PeptiCKDdb-peptide- and protein-centric database for the investigation of genesis and progression of chronic kidney disease

The peptiCKDdb is a publicly available database platform dedicated to support research in the field of chronic kidney disease (CKD) through identification of novel biomarkers and molecular features of this complex pathology. PeptiCKDdb collects peptidomics and proteomics datasets manually extracted from published studies related to CKD. Datasets from peptidomics or proteomics, human case/control studies on CKD and kidney or urine profiling were included. Data from 114 publications (studies of body fluids and kidney tissue: 26 peptidomics and 76 proteomics manuscripts on human CKD, and 12 focusing on healthy proteome profiling) are currently deposited and the content is quarterly updated. Extracted datasets include information about the experimental setup, clinical study design, discovery-validation sample sizes and list of differentially expressed proteins (P-value < 0.05). A dedicated interactive web interface, equipped with multiparametric search engine, data export and visualization tools, enables easy browsing of the data and comprehensive analysis. In conclusion, this repository might serve as a source of data for integrative analysis or a knowledgebase for scientists seeking confirmation of their findings and as such, is expected to facilitate the modeling of molecular mechanisms underlying CKD and identification of biologically relevant biomarkers.Database URL: www.peptickddb.com

Exploiting i–vector posterior covariances for short–duration language recognition

Linear models in i-vector space have shown to be an effective solution not only for speaker identification, but also for language recogniton. The i-vector extraction process, however, is affected by several factors, such as noise level, the acoustic content of the utterance and the duration of the spoken segments. These factors influence both the i-vector estimate and its uncertainty, represented by the i-vector posterior covariance matrix. Modeling of i-vector uncertainty with Probabilistic Linear Discriminant Analysis has shown to be effective for short-duration speaker identification. This paper extends the approach to language recognition, analyzing the effects of i-vector covariances on a state-of-the-art Gaussian classifier, and proposes an effective solution for the reduction of the average detection cost (Cavg) for short segments

Modelagem CESM para um sistema de recomendações: o caso de uma livraria virtual

Recommender systems aim to create personalized recommendations for guiding the user in choosing the most useful product, service, or content in a large space of possible options. Through content filtering and collaborative filtering, a recommender system can select appropriate options for the user, alleviating the information overload in the context of virtual bookstores. In this sense, this work’s aim is to present the modeling and analysis of a recommender system for a virtual bookstore seen as a sociotechnological system. A sociotechnological system involves human agents and artificial agents, the interconnections between them, their interaction dynamics, subjection to changes in relation to environmental demands and the emergence of behaviors. In this context, the methodological approach is based on the systemic view of Mario Bunge, through the application of the CESM model, according to which a system can be represented by its components, environment, structure, and mechanism. Mechanisms involved in the recommender system are outlined as cause-effect diagrams. The CESM modeling offers an integrated view of the interconnections and interactions between the components of the system and their context, and the identification of the involved processes. The modeling study precedes its computational implementation and actions for improvements and corrections of non-functional aspects related to the mechanisms can be designed before its deployment in the context of a virtual bookstore

A Contextual Topic Modeling and Content Analysis of Iranian laws and Regulations

A constitution is the highest legal document of a country and serves as a guide for the establishment of other laws. The constitution defines the political principles, structure, hierarchy, position, and limits of the political power of a country's government. It determines and guarantees the rights of citizens. This study aimed at topic modeling of Iranian laws. As part of this research, 11760 laws were collected from the Dotic website. Then, topic modeling was conducted on the title and content of the regularizations using LDA. Data analysis with topic modeling led to the identification of 10 topics including Economic, Customs, Housing and Urban Development, Agriculture, Insurance, Legal and judicial, Cultural, Information Technology, Political, and Government. The largest topic, Economic, accounts for 29% of regulations, while the smallest are Political and Government, accounting for 2%. This research utilizes a topic modeling method in exploring law texts and identifying trends in regularizations from 2016-2023. In this study, it was found that regularizations constitute a significant percentage of law, most of which are related to economics and customs. Cultural regularizations have increased in 2023. It can be concluded any law enacted each year can reflect society's conditions and legislators' top concerns

Multimedia Protection using Content and Embedded Fingerprints

Improved digital connectivity has made the Internet an important medium for multimedia distribution and consumption in recent years. At the same time, this increased proliferation of multimedia has raised significant challenges in secure multimedia distribution and intellectual property protection. This dissertation examines two complementary aspects of the multimedia protection problem that utilize content fingerprints and embedded collusion-resistant fingerprints. The first aspect considered is the automated identification of multimedia using content fingerprints, which is emerging as an important tool for detecting copyright violations on user generated content websites. A content fingerprint is a compact identifier that captures robust and distinctive properties of multimedia content, which can be used for uniquely identifying the multimedia object. In this dissertation, we describe a modular framework for theoretical modeling and analysis of content fingerprinting techniques. Based on this framework, we analyze the impact of distortions in the features on the corresponding fingerprints and also consider the problem of designing a suitable quantizer for encoding the features in order to improve the identification accuracy. The interaction between the fingerprint designer and a malicious adversary seeking to evade detection is studied under a game-theoretic framework and optimal strategies for both parties are derived. We then focus on analyzing and understanding the matching process at the fingerprint level. Models for fingerprints with different types of correlations are developed and the identification accuracy under each model is examined. Through this analysis we obtain useful guidelines for designing practical systems and also uncover connections to other areas of research. A complementary problem considered in this dissertation concerns tracing the users responsible for unauthorized redistribution of multimedia. Collusion-resistant fingerprints, which are signals that uniquely identify the recipient, are proactively embedded in the multimedia before redistribution and can be used for identifying the malicious users. We study the problem of designing collusion resistant fingerprints for embedding in compressed multimedia. Our study indicates that directly adapting traditional fingerprinting techniques to this new setting of compressed multimedia results in low collusion resistance. To withstand attacks, we propose an anti-collusion dithering technique for embedding fingerprints that significantly improves the collusion resistance compared to traditional fingerprints

Consumer-company Identification: Development and Validation of a Scale

Consumer-Company Identification is a relatively new issue in the marketing academia. Bhattacharya and Sen(2003) explored the Social Identity theory and established Consumer-Company Identification as the primary psychological substrate for deep relationships between the organization and its customers. In the present study a new instrument was constructed and validated that permits the empirical verification of the phenomenon described by Bhattacharya and Sen (2003). The scale validated in the present study is the first to embrace the idiosyncrasies of the identification between consumers and organizations. The process was conducted through 3 independent data collections. The first one was collected using literature search and in-depth interviews with 12 undergraduate students and bachelors from different professional fields. The second data base was obtained from a survey of 226 undergraduate students from 3 universities in 2 big Brazilian cities. This data base was used for purification purposes using Explanatory Factorial Analysis. Finally, the Structural Equation Modeling technique was applied to analyze a third data base composed of 387 observations collected from the same 3 universities of the second study. The results confirm the content, convergent and discriminant validity of the new scale proposed

Multi-Parametric Analysis and Modeling of Relationships between Mitochondrial Morphology and Apoptosis

Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis

Eliciting Behavior From Interactive Narratives: Isolating the Role of Agency in Connecting With and Modeling Characters

A key component differentiating interactive storytelling from non-interactive media is agency, or control over character choices. A series of experiments show that providing agency over a character increased the user-character connection, which then increased engagement in a character-consistent charitable act. Findings were observed in technologically simple online narratives that controlled for navigation/controller differences, graphics, sounds, lengthy play, and avatar customization. Effects emerged even though users did not practice these acts by making their character behave charitably. Findings were robust across happy and unfortunate endings and across first-, second-, and third-person narrative perspectives. Findings suggest promise for developing inexpensive ‘‘storygames’’ to encourage supportive behaviors