A Computational Solution to Bolster Epigenetic Clock Reliability for Clinical Trials and Longitudinal Tracking

Epigenetic clocks are widely used aging biomarkers, but they are calculated from methylation data for individual CpGs that can be surprisingly unreliable. We report that technical noise causes six major epigenetic clocks to deviate by 3 to 9 years between replicates. We present a novel computational solution: we perform principal component analysis followed by biological age prediction using principal components, extracting shared age-related changes across CpGs while ignoring noise from individual CpGs. Our novel principal-component versions of six clocks show agreement between most technical replicates within 1 year, and increased stability in short- and long-term longitudinal studies. This requires only one additional step compared to traditional clocks, does not require prior knowledge of CpG reliabilities, and can improve the reliability of any existing or future epigenetic biomarker. The extremely high reliability of principal component epigenetic clocks makes them particularly useful for personalized medicine and clinical trials evaluating novel aging interventions.

DETECÇÃO E RECONHECIMENTO DE PLACAS DE LICENCIAMENTO VEICULAR EM TEMPO REAL USANDO CNN

Brazil has a large fleet of vehicles running daily along urban roads and highways, which requires the use of some computational solution to assist in control and management. In this work we developed an application to detect and recognize real-time licenseplates with various application possibilities. The methodology developed in this work has three main stages: plate detection, character segmentation and recognition. For the detection step we used the YOLO library, which makes use of machine learning techniques to detect objects in real time. YOLO was trained using a dataset with plate images in different environments. In the segmentation stage, the individual characters contained in the plate were separated, using image processing methods. In the last stage, character recognition was performed using two convolutional neural networks, obtaining a hit rate of 83.33%.

Balanced Academic Curriculum: Looking For An Optimal Solution With Metaheuristics And Functional Programming

The curriculum design is quite a challenge in the academy, mainly because it requires an adequate distribution of content for the development of the expected professional competencies regarding the available time, the necessary academic load, and their gradual progress in the higher educational institutions. Considering the above, the main objective of this work is to present and exemplify a computational solution to minimize the cost of designing curriculum plans using bio-inspired algorithms to automate and reduce errors in such a process. Specifically, the purpose of this research focuses on solving the Curriculum Mesh Balancing (BACP) problem through metaheuristic optimization based on the behavior or algorithm of fireflies and the use of functional programming in the Haskell lang curricular meshes, rolling of curricular meshes, metaheuristics; firefly algorithm, functional programming in Haskell programming language. The firefly algorithm will be applied to a set of test instances to demonstrate its effectiveness. According to the obtained results, this proposal allows the efficient gathering of solutions to the problem under study.

Constituent order in silent gesture reflects the perspective of the producer

Understanding the relationship between human cognition and linguistic structure is a central theme in language evolution research. Numerous studies have investigated this question using the silent gesture paradigm in which participants describe events using only gesture and no speech. Research using this paradigm has found that Agent–Patient–Action (APV) is the most commonly produced gesture order, regardless of the producer’s native language. However, studies have uncovered a range of factors that influence ordering preferences. One such factor is salience, which has been suggested as a key determiner of word order. Specifically, humans, who are typically agents, are more salient than inanimate objects, so tend to be mentioned first. In this study, we investigated the role of salience in more detail and asked whether manipulating the salience of a human agent would modulate the tendency to express humans before objects. We found, first, that APV was less common than expected based on previous literature. Secondly, salience influenced the relative ordering of the patient and action, but not the agent and patient. For events involving a non-salient agent, participants typically expressed the patient before the action and vice versa for salient agents. Thirdly, participants typically omitted non-salient agents from their descriptions. We present details of a novel computational solution that infers the orders participants would have produced had they expressed all three constituents on every trial. Our analysis showed that events involving salient agents tended to elicit AVP; those involving a non-salient agent were typically described with APV, modulated by a strong tendency to omit the agent. We argue that these findings provide evidence that the effect of salience is realized through its effect on the perspective from which a producer frames an event.

Computational solution of networks versus cluster grouping for social network contact recommender system

<span lang="EN-US">Graphs have become the dominant life-form of many tasks as they advance a structural system to represent many tasks and their corresponding relationships. A powerful role of networks and graphs is to bridge local feats that exist in vertices or nodal agents as they blossom into patterns that helps explain how nodes and their corresponding edges impacts a complex effect that ripple via a graph. User cluster are formed as a result of interactions between entities – such that many users today, hardly categorize their contacts into groups such as “family”, “friends”, “colleagues”. The need to analyze such user social graph via implicit clusters, enables the dynamism in contact management. Study seeks to implement this dynamism via a comparative study of the deep neural network and friend suggest algorithm. We analyze a user’s implicit social graph and seek to automatically create custom contact groups using metrics that classify such contacts based on a user’s affinity to contacts. Experimental results demonstrate the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.</span>