Optical Drug Monitoring: Photoacoustic Imaging of Nanosensors to Monitor Therapeutic Lithium in Vivo

Personalized medicine could revolutionize how primary care physicians treat chronic disease and how researchers study fundamental biological questions. To realize this goal, we need to develop more robust, modular tools and imaging approaches for in vivo monitoring of analytes. In this report, we demonstrate that synthetic nanosensors can measure physiologic parameters with photoacoustic contrast, and we apply that platform to continuously track lithium levels in vivo. Photoacoustic imaging achieves imaging depths that are unattainable with fluorescence or multiphoton microscopy. We validated the photoacoustic results that illustrate the superior imaging depth and quality of photoacoustic imaging with optical measurements. This powerful combination of techniques will unlock the ability to measure analyte changes in deep tissue and will open up photoacoustic imaging as a diagnostic tool for continuous physiological tracking of a wide range of analytes

Optical Drug Monitoring: Photoacoustic Imaging of Nanosensors to Monitor Therapeutic Lithium in Vivo

Personalized medicine could revolutionize how primary care physicians treat chronic disease and how researchers study fundamental biological questions. To realize this goal, we need to develop more robust, modular tools and imaging approaches for in vivo monitoring of analytes. In this report, we demonstrate that synthetic nanosensors can measure physiologic parameters with photoacoustic contrast, and we apply that platform to continuously track lithium levels in vivo. Photoacoustic imaging achieves imaging depths that are unattainable with fluorescence or multiphoton microscopy. We validated the photoacoustic results that illustrate the superior imaging depth and quality of photoacoustic imaging with optical measurements. This powerful combination of techniques will unlock the ability to measure analyte changes in deep tissue and will open up photoacoustic imaging as a diagnostic tool for continuous physiological tracking of a wide range of analytes

Modelling to bridge many boundaries: the Colorado and Murray-Darling River basins

Increasing pressure on shared water resources has often been a driver for the development and utilisation of water resource models (WRMs) to inform planning and management decisions. With an increasing emphasis on regional decision-making among competing actors as opposed to top-down and authoritative directives, the need for integrated knowledge and water diplomacy efforts across federal and international rivers provides a test bed for the ability of WRMs to operate within complex historical, social, environmental, institutional and political contexts. This paper draws on theories of sustainability science to examine the role of WRMs to inform transboundary water resource governance in large river basins. We survey designers and users of WRMs in the Colorado River Basin in North America and the Murray-Darling Basin in southeastern Australia. Water governance in such federal rivers challenges inter-governmental and multi-level coordination and we explore these dynamics through the application of WRMs. The development pathways of WRMs are found to influence their uptake and acceptance as decision support tools. Furthermore, we find evidence that WRMs are used as boundary objects and perform the functions of ‘boundary work’ between scientists, decision-makers and stakeholders in the midst of regional environmental changes

Global distribution of Leptospira serovar isolations and detections from animal host species: A systematic review and online database

Objectives Leptospira, the spirochaete causing leptospirosis, can be classified into >250 antigenically distinct serovars. Although knowledge of the animal host species and geographic distribution of Leptospira serovars is critical to understand the human and animal epidemiology of leptospirosis, current data are fragmented. We aimed to systematically review, the literature on animal host species and geographic distribution of Leptospira serovars to examine associations between serovars with animal host species and regions and to identify geographic regions in need of study. Methods Nine library databases were searched from inception through 9 March 2023 using keywords including Leptospira, animal, and a list of serovars. We sought reports of detection of Leptospira, from any animal, characterised by cross agglutinin absorption test, monoclonal antibody typing, serum factor analysis, or pulsed-field gel electrophoresis to identify the serovar. Results We included 409 reports, published from 1927 through 2022, yielding data on 154 Leptospira serovars. The reports included data from 66 (26.5%) of 249 countries. Detections were from 144 animal host species including 135 (93.8%) from the class Mammalia, 5 (3.5%) from Amphibia, 3 (2.1%) from Reptilia, and 1 (0.7%) from Arachnida. Across the animal host species, Leptospira serovars that were detected in the largest number of animal species included Grippotyphosa (n = 39), Icterohaemorrhagiae (n = 29), Pomona (n = 28), Australis (n = 25), and Ballum (n = 25). Of serovars, 76 were detected in a single animal host species. We created an online database to identify animal host species for each serovar by country. Conclusions We found that many countries have few or no Leptospira serovars detected from animal host species and that many serovars were detected from a single animal species. Our study highlights the importance of efforts to identify animal host species of leptospirosis, especially in places with a high incidence of human leptospirosis. We provide an updated resource for leptospirosis researchers

Seasonal forecasts of North Atlantic tropical cyclone activity in the North American Multi-Model Ensemble

The North American Multi-Model Ensemble (NMME)-Phase II models are evaluated in terms of their retrospective seasonal forecast skill of the North Atlantic (NA) tropical cyclone (TC) activity, with a focus on TC frequency. The TC identification and tracking algorithm is modified to accommodate model data at daily resolution. It is also applied to three reanalysis products at the spatial and temporal resolution of the NMME-Phase II ensemble to allow for a more objective estimation of forecast skill. When used with the reanalysis data, the TC tracking generates realistic climatological distributions of the NA TC formation and tracks, and represents the interannual variability of the NA TC frequency quite well. Forecasts with the multi-model ensemble (MME) when initialized in April and later tend to have skill in predicting the NA seasonal TC counts (and TC days). At longer leads, the skill is low or marginal, although one of the models produces skillful forecasts when initialized as early as January and February. At short lead times, while demonstrating the highest skill levels the MME also tends to significantly outperform the individual models and attain skill comparable to the reanalysis. In addition, the short-lead MME forecasts are quite reliable. At regional scales, the skill is rather limited and mostly present in the western tropical NA and the Caribbean Sea. It is found that the overall MME forecast skill is limited by poor representation of the low-frequency variability in the predicted TC frequency, and large fluctuations in skill on decadal time scales. Addressing these deficiencies is thought to increase the value of the NMME ensemble in providing operational guidance

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer\u27s disease

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer\u27s disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score\u27s predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials

Automatically Harnessing Sparse Acceleration

Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it well. High-performance libraries are available, but adoption costs are significant. Moreover, libraries tie programs into vendor-specific software and hardware ecosystems, creating non-portable code. In this paper, we develop a new approach based on our specification Language for implementers of Linear Algebra Computations (LiLAC). Rather than requiring the application developer to (re)write every program for a given library, the burden is shifted to a one-off description by the library implementer. The LiLAC-enabled compiler uses this to insert appropriate library routines without source code changes. LiLAC provides automatic data marshaling, maintaining state between calls and minimizing data transfers. Appropriate places for library insertion are detected in compiler intermediate representation, independent of source languages. We evaluated on large-scale scientific applications written in FORTRAN; standard C/C++ and FORTRAN benchmarks; and C++ graph analytics kernels. Across heterogeneous platforms, applications and data sets we show speedups of 1.1$\times$ to over 10$\times$ without user intervention.Comment: Accepted to CC 202

Intellectual enrichment and genetic modifiers of cognition and brain volume in Huntington's disease

An important step towards the development of treatments for cognitive impairment in ageing and neurodegenerative diseases is to identify genetic and environmental modifiers of cognitive function and understand the mechanism by which they exert an effect. In Huntington’s disease, the most common autosomal dominant dementia, a small number of studies have identified intellectual enrichment, i.e. a cognitively stimulating lifestyle and genetic polymorphisms as potential modifiers of cognitive function. The aim of our study was to further investigate the relationship and interaction between genetic factors and intellectual enrichment on cognitive function and brain atrophy in Huntington’s disease. For this purpose, we analysed data from Track-HD, a multi-centre longitudinal study in Huntington’s disease gene carriers and focused on the role of intellectual enrichment (estimated at baseline) and the genes FAN1, MSH3, BDNF, COMT and MAPT in predicting cognitive decline and brain atrophy. We found that carrying the 3a allele in the MSH3 gene had a positive effect on global cognitive function and brain atrophy in multiple cortical regions, such that 3a allele carriers had a slower rate of cognitive decline and atrophy compared with non-carriers, in agreement with its role in somatic instability. No other genetic predictor had a significant effect on cognitive function and the effect of MSH3 was independent of intellectual enrichment. Intellectual enrichment also had a positive effect on cognitive function; participants with higher intellectual enrichment, i.e. those who were better educated, had higher verbal intelligence and performed an occupation that was intellectually engaging, had better cognitive function overall, in agreement with previous studies in Huntington’s disease and other dementias. We also found that intellectual enrichment interacted with the BDNF gene, such that the positive effect of intellectual enrichment was greater in Met66 allele carriers than non-carriers. A similar relationship was also identified for changes in whole brain and caudate volume; the positive effect of intellectual enrichment was greater for Met66 allele carriers, rather than for non-carriers. In summary, our study provides additional evidence for the beneficial role of intellectual enrichment and carrying the 3a allele in MSH3 in cognitive function in Huntington’s disease and their effect on brain structure