Microbial communities underpin the Earth's biological and geochemical processes, but their complexity hampers understanding. Motivated by the challenge of diversity and the need to forge ways of capturing dynamical behaviour connecting genes to function, biologically independent experimental communities comprising hundreds of microbial genera were established from garden compost and propagated on nitrogen-limited minimal medium with cellulose (paper) as sole carbon source. After 1 year of bi-weekly transfer, communities retained hundreds of genera. To connect genes to function, we used a simple experimental manipulation that involved the periodic collection of selfish genetic elements (SGEs) from separate communities, followed by pooling and redistribution across communities. The treatment was predicted to promote amplification and dissemination of SGEs and thus horizontal gene transfer. Confirmation came from comparative metagenomics, which showed the substantive movement of ecologically significant genes whose dynamic across space and time could be followed. Enrichment of genes implicated in nitrogen metabolism, and particularly ammonification, prompted biochemical assays that revealed a measurable impact on community function. Our simple experimental strategy offers a conceptually new approach for unravelling dynamical processes affecting microbial community function.This article is part of the theme issue ‘Conceptual challenges in microbial community ecology’. ApproprNew strategies for investigation are required that are process-focused . IdeallyEqually desirable are approaches that provide a link between genes, their spatial and temporal dynamics, and community function, achieved using strategies that leave as far as possible the natural complexity of communities undisturbed. Here we show—in a proof-of-principle experiment—that this can be achieved via an experimental strategy that combines theory governing the behaviour of selfish genetic elements –23 (SGEs2.(a)3 compost heap and each placed in one of ten 140 ml bottles (mesocosms) containing 20 ml nitrogen-limited minimal M9 medium plus cellulose (a 4 cm2 piece of paper) as sole carbon source. Mesocosms were incubated on a laboratory bench without shaking and with lids left unsealed. After a two-week period, the initial piece of paper from each mesocosm was separately transferred to a new bottle containing fresh M9 medium plus a new piece of paper. Following a further two-week incubation period, we considered the communities to have adjusted to laboratory conditions. At this time point, mesocosms were vortex-mixed and 1 ml of cellulosic slurry from each bottle was transferred to two new mesocosms containing 19 ml fresh M9 medium plus paper giving a total of 20 paired communities. This transfer, four weeks after initial establishment, was deemed time point zero (T0). Thereafter and for the ensuing 48 weeks, communities were serially propagated every two weeks (T1 to T24) by transferring 1 ml of slurry to fresh bottles. One set of each pair of bottles was labelled ‘vertical’ ) and the second of each pair labelled ‘horizontal’ ). Details of the treatments and their significance are elaborated below, but at this stage, with a focus solely on the question of diversity in cellulose-based mesocosms, we acknowledge treatment effects by name only.Experimental communities were established from a diverse primary source: garden compost. Ten independent 1 g samples of compost were taken from 10 different regions of a single 1 m0, and 20 T1 and T24 communities and the data interrogated for sequence reads mapping to 16S rDNA . Communities at T0 harboured on average approximately 140 bacterial genera (s.d. ± 23). At T24 the number of genera detected increased in all communities to an average of approximately 200 (s.d. ± 32), indicating an increase in abundance of rare types . Diversity levels were highly similar in both HCs and VCs . Rank abundance distributions from pooled HCs and VCs at T1 and T24 are shown in 24 distribution to be markedly flatter in both cases, consistent with an overall tendency for rare types to have increased during the course of the year-long selection. Rank abundance distributions for each T1 and T24 HC and VC, plus the rank order (and change therein) of the most common genera are shown in the electronic supplementary material, figure S2a,b. Electronic supplementary material, figure S3 is a multidimensional scaling plot showing that communities at the start and end of the experiment differed markedly in genera composition and there was no sign of convergence after 48 weeks. Among the most abundant genera, only two harbour species commonly associated with cellulolytic ability [Cytophaga and Cellvibrio), while others are better known for roles in ammonification [Azospirillum and Paenibacillus (nitrogen fixation); Rhodanobacter and Pseudomonas (dissimilatory nitrate reduction/denitrification); electronic supplementary material, figure S2a,b). Single-celled eukaryotes were also present in all mesocosms with several unexpectedly maintaining populations of nematodes through the 48 week selection period.Diversity through 48 weeks of propagation was addressed by metagenomics: total DNA was extracted from each of the 10 T ability  (Cytophafication  (Azospir(b)An important goal is the development of strategies that link genes to community function by way of the process. An ideal experiment is one that alters a single ecological or evolutionary process and compares the outcome to a set of replicates in which the process is unaffected. For example, evolutionary biologists interested in the evolutionary consequences of sex have taken advantage of yeast in which it is possible to genetically engineer sexual and asexual types that are otherwise isogenic ,30. DiffWe reasoned that a similar manipulation might be possible in the context of microbial communities. Recombination mediated by HGT is loosely analogous to sex, albeit to a form of sex that takes place at the level of communities. Moreover, like genetic manipulations affecting sexual reproduction in yeast, the extent of HGT can be controlled by manipulating the evolutionary fate of SGEs ,23. SuchThe critical manipulation is one that affects the likelihood that SGEs encounter new hosts. Frequent exposure to new hosts drives dissemination and evolution of SGEs and linked genes . Conversvertical and horizontal treatment designations referred to above reflect manipulations that are predicted to either restrict, or facilitate, respectively, HGT mediated by SGEs. The vertical regime involved serial transfer of material from mesocosm to mesocosm. SGEs within these communities were denied opportunity to encounter new hosts: transfer of SGEs was vertical and the extent of HGT is expected to be limited 0 community and each of the 20 T1 communities  and sequenced (0 and descendant HCs and VCs (e.g. T0-1 and descendants T1V-1 and T1H-1) DNA sequence data were interrogated to identify reads found solely in (and therefore unique to) the horizontal mesocosm (e.g. T1H-1). Such unique reads are likely to stem from SGEs, or be associated with SGE activity, originating from an allopatric community.To determine whether the horizontal regime promoted the movement of SGEs, total DNA was extracted from each TAnalysis of the sequences unique to each horizontal community resulted in identification of approximately 26 million unique reads from a total of approximately 152 million reads across all 10 communities, with an average of approximately 2.6 million unique reads per community . Assembly of reads into contigs revealed an average of 3352 contigs per HC greater than 1 kb in length. The mean maximum contig size per assembly was 82 kb .1H-1_35969) containing numerous genes characteristic of phages is shown in the electronic supplementary material, figure S4a.After extracting open reading frames (ORFs), interrogation of the Conserved Domain Database (CDD)  showed 11H-1_35969 and 19 other representative phage-like entities  across independent mesocosms was determined by mapping total reads from each horizontal and vertical metagenome onto each of the 20 phage-like contigs . Within two weeks, this contig had amplified and spread to HC 1, 5 and 6 . These include genes implicated in cellulose degradation, defence against SGE invasion, nitrogen metabolism and siderophore biosynthesis  –38. Part(d)0 communities into VCs and HCs, we complemented existing DNA sequence from T0 and T1 and T24 with DNA extracted from all HCs and VCs at time points T2, T3, T4, T10, T16 and T20 yielding a total of 180 metagenomes . These 180 metagenomic datasets ) spread across 48 weeks allowed the abundance and distribution of SGEs and associated genes to be determined by read mapping to contigs assembled from T0 data. The spatial and temporal dynamics of two phage-like contigs and two contigs encoding β-glucosidases are shown in figure 4a–d.Given evidence indicative of the amplification and dissemination of DNA via the horizontal treatment just two weeks after splitting T1H7_13351 (a), this phage-like element was detected only in community C4 at T0, but was probably present also in communities C1 and C2, although below the limit of detection. By week 3, the phage-like element was present in all HCs. In VCs, the element persisted in community C4 for 22 weeks and transiently in C1 and C2, but was present at 48 weeks in all but one HC. Within HCs, a pattern observed on four occasions saw the phage-like element go extinct , but re-emerge and persist at latter time points.Focusing on PhageContigTH7_13351 a, this p(e)0 and all T24 HCs and VCs . For the ensuing analysis, we have avoided standard statistical testing because horizontal microcosms, by virtue of the movement of SGEs and linked genes among communities, cannot be treated as independent entities. Nonetheless, trends are clearly evident from the data displayed in 0 community—median value, interquartile range and full data range.To determine whether the horizontal transfer regime affected the abundance and distribution of functional genes, the MG-RAST database was interrogated with sequence reads from all TThe relative abundance of reads assigned to 13 of 28 functional categories at subsystems level 1 , includia). Changes were observed in the relative abundance of reads mapping to genes involved in both ammonia assimilation and dissimilation (ammonification). Such changes are consistent with the nature of the selective environment. This prompted a query of the extent to which these changes may have been influenced by the movement of genes via SGEs.The nitrogen-limited nature of the culture medium, combined with evidence of selection favouring genes involved in nitrogen metabolism, led to closer focus on this essential resource. The MG-RAST database was again interrogated, but this time at subsystems level 2 , which pfigure 524 metagenomes. T24 HC metagenomes were compared to their paired T24 VC metagenomes as well as to all metagenomes from VCs from earlier time points . This showed a greater number of unique sequences in the HCs, but also showed a fraction of unique sequences present in VCs. The latter is to be expected given that T0 communities must contain rare sequences that through the course of the selection experiment became common—and is consistent with data on changes in abundance of genera . Nonetheless, the ‘deep’ T0 metagenomes were used in all subsequent analyses.A modified version of the bioinformatic pipeline developed to identify sequences unique to HCs (Methods) was applied to the Tf genera . To see b; electronic supplementary material, table S9).To determine the fraction of unique reads that mapped to genes involved in nitrogen metabolism, the final sets of unique reads were obtained from HCs and VCs; they were assembled, ORFs identified and functionally categorized using the CDD. HCs contained more unique ORFs predicted to be involved in nitrogen metabolism compared to VCs . HCs were enriched in several functional classes of gene, especially those with predicted roles in regulation and ammonification  the concentration of nitrate, nitrite and ammonia were determined in the Tb; electronic supplementary material, table S11). A comparison between T0 communities and T24 VCs showed no change  supporting the conclusion that movement of ecologically significant genes via SGEs was responsible for the functional difference between HC and VC.To check the robustness of this finding, the concentration of ammonia was measured during the course of a two-week period. Despite substantive variability in the composition of the independent communities , a significantly greater concentration of ammonia was detected in HCs at each of nine sampling occasions throughout the two-week period  genotypes are propagated under defined laboratory conditions with variation arising solely by de novo mutation and where selection is the primary arbiter of mutation success ,25. ThisWe sought to apply the same kind of simplifying experimental approach to the study of microbial communities, however, doing so is not straightforward. Simply substituting a community of microbes in place of a single bacterial genotype, followed by recapitulation of a standard serial transfer experiment, would not be analogous to propagating a single bacterial genotype as done, for example, in the work of Lenski . In the If it makes no sense to assume that communities adapt in a manner analogous to single bacterial populations, then attention shifts away from the focus on mutation and selection affecting individual units, and turns to possibilities that might stem from manipulation of evolutionary processes such as recombination, or migration that could conceivably have community-level effects. We chose to manipulate the former, seeing the possibility to promote, or retard HGT, via a simple mixing regime predicted to affect the evolutionary fate of SGEs, which in turn, might connect genes to community function.in vitro environment not too dissimilar from that encountered in nature. Additionally, component species can in principle be isolated and cultured on laboratory agar with glucose as sole carbon source (glucose being the utilizable metabolic break-down product of cellulose). However, the primary consideration for use of cellulose was that it is difficult to degrade [r-selected types. In effect, while the means and mode of propagation involved serial transfer, communities probably experienced culture conditions more reminiscent of those provided in chemostat culture.Our focal communities were established from a highly diverse environment (garden compost) and propagated on cellulose as sole carbon source. The decision to use cellulose was in part a nod to simplification, but was also motivated by the fact that garden compost is rich in this material, thus ensuring an  degrade , thus alLevels of diversity recorded from analysis of metagenomic data were remarkably high, remaining stable—and even increasing—through the course of the 48 week period of propagation . This isAlthough noted only in passing, and from examination of the mapping of DNA sequence reads to genes connected with nutrient cycling (data not shown), cycles for replenishing all major nutrients appeared to have been established within each mesocosm, with that involving nitrogen being most evident. Each community harboured a single dominant free-living nitrogen-fixing bacterium, but the identity—even at the genus level—differed between mesocosms. Attempts to establish such complex communities by choosing key players before the fact, would probably fail. That such diversity and stability can arise from a single gram of compost speaks to an abundance of redundancy in these natural systems ,53.Given the experimental design with vertical and horizontal treatment regimes, it is natural to query the relationship between treatment effects and patterns of diversity. At the level of number of bacterial genera and shape of rank abundance curves, no difference was detected and in fact communities showed remarkably similar overall trends. However, differences are apparent when focusing on the rank of individual genera. These are evident in the butterfly plots in the electronic supplementary material, figure S2b and show difference both between independent communities but also differences between vertical and horizontal treatments within communities. Because our experimental design did not include technical replicates  no statistical analysis is possible. Nonetheless, notable is the similarity of changes in genus rankings between vertical and horizontal treatments from the same communities. Also notable is the fact that there was no evidence of convergence in diversity in the horizontal treatment after 48 weeks . Although unsurprising, this emphasizes the distinctness of the communities despite being connected via movement of SGEs and linked genes.The pooling of material from each mesocosm that passed through a 0.2 µm filter to produce an SGE-cocktail, followed by its mixing and regular distribution among HCs, was evidently effective in mobilizing SGEs and promoting horizontal transfer of genes of ecological significance. In fact, the magnitude of the effect, combined with capacity to detect it via identification of DNA sequence reads unique to each horizontal community, exceeded expectation. Within just two weeks of implementation, approximately 17% of DNA sequence reads within HCs were derived from allopatric communities. That these reads could be assembled into contigs containing genes of ecological relevance, including those involved in cellulose degradation, emphasizes the dynamism of processes driven by SGEs.One consequence of this dynamism is the application of the protocol to detect—at the community level—traits under selection. The suite of unique DNA sequences amplified and disseminated across communities suggests that these genes matter. In the context of conditions experienced by the compost-derived communities, this makes sense. Community members are expected to have experienced selection for the ability to degrade cellulose: this is indicated by the abundance of horizontally transferred genes predicted to be involved cellulose degradation . Similarly, as mesocosms were nitrogen-limited, selection was expected to favour the movement of genes involved in nitrogen metabolism. That this happened is indicated by the prevalence of genes implicated in nitrogen metabolism among the set of DNA sequence reads unique to HCs. Not appreciated at the outset was that communities, according to the signature acquired from unique sequence reads, were iron limited. With hindsight, this was to be expected. That genes encoding CRISPR are also enriched is especially significant and suggests an unappreciated dynamic between hosts, the defence systems maintained to protect bacterial hosts against phages, and benefits that accrue from the horizontal dissemination of these systems .Without further experimentation, the identity of the elements responsible for horizontal spread of genes is unknown, and so too is the relationship between the genes amplified and disseminated across mesocosms, and the vehicles for their dissemination. While some assembled contigs show features typical of phages, many do not. This may be a consequence of working with metagenomic data, but may also reflect the existence of elements whose nature and dynamic remains to be discovered.One of the larger assembled contigs shown in the electronic supplementary material, figure S4b is a case in point: at approximately 80 kb, hints as to its identity are expected, and yet while carrying genes predicted to encode glycoside hydrolyses—implicated in cellulose degradation—the only suggestion of ability to be both mobilized and disseminated is via linkage to a single integrase. Acquisition of long-read sequence data may aid understanding, but it is also possible that the captured dynamic—the rapid amplification and dissemination of ecologically significant genes—is attributable to ‘fortunate accidents’ mediated by, for example, lytic phages or other elements that happen on rare occasion to capture genes of utility to hosts, with the feedback between genes that prove useful to hosts and the amplifying effects of selection driving their dynamic. This points to the possibility of processes that unfold at the level of diverse communities that remain to be understood.1H7_13351 (a) raise numerous questions. Extinction within VC4, but persistence in all HCs at 48 weeks (with the exception of H1) raises questions as to the causes. Using recently developed Hi-C [Irrespective of the nature of the disseminating vehicles, the experimental approach outlined allows the dynamics of elements amplified and disseminated to be tracked. Examples are shown in figures H7_13351 a raise nped Hi-C ,19,55 anped Hi-C , it ough0 communities) in VCs . Additionally, increased ammonia in the HCs is consistent with the prediction that the horizontal movement of DNA will increase the rate at which community function improves.The association established between genes involved in nitrogen metabolism and enhanced activity of SGEs in HCs is correlative, but is nonetheless linked to horizontally transferred sequences that map to genes with predicted roles in ammonification. The association is further linked to data that demonstrate a significant effect of the experimental treatment on concentrations of ammonia in HCs , but no The two processes that generate ammonia: nitrogen fixation and nitrate ammonification, both require environments devoid of oxygen (or mostly so in the case of nitrate ammonification) ,56. It iThe genomic era has done much to resolve the controversy surrounding the importance of HGT as a driver of evolutionary change –60. DireMicrobial communities are dynamical systems defined by complex ecological interactions and evolutionary feedbacks . The cha4.(a)2 piece of cellulose paper (Whatman cellulose filter paper). All incubations were performed in 140 ml sterile glass bottles with loosened screw caps allowing for gas exchange between the community and the environment. Incubation was at room temperature without shaking. Compost was sampled from the Square Theodore-Monod compost heap  in February 2016. During the primary incubation period of two weeks, the piece of cellulose was suspended by a wire from the screw cap into the media allowing for paper colonization. After two weeks, the community was transferred by moving the wire-suspended cellulose paper into 20 ml of fresh M9 minimal media that also contained a new 4 cm2 piece of cellulose paper in suspension. Two more weeks of incubation provided the opportunity for colonization of the new piece of suspended cellulose paper thus eliminating the need for a wire-suspended community for each transfer. All community transfers were performed by vortexing each bottle at maximum speed until the cellulose fibres were disaggregated into a slurry.Microbial communities were initially established by placing 10 independent 1 g samples of fresh compost into 20 ml of M9 minimal media (the nitrogen source was ammonium chloride (0.935 mM)) supplemented with a 4 cm(b)g for 10 min and the pellet was stored at −80°C for later processing. DNA was extracted from the founding T0 communities  as well as transfers 1, 2, 3, 4, 10, 16, 20, 24  using the soil DNA extraction kit (Norgen Biotek). DNA sequencing was performed using the MiSeq and NextSeq platforms for all time points and T0 communities with additional deep sequencing of the T0 communities using the HiSeq platform.Vertical and horizontal transfers were performed at exactly the same time every two weeks. Before each transfer aliquots of the community were taken for glycerol stocks, DNA extraction and phage storage. Glycerol stocks were created by mixing 500 µl of cellulose-microbial slurry with 500 µl of 80% glycerol and stored at −80°C. For DNA extractions 2 ml of cellulose-microbial slurry was centrifuged at 13 000n = 10) followed by centrifugation and passage through a 0.2 µm filter to remove microbial cells. Recognizing the possibility that ultra micro-bacteria might escape sedimentation and pass through the filter, metagenomic sequence reads were recruited to seven available reference genomes. A maximum of 0.018% of reads across all horizontal and vertical mesocosms at 48 weeks matched these reference genomes .Transfers at two-week intervals were performed as described in (c)rinSeq [−5, minimum alignment length of 100 base pairs, and a minimum percentage identity of 90%. ‘Unique’ horizontal and vertical sequences were assembled using MEGAHIT [r-Hit [0 metagenome  and the paired vertical metagenome using BLASTn. Horizontal-T0 and horizontal–vertical sequence matches were removed, thus leaving a set of unique horizontal sequences that could not be identified in neither the founding T0-metagenome, nor the paired vertical metagenome. Sequence matches were determined using a minimum e-value threshold of 1 × 10−5, minimum alignment length of 100 base pairs, and a minimum per cent identity of 90%. The unique horizontal sequences were then assembled, ORFs were extracted, and predicted protein domains were determined through comparison to the CDD. To determine the impact of increasing the sequencing depth of the T0 communities on the final percentage of unique sequences, T0 metagenomes were sequenced using the HiSeq platform and reads from each mesocosm split arbitrarily into eight metagenomes each consisting of about approximately 40 million sequences. The unique analysis pipeline was then applied using the first arbitrary T0 metagenome and any matching sequences were removed. The process was repeated with all eight T0 arbitrarily defined metagenomes thus revealing the extent to which additional sequencing affected the ability to detect rare sequences.DNA sequences were demultiplexed using bcl2fastq, paired ends were joined using FLASh  and initrinSeq  with a mrinSeq . SequencrinSeq  with a m MEGAHIT , ORFs wi MEGAHIT , compare MEGAHIT  using RPT [r-Hit . Sequenc(d)24) and T0 were re-established by placing 100 µl of glycerol stock into 20 ml of M9 minimal media supplemented with a 4 cm2 piece of cellulose paper and incubated for two weeks. One millilitre of the cellulose-community slurry was transferred to 19 ml of M9 minimal media with a fresh piece of cellulose and incubated for an additional two weeks. One millilitre of the community was then transferred to a new bottle and 100 µl of surrounding media was sampled at various time points during the two-week incubation period to determine ammonia/ammonium, nitrate and nitrite concentrations. The concentrations of all three nitrogen species were determined using fluorometric assay kits according to the manufacturer's protocol (Ammonia Assay Kit, Sigma-Aldrich, and nitrate/nitrite fluorometric assay kit; Vertical and HC from the 1-year time point (24 transfers, T


Cupiennius salei); however, much less is known about the distribution and function of chemosensory organs. Furthermore, our knowledge on how the sense organ pattern develops on the spider appendages is limited. Here we analyse the development of the pattern and distribution of six different external mechano- and chemosensory organs in all postembryonic stages and in adult male and female spiders of the species Parasteatoda tepidariorum. We show that except for small mechanosensory setae, external sense organs appear in fixed positions on the pedipalps and first walking legs, arranged in longitudinal rows along the proximal-distal axis or in invariable positions relative to morphological landmarks . A comparison to other Entelegynae spiders shows that these features are conserved. We hope that this study lays the foundation for future molecular analysis to address the question how this conserved pattern is generated.Spiders are equipped with a large number of innervated cuticular specializations, which respond to various sensory stimuli. The physiological function of mechanosensory organs has been analysed in great detail in some model spider species (e.g. The online version of this article (10.1007/s00427-020-00655-8) contains supplementary material, which is available to authorized users. Arthropods have a sophisticated sensory system which mediates environmental interactions and supports essential activities such as foraging and mating Stevens . BesidesParasteatoda tepidariorum, with specific focus on the chemosensory (CS) setae, which are understudied in spiders. Previous publications suggest that CS setae are more abundant on the anterior appendages  on the appendages of the spider s Foelix  and we ts Foelix . The peds Foelix .In arthropods, ESOs originate from precursor cells in the epidermis, which form cuticular specializations such as bristles, sockets and slits, in addition to the neural part of the sense organ , or sturdy (bristle-like). Many exhibit a serrated surface Table  but smooThe tarsal organ is an open pit located on the dorsal tarsus. It contains several sensilla with single pores at the tip suggesting an olfactory function but electrophysiological recordings also indicate responses to humidity and thermal stimuli Barth . The CS P. tepidariorum in all postembryonic and adult stages and both sexes. The aim of the paper is to contribute to our understanding of the evolution of sense organ patterns and to provide the morphological basis for future molecular genetic studies in this model spider species.Here, we describe the distribution of these 6 types of sensilla in the pedipalps and L1 of P. tepidariorum shows a dome-like shape and a central pit  setae are present on the distal most 2–4 podomeres of the pedipalp and L1, respectively, in addition to the pedipalp coxa Fig. , followeOn the tibia of L1, the first couple of CS setae appear in fixed positions on the dorso-posterior side close to the metatarsal joint (2nd instar) and on the dorso-anterior side in the middle of the podomere between the landmark MS setae 3rd instar) Fig. . In the rd instarIn the 1st instar, most of the tarsal CS setae are located at the distal tip Fig. . The firP. tepidariorum, the CS setae of the walking leg are mainly found on the distal podomeres  in all Entelegynae spiders analysed , has three CS rows on each side of the leg in addition to a dorsal and a ventral row . In contrast to other spider species, the trichobothria are not clustered in P. tepidariorum, rather, they are aligned in an offset dorsal row   Barth .Slit sensilla are present from the 1st larval stage onward, initially only in the distal podomeres . Most slit sensilla cannot be identified individually throughout development due to a lack of landmarks and intercalary growth of the appendages. However, two slit sensilla located at the distal tip of the tarsus are visible in all postembryonic stages and in the adult, both in L1 and the pedipalps Figs. b. The slP. tepidariorum is similar to the pattern in C. salei and A. bruennichi  Fig. b. There C. salei and A. bruennichi  and in L1  of P. tepidariorum, considering the different sizes of the appendage joints. In all three spider species, there is a single lyriform organ on the coxa and on the metatarsus of L1 but the numbers vary in the remaining joints. A. bruennichi and P. tepidariorum also show sex-specific differences in the number of lyriform organs on the tibia of the pedipalps. While there is a lyriform organ at the distal tibial joint in A. bruennichi males, the sense organ is missing in females. The reverse is the case in P. tepidariorum.Compared to P. tepidariorum compared to other Entelegynae spider species from which data are available  in late postembryonic and adult stages, and cannot be individually identified, except for a few large bristle-like setae. In contrast, all other ESOs seem to appear in fixed positions. CS setae, slit sensilla and trichobothria are arranged in longitudinal rows along the proximal-distal axis. In addition, there is a dense field of CS setae at the tip of the pedipalpal and L1 tarsi and a cluster of slit sense organs on the pedipalpal coxa and L1 trochanter  with lactic acid (1:1 lactic acid to Hoyer’s medium) was used as a mounting solution clearing the cuticles of soft tissues and pigments. The mounted samples were placed into an oven at 65 °C overnight. An inverse Leica microscope (DM IL) and corresponding LAS software (version 2.8.1) were used for image acquisition of cuticle preparations.For the analysis of the external sense organs, we used either the exuviae, which were preferred because of their transparency, or whole mounts of the spider appendages. Leg and pedipalp preparations of spiders were obtained by placing them for a few minutes at − 20 °C to anaesthetize them. Using forceps and a stereomicroscope, pedipalps and 1st walking legs were removed. The specimens were mounted between two coverslips enabling the imaging from both sides (anterior and posterior). Hoyer’s medium  and the 1st walking legs of adult female spiders (removed from anaesthetized spiders as described above) were fixed in 1:1 heptane and glutaraldehyde/paraformaldehyde (3%) and 0.01% Triton X-100. The detergent reduces the surface tension which enables the samples to submerge. The samples were fixed overnight at 4 °C and dehydrated using an ascending alcohol series . Samples were incubated for 15′ at each concentration and then chemically dried using HMDS (Sigma-Aldrich) by transferring the samples into a glass dish. The samples were washed once with 1:1100% EtOH and HMDS and replaced with 100% HMDS, which was left in the glass dish for evaporation overnight under the fume hood. Dried samples were carefully mounted onto aluminium stubs prepared with sticky tape. For SEM imaging (FEI Quanta 3DFEG and the FEI Inspect F electron microscopes), the samples were gold sputtered (Agar auto sputter coater model 108A).Light microscope images were used to count the 6 different ESOs on each side of the podomeres . For the 1st to 5th instars, we counted the ESOs of 6 appendages each for pedipalps and L1. For the subadult and adult stages, we counted 2 appendages each for pedipalps and L1, and for females and males, respectively.Microsoft Excel  was used to document data, calculate the mean and standard deviation (SD), as well as for creating bar charts. The schematic drawings are based on light microscopic images and were generated with Inkscape (version 0.92.3) and Adobe Illustrator (version 16.0.0).ESM 1(PDF 22718 kb)


Nephrogenic diabetes insipidus (NDI), which can be congenital or acquired, results from the failure of the kidney to respond to the anti-diuretic hormone (ADH). This will lead to excessive water loss from the body in the form of urine. The kidney, therefore, has a crucial role in maintaining water balance and it is vital to restore this function in an artificial kidney. Herein, an ultrasensitive and highly selective aptameric graphene-based field-effect transistor (GFET) sensor for ADH detection was developed by directly immobilizing ADH-specific aptamer on a surface-modified suspended graphene channel. This direct immobilization of aptamer on the graphene surface is an attempt to mimic the functionality of collecting tube  Anti-diuretic hormone (ADH), also known as arginine-vasopressin or neurohypophysial hormone, is an imperative peptide for maintaining the plasma osmolality in the human body . TheoretFrom 1950 to the present, several approaches have been developed and implemented for detecting ADH, namely bioassays, immunoassays, radioimmunoassays and enzyme-linked immunoassays (ELISAs). The extensive time consumption, extravagant procedure cost and low specificity of bioassays lead to the development of the immunoassay approach . HoweverCurrently, there are many ADH assay kits available on the market, mostly dominated by ELISA-type kits such as the ADH/ vasopressin (VP)/ arginine-vasopressin (AVP) ELISA Kit from Biomatik. The lowest threshold exhibited by these kits is 0.5 pg/mL . Althougn−1·kg−1 , the limn−1·kg−1 . TherefoOne of the most promising tools to resolve these issues is the biosensor. Among the variety of electrical biosensing architectures reported ,15, deviA graphene-based field-effect transistor (GFET) with suspended graphene (the suspended graphene bridges the source and drain electrodes) holds more advantages than an embedded architecture (graphene supported by silicon dioxide substrate). This is because charge carriers in a suspended graphene FET offer high mobilities up to ded GFET ,20. In aded GFET . Howeverded GFET . This leIn this work, a FET-based biosensor was developed to detect ADH in experimental setting. A suspended graphene-based FET was fabricated for the ultrasensitive, selective and rapid detection of ADH. Moreover, ADH-specific aptamers were immobilized on the graphene channel as an attempt to mimic the functionality of the collecting tube Field-effect transistor (FET) devices were fabricated by using the conventional photolithography process and metal lift-off technique as described previously . Single Two microliters of 1 M of potassium hydroxide (KOH) with pH 9.0 acquired from Qrec (Asia) Sdn. Bhd. was pipetted onto the graphene layer at the channel area. A phosphate-buffered saline ) was used as the washing buffer. The device was characterized using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to inspect for the successful chemical activation of the graphene layer. The process then proceeded with the functionalization of the graphene layer that comprises of two main stages which are surface modification and immobilization of aptamers.The surface modification began with the introduction of 2% (3-aminopropyl) triethoxysilane (APTES) in 30% ethanol solvent to formulate an amine functionalized surface which was then left for 2 h at ambient condition. PBS buffer was used to rinse and wash the device three times to remove unbounded APTES residues from the surface. Subsequently, 2.5% glutaraldehyde (GA) was applied to the chemically modified graphene surface. This was left for an hour for the molecules to establish covalent bonding with the amine functionalized surface. The surface was then rinsed, washed and left till dry. After that, 1 µM of ADH specific-aptamer with the sequence of 5’ TCACGTGCATGATAGACGGCGAAGCCGTCGAGTTGCTGTGTGCCGATGCACGTGA-3’  , urease, glucose oxidase and human serum without ADH hormone was independently applied at the channel area and electrical measurements were recorded using the Keithley 2400 Sourcemeter SMU Instruments. Similarly, human serum with ADH was tested to check for the specific functioning of the biosensor in human body conditions.The image of the fabricated device obtained from the optical microscope is illustrated in The chemical composition of elements that made up the fabricated GFET was inspected and verified by performing the energy-dispersive X-ray analysis (EDX) on the GFET area comprising the source, drain and transducing channel. The 3D image from the characterization of surface morphology by the atomic force microscope (AFM) is illustrated in Raman spectroscopy was performed to analyze the structural and electronic properties of the graphene before and after transferring onto the device . A 532 nectively . The ratBased on the data obtained and tabulated in 350 cm−1 c. This pl doping ,29. The image of the FET after graphene transfer is shown in graphene . HoweverIt is also worth mentioning that there is a sharp peak at 2450 orted by  in 2005.orted by ,33,34. TThe ATR-FTIR transmission spectra of the graphene layer for four consecutive surface functionalization processes that begins with chemical activation a, surfacFour new peaks emerged after the graphene layer was activated with 1 M of KOH in comparison with bare graphene a. A peakgraphene . Valanced out by . The pea bending  and out- bending . Therefo bending . The KOH-activated graphene was then modified with APTES. From Subsequently, 2 µL of GA was added on the transducing layer. GA is a homo-bifunctional cross-linker with aldehyde groups on both ends with carbon chain spacer . The alds report . Peaks aIn the last stage, ADH-specific aptamer which is the bioreceptor for the ADH biosensor was immobilized on the surface-modified graphene. The measurement was taken only after 15 min to ensure the complete immobilization of ADH-specific aptamer on the surface-modified graphene at the channel area . Based oThus, the successful surface modification and immobilization of ADH-specific aptamers on the KOH-modified graphene surface were verified through ATR-FTIR characterization. The schematic illustration of the stages involved in the surface functionalization of graphene is illustrated in The electrical characteristics of the GFET device were monitored and measured as each individual surface functionalization step was carried out during the functionalization of the device a. Figure3-point probe measurements were carried out and changes in drain current in the range of 6 to 8 V were observed a. At thiThe value of This drastic decrement of orted by . The drain current, The increase of drain current in conjunction with the increase of ADH concentration clearly authenticates that the binding of positively charged ADH hormone with ADH-specific aptamer increases the number of positively charged biomarkers at the surface of the transducing channel. This, in turn, exerts an attractive force to attract negatively-charged charge carriers to accumulate in the channel area. Thus, the concentration of electrons increases in the transducing channel area (the area between the source and drain electrodes). This, in turn, increases the conductivity of graphene which is proved by the increase of drain current in the biosensor. This increase in ADH concentration leads to the increase of concentration of charge carriers in the transducing channel which causes a significant increase in c + 0.0009) with a correlation coefficient of 0.9613. Therefore, based on the slope of the calibration curve, the sensitivity of this biosensor to detect ADH spiked in PBS buffer is 5.00 × The graph of  plotted c. The liThe graph of the ratio of relative change in drain current versus logarithmic concentration of ADH spiked in PBS buffer is illustrated in The limit of detection (LOD) was considered with the lowest concentration of an analyte  against the background signal (S/N = 3:1), in other words, LOD = standard deviation of the baseline + 3σ. Based on 3σ calculation, the ADH biosensor exhibits a limit of detection as low as 3.55 ag/mL. LOD in the range of Therefore, LOD as low as 3.55 ag/mL shows an improvement in the detection limit compared to the research work that has been previously reported ,45,46. TThe specificity analysis on the ADH biosensor was carried out by dropping different biomolecules independently on the ADH-specific aptamer-immobilized transducing channel . The graGlucose oxidase (GOx) is an enzyme conventionally found in the human body, whereas urease is also an enzyme that could not be synthesised by mammalian cells. However, it can be produced by myriad bacteria found in the body, particularly in the intestine . The intFinally, human serum without ADH was pipetted on the functionalized transducing channel and the drain current was recorded. Based on When 1 pg/mL of ADH spiked with human serum was tested on the ADH-specific aptamer immobilized transducing channel, the drain current reading was recorded after a wait of 10 min. The drain current was then increased up to 557.89 µA when it indicated the successful binding of ADH-specific aptamer with ADH-spiked in human serum. This strongly suggested and proved that ADH biosensor is highly specific and selective. ADH spiked in PBS buffer and human serum demonstrated a large change in current and recorded a higher current compared to other tested biomolecules. This shows that specific interaction only takes place with ADH-spiked in PBS buffer and ADH-spiked in human serum which increases the charge carrier density at the transducing channel and eventually leads to a drastic change in current. Therefore, the ADH biosensor is highly selective and specific in interacting with ADH molecules which is promising for future applications in an artificial kidney.In conclusion, the ADH biosensor has a sensitivity of 50.00 µA·


Generation of electric voltage in a conductor by applying a temperature gradient is a fundamental phenomenon called the Seebeck effect. This effect and its inverse is widely exploited in diverse applications ranging from thermoelectric power generators to temperature sensing. Recently, a possibility of thermoelectricity arising from the interplay of the non-local Cooper pair splitting and the elastic co-tunneling in the hybrid normal metal-superconductor-normal metal structures was predicted. Here, we report the observation of the non-local Seebeck effect in a graphene-based Cooper pair splitting device comprising two quantum dots connected to an aluminum superconductor and present a theoretical description of this phenomenon. The observed non-local Seebeck effect offers an efficient tool for producing entangled electrons. Thermoelectricity due to the interplay of the nonlocal Cooper pair splitting and the elastic co-tunneling in normal metal-superconductor-normal metal structure is predicted. Here, the authors observe the non-local Seebeck effect in a graphene-based Cooper pair splitting device. Thermoelectric effects in the superconducting systems22, in particular those dealing with non-local thermoelectric currents in superconductor–ferromagnet devices25 and in bulk non-magnetic hybrid NSN structures28 have attracted special attention. The connection between thermoelectric effects and the Cooper pair splitting (CPS)2, proposed in ref. 29, established a mechanism for the coherent non-local thermoelectric effect in hybrid superconducting systems. This connection was further studied and explicitly described for a ballistic NSN structure4. It was revealed analytically in ref. 4 that the electric transport in the NSN structures depends on the elastic co-tunneling (EC) process on par with the CPS. Contrary to intuitive expectations, together these two processes may enable the transfer of heat through the superconductor5. The EC and CPS probabilities can in turn be made energy dependent by placing quantum dots between each normal lead and the superconducting region2.Mesoscopic thermoelectric effects have been investigated in a variety of condensed matter systems that, besides fundamental normal metal–superconductor–normal metal (NSN) systemsHere we present the experimental observation of the non-local thermoelectric current generated by imposing thermal gradient across a quantum dot–superconductor–quantum dot (QD-S-QD) splitter. We find that both CPS and EC processes contribute to the non-local thermoelectric current and that their relative contributions can be tuned by the gate potentials. The ability to tune between the CPS and EC allows for testing of fundamental theoretical concepts relating entanglement and heat transport in the graphene CPS systems.E∣ < Δ, we find, see Supplementary Note τEC(E), and CPS, τCPS(E), probabilities are given by the expressionsτL(R)(E) is the transmission probability of the left (right) quantum dot renormalized by Coulomb interaction, which depends on the energy of an electron and on the side gate potentials applied to the dots Vsg,L(R) (τL(R)(E) is given by the sum of Lorentzian peaks or Fano resonances associated with discrete energy levels, see Supplementary Note τS is the effective transmission probability of the superconducting lead. The latter corresponds to the probability for an electron coming out of one dot so that, instead of escaping into the bulk of the superconducting electrode, it reaches the other dot. It becomes independent on the electron energy E if the dots are separated by a distance shorter than the superconducting coherence length. This condition is reasonably well fulfilled in our experiment. The non-local thermoelectric currents in the dots can, in turn, be expressed in terms of the EC and CPS contributions, TL(R).Let us consider an QD-S-QD device within the Landauer formalism. Taking that the non-local transport is primarily coherent and that the electron energies are smaller than the superconducting gap, ∣37, and particularly promising results have been obtained in carbon nanotube, graphene, and nanowire settings, where the splitting efficiencies approaching 90% have been observed. Our present device, depicted in Fig. Isw(T)38. The resistive heater comprises the graphene nanoribbon and two attached aluminum leads. The heater is distinctly apart and electrically isolated from the rest of the device, the heat to the Cooper pair splitter being transmitted through the substrate.Now, we turn to experimental realization of CPS and EC. Several material platforms have been employed in the experimentsT = TL − TR between the leads of the two-terminal device induces the thermoelectric current I = αGΔT, where G is the conductance of the device and α is the Seebeck coefficient39. For typical metals, such as aluminum or copper, the Seebeck coefficient is quite small, α ~ 3−7 μV/K. For graphene, α is inversely proportional to the square root of charge density, and it can reach much higher values close to the charge neutrality point41. In quantum dots with energy-dependent electron transmission probability42, and in superconductor–ferromagnet tunnel junctions25 large α up to a few kB/e ~ 100 μV/K can be achieved. In our experiment, we observe similar values of the Seebeck coefficient in graphene quantum dots. We operate the graphene heater at frequency f = 2.1 Hz and record thermoelectric currents through both quantum dots at the double frequency 2f (see “Methods”). Thermal gradient induced by the heater is measured by SGS thermometers, which were calibrated separately as discussed in Supplementary Note The temperature difference ΔIL(R), we subtract off its slowly varying average local background, 〈IL(R)〉 (see “Methods”):Vh = 5 mV. In Fig. gL(R) = hGL(R)/e2 is the dimensionless conductance of the left (right) quantum dot. We find that gL/dVsg,L]gR.The thermoelectric current induced by the heater in the left (right) quantum dot is given by the sum of dominating local  between the nth energy level of the dot  and, respectively, normal and superconducting leads; we also assume that the nth level is coupled to a dark energy level, having the energy tj,n. This results in the transmission probabilities of the dots 43, exhibit splitted peaks for tj,n ≠ 0. In our model, we ignore Coulomb interaction because the charging energies of the dots are relatively small, EjC, ≲ γj,n + Γj,n.Before proceeding to our main result, note that some conductance peaks are split into two closely located peaks see Fig. . The splVsg,R = −1.24 V. Although in order to reproduce this behavior we had to take hopping amplitude, tR, larger than required by the perfect fit to the conductance peak /2 in evaluating 44; the same theory was earlier employed in deriving the non-local contributions using Eq.  is inferred from the experimentally measured conductance gL, as explained in Supplementary Note Since, as noted, the non-local currents are relatively small, one can treat the measured currents foremost as local, entclass1pt{minimaIn the low temperature regime, the coherent model predicts very small current due to lack of quasiparticles, while the experimental thermoelectric current remains significant and exhibits additional sign changes in the vicinity of some of the conductance peaks. These features can originate from non-zero, thermally induced voltages across the dots. To capture these effects, we propose that electrons may undergo quick inelastic relaxation, see Supplementary Note 5 as the non-local thermoelectric effect is shown to develop from single peak to double peak structure when temperature is lowered5. The calculated double peak structure is similar to our local thermoelectric signal observed at low temperatures. However, the resonance energy dependence of the non-local signal with Coulomb blockade agrees poorly with the experimental results in Fig. Alternatively, the additional peak in the local thermoelectricity could originate from Coulomb blockadeThis work has demonstrated the use of thermal gradient as primus motor for generating entangled electrons in graphene Cooper pair splitter. As the quantum dots in the device can be tuned individually, we are able to tune the device operation between EC and CPS regimes, thereby accomplishing direct control of two streams of entangled electrons. This type of scheme is useful not only for enabling devices where electrical drive is neither possible nor desired but also as a platform for realizing quantum thermodynamical experiments.++ doped silicon wafer, coated by 280-nm-thick thermal silicon dioxide. The conducting substrate was employed as a backgate for coarse tuning of the graphene quantum dots, while fine tuning was performed by adjusting the side gates. Electron beam lithography (EBL) on PMMA resist was used to pattern a mask for plasma etching of the graphene structures. A second EBL step was carried out to expose the pattern for electrode structures, followed by deposition of Ti/Al  leads using an e-beam evaporator. Normal contacts to the graphene quantum dots were made using etched graphene nanoribbons with a small number of conductance channels at the operating point45.Our graphene films were manufactured using mechanical exfoliation of graphite  and placed on a highly p++ doping and the interfacial scattering at the Si/SiO2 interface reduce the phonon mean free path in the substrate to one micron range, which facilitates the use of the heat diffusion equation for estimating thermal gradients along the substrate near the graphene ribbon heater and the splitter. Heat transport analysis was done separately for each component involved in the operation of the CPS, as well as a COMSOL simulation, see Supplementary Note The strong p6 V/A, which provides a virtual ground of 20 Ω. The lead resistance including filters was approximately 100 Ω, which is much less than the quantum dot resistance ~h/e2, the quantum resistance. The galvanically separated heater was driven at f = 2.1 Hz, with an ac voltage amplitude ranging between 1 and 40 mV  were experimentally found to provide good thermometers over the relevant range of temperatures in our work.The local temperature was monitored using two SGS junctions. At low temperature, because of the proximity effect, graphene becomes superconducting, with a supercurrent exponentially proportional to temperature: T to infer the effective local temperature within the graphene sample. The SGS temperature under the voltage bias Vh in the graphene ribbon heater was obtained by direct comparison between L temperature and the heating voltage (Vh in Volts). For the SGSR thermometer, we obtained an estimate We have used the amplitude of the differential resistance peak entclass1pt{minimaIL(R), constituting the sum of local, IL(R) averaged over the gate voltage on the opposite dot; IL(R). On practice, however, the gate electrodes may be subject to cross-talk, which the described simple processing does not account for. For the most part, the cross-talk was eliminated by a small rotation of the data array. Bearing in mind the remaining residual cross-talk, we construct a slowly varying background Vsg,R. The non-local contribution is then obtained using the formulaVsg,L, Vsg,R} plane.The direct experimental measurement allows to obtain the total electric current through the left (right) dot, 46 for calculating the local thermoelectric current; Lorentzian resonance line shapes are employed for transport in the quantum dots48. For the non-local current, we employ a standard crossed Andreev reflection formalism51. In our incoherent theory, based on scattering matrix formalism52, we also include the influence of internal thermally generated current sources and their back-action effect owing to the environmental impedance caused by graphene ribbons. The inclusion of the back-action-induced voltage sources makes the incoherent calculation self-consistent. For details of the calculations, we refer to Supplementary Notes Our theoretical calculations are based on both coherent and incoherent modeling of transport. In coherent modeling, we employ the Landauer approach with Andreev reflectionSupplementary InformationPeer Review File


Background. Muscle synergy analysis is an approach to understand the neurophysiological mechanisms behind the hypothesized ability of the Central Nervous System (CNS) to reduce the dimensionality of muscle control. The muscle synergy approach is also used to evaluate motor recovery and the evolution of the patients’ motor performance both in single-session and longitudinal studies. Synergy-based assessments are subject to various sources of variability: natural trial-by-trial variability of performed movements, intrinsic characteristics of subjects that change over time , as well as experimental factors such as different electrode positioning. These sources of variability need to be quantified in order to resolve challenges for the application of muscle synergies in clinical environments. The objective of this study is to analyze the stability and similarity of extracted muscle synergies under the effect of factors that may induce variability, including inter- and intra-session variability within subjects and inter-subject variability differentiation. The analysis was performed using the comprehensive, publicly available hand grasp NinaPro Database, featuring surface electromyography (EMG) measures from two EMG electrode bracelets. Methods. Intra-session, inter-session, and inter-subject synergy stability was analyzed using the following measures: variance accounted for (VAF) and number of synergies (NoS) as measures of reconstruction stability quality and cosine similarity for comparison of spatial composition of extracted synergies. Moreover, an approach based on virtual electrode repositioning was applied to shed light on the influence of electrode position on inter-session synergy similarity. Results. Inter-session synergy similarity was significantly lower with respect to intra-session similarity, both considering coefficient of variation of VAF  and coefficient of variation of NoS  as well as synergy similarity . Virtual electrode repositioning revealed that a slightly different electrode position can lower similarity of synergies from the same session and can increase similarity between sessions. Finally, the similarity of inter-subject synergies has no significant difference from the similarity of inter-session synergies (both on average approximately 84–90% depending on selected VAF). Conclusion. Synergy similarity was lower in inter-session conditions with respect to intra-session. This finding should be considered when interpreting results from multi-session assessments. Lastly, electrode positioning might play an important role in the lower similarity of synergies over different sessions. The way in which the central nervous system (CNS) controls and coordinates many muscles in voluntary movements is a longstanding question . Muscle Referring to neurorehabilitation, several efforts were made in previous studies to characterize the patients’ EMG patterns with muscle synergies. It was shown that lower motor function (reduced Fugl–Meyer assessment scores) might be coupled with a reduction in the number of available synergies ,16. FurtIn this context, it was shown that several aspects related to the evaluation of motor recovery and the evolution of the patients’ motor performance may be quantified with muscle synergies, both in single-session and longitudinal studies. In fact, synergy-based assessments require extracting muscle synergies from data recorded in different experimental conditions and/or sessions and subsequently matching and comparing them for purposeful assessments. Thus, the results need to be interpreted considering the effect of intra-subject and inter-subject variability as well as intra-session and inter-session variability. Variability can be defined in this context as a set of similarity-based metrics to compare synergies across experimental conditions. Intra-session factors for variability include natural trial-by-trial variability of performed movements . On the These challenges have only been partially addressed in literature. The effect of some of these sources of variability on extracted muscle synergies was already emphasized in several studies on healthy people, when assessing upper-limb intra- and inter-subject synergy composition similarity ,21,22 orSeveral aspects related to the variability found in muscle synergies can be analyzed in more detail to provide an assessment preparatory to rehabilitation applications. More specifically, this study aims at quantifying the sources of variability of muscle synergy composition that are related to inter-subject variability as well as intra-subject variability separated into intra-session (different movement repetitions) and inter-session (different acquisition sessions). We decided to assess these features first on a dataset of healthy participants under the assumption that, in each session, the motor capabilities of the subjects recruited were the same. The analysis was performed using as a testing scenario the comprehensive, publicly available hand grasp NinaPro Database (dataset number 6), featuring surface EMG measures from two EMG electrode bracelets, placed at the forearm level ,29. At tBy analyzing robustness, stability and similarity of extracted muscle synergies under the effect of factors that may induce variability , this work contributes to the knowledge available regarding the clinical usability of muscle synergies. The presented results are application specific, since electrode repositioning is simulated and constrained to a bracelet configuration of electrodes that was used for the NinaPro database. Nevertheless, through this methodology, we give a preliminary assessment of the possible influence of electrode position on the stability of muscle synergies in hand grasps, possibly estimating the effects of electrode movement and subject variability.A graphical overview of the data analysis performed in the study is shown in The NinaPro database  is availThe set of hand grasps was chosen from the robotic and rehabilitation literature with the goal of covering several hand movements exploited in activities of daily living (ADL) ,35,36. TSurface EMG was recorded with 14 Delsys Trigno sEMG wireless electrodes as shown in EMG data were sampled at 2000 Hz. The recorded signals correspond to the entire movements. The subjects were asked to sit in front of a table with the forearm leaning on it. The grasp to be executed was shown in two videos and then a set of audio indications explained the tasks to be performed . A fixed image representing the grasp was shown on the screen of the laptop during the experiment. The number of repetitions was equally distributed among two different objects. Each repetition lasted for 4 s and was followed by 4 s of rest .The first step of data analysis was detecting the movement phases and aligning movement repetitions for each condition, grasp and subject considered. Moreover, data were preprocessed in several steps, with a preprocessing pipeline similar to the one employed in recent studies ,41. EMG For each condition  and subject, muscle synergies were extracted from concatenated EMG data of all exercises using nonnegative matrix factorization (NNMF) ,43.NNMF was repeated for a fixed number of extracted synergies ranging from 1 to the number of recorded EMG channels (14). Since NNMF is stochastic, analysis was repeated 50 times ,44 with In order to compare synergies from different conditions , we set up a brute force algorithm enabling to produce meaningful synergy matching after NNMF . The algWe classified several aspects of synergy stability and robustness, which we achieved by decomposing the NinaPro dataset as illustrated in Intra-session analysis focused on a comparison of synergies that were extracted from movement repetitions within the same session. Synergies were extracted from 6 randomly selected repetitions of movements out of the 12 available that were recorded in each session see . ExtractInter-session analysis focused on comparing metrics related to extracted synergies from different sessions. To achieve this result, all 12 repetitions of movements from the same session were concatenated and used to extract synergies of that session. The synergy sets of 5 sessions of each subject were compared see .The aim of this analysis was to assess the role of electrode positioning as a source of difference between inter-session and intra-session similarity of synergies. Placing electrodes in a slightly nonrepeatable way between sessions can happen in real scenarios and has been mentioned already as a possible reason for lower inter-session similarity and stability of synergies with respect to intra-session assessments .The NinaPro dataset 6 (Ninapro DB6) is a valuable resource to study the effect of electrode positioning on muscular hand synergies, and to our knowledge, no other publicly available datasets include the repetition of the same electromyography acquisition protocol in different sessions. The NinaPro DB6 contains recordings performed during 10 acquisition sessions with the EMG electrodes positioned in a bracelet configuration. The electrode configuration allows to simulate slight electrode displacements by artificially calculating values of EMG data from the recorded channels. The electrodes in each bracelet were equally spaced, and the bracelets were positioned according to anatomical landmarks. A first approximation to simulate the misplacement of the electrodes is to assume that each electrode in the same bracelet was rotated medially or laterally by the same distance. Thus, expected differences in rotation could be due to the possibility that the electrodes were misplaced by a few millimeters to the medial or lateral side according to the anatomical landmark. Again, a preliminary approximation to simulate the misplacement of the electrodes could be to assume that the misplacement can be approximated by linear interpolation of the EMG activity from adjacent electrodes. Similarly, in fact, each Delsys Trigno electrode detects sEMG at the skin surface by 4 silver bar contacts that are 5 mm large and positioned a few millimeters from each other. Obviously, this approach is a strong simplification of the electrode misplacement problem. The real change in the recorded muscle activity is likely nonlinear due to muscle physiology, as well each electrode is likely moved singularly further or closer to the anatomical landmark. Nevertheless, the simplification is justified by the limits of the acquisition setup itself , and it was sufficient for the aim of this analysis .Five simulated step sizes were tested  accordinDuring the recordings of Ninapro DB6, the equipment consisted of two electrode bracelets; both of them could, in theory, be moved for different step sizes or even in different directions. It follows that the number of possible movement combinations exponentially increases. Thus, at the present stage, we decided to limit our analyses to the more proximal bracelet including only the first 8 electrodes  and assume that all 8 electrodes were moved by an equal step size in the same direction.New artificial EMG data were created by linearly interpolating EMG recordings from two neighboring electrodes using the formula and schematics shown in The analysis was performed as follows: (1) From the original data, new artificial data were created by interpolating new channels for steps of −8 mm, −4 mm, 0 mm, 4 mm, and 8 mm. This simulation was performed for each participant and each session. (2) For each electrode displacement step, synergies were extracted with NNMF from all steps of all sessions of each participant. (3) Matching was performed between extracted synergies. Depending on the purpose of the analyses, synergies were matched according to specific criteria  as shown in First, we computed variance accounted for (VAF) curves depicting how VAF changes, when an increasing number of synergies was extracted with the NNMF algorithm for different sessions (inter-session) and different sub-selections of movements for the same sessions (intra-session). VAF values were used as a measure of the quality of reconstruction of the data. Furthermore, we analyzed the number of synergies (NoS), which was also employed as a measure of synergy stability . Since iIn order to quantify variability of VAF and NoS among sub-selections of movements or between sessions, the coefficient of variation (CoV) was used. It is a measure of the dispersion of data points around the mean. Subsequently, CoV was calculated for inter-session and intra-session VAF curves and NoS values. The inter-session dataset was created by computing the VAF (and NoS) values for each of the 5 sessions of each subject, whereas intra-session dataset was achieved by computing the VAF (and NoS) values for 5 movement sub-selections (containing 6 movement repetitions) inside the same session . To get representative intra-session CoV for each subject, 5 CoV from 5 sessions were averaged. In this way, our dataset was composed of 10 CoV for VAF values (one per subject) for each number of extracted synergies (1 to 14) and for each experimental condition (intra and inter) as well as 10 CoV for NoS values (one per subject) for each VAF value and for each experimental condition (intra and inter). In order to investigate the difference between the amount of variability intra-session compared to inter-session condition, the difference in the CoV values was assessed using two-way repeated measures ANOVA using condition type (inter and intra) and VAF (or NoS) as factors.Synergies in intra- and inter-session conditions were compared also according on their spatial composition.The inter-session synergy similarity assessed how similar is the spatial composition of synergies between different sessions in which an individual performed the same sets of movements. EMG data were extracted and filtered from each session individually, followed by the matching of synergies from 5 sessions. The most similar synergies between days, found with the “brute force” matching algorithm  was performed. Analysis was repeated for each session and each participant. Intra-session similarity of each subject was determined as the average of intra-session similarities of all 5 sessions for this subject. In this way, our intra-session dataset for spatial synergy similarity was composed of 10 average matching values (one per subject representing average of 5 sessions) for each VAF value. Since the aim of these analyses was to investigate the relationship between the amount of variability due to intra-session variability and inter-session variability, we tested our dataset to determine the difference in the average spatial synergy matching values between the two conditions (inter and intra) using condition and VAF as factors.Intra-session virtual electrode repositioning (IntraSessVER)The first step for our analysis was to investigate if a different synergy similarity is detected when slightly repositioning electrodes within the same session. This finding can confirm that electrode repositioning might influence synergy similarity and serves as motivation for the following steps of the analyses.To achieve this, we created a simulated dataset to compare muscle synergy similarity from different virtual electrode positions for the same session. We compared average similarities for all possible repositioning distances . This was repeated for all VAF values. Maximal similarity of synergies (MAX) between any two steps of rotations for each session (and subjects), average similarities (AVRG) between all step pairs, and minimal similarities (MIN) were calculated. MAX values represented, for each subject, the average (computed across 5 sessions) of maximum similarities detected between different electrode positions within each session. To achieve AVRG value similarity, values of all step pairs in each of the sessions were averaged (for each subject). Finally, MIN values represented the average (computed across 5 sessions) of minimum similarities for each subject detected for any combination of electrode positions within same session. In this way, our IntraSessVER dataset was composed of 10 AVRG, 10 MIN, and 10 MAX matching values (one for each subject representing an average of 5 sessions) for each VAF value.Inter-session virtual electrode repositioning (InterSessVER)Here, we aimed to assess if repositioning electrodes virtually between sessions can lead to higher similarity between sessions. If this condition was met, a part of the variability in the muscle synergy similarity that existed between sessions can be due to slightly different positions of electrodes. For each possible combination of two sessions, we created a simulated dataset computing the maximum muscle synergy similarity by calculating all combinations of steps for those two sessions. Maximum, minimum, and average similarities between different step pairs (25 combinations) and session pairs (10 combinations) were calculated for all the subjects. MAX values represented the highest similarity that each subject achieved between any two sessions having the option to also shift electrodes between sessions. MIN values reported the minimum value that each subject had between any two sessions. AVRG values were the average of MAX values found for each session pair, calculated for each subject. In this way, our InterSessVER dataset was composed of 10 ARVG, 10 MIN, and 10 MAX matching values (one for each subject) for each VAF value.Intra-session without electrode repositioning (IntraSessORIG)The intra-session dataset was created by computing the similarity of the synergies extracted from a different selection of movement repetitions but from the same session (no electrode repositioning). This was the same analysis as already described in Inter-session without electrode repositioning (InterSessORIG)The intersession dataset was created by computing the similarity of synergies between sessions. The dataset was analogous to the one described in Inter-subject (InterSUBJ)Finally, we considered a fifth condition that we labelled inter-subject . The following datasets were considered:Comp#1: to test whether significant differences existed between similarities of synergies extracted from the same sessions (IntraSessORIG) and synergies from different sessions (InterSessORIG) both without virtually repositioning of electrodes. This was analogous to the comparison in Comp#2: to test whether a significant difference exists in average values of similarities between intra-session (IntraSessORIG) and intra-session with different steps in virtual electrode repositioning (IntraSessVER). This allowed to show whether higher, lower, or comparable intra-session synergy similarity was achievable when electrodes are virtually repositioned.Comp#3: to test whether a significant difference exists between the average value of similarities of sessions with original positions (InterSessORIG) and sessions with the best electrode positioning combination (InterSessVER). This allows us to test whether on average it is possible to get higher inter-session similarity when allowing electrode displacement between sessions. In other words, is it possible that slight electrode repositioning between sessions is one of the factors influencing lower synergy similarity?Comp#4: to test whether a significant difference exists between maximal similarities that can be achieved for inter (InterSessVER) and intra-session (IntraSessVER) conditions while allowing electrode repositioning. This allows to test if, by moving electrodes between sessions, for any combination of sessions, it is possible to get similarity as high as with intra-session data.Comp#5: to test whether a significant difference exists between synergies of the same subject extracted in different sessions (InterSessORIG) when compared to synergies of other subjects (InterSUBJ). This investigation tests how well muscle synergies generalize between individuals with respect to the average inter-session similarity. The analysis was done in order to assess whether lower inter-session (with respect to intra-session) similarities impose serious concern for multi-session or longitudinal assessments. If inter-subject similarities were lower than inter-session ones, synergies contained subject specific information, but generalization of synergies between subjects may not be very high. On the other hand, if inter-subject similarities were on the same level (or higher) than inter-session, then the synergies generalize well between subjects but it is not possible to distinguish whether synergies are from the same subject but recorded during different sessions or from a completely different subject. This can be a serious point to consider for multi-session assessments.Given these 5 sub-datasets, the following comparisons were analyzed :For all the proposed objectives, the dataset was composed of 10 values for MAX, AVRG, and MIN value (one for each subject) for each VAF and condition. All the comparisons were repeated for 4 VAF levels: 80, 85, 90, and 95%.p-values were corrected if sphericity was not satisfied. All tests were performed in the statistical software R [Two-way repeated ANOVA measures were applied in the analyses. The used factors are the condition of the analysis  and VAF or NoS that were used as a criterion for extraction of muscle synergies. Mauchly’s test for sphericity was conducted, and ftware R  . The number of synergies also influenced stability (CoV) of VAF values  = 83.14, p < 0.001). The interaction between the type of comparison (inter vs. intra) and the number of synergies was also significant  = 48.28, p < 0.001).VAF and NoS vary between sessions and even for different movement repetition selections (not shown). In order to assess significance of this variation and to quantify the difference between inter- and intra-session variation, the coefficient of variation (CoV) was used. In p < 0.001). Similarly, VAF  = 13.71, p < 0.001) and interaction  = 10.8, p < 0.001) effects were also significant.Both from In p < 0.001). The VAF also has significant influence  = 7.34, p < 0.001) while interaction after sphericity correction is not significant  = 2.49, p = 0.12).In p < 0.001) after sphericity correction, as the distance is increased and stabilizes with bigger distances. There is also significant influence of VAF  = 37.74, p < 0.001) after sphericity correction and an interaction between VAF and distance  = 37.82, p < 0.001) after sphericity correction.In order to investigate why the inter-session similarity is significantly lower than intra-session similarity, a possible influence of electrode positioning was tested by simulating data from slightly different positions of the electrodes. Results are shown in The comparison of the similarity between different conditions and how they relate to each other is given as an example for a VAF value of 80%. For inter-session (InterSessORIG), average values (black) of synergy similarity between pairs of sessions were 0.841 ± 0.052, maximal values (red) of similarity between two sessions for different subjects were 0.942 ± 0.021, while minimal values (blue) were 0.722 ± 0.093. For intra-session (IntraSessORIG), these values were much higher, all above 0.9 . Average similarities of synergies between two people (InterSUBJ) were 0.837 ± 0.026. The highest similarity between two subjects was 0.940 ± 0.024. The lowest similarity between two subjects was 0.754 ± 0.018.When assessing changes in similarity, when electrodes were virtually moved within the same session (IntraSessVER), the highest similarity (for the given 5 steps) was 0.989 ± 0.003, while average values for different combinations of the steps were 0.824 ± 0.028 and minimal ones were 0.680 ± 0.052. When assessing whether similarity can be increased between sessions by comparing synergies from various positions (InterSessVER), the highest values obtained were 0.961 ± 0.020, average values were 0.912 ± 0.033, and minimal ones were 0.839 ± 0.063.p < 0.001) but without influence of VAF  = 0.94, p = 0.4 after sphericity correction) and significant interaction  = 7.96, p < 0.001).Comparing inter-session (InterSessORIG) and intra-session (IntraSessORIG) similarities without option to reposition electrodes (reported in p < 0.001). Also the VAF value  = 16.78, p < 0.001) and the interaction between type of comparison and VAF  = 36.79, p < 0.001 after sphericity correction) had a significant effect.A lower average similarity was found when electrodes were virtually moved (IntraSessVER) inside the same session with respect to the original inter-session similarity (IntraSessORIG) without moving electrodes. Similarity values for IntraSessORIG and IntraSessVER (reported in p < 0.001). While VAF also had a significant effect  = 4.89, p < 0.05 after sphericity correction), interaction was not significant  = 1.07, p = 0.36). This result suggests that it is possible that the lower similarity  was compared , Comp#3 milarity  Comp#1 tp < 0.001). Interaction had significant influence  = 6.55, p < 0.05 after sphericity correction), but VAF did not  = 0.91, p = 0.41).Even when optimally matching synergies between two sessions with virtual electrode repositioning (InterSessVER), it was not possible to achieve the similarity obtained in synergies computed from the same session after virtual electrode repositioning . VAF influenced the similarity significantly  = 8.37, p < 0.001), but there was no interaction between VAF and the type of analysis  = 0.23, p = 0.055).The last analysis proposed was to see how synergies from the same movements generalize between people (InterSUBJ). A comparison between the average subject-specific inter-session similarity  and the inter-subject similarity , Comp#5 An overview of previous studies revealed limited knowledge considering both inter-session and intra-session stability of synergies in healthy subjects. The novel contribution of this article is to clarify this difference using an appropriate dataset for both analyses. Intra-session stability of synergies is significantly higher than inter-session. There is a continuously decreasing trend of VAF CoV as the number of synergies increases, which stabilizes for a high number of synergies. A decreasing trend was observed as well by Taborri and colleagues  who studA significant difference between inter- and intra-session and a similar decreasing trend was detected also when considering stability of NoS that are needed to explain at least 80, 85, 90, and 95% of variance of original EMG data. Variation of VAF was higher for smaller NoS, and variation of NoS was higher for smaller VAF. This result suggests that, choosing a higher VAF as criteria for selection of the number of synergies, the number of synergies is expected to be relatively stable in both experimental conditions. Instead, increasing the VAF value and accepting a “high” decomposition order reduces the potential of the muscle synergy approach. Thus, according to our results, using the VAF threshold as a method to determine the number of synergies across several sessions (possibly for inter-session comparison) might be a trade-off between stability of synergies and parsimonious number of modules.The stability of VAF and NoS were highly informative but still could not provide details about the spatial synergy composition and the stability needed for multi-session assessments. Thus, the spatial synergy composition through sessions was compared using cosine similarity and matching.The synergy similarity between sessions had a broad range from 0.7 to 0.85 (cosine similarity) depending on the subject and VAF. Possible explanations for differences between subjects are that some subjects had higher movement repeatability because of redundancy (or abundancy) of the musculoskeletal system that allows to perform a movement using different muscle recruitment options as suggested by Latash and colleagues  or becauWe found that intra-session synergy similarity was higher than inter-session similarity. There are several possible reasons for finding significantly higher intra-session values compared to inter-session. First, the repeatability of EMG patterns can be higher inside the same session than between sessions. Since the subjects were instructed to perform exactly the same tasks during each session, this might happen because of natural variability of subjects or due to daily conditions, fatigue, or other factors related to each session. A second possible source of variability is slight electrode misplacement between sessions. Since even in the most accurate experimentation both factors can be found, it is challenging to control all the variables that may induce variability.When synergies from the same session but different combinations of movement repetitions were used and compared (assessing intra-session variability), average similarity  was around 0.962 ± 0.015. This value can be considered as the maximal similarity that we can expect between synergies from the same movement repeated several times. This is a very high similarity and such results have been reported previously ,31 but uIn the existing literature, studies analyzing inter-session or intra-session similarities were not performed on the same data and same muscles of interest. In all studies, it is claimed that good repeatability is found, both at the intra-session and inter-session levels. Kristiansen and colleagues  found veSince the assessment of variability in stability and similarity of synergies comparing intra-session and inter-session conditions revealed significant differences, we also provided a simulation of displacement of the proximal bracelet of electrodes to question whether repositioning might influence these features of muscle synergies.Comparing intra-session synergy similarity without electrode repositioning (IntraSessORIG) with electrode repositioning (IntraSessVER) allowed to detect a significant drop in average similarities when electrodes are repositioned (Comp#2). This leads to the conclusion that, if electrodes are displaced, we cannot expect that synergies can reach the similarity limit of 0.95 .Inter-session synergy similarity significantly increased with virtual electrode repositioning compared to inter-session similarity with the original positions of the electrodes (Comp#3). We found that similarity of synergies between two sessions can be increased if electrodes of one session were virtually repositioned with the goal to achieve higher similarities. This opens a possibility that the lower inter-session synergy similarity found when compared with intra-session (Comp#1) similarity was due to the fact that electrodes were not positioned in exactly the same way for all sessions. This is further clarified in Comp#4. Observing that the highest scores in InterSessVER were smaller than the highest scores in IntraSessVER, we conclude that electrode repositioning cannot account for all the differences that exist between sessions. Possibly, residual differences can be explained by variability induced by changing conditions between sessions  or because even more accurate simulations for electrode repositioning need to be used. For instance, while the simulated data accounted for linear, constant differences among the position of the electrodes, in reality, the variability of the positioning of the electrodes is most likely nonlinear and nonconstant also due to muscle activity and physiology.We consider this result to be of interest in muscle synergy analysis. We found that, in applications that involve distal upper-limb coordination, part of the inter-session variability can be attributed to effects that are probably not related to real physiological variability in motor coordination. While this effect can be relevant in this application that involves fine control compared to others targeting other body segments, the inter-session protocol-related variability should induce researchers to interpret results collected at different time stages with particular care. Only after considering the difference in synergies between sessions due to such protocol-related reasons, one can focus on quantifying other changes of interest, such as recovery in case of patients.Finally, after showing that the intra-session similarity is higher than inter-session similarity, we addressed whether synergy similarity can be distinguished when extracting synergies from the same person but in different sessions (inter-session) or extracting synergies from different people across sessions (inter-subject). We found the unexpected and very interesting result that there was no significant difference between the similarity of synergies from different people and the similarity of synergies from the same person, extracted in different sessions (Comp#5). This result can be interpreted under two perspectives: on one side, it confirms that synergies are meaningful and similar across subjects; on the other hand, while these results cannot be generalized to other scenarios and should be considered as specific of our application, they suggest caution for multi-session assessments and longitudinal analysis.These results also suggest that, across sessions, the subjects’ synergies may have a comparable variability with respect to other people ,25,50. TAll the mentioned results suggest that the synergy-based evaluation of a person within the same session should consider the natural variability that underlies human motor control, as previously shown by other authors . Even moIn this paper, we analyzed robustness, stability, and similarity of the extracted muscle synergies under the effect of factors that may induce variability, including inter-session measurements and natural intra-session variability. These analyses can help shed light on the applicability and challenges of applying muscle synergies in clinical environments as well as to more deeply understand their functioning.The main finding was that spatial similarity is significantly higher in intra-session conditions (synergies extracted from the same data acquisition) rather than in inter-session conditions (synergies extracted from the different data acquisitions). We also found that virtual electrode repositioning can significantly decrease the similarity of intra-session synergies, while it can also increase the similarity of synergies extracted in different sessions. Nevertheless, comparing the highest similarities achieved with virtual electrode repositioning for intra- and inter-sessions reveals that the electrode position does not fully explain the difference in similarity of inter-session compared to intra-session conditions. Future studies could explain if this is due to the strong simplification of the adopted modelling approach or to other experimental factors. Furthermore, the assessment of how well synergies generalize between healthy subjects while performing the same set of movements revealed no significant difference between the similarity of synergies from different subjects and the similarity of synergies from different sessions of the same subject. This point can have an impact on longitudinal studies where synergies can be used for the evaluation of neurological diseases at different time stages and for the assessment of treatment effects: if the changes we want to assess with the muscle synergy approach are smaller than this variability, then we will probably not be able to detect them. Lastly, the results are relative to the specific exercises and EMG channels recorded in the NinaPro hand grasp database 6 and cannot be extended to other movement tasks, electrode positions, and body segments. Nevertheless, they create a new understanding of the supporting evidence that muscle synergy analysis based on nonnegative matrix factorization is clinically and experimentally applicable with the limitations discussed in the paper. Further applications of our analysis need to be considered in longitudinal trials and clinical applications.