Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal interpolation schemes to constrain pmPM2.5 in regional modeling over the United States

This thesis presents the use of data assimilation with optimal interpolation (OI) to develop atmospheric aerosol concentration estimates for the United States at high spatial and temporal resolutions. Concentration estimates are highly desirable for a wide range of applications, including visibility, climate, and human health. OI is a viable data assimilation method that can be used to improve Community Multiscale Air Quality (CMAQ) model fine particulate matter (PM2.5) estimates. PM2.5 is the mass of solid and liquid particles with diameters less than or equal to 2.5 m suspended in the gas phase. OI was employed by combining model estimates with satellite and surface measurements. The satellite data assimilation combined 36 x 36 km aerosol concentrations from CMAQ with aerosol optical depth (AOD) measured by MODIS and AERONET over the continental United States for 2002. Posterior model concentrations generated by the OI algorithm were compared with surface PM2.5 measurements to evaluate a number of possible data assimilation parameters, including model error, observation error, and temporal averaging assumptions. Evaluation was conducted separately for six geographic U.S. regions in 2002. Variability in model error and MODIS biases limited the effectiveness of a single data assimilation system for the entire continental domain. The best combinations of four settings and three averaging schemes led to a domain-averaged improvement in fractional error from 1.2 to 0.97 and from 0.99 to 0.89 at respective IMPROVE and STN monitoring sites. For 38% of OI results, MODIS OI degraded the forward model skill due to biases and outliers in MODIS AOD. Surface data assimilation combined 36 × 36 km aerosol concentrations from the CMAQ model with surface PM2.5 measurements over the continental United States for 2002. The model error covariance matrix was constructed by using the observational method. The observation error covariance matrix included site representation that scaled the observation error by land use (i.e. urban or rural locations). In theory, urban locations should have less effect on surrounding areas than rural sites, which can be controlled
Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.
Sex-related differences in the associations between diurnal cortisol pattern and social and emotional loneliness in older adults

Introduction Loneliness is a distressful feeling that can affect mental and physical health, particularly among older adults. Cortisol, the primary hormone of the Hypothalamic-Pituitary-Adrenal axis (HPA-axis), may act as a biological transducer through which loneliness affects health. While most previous studies have evaluated the association between loneliness, as a unidimensional construct, and diurnal cortisol pattern, no research has examined this relationship discriminating between social and emotional loneliness in older adults. As sex differences in the negative mental health outcomes of loneliness have been reported, we also investigated whether diurnal cortisol indices and loneliness associations occur in a sex-specific manner. Methods We analyzed the diurnal cortisol- pattern in 142 community-dwelling, non-depressed, Caucasian older adults (55,6% female) aged 60-90. Social and emotional (family and romantic) loneliness scores were assessed using the Spanish version of the Social and Emotional Loneliness Scale for Adults (SELSA). Five salivary cortisol samples were used to capture key features of the diurnal cortisol pattern, including: awakening and bedtime cortisol levels, awakening response (CAR), post-awakening cortisol output (post-awakening cortisol [i.e., the area under the curve with reference to the ground: AUCG]), total diurnal cortisol release (AUCG), and diurnal cortisol slope (DCS). Results After controlling for sociodemographic variables, the hierarchical linear multiple regression analyses revealed that in male older adults, higher scores on social and family loneliness were associated with elevated awakening cortisol levels, total diurnal cortisol output, and a steeper diurnal cortisol slope (DCS). However, these associations were not observed in female older adults. In addition, feelings of romantic loneliness were positively associated with bedtime cortisol levels and AUCG in older males. Multilevel growth curve modeling showed that experiencing more social and emotional loneliness predicted higher diurnal cortisol output throughout the day in older male adults. Discussion The presence of sex differences in the relationship between cortisol indices and loneliness among older adults holds particular significance for diagnostic and screening procedures. Combining loneliness scales as screening tools with diurnal cortisol measures has the potential to be an effective and cost-efficient approach in identifying higher-risk individuals at early stages.
The Effect of Orally Administered Δ9-Tetrahydrocannabinol (THC) and Cannabidiol (CBD) on Obesity Parameters in Mice

Prolonged cannabis users show a lower prevalence of obesity and associated comorbidities. In rodent models, Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) from the plant Cannabis sativa L. have shown anti-obesity properties, suggesting a link between the endocannabinoid system (ECS) and obesity. However, the oral administration route has rarely been studied in this context. The aim of this study was to investigate the effect of prolonged oral administration of pure THC and CBD on obesity-related parameters and peripheral endocannabinoids. C57BL/6 male mice were fed with either a high-fat or standard diet and then received oral treatment in ramping doses, namely 10 mg/kg of THC or CBD for 5 weeks followed by 30 mg/kg for an additional 5 weeks. Mice treated with THC had attenuated weight gain and improved glucose tolerance, followed by improvement in steatosis markers and decreased hypertrophic cells in adipose epididymal tissue. Mice treated with CBD had improved glucose tolerance and increased markers of lipid metabolism in adipose and liver tissues, but in contrast to THC, CBD had no effect on weight gain and steatosis markers. CBD exclusively decreased the level of the endocannabinoid 2-arachidonoylglycerol in the liver. These data suggest that the prolonged oral consumption of THC, but not of CBD, ameliorates diet-induced obesity and metabolic parameters, possibly through a mechanism of adipose tissue adaptation.
Genotype and Phenotype Characterization of Rhinolophus sp. Sarbecoviruses from Vietnam: Implications for Coronavirus Emergence

Bats are a major reservoir of zoonotic viruses, including coronaviruses. Since the emergence of SARS-CoV in 2002/2003 in Asia, important efforts have been made to describe the diversity of Coronaviridae circulating in bats worldwide, leading to the discovery of the precursors of epidemic and pandemic sarbecoviruses in horseshoe bats. We investigated the viral communities infecting horseshoe bats living in Northern Vietnam, and report here the first identification of sarbecoviruses in Rhinolophus thomasi and Rhinolophus siamensis bats. Phylogenetic characterization of seven strains of Vietnamese sarbecoviruses identified at least three clusters of viruses. Recombination and cross-species transmission between bats seemed to constitute major drivers of virus evolution. Vietnamese sarbecoviruses were mainly enteric, therefore constituting a risk of spillover for guano collectors or people visiting caves. To evaluate the zoonotic potential of these viruses, we analyzed in silico and in vitro the ability of their RBDs to bind to mammalian ACE2s and concluded that these viruses are likely restricted to their bat hosts. The workflow applied here to characterize the spillover potential of novel sarbecoviruses is of major interest for each time a new virus is discovered, in order to concentrate surveillance efforts on high-risk interfaces.
Molecular Docking and Dynamics of SARS-CoV-2 Programmed Ribosomal Frameshifting RNA and Ligands for RNA-Targeting Alkaloids Prospecting

RNA-ligand docking is a part of computational biology, which is currently lowly recognized compared to the protein-ligand docking procedure commonly applied for drug discovery. This in silico study aims to create a simplified protocol for RNA-ligand docking, which is applicable to RNA-targeting small molecular drug screening. Four alkaloids (berberine, colchicine, nicotine, and tomatine) were subjected to this study and contended against the SARS-CoV-2 genomic RNA -1 PRF component targeting control drug, merafloxacin, including two known intercalator berberine and colchicine, a small alkaloid nicotine and a large alkaloid tomatine. The alkaloids were screened for drug-likeness properties (Lipinski’s Rules of 5 or LRo5), bioavailability indexes, and synthetic accessibility values using SwissADME before docking. The docking used PyRx – Autodock Vina and re-scored for RNA-ligand scoring using AnnapuRNA. The docking results have the interactions mapped using fingeRNAt and visualized using Discovery Studio. Molecular dynamics using CHARMM36 and AMBER forcefields were simulated in NAMD. The molecular dynamics 1 ns simulation results showed that the ligand interaction over time did not cause much interference with the RNA, indicated by the low number of RMSD changes between RNA itself and the RNA-ligand complex. Additionally, CHARMM36 forcefield provided more stable fluctuation compared to AMBER. The results indicated that tomatine disobeyed LRo5 and had a low bioavailability index and bad synthetic accessibility value, while the rest alkaloids passed. In the end, berberine has an even higher docking score than the control drug. The study also shows that this protocol can be useful for future RNA-ligand computational studies.
Discriminant Analysis of Gentiana Headspace Gas Chromatography – Mass Spectrometry Extract Data from In Vitro Cultures

A set of Gentiana L. species was successfully grown in vitro under the same conditions, and 72 samples from various cultures of these species (root, shoot, cotyledon callus, hypocotyl callus, and root callus) were obtained. The investigated species were G. affinis, G. andrewsii, G. bhutanica, G. burseri, G. cachemirica, G. capitata, G. crassicaulis, G. dahurica, G. decumbens, G. freyniana, G. frigida, G. gelida, G. grossheimii, G. kurroo, G. macrophylla, G. paradoxa, G. robusta, G. scabra, G. septemfida, G. siphonantha, and G. tianschanica. The obtained samples were extracted with a methanol-acetone-water (3:1:1) mixture, evaporated to dryness, and subjected to thin layer chromatography (TLC) on silica gel in sandwich mode with ethyl acetate-methanol-water (8:2:2) as the mobile phase. The resulting dry extracts were subjected to gas chromatography – mass spectrometry (GC-MS) fingerprinting of the headspace volatile fraction. Total ion count and average mass spectrum vectors were collected as two blocks and scaled independently to form a complex dataset. The major direction separating root or shoot samples from callus samples was found not to be fully associated with the highest variance as this information was placed in the first and fourth principal components of the principal component analysis (PCA). Therefore, linear discriminant analysis was performed on the first four (only the informative) components to reveal features responsible for the separation of culture types in the multivariate space.
Anti-inflammatory effect of interleukin-6 highly enriched in secretome of two clinically relevant sources of mesenchymal stromal cells

Despite several advances in the field of regenerative medicine, clinical management of extensive skin wounds or burns remains a major therapeutic issue. During the past few years, Mesenchymal Stromal Cells (MSCs) have emerged as a novel therapeutic tool to promote tissue repair through their anti-inflammatory, pro-trophic and pro-remodeling effects. They exert their biological activity mainly via the secretion of soluble bioactive molecules such as cytokines, growth factors, proteins and microRNAs which can be encapsulated within extracellular vesicles (EV). The recent discovery of their high plasticity to external stimuli has fostered the development of new targeted therapies known as priming strategies, to enhance their potential. Our team recently showed that Interleukin-1β (IL-1β)-primed gingival MSCs promote wound healing and epidermal engraftment in vitro, and in vivo through their secreted products that contain extracellular vesicles. In the present work, we investigated whether two common sources of MSCs, gingiva and bone marrow, could respond similarly to IL-1β to favor pro-healing capabilities of their secretome. We showed that both primed-MSC sources, or their related secreted products, are able to reduce inflammation in LPS-challenged human monocytic THP-1 cell line. IL-1β priming enhanced MSC secretion of wound healing-related growth factors, cytokines and miRNAs in both sources. Among them, interleukin 6 was shown to be involved in the anti-inflammatory effect of MSC secreted products. Overall, these results underline the pro-healing properties of both MSC sources and their secretome upon IL-1β priming and their potential to improve the current medical treatment of severe wounds.
A trajectory planning scheme for spacecraft in the space station environment. M.S. Thesis - University of California

Simulated annealing is used to solve a minimum fuel trajectory problem in the space station environment. The environment is special because the space station will define a multi-vehicle environment in space. The optimization surface is a complex nonlinear function of the initial conditions of the chase and target crafts. Small permutations in the input conditions can result in abrupt changes to the optimization surface. Since no prior knowledge about the number or location of local minimum on the surface is available, the optimization must be capable of functioning on a multimodal surface. It has been reported in the literature that the simulated annealing algorithm is more effective on such surfaces than descent techniques using random starting points. The simulated annealing optimization was found to be capable of identifying a minimum fuel, two-burn trajectory subject to four constraints which are integrated into the optimization using a barrier method. The computations required to solve the optimization are fast enough that missions could be planned on board of the space station. Potential applications for on board planning of missions are numerous. Future research topics may include optimal planning of multi-waypoint maneuvers using a knowledge base to guide the optimization, and a study aimed at developing robust annealing schedules for potential on board missions. PRECEDING PAGE BLANK NOT FILMED
Why do users keep coming back to TikTok? Understanding users’ motivation toward the continuous use intention of Chinese adolescent social media users

In recent years, the TikTok platform has made a significant impact in the social media market worldwide, now holding a large market share in the short video service market popular with young people. Despite all this success, there is little research on how Internet users become loyal to TikTok. Hence, this study proposes a framework based on the use and gratification theory to evaluate the antecedents of adolescents’ continuous use motivations and behavior, and how they influence teenagers’ continuous use of TikTok. The survey population for this study was reached both online and offline. A total of 362 adolescent Chinese users of the TikTok platform were surveyed. The results show that self-expression, entertainment, information seeking, communication, and passing time were derived as adolescents’ motivations to use TikTok. Among these motivations, entertainment, communication, and passing time positively affect user satisfaction with TikTok. Furthermore, user satisfaction and loyalty positively affect the intention to continue using TikTok. Finally, this study advances understanding of adolescents’ use motivation of TikTok and provides new reference material for the continuous innovation of the short video format. AcknowledgmentThis study appreciated the partial financial support from Research Project of Anhui University of Science and Technology in 2023 (Project topic: Research on the construction and improvement strategy of mobile shopping experience evaluation system from the perspective of experience economy; Grant number: 2023yjrc30) and Key Research Projects of Humanities and Social Sciences in Colleges and Universities in Anhui Province (Project topic: Research on the Management Mechanism and Practical Path of Anhui Free Trade Zone; Grant number: SK2020A0199).
Domain Adaptation Using Class-Balanced Self-Paced Learning for Soil Classification With LIBS

Laser-induced breakdown spectroscopy (LIBS) is a promising technology for soil analysis due to its simple setup, cost-effectiveness, and rapid (few seconds) analysis time per sample. The recent rise in machine learning (ML) techniques for processing LIBS spectra has made LIBS more attractive. However, because of the soil samples’ varied physical properties and chemical composition, the emission lines’ distribution can be highly variable. It may cause spectra distribution change and make the training spectra not representative of the test spectra. Hence, applying an ML model trained with only samples from the training distribution to the test distribution will likely experience performance degradation. To solve the spectra distribution problem, we propose using self-learning, a domain adaptation (DA) method to self-adapt to the domain shift. It involves an iterative process of predicting on the target domain with the model trained by the source domain and then taking the confident predictions as pseudolabels for co-training the model. On top of self-learning, we also propose a novel class-balanced self-paced learning method. It balances the classes in the co-training process by ignoring the easy classes, which has a large predictive proportion to avoid the gradual dominance of these classes in pseudolabel generation. Instead of using universal selection proportion and in addition to achieve various confidence thresholds for classes, the proposed method balances and self-paces the other classes by customizing the class selection proportion and increment to avoid model bias in the self-training process. The class selection proportion and addition are tuned by validation, in which the validation set is generated by decision fusion of convolutional neural networks and partial least-squares discriminant analysis. Our method is tested with the Euro-Mediterranean symposium on LIBS (EMSLIBS) dataset, which shows the proposed method can effectively handle the spectra distribution change and achieves 90.2% test accuracy. It is comparable to the EMSLIBS contest winners with the same dataset, which uses test data calibration.
A simple and rapid DNA extraction method from meat by microneedle patch for detection of adulterated components

ABSTRACT The simple and rapid identification of meat via DNA-based methods is still subject to the conditions of complex and time-consuming preprocessing. It usually takes 3–4 h to obtain DNA sample using conventionally available DNA extraction kits. The existing approaches are cumbersome, require large equipment, and are not suitable for field operation. This study developed a swelling microneedle patch, constituted by micron-sized copolymers of methyl vinyl ether and maleic acid (PMVE/MA). The microneedle patch was applied to the meat sample for 1 min before being pulled out and rinsed with Tris-EDTA (TE) buffer to obtain DNA, which could be used for polymerase chain reaction (PCR) without purification. The DNA extracted by the microneedle patch could meet the needs for amplification and identification of meat samples. The developed approach which combines the patch-based extraction method and conventional PCR amplification is quick, simple, and reliable for DNA extraction and identification of meat samples.
Vietnamese EFL high school teachers’ perceptions of difficulties when implementing competency-based English teaching curriculum and their proposed solutions

This study aims to examine the perceptions of Vietnamese English as a foreign language (EFL) high school teachers regarding the implementation of the competency-based English teaching curriculum (CETC) in the Mekong Delta region of Vietnam. Utilizing a qualitative approach, this study sought to delve into the challenges high school EFL teachers faced while implementing the CETC in Vietnam. Semi-structured interviews with 10 teachers were conducted to gain insights into their experiences, specifically targeting their perceptions of difficulties and their suggestions for potential solutions derived from these experiences. The findings reveal teachers’ perceptions of their perceived difficulties related to student-related factors (low English proficiency, and limited self-directed learning abilities), teacher-related factors (traditional teaching methods, inadequate language and pedagogical skills, and insufficient training), and institution-related factors (large class sizes, limited teaching time, and inadequate facilities). In addition, the teachers recommended strategic solutions for a better implementation of the CETC including providing constructive feedback, promoting collaborative learning, enhancing teachers’ language and pedagogical proficiency, reducing class sizes, and improving facilities. Accordingly, the study contributes to the understanding of CETC implementation and provides practical implications for teachers, students, and policymakers in Vietnam and other similar contexts.