Mcdonaldmunk8781

In addition, the novel strategy reported here could be similarly used to develop many other types of self-supporting electrodes with further improved HER performance.

High frequency oscillations (HFOs) are a promising biomarker of tissue that instigates seizures. However, ambiguous data and random background fluctuations can cause any HFO detector (human or automated) to falsely label non-HFO data as an HFO (a false positive detection). The objective of this paper was to identify quantitative features of HFOs that distinguish between true and false positive detections.

Feature selection was performed using background data in multi-day, interictal intracranial recordings from ten patients. We selected the feature most similar between randomly selected segments of background data and HFOs detected in surrogate background data (false positive detections by construction). We then compared these results with fuzzy clustering of detected HFOs in clinical data to verify the feature's applicability. We validated the feature is sensitive to false versus true positive HFO detections by using an independent data set (six subjects) scored for HFOs by three human reviewers. Lastly,e.Condensed Matter Physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many other important branches of science, such as Chemistry, Materials Science, Statistical Physics, and High-Performance Computing. With the advancements in modern Machine Learning (ML) technology, a keen interest in applying these algorithms to further CMP research has created a compelling new area of research at the intersection of both fields. In this review, we aim to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML algorithms. We also discuss the main challenges and outlooks for future developments.The properties of dense hot hydrogen, in particular the phase transition between the molecular insulating and atomic conductive states, are important in the fields of astrophysics and high-pressure physics. Previous ab initio calculations suggested the metallization in liquid hydrogen, accompanied by dissociation, is a first-order phase transition and ends at a critical point in temperature range between 1500 and 2000 K and pressure close to 100 GPa. Using density functional theoretical molecular dynamics simulations, we report a first-principles equation of state of hydrogen that covers dissociation transition conditions at densities ranging from 0.20 to 1.00 g/cc and temperatures of 600-9000 K. Our results clearly indicate that a drop in pressure and a sharp structural change still occur as the system transforms from a diatomic to monoatomic phase at temperatures above 2000 K, and support the first-order phase transition in liquid hydrogen would end in the temperature about 4500 K.

The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse.

Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedbaunderlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in injury or disease.

To our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in injury or disease.

In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing.

The approach leverages digital biomarkers and recent advances in machine learning (ML) for mass AF diagnosis from overnight-hours of single-channel electrocardiogram (ECG) recording. Four databases, totaling n = 3088 patients and p = 26 913 h of continuous single-channel electrocardiogram raw data were used. Three of the databases (n = 125, p = 2513) were used for training a ML model in recognizing AF events from beat-to-beat time series. Visit 1 of the sleep heart health study database (SHHS1, n = 2963, p = 24 400) was used as the test set to evaluate the feasibility of identifying prominent AF from polysomnographic recordings. By combining AF diagnosis history and a cardiologist's visual inspection of individuals suspected of having AF (n = 118), a total of 70 patients were diagnosed with prominent AF in SHHS1.

Model prediction on SHHS1 showed an overall [Formula see text]and [Formula see text] in classifying individuals with or without prominent AF. [Formula see text] was non-inferior (p = 0.03) for individuals with an apnea-hypopnea index (AHI) ≥15 versus AHI < [Formula see text]. Over 22% of correctly identified prominent AF rhythm cases were not previously documented as AF in SHHS1.

Individuals with prominent AF can be automatically diagnosed from an overnight single-channel ECG recording, with an accuracy unaffected by the presence of moderate-to-severe obstructive sleep apnea. This approach enables identifying a large proportion of AF individuals that were otherwise missed by regular care.

Individuals with prominent AF can be automatically diagnosed from an overnight single-channel ECG recording, with an accuracy unaffected by the presence of moderate-to-severe obstructive sleep apnea. Selleckchem Nuciferine This approach enables identifying a large proportion of AF individuals that were otherwise missed by regular care.