Anxiety & Stress

A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits.

Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lodging resistance. However, the traditional method of measuring rice c...

Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables.

OBJECTIVES: Research during prior virus outbreaks has examined vulnerability factors associated with increased anxiety and fear.

Mediating artificial intelligence developments through negative and positive incentives.

The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, busines...

Conversational Agents for Chronic Disease Self-Management: A Systematic Review.

We conducted a systematic literature review to assess how conversational agents have been used to facilitate chronic disease self-management. The Pref...

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to...

Stress detection using deep neural networks.

BACKGROUND: Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and...

Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years.

BACKGROUND: Recent studies have demonstrated that passive smartphone and wearable sensor data collected throughout daily life can predict anxiety symp...

Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.

Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanis...

Machine Learning Revealed New Correlates of Chronic Pelvic Pain in Women.

Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its associated significant costs and to improve women'...

Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial.

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preempti...

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning.

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infr...

Monosodium glutamate induces memory and hepatic dysfunctions in mice: ameliorative role of Jobelyn through the augmentation of cellular antioxidant defense machineries.

This study investigated the effect of high doses of monosodium glutamate (MSG), a known food additive on hepatic, memory and locomotor functions in mi...

On the use of AI for Generation of Functional Music to Improve Mental Health.

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reductio...

Do We Take a Robot's Needs into Account? The Effect of Humanization on Prosocial Considerations Toward Other Human Beings and Robots.

Robots are becoming an integral part of society, yet the extent to which we are prosocial toward these nonliving objects is unclear. While previous re...

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data.

To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for ...

Effect of Social Anxiety on the Adoption of Robotic Training Partner.

With the development of artificial intelligence technologies, robotic training partner is becoming a reality, which is a substitute for human training...

Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.

Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery assoc...

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.

Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and manageme...

Predicting dairy cattle heat stress using machine learning techniques.

The objectives of the study were to use a heat stress scoring system to evaluate the severity of heat stress on dairy cows using different heat abatem...

Deep learning with wearable based heart rate variability for prediction of mental and general health.

The ubiquity and commoditisation of wearable biosensors (fitness bands) has led to a deluge of personal healthcare data, but with limited analytics ty...