DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features

The influence of negatively charged heavy ions on Alfven waves in a cometary environment

Alfven waves are important in a wide variety of areas like astrophysical, space and laboratory plasmas. In cometary environments, waves in the hydromagnetic range of frequencies are excited predominantly by heavy ions. We, therefore, study the stability of Alfven waves in a plasma of hydrogen ions, positively and negatively charged oxygen ions and electrons. Each species has been modeled by drifting distributions in the direction parallel to the magnetic field; in the perpendicular direction the distribution is  simulated with a loss cone type distribution obtained through the subtraction of two Maxwellian distributions with different temperatures.  We find that for frequencies  ( andbeing respectively the Doppler shifted and hydrogen ion gyro-frequencies ), the peak growth  rate increases with increasing negatively charged oxygen ion densities. On the other hand, for frequencies (being the oxygen ion gyro-frequencies) the region of wave growth increases with increasing negatively charged oxygen ion densities

Extraction, characterization and antibacterial activity of medicinal plants for the control of food pathogens

Preservatives are substances that are added to food or used in cooking in order to stop it from going bad or growing bacteria that could make people sick. On the other hand, although these compounds are designed to prevent microbial spoilage of food, they frequently have unintended consequences for human health, the distribution network for food, and the evolution of microbial resistance. As a result of these challenges, it is becoming increasingly vital to find a natural preservative that is both safe and healthy for human consumption. In some circumstances, plant extracts are utilised both to treat and prevent food-borne illnesses. In the present study, Tinospora Cordifolia, Vitex Negundo, and Syzgium Cumini were used as a medicinal plants. Among the 3 different plant extracts, it was discovered that only one of them, the Vitex Negundo extract 400µg/ml plant extract, was shown high inhibition activity against the food pathogens. For the purpose of preventing food poisoning and preserving food, natural alternatives based on these potentially useful plant extracts can serve as an effective replacement for antibacterial agents that are generated using chemical processes. &nbsp

Short-term consumption of highly processed diets varying in macronutrient content impair the sense of smell and brain metabolism in mice

Objective: Food processing greatly contributed to increased food safety, diversity, and accessibility. However, the prevalence of highly palatable and highly processed food in our modern diet has exacerbated obesity rates and contributed to a global health crisis. While accumulating evidence suggests that chronic consumption of such foods is detrimental to sensory and neural physiology, it is unclear whether its short-term intake has adverse effects. Here, we assessed how short-term consumption (<2 months) of three diets varying in composition and macronutrient content influence olfaction and brain metabolism in mice. Methods: The diets tested included a grain-based standard chow diet (CHOW; 54% carbohydrate, 32% protein, 14% fat; #8604 Teklad Rodent diet , Envigo Inc.), a highly processed control diet (hpCTR; 70% carbohydrate, 20% protein, 10% fat; #D12450B, Research Diets Inc.), and a highly processed high-fat diet (hpHFD; 20% carbohydrate, 20% protein, 60% fat; #D12492, Research Diets Inc.). We performed behavioral and metabolic phenotyping, electro-olfactogram (EOG) recordings, brain glucose metabolism imaging, and mitochondrial respirometry in different brain regions. We also performed RNA-sequencing (RNA-seq) in the nose and across several brain regions, and conducted differential expression analysis, gene ontology, and network analysis. Results: We show that short-term consumption of the two highly processed diets, but not the grain-based diet, regardless of macronutrient content, adversely affects odor-guided behaviors, physiological responses to odorants, transcriptional profiles in the olfactory mucosa and brain regions, and brain glucose metabolism and mitochondrial respiration. Conclusions: Even short periods of highly processed food consumption are sufficient to cause early olfactory and brain abnormalities, which has the potential to alter food choices and influence the risk of developing metabolic disease