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The increasing amount of interconnected data has given rise to a need among researchers and practitioners to develop new approaches to visualizing network structures. The intricacy of such structures vastly exceeds the capacity of most conventional approaches to network visualization in terms of dimensional and resolution restrictions, as they are mostly presented as two-dimensional with on a limited size screen. An additional limitation of traditional network visualization tools from a human-computer interaction standpoint is the limited interaction itself where immersion and "deep-diving" into high-dimensional data is not possible. We built NetImmerse, an application to visualize network data in a virtual environment with the ability to overview, zoom, and request details on-demand. Within the virtual space, users can either walk around the 3D data representation or rotate and move the representation using the two controllers. We tested the application with users and simulated a representative use case. NetImmerse enabled the participants to gain accurate insights based on the defined task. Participants indicated a PU of 5.25 and a PEOU of 5.46. We believe that NetImmerse is an engaging platform for multi-dimensional data exploration and may result in better insights and enhanced network data exploration.

Rising within-country differences in house values are a much debated trend in the U.S. and internationally. Using new long-run regional data for 15 advanced economies, we first show that standard explanations linking growing price dispersion to rent dispersion are contradicted by an important stylized fact: rent dispersion has increased far less than price dispersion. We then propose a new explanation: a uniform decline in real risk-free interest rates can have heterogeneous spatial effects on house values. Falling real safe rates disproportionately push up prices in large agglomerations where initial rent-price ratios are low, leading to housing market polarization on the national level.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets., Scientific Data, 9 (1), ISSN:2052-4463

Over the past decades, institutional landlords – from real estate companies like the German giant Vonovia to private equity companies like Blackstone, or pension funds like ABP, the Dutch pension fund for government and education employees – have minted EUR 40bn of Berlin’s houses into assets that they rent out. This is roughly double the combined value of London’s and Amsterdam's institutionally owned houses and it is a trend that has accelerated since the COVID19 pandemic. Europe’s residential real estate has become an attractive asset class for investors worldwide, supported by a range of government policies that are ostensibly aimed at homeowners: support for housing markets pushes up house prices and reduces affordability for citizens, whereas income support for rent-paying households ensures stable returns for investors. In response, citizens across Europe – from Berlin to Dublin and Madrid – have mobilized to pressure governments into taking action. From rent controls to better regulation or even expropriation of institutional landlords, the political tide seems to be turning against a decades-old phenomenon known as the financialization of housing. A mega-trend across housing markets everywhere, it can be understood as (1) the disproportionate growth of housing finance relative to the underlying housing economy or (2) the turn to Housing as an Asset Class (HAC), captured by the increasing for-profit and financial orientation of actors in housing markets, and encouraged in Europe by a broad range of European-level financial legislation. In this report, we explore the growing importance of institutional landlords such as Blackstone, focusing in particular on the mechanisms through which European legislation has accommodated their strategies to transform housing into asset classes. We use data to map the complex financial ecosystem behind private equity landlords. We then propose a set of reforms that would de-financialize housing for the public good. 1. Housing as an asset class - The age of the institutional landlord 2. Housing and mortgage markets in Europe 3. The financialization of housing 3.1 Housing as an Asset Class (HAC) in Institutional Portfolios 3.2 Securitization of housing loans 3.3 Private equity real estate (PERE) funds 3.4 Public equity: REITs in Spain and Ireland 3.4 Public equity: Listed Real Estate Companies 3.5 Institutional investors Insurance Companies 3.6 Pension funds 4. The contribution of EU policies to the financialization of housing 4.1 Insurance companies 4.2 Pension Funds 4.3 Credit institutions 4.4 Residential/RE funds 5. Recommendations for EU legislative action to slow down and tackle finacialization: A European housing framework 5.1 A Housing Upgrade in the Social Taxonomy to underpin mandatory disclosure and regulation of institutional landlords 5.2 The European Housing Fund 5.3 A Housing Red Flag Rule on new European-level regulatory initiatives References

The comparative study of housing decommodification lags behind classical welfare state research, while housing research itself is rich in homeownership studies but lacks comparative accounts of private and social rentals due to missing comparative data. Building on existing works and various primary sources, this study presents a new collection of up to forty-eight countries’ social housing shares in stock and new construction since the first housing laws around 1900. The interpolated benchmark time series generally describe the rise and fall of social housing across a residual, a socialist, and a Northern-European housing group. The decline was steeper than for the classical welfare state, but the degree of erosion was surprisingly small in some countries where public housing associations remained resilient. Within the broader housing welfare state, social housing correlates positively with rent regulation and allowances, but negatively with homeownership subsidies and liberal mortgage regulation. A multivariate analysis shows that social housing is rather explained by housing shortages and complementarities with rental and welfare policies than by typical welfare state theories (GDP, political parties). Generally, the paper shows that conventional housing typologies are difficult to defend over time and argues more generally for including housing decommodification in welfare state research. Die vergleichende Forschung zur Dekommodifizierung des Gutes Wohnen ist bisher von der klassischen Wohlfahrtsstaatsforschung vernachlässigt worden. Die Wohnungsforschung selbst ist wiederum reich an Studien zum Wohneigentum, aber vergleichende Darstellungen zu privaten und sozialen Mietwohnungen sind aufgrund fehlender komparativer Daten wenig erforscht. Aufbauend auf bestehenden Arbeiten und verschiedenen Primärquellen stellt diese Studie daher zunächst eine neue Datensammlung von bis zu 48 Ländern vor, die den Anteil der Sozialwohnungen an den Beständen und Neubauten seit den ersten Wohnungsbaugesetzen um 1900 erfasst. Die interpolierten Benchmark-Zeitreihen beschreiben im Allgemeinen den Aufstieg und Fall des sozialen Wohnungsbaus in einem residualen, sozialistischen und einem nordeuropäischen Wohnungsregime. Der Rückgang war steiler als beim klassischen Wohlfahrtsstaat, aber überraschend resilient in Ländern mit öffentlichen Wohnungsbaugesellschaften. Innerhalb des umfassenderen Wohnungswohlfahrtsstaates korreliert der soziale Wohnungsbau positiv mit der Regulierung von Mieten und Wohngeldzahlungen, aber negativ mit Wohneigentumssubventionen und liberalen Hypothekenregelungen. Eine multivariate Analyse zeigt, dass der soziale Wohnungsbau eher durch Wohnungsknappheit und funktionale Komplementarität mit Miet- und Sozialpolitik als mit typischen wohlfahrtsstaatlichen Faktoren (BIP, politische Parteien) erklärt wird. Generell zeigt der Beitrag, dass herkömmliche Wohnungstypologien im Laufe der Zeit nur schwer zu verteidigen sind, und plädiert dafür, die Dekommodifizierung von Wohnraum stärker in die Wohlfahrtsstaatsforschung einzubeziehen. Contents 1 Introduction 2 Social housing: What it is and how to measure it? 3 Descriptive results 4 Bivariate findings: Social housing and the broader housing welfare state 5 Multivariate: The determinants of social housing provision 6 Conclusion Appendix References

Blue cone monochromacy (BCM) is an X-linked retinal disorder characterized by low vision, photoaversion, and poor color discrimination. BCM is due to the lack of long-wavelength-sensitive and middle-wavelength-sensitive cone photoreceptor function and caused by mutations in the OPN1LW/OPN1MW gene cluster on Xq28. Here, we investigated the prevalence and the landscape of submicroscopic structural variants (SVs) at single-base resolution in BCM patients. We found that about one-third (n = 73) of the 213 molecularly confirmed BCM families carry an SV, most commonly deletions restricted to the OPN1LW/OPN1MW gene cluster. The structure and precise breakpoints of the SVs were resolved in all but one of the 73 families. Twenty-two families-all from the United States-showed the same SV, and we confirmed a common ancestry of this mutation. In total, 42 distinct SVs were identified, including 40 previously unreported SVs, thereby quadrupling the number of precisely mapped SVs underlying BCM. Notably, there was no "region of overlap" among these SVs. However, 90% of SVs encompass the upstream locus control region, an essential enhancer element. Its minimal functional extent based on deletion mapping in patients was refined to 358 bp. Breakpoint analyses suggest diverse mechanisms underlying SV formation as well as in one case the gene conversion-based exchange of a 142-bp deletion between opsin genes. Using parsimonious assumptions, we reconstructed the composition and copy number of the OPN1LW/OPN1MW gene cluster prior to the mutation event and found evidence that large gene arrays may be predisposed to the occurrence of SVs at this locus.

Despite the importance of team communication for successful collaborative problem solving, automated solutions for teams are notably absent from the literature. One promising avenue of research has been the development and integration of speech-based technology for team meetings. However, these technologies often fall short of meeting the needs of the teams as they do not take meeting context into consideration. In this paper, we demonstrate the efficacy of context detection with data collected during real team meetings. By capturing and analyzing social signals of rotation in team dynamics, we can demonstrate that different stages of collaborative problem solving using the design thinking methodology differ in their dynamics. Using supervised machine learning, we successfully predict design thinking mode with an overall F1 score of 0.68 and a best-performing sub-class model of 0.94. We believe this to be an essential step towards improving speech-based technology that aims to assist teams during meetings. Making these automated systems context-aware will enable them to provide teams with relevant information, such as resources or guidance.

Background Rare Eye Diseases (RED) are the leading cause of visual impairment and blindness for children and young adults in Europe. This heterogeneous group of conditions includes over 900 disorders ranging from relatively prevalent disorders such as retinitis pigmentosa to very rare entities such as developmental eye anomalies. A significant number of patients with RED have an underlying genetic etiology. One of the aims of the European Reference Network for Rare Eye Diseases (ERN–EYE) is to facilitate improvement in diagnosis of RED in European member states. Main body Technological advances have allowed genetic and genomic testing for RED. The outcome of genetic testing allows better understanding of the condition and allows reproductive and therapeutic options. The increase of the number of clinical trials for RED has provided urgency for genetic testing in RED. A survey of countries participating in ERN-EYE demonstrated that the majority are able to access some forms of genomic testing. However, there is significant variability, particularly regarding testing as part of clinical service. Some countries have a well-delineated rare disease pathway and have a national plan for rare diseases combined or not with a national plan for genomics in medicine. In other countries, there is a well-established organization of genetic centres that offer reimbursed genomic testing of RED and other rare diseases. Clinicians often rely upon research-funded laboratories or private companies. Notably, some member states rely on cross-border testing by way of an academic research project. Consequently, many clinicians are either unable to access testing or are confronted with long turnaround times. Overall, while the cost of sequencing has dropped, the cumulative cost of a genomic testing service for populations remains considerable. Importantly, the majority of countries reported healthcare budgets that limit testing. Short conclusion Despite technological advances, critical gaps in genomic testing remain in Europe, especially in smaller countries where no formal genomic testing pathways exist. Even within larger countries, the existing arrangements are insufficient to meet the demand and to ensure access. ERN-EYE promotes access to genetic testing in RED and emphasizes the clinical need and relevance of genetic testing in RED.

ESRETNET Study Group, The ERDC Study Group, The Associated Clinical Study Group., Inherited retinal diseases (IRDs), defined by dysfunction or progressive loss of photoreceptors, are disorders characterized by elevated heterogeneity, both at the clinical and genetic levels. Our main goal was to address the genetic landscape of IRD in the largest cohort of Spanish patients reported to date. A retrospective hospital-based cross-sectional study was carried out on 6089 IRD affected individuals (from 4403 unrelated families), referred for genetic testing from all the Spanish autonomous communities. Clinical, demographic and familiar data were collected from each patient, including family pedigree, age of appearance of visual symptoms, presence of any systemic findings and geographical origin. Genetic studies were performed to the 3951 families with available DNA using different molecular techniques. Overall, 53.2% (2100/3951) of the studied families were genetically characterized, and 1549 different likely causative variants in 142 genes were identified. The most common phenotype encountered is retinitis pigmentosa (RP) (55.6% of families, 2447/4403). The most recurrently mutated genes were PRPH2, ABCA4 and RS1 in autosomal dominant (AD), autosomal recessive (AR) and X-linked (XL) NON-RP cases, respectively; RHO, USH2A and RPGR in AD, AR and XL for non-syndromic RP; and USH2A and MYO7A in syndromic IRD. Pathogenic variants c.3386G > T (p.Arg1129Leu) in ABCA4 and c.2276G > T (p.Cys759Phe) in USH2A were the most frequent variants identified. Our study provides the general landscape for IRD in Spain, reporting the largest cohort ever presented. Our results have important implications for genetic diagnosis, counselling and new therapeutic strategies to both the Spanish population and other related populations., This work was supported by the Instituto de Salud Carlos III (ISCIII) of the Spanish Ministry of Health (FIS; PI16/00425 and PI19/00321), Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER, 06/07/0036), IIS-FJD BioBank (PT13/0010/0012), Comunidad de Madrid (CAM, RAREGenomics Project, B2017/BMD-3721), European Regional Development Fund (FEDER), the Organización Nacional de Ciegos Españoles (ONCE), Fundación Ramón Areces, Fundación Conchita Rábago and the University Chair UAM-IIS-FJD of Genomic Medicine. Irene Perea-Romero is supported by a PhD fellowship from the predoctoral Program from ISCIII (FI17/00192). Ionut F. Iancu is supported by a grant from the Comunidad de Madrid (CAM, PEJ-2017-AI/BMD7256). Marta del Pozo-Valero is supported by a PhD grant from the Fundación Conchita Rábago. Berta Almoguera is supported by a Juan Rodes program from ISCIII (JR17/00020). Pablo Minguez is supported by a Miguel Servet program from ISCIII (CP16/00116). Marta Corton is supported by a Miguel Servet program from ISCIII (CPII17/00006).