Single Observation Research Articles

Analysis of Growth Variation in Maize Leaf Area Index Based on Time-Series Multispectral Images and Random Forest Models

The leaf area index (LAI) is a direct indicator of crop canopy growth and serves as an indirect measure of crop yield. Unmanned aerial vehicles (UAVs) offer rapid collection of crop phenotypic data across multiple time points, providing crucial insights into the evolving dynamics of the LAI essential for crop breeding. In this study, the variation process of the maize LAI was investigated across two locations (XD and KZ) using a multispectral sensor mounted on a UAV. During a field trial involving 399 maize inbred lines, LAI measurements were obtained at both locations using a random forest model based on 28 variables extracted from multispectral imagery. These findings indicate that the vegetation index computed by the near-infrared band and red edge significantly influences the accuracy of the LAI prediction. However, a prediction model relying solely on data from a single observation period exhibits instability (R2 = 0.34–0.94, RMSE = 0.02–0.25). When applied to the entire growth period, the models trained using all data achieved a robust prediction of the LAI (R2 = 0.79–0.86, RMSE = 0.12–0.18). Although the primary variation patterns of the maize LAI were similar across the two fields, environmental disparities changed the variation categories of the maize LAI. The primary factor contributing to the difference in the LAI between KZ and XD lies in soil nutrients associated with carbon and nitrogen in the upper soil. Overall, this study demonstrated that UAV-based time-series phenotypic data offers valuable insight into phenotypic variation, thereby enhancing the application of UAVs in crop breeding.

Study on the Feasibility and Performance Evaluation of High-Orbit Spacecraft Orbit Determination Based on GNSS/SLR/VLBI

Deep space exploration utilizing high-orbit vehicles is a vital approach for extending beyond near-Earth space, with orbit information serving as the foundation for all functional capabilities. The performance of orbit determination is primarily influenced by observation types, errors, geometrical structures, and physical perturbations. Currently, research on orbit determination for high-orbit spacecraft predominantly focuses on single observation methods, error characteristics, multi-source fusion techniques, and algorithms. However, these approaches often suffer from low observation accuracy and increased costs. This paper advocates for the comprehensive utilization of existing multi-source observation methods, such as GNSS (Global Navigation Satellite System), SLR (Satellite Laser Ranging), and VLBI (Very Long Baseline Interferometry), in research. The decoupled Kalman filter reveals a positive correlation between measurement positioning accuracy and orbit determination accuracy, and it derives a simple orbit performance evaluation model that considers the influence of observation value types and geometric configurations, without the need to introduce complex dynamic models. Simulations are then employed to verify and analyze antenna gain, observation values, and performance evaluation. The results indicate the following: (1) Under simulated conditions, the optimal strategy involves employing the SLR/VLBI dual system during periods when VLBI orbit determination is feasible, yielding an average Weighted Position Dilution of Precision (WPDOP) of 26.79. (2) For periods when VLBI orbit determination is not feasible, the optimal approach is to utilize the GNSS/SLR/VLBI triple system, resulting in an average WPDOP of 16.32. (3) The orbit determination performance of the triple system is not significantly impacted by the use of global SLR stations compared to using only Chinese SLR stations. However, the global network enables continuous, round-the-clock orbit determination capabilities.

Talin and vinculin combine their activities to trigger actin assembly

Focal adhesions (FAs) strengthen their link with the actin cytoskeleton to resist force. Talin-vinculin association could reinforce actin anchoring to FAs by controlling actin polymerization. However, the actin polymerization activity of the talin-vinculin complex is not known because it requires the reconstitution of the mechanical and biochemical activation steps that control the association of talin and vinculin. By combining kinetic and binding assays with single actin filament observations in TIRF microscopy, we show that the association of talin and vinculin mutants, mimicking mechanically stretched talin and activated vinculin, triggers a sequential mechanism in which filaments are nucleated, capped and released to elongate. In agreement with these observations, FRAP experiments in cells co-expressing the same constitutive mutants of talin and vinculin revealed accelerated growth of stress fibers. Our findings suggest a versatile mechanism for the regulation of actin assembly in FAs subjected to various combinations of biochemical and mechanical cues.

Hints of a Sulfur-rich Atmosphere around the 1.6 R ⊕ Super-Earth L98-59 d from JWST NIRspec G395H Transmission Spectroscopy

Detecting atmospheres around planets with a radius below 1.6 R ⊕, commonly referred to as rocky planets, has proven to be challenging. However, rocky planets orbiting M dwarfs are ideal candidates due to their favorable planet-to-star radius ratio. Here, we present one transit observation of the Super-Earth L98-59 d (1.58 R ⊕ and 2.31 M ⊕), at the limit of rocky/gas-rich, using the JWST NIRSpec G395H mode covering the 2.8–5.1 μm wavelength range. The extracted transit spectrum from a single transit observation deviates from a flat line by 2.6σ–5.6σ, depending on the data reduction and retrieval setup. The hints of an atmospheric detection are driven by a large absorption feature between 3.3 and 4.8 μm. A stellar contamination retrieval analysis rejected the source of this feature as being due to stellar inhomogeneities, making the best fit an atmospheric model including sulfur-bearing species, suggesting that the atmosphere of L98-59 d may not be at equilibrium. This result will need to be confirmed by the analysis of the second NIRSpec G395H visit in addition to the NIRISS SOSS transit observation.

Inclined bending seismic reflection layer in the crust illuminated by distributed fibre-optic-sensing measurements in western Japan

Distributed acoustic sensing (DAS), which enables a single fibre-optic cable to function as multiple sensors, is a technique to measure the strain rate distributed along the cable. This technique is applied to record ground motions in western Japan via a 50 km-long fibre-optic cable beneath a national road. The measured values are strain changes along the cable every 5 m, corresponding to 9788 sensor deployments. This high-density measurement along the long cable successfully recorded the 2021 M2.8 and M3.2 earthquakes that occurred in the crust within the distance of the cable in southern Kyoto. The direct S waves were followed by seismic waves approximately 8–14 s later, which were reflected by lower crustal structures. These waveforms were previously reported by observing many earthquakes via multiple seismometers, but the DAS observations clearly illuminate reflected wavefields from single earthquake observations for the first time. The numerical simulation of the strain-rate wavefields of these earthquakes reveals the existence of a north-dipping thin layer with a slow seismic velocity in the lower crust, which becomes steeper in the shallower part. This layer might represent the path of slab-derived fluid to the shallow fault zone.

Mesiri: Mephisto Early Supernovae Ia Rapid Identifier

The early time observations of Type Ia supernovae (SNe Ia) play a crucial role in investigating and resolving longstanding questions about progenitor stars and the explosion mechanisms of these events. Colors of supernovae (SNe) in the initial days after the explosion can help differentiate between different types of SNe. However, the use of true color information to identify SNe Ia at the early-time explosion is still in its infancy. The Multi-channel Photometric Survey Telescope (Mephisto) is a photometric survey telescope equipped with three CCD cameras, capable of simultaneously imaging the same patch of sky in three bands (u, g, i or v, r, z), yielding real-time colors of astronomical objects. In this paper, we introduce a new time-series classification tool named Mephisto Early Supernovae Ia Rapid Identifier (Mesiri), which, for the first time, utilizes real-time color information to distinguish early-time SNe Ia from core-collapse supernovae. Mesiri is based on the deep learning approach and can achieve an accuracy of 96.75% ± 0.79%, and AUC of 98.87% ± 0.53% in case of single epoch random observation before the peak brightness. These values reach towards perfectness if additional data points on several night observations are considered. The classification with real-time color significantly outperforms that with pseudo-color, especially at the early time, i.e., with only a few points of observations. The BiLSTM architecture shows the best performance compared to others that have been tested in this work.

High-resolution spectroscopy of detached eclipsing binaries during total eclipses

We present results of high-resolution spectroscopic observations of detached eclipsing binaries (DEBs) with total eclipses, for which UVES spectra were obtained during the phase of totality. These observations serve as a key way to determine the age and initial metallicity of the systems and to verify evolutionary phases of their components and distances. With the additional, independent information concerning the effective temperature and metallicity of one of the components, we aim to estimate the precise ages of the studied binaries and show the usefulness of totality spectra. The second goal was to provide precise orbital and physical stellar parameters of the components of systems in question. Using the VLT/UVES, we obtained high-resolution spectra of 11 DEBs during their total-eclipse phase. Atmospheric parameters of then-visible (larger) components were obtained with With additional spectroscopy from the Comprehensive Research with \'Echelles on the Most interesting Eclipsing binaries ( project, public archives, and literature, we obtained radial-velocity (RV) measurements, from which orbital parameters were calculated. Photometric time-series observations from TESS and ASAS were modelled with the JKTEBOP code, and, combined with RV-based results, they allowed us to obtain physical parameters for nine double-lined systems from our sample. All the available data were used to constrain the ages with our own approach, utilising MESA isochrones. Reddening-free, isochrone-based distances were also estimated and confronted with Gaia Data Release 3 (GDR3) results. We show that single spectroscopic observations taken during a total eclipse can break the age-metallicity degeneracy and allow for the precise determination of the age of a DEB. With high-quality spectroscopic and photometric data, we are able to reach a 5-10<!PCT!> level of uncertainty (e.g. $724^ $ Myr). Even for single-lined DEBs, where absolute masses are not possible to obtain, the spectroscopic analysis of one of the components allows one to put strong constraints on the properties of both stars. For some cases, we noted inconsistencies between isochrone-based and GDR3 distances. For one binary, which could not be fitted with a single isochrone (RZ Eri), we suggest a new explanation.

Changepoint detection on daily home activity pattern: a sliced Poisson process method.

The problem of health and care of people is being revolutionized. An important component of that revolution is disease prevention and health improvement from home. A natural approach to the health problem is monitoring changes in people's behavior or activities. These changes can be indicators of potential health problems. However, due to a person's daily pattern, changes will be observed throughout each day, with, eg, an increase of events around meal times and fewer events during the night. We do not wish to detect such within-day changes but rather changes in the daily behavior pattern from one day to the next. To this end, we assume the set of event times within a given day as a single observation. We model this observation as the realization of an inhomogeneous Poisson process where the rate function can vary with the time of day. Then, we propose to detect changes in the sequence of inhomogeneous Poisson processes. This approach is appropriate for many phenomena, particularly for home activity data. Our methodology is evaluated on simulated data. Overall, our approach uses local change information to detect changes across days. At the same time, it allows us to visualize and interpret the results, changes, and trends over time, allowing the detection of potential health decline.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.

Functional Time Series Analysis and Visualization Based on Records

In many phenomena, data are collected on a large scale and at different frequencies. In this context, functional data analysis (FDA) has become an important statistical methodology for analyzing and modeling such data. The approach of FDA is to assume that data are continuous functions and that each continuous function is considered as a single observation. Thus, FDA deals with large-scale and complex data. However, visualization and exploratory data analysis, which are very important in practice, can be challenging due to the complexity of the continuous functions. Here we introduce a type of record concept for functional data, and we propose some nonparametric tools based on the record concept for functional data observed over time (functional time series). We study the properties of the trajectory of the number of record curves under different scenarios. Also, we propose a unit root test based on the number of records. The trajectory of the number of records over time and the unit root test can be used for visualization and exploratory data analysis. We illustrate the advantages of our proposal through a Monte Carlo simulation study. We also illustrate our method on two different datasets: Daily wind speed curves at Yanbu, Saudi Arabia and annual mortality rates in France. Overall, we can identify the type of functional time series being studied based on the number of record curves observed. Supplementary materials for this article are available online.

Arbuscular mycorrhizal fungal spore communities and co-occurrence networks demonstrate host-specific variation throughout the growing season.

Microbial community assembly involves a series of ecological filtering mechanisms that determine the composition of microbial communities. While the importance of both broad and local level factors on microbial communities has been reasonably well studied, this work often is limited to single observations and neglects to consider how communities change over time (i.e., seasonal variation). Because seasonal variation is an important determinant of community assembly and determines the relative importance of community assembly filters, this represents a key knowledge gap. Due to their close associations with seasonal variation in plant growth and fitness, arbuscular mycorrhizal (AM) fungi are useful groups for assessing the importance of seasonal dynamics in microbial community assembly. We tested how seasonal variation (spring vs. summer), plant life history stage (vegetative vs. flowering), and host plant species (Baptisia bracteata var. leucophaea & Andropogon gerardii) influenced AM fungal spore community assembly. AM fungal spore community temporal dynamics were closely linked to plant host species and life history stage. While AM fungal spore communities demonstrated strong turnover between the spring (e.g., higher sporulation) and late summer (e.g., higher diversity), the strength and direction of these changes was modified by host plant species. Here we demonstrate the importance of considering temporal variation in microbial community assembly, and also show how plant-microbe interactions can modify seasonal trends in microbial community dynamics.

Comparison of engineered nanoparticle transport in columns of three different lengths: Transport experiments and multi-observation point modeling

This study focuses on the effectiveness of commonly-used 15 cm column lengths for investigating nanoparticle transport in porous media. Experimental tests examined the transport and retention behaviors of two types of nanoparticles, graphene oxide (GO) and titanium dioxide (TiO2) nanoparticles, in saturated sand columns of different lengths (15, 30 and 45 cm), while considering key environmental factors like ionic strength (IS, 1–50 mM), flow rate (1–3 mL min−1), and grain size (150–850 μm). In the 15 cm columns, both GO and TiO2 transport decreased with higher IS and lower flow rate; grain size affected GO and TiO2 differently. Smaller grain size increased GO retention in the sand columns through straining, thus weakening GO mobility, whereas increased fluid shear force suppressed the ripening of TiO2, enhancing its migration. Similar environmental effects were noted in longer columns (30 and 45 cm), but fitted transport parameters (Smax and k) and predicted long-term mobility (Lmax) indicated that 15 cm columns might underestimate nanoparticle mobility. Blocking and ripening models based on single and multiple observation points to simulate nanoparticle transport and retention showed that predictions aligned well with experimental data. These results indicate that using combinations of columns of different lengths to achieve multiple observation points improves model prediction accuracy; in single-column experiments, the 45 cm and 30 cm columns respectively better predict the mobility range of GO and TiO2 compared to 15 cm columns.

Time series prediction method for multi-source observation data

Traditional time series analysis focuses on modeling and predicting time series data obtained from a single observation source. However, in a confrontational environment, one party often creates biased data to interfere with the other party's predictions. To mitigate the observation error inherent in single-source data, there is a growing emphasis on the importance of utilizing and studying multi-source observation data, which motivates this paper to propose methods for analyzing and predicting this kind of data. First, this paper proves that the accuracy of the estimation can be improved by introducing multiple sets of time-invariant data with bias. Subsequently, when this approach is extended to include time-varying data, these findings continue to hold. For time-varying data with bias, this paper proposes a comprehensive prediction procedure to provide the uncertainty distribution for the forthcoming moment. Finally, three examples are proposed to illustrate the effectiveness and efficiency of the methods.

State estimation for constant-time labeled automata under dense time

In this paper, we focus on state estimation for constant-time labeled automata in a dense time context, i.e., the time constraints of the automata can be given according to real numbers. Given a sequence of timed observations (i.e., pairs of logical observations with their time stamps) collected from a system within a finite time window, a state estimation method is proposed to find the set of states in which the system might reside by the end of the time window. By using both labeling and timing information as well as the structure of the system, we can express any finite time evolution from one state to another into constraint satisfaction problems (CSPs). This structural analysis is independent of all collected sequences of timed observations and can be achieved offline, although its cost is exponential with respect to the number of states in the system. Consequently, two algorithms are designed to perform state estimation under a single observation and no observation, respectively, by solving a finite number of CSPs generated according to the system’s structural information. Both algorithms can be jointly used in an iterative approach to perform state estimation for any sequence of timed observations. In such a case, the number of generated CSPs in the algorithms increases linearly with respect to the length of the observed sequence.

Comparison of test-retest reproducibility of DESPOT and 3D-QALAS for water T 1 and T 2 mapping.

Relaxometry, specifically T 1 and T 2 mapping, has become an essential technique for assessing the properties of biological tissues related to various physiological and pathological conditions. Many techniques are being used to estimate T 1 and T 2 relaxation times, ranging from the traditional inversion or saturation recovery and spin-echo sequences to more advanced methods. Choosing the appropriate method for a specific application is critical since the precision and accuracy of T 1 and T 2 measurements are influenced by a variety of factors including the pulse sequence and its parameters, the inherent properties of the tissue being examined, the MRI hardware, and the image reconstruction. The aim of this study is to evaluate and compare the test-retest reproducibility of two advanced MRI relaxometry techniques (Driven Equilibrium Single Pulse Observation of T 1 and T 2, DESPOT, and 3D Quantification using an interleaved Look-Locker acquisition Sequence with a T 2 preparation pulse, QALAS), for T 1 and T 2 mapping in a healthy volunteer cohort. 10 healthy volunteers underwent brain MRI at 1.3 mm3 isotropic resolution, acquiring DESPOT and QALAS data (~11.8 and ~5 minutes duration, including field maps, respectively), test-retest with subject repositioning, on a 3.0 Tesla Philips Ingenia Elition scanner. To reconstruct the T 1 and T 2 maps, we used an equation-based algorithm for DESPOT and a dictionary-based algorithm that incorporates inversion efficiency and B 1 -field inhomogeneity for QALAS. The test-retest reproducibility was assessed using the coefficient of variation (CoV), intraclass correlation coefficient (ICC) and Bland-Altman plots. Our results indicate that both the DESPOT and QALAS techniques demonstrate good levels of test-retest reproducibility for T 1 and T 2 mapping across the brain. Higher whole-brain voxel-to-voxel ICCs are observed in QALAS for T 1 (0.84 ± 0.039) and in DESPOT for T 2 (0.897 ± 0.029). The Bland-Altman plots show smaller bias and variability of T 1 estimates for QALAS (mean of -0.02 s, and upper and lower limits of -0.14 and 0.11 s, 95% CI) than for DESPOT (mean of -0.02 s, and limits of -0.31 and 0.27 s). QALAS also showed less variability (mean 1.08 ms, limits -1.88 to 4.04 ms) for T 2 compared to DESPOT (mean of 2.56 ms, and limits -17.29 to 22.41 ms). The within-subject CoVs for QALAS range from 0.6% (T 2 in CSF) to 5.8% (T 2 in GM), while for DESPOT they range from 2.1% (T 2 in CSF) to 6.7% (T 2 in GM). The between-subject CoVs for QALAS range from 2.5% (T 2 in GM) to 12% (T 2 in CSF), and for DESPOT they range from 3.7% (T 2 in WM) to 9.3% (T 2 in CSF). Overall, QALAS demonstrated better reproducibility for T 1 and T 2 measurements than DESPOT, in addition to reduced acquisition time.

Current distribution of the Reeves’ muntjac Muntiacus reevesi (Ogilby, 1839) in France

Abstract The Reeves’ muntjac Muntiacus reevesi is an ungulate native to China and Taiwan that has been released into the wild in several continental European countries and the United Kingdom. We present recent records of the species in mainland France. We also investigated the dispersal rate and density of the established population in the Centre-Val de Loire region. The number of observations of M. reevesi was low in the period 2008–2016, increased sharply until 2019 and then decreased until 2023. From a comparison between the distribution in 2008–2012 and 2013–2017, the dispersal rate was estimated at an average of 3.2 km/year, and 7.6 km/year between 2013–2017 and 2018–2023. Based on a camera trap protocol, the population density was assumed to be very low in forested areas but estimated with a lack of robustness due to a single observation. Further efforts must be made to better assess the risk of an increase in the wild population of Reeves’ muntjac in the Centre-Val de Loire region. To prevent potential impacts, rapid control measures can be put in place in the large area (10,800 km2) where muntjacs are now rapidly spreading.

Helmert variance component estimation for mixed additive and multiplicative random error model

With the development of surveying and mapping science, the object of measurement adjustment has been extended from a single observation of the same kind in the past to different precisions of the same kind, or different kinds of observations. There are additive and multiplicative errors related to electronic instrument measurements, which affect the quality of the adjustment results. A single additive error model is difficult to meet the accuracy requirements, so a mixed additive and multiplicative random error model (MAMREM) is needed. Aiming at the problem of inaccurate MAMREM stochastic model, this paper proposes Helmert Variance Component Estimation (VCE) to determine the weight matrix in different types of observations under MAMREM. In this paper, the formula and iterative algorithm of Helmert variance component estimation applied to MAMREM are derived, and the weights of different kinds of observations in the adjustment process are estimated. In order to verify the effectiveness of the method, the digital elevation model experiment and the side network experiment are used to verify the method. The results prove the effectiveness of the method.

Wind-line Variability and Intrinsic Errors in Observational Mass-loss Rates

UV wind-line variability in OB stars appears to be universal. In order to quantify this variation and to estimate its effect on a mass-loss rate determined from a single observation, we use the International Ultraviolet Explorer archive to identify nonpeculiar OB stars with well-developed but unsaturated Si iv λ1400 doublets and at least 10 independent observations. This resulted in 1699 spectra of 25 stars. A simple model was used to translate the observed profile variations into optical depth variations and, hence, variations in measured mass-loss rates. These variations quantify the intrinsic error inherent in any single mass-loss rate derived from a single observation. The derived rates have an overall 1σ variation of about 22%, but this appears to differ with T eff, being as small at 8% for the hottest stars and up to 45% for the cooler ones. Furthermore, any single determination can differ from the mean by a factor of 2 or more. Our results also imply that mass-loss rates determined from nonsimultaneous observations (such as UV and ground-based data) need not agree. In addition, we use our results to examine the nature of the structures responsible for the variability. Our findings suggest that the optical depth variations result from optically very thick structures occulting more or less of the line of sight to the stellar disk. Further, the smaller optical depth variations in the hottest stars suggest that the structures responsible for the variations are disrupted in their more powerful winds.

X-Ray Source Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on systematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reliable classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed Random Forest classifier, built on selected features derived from light curves, energy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classification during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

Muscle Healing in Sports Injuries: MRI Findings and Proposed Classification Based on a Single Institutional Experience and Clinical Observation.

MRI plays a crucial role in assessment of patients with muscle injuries. The healing process of these injuries has been studied in depth from the pathophysiologic and histologic points of view and divided into destruction, repair, and remodeling phases, but the MRI findings of these phases have not been fully described, to our knowledge. On the basis of results from 310 MRI studies, including both basal and follow-up studies, in 128 athletes with muscle tears including their clinical evolution, the authors review MRI findings in muscle healing and propose a practical imaging classification based on morphology and signal intensity that correlates with histologic changes. The proposed phases, which can overlap, are destruction (phase 1), showing myoconnective tissue discontinuity and featherlike edema; repair (phase 2), showing filling in of the connective tissue gaps by a hypertrophic immature scar; and remodeling (phase 3), showing scar maturation and regression of the edema. A final healed stage can be identified with MRI, which is characterized by persistence of a slight fusiform thickening of the connective tissue. This information can be obtained from a truncated MRI protocol with three acquisitions, preferably performed with a 3-T magnet. During MRI follow-up of muscle injuries, other important features to be assessed are changes in muscle edema and specific warning signs, such as persistent intermuscular edema, new connective tear, and scar rupture. An understanding of the MRI appearance of normal and abnormal muscle healing and warning signs, along with cooperation with a multidisciplinary team, enable optimization of return to play for the injured athlete. ©RSNA, 2024 See the invited commentary by Flores in this issue.