Refugees and asylum seekers living in the Australian Community: the importance of work rights and employment support

While Australian legislation allows for the mandatory detention of asylum seekers arriving without a valid visa, in recent years the Australian Government has released thousands from immigration detention prior to their protection claims being finalised. This article outlines the results of interviews with eleven men who had been released into such community-based arrangements after long periods of immigration detention. The major challenge for most of the men who had been granted the right to work upon their release was securing employment, while being denied the right to work was the major challenge for those released without this right. This article explores the social and personal benefits that employment can offer asylum seekers and refugees and the implications it has for integration into their host country

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potential of multiple data types to create a shared feature space with aligned semantic meaning across inputs of drastically varying sizes (i.e. images, text, sound). Most current MM architectures fuse these representations in parallel, which not only limits their interpretability but also creates a dependency on modality availability. We present MultiModN, a multimodal, modular network that fuses latent representations in a sequence of any number, combination, or type of modality while providing granular real-time predictive feedback on any number or combination of predictive tasks. MultiModN's composable pipeline is interpretable-by-design, as well as innately multi-task and robust to the fundamental issue of biased missingness. We perform four experiments on several benchmark MM datasets across 10 real-world tasks (predicting medical diagnoses, academic performance, and weather), and show that MultiModN's sequential MM fusion does not compromise performance compared with a baseline of parallel fusion. By simulating the challenging bias of missing not-at-random (MNAR), this work shows that, contrary to MultiModN, parallel fusion baselines erroneously learn MNAR and suffer catastrophic failure when faced with different patterns of MNAR at inference. To the best of our knowledge, this is the first inherently MNAR-resistant approach to MM modeling. In conclusion, MultiModN provides granular insights, robustness, and flexibility without compromising performance.Comment: Accepted as a full paper at NeurIPS 2023 in New Orleans, US

Modular Clinical Decision Support Networks (MoDN)-Updatable, interpretable, and portable predictions for evolving clinical environments.

Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data

Leishmaniavirus-dependent metastatic leishmaniasis is prevented by blocking IL-17A

Cutaneous leishmaniasis has various outcomes, ranging from self-healing reddened papules to extensive open ulcerations that metastasise to secondary sites and are often resistant to standard therapies. In the case of L. guyanensis (L.g), about 5-10% of all infections result in metastatic complications. We recently showed that a cytoplasmic virus within L.g parasites (LRV1) is able to act as a potent innate immunogen, worsening disease outcome in a murine model. In this study, we investigated the immunophenotype of human patients infected by L.g and found a significant association between the inflammatory cytokine IL-17A, the presence of LRV1 and disease chronicity. Further, IL-17A was inversely correlated to the protective cytokine IFN-γ. These findings were experimentally corroborated in our murine model, where IL-17A produced in LRV1+ L.g infection contributed to parasite virulence and dissemination in the absence of IFN-γ. Additionally, IL-17A inhibition in mice using digoxin or SR1001, showed therapeutic promise in limiting parasite virulence. Thus, this murine model of LRV1-dependent infectious metastasis validated markers of disease chronicity in humans and elucidated the immunologic mechanism for the dissemination of Leishmania parasites to secondary sites. Moreover, it confirms the prognostic value of LRV1 and IL-17A detection to prevent metastatic leishmaniasis in human patients

Severe Cutaneous Leishmaniasis in a Human Immunodeficiency Virus Patient Coinfected with Leishmania braziliensis and Its Endosymbiotic Virus.

Leishmania parasites cause a broad range of disease, with cutaneous afflictions being, by far, the most prevalent. Variations in disease severity and symptomatic spectrum are mostly associated to parasite species. One risk factor for the severity and emergence of leishmaniasis is immunosuppression, usually arising by coinfection of the patient with human immunodeficiency virus (HIV). Interestingly, several species of Leishmania have been shown to bear an endogenous cytoplasmic dsRNA virus (LRV) of the Totiviridae family, and recently we correlated the presence of LRV1 within Leishmania parasites to an exacerbation murine leishmaniasis and with an elevated frequency of drug treatment failures in humans. This raises the possibility of further exacerbation of leishmaniasis in the presence of both viruses, and here we report a case of cutaneous leishmaniasis caused by Leishmania braziliensis bearing LRV1 with aggressive pathogenesis in an HIV patient. LRV1 was isolated and partially sequenced from skin and nasal lesions. Genetic identity of both sequences reinforced the assumption that nasal parasites originate from primary skin lesions. Surprisingly, combined antiretroviral therapy did not impact the devolution of Leishmania infection. The Leishmania infection was successfully treated through administration of liposomal amphotericin B

Erratum for Williams et al., "Investigation of the Plasma Virome from Cases of Unexplained Febrile Illness in Tanzania from 2013 to 2014: a Comparative Analysis between Unbiased and VirCapSeq-VERT High-Throughput Sequencing Approaches"

High-throughput sequencing can provide insights into epidemiology and medicine through comprehensive surveys of viral genetic sequences in environmental and clinical samples. Here, we characterize the plasma virome of Tanzanian patients with unexplained febrile illness by using two high-throughput sequencing methods: unbiased sequencing and VirCapSeq-VERT (a positive selection system). Sequences from dengue virus 2, West Nile virus, human immunodeficiency virus type 1, human pegivirus, and Epstein-Barr virus were identified in plasma. Both sequencing strategies recovered nearly complete genomes in samples containing multiple viruses. Whereas VirCapSeq-VERT had better sensitivity, unbiased sequencing provided better coverage of genome termini. Together, these data demonstrate the utility of high-throughput sequencing strategies in outbreak investigations. &lt;b&gt;IMPORTANCE&lt;/b&gt; Characterization of the viruses found in the blood of febrile patients provides information pertinent to public health and diagnostic medicine. PCR and culture have historically played an important role in clinical microbiology; however, these methods require a targeted approach and may lack the capacity to identify novel or mixed viral infections. High-throughput sequencing can overcome these constraints. As the cost of running multiple samples continues to decrease, the implementation of high-throughput sequencing for diagnostic purposes is becoming more feasible. Here we present a comparative analysis of findings from an investigation of unexplained febrile illness using two strategies: unbiased high-throughput sequencing and VirCapSeq-VERT, a positive selection high-throughput sequencing system

Exacerbated leishmaniasis caused by a viral endosymbiont can be prevented by immunization with Its viral capsid

Recent studies have shown that a cytoplasmic virus called Leishmaniavirus (LRV) is present in some Leishmania species and acts as a potent innate immunogen, aggravating lesional inflammation and development in mice. In humans, the presence of LRV in Leishmania guyanensis and in L. braziliensis was significantly correlated with poor treatment response and symptomatic relapse. So far, no clinical effort has used LRV for prophylactic purposes. In this context, we designed an original vaccine strategy that targeted LRV nested in Leishmania parasites to prevent virus-related complications. To this end, C57BL/6 mice were immunized with a recombinant LRV1 Leishmania guyanensis viral capsid polypeptide formulated with a T helper 1-polarizing adjuvant. LRV1-vaccinated mice had significant reduction in lesion size and parasite load when subsequently challenged with LRV1+ Leishmania guyanensis parasites. The protection conferred by this immunization could be reproduced in naïve mice via T-cell transfer from vaccinated mice but not by serum transfer. The induction of LRV1 specific T cells secreting IFN-γ was confirmed in vaccinated mice and provided strong evidence that LRV1-specific protection arose via a cell mediated immune response against the LRV1 capsid. Our studies suggest that immunization with LRV1 capsid could be of a preventive benefit in mitigating the elevated pathology associated with LRV1 bearing Leishmania infections and possibly avoiding symptomatic relapses after an initial treatment. This novel anti-endosymbiotic vaccine strategy could be exploited to control other infectious diseases, as similar viral infections are largely prevalent across pathogenic pathogens and could consequently open new vaccine opportunities