bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intratumoral tertiary lymphoid structures promote patient survival and immunotherapy response in head neck squamous cell carcinoma
Authors: Liu Zhonglong , Meng Xiaoyan , Tang Xiao , Zou Weili , He Yue 
Published: December 2022

AbstractTertiary lymphoid structures (TLSs) hold the potential role in the prediction of immunotherapy response in several clinical trials. TLSs in head neck squamous cell carcinoma (HNSCC) have been investigated through IHC analysis, whereas there is no TLS gene signature to evaluate the level of TLS neogenesis. We here proposed a TLS signature containing 13 chemokines and determined TLS-hi and TLS-low groups in HNSCC samples from The Cancer Genome Atlas. TLS-hi condition signified a better overall survival. A more inflamed immune infiltrative landscape was identified in the TLS-hi tumors characterized by higher proportion of T cells, TCR/BCR activation and antigen processing. High level of TLSs has a determined role in the clinical significance of T cells. Interesting discovery was that innate lymphoid cells and cancer-associated fibroblasts were positively associated with TLS neogenesis in TME of HNSCC. Furthermore, by integrated TLSs with stromal cells and score, immune cells and score, TMB and malignant cells, we proposed a novel HNSCC TME classifications (HNSCC-TCs 1–5), unravelling the counteracted role of stromal cells and score in inflamed immune landscape, which may provide a novel stromal targeted modality in HNSCC therapy. Finally, we verified that TLS statue is an ideal predictor for immune checkpoint blockade immunotherapy. Current study indicated that the TLSs serve as a novel prognostic biomarker and predictor for immunotherapy, which may provide directions to the current investigations on immunotherapeutic strategies for HNSCC.
Risk of postpartum depressive symptoms is influenced by psychological burden related to the COVID-19 pandemic and dependent of individual stress coping
Authors: Meister Sarah , Dreyer Eva-Maria , Hahn Laura , Thomann Marilena , Keilmann Lucia , Beyer Susanne , Mayer Clarissa , Prins Gwendolin , Hasbargen Uwe , Mahner Sven , Jeschke Udo , Kolben Thomas , Burges Alexander 
Published: December 2022

Abstract
Purpose
There are different studies worldwide, which have shown a higher risk of mental disorders due to the COVID-19 pandemic. One aim of this study was to identify influencing factors of the psychological burden related to the COVID-19 pandemic and the impact on the development of postpartum depression. Further, the role of individual stress and coping strategies was analyzed in this context.

Materials and methods
Between March and October 2020, 131 women in obstetric care at the LMU Clinic Munich completed a questionnaire at consecutive stages during their perinatal period. The times set for the questionnaire were before birth, 1 month, 2 months, and 6 months after birth. The questionnaire was designed to evaluate the psychological burden related to the COVID-19 pandemic. For this a modified version of the Stress and coping inventory (SCI) and the Edinburgh Postnatal Depression Scale (EPDS) was used.

Results
We could show that the psychological burden related to the COVID-19 pandemic influenced the EPDS score 1, 2 and 6 months after birth. In addition, the prenatal stress and individual coping strategies affected the EPDS and the burden related to the COVID-19 pandemic before and after birth significantly.

Conclusion
An association of the psychological burden related to the COVID-19 pandemic with the risk of developing postpartum depressive symptoms could be shown in this study. In this context, the separation of the partner and the family was recognized as an important factor. Furthermore, the SCI was identified as an effective screening instrument for identifying mothers with an increased risk of postpartum depression. Hereby allowing primary prevention by early intervention or secondary prevention by early diagnosis.
Anterior surgical treatment for cervical degenerative radiculopathy: a prediction model for non-success
Authors: Mjåset Christer , Solberg Tore K. , Zwart John-Anker , Småstuen Milada C. , Kolstad Frode , Grotle Margreth 
Published: December 2022

Abstract
Purpose
By using data from the Norwegian Registry for Spine Surgery, we wanted to develop and validate prediction models for non-success in patients operated with anterior surgical techniques for cervical degenerative radiculopathy (CDR).

Methods
This is a multicentre longitudinal study of 2022 patients undergoing CDR surgery and followed for 12 months to find prognostic models for non-success in neck disability and arm pain using multivariable logistic regression analysis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC) and a calibration test. Internal validation by bootstrapping re-sampling with 1000 repetitions was applied to correct for over-optimism. The clinical usefulness of the neck disability model was explored by developing a risk matrix for individual case examples.

Results
Thirty-eight percent of patients experienced non-success in neck disability and 35% in arm pain. Loss to follow-up was 35% for both groups. Predictors for non-success in neck disability were high physical demands in work, low level of education, pending litigation, previous neck surgery, long duration of arm pain, medium-to-high baseline disability score and presence of anxiety/depression. AUC was 0.78 (95% CI, 0.75, 0.82). For the arm pain model, all predictors for non-success in neck disability, except for anxiety/depression, were found to be significant in addition to foreign mother tongue, smoking and medium-to-high baseline arm pain. AUC was 0.68 (95% CI, 0.64, 0.72).

Conclusion
The neck disability model showed high discriminative performance, whereas the arm pain model was shown to be acceptable. Based upon the models, individualized risk estimates can be made and applied in shared decision-making with patients referred for surgical assessment.
Cortical Deficits are Correlated with Impaired Stereopsis in Patients with Strabismus
Authors: Xi Sida , Zhou Yulian , Yao Jing , Ye Xinpei , Zhang Peng , Wen Wen , Zhao Chen 
Published: December 2022

AbstractIn this study, we explored the neural mechanism underlying impaired stereopsis and possible functional plasticity after strabismus surgery. We enrolled 18 stereo-deficient patients with intermittent exotropia before and after surgery, along with 18 healthy controls. Functional magnetic resonance imaging data were collected when participants viewed three-dimensional stimuli. Compared with controls, preoperative patients showed hypoactivation in higher-level dorsal (visual and parietal) areas and ventral visual areas. Pre- and postoperative activation did not significantly differ in patients overall; patients with improved stereopsis showed stronger postoperative activation than preoperative activation in the right V3A and left intraparietal sulcus. Worse stereopsis and fusional control were correlated with preoperative hypoactivation, suggesting that cortical deficits along the two streams might reflect impaired stereopsis in intermittent exotropia. The correlation between improved stereopsis and activation in the right V3A after surgery indicates that functional plasticity may underlie the improvement of stereopsis. Thus, additional postoperative strategies are needed to promote functional plasticity and enhance the recovery of stereopsis.
Sensor-based jump detection and classification with machine learning in trampoline gymnastics
Authors: Woltmann Lucas , Hartmann Claudio , Lehner Wolfgang , Rausch Paul , Ferger Katja 
Published: December 2022

AbstractThe task of the judge of difficulty in trampoline gymnastics is to check the elements and difficulty values entered on the competition cards and the difficulty of each element according to a numeric system. To do this, the judge must count all somersaults and twists for each jump during a routine and thus record the difficulty of the routine. This assessment can be automated with the help of inertial measurement units (IMUs) and facilitate the judges’ task during the competition. Currently, there is no known reliable method for the automated detection and recognition of the various elements to determine the difficulty of an exercise in trampoline gymnastics. Accordingly, a total of 2076 jumps and 50 different jump types were recorded over the course of several training sessions. In the first instance, 10 different jump types were used to train different machine learning (ML) models. Eight ML models were used for the automatic jump classification. Supervised learning approaches include a naive classifier, deep feedforward neural network, convolutional neural network, k‑nearest neighbors, Gaussian naive Bayes, support-vector classification, gradient boosting classifier, and stochastic gradient descent. When all classifiers were compared for accuracy, i.e., how many jumps were correctly detected by the ML model, the deep feedforward neural network and the convolutional neural network provided the best matches with 96.4 and 96.1%, respectively. The findings of this study will help to develop the automated classification of sensor-based data to support the judge and, simultaneously, for automated training logging.
Atorvastatin improves cisplatin sensitivity through modulation of cholesteryl ester homeostasis in breast cancer cells
Authors: Zipinotti dos Santos Diandra , Santos Guimaraes Isabella dos , Hakeem-Sanni Mariam F. , Cochran Blake J. , Rye Kerry-Anne , Grewal Thomas , Hoy Andrew J. , Rangel Leticia B. A. 
Published: December 2022

Abstract
Background
Acquired treatment resistance is a significant problem in breast cancer management, and alterations in lipid metabolism have been proposed to contribute to the development of drug resistance as well as other aspects of tumor progression. The present study aimed to identify the role of cholesterol metabolism in MCF-7 and MDA-MB-231 breast cancer cell response to cisplatin (CDDP) treatment in the acute setting and in a model of CDDP resistance.

Methods
MCF-7 (luminal A), MDA-MB-231 (triple-negative) and CDDP-resistant MDA-MB-231 (MDACR) cell lines were grown in the presence or absence of CDDP in combination with atorvastatin (ATV), lipid depletion or low-density lipoprotein loading and were analyzed by a variety of biochemical and radiometric techniques.

Results
Co-administration of CDDP and ATV strongly reduced cell proliferation and viability to a greater extent than CDDP alone, especially in MDA-MB-231 cells. These findings were associated with reduced cholesteryl ester synthesis and storage in MDA-MB-231 cells. In MDACR cells, acetyl-CoA acetyltransferase 1 (ACAT-1) was upregulated compared to naïve MDA-MB-231 cells and ATV treatment restored CDDP sensitivity, suggesting that aberrant ACAT-1 expression and associated changes in cholesterol metabolism contribute to CDDP resistance in MDA-MB-231 cells.

Conclusion
These findings indicate that the elevated susceptibility of MDA-MB-231 cells to co-administration of CDDP and ATV, is associated with an increased reliance on cholesteryl ester availability. Our data from these cell culture-based studies identifies altered cholesterol homeostasis as an adaptive response to CDDP treatment that contributes to aggressiveness and chemotherapy resistance.
Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery
Authors: Aggour Kareem S. , Detor Andrew , Gabaldon Alfredo , Mulwad Varish , Moitra Abha , Cuddihy Paul , Kumar Vijay S. 
Published: December 2022

AbstractMaterials scientists are facing increasingly challenging multi-objective performance requirements to meet the needs of modern systems such as lighter-weight and more fuel-efficient aircraft engines, and higher heat and oxidation-resistant steam turbines. While so-called second wave statistical machine learning techniques are beginning to accelerate the materials development cycle, most materials science applications are data-deprived when compared to the vastness and complexity of the search space of possible solutions. In line with DARPA’s vision of third wave AI approaches, we believe a combination of data-driven statistical machine learning and domain knowledge will be required to achieve a true revolution in materials discovery. To that end, we envision and have begun reducing to practice a system that fuses three forms of knowledge—factual scientific knowledge, physics-based and/or data-driven analytical models, and domain expert knowledge—into a single ‘Compound Knowledge Graph’ in which contextual reasoning and adaptation can be performed to answer increasingly complex questions. We believe this Compound Knowledge Graph-based system can be the nucleus of a collaborative AI assistant that supports stateful natural language back-and-forth dialogs between materials scientists and the AI to accelerate the development and discovery of new materials. This paper details our vision, summarizes our progress to date on a steam turbine blade coating use case, and outlines our thoughts on the key challenges in making this vision a reality.
Summing $$\mu (n)$$: a faster elementary algorithm
Authors: Helfgott Harald Andrés , Thompson Lola 
Published: December 2022

AbstractWe present a new elementary algorithm that takes $$ \textrm{time} \ \ O_\epsilon \left( x^{\frac{3}{5}} (\log x)^{\frac{8}{5}+\epsilon } \right) \ \ \textrm{and} \ \textrm{space} \ \ O\left( x^{\frac{3}{10}} (\log x)^{\frac{13}{10}} \right) $$

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A Friend in Need Is a Friend Indeed? Analysis of the Willingness to Share Self-Produced Electricity During a Long-lasting Power Outage
Authors: Kurz Konstantin , Bock Carolin , Knodt Michèle , Stöckl Anna 
Published: December 2022

AbstractWill private households owning a photovoltaic system share their electricity during a long-lasting power outage? Prior research has shown that our energy systems need to become more resilient by using dispersed energy sources—a role that could well be performed by these private photovoltaic systems, but only if their owners decide to share the produced electricity, and not consume it themselves. Considering the potential of this approach, it is indispensable to better understand incentives and motives that facilitate such cooperative behaviour. Drawing on theories of social dilemmas as well as prosocial behaviour, we hypothesize that both, structural solutions such as increased rewards as well as individual motives such as empathy-elicited altruism and norms predict cooperation. We test these hypotheses against a dataset of 80 households in Germany which were asked about their sharing behaviour towards four different recipient groups. We show that the effectiveness of motives differs significantly across recipient groups: Individual (intrinsic) motivations such as empathy-elicited altruism and altruistic norms serve as a strong predictor for cooperative behaviour towards related recipients as well as critical infrastructure, whereas higher rewards partially even reduce cooperation depending on the donor’s social value orientation. For the recipient groups neighbours and public infrastructure, no significant effect for any of the tested incentives is found. Contributing to literature on social dilemmas and energy resilience, these results demonstrate the relevance of individual rather than structural incentives for electricity sharing during a power outage to render our energy provision more resilient. Practical implications for policymakers are given.
Variability in the inorganic composition of colored acrylonitrile–butadiene–styrene and polylactic acid filaments used in 3D printing
Authors: Peloquin Derek M. , Rand Logan N. , Baumann Eric J. , Gitipour Ali , Matheson Joanna , Luxton Todd P. 
Published: December 2022

AbstractFused filament fabrication is a 3D printing technique that has gained widespread use from homes to schools to workplaces. Thermoplastic filaments, such as acrylonitrile–butadiene–styrene (ABS) and polylactic acid (PLA), are extruded at temperatures near their respective glass transition temperature or melting point, respectively. Little has been reported on the inorganic elemental composition and concentrations present in these materials or the methods available for extracting that information. Because inorganic constituents may be included in the aerosolized particulates emitted during the printing process, identifying elements that could be present and at what specific concentrations is critical. The objective of the current research is to determine the range of metals present in thermoplastic filaments along with their relative abundance and chemical speciation as a function of polymer type, manufacturer, and color. A variety of filaments from select manufacturers were digested using a range of techniques to determine the optimal conditions for metal extraction from ABS and PLA polymers. The extraction potential for each method was quantified using by ICP-MS analysis. When possible, further characterization of the chemical composition of the filaments was investigated using X-ray Absorption spectroscopy to determine chemical speciation of the metal. Optimal digestion conditions were established using a high temperature, high pressure microwave-assisted acid digestion method to produce the most complete and repeatable extraction results. The composition and abundance of metals in the filaments varied greatly as a function of polymer, manufacturer, and color. Potential elements of concern present in the filaments at elevated concentration included that could pose a respiratory risk included Si, Al, Ti, Cu, Zn, and Sn. XAS analysis revealed a mixture of metal oxides, mineral, and organometallic compounds were present in the filaments that were being used to increase opaqueness impart color (dyes), polymeric catalysts, and flame retardants. This work shows that a variety of metals are present in the starting materials used for 3D printing and depending on their partitioning into 3D printed products and byproducts as well as the exposure route, may pose a health risk which merits further investigation.
Linearized layering method for stress intensity factor determination
Authors: Storm Bradley , Abou-Hanna Jeries J. 
Published: December 2022

AbstractThis paper looks to reduce the complexity of determining stress intensity factors while maintaining high levels of accuracy by the use of a linearized layering approach. Many techniques for stress intensity factor determination exist, but they can be limited by conservative results, requiring too many user parameters, or by being too computationally intensive. Multiple notch geometries with various crack lengths were investigated in this study to better understand the effectiveness of the proposed method. By linearizing the average stresses in radial layers around the crack opening, stress intensity factors were found to have error ranging from −10.03 to 8.94% when compared to analytically exact solutions. This approach proved to be a robust and efficient method of accurately determining stress intensity factors.
Transient coupled thermo-elasticity analysis of a temperature-dependent thick-walled cylinder under cyclic thermo-mechanical loads
Authors: Elfar Mohamed , Sedaghati Ramin , Abdelsalam Ossama R. 
Published: December 2022

AbstractSeveral studies have reported the solution of the classical coupled thermo-elasticity for thick-walled cylinders under different boundary conditions; however very limited studies have been conducted on the analysis of temperature-dependent thick-walled cylinders under cyclic nonlinear boundary conditions. Therefore, this study investigates the transient response of temperature-dependent thick-walled cylinders under cyclic nonlinear thermo-mechanical loads based on classical coupled thermo-elasticity. Also, it studies the impact of considering temperature-dependent material properties (TDMP) and temperature-independent material properties (TIMP) hypotheses on the accuracy of the results. The governing equations of the classical nonlinear coupled thermo-elasticity are numerically solved based on the finite-difference method, namely the Crank-Nicolson method. Then, an experimental setup is designed to further investigate the impact of considering TDMP and TIMP through a temperature measurement of the outer surface of a machine gun barrel under burst shooting. It has been found that considering TIMP overestimates the residual temperature and residual stress values through the cylinder thickness compared with TDMP. Moreover, based on the temperature measurement, TDMP estimates results with higher accuracy of 7% than TIMP.
Improving order-picking response time at retail warehouse: a case of sugar company
Authors: Alqahtani Ammar Y. 
Published: December 2022

AbstractReducing customer response time, lowering overall logistic costs, and raising customer service standards are all partly attributable to innovative methods for increasing order-picking efficiency. This paper focuses on the storage allocation issue to determine the optimal stock-keeping-unit (SKU) inventory level and the storage assignment problem to place SKUs in the most efficient locations. In this research, we describe a hierarchical top-down technique for merging separate decision-making processes related to allocation and assignment. In this example, we use the suggested method, and show how the Analytic Hierarchical Process (AHP) yields useful insights for solving the case study at hand. The Radio Shuttle, a rack system with the best evaluation and the shortest product retrieval time, was put forth as an alternative. The proposed solution was estimated to save 50 h of travel time and 48 h of lost work time.