Emergent patterns in global health diplomacy: a network analysis of the resolutions adopted by the World Health Assembly from 1948 to 2022.

From a complexity perspective on governance, multilateral diplomacy is based on interactions between people, ideas, norms, policies and institutions. This article uses a computer-assisted methodology to better understand governance systems as a network of norms. All World Health Assembly (WHA) resolutions that were available from 1948 to 2022 were collected from the WHO Institutional Repository for Information Sharing (IRIS) database. Regular expressions were used to identify how resolutions cite other resolutions and the resulting relationships were analysed as a normative network. The findings show that WHA resolutions constitute a complex network of interconnected global health issues. This network is characterised by several community patterns. While chain-like patterns are associated with specific diseases programmes, radial patterns are characteristic of highly important procedural decisions that member states reaffirm in similar situations. Finally, densely connected communities correspond to contested topics and emergencies. While these emergeng patterns suggest the relevance of using network analysis to understand global health norms in international organisations, we reflect on how this computational approach can be extended to provide new understandings of how multilateral governance systems work, and to address some important contemporary questions about the effects of regime complexity on global health diplomacy.

Choreography Controlled (ChoCo) brain MRI artifact generation for labeled motion-corrupted datasets.

MRI is a non-invasive medical imaging modality that is sensitive to patient motion, which constitutes a major limitation in most clinical applications. Solutions may arise from the reduction of acquisition times or from motion-correction techniques, either prospective or retrospective. Benchmarking the latter methods requires labeled motion-corrupted datasets, which are uncommon. Up to our best knowledge, no protocol for generating labeled datasets of MRI images corrupted by controlled motion has yet been proposed. Hence, we present a methodology allowing the acquisition of reproducible motion-corrupted MRI images as well as validation of the system's performance by motion estimation through rigid-body volume registration of fast 3D echo-planar imaging (EPI) time series. A proof-of-concept is presented, to show how the protocol can be implemented to provide qualitative and quantitative results. An MRI-compatible video system displays a moving target that volunteers equipped with customized plastic glasses must follow to perform predefined head choreographies. Motion estimation using rigid-body EPI time series registration demonstrated that head position can be accurately determined (with an average standard deviation of about 0.39 degrees). A spatio-temporal upsampling and interpolation method to cope with fast motion is also proposed in order to improve motion estimation. The proposed protocol is versatile and straightforward. It is compatible with all MRI systems and may provide insights on the origins of specific motion artifacts. The MRI and artificial intelligence research communities could benefit from this work to build in-vivo labeled datasets of motion-corrupted MRI images suitable for training/testing any retrospective motion correction or machine learning algorithm.

Cohort profile: Actionable Register of Geneva Outpatients and inpatients with SARS-CoV-2 (ARGOS).

PURPOSE: The Actionable Register of Geneva Outpatients and inpatients with SARS-CoV-2 (ARGOS) is an ongoing prospective cohort created by the Geneva Directorate of Health. It consists of an operational database compiling all SARS-CoV-2 test results recorded in the Geneva area since late February 2020. This article aims at presenting this comprehensive cohort, in light of some of the varying public health measures in Geneva, Switzerland, since March 2020. PARTICIPANTS: As of 1 June 2021, the database included 360 525 patients, among which 65 475 had at least one positive test result for SARS-CoV-2. Among all positive patients, 37.6% were contacted only once, 10.6% had one follow-up call, 8.5% had two and 27.7% had three or more follow-up calls. Participation rate among positive patients is 94%. Data collection is ongoing. FINDINGS TO DATE: ARGOS data illustrates the magnitude of COVID-19 pandemic in Geneva, Switzerland, and details a variety of population factors and outcomes. The content of the cohort includes demographic data, comorbidities and risk factors for poor clinical outcome, self-reported COVID-19 symptoms, environmental and socioeconomic factors, prospective and retrospective contact tracing data, travel quarantine data and deaths. The registry has already been used in several publications focusing on symptoms and long COVID-19, infection fatality rate and re-infection. FUTURE PLANS: The data of this large real-world registry provides a valuable resource for various types of research, such as clinical research, epidemiological research or policy assessment as it illustrates the impact of public health policies and overall disease burden of COVID-19.

Sequence-Directed Action of RSC Remodeler and General Regulatory Factors Modulates +1 Nucleosome Position to Facilitate Transcription.

Accessible chromatin is important for RNA polymerase II recruitment and transcription initiation at eukaryotic promoters. We investigated the mechanistic links between promoter DNA sequence, nucleosome positioning, and transcription. Our results indicate that positioning of the transcription start site-associated +1 nucleosome in yeast is critical for efficient TBP binding and is driven by two key factors, the essential chromatin remodeler RSC and a small set of ubiquitous general regulatory factors (GRFs). Our findings indicate that the strength and directionality of RSC action on promoter nucleosomes depends on the arrangement and proximity of two specific DNA motifs. This, together with the effect on nucleosome position observed in double depletion experiments, suggests that, despite their widespread co-localization, RSC and GRFs predominantly act through independent signals to generate accessible chromatin. Our results provide mechanistic insight into how the promoter DNA sequence instructs trans-acting factors to control nucleosome architecture and stimulate transcription initiation.

Nucleosome Stability Distinguishes Two Different Promoter Types at All Protein-Coding Genes in Yeast.

Previous studies indicate that eukaryotic promoters display a stereotypical chromatin landscape characterized by a well-positioned +1 nucleosome near the transcription start site and an upstream -1 nucleosome that together demarcate a nucleosome-free (or -depleted) region. Here we present evidence that there are two distinct types of promoters distinguished by the resistance of the -1 nucleosome to micrococcal nuclease digestion. These different architectures are characterized by two sequence motifs that are broadly deployed at one set of promoters where a nuclease-sensitive ("fragile") nucleosome forms, but concentrated in a narrower, nucleosome-free region at all other promoters. The RSC nucleosome remodeler acts through the motifs to establish stable +1 and -1 nucleosome positions, while binding of a small set of general regulatory (pioneer) factors at fragile nucleosome promoters plays a key role in their destabilization. We propose that the fragile nucleosome promoter architecture is adapted for regulation of highly expressed, growth-related genes.

Motifs tree: a new method for predicting post-translational modifications.

MOTIVATION: Post-translational modifications (PTMs) are important steps in the maturation of proteins. Several models exist to predict specific PTMs, from manually detected patterns to machine learning methods. On one hand, the manual detection of patterns does not provide the most efficient classifiers and requires an important workload, and on the other hand, models built by machine learning methods are hard to interpret and do not increase biological knowledge. Therefore, we developed a novel method based on patterns discovery and decision trees to predict PTMs. The proposed algorithm builds a decision tree, by coupling the C4.5 algorithm with genetic algorithms, producing high-performance white box classifiers. Our method was tested on the initiator methionine cleavage (IMC) and N(α)-terminal acetylation (N-Ac), two of the most common PTMs. RESULTS: The resulting classifiers perform well when compared with existing models. On a set of eukaryotic proteins, they display a cross-validated Matthews correlation coefficient of 0.83 (IMC) and 0.65 (N-Ac). When used to predict potential substrates of N-terminal acetyltransferaseB and N-terminal acetyltransferaseC, our classifiers display better performance than the state of the art. Moreover, we present an analysis of the model predicting IMC for Homo sapiens proteins and demonstrate that we are able to extract experimentally known facts without prior knowledge. Those results validate the fact that our method produces white box models. AVAILABILITY AND IMPLEMENTATION: Predictors for IMC and N-Ac and all datasets are freely available at http://terminus.unige.ch/.

Optimization of strut placement in flow diverter stents for four different aneurysm configurations.

A modern technique for the treatment of cerebral aneurysms involves insertion of a flow diverter stent. Flow stagnation, produced by the fine mesh structure of the diverter, is thought to promote blood clotting in an aneurysm. However, apart from its effect on flow reduction, the insertion of the metal device poses the risk of occlusion of a parent artery. One strategy for avoiding the risk of arterial occlusion is the use of a device with a higher porosity. To aid the development of optimal stents in the view point of flow reduction maintaining a high porosity, we used lattice Boltzmann flow simulations and simulated annealing optimization to investigate the optimal placement of stent struts. We constructed four idealized aneurysm geometries that resulted in four different inflow characteristics and employed a stent model with 36 unconnected struts corresponding to the porosity of 80%. Assuming intracranial flow, steady flow simulation with Reynolds number of 200 was applied for each aneurysm. Optimization of strut position was performed to minimize the average velocity in an aneurysm while maintaining the porosity. As the results of optimization, we obtained nonuniformed structure as optimized stent for each aneurysm geometry. And all optimized stents were characterized by denser struts in the inflow area. The variety of inflow patterns that resulted from differing aneurysm geometries led to unique strut placements for each aneurysm type.