Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of exoplanets: exploiting each property of light

Up to now and probably for still a long time, the only support of information used to detect exoplanets has been the analysis of light, either visible or infrared. In the vast majority of cases it is the light from a star and not the light from the planet itself which is used, because the huge contrast in brightness between the star and a planet orbiting it as well as the extremely short angular distance between them makes direct imaging a real challenge. It is then a subtle effect detected on the starlight that in general indicates the planet’s presence and provides information on some of its characteristics: mass, radius, distance to the star, temperature, etc. As an introduction to the different contributions appearing in this volume, this article proposes a kind of brief review of the various methods imagined by astronomers to exploit one of the properties of the light to succeed in detecting and characterizing exoplanets. We’ll show that even direct detection became a reality and contributes to the more than 5000 exoplanets detected today. Résumé. Jusqu’à présent et probablement pour encore longtemps, le seul support d’information utilisé pour détecter les exoplanètes est l’analyse de la lumière, qu’elle soit visible ou infrarouge. Dans la grande majorité des cas, c’est la lumière d’une étoile et non celle de la planète elle-même qui est utilisée, car l’énorme contraste de luminosité entre l’étoile et une planète en orbite autour d’elle ainsi que la distance angulaire extrêmement courte qui les sépare font de l’imagerie directe un véritable défi. C’est alors un effet subtil détecté sur la lumière de l’étoile qui indique en général la présence de la planète et fournit des informations sur certaines de ses caractéristiques : masse, rayon, distance à l’étoile, température, etc. En guise d’introduction aux différentes contributions figurant dans ce volume, cet article propose une sorte de brève revue des différentes méthodes imaginées par les astronomes pour exploiter une des propriétés de la lumière afin de parvenir à détecter et caractériser des exoplanètes. Nous montrerons que même la détection directe est devenue une réalité et contribue aux plus de 5000 exoplanètes détectées aujourd’hui.
Apelin prevents diabetes-induced poor collateral vessel formation and blood flow reperfusion in ischemic limb

Introduction Peripheral arterial disease (PAD) is a major risk factor for lower-extremity amputation in diabetic patients. Unfortunately, previous clinical studies investigating therapeutic angiogenesis using the vascular endothelial growth factor (VEGF) have shown disappointing results in diabetic patients, which evokes the necessity for novel therapeutic agents. The apelinergic system (APJ receptor/apelin) is highly upregulated under hypoxic condition and acts as an activator of angiogenesis. Apelin treatment improves revascularization in nondiabetic models of ischemia, however, its role on angiogenesis in diabetic conditions remains poorly investigated. This study explored the impact of Pyr-apelin-13 in endothelial cell function and diabetic mouse model of hindlimb ischemia. Methods Nondiabetic and diabetic mice underwent femoral artery ligation to induce limb ischemia. Diabetic mice were implanted subcutaneously with osmotic pumps delivering Pyr-apelin-13 for 28 days. Blood flow reperfusion was measured for 4 weeks post-surgery and exercise willingness was assessed with voluntary wheels. In vitro, bovine aortic endothelial cells (BAECs) were exposed to normal (NG) or high glucose (HG) levels and hypoxia. Cell migration, proliferation and tube formation assays were performed following either VEGF or Pyr-apelin-13 stimulation. Results and Discussion Following limb ischemia, blood flow reperfusion, functional recovery of the limb and vascular density were improved in diabetic mice receiving Pyr-apelin-13 compared to untreated diabetic mice. In cultured BAECs, exposure to HG concentrations and hypoxia reduced VEGF proangiogenic actions, whereas apelin proangiogenic effects remained unaltered. Pyr-apelin-13 induced its proangiogenic actions through Akt/AMPK/eNOS and RhoA/ROCK signaling pathways under both NG or HG concentrations and hypoxia exposure. Our results identified the apelinergic system as a potential therapeutic target for angiogenic therapy in diabetic patients with PAD.
Penetration characteristics of water in calcium silicate hydrate nanoslit and temperature effect

ABSTRACT This study investigates the penetration characteristics of water and temperature effect, aiming to understand the mechanism of water intrusion and provide a basis for studying the transport of aggressive ions in concrete. Molecular dynamics simulations were employed to simulate the capillary motion of water in hydrated calcium silicate (C–S-H) slit. The effects of temperature on water density, chemical bond of atomic pairs, flow velocity distribution, and slip length during the flow process were analysed, and the flow equations were modified. It was found that the density of water in the region of 0.55 nm from the interior wall of the C–S-H slit manifests significant inhomogeneity. The stronger the chemical bond of atomic pairs, the less the residence time is influenced by temperature. Increasing the temperature from 280 K to 360 K results in a significant increase in the cross-sectional flow velocity within the nanoslit, while the changes in the size of the inhomogeneous region and the slip length are insignificant. The modified Bosanquet equation is suitable for describing the second stage of nanocapillary flow, while the Lucas-Washburn equation is suitable for describing the third stage.
Risk factors for death and illness severity in vaccinated versus unvaccinated COVID-2019 inpatients: a retrospective cohort study

ABSTRACT Objective: To determine the clinical profile of COVID-19 inpatients who were vaccinated prior to hospitalization and to compare the risk factors for death and the 28-day survival rate of between those inpatients vaccinated with one, two, or three doses and unvaccinated COVID-19 inpatients. Methods: This was a retrospective observational cohort study involving COVID-19 patients admitted to a referral hospital in the city of Recife, Brazil, between July of 2020 and June of 2022. Results: The sample comprised 1,921 inpatients, 996 of whom (50.8%) were vaccinated prior to hospitalization. After adjusting the mortality risk for vaccinated patients, those undergoing invasive mechanical ventilation (IMV) had the highest mortality risk (adjusted OR [aOR] = 7.4; 95% CI, 3.8-14.1; p < 0.001), followed by patients > 80 years of age (aOR = 7.3; 95% CI, 3.4-15.4; p < 0.001), and those needing vasopressors (aOR = 5.6; 95% CI, 2.9-10.9; p < 0.001). After adjusting the mortality risk for all patients, having received three vaccine doses (aOR = 0.06; 95% CI, 0.03-0.11; p < 0.001) was the most important protective factor against death. There were progressive benefits of vaccination, reducing the frequency of ICU admissions, use for IMV, and death (respectively, from 44.9%, 39.0% and 39.9% after the first dose to 16.7%, 6.2% and 4.4% after the third dose), as well as significant improvements in survival after each subsequent dose (p < 0.001). Conclusions: Vaccines were effective in reducing illness severity and death in this cohort of COVID-19 inpatients, and the administration of additional doses conferred them with accumulative vaccine protection.
Grit and Academic Self-Efficacy as Serial Mediation in the Relationship Between Growth Mindset and Academic Delay of Gratification: A Cross-Sectional Study

Purpose With a growth mindset, individuals focus on the process of growth, actively seek challenges, recognise and accept failures, and apply more effort and monitor themselves to overcome difficulties. Doing so translates into excellent academic performance. However, it has not yet been fully clarified how growth mindset affects academic delay of gratification (ADG) and the mechanisms underlying their interactions. In this study, grit and academic self-efficacy were tested as mediating mechanisms between growth mindset and the ADG using a serial mediation effect model based on self-determination theory (SDT). Methods A cross-sectional design was conducted with 759 Chinese junior high school students using the following tools: Growth Mindset Scale, Short Grit Scale, Academic Self-efficacy Scale, and Academic Delay of Gratification Scale. Hypotheses were tested using structural equation modeling. Results The results showed that: (1) by gender and grade control, growth mindset indicated a positive significance in the prediction of ADG; (2) grit and academic self-efficacy played a mediating role in the influence of a growth mindset on the ADG. Grit and academic self-efficacy also have a serial mediation effect between growth mindset and ADG. Conclusion The results showed that a growth mindset does not only directly affect the ADG but also indirectly affects it through grit and academic self-efficacy. Based on SDT, this study further revealed the potential mechanism of a growth mindset affecting the ADG. Furthermore, it provides practical guidance for the cultivation of ADG for junior high school students.
Bidirectional Dilation Transformer for Multispectral and Hyperspectral Image Fusion

Transformer-based methods have proven to be effective in achieving long-distance modeling, capturing the spatial and spectral information, and exhibiting strong inductive bias in various computer vision tasks. Generally, the Transformer model includes two common modes of multi-head self-attention (MSA): spatial MSA (Spa-MSA) and spectral MSA (Spe-MSA). However, Spa-MSA is computationally efficient but limits the global spatial response within a local window. On the other hand, Spe-MSA can calculate channel self-attention to accommodate high-resolution images, but it disregards the crucial local information that is essential for low-level vision tasks. In this study, we propose a bidirectional dilation Transformer (BDT) for multispectral and hyperspectral image fusion (MHIF), which aims to leverage the advantages of both MSA and the latent multiscale information specific to MHIF tasks. The BDT consists of two designed modules: the dilation Spa-MSA (D-Spa), which dynamically expands the spatial receptive field through a given hollow strategy, and the grouped Spe-MSA (G-Spe), which extracts latent features within the feature map and learns local data behavior. Additionally, to fully exploit the multiscale information from both inputs with different spatial resolutions, we employ a bidirectional hierarchy strategy in the BDT, resulting in improved performance. Finally, extensive experiments on two commonly used datasets, CAVE and Harvard, demonstrate the superiority of BDT both visually and quantitatively. Furthermore, the related code will be available at the GitHub page of the authors.
Exploring the transformative influence of neuroplasticity on stroke rehabilitation: a narrative review of current evidence

This review aims to assess the role of neuroplasticity in facilitating stroke recovery and identify the challenges and limitations associated with its implementation. A comprehensive literature search was conducted to identify relevant studies, which were meticulously evaluated to determine the potential solutions for effectively harnessing neuroplasticity. The results indicate that neuroplasticity holds significant promise in stroke rehabilitation; however, individual variability in response to interventions, timing and duration of interventions and sociocultural and clinical factors pose challenges. Tailoring interventions to individual patient characteristics is crucial for optimising the impact of neuroplasticity. Despite challenges and limitations, the transformative potential of neuroplasticity in stroke rehabilitation is undeniable. The abstract concludes by emphasising the importance of a comprehensive understanding of individual variability, optimising intervention timing and duration and considering sociocultural and clinical factors. Future research and clinical practice should prioritise personalised interventions and interdisciplinary collaborations to fully exploit the vast potential of neuroplasticity in stroke recovery.
Extrapolating for attaining high precision solutions for fractional partial differential equations

Abstract This paper aims at obtaining a high precision numerical approximation for fractional partial differential equations. This is achieved through appropriate discretizations: firstly we consider the application of shifted Legendre or Chebyshev polynomials to get a spatial discretization, followed by a temporal discretization where we use the Implicit Euler method (although any other temporal integrator could be used). Finally, the use of an extrapolation technique is considered for improving the numerical results. In this way a very accurate solution is obtained. An algorithm is presented, and numerical results are shown to demonstrate the validity of the present technique.
Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress

Through conversion of land cover to more built-up, impervious surfaces, cities create hotter environments than their surroundings for urban residents, with large differences expected between different parts of the city. Existing measurements of ambient air temperature and heat stress, however, are often insufficient to capture the intra-urban variability in heat exposure. This study provides a replicable method for modeling air temperature, humidity, and moist heat stress over the urban area of Chapel Hill while engaging citizens to collect high-temporal and spatially-resolved air temperature and humidity measurements. We use low-cost, consumer-grade sensors combined with satellite remote sensing data and machine learning to map urban air temperature and relative humidity over various land-cover classes to understand intra-urban spatial variability of ambient heat exposure at a relatively high resolution (10 m). Our findings show that individuals may be exposed to higher levels of air temperature and moist heat stress than weather station data suggest, and that the ambient heat exposure varies according to land cover type, with tree-covered land the coolest and built-up areas the warmest, and time of day, with higher air temperatures observed during the early afternoon. Combining our resulting dataset with sociodemographic data, policymakers and urban planners in Chapel Hill can use data output from this method to identify areas exposed to high temperature and moist heat stress as a first step to design effective mitigation measures.
Mycorrhizal Fungal Effects on Plant Growth, Osmolytes, and CsHsp70s and CsPIPs Expression in Leaves of Cucumber under a Short-Term Heat Stress

Arbuscular mycorrhizal (AM) fungi enhance plant stress tolerance, but it is unclear whether AM fungi affect heat tolerance in cucumbers. This study aimed to analyze how an AM fungus, Diversispora versiformis, affected growth, chlorophyll, five osmolytes, and plasma membrane intrinsic proteins (PIPs) and heat shock protein 70 (Hsp70) gene expression in cucumber leaves after a short-term (80 h) heat stress. Heat treatment significantly reduced root AM fungal colonization rate (0.26 folds). Heat treatment also distinctly suppressed plant height, stem diameter, and biomass, whereas AM fungal inoculation improved these growth variables as well as the chlorophyll index, with the benefit being more obvious under heat than under no-heat stress conditions. Heat treatment triggered differential changes in osmolytes (sucrose, fructose, and betaine) of inoculated and uninoculated cucumbers, whereas inoculation with AM fungus significantly raised leaf sucrose, fructose, glucose, betaine, and proline levels when compared to non-AM fungal inoculation. Heat treatment increased the expression of two (CsPIP1;6 and CsPIP2;1) of eight CsPIPs in inoculated and uninoculated plants, whereas AM fungal inoculation up-regulated the expression of CsPIP1;6, CsPIP2;1, and CsPIP2;6 under heat stress conditions. Hsp70s expressed differently in inoculated and uninoculated plants under heat versus no-heat stress, with 6 of 11 CsHsp70s down-regulated in inoculated plants. Under heat stress conditions, AM fungus only up-regulated CsHsp70-8 expression in 11 Hsp70s, while another eight CsHsp70s were down-regulated. Heat treatment and AM fungal inoculation both increased the expression of CsHsp70-8 and CsPIP1;6. It was concluded that AM fungus-inoculated cucumbers have high levels of growth, chlorophyll, and osmolytes under heat stress and do not require high CsPIPs and CsHsp70s expression to tolerate a short-term heat treatment.
Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information

MOTIVATION Reconstruction of gene regulatory networks (GRNs), which explicitly represent the causality of developmental or regulatory process, is of utmost interest and has become a challenging computational problem for understanding the complex regulatory mechanisms in cellular systems. However, all existing methods of inferring GRNs from gene expression profiles have their strengths and weaknesses. In particular, many properties of GRNs, such as topology sparseness and non-linear dependence, are generally in regulation mechanism but seldom are taken into account simultaneously in one computational method. RESULTS In this work, we present a novel method for inferring GRNs from gene expression data considering the non-linear dependence and topological structure of GRNs by employing path consistency algorithm (PCA) based on conditional mutual information (CMI). In this algorithm, the conditional dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles. The method is validated on the benchmark GRNs from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The cross-validation results confirmed the effectiveness of our method (PCA-CMI), which outperforms significantly other previous methods. Besides its high accuracy, our method is able to distinguish direct (or causal) interactions from indirect associations. AVAILABILITY All the source data and code are available at: http://csb.shu. edu.cn/subweb/grn.htm. CONTACT lnchen@sibs.ac. cn; zpliu@sibs.ac. cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
End-Cloud Collaboration Navigation Planning Method for Unmanned Aerial Vehicles Used in Small Areas

Unmanned aerial vehicle (UAV) collaboration has become the main means of indoor and outdoor regional search, railway patrol, and other tasks, and navigation planning is one of the key, albeit difficult, technologies. The purpose of UAV navigation planning is to plan reasonable trajectories for UAVs to avoid obstacles and reach the task area. Essentially, it is a complex optimization problem that requires the use of navigation planning algorithms to search for path-point solutions that meet the requirements under the guide of objective functions and constraints. At present, there are autonomous navigation modes of UAVs relying on airborne sensors and navigation control modes of UAVs relying on ground control stations (GCSs). However, due to the limitation of airborne processor computing power, and background command and control communication delay, a navigation planning method that takes into account accuracy and timeliness is needed. First, the navigation planning architecture of UAVs of end-cloud collaboration was designed. Then, the background cloud navigation planning algorithm of UAVs was designed based on the improved particle swarm optimization (PSO). Next, the navigation control algorithm of the UAV terminals was designed based on the multi-objective hybrid swarm intelligent optimization algorithm. Finally, the computer simulation and actual indoor-environment flight test based on small rotor UAVs were designed and conducted. The results showed that the proposed method is correct and feasible, and can improve the effectiveness and efficiency of navigation planning of UAVs.
NGR-Based Radiopharmaceuticals for Angiogenesis Imaging: A Preclinical Review

Angiogenesis plays a crucial role in tumour progression and metastatic spread; therefore, the development of specific vectors targeting angiogenesis has attracted the attention of several researchers. Since angiogenesis-associated aminopeptidase N (APN/CD13) is highly expressed on the surface of activated endothelial cells of new blood vessels and a wide range of tumour cells, it holds great promise for imaging and therapy in the field of cancer medicine. The selective binding capability of asparagine-glycine-arginine (NGR) motif containing molecules to APN/CD13 makes radiolabelled NGR peptides promising radiopharmaceuticals for the non-invasive, real-time imaging of APN/CD13 overexpressing malignancies at the molecular level. Preclinical small animal model systems are major keystones for the evaluation of the in vivo imaging behaviour of radiolabelled NGR derivatives. Based on existing literature data, several positron emission tomography (PET) and single-photon emission computed tomography (SPECT) radioisotopes have been applied so far for the labelling of tumour vasculature homing NGR sequences such as Gallium-68 (68Ga), Copper-64 (64Cu), Technetium-99m (99mTc), Lutetium-177 (177Lu), Rhenium-188 (188Re), or Bismuth-213 (213Bi). Herein, a comprehensive overview is provided of the recent preclinical experiences with radiolabelled imaging probes targeting angiogenesis.
Gut Microbiota Interventions to Retain Residual Kidney Function

Residual kidney function for patients with chronic kidney disease (CKD) is associated with better quality of life and outcome; thus, strategies should be implemented to preserve kidney function. Among the multiple causes that promote kidney damage, gut dysbiosis due to increased uremic toxin production and endotoxemia need attention. Several strategies have been proposed to modulate the gut microbiota in these patients, and diet has gained increasing attention in recent years since it is the primary driver of gut dysbiosis. In addition, medications and faecal transplantation may be valid strategies. Modifying gut microbiota composition may mitigate chronic kidney damage and preserve residual kidney function. Although various studies have shown the influential role of diet in modulating gut microbiota composition, the effects of this modulation on residual kidney function remain limited. This review discusses the role of gut microbiota metabolism on residual kidney function and vice versa and how we could preserve the residual kidney function by modulating the gut microbiota balance.