VLSI top-down design based on the separation of hierarchies

Despite the presence of structure, interactions between the three views on VLSI design still lead to lengthy iterations. By separating the hierarchies for the respective views, the interactions are reduced. This separated hierarchy allows top-down design with functional abstractions as exemplified by an experimental self-timed CMOS RISC computer design

No substantial change in the balance between model-free and model-based control via training on the two-step task

Human decisions can be habitual or goal-directed, also known as model-free (MF) or model-based (MB) control. Previous work suggests that the balance between the two decision systems is impaired in psychiatric disorders such as compulsion and addiction, via overreliance on MF control. However, little is known whether the balance can be altered through task training. Here, 20 healthy participants performed a well-established two-step task that differentiates MB from MF control, across five training sessions. We used computational modelling and functional near-infrared spectroscopy to assess changes in decision-making and brain hemodynamic over time. Mixed-effects modelling revealed overall no substantial changes in MF and MB behavior across training. Although our behavioral and brain findings show task-induced changes in learning rates, these parameters have no direct relation to either MF or MB control or the balance between the two systems, and thus do not support the assumption of training effects on MF or MB strategies. Our findings indicate that training on the two-step paradigm in its current form does not support a shift in the balance between MF and MB control. We discuss these results with respect to implications for restoring the balance between MF and MB control in psychiatric conditions

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal diferences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This fnding requires replication in a larger dataset

European Stakeholder Learnings Regarding Biosimilars: Part I—Improving Biosimilar Understanding and Adoption

Background Despite the benefts ofered by biosimilars in terms of cost savings and improved patient access to biological therapies, and an established regulatory pathway in Europe, biosimilar adoption is challenged by a lack of knowledge and understanding among stakeholders such as healthcare professionals and patients about biosimilars, impacting their trust and willingness to use them. In addition, stakeholders are faced with questions about clinical implementation aspects such as switching. Objective This study aims to provide recommendations on how to improve biosimilar understanding and adoption among stakeholders based on insights of healthcare professionals (physicians, hospital pharmacists, nurses), patient(s) (representatives) and regulators across Europe. Method The study consists of a structured literature review gathering original research data on stakeholder knowledge about biosimilars, followed by semi-structured interviews across fve stakeholder groups including physicians, hospital pharmacists, nurses, patient(s) (representatives) and regulators across Europe. Results Although improvement in knowledge was observed over time, generally low to moderate levels of awareness, knowledge and trust towards biosimilars among healthcare professionals and patients are identifed in literature (N studies = 106). Based on the provided insights from interviews with European experts (N = 44), a number of challenges regarding biosimilar stakeholder understanding are identifed, including a lack of practical information about biosimilars and their use, a lack of understanding about biosimilar concepts and a lack of knowledge about biologicals in general. Misinformation by originator industry is also believed to have impacted stakeholder trust. In terms

European Stakeholder Learnings Regarding Biosimilars: Part II—Improving Biosimilar Use in Clinical Practice

Background Despite the benefts biosimilars ofer in terms of cost savings and patient access, healthcare professionals and patients have been reluctant to use them. Next to insufcient understanding of and trust in biosimilars, healthcare professionals and patients have questions about switching and the nocebo efect when using biosimilars in clinical practice. In addition, clear motivation to use biosimilars may be lacking among these stakeholders. Objective This study aims to provide recommendations on how to improve biosimilar use on both a clinical and a practical level based on insights from healthcare professionals (physicians, hospital pharmacists, nurses), patients (or their representatives), and regulators across Europe. Methods We conducted 44 semi-structured interviews with experts from fve stakeholder groups across Europe: physicians, hospital pharmacists, nurses, regulators, and patients/representatives. Interviews were transcribed ad verbatim and transcripts analysed according to the thematic framework method. Results Based on the insights and considerations of the experts interviewed, we identifed a number of recommendations to improve the use of biosimilars in clinical practice. Regarding switch implementation, the experts voiced support for the following actions: (1) disseminate evidence from and experience with (multiple) switching; (2) provide clear, one-voice regulatory guidance about the interchangeability of biosimilars and their reference product; (3) apply a multi-stakeholder implementation and communication protocol to guide switching in clinical practice; (4) apply a pragmatic approach when taking switch decisions; and (5) avoid mandated switching, allowing stakeholder communication and alignment. When discussing approaches to increas

Computational neuroimaging strategies for single patient predictions

AbstractNeuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches – Bayesian model selection and generative embedding – which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning

Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons

Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance:A Case Study in Lung Cancer Patient Preferences

Introduction: Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR. Methods: A total of 307 patients with non–small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen’s weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected. Results: Most respondents (&gt;65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC (P &lt; 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR (P &lt; 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53–0.55). Conclusion: Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions. Both DCEs and SW tasks can be used to determine attribute relative importance rankings and weights; however, little evidence exists empirically comparing these methods in terms of outcomes or respondent usability. Most respondents found the DCE and SW tasks very easy or easy to understand and answer. A direct comparison of DCE and SW found significant differences in attribute importance rankings and weights as well as a greater spread in the DCE-derived attribute relative importance weights.</p