bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-Invasive Prenatal Diagnosis of Single Gene Disorders with enhanced Relative Haplotype Dosage Analysis for diagnostic implementation
Authors: Pacault Mathilde , Verebi Camille , Champion Magali , Orhant Lucie , Perrier Alexandre , Girodon Emmanuelle , Leturcq France , Vidaud Dominique , Férec Claude , Bienvenu Thierry , Daveau Romain , Nectoux Juliette 
Published: January 2023

AbstractNon-invasive prenatal diagnosis for single-gene disorders (SGD-NIPD) has been widely adopted by patients, but is mostly limited to the exclusion of paternal or de novo mutations. Indeed, it is still difficult to infer the inheritance of maternal allele from cell free DNA (cfDNA) analysis. Based on the study of maternal haplotypes imbalance in cfDNA, relative haplotype dosage (RHDO) was developed to address this challenge. Although RHDO has proven to be reliable, robust control of statistical error and explicit delineation of critical parameters for assessing the quality of analysis have not been fully considered yet.Here we propose a universal and adaptable enhanced-RHDO procedure (eRHDO) through an automated bioinformatics pipeline with a didactical visualization of results that aims to be applied for any SGD-NIPD in routine care. A training cohort of 43 families carriers forCFTR, NF1, DMD, or F8mutations allowed the characterization and optimal setting of several adjustable data variables, such as minimal sequencing depth and type 1 and type 2 statistical errors, as well as the quality assessment for intermediate steps and final result through block score and concordance score. Validation was successfully carried out on 56 pregnancies of the test cohort. Finally, computer simulations were used to estimate the effect of fetal-fraction, sequencing depth and number of informative SNPs on the quality of results. Our workflow proved to be robust, as we obtained 94.9% conclusive and correctly inferred fetal genotypes, without any false negative or false positive result.By standardizing data generation and analysis, we fully describe a turnkey protocol for laboratories wishing to offer eRHDO-based non-invasive prenatal diagnosis for single-gene disorders as an alternative to conventional prenatal diagnosis.
Germination timing under climate change: warmer springs favor early germination of range-wide cork oak populations
Authors: Benito Garzón Marta , Baillou Fany , e Silva Filipe Costa , Faria Carla , Marchi Maurizio , Stiti Bouthenia , Vendramin Giovanni Giuseppe , Vizcaíno-Palomar Natalia 
Published: January 2023

AbstractClimate change is favoring the northward shift of Mediterranean species which are expanding their ranges at their leading edges, becoming natural candidates for increasing forest biodiversity in these regions. However, current knowledge on tree populations’ responses to climate change is mostly based on adult trees, even if tree early developmental stages are far more sensitive to climate and tightly linked to fitness. To fill this knowledge gap, we investigated the potential adaptation of cork oak range-wide populations to increasing spring temperature in germination and post-germination traits. We sowed 701 acorns from 11 populations at 15, 20 and 25°C, monitored germination daily and measured post-germination traits. We model germination timing through Cox’s proportional-hazards models, assess populations’ adaptation to spring temperature transfer distances and quantify the effect of acorn mass and storage duration on all considered traits with fixed-effects models. We predict germination and post-germination climate niches under current and RCP 8.5 2080 scenarios. Large differences in germination timing are due to both the population origin and temperature treatment; germination and survival rates showed a sub-optimality towards warmer-than-origin temperatures and heavier acorns produced faster growing seedlings. The timing of germination is the early stage trait most affected by increasing spring temperatures, with germination in 2080 predicted to be 12 days earlier than to date in the northern part of the species’ range. Warmer spring temperatures will significantly accelerate the germination of other recalcitrant Mediterranean species, which could alter seedlings developmental environment and ultimately populations’ regeneration and species composition. As such, germination timing should receive more attention by scientists and stakeholders, and should be included in forest vulnerability assessments and assisted migration programs aiming at long-term forest regeneration to adapt forests to climate change.
RNA-to-image multi-cancer synthesis using cascaded diffusion models
Authors: Carrillo-Perez Francisco , Pizurica Marija , Zheng Yuanning , Nandi Tarak Nath , Madduri Ravi , Shen Jeanne , Gevaert Olivier 
Published: January 2023

AbstractData scarcity presents a significant obstacle in the field of biomedicine, where acquiring diverse and sufficient datasets can be costly and challenging. Synthetic data generation offers a potential solution to this problem by expanding dataset sizes, thereby enabling the training of more robust and generalizable machine learning models. Although previous studies have explored synthetic data generation for cancer diagnosis, they have predominantly focused on single modality settings, such as whole-slide image tiles or RNA-Seq data. To bridge this gap, we propose a novel approach, RNA-Cascaded-Diffusion-Model or RNA-CDM, for performing RNA-to-image synthesis in a multi-cancer context, drawing inspiration from successful text-to-image synthesis models used in natural images. In our approach, we employ a variational auto-encoder to reduce the dimensionality of a patient’s gene expression profile, effectively distinguishing between different types of cancer. Subsequently, we employ a cascaded diffusion model to synthesize realistic whole-slide image tiles using the latent representation derived from the patient’s RNA-Seq data. Our results demonstrate that the generated tiles accurately preserve the distribution of cell types observed in real-world data, with state-of-the-art cell identification models successfully detecting important cell types in the synthetic samples. Furthermore, we illustrate that the synthetic tiles maintain the cell fraction observed in bulk RNA-Seq data and that modifications in gene expression affect the composition of cell types in the synthetic tiles. Next, we utilize the synthetic data generated by RNA-CDM to pretrain machine learning models and observe improved performance compared to training from scratch. Our study emphasizes the potential usefulness of synthetic data in developing machine learning models in sarce-data settings, while also highlighting the possibility of imputing missing data modalities by leveraging the available information. In conclusion, our proposed RNA-CDM approach for synthetic data generation in biomedicine, particularly in the context of cancer diagnosis, offers a novel and promising solution to address data scarcity. By generating synthetic data that aligns with real-world distributions and leveraging it to pretrain machine learning models, we contribute to the development of robust clinical decision support systems and potential advancements in precision medicine.
Cancer-associated fibroblasts produce matrix-bound vesicles that influence endothelial cell function
Authors: Santi Alice , Kay Emily J , Neilson Lisa J , McGarry Lynn , Lilla Sergio , Mullin Margaret , Paul Nikki R , Fercoq Frédéric , Koulouras Grigorios , Blanco Giovanny Rodriguez , Athineos Dimitris , Mason Susan , Hughes Mark , Kieffer Yann , Nixon Colin , Blyth Karen , Mechta-Grigoriou Fatima , Carlin Leo M , Zanivan Sara 
Published: January 2023

AbstractIntercellular communication between different cell types in solid tumors contributes to tumor growth and metastatic dissemination. The secretome of cancer-associated fibroblasts (CAFs) plays major roles in these processes. Using human mammary CAFs, we unveil a mechanism of cell-cell communication between CAFs with myofibroblast phenotype and endothelial cells (ECs) based on intercellular protein transfer through extracellular vesicles (EVs). CAFs transfer proteins to ECs, including plasma membrane receptors, which we have identified by using mass spectrometry- based proteomics. Using THY1 as an example of transferred plasma membrane-bound protein, we show that CAF-derived proteins can influence how ECs interact with other cell types. Here, we show that CAFs produce high amounts of matrix-bound EVs that have a key role in protein transfer. Hence, our work paves the way for further studies to understand how CAF-derived matrix-bound EVs influence tumor pathology by regulating functions of neighboring cancer, stromal and immune cells.One sentence summaryCAFs with a myofibroblastic-like phenotype transfer proteins to ECs, including plasma membrane receptors, through matrix-bound EVs
Manifold Learning for fMRI time-varying FC
Authors: Gonzalez-Castillo Javier , Fernandez Isabel , Lam Ka Chun , Handwerker Daniel A , Pereira Francisco , Bandettini Peter A 
Published: January 2023

AbstractWhole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolve over time in meaningful ways at temporal scales going from years (e.g., development) to seconds (e.g., within-scan time-varyingFC(tvFC)). Yet, our ability to exploretvFCis severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers seek to generate low dimensional representations (e.g.,2Dand3Dscatter plots) expected to retain its most informative aspects (e.g., relationships to behavior, disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)—namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies—are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFCdata to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (i.e., minimum number of latent dimensions;ID) oftvFCdata manifolds. Third, we describe the inner workings of three state-of-the-artMLTs: Laplacian Eigenmaps (LE), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations oftvFCdata, as well as their robustness against hyper-parameter selection. Our results show thattvFCdata has anIDthat ranges between 4 and 26, and thatIDvaries significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed:UMAPandT-SNEcan capture these two levels of detail concurrently, but LEcould only capture one at a time. We observed substantial variability in embedding quality acrossMLTs, and within-MLTas a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application ofMLTstotvFCdata. Overall, we conclude that whileMLTscan be useful to generate summary views of labeledtvFCdata, their application to unlabeled data such as resting-state remains challenging.
SYNGAP1 deficiency disrupts neoteny in human cortical neurons in vivo
Authors: Vermaercke Ben , Iwata Ryohei , Weirda Keimpe , Boubakar Leïla , Rodriguez Paula , Ditkowska Martyna , Bonin Vincent , Vanderhaeghen Pierre 
Published: January 2023

AbstractIntellectual deficiency (ID) and autism spectrum disorder (ASD) originate from disrupted development of human-specific cognitive functions. Human brain ontogeny is characterized by a considerably prolonged, neotenic, cortical neuron development. Neuronal neoteny could be disrupted in ID/ASD, but this was never tested because of the difficulties to study developing human cortical circuits. Here we use xenotransplantation of human cortical neurons into the mouse cortex to study the in vivo neuronal consequences of SYNGAP1 haploinsufficiency, a frequent cause of ID/ASD. We find that SYNGAP1 deficient neurons display strong acceleration of morphological and functional synaptic development. At the circuit level, SYNGAP1 haploinsufficient neurons display disrupted neoteny, with faster integration into cortical circuits and acquisition of sensory responsiveness months ahead of time. These data link neuronal neoteny to ID/ASD, with important implications for diagnosis and treatments.
Dally is not essential for Dpp spreading or internalization but for Dpp stability by antagonizing Tkv-mediated Dpp internalization
Authors: Simon Niklas , Safyan Abu , Pyrowolakis George , Matsuda Shinya 
Published: January 2023

AbstractDpp/BMP acts as a morphogen to provide positional information in the Drosophila wing disc. Key cell-surface molecules to control Dpp morphogen gradient formation and signaling are heparan sulfate proteoglycans (HSPGs). In the wing disc, two HSPGs, the glypicans Division abnormally delayed (Dally) and Dally-like (Dlp) have been suggested to act redundantly to control these processes through direct interaction of their heparan sulfate (HS) chains with Dpp. Based on this assumption, a number of models on how glypicans control Dpp gradient formation and signaling have been proposed, including facilitating or hindering Dpp spreading, stabilizing Dpp on the cell surface, or recycling Dpp. However, how distinct HSPGs act remains largely unknown. Here we generate genome-engineering platforms for the two glypicans and find that only Dally is critical for Dpp gradient formation and signaling through interaction of its core protein with Dpp. We also find that this interaction is not sufficient and that the HS chains of Dally are essential for these functions largely without interacting with Dpp. We provide evidence that the HS chains of Dally are not essential for spreading or recycling of Dpp but for stabilizing Dpp on the cell surface by antagonizing receptor-mediated Dpp internalization. These results provide new insights into how distinct HSPGs control morphogen gradient formation and signaling during development.
PML mutants resistant to arsenic induced degradation fail to generate the appropriate SUMO and ubiquitin signals required for RNF4 and p97 recruitment
Authors: Jaffray Ellis G. , Tatham Michael H. , Rojas-Fernandez Alejandro , Ibrahim Adel , Ball Graeme , Hay Ronald T. 
Published: January 2023

ABSTRACTArsenic is an effective treatment for Acute Promyelocytic Leukaemia as it induces degradation of the Promyelocytic Leukaemia (PML) – retinoic acid receptor alpha (RARA) oncogenic fusion protein. Some patients relapse with arsenic resistant disease because of missense mutations in PML. To determine the mechanistic basis for arsenic resistance we reconstituted PML-/- cells with YFP fusions of wild type (WT) and two mutant forms of PMLV found in patients refractory to arsenic, A216T and L217F. Both mutants formed PML bodies that were larger, but less numerous than WT and neither responded to arsenic by degradation. Analysis of immunoprecipitated PML bodies indicated that while WT PML experiences increased SUMO1, SUMO2/3 and ubiquitin conjugation, A216T PML is almost completely unresponsive and therefore does not recruit the SUMO targeted ubiquitin E3 ligase RNF4. Compared to WT PML, L217F PML was modified to a similar extent by SUMO2 but not SUMO1 and although it recruited RNF4, it failed to develop the appropriate poly-ubiquitin conjugates required to recruit the segregase p97, which is essential for PML degradation.
Water stabilizes an alternate turn conformation in horse heart myoglobin
Authors: Bronstein Alex , Marx Ailie 
Published: January 2023

SummaryComparison of myoglobin structures reveals that protein isolated from horse heart consistently adopts an alternate turn conformation in comparison to its homologues. Analysis of hundreds of high-resolution structures discounts crystallization conditions or the surrounding amino acid protein environment as explaining this difference, that is also not captured by the AlphaFold prediction. Rather, a water molecule is identified as stabilizing the conformation in the horse heart structure, which immediately reverts to the whale conformation in molecular dynamics simulations excluding that structural water.
Stable and accurate tracking control of tail-sitter aircraft in all flight modes
Authors: Zou Xu , Liu Zhenbao , Dang Qingqing , Wang Lina 
Published: January 2023

Purpose
This paper aims to design a global controller that is operational throughout all flight modes and less dependent on an accurate model.

Design/methodology/approach
By adopting the interconnection and damping assignment passivity-based control (IDA-PBC) technology and compensating extra inputs for handling the unknown dynamics and time-varying disturbances, a model-free control (MFC)-based global controller is proposed.

Findings
Test results indicate that the designed controllers are more suitable for actual flight as they have smaller position tracking errors and energy consumption in all flight phases than the excellent model-free controller intelligent-PID.

Practical implications
The designed global controller, which works in all flight modes without adjusting its structure and parameters, can realize a stable and accurate tracking control of a tail-sitter and improve the resistance to unknown disturbances and model uncertainties.

Originality/value
The newly-designed controller is considered as an enhanced version of the traditional MFC. It further improves the control effect by using the poorly known dynamics of the system and choosing the IDA-PBC as the control auxiliary input. This method eliminates the unnecessary dynamics to continuously stabilize the vehicle with suitable energy consumption covering its entire flight envelope.
Credit risk management in small-scale farming by formal financial institutions during the COVID-19 era: Nigerian perspective
Authors: Okpukpara Victoria , Okpukpara Benjamin Chiedozie , Omeje Emmanuel Ejiofor , Ukwuaba Ikenna Charles , Ogbuakanne Maryann 
Published: January 2023

PurposeProviding loans, particularly to small-scale farmers, is one of the roles of formal financial institutions. Lending to small farmers is risky. An institution's health is closely related to the institution's ability to manage credit and portfolio risk. Expanding smallholder farmers' access to finance while maintaining a sustainable financial system is essential; however, pandemics present additional challenges. Accordingly, as reported in the literature, the pandemic's high loan default rates and decreases in return on assets (ROAs) call for further credit risk management research. There have been limited studies on credit risk management during coronavirus disease 2019 (COVID-19), so this article aims to provide useful information on its influences.Design/methodology/approachResearchers used data from formal financial institutions in 2018 (before COVID-19) and in 2021 (during COVID-19) to accomplish the study's broad objective. Descriptive and inferential statistics were the main analytical tools. The credit risk management indicators were categorized into collateral management, loan management, loan recovery management, governance and Information and Communication Technology (ICT). Weights were assigned to each category based on the importance to credit risk management. A binary logit model was employed in assessing the factors influencing credit risk management as proxied to loan repayment, while Ordinary Least Square (OLS) was used to examine factors that influence ROAs.FindingsOne of the most noteworthy findings is that credit risk management is affected by different factors and magnitudes before and during the COVID-19 era. Loan recovery and ICT management indicators were most influential during the pandemic. In addition, the study noted that low agricultural productivity during the pandemic contributed to an additional challenge in loan default rates because of various COVID-19-containing measures. Additionally, there was a lack of governance and ICT management capacity to drive credit and portfolio risk management during the epidemic.Originality/valueThe paper presents new empirical findings on credit risk management during the COVID-19 era. The study used a methodology which has not been used previously in credit risk management in Nigerian financial institutions. Therefore, this research could become the cornerstone of further academic research in other developing countries using this methodology.
A bundle of human resource practices and employee resilience: the role of employee well-being
Authors: Rurkkhum Suthinee 
Published: January 2023

PurposeThe purpose of this study was to examine the mediating effect of employee well-being on the relationship between a bundle of human resource practices (HR practices) and employee resilience among Thai employees in an insurance company.Design/methodology/approachSelf-report questionnaires were received from 317 employees. Confirmatory factor analysis (CFA) was conducted, followed by structural equation model (SEM) to test all hypotheses.FindingsA partial mediator role of employee well-being on the relationship was detected, highlighting the significant consequences of a bundle of HR practices during the recent pandemic.Research limitations/implicationsThe study's limitations were its self-report questionnaires and cross-sectional design.Practical implicationsThis study highlights the importance of a bundle of HR practices under the unusual situation, i.e. during the data collection process, telecommuting was implemented in the organization to comply with the government regulations. A bundle of HR practices can be perceived as resources that can help individual employees overcome this challenging situation, which supports organizational performance.Originality/valueHigh environmental uncertainty requires today's organizations to be aware of the importance of employee resilience since this can contribute to organizational resilience. Additionally, employees rank their well-being as one of the top factors they seek from an organization. Thus, this study empirically extended the benefits of a bundle of HR practices in the context of COVID-19, supporting the mutual gains model.
Do entrepreneurial self-efficacy, entrepreneurial motivation, and family support enhance entrepreneurial intention? The mediating role of entrepreneurial education
Authors: Saoula Oussama , Shamim Amjad , Ahmad Munawar Javed , Abid Muhammad Farrukh 
Published: January 2023

Purpose
Entrepreneurship is an important paradigm for enhancing the economic well-being of nations. However, despite heated debate about the significant role of entrepreneurial education (EE) in developing favourable entrepreneurial intention (EI), little is known about the role of individuals’ entrepreneurial self-efficacy (ES), entrepreneurial motivation (EM) and family support (FS), which the authors investigated in this study.

Design/methodology/approach
This study has used a quantitative research design to collect data from 334 young people from various Malaysian higher education institutes using a purposive sampling technique and a deductive approach based on the theory of planned behaviour (TPB).

Findings
The findings revealed interesting insights into the criticality of young people’s ES, EM and FS in learning methods, techniques and skills to start new enterprises. Moreover, EE was a significant mediator of the relationship between individual self-efficacy, FS, EM and EI.

Originality/value
This study is among the few to contribute to strategic management scholarship by designing a framework based on the idea that EE relies on diverse factors, particularly ES, EM and FS. These factors encourage Malaysian young people to seek the necessary education to develop favourable EI and launch successful businesses.