Online prediction of sustained muscle force from individual motor unit activities using adaptive surface EMG decomposition.

Decoding movement intentions from motor unit (MU) activities to represent neural drive information plays a central role in establishing neural interfaces, but there remains a great challenge for obtaining precise MU activities during sustained muscle contractions. In this paper, we presented an online muscle force prediction method driven by individual MU activities that were decomposed from prolonged surface electromyogram (SEMG) signals in real time. In the training stage of the proposed method, a set of separation vectors was initialized for decomposing MU activities. After transferring each decomposed MU activity into a twitch force train according to its action potential waveform, a neural network was designed and trained for predicting muscle force. In the subsequent online stage, a practical double-thread-parallel algorithm was developed. One frontend thread predicted the muscle force in real time utilizing the trained network and the other backend thread simultaneously updated the separation vectors. To assess the performance of the proposed method, SEMG signals were recorded from the abductor pollicis brevis muscles of eight subjects and the contraction force was simultaneously collected. With the update procedure in the backend thread, the force prediction performance of the proposed method was significantly improved in terms of lower root mean square deviation (RMSD) of around 10% and higher fitness (R2) of around 0.90, outperforming two conventional methods. This study provides a promising technique for real-time myoelectric applications in movement control and health.

Diphthamide deficiency promotes association of eEF2 with p53 to induce p21 expression and neural crest defects.

Diphthamide is a modified histidine residue unique for eukaryotic translation elongation factor 2 (eEF2), a key ribosomal protein. Loss of this evolutionarily conserved modification causes developmental defects through unknown mechanisms. In a patient with compound heterozygous mutations in Diphthamide Biosynthesis 1 (DPH1) and impaired eEF2 diphthamide modification, we observe multiple defects in neural crest (NC)-derived tissues. Knockin mice harboring the patient's mutations and Xenopus embryos with Dph1 depleted also display NC defects, which can be attributed to reduced proliferation in the neuroepithelium. DPH1 depletion facilitates dissociation of eEF2 from ribosomes and association with p53 to promote transcription of the cell cycle inhibitor p21, resulting in inhibited proliferation. Knockout of one p21 allele rescues the NC phenotypes in the knockin mice carrying the patient's mutations. These findings uncover an unexpected role for eEF2 as a transcriptional coactivator for p53 to induce p21 expression and NC defects, which is regulated by diphthamide modification.

Online Decomposition of Surface Electromyogram Into Individual Motor Unit Activities Using Progressive FastICA Peel-Off.

Surface electromyogram (SEMG) decomposition provides a promising tool for decoding and understanding neural drive information non-invasively. In contrast to previous SEMG decomposition methods mainly developed in offline conditions, there are few studies on online SEMG decomposition. A novel method for online decomposition of SEMG data is presented using the progressive FastICA peel-off (PFP) method. The proposed online method utilized a two-stage approach, consisting of an offline prework stage for initializing high-quality separation vectors through the offline PFP algorithm, and an online decomposition stage for estimating source signals of different motor units by applying these vectors to the input SEMG data stream. Specifically, a new successive multi-threshold Otsu algorithm was developed in the online stage for determining each motor unit spike train (MUST) precisely with fast and simple computations (to replace a time-consuming iterative threshold setting in the original PFP method). The performance of the proposed online SEMG decomposition method was evaluated by both simulation and experimental approaches. When processing simulated SEMG data, the online PFP method achieved a decomposition accuracy of 97.37%, superior to that (95.1%) of an online method with a traditional k-means clustering algorithm for MUST extraction. Our method was also found to achieve superior performance at higher noise levels. For decomposing experimental SEMG data, the online PFP method was able to extract an average of 12.00 ± 3.46 MUs per trial, with a matching rate of 90.38%, with respect to the expert-guided offline decomposition results. Our study provides a valuable way of online decomposition of SEMG data with advanced applications in movement control and health.

Adaptive Online Decomposition of Surface EMG Using Progressive FastICA Peel-Off.

This study presents a method for adaptive online decomposition of high-density surface electromyogram (SEMG) signals to overcome the performance degradation during long-term recordings. The proposed method utilized the progressive FastICA peel-off (PFP) method and integrated a practical double-thread-parallel algorithm into the conventional two-stage calculation approach. During the offline initialization stage, a set of separation vectors was computed. In the subsequent online decomposition stage, a backend thread was implemented to periodically update the separation vectors using the constrained FastICA algorithm and the automatic PFP method. Concurrently, the frontend thread employed the newly updated separation vectors to accurately extract motor unit (MU) spike trains in real time. To assess the effectiveness of the proposed method, simulated and experimental SEMG signals from abductor pollicis brevis muscles of ten subjects were used for evaluation. The results demonstrated that the proposed method outperformed the conventional method, which relies on fixed separation vectors. Specifically, the proposed method showed an improved matching rate by 3.63% in simulated data and 1.98% in experimental data, along with an increased motor unit number by 2.39 in simulated data and 1.30 in experimental data. These findings illustrated the feasibility of the proposed method to enhance the performance of online SEMG decomposition. As a result, this work holds promise for various applications that require accurate MU firing activities in decoding neural commands and building neural-machine interfaces.

Nobachelins, new siderophores from Nocardiopsisbaichengensis protecting Caenorhabditiselegans from Pseudomonasaeruginosa infection.

The biosynthetic potential of actinobacteria to produce novel natural products is still regarded as immense. In this paper, we correlated a cryptic biosynthetic gene cluster to chemical molecules by genome mining and chemical analyses, leading to the discovery of a new group of catecholate-hydroxamate siderophores, nobachelins, from Nocardiopsisbaichengensis DSM 44845. Nobachelin biosynthesis genes are conserved in several bacteria from the family Nocardiopsidaceae. Structurally, nobachelins feature fatty-acylated hydroxy-ornithine and a rare chlorinated catecholate group. Intriguingly, nobachelins rescued Caenorhabditiselegans from Pseudomonasaeruginosa-mediated killing.

Uncovering a Subtype of Microviridins via the Biosynthesis Study of FR901451.

Microviridins are a class of ribosomally synthesized and post-translationally modified peptides originally discovered from cyanobacteria, featured by intramolecular ω-ester and ω-amide bonds catalyzed by two ATP-grasp ligases. In this study, 104 biosynthetic gene clusters of microviridins from Bacteroidetes were bioinformatically analyzed, which unveiled unique features of precursor peptides. The analysis of core peptides revealed a microviridin-like biosynthetic gene cluster from Chitinophagia japonensis DSM13484 consisting of two potential precursors ChiA1 and ChiA2. Unexpectedly, the core peptide sequence of ChiA1 is consistent with the backbone of the elastase-inhibiting peptide FR901451, while ChiA2 is likely to be a precursor of an unknown product. However, an unusual C-terminal follower cleavage compared to the previously known microviridin pathways was observed and found to be dispensable for other modifications. To confirm the biosynthetic origin of FR901451, ATP-grasp ligases ChiC and ChiB were biochemically characterized to be responsible for the intramolecular ester and amide bond formation, respectively. In vitro reconstitution of the pathway showed the three-fold dehydrations of ChiA1 while unusual four-fold dehydrations were observed for ChiA2. Furthermore, in vivo gene coexpression facilitated the production of chitinoviridin A1 (FR901451) and two novel microviridin-class compounds chitinoviridin A2A and chitinoviridin A2B, with an extra macrolactone ring. All of these peptides showed potent inhibitory effects against elastase and chymotrypsin independently.

ß-C-Glycosylation with 2-Oxindole Acceptors via Palladium-Catalyzed Decarboxylative Reactions.

This report describes a highly efficient ß-selective C-glycosylation of bicyclic galactals with 2-oxindoles through a palladium-catalyzed decarboxylative pathway. A variety of substrates representing both glycosyl donors and acceptors could be transformed in greater than 90% yields under mild reaction conditions. The decarboxylation intermediate of galactal could serve as an efficient base to deprotonate the enol tautomer of 2-oxindole and enhance its nucleophilicity. The ß-selective nucleophilic addition at the anomeric center originates from the steric hindrance imposed by the palladium and bulky ligand.

Two new mohangic acid derivatives from the deep-sea bacteria Alcanivorax dieselolei BC-5.

Mohangic acids are a class of p-aminoacetophenonic acids that contain a conjugated triene or diene moiety. Herein, this paper reports two new mohangic acids E and F (1-2) together with a known compound mohangic acid A (3), which were isolated from the deep-sea sediment-derived bacteria Alcanivorax dieselolei BC-5. The structures of 1 and 2 were established by HRESIMS, 1 D and 2 D NMR, and IR spectroscopy.

Decoding finger movement patterns from microscopic neural drive information based on deep learning.

Recent development of surface electromyogram (sEMG) decomposition technique provides a good basis of decoding movements from individual motor unit (MU) activities that directly representing microscopic neural drives. How to interpret the function and contribution of each decomposed MU to macroscopic movements remains unclear. The objective of this study is to decode finger movement patterns by establishing a relationship between individual MU activities and movements. In this study, high-density sEMG (HD-sEMG) data were recorded by a 16 × 8 electrode array from finger extensor muscles of 10 subjects performing 10 finger movement patterns. The progressive FastICA peel-off algorithm was first applied to decompose the HD-sEMG data to obtain microscopic neural drives in terms of MU firing sequences and their corresponding action potential waveforms. Then, convolutional neural network was used for classification of the decomposed MUs by characterizing their spatial waveforms spanned over all channels of the array. On this basis, a fuzzy weighted decision strategy was designed to give a final decision of movement pattern recognition, where function of an individual MU was measured in the form of contributing into all movement patterns with different weights to solve the issue of MUs shared among multiple patterns due to muscle co-activation. The proposed method yielded an average accuracy approximating to 100%, and it outperformed other common MU-based methods or conventional myoelectric classification methods using macroscopic sEMG features (p <  0.05). The proposed method has a wide application prospect in the field of human-machine interaction and precise motor control.

Transition Metals Induce Quenching of Monomeric Near-Infrared Fluorescent Proteins.

Transition metals such as zinc and copper are essential in numerous life processes, and both deficiency and toxic overload of these metals are associated with various diseases. Fluorescent metal sensors are powerful tools for studying the roles of metal ions in the physiology and pathology of biological systems. Green fluorescent protein (GFP) and its derivatives are highly utilized for protein-based sensor design, but application to anaerobic systems is limited because these proteins require oxygen to become fluorescent. Bacteriophytochrome-based monomeric near-infrared fluorescent proteins (miRFPs) covalently bind a bilin cofactor, which can be added exogenously for anaerobic cells. miRFPs can also have emission wavelengths extending to >700 nm, which is valuable for imaging applications. Here, we evaluated the suitability of miRFP670 and miRFP709 as platforms for single fluorescent protein metal ion sensors. We found that divalent metal ions like Zn2+, Co2+, Ni2+, and Cu2+ can quench from ∼6-20% (Zn2+, Co2+, and Ni2+) and up to nearly 90% (Cu2+) of the fluorescence intensity of pure miRFPs and have similar impacts in live Escherichia coli cells expressing miRFPs. The presence of a 6× histidine tag for purification influences metal quenching, but significant Cu2+-induced quenching and a picomolar binding affinity are retained in the absence of the His6 tag in both cuvettes and live bacterial cells. By comparing the Cu2+ and Cu+-induced quenching results for miRFP670 and miRFP709 and through examining absorption spectra and previously reported crystal structures, we propose a surface metal binding site near the biliverdin IXα chromophore.

Chemical constituents from salt lake-derived Streptomyces sp. QHA10.

Two new alkaloids (1,2) and one new enoic acid (3), together with three known piericidins (4-6), were isolated from the liquid fermentation of the salt lake derived Streptomyces sp. QHA10. The structures of 1-3 were elucidated based on extensive spectroscopic data (NMR, HRESIMS) as well as single-crystal X-ray diffraction. Compound 3 showed potential anti-inflammatory activity by inhibiting the production of nitric oxide (NO) in lipopolysaccharide (LPS)-induced RAW 264.7 mouse macrophages with the IC50 value of 24.5 µM.

Low Power Consumption 3D-Inverted Ridge Thermal Optical Switch of Graphene-Coated Polymer/Silica Hybrid Waveguide.

An inverted ridge 3D thermal optical (TO) switch of a graphene-coated polymer/silica hybrid waveguide is proposed. The side electrode structure is designed to reduce the mode loss induced by the graphene film and by heating the electrode. The graphene layer is designed to be located on the waveguide to assist in the conduction of heat produced by the electrode. The inverted ridge core is fabricated by etching and spin-coating processes, which can realize the flat surface waveguide. This core improves the transfer of the graphene layer and the compatibility of the fabrication processes. Because of the opposite thermal optical coefficient of polymer and silica and the high thermal conductivity of the graphene layer, the 3D hybrid TO switch with low power consumption and fast response time is obtained. Compared with the traditional TO switch without graphene film, the power consumption of the proposed TO switch is reduced by 41.43% at the wavelength of 1550 nm, width of the core layer (a) of 3 µm, and electrode distance (d) of 4 µm. The rise and fall times of the proposed TO switch are simulated to be 64.5 µs and 175 µs with a d of 4 µm, and a of 2 µm, respectively.

Rapid access to 3-aminoindazoles from nitriles with hydrazines: a strategy to overcome the basicity barrier imparted by hydrazines.

A practical and efficient base mediated synthesis of free 3-aminoindazoles has been developed from the reaction of nitriles with hydrazines, which successfully overcomes the difficulty of using aromatic hydrazines as substrates and allows for the synthesis of a wide range of N-aryl substituted free 3-aminoindazoles in moderate to excellent yields under mild conditions in one-pot. This finding provides a rapid and useful strategy for the synthesis of various functionalized 3-aminoindazole derivatives.

Dispersed-Monolayer Graphene-Doped Polymer/Silica Hybrid Mach-Zehnder interferometer (MZI) Thermal Optical Switch with Low-Power Consumption and Fast Response.

This article demonstrates a dispersed-monolayer graphene-doped polymer/silica hybrid Mach-Zehnder interferometer (MZI) thermal optical switch with low-power consumption and fast response. The polymer/silica hybrid MZI structure reduces the power consumption of the device as a result of the large thermal optical coefficient of the polymer material. To further decrease the response time of the thermal optical switch device, a polymethyl methacrylate, doped with monolayer graphene as a cladding material, has been synthesized. Our study theoretically analyzed the thermal conductivity of composites using the Lewis-Nielsen model. The predicted thermal conductivity of the composites increased by 133.16% at a graphene volume fraction of 0.263 vol %, due to the large thermal conductivity of graphene. Measurements taken of the fabricated thermal optical switch exhibited a power consumption of 7.68 mW, a rise time of 40 µs, and a fall time of 80 µs at a wavelength of 1550 nm.

Porous organic polymer supported rhodium as a heterogeneous catalyst for hydroformylation of alkynes to α,ß-unsaturated aldehydes.

A new porous organic polymer supported rhodium catalyst (Rh/POL-BINAPa&PPh3) has been developed for the hydroformylation of various alkynes to afford the corresponding α,ß-unsaturated aldehydes with high chem- and stereoselectivity, excellent catalytic activity and good reusability (10 cycles). The heterogeneous catalyst exhibited more catalytic activity than the comparable homogeneous Rh/BINAPa/PPh3 system.