A discrete approach for modeling cell–matrix adhesions

During recent years the interaction between the extracellular matrix and the cytoskeleton of the cell has been object of numerous studies due to its importance in cell migration processes. These interactions are performed through protein clutches, known as focal adhesions. For migratory cells these focal adhesions along with force generating processes in the cytoskeleton are responsible for the formation of protrusion structures like lamellipodia or filopodia. Much is known about these structures: the different proteins that conform them, the players involved in their formation or their role in cell migration. Concretely, growth-cone filopodia structures have attracted significant attention because of their role as cell sensors of their surrounding environment and its complex behavior. On this matter, a vast myriad of mathematical models has been presented to explain its mechanical behavior. In this work, we aim to study the mechanical behavior of these structures through a discrete approach. This numerical model provides an individual analysis of the proteins involved including spatial distribution, interaction between them, and study of different phenomena, such as clutches unbinding or protein unfolding

Sentiment analysis of Turkish Twitter data

In this paper, we present a mechanism to predict the sentiment on Turkish tweets by adopting two methods based on polarity lexicon (PL) and artificial intelligence (AI). The method of PL introduces a dictionary of words and matches the words to those in the harvested tweets. The tweets are then classified to be either positive, negative, or neutral based on the result found after matching them to the words in the dictionary. The method of AI uses support vector machine (SVM) and random forest (RF) classifiers to classify the tweets as either positive, negative or neutral. Experimental results show that SVM performs better on stemmed data by achieving an accuracy of 76%, whereas RF performs better on raw data with an accuracy of 88%. The performance of PL method increases continuously from 45% to 57% as data are being modified from a raw data to a stemmed data. © 2019 Author(s)

Sentiment analysis of turkish twitter data using polarity lexicon and artificial intelligence

Sentiment analysis is a process of computationally detecting and classifying opinions written in a piece of writer’s text. It determines the writer’s impression as achromatic or negative or positive. Sentiment analysis became unsophisticated due to the invention of Internet-based societal media. At present, usually people express their opinions by dint of Twitter. Henceforth, Twitter is a fascinating medium for researchers to perform data analysis. In this paper, we address a handful of methods to prognosticate the sentiment on Turkish tweets by taking up polarity lexicon as well as artificial intelligence. The polarity lexicon method uses a dictionary of words and accords with the words among the harvested tweets. The tweets are then grouped into either positive tweets or negative tweets or neutral tweets. The methods of artificial intelligence use either individually or combined classifiers e.g., support vector machine (SVM), random forest (RF), maximum entropy (ME), and decision tree (DT) for categorizing positive tweets, negative tweets, and neutral tweets. To analyze sentiment, a total of 13000 Turkish tweets are collected from Twitter with the help of Twitter’s application programming interface (API). Experimental results show that the mean performance of our proposed methods is greater than 72%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Computational insights into the self-assembly of phenylalanine-based molecules

In a recent paper “Self-Assembly of Phenylalanine-Based Molecules”, we have studied the formation and stability of phenylalanine and diphenylalanine constructs. In the case of diphenylalanine we observe nanotubes, however, phenylalanine molecules aggregate in layers of four, not six, molecules. In the preset paper, we extend this previous work and compare the energetics of all experimentally observed structures, simulated structures, and designed structures, by way of single point Density Functional Theory ( DFT) calculations. We take a detailed look at water content, pore size and dipole moments inside our phenylalaninecontaining tubes and analyze stabilizing factors in the nanostructures