Multi-habitat landscapes are more diverse and stable with improved function.

Conservation, restoration and land management are increasingly implemented at landscape scales1,2. However, because species interaction data are typically habitat- and/or guild-specific, exactly how those interactions connect habitats and affect the stability and function of communities at landscape scales remains poorly understood. We combine multi-guild species interaction data (plant-pollinator and three plant-herbivore-parasitoid communities, collected from landscapes with one, two or three habitats), a field experiment and a modelling approach to show that multi-habitat landscapes support higher species and interaction evenness, more complementary species interactions and more consistent robustness to species loss. These emergent network properties drive improved pollination success in landscapes with more habitats and are not explained by simply summing component habitat webs. Linking landscape composition, through community structure, to ecosystem function, highlights mechanisms by which several contiguous habitats can support landscape-scale ecosystem services.

Using artificial intelligence to identify drugs for repurposing to treat l-DOPA-induced dyskinesia.

Repurposing regulatory agency-approved molecules, with proven safety in humans, is an attractive option for developing new treatments for disease. We identified and assessed the efficacy of 3 drugs predicted by an in silico screen as having the potential to treat l-DOPA-induced dyskinesia (LID) in Parkinson's disease. We analysed ∼1.3 million Medline abstracts using natural language processing and ranked 3539 existing drugs based on predicted ability to reduce LID. 3 drugs from the top 5% of the 3539 candidates; lorcaserin, acamprosate and ganaxolone, were prioritized for preclinical testing based on i) having a novel mechanism of action, ii) having not been previously validated for the treatment of LID, iii) being blood-brain-barrier penetrant and orally bioavailable and iv) being clinical trial ready. We assessed the efficacy of acamprosate, ganaxolone and lorcaserin in a rodent model of l-DOPA-induced hyperactivity, with lorcaserin affording a 58% reduction in rotational asymmetry (P < 0.05) compared to vehicle. Acamprosate and ganaxolone failed to demonstrate efficacy. Lorcaserin, a 5HT2C agonist, was then further tested in MPTP lesioned dyskinetic macaques where it afforded an 82% reduction in LID (P < 0.05), unfortunately accompanied by a significant increase in parkinsonian disability. In conclusion, although our data do not support the repurposing of lorcaserin, acamprosate or ganaxolone per se for LID, we demonstrate value of an in silico approach to identify candidate molecules which, in combination with an in vivo screen, can facilitate clinical development decisions. The present study adds to a growing literature in support of this paradigm shifting approach in the repurposing pipeline.

Identifying laboratory sources of microplastic and nanoplastic contamination from the air, water, and consumables.

Microplastic and nanoplastic research has proliferated in recent years in response to the escalating plastic pollution crisis. However, a lack of optimised methods for sampling and sample processing has potential implications for contaminating samples resulting in an overestimation of the quantity of microplastics and nanoplastics present in environmental samples. In response, a series of recommendations have been made, but most have not been quantified or validated sources of contamination. In the present study, we investigated sources of plastic contamination in common laboratory procedures including water sources (e.g., Milli-Q), consumables (e.g., unburnt glassware), airflow (e.g., fume hood) and dust. Using flow cytometry, we identified water, air flow and dust as sources of significant contamination. Milli-Q and reverse osmosis were the least contaminated sources when compared with tap water. Interestingly, current recommendations are to use glass consumables in replacement of plastic consumables, however, we have identified glassware and glass consumables as a significant source of contamination. Current best practice is to cover the glass tube with aluminium foil to reduce airborne contamination, but we found fresh aluminium foil to be a significant source of contamination, bringing light to the limitations foil has as a contamination control measure. Lastly, we identified significant quantities of microplastics and nanoplastics present in dust collected within the laboratory, suggesting this is a widespread and underestimated source of contamination. We have provided validated sources of contamination for both consumables and common laboratory procedures and provided mitigation strategies based on these. Additional recommendations include the appropriate design of experimental controls to quantify levels of introduced contamination based on methods and the detection techniques utilised. The application of these mitigation strategies and appropriate experimental design will allow for more accurate estimations on the level of microplastic and nanoplastic contamination within environmental samples.

The understudied global experiment of pollution's impacts on wildlife and human health: The ethical imperative for interdisciplinary research.

The global impact of pollution on human and wildlife health is a growing concern. The health impacts of pollution are significant and far-reaching yet poorly understood as no one field of research has the practices and methodologies required to encapsulate the diversity of these consequences. This paper advocates that interdisciplinary research is essential to comprehend the full extent of the impact of pollution. Medical and ecological research play a key role in investigating the health consequences of the pollution crisis, yet the wildlife experience is often neglected. This paper outlines how applying advanced techniques and expertise adapted in medical research to wildlife exposed to pollutants offers a unique perspective to understanding the full diversity of impacts to health. The challenges that impede the progress of this research include the lack of support for interdisciplinary research among funding streams, limitations in field-specific techniques, and a lack of communication between researchers from different disciplines. Of awarded funding from major national research councils across Australia, Europe, and the United States of America, only 0.5% is dedicated to pollution focused research. This is inclusive of laboratory equipment, mitigation strategies, quantification of environmental samples and health consequences research. Of that, 0.03% of funding is awarded to explaining the wildlife experience and documenting the health consequences observed despite being model organisms to environmentally and biologically relevant models for pollution exposure. This calls for a coordinated effort to overcome these hurdles and to promote interdisciplinary research in order to fully comprehend the consequences of pollution exposure and protect the health of humans, wildlife, and the environment. An interdisciplinary approach to this problem is timely given the magnitude of negative health consequences associated with exposure, the number of pollutants already present within the environment and the continual development of new compounds.

Impact of Treating Depression on Associated Comorbidities: A Systematic Literature Review.

Objective: To identify and summarize data that describe the impact of effectively treating major depressive disorder (MDD) on the severity or risk of serious comorbidities.Data Sources: MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, and several congresses were searched. Searches included terms related to MDD, randomized controlled trials (RCTs), and physical comorbidities and were restricted to English-language publications. Searches were conducted in November 2019 for the previous 2 years for conference proceedings; no date restriction was applied to the database searches.Study Selection: Included studies were RCTs or meta-analyses that assessed depression therapies. Studies were required to report a statistically significant improvement in depression scores as well as the concurrent impact on comorbidities. A total of 1,997 articles were initially identified for screening.Data Extraction: Two investigators extracted data and assessed study quality.Results: A total of 30 studies, including 24 RCTs (N = 6,333) and 6 meta/pooled analyses of RCTs, were included. Findings in several comorbidity categories were mixed; for example, in half (4 of 8) of the identified studies in people with cardiovascular disease and depression, individuals who received treatment leading to reduced depressive symptoms compared with a control arm also had a significantly decreased incidence of cardiovascular events or significantly improved cardiac disease symptom/severity scores compared with controls. Significant improvements in comorbid disease severity observed alongside improvements in depressive symptoms were also noted in studies of comorbid Parkinson's disease, multiple sclerosis, chronic pain and fibromyalgia, and chronic obstructive pulmonary disease.Conclusions: Effective treatment of MDD may lead to a reduction in the severity of certain serious comorbidities. These results highlight the importance of appropriate and timely treatment of MDD.

Impact of Major Depressive Disorder on Comorbidities: A Systematic Literature Review.

Objective: To summarize the breadth of data exploring the relationship between major depressive disorder (MDD) and both the incidence and the disease course of a range of comorbidities.Data Sources: The authors searched MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, and several prespecified congresses. Searches included terms related to MDD and several comorbidity categories, restricted to those published in the English language from 2005 onward.Study Selection: Eligibility criteria included observational studies within North America and Europe that examined the covariate-adjusted impact of MDD on the risk and/or severity of comorbidities. A total of 6,811 articles were initially identified for screening.Data Extraction: Two investigators extracted data and assessed study quality.Results: In total, 199 articles were included. Depression was significantly (P < .05) associated with an increased incidence of dementia and Alzheimer's disease as well as cognitive decline in individuals with existing disease; increased incidence and worsening of cardiovascular disease/events (although mixed results were found for stroke); worsening of metabolic syndrome; increased incidence of diabetes, particularly among men, and worsening of existing diabetes; increased incidence of obesity, particularly among women; increased incidence and worsening of certain autoimmune diseases; increased incidence and severity of HIV/AIDS; and increased incidence of drug abuse and severity of both alcohol and drug abuse.Conclusions: The presence of MDD was identified as a risk factor for both the development and the worsening of a range of comorbidities. These results highlight the importance of addressing depression early in its course and the need for integrating mental and general health care.

Ingested plastics in beach-washed Fairy Prions Pachyptila turtur from Tasmania.

Plastic is an omnipresent pollutant in marine ecosystems and is widely documented to be ingested among seabird species. Procellariiformes are particularly vulnerable to plastic ingestion, which can cause internal damage, starvation, and occasionally mortality. In this study, 34 fledgling Fairy Prions (Pachyptila turtur) recovered during a wreck event in south-eastern Tasmania in 2022 were examined for ingested plastics and body condition (e.g., wing chord length). While many of the birds exhibited poor body condition, this was not correlated with the count or mass of ingested plastics. We hypothesise the marine heatwave event, and resulting lack of prey, contributed to bird body condition and subsequent mortality. We provide some of the first data on the size of individual plastic particles ingested by seabirds and make recommendations for future studies to report this important metric in a consistent manner that ensures data are comparable.

Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19.

The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.

College students' knowledge and management of food allergies.

OBJECTIVE: This study explored predictors of food allergy management in college students, including participants' reported allergy severity, history of allergic reactions, and allergy knowledge. Further, we compared allergy knowledge in participants with food allergy to a matched sample of college students without food allergy. Method: Participants were recruited from a larger nationwide study of knowledge and attitudes toward food allergy in college students, with purposeful oversampling of students with food allergies. Participants completed measures assessing their food allergy(ies), symptoms, history of reactions, and current allergy management behaviors. Participants with food allergies and control participants without food allergies completed a measure of food allergy knowledge. Results: Hierarchical regression revealed that food allergy knowledge accounted for an additional 20% of variance in students' allergy management behaviors, above and beyond severity and allergic reactions, R2=.39, F(3,48)=10.09, p<.001. There was not a statistically significant difference in food allergy knowledge between participants with food allergy and matched controls, t(49)=-1.85, p=.07, 95% CI=-1.42 to 0.06. Conclusions: This study suggests allergy knowledge is an important factor in food allergy management. Knowledge significantly predicted food allergy management behaviors above and beyond food allergy severity and recent food allergy reactions. College students with food allergies did not demonstrate greater knowledge than controls, suggesting a need for psychoeducational intervention to target college students' allergy knowledge as they transition to independent allergy management.

Using artificial intelligence to identify anti-hypertensives as possible disease modifying agents in Parkinson's disease.

PURPOSE: Drug repurposing is an effective means of increasing treatment options for diseases, however identifying candidate molecules for the indication of interest from the thousands of approved drugs is challenging. We have performed a computational analysis of published literature to rank existing drugs according to predicted ability to reduce alpha synuclein (aSyn) oligomerization and analyzed real-world data to investigate the association between exposure to highly ranked drugs and PD. METHODS: Using IBM Watson for Drug Discoveryâ (WDD) we identified several antihypertensive drugs that may reduce aSyn oligomerization. Using IBM MarketScanâ Research Databases we constructed a cohort of individuals with incident hypertension. We conducted univariate and multivariate Cox proportional hazard analyses (HR) with exposure as a time-dependent covariate. Diuretics were used as the referent group. Age at hypertension diagnosis, sex, and several comorbidities were included in multivariate analyses. RESULTS: Multivariate results revealed inverse associations for time to PD diagnosis with exposure to the combination of the combination of angiotensin receptor II blockers (ARBs) and dihydropyridine calcium channel blockers (DHP-CCB) (HR = 0.55, p < 0.01) and angiotensin converting enzyme inhibitors (ACEi) and diuretics (HR = 0.60, p-value <0.01). Increased risk was observed with exposure to alpha-blockers alone (HR = 1.81, p < 0.001) and the combination of alpha-blockers and CCB (HR = 3.17, p < 0.05). CONCLUSIONS: We present evidence that a computational approach can efficiently identify leads for disease-modifying drugs. We have identified the combination of ARBs and DHP-CCBs as of particular interest in PD.

Evidence that Bacteria Packaging by Tetrahymena Is a Widespread Phenomenon.

Protozoa are natural predators of bacteria, but some bacteria can evade digestion once phagocytosed. Some of these resistant bacteria can be packaged in the fecal pellets produced by protozoa, protecting them from physical stresses and biocides. Depending on the bacteria and protozoa involved in the packaging process, pellets can have different morphologies. In the present descriptive study, we evaluated the packaging process with 20 bacteria that have never been tested before for packaging by ciliates. These bacteria have various characteristics (shape, size, Gram staining). All of them appear to be included in pellets produced by the ciliates Tetrahymena pyriformis and/or T. thermophila in at least one condition tested. We then focused on the packaging morphology of four of these bacteria. Our results demonstrated that, as shown previously for Mycobacterium smegmatis, the packaging of Microbacterium oxydans, Micrococcus luteus, and Cupriavidus sp. was formed of a single layer of material. The packaging of Cellulosimicrobiumfunkei was made of indistinguishable material. A different pellet morphology was obtained for each of the four bacterial strains studied. The ingestion of small bacteria resulted in rounder, denser, and more regular pellets. These results support the idea that bacteria packaging is a relatively widespread phenomenon.

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.

Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the patient population present massive challenges to the development of early diagnostic tools and effective treatments for these diseases. Machine learning, a subfield of artificial intelligence, is enabling scientists, clinicians and patients to address some of these challenges. In this Review, we discuss how machine learning can aid early diagnosis and interpretation of medical images as well as the discovery and development of new therapies. A unifying theme of the different applications of machine learning is the integration of multiple high-dimensional sources of data, which all provide a different view on disease, and the automated derivation of actionable insights.

Identifying drugs with disease-modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology.

PURPOSE: The aim of the study was to assess the feasibility of an approach combining computational methods and pharmacoepidemiology to identify potentially disease-modifying drugs in Parkinson's disease (PD). METHODS: We used a two-step approach; (a) computational method using artificial intelligence to rank 620 drugs in the Ontario Drug Benefit formulary based on their predicted ability to inhibit alpha-synucleinaggregation, a pathogenic hallmark of PD; and (b) case-control study using administrative databases in Ontario, Canada. Persons aged 70-110 years with incident PD from April 2002-March 2013. Controls were randomly selected from persons with no previous diagnosis of PD. RESULTS: A total of 15 of the top 50 drugs were deemed feasible for pharmacoepidemiologic analysis, of which seven were significantly associated with incident PD after adjustment, with five of these seven associated with a decreased odds of PD. Methylxanthine drugs pentoxifylline (OR, 0.72; 95% CI, 0.59-0.89) and theophylline (OR, 0.77; 95% CI, 0.66-0.91), and the corticosteroid dexamethasone (OR, 0.72; 95% CI, 0.61-0.85) were associated with decreased odds of PD. CONCLUSIONS: Our findings demonstrate the feasibility of this approach to focus the search for disease-modifying drugs. Corticosteroids and methylxanthines should be further investigated as potential disease-modifyingdrugs in PD.

From winter to summer and back: Lessons from the parameterization of a seasonal food web model for the Bialowieza forest.

Dynamic food web models describe how species abundances change over time as a function of trophic and life-history parameters. They are essential to predicting the response of ecosystems to perturbations. However, they are notoriously difficult to parameterize, so that most models rely heavily either on allometric scaling of parameters or inverse estimation of biomass flows. The allometric approach makes species of comparable body mass have near-identical parameters which can generate extinctions within a trophic level. The biomass flow approach is more precise, but is restricted to steady-states, which is not appropriate for time-varying environments. Adequately parameterizing large food webs of temperate and arctic environments requires dealing both with many species of similar sizes and a strongly seasonal environment. Inspired by the rich empirical knowledge on the vertebrate food web of the Bialowieza forest, we parameterize a bipartite food web model comprising 21 predators and 124 prey species. Our model is a non-autonomous coupled ordinary differential equations system that allows for seasonality in life-history and predation parameters. Birth and death rates, seasonal descriptions of diet for each species, food requirements and biomass information are combined into a seasonal parameterization of a dynamic food web model. Food web seasonality is implemented with time-varying intrinsic growth rate and interaction parameters, while predation is modelled with both type I and type II functional responses. All our model variants allow for >80% persistence in spite of massive apparent competition, and a quantitative match to observed (seasonal) biomasses. We also identify trade-offs between maximizing persistence, reproducing observed biomasses, and ensuring model robustness to sampling errors. Although multi-annual cycles are expected with type II functional responses, they are here prevented by a strong predator self-regulation. We discuss these results and possible improvements on the model. We provide a general workflow to parameterize dynamic food web models in seasonal environments, based on a real case study. This may help to better predict how biodiverse food webs respond to changing environments.

The effect of seasonal strength and abruptness on predator-prey dynamics.

Coupled dynamical systems in ecology are known to respond to the seasonal forcing of their parameters with multiple dynamical behaviours, ranging from seasonal cycles to chaos. Seasonal forcing is predominantly modelled as a sine wave. However, the transition between seasons is often more sudden as illustrated by the effect of snow cover on predation success. A handful of studies have mentioned the robustness of their results to the shape of the forcing signal but did not report any detailed analyses. Therefore, whether and how the shape of seasonal forcing could affect the dynamics of coupled dynamical systems remains unclear, while abrupt seasonal transitions are widespread in ecological systems. To provide some answers, we conduct a numerical analysis of the dynamical response of predator-prey communities to the shape of the forcing signal by exploring the joint effect of two features of seasonal forcing: the magnitude of the signal, which is classically the only one studied, and the shape of the signal, abrupt or sinusoidal. We consider both linear and saturating functional responses, and focus on seasonal forcing of the predator's discovery rate, which fluctuates with changing environmental conditions and prey's ability to escape predation. Our numerical results highlight that a more abrupt seasonal forcing mostly alters the magnitude of population fluctuations and triggers period-doubling bifurcations, as well as the emergence of chaos, at lower forcing strength than for sine waves. Controlling the variance of the forcing signal mitigates this trend but does not fully suppress it, which suggests that the variance is not the only feature of the shape of seasonal forcing that acts on community dynamics. Although theoretical studies may predict correctly the sequence of bifurcations using sine waves as a representation of seasonality, there is a rationale for applied studies to implement as realistic seasonal forcing as possible to make precise predictions of community dynamics.

Zebrafish oxytocin neurons drive nocifensive behavior via brainstem premotor targets.

Animals have evolved specialized neural circuits to defend themselves from pain- and injury-causing stimuli. Using a combination of optical, behavioral and genetic approaches in the larval zebrafish, we describe a novel role for hypothalamic oxytocin (OXT) neurons in the processing of noxious stimuli. In vivo imaging revealed that a large and distributed fraction of zebrafish OXT neurons respond strongly to noxious inputs, including the activation of damage-sensing TRPA1 receptors. OXT population activity reflects the sensorimotor transformation of the noxious stimulus, with some neurons encoding sensory information and others correlating more strongly with large-angle swims. Notably, OXT neuron activation is sufficient to generate this defensive behavior via the recruitment of brainstem premotor targets, whereas ablation of OXT neurons or loss of the peptide attenuates behavioral responses to TRPA1 activation. These data highlight a crucial role for OXT neurons in the generation of appropriate defensive responses to noxious input.

Reshaping our understanding of species' roles in landscape-scale networks.

In network ecology, landscape-scale processes are often overlooked, yet there is increasing evidence that species and interactions spill over between habitats, calling for further study of interhabitat dependencies. Here, we investigate how species connect a mosaic of habitats based on the spatial variation of their mutualistic and antagonistic interactions using two multilayer networks, combining pollination, herbivory and parasitism in the UK and New Zealand. Developing novel methods of network analysis for landscape-scale ecological networks, we discovered that few plant and pollinator species acted as connectors or hubs, both within and among habitats, whereas herbivores and parasitoids typically have more peripheral network roles. Insect species' roles depend on factors other than just the abundance of taxa in the lower trophic level, exemplified by larger Hymenoptera connecting networks of different habitats and insects relying on different resources across different habitats. Our findings provide a broader perspective for landscape-scale management and ecological community conservation.

Exploring the Role of Medial Olivocochlear Efferents on the Detection of Amplitude Modulation for Tones Presented in Noise.

The medial olivocochlear reflex has been hypothesized to improve the detection and discrimination of dynamic signals in noisy backgrounds. This hypothesis was tested here by comparing behavioral outcomes with otoacoustic emissions. The effects of a precursor on amplitude-modulation (AM) detection were measured for a 1- and 6-kHz carrier at levels of 40, 60, and 80 dB SPL in a two-octave-wide noise masker with a level designed to produce poor, but above-chance, performance. Three types of precursor were used: a two-octave noise band, an inharmonic complex tone, and a pure tone. Precursors had the same overall level as the simultaneous noise masker that immediately followed the precursor. The noise precursor produced a large improvement in AM detection for both carrier frequencies and at all three levels. The complex tone produced a similarly large improvement in AM detection at the highest level but had a smaller effect for the two lower carrier levels. The tonal precursor did not significantly affect AM detection in noise. Comparisons of behavioral thresholds and medial olivocochlear efferent effects on stimulus frequency otoacoustic emissions measured with similar stimuli did not support the hypothesis that efferent-based reduction of cochlear responses contributes to the precursor effects on AM detection.

Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson.

BACKGROUND: Pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms. A biological pathway network typically includes entities (e.g. genes, proteins and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrence data. We propose a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship. METHODS: Through natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases. Using Bruton's tyrosine kinase as a case study, Watson for Drug Discovery's automatically extracted relationship network was compared with a prominent manually curated physical interaction network. Additionally, potential biomarkers for Bruton's tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers. RESULTS: Watson's natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of Bruton's tyrosine kinase in a prominent manually curated physical interaction network. Matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery's 13,595 ranked genes, with 7 in the top 5%. CONCLUSION: Taken together, these results suggest that Watson for Drug Discovery's automatic relationship network identifies the majority of upstream and downstream genes in biological pathway networks and can be used to help with the identification and prioritization of pharmacodynamic biomarker evaluation, accelerating the early phases of disease hypothesis generation.

Repurposing drugs to treat l-DOPA-induced dyskinesia in Parkinson's disease.

In this review, we discuss the opportunity for repurposing drugs for use in l-DOPA-induced dyskinesia (LID) in Parkinson's disease. LID is a particularly suitable indication for drug repurposing given its pharmacological diversity, translatability of animal-models, availability of Phase II proof-of-concept (PoC) methodologies and the indication-specific regulatory environment. A compound fit for repurposing is defined as one with appropriate human safety-data as well as animal safety, toxicology and pharmacokinetic data as found in an Investigational New Drug (IND) package for another indication. We first focus on how such repurposing candidates can be identified and then discuss development strategies that might progress such a candidate towards a Phase II clinical PoC. We discuss traditional means for identifying repurposing candidates and contrast these with newer approaches, especially focussing on the use of computational and artificial intelligence (AI) platforms. We discuss strategies that can be categorised broadly as: in vivo phenotypic screening in a hypothesis-free manner; in vivo phenotypic screening based on analogy to a related disorder; hypothesis-driven evaluation of candidates in vivo and in silico screening with a hypothesis-agnostic component to the selection. To highlight the power of AI approaches, we describe a case study using IBM Watson where a training set of compounds, with demonstrated ability to reduce LID, were employed to identify novel repurposing candidates. Using the approaches discussed, many diverse candidates for repurposing in LID, originally envisaged for other indications, will be described that have already been evaluated for efficacy in non-human primate models of LID and/or clinically. This article is part of the Special Issue entitled 'Drug Repurposing: old molecules, new ways to fast track drug discovery and development for CNS disorders'.