Extremity Deep Research Articles

Explainable machine learning identifies a polygenic risk score as a key predictor of pancreatic cancer risk in the UK Biobank

BackgroundPredicting the risk of developing pancreatic ductal adenocarcinoma (PDAC) is of paramount importance, given its high mortality rate. Current PDAC risk prediction models rely on a limited number of variables, do not include genetics, and have a modest accuracy. AimThis study aimed to develop an interpretable PDAC risk prediction model, based on machine learning (ML). MethodsFive ML models (Adaptive Boosting, eXtreme Gradient Boosting, CatBoost, Deep Forest and Random Forest) built on 56 exposome variables and a polygenic risk score (PRS) were tested in 654 PDAC cases and 1,308 controls of the UK Biobank. Additionally, SHapley Additive exPlanation (SHAP) and Global model Interpretation via the Recursive Partitioning (Girp) were employed to explain the models. ResultsAll models provided similar performance, but based on recall the best was CatBoost (77.10 %). SHAP highlighted age and the PRS as primary contributors across all models. Girp developed rules to discern cases from controls, identifying age, PRS, and pancreatitis in most of the rules. ConclusionThe predictive models tested have exhibited good performance, indicating their potential application in the clinical field in the near future, with the PRS playing a key role in identifying high-risk individuals as demonstrated by the explainers.

Early Thrombus Removal for Acute Lower Extremity Deep Vein Thrombosis: Update on Inclusion, Technical Aspects, and Postprocedural Management.

Acute lower extremity deep vein thrombosis (DVT) is a common disorder with significant associated morbidity, including pain and swelling, as well as the risk of pulmonary embolism (PE), recurrent venous thromboembolism (VTE) and chronic debilitating post-thrombotic syndrome (PTS). Anticoagulation is standard of care for DVT treatment. It assists in reducing thrombus progression and the occurrence of PE, but incomplete DVT resolution increases the risk of recurrent VTE, valvular insufficiency, and PTS. Endovascular DVT interventions, such as catheter-directed thrombolysis, pharmacomechanical thrombectomy, and large-bore mechanical thrombectomy offer an alternative therapeutic strategy for DVT management. This paper will discuss technical factors and current issues when performing lower extremity DVT interventions including patient selection, anticoagulation, choice of device for endovascular thrombus removal, adjunctive techniques, and venous stent management.Level of Evidence: No level of evidence for: review articles, basic science, laboratory investigations, and experimental study articles.

US of Lower Extremity Deep Vein Thrombosis: A Review.

The standard protocol for lower extremity venous US extends from the common femoral vein through the deep calf veins with color and spectral Doppler sampling, allowing evaluation of acute deep vein thrombosis and differentiation from postthrombotic change.

Spatially resolved spectroscopic analysis of Lyα haloes

The extended Lyα haloes (LAHs) have been found to be prevalent around high-redshift star-forming galaxies. However, the origin of the LAHs is still a subject of debate. The spatially resolved analysis of Lyα profiles provides an important diagnostic. We analyse the average spatial extent and spectral variation of the circumgalactic LAHs by stacking a sample of 155 Lyα emitters (LAEs) at redshifts of 3 < z < 4 in the MUSE Extremely Deep Field. Our analysis reveals that, with respect to the Lyα line of the target LAE, the peak of the Lyα line at large distances becomes increasingly more blueshifted up to a projected distance of 60 kpc (≈3× virial radius), with a velocity offset of ≈250 km/s. This trend is evident in both the mean and median stacks, suggesting that it is a general property of our LAE sample, which typically has a Lyα luminosity of ≈1041.1 erg s−1. However, due to the absence of systemic redshift data, it remains unclear whether the Lyα line peak at large projected distances is less redshifted compared to the inner regions or truly blueshifted with respect to the systemic velocity. We explore various scenarios to explain the large-scale kinematics of the Lyα line.

A 12-Year Review of Upper Extremity Deep Vein Thrombosis-Are They the Same as Lower Extremity Deep Vein Thrombosis?

Background: Upper extremity deep vein thrombosis (UEDVT) is uncommon but not insignificant. The current literature is limited, and the management is largely extrapolated from the treatment of lower extremity DVTs (LEDVT). Methods: A retrospective review was conducted on patients diagnosed with UEDVT at Northern Health, Victoria, Australia, between December 2010 and December 2022. Medical records were reviewed to assess baseline characteristics and treatment outcomes. The results were compared to our previously collected data for LEDVTs. Results: 137 patients with UEDVT were identified (52.6% females; median age 62 years, IQR 46-74 years). A total of 105 patients (76.6%) had at least one provoking factor at the time of diagnosis, most commonly malignancy (45.7%) and/or indwelling venous devices (58.1%). Fourteen patients (10.1%) were subsequently diagnosed with Paget-Schroetter syndrome, with nine receiving endovascular or surgical intervention. A total of 109 patients (79.6%) received limited therapeutic anticoagulation (median 3 months, IQR 1.5-6.0 months) with enoxaparin, the most common anticoagulant used. Six patients had major bleeding (5.2/100-patient-years), and seven developed clot progression while on anticoagulation (6.0/100-patient-years). Ten patients had recurrent VTE following anticoagulation cessation (4.6/100-patient-years). There were no significant differences seen in the complication rate between catheter-related UEDVT and other UEDVTs. Compared to LEDVT, UEDVT was more likely provoked with comparable complication rates. Conclusions: UEDVTs were commonly associated with a provoking factor, with indwelling catheters and/or malignancies being the most common. Interestingly, catheter-related UEDVT had comparable clot progression/recurrence and major bleeding compared to other UEDVTs and LEDVTs, which may be confounded by relatively high rates of malignancy.

An Asymptomatic Giant Endometrioma Presented as Lower Extremity Deep Vein Thrombosis with Early Pregnancy: A Case Report and Review of the Literature

An Asymptomatic Giant Endometrioma Presented as Lower Extremity Deep Vein Thrombosis with Early Pregnancy: A Case Report and Review of the Literature

Calibration of Typhoon Track Forecasts Based on Deep Learning Methods

An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by correcting WRF-forecasted tracks using deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) + Convolutional Long Short-Term Memory (ConvLSTM) + Wide and Deep Learning (WDL), BiLSTM + Convolutional Gated Recurrent Unit (ConvGRU) + WDL, and BiLSTM + ConvLSTM + Extreme Deep Factorization Machine (xDeepFM), with a comparison to the Kalman Filter. The results demonstrate that the BiLSTM + ConvLSTM + WDL model reduces the 72 h track prediction error (TPE) from 255.18 km to 159.23 km, representing a 37.6% improvement over the original WRF model, and exhibits significant advantages across all evaluation metrics, particularly in key indicators such as Bias2, Mean Squared Error (MSE), and Sequence. The decomposition of MSE further validates the importance of the BiLSTM, ConvLSTM, WDL, and Temporal Normalization (TN) layers in enhancing the model’s spatio-temporal feature-capturing ability.

Prediction and Risk Factors of Lower Extremity Deep Vein Thrombosis after Total Joint Arthroplasty.

Prediction and Risk Factors of Lower Extremity Deep Vein Thrombosis after Total Joint Arthroplasty.

Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability

A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Combining Machine Learning techniques and Genetic Algorithm for predicting run times of High Performance Computing jobs

This study proposes a novel approach combining Machine Learning (ML) techniques and Genetic Algorithms (GA) for predicting High-Performance Computing (HPC) job run times. The objective is to create a prediction method universally applicable to any HPC system, irrespective of workload characteristics, application specific parameters, user behavior, or hardware architecture. Since user-supplied run time estimates are often inaccurate, we aim to categorize job runtimes into several classes, allowing users to select appropriate classes for their jobs. A Genetic Algorithm is developed to optimally define these runtime classes, determining both the number of classes and the time intervals represented by them. Four Machine Learning algorithms (K Nearest Neighbours, Support Vector Regression, Extreme Gradient Boosting and Deep Neural Networks) are implemented for run time prediction. A unique set of features extracted from historical job data serves as input to the Machine Learning models. The generalized nature of our method is demonstrated by validating its performance on data from six clusters with distinct configurations, applications and runtime distributions. Our results illustrate the superior performance of Machine Learning models incorporating GA-defined runtime classes for all datasets. Across all six datasets, our method achieves R2 scores exceeding 0.8, and accuracy greater than 0.7.

The Effectiveness and Safety of Rivaroxaban and Edoxaban in the Treatment of Lower Extremity Deep Vein Thrombosis

IntroductionDeep vein thrombosis (DVT) is a medical condition characterized by forming a blood clot, or thrombus, in one of the deep veins, typically in the legs. It is a type of venous thromboembolism (VTE), which refers to the formation of blood clots in the veins. It is caused by Virchow's triad (stasis, hypercoagulation, and endothelial injury). ObjectiveOur main objective is to explore the effectiveness and safety of Rivaroxaban and Edoxaban in treating lower extremity deep vein thrombosis. MethodsWe conducted a retrospective study involving 406 patients subjected to DVT treatment using DOACs (Edoxaban and Rivaroxaban) at our hospital. We recruited adult patients (18 years and above) diagnosed with lower extremity deep vein thrombosis and received treatment with either Rivaroxaban or Edoxaban as the primary anticoagulant therapy for DVT. We excluded patients who received treatment with other anticoagulant medications (warfarin heparin) as the primary therapy for DVT. ResultsThe groups showed statistically significant differences in red blood cell count and haemoglobin levels, with the Edoxaban group having high values. However, the two groups observed no statistically significant differences in creatinine clearance, white blood cell count, platelet count, C-reactive protein, and D-dimer levels. The difference in the incidence of PE between the two groups was statistically significant (P value < 0.001). The Edoxaban group had fewer PE patients than the rivaroxaban group. The reduction in recurrent thrombosis was significantly higher in the rivaroxaban group compared to the Edoxaban group. There were no significant differences in the major bleeding at various sites across the two treatment groups (p > 0.05). ConclusionRivaroxaban's pharmacokinetic profile includes rapid absorption and a relatively short half-life. It means that once administered, Rivaroxaban quickly reaches its peak concentration in the blood and is subsequently eliminated from the body within a relatively short period. Edoxaban's pharmacokinetic profile may include slower absorption and a longer half-life than Rivaroxaban. It can result in a slower rate of achieving peak concentration and a more prolonged presence in the bloodstream. These results emphasize the need for careful consideration of anticoagulant therapy in patients with underlying cancer and underscore the importance of managing risks while providing adequate anticoagulation to prevent thrombotic events.

Comparative Study of Two Different Access Points, One Tibial Vein and the Popliteal Vein, for Catheter-Directed Thrombolysis in the Treatment of Acute Mixed Lower Extremity Deep Vein Thrombosis

Although the popliteal vein approach is commonly used for catheter-directed thrombolysis (CDT) treatment in patients with acute lower extremity deep vein thrombosis (DVT), CDT via a new access route, the posterior tibial vein, is also used and has demonstrated good results. However, this tibial approach has not been tested in large samples. In this article, we compare the early efficacy of CDT using the tibial and popliteal vein approaches for the treatment of acute mixed lower extremity DVT. In this retrospective cohort study, 87 patients with acute mixed lower extremity DVT treated at the Department of Interventional Medicine of Zhuhai People's Hospital were enrolled; those with tibial vein access and popliteal vein access were included in the observation (n=55) and control (n=32) groups, respectively. The safety and efficacy of CDT via tibial vein access were investigated by collecting and comparing indicators such as venous patency, thrombus removal effect, thigh and calf circumference difference, swelling reduction rate of the affected limb, surgical complications, and postdischarge complication rate of the patients in the 2 groups. The postoperative thrombus clearance effect of the observation group was significantly better than that of the control group (P<0.05), and the postoperative venous patency rate of the observation group was 83.2±15.7%, which was higher than that of the control group (62.2±38.2%) (P=0.005). The swelling reduction rate of the lower extremity was 74.0±33.8% in the observation group and 51.4±30.0% in the control group, with a statistically significant difference (P=0.002). However, there was no statistically significant difference (P>0.05) in the rates of thigh swelling reduction, bleeding-related complications, or postoperative complications between the 2 groups of patients. CDT via the tibial vein approach is safe, effective, and may be a better approach for CDT access, offering superior thrombus clearance, venous patency, and lower extremity swelling reduction postoperatively.

MIDIS: The Relation between Strong (Hβ + [O iii]) Emission, Star Formation, and Burstiness around the Epoch of Reionization

We investigate the properties of strong (Hβ + [O iii]) emitters before and after the end of the “Epoch of Reionization” from z = 8 to z = 5.5. We make use of ultradeep JWST/NIRCam imaging in the parallel field (P2) of the MIRI Deep Imaging Survey (MIDIS) in the Hubble eXtreme Deep Field (H-XDF), in order to select prominent (Hβ + [O iii]) emitters (with rest-frame equivalent width (EW0) ≳ 100 Å) at z = 5.5–7, based on their flux density enhancement in the F356W band with respect to the spectral energy distribution continuum. We complement our selection with other (Hβ + [O iii]) emitters from the literature at similar and higher (z = 7−8) redshifts. We find (nonindependent) anticorrelations between EW0(Hβ + [O iii]) and both galaxy stellar mass and age, in agreement with previous studies, and a positive correlation with specific star formation rate (sSFR). On the SFR–M ⋆ plane, the (Hβ + [O iii]) emitters populate both the star formation main sequence and the starburst region, which become indistinguishable at low stellar masses ( log10(M⋆)<7.5 ). We find tentative evidence for a nonmonotonic relation between EW0(Hβ + [O iii]) and SFR, such that both parameters correlate with each other at SFR ≳ 1 M ⊙ yr−1, while the correlation flattens out at lower SFRs. This suggests that low metallicities producing high EW0(Hβ + [O iii]) could be important at low SFR values. Interestingly, the properties of the strong emitters and other galaxies (33% and 67% of our z = 5.5–7 sample, respectively) are similar, including, in many cases, high sSFR. Therefore, it is crucial to consider both emitters and nonemitters to obtain a complete picture of the cosmic star formation activity around the Epoch of Reionization.

MIDIS: Unveiling the Role of Strong Hα Emitters During the Epoch of Reionization with JWST

By using an ultradeep JWST/MIRI image at 5.6 μm in the Hubble eXtreme Deep Field, we constrain the role of strong Hα emitters (HAEs) during “cosmic reionization” at z ≃ 7–8. Our sample of HAEs is comprised of young (<35 Myr) galaxies, except for one single galaxy (≈300 Myr), with low stellar masses (≲109 M ⊙). These HAEs show a wide range of rest-frame UV continuum slopes (β), with a median value of β = −2.15 ± 0.21, which broadly correlates with stellar mass. We estimate the ionizing photon production efficiency (ξ ion,0) of these sources (assuming f esc,LyC = 0%), which yields a median value log10(ξion,0/(Hzerg−1))=25.50−0.12+0.10 . We show that ξ ion,0 positively correlates with Hα equivalent width and specific star formation rate. Instead ξ ion,0 weakly anticorrelates with stellar mass and β. Based on the β values, we predict fesc,LyC=4%−2+3 , which results in log10(ξion/(Hzerg−1))=25.55−0.13+0.11 . Considering this and related findings from the literature, we find a mild evolution of ξ ion with redshift. Additionally, our results suggest that these HAEs require only modest escape fractions (f esc,rel) of 6%–15% to reionize their surrounding intergalactic medium. By only considering the contribution of these HAEs, we estimated their total ionizing emissivity ( Ṅion ) as Ṅion=1050.53±0.45s−1Mpc−3 . When comparing their Ṅion with non-HAE galaxies across the same redshift range, we find that that strong, young, and low-mass emitters may have played an important role during cosmic reionization.

Advanced optimization-based weighted features for ensemble deep learning smart occupancy detection network for road traffic parking

In day-to-day activities, the advanced technology like Internet of Things (IoT) emerges to improve the lifestyle of people. In metropolitan cities, real-time parking is the long-lasting problem that we face in our daily life activities. Urban parking regulation gained more attention because of its capability to diminish energy consumption, congested traffic, and manifestation. The parking space detection for vehicles in real-time is a crucial role for on-street parking control models where the data is delivered to the drivers through internet with the help of Global Positioning System (GPS). Hence, the network congestion gets increased, where the existing model does not performed well in the parking space effectively. This research paper recommends a novel strategy for detecting occupancy in road traffic parking by IoT and deep networks to overcome these difficulties. At first, the required information is gathered from the standard datasets as CNRPark + EXT for further processing. After the data collection process, the preprocessing phase is executed, where the data cleaning and data transformation approaches are done with a multi-scale retinex network. The pre-processing technique is effectively performed to reduce background noise. Further, the feature extraction is progressed through the Residual Attention Network, where the relevant features are extracted from the pre-processed data. Subsequently, the optimal features and weights are selected by the novel Advanced Pelican Optimization Algorithm (APOA) to get the optimal weighted feature selection. This optimal weighted feature is further given to the Ensemble Deep Networks (EDN) by integrating the Deep Conditional Random Field (DCRF), and Extreme Deep Learning (EDL) model. In final process, the detection of occupancy in road traffic parking is determined by averaging both classifiers outcomes. Finally, the results are compared with numerous optimization algorithms and classifiers to ensure the developed system's efficacy. Throughout the validation, the developed model outperforms with an accuracy as 96.6, precision as 92.9 and Mathew's Correlation Coefficient (MCC) as 92.3. Thus, the developed model shows better performance than the existing traditional approaches.

Median surface-brightness profiles of Lyman-alpha haloes in the MUSE Extremely Deep Field

We present the median surface brightness profiles of diffuse haloes (LAHs) around star-forming galaxies by stacking 155 spectroscopically confirmed emitters (LAEs) at $3&lt;z&lt;4$ in the MUSE Extremely Deep Field (MXDF) with a median luminosity of $ L_ Ly alpha $. After correcting for a systematic surface brightness offset we identified in the data cube, we detect extended emission out to a distance of approx 270 kpc. The median surface-brightness profile shows a power-law decrease in the inner 20 kpc and a possible flattening trend at a greater distance. This shape is similar for LAEs with different luminosities, but the normalisation of the surface-brightness profile increases with luminosity. At distances over 50 kpc, we observe a strong overlap of adjacent LAHs, and the surface brightness is dominated by the LAHs of nearby LAEs. We find no clear evidence of redshift evolution of the observed profiles when comparing with samples at $4&lt;z&lt;5$ and $5&lt;z&lt;6$. Our results are consistent with a scenario in which the inner 20 kpc of the LAH is powered by star formation in the central galaxy, while the LAH beyond a radius of 50 kpc is dominated by photons from surrounding galaxies.

Optimizing Venous Stenting: Consensus Recommendations for Enhanced Management of Lower Extremity Deep Vein Thrombosis.

Deep vein thrombosis (DVT) poses a complex challenge and often leads to postthrombotic syndrome (PTS), a debilitating complication. The emergence of venous stents offers a potential preventive avenue against this complication. This study aimed to provide consensus recommendations on the use of venous stent for DVT. From June to July 2023, 20 internal medicine, angiology and vascular surgery, and vascular and interventional radiology experts were involved in the Delphi process. Thirty-one recommendations, categorized into three thematic areas, were rigorously evaluated: indications for stent use, stent selection and placement, and monitoring and prevention of complications. Agreement was evaluated using a Likert scale, with consensus defined as agreement by two-thirds of the participants. Consensus was reached for 23 (74.2%) of 31 recommendations. The agreement was centered on considerations, such as stent placement in specific acute DVT scenarios, emphasizing pivotal stent characteristics. However, there were divergences in the recommended stent length to prevent migration and stent characteristics based on iliocaval bifurcation morphology. Notably, there was no consensus on whether patients with DVT caused by a major transient risk factor need more than 3 months of anticoagulation therapy or whether aspirin should be added to anticoagulant treatment after venous stenting. These consensus recommendations offer practical insights into optimizing venous stent use to prevent PTS in DVT patients. Addressing the critical aspects of stent selection, placement, and postprocedural care, these recommendations contribute to clinical decision-making. The identified divergences underscore the importance of consensus and thus indicate the need for further investigation.

Application of machine learning to predict unbound drug bioavailability in the brain

Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.

Sex Differences in Clinical Presentation of Lower Extremity Deep Vein Thrombosis.

Background: Differences between men and women in the clinical features and extent of lower limb deep vein thrombosis (DVT) may influence DVT diagnostic algorithms involving pretest clinical probability (PTP) assessment, D-dimer, and compression ultrasonography (CUS). Aims: To assess differences in DVT clinical presentation between men and women and their effect on PTP and D-dimer. Methods: We conducted a retrospective study in outpatients referred for suspected DVT of the lower limbs to our vascular emergency department from January 2005 to December 2019. Patients underwent PTP assessment with the Wells score, D-dimer testing, and CUS. Results: More women were referred for suspected DVT than men (M/F: 1,785/2,821; F: 61.4%; p < 0.0001). Women were older than men (median age: 71 vs. 67 years; p = 0.0001), DVT was diagnosed in 436 patients (9.4%) but in more men than women (M: 210 [11.8%] vs. F: 226 [8%]; p = 0.0002), with more proximal DVT in men than women (M: 131 7.3% vs. F: 124 [4.4%]; p = 0.00021). PTP was more likely in men (355 [19.9%]) than women (455 [16.2%]) (p = 0.0011); more men had swelling in the entire limb, increased calf circumference by >3 cm compared with the contralateral limb, and pitting edema, than women. D-dimer levels (available in 65% of patients) were more frequently positive in women with DVT than in men (94.6% vs. 85.7%; p = 0.016). However, a positive D-dimer and/or likely PTP was similarly frequent in men (92%) and women (96%) with DVT. Conclusions: More women than men are referred for suspected DVT, and men have a higher prevalence of proximal DVT. However, current algorithms for DVT diagnosis perform similarly in men and in women.

MIDIS: JWST NIRCam and MIRI Unveil the Stellar Population Properties of Lyα Emitters and Lyman-break Galaxies at z ≃ 3–7

We study the stellar population properties of 182 spectroscopically confirmed (MUSE/VLT) Lyα emitters (LAEs) and 450 photometrically selected Lyman-break galaxies (LBGs) at z = 2.8–6.7 in the Hubble Extreme Deep Field. Leveraging the combined power of Hubble Space Telescope and JWST NIRCam and MIRI observations, we analyze their rest-frame UV-through-near-IR spectral energy distributions, with MIRI playing a crucial role in robustly assessing the LAEs’ stellar masses and ages. Our LAEs are low-mass objects (log10(M⋆/M⊙)≃7.5) with little or no dust extinction (E(B − V) ≃ 0.1) and a blue UV continuum slope (β ≃ −2.2). While 75% of our LAEs are young (<100 Myr), the remaining 25% have significantly older stellar populations (≥100 Myr). These old LAEs are statistically more massive, less extinct, and have lower specific star formation rate than young LAEs. Besides, they populate the plane of M ⋆ versus star formation rate along the main sequence of star-forming galaxies, while young LAEs populate the starburst region. The comparison between the LAEs’ properties and those of a stellar-mass-matched sample of LBGs shows no statistical difference between these objects, except for the LBGs’ redder UV continuum slope and marginally larger E(B − V) values. Interestingly, 48% of the LBGs have ages <10 Myr and are classified as starbursts, but lack detectable Lyα emission. This is likely due to H i resonant scattering and/or dust-selective extinction. Overall, we find that JWST observations are crucial in determining the properties of LAEs and shedding light on their comparison with LBGs.