Despite these many scientific advances, there is no reason to expect that the ligand field will not continue to evolve. From development of ligands based on novel or existing biomarkers to linking ligands to drugs and gene and antisense delivery systems, several fields have coalesced to facilitate ligand-directed siRNA therapeutics. In this review, we discuss the major categories of ligand-targeted siRNA therapeutics for tumors, as well as the different strategies to identify new ligands. Over the past 20 years, a diverse group of ligands targeting surface biomarkers or receptors has been identified with several investigated to target siRNA to tumors. Many approaches to developing tumor-homing peptides, RNA and DNA aptamers, and single-chain variable fragment antibodies by using phage display, In tumor-bearing mice, nanoparticles (NPs) greater than 10 nm in diameter have a proclivity to accumulate in the tumor. Accumulation of these NPs in tumors, however, is modest and only about 20 to 40% greater than its accumulation by normal tissues . When NPBuilding on accumulation of NPs in tumors from the EPR effect, researchers have sought to increase their tumor delivery by coating the particles with tumor-localizing ligands. The mechanism by which ligands increase the antitumor efficacy of their cargo (in our case siRNA) is somewhat controversial. Most investigators have determined that increased efficacy of targeted ligand-siRNA NPs is due to enhanced binding to the tumor surface marker and accumulation of NPs in the tumor compared to that in nontargeted tissues. Some investigators, however, have found that accumulation of targeted and nontargeted NPs within tumors was similar and found that increased efficacy of the targeted NP was due to enhanced receptor-mediated endocytosis and increased intracellular localization of the siRNA therapeutic . Most liIn this review, we describe various strategies that have been developed for ligand-siRNA therapeutics to increase their selectivity toward tumors . “DecoraIn addition, the efficacy (or lack thereof) of the siRNA-NP may interfere with the independent evaluation of ligand-directed therapies. It is our goal in this review not only to cover the array of ligand-targeted siRNA NPs, but also to indicate possible flaws in the particular study and alert the reader to the potential of the ligand, independent of the efficacy of the NP. This determination will be particularly important in cases in which there has been reduced antitumor efficacy with the nanoparticle.Ligands targeting tumor cells and their angiogenic vessels have primarily been peptides isolated by the phage display method  Figure  7–15] . Interestingly, αvβ3 and αvβ5 both have important roles in tumor angiogenesis, yet they control different aspects of angiogenesis. Whereas αvβ3 has an important role in the bFGF-mediated angiogenic pathway, αvβ5 has a more critical role with the VEGF-mediated pathway .–86.81–86 EpCAM siRNA and aptamer targeting the EpCAM adhesion molecule on tumor cells –10494–10particle , this il+ xenografts but with a scFv-Ab NP in which the siRNA targeted Polo-like kinase-1 (PLK1) . U. U135]. The CP15 peptide, identified with a phage display library, is able to target colon cancer cells yet it is not able to recognize normal intestinal epithelial cells. The C15 peptide (VHLGYAT) was conjugated to the PEG-chitosan (CS) copolymer prior to addition of a cross-linker (sodium tripolyphosphate) and siRNA. Silencing of the targeted PLK1 mRNA and protein in human tumor xenografts with the C15-PEG-CS NPs was approximately 50% of that in the control siRNA group . Althougβ (33–55)-PEG-PEI and the cytokine siGro-α against ovarian tumor cells [Gro-α, the numbers of cells and migration distance of cells were reduced by about 35% and 75%, respectively. Although these studies with siRNA were in vitro, the same laboratory had previously demonstrated that tumor volume and weight were significantly reduced with the FSH-targeting ligand NPs containing paclitaxel compared to the nontargeting NPs [Both FSH and LH ligands bind receptors present on the normal ovary as well as on ovarian cancer cells, and peptide fragments of these hormone ligands have been used to target NPs. Hong and colleagues examined the efficacy of NPs composed of FSHor cells . Compareting NPs . in vivo experiment, the LHRH-peptide-NP-Pax/siCD44 prevented growth and indeed caused regression of the ovarian tumor xenografts that were about 400 mm3 prior to initiation of therapy by about 50% (after the 8th treatment). The targeted NP containing paclitaxel or siCD44 reduced tumor growth markedly more than did the untargeted NP groups. Moreover, the CD44 protein in the xenografts was clearly downregulated by the siRNA when the LHRH-NP-paclitaxel and the LHRH-NP-paclitaxel/siCD44 groups were compared.Similarly, Shah et al. targeted ovarian cancer with a LHRH peptide-polypropylenimine-NP containing paclitaxel and a siRNA that downregulates CD44 . The LHRThe gastrin-releasing peptide, isolated from the peptide phage library, binds to the BB2 receptor on several malignancies including colon, breast, lung, and prostate cancer cell lines. The authors found that the GRP peptide did bind to the MDA-MB-231 cells but did not bind to endothelial cells . Moreove in vitro study) examining RVG-directed NPs with siRNA targeting cancer [ siGADPH NP than with the untargeted NP.Similar to the transferrin-mediated NPs, the rabies virus glycoprotein- (RVG-) derived peptides may augment transport of NPs across the blood-brain barrier to the central nervous system . To dateg cancer . That thDiverse numbers of ligands and approaches to augment siRNA delivery to tumor cells and to tumors in mouse models have been described in this review. More complex ligand targeting NPs, no matter how promising in preclinical trials, must be justified economically and overcome barriers with production scale-up for commercial manufacturing. It is thus not surprising that the less complex ligands  are investigated initially in clinical trials. For example, aptamers are being tested in clinical trials and CenR peptides with their recent demonstration of preclinical efficacy may not be far behind. In addition, antibodies directed against Her-2/neu or EGFR (Her-1) tumor-associated receptors or antigens have already demonstrated marked clinical efficacy. Nonetheless, the progressive shift to the use of mAb as targeting ligands for toxins or chemotherapeutics certainly supports development of ligand-targeted forms of siRNA.Of course, the economically more feasible targeted siRNA therapies (antibody-siRNA or aptamer-siRNA conjugates) could be less effective in some cases than more complex NPs. Multicomponent NPs may have significant advantages due to greater tumor specificity , reduced need to use chemically modified siRNA, and enhanced intracellular siRNA delivery avoiding lysosomal degradation. Nevertheless, because of the challenges with their manufacturing, the more complex multicomponent NPs may not be tested in patients despite their preclinical efficacy. This remains a major challenge in the field for the most promising multicomponent NPs to be tested for clinical efficacy.μM).The type of ligand to conjugate on the NP may also be challenging. This is illustrated by the number of approaches to identify a ligand targeting the TfR . Investi in vivo. Silencing experiments done in the presence of medium plus serum reduced the targeting ability of the transferrin-linked NP, by enabling a corona of proteins to surround the NP [An additional layer of complexity is the interaction between the ligand-siRNA therapeutic and the blood components. Although considerable attention has been given to measuring the biophysical parameters of the NP, less has been given to what may occur to the NPd the NP . This fi in vitro and/or in vivo for enhanced siRNA efficacy against tumors is certainly challenging. Nevertheless, we think it is likely that the many permutations of the ligand, siRNA, and NPs will yield promising drug candidates for improved treatment of a wide range of life-threatening cancers.In addition to the many promising ligands that have been investigated for targeting siRNA to tumors, many other ligands that have considerable potential have not been tested with siRNA. One example is the urokinase plasminogen activator (uPA) ligand for its receptor, which is upregulated in the majority of pancreatic (about 90%) and breast cancers (60–90%). With a recombinant N-terminal peptide of the uPA, high levels of the NP injected via the tail vein were found in tumors with low levels in normal tissue . Indeed,


Trypanozoon) are eukaryotic parasitesthat cause disease in either humans or livestock. The development of genomic resources can be of great use to those interested in studying and controlling the spread of these trypanosomes. Here we present a large comparative analysis of Trypanozoon whole genomes, 83 in total, including human and animal infective African trypanosomes: 21 T. brucei brucei, 22 T. b. gambiense, 35 T. b. rhodesiense and 4 T. evansi strains, of which 21 were from Uganda. We constructed a maximum likelihood phylogeny based on 162,210 single nucleotide polymorphisms (SNPs.) The three Trypanosoma brucei sub-species and Trypanosoma evansi are not monophyletic, confirming earlier studies that indicated high similarity among Trypanosoma “sub-species”. We also used discriminant analysis of principal components (DAPC) on the same set of SNPs, identifying seven genetic clusters. These clusters do not correspond well with existing taxonomic classifications, in agreement with the phylogenetic analysis. Geographic origin is reflected in both the phylogeny and clustering analysis. Finally, we used sparse linear discriminant analysis to rank SNPs by their informativeness in differentiating the strains in our data set. As few as 84 SNPs can completely distinguish the strains used in our study, and discriminant analysis was still able to detect genetic structure using as few as 10 SNPs. Our results reinforce earlier results of high genetic similarity between the African Trypanozoon. Despite this, a small subset of SNPs can be used to identify genetic markers that can be used for strain identification or other epidemiological investigations.African trypanosomes of the sub-genus  Trypanosomes are a major health threat to the people and livestock of Sub-Saharan Africa. Building genomic resources and understanding the genetic structure of these parasites will aid researchers trying to control their spread. To this end, we compared the genomes from 83 trypanosome strains, identifying 162,210 single nucleotide polymorphisms (SNPs) between them. Our analysis shows high genetic similarity between the trypanosomes, and confirms earlier results indicating that the traditional taxonomic classifications do not correspond well with genetic data. Further, we demonstrate that, despite the high genetic similarity, each strain in the study can be distinguished using as few as 84 SNPs, suggesting that a small number of SNPs can be useful for tracking and classifying populations of African trypanosomes. Trypanosoma brucei are vectored between human and animal hosts by the tsetse fly. Trypanosoma brucei brucei (Tbb) causes nagana in livestock, while Trypanosoma brucei gambiense (Tbg) causes chronic sleeping sickness and Trypanosoma brucei rhodesiense (Tbr) causes acute sleeping sickness in humans. This species, along with Trypanosoma evansi (Tev) and Trypanosoma equiperdum (Teq) comprise the sub-genus Trypanozoon. Both Tev and Teq are also found outside of sub-Saharan Africa and are recognized as being highly similar to Trypanosoma brucei [Tev causes surra, a disease of livestock, and Teq causes dourine, which affects horses. Most of the African Trypanozoon taxa are morphologically indistinguishable, with the main differences between them being the host in which the trypanosome causes disease or the insect vector that enables their distribution . In addition, both Tev and Teq have lost all or part of their kinetoplastid DNA  gene. This transition apparently evolved multiple times from different Tbb strains [Trypanosoma brucei gambiense have been divided into Tbg group 1 (Tbg-1) and Tbg group 2 (Tbg-2). The first one comprises most Tbg isolates. They are genetically distinct from the single Tbg-2 isolate found originally in Ivory Coast and now thought to be extinct [Tbg types are more distinct from Tbb than Tbr still harbors a large amount of genetic similarities to it [T. evansi also harbor further genetic subdivisions, with Tev strains being classified as Type A or Type B, according to their mini-circle DNA sequences [Tbg-1 range and the Western edge of the Tbr range [Tbb and the tsetse fly vector occur throughout the country, overlapping with the Tbr and Tbg-1 ranges. Sleeping sickness cases caused by a co-infection of both Tbg-1 and Tbr could be a possibility. The epidemiological consequences of the overlap in ranges are difficult to predict but would certainly pose challenges to healthcare professionals.The African trypanosomes belonging to the enturies . Being mplastids . Similar strains ,9,10,11. extinct ,13. Bothes to it ,11,14,15equences . Taken tbr range . Becausebr range , and TbbTrypanozoon by analyzing whole genome data from 83 strains from across sub-Saharan Africa, representing a range of human and animal infective types . Nineteen of the Tbg-1 strains were isolated from a hospital in the Democratic Republic of the Congo in response to a sleeping sickness outbreak [Tbr and Tbg-1 co-occur and where methods of strain discovery can be extremely useful [The purpose of the present study is twofold. First, we provide a large comparative genomic analysis of African trypanosomes of the sub-genus ve types , and comoutbreak . Eleven y useful . This day useful , 19,20.Trypanozoon strains into genetic clusters, identified using whole genome data. Minimizing the number of SNPs necessary to diagnose specific strains of T. brucei has practical implications, as being able to define strains using a subset of markers significantly reduces the cost and effort to do so, allowing for the development of field portable diagnostics [For the second aim, we used the genome data from these 83 strains in combination with a powerful statistical technique, sparse linear discriminant analysis (SLDA), to select a subset of SNPs to facilitate strain identification. SLDA calculates linkage disequilibrium across all SNPs and, unlike existing procedures which generally consider the association between a phenotype and a single SNP, simultaneously selects informative variants across the whole genome –25. In tgnostics . SelectiT. brucei versus other African trypanosomes. A second PCR test for the presence of the serum resistance-associated (SRA) gene diagnostic of Tbr  was alsin TgsGP  was condribed in , with alribed in  and aligrain 927 . To preprain 927 . SNPs werain 927 . SNPs wirain 927 . SNPs ocrain 927 , or in train 927 , as descrain 927 , due to Trypanozoon strains could be grouped; (2) several sub-sets of the main dataset that included either a smaller number of either SNPs or strains (see below). A-score optimization was performed to avoid over-fitting for each DAPC analysis, following the recommendations of the package authors [We carried out several clustering and phylogenetic analyses to investigate the relative evolutionary affinities of the 83 strains included in the study. A maximum-likelihood phylogeny was constructed based on SNPs. The SNPhylo pipeline was used to filter and align SNPs, as a first step in phylogeny construction . The min authors .To identify subsets of SNPs that are especially useful and informative to classify an unknown strain to one of the identified genetic groups (see above), we carried out logistic regression and Sparse Linear Discriminant Analysis (SLDA). Starting with the main set of SNPs and the clusters identified by k-means clustering, we used a four-fold cross validation strategy, where four replicates of the data were used, with each having one-fourth of the initial data removed from the analysis to avoid over-fitting. This resulted in four data sets, each comprised of three quarters of the total data. Within each sub-dataset, dichotomized variables were generated to indicate the cluster label for each individual. An LD filtering step was carried out using the PLINK clump procedure (LD threshold = 0.2), to eliminate highly similar SNPs.On each data set, we carried out logistic regression analysis to identify SNPs significantly associated with each of the genetic clusters identified by k-means clustering . This was done using the software PLINK association procedure . SNPs paWe then examined the number of unique genotypes obtained only by considering combinations of the top-ranked SLDA-SNPs. Starting with the top ten ranked SLDA-SNPs, we concatenated the genotypes for each strain and calculated the number of unique combination genotypes obtained, using R. This procedure was repeated while adding the next highest-ranking SNPs until 83 unique combination genotypes were obtained.To further test the classifying ability of the SLDA-selected SNPs (SLDA-SNPS), we repeated the DAPC and k-means clustering analysis on the top X ranked SLDA-SNPs, where X is equal to 10,000, 1000, 500, 250, 100, 50, 20, or 10 out of the total 31,164 SLDA-SNPs. We compared the clustering results obtained when using these eight subsets of SLDA-SNPs to the entire 162,210 SNP dataset by calculating the percentage of overlap in cluster membership for each strain, then calculating the average percentage for all strains. As an example, consider if Strain-X is assigned to cluster 1 based on the whole dataset, and cluster 3 based on the top 10 SLDA-SNPs (the clusters are arbitrarily named). The number of other strains assigned to both cluster 3 (based on the top 10 SLDA-SNPs), and cluster 1 (based on the entire SNP dataset), is 4. The total number of strains assigned to cluster 3 is 5 (not counting Strain-X). The percentage of overlap is 80% (4/5) for Strain-X. This metric is calculated for all strains, measuring how frequently the strains are grouped together in different k-means analyses.To further assess the classification ability of the SLDA-SNPS we carried out an additional test. First, we removed 21 randomly selected strains from the original dataset. Then, we carried out DAPC analyses with each of the eight SLDA-SNP subsets, using the cluster assignment from the k-means clustering analysis done on the whole data set . Then, we used the predict.dapc function of the adegenet R package to assign the 21 previously removed strains to a cluster, based on the discriminant function produced by the new DAPC. The assignment was considered successful if the correct cluster was given the highest probability score by the predict.dapc function. This process was repeated with 10 sets of 21 randomly selected strains for each of the 8 SLDA-SNP subsets.Tbb, and applied a minor allele frequency filter.We jointly analyzed the genome sequences of 83 trypanosome strains, 25 of which were previously unpublished. To characterize genomic variation among these strains, we identified 931,876 SNPs. Filtering for minor allele frequency (0.05), missing data, indels and informativeness yielded 162,210 SNPs for clustering and phylogenetic analysis. This reduced set is smaller than the one identified by Sistrom et al. 2014 [Tbr), or outside of Africa (Tev strains Type A: E110 and STIB 810). The Western strains are confined to two of the major clades, indicating some geographic separation among strains. In contrast, the Tbb and Tbr strains are broadly paraphyleitc. All Tbg-1 strains are in a single clade, while the Tbg-2 (Th126) is the sister taxon of a clade including other Tbb strains from Western Africa. This is likely a reflection of shared ancestral polymorphisms between some Tbb strains and this Tbg-2 strain, confirming previous studies [Tev, the 3 type A strains cluster in a clade with Tbb strains, while the single type B strain in this study (KETRI 2479) falls in a relatively distant branch of the ML tree [We used multiple phylogenetic and clustering methods to clarify the underlying genetic relationship of the 83 African trypanosome strains. We constructed a phylogenetic tree by first thinning the SNPs with more than 90% correlation to account for linkage, leaving 37,893 SNPs. The SNPhylo pipeline was used to concatenate and align the SNPs, and the phangorn R package was used to perform maximum-likelihood and bootstrapping analyses . Bootstr ML tree ,42. The Tbg-1 strains  contains at least one Tbr and Tbb strain. Thus, while DAPC detected genetic structure in the 83 strains, as it identifies at least 7 distinct genetic clusters, this structure does not coincide with the traditional taxonomy, with the exception of Tbg-1. The DAPC for K = 8 through K = 11 are qualitatively similar . We used Discriminant Analysis of Principal Components (DAPC), as it provides a way to visualize differences between clusters of strains. K-means clustering based on principal components built from SNPs was used to group the strains . To further assess the congruence of the DAPC results on the SLDA-SNPs data sets with the one from the whole data set, we checked how many times a certain strain grouped together with the same strains in the SLDA-SNPs and in the full SNP dataset DAPC analyses. We did this by examining each strain in the a SLDA-SNP data set, and calculating the percentage of strains that co-occur with that strain in its cluster assigned by the SLDA-SNP DAPC and the full data-set DAPC  to see how often the removed strains fell into one of the 7 clusters they were assigned to by the full data set. We used the “predict.dapc” function to use the discriminant analysis made by DAPC to classify the excluded strains. This process was repeated with 10 sets of 21 randomly chosen strains. The average number classified correctly for each SLDA-SNP set is shown in We next examined the genotypes of each strain for the top-ranked SLDA-SNPs to see how many SLDA-SNPs were required to generate a unique combination of genotypes for each strain. The genotypes of the top 84 SLDA-SNPs combined to generate a unique genotype for each strain, suggesting that using SNPs ranked by SLDA provides a powerful way to identify strains using a select few informative SNPs  that represents the genetic relationships of the strains based on whole genome data .Trypanozoon samples in our study. The phylogenetic and clustering analyses elucidate the evolutionary relationship among the strains  is in a relatively distant branch of the phylogeny.In the ML phylogeny  none of l nature . This reanalyzed  cluster Tbg-1 strains, which come largely from the DRC. In this instance, sub-species classification as Tbg-1 rather than geographic origin may drive membership in this clade. The only other strain from DRC  is found in the clade with the other Western African strains, supporting this observation. Tbb and Tbr strains from Eastern Africa are found throughout the rest of the phylogeny and do not seem to group according to country of origin. Therefore, while there does appear to be a geographic signal in the data, it does not fully explain similarities between the strains, and none of the regions could be assigned exclusively to a single clade. Sistrom et al. [Tbr and Tbb strains, including some of those studied in our analysis, also found some evidence of geographic structure. They performed a clustering analysis and found a significant association between geographic origin and cluster membership. As an example, all of the strains from the Ivory Coast in our analysis were also included in [Geographic origin is reflected weakly in the ML phylogeny. The strains from Western Africa  and allTbg-1 strains and includes only one other non-Tbg strain (strain D16). Cluster 7 groups all four Tev strains analyzed in this study, a result at odds with the topological assignment in Tev strain falls in a differ clade from the other 3 Tev strains . This flciparum , where Tlciparum ,45.In conclusion, this analysis takes advantage of genomic data coupled with statistical methods not generally used for strain identification to select a small subset of SNPs that have similar information content as the whole genome data to uniquely identify strains. This approach can be used in a variety of contexts and with different types of organisms, as long as genome data for a representative group of individuals is available, allowing the development of efficient and relatively inexpensive ways to screen for genomic variation a large number of samples, while still retaining the information content provided by whole genome analyses.S1 TableTrypanosoma brucei brucei, Tev-Type A = Trypanosoma evansi Type A, Tev-Type B = Trypanosoma evansi Type B, Tbg-1 = Trypanosoma brucei gambiense group 1, Tbg-2 = Trypanosoma brucei gambiense group 2, Tbr = Trypanosoma brucei rhodesiense. The final column lists the reference where the genome was first published. NA indicates this is the first time the genome is described.Tbb = (XLS)Click here for additional data file.S1 FigTbb, red: Tbr, green: Tbg, purple: Tev.Based on 162,210 SNPs. Color of strain names indicates the sub-species: blue: (TIFF)Click here for additional data file.S2 FigBIC plotted for k = 1–40. Clustering based on the 162,210 SNP dataset.(TIF)Click here for additional data file.S3 FigPart A shows strains colored by cluster, and are connected by lines to the cluster’s centroid. The region enclosed by the dashed square is expanded in the inset for clarity. Part B shows the same data as in A, but with the strains colored according to their named taxon. The circles representing the clusters are the same as in part A for comparison.(TIF)Click here for additional data file.


Impaired hand dexterity is a major disability of the upper limb after stroke. An electromyography (EMG)-driven neuromuscular electrical stimulation (NMES) robotic hand was designed previously, whereas its rehabilitation effects were not investigated.This study aims to investigate the rehabilitation effectiveness of the EMG-driven NMES-robotic hand-assisted upper-limb training on persons with chronic stroke.n = 15) who received 20 sessions of EMG-driven NMES-robotic hand-assisted upper-limb training. The training effects were evaluated by pretraining, posttraining, and 3-month follow-up assessments with the clinical scores of the Fugl-Meyer Assessment (FMA), the Action Research Arm Test (ARAT), the Wolf Motor Function Test, the Motor Functional Independence Measure, and the Modified Ashworth Scale (MAS). Improvements in the muscle coordination across the sessions were investigated by EMG parameters, including EMG activation level and Co-contraction Indexes (CIs) of the target muscles in the upper limb.A clinical trial with single-group design was conducted on chronic stroke participants (P < 0.05), the ARAT (P < 0.05), and in the MAS (P < 0.05) were observed after the training and sustained 3 months later. The EMG parameters indicated a significant decrease of the muscle activation level in flexor digitorum (FD) and biceps brachii (P < 0.05), as well as a significant reduction of CIs in the muscle pairs of FD and triceps brachii and biceps brachii and triceps brachii (P < 0.05).Significant improvements in the FMA shoulder/elbow and wrist/hand scores (The upper-limb training integrated with the assistance from the EMG-driven NMES-robotic hand is effective for the improvements of the voluntary motor functions and the muscle coordination in the proximal and distal joints. Furthermore, the motor improvement after the training could be maintained till 3 months later.ClinicalTrials.gov. NCT02117089; date of registration: April 10, 2014. Stroke is one of the leading causes of adult disability, and patients with stroke require continuous long-term medical care for reducing physical impairments . Only 18According to the traditional viewpoint on neurorehabilitation after stroke, significant motor improvements are usually observed during the subacute period and are associated with a spontaneous recovery in the early period, and motor recovery is expected to be minimal or plateaued during the chronic period . HoweverWhile the EMG-driven strategy has been widely adopted, the use of robot-assisted therapy remains suboptimal. For example, a robot cannot directly activate the desired muscle groups because the target muscles of patients with stroke usually cooperate with compensatory motions from other muscular activities . In contNevertheless, previous studies on combinations of NMES and robotic systems have mainly focused on motor recovery of the elbow and wrist joints –26. ThusThe EMG-driven NMES-robotic hand system used in this study is shown in Figure The wearable robotic hand  provided individual mechanical assistance to the five fingers, and each finger was actuated by a linear actuator Figure , 29. TheThe NMES electrode pair , which provided stimulation during finger extension, was attached over the extensor digitorum (ED) muscle. The configuration for the EMG and NMES electrodes on the ED muscle is shown in Figure The abductor pollicis brevis (APB) and ED muscles were used as voluntary neuromuscular drives to control robot assistance and NMES assistance from the system to facilitate performance of phasic and sequential limb tasks, namely, hand closing and hand opening. The APB was selected as the driving muscle in the hand closing phase, since the EMG signals from the APB of the paretic limb after stroke are less affected by spasticity and are relatively easier to be controlled than the flexor digitorum (FD) muscle for finger movements in chronic stroke . EMG-triThe EMG signals from the driving muscles captured using EMG electrodes  were first amplified 1,000 times , sampled at 1,000 Hz by using a data acquisition card  and filtered by using a band-pass filter in the range 10–500 Hz. After digitization, the EMG signals from the APB and ED muscles were rectified and low-pass filtered  to obtain an envelope of EMG signals  in the real-time control.After obtaining ethical approval from the Human Subjects Ethics Subcommittee of the Hong Kong Polytechnic University, participants in this study were recruited from the local districts through advertisement. A total of 20 patients were screened for the training during the subject recruitment. Fifteen participants with chronic poststroke hemiparesis met the inclusion criteria and were recruited in this study after obtaining their written consents. Inclusion criteria were as follows: (1) aged from 18 to 78 years , 33, (2)All participants received the EMG-driven NMES-robotic hand-assisted upper-limb training, which consisted of 20 training sessions with the intensity of 3–5 sessions/week, within 7 consecutive weeks.An evaluation was conducted at the beginning of each training session. Each participant was first subjected to a maximum voluntary contraction (MVC) test for the following five target muscles: APB, ED, FD, biceps brachii (BIC), and triceps brachii (TRI). EMG electrode pairs with center separation of 2 cm were attached to the skin surface of the muscles of interest according to the configuration specified in Cram’s work . Then, eWhile conducting the MVC test on the ED and FD muscles, participants were seated at a table and the paretic upper limb was placed on the table with elbow joint extended at an angle of 130°, the wrist was held by an experimental operator and positioned around its neutral position, and the finger positions were set by the operator to obtain an angle around 150° at the MCP joints of the index, middle, ring, and little fingers. During the maximum extension of the four fingers, the ED EMG signals were recorded; and during the maximum flexion, the FD EMG signals were captured. For the MVC test on the APB, the operator held the thumb in an extended position (around 30°) and asked the participants to conduct maximum thumb palmar abduction with the same configuration of the wrist and elbow joints as in the ED and FD MVC tests. During the MVC test on the BIC and TRI muscles, the paretic upper limb was positioned with the shoulder abducted at 70° and the elbow flexed at 90°. The MVC test on each target muscle was repeated twice and was maintained for 5 s. A 2-min interval was maintained between two consecutive contractions to prevent muscle fatigue. The maximum EMG amplitude recorded in the two repetitions was selected as the EMG amplitude of MVC for the target muscle.The bare hand evaluation task, which was conducted after the MVC test, involved lateral and vertical arm reaching-grasping tasks . The parThe EMG recording was started when the participant began to grasp the sponge (as soon as one finger touched the sponge) to when the participant released the sponge at the starting point . The EMG signals from the target muscles  were first amplified 1,000 times, filtered by a band-pass filter in the range 10–500 Hz, and full-wave rectified. The EMG signals were sampled at 1,000 Hz by the data acquisition card and stored in the computer for off-line processing as we did previously , 17. In n = 12) could not sustain the weight of the paretic limb and the robotic hand without assistance. This was mainly due to weakness of the shoulder and elbow joints. Therefore, during the arm transportation, these participants were allowed to use their unaffected limb to provide self-aware minimal support at the wrist joint of the paretic limb. During the last session of the training, 10 participants could lift the affected limb while wearing the robotic hand.Participants were required to perform lateral and vertical arm reaching–grasping tasks with the EMG-driven NMES-robotic hand on the paretic side with same seating arrangement and movements as the previous evaluation. In each training session, the participants performed 30-min lateral and vertical tasks, respectively, with a 10-min interval between the tasks to prevent muscle fatigue. However, most of the participants  was the EMG envelope signal after the normalization with respect to the EMG MVC value of the muscle, and T was the length of the signal. Figure N was the normalized EMG activation level of muscle i. The i. The For the cross-sessional monitoring, two EMG parameters were calculated which were (1) the normalized EMG activation level of each target muscle and 2) the normalized EMG Co-contraction Index (CI) between a muscle pairs , 17. The the normijA(t) was overlapping activity of EMG linear envelopes for muscles i and j, and T was the length of the signal. An increase in CI value represents an increased co-contraction phase of a muscle pair (broadened overlapping area), and a decrease in CI value indicates a decreased co-contraction phase of a muscle pair (lessened overlapping area). The CI value was also further normalized, similar to the EMG activation level, for obtaining the tendency of muscle coordination, which considers the maximal and minimal CI values of a participant recorded across the 20 training sessions and its equation . One-way analysis of variance (ANOVA) with repeated measures (Bonferroni post hoc test) were used to evaluate the differences on the clinical assessments across different time points  and the EMG parameters  across the 20 training sessions. The levels of statistical significance were indicated at 0.05, 0.01, and 0.001 in this study.The normality tests on the clinical scores and the EMG data by Lilliefors method were performed with a significant level of 0.05 . It founn = 15) completed the EMG-driven NMES-robotic hand-assisted upper-limb training. The demographic data of the participants are shown in Table P < 0.05, one-way ANOVA with Bonferroni post hoc test) are illustrated in Figure P < 0.001, EF = 0.313, F = 7.96, one-way ANOVA with Bonferroni post hoc test). In Figure P < 0.001, EF = 0.228, F = 5.18, one-way ANOVA with Bonferroni post hoc test). A significant increase in the FMA shoulder/elbow score after the training was observed compared with the pretraining values, and the increase was maintained during the assessment at the 3-month follow-up . The variation in ARAT score at five time points is shown in Figure P < 0.01, EF = 0.226, F = 5.12, one-way ANOVA with Bonferroni post hoc test). Figure P < 0.01, EF = 0.214, F = 4.77, one-way ANOVA with Bonferroni post hoc test). Significant decreases were observed in the MAS score at the wrist  and finger  after the training, and these deductions with respect to the pretraining values were maintained during the 3-month follow-up.All recruited participants n = 5 completP < 0.001, EF = 0.331, F = 7.29, one-way ANOVA with post hoc tests) and BIC muscles . Regarding the variation patterns of the EMG activation level of the FD muscle, the EMG level showed a rapid decrease of 50% over the first four sessions and was further declined by 19% from the 5th to 20th sessions. Concerning the variation patterns of the EMG activation level of the BIC muscle, the EMG level steadily decreased over the 20 training sessions, with a total decrease of 50%. No descending plateau was reached for the EMG levels of the FD and BIC muscles within the 20 training sessions. Figure P < 0.001, EF = 0.148, F = 2.56, one-way ANOVA with post hoc tests) and BIC and TRI muscle pair  during the evaluation across the 20 sessions of the training. Regarding the variation patterns of CI of the FD and TRI muscles and the BIC and TRI muscle pair, the CIs gradually declined and did not reach a plateau over the 20 training sessions. No significant increases or decreases were observed in the EMG parameters of other target muscles and muscle pairs.Figure In this study, the recruited participants with chronic stroke showed stable baselines without significant variations in all clinical scores before the training. After 20 sessions of the upper-limb training assisted with EMG-driven NMES-robotic hand, motor function improvements associated with the improved clinical scores and cross-session recorded EMG parameters were observed in all the participants, and the improvements after the training could be maintained 3 months later.Results from the clinical assessments revealed that the voluntary motor functions and muscle coordination of the affected upper limb significantly improved after the training. The significant increase in the FMA (shoulder/elbow and wrist/hand) score after the training indicated an improvement in voluntary motor control at the joints of the entire paretic upper limb, and these motor function improvements were maintained at 3-month follow-up. A significant increase of 6 points in the FMA wrist/hand (max 24) score was observed after the training (mean admission score was 8 points). Compared with a similar study on robot-assisted hand training by using HapticKnob , motor iA review on robot-assisted poststroke upper-limb rehabilitation  indicateThe cross-sessional EMG monitoring reflected the recovery progress of the muscle coordination during the training program, which also monitored individual muscle activation and coordination patterns among the contracting muscles. The significantly improved muscular coordination of the proximal and distal joints also was achieved through the EMG-driven NMES-robotic hand assisted training, as revealed by both clinical scores and the EMG parameters . The decrease in the EMG activation levels could have two major reasons: (1) the reduced spasticity, which reduced the extra muscle activities , and (2)In addition to the EMG activation levels, the CI revealed the coactivity of a muscle pair and the recovery progress on muscular coordination. Dewald et al. indicated that discoordination among muscles is one of the major factors for motor disability after stroke and highly related to the muscle spasticity and compensatory motions in the affected limb . CompensIn this work, the motor function improvement was obtained at the elbow, wrist and fingers as reflected by the clinical scores and the EMG parameters. During the training, the assistance from the EMG-driven NMES robot was incorporated in the coordinated tasks related to the arm reaching/withdrawing and hand open/close of the whole upper limb. Multi-joint coordinated upper limb practice simulating daily activities is necessary for stroke survivors to regain meaningful motor functions after training, since the task practiced would be the motor function restored, e.g., task-oriented rehabilitation . In the It was understood that the combined treatment of NMES and robot could introduce additional muscle fatigue to the target muscle under stimulation, i.e., the ED muscle in this work, in a training program with multiple sessions. Accumulated fatigue in the stimulated muscle might result in an increase in the EMG amplitude of the target muscle across the sessions. Although normalized EMG signals were adopted in this work to minimize the cross-sessional difference in EMG detection, more sensitive EMG representations which are less affected by the muscle fatigue will be explored in our future study. In this work, there was no significant change observed in the ED EMG level, nor in the CIs related to the ED muscle across the training sessions. Randomized controlled trials will be conducted in future studies to compare the rehabilitation effectiveness of the EMG-driven NMES-robotic hand with other device assisted programs  and with the conventional manual treatment on the upper limb.In this study, the training effects of the poststroke upper-limb training assisted with EMG-driven NMES-robotic hand were investigated through a single-group clinical trial on patients with chronic stroke. The measured outcomes  indicated that significant motor function improvements were achieved after the training, which included an increase in the voluntary motor effort on the entire upper limb, improved muscular coordination, and released muscle spasticity in the proximal and distal joints, and the motor improvements could be maintained till 3 months later after the training. Evidence suggests that intensive and repetitive upper-limb training with coordinated hand movements assisted by the voluntary EMG-driven NMES-robotic hand facilitates hand function recovery and improves muscular coordination in the upper limb with long sustainability in patients with chronic stroke.The study was carried out in accordance with the human ethic guidelines of the Human Subjects Ethics Subcommittee of Hong Kong Polytechnic University.CN contributed in the NMES-robot hand-assisted training experiment, data collection and analysis, and manuscript drafting. WR and WL contributed in the NMES-robot design and maintenance. YX contributed in the training and data collection. XH conceived of the study and coordinated the whole project, including the trial design, human subject experiments, and manuscript drafting. YZ contributed in the data analysis and manuscript drafting.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Face-to-face communication is multimodal; it encompasses spoken words, facial expressions, gaze, and co-speech gestures. In contrast to linguistic symbols  relying on mostly explicit conventions, gestures vary in their degree of conventionality. Bodily signs may have a general accepted or conventionalized meaning  or less so . We hypothesized that subjective perception of conventionality in co-speech gestures relies on the classical language network, i.e., the left hemispheric inferior frontal gyrus  and the posterior superior temporal gyrus  and studied 36 subjects watching video-recorded story retellings during a behavioral and an functional magnetic resonance imaging (fMRI) experiment. It is well documented that neural correlates of such naturalistic videos emerge as intersubject covariance (ISC) in fMRI even without involving a stimulus . The subjects attended either to perceived conventionality or to a control condition (any hand movements or gesture-speech relations). Such tasks modulate ISC in contributing neural structures and thus we studied ISC changes to task demands in language networks. Indeed, the conventionality task significantly increased covariance of the button press time series and neuronal synchronization in the left IFG over the comparison with other tasks. In the left IFG, synchronous activity was observed during the conventionality task only. In contrast, the left pSTG exhibited correlated activation patterns during all conditions with an increase in the conventionality task at the trend level only. Conceivably, the left IFG can be considered a core region for the processing of perceived conventionality in co-speech gestures similar to spoken language. In general, the interpretation of conventionalized signs may rely on neural mechanisms that engage during language comprehension. Languages are generally seen as conventional sign systems par excellence. Linguistic signs are primarily spoken and written words, or manual signs in the case of sign language. However, face-to-face communication typically not only includes symbolic linguistic signs, but also facial expressions, eye gaze and co-speech gestures, all of which contribute information to the overall message . On a cognitive-semiotic level, conventionality may be accounted for by drawing on Peirce's model of Universal Categories  pertains to rules, laws, patterns, and habits such as underpinning conventional meanings of spoken words and emblematic gestures, but also social norms, rituals, etc. Peirce, . For theTasks may not only induce different levels of attention or distraction  Detection of conventional aspects in co-speech gestures is consistent across study participants and distinct from attending to non-conventional gestures. Therefore, we expected increased synchronization of button-press time series and of functional brain activation during the perception of conventionality in co-speech gestures. This tests the reliability of the conventionality detection task.(2) The task to detect conventional co-speech gestures increases neural synchronization in key nodes of the language networks, that is, the left IFG (Broca's area) and the left pSTG (Wernicke's area). Further, we explored whether other neural structures may be involved in the detection of conventional aspects in co-speech gestures.Thirty-six right-handed German native speakers participated in the present study . Participants had normal or corrected to normal vision, normal hearing, no history of psychiatric or neurological illness, no current psychopharmacological treatment, and no contraindication against MR investigations. The experiments were designed according to the Code of Ethics of the World Medical Association  and the study protocol was approved by the local Ethics Committee. After detailed briefing and instruction, all participants gave written informed consent.Video clips of short narrations, comprising spontaneously produced co-speech gestures, served as stimuli. Therefore, 14 right-handed German native speakers (6 women) were video-recorded during freely retelling the narrative of 3 short movies . The 2 narrations in each video clip were separated by a 4-s fixation-cross phase.Although we analyzed the fMRI data with model-free ISC, gesture units, phrases, and phases were annotated . Therefore, we separated the behavioral data acquisition  from the neuroimaging data acquisition (fMRI experiment). The 12 video clips were divided into 2 sets of 6 videos clips each. Half of the participants  watched the first 6 video clips during the behavioral part and the other 6 during the fMRI part; in the other half of the participants , this relation was switched. This way, both behavioral and fMRI data was obtained for all video clips and participants watched every narration only once. Across the experiments each of the 12 video clips was matched to 2 of the 3 tasks (see section Task Instructions for details on the tasks), yielding 24 video clip—task combinations. Each task was thereby assigned to 8 video clips. By matching 2 tasks to 1 video clip we were able to perform an across-task comparison while keeping the stimuli identical. Differences in functional covariance could thus be ascribed to task effects rather than video clip effects. Matching only 2 of the three tasks to a video clip instead of performing complete permutation reduced the number of video clip-task combinations and, therefore, increased the number of participants watching the same combination yielding increased power. Effects of different videos were fully accounted for because covariance calculation compared only data from the same video clip. In total, 1 video clip—task combination was seen by 9 participants. To compensate sequence effects, the order of the video clip—task combinations was balanced across experiments.Before a video clip, task instructions were given for 6 s. After the clip, a question was presented (10 s) inquiring in which of the 2 narration the participant discerned more of the task-relevant gestures. Responses were given via a button press on a keyboard during the behavioral experiment and on an MRI-compatible keyboard (Lumitouch) during fMRI. Each question was followed by a 10-s fixation cross , expert definition of gesture types (such as based on McNeil's typology), or gesture morphology . During the behavioral experiment the wording was “Please press the button when the speaker produces a hand movement, which is in common use.” . This wording directly relates to Peirce's definition of Thirdness encompassing habits, patterns and rules in sign use. During the control conditions, participants focused on the occurrence of any hand movements , and on gestures relating to the spoken content . With the first control task (ANY) we controlled for the processing of hand movements, while with the second control task (REL) we controlled for the audiovisual integration of gestures and concurrent speech.During fMRI, the instruction was to only attend to the task-instructed aspects and not to press a button during watching. In both experiments, after each video clip the participants answered the control question by deciding in which of the 2 narrations they detected more of the indicated gestures. Task instructions were shown to the participants directly before the presentation of each video clip. Additionally, the tasks were explained in detail during a standardized briefing prior to the experiments. During this briefing, the instructor carefully refrained from moving her own hands and from giving gesture examples.www.neurobs.com) was used to present the stimuli and to record button presses.Participants were seated in front of a notebook with 15′ screen in a dimmed and quiet room. Sound was presented via headphones; the volume was adjusted to a comfortable hearing level. Presentation software® . Covariance analysis compares the time courses of button press responses. If a pair of response patterns was similar throughout the video clip, the assigned covariance value was high. Conversely, if the response time courses differed, covariance was close to zero. This resulted in 36 values per video clip—task combination and, since each task was matched with 8 video clips, a total of 288 covariance values for each task. To control for video clip effects, across-task covariance was assessed. Here, covariance between time series obtained for the same video clip but for different tasks was calculated. The across-task covariance depended on gesture patterns in each video clip. Therefore, the comparison between same-task and across-task covariance revealed task-effects. Across-task comparisons resulted in 81 values per video clip and, since 12 video clips were presented, a total of 972 covariance values. The resulting 1836 covariance values  were submitted to a 1-factor ANOVA with the 4-level factor “task comparison.” Subsequently, To control for global task performance, the binary responses to the control task were evaluated for each video clip—task combination. Percentage agreements were computed and averaged. A 2 by 2 ANOVA discerned differences between experiments  and the groups with reciprocal experiment-video combinations .During fMRI, stimuli were presented with MR-compatible headphones and on a screen viewed via an angled mirror. Presentation and recording of button-press answers to control questions was conducted with Presentation software.3). Slices were positioned oblique-transversally to achieve maximal brain coverage. Session length varied depending on the balanced video clip combination (average 891 volumes). Head movement was minimized using foam wedges to support the head position in a 20-channel head coil.MR imaging was conducted on a 3 Tesla MR Scanner  at the Department of Psychiatry, Psychotherapy, and Psychosomatics of the RWTH Aachen University (Germany). Echo planar imaging (EPI) collected functional images sensitive to the blood oxygenation level dependent (BOLD) contrast . The first 4 volumes were discarded to remove the influence of T1 saturation effects. Images were spatially realigned to the mean image, normalized to the stereotaxic anatomical MNI space  with 2 mm isotropic voxels, spatially smoothed with a Gaussian kernel , and high-pass filtered at 0.008 Hz.Functional MRI data analysis was conducted using the software Statistical Parametric Mapping  mapping. Each video clip yielded an average of 275 volumes starting 6 s after the beginning of a narration. ISC values were calculated between pairs of time series obtained for the same video clip-task combination (9 time series for each combination). ISC analysis compares the time courses of neural responses. If, in a given voxel, a brain region showed similar response patterns throughout the video clip, the assigned covariance value was high. Conversely, if the activation time courses differed, covariance was low. Analogous to the behavioral experiment, this resulted in 36 values for each video clip-task combination and, since each task was matched with 8 video clips, a total of 288 covariance values for each task. Across-task covariance controlled for video-clip effects by calculating covariance between time series obtained for the same video clip but watched with across-task comparisons resulted in 81 values per video clip and, since 12 video clips were presented, a total of 972 across task covariance values in each voxel. In total  1,836 comparisons (ISC maps) were submitted to the analysis The covariance maps resulting from each data pair entered ANalysis of COVAriance (ANCOVA). The ANCOVA assessed task effects on regional covariance . Gender and age differences (1 predictor each), group, as well as indicator variables for the video clips (with 10 predictors) were included in the model as predictors of no interest.F-tests investigated effects between the 4 task levels. First, an ANOVA including the four predictors assessed overall task effects. Second, an F-test across task differences assessed task-specific effects for the CON task. Post-hoc t-tests assessed whether the conventionality task would increase local stimulus-induced BOLD fluctuation compared to the control tasks and the across-task condition. Correction for multiple testing was conducted according to random-field theory's family-wise error (FWE) correction at a significance threshold of pFWE < 0.05. To further explore the task effects, the F-contrast was additionally displayed at an uncorrected threshold of p < 0.001 .1 Figure . FurtherT-tests discerned the contribution of each factor level post-hoc (p < 0.05).To investigate the localization within the language network, region-of-interest (ROI) analyses targeted the left IFG (Broca's area) and the left pSTG (Wernicke's area). The IFG ROI encompassed the anatomical regions IFG pars triangularis and IFG pars opercularis of the predefined AAL atlas  and pressed a button for each gesture they perceived as task-relevant. Participants gave 55.8 ± 62.1 (mean ± SD) responses per video clip. The CON task elicited the least amount of responses with on average 16.6 ± 22.2 button presses per video clip; the REL task elicited 41.1 ± 32.1 button presses; and the ANY task elicited most responses (109.6 ± 73.8).F = 27.50, p < 0.001; Figure T = 3.63, p < 0.001] and the REL task  but only on a trend level within the ANY task . Thus, tasks CON and REL yielded a specific response profile across participants.Covariances were calculated between each time series obtained for the same videos. A 1-factor ANOVA confirmed a significant main effect for the 4-level factor “task comparison”  but clearly neither a difference between behavioral and fMRI experiment  nor an interaction emerged . This indicates similar task performance during both parts of the experiment and a moderate reliability.During both the behavioral and the fMRI experiment participants decided after each video clip, which of the 2 narrations comprised more of the task-relevant gestures. Agreement among participants was 74.9 ± 15.4% during the behavioral experiment and 77.8 ± 13.5% during the fMRI experiment. The high agreement indicated that participants watched the video clips attentively and adhered to the tasks throughout both experiments. Furthermore, the finding indicates that participants interpreted the tasks in a comparable way. Half of the participants (Group 1) viewed the first half of the videos in the behavioral experiment and the other half during the fMRI experiment, whereas for the other half of the participants (Group 2) the allocation was switched. These groups differed on a trend level in the percentage of agreement rating  and, on a trend level, in the pSTG . Further, for the IFG ROI, post-hoc t-tests demonstrated increased values for the CON task (4.24 ± 0.66) in comparison to the ANY task , to the REL task , and to the across-task condition . All other comparisons failed significance . Thus, direct hypothesis testing in the ROI analysis confirmed the role of the IFG in the conventionality encoding of co-speech gestures and further indicated the same preference for pSTG.The hypothesis-driven ROIs were selected from anatomical templates of left IFG and left pSTG. ROI analyses confirmed a selective response of the IFG. The ANOVA of the extracted ISC values yielded a significant effect of “task comparison” in the IFG [In summary, the conventionality tasks increased synchronization of behavioral responses, i.e., button presses mark co-speech gestures perceived as conventional. In addition, the CON task increased functional synchronization in the left IFG and, at a lower threshold, in the left pSTG. These cortex areas were not affect by the control tasks (task REL and task ANY).Task instructions to detect conventionality in co-speech gestures increased synchronous activity in frontal language areas. Watching spontaneously produced narrations, the participants marked  or attended to (fMRI experiment) conventional aspects and to 2 control attributes of the observed gestures: the occurrence of any hand movement and the gesture-speech relation. Inter-subject synchronization during watching the multimodal narrations yielded patterns typically found for naturalistic stimuli. The conventionality task led to increased behavioral synchronization and thus aligned the perception of conventional co-speech gestures across participants. In the same vein, synchrony of neural activation increased in left IFG (Broca's area) and, on a trend level only, in pSTG (Wernicke's area) during the conventionality task over the detection of hand movements or gesture-speech relations. No other network was modulated by the detection of conventionality in co-speech gestures. Conceivably, the left IFG can be considered a core region for the processing of perceived conventionality in co-speech gestures during communication. In the following, we discuss the task-evoked attention to conventional aspects in co-speech gestures on the basis of the behavioral data. Then, we briefly consider the results regarding the ISC map across all tasks before turning to the task-based recruitment of the language networks. In particular, the left IFG and pSTG are connected to conventionality processing. Finally, we discuss possible implications of our results for the gestural origin of language hypothesis and for empirical research on Peirce's Universal Categories.For the detection of conventional aspects in co-speech gestures, no empirically-tested theory was available. Therefore, we designed tasks to match the theoretical concept of conventionality in co-speech gestures as inspired by Peirce's UCs and, in particular, the category of Thirdness. Our data confirm that task-induced modulation of neural processes during naturalistic stimulation may reflect complex cognition. In the behavioral experiment, the task had an effect on the covariance of response time series. In particular, indications of conventionality covaried across subjects more than with other tasks. Thus, participants agreed in parts on the occurrence of conventional gestures, indicating task reliability. Conceivably, the interpretation of gestural signs was reflected in the neural signature during the fMRI recording.Participants decided similarly which of the two narrations comprised more of the task-relevant gestures. This reliability measure indicates that the participants understood the task in a comparable way. Further, the similarity of agreement scores between behavioral and fMRI experiment suggests that the task-according perception of gestures was similar; even in the absence of the behavioral on-line monitoring.Behavioral covariance during the conventionality and the REL (relation between speech and gesture) tasks was significantly higher than the across-task covariance. The ANY task (any hand movement) did not increase covariance of response patterns as compared to across-task covariance. Possibly, the phrasing of this task was too vague to account for the complexity of gestural movements. In particular, co-speech gestures comprised not only strokes, but also other gesture phases such as the preparation phase, retraction phase, and various holds, i.e., pre-stroke and post-stroke holds  vocal exclamations but in voluntary gestural behavior  and, to a lesser degree, in the left pSTG (Wernicke's area). In line with studies on highly conventionalized manual signs such as emblems and sign language, conventional aspects of discourse-integrated gestures seem to be processed in the language networks. In general, the interpretation of conventional signs, based on either rules or habits, may rely on neural mechanisms subserving language comprehension and may even be a central building block of the human language facility.This study was carried out in accordance with the Code of Ethics of the World Medical Association . All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the local Ethics Committee (Uniklinik RWTH Aachen).DW: planned the study, collected the data, analyzed the data, and wrote the manuscript; L-MR: planned the study, assisted in data collection and edited the manuscript; IM: planned the study and edited the manuscript; MK: assisted in data collection and data analysis; KM: planned the study, assisted in data analysis and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Potato virus Y has emerged as a threatening problem in all potato growing areas around the globe. PVY reduces the yield and quality of potato cultivars. During the last 30 years, significant genetic changes in PVY strains have been observed with an increased incidence associated with crop damage. In the current study, computational approaches were applied to predict Potato derived miRNA targets in the PVY genome. The PVY genome is approximately 9 thousand nucleotides, which transcribes the following 6 genes:CI, NIa, NIb-Pro, HC-Pro, CP, and VPg. A total of 343 mature miRNAs were retrieved from the miRBase database and were examined for their target sequences in PVY genes using the minimum free energy (mfe), minimum folding energy, sequence complementarity and mRNA-miRNA hybridization approaches. The identified potato miRNAs against viral mRNA targets have antiviral activities, leading to translational inhibition by mRNA cleavage and/or mRNA blockage. We found 86 miRNAs targeting the PVY genome at 151 different sites. Moreover, only 36 miRNAs potentially targeted the PVY genome at 101 loci. The CI gene of the PVY genome was targeted by 32 miRNAs followed by the complementarity of 26, 19, 18, 16, and 13 miRNAs. Most importantly, we found 5 miRNAs  that could target the CI, NIa, NIb-Pro, HC-Pro, CP, and VPg genes of PVY. The predicted miRNAs can be used for the development of PVY-resistant potato crops in the future. PVY has emerged as one of the most alarming pathogens in potatoes around the world; it affects the yield and quality of potatoes by inducing ringspot disease   bearing accession number AB714135.Mature microRNA sequences of potato were obtained from the miRNA database PVY genome were calculated through CLC Genomics Workbench v8.Genome organization, ORFs and nucleotide distribution of In silico tools often predict numerous target sites within the target sequence, and only a limited number of them have been validated experimentally. In plants, miRNA-mRNA attachment is less complicated than it is in animals and it mostly depends on a higher quality match between the target sequence and the miRNA than it does in animals , and the PVY genome was assessed to determine whether there were any possible targets for Solanum tuberosum miRNAs. The analysis was performed by miRanda at its default settings .As the first miRNA target prediction software, miRanda  .RNAhybrid predicts miRNA and mRNA hybridization based on minimum free energy and site complementary. “Tapirhybrid,” another tool rated as one of the best tools for miRNA target prediction  algorithm referenced in protein databases. Fifty of the most homologous sequences were retrieved and aligned using the Muscle alignment tool ; Eight of these miRNAs were shown to have multiple loci interactions at different nucleotide positions . It is imperative to discuss that only one miRNA (miR162b-3p) targeted the NIb gene at four different positions, while the other three miRNAs targeted the gene at two loci . miR164-3p targeted the HC-Pro gene at 3 different loci, and each miRNA targeted HC-Pro at 2 different positions . The former miRNA targeted the NIa-Pro gene at three loci, and the latter targeted at two loci. The NIa-Pro gene was observed to be attacked by 18 different miRNAs; only three of the above-mentioned miRNAs targeted the gene at multiple loci, while the other 15 miRNAs  targeted at a single locus as shown in Figure The VPg gene at a single locus  that could target the genes of PVY at multiple loci. On the other hand, on the basis of the polyprotein delta blast, we found other virus stains, which were genetically similar to PVY  gene was targeted by 32 miRNAs. The CI gene consists of 1901 nucleotides encoding cylindrical inclusion protein. The gene has helicase activity  gene was targeted by 26 miRNAs, and four of the miRNAs  were found to be more important because they targeted the gene at 10 different positions. miR162b-3p potentially targeted the gene at four multiple positions, and the other three miRNAs  targeted the gene at 6 possible sites. NIb gene nucleotides are present at the 5′ terminus from 7015 to 8571 nt. The gene encodes the RNA-dependent RNA polymerase, and it is involved in viral replication  gene. Among the 19 miRNAs, 4  were found to be more critical because of their potential interaction at nine loci of the HC-Pro gene. The product of HC-Pro is proteinase  that potentially targeted the The current bioinformatic approach revealed 18 potential miRNAs that targeted the NIa-pro  gene at multiple loci. Their potential attachment to NIa-Pro not only interferes with the normal functioning of NIa-Pro but is also responsible for the abnormal activity of Nib because both genes work in collaborative manner. We found that only two miRNAs (miR160a-5p and miR7997b) interact with NIa-pro at five different positions. The other 16 miRNAs targeted this gene at a single locus. Proteinase by nature cleaves approximately two-thirds of the viral polypeptide at multiple positions to make them functional  gene was targeted by 13 miRNAs. The CP encodes the capsid protein, which encapsulates the single-stranded RNA genome of PVY. It also facilitates cell-to-cell and long-distance movement  gene.We found 16 miRNAs with potential to inactivate the VPg gene encodes the viral genome-linked protein, which facilitates RNA replication . Apart from perfect complementarities in miRNAs and their targeted regions with no mismatch, up to 1–24 nucleotides were focused upon, and only un-gapped miRNA-mRNA predicted targeted models were considered as best hits. Finally, we selected only those miRNAs that were found to be potential targets by all three software.Turnip yellow mosaic virus and Turnip mosaic virus infection, respectively. Here, we may use miRNAs against PVY in transgenic plants.Controlling for viral infection, following viral mRNA degradation is the simplest approach. Niu and colleagues used a 273 bp sequence of Arabidopsis miR159a per-miRNA transcript expression amiRNAs against the viral suppressor genes P69 and HC-Pro to generate resistance against PVY, are more likely to hit by cloned miRNA.This study provides a better way to computationally analyze the best-candidate miRNAs against viruses, prior to cloning. As our approach allows a narrow-range of match-mismatch in microRNA-mRNA attachment, it screens most of the falsely predicted attachments. The phylogenetic tree helps to determine whether there is any possibility for cloned miRNAs to act off-target and affect other virus species. The more viral species that are discovered to be closer to The main idea was developed by MI and bioinformatics analysis were done by MI, MH, and AA, data interpretation was done by MS and JW and manuscript was written and proof-read jointly by all authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The current experiment investigated generalizability of motor learning in proximal versus distal effectors in upper extremities. Twenty-eight participants were divided into three groups: training proximal effectors, training distal effectors, and no training control group (CG). Performance was tested pre- and post-training for specific learning and three learning transfer conditions: (1) bilateral learning transfer between homologous effectors, (2) lateral learning transfer between non-homologous effectors, and (3) bilateral learning transfer between non-homologous effectors. With respect to specific learning, both training groups showed significant, similar improvement for the trained proximal and distal effectors, respectively. In addition, there was significant learning transfer to all three transfer conditions, except for bilateral learning transfer between non-homologous effectors for the distal training group. Interestingly, the proximal training group showed significantly larger learning transfer to other effectors compared to the distal training group. The CG did not show significant improvements from pre- to post-test. These results show that learning is partly effector independent and generalizable to different effectors, even though transfer is suboptimal compared to specific learning. Furthermore, there is a proximal-distal gradient in generalizability, in that learning transfer from trained proximal effectors is larger than from trained distal effectors, which is consistent with neuroanatomical differences in activation of proximal and distal muscles. In genelateral transfer of learning between non-homologous effectors. Such intra-limb transfer has been studied previously in writing skills  effector system at the other side, and whether the transfer effects follow a proximal-distal gradient. We expected to find more transfer for homologous proximal effectors compared to homologous distal effectors because of their differences in peripheral and central neural interaction and communication. In addition, we examined both lateral and bilateral transfer between non-homologous effectors, where we also expected to find a proximal-to-distal gradient. Finally, we also investigated specific learning in order to confirm the superior effect of specific training compared to the transfer conditions.In total 28 university students (mean age 23.1 ± 1.9 years) with no known neuromuscular disorders or functional limitations participated in this study. The participants were assigned to three groups: (1) the proximal training group (5 men and 5 women), (2) the distal training group (5 men and 5 women), and (3) the control group (CG) (4 men and 4 women). All participants were right-handed as indicated by the Edinburgh Handedness Inventory . All parFigure 1). The criterion waveform was the same in every trial. In each condition, the subjects were instructed to position the center of the crosshair at the head of the snake and follow the undulating moving snake head as closely as possible. When the center of the crosshair was perfectly located on the head of the snake, the color of the snake head changed, thereby functioning as online feedback to the subjects. The moving snake task was made using the Unity3D game engine and programmed using C#. The sampling frequency of the task was 100 Hz. For each sample the following information was stored: time since the game started, the target point’s x- and y- coordinates, and the crosshair’s x- and y-coordinates. Two different customized joysticks were used in order to dissociate and perform isolated movements of the proximal and distal effectors, a customized proximal joystick controlled by shoulder and elbow, and a customized distal joystick controlled by wrist and index finger. The joysticks were operated with both dominant and non-dominant side, in a total of four different conditions.A customized 2D virtual “moving snake” task was designed for the purpose of the present experiment. The moving snake task consisted of a criterion waveform made by the head of the snake and a controllable crosshair with which the participants had to track the target (head of the snake) as precisely as possible . In addition, the height of the seat was elevated in order to eliminate activation of the feet. In order to isolate the use of wrist and index fingers, the forearm rested on a platform to which it was strapped . The screen had a width of 148 cm and a height of 110 cm. The amplitude of the function was adjusted to be within 1/3 of the height of the screen.Furthermore, a custom-made chair and apparatus were used to prevent postural instability and activation of other postural core muscles, thereby limiting activation to only shoulder-elbow in the proximal condition and only wrist-index finger in the distal condition. To ensure the use of only shoulder and elbow movements in the proximal condition, the trunk and the upper body were strapped to the chair  Proximal effectors dominant side, (2) Distal effectors dominant side, (3) Proximal effectors non-dominant side and (4) Distal effectors non-dominant side.The participants had one practice trial in each condition first in order to familiarize themselves with the apparatus, the task, and the different conditions. After the participants were accustomed to the task and each condition, baseline performance in the different conditions was captured (pre-test). Each of the four conditions was performed three times, resulting in a total of twelve trials. The order of the respective conditions was counterbalanced across participants.After the pre-test, the participants were divided into three groups based on their mean scores on the four pre-test conditions. The criterion score for each participant was calculated by summarizing mean performance score from each pre-test condition, and divided by the number of conditions (4 conditions). The Proximal Effector Training Group (PETG) controlled the joystick during the training period with dominant shoulder-elbow joints, while the Distal Effector Training Group (DETG) controlled the joystick with dominant wrist-index finger. The CG did not receive training and only performed the pre- and post-tests.The two training groups trained on five consecutive days, with each daily session consisting of 25 trials of 30 s each. Each training session was subdivided in 5 blocks with five trials each, followed by a 2 min rest period in order to maintain motivation and prevent fatigue. At the end of each training session the experimenter provided verbal feedback, accompanied by a visual graphic feedback of the last trial on the screen in front of the participants. The CG did not practice between the pre-test and post-test. The day after the last training session for the experimental groups, the post-test was conducted on all three groups. The post-test was identical to the pre-test except for the initial practice trials.Figure 1):The dependent variable was the average Absolute Spatial Error (ASE) between the target and the crosshair. The unit of measurement was in virtual meters as defined in the customized software. ASE was measured as the distance between the head of the snake and the crosshair, calculated by the Pythagoras equation  on the post-test.Dependent variables were the ASE and relative improvement between pre- and post-test (ΔASE index) for the four effectors . All variables were normally distributed, as indicated by Kolmogorov–Smirnov tests. To test equality between the three groups at pre-test, a one-way ANOVA Group  was performed on ASE for each of the four conditions.t-tests were performed on ASE.In order to test performance differences between pre- and post-test in the three groups in the four conditions, paired-samples To test potential pre-existing effector and side differences, a two-way repeated measures ANOVA Effector  by Side Dominance  was performed on pre-test ASE.Post hoc follow-up of significant effects consisted of pairwise comparisons with Bonferroni corrections for multiple comparisons.To evaluate potential differences in learning conditions between the proximal and distal training groups, a two-way repeated measures mixed model ANOVA on Training Group (2) by Learning Type (4) was performed on ΔASE index. As described above, the four learning types were Specific learning, and three learning transfer conditions: Bilateral learning transfer between homologous effectors, Lateral learning transfer between non-homologous effectors, and Bilateral learning transfer between non-homologous effectors. p < 0.05 was used for statistical significance.All statistical analyses were performed in SPSS , and a criterion alpha level of Table 1. A one-way ANOVA confirmed that the three groups did not differ in ASE at pre-test in any of the conditions .Descriptive analyses of the ASE at pre- and post-test are presented in t-tests on ASE. PETG had significant improvement from pre- to post-test in all four conditions , while there was significant improvement in three out of four conditions for the distal training group . No significant differences in performance between pre- and post-test were shown in any of the conditions for the CG .Performance improvement between pre- and post-tests was tested by paired-samples F = 62.53, p < 0.0005] and Side Dominance , confirming that the ASE is significantly less for distal effectors compared to proximal effectors, and less for the preferred side compared to the non-preferred side. Therefore, the relative ASE, ΔASE index, will be used below to investigate potential differences between the different conditions in the amount of learning.To test for potential pre-existing differences between effectors and side-dominance, a two-way repeated measures ANOVA Effector  by Side Dominance  was performed on pre-test ASE. It confirmed a main effect for both Effector  and a main effect of Learning Type . Pairwise comparisons between the four learning types with Bonferroni corrections indicated that specific learning was significantly more effective compared to the three transfer types, that bilateral learning transfer between homologous effectors was not significantly different from bilateral learning transfer between non-homologous effectors, and that lateral learning transfer between non-homologous effectors was significantly less effective than the other types .Significant learning effects were found for 7 out of 8 comparisons in the two training groups, but the amount of learning might differ between the different types of learning for the proximal versus distal training groups. To test for potential differences in learning transfer effects for the training groups, a two-way repeated measures mixed model ANOVA on Training Group  by Learning Type  was performed on ΔASE index. It confirmed a main effect for Training Group on ΔASE index , indicating that the ΔASE index may be larger for PETG than for DETG in some conditions but not all. The interaction was followed up by post hoc independent samples t-tests on each condition, confirming that the proximal training group showed larger learning effects than the distal training group in all learning types  except for specific learning where both groups performed equal . Below, we describe in more detail the effects of specific training and the three transfer conditions.In addition, there was a significant Training Group by Learning Type interaction [Figure 3). The post hoc follow-up of the main effect of Learning Type described above showed that specific learning was more effective than all three types of learning transfer, all p’s < 0.0005. There was no significant difference between the proximal and distal training groups regarding specific learning, t(18) = -0.143, p = 0.89.For the specific learning type, training occurred on the same effector as tested in the post-test condition. Training of both proximal and distal effectors resulted in large improvements of the ΔASE index, namely 39.77 ± 12.44% and 38.97 ± 12.61%, respectively , but significantly more effective compared to bilateral learning transfer to the non-homologous effectors (p = 0.001). No differences were shown for bilateral learning transfer between homologous effectors versus lateral learning transfer between non-homologous (p = 1.000). An independent samples t-test on ΔASE index showed significantly larger bilateral learning transfer to the contralateral homologous effectors for the proximal versus distal training group, t(18) = -4.062, p < 0.001 .Learning transfer to the contralateral homologous effector systems was demonstrated for both proximal and distal effectors. Improvement of the ΔASE index was 30.70 ± 7.31% and 16.34 ± 8.46%, respectively. The post hoc follow-up of Learning Type showed that lateral learning transfer between non-homologous effectors was less effective compared to specific learning (p < 0.0005), but more effective compared to bilateral learning transfer between non-homologous effectors (p = 0.002). No differences were shown for lateral learning transfer between non-homologous effectors and bilateral learning transfer between homologous effectors (p = 1.000). An independent samples t-test on ΔASE index showed significantly larger lateral learning transfer between non-homologous effectors for the proximal versus distal training group, t(18) = -3.254, p = 0.004 .Lateral learning transfer between non-homologous effectors was demonstrated for both proximal and distal effectors, with improvements of the ΔASE index being 28.68 ± 11.40%, and 14.02 ± 8.54%, respectively. The post hoc follow-up of Learning Type showed that bilateral learning transfer between non-homologous effectors was the least effective compared to the other three learning types, all p-values < 0.002. An independent samples t-test on ΔASE index showed significantly larger bilateral learning transfer between non-homologous effectors for the proximal versus distal training group, t(18) = 2.988, p = 0.008 .Training with the dominant proximal effector resulted in significant bilateral learning transfer to the distal non-homologous effector, while training with the dominant distal effector system did not show significant bilateral learning transfer to the proximal non-homologous effector. Improvement of the ΔASE index was 17.78 ± 11.55% and 4.36 ± 8.27%, respectively. The The main purpose of the current study was to elucidate potential differences in learning transfer after training proximal versus distal effectors in the upper extremities. In addition, we investigated specific learning effects in the same effectors. The theoretical background for the study was derived from motor learning theories about learning transfer combined with models of morphological and functional differences between proximal and distal effectors. In line with the theories of motor learning and learning transfer, it was hypothesized that specific training would be the most effective type of training irrespective of trained effector. Furthermore, grounded in the models of neural communication and transmission of motor control, we expected to find a proximal-distal gradient in motor learning transfer. The results confirmed specific training to be the most effective for performance improvement for both proximal and distal effectors, and demonstrated a general transfer and thus generalizability of performance when training with both proximal and distal effectors to other non-trained effectors in the upper extremities. Most importantly, the results supported our hypothesis of a proximal-distal gradient in motor learning transfer, in that we found more pronounced learning transfer to non-trained effectors in upper extremities after training with proximal effectors versus training with distal effectors. No significant improvement was shown for the non-training CG in any of the conditions.The superior effect of specific motor learning for both the proximal and distal training groups confirms that learning effects are highly specific to the particular task trained, as proposed by the specificity of learning hypothesis , 1968, aThe most interesting transfer effect explored in the present study was the potential difference in bilateral motor learning transfer between proximal and distal homologous effectors. Based on the theory of bilateral transmission and communication between hemispheres and in the spinal cord, we expected to find more pronounced bilateral motor learning transfer for proximal versus distal homologous effectors. A behavioral approach was selected to study how unilaterally practiced upper limb skills transfer to the performance of the same effector on the opposite, untrained side. Behavioral studies of bilateral transfer have shown consistency of this phenomenon across both homogenous proximal and distal effectors. Already in 1903, Swift conducted a study of bilateral transfer for gross motor skills , while The morphological differences in transmission and communication between hemispheres and in the spinal cord for proximal and distal muscles are well documented e.g., , but theIn addition to the bilateral learning transfer between homologous effectors described above, the current results revealed positive lateral learning transfer between non-homologous effectors (indicating intra-limb transmission of learning effects) for both the proximal and distal effectors. These results are consistent with previous unimanual and bimanual transfer studies e.g., . InteresTransfer of learning effects to inter-limb non-homologous effectors was shown for dominant proximal effectors to non-dominant distal effectors, but not for dominant distal effectors to non-dominant proximal effectors. These results are consistent with what earlier studies demonstrated in unimanual skills such as writing and drawing , and areAs the results of the current study illustrate, specific training of effectors is superior to transfer of learning effects to untrained effectors, confirming that motor control is characterized by a relatively high level of neuromuscular specificity. More interestingly, the transfer or generalizability of learning effects is larger for proximal effectors compared to distal effectors, which is consistent with the neuroanatomical differences between proximal and distal effectors. These results pave the way for further studies that combine a behavioral study with direct measures of neural activation to illuminate the potential link of communication and transfer of learning in motor performance between different effectors in general, and potential differences between proximal and distal effectors in particular.The functional and behavioral data from the current study do not evaluate the predictions of transmission of information directly, but encourage further research to include measures both of brain activity, e.g., electroencephalography (EEG) or functional magnetic resonance imaging (fMRI), and of muscle activity measures through, e.g., electromyography (EMG). Such measures could provide additional insights into the interhemispheric communication of proximal and distal effectors and the potential effects on transfer of learning. They can also illuminate whether learning transfer is caused by interhemispheric interactions alone, or whether spinal contributions should be considered in addition. Similarly, brain activity measurements can elucidate to what extent learning transfer has a neural origin, or whether more general principles for learning need to be taken into account. Incorporating such measures could give further and more detailed information about the differences in learning transfer for proximal versus distal effectors. The present study does not evaluate potential asymmetries of transfer after practicing the non-dominant proximal and distal effectors. It would be an interesting follow-up study to examine potential asymmetries in the direction of transfer effects when training with dominant versus non-dominant proximal and distal effectors.To conclude, the current study contributes to our understanding of motor control and learning processes as it addresses both the effect of specific training of effector systems and learning transfer to different effector systems. It was hypothesized that the morphological differences between proximal and distal effectors would result in more pronounced transfer of learning in proximal compared to distal effectors, which was confirmed by the observed proximal-distal gradient in our results. As such, the study informs about potential neuromuscular constraints for the motor control system of proximal and distal effectors, the possibilities and limitations regarding transfer of motor learning capabilities in general, and the differences in transfer between proximal and distal effector systems in upper extremities in particular.This study was evaluated by and carried out in accordance with the recommendations of the Norwegian regional ethical committee. All subjects gave written informed consent in accordance with the Declaration of Helsinki.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The spins in the conical and skyrmion phases were excited by the impulsive magnetic field from the inverse-Faraday effect, and resultant spin dynamics were detected by using time-resolved magneto-optics. Clear dispersions of the helimagnon were observed, which is accompanied by a distinct transition into the skyrmion phase, by sweeping temperature and magnetic field. In addition to the collective excitations of skyrmions, i.e., rotation and breathing modes, several spin precession modes were identified, which would be specific to optical excitation. The ultrafast, nonthermal, and local excitation of the spin systems by photons would lead to the efficient manipulation of nano-magnetic structures.Magnetic skyrmions in an insulating chiral magnet Cu Magnetic skyrmion is a nano-scale spin-swirling object protected by topology, discovered to deliver emergent electromagnetic interactions for various potential applications159102OSeO3 is a helimagnet with the noncentrosymmetric P213 structure, and is a unique insulating crystal known to host skyrmions with the Dzyaloshinskii-Moriya interaction131617Cuk-vector in the solid, with its amplitude related to the electronic structure and spin-orbit interactions. For the case of insulating samples, one can avoid thermal excitations by using the photons with the energy below the optical gap, thus enabling pure spin excitations. By focusing or structuring the light spot, the spin excitation can be spatially-controlled19To further control the skyrmions in local  and ultrafast manner, it is promising to utilize optical techniques, since single skyrmions have already been created by pulsed laser excitation in the past192OSeO3 as a first step. It is found that the non-thermal optical pulse leads to the excitations of helimagnons and rotation/breathing of the skyrmions. Clear beating features in the spin precession signals revealed several fundamental spin wave modes, with mode-dependent decay constants, which are compared to those found in the microwave resonance experiments17In this report, we demonstrate inverse-Faraday excitation of the collective spin dynamics in Cu2OSeO3 and experimental setups are shown in exHk-vector) ) are expected. The latter generates two magnons of opposite wavevectors (±k ≠ 0) by the ellipticity of the precession orbitexH > 220 Oe ||[110] [k, as has been discussed in the case of magnetic bubble latticesexH region can be ascribed to the multi-domain states, while those in the ferrimagnetic phase  planes. The conical spin and SkX phases were checked by the magnetic susceptibility measurements [μmφ spot) excite the spin system at 0.95 eV  by the inverse-Faraday effect, and the induced spin precession was detected by the Faraday rotation of the linearly-polarized probe pulse at 2.2 eV [exH) was applied by a pair of permanent magnets, which limited the phase space studied in our experiment.Single crystals of Cuurements , in whicurements . The pumt 2.2 eV . Thus th