bluuebunny/crossref_metadata_2025_split huggingface dataset:
Benefit-finding in children with advanced cancer and their parents
Authors: Siefring Emma , Olsavsky Anna L. , Schaefer Megan , Sutherland-Foggio Malcolm , Himelhoch Alexandra C. , Hill Kylie N. , Kenney Ansley E. , Humphrey Lisa , Olshefski Randal , Gerhardt Cynthia A. 
Published: November 2024

Abstract

Objectives
Although pediatric cancer often causes significant stress for families, most childhood cancer survivors are resilient and do not exhibit severe or lasting psychopathology. Research demonstrates some survivors may report benefit-finding or positive outcomes following this stressful life event. However, considerably less research has included families of children who are unlikely to survive their illness. Thus, this study investigated benefit-finding among parents and their children with advanced cancer, as well as associated demographic and medical factors.

Methods
Families (N = 72) of children with advanced cancer (ages 5–25) were recruited from a large pediatric hospital. Advanced cancer was defined as relapsed or refractory disease, an estimated prognosis of <60%, or referral to end-of-life care. Participants completed a demographic survey and the Benefit Finding Scale at enrollment.

Results
Children, mothers, and fathers reported moderate to high benefit-finding scores. Correlations between family members were weak and non-significant. Children reported significantly higher benefit-finding than fathers. Demographic and medical factors were not associated with benefit-finding in children, mothers, or fathers.

Significance of results
Families of children with advanced cancer reported moderate to high benefit-finding regardless of background or medical factors. Children identified benefits of their cancer experience independent of the experiences of their mothers and fathers. Larger studies should continue to examine factors associated with positive and negative outcomes in the context of childhood cancer to inform interventions.
Polycentric governance in collusive agreements
Authors: Schmal W. Benedikt 
Published: November 2024

Abstract
Collusive agreements in the form of corporate cartels are complex structures. The involved firms need to agree on terms that are legally not enforceable. However, the interplay between the involved firms in a collusive agreement, i.e., the governance dimension within a cartel, has received surprisingly low attention. Using a comprehensive OECD dataset of 191 cartels from 2012 to 2018, this paper empirically demonstrates how polycentric governance within a cartel may possibly contribute to understanding its stability. It may be beneficial for the duration and lower sanctions imposed by competition authorities, especially for large cartels. By that, the paper sheds new light on two aspects: the entangled governance structures of corporate cartels and the relevance of the concept of polycentricity beyond public administration.
Mutual coupling reduction of two-port dielectric resonator MIMO antenna using defected ground structure
Authors: Sharma Gouri Shankar , Gupta Anshul 
Published: November 2024

AbstractThis article focuses on reducing mutual coupling between the ports of dielectric resonator antenna (DRA) using defected ground structures (DGSs). The antenna has the dimension of 50 mm × 50 mm × 8.5 mm. The resonating element in the proposed two-port radiator consists of a cylindrical structure of alumina ceramic (ɛr = 9.8). The rectangular-shaped aperture is utilized to excite both of the resonating elements. The resonating ceramic elements acting as radiators are offset-fed to enhance the antenna’s coupling. Combining interdigital-shaped and semicircular arc-shaped DGSs improves isolation between two resonating elements, embodying the structural novelty. The measured operating frequency range of Port-1 and Port-2 is 5.19–6.7 and 5.15–6.68 GHz, resonating at 5.58 and 5.56 GHz, respectively. The measured mutual coupling between the two ports is −35.5 dB. The measured gain for Port-1 is depicted to be 5.5 dB. The presented multiple-input–multiple-output (MIMO) radiator in this article is an appropriate candidate for WLAN (5.25–5.35, 5.47–5.725, 5.725–5.85, 5.850–5.925 GHz) and WiMAX(5.5 GHz) applications. All the simulated and experimentally observed MIMO parameters of the radiator are discovered to be within optimal bounds.
Deep-blur: Blind identification and deblurring with convolutional neural networks
Authors: Debarnot Valentin , Weiss Pierre 
Published: November 2024

Abstract
We propose a neural network architecture and a training procedure to estimate blurring operators and deblur images from a single degraded image. Our key assumption is that the forward operators can be parameterized by a low-dimensional vector. The models we consider include a description of the point spread function with Zernike polynomials in the pupil plane or product-convolution expansions, which incorporate space-varying operators. Numerical experiments show that the proposed method can accurately and robustly recover the blur parameters even for large noise levels. For a convolution model, the average signal-to-noise ratio of the recovered point spread function ranges from 13 dB in the noiseless regime to 8 dB in the high-noise regime. In comparison, the tested alternatives yield negative values. This operator estimate can then be used as an input for an unrolled neural network to deblur the image. Quantitative experiments on synthetic data demonstrate that this method outperforms other commonly used methods both perceptually and in terms of SSIM. The algorithm can process a 512

$ \times $

512 image under a second on a consumer graphics card and does not require any human interaction once the operator parameterization has been set up.1
A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks
Authors: Penna Michele Francesco , Giordano Luca , Tortora Stefano , Astarita Davide , Amato Lorenzo , Dell’Agnello Filippo , Menegatti Emanuele , Gruppioni Emanuele , Vitiello Nicola , Crea Simona , Trigili Emilio 
Published: November 2024

Abstract
This work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder–elbow exoskeleton. Features related to muscle synergies are used in a Gaussian Mixture Model and probability accumulation-based logic to infer the user’s movement direction. The performance of the algorithm was verified by a feasibility study including eight healthy participants. The experiments comprised a transparent session, during which the exoskeleton did not provide any assistance, and an assistive session in which the Syn-ID strategy was employed. Participants were asked to reach eight targets equally spaced on a circumference of 25 cm radius (adjusted chance level: 18.1%). The results showed an average accuracy of 48.7% after 0.6 s from the EMG onset. Most of the confusion of the estimate was found along directions adjacent to the actual one (type 1 error: 33.4%). Effects of the assistance were observed in a statistically significant reduction in the activation of Posterior Deltoid and Triceps Brachii. The final positions of the movements during the assistive session were on average 1.42 cm far from the expected ones, both when the directions were estimated correctly and when type 1 errors occurred. Therefore, combining accurate estimates with type 1 errors, we computed a modified accuracy of 82.10±6.34%. Results were benchmarked with respect to a purely kinematics-based approach. The Syn-ID showed better performance in the first portion of the movement (0.14 s after EMG onset).
Several Mechanisms Drive the Heterogeneity in Browning Across a Boreal Stream Network
Authors: Zhu Xudan , Berninger Frank , Chen Liang , Larson Johannes , Sponseller Ryan A. , Laudon Hjalmar 
Published: November 2024

AbstractOver the past few decades, many catchments in Northern hemisphere have experienced increases in dissolved organic carbon (DOC) concentrations, resulting in a brownish color of the water, known as aquatic browning. Several mechanisms have been proposed to explain browning, but consensus regarding the relative importance of recovery from acid deposition, climate change, and land management remains elusive. To advance our understanding of browning mechanisms, we explored DOC trends across 13 nested boreal catchments, leveraging concurrent hydrological, chemical, and terrestrial ecosystem data to quantify the contributions of different drivers on observed trends. We first identified the related environmental factors, then attributed the individual trends of DOC to potential drivers across space and time. Our results showed that all catchments exhibited increased DOC trends from 2003 to 2021, but the DOC response rates differed by five‐fold. No single mechanism could fully explain the browning; instead, sulfate deposition, climate‐related factors, and site properties jointly controlled the variation in DOC trends. Specifically, the long‐term increases in DOC were primarily driven by recovery from sulfate deposition, followed by increases in terrestrial productivity, temperature, and discharge. However, catchment area and landcover type also regulated the response rate of DOC to these drivers, creating spatial heterogeneity in browning among sub‐catchments despite similar deposition and climate forcing. Interestingly, browning has weakened in the last decade as sulfate deposition has fully recovered and other current drivers are insufficient to sustain the long‐term increases. Our results highlight that multifaceted, spatially structured, and nonstationary drivers must be accounted for to predict future DOC changes.
Ionospheric Disturbances Observed Over the Peruvian Sector During the Mother's Day Storm (G5‐Level) on 10–12 May 2024
Authors: Singh Ram , Scipión Danny E. , Kuyeng Karim , Condor Percy , De La Jara Cesar , Velasquez Juan Pablo , Flores Roberto , Ivan Edwar , Souza Jonas R. , Migliozzi Michael 
Published: November 2024

AbstractThis article presents the recent extreme and rare G5‐level geomagnetic storm (Mother's Day Storm) effects on the equatorial and low‐latitude ionosphere observed at the Peruvian sector by the Jicamarca (11.9°S, 76.8°W, magnetic dip 1°N) incoherent scatter radar and associated instruments. This storm was produced by multiple Earth‐directed coronal mass ejections, which generated significant modifications in the Earth's magnetic field, leading to the Sym‐H of ∼−518 nT. On the dayside, due to the strong eastward penetration electric field, vertical plasma drift and equatorial electrojet (EEJ) enhanced for 2–3 hr and remained consistent at values of ∼95 m/s and 260 nT between 1700 and 1900 UT (1200 and 1400 LT). At the same time, vertical E B plasma drift uplifted the equatorial ionosphere, producing the dusk‐side super plasma fountain and transferring electron density to higher latitudes. A huge increase (∼1,325%) in electron density (from 11 to 142 TECu) is observed at low and mid‐latitudes from ∼20°S to 50°S between 2000 and 0400 UT (1500–2300 LT). The strong westward penetration electric field suppressed pre‐reversal enhancement, leading to downward plasma drift (∼−96 m/s) at around 2400 UT (1900 LT). Overnight, vertical plasma drift fluctuated between ±90 m/s, and the combined effect of penetration and disturbance dynamo electric fields caused a significant increase (∼530 km) in ionospheric virtual height. In the main and early recovery phase, consistent short‐ and long‐duration electric field disturbances persisted for approximately 30 hr, with periods of ∼48 and 90 min.
Lower‐Hybrid Wave‐Induced Plasma Mixing Related to Kelvin‐Helmholtz Vortices During Southward IMF
Authors: Blasl K. A. , Settino A. , Nakamura R. , Hasegawa H. , Nakamura T. K. M. , Hosner M. 
Published: November 2024

AbstractWe examine characteristics of the boundaries of 11 Kelvin‐Helmholtz vortex crossings observed by MMS on 23 September 2017 under southward IMF conditions. At both the leading and trailing edges, boundary regions of mixed plasma are observed together with lower‐hybrid wave activity. We found that thicker boundary regions feature a higher number of sub‐ion scale current sheets, of which only one shows clear reconnection signatures. Moreover, the lower‐hybrid waves along the vortex spine region are identified as an effective mechanism for plasma transport with an estimated diffusion coefficient of s. Comparisons with 3D simulations performed under the same conditions as the MMS event suggest that the extension of the boundary regions as well as the number of current sheets are related to different evolutionary stages of the vortices. Such observations can be explained by changes in the upstream magnetic field conditions.
Dissolved Nitrous Oxide and Methane in the Taiwan Strait: Distribution, Seasonal Variation, and Emission
Authors: Wang Xue , Zhan Liyang , Liu Jian , Wang Aijun , Zhang Jiexia , Chen Weicong , Wu Man , Wen Wenjian , Zhang Ruijie , Ye Wangwang 
Published: November 2024

AbstractThe global ocean plays an important role in the overall budgets of nitrous oxide (N2O) and methane (CH4), especially in continental estuaries and shelf areas. Four cruises were conducted between 2021 and 2022, covering the spring, summer, and fall seasons, to study the spatial and seasonal characteristics of N2O and CH4 distributions and emissions in the Taiwan Strait (TWS). The surface N2O and CH4 concentrations gradually decreased from the coast to the open sea, with maximum values (14.3 and 15.6 nmol L−1) occurring near the Jiulong and Minjiang estuaries, respectively. The mean surface N2O concentration (8.2 nmol L−1) was highest in the spring and approximately the same in the summer as in the fall. The mean surface concentrations of CH4 (8.8 nmol L−1) were greater in summer than in spring and fall, probably because of the high freshwater input in summer. Except for several stations in fall, surface waters were oversaturated with N2O and CH4 relative to the atmosphere in other seasons, and the TWS was a net source of atmospheric N2O and CH4 in the spring, summer, and fall. In situ production is the main source of N2O and CH4 in the TWS, with nitrification being the dominant mechanism of N2O production in the TWS. In contrast, physical influences (riverine inputs and water mass mixing) reshape the distributions of N2O and CH4. The annual emissions of N2O and CH4 from the TWS were estimated to be 0.9 × 10−3 ± 2.9 × 10−3 Tg yr−1 and 1.6 × 10−3 ± 3.0 × 10−3 Tg yr−1, respectively. Taken together, the TWS accounts for 0.025% of the surface area of the world's oceans and 0.022% ± 0.07% and 0.017% ± 0.033% of global oceanic N2O and CH4 emissions, respectively.
Using Explainable AI and Transfer Learning to Understand and Predict the Maintenance of Atlantic Blocking With Limited Observational Data
Authors: Zhang Huan , Finkel Justin , Abbot Dorian S. , Gerber Edwin P. , Weare Jonathan 
Published: November 2024

AbstractBlocking events are an important cause of extreme weather, especially long‐lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic (QG) model developed by Marshall and Molteni (1993), https://doi.org/10.1175/1520‐0469(1993)050<1792:taduop>2.0.co;2. We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high‐pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high‐pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre‐training the CNN on the plentiful data of the Marshall‐Molteni model, and then using Transfer learning (TL) to achieve better predictions than direct training. SHAP analysis before and after TL allows a comparison between the predictive features in the reanalysis and the QG model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.
Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization
Authors: Christopoulos Costa , Lopez‐Gomez Ignacio , Beucler Tom , Cohen Yair , Kawczynski Charles , Dunbar Oliver R. A. , Schneider Tapio 
Published: November 2024

AbstractThis work integrates machine learning into an atmospheric parameterization to target uncertain mixing processes while maintaining interpretable, predictive, and well‐established physical equations. We adopt an eddy‐diffusivity mass‐flux (EDMF) parameterization for the unified modeling of various convective and turbulent regimes. To avoid drift and instability that plague offline‐trained machine learning parameterizations that are subsequently coupled with climate models, we frame learning as an inverse problem: Data‐driven models are embedded within the EDMF parameterization and trained online in a one‐dimensional vertical global climate model (GCM) column. Training is performed against output from large‐eddy simulations (LES) forced with GCM‐simulated large‐scale conditions in the Pacific. Rather than optimizing subgrid‐scale tendencies, our framework directly targets climate variables of interest, such as the vertical profiles of entropy and liquid water path. Specifically, we use ensemble Kalman inversion to simultaneously calibrate both the EDMF parameters and the parameters governing data‐driven lateral mixing rates. The calibrated parameterization outperforms existing EDMF schemes, particularly in tropical and subtropical locations of the present climate, and maintains high fidelity in simulating shallow cumulus and stratocumulus regimes under increased sea surface temperatures from AMIP4K experiments. The results showcase the advantage of physically constraining data‐driven models and directly targeting relevant variables through online learning to build robust and stable machine learning parameterizations.
Characterizing Subsurface Structures From Hard and Soft Data With Multiple‐Condition Fusion Neural Network
Authors: Cui Zhesi , Chen Qiyu , Luo Jian , Ma Xiaogang , Liu Gang 
Published: November 2024

AbstractAccurately inferring realistic subsurface structures poses a considerable challenge due to the impact of morphology on flow and transport behaviors. Traditional subsurface characterization relies on two primary types of data: hard data, derived from direct subsurface measurements, and soft data, encompassing remotely sensed geophysical information and its interpretation. Existing deep‐learning‐based methodologies predominantly focus on the transition from multiple observations to subsurface structures. However, implicit non‐linear correlations among diverse data sources often remain underutilized, leading to potential bias and errors. In this study, we introduce a multiple‐condition fusion network (MCF‐Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple‐source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. The integration of these features is achieved through multiple‐condition fusion blocks, designed to capture representative characteristics. These blocks are also adept at reconstructing heterogeneous structures and facilitating hydrological parameterization. MCF‐Net exhibits accuracy in estimating subsurface structures across various types of subsurface observations. Experimental results underscore the utility and superiority of MCF‐Net in applications of hydrogeological modeling.
Comparing stem cell mobilization with chemotherapy and cytokine (G-CSF) versus cytokine alone in myeloma patients (MOCCCA): a randomized phase II, open-label, non-inferiority trial
Authors: Jeker Barbara , Thalmann Laura , Bacher Ulrike , Nilius Henning , Rhyner Gaëlle , Sökler Martin , Soltermann Susanne , Winkler Annette , Vorburger Corinne , Daskalakis Michael , Hoffmann Michèle , Pabst Thomas 
Published: November 2024

AbstractIn fit patients with newly diagnosed myeloma, high-dose chemotherapy (HDCT) followed by autologous stem cell transplantation (ASCT) is considered standard of care. For mobilization of CD34+ cells for ASCT, combined cytotoxic chemotherapy and G-CSF is commonly used. However, the importance of cytostatic chemotherapy for reliable mobilization remains unclear. This prospective randomized phase II non-inferiority trial compared G-GSF only (G) compared to standard chemotherapy/G-CSF (CG) for CD34+ mobilization. The primary endpoint was a less than 15% difference in successful stem cell collection ( ≥ 5.0 × 106 CD34+ cells/kg b.w. in a single day collection procedure without additional stimulation with plerixafor) with the G regimen. 136 patients were 1:1 randomized. With an 18% difference in favor of the CG therapy, the non-inferiority margin was not maintained (95% CI 1%, 34%, p = 0.04). The median total CD34+ yield was 9.99 × 106/kg b.w. in CG patients and 7.42 × 106/kg b.w. in patients with G-CSF alone (p < 0.001). Ultimately, 130 (96%) patients proceeded to HDCT with ASCT. There were no differences in adverse events, hematologic engraftment, quality of life, or pain perception between the groups. Our data indicate that G-CSF only is inferior to chemotherapy with G-CSF for peripheral CD34+ stem cell mobilization. Trial registration SNCTP #: SNCTP000002952; Trials.gov #: NCT03442673.
NINJ1: Bridging lytic cell death and inflammation therapy
Authors: Shen Jinze , Chen Ruixiu , Duan Shiwei 
Published: November 2024

AbstractNINJ1, a critical transmembrane protein in inflammation, governs diverse biological processes. Recent breakthroughs revealed NINJ1’s structural basis for plasma membrane rupture, which is directly linked to lytic cell death. This discussion explores NINJ1’s functions, focusing on its pivotal role in lytic cell death regulation and the latest advancements in targeted therapeutic interventions.