Combinations of the azaquinazoline anti-Wolbachia agent, AWZ1066S, with benzimidazole anthelmintics synergise to mediate sub-seven-day sterilising and curative efficacies in experimental models of filariasis.

Lymphatic filariasis and onchocerciasis are two major neglected tropical diseases that are responsible for causing severe disability in 50 million people worldwide, whilst veterinary filariasis (heartworm) is a potentially lethal parasitic infection of companion animals. There is an urgent need for safe, short-course curative (macrofilaricidal) drugs to eliminate these debilitating parasite infections. We investigated combination treatments of the novel anti-Wolbachia azaquinazoline small molecule, AWZ1066S, with benzimidazole drugs (albendazole or oxfendazole) in up to four different rodent filariasis infection models: Brugia malayi-CB.17 SCID mice, B. malayi-Mongolian gerbils, B. pahangi-Mongolian gerbils, and Litomosoides sigmodontis-Mongolian gerbils. Combination treatments synergised to elicit threshold (>90%) Wolbachia depletion from female worms in 5 days of treatment, using 2-fold lower dose-exposures of AWZ1066S than monotherapy. Short-course lowered dose AWZ1066S-albendazole combination treatments also delivered partial adulticidal activities and/or long-lasting inhibition of embryogenesis, resulting in complete transmission blockade in B. pahangi and L. sigmodontis gerbil models. We determined that short-course AWZ1066S-albendazole co-treatment significantly augmented the depletion of Wolbachia populations within both germline and hypodermal tissues of B. malayi female worms and in hypodermal tissues in male worms, indicating that anti-Wolbachia synergy is not limited to targeting female embryonic tissues. Our data provides pre-clinical proof-of-concept that sub-seven-day combinations of rapid-acting novel anti-Wolbachia agents with benzimidazole anthelmintics are a promising curative and transmission-blocking drug treatment strategy for filarial diseases of medical and veterinary importance.

Symptomatic carotid webs require aggressive intervention.

OBJECTIVE: Carotid web (CaWeb) is a rare form of fibromuscular dysplasia that can produce embolic stroke. Misdiagnosis of symptomatic CaWeb as "cryptogenic stroke" or "embolic stroke of unknown source" is common and can lead to recurrent, catastrophic neurologic events. Reports of CaWeb in the literature are scarce, and their natural history is poorly understood. Appropriate management remains controversial. METHODS: CaWeb was defined as a single, shelf-like, linear projection in the posterolateral carotid bulb causing a filling defect on computed tomography angiography (CTA) or cerebral angiography. Cases of symptomatic CaWeb at a single institution with a high-volume stroke center were identified through collaborative evaluation by vascular neurologists and vascular surgeons. RESULTS: Fifty-two patients with symptomatic CaWeb were identified during a 6-year period (2016-2022). Average age was 49 years (range, 29-73 years), 35 of 52 (67%) were African American, and 18 of 52 (35%) were African American women under age 50. Patients initially presented with stroke (47/52; 90%) or transient ischemic attack (5/52; 10%). Stenosis was <50% in 49 of 52 patients (94%) based on NASCET criteria, and 0 of 52 (0%) CaWebs were identified with carotid duplex. Definitive diagnosis was made by CTA examined in multiple planes or cerebral angiography examined in a lateral projection to adequately assess the posterolateral carotid bulb, where 52 of 52 (100%) of CaWebs were seen. Early in our institutional experience, 10 of 52 patients (19%) with symptomatic CaWeb were managed initially with dual antiplatelet and statin therapy or systemic anticoagulation; all suffered ipsilateral recurrent stroke at an average interval of 43 months (range, 1-89 months), and five were left with permanent deficits. Definitive treatment included carotid endarterectomy in 27 of 50 (56%) or carotid stenting in 23 of 50 (46%). Two strokes were irrecoverable, and intervention was deferred. Web-associated thrombus was observed in 20 of 50 (40%) on angiography or grossly upon carotid exploration. Average interval from initial stroke to intervention was 39 days. After an average follow-up of 38 months, there was no reported postintervention stroke or mortality. CONCLUSIONS: To our knowledge, this is the largest single-institution analysis of symptomatic CaWeb yet reported. Our series demonstrates that carotid duplex is inadequate for diagnosis, and that medical management is unacceptable for symptomatic CaWeb. Recurrent stroke occurred in all patients managed early in our experience with medical therapy alone. We have since adopted an aggressive interventional approach in cases of symptomatic CaWeb, with no postoperative stroke reported over an average follow-up of 38 months. In younger patients presenting with cryptogenic stroke, especially African American women, detailed review of lateral cerebral angiography or multi-planar, fine-cut CTA images is required to accurately rule out or diagnose CaWeb and avoid recurrent neurologic events.

Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation.

Restricted latent class models (RLCMs) provide an important framework for diagnosing and classifying respondents on a collection of multivariate binary responses. Recent research made significant advances in theory for establishing identifiability conditions for RLCMs with binary and polytomous response data. Multiclass data, which are unordered nominal response data, are also widely collected in the social sciences and psychometrics via forced-choice inventories and multiple choice tests. We establish new identifiability conditions for parameters of RLCMs for multiclass data and discuss the implications for substantive applications. The new identifiability conditions are applicable to a wealth of RLCMs for polytomous and nominal response data. We propose a Bayesian framework for inferring model parameters, assess parameter recovery in a Monte Carlo simulation study, and present an application of the model to a real dataset.

A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy.

Cognitive diagnostic models provide a framework for classifying individuals into latent proficiency classes, also known as attribute profiles. Recent research has examined the implementation of a Pólya-gamma data augmentation strategy binary response model using logistic item response functions within a Bayesian Gibbs sampling procedure. In this paper, we propose a sequential exploratory diagnostic model for ordinal response data using a logit-link parameterization at the category level and extend the Pólya-gamma data augmentation strategy to ordinal response processes. A Gibbs sampling procedure is presented for efficient Markov chain Monte Carlo (MCMC) estimation methods. We provide results from a Monte Carlo study for model performance and present an application of the model.

Multiple-biomarkers show the importance of blue carbon to commercially important fishery species.

Coastal blue carbon ecosystems (BCE) support nearshore food webs and provide habitat for many commercially important fish and crustacean species. However, the complex links between catchment vegetation and the carbon food-base of estuarine systems are difficult to disern. We employed a multi-biomarker approach (stable isotope ratios - δ13C and δ15N, fatty acid trophic markers - FATMs and metabolomics - central carbon metabolism metabolites) to test links between estuarine vegetation and the food sources available to commercially important crabs and fish occurring within the river systems of the near-pristine eastern coastline of the Gulf of Carpentaria, Australia. Stable isotope analysis confirmed the dietary importance of fringing macrophytes to consumer diet, but showed that this is modulated by their dominance along the riverbank. FATMs indicative of specific food sources further confirmed the differences among upper intertidal macrophytes (driven by concentrations of 16: 1ω7, 18:1ω9, 18:2ω6, 18:3ω3 & 22.0) and seagrass (driven by 18:2ω6, 18:3ω3). These dietary patterns were also reflected in the concentration of central carbon metabolism metabolites. Overall, our study demonstrates the congruence of different biomarker approaches to resolve biochemical links between blue carbon ecosystems and important nekton species, and provides fresh insights into the pristine tropical estuaries of northern Australia.

Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis.

Hidden Markov models (HMMs) have been applied in various domains, which makes the identifiability issue of HMMs popular among researchers. Classical identifiability conditions shown in previous studies are too strong for practical analysis. In this paper, we propose generic identifiability conditions for discrete time HMMs with finite state space. Also, recent studies about cognitive diagnosis models (CDMs) applied first-order HMMs to track changes in attributes related to learning. However, the application of CDMs requires a known [Formula: see text] matrix to infer the underlying structure between latent attributes and items, and the identifiability constraints of the model parameters should also be specified. We propose generic identifiability constraints for our restricted HMM and then estimate the model parameters, including the [Formula: see text] matrix, through a Bayesian framework. We present Monte Carlo simulation results to support our conclusion and apply the developed model to a real dataset.

Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park.

Continuous monitoring of oil discharges in coastal and open ocean waters using Earth Observation (EO) has undeniably contributed to diminishing their occurrence wherever a detection system was in place, such as in Europe (EMSA's CleanSeaNet) or in the United States (NOAA's OR&R). This study describes the development and testing of a semi-automated oil slick detection system tailored to the Great Barrier Reef (GBR) marine park solely based on EO data as no such service was routinely available in Australia until recently. In this study, a large, curated, historical global dataset of SAR imagery acquired by Sentinel-1 SAR, now publicly available, is used to assess classification techniques, namely an empirical approach and a deep learning model, to discriminate between oil-like features and look-alikes in the scenes acquired over the marine park. An evaluation of this detection system on 10 Sentinel-1 SAR images of the GBR using two performance metrics - the detection accuracy and the false-positive rate (FPR) - shows that the classifiers perform best when combined (accuracy >98 %; FPR 0.01) rather than when used separately. This study demonstrates the benefit of sequentially combining classifiers to improve the detection and monitoring of unreported oil discharge events in SAR imagery. The workflow has also been tested outside the GBR, demonstrating its robustness when applied to other regions such as Australia's Northwest Shelf, Southeast Asia and the Pacific.

Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models.

The specification of the [Formula: see text] matrix in cognitive diagnosis models is important for correct classification of attribute profiles. Researchers have proposed many methods for estimation and validation of the data-driven [Formula: see text] matrices. However, inference of the number of attributes in the general restricted latent class model remains an open question. We propose a Bayesian framework for general restricted latent class models and use the spike-and-slab prior to avoid the computation issues caused by the varying dimensions of model parameters associated with the number of attributes, K. We develop an efficient Metropolis-within-Gibbs algorithm to estimate K and the corresponding [Formula: see text] matrix simultaneously. The proposed algorithm uses the stick-breaking construction to mimic an Indian buffet process and employs a novel Metropolis-Hastings transition step to encourage exploring the sample space associated with different values of K. We evaluate the performance of the proposed method through a simulation study under different model specifications and apply the method to a real data set related to a fluid intelligence matrix reasoning test.

Metabolites as signalling molecules.

Traditional views of cellular metabolism imply that it is passively adapted to meet the demands of the cell. It is becoming increasingly clear, however, that metabolites do more than simply supply the substrates for biological processes; they also provide critical signals, either through effects on metabolic pathways or via modulation of other regulatory proteins. Recent investigation has also uncovered novel roles for several metabolites that expand their signalling influence to processes outside metabolism, including nutrient sensing and storage, embryonic development, cell survival and differentiation, and immune activation and cytokine secretion. Together, these studies suggest that, in contrast to the prevailing notion, the biochemistry of a cell is frequently governed by its underlying metabolism rather than vice versa. This important shift in perspective places common metabolites as key regulators of cell phenotype and behaviour. Yet the signalling metabolites, and the cognate targets and transducers through which they signal, are only beginning to be uncovered. In this Review, we discuss the emerging links between metabolism and cellular behaviour. We hope this will inspire further dissection of the mechanisms through which metabolic pathways and intermediates modulate cell function and will suggest possible drug targets for diseases linked to metabolic deregulation.

Extending exploratory diagnostic classification models: Inferring the effect of covariates.

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.

Inferring Latent Structure in Polytomous Data with a Higher-Order Diagnostic Model.

Researchers continue to develop and advance models for diagnostic research in the social and behavioral sciences. These diagnostic models (DMs) provide researchers with a framework for providing a fine-grained classification of respondents into substantively meaningful latent classes as defined by a multivariate collection of binary attributes. A central concern for DMs is advancing exploratory methods for uncovering the latent structure, which corresponds with the relationship between unobserved binary attributes and observed polytomous items with two or more response options. Multivariate behavioral polytomous data are often collected within a higher-order design where general factors underlying first-order latent variables. This study advances existing exploratory DMs for polytomous data by proposing a new method for inferring the latent structure underlying polytomous response data using a higher-order model to describe dependence among the discrete latent attributes. We report a novel Bayesian formulation that uses variable selection techniques for inferring the latent structure along with a higher-order factor model for attributes. We report evidence of accurate parameter recovery in a Monte Carlo simulation study and present results from an application to the 2012 Programme for International Student Assessment (PISA) problem-solving vignettes to demonstrate the method.

A Note on Weaker Conditions for Identifying Restricted Latent Class Models for Binary Responses.

Restricted latent class models (RLCMs) are an important class of methods that provide researchers and practitioners in the educational, psychological, and behavioral sciences with fine-grained diagnostic information to guide interventions. Recent research established sufficient conditions for identifying RLCM parameters. A current challenge that limits widespread application of RLCMs is that existing identifiability conditions may be too restrictive for some practical settings. In this paper we establish a weaker condition for identifying RLCM parameters for multivariate binary data. Although the new results weaken identifiability conditions for general RLCMs, the new results do not relax existing necessary and sufficient conditions for the simpler DINA/DINO models. Theoretically, we introduce a new form of latent structure completeness, referred to as dyad-completeness, and prove identification by applying Kruskal's Theorem for the uniqueness of three-way arrays. The new condition is more likely satisfied in applied research, and the results provide researchers and test-developers with guidance for designing diagnostic instruments.

NKp46+ natural killer cells develop an activated/memory-like phenotype and contribute to innate immunity against experimental filarial infection.

Lymphatic filariasis and onchocerciasis are major neglected tropical diseases affecting over 90 million people worldwide with painful and profoundly disfiguring pathologies (such as lymphoedema or blindness). Type 2 inflammation is a hallmark of filarial nematode tissue infection and is implicated both in eosinophil dependent immunity and lymphatic or ocular immunopathologies. Type-2 innate lymphoid cells (ILC2) are known to play an important role in the initiation of type 2 inflammation in helminth infection. We therefore tracked comparative IL-12Rß2+ ILC1, ST2+ ILC2 and NKp46+ natural killer (NK) innate lymphoid cell population expansions during Brugia malayi experimental peritoneal filarial infections using either immunocompetent or immunodeficient mice. In immunocompetent BALB/c animals, NKp46+ NK cells rapidly expanded representing over 90% of the ILC population in the first week of infection, whereas, surprisingly, ST2+ ILC2 failed to expand. NKp46+ NK cell expansions were confirmed in RAG2 deficient mice lacking adaptive immunity. Ablation of the NKp46+ NK cell compartment in RAG2 common gamma chain (gc) mice led to increased susceptibility to chronic adult B. malayi infection. This data was recapitulated using an Onchocerca ochengi male worm peritoneal implant model. When NKp46+ NK cells were depleted in RAG2 deficient mice using anti-NKp46 or asialo GM1 antibody injections over the first five weeks of B. malayi infection, susceptibility to adult B. malayi infection was significantly increased by 2-3 fold with concomitant impairment in eosinophil or neutrophil recruitments. Finally, we demonstrate that in RAG2 deficient mice, drug clearance of a primary adult B. malayi infection followed by challenge infection leads to resistance against early larval B. malayi establishment. This innate resistance is associated with bolstered NK and eosinophils whereby NKp46+ NK cells express markers of memory-like/enhanced activation (increased expression of interferon gamma and Ly6C). Our data promotes a novel functional role for NKp46+ NK cells in immunoprotection against experimental primary and secondary filarial infection which can proceed in the absence of adaptive immune regulation.

Validated transport conditions maintain the quality of washed red blood cells.

BACKGROUND: Washing red blood cell (RBC) units prior to transfusion is indicated for certain patients. In the United States, units stored at 1°C-6°C or transported at 1°C-10°C are available for issue up to 24 h, if not used immediately. The washing procedure commonly utilizes room temperature saline resulting in units starting out above the allowed temperature range. This leads to wastage if units are issued and returned too quickly before having a chance to equilibrate in a transport cooler. STUDY DESIGN AND METHODS: Here we performed an experimental study of washed RBC quality comparing "ideal" storage conditions in a blood bank refrigerator to a "real-world" simulation of unit transport, including holding in a transport cooler. Twelve RBC units were washed and allocated evenly into either condition. RESULTS: Measurements at 0, 1, 3, 6, 12, and 24 h post-washing revealed that placement in a transport cooler was associated with higher unit temperature prior to 12 h (p = .013) with a maximum difference of 9.3°C. Despite this difference, several measures of unit quality including extracellular potassium, pH, lactate, and free hemoglobin were indistinguishable between conditions (p = .382, .224, .286, .691, respectively). We selected half of the tested units from our irradiated inventory and confirmed increased potassium leak (p < .001) and accumulation of free hemoglobin (p = .012) in irradiated units. DISCUSSION: Washed units stored under approved transport conditions are acceptable to return to inventory up to 24 h after washing and we provide a prediction interval-based temperature threshold for rejecting these units, permitting reduced waste.

A mouse infection model and long-term lymphatic endothelium co-culture system to evaluate drugs against adult Brugia malayi.

The development of new drugs targeting adult-stage lymphatic filarial nematodes is hindered by the lack of a robust long-term in vitro culture model. Testing potential direct-acting and anti-Wolbachia therapeutic candidates against adult lymphatic filariae in vitro requires their propagation via chronic infection of gerbils. We evaluated Brugia malayi parasite burden data from male Mongolian gerbils compared with two immune-deficient mouse strains highly susceptible to B. malayi: CB.17 Severe-Combined Immmuno-Deficient (SCID) and interleukin-4 receptor alpha, interleukin-5 double knockout (IL-4Rα-/-IL-5-/-) mice. Adult worms generated in IL-4Rα-/-IL-5-/- mice were tested with different feeder cells (human embryonic kidney cells, human adult dermal lymphatic endothelial cells and human THP-1 monocyte differentiated macrophages) and comparative cell-free conditions to optimise and validate a long-term in vitro culture system. Cultured parasites were compared against those isolated from mice using motility scoring, metabolic viability assay (MTT), ex vivo microfilariae release assay and Wolbachia content by qPCR. A selected culture system was validated as a drug screen using reference anti-Wolbachia (doxycycline, ABBV-4083 / flubentylosin) or direct-acting compounds (flubendazole, suramin). BALB/c IL-4Rα-/-IL-5-/- or CB.17 SCID mice were superior to Mongolian gerbils in generating adult worms and supporting in vivo persistence for periods of up to 52 weeks. Adult females retrieved from BALB/c IL-4Rα-/-IL-5-/- mice could be cultured for up to 21 days in the presence of a lymphatic endothelial cell co-culture system with comparable motility, metabolic activity and Wolbachia titres to those maintained in vivo. Drug studies confirmed significant Wolbachia depletions or direct macrofilaricidal activities could be discerned when female B. malayi were cultured for 14 days. We therefore demonstrate a novel methodology to generate adult B. malayi in vivo and accurately evaluate drug efficacy ex vivo which may be adopted for drug screening with the dual benefit of reducing overall animal use and improving anti-filarial drug development.

Wolbachia depletion blocks transmission of lymphatic filariasis by preventing chitinase-dependent parasite exsheathment.

Lymphatic filariasis is a vector-borne neglected tropical disease prioritized for global elimination. The filarial nematodes that cause the disease host a symbiotic bacterium, Wolbachia, which has been targeted using antibiotics, leading to cessation of parasite embryogenesis, waning of circulating larvae (microfilariae [mf]), and gradual cure of adult infection. One of the benefits of the anti-Wolbachia mode of action is that it avoids the rapid killing of mf, which can drive inflammatory adverse events. However, mf depleted of Wolbachia persist for several months in circulation, and thus patients treated with antibiotics are assumed to remain at risk for transmitting infections. Here, we show that Wolbachia-depleted mf rapidly lose the capacity to develop in the mosquito vector through a defect in exsheathment and inability to migrate through the gut wall. Transcriptomic and Western blotting analyses demonstrate that chitinase, an enzyme essential for mf exsheathment, is down-regulated in Wolbachia-depleted mf and correlates with their inability to exsheath and escape the mosquito midgut. Supplementation of in vitro cultures of Wolbachia-depleted mf with chitinase enzymes restores their ability to exsheath to a similar level to that observed in untreated mf. Our findings elucidate a mechanism of rapid transmission-blocking activity of filariasis after depletion of Wolbachia and adds to the broad range of biological processes of filarial nematodes that are dependent on Wolbachia symbiosis.

Exploratory Restricted Latent Class Models with Monotonicity Requirements under PÒLYA-GAMMA Data Augmentation.

Restricted latent class models (RLCMs) provide an important framework for supporting diagnostic research in education and psychology. Recent research proposed fully exploratory methods for inferring the latent structure. However, prior research is limited by the use of restrictive monotonicity condition or prior formulations that are unable to incorporate prior information about the latent structure to validate expert knowledge. We develop new methods that relax existing monotonicity restrictions and provide greater insight about the latent structure. Furthermore, existing Bayesian methods only use a probit link function and we provide a new formulation for using the exploratory RLCM with a logit link function that has an additional advantage of being computationally more efficient for larger sample sizes. We present four new Bayesian formulations that employ different link functions (i.e., the logit using the Pòlya-gamma data augmentation versus the probit) and priors for inducing sparsity in the latent structure. We report Monte Carlo simulation studies to demonstrate accurate parameter recovery. Furthermore, we report results from an application to the Last Series of the Standard Progressive Matrices to illustrate our new methods.

Omics-based ecosurveillance uncovers the influence of estuarine macrophytes on sediment microbial function and metabolic redundancy in a tropical ecosystem.

Vertical zonation within estuarine ecosystems can strongly influence microbial diversity and function by regulating competition, predation, and environmental stability. The degree to which microbial communities exhibit horizontal patterns through an estuary has received comparatively less attention. Here, we take a multi-omics ecosurveillance approach to study environmental gradients created by the transition between dominant vegetation types along a near pristine tropical river system (Wenlock River, Far North Queensland, Australia). The study sites included intertidal mudflats fringed by saltmarsh, mangrove or mixed soft substrata habitats. Collected sediments were analyzed for eukaryotes and prokaryotes using small sub-unit (SSU) rRNA gene amplicons to profile the relative taxonomic composition. Central carbon metabolism metabolites and other associated organic polar metabolites were analyzed using established metabolomics-based approaches, coupled with total heavy metals analysis. Eukaryotic taxonomic information was found to be more informative of habitat type. Bacterial taxonomy and community composition also showed habitat-specificity, with phyla Proteobacteria and Cyanobacteria strongly linked to mangroves and saltmarshes, respectively. In contrast, metabolite profiling was critical for understanding the biochemical pathways and expressed functional outputs in these systems that were tied to predicted microbial gene function (16S rRNA). A high degree of metabolic redundancy was observed in the bacterial communities, with the metabolomics data suggesting varying degrees of metabolic criticality based on habitat type. The predicted functions of the bacterial taxa combined with annotated metabolites accounted for the conservative perspective of microbial community redundancy against the putative metabolic pathway impacts in the metabolomics data. Coupling these data demonstrates that habitat-mediated estuarine gradients drive patterns of community diversity and metabolic function and highlights the real redundancy potential of habitat microbiomes. This information is useful as a point of comparison for these sensitive ecosystems and provides a framework for identifying potentially vulnerable or at-risk systems before they are significantly degraded.