Search

The long-term health consequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are still being understood. The molecular and phenotypic properties of SARS-CoV-2 antigen-specific T cells suggest a dysfunctional profile that persists in convalescence in those who were severely ill. By contrast, the antigen-specific memory B-cell (MBC) population has not yet been analyzed to the same degree, but phenotypic analysis suggests differences following recovery from mild or severe coronavirus disease 2019 (COVID-19). Here, we performed single-cell molecular analysis of the SARS-CoV-2 receptor-binding domain (RBD)-specific MBC population in three patients after severe COVID-19 and four patients after mild/moderate COVID-19. We analyzed the transcriptomic and B-cell receptor repertoire profiles at ~2 months and ~4 months after symptom onset. Transcriptomic analysis revealed a higher level of tumor necrosis factor-alpha (TNF-α) signaling via nuclear factor-kappa B in the severe group, involving CD80, FOS, CD83 and TNFAIP3 genes that was maintained over time. We demonstrated the presence of two distinct activated MBCs subsets based on expression of CD80

Phagocytic responses by effector cells to opsonized viruses have been recognized to play a key role in antiviral immunity. Limited data on coronavirus disease 2019 suggest that the role of Ab-dependent and -independent phagocytosis may contribute to the observed immunological and inflammatory responses; however, their development, duration, and role remain to be fully elucidated. In this study of 62 acute and convalescent patients, we found that patients with acute coronavirus disease 2019 can mount a phagocytic response to autologous plasma-opsonized Spike protein–coated microbeads as early as 10 d after symptom onset, while heat inactivation of this plasma caused 77–95% abrogation of the phagocytic response and preblocking of Fc receptors showed variable 18–60% inhibition. In convalescent patients, phagocytic response significantly correlated with anti-Spike IgG titers and older patients, while patients with severe disease had significantly higher phagocytosis and neutralization functions compared with patients with asymptomatic, mild, or moderate disease. A longitudinal subset of the convalescent patients over 12 mo showed an increase in plasma Ab affinity toward Spike Ag and preservation of phagocytic and neutralization functions, despite a decline in the anti-Spike IgG titers by >90%. Our data suggest that early phagocytosis is primarily driven by heat-liable components of the plasma, such as activated complements, while anti-Spike IgG titers account for the majority of observed phagocytosis at convalescence. Longitudinally, a significant increase in the affinity of the anti-Spike Abs was observed that correlated with the maintenance of both the phagocytic and neutralization functions, suggesting an improvement in the quality of the Abs.

MotivationIn multi-cohort machine learning studies, it is critical to differentiate between effects that are reproducible across cohorts and those that are cohort-specific. Multi-task learning (MTL) is a machine learning approach that facilitates this differentiation through the simultaneous learning of prediction tasks across cohorts. Since multi-cohort data can often not be combined into a single storage solution, there would be the substantial utility of an MTL application for geographically distributed data sources.ResultsHere, we describe the development of ‘dsMTL’, a computational framework for privacy-preserving, distributed multi-task machine learning that includes three supervised and one unsupervised algorithms. First, we derive the theoretical properties of these methods and the relevant machine learning workflows to ensure the validity of the software implementation. Second, we implement dsMTL as a library for the R programming language, building on the DataSHIELD platform that supports the federated analysis of sensitive individual-level data. Third, we demonstrate the applicability of dsMTL for comorbidity modeling in distributed data. We show that comorbidity modeling using dsMTL outperformed conventional, federated machine learning, as well as the aggregation of multiple models built on the distributed datasets individually. The application of dsMTL was computationally efficient and highly scalable when applied to moderate-size (n Availability and implementationdsMTL is freely available at https://github.com/transbioZI/dsMTLBase (server-side package) and https://github.com/transbioZI/dsMTLClient (client-side package).Supplementary informationSupplementary data are available at Bioinformatics online.

Prediction models of brain texture changes in recent-onset psychosis (ROP) and recent-onset depression (ROD) have lately been proposed. The validation of these models transdiagnostically at the individual level and the investigation of the variability in clinical profiles are still missing. Established prevention and treatment approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of patients. We aimed to investigate the utility of brain texture changes for a) identification of the psychopathological state (ROP and ROD) and b) the association of individualized brain texture maps with clinical symptom severity and outcome profiles. We developed transdiagnostic models based on structural MRI data on 116 patients with ROD, 122 patients with ROP, and 197 healthy controls (HC) from the Personalised pROgNostic tools for early psychosIs mAnagement (PRONIA) study by applying explainable artificial intelligence and clustering analysis. We investigated the contrast texture feature as the key feature for the identification of a general psychopathological state. The discrimination power of the trained prediction model was > 72% and validated in a second independent age and sex-matched sample of 137 ROP, 94 ROD, and 159 HC. Clustering analysis was implemented to map the texture brain changes produced from an explainable artificial intelligence algorithm, in a group fashion. The explained individualized brain contrast map grouped into 8 homogeneous clusters. In each group, we investigated the association between the explained brain contrast texture map and clinical symptom severity as well as outcome profiles. Different patterns in the explained brain contrast texture map showed unique associations of brain alterations with clinical symptom severity and clinical outcomes, i.e., age, positive, negative and depressive symptoms, and functionality. In some clusters, the mean explained brain contrast texture map values and/or brain contrast texture voxels significantly contribute to the classification decision significantly predicted PANSS scores, functionality and change in functionality over time. In conclusion, we created homogeneous clusters which statistically significant predict the clinical severity and outcome profile.

Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.

Background Human metapneumovirus (hMPV), first described in 2001, is a cause of acute respiratory tract infection in the elderly, with symptoms ranging from mild to severe, including pneumonia, but outbreaks are rarely described. Methods Between 1 July and 31 December 2018, there were three outbreaks of Influenza-like Illness (ILI) where hMPV was the primary pathogen observed, among 64 aged-care facilities (ACFs) in Western Sydney. These outbreaks were investigated by the Western Sydney Local Health District (WSLHD) Public Health Unit (PHU); multiplex polymerase chain reaction (PCR) testing was performed on nasopharyngeal swabs collected by the ACF. Results hMPV was the main causative pathogen in three outbreaks (27, 28, and 15 symptomatic cases, respectively) in late winter and early spring. Fifty-five residents and 15 staff cases (70 total cases) were identified; hMPV was detected in 12 of 63 specimens submitted. Of the cases in residents, eight were hospitalised (8/63; 15%), including five with confirmed hMPV and a further one epidemiologically linked to a positive case. Six residents died (6/63; 11%) during the hMPV outbreaks; four of these had laboratory-confirmed hMPV, with a further case epidemiologically linked to a hMPV case, with a primary diagnosis of pneumonia/viral pneumonia. The sixth death was not epidemiologically linked, nor did this case exhibit any respiratory symptoms during the outbreak; however, it was reported in line with public health guidelines. Conclusion A major challenge in 2018 was the incompleteness of testing for, and awareness of, hMPV as a viral cause of ILI by facilities, laboratories, and emergency departments, which generally opted for rapid testing for influenza and RSV only. There is no licensed vaccine or approved treatment for hMPV, so efficient infection control measures are most important.

Clustering models or cluster analyses have been widely used to explore individual heterogeneity in mental health research and psychology. Despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by first providing an introduction to the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research with examples in the R software language. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. A rapid review of publications (December 2020-December 2021) focusing on the top six psychology and psychiatry journals publishing most of the clustering papers is also presented. The review highlights a few issues, including a lack of diversity in the algorithm of choice, robust internal and external validation processes (e.g., via resampling), and available data and analysis code to improve reproducibility. This comprehensive paper offers researchers (in both mental health and other broader health application areas) advanced tools and guidelines to use these methods efficiently, robustly and transparently.

Background: cancer services worldwide had to adapt in response to the COVID-19 pandemic to minimise risk to patients and staff. We aimed to assess the national impact of COVID-19 on the prescribing of systemic anticancer treatment in England, immediately after lockdown and after the introduction of new treatments to reduce patient risk. Methods: we did a retrospective analysis using data from a central National Health Service England web database mandated for clinicians to register intention to start all new systemic anticancer treatments approved for use in England since 2016. We analysed the monthly number of treatment registrations in April, 2020, after the implementation of societal lockdown on March 23, 2020, and after implementation of treatment options to reduce patient risk such as oral or less immunosuppressive drugs, in May and June, 2020. We compared the number of registrations in April–June, 2020, with the mean number of registrations and SD during the previous 6 months of unaffected cancer care (September, 2019, to February, 2020). We calculated the percentage change and absolute difference in SD units for the number of registrations overall, by tumour type, and by type and line of therapy. Findings: in April, 2020, 2969 registrations were recorded, representing 1417 fewer registrations than in the control period (monthly mean 4386; 32% reduction, absolute difference 4·2 SDs, pInterpretation: after the onset of the COVID-19 pandemic, there was a reduction in systemic anticancer treatment initiation in England. However, following introduction of treatment options to reduce patient risk, registrations began to increase in May, 2020, and reached higher numbers than the pre-pandemic mean in June, 2020, when other clinical and societal risk mitigation factors (such as telephone consultations, facemasks and physical distancing) are likely to have contributed. However, outcomes of providing less treatment or delaying treatment initiation, particularly for advanced cancers and neoadjuvant therapies, require continued assessment. Funding: none.

Autism spectrum disorder (ASD) is associated with high structural heterogeneity in magnetic resonance imaging (MRI). This work uncovers three neuroanatomical dimensions of ASD (N = 307) using machine learning methods and constructs their characteristic MRI signatures. The presence of these signatures, along with their clinical profiles and genetic architectures, are investigated in the general population. High expression of the first dimension (A1, “aging-related”) is associated with globally reduced brain volume, cognitive dysfunction, and aging-related genetic variants. The second dimension (A2, “schizophrenia-like”) is characterized by enlarged subcortical volume, antipsychotic medication use, and partially overlapping genetic underpinnings to schizophrenia. The third dimension (A3, “classical ASD”) is distinguished by enlarged cortical volume, high non-verbal cognitive performance, and genes and biological pathways implicating brain development and abnormal apoptosis. Thus, we propose a three-dimensional endophenotypic representation to construe the heterogeneity in ASD, which can support precision medicine and the discovery of the biological mechanisms of ASD.

The emergence of resistance to antiviral drugs increasingly used to treat SARS-CoV-2 infections has been recognised as a significant threat to COVID-19 control. In addition, some SARS-CoV-2 variants of concern appear to be intrinsically resistant to several classes of these antiviral agents. Therefore, there is a critical need for rapid recognition of clinically relevant polymorphisms in SARS-CoV-2 genomes associated with significant reduction of drug activity in virus neutralisation experiments. Here we present SABRes, a bioinformatic tool, which leverages on expanding public datasets of SARS-CoV-2 genomes and allows detection of drug resistance mutations in consensus genomes as well as in viral subpopulations. We have applied SABRes to detect resistance-conferring mutations in over 25,000 genomes generated over the course of the SARS-CoV-2 pandemic in Australia.

SummaryMachine-learning techniques are used in this BJPsych special issue on precision medicine in attempts to create statistical models that make clinically relevant predictions for individual patients. In this primer, we outline five key points that are helpful for a new reader to consider in order to engage with the field and evaluate the literature. These points include the consideration of why we are interested in new statistical approaches, how they may produce individualised predictions, what caveats need to be kept in-mind and why the interest and engagment of clinicians and clinical researchers is critical to successful model development and implementation. We hope that the following primer will provide shared understanding to encourage dialogue between clinical and methodological fields.

BackgroundClinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.AimsWe aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.MethodBaseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).ResultsModels combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.ConclusionsFindings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.

BACKGROUND Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. METHODS HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). RESULTS The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. CONCLUSIONS We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Phagocytic responses by effector cells to antibody or complement-opsonised viruses have been recognized to play a key role in anti-viral immunity. These include antibody dependent cellular phagocytosis mediated via Fc-receptors, phagocytosis mediated by classically activated complement-fixing IgM or IgG1 antibodies and antibody independent phagocytosis mediated via direct opsonisation of viruses by complement products activated via the mannose-binding lectin pathway. Limited data suggest these phagocytic responses by effector cells may contribute to the immunological and inflammatory responses in SARS-CoV-2 infection, however, their development and clinical significance remain to be fully elucidated. In this cohort of 62 patients, acutely ill individuals were shown to mount phagocytic responses to autologous plasma-opsonised SARS-CoV-2 Spike protein-coated microbeads as early as 10 days post symptom onset. Heat inactivation of the plasma prior to use as an opsonin caused 77-95% abrogation of the phagocytic response, and pre-blocking of Fc-receptors on the effector cells showed only 18-60% inhibition. These results suggest that SARS-CoV-2 can provoke early phagocytosis, which is primarily driven by heat labile components, likely activated complements, with variable contribution from anti-Spike antibodies. During convalescence, phagocytic responses correlated significantly with anti-Spike IgG titers. Older patients and patients with severe disease had significantly higher phagocytosis and neutralisation functions when compared to younger patients or patients with asymptomatic, mild, or moderate disease. A longitudinal study of a subset of these patients over 12 months showed preservation of phagocytic and neutralisation functions in all patients, despite a drop in the endpoint antibody titers by more than 90%. Interestingly, surface plasmon resonance showed a significant increase in the affinity of the anti-Spike antibodies over time correlating with the maintenance of both the phagocytic and neutralisation functions suggesting that improvement in the antibody quality over the 12 months contributed to the retention of effector functions.Author SummaryLimited data suggest antibody dependent effector functions including phagocytosis may contribute to the immunological and inflammatory responses in SARS CoV-2 infection, however, their development, maintenance, and clinical significance remain unknown. In this study we show: Patients with acute SARS CoV-2 infection can mount phagocytic responses as early as 10 days post symptom onset and these responses were primarily driven by heat labile components of the autologous plasma. These results indicate that the current approach of studying phagocytosis using purified or monoclonal antibodies does not recapitulate contribution by all components in the plasma.In convalescent patients, high phagocytic responses significantly correlated with increasing age, increasing disease severity, high neutralisation functions and high anti-Spike antibody titers, particularly IgG1.Longitudinal study of convalescent patients over a 12-month period showed maintenance of phagocytic and neutralisation functions, despite a drop in the anti-Spike endpoint antibody titers by more than 90%. However, we found significant increase in the affinity of the anti-Spike antibodies over the 12-month period and these correlated with the maintenance of functions suggesting that improvement in the antibody quality over time contributed to the retention of effector functions. Clinically, measuring antibody titers in sera but not the quality of antibodies is considered a gold standard indicator of immune protection following SARS-CoV 2 infection or vaccination. Our results challenge this notion and recommends change in the current clinical practice.

Background Precise prognosis of clinical outcomes in individuals at clinical high-risk (CHR) of developing psychosis is imperative to guide treatment selection. While much effort has been put into the prediction of transition to psychosis in CHR individuals, prognostic models focusing on negative symptom progression in this population are widely missing. This is a major oversight bearing in mind that 82% of CHR individuals exhibit at least one negative symptom in the moderate to severe range at first clinical presentation, whereas 54% still meet this criteria after 12 months. Negative symptoms are strong predictors of poor functional outcome irrespective of other symptoms such as depression or anxiety. Prognostic tools are therefore urgently required to track negative symptom progression in CHR individuals in order to guide early personalized interventions. Here, we applied machine-learning to multi-site data from five European countries with the aim of predicting negative symptoms of at least moderate severity 9-month after study inclusion. Methods We analyzed data from the ‘Personalized Prognostic Tools for Early Psychosis Management’ (PRONIA; www.pronia.eu) study, which consisted of 94 individuals at clinical high-risk of developing psychosis (CHR). Predictive models either included baseline level of negative symptoms, measured with the Structured Interview for Prodromal Syndromes, whole-brain gyrification pattern, or both to forecast negative symptoms of moderate severity or above in CHR individuals. Using data from the clinical and gyrification model, further sequential testing simulations were conducted to stratify CHR individuals into different risk groups. Lastly, we assessed the models’ ability to predict functional outcomes in CHR individuals. Results Baseline negative symptom severity alone predicted moderate to severe negative symptoms with a balanced accuracy (BAC) of 68%, whereas predictive models trained on gyrification measures achieved a BAC of 64%. Stacking the two modalities allowed for an increased BAC of 72%. Additional sequential testing simulations suggested, that CHR patients could be stratified into a high risk group with 83% probability of experiencing at least moderate negative symptoms at follow-up and a medium/low risk group with a risk ranging from 25 to 38%, when using the two models sequentially. Furthermore, the models trained to predict negative symptom severity from baseline symptoms were less predictive of role (60% BAC) and social (62% BAC) functioning at follow-up. However, the model trained on gyrification data also predicted role (74% BAC) and social (73% BAC) functioning later on. The stacking model predicted role, and social functioning with 64% BAC and 66% BAC respectively. Discussion To the best of our knowledge this is the first study using state-of-the-art predictive modelling to prospectively identify CHR subjects with negative symptoms in the moderate to above moderate severity range who potentially require further therapeutic consideration. While the predictive performance will need to be validated in other samples and may be improved by expanding the models with additional predictors, we believe that this pragmatic approach will help to stratify individual risk profiles and optimize personal interventions in the future.

Summary Background Influenza continues to have a substantial socioeconomic and health impact despite a long established vaccination programme and approved antivirals. Preclinical data suggest that combining antivirals might be more effective than administering oseltamivir alone in the treatment of influenza. Methods We did a randomised, double-blind, multicentre phase 2 trial of a combination of oseltamivir, amantadine, and ribavirin versus oseltamivir monotherapy with matching placebo for the treatment of influenza in 50 sites, consisting of academic medical centre clinics, emergency rooms, and private physician offices in the USA, Thailand, Mexico, Argentina, and Australia. Participants who were aged at least 18 years with influenza and were at increased risk of complications were randomly assigned (1:1) by an online computer-generated randomisation system to receive either oseltamivir (75 mg), amantadine (100 mg), and ribavirin (600 mg) combination therapy or oseltamivir monotherapy twice daily for 5 days, given orally, and participants were followed up for 28 days. Blinded treatment kits were used to achieve masking of patients and staff. The primary endpoint was the percentage of participants with virus detectable by PCR in nasopharyngeal swab at day 3, and was assessed in participants who were randomised, had influenza infection confirmed by the central laboratory on a baseline nasopharyngeal sample, and had received at least one dose of study drug. Safety assessment was done in all patients in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, number NCT01227967. Findings Between March 1, 2011, and April 29, 2016, 633 participants were randomly assigned to receive combination antiviral therapy (n=316) or monotherapy (n=317). Seven participants were excluded from analysis: three were not properly randomised, three withdrew from the study, and one was lost to follow-up. The primary analysis included 394 participants, excluding 47 in the pilot phase, 172 without confirmed influenza, and 13 without an endpoint sample. 80 (40·0%) of 200 participants in the combination group had detectable virus at day 3 compared with 97 (50·0%) of 194 (mean difference 10·0, 95% CI 0·2–19·8, p=0·046) in the monotherapy group. The most common adverse events were gastrointestinal-related disorders, primarily nausea (65 [12%] of 556 reported adverse events in the combination group vs 63 [11%] of 585 reported adverse events in the monotherapy group), diarrhoea (56 [10%] of 556 vs 64 [11%] of 585), and vomiting (39 [7%] of 556 vs 23 [4%] of 585). There was no benefit in multiple clinical secondary endpoints, such as median duration of symptoms (4·5 days in the combination group vs 4·0 days in the monotherapy group; p=0·21). One death occurred in the study in an elderly participant in the monotherapy group who died of cardiovascular failure 13 days after randomisation, judged by the site investigator as not related to study intervention. Interpretation Although combination treatment showed a significant decrease in viral shedding at day 3 relative to monotherapy, this difference was not associated with improved clinical benefit. More work is needed to understand why there was no clinical benefit when a difference in virological outcome was identified. Funding National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA.

It is becoming accepted that the associative strength of a cue can change in its absence. However, the rules that govern the association of the representations of absent cues are in dispute. There is evidence to support both the idea that the representation of a cue is associated in the same fashion as the cue itself (e.g. Holland, 1990) and the contrary idea that the representation will be associated in the opposite manner to the cue itself (e.g. DickinsonBurke, 1996). In three experiments an aversion to almond was formed by the pairing of the induction of nausea by lithium chloride injection with a context previously paired with almond. In addition in the third experiment an aversion was formed to almond when a flavoured solution previously paired with quinine was drunk in a context previously paired with almond. These results suggest that the rules governing the association of the representations of absent cues depend on the type of reinforcer.

Detection of flavivirus seroconversions in sentinel chicken flocks located throughout Australia is used to provide an early warning of increased levels of Murray Valley encephalitis (MVE) and Kunjin virus activity in the region. During the 2001/2002 season low levels of flavivirus activity were detected in northern Australia compared to previous years. MVE and Kunjin virus activity was detected in the Kimberley and Pilbara regions of Western Australia and the Northern Territory but not in north Queensland, New South Wales or Victoria. This is in contrast to the previous season when MVE activity was detected both in northern Australia and, for the first time in over 20 years, in New South Wales. Two cases of Murray Valley encephalitis were reported from the north of Western Australia during the 2001/2002 wet season.

A system is described involving an audio-video tape recorder, a control device, and a delay timer that dubs speech sounds onto prerecorded lip movements on videotape. Accuracy of synchrony or measured asynchrony between the sound and lip movements is a few milliseconds.

BACKGROUND: Global genetic diversity of HIV-1 is a major challenge to the development of HIV vaccines. We aimed to estimate the regional and global distribution of HIV-1 subtypes and recombinants during 1990-2015. METHODS: We searched PubMed, EMBASE (Ovid), CINAHL (Ebscohost), and Global Health (Ovid) for HIV-1 subtyping studies published between Jan 1, 1990, and Dec 31, 2015. We collected additional unpublished HIV-1 subtyping data through a global survey. We included prevalence studies with HIV-1 subtyping data collected during 1990-2015. We grouped countries into 14 regions and analysed data for four time periods (1990-99, 2000-04, 2005-09, and 2010-15). The distribution of HIV-1 subtypes, circulating recombinant forms (CRFs), and unique recombinant forms (URFs) in individual countries was weighted according to the UNAIDS estimates of the number of people living with HIV (PLHIV) in each country to generate regional and global estimates of HIV-1 diversity in each time period. The primary outcome was the number of samples designated as HIV-1 subtypes A, B, C, D, F, G, H, J, K, CRFs, and URFs. The systematic review is registered with PROSPERO, number CRD42017067164. FINDINGS: This systematic review and global survey yielded 2203 datasets with 383 519 samples from 116 countries in 1990-2015. Globally, subtype C accounted for 46·6% (16 280 897/34 921 639 of PLHIV) of all HIV-1 infections in 2010-15. Subtype B was responsible for 12·1% (4 235 299/34 921 639) of infections, followed by subtype A (10·3%; 3 587 003/34 921 639), CRF02_AG (7·7%; 2 705 110/34 921 639), CRF01_AE (5·3%; 1 840 982/34 921 639), subtype G (4·6%; 1 591 276/34 921 639), and subtype D (2·7%; 926 255/34 921 639). Subtypes F, H, J, and K combined accounted for 0·9% (311 332/34 921 639) of infections. Other CRFs accounted for 3·7% (1 309 082/34 921 639), bringing the proportion of all CRFs to 16·7% (5 844 113/34 921 639). URFs constituted 6·1% (2 134 405/34 921 639), resulting in recombinants accounting for 22·8% (7 978 517/34 921 639) of all global HIV-1 infections. The distribution of HIV-1 subtypes and recombinants changed over time in countries, regions, and globally. At a global level during 2005-15, subtype B increased, subtypes A and D were stable, and subtypes C and G and CRF02_AG decreased. CRF01_AE, other CRFs, and URFs increased, leading to a consistent increase in the global proportion of recombinants over time. INTERPRETATION: Global and regional HIV diversity is complex and evolving, and is a major challenge to HIV vaccine development. Surveillance of the global molecular epidemiology of HIV-1 remains crucial for the design, testing, and implementation of HIV vaccines. FUNDING: None. ispartof: LANCET INFECTIOUS DISEASES vol:19 issue:2 pages:143-155 ispartof: location:United States status: published