Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary Practices and Anthropometric Profile of Professional Male Surfers

Aims: To identify dietary practices together with anthropometric variables of a group of professional surfers. Subjects: Participants of a National Surf Championship in Brazil. Evaluations: (1) Anthropometric measures, indexes and calculations (body mass, height, arm span, body circumferences and nine skinfold thicknesses, arm spam/height ratio, waist-to-hip-ratio, conicity index and % body fat); (2) One 24h food-recall, describing all the food and drinks consumed at the previous day, and analyzed for energy, macro-nutrients, some micro-nutrients and food groups. The values were compared with some references and position statements and with the Brazilian Food Guide; (3) The basal and daily energy expenditure was estimated from specific equations; the energy intake was estimated from the information on diet and the energy balance was estimated from the difference between these values. Main results: The intake of carbohydrate is below the position statements (2.5-9.7g/kg and 6-10g/kg body weight, respectively). Possibly the intake of supplements such as maltodextrin fulfilled the requirements (30.3% of the athletes referred the intake of this supplement); The intake of BCAA (Branched-Chain Amino Acids) showed to be high (24.2% of the athletes); The low intake of vegetables possibly contributes to the low intake of micronutrients (0.0-6.0 servings, against the recommendation of 4-5 servings); The high intake of fried food (added lipid = 0.0-5.9 servings, against the recommendation of 1.0-2.0 servings) overlapped healthy habits such as beans and nuts consumption (0.0-13.6 servings against the recommendation of 1.0 serving); The percentage of body fat seems to be similar to swimmers (around 12% of body weight). As a conclusion, the dietary practices of surfers in the day prior a competition need to be improved, especially with respect to carbohydrates and lipids. The balance between energy intake and energy expenditure needs to be investigated more deeply. Studies investigating the role of certain amino acids supplement, such as BCAA, in surf practice could allow a better interpretation of this practice.
Spatial and temporal dynamics of groundwater flow across a wet meadow, Polar Bear Pass, Bathurst island, Nunavut

Interest is growing about how groundwater supplies will shift in warming northern terrains. We evaluated the seasonal and spatial pattern of groundwater flow in a wet meadow bordered by a late‐lying snowbed and tundra ponds at Polar Bear Pass, Bathurst Island (75.7°N, 98.7°W). A water budget approach signalled the relative importance of groundwater inflow to tundra ponds. Groundwater flow across the wet meadow was estimated using a modified Darcy's equation, which requires information on both water and frost tables, and hydraulic conductivity. These data were obtained from 2007 to 2009 along a series of water wells extending from a late‐lying snowbed across the wet meadow to a nearby study pond. Groundwater fluxes across the wet meadow were limited in magnitude and duration in a warm/dry year (2007), when the late‐lying snowbed was the main external water source, a response differing from rainy/cool years (2008 and 2009). Overall, seasonal water budgets indicate that groundwater fluxes were minimal in the wet meadow and an adjacent tundra pond. Late‐lying snowbeds play a limited role in sustaining wet meadows and ponds here. Summer precipitation and evaporation continue to drive wet meadow and tundra pond hydrological response in low‐gradient wetlands, especially in the post‐snowmelt season. Copyright © 2016 John Wiley & Sons, Ltd.
Noise Removal and Fracture Analysis in Borehole Images Using Mathematical Morphology and Compressive Sensing

In this study, we suggest an automatic processing method of borehole acoustic or conductivity images based on the mathematical morphology and compressive sensing. In our method, there are four major steps, the first is the random noise attenuation of the field acoustic amplitude or conductivity borehole image, the second step implements the edge detection of fractures and vugs, the third step includes reconstruction of fracture and vug by using polynomial functions, and the final step carries out quantitative evaluation of the fracture and vug attributes. Other than conventional de-noising of the acoustic image with smooth filtering and median filtering which eliminate the noise as well as smooth away some useful edge details, we provide a de-noising method based on mathematical morphology and compressive sensing to remove some undesired details on the FMI or acoustic images that are related to acquisition noise or electric random noise on conductivity images of the borehole wall. The test example on real data has been provided and showed that the combination of two theories can achieve a satisfying result.
Top quark anomalous couplings from top-pair tagged photoproduction at pe− colliders

We summarize the quantitative results of our analysis [1] of top-pair photoproduction in semileptonic mode in pe collisions at the LHeC and FCC-he. We define three photoproduction regions, based on the rapidity acceptance range of the electron tagger, that provide different degrees of sensitivity to top-quark effective couplings. We focus on the ttγ dipole couplings and the left-handed vector tbW coupling, for which we determine limits at both energies in the different photoproduction regions. Furthermore, we find that the LHeC and FCC-he will yield tight direct bounds on top dipole moments, greatly improving on current direct limits from hadron colliders, and direct limits on the tbW coupling as restrictive as those expected from the HL-LHC. We also consider indirect limits from b → sγ branching ratio and CP asymmetry, that are well known to be very sensitive probes of top electromagnetic dipole moments.
Three-Dimensional Modeling with Osteoblast-like Cells under External Magnetic Field Conditions Using Magnetic Nano-Ferrite Particles for the Development of Cell-Derived Artificial Bone

The progress in artificial bone research is crucial for addressing fractures and bone defects in the aging population. However, challenges persist in terms of biocompatibility and structural complexity. Nanotechnology provides a promising avenue by which to overcome these challenges, with nano-ferrite particles (NFPs) exhibiting superparamagnetic properties. The ability to control cell positioning using a magnetic field opens up new possibilities for customizing artificial bones with specific shapes. This study explores the biological effects of NFPs on osteoblast-like cell lines (MC3T3-E1), including key analyses, such as cell viability, cellular uptake of NFPs, calcification processes, cell migration under external magnetic field conditions, and three-dimensional modeling. The results indicate that the impact of NFPs on cell proliferation is negligible. Fluorescence and transmission electron microscopy validated the cellular uptake of NFPs, demonstrating the potential for precise cell positioning through an external magnetic field. Under calcification-inducing conditions, the cells exhibited sustained calcification ability even in the presence of NFPs. The cell movement analysis observed the controlled movement of NFP-absorbing cells under an external magnetic field. Applying a magnetic field along the z-axis induced the three-dimensional shaping of cells incorporating NFPs, resulting in well-arranged z-axis directional patterns. In this study, NFPs demonstrated excellent biocompatibility and controllability under an external magnetic field, laying the foundation for innovative treatment strategies for customizing artificial bones.
Mass Spectrometry Investigation of Some ATP-Binding Cassette (ABC) Proteins

Drug resistance remains one of the main causes of poor outcome in cancer therapy. It is also becoming evident that drug resistance to both chemotherapy and to antibiotics is driven by more than one mechanism. So far, there are at least eight recognized mechanisms behind such resistance. In this review, we choose to discuss one of these mechanisms, which is known to be partially driven by a class of transmembrane proteins known as ATP-binding cassette (ABC) transporters. In normal tissues, ABC transporters protect the cells from the toxic effects of xenobiotics, whereas in tumor cells, they reduce the intracellular concentrations of anticancer drugs, which ultimately leads to the emergence of multidrug resistance (MDR). A deeper understanding of the structures and the biology of these proteins is central to current efforts to circumvent resistance to both chemotherapy, targeted therapy, and antibiotics. Understanding the biology and the function of these proteins requires detailed structural and conformational information for this class of membrane proteins. For many years, such structural information has been mainly provided by X-ray crystallography and cryo-electron microscopy. More recently, mass spectrometry-based methods assumed an important role in the area of structural and conformational characterization of this class of proteins. The contribution of this technique to structural biology has been enhanced by its combination with liquid chromatography and ion mobility, as well as more refined labelling protocols and the use of more efficient fragmentation methods, which allow the detection and localization of labile post-translational modifications. In this review, we discuss the contribution of mass spectrometry to efforts to characterize some members of the ATP-binding cassette (ABC) proteins and why such a contribution is relevant to efforts to clarify the link between the overexpression of these proteins and the most widespread mechanism of chemoresistance.
Three New Species of Microsphecodes s.str. (Hymenoptera: Halictidae) with an updated key to species of the subgenus.

We diagnose, describe and illustrate three new species of Microsphecodes Eickwort and Stage, all from the nominate subgenus: M. peckorum Graham and Packer n. sp. from Venezuela, M. fernandoi Graham and Packer n. sp. from French Guiana, and M. septentrionalis Graham and Packer n. sp. from Guatemala. The latter extends the known range of the subgenus further north by approximately 500 km. We provide an updated, illustrated, key to the species of Microsphecodes s.str.
Mining the Micro-Trajectory of Two-Wheeled Non-Motorized Vehicles Based on the Improved YOLOx

Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model’s feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads.
A Note on Taxonomy and Genetic Resource Potential of Oryza meyeriana var. indandamanica, a Rare Wild Relative of Rice from the Andaman Islands

The narrow endemic taxon, Oryza meyeriana (Zoll. & Moritzi) Baill. var. indandamanica (J.L.Ellis) Veldkamp, was often merged with either O. meyeriana (Zoll. & Moritzi) Baill. or O. granulata Nees & Arn. ex Watt. or all three taxa were subsumed to O. meyeriana. A critical analysis of protologues, herbarium specimens and earlier taxonomic works pertaining to these three taxa coupled with the detailed morphological study of germplasm (IC641181) of var. indandamanica, which was originally collected from the Saddle Peak National Park of the North Andaman Island, was undertaken. Morphological studies indicated that var. indandamanica fell well within the circumscription of var. meyeriana. However, chloroplast genome sequence data reveals its closeness towards var. granulata, compared with var. meyeriana. Our views agree to the merger of all these three taxa into one are presented. Also, the genetic resource potential of the studied accession is discussed.
Untargeted metabolomics profiling of oat (Avena sativa L.) and wheat (Triticum aestivum L.) infested with wheat stem sawfly (Cephus cinctus Norton) reveals differences associated with plant defense and insect nutrition

Introduction Wheat stem sawfly (WSS), Cephus cinctus Norton, is a major pest of common bread wheat (Triticum aestivum L.) and other cultivated cereals in North America. Planting of cultivars with solid stems has been the primary management strategy to prevent yield loss due to WSS infestation, however expression of this phenotype can vary depending on environmental conditions and solid stems hinder biological control of WSS via braconid parasitoids Bracon cephi (Gahan) and Bracon lissogaster Muesebeck. In the hollow stems of oat (Avena sativa L.), WSS larvae experience 100% mortality before they reach late instars, but the mechanisms for this observed resistance have not been characterized. Objective The objective of this study was to explore additional sources of resistance outside of the historic solid stem phenotype. Methods Here, we use an untargeted metabolomics approach to examine the response of the metabolome of two cultivars of oat and four cultivars of spring wheat to infestation by WSS. Using liquid chromatography-mass spectrometry (LC-MS), differentially expressed metabolites were identified between oat and wheat which were associated with the phenylpropanoid pathway, phospholipid biosynthesis and signaling, the salicylic acid signaling pathway, indole-3-acetic acid (IAA) degradation, and biosynthesis of 1,4-benzoxazin-3-ones (Bxs). Several phospho- and galacto- lipids were found in higher abundance in oat, and with the exception of early stem solidness cultivar Conan, both species experienced a decrease in abundance once infested. In all wheat cultivars except Conan, an increase in abundance was observed for Bxs HMDBOA-glc and DIBOA-β-D-glucoside after infestation, indicating that this pathway is involved in wheat response to infestation in both solid and hollow stemmed cultivars. Differences between species in compounds involved in IAA biosynthesis, degradation and inactivation suggest that wheat may respond to infestation by inactivating IAA or altering the IAA pool in stem tissue. Conclusion We propose that the species differences found here likely affect the survival of WSS larvae and may also be associated with differences in stem architecture at the molecular level. Our findings suggest pathways to focus on for future studies in elucidating plant response to WSS infestation.
Determinants of Personal Financial Management Behavior: Moderating Role of Financial Literacy

This study aims to investigate the effect of subjective norms and propensity to plan on personal financial management behavior, with financial literacy as a moderation. This study is a quantitative approach and data collection were done using questionnaires. The population of this study were students of Economics Education (Accounting) Department on one of University in Indonesia with a total of 235 students. In this paper, we used partial least squares to measure potential constituents as a composite index-path modeling. We used WarpPLS software version 7.0 to explore the relationships contained in the model. The structural equation model results show that subjective norms and the propensity to plan have a positive effect on personal financial management behavior. Additionally, financial literacy was able to moderate the influence of subjective norms on personal financial management behavior. However, moderating effects do not happen on the propensity to plan. This research implies that personal financial management behavior can depend on psychological aspects. Keywords: subjective norms, propensity to plan, personal financial management behavior, financial literacy
RECCIPE: A new framework assessing localized cell-cell interaction on gene expression in multicellular ST data

Cell-cell interaction (CCI) plays a pivotal role in cellular communication within the tissue microenvironment. The recent development of spatial transcriptomics (ST) technology and associated data analysis methods has empowered researchers to systematically investigate CCI. However, existing methods are tailored to single-cell resolution datasets, whereas the majority of ST platforms lack such resolution. Additionally, the detection of CCI through association screening based on ST data, which has complicated dependence structure, necessitates proper control of false discovery rates due to the multiple hypothesis testing issue in high dimensional spaces. To address these challenges, we introduce RECCIPE, a novel method designed for identifying cell signaling interactions across multiple cell types in spatial transcriptomic data. RECCIPE integrates gene expression data, spatial information and cell type composition in a multivariate regression framework, enabling genome-wide screening for changes in gene expression levels attributed to CCIs. We show that RECCIPE not only achieves high accuracy in simulated datasets but also provides new biological insights from real data obtained from a mouse model of Alzheimer’s disease (AD). Overall, our framework provides a useful tool for studying impact of cell-cell interactions on gene expression in multicellular systems.
Gravity gradient data filtering using translation invariant wavelet

Full tensor gradient (FTG) data is highly useful in hydrocarbon exploration and the detection of some geological targets with small size as its higher detailed information abundance and finer resolution. At the same time, there are some high-frequency Gaussian white noise mixed in the target signal and which has closer frequency range than the conventional gravity data. Thus, one key step before inversion is to remove as much Gaussian white noise as possible and reserve the subtle details. For this pre-processing step, several effective methods have been used, including low-pass filters, least square fitting methods based on Laplace equation and wavelet filtering methods. In this paper, we would utilize the translation invariant wavelet for the reason that it can suppress Gaussian white noise through multi-resolution analysis and at the same time can avoid pseudo-Gibbs phenomenon. The other point different from wavelet method used before is that we applied a mixed threshold constructed according to the curve of both soft threshold and hard threshold. Compared to soft and hard threshold, mixed threshold can keep more details and remove more noise respectively in terms of the energy distribution of signal and noise. Then we process wavelet coefficients with mixed threshold and do inverse transform to recover the data. The results demonstrate that translation invariant wavelet can not only remove the major part of Gaussian white noise, but also reserves high-frequency detailed information of FTG data. Obviously, translation invariant wavelet with mixed thresholding has preferable application effect in filtering FTG data.
Solving Nonlinear Fractional PDEs with Applications to Physics and Engineering Using the Laplace Residual Power Series Method

The Laplace residual power series (LRPS) method uses the Caputo fractional derivative definition to solve nonlinear fractional partial differential equations. This technique has been applied successfully to solve equations such as the fractional Kuramoto–Sivashinsky equation (FKSE) and the fractional generalized regularized long wave equation (GRLWE). By transforming the equation into the Laplace domain and replacing fractional derivatives with integer derivatives, the LRPS method can solve the resulting equation using a power series expansion. The resulting solution is accurate and convergent, as demonstrated in this paper by comparing it with other analytical methods. The LRPS approach offers both computational efficiency and solution accuracy, making it an effective technique for solving nonlinear fractional partial differential equations (NFPDEs). The results are presented in the form of graphs for various values of the order of the fractional derivative and time, and the essential objective is to reduce computation effort.
Conditional Diffusion Model for X-Ray Segmentation Data Generation

: Nowadays training a well-functioning deep learning AI model requires a large amount of data, while in the field of medicine many scenarios lack training data due to privacy issues and legal reasons. In this essay, we propose to use ControlNet, a novel approach that leverages stable diffusion models and conditional control to produce realistic and diverse medical images. ControlNet allows us to specify extra conditions that the diffusion model should follow, such as edge maps, depth maps, segmentation masks, or CLIP image embeddings. These conditions can help us to preserve the structure, shape, and semantics of the target organs or tissues, as well as to manipulate the appearance, style, and context of the generated images. Specifically, we will use ControlNet to generate X-ray of a patient with pulmonary nodules and show the improvement.