Automatic detection of influential actors in disinformation networks

The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50,000 accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the precision-recall (P-R) curve; maps out salient network communities; and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from US Congressional reports, investigative journalism, and IO datasets provided by Twitter.

Object detection networks and augmented reality for cellular detection in fluorescence microscopy

Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines.

Improving the Automobile Purchasing Behavior of Customer: Classification Techniques

Data mining (DM) is the automate detection of relevant pattern from the database. E-Commerce is a very famous as well as frequently used new technique in the real world applications. DM is an automate detection of relevant patterns from large amount of information repositories. E-Commerce is a Killer-domain for data mining. DM is often a complex process and may require a variety of steps before some results are obtained. To predict behaviors and future trends many tools are available in DM, also allowing the businesses to make proactive pathways for the customer. In this research work, it is taken online shoppers purchasing vehicle data set and find accuracy in terms of its purchasing behavior using some of the classification algorithms. The classification algorithms namely Bayes Net and NavieBayse are utilized for the analysis and a comparative study of both the algorithms are carried out. Finally, the performance of the chosen algorithm is suggested for analyzing the vehicle data set based on the purchasing behavior of the customer and predicts some accuracy.

Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin & Eosin (H&E)-stained whole slide images (WSIs) from 25 Balb/c mice with various level of brain metastatic tumor burden. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue. Our approach resulted in a robust and highly concordant correlation between automated metastases quantification of brain metastases and manual human assessment (R2 = 0.8783; P < 0.0001). This simple approach is amenable to other similar analyses, including that of human tissues. Widespread adoption of these tools aims to democratize ML and improve precision in traditionally qualitative tasks in histopathology-based research.

Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis

In this paper we demonstrate the application of object detection networks for the classification and localization of cells in fluorescence microscopy. We benchmark two leading object detection algorithms across multiple challenging 2-D microscopy datasets as well as develop and demonstrate an algorithm which can localize and image cells in 3-D, in real-time. Furthermore, we exploit the fast processing of these algorithms and develop a simple and effective Augmented Reality (AR) system for fluorescence microscopy systems. Object detection networks are well-known high performance networks famously applied to the task of identifying and localizing objects in photography images. Here we show their application and efficiency for localizing cells in fluorescence microscopy images. Object detection algorithms are typically trained on many thousands of images, which can be prohibitive within the biological sciences due to the cost of imaging and annotating large amounts of data. Through taking different cell types and assays as an example, we show that with some careful considerations it is possible to achieve very high performance with datasets with as few as 26 images present. Using our approach, it is possible for relatively non-skilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of conventionally manual fluorescence microscopy acquisition pipelines.

Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D

Particle tracking is a powerful biophysical tool that requires conversion of large video files into position time series, i.e., traces of the species of interest for data analysis. Current tracking methods, based on a limited set of input parameters to identify bright objects, are ill-equipped to handle the spectrum of spatiotemporal heterogeneity and poor signal-to-noise ratios typically presented by submicron species in complex biological environments. Extensive user involvement is frequently necessary to optimize and execute tracking methods, which is not only inefficient but introduces user bias. To develop a fully automated tracking method, we developed a convolutional neural network for particle localization from image data, comprising over 6,000 parameters, and used machine learning techniques to train the network on a diverse portfolio of video conditions. The neural network tracker provides unprecedented automation and accuracy, with exceptionally low false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species.

Long-term dopamine neurochemical monitoring in primates

Many debilitating neuropsychiatric and neurodegenerative disorders are characterized by dopamine neurotransmitter dysregulation. Monitoring subsecond dopamine release accurately and for extended, clinically relevant timescales is a critical unmet need. Especially valuable has been the development of electrochemical fast-scan cyclic voltammetry implementing microsized carbon fiber probe implants to record fast millisecond changes in dopamine concentrations. Nevertheless, these well-established methods have only been applied in primates with acutely (few hours) implanted sensors. Neurochemical monitoring for long timescales is necessary to improve diagnostic and therapeutic procedures for a wide range of neurological disorders. Strategies for the chronic use of such sensors have recently been established successfully in rodents, but new infrastructures are needed to enable these strategies in primates. Here we report an integrated neurochemical recording platform for monitoring dopamine release from sensors chronically implanted in deep brain structures of nonhuman primates for over 100 days, together with results for behavior-related and stimulation-induced dopamine release. From these chronically implanted probes, we measured dopamine release from multiple sites in the striatum as induced by behavioral performance and reward-related stimuli, by direct stimulation, and by drug administration. We further developed algorithms to automate detection of dopamine. These algorithms could be used to track the effects of drugs on endogenous dopamine neurotransmission, as well as to evaluate the long-term performance of the chronically implanted sensors. Our chronic measurements demonstrate the feasibility of measuring subsecond dopamine release from deep brain circuits of awake, behaving primates in a longitudinally reproducible manner.