional activity of the MUC1 promoter and role of estrogen receptor alpha (ERα) in regulation of the MUC1 gene expression

BACKGROUND: The MUC1 gene encodes a mucin glycoprotein(s) which is basally expressed in most epithelial cells. In breast adenocarcinoma and a variety of epithelial tumors its transcription is dramatically upregulated. Of particular relevance to breast cancer, steroid hormones also stimulate the expression of the MUC1 gene. The MUC1 gene directs expression of several protein isoforms, which participate in many crucial cell processes. Although the MUC1 gene plays a critical role in cell physiology and pathology, little is known about its promoter organization and transcriptional regulation. The goal of this study was to provide insight into the structure and transcriptional activity of the MUC1 promoter. RESULTS: Using TRANSFAC and TSSG soft-ware programs the transcription factor binding sites of the MUC1 promoter were analyzed and a map of transcription cis-elements was constructed. The effect of different MUC1 promoter regions on MUC1 gene expression was monitored. Different regions of the MUC1 promoter were analyzed for their ability to control expression of specific MUC1 isoforms. Differences in the expression of human MUC1 gene transfected into mouse cells (heterologous artificial system) compared to human cells (homologous natural system) were observed. The role of estrogen on MUC1 isoform expression in human breast cancer cells, MCF-7 and T47D, was also analyzed. It was shown for the first time that synthesis of MUC1/SEC is dependent on estrogen whereas expression of MUC1/TM did not demonstrate such dependence. Moreover, the estrogen receptor alpha, ERα, could bind in vitro estrogen responsive cis-elements, EREs, that are present in the MUC1 promoter. The potential roles of different regions of the MUC1 promoter and ER in regulation of MUC1 gene expression are discussed. CONCLUSION: Analysis of the structure and transcriptional activity of the MUC1 promoter performed in this study helps to better understand the mechanisms controlling transcription of the MUC1 gene. The role of different regions of the MUC1 promoter in expression of the MUC1 isoforms and possible function of ERα in this process has been established. The data obtained in this study may help in development of molecular modalities for controlled regulation of the MUC1 gene thus contributing to progress in breast cancer gene therapy

Automatic Topic-Guided Segmentation of Holocaust Survivor Testimonies

In recent decades, efforts have been made to gather and digitize the testimonies of living Holocaust survivors. The challenge we now face is attending to those thousands of human stories, which while safely stored in archives, may nevertheless disappear into oblivion. Despite recent advances in narrative analysis in the fields of Computational Literature (CL) and Natural Language Processing (NLP), existing language model technology still faces challenges in analyzing elaborate narratives and long texts. One such challenge is text segmentation – a long-standing issue in the area of CL and NLP. In our work, we propose a computational method to approach this problem. Our research draws on testimony transcripts from the Shoah Foundation (SF) Holocaust archive for supervised topic classification, which is then used as topics guidance for automatic segmentation

Quantitative measurement of odor detection thresholds using an air dilution olfactometer, and association with genetic variants in a sample of diverse ancestry

Genetic association studies require a quantitative and reliable method for odor threshold assessment in order to examine the contribution of genetic variants to complex olfactory phenotypes. Our main goal was to assess the feasibility of a portable Scentroid air dilution olfactometer for use in such studies. Using the Scentroid SM110C and the SK5 n-butanol Sensitivity Kit (IDES Canada Inc.), n-butanol odor thresholds were determined for 182 individuals of diverse ancestry (mean age: 20.4 ± 2.5 years; n = 128 female; n = 54 male). Threshold scores from repeat participants were used to calculate a test–retest reliability coefficient, which was statistically significant (r = 0.754, p < 0.001, n = 29), indicating that the Scentroid provides reliable estimates of odor thresholds. In addition, we performed a preliminary genetic analysis evaluating the potential association of n-butanol odor thresholds to six single-nucleotide polymorphisms (SNPs) putatively involved in general olfactory sensitivity (GOS). The results of multiple linear regression analysis revealed no significant association between the SNPs tested and threshold scores. However, our sample size was relatively small, and our study was only powered to identify genetic markers with strong effects on olfactory sensitivity. Overall, we find that the Scentroid provides reliable quantitative measures of odor detection threshold and is well suited for genetic studies of olfactory sensitivity