Serum Apolipoproteins C-I and C-III Are Reduced in Stomach Cancer Patients: Results from MALDI-Based Peptidome and Immuno-Based Clinical Assays

Finding new peptide biomarkers for stomach cancer in human sera that can be implemented into a clinically practicable prediction method for monitoring of stomach cancer. We studied the serum peptidome from two different biorepositories. We first employed a C8-reverse phase liquid chromatography approach for sample purification, followed by mass-spectrometry analysis. These were applied onto serum samples from cancer-free controls and stomach cancer patients at various clinical stages. We then created a bioinformatics analysis pipeline and identified peptide signature discriminating stomach adenocarcinoma patients from cancer-free controls. Matrix Assisted Laser Desorption/Ionization–Time of Flight (MALDI-TOF) results from 103 samples revealed 9 signature peptides; with prediction accuracy of 89% in the training set and 88% in the validation set. Three of the discriminating peptides discovered were fragments of Apolipoproteins C-I and C-III (apoC-I and C-III); we further quantified their serum levels, as well as CA19-9 and CRP, employing quantitative commercial-clinical assays in 142 samples. ApoC-I and apoC-III quantitative results correlated with the MS results. We then employed apoB-100-normalized apoC-I and apoC-III, CA19-9 and CRP levels to generate rules set for stomach cancer prediction. For training, we used sera from one repository, and for validation, we used sera from the second repository. Prediction accuracies of 88.4% and 74.4% were obtained in the training and validation sets, respectively. Serum levels of apoC-I and apoC-III combined with other clinical parameters can serve as a basis for the formulation of a diagnostic score for stomach cancer patients

Counting the Founders: The Matrilineal Genetic Ancestry of the Jewish Diaspora

The history of the Jewish Diaspora dates back to the Assyrian and Babylonian conquests in the Levant, followed by complex demographic and migratory trajectories over the ensuing millennia which pose a serious challenge to unraveling population genetic patterns. Here we ask whether phylogenetic analysis, based on highly resolved mitochondrial DNA (mtDNA) phylogenies can discern among maternal ancestries of the Diaspora. Accordingly, 1,142 samples from 14 different non-Ashkenazi Jewish communities were analyzed. A list of complete mtDNA sequences was established for all variants present at high frequency in the communities studied, along with high-resolution genotyping of all samples. Unlike the previously reported pattern observed among Ashkenazi Jews, the numerically major portion of the non-Ashkenazi Jews, currently estimated at 5 million people and comprised of the Moroccan, Iraqi, Iranian and Iberian Exile Jewish communities showed no evidence for a narrow founder effect, which did however characterize the smaller and more remote Belmonte, Indian and the two Caucasus communities. The Indian and Ethiopian Jewish sample sets suggested local female introgression, while mtDNAs in all other communities studied belong to a well-characterized West Eurasian pool of maternal lineages. Absence of sub-Saharan African mtDNA lineages among the North African Jewish communities suggests negligible or low level of admixture with females of the host populations among whom the African haplogroup (Hg) L0-L3 sub-clades variants are common. In contrast, the North African and Iberian Exile Jewish communities show influence of putative Iberian admixture as documented by mtDNA Hg HV0 variants. These findings highlight striking differences in the demographic history of the widespread Jewish Diaspora

Specific Deficit in Implicit Motor Sequence Learning following Spinal Cord Injury.

Physical and psychosocial rehabilitation following spinal cord injury (SCI) leans heavily on learning and practicing new skills. However, despite research relating motor sequence learning to spinal cord activity and clinical observations of impeded skill-learning after SCI, implicit procedural learning following spinal cord damage has not been examined.To test the hypothesis that spinal cord injury (SCI) in the absence of concomitant brain injury is associated with a specific implicit motor sequence learning deficit that cannot be explained by depression or impairments in other cognitive measures.Ten participants with SCI in T1-T11, unharmed upper limb motor and sensory functioning, and no concomitant brain injury were compared to ten matched control participants on measures derived from the serial reaction time (SRT) task, which was used to assess implicit motor sequence learning. Explicit generation of the SRT sequence, depression, and additional measures of learning, memory, and intelligence were included to explore the source and specificity of potential learning deficits.There was no between-group difference in baseline reaction time, indicating that potential differences between the learning curves of the two groups could not be attributed to an overall reduction in response speed in the SCI group. Unlike controls, the SCI group showed no decline in reaction time over the first six blocks of the SRT task and no advantage for the initially presented sequence over the novel interference sequence. Meanwhile, no group differences were found in explicit learning, depression, or any additional cognitive measures.The dissociation between impaired implicit learning and intact declarative memory represents novel empirical evidence of a specific implicit procedural learning deficit following SCI, with broad implications for rehabilitation and adjustment

A Weakly Supervised and Deep Learning Method for an Additive Topic Analysis of Large Corpora

The collaborative effort of a theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. Additivity also enables the reuse of previous efforts or the merging of separate research projects, thereby increasing the accessibility of such methods and the ability of the discipline to create shareable content analysis capabilities. This paper proposes a weakly supervised topic analysis method, which combines a low-cost unsupervised method to compile a training-set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method is a solid starting point for a low-cost and additive solution for a large-scale topic analysis

Role-based association of verbs, actions, and sentiments with entities in political discourse

A crucial challenge in measuring how text represents an entity is the need to associate each representative expression with a relevant entity to generate meaningful results. Common solutions to this problem are usually based on proximity methods that require a large corpus to reach reasonable levels of accuracy. We show how such methods for the association between an entity and a representation yield a high percentage of false positives at the expression level and low validity at the document level. We introduce a solution that combines syntactic parsing, semantic role labeling logic, and a machine learning approach—the role-based association method. To test our method, we compared it with prevalent methods of association on the news coverage of two entities of interest—the State of Israel and the Palestinian Authority. We found that the role-based association method is more accurate at the expression and the document levels

Clause analysis:Using syntactic information to automatically extract source, subject, and predicate from texts with an application to the 2008-2009 Gaza War

This article presents a new method and open source R package that uses syntactic information to automatically extract source-subject-predicate clauses. This improves on frequency-based text analysis methods by dividing text into predicates with an identified subject and optional source, extracting the statements and actions of (political) actors as mentioned in the text. The content of these predicates can be analyzed using existing frequency-based methods, allowing for the analysis of actions, issue positions and framing by different actors within a single text. We showthat a small set of syntactic patterns can extract clauses and identify quotes with good accuracy, significantly outperforming a baseline system based on word order. Taking the 2008-2009 Gaza war as an example, we further show how corpus comparison and semantic network analysis applied to the results of the clause analysis can show differences in citation and framing patterns between U.S. and English-language Chinese coverage of this war