bluuebunny/crossref_metadata_2025_split huggingface dataset:
ALKBH5-mediated m6A modification of IL-11 drives macrophage-to-myofibroblast transition and pathological cardiac fibrosis in mice
Authors: Zhuang Tao , Chen Mei-Hua , Wu Ruo-Xi , Wang Jing , Hu Xi-De , Meng Ting , Wu Ai-Hua , Li Yan , Yang Yong-Feng , Lei Yu , Hu Dong-Hua , Li Yan-Xiu , Zhang Li , Sun Ai-Jun , Lu Wei , Zhang Guan-Nan , Zuo Jun-Li , Ruan Cheng-Chao 
Published: March 2024

AbstractCardiac macrophage contributes to the development of cardiac fibrosis, but factors that regulate cardiac macrophages transition and activation during this process remains elusive. Here we show, by single-cell transcriptomics, lineage tracing and parabiosis, that cardiac macrophages from circulating monocytes preferentially commit to macrophage-to-myofibroblast transition (MMT) under angiotensin II (Ang II)-induced hypertension, with accompanying increased expression of the RNA N6-methyladenosine demethylases, ALKBH5. Meanwhile, macrophage-specific knockout of ALKBH5 inhibits Ang II-induced MMT, and subsequently ameliorates cardiac fibrosis and dysfunction. Mechanistically, RNA immunoprecipitation sequencing identifies interlukin-11 (IL-11) mRNA as a target for ALKBH5-mediated m6A demethylation, leading to increased IL-11 mRNA stability and protein levels. By contrast, overexpression of IL11 in circulating macrophages reverses the phenotype in ALKBH5-deficient mice and macrophage. Lastly, targeted delivery of ALKBH5 or IL-11 receptor α (IL11RA1) siRNA to monocytes/macrophages attenuates MMT and cardiac fibrosis under hypertensive stress. Our results thus suggest that the ALKBH5/IL-11/IL11RA1/MMT axis alters cardiac macrophage and contributes to hypertensive cardiac fibrosis and dysfunction in mice, and thereby identify potential targets for cardiac fibrosis therapy in patients.
Distinct pathways drive anterior hypoblast specification in the implanting human embryo
Authors: Weatherbee Bailey A. T. , Weberling Antonia , Gantner Carlos W. , Iwamoto-Stohl Lisa K. , Barnikel Zoe , Barrie Amy , Campbell Alison , Cunningham Paula , Drezet Cath , Efstathiou Panagiota , Fishel Simon , Vindel Sandra Gutiérrez , Lockwood Megan , Oakley Rebecca , Pretty Catherine , Chowdhury Nabiha , Richardson Lucy , Mania Anastasia , Weavers Lauren , Christie Leila , Elder Kay , Snell Phillip , Zernicka-Goetz Magdalena 
Published: March 2024

AbstractDevelopment requires coordinated interactions between the epiblast, which generates the embryo proper; the trophectoderm, which generates the placenta; and the hypoblast, which forms both the anterior signalling centre and the yolk sac. These interactions remain poorly understood in human embryogenesis because mechanistic studies have only recently become possible. Here we examine signalling interactions post-implantation using human embryos and stem cell models of the epiblast and hypoblast. We find anterior hypoblast specification is NODAL dependent, as in the mouse. However, while BMP inhibits anterior signalling centre specification in the mouse, it is essential for its maintenance in human. We also find contrasting requirements for BMP in the naive pre-implantation epiblast of mouse and human embryos. Finally, we show that NOTCH signalling is important for human epiblast survival. Our findings of conserved and species-specific factors that drive these early stages of embryonic development highlight the strengths of comparative species studies.
Social perception of robots is shaped by beliefs about their minds
Authors: Momen Ali , Hugenberg Kurt , Wiese Eva 
Published: March 2024

AbstractRoboticists often imbue robots with human-like physical features to increase the likelihood that they are afforded benefits known to be associated with anthropomorphism. Similarly, deepfakes often employ computer-generated human faces to attempt to create convincing simulacra of actual humans. In the present work, we investigate whether perceivers’ higher-order beliefs about faces (i.e., whether they represent actual people or android robots) modulate the extent to which perceivers deploy face-typical processing for social stimuli. Past work has shown that perceivers’ recognition performance is more impacted by the inversion of faces than objects, thus highlighting that faces are processed holistically (i.e., as Gestalt), whereas objects engage feature-based processing. Here, we use an inversion task to examine whether face-typical processing is attenuated when actual human faces are labeled as non-human (i.e., android robot). This allows us to employ a task shown to be differentially sensitive to social (i.e., faces) and non-social (i.e., objects) stimuli while also randomly assigning face stimuli to seem real or fake. The results show smaller inversion effects when face stimuli were believed to represent android robots compared to when they were believed to represent humans. This suggests that robots strongly resembling humans may still fail to be perceived as “social” due pre-existing beliefs about their mechanistic nature. Theoretical and practical implications of this research are discussed.
Investigating the mechanisms underlying resistance to chemotherapy and to CRISPR-Cas9 in cancer cell lines
Authors: Tomasi Francesca , Pozzi Matteo , Lauria Mario 
Published: March 2024

AbstractCancer is one of the major causes of death worldwide and the development of multidrug resistance (MDR) in cancer cells is the principal cause of chemotherapy failure. To gain insights into the specific mechanisms of MDR in cancer cell lines, we developed a novel method for the combined analysis of recently published datasets on drug sensitivity and CRISPR loss-of-function screens for the same set of cancer cell lines. For our analysis, we first selected cell lines that consistently exhibit drug resistance across several classes of compounds. We then identified putative resistance genes for each class of compound and used inferred gene regulatory networks (GRNs) to study possible mechanisms underlying the development of MDR in the identified cancer cell lines. We show that the same method of analysis can also be used to identify cell lines that consistently exhibit resistance to the gene knockout effect of the CRISPR-Cas9 technique and to study the possible underlying mechanisms. In the GRN associated to the drug resistant cell lines, we identify genes previously associated with resistance (UHMK1, RALYL, MGST3, USP9X, and ESRG), genes for which an indirect association can be identified (SPINK13, LINC00664, MRPL38, and EMILIN3), and genes that are found to be overexpressed in non-resistant cancer cell lines (MRPL38, EMILIN3 and RALYL). In the GRNs associated to the CRISPR-Cas9 resistance mechanism, none of the identified genes has been previously reported in the admittedly sparse literature on the subject. However, some of these genes have a common role: APBB2, RUNX1T1, ZBTB7C, and ISX regulate transcription, while APBB2, BTG3, ZBTB7C, SZRD1 and LEF1 have a function in regulating proliferation, suggesting a role for these two pathways. While our results are specific for the lung cancer cell lines we selected for this work, our method of analysis can be applied to cell lines from other tissues and for which the required data is available.
Machine learning and XAI approaches highlight the strong connection between $$O_3$$ and $$NO_2$$ pollutants and Alzheimer’s disease
Authors: Fania Alessandro , Monaco Alfonso , Amoroso Nicola , Bellantuono Loredana , Cazzolla Gatti Roberto , Firza Najada , Lacalamita Antonio , Pantaleo Ester , Tangaro Sabina , Velichevskaya Alena , Bellotti Roberto 
Published: March 2024

AbstractAlzheimer’s disease (AD) is the most common type of dementia with millions of affected patients worldwide. Currently, there is still no cure and AD is often diagnosed long time after onset because there is no clear diagnosis. Thus, it is essential to study the physiology and pathogenesis of AD, investigating the risk factors that could be strongly connected to the disease onset. Despite AD, like other complex diseases, is the result of the combination of several factors, there is emerging agreement that environmental pollution should play a pivotal role in the causes of disease. In this work, we implemented an Artificial Intelligence model to predict AD mortality, expressed as Standardized Mortality Ratio, at Italian provincial level over 5 years. We employed a set of publicly available variables concerning pollution, health, society and economy to feed a Random Forest algorithm. Using methods based on eXplainable Artificial Intelligence (XAI) we found that air pollution (mainly $$O_3$$

O
3

and $$NO_2$$

N

O
2

) contribute the most to AD mortality prediction. These results could help to shed light on the etiology of Alzheimer’s disease and to confirm the urgent need to further investigate the relationship between the environment and the disease.
SARS-CoV-2 infection prevalence and associated factors among primary healthcare workers in France after the third COVID-19 wave
Authors: Pouquet Marie , Decarreaux Dorine , Di Domenico Laura , Sabbatini Chiara E. , Prévot-Monsacre Pol , Fourié Toscane , Villarroel Paola Mariela Saba , Priet Stephane , Blanché Hélène , Sebaoun Jean-Marc , Deleuze Jean-François , Turbelin Clément , Rossignol Louise , Werner Andréas , Kochert Fabienne , Grosgogeat Brigitte , Rabiega Pascaline , Laupie Julien , Abraham Nathalie , Noël Harold , van der Werf Sylvie , Colizza Vittoria , Carrat Fabrice , Charrel Remi , de Lamballerie Xavier , Blanchon Thierry , Falchi Alessandra 
Published: March 2024

AbstractData on the SARS-CoV-2 infection among primary health care workers (PHCWs) are scarce but essential to reflect on policy regarding prevention and control measures. We assessed the prevalence of PHCWs who have been infected by SARS-CoV-2 in comparison with modeling from the general population in metropolitan France, and associated factors. A cross-sectional study was conducted among general practitioners (GPs), pediatricians, dental and pharmacy workers in primary care between May and August 2021. Participants volunteered to provide a dried-blood spot for SARS-CoV-2 antibody assessment and completed a questionnaire. The primary outcome was defined as the detection of infection-induced antibodies (anti-nucleocapsid IgG, and for non-vaccinees: anti-Spike IgG and neutralizing antibodies) or previous self-reported infection (positive RT-qPCR or antigenic test, or positive ELISA test before vaccination). Estimates were adjusted using weights for representativeness and compared with prediction from the general population. Poisson regressions were used to quantify associated factors. The analysis included 1612 PHCWs. Weighted prevalences were: 31.7% (95% CI 27.5–36.0) for GPs, 28.7% (95% CI 24.4–33.0) for pediatricians, 25.2% (95% CI 20.6–31.0) for dentists, and 25.5% (95% CI 18.2–34.0) for pharmacists. Estimates were compatible with model predictions for the general population. PHCWs more likely to be infected were: GPs compared to pharmacist assistants (adjusted prevalence ratio [aPR] = 2.26; CI 95% 1.01–5.07), those living in Île-de-France (aPR = 1.53; CI 95% 1.14–2.05), South-East (aPR = 1.57; CI 95% 1.19–2.08), North-East (aPR = 1.81; CI 95% 1.38–2.37), and those having an unprotected contact with a COVID-19 case within the household (aPR = 1.48; CI 95% 1.22–1.80). Occupational factors were not associated with infection. In conclusion, the risk of SARS-CoV-2 exposure for PHCWs was more likely to have occurred in the community rather than at their workplace.
Modeling protective action decision-making in earthquakes by using explainable machine learning and video data
Authors: Zhang Xiaojian , Zhao Xilei , Baldwin Dare , McBride Sara , Bellizzi Josephine , Cochran Elizabeth S. , Luco Nicholas , Wood Matthew , Cova Thomas J. 
Published: March 2024

AbstractEarthquakes pose substantial threats to communities worldwide. Understanding how people respond to the fast-changing environment during earthquakes is crucial for reducing risks and saving lives. This study aims to study people’s protective action decision-making in earthquakes by leveraging explainable machine learning and video data. Specifically, this study first collected real-world CCTV footage and video postings from social media platforms, and then identified and annotated changes in the environment and people’s behavioral responses during the M7.1 2018 Anchorage earthquake. By using the fully annotated video data, we applied XGBoost, a widely-used machine learning method, to model and forecast people’s protective actions (e.g.,drop and cover,hold on, andevacuate) during the earthquake. Then, explainable machine learning techniques were used to reveal the complex, nonlinear relationships between different factors and people’s choices of protective actions. Modeling results confirm that social and environmental cues played critical roles in affecting the probability of different protective actions. Certain factors, such as the earthquake shaking intensity and number of people shown in the environment, displayed evident nonlinear relationships with the probability of choosing toevacuate. These findings can help emergency managers and policymakers design more effective protective action recommendations during earthquakes.
A novel equation for the estimation of low-density lipoprotein cholesterol in the Saudi Arabian population: a derivation and validation study
Authors: Nuwaylati Dena A. , Awan Zuhier A. 
Published: March 2024

AbstractLow-density lipoprotein cholesterol (LDL-C) is typically estimated by the Friedewald equation to guide atherosclerotic cardiovascular disease (ASCVD) management despite its flaws. Martin–Hopkins and Sampson-NIH equations were shown to outperform Friedewald’s in various populations. Our aim was to derive a novel equation for accurate LDL-C estimation in Saudi Arabians and to compare it to Friedewald, Martin–Hopkins and Sampson-NIH equations. This is a cross-sectional study on 2245 subjects who were allocated to 2 cohorts; a derivation (1) and a validation cohort (2). Cohort 1 was analyzed in a multiple regression model to derive an equation (equationD) for estimating LDL-C. The agreement between the measured (LDL-CDM) and calculated levels was tested by Bland–Altman analysis, and the biases by absolute error values. Validation of the derived equation was carried out across LDL-C and triglyceride (TG)-stratified groups. The mean LDL-CDM was 3.10 ± 1.07 and 3.09 ± 1.06 mmol/L in cohorts 1 and 2, respectively. The derived equation is: LDL-CD = 0.224 + (TC × 0.919) – (HDL-C × 0.904) – (TG × 0.236) – (age × 0.001) – 0.024. In cohort 2, the mean LDL-C (mmol/L) was estimated as 3.09 ± 1.06 by equationD, 2.85 ± 1.12 by Friedewald, 2.95 ± 1.09 by Martin–Hopkins, and 2.93 ± 1.11 by Sampson-NIH equations; statistically significant differences between direct and calculated LDL-C was observed with the later three equations (P < 0.001). Bland–Altman analysis showed the lowest bias (0.001 mmol/L) with equationD as compared to 0.24, 0.15, and 0.17 mmol/L with Friedewald, Martin–Hopkins, and Sampson-NIH equations, respectively. The absolute errors in all guideline-stratified LDL-C categories was the lowest with equationD, which also showed the best classifier of LDL-C according to guidelines. Moreover, equationD predicted LDL-C levels with the lowest error with TG levels up to 5.63 mmol/L. EquationD topped the other equations in estimating LDL-C in Saudi Arabians as it could permit better estimation when LDL-C is < 2.4 mmol/L, in familial hyperlipidemia, and in hypertriglyceridemia, which improves cardiovascular outcomes in high-risk patients. We recommend further research to validate equationD in a larger dataset and in other populations.
Hydrophobic solution functions as a multifaceted mosquito repellent by enhancing chemical transfer, altering object tracking, and forming aversive memory
Authors: Wiedemann Bianca M. , Takeuchi Kohei , Ohta Kazumi , Kato-Namba Aya , Yabuki Masayuki , Kazama Hokto , Nakagawa Takao 
Published: March 2024

AbstractDeveloping a safe and potent repellent of mosquitoes applicable to human skins is an effective measure against the spread of mosquito-borne diseases. Recently, we have identified that hydrophobic solutions such as low viscosity polydimethylsiloxane (L-PDMS) spread on a human skin prevent mosquitoes from staying on and biting it. This is likely due to the ability of L-PDMS in wetting mosquito legs and exerting a capillary force from which the mosquitoes attempt to escape. Here we show three additional functions of L-PDMS that can contribute to repel Aedes albopictus, by combining physicochemical analysis and behavioral assays in both an arm cage and a virtual flight arena. First, L-PDMS, when mixed with topical repellents and applied on a human skin, enhances the effect of topical repellents in reducing mosquito bites by efficiently transferring them to mosquito legs upon contact. Second, L-PDMS applied to mosquito tarsi compromises visual object tracking during flight, exerting an influence outlasting the contact. Finally, L-PDMS applied to mosquito tarsi acts as an aversive reinforcer in associative learning, making mosquitoes avoid the conditioned odor. These results uncover a multifaceted potential of L-PDMS in altering a sequence of mosquito behaviors from biting a human skin, visual object tracking following takeoff, to the response to an odor linked with L-PDMS.
Normal stress distribution in built-up cold-formed column in relation to interconnecting bolt spacing
Authors: Deniziak Patryk , Urbańska-Galewska Elżbieta , Gordziej-Zagórowska Małgorzata 
Published: March 2024

AbstractIn order to increase a stiffness of cold-formed steel (CFS) elements it is practised to built-up the cross-section. In the analysed case, a main element is strengthened by adding extra chord in contact partially along the column. This additional chord acts as a longitudinal stiffener connected with the main section by series of bolts. Authors check whether rules applied over the years, for hot-rolled elements, can be indiscriminately used in the analysed CFS element. The aim of this study is to experimentally and computationally recognize the normal stress distribution in axially compressed CFS built-up column chords and to evaluate the element load-bearing capacity.
New perspective for the upscaling of plant functional response to flooding stress in salt marshes using remote sensing
Authors: Vuerich Marco , Cingano Paolo , Trotta Giacomo , Petrussa Elisa , Braidot Enrico , Scarpin Dora , Bezzi Annelore , Mestroni Michele , Pellegrini Elisa , Boscutti Francesco 
Published: March 2024

AbstractUnderstanding the response of salt marshes to flooding is crucial to foresee the fate of these fragile ecosystems, requiring an upscaling approach. In this study we related plant species and community response to multispectral indices aiming at parsing the power of remote sensing to detect the environmental stress due to flooding in lagoon salt marshes. We studied the response of Salicornia fruticosa (L.) L. and associated plant community along a flooding and soil texture gradient in nine lagoon salt marshes in northern Italy. We considered community (i.e., species richness, dry biomass, plant height, dry matter content) and individual traits (i.e., annual growth, pigments, and secondary metabolites) to analyze the effect of flooding depth and its interplay with soil properties. We also carried out a drone multispectral survey, to obtain remote sensing-derived vegetation indices for the upscaling of plant responses to flooding. Plant diversity, biomass and growth all declined as inundation depth increased. The increase of soil clay content exacerbated flooding stress shaping S. fruticosa growth and physiological responses. Multispectral indices were negatively related with flooding depth. We found key species traits rather than other community traits to better explain the variance of multispectral indices. In particular stem length and pigment content (i.e., betacyanin, carotenoids) were more effective than other community traits to predict the spectral indices in an upscaling perspective of salt marsh response to flooding. We proved multispectral indices to potentially capture plant growth and plant eco-physiological responses to flooding at the large scale. These results represent a first fundamental step to establish long term spatial monitoring of marsh acclimation to sea level rise with remote sensing. We further stressed the importance to focus on key species traits as mediators of the entire ecosystem changes, in an ecological upscaling perspective.
Secular evolution of resonant planets in the coplanar case
Authors: Pons J. , Gallardo T. 
Published: March 2024

Aims. We study the secular evolution of two planets in mutual deep mean-motion resonance (MMR) in the planar elliptic three-body problem framework for different mass ratios. We do not consider any restriction in the eccentricity of the inner planet e1 or in the eccentricity of the outer planet e2.
Methods. The method we used is based on a semi-analytical model that consists of calculating the averaged resonant disturbing function numerically. It is assumed for this that all the orbital elements (except for the mean longitudes) of both planets are constant on the resonant timescale. In order to obtain the secular evolution inside the MMR, we make use of the adiabatic invariance principle, assuming a zero-amplitude resonant libration. We constructed two phase portraits, called the ℋ1 and ℋ2 surfaces, in the three-dimensional spaces (e1, Δϖ, σ) and (e2, Δϖ, σ), where Δϖ is the difference between the planetary longitude of perihelia and σ is the critical angle. These surfaces, which are related through the angular moment conservation, allow us to find the apsidal corotation resonances (ACRs) and to predict the secular evolution of e1, e2, Δϖ, and σ (libration center).
Results. While studying the 1:1, 2:1, 3:1, and 3:2 MMR, we found that large excursions in eccentricity can exist in some particular cases. We compared the secular variations of e1, e2, Δϖ, and σ predicted by the model with a numerical integration of the exact equations of motion for different mass ratios. We obtained good matches. Finally, the model was applied to study the secular evolution of the resonant exoplanet systems HD 73526 and HD 31527. They both have a pair of planets and are very close to the deep MMR condition. In the first system, we found that the pair of planets that constitutes the system evolves in a symmetrical ACR, whereas in the second system, we found that planets c and d, which are in an unusual 16:3 MMR, are close to an ACR, but outside its dynamical region, where Δϖ circulates.