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Brain disorders are often associated with changes in brain structure and function, where functional changes may be due to underlying structural variations. Gray matter (GM) volume segmentation from 3D structural MRI offers vital structural information for brain disorders like schizophrenia, as it encompasses essential brain tissues such as neuronal cell bodies, dendrites, and synapses, which are crucial for neural signal processing and transmission; changes in GM volume can thus indicate alterations in these tissues, reflecting underlying pathological conditions. In addition, the use of the ICA algorithm to transform high-dimensional fMRI data into functional network connectivity (FNC) matrices serves as an effective carrier of functional information. In our study, we introduce a new generative deep learning architecture, the conditional efficient vision transformer generative adversarial network (cEViT-GAN), which adeptly generates FNC matrices conditioned on GM to facilitate the exploration of potential connections between brain structure and function. We developed a new, lightweight self-attention mechanism for our ViT-based generator, enhancing the generation of refined attention maps critical for identifying structural biomarkers based on GM. Our approach not only generates high quality FNC matrices with a Pearson correlation of 0.74 compared to real FNC data, but also uses attention map technology to identify potential biomarkers in GM structure that could lead to functional abnormalities in schizophrenia patients. Visualization experiments within our study have highlighted these structural biomarkers, including the medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (DL-PFC), and cerebellum. In addition, through cross-domain analysis comparing generated and real FNC matrices, we have identified functional connections with the highest correlations to structural information, further validating the structure-function connections. This comprehensive analysis helps to understand the intricate relationship between brain structure and its functional manifestations, providing a more refined insight into the neurobiological research of schizophrenia.

The breeding biology of the Luzon flameback Chrysocolaptes haematribon is little known. Here we provide insights on the breeding of the species from two nests we located. Cavity nests were in dead trees and were 15-19 cm deep; entrances were vertical ovals and had dimensions (L×W) of 11-12×8-10 cm. One nest that was observed in April during the days around fledging had one male and one female nestling in it, which left and re-entered the nest on several occasions. The adult female was observed to solely provision food on six occasions, with food items including a moth, a butterfly, beetle larvae, and a small lizard. Attempts by dollarbird Eurystomus orientalis to usurp the nest were observed on each of the three observation days. Both the adult male and adult female flamebacks defended the nest either from the inside, outside or a combination of both. This paper serves as the first detailed report on the breeding biology and nest defence of the species and adds valuable information to the scarce knowledge on woodpeckers in the Philippines.

BackgroundInferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information.MethodsWe propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude–magnitude and phase–phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude–phase and phase–magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals.ResultsSimulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2–20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features.ConclusionThe proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case.

Background The role of rescue stenting (RS) in acute ischemic strokes due to intracranial atherosclerotic disease–related large vessel occlusion is an area of active investigation. This study evaluates the efficacy and safety of RS under these circumstances. Methods A systematic literature review, conforming to Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, was conducted across PubMed, EMBASE, Cochrane, and OVID databases. Common and random effects analyses were conducted to compare outcomes including modified Rankin Scale score, 90‐day mortality, and symptomatic intracranial hemorrhage between patients receiving and those not receiving RS after failed endovascular thrombectomy within posterior circulation. Results Ten studies comprising 1202 patients (730 RS, 472 control) were analyzed. RS was associated with a statistically significant improvement in functional outcomes, evidenced by a higher proportion of patients achieving modified Rankin Scale score ≤2 at 90 days (32.74% versus 21.19% in controls; P

Recent developments in molecular genetic testing methods (e.g. next-generation sequencing [NGS]-panels) largely accelerated the process of finding the most appropriate targeted therapeutic intervention for cancer patients based on molecularly targetable genetic alterations. In Hungary, a centralized approval system following the recommendation of the National Molecular Tumor Board was launched for the coordination of all aspects of comprehensive genetic profiling (CGP) including patient selection and therapy reimbursement. Aim: The study aims to evaluate the clinical benefit of CGP in our Comprehensive Cancer Center Methods and patients: CGP was introduced into our routine clinical practice in 2021. An NGS-based large (> 500 genes) gene panel was used for cases where molecular genetic testing was approved by the National Molecular Tumor Board. From 2021 until August 2023 163 cases were tested. The majority of them were ECOG 0–1 patients with advanced-stage diseases, histologically rare cancer, or cancers with unknown primary tumours. Results: Seventy-four cases (74 of 163, 45%) had clinically relevant genetic alterations. In 34 patients, the identified variants represented an indication for an approved therapy (approved by the Hungarian authorities, on-label indication), while in 40 cases the recommended therapy did not have an approved indication in Hungary for certain tumour types, but off-label indication could be recommended. Based on our CGP results, 24 patients (24/163; 14.7%) received targeted therapy. Treatment duration was between 1 and 60 months. In total 14 (14/163; 8.5% of the tested cases) patients had a positive clinical response (objective response or stable disease) and were treated for more than 16 weeks. Interpretation: NGS-based CGP was successfully introduced in our institution and a significant number of patients benefited from comprehensive genetic tests. Our preliminary results can serve as the starting point of Drug Rediscovery Protocol (DRUP) studies.

BackgroundSchizophrenia (SZ) is a psychiatric condition that adversely affects an individual’s cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises.MethodsOur proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC).ResultsExperimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%.ConclusionWe proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings.

Introduction: The human cerebellum emerges as a posterior brain structure integrating neural networks for sensorimotor, cognitive, and emotional processing across the lifespan. Developmental studies of the cerebellar anatomy and function are scant. We examine age-dependent MRI morphometry of the anterior cerebellar vermis, lobules I-V and posterior neocortical lobules VI-VII and their relationship to sensorimotor and cognitive functions. Methods: Typically developing children (TDC; n=38; age 9–15) and healthy adults (HAC; n=31; 18–40) participated in high-resolution MRI. Rigorous anatomically informed morphometry of the vermis lobules I-V and VI-VII and total brain volume (TBV) employed manual segmentation computer-assisted FreeSurfer Image Analysis Program [http://surfer.nmr.mgh.harvard.edu]. The neuropsychological scores (WASI-II) were normalized and related to volumes of anterior, posterior vermis, and TBV. Results: TBVs were age independent. Volumes of I-V and VI-VII were significantly reduced in TDC. The ratio of VI-VII to I-V (∼60%) was stable across age-groups; I-V correlated with visual-spatial-motor skills; VI-VII with verbal, visual-abstract and FSIQ. Conclusions: In TDC neither anterior I-V nor posterior VI-VII vermis attained adult volumes. The ''inverted U'' developmental trajectory of gray matter peaking in adolescence does not explain this finding. The hypothesis of protracted development of oligodendrocyte/myelination is suggested as a contributor to TDC's lower cerebellar vermis volumes.

Summary: Germline pathogenic variants in BRCA1 and BRCA2 (gpath(BRCA1/2)) represent genetic susceptibility for hereditary breast and ovarian cancer syndrome. Tumor-immune interactions are key contributors to breast cancer pathogenesis. Although earlier studies confirmed pro-tumorigenic immunological alterations in breast cancer patients, data are lacking in healthy carriers of gpath(BRCA1/2). Peripheral blood mononuclear cells of 66 women with or without germline predisposition or breast cancer were studied with a mass cytometry panel that identified 4 immune subpopulations of altered frequencies between healthy controls and healthy gpath(BRCA1) carriers, while no difference was observed in healthy gpath(BRCA2) carriers compared to controls. Moreover, 3 (one IgD-CD27+CD95+ B cell subpopulation and two CD45RA-CCR7+CD38+ CD4+ T cell subpopulations) out of these 4 subpopulations were also elevated in triple-negative breast cancer patients compared to controls. Our results reveal an activated peripheral immune phenotype in healthy carriers of gpath(BRCA1) that needs to be further elucidated to be leveraged in risk-reducing strategies.

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

The induction of proinflammatory T cells by dendritic cell (DC) subtypes is critical for antitumor responses and effective immune checkpoint blockade (ICB) therapy. Here, we show that human CD1c+CD5+ DCs are reduced in melanoma-affected lymph nodes, with CD5 expression on DCs correlating with patient survival. Activating CD5 on DCs enhanced T cell priming and improved survival after ICB therapy. CD5+ DC numbers increased during ICB therapy, and low interleukin-6 (IL-6) concentrations promoted their de novo differentiation. Mechanistically, CD5 expression by DCs was required to generate optimally protective CD5hi T helper and CD8+ T cells; further, deletion of CD5 from T cells dampened tumor elimination in response to ICB therapy in vivo. Thus, CD5+ DCs are an essential component of optimal ICB therapy.

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p

IntroductionResting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations. In this study, we propose a framework that focuses on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA).MethodsMotivated by earlier work suggesting significant spectral differences in people with schizophrenia, we developed an approach to evaluate time-resolved spectral coupling (trSC). To do this, we first calculated the correlation between the power spectra of windowed time-courses pairs of brain components. Then, we subgrouped each correlation map into four subgroups based on the connectivity strength utilizing quartiles and clustering techniques. Lastly, we examined clinical group differences by regression analysis for each averaged count and average cluster size matrices in each quartile. We evaluated the method by applying it to resting-state data collected from 151 (114 males, 37 females) people with schizophrenia (SZ) and 163 (117 males, 46 females) healthy controls (HC).ResultsOur proposed approach enables us to observe the change of connectivity strength within each quartile for different subgroups. People with schizophrenia showed highly modularized and significant differences in multiple network domains, whereas males and females showed less modular differences. Both cell count and average cluster size analysis for subgroups indicate a higher connectivity rate in the fourth quartile for the visual network in the control group. This indicates increased trSC in visual networks in the controls. In other words, this shows that the visual networks in people with schizophrenia have less mutually consistent spectra. It is also the case that the visual networks are less spectrally correlated on short timescales with networks of all other functional domains.ConclusionsThe results of this study reveal significant differences in the degree to which spectral power profiles are coupled over time. Importantly, there are significant but distinct differences both between males and females and between people with schizophrenia and controls. We observed a more significant coupling rate in the visual network for the healthy controls and males in the upper quartile. Fluctuations over time are complex, and focusing on only time-resolved coupling among time-courses is likely to miss important information. Also, people with schizophrenia are known to have impairments in visual processing but the underlying reasons for the impairment are still unknown. Therefore, the trSC approach can be a useful tool to explore the reasons for the impairments.

Brain functional networks identified from resting functional magnetic resonance imaging (fMRI) data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale (ICA dimensionality, i.e., ICA model order). In this work, we present an approach using multi-objective optimization scICA with reference algorithm (MOO-ICAR) to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales, which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N > 1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into scICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. The classification model built on the msFNC features obtained up to 85% F1 score, 83% precision, and 88% recall, indicating the strength of the proposed framework in detecting group differences between schizophrenia and the control group. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

This study examined the extent to which boys fell into clusters comprised of different levels of motivations and costs. In turn, the antecedents of these clusters and associations with engagement and wellbeing outcomes were considered. Based on survey responses from 168 students across Years 5, 7 and 9 from an all-boys' school in Sydney, Australia, three clusters were identified: "Positively Engaged," "Disengaged," and "Struggling Ambitious." Performance-approach and avoidance achievement goals, mastery classroom goal structure, perceived peer valuing of mathematics and teacher enthusiasm differentially predicted profile membership. Clusters were also found to differ in terms of both wellbeing and engagement, such that students within maladaptive profiles evidenced the most negative outcomes. The study reaffirms prior work, holds implications for addressing student motivation in mathematics, and adds to understanding of the interplay of individual and classroom goal structures in relation to students' mathematics expectancies, values and resultant outcomes.

Abstract Background During the COVID-19 pandemic swift implementation of research cohorts was key. While many studies focused exclusively on infected individuals, population based cohorts are essential for the follow-up of SARS-CoV-2 impact on public health. Here we present the CON-VINCE cohort, estimate the point and period prevalence of the SARS-CoV-2 infection, reflect on the spread within the Luxembourgish population, examine immune responses to SARS-CoV-2 infection and vaccination, and ascertain the impact of the pandemic on population psychological wellbeing at a nationwide level. Methods A representative sample of the adult Luxembourgish population was enrolled. The cohort was followed-up for twelve months. SARS-CoV-2 RT-qPCR and serology were conducted at each sampling visit. The surveys included detailed epidemiological, clinical, socio-economic, and psychological data. Results One thousand eight hundred sixty-five individuals were followed over seven visits (April 2020—June 2021) with the final weighted period prevalence of SARS-CoV-2 infection of 15%. The participants had similar risks of being infected regardless of their gender, age, employment status and education level. Vaccination increased the chances of IgG-S positivity in infected individuals. Depression, anxiety, loneliness and stress levels increased at a point of study when there were strict containment measures, returning to baseline afterwards. Conclusion The data collected in CON-VINCE study allowed obtaining insights into the infection spread in Luxembourg, immunity build-up and the impact of the pandemic on psychological wellbeing of the population. Moreover, the study holds great translational potential, as samples stored at the biobank, together with self-reported questionnaire information, can be exploited in further research. Trial registration Trial registration number: NCT04379297, 10 April 2020.

Abstract The goals of this study are to describe machine learning techniques employing computer-vision movement algorithms to automatically evaluate infants’ general movements (GMs) in the writhing stage. This is a retrospective study of infants admitted 07/2019 to 11/2021 to a level IV neonatal intensive care unit (NICU). Infant GMs, classified by certified expert, were analyzed in two-steps (1) determination of anatomic key point location using a NICU-trained pose estimation model [accuracy determined using object key point similarity (OKS)]; (2) development of a preliminary movement model to distinguish normal versus cramped-synchronized (CS) GMs using cosine similarity and autocorrelation of major joints. GMs were analyzed using 85 videos from 74 infants; gestational age at birth 28.9 ± 4.1 weeks and postmenstrual age (PMA) at time of video 35.9 ± 4.6 weeks The NICU-trained pose estimation model was more accurate (0.91 ± 0.008 OKS) than a generic model (0.83 ± 0.032 OKS, p

Identifying relationships among multiple datasets is an effective way to summarize information and has been growing in importance. In this paper, we propose a robust 3-step method for identifying the relationship structure among multiple datasets based on Independent Vector Analysis (IVA) and bootstrap-based hypothesis testing. Unlike previous approaches, our theory-backed method eliminates the need for user-defined thresholds and can effectively handle non-Gaussian data. It achieves this by incorporating higher-order statistics through IVA and employing an eigenvalue decomposition-based feature extraction approach without distributional assumptions. This way, our method estimates more interpretable components and effectively identifies the relationship structure using hierarchical clustering. Simulation results demonstrate the effectiveness of our method, as it achieves perfect Adjusted Mutual Information (AMI) for different values of the correlation between the components. When applied to multi-task fMRI data from patients with schizophrenia and healthy controls, our method successfully reveals activated brain regions associated with the disorder, and identifies the relationship structure of task datasets that matches our prior knowledge of the experiment. Moreover, our proposed method extends beyond task datasets, offering broad applicability in subgroup identification in neuroimaging and other domains.

In late 2020, a private volunteer group operating through a Facebook group approached Toronto and Region Conservation Authority (TRCA) with an interest in participating in the Citizen Science Nest Protection and Monitoring Volunteer Program. During the 2021 season, TRCA staff virtually trained volunteers in turtle nest detection, protection, and monitoring due to COVID-19 social constraints, and provided necessary supplies and equipment. In 2021 volunteers and staff detected 181 turtle nests and were able to protect 75 nests with simple predator-exclusion devices, while in 2022, 165 nests were detected, and 155 were protected. The volunteers also collected data, including nesting location, date, and species in addition to the number of successful hatchlings (2021 only). In 2021, the 75 protected nests yielded 81 Midland Painted Turtle and 665 Snapping Turtle hatchlings. Five Midland Painted Turtle’s eggs were also recovered from an injured turtle, and 44 Snapping Turtle eggs were recovered from a partially predated nest, transported to the Ontario Turtle Conservation Centre, incubated, hatched, and overwintered, and then released back into the local wetlands. The partnership with a local community group proved effective, and we would recommend this practice to other conservation organizations interested in turtle nest protection.

Adapting agriculture to climate change is essential for sustainable food production. However, the development of suitable adaptation mechanisms requires a clear understanding of the plant-climate interaction. The number of growing degree days (GDD) is a good proxy for understanding plant-climate interrelationship and farm productivity. Here, using a 2-year experiment of barley-sweet corn double cropping (DC) system and 20-year climate and yield data, we found that barley and sweet corn GDD and productivity were strongly related to changes in climate patterns. Furthermore, we found a positive effect of the barley-sweet corn DC system on farm productivity (18.5 and 5.6 tonnes. ha−1 for DC and single cropping respectively) and return on investment (1.8; 1.4 as benefit:cost ratios for DC and single cropping system respectively). Altogether the results of this study suggest that the winter barley-sweet corn double cropping system is a potential strategy to boost farm productivity as well as an adaptation mechanism to be considered for the changing climate in the study region.

This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0.