The Nonlinear Dynamic Conversion of Analog Signals into Excitation Patterns

Local periodic perturbations induce frequency-dependent propagation waves in an excitable spatio-temporally chaotic system. We show how segments of noise-contaminated and chaotic perturbations induce characteristic sequences of excitations in the model system. Using a set of tuned excitable systems, it is possible to characterize signals by their spectral composition of excitation pattern. As an example we analyze an epileptic spike-and-wave time series.Comment: 14 pages, 5 figure

Coupling Broadband Terahertz Dipoles to Microscale Resonators

Optically driven spintronic emitters are a unique class of terahertz (THz) sources due to their quasi-two-dimensional geometry and thereby their capability to effectively couple to resonator near fields. Global excitation of the emitters often obstructs the intricate details of the coupling mechanisms between local THz dipoles and the individual modes of resonator structures. Here, we demonstrate the spatial mapping of the coupling strength between a micrometer-scale terahertz source on a spintronic emitter and far-field light mediated by a structured metallic environment. For a bow-tie geometry, experimental results are reproduced by a numerical model, providing insights into the microscopic coupling mechanisms. The broad applicability of the technique is showcased by extracting the THz mode structure in split-ring resonator metasurfaces and linear arrays. With these developments, planar THz sources with tailored spectral and angular emission profiles become accessible

Quantum physics meets biology

Quantum physics and biology have long been regarded as unrelated disciplines, describing nature at the inanimate microlevel on the one hand and living species on the other hand. Over the last decades the life sciences have succeeded in providing ever more and refined explanations of macroscopic phenomena that were based on an improved understanding of molecular structures and mechanisms. Simultaneously, quantum physics, originally rooted in a world view of quantum coherences, entanglement and other non-classical effects, has been heading towards systems of increasing complexity. The present perspective article shall serve as a pedestrian guide to the growing interconnections between the two fields. We recapitulate the generic and sometimes unintuitive characteristics of quantum physics and point to a number of applications in the life sciences. We discuss our criteria for a future quantum biology, its current status, recent experimental progress and also the restrictions that nature imposes on bold extrapolations of quantum theory to macroscopic phenomena.Comment: 26 pages, 4 figures, Perspective article for the HFSP Journa

Cytochrome P450 2B6 (CYP2B6) and constitutive androstane receptor (CAR) polymorphisms are associated with early discontinuation of efavirenz-containing regimens

Objectives Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolic clearance of efavirenz and single nucleotide polymorphisms (SNPs) in the CYP2B6 gene are associated with efavirenz pharmacokinetics. Since the constitutive androstane receptor (CAR) and the pregnane X receptor (PXR) correlate with CYP2B6 in liver, and a CAR polymorphism (rs2307424) and smoking correlate with efavirenz plasma concentrations, we investigated their association with early (<3 months) discontinuation of efavirenz therapy. Methods Three hundred and seventy-three patients initiating therapy with an efavirenz-based regimen were included (278 white patients and 95 black patients; 293 male). DNA was extracted from whole blood and genotyping for CYP2B6 (516G → T, rs3745274), CAR (540C → T, rs2307424) and PXR (44477T → C, rs1523130; 63396C → T, rs2472677; and 69789A → G, rs763645) was conducted. Binary logistic regression using the backwards method was employed to assess the influence of SNPs and demographics on early discontinuation. Results Of the 373 patients, 131 withdrew from therapy within the first 3 months. Black ethnicity [odds ratio (OR) = 0.27; P = 0.0001], CYP2B6 516TT (OR = 2.81; P = 0.006), CAR rs2307424 CC (OR = 1.92; P = 0.007) and smoking status (OR = 0.45; P = 0.002) were associated with discontinuation within 3 months. Conclusions These data indicate that genetic variability in CYP2B6 and CAR contributes to early treatment discontinuation for efavirenz-based antiretroviral regimens. Further studies are now required to define the clinical utility of these association

Increased Number of Cerebellar Granule Cells and Astrocytes in the Internal Granule Layer in Sheep Following Prenatal Intra-amniotic Injection of Lipopolysaccharide

Chorioamnionitis is an important problem in perinatology today, leading to brain injury and neurological handicaps. However, there are almost no data available regarding chorioamnionitis and a specific damage of the cerebellum. Therefore, this study aimed at determining if chorioamnionitis causes cerebellar morphological alterations. Chorioamnionitis was induced in sheep by the intra-amniotic injection of lipopolysaccharide (LPS) at a gestational age (GA) of 110 days. At a GA of 140 days, we assessed the mean total and layer-specific volume and the mean total granule cell (GCs) and Purkinje cell (PC) number in the cerebelli of LPS-exposed and control animals using high-precision design-based stereology. Astrogliosis was assessed in the gray and white matter (WM) using a glial fibrillary acidic protein staining combined with gray value image analysis. The present study showed an unchanged volume of the total cerebellum as well as the molecular layer, outer and inner granular cell layers (OGL and IGL, respectively), and WM. Interestingly, compared with controls, the LPS-exposed brains showed a statistically significant increase (+20.4%) in the mean total number of GCs, whereas the number of PCs did not show any difference between the two groups. In addition, LPS-exposed animals showed signs of astrogliosis specifically affecting the IGL. Intra-amniotic injection of LPS causes morphological changes in the cerebellum of fetal sheep still detectable at full-term birth. In this study, changes were restricted to the inner granule layer. These cerebellar changes might correspond to some of the motor or non-motor deficits seen in neonates from compromised pregnancies

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI