351 research outputs found
A web of harms: serious and organised crime and its impact on Australian interests
No full textOverview
This report analyses serious, transnational and organised crime and the harms it causes to Australia’s interest, with the aim of reinvigorating a discussion of this critical matter amongst Australians.
This web impacts on our national interests to the sum of an estimated $15 billion per year. That very conservative estimate includes costs to government through denied revenue and increased law enforcement costs. But there are also social, health and economic harms to individuals, community and business.
The report poses a series of questions to be considered by the community, business and government
If Supply-Oriented Drug Policy is Broken, Can Harm Reduction Help Fix It?—Melding Disciplines and Methods to Advance International Drug Control Policy
No full textCritics of the international drug control regime contend that supply-oriented policy interventions are not just ineffective, but they also produce unintended adverse consequences. Research suggests their claims have merit. Lasting local reductions in opium production are possible, albeit rare; but, unless global demand shrinks, production will shift elsewhere, with little or no effect on the aggregate supply of heroin and, potentially, at some expense to exiting and newly emerging suppliers. The net consequences of the international drug control regime and related national policies are as yet unknown. In this paper, we consider whether “harm reduction,” a subject of intense debate in the demand-oriented drug policy community, can provide a unifying foundation for supply-oriented drug policy, one capable of speaking more directly to policy goals. Despite substantial conceptual and technical challenges, we find that harm reduction can provide a basis for assessing the net consequences of supply-oriented drug policy, choosing more rigorously among policy options, and identifying new policy options. In addition, we outline a practical path forward for assessing harms and policy options.
Digital biomarkers and sex impacts in Alzheimer’s disease management — potential utility for innovative 3P medicine approach
No full textDigital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer’s, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida’s digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida’s application, achieved a 75% ROC-AUC (receiver operating characteristic — area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline.RLH and IT were supported by Altoida Inc. JM was supported in this work by the Charles University Grant Agency (GA UK) project no. 436119 at Charles University, Second Faculty of Medicine, Prague, Czech Republic.Peer Reviewed"Article signat per 16 autors/es: Robbert L. Harms, Alberto Ferrari, Irene B. Meier, Julie Martinkova, Enrico Santus, Nicola Marino, Davide Cirillo, Simona Mellino, Silvina Catuara Solarz, Ioannis Tarnanas, Cassandra Szoeke, Jakub Hort, Alfonso Valencia, Maria Teresa Ferretti, Azizi Seixas & Antonella Santuccione Chadha "Postprint (published version
Robust and Fast Markov Chain Monte Carlo Sampling of Diffusion MRI Microstructure Models
No full textIn diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200
Correction to: Seismic glitchness at Sos Enattos site: impact on intermediate black hole binaries detection efficiency (The European Physical Journal Plus, (2021), 136, 5, (511), 10.1140/epjp/s13360-021-01450-8)
No full textThe original version of this paper was inadvertently published with an incorrect author name for the author L. Naticchioni. The correct name is: L. Naticchioni. The original article has been corrected
The New Conflicts Law
No full textThe deterrent and remedial power of civil litigation in U.S. courts is justifiably famous. But as Kiobel and other cases underscore, such litigation is only one of many possible ways to regulate harms that affect multiple sovereigns. Globalization, increased cross-border activity, and the lightweight limits on extraterritorial jurisdiction imposed by international law combine to create an environment in which it is common for multiple legal systems to regulate a single course of conduct. When sovereigns disagree over how to regulate harm, the ensuing conflicts expose U.S. legal systems to a new and unfamiliar form of political backlash.
This Article identifies, explains, and critically analyzes a new body of law that responds to these conflicts in a novel and problematic way. Beginning in the 1980s and accelerating in recent terms, the Supreme Court has interpreted indeterminate legal materials that are not obviously about regulatory conflict to create a set of clear, ex ante rules restricting private regulatory enforcement in U.S. courts. This set of rules--"the new conflicts law"-- prevents conflicts between domestic litigation and other nations' approaches to regulating harm and transfers authority for regulatory conflict from frontline decisionmakers to the U.S. Supreme Court. But in seeking to limit interference with foreign regulation, the new law undermines U.S. regulatory systems with no clear welfare payoff. And it often precludes democratically accountable policymakers from revisiting the Supreme Court's conclusions about the appropriate relationship between U.S. litigation and foreign regulation.
To address these concerns, the Article proposes incremental changes to four doctrines within the new conflicts law. The more basic and urgent task, however, is to recognize the new conflicts law for the significant development it is. With little fanfare, the Supreme Court has dramatically changed the way in which the U.S. legal system manages regulatory conflict
Data_Sheet_2_Robust and Fast Markov Chain Monte Carlo Sampling of Diffusion MRI Microstructure Models.pdf
No full textIn diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200.</p
Data_Sheet_1_Robust and Fast Markov Chain Monte Carlo Sampling of Diffusion MRI Microstructure Models.PDF
No full textIn diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200.</p
Combating escalating harms associated with pharmaceutical opioid use in Australia: The POPPY II study protocol
No full text© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. Introduction Opioid prescribing has increased 15-fold in Australia in the past two decades, alongside increases in a range of opioid-related harms such as opioid dependence and overdose. However, despite concerns about increasing opioid use, extramedical use and harms, there is a lack of population-level evidence about the drivers of long-term prescribed opioid use, dependence, overdose and other harms. Methods and analysis We will form a cohort of all adult residents in New South Wales (NSW), Australia, who initiated prescribed opioids from 2002 using Pharmaceutical Benefits Scheme dispensing records. This cohort will be linked to a wide range of other datasets containing information on sociodemographic and clinical characteristics, health service use and adverse outcomes (eg, opioid dependence and non-fatal and fatal overdose). Analyses will initially examine patterns and predictors of prescribed opioid use and then apply regression and survival analysis to quantify the risks and risk factors of adverse outcomes associated with prescribed opioid use. Ethics and dissemination This study has received full ethical approval from the Australian Institute of Health and Welfare Ethics Committee, the NSW Population and Health Services Research Committee and the ACT Health Human Research Ethics Committee. This will be the largest postmarketing surveillance study of prescribed opioids undertaken in Australia, linking exposure and outcomes and examining risk factors for adverse outcomes of prescribed opioids. As such, this work has important translational promise, with direct relevance to regulatory authorities and agencies worldwide. Project findings will be disseminated at scientific conferences and in peer-reviewed journals. We will also conduct targeted dissemination with policy makers, professional bodies and peak bodies in the pain, medicine and addiction fields through stakeholder workshops and advisory groups. Results will be reported in accordance with the REporting of studies Conducted using Observational Routinely collected Data (RECORD) Statement
Studies in the Bromeliaceae, XII
No full textIn the preliminary part the author publishes the news: Hohenbergia guatenialensis L. B. Smith, Piteairnia heterophylla (Lindl.) Beer forma albiflora Standley et L. B. Smith, P. nutckheimii Donn. Smith var. macrolepis L. B. Smith, Tillandsia ionantha Planch. var. scaposa L. B. Smith, T. penlandii L. B. Smith and their var. pedunculata L. B. Smith, T. polita L. B. Smith, Vriesia lancifolia (Baker) L. B. Smith, V. pectinata L. B. Smith and V. racinae L. B. Smith.
In the second part the author continues his synopses of the Tribe Tillandsieae (now subfamily Tillandsioideae according to Harms). In this part he studies the species with simple inflorescences and flowers that all go to one side, with a total of 26 of the genus Vriesia. The first part of the synopses has been published in numbers LXXXVI and CVI of ?Contributions from the Gray Herbariam of Harvard University?
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