Neuroleptic Malignant Syndrome: An Intensive Care Unit Case of Exceptionally High Creatinine Kinase and Myoglobin Levels.

Neuroleptic Malignant Syndrome (NMS) is a rare, life-threatening neurologic emergency known to be related to the administration or sudden withdrawal of dopaminergic medications. The clinical course, symptoms, and bloodwork are very heterogeneous, making this syndrome difficult to identify. Thus, NMS is a diagnosis of exclusion. We present a case of severe NMS with exceptionally high creatinine kinase (CK) and myoglobin levels with unclear etiology and a challenging differential diagnosis. Also, our case stands out because it was serious, unique, and had a favorable outcome, which could contribute to the management of future similar cases.

Retinal Metastases as the Initial Presentation of Advanced Lung Adenocarcinoma.

Introduction: Most lung cancers are diagnosed at an advanced stage. Common metastatic sites include the brain, bone, liver and adrenal glands. Ocular metastases, however, are extremely rare. We present a case of advanced lung adenocarcinoma presenting exclusively with photopsias attributable to retinal metastases. Case description: We describe a woman in her fifties, a lifetime non-smoker with an unremarkable medical and family history, who presented to the emergency department with photopsias for a week. Ophthalmology evaluation revealed decreased visual acuity bilaterally, and a fundus examination disclosed lesions suggestive of bilateral retinal metastases. A comprehensive evaluation diagnosed a stage IVb lung adenocarcinoma with exon 19 mutation on epidermal growth factor receptor gene. Subsequently, she developed complaints of headaches and dizziness. She received frontline osimertinib 80 mg daily, preceded by upfront whole-brain radiation therapy with partial orbital inclusion for symptomatic ocular and brain metastases. After ten radiation therapy sessions, her complaints were resolved and an ophthalmology revaluation revealed improvement in visual acuity and resolution of photopsia complaints. The patient is currently on osimertinib and preserves an ECOG score of 0. Conclusion: Retinal metastases usually indicate advanced disease, so presenting with isolated ocular symptoms is exceedingly rare. Especially in cases of uncommon metastases, a multidisciplinary approach is fundamental for a prompt diagnosis and timely treatment, impacting prognosis and quality of life. LEARNING POINTS: Ocular metastases in lung cancer are usually a sign of advanced disease.Advanced lung adenocarcinoma presenting solely with retinal metastases is extremely rare.A multidisciplinary team is essential for the diagnosis and treatment of lung cancer with uncommon metastases.

Hydrogen peroxide as a mitigation against Microcystis sp. bloom.

Microcystis sp. is a harmful cyanobacterial species commonly seen in earthen ponds. The overgrowth of these algae can lead to fluctuations in water parameters, including DO and pH. Also, the microcystins produced by these algae are toxic to aquatic animals. This study applied hydrogen peroxide (7 mg/L) to treat Microcystis sp. in a laboratory setting and in three earthen pond trials. In the lab we observed a 64.7% decline in Microcystis sp. And in our earthen pond field experiments we measured, on average, 43% reductions in Microcystis sp. cell counts within one hour. The treatment was found to eliminate specifically Microcystis sp. and did not reduce the cell count of the other algae species in the pond. A shift of the algae community towards the beneficial algae was also found post-treatment. Lastly, during the pond trials, the gill status of Tilapia and Giant tiger prawn were not affected by the H2O2 treatment suggesting this may be a good mitigation strategy for reducing cyanobacteria in pond aquaculture.

Association of health, nutrition, and socioeconomic variables with global antimicrobial resistance: a modelling study.

BACKGROUND: Although antimicrobial use is a key selector for antimicrobial resistance, recent studies have suggested that the ecological context in which antimicrobials are used might provide important factors for the prediction of the emergence and spread of antimicrobial resistance. METHODS: We used 1547 variables from the World Bank dataset consisting of socioeconomic, developmental, health, and nutritional indicators; data from a global sewage-based study on antimicrobial resistance (abundance of antimicrobial resistance genes [ARGs]); and data on antimicrobial usage computed from the ECDC database and the IQVIA database. We characterised and built models predicting the global resistome at an antimicrobial class level. We used a generalised linear mixed-effects model to estimate the association between antimicrobial usage and ARG abundance in the sewage samples; a multivariate random forest model to build predictive models for each antimicrobial resistance class and to select the most important variables for ARG abundance; logistic regression models to test the association between the predicted country-level antimicrobial resistance abundance and the country-level proportion of clinical resistant bacterial isolates; finite mixture models to investigate geographical heterogeneities in the abundance of ARGs; and multivariate finite mixture models with covariates to investigate the effect of heterogeneity in the association between the most important variables and the observed ARG abundance across the different country subgroups. We compared our predictions with available clinical phenotypic data from the SENTRY Antimicrobial Surveillance Program from eight antimicrobial classes and 12 genera from 56 countries. FINDINGS: Using antimicrobial use data from between Jan 1, 2016, and Dec 31, 2019, we found that antimicrobial usage was not significantly associated with the global ARG abundance in sewage (p=0·72; incidence rate ratio 1·02 [95% CI 0·92-1·13]), whereas country-specific World Bank's variables explained a large amount of variation. The importance of the World Bank variables differed between antimicrobial classes and countries. Generally, the estimated global ARG abundance was positively associated with the prevalence of clinical phenotypic resistance, with a strong association for bacterial groups in the human gut. The associations between bacterial groups and ARG abundance were positive and significantly different from zero for the aminoglycosides (three of the four of the taxa tested), ß-lactam (all the six microbial groups), fluoroquinolones (seven of nine of the microbial groups), glycopeptide (one microbial group tested), folate pathway antagonists (four of five microbial groups), and tetracycline (two of nine microbial groups). INTERPRETATION: Metagenomic analysis of sewage is a robust approach for the surveillance of antimicrobial resistance in pathogens, especially for bacterial groups associated with the human gut. Additional studies on the associations between important socioeconomic, nutritional, and health factors and antimicrobial resistance should consider the variation in these associations between countries and antimicrobial classes. FUNDING: EU Horizon 2020 and Novo Nordisk Foundation.

Identification of Neurotransmission and Synaptic Biological Processes Disrupted in Autism Spectrum Disorder Using Interaction Networks and Community Detection Analysis.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by communication deficits and repetitive behavioral patterns. Hundreds of candidate genes have been implicated in ASD, including neurotransmission and synaptic (NS) genes; however, the genetic architecture of this disease is far from clear. In this study, we seek to clarify the biological processes affected by NS gene variants identified in individuals with ASD and the global networks that link those processes together. For a curated list of 1216 NS candidate genes, identified in multiple databases and the literature, we searched for ultra-rare (UR) loss-of-function (LoF) variants in the whole-exome sequencing dataset from the Autism Sequencing Consortium (N = 3938 cases). Filtering for population frequency was carried out using gnomAD (N = 60,146 controls). NS genes with UR LoF variants were used to construct a network of protein-protein interactions, and the network's biological communities were identified by applying the Leiden algorithm. We further explored the expression enrichment of network genes in specific brain regions. We identified 356 variants in 208 genes, with a preponderance of UR LoF variants in the PDE11A and SYTL3 genes. Expression enrichment analysis highlighted several subcortical structures, particularly the basal ganglia. The interaction network defined seven network communities, clustering synaptic and neurotransmitter pathways with several ubiquitous processes that occur in multiple organs and systems. This approach also uncovered biological pathways that are not usually associated with ASD, such as brain cytochromes P450 and brain mitochondrial metabolism. Overall, the community analysis suggests that ASD involves the disruption of synaptic and neurotransmitter pathways but also ubiquitous, but less frequently implicated, biological processes.

In situ generation of highly localized chlorine by laser-induced graphene electrodes during electrochemical disinfection.

Laser-induced graphene (LIG) has gained popularity for electrochemical water disinfection due to its efficient antimicrobial activity when activated with low voltages. However, the antimicrobial mechanism of LIG electrodes is not yet fully understood. This study demonstrated an array of mechanisms working synergistically to inactivate bacteria during electrochemical treatment using LIG electrodes, including the generation of oxidants, changes in pH-specifically high alkalinity associated with the cathode, and electro-adsorption on the electrodes. All these mechanisms may contribute to the disinfection process when bacteria are close to the surface of the electrodes where inactivation was independent of the reactive chlorine species (RCS); however, RCS was likely responsible for the predominant cause of antibacterial effects in the bulk solution (i.e., ≥100 mL in our study). Furthermore, the concentration and diffusion kinetics of RCS in solution was voltage-dependent. At 6 V, RCS achieved a high concentration in water, while at 3 V, RCS was highly localized on the LIG surface but not measurable in water. Despite this, the LIG electrodes activated by 3 V achieved a 5.5-log reduction in Escherichia coli (E.coli) after 120-min electrolysis without detectable chlorine, chlorate, or perchlorate in the water, suggesting a promising system for efficient, energy-saving, and safe electro-disinfection.

Antimicrobial resistance monitoring in the Danish swine production by phenotypic methods and metagenomics from 1999 to 2018.

BackgroundIn Denmark, antimicrobial resistance (AMR) in pigs has been monitored since 1995 by phenotypic approaches using the same indicator bacteria. Emerging methodologies, such as metagenomics, may allow novel surveillance ways.AimThis study aimed to assess the relevance of indicator bacteria (Escherichia coli and Enterococcus faecalis) for AMR surveillance in pigs, and the utility of metagenomics.MethodsWe collated existing data on AMR and antimicrobial use (AMU) from the Danish surveillance programme and performed metagenomics sequencing on caecal samples that had been collected/stored through the programme during 1999-2004 and 2015-2018. We compared phenotypic and metagenomics results regarding AMR, and the correlation of both with AMU.ResultsVia the relative abundance of AMR genes, metagenomics allowed to rank these genes as well as the AMRs they contributed to, by their level of occurrence. Across the two study periods, resistance to aminoglycosides, macrolides, tetracycline, and beta-lactams appeared prominent, while resistance to fosfomycin and quinolones appeared low. In 2015-2018 sulfonamide resistance shifted from a low occurrence category to an intermediate one. Resistance to glycopeptides consistently decreased during the entire study period. Outcomes of both phenotypic and metagenomics approaches appeared to positively correlate with AMU. Metagenomics further allowed to identify multiple time-lagged correlations between AMU and AMR, the most evident being that increased macrolide use in sow/piglets or fatteners led to increased macrolide resistance with a lag of 3-6 months.ConclusionWe validated the long-term usefulness of indicator bacteria and showed that metagenomics is a promising approach for AMR surveillance.

Disease similarity network analysis of Autism Spectrum Disorder and comorbid brain disorders.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with heterogeneous clinical presentation, variable severity, and multiple comorbidities. A complex underlying genetic architecture matches the clinical heterogeneity, and evidence indicates that several co-occurring brain disorders share a genetic component with ASD. In this study, we established a genetic similarity disease network approach to explore the shared genetics between ASD and frequent comorbid brain diseases (and subtypes), namely Intellectual Disability, Attention-Deficit/Hyperactivity Disorder, and Epilepsy, as well as other rarely co-occurring neuropsychiatric conditions in the Schizophrenia and Bipolar Disease spectrum. Using sets of disease-associated genes curated by the DisGeNET database, disease genetic similarity was estimated from the Jaccard coefficient between disease pairs, and the Leiden detection algorithm was used to identify network disease communities and define shared biological pathways. We identified a heterogeneous brain disease community that is genetically more similar to ASD, and that includes Epilepsy, Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder combined type, and some disorders in the Schizophrenia Spectrum. To identify loss-of-function rare de novo variants within shared genes underlying the disease communities, we analyzed a large ASD whole-genome sequencing dataset, showing that ASD shares genes with multiple brain disorders from other, less genetically similar, communities. Some genes (e.g., SHANK3, ASH1L, SCN2A, CHD2, and MECP2) were previously implicated in ASD and these disorders. This approach enabled further clarification of genetic sharing between ASD and brain disorders, with a finer granularity in disease classification and multi-level evidence from DisGeNET. Understanding genetic sharing across disorders has important implications for disease nosology, pathophysiology, and personalized treatment.

A Role for Gene-Environment Interactions in Autism Spectrum Disorder Is Supported by Variants in Genes Regulating the Effects of Exposure to Xenobiotics.

Heritability estimates support the contribution of genetics and the environment to the etiology of Autism Spectrum Disorder (ASD), but a role for gene-environment interactions is insufficiently explored. Genes involved in detoxification pathways and physiological permeability barriers (e.g., blood-brain barrier, placenta and respiratory airways), which regulate the effects of exposure to xenobiotics during early stages of neurodevelopment when the immature brain is extremely vulnerable, may be particularly relevant in this context. Our objective was to identify genes involved in the regulation of xenobiotic detoxification or the function of physiological barriers (the XenoReg genes) presenting predicted damaging variants in subjects with ASD, and to understand their interaction patterns with ubiquitous xenobiotics previously implicated in this disorder. We defined a panel of 519 XenoReg genes through literature review and database queries. Large ASD datasets were inspected for in silico predicted damaging Single Nucleotide Variants (SNVs) (N = 2,674 subjects) or Copy Number Variants (CNVs) (N = 3,570 subjects) in XenoReg genes. We queried the Comparative Toxicogenomics Database (CTD) to identify interaction pairs between XenoReg genes and xenobiotics. The interrogation of ASD datasets for variants in the XenoReg gene panel identified 77 genes with high evidence for a role in ASD, according to pre-specified prioritization criteria. These include 47 genes encoding detoxification enzymes and 30 genes encoding proteins involved in physiological barrier function, among which 15 are previous reported candidates for ASD. The CTD query revealed 397 gene-environment interaction pairs between these XenoReg genes and 80% (48/60) of the analyzed xenobiotics. The top interacting genes and xenobiotics were, respectively, CYP1A2, ABCB1, ABCG2, GSTM1, and CYP2D6 and benzo-(a)-pyrene, valproic acid, bisphenol A, particulate matter, methylmercury, and perfluorinated compounds. Individuals carrying predicted damaging variants in high evidence XenoReg genes are likely to have less efficient detoxification systems or impaired physiological barriers. They can therefore be particularly susceptible to early life exposure to ubiquitous xenobiotics, which elicit neuropathological mechanisms in the immature brain, such as epigenetic changes, oxidative stress, neuroinflammation, hypoxic damage, and endocrine disruption. As exposure to environmental factors may be mitigated for individuals with risk variants, this work provides new perspectives to personalized prevention and health management policies for ASD.

Gene Variants Involved in Nonsense-Mediated mRNA Decay Suggest a Role in Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with unclear etiology. Many genes have been associated with ASD risk, but the underlying mechanisms are still poorly understood. An important post-transcriptional regulatory mechanism that plays an essential role during neurodevelopment, the Nonsense-Mediated mRNA Decay (NMD) pathway, may contribute to ASD risk. In this study, we gathered a list of 46 NMD factors and regulators and investigated the role of genetic variants in these genes in ASD. By conducting a comprehensive search for Single Nucleotide Variants (SNVs) in NMD genes using Whole Exome Sequencing data from 1828 ASD patients, we identified 270 SNVs predicted to be damaging in 28.7% of the population. We also analyzed Copy Number Variants (CNVs) from two cohorts of ASD patients (N = 3570) and discovered 38 CNVs in 1% of cases. Importantly, we discovered 136 genetic variants (125 SNVs and 11 CNVs) in 258 ASD patients that were located within protein domains required for NMD. These gene variants are classified as damaging using in silico prediction tools, and therefore may interfere with proper NMD function in ASD. The discovery of NMD genes as candidates for ASD in large patient genomic datasets provides evidence supporting the involvement of the NMD pathway in ASD pathophysiology.

Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning.

The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype makes molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine-learning integrative approach, which seeks to delineate associations between patients' clinical profiles and disrupted biological processes, inferred from their copy number variants (CNVs) that span brain genes. Clustering analysis of the relevant clinical measures from 2446 ASD cases in the Autism Genome Project identified two distinct phenotypic subgroups. Patients in these clusters differed significantly in ADOS-defined severity, adaptive behavior profiles, intellectual ability, and verbal status, the latter contributing the most for cluster stability and cohesion. Functional enrichment analysis of brain genes disrupted by CNVs in these ASD cases identified 15 statistically significant biological processes, including cell adhesion, neural development, cognition, and polyubiquitination, in line with previous ASD findings. A Naive Bayes classifier, generated to predict the ASD phenotypic clusters from disrupted biological processes, achieved predictions with a high precision (0.82) but low recall (0.39), for a subset of patients with higher biological Information Content scores. This study shows that milder and more severe clinical presentations can have distinct underlying biological mechanisms. It further highlights how machine-learning approaches can reduce clinical heterogeneity by using multidimensional clinical measures, and establishes genotype-phenotype correlations in ASD. However, predictions are strongly dependent on patient's information content. Findings are therefore a first step toward the translation of genetic information into clinically useful applications, and emphasize the need for larger datasets with very complete clinical and biological information.

Clinical insights gained by refining the 2016 WHO classification of diffuse gliomas with: EGFR amplification, TERT mutations, PTEN deletion and MGMT methylation.

BACKGROUND: Significant advances in the molecular profiling of gliomas, led the 2016 World Health Organization (WHO) Classification to include, for the first-time, molecular biomarkers in glioma diagnosis: IDH mutations and 1p/19q codeletion. Here, we evaluated the effect of this new classification in the stratification of gliomas previously diagnosed according to 2007 WHO classification. Then, we also analyzed the impact of TERT promoter mutations, PTEN deletion, EGFR amplification and MGMT promoter methylation in diagnosis, prognosis and response to therapy in glioma molecular subgroup. METHODS: A cohort of 444 adult gliomas was analyzed and reclassified according to the 2016 WHO. Mutational analysis of IDH1 and TERT promoter mutations was performed by Sanger sequencing. Statistical analysis was done using SPSS Statistics 21.0. RESULTS: The reclassification of this cohort using 2016 WHO criteria led to a decrease of the number of oligodendrogliomas (from 82 to 49) and an increase of astrocytomas (from 49 to 98), while glioblastomas (GBM) remained the same (n = 256). GBM was the most common diagnosis (57.7%), of which 55.2% were IDH-wildtype. 1p/19q codeleted gliomas were the subgroup associated with longer median overall survival (198 months), while GBM IDH-wildtype had the worst outcome (10 months). Interestingly, PTEN deletion had poor prognostic value in astrocytomas IDH-wildtype (p = 0.015), while in GBM IDH-wildtype was associated with better overall survival (p = 0.042) as well as MGMT promoter methylation (p = 0.009). EGFR amplification and TERT mutations had no impact in prognosis. Notably, EGFR amplification predicted a better response to radiotherapy (p = 0.011) and MGMT methylation to chemo-radiotherapy (p = 0.003). CONCLUSION: In this study we observed that the 2016 WHO classification improved the accuracy of diagnosis and prognosis of diffuse gliomas, although the available biomarkers are not enough. Therefore, we suggest MGMT promoter methylation should be added to glioma classification. Moreover, we found two genetic/clinical correlations that must be evaluated to understand their impact in the clinical setting: i) how is PTEN deletion a favorable prognostic factor in GBM IDH wildtype and an unfavorable prognostic factor in astrocytoma IDH wildtype and ii) how EGFR amplification is an independent and strong factor of response to radiotherapy.

Identification of Sindhi cows that are susceptible or resistant to Haematobia irritans.

To identify susceptible and resistant Haematobia irritans cows, horn flies were counted biweekly for 3 years in a herd of 25 Sindhi cows. Repeated measures linear mixed models were created including cow as a random factor. The results were analyzed by: 1) observing horn fly counts, considering fly-susceptible cows with infestations appearing in the upper quartile more than 50% of the weeks and in the lower quartile less than 20% of the weeks, and fly-resistant cows those that the number of flies was in the lower quartile more than 50% of the weeks and in the upper quartile less than 20%; 2) by the best linear unbiased predictions (BLUPs), to evaluate the cow effect on fly counts. Fly-susceptible cows were those in which the infestation appeared in the 90th percentile of the BLUPs, whereas fly-resistant cows appeared in the 10th percentile. For the observational method the individuals identified as resistant varied between 8% and 20% and 8% to 12% were susceptible. For the BLUP method, the rates of susceptible and resistant cows were 12%. The agreement among methods suggests that susceptible cows can be identified by observations of fly counts, allowing for selective breeding, culling or treatment.

Nonbacterial thrombotic endocarditis. / Endocardite trombótica não bacteriana.

An 83-year-old woman with a 10-year history of rheumatoid arthritis was admitted for urinary tract infection with exacerbation of chronic kidney disease and decompensated heart failure of unknown etiology. Transesophageal echocardiography (TEE) showed a vegetation involving the posterior mitral valve leaflet, and a hypothesis of infective endocarditis was proposed. Empirical antibiotic therapy was initiated. TEE was repeated after antibiotic therapy, and showed persistence of the original vegetation and revealed the presence of another, smaller vegetation. Clinical investigation revealed no infectious process, and so a diagnosis of nonbacterial thrombotic endocarditis (NBTE) was established. Anticoagulant therapy was started immediately. The NBTE lesion had disappeared on the follow-up echocardiogram two months after anticoagulant therapy.

Identification of Sindhi cows that are susceptible or resistant to Haematobia irritans / Identificação de vacas Sindhi que são suscetíveis ou resistentes à Haematobia irritans

Abstract To identify susceptible and resistant Haematobia irritans cows, horn flies were counted biweekly for 3 years in a herd of 25 Sindhi cows. Repeated measures linear mixed models were created including cow as a random factor. The results were analyzed by: 1) observing horn fly counts, considering fly-susceptible cows with infestations appearing in the upper quartile more than 50% of the weeks and in the lower quartile less than 20% of the weeks, and fly-resistant cows those that the number of flies was in the lower quartile more than 50% of the weeks and in the upper quartile less than 20%; 2) by the best linear unbiased predictions (BLUPs), to evaluate the cow effect on fly counts. Fly-susceptible cows were those in which the infestation appeared in the 90th percentile of the BLUPs, whereas fly-resistant cows appeared in the 10th percentile. For the observational method the individuals identified as resistant varied between 8% and 20% and 8% to 12% were susceptible. For the BLUP method, the rates of susceptible and resistant cows were 12%. The agreement among methods suggests that susceptible cows can be identified by observations of fly counts, allowing for selective breeding, culling or treatment.

Resumo Para identificar vacas susceptíveis e resistentes à Haematobia irritans, moscas-dos-chifres foram contadas quinzenalmente durante três anos em 25 vacas de um rebanho Sindhi. Modelos lineares de medidas repetidas foram criados, analisando os resultados de duas formas: 1) pela contagem das moscas, considerando susceptíveis as vacas nas quais a infestação aparecia no quartil superior mais de 50% das semanas e no quartil inferior menos de 20% das semanas. Vacas resistentes foram consideradas aquelas nas quais o número de moscas apareceu no quartil inferior mais de 50% das semanas e no quartil superior menos de 20% das semanas; 2) pela melhor predição linear não-viesada (BLUP), para avaliar o efeito das vacas na contagem de moscas. As vacas foram consideradas susceptíveis quando apareciam no percentil 90 dos BLUPs e resistentes quando apareciam no percentil 10. O método observacional identificou 8% a 20% de indivíduos resistentes e 8% a 12% de susceptíveis. O método dos BLUPs identificou igual taxa de 12% de indivíduos susceptíveis e resistentes. A forte concordância entre estes dois métodos sugere que as vacas susceptíveis podem ser identificadas pela contagem das moscas, o que permite estabelecer seleção dos animais resistentes ou tratamento ou eliminação dos mais susceptíveis.

Time-series clustering of cage-level sea lice data.

Sea lice Lepeophtheirus salmonis (Krøyer) are a major ectoparasite affecting farmed Atlantic salmon in most major salmon producing regions. Substantial resources are applied to sea lice control and the development of new technologies towards this end. Identifying and understanding how sea lice population patterns vary among cages on a salmon farm can be an important step in the design and analysis of any sea lice control strategy. Norway's intense monitoring efforts have provided salmon farmers and researchers with a wealth of sea lice infestation data. A frequently registered parameter is the number of adult female sea lice per cage. These time-series data can be analysed descriptively, the similarity between time-series quantified, so that groups and patterns can be identified among cages, using clustering algorithms capable of handling such dynamic data. We apply such algorithms to investigate the pattern of female sea lice counts among cages for three Atlantic salmon farms in Norway. A series of strategies involving a combination of distance measures and prototypes were explored and cluster evaluation was performed using cluster validity indices. Repeated agreement on cluster membership for different combinations of distance and centroids was taken to be a strong indicator of clustering while the stability of these results reinforced this likelihood. Though drivers behind clustering are not thoroughly investigated here, it appeared that fish weight at time of stocking and other management practices were strongly related to cluster membership. In addition to these internally driven factors it is also possible that external sources of infestation may drive patterns of sea lice infestation in groups of cages; for example, those most proximal to an external source. This exploratory method proved useful as a pattern discovery tool for cages in salmon farms.