Rationale and design of the PACIFIC-PRESERVED (PhenomApping, ClassIFication and Innovation for Cardiac dysfunction in patients with heart failure and PRESERVED left ventricular ejection fraction) study.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome that is poorly defined, reflecting an incomplete understanding of its pathophysiology. AIM: To redefine the phenotypic spectrum of HFpEF. METHODS: The PACIFIC-PRESERVED study is a prospective multicentre cohort study designed to perform multidimensional deep phenotyping of patients diagnosed with HFpEF (left ventricular ejection fraction≥50%), patients with heart failure with reduced ejection fraction (left ventricular ejection fraction≤40%) and subjects without overt heart failure (3:2:1 ratio). The study proposes prospective investigations in patients during a 1-day hospital stay: physical examination; electrocardiogram; performance-based tests; blood samples; cardiac magnetic resonance imaging; transthoracic echocardiography (rest and low-level exercise); myocardial shear wave elastography; chest computed tomography; and non-invasive measurement of arterial stiffness. Dyspnoea, depression, general health and quality of life will be assessed by dedicated questionnaires. A biobank will be established. After the hospital stay, patients are asked to wear a connected garment (with digital sensors) to collect electrocardiography, pulmonary and activity variables in real-life conditions (for up to 14 days). Data will be centralized for machine-learning-based analyses, with the aim of reclassifying HFpEF into more distinct subgroups, improving understanding of the disease mechanisms and identifying new biological pathways and molecular targets. The study will also serve as a platform to enable the development of innovative technologies and strategies for the diagnosis and stratification of patients with HFpEF. CONCLUSIONS: PACIFIC-PRESERVED is a prospective multicentre phenomapping study, using novel analytical techniques, which will provide a unique data resource to better define HFpEF and identify new clinically meaningful subgroups of patients.

Early detection of white matter hyperintensities using SHIVA-WMH detector.

White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.

The PACIFIC ontology for heterogeneous data management in cardiology.

With the emergence of health data warehouses and major initiatives to collect and analyze multi-modal and multisource data, data organization becomes central. In the PACIFIC-PRESERVED (PhenomApping, ClassIFication, and Innovation for Cardiac Dysfunction - Heart Failure with PRESERVED LVEF Study, NCT04189029) study, a data driven research project aiming at redefining and profiling the Heart Failure with preserved Ejection Fraction (HFpEF), an ontology was developed by different data experts in cardiology to enable better data management in a complex study context (multisource, multiformat, multimodality, multipartners). The PACIFIC ontology provides a cardiac data management framework for the phenomapping of patients. It was built upon the BMS-LM (Biomedical Study -Lifecycle Management) core ontology and framework, proposed in a previous work to ensure data organization and provenance throughout the study lifecycle (specification, acquisition, analysis, publication). The BMS-LM design pattern was applied to the PACIFIC multisource variables. In addition, data was structured using a subset of MeSH headings for diseases, technical procedures, or biological processes, and using the Uberon ontology anatomical entities. A total of 1372 variables were organized and enriched with annotations and description from existing ontologies and taxonomies such as LOINC to enable later semantic interoperability. Both, data structuring using the BMS-LM framework, and its mapping with published standards, foster interoperability of multimodal cardiac phenomapping datasets.

Uncovering the origin of radiationless losses in CuLaO2 by rationalizing the action of temperature and pressure on luminescence.

CuLaO2 is a rare-earth and dopant-free inorganic compound able to emit green light upon blue excitation. Its absorption amounts to 90% but its internal quantum efficiency is poor (<17%). The origin of this deleterious radiationless behavior is addressed by investigating the spectroscopic properties of this compound under the action of temperature and hydrostatic pressure in the 15-400 K and 1 bar-40 kbar intervals, and by combining the spectroscopic data with earlier results of DFT calculations. A two-step radiationless process is demonstrated, involving radiative re-absorption and cross-over to excitonic states.

Luminescence-structure relationships in solids doped with Bi3.

The luminescence properties of Bi3+ in 177 inorganic compounds are reviewed and rationalized based on two simple and easy-to-use models. These models are applicable to any kind of compound whose crystal structure is known. The calculation of the nephelauxetic function, he, involved in one of the models is described step-by-step for an easy implementation in a home-made calculator or machine-learning algorithm. Finally, a criterion based on the experimental value of the Stokes shift is introduced to help in fixing the origin of the Bi3+-related emission.

Emission Enhancement and Energy Transfers in YV0.5P0.5O4 Nanoparticles Codoped with Eu3+ and Bi3+ Ions.

In this study, solid-state solutions of yttrium orthovanadate-phosphate with varying concentrations of codopants (Eu3+, Bi3+) have been obtained via coprecipitation. An ionic radii mismatch between V5+ and P5+ substituents is manifested in broad XRD lines. The sharpening of the XRD lines is observed with increasing bismuth ions concentration in the Eu3+ codoped YV0.5P0.5O4 matrix. The difference in the number of the Stark components for the 5D0 → 7FJ transitions indicates changes in the lattice and a number of possible Eu3+ sites. A thorough, systematic spectroscopic analysis of YV0.5P0.5O4: x mol % Eu3+, y mol % Bi3+ was conducted at room temperature and 5 K. Metal-to-metal energy transfers occurring between Eu3+, V5+, and Bi3+ optically active ions have been investigated. Additionally, efficiency of the Bi3+-Eu3+ energy transfer (ET) was calculated.

The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology.

Biomedical research data reuse and sharing is essential for fostering research progress. To this aim, data producers need to master data management and reporting through standard and rich metadata, as encouraged by open data initiatives such as the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines. This helps data re-users to understand and reuse the shared data with confidence. Therefore, dedicated frameworks are required. The provenance reporting throughout a biomedical study lifecycle has been proposed as a way to increase confidence in data while reusing it. The Biomedical Study - Lifecycle Management (BMS-LM) data model has implemented provenance and lifecycle traceability for several multimodal-imaging techniques but this is not enough for data understanding while reusing it. Actually, in the large scope of biomedical research, a multitude of metadata sources, also called Knowledge Organization Systems (KOSs), are available for data annotation. In addition, data producers uses local terminologies or KOSs, containing vernacular terms for data reporting. The result is a set of heterogeneous KOSs (local and published) with different formats and levels of granularity. To manage the inherent heterogeneity, semantic interoperability is encouraged by the Research Data Management (RDM) community. Ontologies, and more specifically top ontologies such as BFO and DOLCE, make explicit the metadata semantics and enhance semantic interoperability. Based on the BMS-LM data model and the BFO top ontology, the BioMedical Study - Lifecycle Management (BMS-LM) core ontology is proposed together with an associated framework for semantic interoperability between heterogeneous KOSs. It is made of four ontological levels: top/core/domain/local and aims to build bridges between local and published KOSs. In this paper, the conversion of the BMS-LM data model to a core ontology is detailed. The implementation of its semantic interoperability in a specific domain context is explained and illustrated with examples from small animal preclinical research.

A First Wide-Open LDH Structure Hosting InP/ZnS QDs: A New Route Toward Efficient and Photostable Red-Emitting Phosphor.

The architecture of Zn-Al layered double hydroxides (LDHs), organo-modified with bola-amphiphiles molecules, is matching its interlayer space to the size of narrow-band red-emitting InP/ZnS core-shell quantum dots (QDs) to form original high-performance functional organic-inorganic QD-bola-LDH hybrids. The success of size-matching interlayer space (SMIS) approach is confirmed by X-ray diffraction, small angle X-ray scattering (SAXS), TEM, STEM-HAADF, and photoluminescence investigations. The QD-Bola-LDH hybrid exhibits a photoluminescence quantum yield three times higher than that of pristine InP/ZnS QDs and provides an easy dispersion into silicone-based resins, what makes the SMIS approach a change of paradigm compared to intercalation chemistry using common host structures. Moreover, this novel hybrid presents low QD-QD energy transfer comparable to that obtained for QDs in suspension. Composite silicone films incorporating InP/ZnS (0.27 wt%) QD-bola-LDH hybrids further show remarkable improved photostability relative to pristine QDs. An LED overlay consisting of a blue LED chip and silicone films loaded with QD-bola-LDH hybrids and YAG:Ce phosphors exhibits a color rendering index close to 94.

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network.

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis.

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92 %. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96 % of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.

Origin of Luminescence in La2MoO6 and La2Mo2O9 and Their Bi-Doped Variants.

In this article, lanthanum molybdenum oxides (La2MoO6 and La2Mo2O9) and their Bi-doped derivatives were investigated as potential rare-earth-free phosphors. An X-ray diffraction analysis coupled with an EDX study confirmed the purity of the samples and the insertion of bismuth in a 1 molar % amount. Kubelka-Munk-transformed reflectance spectra clearly indicated that the insertion of Bi induces a shortening of the optical gap in La2MoO6 but has no impact on that of La2Mo2O9. Moreover, excitation and emission spectra evidenced a strong temperature quenching effect in all materials. Also, the CIEx,y parameters at 77 K are almost identical with or without Bi doping for the two host lattices. Clearly, it was shown, by combining experimental data, ab initio calculations, and the empirical positioning of absorption bands that the luminescence of the Bi-doped La2MoO6 sample is mainly related to the host lattice itself and distortions induced by La/Bi substitution. The role of the Bi3+ dopant is indirect, and the luminescence is mainly due to a Mo-O charge transfer rather than an on-site Bi3+ 3P1,0 → 1S0 transition. Concerning La2Mo2O9, there is no effect following the insertion of Bi, implying that the role of Bi is insignificant.

Forgotten and Resurrected Chernovite-(Y): YAsO4 Doped with Eu3+ Ions as a Potential Nanosized Luminophore.

In the present work, a precipitation method was employed to prepare nanosized YAsO4 doped with Eu3+ ions. The raw nanomaterials have been thermally treated in a temperature range between 500 and 900 °C for 3 h. The XRD analysis demonstrated that the powders were single-phase nanopowders with high crystallite dispersion. Our studies were focused on relating the luminescence properties of the Eu3+ dopant to the nanocrystallite (NC) size. The average NC size varied accordingly between 15 and 45 nm. We have found that the size effect is manifested mainly in the expansion of the cell volume and broadening of XRD peaks, as indicated by Rietveld analysis. Moreover, the emission and excitation spectra, although typical for Eu3+ ions, demonstrated some degree of variability with calcination temperatures and doping concentration. To explain these differences, a detailed analysis of luminescence spectra by the Judd-Ofelt theory has been performed.

Tunable luminescence of Bi(3+)-doped YP(x)V(1-x)O4 (0 ≤ x ≤1).

A systematic investigation of the luminescence spectroscopy of Y(P,V)O4:Bi(3+) is presented. The emission spectra and the decay curves are measured as a function of the host morphology, composition, temperature, excitation wavelength, and doping concentration. On this basis, the nature of the excited states and the radiative and non-radiative relaxation processes are discussed. Colour coordinates and quantum yield measurements are also carried out to provide information about the potential applications of the studied materials.

Optical processes in (Y,Bi)VO4 doped with Eu3+ or Pr3+.

Zircon and fergusonite-type vanadates either undoped or doped with Eu(3+) or Pr(3+) are synthesized in the system (Y,Bi)2O3-V2O5 by solid state and coprecipitation procedures. Their optical properties are investigated at 300 and 77 K and the luminescence mechanisms are discussed on the basis of energy level schemes that combine the host and the dopant states. Fergusonite BiVO4 is shown to glow in the deep red region at 77 K upon excitation at 450 nm and shorter wavelengths. Host sensitization is demonstrated in Eu(3+)-doped fergusonite BiVO4 and zircon BiVO4 at 77 K, but lost as temperature is raised to 300 K. The origin of this effect is addressed by considering the nature of the host-band edge states and self-quenching processes. The near-UV excited luminescence in the system (Y, Bi)VO4:Pr(3+) (zircon) consists of the yellow bandlike emission of the zircon host and of the characteristic red (1)D2 → (3)H4 emission lines of Pr(3+) in vanadates. The relative contribution of these features can be fine-tuned at room temperature by adjusting the composition of the materials or the excitation wavelength.

Revisiting the spectroscopy of the Bi3+ ion in oxide compounds.

A model is introduced to predict the energy of metal-to-metal charge-transfer transitions in oxide compounds containing Bi(3+) ions and d(0) or d(10) metals (M(n+)). The model takes into account the structural characteristics of the host lattices, the anion relaxation resulting from Bi(3+) doping, and the electronegativities and coordination numbers of the Bi(3+) and M(n+) ions in the compounds. It is shown, through a critical review of the archival literature, that this model provides new insights on the assignment of the luminescence spectra and the related interpretation of the spectroscopic behaviors.

Luminescence dynamics in Tb(3+)-doped CaWO(4) and CaMoO(4) crystals.

Single crystals of CaWO(4) and CaMoO(4) doped with Tb(3+) have been grown by the flux growth method. Their luminescence properties have been investigated in the 10-600 K temperature range under different experimental conditions. In spite of very similar spectra at low temperature upon excitation at 365 nm, the crystals show a very different behavior as the temperature is raised or the excitation wavelength is changed. These differences have been accounted for on the basis of models that take into consideration the position of the energy levels of the rare earth relative to the bandgap of the host material.

Lanthanide 4f-level location in AVO(4):Ln(3+) (A = La, Gd, Lu) crystals.

The spectral properties of LaVO(4), GdVO(4) and LuVO(4) crystals doped with Ce(3+), Pr(3+), Eu(3+) or Tb(3+) have been investigated in order to determine the position of the energy levels relative to the valence and conduction bands of the hosts along the trivalent and divalent lanthanide series. Pr(3+) and Tb(3+) ground state levels are positioned based on the electron transfer energy from those states to the conduction band, the so-called intervalence charge transfer (IVCT). This approach is compared with an alternative model that is based on electron transfer from the valence band to a lanthanide.