NADH dehydrogenase-like complex L subunit improves salt tolerance by enhancing photosynthetic electron transport.

Cyclic electron transport (CET) around photosystem I (PSI) mediated by the NADH dehydrogenase-like (NDH) complex is closely related to plant salt tolerance. However, whether overexpression of a core subunit of the NDH complex affects the photosynthetic electron transport under salt stress is currently unclear. Here, we expressed the NDH complex L subunit (Ndhl) genes ZmNdhl1 and ZmNdhl2 from C plant maize (Zea mays) or OsNdhl from C plant rice (Oryza sativa) using a constitutive promoter in rice.

Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.

Objective: Psychiatric evaluation suffers from subjectivity and bias, and is hard to scale due to intensive professional training requirements. In this work, we investigated whether behavioral and physiological signals, extracted from tele-video interviews, differ in individuals with psychiatric disorders.

Methods: Temporal variations in facial expression, vocal expression, linguistic expression, and cardiovascular modulation were extracted from simultaneously recorded audio and video of remote interviews.

Disentangling Visual Exploration Differences in Cognitive Impairment.

Objective: Individuals with cognitive impairment (CI) exhibit different oculomotor functions and viewing behaviors. In this work we aimed to quantify the differences in these functions with CI severity, and assess general CI and specific cognitive functions related to visual exploration behaviors.

Methods: A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals.

Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study.

Background: Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit.

Inverse design of chiral functional films by a robotic AI-guided system.

Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum.

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns.

Objective: The current clinical practice of psychiatric evaluation suffers from subjectivity and bias, and requires highly skilled professionals that are often unavailable or unaffordable. Objective digital biomarkers have shown the potential to address these issues. In this work, we investigated whether behavioral and physiological signals, extracted from remote interviews, provided complimentary information for assessing psychiatric disorders.

Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight).

Significance: We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature.

Aim: Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research.

Disentangling visual exploration differences in cognitive impairment.

Objective: Compared to individuals without cognitive impairment (CI), those with CI exhibit differences in both basic oculomotor functions and complex viewing behaviors. However, the characteristics of the differences and how those differences relate to various cognitive functions have not been widely explored. In this work we aimed to quantify those differences and assess general cognitive impairment and specific cognitive functions.

An investigation of privacy preservation in deep learning-based eye-tracking.

Background: The expanding usage of complex machine learning methods such as deep learning has led to an explosion in human activity recognition, particularly applied to health. However, complex models which handle private and sometimes protected data, raise concerns about the potential leak of identifiable data. In this work, we focus on the case of a deep network model trained on images of individual faces.

Chiral biosensing at both interband transition and plasmonic extinction regions using twisted-stacked nanowire arrays.

Chiral metal nanostructures that exhibit strong chiroptical properties and enhanced light-matter interactions have recently attracted great interest due to their potential applications including chiral sensing and asymmetric synthesis. Most studies in this field focused on chiral sensing using circular dichroism (CD) responses at the plasmonic extinction region. In comparison, little is known about their CD responses at interband transition regions and their utility in chiral biosensing.

Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study.

Background: Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient's outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician's interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders.

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments.

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware.

Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review.

Background: Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient's self-report, what the clinician observes directly, and what the clinician gathers from collateral informants, but these elements are prone to subjectivity. Utilizing computer vision to measure facial expressions is a promising approach to adding more objectivity in the evaluation and diagnosis of schizophrenia.

Automated analysis of facial emotions in subjects with cognitive impairment.

Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.

Efficient and accurate identification of ear diseases using an ensemble deep learning model.

Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in clinical cases to achieve an automatic diagnosis of ear diseases in real time.

Classifying Major Depressive Disorder and Response to Deep Brain Stimulation Over Time by Analyzing Facial Expressions.

Objective: Major depressive disorder (MDD) is a common psychiatric disorder that leads to persistent changes in mood and interest among other signs and symptoms. We hypothesized that convolutional neural network (CNN) based automated facial expression recognition, pre-trained on an enormous auxiliary public dataset, could provide improve generalizable approach to MDD automatic assessment from videos, and classify remission or response to treatment.

Methods: We evaluated a novel deep neural network framework on 365 video interviews (88 hours) from a cohort of 12 depressed patients before and after deep brain stimulation (DBS) treatment.

An Energy Efficient AdaBoost Cascade Method for Long-Term Seizure Detection in Portable Neurostimulators.

Responsive neurostimulation (RNS) is becoming a promising therapy in refractory epilepsy control. In a RNS system, a critical challenge is how to detect seizure onsets accurately with low computational costs. In this study, an energy efficient AdaBoost cascade method for robust long-term seizure detection from local field potential (LFP) signals was proposed and evaluated in a portable neurostimulator.

Acute Seizure Control Efficacy of Multi-Site Closed-Loop Stimulation in a Temporal Lobe Seizure Model.

The closed-loop electrical stimulation is emerging as a promising neural modulation therapy for refractory epilepsy. However, the efficacy of electrical stimulation is less than optimal and the mechanism of seizure control is still unclear. In this paper, we evaluated the acute seizure control efficacy of the multi-site closed-loop stimulation (MSCLS) in a rodent model with a custom designed closed-loop neurostimulator.