Publications

Replication studies that contradict prior findings may facilitate scientific self-correction by triggering a reappraisal of the original studies; however, the research community's response to replication results has not been studied systematically. One approach for gauging responses to replication results is to examine how they impact citations to original studies. In this study, we explored post-replication citation patterns in the context of four prominent multi-laboratory replication attempts published in the field of psychology that strongly contradicted and outweighed prior findings. Generally, we observed a small post-replication decline in the number of favourable citations and a small increase in unfavourable citations. This indicates only modest corrective effects and implies considerable perpetuation of belief in the original findings. Replication results that strongly contradict an original finding do not necessarily nullify its credibility; however, one might at least expect the replication results to be acknowledged and explicitly debated in subsequent literature. By contrast, we found substantial citation bias: the majority of articles citing the original studies neglected to cite relevant replication results. Of those articles that did cite the replication, but continued to cite the original study favourably, approximately half offered an explicit defence of the original study. Our findings suggest that even replication results that strongly contradict original findings do not necessarily prompt a corrective response from the research community.

Replication and the reported crises impacting many fields of research have become a focal point for the sciences. This has led to reforms in publishing, methodological design and reporting, and increased numbers of experimental replications coordinated across many laboratories. While replication is rightly considered an indispensable tool of science, financial resources and researchers’ time are quite limited. In this perspective, we examine different values and attitudes that scientists can consider when deciding whether to replicate a finding and how. We offer a conceptual framework for assessing the usefulness of various replication tools, such as preregistration.

Patients with isolated NNRTI TDR experienced low VF rates with INSTIs and bPIs. INSTIs were noninferior to bPIs in an analysis of VF but superior to bPIs when frequency of switching and loss to follow-up were also considered.

The objectives of the study were to calculate the standardised mortality rates (SMRs) for COVID-19 in European Union/European Economic Area countries plus the UK and Switzerland and to evaluate the correlation between SMRs and selected indicators in the first versus the subsequent waves until 23 June 2021. We used indirect standardisation (using Italy as the reference) to compute SMRs and considered 16 indicators of health and social well-being, health system capacity and COVID-19 response. The highest SMRs were in Belgium, the UK and Spain in the first wave (1.20–1.84) and in Hungary, Czechia and Slovakia in the subsequent waves (2.50–2.69). Human Development Index (HDI), life expectancy, urbanisation and healthcare expenditure had positive correlations with SMR in the first wave (rho=0.30–0.46), but negative correlations (rho=−0.67 to −0.47) in the subsequent waves. Retail/recreation mobility and transit mobility were negatively correlated with SMR in the first wave, while transit mobility was inversely correlated with SMR in the subsequent waves. The first wave hit most hard countries with high HDI, high life expectancy, high urbanisation, high health expenditures and high tourism. This pattern may reflect higher early community seeding and circulation of the virus. Conversely, in the subsequent waves, this pattern was completely inversed: countries with more resources and better health status did better than eastern European countries. While major SMR differences existed across countries in the first wave, these differences largely dissipated by 23 June 2021, with few exceptions.

In this paper, we examine the area-performance design space of a processing core for a chip multiprocessor (CMP), considering both the architectural design space and the tradeoffs of the physical design on which the architecture relies. We first propose a methodology for performing an integrated optimization of both the micro-architecture and the physical circuit design of a microprocessor. In our approach, we use statistical and convex fitting methods to capture a large micro-architectural design space. We then characterize the area-delay tradeoffs of the underlying circuits through RTL synthesis. Finally, we establish the relationship between the architecture and the circuits in an integrative model, which we use to optimize the processor. As a case study, we apply this methodology to explore the performance-area tradeoffs in a highly parallel accelerator architecture for visual computing applications. Based on some early circuit tradeoff data, our results indicate that two separate designs are performance/area optimal for our set of benchmarks: a simpler single-issue, 2-way multithreaded core running at high-frequency, and a more aggressively tuned dual-issue 4-way multithreaded design running at a lower frequency.

Markov Random Field Based Automatic Image Alignment for Electron Tomography Farshid Moussavi ∗ Department of Electrical Engineering Stanford University Stanford, CA 94305 farshid1@stanford.edu Luis R. Comolli Life Sciences Division Lawrence Berkeley National Laboratory Berkeley, CA 94704 lrcomolli@lbl.gov Kenneth H. Downing Life Sciences Division Lawrence Berkeley National Laboratory Berkeley, CA 94704 khdowning@lbl.gov Fernando Amat ∗ Department of Electrical Engineering Stanford University Stanford, CA 94305 famat@stanford.edu Gal Elidan Department of Computer Science Stanford University Stanford, CA 94305 galel@cs.stanford.edu Mark Horowitz Department of Electrical Engineering Stanford University Stanford, CA 94305 horowitz@stanford.edu Introduction Cryo electron tomography (cryo-ET) is the primary method for obtaining 3D reconstructions of intact bacteria, viruses, and complex molecular machines ([7],[2]). It first flash freezes a specimen in a thin layer of ice, and then rotates the ice sheet in a transmission electron microscope (TEM) recording images of different projections through the sample. The resulting images are aligned and then back projected to form the desired 3-D model. The typical resolution of biological electron microscope is on the order of 1 nm per pixel which means that small imprecision in the microscope’s stage or lenses can cause large alignment errors. To enable a high precision alignment, biologists add a small number of spherical gold beads to the sample before it is frozen. These beads generate high contrast dots in the image that can be tracked across projections. Each gold bead can be seen as a marker with a fixed location in 3D, which provides the reference points to bring all the images to a common frame as in the classical structure from motion problem. A high accuracy alignment is critical to obtain a high resolution tomogram (usually on the order of 5-15nm resolution). While some methods try to automate the task of tracking markers and aligning the images ([8],[4]), they require user intervention if the SNR of the image becomes too low. Unfortunately, cryogenic electron tomography (or cryo-ET) often has poor SNR, since the samples are relatively thick (for TEM) and the restricted electron dose usually results in projections with SNR under 0 dB. This paper shows that formulating this problem as a most-likely estimation task yields an approach that is able to automatically align with high precision cryo-ET datasets using inference in graphical models. This approach has been packaged into a publicly available software called RAPTOR-Robust Alignment and Projection estimation for Tomographic Reconstruction. 1 These authors contributed equally to this work. [1] presents an extended version of the results reported in this abstract.