Protocols for spatial allocation of farm types

Farm Management,

Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array

Interacting fermions on a lattice can develop strong quantum correlations, which lie at the heart of the classical intractability of many exotic phases of matter. Seminal efforts are underway in the control of artificial quantum systems, that can be made to emulate the underlying Fermi-Hubbard models. Electrostatically confined conduction band electrons define interacting quantum coherent spin and charge degrees of freedom that allow all-electrical pure-state initialisation and readily adhere to an engineerable Fermi-Hubbard Hamiltonian. Until now, however, the substantial electrostatic disorder inherent to solid state has made attempts at emulating Fermi-Hubbard physics on solid-state platforms few and far between. Here, we show that for gate-defined quantum dots, this disorder can be suppressed in a controlled manner. Novel insights and a newly developed semi-automated and scalable toolbox allow us to homogeneously and independently dial in the electron filling and nearest-neighbour tunnel coupling. Bringing these ideas and tools to fruition, we realize the first detailed characterization of the collective Coulomb blockade transition, which is the finite-size analogue of the interaction-driven Mott metal-to-insulator transition. As automation and device fabrication of semiconductor quantum dots continue to improve, the ideas presented here show how quantum dots can be used to investigate the physics of ever more complex many-body states

Qu’en penseraient mes pairs? Comparaison entre la méthode fondée sur l'opinion et celle fondée sur la prédiction dans l'évaluation de cours de formation médicale continue

Background: Although medical courses are frequently evaluated via surveys with Likert scales ranging from “strongly agree” to “strongly disagree,” low response rates limit their utility. In undergraduate medical education, a new method with students predicting what their peers would say, required fewer respondents to obtain similar results. However, this prediction-based method lacks validation for continuing medical education (CME), which typically targets a more heterogeneous group than medical students. Methods: In this study, 597 participants of a large CME course were randomly assigned to either express personal opinions on a five-point Likert scale (opinion-based method; n = 300) or to predict the percentage of their peers choosing each Likert scale option (prediction-based method; n = 297). For each question, we calculated the minimum numbers of respondents needed for stable average results using an iterative algorithm. We compared mean scores and the distribution of scores between both methods. Results: The overall response rate was 47%. The prediction-based method required fewer respondents than the opinion-based method for similar average responses. Mean response scores were similar in both groups for most questions, but prediction-based outcomes resulted in fewer extreme responses (strongly agree/disagree). Conclusions: We validated the prediction-based method in evaluating CME. We also provide practical considerations for applying this method.Contexte : Bien que les cours de médecine soient fréquemment évalués au moyen d'enquêtes avec des échelles de Likert allant de « totalement d'accord » à « totalement en désaccord », les faibles taux de réponse en limitent l'utilité. Dans l'enseignement médical prédoctoral, une nouvelle méthode dans laquelle les étudiants prédisent ce que leurs pairs diraient, nécessite moins de répondants pour obtenir des résultats similaires. Cependant, cette méthode fondée sur la prédiction n'est pas validée pour la formation médicale continue (FMC), qui cible généralement un groupe plus hétérogène que les étudiants en médecine. Méthodes : Dans cette étude, 597 participants à un grand cours de FMC ont été choisis au hasard pour exprimer leur opinion personnelle sur une échelle de Likert en cinq points (méthode fondée sur l'opinion; n = 300) ou à prédire le pourcentage de leurs pairs choisissant chaque option de l'échelle de Likert (méthode fondée sur la prédiction; n = 297). Pour chaque question, nous avons calculé le nombre minimum de répondants nécessaire pour obtenir des résultats moyens stables à l'aide d'un algorithme itératif. Nous avons comparé les scores moyens et la distribution des scores entre les deux méthodes. Résultats : Le taux de réponse global était de 47 %. La méthode fondée sur la prédiction a nécessité moins de répondants que celle fondée sur l'opinion pour des réponses moyennes similaires. Les scores moyens des réponses étaient similaires dans les deux groupes pour la plupart des questions, mais les résultats fondés sur la prédiction ont donné lieu à moins de réponses extrêmes (totalement d'accord/totalement en désaccord). Conclusions : Nous avons validé la méthode fondée sur la prédiction dans l'évaluation de la FMC. Nous présentons également des considérations pratiques pour la mise en œuvre de cette méthode

Novel multi-marker proteomics in phenotypically matched patients with ST-segment myocardial infarction:association with clinical outcomes

Early prediction of significant morbidity or mortality in patients with acute ST-segment elevation myocardial infarction (STEMI) represents an unmet clinical need. In phenotypically matched population of 139 STEMI patients (72 cases, 67 controls) treated with primary percutaneous coronary intervention, we explored associations between a 24-h relative change from baseline in the concentration of 91 novel biomarkers and the composite outcome of death, heart failure, or shock within 90 days. Additionally, we used random forest models to predict the 90-day outcomes. After adjustment for false discovery rate, the 90-day composite was significantly associated with concentration changes in 14 biomarkers involved in various pathophysiologic processes including: myocardial fibrosis/remodeling (collagen alpha-1, cathepsin Z, metalloproteinase inhibitor 4, protein tyrosine phosphatase subunits), inflammation, angiogenesis and signaling (interleukin 1 and 2 subunits, growth differentiation factor 15, galectin 4, trefoil factor 3), bone/mineral metabolism (osteoprotegerin, matrix extracellular phosphoglycoprotein and tartrate-resistant acid phosphatase), thrombosis (tissue factor pathway inhibitor) and cholesterol metabolism (LDL-receptor). Random forest models suggested an independent association when inflammatory markers are included in models predicting the outcomes within 90 days. Substantial heterogeneity is apparent in the early proteomic responses among patients with acutely reperfused STEMI patients who develop death, heart failure or shock within 90 days. These findings suggest the need to consider synergistic multi-biomarker strategies for risk stratification and to inform future development of novel post-myocardial infarction therapies

Distinct contribution of cone photoreceptor subtypes to the mammalian biological clock

Ambient light detection is important for the synchronization of the circadian clock to the external solar cycle. Light signals are sent to the suprachiasmatic nuclei (SCN), the site of the major circadian pacemaker. It has been assumed that cone photoreceptors con-tribute minimally to synchronization. Here, however, we find that cone photoreceptors are sufficient for mediating entrainment and transmitting photic information to the SCN, as evaluated in mice that have only cones as functional photoreceptors. Using in vivo electrophysiological recordings in the SCN of freely moving cone-only mice, we observed light responses in SCN neuronal activity in response to 60-s pulses of both ultraviolet (UV) (lambda(max) 365 nm) and green (lambda(max) 505 nm) light. Higher irradiances of UV light led to irradiance-dependent enhancements in SCN neuronal activity, whereas higher irradiances of green light led to a reduction in the sustained response with only the transient response remain-ing. Responses in SCN neuronal activity decayed with a half-max time of similar to 9 min for UV light and less than a minute for green light, indicating differential input between short-wavelength-sensitive and mid-wavelength-sensitive cones for the SCN responsiveness. Furthermore, we show that UV light is more effective for photo-entrainment than green light. Based on the lack of a full sustained response in cone-only mice, we confirmed that rapidly alternating light levels, rather than slowly alternating light, caused substantial phase shifts. Together, our data provide strong evidence that cone types contribute to photoentrainment and differentially affect the electrical activity levels of the SCN.Circadian clocks in health and diseas

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection

Background and objective: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data. Methods: In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy. Results: VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p &lt; 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p &lt; 0.01). Conclusions: Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.</p

Methodological concepts for integrated assessment of agricultural and environmental policies in SEAMLESS-IF

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,

Prediction meets causal inference: the role of treatment in clinical prediction models

In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a ‘predictimand’ framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference