Drosophila DIP and Dpr proteins mediate neuronal targeting in the fly through highly specific protein-protein interactions. We show here that DIPs/Dprs segregate into seven specificity subgroups defined by binding preferences between their DIP and Dpr members. We then describe a sequence-, structure- and energy-based computational approach, combined with experimental binding affinity measurements, to reveal how specificity is coded on the canonical DIP/Dpr interface. We show that binding specificity of DIP/Dpr subgroups is controlled by “negative constraints”, which interfere with binding. To achieve specificity, each subgroup utilizes a different combination of negative constraints, which are broadly distributed and cover the majority of the protein-protein interface. We discuss the structural origins of negative constraints, and potential general implications for the evolutionary origins of binding specificity in multi-protein families.Differential binding affinities among closely related protein family members underlie many biological phenomena, including cell-cell recognition.  Drosophila development. Here, the authors combine a computational approach with binding affinity measurements and find that DIP/Dpr binding specificity is controlled by negative constraints that interfere with non-cognate binding.Dpr (Defective proboscis extension response) and DIP (Dpr Interacting Proteins) are immunoglobulin-like cell-cell adhesion proteins that form highly specific pairwise interactions, which control synaptic connectivity during  While one Dscam (or Pcdh) isoform may have >90% sequence identity to another family member, the few differences between them ensure that only homophilic recognition occurs. This is critical to their function in the diversification of neuronal identities and self/non-self-discrimination4. In other cases, for example the type I and type II classical cadherins which pattern epithelia and other tissue structures, specificity is less strict7. While classical cadherins also exhibit homophilic binding, in addition they show strong heterophilic binding to other select family members7. In another example, the nectin adhesion protein family encodes interactions between members that are mainly heterophilic, contributing to the formation of a checkerboard pattern between two cell types, each expressing a cognate nectin8. The specificity patterns of these protein families underlie their biological functions, and are conserved in evolution. Understanding how specificity is coded on multi-protein adhesion protein families is thus critical.Over the course of evolution, gene duplications followed by sequence divergence have generated numerous protein families whose homologous members have distinct binding specificities. In many cases there are only subtle differences in the sequence and structure of closely related family members, yet these can have profound functional consequences. Families of cell–cell adhesion proteins offer many such examples, where seemingly small changes in sequence generate precise protein–protein interaction specificities. For example, invertebrate DscamDrosophila melanogaster neuronal recognition proteins, the 21-member Dpr (Defective proboscis extension response) and the 11-member DIP (Dpr Interacting Proteins). These proteins have been extensively characterized structurally11, and their interactions were characterized quantitatively with biophysical measurements11. They thus offer an ideal system to study the evolutionary design of specificity on protein–protein interfaces. DIPs and Dprs are expressed in cell-specific patterns throughout the developing nervous system12. DIPs preferentially bind Dprs, and a network of specific heterophilic interactions is formed between members of the two families. This molecular binding network is correlated with synaptic specificity in the fly retina, suggesting that DIP/Dpr interactions play an important role in neuronal patterning13.Here, we carry out a comprehensive computational and experimental study of specificity determinants in two interacting families of 13. Homodimerization is observed for some DIPs and Dprs, and homo-dimerization and hetero-dimerization is mediated by an interface formed between the membrane-distal Ig1 domains .The extracellular regions of DIP and Dpr family members consist of three and two tandem Ig-like domains, respectivelyins Fig. . SurfaceDrosophila melanogaster DIPs and Dprs have intra-family pairwise sequence identities greater than about 50% and 40%, respectively, while the average identity between individual DIPs and Dprs is about 30%. Binding interfaces for crystallographically determined hetero-dimer structures are essentially identical—superimposing to within 1 Å (ref.11)  Fig. . The cen2 buried surface area and comprising 33 interfacial residues on the DIP side and 33 interfacial residues on the Dpr side. We recognize that non-interfacial residues may also contribute to specificity but the primary determinants will, in most cases, be part of the interface and these are the focus of the current work.Since there are a total of forty-nine possible DIP/Dpr subfamily pairs and only seven bind strongly, there must be forty-two sets of negative constraints that preclude incorrect pairing. These are coded on a pseudo-symmetric Ig1–Ig1 interface of about 1900 ÅGs) resulting from mutations and found that FoldX18 offered the best combination of accuracy and computational efficiency. Using FoldX, we identified negative constraints for every DIP/Dpr pair and then confirmed a subset of our predictions using SPR.We begin by asking what can be learned from sequence alone and find that this information is useful but incomplete. Our structure-based approach involves building homology models of hypothetical complexes formed between all DIPs and Dprs and, using solved complexes where available, to calculate the energetic consequences of mutating interfacial residues in one family member to those of every other. We tested various programs that calculate binding free energy changes  used to weaken undesired complex formation between different subgroup pairs. Some locations on the interface are used by multiple pairs while others are used in only a small number of cases. In general, more than one negative constraint is required to significantly weaken binding. We also discuss the trade-off between specificity and affinity, a phenomenon that appears essential for the generation of multiple specificity groupsInsecta orthologs (see Methods section for details). Interfacial positions of DIPs and Dprs were assigned based on non-zero change in accessible surface area per residue upon complex formation in experimentally solved crystal structures. Interfacial positions of DIP/Dpr complexes of unknown structure were inferred from multiple sequence alignment . The family-wide numbering of interfacial positions  has both DIP and Dpr members, for purposes of discussion we can define four sets of proteins, DIP-i, DIP-j, Dpr-i, and Dpr-j. We need to explain why members of set DIP-i don’t bind to members of set Dpr-j and, in turn, why members of set DIP-j don’t bind to members of set Dpr-i. Since Dpr-i binds to DIP-i while Dpr-j does not, we first use FoldX calculations to identify residues of Dpr-i that weaken binding to DIP-i when mutated into a residue of Dpr-j. Thus, we identify negative constraints of Dpr-j by studying their predicted effects on the binding of mutated Dpr-i to DIP-i. The experimental tests follow the same logic: the 10; purple subgroup—crystal structure of Dpr11/DIP-ɣ10; green subgroup—crystal structures of Dpr4/DIP-η and for two conformations each for Dpr1/DIP-η and Dpr2/DIP-θ)11; pink subgroup—homology model of Dpr7/DIP-κ; orange subgroup—homology model of Dpr8/DIP-β; brown subgroup—homology model of Dpr12/DIP-δ; red subgroup—homology model of Dpr13/DIP-ε. Overall, we predicted the effects 3044 mutations of 33 interfacial residues of 13 template complexes on the DIP and Dpr side into residues occurring in DIPs and Dprs of other subfamilies  and 1) to mutate every interfacial residue in the DIP protomer to all residues appearing at the same position in DIPs of other specificity subgroups and then (2) calculate ΔΔGs for the binding of each DIP to the Dpr in the complex. (3) Repeat the procedure for the Dpr in the complex and then calculate ΔΔGs for its binding to each DIP. Templates that were used include: blue subgroup—crystal structure of Dpr6/DIP-α and two conformations of Dpr10/DIP-α to mutatInsecta species so as to focus on negative constraints that are evolutionarily conserved. The evolutionary filter assumes that orthologs in the same subfamily conserve their interaction specificity (see Methods section for data supporting the assumption). To clarify, the energy-based filter is based on FoldX calculations of structures of fly proteins whereas the evolutionary filter is based on comparison of ortholog sequences of different species of insects. The evolutionary filter involved an analysis of interfacial residues at corresponding positions of DIP-i and DIP-j (or Dpr-i and Dpr-j) in Insecta multiple sequence alignments to see if the identities, size, or biophysical properties of amino acids in all members of subfamily “i” were different from those of subfamily “j” at the predicted position (see “evolutionary filter” in Methods section for details).Negative constraints were defined as positions predicted to cause destabilizing effects for every DIP or Dpr fly subgroup member in the context of binding to a member of another Dpr or DIP subgroup, respectively. Our energy-based filter also requires that the effect is destabilizing for every subfamily template complex used in the FoldX calculations (see “energy filter” in Methods section for details). In addition, we introduced a second filtering step, whereby we only defined a negative constraint as a position that would also be predicted for other A subset of our predictions (see below) was tested experimentally. Overall, we identified negative constraints for 42 combinations of non-interacting DIP and Dpr subgroups (Supplementary Data Gs) based on reports of their performance in the literature30. These include FoldX31, mCSM30, BeAtMusic27, MutaBind29, Rosetta flex ddG24, and BindProfX32. FoldX, mCSM, and BeAtMusic require from seconds to few minutes of CPU time per single point mutation, while MutaBind, Rosetta flex ddG, and BindProfX take more than an hour. Our evaluation tests were based on 25 experimental ΔΔG values obtained via SPR or AUC measurements of DIP/Dpr and DIP/DIP dimers. Of these, two were published11 and twenty-three are new  On purple subgroup Dprs; (b) On blue subgroup DIP-α; (c) On blue subgroup Dprs; and (d) On purple subgroup DIP-γ.Gs for binding to DIP-α. The protocol is applied for every template complex of the blue subfamily listed above , 2.0 ± 0.2 μM for  and 7.9 ± 0.9 μM for , respectively. A similar level of experimental error (10–15%) is expected for the KDs of all other Dpr or DIP mutants determined in this study, where each concentration was tested in duplicate but in a single experiment. Fitting errors and experimental errors are displayed in each figure containing SPR data.We tested these predictions with SPR measurements of wild type and mutant proteins. At least five independent measurements for each of three wild type interactions, Dpr10/DIP-α, Dpr6/DIP-α, and Dpr11/DIP-γ, resulted in average KD relative to wild-type by about an order of magnitude; adding a second mutation, Q29E, further increases the KD by about a factor of 2 while adding a third mutation L10G results in a triple mutant with a KD > 500 μM. Thus, introducing Dpr11-like negative constraints to Dpr10 at these three positions is sufficient to kill binding between Dpr10 and DIP-α. We then carried out a reverse set of experiments aimed at removing negative constraints on Dpr11 to enhance its binding to DIP-α. As can be seen in Fig. KD of 18.3 μM—weaker than wild-type Dpr10 but still quite strong. Overall, our results indicate that we have successfully identified the negative constraints on the Dpr side of the interface that preclude binding of purple group Dprs to blue group DIPs.Figure KD of DIP-γ/Dpr11 binding from 7.9 to 37.2 μM  and Dpr11/DIP-γ (purple subgroup) in detail since these pairwise DIP/Dpr interactions have revealed clear associated phenotypes in the central and peripheral nervous systems of 11. The relevant SPR data from that study are compared to our current FoldX predictions in Supplementary Table In a previous study, we tested mutants designed to switch the binding preference of the blue subgroup Dpr6 from its cognate DIP-α to the green subgroup DIP-η and, in turn, switch the binding preference of the green subgroup Dpr4 from DIP-η to DIP-α2 of accessible surface area upon complex formation. Each non-cognate DIP/Dpr subfamily pair was found to be destabilized by a set of about 7 interfacial residues involving both sides of the interface. The physical source of the negative constraint can be shape non-complementarity in the hydrophobic core , Notably, G10L increased cognate binding between Dpr11 and DIP-γ by about the same amount, indicating that some cognate binding affinity has been “sacrificed” so as to weaken non-cognate binding.In some interfacial positions, FoldX predicts that mutations to residues found in other subgroups would actually increase affinity Figs. . As an e−1 (ref.11). By contrast, the K7H {K82H} mutation in Dpr4 strengthens cognate binding to DIP-η by 0.3 kcal mol−1 ; three predicted negative constraints were confirmed and one prediction that a residue was not a negative constraint was also confirmed .The approach we have adopted is essentially to build a large set of homology models of complexes that form and do not form and to ask, using FoldX calculations, SPR measurements and visual inspection what is wrong with those that do not form. Overall, we identified 38 positions which are used for negative constraints and carried out 23 validation tests with SPR at 8 of those positions (more than one mutation was tested at each position). In addition, our predictions are in agreement with previously published SPR experiments on the blue and green subfamilies7. The physical origins of negative constraints include: replacing a charge in an ion pair with one of opposite sign leading to Coulombic repulsion; replacing one member of an ion pair with a neutral group, which has the effect of burying an unsatisfied charge; mutations in the hydrophobic core to larger amino acids that create steric clashes or to smaller ones that create cavities which lead to packing defects and to weaker hydrophobic contributions to binding. Of course, the same “trick” cannot be reused for 42 different subgroup pairs. In fact, every set of negative constraints on the DIP and Dpr side of the 42 non-cognate pairs is unique in the sense that the same set is not reused  while inter-subgroup KDs (with a few exceptions) are undetectable, implying a KD > 500 μM. This suggests that the generation of a new DIP/Dpr specificity requires a change in binding free energy of ~2.5 kcal mol−1, a value that is difficult (but not impossible) to reach with one or two mutations.The negative constraints we identified for the most part exploit strong localized unfavorable energetic contributions rather, for example, than more subtle effects that are distributed over many residues (as is the case for type I cadherins where entropic effects involving the movement of entire domains play a role in determining dimerization affinities)b”, is very strong, then it is more of a challenge, for example, to generate an isoform “c” that is similar in sequence to “b”, but interacts very weakly with “a”. Thus, the higher the specificity requirements, the greater the constraints on absolute affinities. That some residues in the interface are not optimized for cognate binding (see above) may then reflect the need to ensure that binding is not too tight.How is the DIP/Dpr interface designed to achieve affinity and specificity? First, we note that, in common with most cell-cell recognition proteins, DIP/Dpr affinities are in the μM range, a thousand-fold weaker, for example, than many antibody-antigen complexes with substantially smaller binding interfaces. That lower affinities are a general feature of adhesion receptors, even those which are not members of large families, suggest that there are other factors involved that have little to do with negative constraints, for example cellular motility. Moreover, cell–cell avidity involves the interactions of multiple receptors so that there may not be a need to evolve proteins with nanomolar affinities. This may account for the observation above that even cognate interfaces are not optimized for affinity. Moreover, a trade-off between affinity and specificity is to be expected in large protein families with only limited sequence divergence. If an interaction between two family members, say “a” and “36 but this strategy does not appear to have been used by DIPs and Dprs.We find that more than a half of the DIP/Dpr interface  is utilized for negative constraints across different insect species. However, these only play a role in the context of non-canonical subgroup–subgroup interactions, whereas in any given cognate DIP/Dpr complex, a majority of the interfacial residues likely play a stabilizing role. One can imagine that an ancestral DIP/Dpr complex relied entirely on stabilizing interactions in the hydrophobic core along with additional stabilizing or energy neutral interactions in the periphery. As multiple family members evolved the need for specificity was satisfied through the introduction of negative constraints throughout the interface. This evolutionary strategy is likely used by other large protein families. Of note, it has been found that specificity can be achieved through the insertion and/or deletion of loops5, the family can be divided into subgroups whose members exhibit considerable promiscuity in their intra-subgroup binding properties while not binding to members of other subgroups. This behavior is reminiscent of the subgroup characteristics of DIPs and Dprs although, since there are fewer cadherin specificity groups, their evolutionary design is less of a challenge. In both cases binding promiscuity may well be a source of functional redundancy but this has not been established. By contrast, Dscams2 and clustered protocadherins4 are strictly homophilic which, as mentioned above, is an absolute requirement for their role of establishing a unique identity for every neuron. In contrast to DIPs and Dprs, the exquisite specificity of these protein families is established through multi-domain interfaces that allow for a greater degree of fine-tuning41. This could result from simply a larger surface area on which to place negative constraints and/or from constraints39 imposed by a multi-domain structure. Negative constraints are obviously a central component of evolutionary design in Pcdh and DSCAM protein families, but these have not yet been characterized in detail.Detailed binding affinity measurements have now been reported for a number of families of adhesion receptors. In some cases, for example type II cadherins42 but in general very little is known about this topic. The situation becomes more complex for cells that express more than one family member and/or more than one subgroup member. The ability to design mutants of altered affinities and specificities combined with modern gene editing techniques opens the door to a new type of experiment which probes the molecular basis of cell-cell recognition. We have reported preliminary studies on this problem involving DIP and Dpr mutants which abolished homophilic or both homophilic and heterophilic interactions in the Drosophila retina43. The next logical step will be to test how subtle changes in affinity and specificity would affect biological phenotype.The range of DIP/Dpr binding affinities indicated in Fig. 10; purple subgroup—crystal structure of Dpr11/DIP-ɣ (PDBID: 6NRR)10; green subgroup—crystal structures of Dpr4/DIP-η (PDBID: 6EG0) and for two conformations each for Dpr1/DIP-η (PDBID: 6NRW) and Dpr2/DIP-θ (PDBID: 6EG1)11. The structures were aligned and visualized using PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.The DIP/Dpr structures used in alignment presented in Fig. 44 were superimposed onto Dpr4/DIP-η crystal structures yielding 231 DIP/Dpr heterodimers. The side chains of the dimer models were further minimized using the Scwrl4 algorithm45.We built homology models for each DIP and Dpr Ig1 domain . The models are expected in principle to be high quality since there is considerable sequence and structural similarity between intra-family DIP and Dpr Ig1 domains. A few modeled proteins have longer loops than those of the template but these regions do not appear critical for Ig1–Ig1 interface. Homology models of DIP and Dpr monomers built using MODELLERhttp://foldxsuite.crg.eu/command/RepairPDB). This procedure “repairs” improbable dihedral angles and van der Waals clashes in a protein. We then run five rounds of “RepairPDB” and confirm that total energy of a protein complex reaches a plateau. The complex whose structure has now been optimized with respect to the FoldX energy function is then used as a starting point for a computational mutagenesis procedure with “BuildModel” (http://foldxsuite.crg.eu/command/BuildModel). A desired mutation is made to the repaired structure and then energy-minimized by sampling rotamers for the mutated residue and its neighboring amino acids. The wild type protein is then energy-minimized with respect to rotamers of the same set of residues. The difference in interaction energy between the mutant and wild type is then calculated between the two protomers in the complex using the “AnalyseComplex” FoldX utility .Although FoldX can be used as a “black box” we summarize here the procedures used to calculate ΔΔG(binding). We first refine the structure of a protein complex using the FoldX utility called “RepairPDB” (http://foldxsuite.crg.eu/parameter/numberOfRuns), which produces ten pairs of wild type and mutant structures, as different neighbors of the mutated residue could be moved during minimization. The average value of the interaction energy obtained via energy decomposition of the total energy using “AnalyseComplex” defines ΔΔG(binding).To test if FoldX achieves convergence for a given mutation we run “BuildModel” ten times by setting the “numberOfRuns” parameter to 10 .It is important to note that our homology models give comparable FoldX results to those obtained from crystal structures. This is perhaps not surprising since we use high-quality homology models based on high sequence identities and the fact that all DIP/Dpr crystal structures superimpose to within 1 Å. To illustrate this point, we carried out FoldX calculations on homology models and compared the results to those obtained from crystal structures of Dpr11/DIP-γ, Dpr6/DIP-α, and Dpr10/DIP-α. The Pearson correlation coefficient (and root mean square error) with experimental values was 0.56 (1.65) for the crystal structures and 0.53 (1.10) for the homology models based on 25 experimental measurements of binding energy differences.31; mCSM is a machine learning method based on the assumption that the impact of a mutation is correlated with atomic-distance patterns in surrounding amino acids30; BeAtMusic relies on a set of coarse-grained statistical potentials derived from known protein structures27. BeAtMusic is the fastest of all the methods we tested—it takes seconds to assess all possible mutations in a protein chain or at the interface; MutaBind utilizes a combination of molecular mechanics force fields, statistical potentials and a fast side-chain optimization algorithm; Rosetta flex ddG24 samples conformational diversity using “backrub” to generate an ensemble of models and then applies torsional minimization, side-chain repacking, and averaging across this ensemble to calculate ΔΔG; BindProfX32 combines a sequence profile conservation score of structural homologs with the FoldX potential. Data on the performance of these methods is summarized in Supplementary Table The following is a summary of the methods evaluated for the calculation of the effects of mutations on binding free energies. FoldX evaluates the effects of mutations using an empirical force-field that allows side chains to move but keeps the backbone rigid46 with an e-value cutoff of 10−35 using in-house developed scripts on non-redundant BLAST database. The resulting 2950 Dpr sequences and 2005 DIP sequences (121 species) were screened using CD-HIT47 with 100% threshold to get rid of redundancy arising from multiple GI (GenInfo Identifier) numbers pointing to the same protein product. The obtained non-redundant set of full-length sequences corresponding to each DIP or Dpr was combined with a reference sequence (DIP-α or Dpr6 respectively), and aligned using Clustal-Omega48. The multiple sequence alignments (MSA) of each DIP or Dpr were further processed in Jalview49 to remove sequences that had non-complete Ig1 domains and to extract interfacial positions based on the reference sequences. Sequence logos based on the MSAs presented in Fig. 50 separately for each Drosophila DIP and Dpr based on 2570 Dpr and 1732 DIP sequences. Sequence logos were also prepared on a subfamily level by combining sequences of all its members  into seven sets .Insecta, the assumption is likely to be correct. To justify this, we calculated the average pairwise sequence identities within Insecta orthologs for interfacial residues of each DIP and Dpr. The average pairwise sequence identity for interfacial residues of DIP (86%) and Dpr (83%) orthologs in Insecta is 84%. This value is similar to the average intra-subgroup pairwise sequence identity for each DIP and Dpr Insecta subgroup (78%) and the intra-subgroup pairwise sequence identities of interfacial residues in Drosophila melanogaster (~80%). These values are also consistently higher than inter-subgroup pairwise sequence identities in Drosophila melanogaster or Insecta (~50%). Only one subfamily (red Dpr) is an exception to the rule—it has relatively low conservation both in Insecta (48%), and in Drosophila melanogaster (40%).We rely on the assumption that orthologs in the same subfamily conserve their interaction specificity. This assumption would not work for comparison of proteins in species that are too far removed from one another on the tree of life, but since we consider orthologs within one class, Drosophila melanogaster where available and on homology models where crystal structures were not available. If mutations were predicted to cause destabilizing effects  for every DIP or Dpr fly subgroup member in the context of binding to a member of another Dpr or DIP subgroup, the position were accepted provided ΔΔG ≥ 0.05 kcal mol−1 for every subfamily template complex used in the FoldX calculations. The cutoff value for ΔΔG was chosen based on the estimated standard deviation we found for mutating an interfacial amino acid into self . Positions that passed the energy filter were further subject to the evolutionary filter (see below).We applied “energy filter” by carrying out FoldX calculations on the effects of mutations on X-ray structures of DIP/Dpr complexes of S < 0.23) and different in identity—the position was accepted. S is given byfi is the fraction (frequency of occurrence) of residues of amino acid type present at a position in MSA and N is the number of amino acid types (base of 21 was picked to account for 20 amino acids types and a gap).If positions in “i” and “j” subgroup were both conserved  and for every amino acid at the same position of subfamily “j” (aaj), we checked if aai and aaj belonged to the same or different biophysical property groups defined above. When aai and aaj were different in property, we included the contribution of the amino acid pair weighted by amino acid occurrences in MSAs (faai × faaj × 100%) to the overall percent difference. When aai and aaj had the same property, the contribution was not included. The sum of all included weighted contributions (N is the number of amino acid pairs where (property of aai) ≠ (property of aaj), while k and l run over all amino acids present at a position in subfamilies “i” and “j”, respectively) defined the total biophysical property difference in %. The positions were accepted at >90% cutoff.If residues in the “i” and “j” subgroups had difference in their biophysical properties. We binned the 20 amino acids into four property groups: hydrophobic , polar , positively charged , and negatively charged . Then, for every amino acid at a position of subfamily “i” . Each percent difference was computed as a sum of the weighted amino acid contributions (N is the number of amino acid pairs where (size of aai group) ≠ (size of aaj group), while k and l run over all amino acids present at a position in subfamilies “i” and “j”, respectively) when the condition above was satisfied (size(aai) > size(aaj) in the first case and size(aai) < size(aaj) in the second case). The maximum of the two percent differences defined the total size difference in %. The positions were accepted at >90% cutoff.If residues in the “i” and “j” subgroups had differences in size , small , medium , large , and bulky . We computed two percent differences—the percent difference when aaWe applied “evolutionary filter” by filtering positions using multiple sequence alignments (MSAs) for DIP and Dpr subfamilies based on sequences of insect orthologs (see above). For every non-cognate DIP-i/Dpr-j family, to filter positions on the DIP side we compared corresponding positions from DIP-i and DIP-j subfamilies in MSAs. To filter positions on the Dpr side we compared positions from Dpr-i and Dpr-j subfamilies in MSAs. The position was accepted if at least one of the three conditions outlined below was true.51) between the NotI and BamHI sites. All primer sequences used in this study are given in Supplementary Table E. coli cells (Thermo Fisher Scientific) were used for plasmid manipulations. Sequence boundaries for each DIP and Dpr were the same as those defined in Cosmanescu et al.11 All sequences were preceded by the signal sequence of human binding immunoglobulin protein BiP (MKLSLVAAMLLLLSAARA), and a kozak sequence (GCCACC). Constructs were followed by a C-terminal hexa-histidine tag. Point mutations were introduced using the QuickChange method (Agilent). HEK293F cells were transiently transfected with each expression construct using the polyethylenimine method52, and cells were expanded and grown in shake flasks in a CO2 incubator for 3–6 days.All proteins were produced by expression in human embryonic kidney cells (Thermo Fisher Scientific). DNA sequences encoding DIP and Dpr extracellular regions were amplified by PCR and sub-cloned into the mammalian expression vector VRC-8400 ref.1 between2, and 5 mM Imidazole pH 8.0 and incubated with Ni2+ charged IMAC Sepharose 6 Fast Flow resin  for 1 hr at 25 °C. Resin was washed with at least 20 column volumes of buffer containing 10 mM Imidazole pH 8.0. SDS gel electrophoresis was used to detect which elution fractions contained the desired protein.Conditioned media was equilibrated to 10 mM Tris-HCl pH 8.0, 500 mM NaCl, 3 mM CaClProteins were further purified by size-exclusion chromatography  on an AKTA pure fast protein liquid chromatography system . Most proteins were stored in a buffer of 10 mM Bis–Tris pH 6.6 and 150 mM NaCl. DIP-α and its mutants were stored in a modified buffer (10 mM Bis–Tris pH 6.0 and 150 mM NaCl) due to stability issues. Most proteins were stored in a buffer of 10 mM Bis–Tris pH 6.6 and 150 mM NaCl. UV absorbance at 280 nm was used to determine protein concentration and verification of purity was determined by gel electrophoresis. Accurate molecular weights were determined through MALDI-TOF mass spectrometry at the Proteomics Shared Resource facility at Columbia University.g. For all runs the lowest speed was held for 20 h and then four scans were taken with a 1 h interval, the second lowest held for 10 h then four scans with a 1 h interval, and the third lowest and highest speed measured as the second lowest speed. Measurements were done at 25 °C, and detection was by UV at 280 nm. Solvent density and protein v-bar were determined using the program SednTerp (Alliance Protein Laboratories). To calculate the KD and apparent molecular weight, data were fit to a global fit model, using HeteroAnalysis software package, obtained from University of Connecticut53 (http://www.biotech.uconn.edu/auf).Experiments were performed in a Beckman XL-A/I analytical ultracentrifuge , utilizing six-cell centerpieces with straight walls, 12 mm path length and sapphire windows. Protein samples were dialyzed to 10 mM Bis–Tris pH 6.6, 150 mM NaCl. The samples were diluted to an absorbance of 0.65, 0.43, and 0.23 at a 10 mm path length and 280 nm wavelength in channels A, B, and C, respectively. Dilution buffer were used as blank. The samples were run at four speeds. Most proteins were run at 16,350, 26,230, 38,440, and 52,980×−1. Dextran surfaces were activated for 7 min using equal volumes of 0.1 M NHS(N-Hydroxysuccinimide) and 0.4 M EDC(1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide). Each protein of interest was immobilized at ~30 μg mL−1 in 10 mM sodium acetate, pH 5.5 until the desired immobilization level was achieved. The immobilized surface was blocked using a 4-min injection of 1.0 M ethanolamine, pH 8.5. Typical immobilization levels ranged between 760-980 RU. To minimize nonspecific binding the reference flow cell was blocked by immobilizing BSA in 10 mM sodium acetate, pH 4.25 for 3 min using a similar amine-coupling protocol as described above. For each experiment where DIP mutants were immobilized to the chip surface, the wild type molecule was immobilized on an adjacent flow cell as a positive control.SPR binding assays were performed using a Biacore T100 biosensor equipped with a Series S CM4 sensor chip. To minimize artificial binding resulting from enhanced-avidity effects of oligomers interacting with an immobilized ligand, DIPs and their respective mutants, were consistently used as ligands rather than analytes, and immobilized over independent flow cells using amine-coupling chemistry in HBS pH 7.4  buffer at 25 °C using a flow rate of 20 μL min−1 BSA and 0.01% (v/v) Tween-20. Analytes were prepared in running buffer and tested at nine concentrations using a three-fold dilution series ranging from 81 to 0.012 μM for data shown in Fig. −1. At the end of the dissociation phase the signal returned to baseline thus eliminating the need for a regeneration step. The last step was buffer wash injection at 100 μL min−1 for 60 s. The analyte was replaced by buffer every two or three binding cycles to double-reference the binding signals by removing systematic noise and instrument drift. The duplicate binding responses were fit globally, using an 1:1 interaction model and a single KD was calculated as the analyte concentration that would yield 0.5 Rmax (ref.54) and a fitting error. KDs < 100 μM were calculated using an independent Rmax. For KDs > 100 μM, the Rmax was fixed to a global value determined by the Rmax of a different Dpr analyte tested over the same DIP surface during the same experiment that showed binding above 50% and therefore produced a more accurate Rmax (ref.54). For KDs > 300 μM, a lower limit is listed since at the analyte concentrations used we could not measure accurate KDs even when the Rmax is fixed. The data was processed using Scrubber 2.0 (BioLogic Software).Binding analysis was performed at 25 °C in a running buffer of 10 mM Tris-HCl, pH 7.2, 150 mM NaCl, 1 mM EDTA, 1 mg mLKDs from independent experiments (see the source data file). For the Dpr10/DIP-α binding pair, the KD from six independent experiments is 1.5 ± 0.1 μM, and similarly for the Dpr6/DIP-α, the KD from six independent experiments is 2.0 ± 0.2 μM. The KD from five independent experiments for Dpr11/DIP-γ is 7.9 ± 0.9 μM. The standard deviation in ΔΔG values computed based on KD measurements of the above three cases do not exceed 0.1 kcal mol−1. Therefore, mutations resulting in ΔΔG (SPR) > | 0.2 kcal mol−1 | are likely to be significant.For several wild type interactions discussed in this manuscript, Dpr10/DIP-α, Dpr6/DIP-α, and Dpr11/DIP-γ, we have also determined 10 recently criticized KDs we previously determined via SPR experiments11, specifically for the interacting pairs Dpr6/DIP-α, Dpr11/DIP-γ, and Dpr1/DIP-η, for being consistently weaker than KDs they reported. The following discussion explains how differences in experimental methodology likely explains these differences and raises issues about the approach used by Cheng et al.10. The underlying challenge in both sets of measurements is that some DIPs homodimerize, a problem that is discussed by Rich and Myszka in their 2006 review of SPR data55. We addressed this problem by immobilizing DIP proteins to the chip surface, using Dprs as analytes, to minimize artificially lower KDs resulting from homodimers used as analytes. It has been suggested that an alternate “fix” is to use the free monomer analyte concentration in KD calculations10. However, the monomer/dimer equilibrium in the analyte can shift the moment the analyte is injected over a surface immobilized with a heterophilic binding partner, which changes the monomeric analyte concentration available for a heterophilic binding reaction over the course of the experiment. Therefore, this correction does not adequately solve the problem.In a note added in proof, Cheng et al.KD of 23 μM and 14 μM, respectively10, compared to our analytical ultracentrifugation-determined KD of 56.2 μM (ref.11). Given the reliability of AUC measurements, this highlights the problems associated with Chang et al.’s approach. Most importantly these numbers suggest that the differences in KD reported in both studies are due in part to the use of different biophysical techniques used to study these molecules.Moreover, to calculate the free monomer analyte concentration of DIP-η, Cheng et al. used size exclusion chromatography and SPR to calculate a homophilic 10, these authors relied on ECIA experiments with artificially multimerized molecules. As previously discussed in detail in Cosmanescu et al.11 although ECIA can be used to detect interactions in a high-throughput fashion and without the need for purified proteins, it involves the use of multimerized forms of both the prey and the bait molecules, and thus introduces artificial avidity to amplify binding signals, enhancing the likelihood of positive detection, but masking the real binding affinities between interactants. In addition, the use of unpurified protein supernatants10 could introduce experimental bias toward identifying interactions between molecules that are more easily expressed at higher levels compared to proteins that can be difficult to express.In addition, for many of the results that differ between our studies and Cheng et al.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary InformationSupplementary Data 1Reporting summary


Within the last decade, robotically-assisted laparoscopic prostatectomy (RALP) has become the standard for treating localized prostate cancer, causing a revival of the 45° Trendelenburg position. In this pilot study we investigated effects of Trendelenburg position on hemodynamics and cerebral oxygenation in patients undergoing RALP.We enrolled 58 patients undergoing RALP and 22 patients undergoing robot-assisted partial nephrectomy (RAPN) (control group) in our study. Demographic patient data and intraoperative parameters including cerebral oxygenation and cerebral hemodynamics were recorded for all patients. Cerebral function was also assessed pre- and postoperatively via the Mini Mental Status (MMS) exam. Changes in parameters during surgery were modelled by a mixed effects model; changes in the MMS result were evaluated using the Wilcoxon signed rank test.Preoperative assessment of patient characteristics, standard blood values and vital parameters revealed no difference between the two groups.Applying a 45° Trendelenburg position causes no difference in postoperative brain function, and does not alter cerebral oxygenation during a surgical procedure lasting up to 5 h. Further studies in larger patient cohorts will have to confirm these findings.https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005094.German Clinical Trial Registry; DRKS00005094; Registered 12th December 2013—Retrospectively registered;  Since Prof. Friedrich Trendelenburg developed the “Raised Pelvic Position” for abdominal and pelvic operations in the 1880s, and its first description by his student Dr. Willy Meyer in 1885, the term “Trendelenburg Position” has become the standard nomenclature to describe a patient lying in supine position  and lateTo perform robot-assisted laparoscopic prostatectomy, an “extreme” 45° Trendelenburg position is often applied, continuing throughout the procedure. In addition to abdominal CO2-insufflation, significantly reduced pulmonary compliance and upper airway edema have been reported .In this study we explore the influence of a prolonged Trendelenburg Position during robotically-assisted radical prostatectomy on intraoperative hemodynamics and cerebral oxygenation, as well as pre- and postoperative cerebral function.We enrolled in our study a total of 58 consecutive male patients (study group) who underwent robot-assisted radical prostatectomy (RALP) for localized prostate cancer therapy and 22 consecutive male patients undergoing robot-assisted partial nephrectomy (RAPN) (control group) for suspected kidney-cell carcinoma at the Wels-Grieskirchen Medical Center. We chose the RAPN procedure for our control group since it is a routine, robotically-assisted procedure at our institution, and a horizontal position is maintained during the entire procedure. Preoperative oncological staging and the indication for the above-mentioned surgical intervention were determined according to the current guidelines of the European Association of Urology (EAU) on prostate cancer and renal cell carcinoma. General anesthesia was induced with 150–200 µg of fentanyl and 150–200 mg of propofol then 0.6 mg/kg of rocuronium was administered followed by orotracheal intubation. General anesthesia was maintained with sevoflurane (MAC 0.9–1.2) and continuous infusion of remifentanil (1–2.5 µg/kg/min). Pneumoperitoneum was induced by insufflating CO2 with gas pressure set to 8 mmHg during the whole operation. After pneumoperitoneum was achieved, study patients were positioned in 45° Trendelenburg by tilting the operating table. Control group patients were brought into flank position prior to inducing pneumoperitoneum with the operation table slightly flexed. Once the procedure started, this patient position was maintained during the entire procedure. All surgeries were performed by experienced urologists (> 100 robot-assisted procedures) using the da Vinci® Si Surgical System  with the surgeon performing the procedure at a control table located away from the operating table. Patients provided informed consent prior to inclusion in our study, which was approved by our local ethics committee (ethics committee of Upper Austria (Study Nr. O-18-13)); it was therefore conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Our sole exclusion criterion was involvement in any other clinical study, but this did not apply since no patients screened met this criterion. The study was designed and conducted in accordance with the consort criteria.Patient demographics and ASA classification was recorded 24–6 h prior to surgery. In addition, mini-mental status test (MMS) was carried out, and a standard blood workup  was obtained. Furthermore, we documented baseline blood pressure and heart frequency. Intraoperative heart frequency (HF), cerebral oxygenation (rSO2), and blood pressure (RR) were obtained and recorded prior to final positioning and consecutively at 10-min intervals throughout the procedure. Also the FiO2 was (= fraction of inspired oxygen) was monitored. To measure rSO2, bilateral cerebral oximetry sensors were placed on the patient’s forehead and rSO2 was continuously monitored using near infrared spectroscopy (NIRS) and the INVOS analytics tool for recording. We also recorded total operating time, time in Trendelenburg position and time of robotic actuation. Within 6–24 h after surgery, standard blood panel was repeated and all patients underwent a MMS Test. Complications were classified according to the Clavien Dindo classification and recorded over a 3 months follow up for all included patients.This trial was designed to explore whether a 45 Degree Trendelenburg Position causes any differences in intraoperative clinical parameters, or changes in the MMS exam result in comparison with patients not in a head-down position. Patients were included in this exploratory study applying the intention-to-treat principle. All statistical calculations were performed by an experienced and licensed statistician (H.W.). Changes in the MMS exam outcome before and after surgery were subjected to the Wilcoxon rank-sum test. To analyze the evolvement of parameters during surgery, we made an exploratory comparison of the different variables at the same time point, followed by a longitudinal analysis of a variable’s evolvement, and investigated the change in a given variable compared to its initial value by linear mixed effects models. To analyze changes, the response variable’s pre-operation value centered at a reasonable value and that variable’s interaction with time were included in the model. All statistical computations were performed using the R statistics software (Version 3.5.2). All tests were two-sided with p < 0.05 considered to indicate statistical significance, but as this is not a confirmatory trial, significant results can be interpreted only as indicating a potential association.All patients included in our study underwent a complete evaluation, and we observed no drop outs. Median age in our study group was 66, ranging from 48 to 79 years. Our control group’s median age was 56 years (45–84). ASA classification showed a median of 2 for both groups. Routine blood panel revealed a median hemoglobin value of 15.3 g/dl (13–17.1) for the RALP group and 14 g/dl (10.20–16.30) for the RAPN group. Median preoperative RR was 137.5 (94–210)/80 (60–110) for our study group and 146.5 (100–180)/80 (60–103) for the control group. The median HF measured 66 bpm (45–92) (RALP) and 70 bpm (60–100) (RAPN). No significant difference was detected in individual preoperative patient characteristics  for RALP and a point value of 30 (IQR = 0.75) for RAPN. There was no statistically significant difference here, either (p = 0.29).Total median operation time was 180 min  for the RALP group and 140.5 min (IQR = 76.50) for the RAPN group, thus the study group’s surgery time was longer (p = 0.01). Also, the RALP group’s robotic-actuation time (= console time) was significantly longer than the RAPN group’s (p = 0.001). RR values exhibited no difference in systolic values, but the prostate group’s diastolic values were significantly higher, although that effect vanished after about 120 min’ operation time. However, since the RAPN group’s operation time was shorter, we had access to fewer measurements after 120 min, with only 1 measurement at 210 min. Our analysis of any statistical change in systolic RR compared to the initial value showed no group difference either. We noted a significant difference between the RALP and RAPN group in diastolic RR compared to the initial valued (p = 0.00), with the prostate group’s RR values significantly higher 120 min into the operation. Age also seemed to reveal an effect on the change in diastolic RR, with the patients over 60 showing significantly lower values (p = 0.00). HF during surgery did not differ significantly between groups in either the longitudinal analysis or analysis of change during the operation. We did, however, observe a tendency for older patients (> 60 years) to present a lower heart frequency. Intraoperative FiO2 measurements revealed very little variation, with no group difference in the intraoperative change versus the initial value. Although the former was also not associated with patient BMI, higher-BMI patients revealed a generally higher FiO2 (p = 0.01). The pCO2  value and pO2  and rSO2 value  showed a significantly smaller change in patients with an initially higher value  and RAPN group (p = 0.19), suggesting no negative effect of the operation and intraoperative positioning on cognitive function Fig. .Fig. 2DiIn the current study we compared intraoperatively recorded hemodynamics of patients undergoing Robot-Assisted Radical Prostatectomy and Robot-Assisted Partial Nephrectomy. We documented no relevant preoperative difference in patient characteristics. Analysis of these data, as well as the comparison of both groups showed decreasing blood pressure and slightly elevated pCO2, as well as a slight decrease in pO2 in both groups, as would be expected during a laparoscopic surgical intervention. However, besides slightly higher diastolic blood pressure, hemodynamic monitoring showed no significant difference within the two groups, which suggests that the Trendelenburg position exerted a negligible effect in the RALP group on patients’ hemodynamics. In addition, noninvasive cerebral oxygenation monitoring revealed no significant change during the operation, and no difference between our two groups. This finding is supported by other studies applying infrared spectroscopy. In a study by Park et al., intraoperative measurements revealed a statistically significant increase in rSO2 when keeping the pCO2 constant . Other sFollow up of all patients including Mini Mental Status evaluation of the cognitive state also showed no negative effect on the intellectual ability of all operated patients in both groups. In addition, we detected no complications related to the Trendelenburg positioning. We therefore conclude that the Trendelenburg position as applied in Robot-Assisted Radical Prostatectomy is safe for the patient and can be applied during surgical interventions requiring extended operating times. Our study however, has some limitations. First of all, it was exploratory and conducted only on a small cohort; further prospective studies will have to confirm these results. In addition, our control group patients did not undergo the same surgical intervention, since Robot-Assisted Radical Prostatectomy is currently only performed in Trendelenburg position, thus a control group undergoing the same procedure was not possible. Furthermore, our control group included, besides partial nephrectomy, various other Robot-Assisted procedures on the kidney including one radical nephrectomy, one adrenalectomy and a pyeloplasty. As the adrenalectomy was due to a benign hormone-inactive tumor, we cannot rule out that surgical adrenal manipulation might have altered our results.The 45° Trendelenburg position demonstrated no negative effect on postoperative brain function and did not alter cerebral oxygenation during a surgical procedure lasting up to 5 h in this exploratory study. Further studies in larger patient cohorts will have to confirm these findings.


Sulfolane-induced corrosion can lead to severe impairment in industrial systems. Therefore, determination of solvent corrosivity is valid. Under standard conditions, pure sulfolane is considered to be thermally stable and chemically inert, hence non-aggressive towards carbon/stainless steel. Unfortunately, the sulfolane-evoked corrosion of the industrial installations is observed for sulfolane-based systems polluted by small quantities of oxygen, water and some oxidizing agents. Moreover, sulfolane decomposition with formation of corrosive (by-)products can be escalated by some process parameters, e.g., temperature. The main objective of this study was to determine the corrosion resistance of AISI 1010 steel immersed in sulfolane at temperatures ranging from 25 to 230 °C. Evaluation of the corrosion damage was carried out using electrochemical techniques and scanning probe/electron microscopy, respectively. The general corrosion tendency, corrosion rate and surface corrosion degree were taken into account as well. It was noticed that the corrosion rate linearly increases with the enhancement of sulfolane temperature. Moreover, the interfacial reaction of steel with sulfolane resulted in the formation of corrosion product layer, which is a physical barrier between the corrosive environment and steel improving corrosion resistance of the latter. In fact, the increment of the sulfolane temperature caused a gradual breakdown of the protective layer and the increase in the corrosion degree of the investigated steel. Finally, it was found that the corrosion degree doubles approximately every 42 °C. A worldwide rise of atmospheric pollution due to the expansion of industrial/agricultural areas and urban settlements is a peculiar ‘landmark’ of the modern civilization. The global ecosystem is being confronted with volatile organic compounds (VOCs) as well as inorganic odorous compounds (VICs) that pose hazards to the health of human beings and plants vegetation forming a significant part of indoors/outdoors pollution ,2,3. The2) [4H8O2S) is a versatile dipolar aprotic solvent with a high oxidation potential (> 6.35 V) that is highly soluble in water (1.266 × 106 mg/L at 20 °C) [14C] or [3H] or [35S] sulfolane was monitored to specify in vivo metabolic path(s) of unchanged sulfolane content or its metabolites following oral/intravenous/dermal administration. Since the toxicokinetic pattern of SFL disposition and clearance cannot be directly foreseen in the human body based on the quantitative ADME-Tox rodent models, it is therefore still not clear whether SFL can be regarded as ‘magic extractor or bad actor’ [An appealing alternative to extensively employed extractive liquids in the dearomatization of petroleum fractions or sweetening of sour gases in the petrol-related industry is sulfolane (SFL) . Althoug2) . On the t 20 °C) . Unfortut 20 °C) . Once int 20 °C) . Due to t 20 °C) . Potentit 20 °C) . The fatd actor’ ,17. In td actor’ : (1)C4H82O2, UV/O3, alkaline ozonation and neutral Fenton reagents coupled with pH, chemical dosage of H2O2 and ethylenediaminetetraacetic acid chelated iron (FeEDTA), O3 flow rate and ultraviolet-C light controlling, respectively. The removal efficiency (RE) of SFL pollutant (>99%) transferred (82–93%) to aqueous phase was found for the scrutinized water/soil fractions along with mixing periods employing UVC irradiation and adequate H2O2 concentration (200 mg/L in undiluted sample), whereas neutral Fenton reagents performed pretty poorly (RE < 50%) [The application of a bio-augmentation strategy that assumes the employment of co-aggregated bacterial consortia considerably improved the SFL removal . The nutE < 50%) . It seemE < 50%) .Due to shortcomings and operational costs of SFL-polluted soil/water treatment a question naturally appears about the main causes for unforeseen/accidental SFL leakage, as it is known that pure liquid is non-aggressive to steel under the standard operating conditions . UnfortuIt should be emphasized that there is a gap on the path leading from data to knowledge pertaining to the detailed quantitative assessment of the effect exerted by individual impurities or operational variables on the SFL-induced corrosion of carbon and alloyed steels. In fact, carbon steel is quite commonly used in the oil and gas industry due to its availability, constructability and relative low cost of production. On the other hand, the susceptibility to corrosion (low corrosion resistance) imposes boundaries on the practical longevity of carbon steel applications, petrochemical pipes are especially prone to corrosion damage due to the erosive/corrosive nature of the produced fluids or gases . Thus, t3) in sulfolane  was conducted in a home-made sealed vessel. The detailed description of the vessel construction and the equipment used in corrosion test can be found elsewhere [The test material was AISI 1010 low carbon steel in the annealed state containing 0.1 wt.% C. Before the corrosion test, the steel samples were degreased in acetone. The corrosion process of as-obtained AISI 1010 steel  placed ca. 0.2 cm from the working electrode using a Luggin capillary. Ohmic drop ΔE = jRS (V) was estimated using measured current density (j) and solution resistance (Rs). Solution resistance was calculated using equation Rs = lκ−1 (Ω·cm2) where l is the distance between Luggin capillary and AISI 1010 steel surface (0.2 cm), κ is the sulfolane conductivity  was registered for 60 min. Next, polarization curves j = f(E)  were recorded in the range ± 150 mVSCE versus OCP using the linear sweep voltammetry technique with the potential sweep rate v = 10 mV·min−1. Using j = f(E) curves with compensated ohmic drop and equation:jcorr), corrosion potential (Ecorr) as well as anodic (βa) and cathodic (βc) Tafel slopes were determined. In addition, the Stern-Geary coefficient (B), polarization resistance (Rp) and corrosion rate (CR) were calculated according to the following equations:ρ is the material density, EW is the material equivalent weight and k is a coefficient which determines the unit of corrosion rate. For the investigated AISI 1010 steel electrodes ρ = 7.86 g·cm−3, EW = 27.923  and k = 3.27·10−3 mm·g·μA−1·cm−1·year−1, which gives the corrosion current density expressed in μA·cm−2 corrosion rate in mm·year−1. Note that low value of the sulfolane conductivity at 25 °C causes the measured current to be very small, i.e., in the of order of nanoamps or smaller, which may lead to some noise. Therefore, to visualize and interpret the obtained data more accurately, noise was removed using Savitzky-Golay smoothing algorithm [A series of electrochemical tests were carried out for AISI 1010 steel covered with corrosion products formed after 96 h of immersion in the sulfolane at temperatures of 25, 95, 180 and 230 °C. Measurements were conducted in a thermostated three-electrode cell filled with sulfolane at the temperature of 25 °C using electrochemical system PARSTAT 2273 . Working and counter electrodes were AISI 1010 steel and platinum plates with geometric surface areas of 8 and 100 cmlgorithm  and OrigCD) was calculated according to the following equation:Qualitative and quantitative surface analysis of AISI 1010 steel electrodes was carried out using scanning electron microscope (SEM) JEOL JSM-6480 , equipped with an energy dispersive X-ray spectroscopy . Obtained SEM images were converted to black and white (black color corresponds to the corroded areas) and next the corrosion degree  maps were registered for the studied electrodes using a scanning electrochemical workstation PAR M370  equipped with a tungsten Kelvin probe . The scanning area was 4 × 4 mm2 and the distance between the sample and the probe was c.a. 90 μm. Approximation of the histograms of the CPD values using Gaussian function given by the equation: CPDav) and its standard deviation (σ) that describe quantitatively the material surface properties. The detailed description of the preparation of histograms can be found elsewhere [Contact potential difference  curves, the average values of OCP and corresponding standard errors (SE) were determined and are reported in OCP decreases with increased sulfolane temperature. In particular, when the temperature increases from 25 to 230 °C, the OCP value decreases by ca. 175 mVSCE and the steel electrode reaches the most negative potential among all tested. Such behavior indicates an increase in the thermodynamic tendency to the corrosion process, thus deterioration of corrosion resistance of AISI 1010 steel. It was found that, up to about 180 °C, standard errors of the mean decrease and then increase, which may be caused by sulfolane decomposition at temperatures over 200 °C. On the one hand, sulfolane decomposition increases the corrosiveness of the environment. On the other hand, the heterogeneity of the surface geometry and/or chemical composition causes that some of its areas are more electrochemically active than others. These two factors generate some distribution of the potential on the material surface. It should be added that corrosion product layer influences the electrochemical parameters as well. Nevertheless, after 60 min relatively stable value of open circuit potential can be observed for all the investigated electrodes.Variations of the Polarization curves for the AISI 1010 steel electrodes immersed in sulfolane at 25, 95, 180, and 230 °C are shown in jcorr) is a kinetic value which is proportional to the corrosion rate (CR) (see Equation (5)). It was found that for the AISI 1010 steel electrodes immersed in sulfolane corrosion potential (Ecorr) corresponds to the open circuit potential. It was also stated that for a sulfolane temperature equal to 230 °C, the value of corrosion current density for tested steel is only ca. 7.5 nA·cm−2, which corresponds to a corrosion rate of 87 nm·year−1. However, an increase in the sulfolane temperature from 25 to 230 °C causes ca. 2.8-fold higher corrosion rate. Parameters jcorr and CR as a function of sulfolane temperature (T) are shown in jcorr = f(T) and CR = f(T) dependencies are linear with the slopes of 0.29(2) nm·year−1/°C and 0.025(1) nA·cm−2/°C, respectively. The calculated slopes can be interpreted as the rate of change of jcorr and CR per degree Celsius. The obtained values of Ecorr, jcorr and CR indicate that the tendency to corrosion and corrosion rate of AISI 1010 steel increase with increased sulfolane temperature.Corrosion potential is a thermodynamic value that provides the information about the corrosion tendency of the material in a specific environment, while corrosion current density , the cathodic process coupled with the anodic may be the reduction of HB) and polarization resistance (Rp) were calculated and are shown in B value is equal to 25 mV, which corresponds to the active corrosion state. However, for electrodes immersed in sulfolane at 95, 180 and 230 °C, relatively high values of the parameter B (70–130 mV) were obtained, indicating the passive corrosion state for those materials. This fact can be explained by the reaction of carbon steel with sulphurous acid during the corrosion process. It causes the formation of ferrous sulfite (FeSO3), which adheres to the steel surface and forms a layer that protects the alloy against further corrosion attack. Note that all obtained Rp values are of the order of MΩ·cm2. However, the highest polarization resistance was determined for the electrode immersed at 95 °C. The obtained results indicate that the protective layer formed on the surface of the AISI 1010 steel may be a physical barrier between the corrosive environment and the material improving the corrosion resistance of the latter. Nevertheless, an increase in the sulfolane temperature to 180 °C and next to 230 °C cause breakdown of the protective layer and increase in the corrosion degree.Using Equations (3) and (4) the Stern–Geary coefficients (CD) of AISI 1010 steel electrodes was estimated using SEM images and corresponding black and white images shown in βa and βc values in The SEM/EDS images of the AISI 1010 steel surface without etching of corrosion products are shown in CPD) maps for the AISI 1010 steel electrodes are shown in CPDav) and its standard deviation σ that are reported in Contact potential difference  results in the discontinuities of the corrosion product layer and a decrease in CPDav value (see CPDav. Moreover, CPDav and Rp as a function of temperature are characterized by a similar trend. It was also found that the spread of the CPD distribution from the average (represented by σ, The AISI 1010 steel electrode immersed in sulfolane at 25 °C a is charat 95 °C b. Note talue see c,d. AISIalue see d is charThermodynamic tendency for the corrosion process, as well as the corrosion rate of the AISI 1010 steel, increase with increased sulfolane temperature. In particular, an increase in the sulfolane temperature from 25 to 230 °C results in a decrease in corrosion potential value by ca. 175 mV , Table 1The highest polarization resistance  and averThe corrosion product layer formed on the surface of the AISI 1010 steel electrodes in sulfolane is a physical barrier between the corrosive environment and the material and partially protects the alloy against further corrosion attack.An increase in the sulfolane temperature caused a gradual breakdown of the protective layer and an increase in the corrosion degree. It was found that the corrosion degree of AISI 1010 steel immersed in sulfolane doubles approximately every 42 °C .


Severe obesity among children and adolescents is a significant global public health concern. The prevalence has markedly increased over the last decades, becoming common in many countries. Overwhelming rates of obesity among youth have prompted efforts to identify an evidence-based immediate- and long-term cardiometabolic risk factor profile in childhood-onset severe obesity, and to highlight gaps that require further investigation. The PubMed database was systematically searched in accordance with PRISMA guidelines. The search yielded 831 results, of which 60 fulfilled stringent criteria and were summarized in this review. The definition of severe obesity was variable, with only one half the publications using the definition BMI > 120% of the 95th percentile. Point estimates of the prevalence of at least one cardiometabolic risk factor in children with severe obesity reportedly range from 67 to 86%. Cross-sectional studies indicate that children and adolescents with severe obesity are at greater risk than those with mild obesity for type 2 diabetes, hypertension, fatty liver disease and dyslipidemia, already at childhood and adolescence. Robust epidemiological data on the long-term risk and actual point estimates in adulthood are lacking for these diseases as well as for other diseases . Recent longitudinal studies indicate an increased risk for cardiomyopathy, heart failure, cardiovascular mortality and all-cause mortality in adulthood for adolescents with severe obesity compared to those with mild obesity. Given the alarming increase in the prevalence of severe obesity, the persistence of adiposity from childhood to adulthood and the precarious course of young adults with chronic comorbidities, the economic and clinical services burden on the healthcare system is expected to rise. The prevalence of childhood obesity has dramatically increased over the last decades , 2. ThisA systematic search was conducted using PubMed, to identify relevant articles published through October 20, 2019. Search terms included “Obesity morbid” [MeSH] OR “severe obesity” OR “class 2 obesity” OR “class 3 obesity” AND “child*” OR “adolescen*” OR “pediat*” AND “diabetes” OR “NAFLD” OR “hypertension” OR “coronary artery disease” OR “stroke” OR “heart failure” OR “mortality” OR “cancer” OR “total cholesterol” OR “HDL” OR “LDL” OR “triglycerides” OR “blood pressure” OR “glycated hemoglobin OR “fasting glucose”. Other cardiometabolic diseases including “cardiomyopathy” OR “arrhythmias” OR “venous thromboembolism” OR “peripheral vascular disease” OR valve disorders OR “chronic kidney disease” were also systematically searched , low-density lipoprotein (LDL), high-density lipoproteins (HDL)) blood pressure (systolic BP and diastolic BP) and glycemic status (fasting glucose and less often glycated hemoglobin). Point estimates of prevalences of abnormal values of these risk factors by BMI group are summarized in Fig. Dyslipidemia that is associated with obesity is typically characterized by increased TG and free fatty acids, decreased HDL levels and a normal or slightly increased LDL level. Most studies that assessed cardiometabolic risk factors according to BMI status reported higher mean values of TG, and lower mean values for HDL –62 Tabl; all werPoint estimates on the prevalence of hypertension in children are mainly based on a single measurement of elevated blood pressure; this was shown to overestimate disease rates . PrevaleP < 0.001) [In parallel to the major rise in pediatric obesity, incidences of youth prediabetes and type 2 diabetes have increased significantly in recent years , 70, 71.< 0.001) . An NHAN< 0.001) .2; about one-third of children with type 2 diabetes were found to have a BMI greater than 40 kg/m2 and 17% a BMI greater than 45 kg/m2 [The most prominent clinical risk factor for type 2 diabetes in children and adolescents appears to be severe obesity. The average BMI of children with type 2 diabetes in published reports ranges from 35 to 39 kg/m45 kg/m2 . In a na45 kg/m2 .Children as young as age 3.5 years were documented with type 2 diabetes. These children were all with severe obesity , 76. ImpFatty liver disease is strongly associated with obesity, and nonalcoholic fatty liver disease (NAFLD) became the most common childhood chronic liver disease in many Western countries . NAFLD pP = 0.009) and fibrosis , and also higher systemic inflammatory markers [The paradigm and rates of progression from NAFLD, through nonalcoholic steatohepatitis (NASH) to cirrhosis and hepatocellular carcinoma, has not been thoroughly studied in the pediatric population. Though biopsy is the gold standard for the diagnosis of NASH, only a few pediatric studies were based on histopathologic data –87. The  markers . Thus, t markers , 89. Fat markers .2), a hazard ratio of 9.2 for hospitalization from heart failure was reported, compared to their peers with normal BMI, with a follow-up of 23.0 years [2 compared to those with normal BMI [Two historical longitudinal cohort studies based on data from the Swedish Conscript Registry showed an association of severe obesity in adolescence with heart failure in adulthood. Among male adolescents with severe obesity 2), a hazThe systematic search described herein did not identify cross-sectional or longitudinal articles that assessed the association between severe obesity in childhood and incident coronary artery disease, stroke, other cardiovascular disease and chronic kidney disease  vs. 3.4 (2.8–4.2), and for all-cause mortality 2.0 (1.7–2.3) vs. 1.7 (1.6–1.9) [An Israeli national longitudinal study that followed 2.3 million male and female conscripts for up to 18.4 years showed an association of severe obesity at adolescence with mortality at adulthood . The haz1.6–1.9)  During the last two decades, the prevalence of severe obesity in children has increased by twofold both in the US –95, and Several studies have reported associations of severe obesity in children with increased prevalence of cardiovascular risk factors. Data of children and adolescents from the US NHANES showed greater prevalence of abnormal TGs, diastolic blood pressure and glycated haemoglobin levels in class 3 than class 2 obesity . In addiType 2 diabetes risk is predominantly affected by obesity class. Higher BMI has been found to be associated with younger age at diabetes diagnosis , 98. YouSociodemographic variables are potential confounders of the association between degree of childhood obesity and cardiometabolic risk. Although some analyses were adjusted for sex and ethnicity, most studies did not adjust the observed risk for socioeconomic confounders that have been shown to be closely associated with both severe obesity and cardiometabolic morbidity . HoweverChildren with early-onset severe obesity display more pronounced obesogenic behaviours than do their peers with overweight or mild obesity . Among tThe above data suggest that cardiovascular disease and risk factors are intensified in severe compared to mild obesity, and appear at earlier ages. The existence of morbidity related to severe obesity already in childhood or youth is especially detrimental since it affects this young population in the most productive years of adulthood. Two pieces of data should be considered in this instance. First, severe obesity has increased among US children, even as young as 2–5 years old . Second,This systematic review has a number of limitations. As noted above, studies that have addressed the risk for cardiometabolic complications in children and adolescents have used highly variable definitions of severe obesity; less than half of them used BMI > 120% of the 95th percentile, which is recommended. Since this definition has a discriminatory advantage in identifying children with severe obesity who are at increased cardiometabolic risk, point estimates may be underestimated . FurtherIn summary, significant cardiovascular morbidity and higher risk of all-cause mortality have been reported in children and adolescents with severe obesity. The alarming increase in the prevalence of severe obesity, specifically among very young children, is likely to pose major challenges for the future burden of cardiometabolic disease. Longitudinal studies with follow-up into adulthood are needed to characterize populations that should be specifically targeted for prevention and early intervention. Accordingly, sex-, ethnic- and other sociodemographic-specific mediators should be delineated. Given the precarious course of young adults with chronic comorbidities such as diabetes, the clinical, financial and public health burden attributed to severe obesity is expected to climb.Additional file 1: Table S1. Search terms used in the systematic search. Table S2. Methods and cut-off points that were used to define abnormal values of cardiometabolic risk factors.


Shedding light on the distribution and ecosystem function of mesopelagic communities in the twilight zone (~200–1000 m depth) of global oceans can bridge the gap in estimates of species biomass, trophic linkages, and carbon sequestration role. Ocean basin-scale bioacoustic data from ships of opportunity programs are increasingly improving this situation by providing spatio-temporal calibrated acoustic snapshots of mesopelagic communities that can mutually complement established global ecosystem, carbon, and biogeochemical models. This data descriptor provides an overview of such bioacoustic data from Australia’s Integrated Marine Observing System (IMOS) Ships of Opportunity (SOOP) Bioacoustics sub-Facility. Until 30 September 2020, more than 600,000 km of data from 22 platforms were processed and made available to a publicly accessible Australian Ocean Data Network (AODN) Portal. Approximately 67% of total data holdings were collected by 13 commercial fishing vessels, fostering collaborations between researchers and ocean industry. IMOS Bioacoustics sub-Facility offers the prospect of acquiring new data, improved insights, and delving into new research challenges for investigating status and trend of mesopelagic ecosystems. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13172516 The acoustic snapshots revealing spatio-temporal sound scattering patterns, deep scattering layer, and diel vertical migration  develop an active bioacoustic data-based ecosystem Essential Ocean Variable (eEOV)63 to complement established and future ocean observing systems measuring physical, chemical, and biological environment of the ocean. These frameworks would help to advance scientific knowledge of marine food chains and manage marine ecosystems sustainably.The main goals and potential uptake values of IMOS Bioacoustics sub-Facility data are: (1) provide calibrated time series acoustic observations for the status and trend of mesopelagic ecosystem, (2) develop a framework synergizing active bioacoustic observations and ecosystem models, et al.3, based mostly on Maclennan, et al.64. All symbols signifying variables are italicized. Any symbol for a variable (x) that is not logarithmically transformed is in lower case. Any symbol for a logarithmically transformed variable, e.g. xref  (dB re xref) is capitalized.The terminology used in this data descriptor follows Demerper (W), with the General Purpose Transceiver (GPT) settings: frequency f (kHz), transmit power pet (W), pulse duration G0 (dB re 1), area backscattering coefficient sa (m2 m–2) correction factor Sa corr (dB re 1), and equivalent two-way beam angle Ψ (dB re 1 sr) of the transducer. These data and associated settings were used to calculate and display volume backscattering strength 2 m−3) for one or more frequency channels as3:–1) is the absorption coefficient, –1) is the sound speed in water, i and j represent vertical sample number and horizontal ping number respectively.In a widely used Simrad echosounder Table , the pro4. This method provides calibrated Echosounder calibration is a prerequisite for quantitative bioacoustic studies. The overall on-axis performance of echosounders installed on the participating platforms was routinely evaluated by established sphere calibration methodmentclass2pt{minim65. The triangle wave error (with a 1 dB peak-to-peak amplitude and a 2720 ping period) was removed from calibration data before calculating The raw data acquired using ES60 and ES70 echosounders were modulated with a triangle wave error sequence20 settings for commonly used Simrad echosounders. The pulse duration was chosen as a trade-off between sample resolution and acceptable signal-to-noise ratio  in the mesopelagic zone, and the logging range was set to provide robust estimates of echosounder background noise (dB re 1 W) levels66.Ensuring the operational need of participating platforms (e.g. fishing), the data acquisition settings in Table Java software suite was developed to assist data management and help identify transects for post processing  files. The inf file is in plain text format that contains user-defined metadata . It also includes key data acquisition settings extracted from the raw data files including frequency, transmit power, pulse duration, and echosounder details (GPT channel identifier and transducer model). The platform navigation details , temporal extent (start and end time of data volume), and geographic extent (limits of latitude and longitude) were also captured in the inf files. These inf files were checked for consistent data acquisition settings, transect selection, and excluding continental shelf water column backscatter data. Raw data files with inconsistent data acquisition or unknown calibration settings were not considered for further processing and archived locally.Depending on the primary purpose of participating platforms, raw data received from operators Table  may coveing Fig. . These t5 designed to remove noise and improve data quality. Transect data files applying related time offset to Coordinated Universal Time (UTC) and calibration parameters were visualized  that includes a sequence of data processing filtersized Eq.  as frequ5.Acoustic data quality from different platforms can vary significantly due to signal attenuation , and signal degradation due to combined transducer motion and noise. Data quality control involved visual inspection of echograms Fig. , followe67 and later Dunford68 developed a single correction function that can be applied for a wide range of circular transducers and related 68 algorithm implemented in Echoview® requires motion data (i.e. pitch and roll of a platform) recorded at a rate above the Nyquist rate of platform’s angular motion69 to avoid temporal aliasing due to an inadequate sampling rate. When platform motion data were available at a suitable sampling rate , transducer motion effects were corrected using Dunford68 algorithm by ensuring time synchronization with recorded acoustic data  contributing to IMOS Bioacoustics sub-Facility were not purposely built for collecting high-quality bioacoustic data. Various factors including inclement weather and vessel design can affect data quality that could cause large biases in derived ata Fig. . TransduData processing filters were applied to each , et al.5) compares each 2 m−3) represents smoothed copy of current ping with a vertical sample number 2 m−3) is an empirically determined impulse noise removal threshold value. Identified noise values were replaced as ‘no data’. The impulse noise removal parameters defined in Echoview® are given in Table Impulse noise affects discrete sections of the data with a duration of less than one ping, for example, transmit pulses originated from other unsynchronized acoustic systems. The impulse noise removal algorithm implemented in Echoview®  compares the percentile score of 2 m−3) is the current ping with a vertical sample number 2 m−3) represents 2 m−3) is an empirically determined attenuated signal removal threshold value. The attenuated signal removal parameters defined in Echoview® are given in Table Signal attenuation is generally caused by air bubbles beneath the transducer that may occur for one ping or can persist over multiple pings. The attenuated signal removal algorithm implemented in Echoview®  compares each 2 m−3) represents smoothed copy of current ping with a vertical sample number 2 m−3) represents smoothed copy of 2 m−3) is an empirically determined transient noise removal threshold value. Identified noise values were replaced as ‘no data’. The transient noise removal parameters defined in Echoview® are given in Table Transient noise is introduced to the received signal that can occur at irregular intervals and persists over multiple pings. The transient noise removal algorithm implemented in Echoview®  is the calibrated 2 m−3) is the calibrated 2 m−3) is the calibrated i and j represent vertical sample number and horizontal ping number respectively.Background noise is introduced to the received signal that can vary in intensity and pattern . According to De Robertis and Higginbottommentclass2pt{minimmentclass2pt{minim2  within an ‘averaging cell’ of The calibrated An empirically determined maximum threshold 2 m−3) values for each vertical sample number The 2 m−3 (an approximation of zero in the linear domain). The background noise removal parameters defined in Echoview® are given in Table An empirically determined threshold 2 m−3 and a time-varied threshold 2 m−3 (defined at 1 m range) was applied to the background noise corrected 2 m-3) were set to ‘−999’ dB re 1 m2 m−3. Similarly, 2 m-3 (note that median filter may replace ‘−999’ with the median of samples in the 7 × 7 neighbourhood). The output of the median filter was used to create a Boolean data range bitmap (between −998 to −20 dB re 1 m2 m−3) with ‘true’ or ‘false’ values for each sample. This Boolean data range bitmap was applied to the background noise corrected 2 m−3.In the final stage, a 7 × 7 median filter was applied to remove residual noise retained in the core filtering stages . A median filter replaces the current Quality-controlled AB® Fig. .−1) and absorption coefficient −1) values estimated using the equations of Mackenzie70 and Francois and Garrison71 respectively  is the uncorrected (but filtered) volume backscattering coefficient values exported from Echoview® at the specified range-axis interval −1) is the cumulative mean sound speed values estimated at each echo-integration cell for the new range −1) is the cumulative mean absorption coefficient values ‘interpolated’ at the new range 2 m−3) is the corrected volume backscattering coefficient values at the new range or in linear terms:Due to changes in cumulative mean sound speed, this correction step creates a grid with irregular 70 and Francois and Garrison71 respectively. Francois and Garrison71 estimate their ‘total absorption equation’ to be accurate within 5% for ocean temperature values of −1.8–30 °C, frequencies of 0.4–1000 kHz, and salinity values of 30–35 PSU. The typical hydrographical conditions  present along the open ocean transects are generally within the reliability limits of Francois and Garrison71 equation.The sound speed and absorption coefficient values for secondary corrections were estimated using the equations of Mackenzie72  or Synthetic Temperature and Salinity (SynTS)73 analyses (http://www.marine.csiro.au/eez_data/doc/synTS.html), but can also be derived from oceanographic reanalysis and ocean circulation models. CARS2009 is a digital climatology or atlas of seasonal ocean water properties. It is based on a comprehensive set of quality‐controlled vertical profiles of in situ ocean properties  collected between 1950 and 2008. CARS2009 NetCDF files contain a gridded mean of these ocean properties and average seasonal cycles generated from the collated observations. CARS2009 covers global oceans on a 0.5 × 0.5 degree grid spatial resolution, and are mapped onto 79 standard depth levels from the sea surface to 5500 m . SynTS is a daily three-dimensional (3D) temperature and salinity product generated by CSIRO, where the CARS temperature and salinity fields are adjusted with daily satellite sea surface temperature (SST) and gridded sea level anomaly (GSLA). SynTS has a 0.2 × 0.2 degree grid spatial resolution, and is mapped onto 66 standard depth levels from the sea surface to 2000 m. Due to limited spatial coverage (60°S–10°N and 90°E–180°E), the SynTS products may not always cover the transect region (e.g. Southern Indian Ocean), in that case CARS climatology values were used for the secondary corrections  file (NetCDF-4 format) with a resolution of 1 km horizontal distance  and 10 m vertical depth  (see ‘Data Records’ section for data contents). This NetCDF file conforms standardized naming conventions and metadata content defined by the Climate and Forecast (CF)74, IMOS75, and International Council for the Exploration of the Sea (ICES)76 published over the years  acquired raw data (.raw files), (2) platform track in CSV format , (3) platform motion data (if recorded) in CSV format , and (4) a snapshot of processed echogram as Portable Network Graphics (PNG) format were packaged and submitted to the publicly accessible AODN Portal Fig. .The primary components of a processed NetCDF file are shown in Fig. Processed NetCDF files are published via the Australian Ocean Data Network (AODN) Portal at:https://portal.aodn.org.au/search?uuid=8edf509b-1481-48fd-b9c5-b95b42247f82.This portal allows transect selection and data download with spatial and temporal subset options implemented for each platform and frequency.A generic metadata record of the project is available via GeoNetwork at:https://catalogue-imos.aodn.org.au/geonetwork/srv/api/records/8edf509b-1481-48fd-b9c5-b95b42247f82.The NetCDF files are also accessible via the AODN THREDDS data server that can be accessed remotely using the OPeNDAP protocol at:http://thredds.aodn.org.au/thredds/catalog/IMOS/SOOP/SOOP-BA/catalog.html.77. Readers are directed to check the AODN Portal for the latest data set.A snapshot of processed NetCDF files at the time of this publication has been assigned a Digital Object Identifier (10.26198/dv5p-t593) and will be maintained in perpetuity by the AODN2 m-3 variation in the derived 78 experiment with two or more platforms simultaneously sailing over the same region, and later comparing the echo-integration results. In suitable circumstances, large uncertainty in the absolute calibrations and platform-specific factors can be quantified. This generic principle was applied to prioritize platforms for potential long-term data collection by comparing data quality metrics between participating platforms. As the spatio-temporal coverage of the data series improves, it will be possible to perform more direct comparisons between platforms and with an acoustic climatology of the regions.In the context of echosounder calibration, it is important to note that respective mentclass2pt{minim79. Keeping Time series calibration results of selected platforms (with consistent echosounder configuration) are shown in Fig. ely Fig. . In contely Fig.  indicatementclass2pt{minimAlthough the established sphere calibration method standardizes bioacoustic data collected by multiple participating platforms, there is a need for an additional diagnostic method to ensure echosounder performance in between routine calibrations. Along with calibration results, the peak values of instantaneous received power mentclass2pt{minim}Per Eq.  highligh}Per Eq.  highlighmentclass2pt{minim2 consider that in fisheries acoustics applications, properly maintained low-frequency scientific echosounders can demonstrate consistent performance within 10% in the long-term. The aim should be to develop a routine or protocol for calibration that would help achieve this accuracy consistently irrespective of the echosounder system used. But in practice, variability in echosounder on-axis sensitivity could result from a combination of factors including system electronics, data acquisition settings, SNR, environmental conditions, and density and composition of the calibration sphere3. The performance of an echosounder may degrade gradually or abruptly  need to be recorded at a sampling rate above the Nyquist rate of platform’s angular motion. The Power Spectral Density (PSD) analyses82 of motion data  reduction in the filtered data  reduction in the filtered data, highlighting range-dependant effect of combined noise5 . Associated data quality metric SNR  reduction in the filtered data, predominantly highlighting range-dependant effect of background noise. Related data quality metric SNR .The difference between variable ‘uncorrected_Sv’ (i.e. filtered data before secondary corrections) and ‘abs_corrected_sv’ (i.e. same data after secondary corrections but before depth interpolation) is calculated (uncorrected_Sv—abs_corrected_sv) to demonstrate the effect of secondary corrections Fig. . This st70 sound speed and Francois and Garrison71 absorption equations following the recommendations by Simmonds and MacLennan2 until more evidence is available to select another formula. Macaulay, et al.85 conducted field measurements of acoustic absorption in seawater from 38 to 360 kHz, indicating consistent results with Francois and Garrison71 equation for frequencies of 200 kHz and lower. Macaulay, et al.85 observed a significant difference around 333 kHz, indicating that Francois and Garrison71 equation is incorrect for some input parameters (note that 333 kHz data is not processed under IMOS Bioacoustics sub-Facility).The processed bioacoustic data sets are consistently corrected based on MackenzieIt is important to note that the percentage correction shown in Fig. The key intermediate variable ‘uncorrected_Sv’ is stored in NetCDF files for recalculating secondary corrections using other equations or data sources (if needed). The equation used for sound absorption calculation is documented in the global attribute section of a NetCDF file as ‘data_processing_absorption_description’ and ‘history’. Similarly, the equation used for sound speed calculation is captured in the global attributes ‘data_processing_soundspeed_description’ and ‘history’.When interpreting bioacoustic data it is important to understand the corrections applied at each processing step particularly calibration, transducer motion correction, data filtering, and secondary corrections for sound speed and absorption variation Fig. . The tra86 highlighted that EK80 raw power measurements were 3–12% lower as compared to EK60, affecting weak scatterer and/or long-range acoustic observations due to nonlinear amplification of low-power signals by the EK60. Presently the users need to correct the data for this bias, and we are in the process of providing a correction update to the data sets with associated metadata. In addition to calibration and unknown methodological uncertainties, this new measurement uncertainty highlights the ongoing challenges in maintaining a diverse data series, and the need for storing fundamental echosounder measurement (i.e. received echo power) and appropriate metadata to enable unforeseen corrections in the future as needed.The widely used Simrad EK60 echosounder is now discontinued and replaced by the Simrad EK80. A recent comparison studyShips of opportunity bioacoustic sampling methods have clear advantages as well as limitations. Their usefulness in resource assessment, ecosystem monitoring, and cost-effective mapping of mesopelagic communities at regional and global scales is established with diverse acoustic-based indicators and metrics, but credible conversion of bioacoustic data (2 m−2) along a transect. Scatterer areal density (number m−2) i.e. the number of organisms (e.g. fish) within the measurement range is calculated by dividing 2) of a representative single fish. Biomass of fish (kg m−2) can be estimated by multiplying this scatterer areal density by the weight in situ measurements and/or 2. Biomass calculations from these equations will be biased if the weight and target strength 2] of the organism are uncertain . For that reason, in situ and/or modelled For example, the processed 87, dominating the received signal. Multiple methods of ecosystem models, net capture, acoustic backscattering models, and in situ profiling acoustic optical systems88 are needed to provide the necessary information to convert basin-scale bioacoustic data into specific biological metrics such as species-specific biomass61.Credible estimation of biomass using a vessel-based echosounder is very difficult for the highly diverse mesopelagic community, where gas inclusions may present in many organisms (depending on the region) that can cause frequency- and depth-dependent resonance scatteringhttp://imos.org.au/facilities/shipsofopportunity/bioacoustic or GitHub https://github.com/CSIRO-Acoustics/Visualize-IMOS-Bioacoustics-data.Generated data are stored in NetCDF files that can be readily imported into a wide variety of cross-platform software programs and programming languages. A custom MATLAB® function ‘viz_sv’ to read and visualize NetCDF files conforming to the conventions described in this data descriptor can be downloaded from the IMOS Bioacoustics sub-Facility web site https://creativecommons.org/licenses/by/4.0/). Any users of IMOS data are required to clearly acknowledge the source of the material in the format: “Data was sourced from Australia’s Integrated Marine Observing System (IMOS) – IMOS is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS). It is operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as Lead Agent.”All NetCDF files are released under the license Creative Commons Attribution 4.0 International (CC-BY 4.0,