bluuebunny/crossref_metadata_2025_split huggingface dataset:
A 2D chiral microcavity based on apparent circular dichroism
Authors: Chen Tzu-Ling , Salij Andrew , Parrish Katherine A. , Rasch Julia K. , Zinna Francesco , Brown Paige J. , Pescitelli Gennaro , Urraci Francesco , Aronica Laura A. , Dhavamani Abitha , Arnold Michael S. , Wasielewski Michael R. , di Bari Lorenzo , Tempelaar Roel , Goldsmith Randall H. 
Published: April 2024

AbstractEngineering asymmetric transmission between left-handed and right-handed circularly polarized light in planar Fabry–Pérot (FP) microcavities would enable a variety of chiral light-matter phenomena, with applications in spintronics, polaritonics, and chiral lasing. Such symmetry breaking, however, generally requires Faraday rotators or nanofabricated polarization-preserving mirrors. We present a simple solution requiring no nanofabrication to induce asymmetric transmission in FP microcavities, preserving low mode volumes by embedding organic thin films exhibiting apparent circular dichroism (ACD); an optical phenomenon based on 2D chirality. Importantly, ACD interactions are opposite for counter-propagating light. Consequently, we demonstrated asymmetric transmission of cavity modes over an order of magnitude larger than that of the isolated thin film. Through circular dichroism spectroscopy, Mueller matrix ellipsometry, and simulation using theoretical scattering matrix methods, we characterize the spatial, spectral, and angular chiroptical responses of this 2D chiral microcavity.
Rotational magnetoelectric switching in orthorhombic multiferroics
Authors: Li Xu , Tian Hao , Chen Lan , Xiang Hongjun , Liu Jun-Ming , Bellaiche L. , Wu Di , Yang Yurong 
Published: April 2024

AbstractControlling the direction of ferromagnetism and antiferromagnetism by an electric field in single-phase multiferroics will open the door to the next generation of devices for spintronics and electronics. The typical magnetoelectric coupling such as the linear magnetoelectric effect is very weak in type-I multiferroics and therefore the magnetoelectric switching is rarely achieved. Here, using first-principles simulations, we propose a magnetoelectric switching mechanism to achieve such highly desired control in orthorhombic multiferroics. One class of two-dimensional proper multiferroics (CrX2Se3 and MnX2Te3, X = Sn, Ge) and perovskite multiferroics (EuTiO3 and BiFeO3/LaFeO3 superlattice) are taken as examples to show the mechanism. In the ferroelectric switching process, the proper polarization rotates its direction by 180° and keeps its magnitude almost unchanged, the ferromagnetic or antiferromagnetic vector is rotationally switched by 180° following the rotation of ferroelectric polarization. This rotational magnetoelectric switching results from in-plane structural anisotropy and magnetic anisotropy, and the process of switching is governed by cosine functions from the phenomenological Landau-type models. This study addresses the challenge of magnetoelectric switching in type-I multiferroics by proposing a general magnetoelectric switching mechanism.
Traumatic spinal cord injury in South Korea for 13 years (2008–2020)
Authors: Noh Sung Hyun , Lee Eunyoung , Kim Kyoung-Tae , Kim Sang Hyun , Cho Pyung Goo 
Published: April 2024

AbstractTraumatic spinal cord injury (TSCI) has significant physical, psychological, and socioeconomic impacts. However, the epidemiological characteristics and treatment patterns of TSCI in South Korea remain unclear. This study aimed to investigate TSCI incidence and treatment behaviors in South Korea from 2008 to 2020. We included data from 30,979 newly diagnosed TSCI patients obtained from the Health Insurance Review and Assessment Service (HIRA). Treatment trends, location of surgery, surgical method, comorbidities, factors affecting hospital stay, and risk factors affecting readmission were analyzed. Patients were divided into the surgery group [n = 7719; (25%)] and the non-surgery group [n = 23,260; (75%)]. Surgical cases involved cervical (64%), thoracic (17%), and lumbar/sacral (19%) lesions. Anterior fusion (38%), posterior fusion (54%), and corpectomy (8%) were the surgical methods. Surgical treatments increased annually. Factors influencing hospital stay included male sex, older age, and higher Charlson comorbidity index (CCI). Female sex and higher CCI scores were associated with readmission. In conclusion, a quarter of all TSCI patients underwent surgery, with an upward trend. Risk factors for longer hospital stays were thoracic spine injury, older age, higher CCI, and male sex. Risk factors for readmission included age range of 40–59 years, lumbar/sacral spine injuries, CCI score of 2, and female sex.
Designing the structure of cationic star-shaped trimeric surfactants most active in micelle formation using molecular connectivity indices
Authors: Mozrzymas Anna 
Published: April 2024

AbstractA model expressing the relationship between the logarithm of critical micelle concentration of cationic star-shaped trimeric surfactants and topological indices was obtained using only molecular connectivity indices. Based on the obtained model, the exemplary compound most active in micelle formation was designed. The analysis of the influence of various structural factors on the value of the critical micelle concentration was supported by atomic charge studies. The obtained model will be used to design new star-shaped trimeric surfactants that are more active in formation of micelle as well as to predict their critical micelle concentration.
Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy
Authors: Lafarge Maxime W. , Domingo Enric , Sirinukunwattana Korsuk , Wood Ruby , Samuel Leslie , Murray Graeme , Richman Susan D. , Blake Andrew , Sebag-Montefiore David , Gollins Simon , Klieser Eckhard , Neureiter Daniel , Huemer Florian , Greil Richard , Dunne Philip , Quirke Philip , Weiss Lukas , Rittscher Jens , Maughan Tim , Koelzer Viktor H. 
Published: April 2024

AbstractThe development of deep learning (DL) models to predict the consensus molecular subtypes (CMS) from histopathology images (imCMS) is a promising and cost-effective strategy to support patient stratification. Here, we investigate whether imCMS calls generated from whole slide histopathology images (WSIs) of rectal cancer (RC) pre-treatment biopsies are associated with pathological complete response (pCR) to neoadjuvant long course chemoradiotherapy (LCRT) with single agent fluoropyrimidine. DL models were trained to classify WSIs of colorectal cancers stained with hematoxylin and eosin into one of the four CMS classes using a multi-centric dataset of resection and biopsy specimens (n = 1057 WSIs) with paired transcriptional data. Classifiers were tested on a held out RC biopsy cohort (ARISTOTLE) and correlated with pCR to LCRT in an independent dataset merging two RC cohorts (ARISTOTLE, n = 114 and SALZBURG, n = 55 patients). DL models predicted CMS with high classification performance in multiple comparative analyses. In the independent cohorts (ARISTOTLE, SALZBURG), cases with WSIs classified as imCMS1 had a significantly higher likelihood of achieving pCR (OR = 2.69, 95% CI 1.01–7.17, p = 0.048). Conversely, imCMS4 was associated with lack of pCR (OR = 0.25, 95% CI 0.07–0.88, p = 0.031). Classification maps demonstrated pathologist-interpretable associations with high stromal content in imCMS4 cases, associated with poor outcome. No significant association was found in imCMS2 or imCMS3. imCMS classification of pre-treatment biopsies is a fast and inexpensive solution to identify patient groups that could benefit from neoadjuvant LCRT. The significant associations between imCMS1/imCMS4 with pCR suggest the existence of predictive morphological features that could enhance standard pathological assessment.
Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis
Authors: Krakowski Isabelle , Kim Jiyeong , Cai Zhuo Ran , Daneshjou Roxana , Lapins Jan , Eriksson Hanna , Lykou Anastasia , Linos Eleni 
Published: April 2024

AbstractThe development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6–80.1) and specificity was 81.5% (95% CI 73.9–87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4–86.5) and specificity was 86.1% (95% CI 79.2–90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
The algorithm journey map: a tangible approach to implementing AI solutions in healthcare
Authors: Boag William , Hasan Alifia , Kim Jee Young , Revoir Mike , Nichols Marshall , Ratliff William , Gao Michael , Zilberstein Shira , Samad Zainab , Hoodbhoy Zahra , Ali Mushyada , Khan Nida Saddaf , Patel Manesh , Balu Suresh , Sendak Mark 
Published: April 2024

AbstractWhen integrating AI tools in healthcare settings, complex interactions between technologies and primary users are not always fully understood or visible. This deficient and ambiguous understanding hampers attempts by healthcare organizations to adopt AI/ML, and it also creates new challenges for researchers to identify opportunities for simplifying adoption and developing best practices for the use of AI-based solutions. Our study fills this gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System. We conducted 20 interviews with the team of engineers and scientists that led the multi-year effort to build the tool, integrate it into practice, and maintain the solution. This “Algorithm Journey Map” enumerates all social and technical activities throughout the AI solution’s procurement, development, integration, and full lifecycle management. In addition to mapping the “who?” and “what?” of the adoption of the AI tool, we also show several ‘lessons learned’ throughout the algorithm journey maps including modeling assumptions, stakeholder inclusion, and organizational structure. In doing so, we identify generalizable insights about how to recognize and navigate barriers to AI/ML adoption in healthcare settings. We expect that this effort will further the development of best practices for operationalizing and sustaining ethical principles—in algorithmic systems.
Back-to-back high category atmospheric river landfalls occur more often on the west coast of the United States
Authors: Zhou Yang , Wehner Michael , Collins William 
Published: April 2024

AbstractThe catastrophic December 2022-January 2023 nine atmospheric rivers in California underscore the urgent need to better understand such high-risk weather extremes. Here we applied a machine learning clustering tool to understand the activity of atmospheric river clusters. Reanalysis results show that clusters with high density, that is the time fraction under atmospheric river conditions within a cluster, exhibit more frequent high-category atmospheric rivers, alongside an increased likelihood for extreme precipitation and severe land surface responses. The key circulation patterns of atmospheric river clusters are primarily attributed to subseasonal variability. Furthermore, the occurrence and density of atmospheric river clusters are modulated by the daily variability of the geopotential height field. Climate model projections suggest that atmospheric river clusters with higher density and higher categories will be more frequent as warming level increases. Our findings emphasize the important role of atmospheric river clusters in the development of climate adaptation and resilience strategies.
Navigating phase diagram complexity to guide robotic inorganic materials synthesis
Authors: Chen Jiadong , Cross Samuel R. , Miara Lincoln J. , Cho Jeong-Ju , Wang Yan , Sun Wenhao 
Published: April 2024

AbstractEfficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.
NT-3 contributes to chemotherapy-induced neuropathic pain through TrkC-mediated CCL2 elevation in DRG neurons
Authors: Sharma Dilip , Feng Xiaozhou , Wang Bing , Yasin Bushra , Bekker Alex , Hu Huijuan , Tao Yuan-Xiang 
Published: April 2024

AbstractCancer patients undergoing treatment with antineoplastic drugs often experience chemotherapy-induced neuropathic pain (CINP), and the therapeutic options for managing CINP are limited. Here, we show that systemic paclitaxel administration upregulates the expression of neurotrophin-3 (Nt3) mRNA and NT3 protein in the neurons of dorsal root ganglia (DRG), but not in the spinal cord. Blocking NT3 upregulation attenuates paclitaxel-induced mechanical, heat, and cold nociceptive hypersensitivities and spontaneous pain without altering acute pain and locomotor activity in male and female mice. Conversely, mimicking this increase produces enhanced responses to mechanical, heat, and cold stimuli and spontaneous pain in naive male and female mice. Mechanistically, NT3 triggers tropomyosin receptor kinase C (TrkC) activation and participates in the paclitaxel-induced increases of C–C chemokine ligand 2 (Ccl2) mRNA and CCL2 protein in the DRG. Given that CCL2 is an endogenous initiator of CINP and that Nt3 mRNA co-expresses with TrkC and Ccl2 mRNAs in DRG neurons, NT3 likely contributes to CINP through TrkC-mediated activation of the Ccl2 gene in DRG neurons. NT3 may be thus a potential target for CINP treatment.
Retrieval Practice and Word Learning by Children With Developmental Language Disorder: Does Expanding Retrieval Provide Additional Benefit?
Authors: Leonard Laurence B. , Christ Sharon L. , Deevy Patricia , Karpicke Jeffrey , Kueser Justin B. 
Published: April 2024

Purpose:
The word learning of preschool-age children with developmental language disorder (DLD) is improved when spaced retrieval practice is incorporated into the learning sessions. In this preregistered study, we compared two types of spacing—an expanding retrieval practice schedule and an equally spaced schedule—to determine if one of these approaches yields better word learning outcomes for the children.

Method:
Fourteen children with DLD aged 4–5 years and 14 same-age children with typical language development (TD) learned eight novel nouns over two sessions. Spacing for half of the novel words was expanded gradually during learning; for the remaining novel words, greater spacing remained at the same level throughout learning. Immediately after the second session and 1 week later, the children's recall of the words was tested.

Results:
The children with TD recalled more novel words than the children with DLD, although this difference could be accounted for by differences in the children's standardized receptive vocabulary test scores. The two groups were similar in their ability to retain the words over 1 week. Initially, the shorter spacing in the expanding schedule resulted in greater retrieval success than the corresponding (longer spaced) retrieval trials in the equally spaced schedule. These early shorter spaced trials also seemed to benefit retrieval of the trials with greater spacing that immediately followed. However, as the learning period progressed, the accuracy levels for the two conditions converged and were likewise similar during final testing.

Conclusion:
We need a greater understanding of how and when short spacing can be helpful to children's word learning, with the recognition that early gains might give a misleading picture of the benefits that short spacing can provide to longer term retention.

Supplemental Material:

https://doi.org/10.23641/asha.25537696
Zero/low overshoot conditions based on maximally‐flatness for PID‐type controller design for uncertain systems with time‐delay or zeros
Authors: Canevi Mehmet , Söylemez Mehmet Turan 
Published: April 2024

AbstractThis paper extends the characteristic ratio approach using novel inequalities to ensure zero/low overshoot for linear‐time‐invariant systems with zeros. The extension provided by this paper is based on the maximally‐flatness property of a transfer function, where the square‐magnitude of the transfer function is ensured to be a low‐pass filter. In order to be able to design low‐order/fixed structure controllers, a partial pole‐assignment approach is used instead of the full pole‐assignment used in the Characteristic Ratio Assignment (CRA) method. The developed inequalities and additional stability conditions are combined into an optimization problem using time domain restrictions when necessary. Although the method given in the paper is general, particular inequalities are developed for PI and PI‐PD controller cases, due to their frequent use in industrial applications. Similarly, First‐Order‐Plus‐Delay‐Time (FOPDT) and Second‐Order‐Plus‐Delay‐Time (SOPDT) systems are considered specifically, since most of the practical systems can be approximated by one of these types. The study is extended to plants with uncertainties where a theorem is developed to decrease computation time dramatically. The benefits of the proposed methods are demonstrated by several examples.
Minimal operation region prediction for networked control robotic manipulators subject to time‐varying delays and disturbances
Authors: Huynh Van Thanh , Lim Chee Peng , Najdovski Zoran , Huong Dinh Cong , Trinh Hieu 
Published: April 2024

AbstractDue to the disturbances and varying latency, a teleoperated robotic manipulator might not comply with the master control commands. Although prior studies on minimising the impact of network latency and disturbances on teleoperated robots were conducted, there has been very little research on the prediction of minimal operation regions of robotic arms, especially in the worst‐case scenarios when the disturbances and time delays still prevail even after impact minimisation. This study investigates the problem and proposes a novel solution to predicting minimal operation regions of networked control robotic manipulators. The proposed method can be used to forecast safe operation regions in which the manipulators will certainly enter and exclude regions that the robots will never penetrate. Leveraging on a Lyanonov Krasovskii criterion, the method performs region prediction by establishing minimal reachable bounding sets of the nonlinear, perturbed robotic arm's state vectors guided via a time‐varying delay‐dominant network. Though predominantly nonlinear, the entire prediction process is formulated as a tractable Linear Matrix Inequality (LMI) optimisation problem, which can be solved efficiently and effectively. Efficacy of the proposed method is validated with simulations where a simulated robotic arm is distorted with time‐varying delays and disturbances.
Tracking control of chained non‐holonomic systems with asymmetric function constraints and external disturbance
Authors: Yang Jing , Wu Yuqiang 
Published: April 2024

AbstractFor a class of chain non‐holonomic systems with external disturbance and function constraints, the tan‐type barrier Lyapunov function is used to solve the constraints of the system, and then the non‐linear disturbance observer is used to deal with the disturbance so that the disturbance error eventually converges exponentially. The control strategy designed by the backstepping method can effectively ensure that signals are bounded without violating the respective constraints. Through the simulation design of a three‐stage wheeled mobile robot, the effectiveness of the control scheme is verified again by the results.
Practical fixed‐time composite‐learning control for full‐state constraint strict‐feedback non‐linear systems: A dynamic regressor extension and mixing based approach
Authors: Cui Man , Wu Zhonghua 
Published: April 2024

AbstractA practical fixed‐time composite learning control scheme, by combining dynamic regressor extension and mixing (DREM) parameter identification algorithm and adaptive dynamic surface control (DSC) technique, is proposed for a class of strict‐feedback non‐linear systems subjected to linear‐in‐parameters uncertainties and full‐state constraint. To address the problem of state constraint, a non‐linear transformation function is introduced to convert the originally constrained non‐linear system into an unconstrained one. Meanwhile, the hyperbolic tangent function is employed to avoid singularity issues that often appeared in the traditional fixed‐time (FXT) control designs. In order to relax the requirement of persistency of excitation condition, a modified FXT‐DREM parameter identification approach with an interval excitation condition is constructed by introducing a three‐layer transformation technique derived from the classical DREM algorithm. Then, the modified FXT‐DREM parameter identification algorithm is seamlessly integrated into the adaptive DSC framework, resulting in a composite‐learning control scheme. By employing Lyapunov stability analysis, the fixed‐time convergence of both the parameter estimation error and the trajectory tracking error is proved. Finally, the effectiveness of the proposed design is demonstrated through simulation test.