Plug-and-play myoelectric control via a self-calibrating random forest common model.

. Electromyographic (EMG) signals show large variabilities over time due to factors such as electrode shifting, user behavior variations, etc substantially degrading the performance of myoelectric control models in long-term use. Previously one-time model calibration was usually required each time before usage.

Posture-invariant myoelectric control with self-calibrating random forests.

Introduction: Myoelectric control systems translate different patterns of electromyographic (EMG) signals into the control commands of diverse human-machine interfaces via hand gesture recognition, enabling intuitive control of prosthesis and immersive interactions in the metaverse. The effect of arm position is a confounding factor leading to the variability of EMG characteristics. Developing a model with its characteristics and performance invariant across postures, could largely promote the translation of myoelectric control into real world practice.

DistaNet: grasp-specific distance biofeedback promotes the retention of myoelectric skills.

An active myoelectric interface responds to the user's muscle signals to enable movements. Machine learning can decode user intentions from myoelectric signals. However, machine learning-based interface control lacks continuous, intuitive feedback about task performance, needed to facilitate the acquisition and retention of myoelectric control skills.

Ion imprinted differential modulation system based on enhanced optic-fiber evanescent wave for sensitive and label-free detection of trace nickel ions.

An optical system with low cost monitoring, high sensitivity, strong selectivity and much lower nickel ion (Ni) content in tap water than the World Health Organization (WHO) standard (1.19 μM) has been prepared by a simple strategy. This proposed ion-imprinted differential modulation system is based on the Bragg grating (FBG) and microfiber interferometer structure, and the interferometer sensing surface is coated with a polydopamine (PDA)/graphene oxide (GO) film to enhance its sensitivity.

One-shot random forest model calibration for hand gesture decoding.

Most existing machine learning models for myoelectric control require a large amount of data to learn user-specific characteristics of the electromyographic (EMG) signals, which is burdensome. Our objective is to develop an approach to enable the calibration of a pre-trained model with minimal data from a new myoelectric user.We trained a random forest (RF) model with EMG data from 20 people collected during the performance of multiple hand grips.

A nonenzymic microfiber optic-biosensor modified phenylboric acid for sensitively and specifically detecting low glucose concentration.

A microfiber interferometer coated with sensitive films formed by amide bond between 3-Carboxy-4-fluorophenylboronic acid (FPBA) and polydopamine (PDA) for the detection of trace glucose concentration is designed and demonstrated. Due to a huge evanescent field, this microfiber interferometer has a very sensitive response to the refractive index (RI) of the surrounding environment, which has excellent sensing performance including RI sensitivity response of 1825.83 nm/RIU and low temperature response of -0.

Biosynthesis of Hybrid Neutral Lipids with Archaeal and Eukaryotic Characteristics in Engineered Saccharomyces cerevisiae.

Discovery of the Asgard superphylum of archaea provides new evidence supporting the two-domain model of life: eukaryotes originated from an Asgard-related archaeon that engulfed a bacterial endosymbiont. However, how eukaryotes acquired bacterial-like membrane lipids with a sn-glycerol-3-phosphate (G3P) backbone instead of the archaeal-like sn-glycerol-1-phosphate (G1P) backbone remains unknown. In this study, we reconstituted archaeal lipid production in Saccharomyces cerevisiae by expressing unsaturated archaeol-synthesizing enzymes.

Performance Analysis of Electromyogram Signal Compression Sampling in a Wireless Body Area Network.

The rapid growth in demand for portable and intelligent hardware has caused tremendous pressure on signal sampling, transfer, and storage resources. As an emerging signal acquisition technology, compressed sensing (CS) has promising application prospects in low-cost wireless sensor networks. To achieve reduced energy consumption and maintain a longer acquisition duration for high sample rate electromyogram (EMG) signals, this paper comprehensively analyzes the compressed sensing method using EMG.

In-depth characterization of nitrogen heterocycles of petroleum by liquid chromatography-energy-resolved high resolution tandem mass spectrometry.

With the increased attention to processing heavy crude oils, a detailed description of chemical composition is critical for the petroleum refining industry. The current analytical technique such as ultrahigh resolution mass spectrometry has been successfully applied for the molecular level characterization of complex petroleum fractions. But the structural characterization of heavy petroleum feedstock is still a great challenge.

A data processing pipeline for petroleomics based on liquid chromatography-high resolution mass spectrometry.

Online liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has attracted much attention in the molecular characterization of crude oil. Neither open access nor commercially available petroleomics tools were developed specifically to process LC-HRMS data. Here, a novel data processing pipeline was specifically designed for LC-HRMS-based petroleomics data.

Long exposure convolutional memory network for accurate estimation of finger kinematics from surface electromyographic signals.

. Estimation of finger kinematics is an important function of an intuitive human-machine interface, such as gesture recognition. Here, we propose a novel deep learning method, named long exposure convolutional memory network (LE-ConvMN), and use it to proportionally estimate finger joint angles through surface electromyographic (sEMG) signals.

Genetic, proteomic and metabolic analysis of the regulation of energy storage in rice seedlings in response to drought.

We used proteomic analysis to determine the response of rice plant seedlings to drought-induced stress. The expression of 71 protein spots was significantly altered, and 60 spots were successfully identified. The greatest down-regulated protein functional category was translation.

Profiling and association mapping of grain metabolites in a subset of the core collection of Chinese rice germplasm (Oryza sativa L.).

In this study, metabolic profiles of a set of 48 rice germplasms from the Chinese core collection were obtained by gas chromatography and time-of-flight mass spectrometry (GC-TOF-MS). Forty-one metabolites were identified and relatively quantified according to the internal standard (IS). Wide ranges of variations for all metabolites were observed among rice accessions.

Secondary metabolic profiling and artemisinin biosynthesis of two genotypes of Artemisia annua.

Artemisinin has been proven to be an effective antimalarial compound, especially for chloroquine-resistant and cerebral malaria. However, its biosynthesis pathway is still not completely clear. In order to get new clues about artemisinin biosynthesis, metabolic profiling by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) was applied to compare the secondary metabolites of two Artemisia annua L.

Metabolic profiling of transgenic rice with cryIAc and sck genes: an evaluation of unintended effects at metabolic level by using GC-FID and GC-MS.

The cryIAc and sck genes were introduced to the rice for the purpose of improving the insect resistance. Metabolic profiles of wild and transgenic rice were compared to assess the unintended effects related to gene modification. Wild samples with different sowing dates or sites were also examined to determine the environmental effects on metabolites.

Determination of radix ginseng volatile oils at different ages by comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry.

Comprehensive 2-D GC (GC x GC) coupled with TOF MS or flame ionization detector (FID) was employed to characterize and quantify the chemical composition of volatile oil in the radixes of Panax ginseng C. A. Mey.

Metabolic fingerprinting investigation of Artemisia annua L. in different stages of development by gas chromatography and gas chromatography-mass spectrometry.

Artemisia annua L. is an annual herb native of Asia and this plant has been famous for the discovery of the anti-malarial drug artemisinin since 1971. In this work, to investigate variety of whole metabolites, metabolic fingerprinting analysis of A.

Analysis of Artemisia annua L. volatile oil by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.

Artemisia annua L. is an annual herb native of Asia, it has been used for many centuries for the treatment of fever and malaria. In this paper, analysis of the volatile oil of Artemisia annua L.

Resolution prediction and optimization of temperature programme in comprehensive two-dimensional gas chromatography.

A model is developed for predicting the resolution of interested component pair and calculating the optimum temperature programming condition in the comprehensive two-dimensional gas chromatography (GC x GC). Based on at least three isothermal runs, retention times and the peak widths at half-height on both dimensions are predicted for any kind of linear temperature-programmed run on the first dimension and isothermal runs on the second dimension. The calculation of the optimum temperature programming condition is based on the prediction of the resolution of "difficult-to-separate components" in a given mixture.

Enhancement of electrically evoked startle-like responses by tetanic stimulation of the superior colliculus.

Single-pulse unilateral electrical stimulation of either the amygdala or the inferior colliculus elicited startle-like responses in chloral hydrate anesthetized rats. EMG responses to intracranial stimulation were recorded from the anterior biceps femoris muscles. The EMG responses were generally enhanced following unilateral tetanic stimulation of the deep layers of the superior colliculus, but the enhancement was stronger for amygdala sites than inferior colliculus sites.