Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

The authors trained a machine learning model to detect kidney stones based on characteristics of urine. This method would allow for detection of kidney stones prior to the onset of noticeable symptoms by the patient.

In this study, the authors test whether providing a larger dataset of glass transition temperatures (T_g) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and T_g is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's T_g, providing valuable improvements to this useful platform.

Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.

In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.

Stress and anxiety have become more prevalent issues in recent years with teenagers especially at risk. Recent studies show that experiencing stress while learning can impair brain-cell communication thus negatively impacting learning. Green tea is believed to have the opposite effect, aiding in learning and memory retention. In this study, the authors used Lymnaea stagnalis , a pond snail, to explore the relationship between green tea and a stressor that impairs memory formation to determine the effects of both green tea and stress on the snails’ ability to learn, form, and retain memories. Using a conditioned taste aversion (CTA) assay, where snails are exposed to a sweet substance followed by a bitter taste with the number of biting responses being recorded, the authors found that stress was shown to be harmful to snail learning and memory for short-term, intermediate, and long-term memory.

In this study, the authors surveyed a number of students in Singapore to determine how their experiences changed after the implementation of home-based learning during the COVID-19 pandemic.

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

In their paper, Kreiman et al. examined what it takes for an artificial neural network to be able to perform well on a new task without forgetting its previous knowledge. By comparing methods that stop task forgetting, they found that longer training times and maintenance of the most important connections in a particular task while training on a new one helped the neural network maintain its performance on both tasks. The authors hope that this proof-of-principle research will someday contribute to artificial intelligence that better mimics natural human intelligence.

Alzheimer's disease (AD) involves the reduction of cholinergic activity due to a decrease in neuronal levels of nAChR α7. In this work, Sanyal and Cuellar-Ortiz explore the role of the nAChR α7 in learning and memory retention, using Drosophila melanogaster as a model organism. The performance of mutant flies (PΔEY6) was analyzed in locomotive and olfactory-memory retention tests in comparison to wild type (WT) flies and an Alzheimer's disease model Arc-42 (Aβ-42). Their results suggest that the lack of the D. melanogaster-nAChR causes learning, memory, and locomotion impairments, similar to those observed in Alzheimer's models Arc-42.