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

OBJECTIVE: 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. APPROACH: We propose DistaNet as a neural network based framework that extracts smooth, continuous, and low-dimensional signatures of the hand grasps from multi-channel myoelectric signals and provides grasp-specific biofeedback to the users. RESULTS: Experimental results show its effectiveness in decoding user gestures and providing biofeedback, helping users retain the acquired motor skills. SIGNIFICANCE: We demonstrates myoelectric skill retention in a pattern recognition setting for the first time. \end{abstract}.

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 (Ni2+) 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. Combined with the ion imprinting technique, the microfiber interferometer sensor sensitivity can reach 0.32 nm/nM with the detection limit of 0.66 nM in the low concentration range (Ni2+ concentration range is 0 nM-100 nM). The experiment not only studies the principle of microfiber interferometer and FBG and their refractive index and temperature performance, but also shows that the FBG power change has a good fitting relationship with wavelength change. In addition, this system performance by the amount of power difference rather than the amount of wavelength shift, which significantly saves on the high cost weight, and size associated with the use of spectral analyzers in traditional inspection systems. This study provides a novel and easy method to develop new sensors with higher comprehensive performance.

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

Objective.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.Approach.We trained a random forest (RF) model with EMG data from 20 people collected during the performance of multiple hand grips. To adapt the decision rules for a new user, first, the branches of the pre-trained decision trees were pruned using the validation data from the new user. Then new decision trees trained merely with data from the new user were appended to the pruned pre-trained model.Results.Real-time myoelectric experiments with 18 participants over two days demonstrated the improved accuracy of the proposed approach when compared to benchmark user-specific RF and the linear discriminant analysis models. Furthermore, the RF model that was calibrated on day one for a new participant yielded significantly higher accuracy on day two, when compared to the benchmark approaches, which reflects the robustness of the proposed approach.Significance.The proposed model calibration procedure is completely source-free, that is, once the base model is pre-trained, no access to the source data from the original 20 people is required. Our work promotes the use of efficient, explainable, and simple models for myoelectric control.

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 -0.04 nm/°C. Due to the good film-forming performance of PDA, whose the amino group coupled with the carboxyl molecule on FPBA to form an amide bond, PDA/FPBA can be attached to the microfiber interferometer for detecting different concentrations of glucose. The concentration range of glucose detection is 0.1-20 mM with a sensitivity of 1.71 nm/mM and a limit of detection of 12.6 ppm. Finally, the sensor is tested in actual samples of human urine to detect different concentrations of glucose and proved to be responsive and reproducible in urine. We can estimate the concentration of glucose in urine by wavelength shift. The sensor has the advantages of simple manufacture, low cost, high sensitivity, and specific recognition glucose in urine. In addition, the success of this sensor shows that the combination of ultrafine fiber and organic chemical materials has broad prospects in the field of optical detection.

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. Using Golden Gate cloning for pathway assembly, modular gene replacement was performed, revealing the potential biosynthesis of both G1P- and G3P-based unsaturated archaeol by uncultured Asgard archaea. Unexpectedly, hybrid neutral lipids containing both archaeal isoprenoids and eukaryotic fatty acids were observed in recombinant S. cerevisiae. The ability of yeast and archaeal diacylglycerol acyltransferases to synthesize such hybrid lipids was demonstrated.

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. A fair comparison is carried out on the performances of 52 ordinary wavelet sparse bases and five widely applied reconstruction algorithms at different compression levels. The experimental results show that the db2 wavelet basis can sparse EMG signals so that the compressed EMG signals are reconstructed properly, thanks to its low percentage root mean square distortion (PRD) values at most compression ratios. In addition, the basis pursuit (BP) reconstruction algorithm can provide a more efficient reconstruction process and better reconstruction performance by comparison. The experiment records and comparative analysis screen out the suitable sparse bases and reconstruction algorithms for EMG signals, acting as prior experiments for further practical applications and also a benchmark for future academic research.

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. In this study, a novel in-depth characterization method of nitrogen heterocycles (N-heterocycles) in heavy petroleum mixtures was proposed by online liquid chromatography coupled with electrospray ionization high resolution energy-resolved mass spectrometry. A series of typical basic aromatic, neutral aromatic and naphtheno-aromatic nitrogen heterocyclic model compounds were synthesized to investigate energy-resolved fragmentation behaviors in high energy collision-induced dissociation at 10-100 eV. Energy-dependent fragmentation pathways were elucidated. Notably, characteristic double bond equivalent (DBE) versus carbon number distributions of N1 ions and all CH ions were discovered, which were closely related to their core structure. Then a workflow to assign core structures of alkyl-substituted N-heterocycles in petroleum was proposed and validated. The developed method was applied to investigate the structural isomers in feed and product vacuum gas oil (VGO) fractions. Core structural differences in feed VGO and subtle structural variations between feed and product VGOs were recognized. This work can distinguish structural isomers of N-heterocycles with the subtle difference in their core structure in heavy petroleum fractions based on global energy dimensional fragmentation characteristics.

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. A customizable formula database was established deriving from the detected sample, which could avoid the interference caused by a large number of redundant molecules in a conventionally theoretical molecular database. Molecular formula candidates were assigned by the formula database using a low noise threshold, and false-positive assignments were eliminated by the chromatographic retention behaviors. Multi-dimensional information was obtained, including heteroatom class, double bond equivalent (DBE), carbon number, retention time, and MS/MS spectra. The developed method was compared with a popular petroleomics software, similar relative abundance class distribution was obtained, and much more formulas of low abundant components were uniquely extracted by the developed method. Finally, it was applied to reveal variation between feed and product oils in hydrodenitrogenation. Significantly compositional and structural differences were revealed. The developed method provides a useful pipeline for molecular data mining of petroleum samples.

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

Objective. 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.Approach. We use a convolution structure to replace the neuron structure of traditional long short-term memory (LSTM) networks, and use the long exposure data structure which retains the spatial and temporal information of the electrodes as input. The Ninapro database, which contains continuous finger gestures and corresponding sEMG signals was used to verify the efficiency of the proposed deep learning method. The proposed method was compared with LSTM and Sparse Pseudo-input Gaussian Process (SPGP) on this database to predict the ten main joint angles on the hand based on sEMG. The correlation coefficient (CC) was evaluated using the three methods on eight healthy subjects, and all the methods adopted the root mean square (RMS) features.Main results.The experimental results showed that the average CC, root mean square error, normalized root mean square error of the proposed LE-ConvMN method (0.82 ± 0.03,11.54 ± 1.89,0.12 ± 0.013) was significantly higher than SPGP (0.65 ± 0.05,p< 0.001; 15.51 ± 2.82,p< 0.001; 0.16 ± 0.01,p< 0.001) and LSTM (0.64 ± 0.06,p< 0.001; 14.77 ± 3.21,p< 0.001; 0.15 ± 0.02,p= < 0.001). Furthermore, the proposed real-time-estimation method has a computation cost of only approximately 82 ms to output one state of ten joints (average value of 10 tests on TitanV GPU).Significance. The proposed LE-ConvMN method could efficiently estimate the continuous movement of fingers with sEMG, and its performance is significantly superior to two established deep learning methods.

Untargeted and targeted metabolomics strategy for the classification of strong aroma-type baijiu (liquor) according to geographical origin using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry.

A metabolomics strategy was developed to differentiate strong aroma-type baijiu (SAB) (distilled liquor) from the Sichuan basin (SCB) and Yangtze-Huaihe River Basin (YHRB) through liquid-liquid extraction coupled with GC×GC-TOFMS. PCA effectively separated the samples from these two regions. The PLS-DA training model was excellent, with explained variation and predictive capability values of 0.988 and 0.982, respectively. As a result, the model demonstrated its ability to perfectly differentiate all the unknown SAB samples. Twenty-nine potential markers were located by variable importance in projection values, and twenty-four of them were identified and quantitated. Discrimination ability is closely correlated to the characteristic flavor compounds, such as acid, esters, furans, alcohols, sulfides and pyrazine. Most of the marker compounds were less abundant in the SCB samples than in the YHRB samples. The quantitated markers were further processed using hierarchical cluster analysis for targeted analysis, indicating that the markers had great discrimination power to differentiate the SAB samples.

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. Up-regulated proteins were mainly related to protein folding and assembly. Additionally, many proteins involved in metabolism (e.g. carbohydrate metabolism) also showed differences in expression. cDNA microarray and GC-MS analysis showed 4756 differentially expressed mRNAs and 37 differentially expressed metabolites. Once these data were integrated with the proteomic analysis, we were able to elucidate the metabolic pathways affected by drought-induced stress. These results suggest that increased energy consumption from storage substances occurred during drought. In addition, increased expression of the enzymes involved in anabolic pathways corresponded with an increase in the content of six amino acids. We speculated that energy conversion from carbohydrates and/or fatty acids to amino acids was increased. Analysis of basic metabolism networks allowed us to understand how rice plants adjust to drought conditions.

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. The maximum/minimum ratios varied from 4.73 to 211.36. The metabolites were categorized into seven groups based on their chemical characteristics. Clustering analysis and a correlation network showed that most of the metabolites had variations among rice accessions in the same direction. Using 218 molecular markers, association mapping was conducted to identify the chromosomal loci influencing the concentrations of identified metabolites. Twenty markers were identified associating with the concentrations of 29 metabolites [-lg(P) > 3]. Allelic effects were investigated in detail in two markers (RM315 and RM541) as examples.

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., genotype SP18 and 001, for some phenotypic and agricultural trait differences, including artemisinin content, existed between the two genotypes. Samples at 7 time points of three growth stages were studied. The data of profiles were subjected to multivariate analysis with partial least squares discriminant analysis (PLS-DA). The results indicated that there were clear differences in terpenoids and artemisinin metabolism between different growth stages and genotypes. Twenty-one compounds, including artemisinin and its related precursors, were selected as the marker compounds of the PLS-DA between the two genotypes. Among them, artemisinic acid, arteannuin B, borneol, beta-farnesene and an unidentified sesquiterpenoid (peak 48) were abundant in 001, while camphor, methyl artemisinic acid and lanceol accumulated mainly in SP18. The relationship between these differences and artemisinin biosynthesis in the two genotypes of A. annua were discussed.

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. The polar compounds of grains were extracted, trimethylsilylated and analyzed by gas chromatography-flame ionization detection (GC-FID). Partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA) were applied to differentiate transgenic and wild rice grains. The significantly distinguishable metabolites were picked out, and then identified by gas chromatography-mass spectrometry (GC-MS). It was found that both the environment and gene manipulation had remarkable impacts on the contents of glycerol-3-phosphate, citric acid, linoleic acid, oleic acid, hexadecanoic acid, 2,3-dihydroxypropyl ester, sucrose, 9-octadecenoic acid (Z)-, 2,3-dihydroxypropyl ester and so on. Sucrose, mannitol and glutamic acid had a significant increase in transgenic grains in contrast to those in non-genetically modified (GM) rice.

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. (ginseng) at different ages. Thirty-six terpenoids were tentatively identified based on the MS library search and retention index in a ginseng sample at the age of 3 years. An obvious group-type separation was obtained in the GC x GC-TOF MS chromatogram. The data collected by GC x GC-FID were processed using a principal component analysis (PCA) method to classify the samples at different ages. The compounds responsible for the significant differentiation among samples were defined. It was found that the relative abundances of alpha-cadinol, alpha-bisabolol, thujopsene, and n-hexadecanoic acid significantly rise with the increase in age.

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. annua L. was carried out by GC and GC-MS coupled with trimethylsilyl derivatisation. Principal component analysis and partial least squares discriminant analysis were employed to classify GC data of A. annua L. samples at five developmental stages. The results indicated that there was no distinct difference of metabolites between control (001) and transgenic strain (F4) from the tender seedling stage to adult seedling stage, but clear differences were detected at pre-flower budding stage, flower budding stage and full flowering stage. Three precursors of artemisinin biosynthesis were studied at five developmental stages and found that a possible bottleneck exists in the conversion from artemisinic acid or dihydroartemisinic acid to artemisinin.

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. was performed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC x GC-TOF MS). Three hundred and three components were tentatively identified and terpene compounds are the main components of Artemisia annua L. volatile oil. Artemisinic acid is tentatively qualified.

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. The resolution of all the neighboring peaks on the first dimension is obtained by the predicted retention time and peak width on the first dimension, the resolution on the second dimension is calculated only for the adjacent components with un-enough resolution on the first dimension and eluted within a same modulation period on the second dimension. The optimum temperature programming condition is acquired when the resolutions of all components of interest by GC x GC separation meet the analytical requirement and the analysis time is the shortest. The validity of the model has been proven by using it to predict and optimize GC x GC temperature programming condition of an alkylpyridine 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. The enhancement of EMG responses to ipsilateral amygdala stimulation was much larger than that for contralateral amygdala stimulation and that for ipsilateral inferior colliculus stimulation. The enhancement of EMG responses to contralateral inferior colliculus stimulation was not significant. The present study provides a motor-output model for studying plasticity in the neural pathways mediating startle facilitation.