bluuebunny/crossref_metadata_2025_split huggingface dataset:
Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model
Authors: Liu Yongkai , Yu Yannan , Ouyang Jiahong , Jiang Bin , Yang Guang , Ostmeier Sophie , Wintermark Max , Michel Patrik , Liebeskind David S. , Lansberg Maarten G. , Albers Gregory W. , Zaharchuk Greg 
Published: July 2023

BACKGROUND:
Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. This study’s goal was to predict ordinal 90-day modified Rankin Scale (mRS) score in acute ischemic stroke patients by fusing a Deep Learning model of diffusion-weighted imaging images and clinical information from the acute period.

METHODS:
A total of 640 acute ischemic stroke patients who underwent magnetic resonance imaging within 1 to 7 days poststroke and had 90-day mRS follow-up data were randomly divided into 70% (n=448) for model training, 15% (n=96) for validation, and 15% (n=96) for internal testing. Additionally, external testing on a cohort from Lausanne University Hospital (n=280) was performed to further evaluate model generalization. Accuracy for ordinal mRS, accuracy within ±1 mRS category, mean absolute prediction error, and determination of unfavorable outcome (mRS score >2) were evaluated for clinical only, imaging only, and 2 fused clinical-imaging models.

RESULTS:
The fused models demonstrated superior performance in predicting ordinal mRS score and unfavorable outcome in both internal and external test cohorts when compared with the clinical and imaging models. For the internal test cohort, the top fused model had the highest area under the curve of 0.92 for unfavorable outcome prediction and the lowest mean absolute error (0.96 [95% CI, 0.77–1.16]), with the highest proportion of mRS score predictions within ±1 category (79% [95% CI, 71%–88%]). On the external Lausanne University Hospital cohort, the best fused model had an area under the curve of 0.90 for unfavorable outcome prediction and outperformed other models with an mean absolute error of 0.90 (95% CI, 0.79–1.01), and the highest percentage of mRS score predictions within ±1 category (83% [95% CI, 78%–87%]).

CONCLUSIONS:
A Deep Learning-based imaging model fused with clinical variables can be used to predict 90-day stroke outcome with reduced subjectivity and user burden.
Modeling the Decay in Probability of Receiving Endovascular Thrombectomy on the Basis of Time From Stroke Onset
Authors: Paydarfar Daniel A. , Holodinsky Jessalyn K. , Mazya Michael V. , Hill Michael , Menon Bijoy , Jayaraman Mahesh , Kamal Noreen 
Published: July 2023

Background
American Heart Association guidelines specify infarct core volume as 1 determinant of eligibility for endovascular thrombectomy. Therefore, it is important to understand how time‐dependent infarct core growth translates to a patient's declining probability of thrombectomy eligibility. Modeling the probability that a patient with suspected large‐vessel occlusion would qualify for thrombectomy on the basis of their expected time from stroke onset to treatment can help inform the optimal prehospital emergency transport protocols, maximizing the likelihood of an excellent patient outcome.

Methods
We extended a published physiological model of infarct core growth to derive a decay curve of thrombectomy eligibility (based on a given infarct core volume threshold) as a function of time from stroke onset. We then adapted an existing model of the time‐dependent probability of an excellent outcome to incorporate this decay curve. Using the adapted model, we determined the optimal prehospital emergency transport protocols in Alberta, Canada, and compared these with the protocols that assumed all patients were thrombectomy eligible.

Results
The probability of qualifying for thrombectomy decays exponentially as time elapses from stroke onset. We found that the area where mothership is the optimal transport protocol increased by 18.6% after incorporating our decay curve of thrombectomy eligibility into the underlying optimization model. The benefit of mothership versus drip‐and‐ship also increased in the areas where mothership was favored, and in areas where drip‐and‐ship was favored, the benefit of drip‐and‐ship weakened. We also performed a number of sensitivity analyses to observe how these results change on the basis of our assumptions for model parameters.

Conclusion
This methodology provides a novel, physiology‐based approach to derive a thrombectomy eligibility curve. These models are necessary to better optimize prehospital transport decisions and consequently improve outcomes of patients with suspected large‐vessel occlusion.
Impacts of Atlantic Multidecadal Oscillation and Volcanic Forcing on the Late Summer Temperature of the Southern Tibetan Plateau
Authors: Nie Wenzheng , Li Mingqi , Deng Guofu , Shao Xuemei 
Published: July 2023

Abstract
In this paper, we present a late summer (August–September) temperature reconstruction over the period 1792–2020 based on a tree-ring maximum latewood density (MXD) chronology for the southern Tibetan Plateau (TP). The reconstruction explained 66.2% of the variance in the instrumental temperature records during the calibration period 1960–2020 and captured the warming trend since the 1960s, which would support the current warming on the TP. In addition, a warming hiatus existed during 2001–12 and the last 20 years (2000–20) were the warmest period in the past two centuries. The reconstruction matched other MXD- and mean latewood density (LWD)-based late summer temperature reconstructions from neighboring regions, and fluctuated in synchrony with the Climatic Research Unit (CRU) Northern Hemisphere land surface temperature during 1850–2020. Multitaper method analysis and wavelet analysis revealed significant periodicities of 2–3, 20–30, and 40–60 years in the reconstructed series. Our reconstructed series was very consistent and highly correlated with the Atlantic multidecadal oscillation (AMO). During the warm phase of the AMO, higher pressure and divergent horizontal winds over the TP contribute to warmer summers in the region. In addition, we found that the southern TP experienced the lowest temperature and downward solar radiation in the second year following large volcanic eruptions. The decrease in downward solar radiation may be directly responsible for the occurrence of the lowest temperatures. The results indicate that the AMO and large volcanic eruptions were impacting factors on temperature in our study area.
Optimal Fingerprinting with Estimating Equations
Authors: Ma Sai , Wang Tianying , Yan Jun , Zhang Xuebin 
Published: July 2023

Abstract
Climate change detection and attribution have played a central role in establishing the influence of human activities on climate. Optimal fingerprinting, a linear regression with errors in variables (EIVs), has been widely used in detection and attribution analyses of climate change. The method regresses observed climate variables on the expected climate responses to the external forcings, which are measured with EIVs. The reliability of the method depends critically on proper point and interval estimations of the regression coefficients. The confidence intervals constructed from the prevailing method, total least squares (TLS), have been reported to be too narrow to match their nominal confidence levels. We propose a novel framework to estimate the regression coefficients based on an efficient, bias-corrected estimating equations approach. The confidence intervals are constructed with a pseudo residual bootstrap variance estimator that takes advantage of the available control runs. Our regression coefficient estimator is unbiased, with a smaller variance than the TLS estimator. Our estimation of the sampling variability of the estimator has a low bias compared to that from TLS, which is substantially negatively biased. The resulting confidence intervals for the regression coefficients have coverage rates close to the nominal level, which ensures valid inferences in detection and attribution analyses. In applications to the annual mean near-surface air temperature at the global, continental, and subcontinental scales during 1951–2020, the proposed method led to shorter confidence intervals than those based on TLS in most of the analyses.

Significance Statement
Optimal fingerprinting is an important statistical tool for estimating human influences on the climate and for quantifying the associated uncertainty. Nonetheless, the estimators from the prevailing practice are not as optimal as believed, and their uncertainties are underestimated, both owing to the unreliable estimation of the optimal weight matrix that is critical to the method. Here we propose an estimation method based on the theory of estimating equations; to assess the uncertainty of the resulting estimator, we propose a pseudo bootstrap procedure. Through extensive numerical studies commonly used in statistical investigations, we demonstrate that the new estimator has a smaller mean-square error, and its uncertainty is estimated much closer to the true uncertainty than the prevailing total least squares method.
Regulation of Southwestern United States Precipitation by non-ENSO Teleconnections and the Impact of the Background Flow
Authors: Dong Cameron , Peings Yannick , Magnusdottir Gudrun 
Published: July 2023

Abstract
In this study, we analyze drivers of non–El Niño–Southern Oscillation (ENSO) precipitation variability in the Southwest United States (SWUS) and the influence of the atmospheric basic state, using atmosphere-only and ocean–atmosphere coupled simulations from the Community Earth System Model version 2 (CESM2) large ensemble. A cluster analysis identifies three main wave trains associated with non-ENSO SWUS precipitation in the experiments: a meridional ENSO-type wave train, an arching Pacific–North American-type (PNA) wave train, and a circumglobal zonal wave train. The zonal wave train cluster frequency differs between models and ENSO phase, with decreased frequency during El Niño and the coupled runs, and increased frequency during La Niña and the atmosphere-only runs. This is consistent with an El Niño–like bias of the atmospheric circulation in the coupled model, with strengthened subtropical westerlies in the central and eastern North Pacific that cause a retraction of the waveguide in the midlatitude eastern North Pacific. As such, zonal wave trains from the East Asian jet stream (EAJS) are more likely to be diverted southward in the east Pacific in the coupled large ensemble, with a consequently smaller role in driving SWUS precipitation variability. This study illustrates the need to reduce model biases in the background flow, particularly relating to the jet stream, in order to accurately capture the role of large-scale teleconnections in driving SWUS precipitation variability and improve future forecasting capabilities.
A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai–Tibet Plateau from Landsat Observations
Authors: Ran Qinwei , Aires Filipe , Ciais Philippe , Qiu Chunjing , Wang Yanfen 
Published: July 2023

Abstract
The Qinghai–Tibet Plateau, known as the Asian Water Tower, has a significant area of water bodies that provide a wide range of valuable ecosystem services. In the context of climate change, the formation condition of surface water and water extent is changing fast. Thus, there is a critical need for monthly detection algorithms at high spatial resolution (∼30 m) with good accuracy. Multiple sensors’ observations are available, but producing reliable long time series surface water mapping at a subannual temporal frequency still remains a challenge, mainly due to data limitations. In this study, we proposed a neural-network-based monthly surface water classification framework relying on Landsat 5/7/8 images in 2000–20 and topographic indices, and retrieved monthly water mask for the year 2020. The surface water was mainly distributed in the central and western parts of the plateau and the maximum area of permanent surface water (water frequency > 60%) was 26.66 × 103 km2 in 2020. The overall, producer, and user accuracies of our surface water map were 0.96, 0.94, and 0.98, respectively, and the kappa coefficient reached 0.90, demonstrating a better performance than existing products [i.e., Joint Research Centre (JRC) Monthly Water History with overall accuracy 0.94, producer accuracy 0.89, user accuracy 0.99, and kappa coefficient 0.89]. Our framework efficiently solved the problem of missing data in Landsat images referring to the JRC and a priori information and performed well in dealing with ice/snow cover issues. We showed that higher uncertainties exist on wetlands and recommended exploring relationships between water and wetlands in the future.

Significance Statement
In this paper, we present a new methodology to estimate surface water and its intra-annual changes using Landsat data. Missing data and retrieval errors in the winter are major issues in the existing products (i.e., JRC dataset). This motivated us to develop a new machine learning algorithm to better improve the retrieval scheme. We show that our approach, based on a neural network classifier, delivers a significant improvement compared to the previous estimates. As shown in the literature, JRC data can hardly be used at the monthly level, whereas our retrieval appears to be exploitable at the monthly scale. This is essential to understand the trend in surface water, one of the key elements of the water cycle.
A Perturbative Solution for Nonlinear Stratified Upwelling over a Frictional Slope
Authors: Choi Jang-Geun , Pringle James , Lippmann Thomas 
Published: July 2023

Abstract
A perturbative solution of simplified primitive equations for nonlinear weakly stratified upwelling over a frictional slope is found that resolves the vertical structure of velocity fields and can satisfy Ertel’s potential vorticity conservation in the stratified inviscid interior. The solution uses assumptions consistent with the model proposed by Lentz and Chapman, including a steady-state, constant cross-shore density gradient, no alongshore gradients, laterally inviscid, and consideration of cross-shore advection of alongshore momentum. The solution resolves the vertical structure of velocity fields (including subsurface maxima of compensational flow, not resolved by Lentz and Chapman) and can satisfy Ertel’s potential vorticity conservation in the stratified inviscid interior. The dynamics are similar to Lentz and Chapman; bottom stress balances alongshore wind stress in a homogeneous density ocean and is replaced by nonlinear cross-shore transport of alongshore momentum as the Burger number (S = αN/f, where α, N, and f are the bottom slope, buoyancy frequency, Coriolis frequency, respectively) increases. When the solution uses the empirical relation between cross-shore and vertical density gradients proposed by Lentz and Chapman, vorticity conservation is not satisfied and the nonlinear momentum transport estimated by the solution linearly increases with S, asymptotically matching Lentz and Chapman for S < 1. When the solution conserves interior potential vorticity, the momentum transport is proportional to S2 for S < 1 and is in better agreement with numerical simulations.
Surface Heating Steers Planetary-Scale Ocean Circulation
Authors: Bhagtani Dhruv , Hogg Andrew McC. , Holmes Ryan M. , Constantinou Navid C. 
Published: July 2023

Abstract
Gyres are central features of large-scale ocean circulation and are involved in transporting tracers such as heat, nutrients, and carbon dioxide within and across ocean basins. Traditionally, the gyre circulation is thought to be driven by surface winds and quantified via Sverdrup balance, but it has been proposed that surface buoyancy fluxes may also contribute to gyre forcing. Through a series of eddy-permitting global ocean model simulations with perturbed surface forcing, the relative contribution of wind stress and surface heat flux forcing to the large-scale ocean circulation is investigated, focusing on the subtropical gyres. In addition to gyre strength being linearly proportional to wind stress, it is shown that the gyre circulation is strongly impacted by variations in the surface heat flux (specifically, its meridional gradient) through a rearrangement of the ocean’s buoyancy structure. On shorter time scales (∼10 years), the gyre circulation anomalies are proportional to the magnitude of the surface heat flux gradient perturbation, with up to ∼0.15 Sv (1 Sv ≡ 106 m3 s−1) anomaly induced per watt per square meter change in the surface heat flux. On time scales longer than a decade, the gyre response to surface buoyancy flux gradient perturbations becomes nonlinear as ocean circulation anomalies feed back onto the buoyancy structure induced by the surface buoyancy fluxes. These interactions complicate the development of a buoyancy-driven theory for the gyres to complement the Sverdrup relation. The flux-forced simulations underscore the importance of surface buoyancy forcing in steering the large-scale ocean circulation.

Significance Statement
Ocean gyres are large swirling circulation features that redistribute heat across ocean basins. It is commonly believed that surface winds are the sole driver of ocean gyres, but recent literature suggests that other mechanisms could also be influential. We perform a series of numerical simulations in which we artificially change either the winds or the heating at the ocean’s surface and investigate how each factor independently affects the ocean gyres. We find that gyres are steered by both winds and surface heating, and that the ocean circulation responds differently to heating on short and long time scales. In addition, the circulation depends on where the heating is applied at the ocean’s surface. Through these simulations, we argue that a complete theory about ocean gyres must consider heating at the ocean’s surface as a possible driver, in addition to the winds.
Linking Future Decent Work Perceptions With Employment Anxiety: A Psychology of Working Perspective
Authors: Wan Wei , Duffy Ryan D. , Xiong Qing , Wang Chenhao 
Published: July 2023

The school-to-work transition can be extraordinarily challenging in the turbulent labor market, and the uncertainty about employment outlooks can cause intense anxiety for college students. Drawing from psychology of working theory (PWT), the current study examined the predictor section of the PWT model in relation to employment anxiety with a sample of impoverished undergraduates in China ( N = 362). Findings suggest that (1) economic constraints were negatively associated with work volition and future decent work perceptions; (2) work volition mediated the relation between economic constraints and future decent work perceptions; (3) future decent work perceptions were negatively related to employment anxiety; and (4) proactive personality and social support attenuated the adverse influence of economic constraints on work volition. Overall, this study contributes to PWT by adding a new outcome (employment anxiety) and also providing evidence of moderation effects. Implications for universities and career counselors are discussed.
Freudian robots: Artificial intelligence and psychodynamics
Authors: Webb Matthew 
Published: July 2023

Objective Artificial intelligence (AI) is rapidly developing, particularly human-interacting and affective AI systems. Psychodynamic concepts important to human development should be considered in the development of AI systems. Conclusion The development of safe and ethical affective artificial intelligence that remains meaningful and flexible should incorporate psychodynamic concepts. Mental health professionals such as psychiatrists and psychotherapists should be involved in the development of AI to provide expert knowledge in the area of psychodynamics.
Cancer Patient Perspectives on the Meaning of Healing and the Clinician as a Healer
Authors: Yang Cindy Y. , Smith Thomas J. , Knowlton Amy R. 
Published: July 2023

Background The experience of living with cancer is marked by suffering and loss, which creates a need for healing. Understanding what healing means to patients and how clinicians can play a role in the healing process is essential to holistic cancer care. Objective The aim of this study was to explore the perspectives of cancer patients on the meaning and experiences of healing and the qualities of a clinician and the clinician-patient relationship that are healing. Methods A qualitative study was conducted using semi-structured interviews with 14 cancer patients. Participants were asked about their illness experience, definition of healing, qualities of a healer, and relationships with clinicians that were healing. Interview transcripts were coded, and qualitative analysis was conducted to identify major themes. Results Participants defined the nature of healing as comprising aspects of physical, mental, emotional, and spiritual well-being. Participants described healing as alleviating pain and symptoms; promoting mental strength, emotional comfort, and spiritual connection; restoring and adapting to losses; and improving quality of life. The qualities of a clinician that contributed to a healing relationship included listening, empathy and compassion, understanding patients’ values and goals, and caring for the patient as a whole person. Conclusion Participants viewed healing as physical, psychosocial, and spiritual in nature and an important part of their cancer experience with an emphasis on quality of life. Clinicians played an important role beyond treating the cancer by helping in the healing process through their humanistic qualities and holistic approach to patient care.
Effect of Capitolunate Positioning on Outcomes in Scaphoid Excision and 4-Bone Fusion Patients
Authors: Hallihan Cameron L. , Goitz Robert J. , Kaufmann Robert A. , Fowler John R. 
Published: July 2023

Background: Scaphoid excision and 4-bone fusion (4BF) is a surgical procedure to treat scapholunate advanced collapse. Some surgeons align the lunate over the capitates, whereas others leave the capitate in its uncovered native position. The capitolunate angle may affect long-term outcomes. This study examined whether postsurgical outcomes differed based on these differences in positioning. Methods: A retrospective analysis was performed for patients that underwent a 4BF between 2006 and 2020. Wrist range of motion; pain (0-10); and Disabilities of the Arm, Shoulder, and Hand (DASH) scores were recorded. The width of the capitate, the width of the lunate contacting the capitate, and the capitolunate angle were measured. Pearson correlations and t tests were performed. For t tests of capitolunate uncovering, patients were divided into 2 groups: patients with 0% capitate uncovering and patients with >0% uncovering. For capitolunate angle, the 2 groups were patients with a capitolunate angle of ≤10° and patients with a capitolunate angle of >10°. Results: There was a significant correlation between capitate coverage and wrist extension, but no correlation for flexion, pain, or DASH scores. Group 1 (0% uncovering) had increased wrist extension and decreased pain compared with group 2 (>0% uncovering). There were no significant correlations or differences in the analyses of capitolunate angle’s impact on outcomes. Conclusions: In patients undergoing 4BF, those who had the lunate aligned to completely cover the capitate head had improved wrist extension and pain compared with patients where the capitate head was left partially uncovered. Capitolunate angle was not predictive of postsurgical outcomes.