Mice harbor ∼2800 intact copies of the retrotransposon Long Interspersed Element 1 (L1). The in vivo retrotransposition capacity of an L1 copy is defined by both its sequence integrity and epigenetic status, including DNA methylation of the monomeric units constituting young mouse L1 promoters. Locus-specific L1 methylation dynamics during development may therefore elucidate and explain spatiotemporal niches of endogenous retrotransposition but remain unresolved. Here, we interrogate the retrotransposition efficiency and epigenetic fate of source (donor) L1s, identified as mobile in vivo. We show that promoter monomer loss consistently attenuates the relative retrotransposition potential of their offspring (daughter) L1 insertions. We also observe that most donor/daughter L1 pairs are efficiently methylated upon differentiation in vivo and in vitro. We use Oxford Nanopore Technologies (ONT) long-read sequencing to resolve L1 methylation genome-wide and at individual L1 loci, revealing a distinctive “smile” pattern in methylation levels across the L1 promoter region. Using Pacific Biosciences (PacBio) SMRT sequencing of L1 5′ RACE products, we then examine DNA methylation dynamics at the mouse L1 promoter in parallel with transcription start site (TSS) distribution at locus-specific resolution. Together, our results offer a novel perspective on the interplay between epigenetic repression, L1 evolution, and genome stability. F, GF, and A  retrotransposon Long Interspersed Element 1 (LINE-1 or L1) is actively mobilizing in both humans and mice, and L1 sequences occupy ∼17% of human DNA and ∼18% of mouse DNA . While hF, and A  which arnd TFIII . Althougelements . The vaselements , leavingelements . Ongoingelements . L1 inseentified .A full-length mouse L1 is ∼6–7 kb long and begins with a 5′ untranslated region (5′ UTR) containing an internal RNA polymerase II promoter . The mouThe L1 5′ UTR is followed by two open reading frames encoding the proteins ORF1p and ORF2p, and a 3′ UTR incorporating a polyadenylation signal . ORF1p ien masse may mask individual L1s whose methylation dynamics differ from those of their subfamily. Indeed, studies of human L1s suggest certain loci can “escape” methylation and thus contribute to somatic retrotransposition throughout development and in cancer  L1 insertions arising in pedigrees of inbred mice , and ideF monomer unit and encoded intact open reading frames (ORFs), and each daughter L1 contained between 0.6 and 1.8 fewer monomer units than the corresponding donor L1 , cloned and capillary sequenced five donor/daughter pairs  to derivdonor L1 . The remdonor L1 . This reF elements  to, the YY1 core binding motif (GCCATCTT)  . The obsspa (spa), with donor and daughter elements showing similar activity. Notably, Insertion 2 which has an amino acid change in ORF1p retrotransposed with the same efficiency as its donor (∼160%) when transcribed from the CMVp, indicating that the mutation does not influence retrotransposition efficiency. In contrast, when driven by the 5′ UTR alone, each daughter element mobilized less efficiently than its donor  also showed partial demethylation in embryo AB15   were >80ryo AB15 E. We alsryo AB15 . Insertiryo AB15 . Howeverryo AB15 . This varyo AB15 . TogetheFI/TFII monomer sequences, which we found to be largely methylated in adult gonads , which generates a heterogeneous population of mESCs in a pluripotent state ; two smaF, GF and A each showed their lowest methylation levels in 2i + LIF ground-state conditions, and reached maximal methylation by day 6 of differentiation (F subfamily was less methylated (<80%) than the other subfamilies (>80%) both during differentiation and in fully differentiated EBs  in d3 EBs compared to other L1 subfamilies , day 3 (d3) EBs, and day 21 (d21) differentiated cells. We achieved ∼15–20× genome-wide depth using an ONT PromethION platform, and surveyed CpG methylation via the methylartist package . Evaluatubfamily A. Averagfamilies A. B1, B2families B. While families C.Next, we quantified the methylation of active L1 subfamily promoters independently of the L1 body . We binned promoters genome-wide based on monomer count, with the majority of young L1 subfamily members containing between 2 and 5 monomer units D, consisFI and TFII elements, and in elements containing three or more monomer units  within each monomer unit, among at least 20 individual L1 TFI loci containing 2, 3, 4, or 5 monomers,  on total RNA isolated from d0, d3, and d21 differentiated mESCs, followed by PCR with an L1 TFI ORF1-specific primer in an approach similar to that used by Deininger et al. to study human L1 expression   B. This fs 80–87) . Plottins 80–87) . While id at d21 A,B, despme point , 5. At aposition . Notablyposition C. The diposition .F element with intact open reading frames  situated in sense orientation within an intron of the gene Cped1  and observed upstream TSSs at all three time points, with most TSSs located within a SINE B2 element . Upstreane Cped1 . For thisplicing . TogetheFI elements containing 2, 3, 4, and 5 monomer units  (Thermo Fisher Scientific) supplemented with 10% heat-inactivated fetal bovine serum (FBS) (Thermo Fisher Scientific), 1% L-glutamine (Thermo Fisher Scientific) and 1% penicillin-streptomycin (Thermo Fisher Scientific). The cells were passaged every 3–4 d after they achieved confluency of 80%–90% using Trypsin 0.25% EDTA (Thermo Fisher Scientific).HeLa-JVM cells were maintained at 37°C and 5% CO2. Cells were maintained in serum + LIF, 2i + serum, and 2i + LIF conditions and were passaged at 70%–80% confluence every 2–3 d using Trypsin 0.25% EDTA (Thermo Fisher Scientific), with media changed every day. The details of mESC culture conditions and media composition are provided in the E14Tg2a mESCs (ATCC CRL-1821) were cultured on gelatinized tissue culture plates and maintained at 37°C and 5% COF insertions among 85 C57BL6/J mice belonging to multigeneration pedigrees, as well as six unfixed polymorphic L1 TF insertions absent from the C57BL6/J reference genome and differentially present/absent among these pedigrees  according to the manufacturer's protocol. The DNA concentration was determined by Qubit 3.0 Fluorometer (Invitrogen) using the Qubit dsDNA HS Assay Kit (Invitrogen) following the manufacturer's instructions.High molecular weight (HMW) genomic DNA from cultured cells was extracted using the Nanobind CBB Big DNA Kit (Circulomics) following the manufacturer's instructions.Mouse L1 reporter constructs were generated via PCR amplification of donor/daughter mouse L1s from genomic DNA, followed by capillary sequencing of multiple clones to identify PCR errors. Constructs containing the full, correct L1 sequence were built from individual PCR clones as described previously . A modifImmunostaining was performed on day 7 (24 h after plating of EBs) and day 21 of mESC differentiation. EBs were plated on gelatinized coverslips in twelve-well plates. Immunostaining was performed using primary antibodies against β-tubulin , Α-fetoprotein (AFP), , and smooth muscle actin . Secondary antibodies used were Alexa Fluor 647 Donkey Anti-Rabbit IgG (Jackson ImmunoResearch 711-606-152); Cy3 Donkey Anti-Goat IgG (Jackson ImmunoResearch 715-165-150), and Alexa Fluor 488 Donkey Anti-Mouse IgG (Jackson ImmunoResearch 715-546-150). Details of the immunostaining and imaging procedures are supplied in the Retrotransposition assays in HeLa-JVM cells were performed as previously described  with somL1 retrotransposition assays in E14Tg2a mESCs (ATCC CRL-1821) were performed as previously described  in antibhttps://github.com/MischaLundberg/extract_sequences). Selected reads were analyzed using QUMA  (Bisulfite conversion was performed using the EZ DNA Methylation-Lightning Kit (Zymo Research), following the manufacturer's instructions. Primers used for target amplification are listed in nalysis) . Full dePurity of HMW DNA was determined using a NanoDrop One Spectrophotometer (Thermo Fisher Scientific) according to the manufacturer's instructions. The DNA concentration was determined by Qubit 3.0 Fluorometer (Invitrogen) using the Qubit dsDNA HS Assay Kit (Invitrogen) and on a TapeStation System (Agilent Technologies). ONT sequencing libraries were created using 1D Ligation (SQK-LSK109), sheared to create an average fragment size of ∼10 kb and sequenced at the Kinghorn Centre for Clinical Genomics at the Garvan Institute of Medical Research  on an ONT PromethION platform.Bases were called using Guppy version 3.2.10 (Oxford Nanopore Technologies) and aligned to the reference genome build mm10 using minimap2 version 2.17  and SAMthttps://www.repeatmasker.org) .OUT track files available for mm10 from the UCSC Genome Browser (https://github.com/adamewing/methylartist) and plotted using the “segplot” function. Reads mapping completely within L1s were excluded from the reference L1 methylation analysis via the “‐‐exclude_ambiguous” option in methylartist segmeth to negate the contribution of ambiguous mappings. Plots categorized by monomer count . 5′ RACE was performed using the 5′RACE module of the SMARTer 5′/3′ RACE Kit  according to the manufacturer's protocol. 1 µg total RNA was used as input for each reaction, and two reactions from biologically independent samples were performed per time point. PCR amplification of 5′ RACE cDNAs was performed using the L1 gene specific primer 5′-catctcttgtattctgttgctgatgctcaa-3′ and the 5′ RACE universal primer provided in the SMARTer 5′/3′ RACE Kit (Takara Bio). 25 PCR cycles were performed as follows: 94°C for 30 sec, 67 °C for 30 sec, 72 °C for 3 min. PCR products were visualized on a 1% agarose gel. Products ranging from 700–5000 bp were excised, and PCR fragments were purified using the MinElute Gel Extraction Kit . Iso-Seq template preparation using the Iso-Seq Express kit followed by PacBio SMRT Cell sequencing on a PacBio Sequel II platform was performed by the Australian Genome Research Facility. Six samples  were multiplexed on a single SMRT cell, generating 3,699,664 CCS reads in total.FI elements they overlapped, with alignments required to terminate within the L1 body. The start positions of these alignments within the 5′ UTR or upstream of the L1 were then recorded as putative TSSs supported by at least one read. These positions were used to generate TSS distributions relative to monomer coordinates or to L1 TFI 5′ UTRs composed of 2, 3, 4 or 5 monomers. Sequence logos surrounding these TSSs were generated using WebLogo  using minimap2  and para WebLogo .https://www.ncbi.nlm.nih.gov/genbank/) under accession numbers OQ856744–OQ856753.All Sanger sequencing data generated in this study have been submitted to the NCBI GenBank database (https://www.ncbi.nlm.nih.gov/bioproject/) under accession number PRJNA763783.The Oxford Nanopore Technologies and PacBio data generated in this study have been submitted to the NCBI BioProject database (F elements with 2, 3, 4, and 5 monomers have been uploaded as New scripts TSSprofile.py and intersect.py used to analyse TSS distribution across L1 TSupplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 6Supplement 7Supplement 8Supplement 9Supplement 10Supplement 11Supplement 12Supplement 13Supplement 14Supplement 15


Pectobacterium atrosepticum (Pba), and it is called, in our study, adaptive proliferation. An important attribute of adaptive proliferation is its timely termination, which is necessary to prevent the waste of endogenous resources when the required level of population density is achieved. However, metabolites that provide the termination of adaptive proliferation remained unidentified. We tested the hypothesis of whether quorum sensing-related autoinducers prime the termination of adaptive proliferation and assessed whether adaptive proliferation is a common phenomenon in the bacterial world. We showed that both known Pba quorum sensing-related autoinducers act synergistically and mutually compensatory to provide the timely termination of adaptive proliferation and formation of cross-protection. We also demonstrated that adaptive proliferation is implemented by bacteria of many genera and that bacteria with similar quorum sensing-related autoinducers have similar signaling backgrounds that prime the termination of adaptive proliferation, enabling the collaborative regulation of this adaptive program in multispecies communities.Bacterial adaptation is regulated at the population level with the involvement of intercellular communication (quorum sensing). When the population density is insufficient for adaptation under starvation, bacteria can adjust it to a quorum level through cell divisions at the expense of endogenous resources. This phenomenon has been described for the phytopathogenic bacterium  Pectobacterium atrosepticum (Pba), it has been shown that, if the cell concentration is below the one that permits intercellular communication, bacteria are able to increase the population density up to a quorum level, even in the absence of an exogenous growth substrate [1–105 CFU/mL until it reaches the value of ~106 CFU/mL. In our study, we refer to this phenomenon as adaptive proliferation. Following adaptive proliferation, Pba cells retain high virulence and develop cross-protection, i.e., become resistant to a number of stressors [Adaptive programs in bacteria are regulated at the population level, involving intercellular signaling ,2,3,4,5.ubstrate . Such a tressors .1 or 105 CFU/mL, the adaptive proliferation yields a population density of ~106 CFU/mL. This means that the adaptive proliferation is terminated not after a particular number of cell divisions, but after the population density reaches a particular level that presumably provides intercellular communication. This is supported by the facts that (1) the termination of the adaptive proliferation is coupled with the accumulation of autoinducers of one of the quorum sensing systems, acyl homoserine lactones (AHL), as well as with the upregulation of AHL-synthase gene, and (2) adaptive proliferation is terminated prematurely (at a level below 106 CFU/mL) in the presence of metabolites accumulated in the starving cultures that had already passed through the process of adaptive proliferation [Importantly, in the absence of exogenous organic carbon, irrespective of whether the initial cell titer is 10Apparently, adaptive proliferation is carried out at the expense of endogenous cell resources, which is evident from cell morphology: cells after adaptive proliferation have reduced sizes and a dramatically reduced volume of cytoplasm . TherefoPba, two quorum sensing systems have been described to date: (1) mediated by 6-oxo- and 8-oxo-AHL—the products of the expI gene and (2) mediated by autoinducer of the second type, AI-2, the product of the luxS gene [Pectobacterium species, but also by species of some other genera, are widely shown to control virulence, motility, biofilm formation, and oxidative stress resistance in Pectobacterium species [Pectobacterium physiological responses, is much less understood [In  species ,10,11. Iderstood ,13.Pba. In addition, we sought to check whether, other than Pba bacterial species, both AHL-producing and AHL-non-producing were able to implement adaptive proliferation, and if so, whether the signaling background that primes the termination of adaptive proliferation is genus-specific or universal for different bacterial genera, enabling the collaborative regulation of this adaptive program in multispecies communities.The aim of our study was to determine whether AHL and AI-2 are involved in the termination of adaptive proliferation in Pba) can cause the premature termination of adaptive proliferation. To check this, starving cultures of low population density (103 CFU/mL) were supplemented with 6-oxo-AHL and/or 8-oxo-AHL. Before this, we determined that, under growth-promoting conditions , the concentration of AHL in Pba cultures reached 10.3 ± 2.3 µM. At this concentration, exogenous AHL did not affect adaptive proliferation, which was terminated at ~106 CFU/mL, as well as in the starving cultures incubated without exogenous AHL. However, at the increased concentrations of 100 µM and 200 µM, exogenous 6-oxo-AHL led to the premature termination of adaptive proliferation, which occurred at CFU titers five- and seven-fold, respectively, lower than that in the cultures incubated without exogenous AHL  of the wild type Pba and its quorum-deficient mutants with knocked out genes encoding AHL-synthase (expI) or AI-2-synthase (luxS). Adaptive proliferation proceeded in a similar way in the wild type and both mutants, and its termination occurred at a similar CFU level (~106 CFU/mL) in all three strains   . This meluxS mutant, and ΔexpI mutant, respectively  resistance following adaptive proliferation.To check this, for the wild type and both mutant strains, the resistance to heat shock  was compared for late exponential phase cells (cultured in LB medium) and starving cells that passed through adaptive proliferation in carbon-deficient medium. No CFU was recovered after heat shock from the suspensions of exponentially growing cells of all three strains A. In turectively B. Thus, Pba cells following adaptive proliferation, tobacco plants were infected with late exponential phase or starving (after passing adaptive proliferation) cells of the wild type strain and its ΔluxS and ΔexpI mutants. In total, 80%, 50%, and 10% of plants displayed disease symptoms following inoculation with exponentially growing cells of the wild type, ΔluxS mutant, and ΔexpI mutant, respectively. In turn, 80%, 20%, and 0% showed disease development following inoculation with the starving cells of the wild type, ΔluxS mutant, and ΔexpI mutant, respectively. Thus, in the case of the wild type, starving cells were as virulent as exponentially growing cells. In contrast, starving cells of the mutant strains were less virulent compared with the exponentially growing cells of the same strain. This means that both AHL- and AI-2 are required for Pba to maintain virulence following adaptive proliferation.To test whether the AHL- or AI-2-deficiency affected not only stress resistance, but also the virulence of Pba cells are incubated at a low population density not in a fresh carbon-deficient medium but in the supernatants of Pba starving cultures that had already passed through adaptive proliferation [expI or luxS genes had no effect on the dynamics of adaptive proliferation or the CFU level at which it was terminated, it can be assumed that neither AHL nor AI-2 are those extracellular metabolites that contribute to the termination of adaptive proliferation. If this is true, then the supernatants of starving cultures (that passed through adaptive proliferation) of all three strains would have a similar effect in terms of causing the premature termination of adaptive proliferation. To check this, cells of the wild type strain, ΔluxS mutant, and ΔexpI mutant were incubated at a low initial population density in a fresh carbon-deficient medium or in the supernatants of cultures that had passed through adaptive proliferation of either the wild type strain or ΔluxS mutant or ΔexpI mutant. The supernatants of the wild type strain theoretically contained both AHL and AI-2, the supernatants of ΔexpI lacked AHL but contained AI-2, and the supernatants of ΔluxS lacked AI-2 but contained AHL.We have previously demonstrated that adaptive proliferation is terminated prematurely if feration . This su4 CFU/mL, whereas, in a fresh medium, the termination of adaptive proliferation occurred at 9 × 105–2 × 106 CFU/mL  effect on causing the premature termination of adaptive proliferation, but this effect was much lower than that of the supernatants of the wild type (expI mutant), the CFU level at which the adaptive proliferation was terminated was lower than that in the supernatants of the ΔluxS mutant but higher than that in the supernatants of the wild type  and AI-2 (strain Vibrio harveyi BB170) in the supernatants of starving cultures of the wild type Pba and ΔluxS and ΔexpI mutants. During the first two days of incubation in carbon-deficient medium at a low initial population density (2–4 × 103 CFU/mL), the concentrations of AHL and AI-2 in all assayed starving cultures were below the level that the reporter strains could detect. By the third day of incubation, when the adaptive proliferation had terminated, the levels of autoinducers were detectable (with the exception of AHL in ΔexpI mutant and AI-2 in ΔluxS mutant). The level of AHL was 2.8-times higher in the supernatants of the ΔluxS mutant than that of the wild type, while the level of AI-2 was 2.5-times higher in the supernatants of the ΔexpI mutant than that of the wild type  implemented adaptive proliferation when cells were transferred into a carbon-deficient medium at a low (2–9 × 103 CFU/mL) initial cell density; the adaptive proliferation proceeded until cultures reached a population density of 1–6 × 106 CFU/mL (Given our findings that AHL play a role in priming the termination of adaptive proliferation in 6 CFU/mL .Pba starving cultures that passed through adaptive proliferation were compared for two AHL-producing (P. ananatis and D. solani) and two AHL-non-producing (X. vesicatoria and E. coli) species  proceeded in a similar way in Pba cultural supernatants and in a fresh carbon-deficient medium  is terminated when the population density reaches a value of 10Pba mutants, one of which with knocked out gene encoding AHL-synthase (ΔexpI) was AHL-deficient, while the other one with knocked out gene encoding AI-2-synthase (ΔluxS) was AI-2-deficient. Unexpectedly, in both mutant strains, the adaptive proliferation proceeded similarly to the wild type strain, including that it was terminated at a population density of 106 CFU/mL. However, in mutant strains, in contrast to the wild type strain, we observed a rather sharp decrease in CFU titers following adaptive proliferation. We proposed that both AHL- and AI-2-mediated quorum sensing systems were required to gain stress resistance following adaptive proliferation. Indeed, following adaptive proliferation, the level of cross-protection and the level of virulence were reduced in both mutants compared with the wild type.Then, we monitored adaptive proliferation in two quorum-related expI and ΔluxS mutants, AHL or AI-2 were required for obtaining the increased stress resistance, but they were unlikely to be involved in priming the termination of adaptive proliferation. Then, we assumed that if these two autoinducers do not indeed participate in the target physiological process, then the supernatants of cultures of mutant strains that passed through adaptive proliferation would cause the premature termination of adaptive proliferation, similar to the supernatants of the wild type. However, compared to the supernatants of the wild type cultures, the supernatants from mutant strains had a reduced ability to prime the premature termination of adaptive proliferation, especially the supernatants of the ΔluxS mutant. This fact clearly shows that both AHL and AI-2  are involved in priming the termination of adaptive proliferation. However, at the same time, it remained unclear why the termination of adaptive proliferation in AHL- and AI-2-deficient strains occurred in a manner similar to the wild type strain.Based on the monitoring of adaptive proliferation in the ΔexpI mutant, we observed an increased level of AI-2 compared to the wild type, while in those of the ΔluxS mutant, an increased level of AHL accumulated. So, if a strain is unable to produce one quorum-related autoinducer during adaptive proliferation, it produces a greater amount of the other one, which presumably contributes to the termination of adaptive proliferation, even in the absence of one of the regulators of this process. This is in accordance with our results showing that only increased concentrations of exogenous AHL could trigger the premature termination of adaptive proliferation in Pba. Thus, the termination of adaptive proliferation is triggered by the combinatory and mutually compensatory actions of two quorum sensing systems and maybe other additional metabolites involved in intercellular communication that remain to be identified. Herewith, the interaction of different regulatory systems during the termination of adaptive proliferation is likely to be organized in such a way as to ensure the successful realization of this physiological process, even in the case of a failure of one of the regulatory systems involved in its regulation.We proposed that the loss of one of the two studied autoinducers (AHL or AI-2) can be compensated for by the enhanced activity of the other or by additional yet unidentified intercellular signaling molecules, which would lead to the termination of adaptive proliferation in AHL- and AI-2-deficient strains. To check this, we determined the relative levels of AHL and AI-2 in the wild type and mutant strains following adaptive proliferation. Indeed, in the supernatants of starving cultures of the ΔPseudomonas aeruginosa and the bioluminescent bacterium Vibrio harveyi [V. harveyi, different quorum-related phenotypes were manifested at different ratios of AI-2 and AHL concentrations. Herewith, the manifestation of some phenotypes required the presence of both autoinducers, while other phenotypes could be induced by only one of the autoinducers, and the second one only enhanced the effect of the first autoinducer [Quorum sensing autoinducer signal integration expressed in the cooperative action of two quorum sensing systems in terms of the activation of various phenotypes has been previously reported for  harveyi ,15,16. I6 CFU/mL) using various treatments, but we were never able to completely repress adaptive proliferation, even when cells were transferred to supernatants of wild type cultures that had passed through adaptive proliferation, despite the fact that these supernatants contained the entire extracellular signaling background for the termination of adaptive proliferation. In our opinion, de novo inoculated cells are unable to perceive this signaling background and, due to this, proceed to the division. However, after several rounds of division, cells acquire the ability to recognize these signals, and adaptive proliferation is terminated prematurely. This scenario implies that bacterial cell sensitivity to quorum sensing signals is not constant and can change depending on the physiological status. Indeed, it has been previously shown that bacteria (V. harveyi) can adjust the sensitivity to quorum sensing signals [The results of the conducted experiments also indicate that the specific extracellular signaling background is not the sole element required for the termination of adaptive proliferation. Additionally, during adaptive proliferation, the sensitivity (competence) of cells to this extracellular signaling background must be increased. We were able to cause the premature termination of adaptive proliferation , as the main quorum sensing signal [In our study, we also found that the phenomenon of adaptive proliferation is not unique to g signal ,20.Pectobacterium wassabiae (a phytopathogenic bacterium closely related to Pba) can sense AHL synthesized by other bacteria in the multispecies community of potato tubers, and an integral AHL pool produced by the whole community determines the level of virulence of this species [It has been previously shown that bacteria within complex communities perceive not only self-produced quorum-related molecules, but also those that are produced by other species ,22,23. T species . Our resThus, our study shows that adaptive proliferation is a widespread phenomenon in the bacterial world. The existence of this phenomenon indicates that bacterial cells contain a significant amount of resources and energy that can be spent to provide multiple cell divisions in the absence of a growth substrate in order to successfully adapt to unfavorable conditions. The consumption of these resources is tightly regulated via intercellular communication to avoid their waste when the quorum level of population density required for adaptation is achieved. Our study provides the first information about the components of the extracellular signaling background that prime the termination of adaptive proliferation. These components are AHL and AI-2, which act synergistically and mutually compensatory (possibly with other as-yet unidentified metabolites) to provide timely termination of adaptive proliferation.Pectobacterium atrosepticum SCRI1043 (Pba) (ATCC BAA-672), Pantoea ananatis LMG20103, Dickeya solani DSM28711, Xanthomonas vesicatoria DSM22252, Escherichia coli K-12 MG1655, Pseudomonas syringae DC3000, and Bacillus subtilis DSM10 were routinely grown in Luria-Bertani (LB) medium on a rotary shaker (180 rpm) at 28 °C. The Pba ΔexpI and ΔluxS mutant strains were grown under similar conditions in the presence of kanamycin (20 μg/mL). E. coli JLD271 was grown at 37 °C in the presence of tetracycline (20 mg/mL). Vibrio harveyi BB170 and BB152 strains were grown in LB medium, containing 20.0 g/L NaCl at 28 °C. To determine the level of AHL in the Pba cultures under growth-promoting conditions, bacteria were cultured on a rotary shaker (180 rpm) at 28 °C in the synthetic medium D5, containing 1.0 g/L NH4Cl, 0.1 mM Na-K phosphate buffer (pH 7.5), 0.3 g/L MgSO4 × 7H2O, and 2.0 g/L sucrose.g, 20 °C, 10 min) at the late logarithmic growth phase (~1–2 × 109 CFU/mL) and washed twice in a mineral carbon-free AB medium, which contained 1.0 g/L NH4Cl, 0.62 g/L MgSO4 × 7H2O, 0.15 g/L KCl, 0.013 g/L CaCl2 × 2H2O, pH 7.5. The cells were then resuspended in AB medium, and 10-fold dilutions were prepared. The resultant starving cultures, with initial population densities of 102–104 CFU/mL, were incubated in glass vials without aeration at 28 °C. To assess the effect of AHL on adaptive proliferation, starving cultures were supplemented with different concentrations of N-(3-oxohexanoyl)-L-homoserine lactone and/or N-(3-oxooctanoyl)-L-homoserine lactone .To obtain starving cultures of the studied bacteria, cells grown in LB medium were harvested (8000× 2–104 CFU/mL) were incubated for four days. After that, first a bulk of cells was harvested , and then the supernatants were filtered through nitrocellulose filters (0.2 μm)  under sterile conditions. The absence of bacteria in the supernatants was confirmed by plating on 1.5% LB-agar. The obtained supernatants were inoculated with 5 × 102–4 × 103 CFU/mL of the exponentially growing cells of either Pba, or Pantoea ananatis, or Dickeya solani, or Escherichia coli, or Xanthomonas vesicatoria, and the cell titers in the obtained cell suspensions were monitored by plating on 1.5% LB-agar.To obtain the supernatants of cultures that passed through adaptive proliferation, starving cultures with low initial population densities (10expI and luxS deletion mutants (ΔexpI and ΔluxS) were constructed by the method described by Kaniga et al. [expI gene in the Pba genome overlapped with the 3’-end of the expR gene (16 bp) located on the complementary DNA strand, during mutagenesis, we removed only that part of the expI gene that did not overlap with the expR gene. The target gene expI (ECA0105 locus), together with the adjacent regions (approximately 1000 bp up- and downstream of expI ORF), were amplified by PCR with primers upexpIF and dnexpIR (expI) was introduced into E. coli NovaBlue by chemical transformation. Transformants carrying the recombinant plasmid were screened by ampicillin resistance and further verified by PCR using plasmid specific primers for the T7 and SP6 polymerase promoters, which flank the multiple cloning regions of pGEM-T Easy.The a et al. . Since t dnexpIR  using Q5expI ORF with the KmR cassette, a part of the pGEM:expI plasmid (including ~1000 bp regions up- and downstream of expI ORF but not expI ORF itself) was amplified with primers dnexpIKmF and upexpIKmR . The KmR cassette was amplified from the pKD4 plasmid with primers KmexpIF and KmexpIR, whose 5′-ends were complementary to Pba DNA regions adjacent to expI ORF. Two obtained PCR fragments (corresponding to pGEM plasmid with ~1000 bp regions up- and downstream of expI ORF and to KmR cassette) were joined by the circular polymerase extension cloning method [expI;KmR) was introduced into E. coli NovaBlue by chemical transformation. The mutant locus was confirmed by DNA sequencing.To replace pexpIKmR , whose 5g method . The obtR cassette and ~1000 bp regions up- and downstream of expI ORF) was amplified with primers upexpIF and dnexpIR and ligated  into the SmaI-digested  suicide vector pKNG101 to generate the recombinant plasmid containing the allelic exchange cassette for the target locus. The obtained plasmid  was introduced into E. coli cc118 by electroporation. The transfer of pKNG101:ΔexpI;KmR plasmid from E. coli cc118 into Pba was achieved by triparental mating using E. coli HH26 as a helper strain. The clones, in which pKNG101:ΔexpI;KmR plasmid was integrated into the chromosome by a single crossover event, were selected by streptomycin and kanamycin resistance. The clones, in which the second crossover event led to the replacement of the target locus with the mutant one and the donor plasmid was eliminated, were selected on M9 agar medium containing 10% sucrose. Then, the clones were tested for sensitivity to streptomycin. Clones without streptomycin resistance were analyzed by PCR with primers CheckexpIF and CheckexpIR to identify ΔexpI mutants. The ΔluxS (ECA3362 locus) mutant was constructed in a similar manner using the primers given in the The mutant locus  for four days (during which the adaptive proliferation had passed). Late exponential phase and starving cells were harvested and resuspended in a carbon-deficient AB medium up to a density of ~106 CFU/mL. For the heat challenge, 100 µL aliquots of cell suspensions were exposed to 48 °C for 5 min. Fifteen min after heat shock, suspensions were plated onto 1.5% LB agar as serial 10-fold dilutions. The plates were incubated at 28 °C for two days before the CFUs were counted.To compare the stress resistance of the late exponential phase and starving (after passing through the adaptive proliferation) cells of the wild type Pba and its ΔexpI and ΔluxS mutants was compared using Nicotiana tabacum cv. Petit Havana SR1 plants. Plants were grown axenically in test tubes placed in a growth chamber with a 16-h light/8-h dark cycle photoperiod. Seeds were surface-sterilized using diluted bleach (0.8% of active chlorine) and 1% sodium dodecyl sulfate for 30 min, washed seven times with sterile distilled water, and then transferred to Murashige and Skoog medium (MS) in Petri dishes. Ten-day-old seedlings were transferred to individual flasks containing MS. Five weeks after planting, the plants were infected with bacteria. Two types of inoculums were used. First, bacteria were grown until the late logarithmic phase (~1 × 109 CFU/mL) in LB medium, washed with sterile 10 mM MgSO4, and resuspended in 10 mM MgSO4 up to a density of ~1 × 107 CFU mL−1. Second, bacteria were incubated under starvation conditions at a low initial population density (~5 × 103) for four days (during which the adaptive proliferation had passed), and then cells were harvested and resuspended up to ~1 × 107 CFU mL−1 in sterile 10 mM MgSO4. Then, sterile 10 mM MgSO4 (control plants) or bacterial suspensions containing ~1 × 105 cells (late log or starving) were placed as 10 μL drops into the bosoms of the leaves in the middle part of the stems using sterile pipette tips, and slight scratches were made simultaneously. Symptoms were scored three days after plant inoculation.The virulence of the late exponential phase and starving (after passing through the adaptive proliferation) cells of the wild type g at 4 °C, and the supernatants were filtered through nitrocellulose filters (0.2 μm) . AHLs were extracted from the obtained cell-free supernatants with an equal volume of ethyl acetate  for 4 h on a rotating mixer. The samples were centrifuged for 5 min at 9000× g at 4 °C, and the ethyl acetate phase was collected. The ethyl acetate was removed by rotary evaporation, and the residual ethyl acetate was evaporated under nitrogen gas [E. coli JLD271, carrying the bioluminescence reporter vector pAL103 or the vector pAL104 as a negative control [600 of 0.5, and the obtained suspension was transferred to a 96-well microtiter plate (180 µL per well). Twenty µL of water-dissolved ethyl acetate extracts of the supernatants of the tested cultures were added to the wells with E. coli JLD271. The plate was incubated for 3 h at 37 °C, and the luminescence from each well was detected using the microplate reader CLARIOstar . Commercial N-(3-oxohexanoyl)-L-homoserine lactone (Sigma) was used as a control.To assess the AHL level, cultures were centrifuged for 10 min at 24,000× ogen gas . The sam control . The repPba cultural supernatants was determined by the bioluminescent method [Vibrio harveyi strains BB170  and BB152  were kindly provided by Dr. B.L. Bassler. V. harveyi cultures were grown overnight as described above, and then the cells were diluted by 1:2000 in the medium, containing 17.5 g/L NaCl, 12.3 g/L MgSO4, 2.0 g/L casamino acids, 10 mM KH2PO4, 1 mM arginine, 1% glycerol, and pH 7.5. An amount of 180 µL aliquots of the reporter strain were added to the wells of a 96-well microtiter plate, and then 20 µL of supernatants from the tested cultures were added to the wells. The plate was incubated for 5 h at 28 °C. The supernatant from V. harveyi BB152 and the sterile medium were used as positive and negative controls, respectively [AI-2 in t method . Vibrio ectively . The lump < 0.05 was considered significant.Statistical analysis was performed in OriginPro 9.8.0.200 using the Mann–Whitney two-sided test. Each experiment was carried out at least in triplicate. For multiple comparisons, a Mann–Whitney two-sided test with Bonferroni correction was used. The value of Pba (AHL and AI-2) are components of the signaling background that “notify” Pba that it is time to terminate adaptive proliferation and proceed to the persistence-related stage of adaptation. AI-2 seems to have a greater contribution to the termination of adaptive proliferation than AHL. However, neither AI-2 nor AHL are strictly required to terminate adaptive proliferation, and these two signaling molecules can compensate for each other’s actions  during the termination of adaptive proliferation, possibly along with some other as-yet unidentified signaling molecules. In turn, both AHL and AI-2 are required for the formation of cross-resistance in the course of adaptive proliferation and for retaining full virulence following adaptive proliferation; herewith, the AHL contributes more to cross-resistance than AI-2. Adaptive proliferation is a phenomenon common to a wide range of bacterial species that use different molecules as quorum sensing signals. The signaling background that primes the termination of adaptive proliferation in one AHL-producing species can be perceived by and terminate adaptive proliferation in  other AHL-producing species, but not in AHL-non-producing species. This indicates that adaptive proliferation can be regulated at the interspecies level in complex bacterial communities. Bacterial cells “prepare” themselves for perceiving the signaling background that primes the termination of adaptive proliferation; the sensitivity to this signaling background is absent (or low) in exponentially growing cells, but it gradually develops during the course of adaptive proliferation.Both known quorum sensing mediators of


The presence of sinkholes has been widely studied due to their potential risk to infrastructure and to the lives of inhabitants and rescuers in urban disaster areas, which is generally addressed in geotechnics and geophysics. In recent years, robotics has gained importance for the inspection and assessment of areas of potential risk for sinkhole formation, as well as for environmental exploration and post-disaster assistance. From the mobile robotics approach, this paper proposes RUDE-AL (Roped UGV DEployment ALgorithm), a methodology for deploying a Mobile Cable-Driven Parallel Robot (MCDPR) composed of four mobile robots and a cable-driven parallel robot (CDPR) for sinkhole exploration tasks and assistance to potential trapped victims. The deployment of the fleet is organized with node-edge formation during the mission’s first stage, positioning itself around the area of interest and acting as anchors for the subsequent release of the cable robot. One of the relevant issues considered in this work is the selection of target points for mobile robots (anchors) considering the constraints of a roped fleet, avoiding the collision of the cables with positive obstacles through a fitting function that maximizes the area covered of the zone to explore and minimizes the cost of the route distance performed by the fleet using genetic algorithms, generating feasible target routes for each mobile robot with a configurable balance between the parameters of the fitness function. The main results show a robust method whose adjustment function is affected by the number of positive obstacles near the area of interest and the shape characteristics of the sinkhole. Sinkholes are hollows in the ground surface formed by the dissolution of limestone or geological features, considered closed cavities drained due to subsoil dissolution in karst rocks . This laPressure wireless sensor network (WSN) technologies and neural network learning databases use UAVs and thermal cameras for sinkhole detection and monitoring . There aThe success of search and rescue (SAR) missions depends on the performance of robotic platforms individually . HoweverRegarding deployment of robot fleets in disaster environments,  details The most important robotic challenges in subway environments are the limitation of wireless communications and detection task such as toxic gas analysis. Robots used for WiSAR tasks must be prepared for difficult weather conditions and interfere minimally with environmental data acquisition , so roboIn terms of SAR tasks using mobile robots, these systems can be considered cooperative Cable-Driven Parallel Robots (CDPR), where each robot represents an anchor point for the wires. This configuration is a major challenge for base localization in the environment, obstacle avoidance, and adaptive control of payload position and orientation .This work is part of a Robotics and Cybernetics Group (ROBCIB) of the Polytechnic University of Madrid (UPM) project, which main objective is to perform the deployment of a Mobile Cable-Driven Parallel Robot (MCDPR) inside a sinkhole automatically, considering mobile robots as mobile bases that will release a parallel cable-driven robot in the region of interest. Therefore, challenges are related to the deployment of the mobile robots (mobile bases) and relative localization of the CDPR. Accordingly, the strategy is focused on two stages: first, the traversability of a path for the mobile bases from the base station to the surroundings of the sinkhole, considering that the mobile bases of the MCDPR will be attached by ropes; then, the release of the CDPR, avoiding contact with positive obstacles. Regarding the relative localization challenge, in order to get a good precision of portable localization systems , using trilateration methods, a first approach presents the use of a fleet of four mobile robots and a CDPR carried on top of one of the mobile platforms, applying two physical configurations: node-edge fleet and roped individual.This paper’s main contribution is RUDE-AL (Roped UGV Deployment Algorithm), an algorithm for the autonomous selection of anchoring points of a mobile robot fleet for sinkhole exploration through computer vision techniques to filter positive and negative obstacles, and genetic algorithms for solving optimization problems.The mission comprises two stages: the first one consists of finding a feasible fleet trajectory in a node-edge configuration with four nodes and three edges; the second corresponds to the deployment of each robot to its corresponding vertex.For this purpose, multiple tests were carried out on 2D images representing 2.5D maps, which were generated manually (using image edition tools) and automatically (images containing splines generated using Bézier curves), evaluating the behavior of the algorithm to produce feasible roped navigation paths for environments with different physical characteristics.Implementing a heuristic method  considerably reduces computational time, allowing to adjust the weights of the variables to be optimized through a weighted fitness function for multi-objective optimization. The algorithm is applied to the navigation of a fleet of four simulated robots in Gazebo.The work is made up of the following sections. CDPRs are emerging as an attractive proposition due to their advantages in covering large volumes with fast movements while maintaining a balance between light weight and high durability .CDPR-related research has been reported since 1984, originally for underwater applications. The RoboCrane project , developAn example of mobile CDPRs are the so-called extended-crane systems, which represent a combination between a cable robot and a conventional crane, where the robotic configuration is intended to separate positioning and balancing tasks. Another example of a reconfigurable structure for cable robots is found in agricultural applications, adding mobile pillars to transport winches while controlling the position of the mobile platform.In terms of CDPR applications, the IPAnema family of robots  is used At conceptual level, the MoPick prototype  presentsA CDPR with three mobile cranes for search and rescue operations is shown by , where eConsidering strategies for motion planning of a fleet of mobile robots for deployment of a cable robot, ref.  proposesIn the related works described in State-of-the-art path planning methods are commonly formulated based on a single-objective optimization for distance cost . HoweverThe use of multi-objective genetic algorithms has been widely studied in building design , balanciOn the application of evolutionary algorithms (EA) for target selection in multi-robot systems, ref.  proposesAs explained in For the conceptualization of the problem, the starting point is a 2D map of free space, positive and negative obstacles. Physically, the self-contained CDPR is on top of one of the mobile robots . HoweverBefore performing path planning, the possibility of finding nearby positive obstacles that affect feasible target points for fleet route planning or deployment route must be considered. For these cases, a range of up to two nearby positive obstacles affecting the planning is defined, dividing into zones of interest where the user selects which area to  explore.Once the feasible candidates are available, the fleet route planning is performed for each candidate point, calculating the distance cost of each route. Then, candidate point ranges are defined for each mobile robot, where for each combination of candidate points the area covered is calculated. The distance cost of the minimum fleet route is minimized and the area covered is maximized for each combination of candidate points using a weighted fit function.Through the optimization of the fitness function, the target points are obtained, with which the deployment routes between them are planned a; subseqPhase 1. Node-edge fleet configuration-Map, obstacle, and start selection for offline planning-Definition, sorting, and choosing of feasible goal candidates based on area coverage and distance cost of fleet deploymentPhase 2. Roped individual configuration-Goal local planning and establishment of deployment configuration based on rope restrictionsThe main contribution of this article is the Roped UGV fleet DEployment ALgorithm (RUDE-AL), designed to provide a feasible goal selection based on the minimization of distance cost of fleet deployment and maximization of covered area, considering rope contact restrictions for negative and positive obstacles. It has been divided into two phases and three stages:The flow diagram of the process of RUDE-AL is shown in In this phase, the four robots are formed in a worm configuration consisting of four nodes and three edges . For navIn this stage, computer vision techniques are used to generate two-dimensional maps of positive and negative obstacles, separating them into individual masks for individual processing. OpenCV tools are used for contour detection, obstacle inflation (erode and dilate tools), and graphical representation of the algorithm. The starting point of the mission and the negative obstacle to be explored are manually selected. The existence of positive obstacles near the undercut that affect the roped deployment process is evaluated, and a group of candidate points is automatically obtained. Then, path planning is performed with a multi-query planner, in this case, a probabilistic roadmap (PRM), in order to obtain the candidate fleet routes from the starting point to the candidate target points of the first stage .Erode (line 12), Dilate (line 13), Range (line 14), Contours (line 16) and Centroids (line 18): are OpenCV functions used to inflate regions of images, define masks, and get characteristics of contours.Remove_dup (line 28): is a function to delete the multiple occurrences of an object in a list.Line(line 35): is a function that returns the slope and the constant “b” for a y = mx + b line between two input pointsCheck_click (line 51): is a function that determines where the user clicks and returns the selected zone to explore (options are 1 or 2).Route (line 51) is a function that performs the prm path planning between two points. Returns the feasible path between them.Algorithm 1 Fleet PlanningInput: map,start  Output:   1://init_candidates: set of initial candidate points2://3://4://index_roi: index of the negative obstacle to explore5://index_pos_obst: list of index of positive obstacles close to ROI6://m: slope of line between two points7://b: constant of line between two points8://y_sup: bool that defines if “y” coordinate of a point is greater that the one described by equation constants9://upper_c: candidate points greater that line defined between two near positive obstacles10://lower_c: candidate points lower that line defined between two near positive obstacles11:12:13:14:15:16:17:18:19:20:fordo21:for do    22:if then        23:           Delete from c_point this neg_contours [i]24:           Append in index_pos_obst this j25:end if        26:end for    27:end for28:29:ifthen30:for do    31:        32:end for    33:end if34:ifthen35:    36:for do    37:        38:if then        39:           Append in upper_c this 40:else        41:           Append in lower_c this 42:end if        43:end for    44:    zone=Check_click45:if then    46:        47:else    48:        49:end if    50:for do    51:        52:end for    53:end if54:returnAlgorithm 1 shows the pseudocode implemented for this stage, using as input variables: the required map, which is an RGB image in png format with a resolution of 1000 × 1000 pixels, whose colors correspond to the requirements of At this stage, the distance cost of the fleet route is minimized and the area covered by the polygon formed by the combination of four points corresponding to the position of the mobile robots is maximized. The candidate points are defined as The calculation of the area covered by the combination of the candidate points is made with the sum of the areas of the triangles that form the quadrilateral 1-2-3-4, shown in d: euclidean distance between pointsFor the calculation of the distance cost, in each fleet route associated to each candidate point , the EquThe cost values associated to each candidate route are normalized with respect to the maximum distance cost. Since the aim is to maximize the fitness function, and the area covered calculated with respect to the area of the obstacle obtained with OpenCV, a weighted fitness function is defined using the scalarized approach (weighted sum method) with the Equation :(3)ffitnTo estimate candidates that maximize the fitness function while reducing the computational cost, genetic algorithms, implemented with the PyGAD package , are useA combination of four candidate points is sought, whose coordinates are defined with values of type “integer”, where each candidate point is contained in a pool of feasible solutions, and has an associated fleet path cost value. For the application of PyGAD to the problem, the indices of the lists of each candidate point are used as solutions, having as objective the maximization of the The value of Get_area (line 13) is a function that gets the area of the contour made by a list of points. The output is the area of the contour.Route_cost (line 14) is a function that calculates the accumulated individual distance cost for a list of paths.Index_range (line 15) is a function that defines the lower and upper limit indexes for each candidate point according to a minimum distance between points. The output is a range of index for each of the four candidate points.Get_candidates (line 16) is a function that defines independent lists of candidate points of a main list according to the given limits. The output is four lists of points.Fitness_function (line 17) is a function that iterates testing different combinations according to Ga_instance parameters. It includes the Area_calc in order to calculate area for every iteration different combination of points.Area_calc (line 17) is a function that calculates the area described between four points according to cross product and sum of areas described in Equation (1).Ga_instance (line 18) is pygad instance to configure the genetic algorithm that include the parameters described in Algorithm 2 Points SelectionInput:   Output: sol_points,fleet_path  1://roi_area: area on negative obstacle in 2://3://4://range_c_points: 2x4 matrix of range of points for candidates 1, 2, 3, 45://c1: list of candidate 1 points6://c2: list of candidate 2 points7://c3: list of candidate 3 points8://c4: list of candidate 4 points9://sol_points: solution of four points10://fit_function: fitness function used in pygad instance11://sol_paths: fleet paths for solution points12://path_cost: list of distance costs related to the input 13:14:path_cost=Route_cost18:ga_instance19:sol_points=ga_instance.best_solution20:fordo21:    sol_paths[i]=22:end for23:fleet_path=min(sol_paths)24:return sol_points,fleet_pathAlgorithm 2 takes as input variables the candidate points In this phase, the robots are freed from the constraints of the worm configuration, where each mobile robot is attached by a rope to the parallel cable robot . In thisAfter having selected the best feasible candidate points, a route planning is performed between the selected points to obtain the deployment routes and propose a feasible deployment configuration considering the roped fleet constraints . By haviThe point corresponding to the fleet robot carrying the cable robot is also taken into account since, in addition to generating a feasible deployment path for UGV navigation, corded fleet constraints for positive obstacles must be considered; see In addition to the previous considerations, it must be evaluated that at each instant of the deployment, there is a rope connecting each mobile robot with the cable robot, where contact with positive obstacles must be avoided; see Finally, the routes of each robot are defined according to the possible configurations for fleet deployment , using tThe criterion for selecting robot routes is to apply the longest route to the leader robot, progressively descending to the shortest route to follower robot 3, in type 1 and type 2 configurations. For type 3 and type 4 configurations, the path to the longest boundary node is assigned to the leader robot, the shortest leg of the path from the leader robot to follower robot 1, the path to the shortest boundary node to follower robot 2, and the shortest path to follower robot 3.Route (line: 10) is a function that performs the PRM path planning between two points. It returns the feasible path between them.Route_check (line: 14) is a function that creates an interpolated line between input point and every interpolated point of the input route, and checks if there are collisions between the interpolated line (rope) and a positive obstacle. Output is a Boolean true if there is collision, and false if there is no collisions.GetRobotPaths (line: 35) is a function in which input is feasibility of each path of the roped configuration deployment check, and it returns the path for each robot according to Algorithm 3 Roped deploymentInput:   Output:   1://init_candidates: set of initial candidate points2://check_path: is a Boolean that indicates if the path is feasible to navigate with roped fleet conditions 3://p1_check: is a list that contains boolean values related to a feasible path between point 1 and a route4://p2_check: is a list that contains boolean values related to a feasible path between point 2 and a route5://p3_check: is a list that contains boolean values related to a feasible path between point 3 and a route6://p4_check: is a list that contains boolean values related to a feasible path between point 4 and a route7:8:9:fordo10:    deployment_paths[i]=Route11:end for12:fordo13:for do    14:        check_path=Route_check15:if then        16:if then           17:               Append in p1_check this check_path18:break               19:end if           20:if then           21:               Append in p2_check this check_path22:break               23:end if           24:if then           25:               Append in p3_check this check_path26:break               27:end if           28:if then           29:               Append in p4_check this check_path30:break               31:end if           32:end if        33:end for    34:end for35:robot1_path, robot2_path, robot3_path, robot4_path=GetRobotPaths36:return robot1_path, robot2_path, robot3_path, robot4_pathThe pseudocode is detailed in Algorithm 3. The functions that help to understand this section are:In order to test the performance of the algorithm on environments with different characteristics, two groups of environments are defined. The first, GLOBAL TEST, is intended to test different combinations of environments with various positive and negative obstacles, to test the robustness of the fleet in node-edge configuration; this group consists of manual generation of maps, using combinations associated with the characteristics of the fleet environment. The second group, SHAPE TEST, modifies the shape features of the region of interest to be explored, in order to test the robustness of the algorithm and identify the mission characteristics  that can affect the performance.For GLOBAL TEST, the experiments are performed on six maps with the characteristics shown in For each map, feasible fleet paths are obtained just once to get the feasible fleet candidate path cost, because the main goal is evaluating the performance of algorithm, not to be a PRM path planner.For GLOBAL TEST, the maps shown in For SHAPE TEST, thirty types of maps are automatically generated for each number of positive obstacles around the area of interest, varying the shape of the sinkhole. These maps are generated through a point connection script using cubic Bezier curves . The criMaps: The range of maps that are auto-generated with the indicated characteristics.Smoothness: Parameter to define the smoothness of the curve.Scale: X and Y pixel rectangle size where the random points will be generated.Where:The total number of generated maps is 90. Tests are performed by planning the routes from four start points , with a total of 360 tests of the algorithm.The 1000 × 1000 pixel resolution maps have a scale of 0.1 m per pixel, thus, the size range of the generated sinkholes goes from 20 m to 50 m approximately. The reference mobile robot size is the SummitXL .Some of the used maps are shown in All experiments were carried on Intel Core i7-9750H PC with 16GB RAM, under the ROS Noetic operating system and Python 3.8, on a computer running Ubuntu 20.04. For simulation, Gazebo is used through Robotnik Stack for Summit-XL , using mThis chapter presents the results of the experiments. Tests are performed from the starting point , obtaining a feasible fleet route, deployment routes, and cable layout. In GLOBAL TEST experiments, the algorithm finds feasible solutions for 100% of the cases.In SHAPE TEST experiments, maps with the features in For the 360 tests, the algorithm returns feasible routes in 357, representing 99.16% efficiency for fleet route and deployment route generation.Since the scope of the paper is delimited to point selection and feasible route generation for a fleet of corded robots, the workspace of the effector mounted on one of the robots is not considered. However, post-fleet navigation issues are discussed, through caveats defined in two groups: limitations of the corded robot workspace, and risk of collision of the cords in the release of the end effector.Selected points representing a limitation to the workspace are obtained eight times , i.e., 2There are also exceptional cases where navigation and deployment of the fleet is feasible, but the release of the effector would present an error. These cases occur in six occasions (1.67%), shown in In order to analyze statistical significance, box and whisker plots associated with the relationships between the fit variables  and map characteristics  are presented.p = 0), fitness vs. shape , and that there are no significant differences of fitness vs. starting point data .p = 0), area weighted cost vs. shape , and that there are no significant differences of area weighted cost vs. start point data .p = 0), weighted fleet route distance cost vs. shape , and that there are no significant differences of fleet route distance vs. start point data .In summary, it can be said that the fitness value obtained as a result of the algorithm is mainly affected by the number of positive obstacles close to the ROI, and secondarily, by the shape of the land depression to be explored. The starting point does not affect in a relevant way the fitness function value.The complexity of an algorithm is usually calculated based on the Big-O notation, divided into time complexity, referring to the execution time of the algorithm, and space complexity, the amount of memory used by an algorithm . In thisWith a value of For the fleet navigation simulation, the Summit XL robot ROS package is adapted to use four robots. The 3D environments are developed by approximating the splines of the generated maps and exporting to a compatible format for import into the Gazebo environment. A script is implemented to track the trajectories of each robot, and a viewer is set up parallel to the execution of the mission. In this work, the use of a fleet of UGV land mobile robots is proposed for the deployment of a mobile cable-driven parallel robot to perform the exploration of sinkholes.For the positioning of the mobile bases of the MCDPR, fleet route planning and corded deployment are performed using maps with different characteristics, generated either manually or automatically.Several experiments have been performed by varying the fleet route environment and the features of the sinkhole to explore. The results of the experiments show the robustness of the algorithm for the generation of feasible routes, in addition to corroborating that both the shape of the sinkhole and the number of positive obstacles in the ROI region of interest present significant differences for the cost function fit. The validity of the routes in a simulation model in Gazebo has been also evaluated.The use of evolutionary algorithms considerably reduces the calculation time of the algorithm. However, parameter settings should be tailored for each task, so it is important to initially consider the scope and scalability of the proposed solution.Future work should focus on optimization of fleet routes, smoothing of planned routes to improve navigation, consideration of physical characteristics of ropes, and integration into real roped robotic fleets. For real-world scenarios, navigation can be improved with the addition of local planning techniques to avoid dynamic obstacles. Simulation can focus on testing the algorithm on rope capable simulators considering the dynamical restrictions of the real roped robotic fleet. In addition, the workspace of the released CDPR as part of the proposed weighted fitness function should be analyzed.


Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neuron function. Although ophthalmic deficits are not considered a classic symptom of ALS, recent studies suggest that changes in retinal cells, similar to those in the spinal cord motor neurons, have been observed in postmortem human tissues and animal models.In this study, we examined by immunofluorescence analysis the retinal cell layers of sporadic ALS patients in post-mortem retinal slices. We evaluated the presence of cytoplasmic TDP-43 and SQSTM1/p62 aggregates, activation of the apoptotic pathway, and microglia and astrocytes reactivity.We found in the retinal ganglion cell layer of ALS patients the increase of mislocalized TDP-43, SQSTM1/p62 aggregates, activation of cleaved caspase-3, and microglia density, suggesting that retinal changes can be used as an additional diagnostic tool for ALS.in vivo retinal biomarkers as an additional diagnostic tool for ALS may provide an opportunity to longitudinally monitor individuals and therapies over time in a noninvasive and cost-effective manner.The retina is considered part of the central nervous system, and neurodegenerative changes in the brain may be accompanied by structural and possibly functional changes in the neuroretina and ocular vasculature. Therefore, using  Today this saying is more relevant than ever, given the growing evidence of retinal manifestations in a range of diseases typical of the central nervous system (CNS). Indeed, being an integral part of the CNS, the retina shares many mechanisms with the brain and often mirrors CNS disease processes. Indeed, the brain, spinal cord, and retina share a common embryological origin and appear to be affected by the same neurodegenerative processes .in vivo with noninvasive techniques, bringing with it the need to identify biomarkers to diagnose and monitor neurological conditions. However, to rationally implement retinal imaging biomarkers in clinical practice, it is necessary to understand the cellular and molecular processes occurring in the retina and their relationship to brain pathology. In recent years, progress has been made in understanding neuropathological changes in the retina of AD and PD patients. Alzheimer’s disease patients and animal models show retinal alterations, such as thinning of the ganglion cell layer, the presence of protein aggregates, glial activation, and alterations in the vasculature , Parkinson’s disease (PD), and Huntington’s disease, multiple sclerosis and amyotrophic lateral sclerosis ALS; . In addiculature , 2019; iculature .In this context, even for ALS, traditionally considered a disease of the pure motor system, there is a growing body of evidence supporting the involvement of other non-motor systems, including the eyes . Indeed,Amyotrophic lateral sclerosis, the most common form of motor neuron disease, is a progressive and ultimately fatal condition, with an average life expectancy of 2–3 years after diagnosis .Despite increasing knowledge, so far the diagnosis of ALS is still made by exclusion and often delayed, as there is no specific test that can confirm the disease . SeveralWhile only a small percentage of ALS cases have a familial origin (fALS), attributable to selective mutations in causative and heritable genetic , the vast majority of cases are believed to have a sporadic origin (sALS), in which genes and environmental factors interact. However, a number of cellular pathways/functions have been associated with the pathogenesis of sALS and fALS, such as RNA processing, dysfunctional protein homeostasis, endoplasmic reticulum stress, dysfunctional mitochondria and/or energy metabolism, increased oxidative stress, altered axonal transport, and dysfunction of RNA-binding proteins .In particular, the presence of protein aggregate inclusions in spinal cord tissue is a hallmark of ALS, present in 97% of familial and sporadic cases. These inclusions may involve different components such as neurofilaments, ubiquitin, RNA-binding proteins such as TDP-43 (coded by TARDBP), and the antioxidant enzyme superoxide dismutase 1 (SOD1). In most cases, these inclusions are positive for ubiquitin/p62, ubiquilin-2, and p-TDP-43 . In famiSOD1 and C9orf72 mice and in a patient with ALS and expansion of the GGGGCC hexanucleotide repeat in the C9orf72 gene (C9-ALS). Notably, SOD1 mutant mice show retinal cell degeneration and protein aggregates have been reported to parallel spinal cord degeneration  and after rehydration in decreasing concentrations of ethanol , then rinsed twice in distilled water and washed with phosphate buffered saline (PBS) solution . Antigen retrieval has been performed by incubating slices at 90°C for 45 min in a solution containing sodium citrate pH 6 (Sigma Aldrich). Slices were allowed to cool down at room temperature for 20–30 min, washed twice in PBS and then incubated for 1 h in a blocking solution containing 5% goat serum and 0.3% Triton in PBS. Primary antibodies diluted in blocking solution were incubated overnight at +4°C: anti-TDP-43 ; anti-SQSTM1 ; anti-Cleaved Caspase-3 ; anti-β-tubulin-III ; anti-Iba1, ; anti-GFAP ; and anti-pTDP-43 ser409 . Negative controls were performed by omitting the primary antibody. On the following day, samples were washed in PBS (3 × 5 min) and then incubated for 45 min with Hoechst  and secondary fluorescent antibody. After 3 × 10 min washes in PBS slices were incubated with Sudan Black (0.3% in EtOH 70%) for 10 min to reduce the level of autofluorescence. The samples were dipped in EtOH 70%, distilled water and mounted using an antifade Fluorescence Mounting Medium (Dako-Agilent). Images were acquired with either a Confocal Laser Scanning or a Confocal Spinning Disk microscope.2.3.For conventional Confocal Laser Scanning analysis of retinal slices, images  were acquired with a Laser Scanning Confocal microscope  equipped with a 60X objective water immersion objective (NA 1.2). Three wavelength diode lasers have been used . When possible, to minimize any putative bias in sampling, six images were collected from the retina of each patient . For each image 20–25 stacks (0.5 μm step each) were acquired and analysis was performed with ImageJ software (2.1.0/1.53c).2.4.Acquisition of the images was also performed using a Nikon Eclipse Ti equipped with a X-Light V3 spinning disk (CrestOptics). The images were acquired with Metamorph software version 7.10.2. (Molecular Devices) using a 60x PlanApo l oil objective  and sectioning the slice in Z with a step size of 0.3 μm each. The acquisitions obtained were analyzed using the ImageJ software.2.5.Fluorescence images of TDP-43 and Hoechst staining were analyzed through a custom algorithm implemented in MATLAB environment. Images were pre-processed as follows: a moving average filter (3 × 3 pixels window) reduced the noise, and a histogram shape-based method automatically identified the background level beyond the peak of the smoothed histograms. Furthermore, for Hoechst signals, a morphological opening operation (disk ray = 6 pixels) was performed to remove small objects and thin lines from the images while preserving nuclei shape. Corrected images were binarized by combining a clustering method with a locally adaptive thresholding. The pixels of the image were divided into two groups  by computing a two-means clustering of the image. Additionally, adaptive thresholds, based on the local mean intensity in the neighborhood of each pixel, are necessary for different lighting conditions in different areas. The union of the two masks returns the binary image of Hoechst and TDP-43 signals used to evaluate the spatial distribution of TDP-43 with respect to the cell nucleus. First, the total area covered by Hoechst signal was divided by the area of a single nucleus to estimate the number of cells in the field of view; then the areas covered by TDP-43 signal outside and inside nuclei, normalized by the number of cells, were computed and compared in control and ALS conditions.2.6.Co-localization between cytoplasmatic TDP-43 with its phosphorylated form pTDP-43 was evaluated by examining fluorescence co-stained images acquired by Confocal Laser Scanning. Images analysis was performed in a Matlab environment. Firstly, we exploit the Hoechst signal to select the region of interest outside nuclei, as explained in the 2.5 paragraph. We then analyzed the average intensity projection of TDP-43 and pTDP-43 stack images within the selected area. We subtracted the local background and applied H-minima transform to suppress non-specific signals, then we binarized images using Otsu’s method, which chooses a threshold that minimizes the intraclass variance of the thresholded black and white pixels. Finally, we calculated Manders overlap coefficient to measures the fraction of the cytoplasmatic TDP signal covered by the pTDP signal.2.7.To estimate co-localization between p62 and TDP-43 staining, we calculated Manders overlap coefficient, which measures the fraction of the p62 signal coincident with the TDP-43 signal. Quantification was performed after subtracting local background to avoid high co-localization values due to the non-specific signal .2.8.Images were acquired by Confocal Spinning Disk microscopy, a Z-projection based on the maximal intensity signal was obtained and after threshold setting, fluorescence intensity value has been recorded. Data are expressed as mean fluorescence integrated density/region of interest (ROI) limited to threshold.2.9.Images of slices immunostained for cleaved Caspase-3, an effector enzyme of the apoptotic pathway, were acquired by Confocal Spinning Disk microscopy. Number of positive cells were manually counted within each image. Results are expressed as the number of cleaved Caspase-3 positive cells divided by the total number of ganglion neurons counted in the region of interest (ROI) and then calculated as percentage of degenerating ganglion cells in each slice.2.10.To quantify the amount of GFAP and Iba1 signals , we used the following methods designed in a Matlab environment. Briefly, for GFAP analysis, we drew a polygonal region of interest (ROI) around the inner nuclear layer on the average intensity projection of the Hoechst channel. Then, the average intensity projection for the GFAP channel stacks was analyzed: after local background subtraction and H-minima transform application, images were binarized using Otsu’s method. The area of the binarized image within the first ROI was measured, normalized on the first ROI, and thus expressed as percentage of area covered. For Iba1 immunostaining we manually count the number of iba1 positive cells over the field of view.2.11.p value <0.05 was considered significant. All statistical analyses were performed using Prism9 software.In order to avoid bias due to thresholding, data have been analyzed by two different experimenters by using a fixed threshold in one case (Triangle method in ImageJ), or using for each image the best threshold according to the experimenter’s judgment. Both methods gave similar results thus reducing the possibility of any bias introduction. Data are shown as the Mean +/− SEM. Statistical significance between controls and ALS patients was assessed with the nonparametric Mann–Whitney U test or T-test as indicated. A 3.3.1.The accumulation of TDP-43 and p62 aggregates in retinal tissue has been reported in genetic mouse models of ALS  suggestiWe analyzed retinal cross sections from 10 clinically and neuropathologically confirmed ALS patients  and 11 healthy controls . To analyze the presence of cytoplasmic TDP-43 inclusions, we stained retinal slices with an antibody against total TDP-43. We quantified the amount of TDP-43 inclusions measuring the areas covered by TDP-43 signal inside and outside the nucleus (stained with Hoechst) and normalized by the number of cells.n = 57/7 fields/individuals; controls 4.4 ± 0.4, n = 50/6 fields/individuals; p = 0.9). TDP-43 immunoreactivity was strikingly and significantly higher in the cytoplasmic compartment of retinal cells of ALS patients compared to age matched controls , thus confirming the accumulation of TDP-43 mislocalization in ALS patients retina . It has to be noticed, however, that both cytoplasmic TDP-43 and pTDP-43 signals were significantly higher in ALS retina compared to age matched controls  compared to controls  respect to age matched controls  thickness associated with various neurological and neurodegenerative diseases. This has been observed in AD, minimal cognitive impairment , multiplhSOD1G93A mice . This is in line with the RNFL thinning in ALS patients recently demonstrated by optical coherence tomography .We also, evaluated the glial reactivity in retinal slices as, among the multifactorial mechanisms underlying ALS-associated neurodegeneration, inflammation is one of the most prominent hallmarks of sporadic and familial forms of ALS , with ovIt should be noted that we found no signs of astrocyte reactivity in the retina of ALS patients contrary to expectations. On the other hand, counting microglia cells, we found increased microglia density in ALS retina.It is possible, however, that the glial phenotype is underestimated in this context. Indeed, the use of coronal retinal slices prevented a morphological analysis of astrocytes and microglia because of the extremely thin thickness of the slices. In addition, inclusion in paraffin prevented a Real Time PCR analysis.In conclusion, our data support the idea that although ALS has traditionally been considered a disorder purely of the motor system, several non-motor systems, including the eyes, are involved and affected by the disease. Furthermore, the presence of protein aggregate inclusions in the retina could become a new fingerprint panel to characterize ALS, supporting the possibility of using ocular biomarkers for early diagnosis of ALS-associated neurodegeneration.Indeed, to date, there is a lack of reliable and noninvasive clinical assessment to monitor ALS patients and document response to therapy in ALS clinical trials. Measurement of retinal changes could become an inexpensive and noninvasive method to assess neurodegeneration and, given its correlation with disease severity, can be used to assess disease progression in ALS patients .However, this will require the development of specific ligands for ALS biomarkers and remote high-resolution imaging techniques to achieve noninvasive and inexpensive diagnosis and monitoring of ALS by scanning the retina.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The use of Human tissue has been approved by the Ethics Committee of Fondazione Santa Lucia I.R.C.C.S. to GR. Written informed consent was not provided because we used tissues obtained from a tissue bank.NP, YG, SC, and MP designed, carried out, and analyzed the immunofluorescence experiments on protein aggregates and neurodegeneration. LM designed, carried out, and analyzed the immunofluorescence experiments on astrocytes and microglia. NP and YG designed, carried out, and analyzed the confocal acquisitions. SG designed and wrote the Matlab-based code. SDA wrote the manuscript with the help of GR and SG and conceived, supervised, and administered the project. SDA and GR acquired funds for the project. All authors contributed to the article and approved the submitted version.This research was funded by the REGIONE LAZIO ; Fulbright ; Rome Technopole PNRR, FP7 (to SDA); ERC-2019-Synergy Grant ; EIC-2022-PathfinderOpen . This research was partially funded by grants from Sapienza University (to SDA).YG and MP are employed by D-Tails s.r.l. SDA is a scientific advisor of D-Tails s.r.l.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We hypothesized that the Hf range would increase and the lateral distance and MOSml minima would decrease during each turn type vs. straight-line gait and during late-cued vs. pre-planned turns. We found that the range of Hf was significantly greater during each turn type vs. straight-line gait and during late-cued vs. pre-planned turns. Also, the lateral distance minima were significantly smaller during turns vs. straight-line gait, and during pre-planned vs. late-cued turns. Our hypotheses about MOSml were partially supported because the MOSml minima patterns were specific to right or left steps and were not significantly different between straight-line gait and pre-planned turns overall, but the right step’s MOSml minima were more negative during late-cued vs. pre-planned turns and between either turn and straight-line gait. Finally, we observed slower gait speeds, fewer footfalls, shorter turn phase duration, and different turn strategies used during late-cued vs. pre-planned turns. Overall, these findings reveal multifaceted control of frontal-plane balance during turns encountered during everyday mobility.This study evaluated frontal-plane dynamic balance control during 90° left turns while walking. Ten healthy young adults performed straight-line gait, pre-planned turns, and turns cued visually (late-cued turns). We quantified rotational balance control via the range of frontal-plane angular momentum (Hf) about the center of mass (COM), and the relative positioning of the COM and the feet using the horizontal distance from the COM to the lateral edge of the base of support  and the mediolateral margin of stability (MOS Of turning tasks, 90° turning is performed every day as we navigate our built environments. The mechanical objectives of turning include (1) redirection of the body’s velocity vector in the transverse plane, (2) rotation of the body’s facing direction in the transverse plane, and (3) balance maintenance  throughout the maneuver. The increased dynamic requirements of turns relative to straight-line gait likely challenges balance during the turn , especiaTurns can be executed in a pre-planned or late-cued manner, depending on the environment and movement intent. Pre-planned turns are performed when the cue to turn is perceived early with respect to movement execution. Late-cued turns are required when the cue to turn is perceived later, therefore the movement planning duration is shorter. There is emerging evidence that turn kinematics may be affected by late cues .Balance maintenance requires control of rotation about the body’s center of mass (COM). One common metric for assessing the rotational component of balance is the angular momentum (H) about the COM, also referred to as the whole-body angular momentum. Herr and Popovic showed that H is tightly controlled near zero during straight-line gait in all three planes to facilitate balance maintenance and reduce energy expenditure . By contml) during 45°, 90°, and 180° pre-planned turns, and late-cued 180° turns, has been shown to reach more extreme values than during straight-line gait  larger ranges of Hf during late-cued vs. pre-planned, and (4) decreased minimum lateral distance and MOSml during late-cued vs. pre-planned turns.The purpose of this study was to understand how frontal-plane balance is regulated during straight-line gait and 90-degree pre-planned and late-cued turns, with respect to rotational control and the relative positioning of the center of mass and feet. We hypothesized that there will be (1) larger ranges of Hf during late-cued and pre-planned turns vs. straight-line gait, (2) decreased minimum lateral distance and MOS2.2.1.Ten healthy adults  provided their informed consent to participate in this study as approved by the Institutional Review Board of Stevens Institute of Technology. All participants indicated that they were free of pathologies and pain that would impair their ability to walk and turn during daily life.We simulated the conditions of a grocery store using tape on the floor in a T-shape to emulate two aisles 0.91 m wide  forming Participants were asked to imagine that they were in a grocery store walking at a comfortable pace in three contexts: walking straight, pre-planned turns, and late-cued turns. They were instructed to walk as if they were walking down a grocery store aisle with no one in front of them, with people behind them such that they should not stop, and that they were not in a rush. First, they performed five trials of straight-line walking down the 10 m aisle. Next, they performed 10 pre-planned turns, followed by 10 late-cued turns, with 15 s rest periods between trials and five-minute instructional periods prior to each of the three conditions. For both turn conditions, we randomly prescribed with which foot to initiate walking, such that each turn condition included five trials starting with each foot. During the pre-planned turn condition, participants were instructed ahead of time that they should turn 90° left to walk down the intersecting aisle because it contained the item of interest, as though they were familiar with its location in this grocery store. The monitor displayed a large image of the item of interest, green broccoli .In the late-cued turn condition, participants knew there was a 50% chance they needed to turn left into the aisle, as though they were unfamiliar with this store and needed to look into the aisle in order to determine to turn. The monitor always started with a black screen, and the participants knew that upon reaching the intersection, the monitor would display either: the green broccoli to cue them to turn, or a “NO” symbol (red circle with a line through it) to cue them to continuing to walk straight. 20 trials were completed, half of which included a late-cued turn.2.2.All marker data were smoothed with a cubic spline filter  and no data were gap-filled. Four trials deriving from two subjects were excluded because of missing data due to marker occlusion. To generate the 15-segment whole-body model of each participant , first a2.2.1.In straight-line gait trials, the phase of interest was when the COM was within the center 3 m of the walkway, bounded by heel strike events to ensure complete gait cycles, to avoid transient behavior at gait initiation and termination. In turn trials, all analyses were performed during the turn phase, defined as follows. We used a person-specific threshold based on when the pelvis heading angle exceeded the mean pelvis heading angle plus or minus three times the standard deviation of the pelvis heading angles during straight-line gait trials. The heel strike before the pelvis threshold was reached was the start of the turn phase. The end of the turn was defined by the first heel strike that occurred after the pelvis heading angle reduced below the threshold relative to the new perpendicular direction of travel  was computed and normalized using methods previously described . H was p2.2.3.We quantified the BOS by which markers are below a height threshold as defined during quiet standing per participant. Four markers on each foot  were used to compute the BOS. To minimize the occurrence of only one marker at heel-strike contributing to a non-anatomical triangular BOS, we buffered the forefoot and hindfoot BOS areas with ellipse planes of best fit. Because the COM velocity vector was always anteriorly directed, we simplified by only using the front foot’s lateral edge during the double support phases. The BOS is the horizontal-plane convex hull outline of the front foot’s markers that are below their threshold height obtained during quiet standing, plus a 1 cm height tolerance. Heel strike and toe-off gait events were detected using the relative positioning of the foot markers and pelvis  modified2.2.4.Lateral distance was defined as the horizontal distance between the COM and the BOS lateral edge, where lateral was defined by the pelvis mediolateral axis . To compBecause only the lateral edges of the BOS were considered, when the COM is within the BOS or medial to it – right of the left foot or left of the right foot –the lateral distance metric is positive. When the COM is lateral of the lateral edge of the BOS, this lateral distance metric is negative event 7, . In each2.2.5.ml was computed from the same BOS position, COM position, and gait event data used in the lateral distance computation. However, both the medial and lateral edges of the BOS were considered when computing the distance from the XCOM to the edges of the BOS. The extrapolated COM (XCOM) position was obtained using an effective pendulum length of 1.34 times the trochanteric height   . Mixed mn 4.1.2) .3.3.1.Spatiotemporal results and p-values are included in 3.2.Hf range was significantly smaller during straight-line gait vs. pre-planned turns (p < 0.0001) and late-cued turns (p < 0.0001) and during pre-planned vs. late-cued turns (p = 0.005) , Fig. 4.3.3.The lateral distance minima were significantly larger during straight-line gait vs. pre-planned (p < 0.0001) and late-cued turns (p < 0.0001) and during late-cued vs. pre-planned turns (p < 0.0001) , Fig. 5.3.4.ml minima were significantly less negative during straight-line gait vs. late-cued turns (p < 0.0001) and during pre-planned vs. late-cued turns (p = 0.002)  when the XCOM was left of the right foot’s medial BOS edge. About 17% of MOSml minima were during left single support . ml maxima, except for one trial when the maximum was during left double support.The MOS= 0.002) , Fig. 6.t phases . The MOSml minima were less negative during straight-line gait vs. pre-planned (p = 0.0002) and late-cued (p < 0.0001) and during pre-planned vs. late-cued turns (p = 0.003) (ml minima were more negative during straight-line gait vs. pre-planned (p < 0.0001) and late-cued turns (p = 0.0001)  , Fig. 6A 0.0001) , Fig. 6B4.ml. Supporting our hypotheses about Hf, its range was greater during each turn type vs. straight-line gait and during late-cued vs. pre-planned turns. Our hypotheses about lateral distance were partially supported because the lateral distance minima were smaller during turns vs. straight-line gait, but they were also smaller during pre-planned vs. late-cued turns. Our hypotheses about MOSml were partially supported because the MOSml minima were not significantly different between straight-line gait and pre-planned turns overall, but were specific to right or left step. The left step’s MOSml minima were smaller negative values during pre-planned turns vs. straight-line gait, but the right step’s MOSml minima were more negative during late-cued vs. pre-planned turns and between either turn and straight-line gait. Finally, relative to pre-planned turns, we observed changes in spatiotemporal patterns during late-cued turns including slower gait speeds, fewer footfalls, shorter turn phase duration, and differing turn strategies. During late-cued turns, 73% were step turns, whereas, during pre-planned turns, step or spin turns each occurred 50% of the time and were linked to the foot with which participants started walking in all trials except two , though they were instructed not to. Second, the space constraints of the lab required a short (4 m) walkway exiting the turn that may have imposed a velocity reduction requirement during the turning tasks, and we only examined left turns to reduce the duration of the experiment. Further, though we randomized within condition block, we did not randomize the condition blocks, so there could be an order effect between conditions. Also, our turn phase definition relied on pelvis rotation, when other preparatory adjustments may have preceded pelvis rotation. We acknowledge that our results are specific to the turn phase and turn strategy computational choices, as well as our experimental design, including the variable nature of the late-cue timing.This study opens many future research questions. For example, we would like to understand how the control of frontal plane balance relates to control in other planes and how balance during turns is controlled by different balance-impaired populations. A more thorough analysis of the effects of gait speed on frontal plane balance measurements is prompted by our initial explorations of gait speed as a covariate. Similarly, a thorough analysis of how the gait phase context when the cue was provided and how that affected turning strategies is warranted, as in .Supplemental Document 1


Caenorhabditis eleganshas been instrumental in studying the underlying mechanisms of TDP-43 toxicityin vivo . Previous studies in C. elegansmodels observed the characteristic features of mutant TDP-43 such as; post-translation modifications, insolubility and cytosolic localization. Models expressing wild-type TDP-43, however, either did not show these features or showed less pronounced phenotypesC. elegansC. elegansmodel for pan-neuronal expression of hTDP-43. We show that altering the thermal environment of hTDP-43 worms enhances characteristic features of TDP-43 proteinopathies, including increased protein levels, cytosolic localization, and phosphorylation . The proteotoxic effects of hTDP-43 translate into numerous movement phenotypes , disrupted sensorimotor functions and decreased fecundity . Our results suggest that the toxicity of wild-type hTDP-43 inC. elegansexceeds the neuromuscular system and affects non-motor systems to a varying degree. Moreover, characteristicC. elegans phenotypes, such as coiling, shrinking and uncoordinated movement, may point at defects in specific neuronal subgroups.Inclusions consisting of transactive response DNA-binding protein 43 (TDP-43) are a characteristic and toxic feature of numerous neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS)The thermal environment enhances pathological features of wild-type hTDP-43C. elegansC. elegans C. elegans.Our findings show that raising the growth temperature of hTDP-43 worms enhances numerous features of TDP-43 pathology . The increased phosphorylation of S409/S410 and cytosolic localization of wild-type hTDP-43 are considered pathological hallmarks of TDP-43 proteinopathies. Previous studies did not readily observe cytosolic localized TDP-43 inWhether our observations represent actual TDP-43 pathology , as previously postulated by Zhang et al.TDP-43 worms show phenotypes associated with defects in cholinergic and GABAergic signalingC. elegans severely impairs spontaneous and induced movement . Since we assayed locomotion and body movement to a new extent, our results add another dimension to the previously described phenotypes in liquid- and on solid medium. For example, hTDP-43 worms are not only uncoordinated but also show context-dependent shrinking and coiling, features that are seen in worms with cholinergic and GABAergic defectsMovement dysfunction is a characteristic phenotype of worms overexpressing (mutant) hTDP-43 . In line with previous studies, we show that neuronal expression of hTDP-43 inSo far, GABAergic dysfunction and degeneration have been considered a specific consequence of neuronal expression of mutant hTDP-43Widespread proteotoxicity of hTDP-43 in extra-motor systemsgoa-1 Gαomediated-pathwayNeuronal circuits integrate various environmental stimuli to alter behavior, including locomotory patterns. Evaluating the behavior of nematodes in the presence of multiple environmental cues provides a way to analyze different components of the nervous system that influence motor output. Both serotonin and dopamine are known to modulate the output of motoneuronsWe show that olfactory-driven movement is abrogated in hTDP-43 worms while the mechanosensory, dopamine-regulated basal slowing response is seemingly intact . Moreover, we find that hTDP-43 worms have lower fecundity and might have a higher basal egg-laying rate in M9 . There is, however, no clear response to 5-HT treatment . The reduction in offspring as a consequence of hTDP-43 expression has been previously describedOur findings do not exclude altered serotonergic or dopaminergic signalling in hTDP-43 worms . Both the  increase in basal egg-laying rate and the reduced effect of food on movement speed hint towards increased serotonergic and dopaminergic signalling respectively as both transmitters can negatively influence movement through the inhibition of cholinergic motoneuronsThe interaction effects of increased hTDP-43 levels and accelerated agingAn increased environmental temperature enhanced most, but not all phenotypic abnormalities in hTDP-43 worms. A higher growth temperature is known to have a major effect on the physiology of the worms since it affects both the rate of development and rate of agingThe inability to separate aging and hTDP-43 specific effects does not preclude the use of temperature to increase the severity of movement dysfunction in hTDP-43 worms for screening purposes. An environmental temperature of 25 °C can be used to completely abolish the thrashing capacity of hTDP-43 worms. Consequently, within thrashing frequency of hTDP-43 worms resides a robust and binary readout that can be used in elegant suppressor screens. At the same time other behaviors that require basal locomotion, like chemotaxis or food-slowing, become inaccessible at 25 °C. Thus, the usefulness of an increased growth temperature highly depends on the feature to be assessed and the question to be answered. Altogether, these findings reinforce the idea that environmental factors can be used to manipulate the genotype-phenotype conversion and, ergo, to shape screenable phenotypes.Table 1: Strains used