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Scientists in Britain have created a machine that uses artificial intelligence (AI) to perform the perfect blow job. The development of 'Autoblow' follows the publication of a paper titled 'Analysis [...]

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and noninvasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to noninvasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell-derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine-learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate noninvasive cell therapy characterization can be achieved with QBAM and machine learning., Introduction Advancements in developmental biology and regenerative medicine have helped produce cell-based therapies to treat retinal degeneration, neurodegeneration, cardiopathies, and other diseases by replacing damaged or degenerative native tissue with [...]

During the last half century, the theory-in-isolation (TiI) approach--within which scholars develop theories by first formulating quantitative models meant to approximate stakeholders' behavior and then testing those models with data acquired for that purpose--has remained the gold standard for publishable marketing scholarship. Unfortunately, the almost universal adoption of this approach has blinded marketing scholars to a viable alternative: the empirical-then-theoretical (EtT) approach, which suggests theories based on observed empirical regularities. However, increasingly pervasive data collection efforts, powerful computer hardware, and sophisticated software-implemented algorithms are fostering a 'big data' (i.e., data sets that are massive, complex, yet quickly replenished from various sources) era far more amenable to the EtT approach. Traditionally, marketing theories emerged from managerial experience and/or scholarly activity in marketing and related disciplines (e.g., economics). Big data represents a complementary source. Nonetheless, revelatory big-data-derived scholarship requires multi-disciplinary research teams, knowledgeable industry experts, and specialized computing capabilities. In addition, big data is prone to biases that multi-disciplinary specialists can mitigate substantially. Thus, marketing scholars contemplating a contemporary EtT approach--which relies on big-data-related analytical tools such as data mining, cognitive computing, neural networks, and artificial intelligence--must have access to skills that extend beyond traditional graduate training in businessm.Essentially, we argue big data is more compatible with an empirical-then-theoretical (EtT) approach than a theoretical-in-isolation (TiI) approach to marketing theory development. Our exposition proceeds as follows. First, we introduce both approaches and big data. Then, we discuss three well-established sources of marketing theory and suggest big data as a fourth source. After presenting an abridged set of published marketing-related big data studies, we close with issues posed by using big data to develop marketing theory and a brief look forward. JEL Classifications: M31 Keywords: Marketing theory, Big Data, Empirical-Then-Theoretical, Theory-In-Isolation Corresponding Author's Email Address: vlukosius@tnstate.edu, INTRODUCTION At marketing science's inception in the early 1960s, data for testing quantifiable theories and estimating model parameters were 'at a premium'. As a result, many initially published tests and [...]

Background: The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal stages. Many neuroimaging studies have focused on case-control classification and predicting conversion from mild cognitive impairment to Alzheimer disease, but predicting scores from clinical assessments (such as the Alzheimer's Disease Assessment Scale or the Mini Mental State Examination) using MRI data has received less attention. Predicting clinical scores can be crucial in providing a nuanced prognosis and inferring symptomatic severity. Methods: We predicted clinical scores at the individual level using a novel anatomically partitioned artificial neural network (APANN) model. The model combined input from 2 structural MRI measures relevant to the neurodegenerative patterns observed in Alzheimer disease: hippocampal segmentations and cortical thickness. We evaluated the performance of the APANN model with 10 rounds of 10-fold cross-validation in 3 experiments, using cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI): ADNI1, ADNI2 and ADNI1 + 2. Results: Pearson correlation and root mean square error between the actual and predicted scores on the Alzheimer's Disease Assessment Scale (ADNI1: r = 0.60; ADNI2: r = 0.68; ADNI1 + 2: r = 0.63) and Mini Mental State Examination (ADNI1: r = 0.52; ADNI2: r = 0.55; ADNI1 + 2: r = 0.55) showed that APANN can accurately infer clinical severity from MRI data. Limitations: To rigorously validate the model, we focused primarily on large cross-sectional baseline data sets with only proof-of-concept longitudinal results. Conclusion: The APANN provides a highly robust and scalable framework for predicting clinical severity at the individual level using high-dimensional, multimodal neuroimaging data., Introduction Machine-learning methods have been used extensively to distinguish people with Alzheimer disease and its prodromes from healthy controls. (1-5) However, predicting symptomatic severity at the individual level remains a [...]

The ocean-atmosphere interface (OAI) is a dynamic boundary of complex energy and chemical exchange and is important to understand mechanisms that influence it. Research is on-going to improve how the OAI is represented within tropical cyclone (TC) prediction models and ensembles. Motivation for improvement stems from a rapidly changing thermodynamic environment caused by climate change. Such changes are not widely understood, as no scientist has observed or measured these changes on long time scales. We assert the possibility of climate change, its underlying uncertainties and modified atmospheric variability can potentially lead to rapid intensification. We argue simplification of OAI to capture model ensemble data uncertainty through probabilistic modeling via Bayesian Neural Network (BNN). We retrieved area--averaged satellite data from NOAA and NASA, created a data set of several parameters--atmospheric air temperature (AirTemp), atmospheric temperature anomaly (ATA), atmospheric carbon dioxide (C[O.sub.2]), sea surface temperature (SST), tropical cyclone heat potential (TCHP), mid- layer wind shear (WindShear), convective available heat potential (CAPE), vertical motions (VerticalMotion), precipitable water content (PWC) and our derived OAI parameter as inputs into a BNN via R programming language. We used the BNN to model the OAI and inferenced potential favorability of an OAI given conditional probabilities. The BNN network rejected ATA and WindShear. Results showed probabilities acceptable within expert interpretations of parameter interactions to predict favorable OAI conditions. Keywords: Tropical Storm, Bayesian Neural Network, Ocean-Atmosphere Interface, INTRODUCTION Uncertainties in tropical cyclone (TC) intensification forecasting continue in modern-day forecasting modelsl. Recent storms such as TC Matthew (2016) and TC Irma (2017) demonstrated our limitations in predicting intensification, [...]