Microorganisms produce secondary metabolites with a remarkable range of bioactive properties. The constantly increasing amount of published genomic data provides the opportunity for efficient identification of biosynthetic gene clusters by genome mining. On the other hand, for many natural products with resolved structures, the encoding biosynthetic gene clusters have not been identified yet. Of those secondary metabolites, the scaffolds of nonribosomal peptides and polyketides (type I modular) can be predicted due to their building block-like assembly. SeMPI v2 provides a comprehensive prediction pipeline, which includes the screening of the scaffold in publicly available natural compound databases. The screening algorithm was designed to detect homologous structures even for partial, incomplete clusters. The pipeline allows linking of gene clusters to known natural products and therefore also provides a metric to estimate the novelty of the cluster if a matching scaffold cannot be found. Whereas currently available tools attempt to provide comprehensive information about a wide range of gene clusters, SeMPI v2 aims to focus on precise predictions. Therefore, the cluster detection algorithm, including building block generation and domain substrate prediction, was thoroughly refined and benchmarked, to provide high-quality scaffold predictions. In a benchmark based on 559 gene clusters, SeMPI v2 achieved comparable or better results than antiSMASH v5. Additionally, the SeMPI v2 web server provides features that can help to further investigate a submitted gene cluster, such as the incorporation of a genome browser, and the possibility to modify a predicted scaffold in a workbench before the database screening. Microorganisms, such as bacteria and fungi, have always been subject to evolutionary pressure. The law of “survival of the fittest” has led to a remarkable diversity of strategies to overcome competitors. At the level of biosynthesis, microorganisms “learned” to produce a vast number of natural products that help them to survive . These sA first general approach in connecting SMs and BGCs was made with our SeMPI v1 web server . The serWhereas SeMPI v1 was dependent on antiSMASH v3  as the BThe prediction and ranking performance was benchmarked using 559 PKS and NRPS BGCs with known substrates from the MIBiG database v2 . Comparehttp://sempi.pharmazie.uni-freiburg.de.The web server provides detailed information about the cluster, including a genome browser which allows visual investigation of the cluster. The predicted SMs can be modified and extended using a molecular workbench before submission to the database screening. Custom molecule libraries can be used to extend the default natural compound screening database. The SeMPI v2 web server is available at BGC detection relies on the correct identification of domains involved in SM biosynthesis. Therefore, the profile-HMMs used to detect biosynthetic domains were benchmarked and sensitive detection thresholds were determined. The cross-validation results and detection thresholds (domT) of the profile-HMMs are shown in The precision for the bACP domains reached only 0.42, meaning that many domains are falsely classified as bACP. A detailed investigation of the cross-validation results showed that only the ACP domains were classified as bACP. In total, 215 ACP domains were classified as bACP, as opposed to 155 truly detected bACP domains. The recall of 0.8 of the DHt domain shows that, in rare cases, this domain is classified wrongly. The cross-validation results revealed that the tAT_d can be detected as a DHt domain. This happened in 21 instances, as opposed to 91 truly detected DHt domains. In one case, the DH2 domain was detected as a DHt domain.To improve the existing PKS domain patterns for module detection and extend the detection scope to NRPS and PKS-NRPS mixed modules, the BGCs in the MIBIG v2 were observed and categorized. A summary of the extracted modules is shown in In order to improve the performance of the A-domain specificity prediction models, a novel, very large training dataset was generated. Overall, 1028 new A-domains could be collected, leading to 2145 sequences altogether. An overview of the phylogenetic composition of the dataset is shown in Different parameters for the RF models were evaluated using a grid search based on the cross-validation described in The RF models with the best parameters determined in The different metrics for the overall performance of each classifier are shown in Pfam domains were correlated to specific PSMs and used to build regression models for PSM prediction. Correlating Pfam domains are listed in The Pearson correlation coefficient is rather average for all observed domains, with less than 0.67. Hence, most PSMs cannot be predicted based on linear correlation to one specific domain. Therefore, regression models that take multiple domains as features were generated to predict the number of PSMs. The performances of the regression models are shown in SeMPI v2 and antiSMASH v5 were benchmarked based on the Tanimoto similarity of the predicted scaffolds to the true SMs of all MIBiG v2 BGCs. The average (arithmetic mean) Tanimoto similarities for each BGC type are shown in Different scoring metrics were combined to allow for the best comparison of natural compounds to the predicted scaffolds. The ranking benchmark shown in The ranking performance of SeMPI v2 and antiSMASH v5 predictions was benchmarked based on the ranking of the MIBiG compounds. The percentages of true SMs ranked into the Top 10 and Top 50 similar compounds are shown in http://sempi.pharmazie.uni-freiburg.de and has processed hundreds of genomic input files without error (except for wrong input files). The processed files include 200 complete streptomyces genomes and all PKS, NRPS, and PKS-NRPS hybrid BGCs from the MIBiG, which were added to the server as a database feature. The server has been running continuously since 26 March 2019, without incidents. A detailed description of its features and usage is provided at the help page of the web server. A selection of screenshots is shown in The SeMPI v2 web server is available at The overall high F-scores above 0.95 indicate that the MIBiG-derived pHMMs are appropriate tools for BGC detection. Only the ACP and bACP domains are wrongly classified in some cases, but since the role of ACP and bACP is identical for the SeMPI v2 module detection algorithm, the misclassification does not pose a problem for the overall scaffold generation. The DHt profile-HMM could be improved, but the detection error is still rather low and the domain is only seldom found in a BGC. The MIBiG v2 contains considerably less DHt domains (133) compared to DH (1469) and DH2 (414) domains.The detection thresholds (domT) were derived from the minimum scores needed to detect the true domain in the MIBiG. It seemed reasonable to use the curated MIBiG as a reference for domain detection. Nevertheless, the threshold should be frequently updated with newly added BGCs in the database. The only core profile-HMM which is not derived from this analysis is the KSQ profile since it was not separately labeled in the GenBank annotation of the MIBIG. The profile-HMM was directly taken from the antiSMASH v5 pipeline. The same threshold as for the KS domain was used.To assign biosynthetic module arrangements with corresponding SM building blocks, a detailed investigation of the extracted modules was performed.A detailed investigation of the carbon methyltransferase (cMT) containing modules revealed that the cMT domain can be found primarily in three possible arrangements in a PKS module. First, the cMT can be positioned between the reduction domains. For most modules containing reduction domains, the cMT was integrated at a conserved position . Second, if there were no reduction domains present, the cMT was located between the AT and the ACP/PCP domains . Third, surprisingly, trans-AT associated modules showed a different cMT arrangement as opposed to the normal (cis-AT) modules: the cMT was positioned between the reductive domains and the ACP domain .Only one example of a trans-AT module with three reductive domains is described in the MIBiG v2 (BGC0001106). Since this module does not include a cMT domain, the correct cMT position for this arrangement could not be inferred. The characteristic cMT position could also be observed for putative trans-AT modules, where the trans-AT docking domain could not be detected , which could be a further marker for trans-AT detection.The module arrangements described above were integrated into the module detection algorithm. The corresponding molecular scaffolds were derived by the addition of a methyl at the α-position of the building block. This could be observed for cis-AT ,18,19 asA special case is represented by the methylmalonyl substrate, where the building block is already α-methylated. In this case, a second α-methylation is also possible . HoweveThe oxygen methyltransferase (oMT) could be found predominantly (16 times) in modules that only contain a KR domain . In those cases, the keto-group in β-position is first reduced to a hydroxyl group and subsequently methylated. The oMT domain could also be observed in modules lacking reduction domains. Trans-AT modules , as well as cis-AT modules  without reduction modules, have been observed harboring an oMT domain. Although biosynthesis routes for BGCs with these module types have been proposed , BGC0000461 (C-A-nMT-PCP-TE), BGC0000384 (A-nMT-PCP), and BGC0000326 (C-A-nMT-PCP-E). The nMT domain usually leads to methylation of the nitrogen in the peptide bond of the inserted building block .In very rare cases, a C-methyltransferase (cMT) is also observed (C-A-cMT-PCP-TE). This cMT is responsible for the methylation of the α-carbon of a thioproline building block in thiazostatin-like metabolites . This reaction is too specific to infer a general biosynthetic rule.A detailed investigation of PKS-NRPS mixed modules showed that, in all cases, the ACP/PCP domain led to the classification as a mixed module . These domains are structurally related and share similar tasks, namely the transport of the building blocks. Therefore, it is probable that mixed forms exist which cannot be reasonably distinguished with pHMMs. In some cases, they might be wrongly detected . In other cases, especially in PKS-NRPS mixed clusters, the ACP/PCP domain could represent the link between the PKS and the NRPS part of the cluster . In regard to the building block generation, this domain swap can be disregarded, since the substrate carrying domains do not lead to modifications of the building block.Based on the module analysis, detection rules for module arrangements with known substrates were derived. For each module, a building block is generated using SMILES. The building block represents the attachment of two carbon atoms to the growing metabolite in the case of PKs. The positions are labeled as described in the article by Keatinge-Clay  and are The PKS reduction pattern and putative methylation are created as described in PKS loading models are defined by the presence of a KSQ domain or the absence of a KS domain. NRPS loading modules are defined by the absence of a condensation domain. Building blocks of loading modules are missing the carbon atom in β-position. Terminal modules for both cluster types are defined by the presence of a TE or TD domain. Additional to the β- and α-position, these modules add a carboxyl group (TE) or a hydroxyl group (TD) to the building block.For each complete block, the building blocks are joined together to generate the final metabolite. An example of the scaffold generation for a hypothetical mixed cluster is shown in The correct order of modules in clusters that deviate from the co-linearity principle is difficult to predict. In some cases, the correct order can be derived by arranging starting modules at the beginning of a block and terminal modules at the end. However, for clusters composed of multiple disjointed modules, this simple approach does not suffice.Although docking domains for PKS allow one to infer the order of modules in some cases, in many clusters, these domains are missing or cannot be identified with the existing profile-HMMs. In the study by Yadav et al. , 17 clusSince clear ab-initio rules are missing, SeMPI v2 infers the order of modules by comparing the overall domain architectures with known clusters. This approach could lead to correct module compositions for various clusters including nigericin (BGC0000114), incednine (BGC0000078), fluvirucin B2 (BGC000157), and even meilingmycin (BGC0000093)—which is composed of a very complex composition of six disjointed blocks. A final assessment of the method would require a large selection of disjointed BGCs, where the correct order is derived from the literature. Since such a collection is not yet available, the ordering algorithm was measured indirectly based on the quality of the predicted scaffolds see .In the last decade, various methods have been proposed for the prediction of the substrate specificity of AT- ,30,31,32For AT-domain substrate predictions with a rather small substrate choice, the accuracy reaches more than 90%. For example, Khayatt et al. could predict 163 domains with 95%  accuracyThe RF models used for substrate prediction in SeMPI v2 yielded comparable performances with the aforementioned state-of-the-art tools. The AT substrates could be predicted with an accuracy of 95% and the A-domain specificity classification reached a micro F1-score of 0.76.The observation of a performance peak in substrate predictability allows the assumption that further improvements should be focused on the training dataset, rather than on the used algorithm. It can be hypothesized that various algorithm-independent parameters are responsible for the difficulty of substrate specificity classification. First, substrate variability hinders the predictability of one outcome class . Second,Therefore, a strong focus was placed on increasing the A-domain training dataset for the SeMPI v2 classifier. The dataset collected by Prieto et al.  comprisePSMs are more challenging to predict than the rather straightforward linear assembly lines of the PKS and NRPS core modules. This is partly because the involved domains are rather arbitrarily located in proximity to the BGC. However, the main reason can be attributed to missing learning data. Although hundreds of BGCs and their SMs are stored in the MIBiG, only a few instances of each PSM can be found—the only exception being carbohydrate scaffolds, where various examples are known, but the type and number of glycosides vary greatly between clusters.2-score.Therefore, the scope of PSMs in SeMPI v2 is mainly targeted by the subsequent database screening. Nevertheless, for some PSMs where at least a few instances are described, a prediction was attempted. Eventually, the addition of PSMs into the screening process can bring researchers closer to the true product of a BGC. The correlation analysis yielded various Pfam domains that were positively correlated to a given PSM. The regression models built based on these domains could predict the number of PSMs for a cluster with a high RFor carbohydrate scaffolds, only the number of glycosides is predicted since the prediction of the glycoside type depends on complex domain arrangements. To the best of our knowledge, only PRISM v3  providesThe comparison of the predicted scaffolds with the true metabolite shows that the predictions made by SeMPI v2 are slightly better compared to the antiSMASH v5 predictions. The best predictions are made for NRPS products (mean Tanimoto similarity: 0.57), which shows that the A-domain specificity classification of SeMPI v2 can compete with the SANDPUMA algorithm implemented in antiSMASH v5.The rather average Tanimoto similarity between prediction and true SM for both tools can be seen as a general indicator of how much structural information is still missing in the prediction scope. Some of this structural information gap can potentially be completed by comparison of the prediction with similar, already described compounds.For the NRPS products, the MCS-based ranking yielded rather similar performance for antiSMASH v5 (Top 10: 45.9%) and SeMPI (Top 10: 45.1%) predictions. However, for the PK dataset, the MCS-based scoring performed much better for SeMPI v2 predictions (Top 10: 36.0%) as compared to antiSMASH v5 (Top 10: 22.5%) predictions. Since the similarity benchmark did not show such a discrepancy, this observation was further investigated.The careful observation of various PK predictions showed that antiSMASH v5 predictions have an offset concerning the reduction patterns of PK products. Furthermore, in the observed scaffolds, the double bonds were assigned at the wrong position. A demonstration based on the abyssomicin BGC is shown in The NRPS–PKS hybrid BGCs led to the poorest rankings in the benchmark. This is not surprising, since NRPS–PKS mixed BGCs can lead to very complex scaffolds, which are difficult to match with the linear predictions. Nevertheless, 40% of the mixed products could be ranked in the Top 10. Considering the Top 50 ranked compounds, which is the default output on the web server, 66% of the true SMs were matched.The ranking benchmark demonstrates that researchers aiming to identify BGC products can gain significant support by using the SeMPI v2 database screening approach.The widely used domain annotations from the MIBIG database v2  were takTo evaluate the detection sensitivity of the created profiles, a benchmark was designed where the profiles were 5-fold cross-validated. Precision, recall, F1-score, and the support for each profile are reported. Additionally, the detection threshold  or the co-synthetic modifications could not be inferred .Third, the non-functional modules comprised all domain combinations which did not allow us to determine a possible building block, because crucial domains for the function of the module were missing .Based on the functional and special modules, patterns were derived, which can be used to screen novel BGCs for known modules.An algorithm was designed which allows us to score possible module combinations in a BGC based on known BGC arrangements.The MIBiG v2 BGCs modules were combined into blocks. Each block represented a unit in which the modules were arranged in succession. Only modules that were located directly next to each other on the same DNA strand were combined into blocks. The blocks were split up if a terminal module (harboring a TE or TD domain) was not located at the end position of the block.Those BGCs in the MIBiG v2 database, where exactly one block could be defined, were taken as reference BGCs. The reference database comprised 252 BGCs.For a novel BGC, the modules are ordered into blocks. For all blocks in a BGC, an all-vs.-all matrix is computed. For each pair in the matrix, an artificial domain set is generated, representing hypothetical combined blocks. Only reasonable block combinations are considered. For example, starting domains can only be at the beginning of a block pair and terminal domains can only be at the end of a pair. The generated domain combinations are aligned with the reference BGCs and scored.The alignment is performed using the Smith-Waterman algorithm  for locaSequences of PKS AT-domains with known substrate specificity were taken as described in SeMPI v1 . The iniFor both sequence datasets, only sequences where at least 10 known specificities could be assigned were used. The A-domain dataset was further trimmed to only include unambiguous specificities. The final datasets comprised 500 AT-domains and 2145 A-domains. The A-domains were furthermore phylogenetically categorized. Therefore, the taxonomy for each sequence was derived from UniProt .The sequences were used to train random forest (RF) classifiers. The sequences were aligned using MAFFT. The alignments were transformed into an array that could be used as a feature set to train RF models. Each position in the alignment can be interpreted as a categorical feature. The RF models were implemented using scikit-learn . The sciTo determine the best parameters for the RF classifiers, an extensive parameter grid search was performed. The parameters are listed in Precision, recall, and F1-score were computed individually for each class. To evaluate the power of the entire model, the metrics were also averaged using micro-, macro-, and weighted-averaging. Similarly, micro- and macro-AUC scores were computed.The GenBank annotation of the MIBIG database v2 was used to extract the number of non-overlapping Pfam domains present in each BGC. The scaffolds of the produced SMs were screened for putative PSMs using the substructure search as implemented in RDKit . SMARTS 2-score (coefficient of determination) [2-score was also computed for a hypothetical model, assuming no PSMs are predicted at all. The models which performed better than the baseline were incorporated into the prediction pipeline. The predicted PSMs were added as additional features to the predicted scaffold.The number of Pfam domains per BGC was correlated with the number of substructures in the related metabolites using the Pearson correlation coefficient. The positively correlating domains were further investigated to derive their function in the PSM. The investigation was based on the Pfam description. For example, domains that correlated with sugar scaffolds were examined for sugar biosynthesis association, such as glycosyltransferase activity. The selected domains were used to build logistic regression models with scikit-learn. The models were evaluated with 5-fold cross-validation. The performance was measured using the Rination) . As a baSMILES of various open-source databases with a focus on natural compounds were collected. The StreptomeDB v2.0 , NANPDB Furthermore, a collection of streptomyces genomes were processed using SeMPI v2, and the results were stored as a database feature on the web server. The predicted BGC products were also included as a possible natural compound screening option. This option allows one to detect the putative BGC for a given streptomyces natural product. The streptomyces genomes included all complete genomes associated with compounds stored in the StreptomeDB v3 . WhereasThe molecules were parsed using RDKit and incorporated into a PostgreSQL  databaseThe database set-up achieves a Tanimoto similarity search of the predicted scaffolds with more than 180,000 molecules in less than a second. The Tanimoto similarity search allows the selection of a group of closely related scaffolds to the query prediction. However, especially repeating patterns such as PK and NRP scaffolds do not allow for good scaffold comparison using molecular fingerprint-based algorithms. Therefore, an additional most common substructure (MCS)-based scoring algorithm was implemented. The MCS algorithm scores the predicted molecules with the compounds in the database, by detection of a MCS. The score is computed by dividing each matching atom and bond in the predicted molecule and the target molecule by the total number of atoms and bonds. Because the MCS algorithm is very time-consuming, it is by default only performed on a preselection of 50 compounds from the similarity search. The RDKit implementation only allows the generation of the MCS between one query and one target molecule. To allow for the matching of incomplete BGCs, the algorithm was extended to allow for the matching of multiple query molecules  in the natural compound targets. The extended MCS scoring algorithm is demonstrated in A third scoring algorithm was implemented to cover the influence of PSMs on the ranking of the metabolites. This scoring function performs a substructure search using RDKit for the predicted PSMs on the preselected natural compounds. The match between the PSMs in the predicted scaffold and the natural compound is computed using the Bray–Curtis similarity . The BraThe scores are used to rank the natural products to present a selection of possible complete SMs that might be produced by the observed BGC. For the final ranking, a mixed score was designed where the individual scores are added with different weights:To determine the weight of each score to allow for the best possible ranking performance, a grid search was performed. Each score was weighted from 0 to 100% in steps of 10%, leading to overall 3992 parameter configurations. The performance of each parameter configuration was measured using the MIBiG-based ranking benchmark.Following the benchmark for SeMPI v1 , where tTwo metrics were defined: First, the Tanimoto similarity  of the predicted scaffold with the known product of each cluster was computed. Second, all MIBiG v2 products of the benchmark BCGs were ranked based on the scoring metrics defined in To the best of our knowledge, the only other pipeline which produces mostly one predicted scaffold per BGC and is actively maintained is antiSMASH v5. We used the predicted scaffolds of antiSMASH v5  as a comOther tools are difficult to compare to our pipeline since the output characteristics are significantly different and hinder comparability. SBSPKS v2  only preThe benchmark was also used to determine the optimal weight of the three different scoring metrics. Therefore, a grid search was performed, where each of the scores was weighted differently.SeMPI v1 was designed as an extension to antiSMASH v3 , with thThe input must be provided in FASTA or GenBank format. The files can contain multiple records. SeMPI creates one result page for each record. If only DNA data are provided, the genes are predicted using Prodigal . If the The biosynthetic domains are detected using the profile-HMMs and thresholds described in The modules are detected based on the rules described in For each scaffold, a set of PSMs is generated using the profile-HMMs and regression models as described in The predicted scaffolds are submitted to the database screening. The screening is performed using a user-selected choice of publicly available databases and/or a custom uploaded database (SDF or SMILES format). A custom database could be, for example, a set of bioactive samples that are assumed to be produced by a specific host. The screening can help to connect the samples with specific BGCs in the host.The predicted scaffolds can also be modified and resubmitted to the natural compound screening. The molecular operations are performed using the JSME Molecule Editor . This alThis feature also allows the usage of the SeMPI v2 database screening backend in combination with other BGC prediction tools such as PRISM v3 and antiSMASH v5.The web server is built using Django  and a PoThe SeMPI v2 web server combines state-of-the-art predictions of PK and NRP scaffolds with a specialized screening in publicly available databases. This allows researchers to further connect the dots between BGCs and SMs. The information generated with SeMPI v2 is presented in a user-friendly web server frontend. The web server allows further customization of the predictions and thus more flexibility for expert users. BGCs can be observed in detail using the visually appealing integrated genome browser.Since SeMPI v2 yields predictions that are complementary to existing pipelines, a comprehensive BGC analysis should include all available resources, such as antiSMASH v5, PRISM v3, SBSPKS v2, and others. Expert users can combine the generated structural features and submit multiple queries to the SeMPI v2 database screening backend to search for natural compound targets in different databases. In this role, SeMPI v2 contributes to further unraveling BGC mysteries and complements the link to putative SMs.


P = 0.8787) or degree (P = 0.0752) of deformation. Significance was demonstrated, regardless of bevel orientation, following multiple needle use with respect to extent of needle tip deformation (P < 0.0001). A clinical recommendation can be made that the dental needle should be routinely replaced when subsequent injections are required during the delivery of a typical IANB.The study aimed to investigate whether any correlation existed between bevel orientation and needle tip deformation following the administration of a standard inferior alveolar nerve block (IANB) technique during patient treatment. Ninety‐three needles of a single brand were collected from a group of eleven similarly trained Australian dentists’ following either single or dual insertion and bone contact. Specimens were examined under scanning electron microscopy at 500x, and both the direction of deformation (either towards or away from lumen) and the extent of deformation were calculated using image processing software. Results showed no correlation between bevel orientation and either the direction ( Local anaesthesia is an integral part of most operative dental procedures due to its effectiveness in the prevention and management of perioperative and postoperative pain Postoperative complications such as trismus and paraesthesia have been reported as a result of IANB injections with some suggested aetiologies being direct needle trauma to the nerve, intraneural haematoma formation and neurotoxicity of the local anaesthetic agent itself The purpose of this cross‐sectional clinical study was to identify whether any correlation exists between bevel orientation following single and multiple (twice) needle use during IANB delivery in terms of direction (towards or away from lumen) and extent of needle tip deformation. Secondarily, these results may be used to guide needle use during dental IANB procedures.This study was fully approved by the Local Health District Human Research and Ethics Committee.This cross‐sectional study collected needle samples from a group of clinicians following IANB delivered during general patient care within a large public teaching hospital. A total of 11 fully registered dentists were voluntarily inducted into the study with eligibility restricted to Australian graduates who completed their studies within the last 5 years. This criterion helped ensure a level of homogeneity amongst study participants, thereby reducing some confounders with respect to IANB technique. Clinicians were aware of the study aims and performed multiple needle insertions dependent on anaesthesia quality. Bevel orientation was not dictated by the study protocol but was to be noted by each participating clinician.All needles collected were identical in brand, gauge and length . Each clinician was asked to administer ten IAN block injections – half with the bevel facing towards the ramus of the mandible and half directed away. Needles were further subcategorised according to single or multiple use (twice only). Clinicians were asked to pay special attention to the handling of needles in order to avoid distortion of the needle tip when recapping the needles for collection. To increase the validity of our sample data, all clinicians were provided with a ‘frequently asked questions’ document, which emphasised attention towards bevel orientation and careful sample collection.Four jars each containing 180 mL of 70% ethanol were used to house the used needles and were marked as follows:Bevel towards bone with only one insertionBevel towards bone with two insertionsBevel away from bone with one insertionBevel away from bone with two insertionsA total of 103 needles were evaluated in this study, inclusive of 10 unused controls. Needle specimens were carefully mounted with the aid of a light microscope onto electron microscope stubs, ensuring the bevel was oriented to the left‐hand side so that the needle tip could be observed laterally. Scanning electron microscopy (SEM) was used to take images of the samples at 500x magnification and needle tip deformation calculated using the microscopy image processing program, Image J.The direction of needle tip deformation was classified as either inward (towards the lumen of the needle) or outward (away from the lumen) facing, relative to the long axis see Fig. . The extt tests, and a two‐way anova was used to investigate the effect of bevel orientation and direction of deflection (inward/outward) on the degree of needle tip distortion measured in degree angles.The variables of bevel orientation and single/dual insertions with their effect on direction and extent of deviation were compared using unpaired P value threshold of 0.05 was selected for significance.The analysis of the extent of distortion was completed in two ways, firstly with raw data and degrees measured to two decimal places (raw angle) and secondly using grouped data, where values falling between a set range of angles were clustered together (clustered angle). A Under SEM, 7 of the 93 clinical samples contained excessive soft tissue debris sufficient enough to obscure the extent of needle tip deformation and hence were excluded from the analysis. Additionally, 3 samples of the 10 unused control needle tips showed varying levels of deformation which may be attributed to the manufacturing process Figs  and 3.All needles examined after having been used for an IANB showed evidence of needle tip deformation.P value = 0.8787, unpaired t test, Table P value = 0.0752, unpaired t test).Bevel orientation during IANB delivery had no significant correlation to the resulting direction of needle tip distortion in terms of inward or outward facing deformation (P < 0.0001)   Fig.  Table 2 Previous clinical human studies have consistently confirmed the presence of needle tip deformation following the use for dental local anaesthesia Firstly, the study is complicated by the influence of manufacturing limitations as evidenced by the SEMs Figs  and 3 shAll needles, following at least one insertion, displayed needle tip deformation after bone contact which seems intuitive and is similar to results in another investigation where 97% of needle tips were also affected Our study results showed no association between bevel orientation relative to the ramus and the direction or the degree of angular deformation. This differs from a previous animal simulation study that suggested that there was a tendency for deformation away from the lumen when the needle bevel was oriented away from the bone P = 0.0752). This is likely attributable to the limited size of the data set and angle spread of results. It is for this reason that a clustered angle approach was taken with results grouped into 10‐degree increments, thus yielding a strong significant association (P < 0.0001) between the angular degree of deformation and the number of insertions. It can therefore be confirmed by this study that following bone contact during delivery of a standard IANB, needle tip deformation occurs and is made worse with subsequent reinsertion of the same needle. Despite the many potential confounders which might affect the direction and/or severity of needle tip distortion following IANB delivery, it appears prudent practice to remind dentists’ to routinely undertake needle replacement between successive IANBs. This in turn may help maintain a more patent needle tip and likely help reduce potential postoperative complications, previously reported in the literature, such as trismus, paraesthesia, pain and tissue damage The raw angle data set Fig.  was unabThe study results may have been affected by several confounders which could be considered in future investigations. Firstly, there may be differences in needle tip patency which could vary between different manufacturers and their choice of materials, bevel angle, metal thickness and bore size. Inclusion of additional needle manufacturers may be beneficial. Secondly, study limitations such as sample size, the unequal number of samples from each dentist, patient and technique variability per clinician, sample preparation and SEM measurement errors may have also affected results. There appears no general consensus as to the most appropriate SEM magnification for such studies and at what point a needle tip deformity becomes clinically relevant. It would be beneficial for future investigations in this area to consider including postoperative outcome measures such as injection site pain and trismus, relative to direction and extent of needle tip deformation.During the delivery of a standard technique IANB, there appears to be no benefit, in minimising needle tip deformation, by having the bevel face either towards or away from the mandibular ramus. There is strong evidence to suggest that needle tip deformation is made worse following subsequent injections and it may be the best practice to routinely change needles between subsequent IANB injections when contact is made with bone.No conflicts of interest or financial interests declared.All authors contributed significantly to various elements of the study including study design, data collection, analysis and manuscript preparation.


The ribosome is a large ribonucleoprotein complex that synthesizes protein in all living organisms. Ribosome biogenesis is a complex process that requires synchronization of various cellular events, including ribosomal RNA (rRNA) transcription, ribosome assembly, and processing and post-transcriptional modification of rRNA. Ribosome biogenesis is fine-tuned with various assembly factors, possibly including nucleotide modification enzymes. Ribosomal small subunit pseudouridine synthase A (RsuA) pseudouridylates U516 of 16S helix 18. Protein RsuA is a multi-domain protein that contains the N-terminal peripheral domain, which is structurally similar to the ribosomal protein S4. Our study shows RsuA preferably binds and pseudouridylates an assembly intermediate that is stabilized by ribosomal protein S17 over the native-like complex. In addition, the N-terminal domain truncated RsuA showed that the presence of the S4-like domain is important for RsuA substrate recognition. Thermodynamic and kinetic cooperativities between early-binding modification enzymes and r-proteins could alter the 30S assembly energy landscape and direct assembly flux through productive and fast pathways.Ribosomes are ribonucleoprotein complexes that synthesize proteins in all living organisms. Accurate and efficient synthesis of new ribosomes is critical for cell survival. During in vitro assembly of the 30S ribosome, ribosomal proteins (r-proteins) bind to 16S rRNA hierarchically that arise from the cooperative addition of r-proteins ,3. AdditE. coli RsuA X-ray crystal structure extends away from the catalytic domain and adopts the open conformation, whereas in that of H. influenza, it is bent towards the catalytic domain to adopt a more compact conformation ..E. coli Fluorescence labeling of RsuA: G127C mutant was carried out as previously described . PurifieE. coli 16S 5′-domain rRNAs  used in FRET (Förster resonance energy transfer)- assays contained AAGGACGACACACUUUGGACAGGACACACAGGACACAGG (E5 extension) added to its 3′-end next to helix 3 as previously mentioned )) to obtain Kd for RsuA-RNA complexes.The 2+ concentrations (0–20 mM) for 15 min at 37 °C. For RsuA titrations that were carried out in the presence of ribosomal proteins, pre-folded 5′-domain RNAs were co-incubated with a 2.5 molar excess of ribosomal proteins for another 15 min at 37 °C. Fluorescence spectra (560–700 nm) of RNA-protein complexes were recorded upon excitation at 560 nm using a PTI Quantum Master-400 fluorometer (Horiba Scientific). FRET efficiencies at each RsuA concentration were calculated with adjusted fluorescence intensities at 565 nm  and 665 nm  using E = I665/(I665 + I565) equation. Titration curves for triplicates were averaged and fitted to the quadratic equation or a single binding isotherm in Origin software to obtain equilibrium dissociation constants for RNA-protein complexes.The helix 3 extension of modified 5′-domain RNA (2 nM) was annealed to a 37-nucleotide long DNA oligomer (2 nM), fluorescently labeled with Cy5 at 3′-end as previously described by Abeysirigunawardena et al. . RNAs we2, 6 mM β-mercaptoethanol)  before incubation at 37 °C for 4 h. Alkaline-treated 5′-domain RNAs were precipitated, pelleted, dried, and resuspended in water. SuperScriptTM III Reverse Transcriptase (Invitrogen) was used to generate cDNA. The manufacturer suggested protocol was followed for these reverse transcription assays. cDNAs from reverse transcription assays were run on an 8% polyacrylamide denaturing gels for 1 h at 55 W per gel. These gels were dried and then exposed to a phosphorimager screen overnight. Radiographs were then recorded using a Typhoon FLA 9500  scanner. Band intensity of the reverse transcriptase pause at the pseudouridylation site was measured with ImageJ (1.48v) software (NIH). The band intensity at the pseudouridylation site was normalized to the band intensity at position 518 to obtain the normalized activity. Standard error was calculated from three independent replicates.The 16S 5′-domain rRNAs (10 pmols) were incubated (at 37 °C for 1 h) with protein RsuA and different combinations of ribosomal proteins S4, S16, S17 and S20 (2.5 molar excess) in HKM4 buffer  . A pheno2+ concentration, the 30S 5′-domain can form most of its native contacts even in the absence of r-proteins [2+ concentration [2+ concentrations to investigate the nature of the 5′-domain structure preferred by RsuA. A FRET-based binding assay was performed to determine equilibrium dissociation constants of various RsuA-5′-domain rRNA complexes (2+ concentrations (0-20 mM) [d). The highest affinity of RsuA to 5′-domain rRNA (Kd = 0.56 ± 0.04 nM) was observed at 8 mM Mg2+ concentration (d was found to be ~30-fold higher (19 ± 5 nM) than at 8 mM Mg2+, clearly indicating the need for magnesium for stable binding of RsuA. Similarly, Kds for RsuA-RNA complexes were found to be approximately 20-fold (11 ± 2 nM) and seven-fold (3.9 ± 0.4 nM) increased at 2 mM and 4 mM Mg2+, respectively. At moderate magnesium concentrations (6 mM ≤ Mg2+ ≤ 10 mM), RsuA-RNA complexes were found to be highly stabilized at the sub-nanomolar range. However, the stability of RsuA complexes decreased as the Mg2+ concentration was increased above 10 mM. Equilibrium dissociation constant increased as the magnesium concentrations were increased from 10 mM–20 mM (7.5 ± 0.4 nM). Similarly, Kds at 12 mM and 16 mM were found to be 10 ± 2 nM, 14.7 ± 0.7 nM, respectively. A similar trend of Kd with increasing magnesium was observed previously for S4-rRNA complexes, in which the lower affinity for S4 at high magnesium concentration was attributed to charge screening effect of Mg2+ ions [Divalent cations are critical for the folding of many naturally occurring RNAs to their active native structures. RNA-backbone contacts between different regions that are required to generate compact and functional RNA structures are stabilized by magnesium ions ,22,23,24proteins . RsuA mantration . The staomplexes . In this0-20 mM) . The FREntration C. At no g2+ ions . Howeverd compared to the complex formed with wild type 5′- domain (Kd = 3.9 ± 0.4 nM). These results indicate that RsuA preferably binds to assembly intermediates in which helix 18 is extended and not forming a pseudoknotted structure.The 16S helix 18 in which Ψ516 modification is present, forms a pseudoknot structure that is essential to position the universally conserved G530 at the 30S decoding site  Figure A,B. Thre2+ to understand how r-proteins that bind to the 5′-domain influence the binding and enzymatic activity of RsuA at physiological Mg2+ concentrations (d = 1.2 ± 0.2 nM) compared to their absence (Kd = 3.9 ± 0.4 nM). A lower binding affinity to RsuA was observed when r-protein S4 was bound to the 5′-domain, suggesting a mild binding anti-cooperativity, perhaps due to the overlap of the binding sites of the two proteins. However, the relatively stable RsuA-5′-domain complex (Kd = 1.2 ± 0.2 nM) was formed by native 30S 5′-domain that also included protein S4. The affinity of RsuA was highest (Kd = 1.0 ± 0.3 nM) in the presence of protein S17 alone. Interestingly, all the 5′-domain complexes that contained S17 illustrated a high affinity to protein RsuA, indicating binding cooperativity between RsuA and S17. Although the affinity of RsuA to 5′-domain rRNA was decreased in the presence of S4 (4.9 ± 0.5 nM), the addition of S17 to RsuA-rRNA-S4 complexes increased its affinity (2.4 ± 0.4 nM). Previous studies on the 5′-domain assembly illustrated that S17 stabilizes non-native assembly intermediate [During ribosome biogenesis, many ribosomal RNA modification enzymes favorably bind to specific ribosome assembly intermediates compared to fully assembled or naked 16S rRNA ,28,29. Strations . Interestrations B. Howevermediate ,34, in wA previous study by Ofengand and co-workers  on RsuA E. coli and Haemophilus influenzae illustrated that the S4-like domain exists in two different conformations [Many classes of pseudouridine synthases contain peripheral domains linked to the core domain that possess their catalytic activity . These prmations ,12  only when RsuA is present in the open conformation. Hence, protein RsuA may recognize the flipped intermediate as its preferred substrate over the native-like 5′-domain complex.Unlike many of the rRNA nucleotide modification enzymes, RsuA may not rely entirely on the sequence-based target recognition. Ribosomal RNA structure at the recognition site may also influence the binding specificity of RsuA. Our data suggest that the N-terminal domain of RsuA may increase its specificity toward 5′-domain RNA, hence play a role in target recognition . This peDespite RsuA being a non-essential protein (like some r-proteins) for bacterial survival , RsuA maIn conclusion, our study illustrates binding cooperativity for proteins S17 and RsuA. The peripheral domain of RsuA is needed for its stable binding, and the binding may follow a mutual structure capture mechanism, in which both the protein and rRNA capture a preferred structure.


One of the most important steps in any viral lifecycle is the production of progeny virions. For retroviruses as well as other viruses, this step is a highly organized process that occurs with exquisite spatial and temporal specificity on the cellular plasma membrane. To facilitate this process, retroviruses encode short peptide motifs, or L domains, that hijack host factors to ensure completion of this critical step. One such cellular machinery targeted by viruses is known as the Endosomal Sorting Complex Required for Transport (ESCRTs). Typically responsible for vesicular trafficking within the cell, ESCRTs are co-opted by the retroviral Gag polyprotein to assist in viral particle assembly and release of infectious virions. This review in the Viruses Special Issue “The 11th International Retroviral Nucleocapsid and Assembly Symposium”, details recent findings that shed light on the molecular details of how ESCRTs and the ESCRT adaptor protein ALIX, facilitate retroviral dissemination at sites of viral assembly. During the final stages of enveloped virus replication, viral proteins are co-assembled with their corresponding viral genome into a heterogeneous ribonucleoprotein complex (RNP). These RNPs are assembled at productive sites of virus release through interactions with many host cell co-factors including the Endosomal Sorting Complex Required for Transport (ESCRT) machinery, with a fraction of these components detected within infectious virions ,2,3,4. IIn parallel, it was recently shown that cytokinesis, a process that displays many parallels with retroviral budding, depends on cooperativity between both TSG101 and ALIX, potentiating a heterogeneous complex that may be ideal for retrovirus budding, as well as other cellular process, when possible . Both prDespite their evolutionary conservation between viruses, these short L domain peptide motifs are not the sole determinants of cellular factor recruitment. Recently, it was shown that HIV Gag and other retroviruses are modified with ubiquitin in their p6 domains, and a C-terminal ubiquitin-Gag fusion construct mimicking a true p6 domain modification, can stimulate and rescue defective virus release ,23. The In order to elucidate the molecular mechanisms behind the concomitant recruitment of the ESCRT-I complex with viral Gag assembly  and subs54EKAYIKD60 located at the start of the alpha helical hairpin in the N-terminus of human VPS28 was essential for forming helical assemblies, in good agreement with the known yeast complex .VPS28 is a 25 kDa protein containing two distinct domains that operate independently to coordinate the events involved in multivesicular body (MVB) trafficking. The N-terminal domain of VPS28 binds to the C-terminal domain of TSG101 while the C-terminal domain of VPS28 binds the ESCRT-II complex, although the role of ESCRT-II in virus release remains debated ,18,29,34 complex D. Based 54EKAYIKD60 sequence is essential for proper ESCRT-I assembly and function. More specifically, the VPS28 EKYK mutant failed to capture TSG101 and formed non-productive aggregates with WT VPS28. In contrast, WT VPS28 readily captured TSG101 while forming very little association with the VPS28 EKYK mutant. Ultimately, this suggested that the 54EKAYIKD60 motif in VPS28 was responsible for productive ESCRT-I formation through the ideal multimerization of VPS28 and recruitment of TSG101 to the VPS28 assembly.This VPS28 mutant (termed EKYK) was excluded from a WT VPS28-TSG101 complex, suggesting that the 54EKAYIKD60 motif as a critical mediator of ESCRT-I helical assembly. This was done through the identification of a triple mutant  within the 54EKAYIKD60 motif, that prevented the VPS28-TSG101 subcomplex from forming higher order assemblies in solution, which also prevented autophagosome closure [At the time of this discovery, Flower and colleagues crystallized the human ESCRT-I complex and remarkably confirmed the  closure . We vali closure . These s317FIYHDR321 motif whose tyrosine and terminal polar residue are completely conserved from yeast to humans. This region within ALIX had been previously identified as a docking site for phosphorylation by Src kinases, but tyrosine phosphorylation is not required for virus release [Operating mainly via the second L domain of HIV Gag is ALIX. ALIX is a three-domain protein consisting of an N-terminal Bro1 domain, a central V domain, and a C-terminal proline rich domain (PRD) . The Bro release ,41.317FIYHDR321 motif was of structural or functional importance. First, we conducted a structural alignment between the ALIX Bro1 domain and the human hetero-tetramer ESCRT-I model to determine any significant topological similarities based on their curiously similar domain architecture. We found that the C-terminal hairpin of TSG101 strongly aligned with the alpha helical hairpin between helices 8 and 9 of the ALIX Bro1 domain which oriented the 317FIYHDR321 motif about the same axis as the VPS28 multimerization domain. This prompted the revisit of our previously resolved structures of the ALIX Bro1 domain protein and Brox. Surprisingly, we found that the 317FIYHDR321 motif was involved in a multitude of higher-order assemblies, in a manner that was strikingly reminiscent of the VPS28 multimerization domain [The recent findings pointing toward a helical nature of ESCRT-I prompted us to investigate ALIX in order to determine whether or not the n domain ,42. Crys317FIY319 in ALIX. Virus release was dramatically abrogated for a HIV virus lacking the ESCRT-I L domain (PTAP-) with no detectable release of mature virus. Ablation of virus production was accompanied with dramatic Gag processing defects and the accumulation of Gag maturation intermediate p15NC-p1-p6. The same ALIX mutant was tested for release with EIAV which is solely reliant on ALIX for release. EIAV also displayed budding defects as severe as those observed with HIV-1. These studies show for the first time that the 317FIY319 is linked to the helical assembly of ALIX, as shown via molecular modeling  observed a substantial increase in processing defects when either the VPS28 or ALIX helical interfaces were disrupted. Remarkably, these same mutants of VPS28 and ALIX also induced severe defects in nuclear envelope repair and delays in cytokinetic abscission. Viral processing and release defects could only be observed by probing cell lysates and virus extracts with antibodies targeting both the N- (CA antibodies) and C-terminal (NC antibodies) regions of Gag. Traditionally, virus processing and release is monitored through the assessment of mature processed CA. However, this approach fails to consider the fate of the RNP assembling on membranes and within the viral particles themselves since processed NC is intimately associated with the RNP. By assessing virus release in such a way, we were able to determine that ESCRT-I and ALIX helical assemblies act to retain the RNP for optimal protease activity within the lumen of budding necks. In doing so, we also demonstrate the requirement for assessing viral maturation products via their NC content in future studies aimed at studying retroviral assembly, for packaging of the RNP ensures that progeny virions will be infectious and capable of establishing a replication-competent reservoir.Ultimately, we propose a model where ESCRT-I and ALIX form a discontinuous co-polymer at sites of scission to act as a structural scaffold for assembling nascent virions, but also as a template for ESCRT-III polymerization and membrane scission . Such a In recent years, several strides have been made in understanding the molecular details underlying Gag assembly and virus budding. In particular, it is the critical advancements in microscopy that have led to these discoveries. The last decade alone has seen astonishing progress with the advent of super-resolution microscopy and exquisite improvements to the capabilities of tomography and electron microscopy. These techniques allowed us to observe molecular details once thought elusive like the spatial and temporal organization of the ESCRTs at sites of membrane scission ,35,48, oNevertheless, the precise nature of host factors at the budding virion-cytoplasm boundary is yet to be determined, perhaps due to its ephemeral nature. Although small molecule inhibitors have already been developed that are aimed at disrupting this critical interface , they in


Actinobacillus pleuropneumonia serotype 2 (APP2) and ApxIV toxin and swine influenza virus were measured. After the slaughter of study pigs, meat inspection results of the batch were gathered from slaughterhouses. Multivariate logistic regression model was built to identify possible risk factors for a herd to be a case herd .Chronic pleurisy is a common finding in slaughtered pigs in post-mortem meat inspection. The prevalence of pleurisy has been increasing during the last decade also in Finland. The aim of this prospective case-control study was to search for environmental, infectious and management-related herd-level risk factors for pleurisy in the slaughterhouse. Altogether 46 Finnish pig herds, including 25 control  and 21 case (high pleurisy) herds, were enrolled in the study and visited during the tenth week of the rearing period of finishing pigs. Herd personnel were asked about basic herd information, management and environmental factors. Selected pigs were examined clinically, environmental parameters were measured and 15 blood samples per herd were taken during herd visits. Antibodies against Finishing herd type and herd size were observed to act as risk factors. None of clinical signs of pigs, management-related factors or environmental measurements were associated with herd status.As previously known, in endemic and subclinical infections such as APP, herd factors are important, but detailed risk factors seem to be difficult to identify. Chronic pleurisy is a common finding in slaughtered pigs in post-mortem meat inspection. Pleurisy prevalence around 20% has been detected in several European countries –4. In FiMycoplasma hyopneumoniae (MHyo). Porcine respiratory disease complex is a multifactorial syndrome with clinical signs caused usually by multiple micro-organisms, both bacteria, (e.g. Actinobacillus pleuropneumoniae (APP), Pasteurella multocida and Glaesserella parasuis), and viruses together with environmental and management-related factors as well as genetics. Both viruses and MHyo have been considered as primary pathogens, which predispose pigs to concomitant bacterial infections such as APP [Most studies investigating pleurisy have been done in countries with many possible pathogens present in the pig population. In Finland, the prevalence of porcine respiratory pathogens differs from the situation in continental Europe. The country has been free from Aujeszky disease virus (ADV), porcine respiratory corona virus (PRCV) and porcine reproductive and respiratory syndrome virus (PRRSV) for decades and nearly free from h as APP . Seroposh as APP , and APPh as APP . Also, sh as APP . Especiah as APP , 10, 11.The type and severity of the disease caused by APP is considered to depend on factors related to pathogen virulence, host, environment and management . JirawatThis study was case-control study with a herd as the unit of interest. A case-control study design was selected to find a clear difference in an outcome that was originally measured in a continuous scale (percentage of pleurisy in study farms). The target population included medium- to large-sized (more than 500 pigs per herd) Finnish herds rearing finishing pigs.Purposive sampling was used. A study herd needed to fulfill the inclusion criteria of 1) at least 1000 finishing pigs sent for slaughter annually and 2) location in south-western Finland within a distance of 250 km from the University of Helsinki ambulatory clinic in Mäntsälä. Three major slaughterhouses were asked to compile a list of finishing pig herds fulfilling the above criteria. This list including 219 herds served as a sampling frame. At the same time, the slaughterhouses provided the research group with the pleurisy percentage of these herds during the year preceding enrolment in the study. The herds were then sorted in descending order according to their pleurisy percentage, separately for each slaughterhouse. Herds were tentatively divided into case and control herds based on their pleurisy values. Herds having a higher pleurisy value than slaughterhouse-specific mean pleurisy plus standard deviation value were considered to be tentative case herds. Similarly, herds having a lower pleurisy value than the slaughterhouse mean minus the standard deviation were considered to be tentative control herds. For slaughterhouse one, pleurisy percentage (±standard deviation) used in tentative classification was 1.9 ± 1.4%, for slaughterhouse two 27.5 ± 16.8% and for slaughterhouse three 4.0 ± 2.9%. These tentative herd classifications were verified based on the post mortem findings of the study batch as described later in materials and methods.Researchers then contacted the tentative case and control herds and requested their consent to participate in the study. During the first contact researchers ascertained that the herd had not suffered from acute respiratory disease outbreak during the last batch of finishing pigs. All voluntary herds were enrolled in the study until the desired number of herds  was fulfilled. Of 93 herds contacted, 29 (31.2%) opted out or were unable to participate. Out of herds that opted out, 48% were not willing to participate, 48% no longer had animals or had changed slaughterhouse, making participation impossible, and 3% reported some other reason. Customers of slaughterhouse 1 and case herds (54% cases vs. 45% controls) were overrepresented in herds opting out. Opted-out herds had approximately twice as many animals as herds that were willing to participate.Researchers visited all study herds once about 10 weeks after the growers (body weight 25 kg) had arrived at the finishing pig herd or a finisher compartment in a farrow-to-finish herd. Visits were carried out during years 2012–2014. At the beginning of the visit, the researchers ascertained that the pig groups had not suffered from acute respiratory disease outbreak after entering the finishing herd or compartment. The herd owner or responsible caretaker was interviewed about basic information of the herd , management and environmental factors using a herd visit questionnaire. The main categories of management-related information included vaccination routines, biosecurity and hygiene, animal flow, mean number of animals per compartment and per pen, mean size of pen, medication routines and feeding. The main categories of environmental factors were ventilation, temperature, heating and floor type.3 concentration  and air temperature, humidity and airflow  were measured in four different spots (two opposite corner pens and two pens in the middle of the row) of the compartment. A herd-level mean of these four measurements was used in further analyses. After environmental measurements, all pigs were gently forced to stand up and their coughing and sneezing episodes were counted for 5 min. A coughing/sneezing episode was defined as a single cough/sneezing or as a set of continuous coughs/sneezings of one animal. The number of soiled pens and the presence of various clinical signs  were registered individually in approximately 100 pigs.In each herd, one finishing pig compartment containing at least 200 but less than 1000 finishing pigs was chosen for clinical inspection of pigs and environmental measurements. After carefully entering the compartment, the researchers counted the number of pens containing pigs lying on top of each other. After that, pen wall temperature , NHAltogether 15 pigs were selected evenly in different parts of the study compartment for blood sampling. These pigs were caught with a snout snare and a blood sample was taken from their vena jugularis with vacuum needles and serum tubes. The samples were centrifuged the next day in the laboratory, and the serum was stored at − 20 °C until analysed.At the end of the visit, outdoor temperature was measured. Before leaving the herd, herd personnel were asked to inform researchers about any major unexpected events, e.g. acute disease outbreak or equipment malfunction, during the remaining rearing period.After the study batch containing clinically examined animals was sent to slaughter, the slaughterhouses provided the researchers with the meat inspection (MI) findings of the batch, including mean carcass weight, kilograms of condemned meat, percentage of whole and partial carcass condemnations, lean meat percentage, abscesses, arthritis, milk spots in liver, organ condemnations, tail biting, pneumonia and pleurisy. Herds having pleurisy percentage higher than the slaughterhouse-specific mean pleurisy in the batches undergoing analysis were considered batch-specific case herds. Similarly, herds having pleurisy percentage lower than the slaughterhouse mean were considered batch-specific control herds. These batch-specific results were used to verify the herd classification. Batch-specific mean pleurisy percentages used for classification were 1.6, 30.2 and 1.8% for slaughterhouses 1, 2 and 3, respectively. Finally, the group of confirmed case herds included only tentative case herds that were also batch-specific case herds. Similarly, confirmed control herds included only those tentative control herds that were also batch-specific control herds.APP antibodies were measured using two commercial test kits: IDEXX APP-ApxIV ELISA  to detect antibodies against ApxIV toxin, which is produced by all known APP serotypes (19), and IDvet ID Screen APP 2 indirect ELISA  to detect antibodies against lipopolysaccharides (LPS) specific to APP serotype 2 (APP2), with a sensitivity of 82.9% and a specificity of 99.6% for IDEXX APP ApxIV ELISA and a specificity of 99.68% for IDVet APP2 ELISA. A pig was considered positive if the test used detected any antibodies in the serum sample.All blood samples were tested with influenza A antibody ELISA  according to the manufacturer’s instructions. A sample was considered unclear when the competition percentage (S/N%) was 46–49% and positive when the competition percentage was ≤45%. If a herd had at least one unclear or positive blood sample (pig) in the ELISA test, blood samples of that herd were further analysed using a haemagglutination inhibition (HI) test according to the European Surveillance Network for Influenza in Pigs. This was done with the antigens H1N1 (SW/Best/96), H1N2 (SW/Gent/7625/99) and H3N2 (SW/St. Oedenrode/96). All antigens were provided by GD Animal Health Service . A sample was considered HI positive if the HI titre was ≥1:20.A required sample size of 24 herds in both groups (control and case) was calculated assuming the proportion of exposed as 40% for controls and as 80% for cases in presumably the most influential variable (herd type). Alpha 0.05 and power 0.8 were used. The least extreme odds ratio to be detected is 6.0 .All gathered data were scrutinized, and all unreliable answers were either checked and corrected, or if this was impossible, removed from the dataset. Most of the variables describing management-related and environmental factors were transformed into meaningful categories. Most of the count variables and measurements were handled as continuous variables.The outcome variable was a categorical variable “confirmed case or control”. Regarding the predictors, the herd-level prevalence (%) of different clinical signs was calculated. Furthermore, the percentage of soiled pens or pens where pigs were lying on top of each other was calculated. The prevalence of diarrhoea, neurological signs or skin scratches and soiled pens was almost zero and these variables were not included in further analyses. For modelling serological results, herd-level APP2, APPIV and SIV antibody prevalences (%) were calculated. Both APP2 and APPIV seroprevalences were categorized in two categories . Descriptive statistics of all predictor variables at herd level  were compiled containing all herds, and a comparison between case and control herds was carried out.Univariate associations between predictor variables and outcome were evaluated using logistic regression. The liberal p-value of 0.2 was used as a keep-in or drop-out threshold. The correlations between predictor values were scrutinized. The herd type (farrow-to-finish or finishing only) was detected to correlate strongly with many predictor variables . The decision to force the variable “herd type” in further models and drop correlating, intervening variables was made. None of the variables related to environmental measurements showed to be statistically significant and therefore were not included in the multivariable model. Furthermore, SIV serology univariate association with herd pleurisy status was clearly insignificant and this variable was not included in the further modeling.Finally, a multivariable logistic regression model was built. The initial model contained predictor variables: herd type (fattening or farrow-to-finish), number of finishing pigs in the herd, compartment disinfection , littering frequency (once or twice per day or continuously available), proportion of slatted flooring (≤50%/> 50%), airspace per pig, feeding type (liquid/dry), piggery temperature when weaners entered the compartment, piggery temperature when finishing pigs left the compartment, heating (yes/no), ventilation system service (yes/no), ventilation adjustment difference in winter, loading corridor (yes/no), handwashing facility for visitors (yes/no), sitting pigs (%), flank biting (%), conjunctivitis (%), herd-level APP2 prevalence and APPIV antibody prevalence (high/low). Backward elimination model building strategy was then utilized. The final model contained only the predictor variables herd type and number of finishing pigs per herd.p = 0.5) as the null hypothesis is “model fits the data”. In addition, residuals, deltabeta and leverage values were scrutinized to observe potential outliers. Furthermore, the area under the ROC curve (0.92) was evaluated to assess predictive ability of model.For model diagnostics, the basic assumptions of logistic models were inspected with regard to data structure and nature of the predictor variables. Observations were independent from each other and the continuous variable in the final model (no. of finishing pigs) had a sufficiently linear relationship with the outcome. The Hosmer-Lemeshow goodness of fit test with seven groups collapsed on quantiles of estimated probabilities was used, and it gave a non-significant result . We removed 18 of the tentative herds because they did not have the same pleurisy status in the batch inspected after the herd visit. Therefore, 46 study herds (21% of eligible herds in sampling frame and 49% of eligible herds contacted) remained, 25 control and 21 case, for data analysis. These herds belonged in three different slaughterhouses . A slight majority of herds reared only finishing pigs  and the rest were farrow-to-finish herds .n = 46) are shown in Table p = 0.02).Basic descriptive statistics of study herds , specifically one control and four case herds, had at least one pig with antibodies against SIV.p = 008) compared with the herd type “only finishing pigs”. Furthermore, for 500 change in numbers of finishing pigs in a herd, the odds for a herd being a case herd changed by 11.6 (p = 0.002). Results of the logistic regression model of for pleurisy are presented in Table Regarding possible management-related risk factors for a herd being a case herd , the herd type “farrow-to-finish” turned out to be a protective factor . In previous studies, contradictory results have been obtained when herd type has been considered in association with pleurisy. Several studies have found that herds that purchase weaners (i.e. finishing pig herds) have a greater risk for respiratory diseases than herds with closed production , 15. On The present study agrees with the previously described debilitating effect of growing herd size on prevalence of pleurisy , 11, 19.Herd type and size are commonly and inextricably associated with specific types of management practices, e.g. farrow-to-finish herds do not buy weaners or growers and finishing herds more commonly are able to practice AIAO production. However, the herd type itself, when used in the sense of sourcing animals, seems to be too vague a definition for choices made on a certain farm regarding infection pressure. To be able to reliably predict risk factors for chronic pleurisy, these management practices need to be defined in more detail. Earlier studies have been able to find several risk factors for pleurisy in the slaughterhouse such as number of pigs per pen , pig denWe did not find any association between serological results and pleurisy prevalence measured in post mortem meat inspection. Controversial results have been previously reported regarding serology and pleurisy registered in meat inspection , 13, 24.In our study, only five herds had antibodies against SIV. Even though four out of these five herds were case herds and only one a control herd, the total number of SIV-positive herds was too small for statistical modelling. SIV, both H1N1 and A(H1N1)pdm09, was first found in Finland in 2008 and 2009 , and botIn this study, herds were defined as cases or controls based on detection of pleurisy for a longer time period than only one batch. Furthermore, we gathered management-related data during herd visits, which enabled us also to inspect the pigs clinically. These choices should have made both the allocation of herds into cases or controls and the collection of data reliable. The results should be more valid than in studies where allocation has been done based on a single batch of slaughtered pigs or where the data have been collected with questionnaires sent to farmers or telephone interviews , 17, 20.Almost 70% of herd owners asked to participate in the study were willing to be involved. As case herds were overrepresented amongst opt-out herds, there is a possibility of selection bias in the data. The most frequent reason for opting out was “not willing to participate”. Almost 60% of these herds were case herds and the reason for refusal was frequently that “participation will not help them to overcome the high pleurisy problem that they have”. The majority of opt-out control herds no longer had pigs, and this was the second most common reason for opting out. Generally, opt-out herds had a larger herd size than opt-in herds. Therefore, it is possible that the herds not willing to participate in the study might have been not only larger, but also not as keen to take active measurements to improve their management. This kind of perceived attitude may have some (possibly negative) influence on overall management of herds. Possible selection bias may have caused study herds to resemble each other more in terms of influential management variables than they actually do in the source population, leading to an underestimation of the severity of risk factors.Lack of statistical power might have caused several factors observed to differ between case and control herds to be seen as statistically non-significant. For example, for the present sample size (at least 24 herds in both groups) the presumed difference in exposed and non-exposed herds should have been 40 and 80% in most influential risk factor (herd type). The realized distribution (36% vs. 80%) fulfilled that criteria well and statistical significance was seen. However, as calculated for another categorical variable (NH3 concentration), the observed proportions of exposed and non-exposed herds were 61 and 52%, respectively, which may lead in a lack of statistical power. Furthermore, quite many possible risk factors were included in the initial multivariable model, which probably exceeded the power of the current study.Finishing herd type and herd size were observed to act as risk factors for a herd to be a case herd . No clinical signs of the pigs, management-related factors or environmental measurements were associated with pleurisy values. As previously known, in endemic and subclinical infections such as APP, herd factors are important, but detailed risk factors seem to be difficult to identify.