Publications

Large trials are uncommon in mental health. Their results are usually comparable with the results of smaller studies, but major disagreements do occur. Both large and smaller trials should be scrutinized as they offer a continuum of randomized evidence.

Importance: Mega-trials can provide large-scale evidence on important questions. Objective: To explore how the results of mega-trails compare to the meta-analysis results of trials with smaller sample sizes. Data Sources: Clinicaltrials.gov was searched for mega-trials until 10.01.2023. PubMed was searched until June 2023 for meta-analyses incorporating the results of the eligible mega-trials. Study Selection: Mega-trials were eligible if they were non-cluster non-vaccine randomized control trials (RCTs); had a sample size over 10,000; and had a peer-reviewed meta-analysis publication presenting results for the primary outcome of the mega-trials and/or all-cause mortality. Data Extraction and Synthesis: For each selected meta-analysis, we extracted results of smaller trials and mega-trials included in the summary effect estimate, and combined them separately using random effects. These estimates were used to calculate the ratio of odds ratios (ROR) between mega-trials and smaller trials in each meta-analysis. Next, the ROR were combined using random-effects. Risk of bias was extracted for each trial included in our analyses (or when not available, assessed only for mega-trials). Main Outcomes and Measures: The main outcomes were the summary ROR for the primary outcome and all-cause mortality between mega-trials and smaller trials. Sensitivity analyses were performed with respect to the time of publishing, masking, weight, type of intervention, and specialty. Results Of 120 mega-trials identified, 39 (33%) had significant benefits for the primary outcome and 18 (15%) had significant benefits for all-cause mortality for the intervention. In 35 comparisons of primary outcomes (including 85 point estimates from 69 unique mega-trials and 272 point estimatesfrom smaller trials) and 26 comparisons of all-cause mortality (including 70 point estimates from 65 unique mega-trials and 267 point estimates from smaller trials), ROR was 1.00 (95% CI, 0.97-1.04) and 1.00 (95% CI, 0.97-1.04), respectively. For the primary outcomes, smaller trials published before the mega-trials had more favorable results than the mega-trials (ROR 1.05, 95% CI, 1.01- 1.10), and than the subsequent smaller trials (ROR 0.91, 95% CI, 0.85-0.96). Conclusions and Relevance: Meta-analyses of smaller studies show in general comparable results with mega-trials, but smaller trials published before the mega-trials give more favorable results than the mega-trials.

Background. Although preregistration can reduce researcher bias and increase transparency in primary research settings, it is less applicable to secondary data analysis. An alternative method that affords additional protection from researcher bias, which cannot be gained from conventional forms of preregistration alone, is an Explore and Confirm Analysis Workflow (ECAW). In this workflow, a data management organization initially provides access to only a subset of their dataset to researchers who request it. The researchers then prepare an analysis script based on the subset of data, upload the analysis script to a registry, and then receive access to the full dataset. ECAWs aim to achieve similar goals as preregistration, but make access to the full dataset contingent on compliance. The present survey aimed to garner information from the research community where ECAWs could be applied—employing the Avon Longitudinal Study of Parents and Children (ALSPAC) as a case example. Methods. We emailed a web-based survey to researchers who had previously applied for access to ALSPAC’s transgenerational observational dataset.Results. We received 103 responses, for a 9% response rate. The results suggest that—at least among our sample of respondents—ECAWs hold the potential to serve their intended purpose and appear relatively acceptable. For example, only 10% of respondents disagreed that ALSPAC should run a study on ECAWs (versus 55% who agreed). However, as many as 26% of respondents agreed that they would be less willing to use ALSPAC data if they were required to use an ECAW (versus 45% who disagreed). Conclusion. Our data and findings provide information for organizations and individuals interested in implementing ECAWs and related interventions.

Axonal microtubule (MT) arrays are the major cytoskeleton substrate for cargo transport. How MT organization, i.e., polymer length, number, and minus-end spacing, is regulated and how it impinges on axonal transport are unclear. We describe a method for analyzing neuronal MT organization using light microscopy. This method circumvents the need for electron microscopy reconstructions and is compatible with live imaging of cargo transport and MT dynamics. Examination of a C. elegans motor neuron revealed how age, MT-associated proteins, and signaling pathways control MT length, minus-end spacing, and coverage. In turn, MT organization determines axonal transport progression: cargoes pause at polymer termini, suggesting that switching MT tracks is rate limiting for efficient transport. Cargo run length is set by MT length, and higher MT coverage correlates with shorter pauses. These results uncover the principles and mechanisms of neuronal MT organization and its regulation of axonal cargo transport.

The best validated susceptibility variants for Parkinson's disease are located in the α-synuclein (SNCA) and microtubule-associated protein tau (MAPT) genes. Recently, a protective p.N551K-R1398H-K1423K haplotype in the leucine-rich repeat kinase 2 (LRRK2) gene was identified, with p.R1398H appearing to be the most likely functional variant. To date, the consistency of the protective effect of LRRK2 p.R1398H across MAPT and SNCA variant genotypes has not been assessed. To address this, we examined 4 SNCA variants (rs181489, rs356219, rs11931074, and rs2583988), the MAPT H1-haplotype-defining variant rs1052553, and LRRK2 p.R1398H (rs7133914) in Caucasian (n = 10,322) and Asian (n = 2289) series. There was no evidence of an interaction of LRRK2 p.R1398H with MAPT or SNCA variants (all p ≥ 0.10); the protective effect of p.R1398H was observed at similar magnitude across MAPT and SNCA genotypes, and the risk effects of MAPT and SNCA variants were observed consistently for LRRK2 p.R1398H genotypes. Our results indicate that the association of LRRK2 p.R1398H with Parkinson's disease is independent of SNCA and MAPT variants, and vice versa, in Caucasian and Asian populations.

Abstract Background Postpandemic surveillance data on coronavirus disease 2019 (COVID-19) infections may help inform future public health policies regarding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing, vaccinations, or other COVID-19 measures. We estimate the total SARS-CoV-2 infections in Austria after the end of the pandemic from wastewater data and utilize these estimates to calculate the average national levels of SARS-CoV-2 infection protection and COVID-19 death protection. Methods We estimated the total SARS-CoV-2 infections in Austria after the end of the pandemic (5 May 2023, per World Health Organization) up to May 2024 from wastewater data using a previously published model. These estimates were used in an agent-based model (ABM) to estimate average national levels of SARS-CoV-2 infection protection and COVID-19 death protection, based on waning immunity estimates of infections and vaccination in previous literature. Results We estimate approximately 3.2 million infections between 6 May 2023 and 23 May 2024, with a total of 17.8 million infections following 12 May 2020. The ABM estimates that the national average death protection was approximately 82% higher in May 2024 than before the pandemic. This represents a relative decrease of 8% since May 2023. It also shows that 95% of people in Austria were infected with SARS-CoV-2 at least once by May 2024. National infection protection remained relatively low after the onset of Omicron. Conclusions These findings should be considered for public health decisions on SARS-CoV-2 testing practices and vaccine booster administrations.

Access to scientific data can enable independent reuse and verification; however, most data are not available and become increasingly irrecoverable over time. This study aimed to retrieve and preserve important datasets from 160 of the most highly-cited social science articles published between 2008–2013 and 2015–2018. We asked authors if they would share data in a public repository—the Data Ark—or provide reasons if data could not be shared. Of the 160 articles, data for 117 (73%, 95% CI [67%–80%]) were not available and data for 7 (4%, 95% CI [0%–12%]) were available with restrictions. Data for 36 (22%, 95% CI [16%–30%]) articles were available in unrestricted form: 29 of these datasets were already available and 7 datasets were made available in the Data Ark. Most authors did not respond to our data requests and a minority shared reasons for not sharing, such as legal or ethical constraints. These findings highlight an unresolved need to preserve important scientific datasets and increase their accessibility to the scientific community.

Algorithms for computational imaging and computer vision are rapidly evolving, and hardware must follow suit: the next generation of image signal processors (ISPs) must be “programmable” to support new algorithms created with high-level frameworks. In this work, we compare flexible ISP architectures, using applications written in the Darkroom image processing language. We target two fundamental architecture classes: programmable in time, as represented by SIMD, and programmable in space, as typified by coarse grain reconfigurable array architectures (CGRA). We consider several optimizations on these two base architectures, such as register file partitioning for SIMD, bus based routing and pipelined wires for CGRA, and line buffer variations. After these optimizations on average, CGRA provides 1.6x better energy efficiency and 1.4x better compute density versus a SIMD solution, and 1.4x the energy efficiency and 3.1x the compute density of an FPGA. However the cost of providing general programmability is still high: compared to an ASIC, CGRA has 6x worse energy and area efficiency, and this ratio would be roughly 10x if memory dominated applications were excluded.