bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mapping of valley splitting by conveyor-mode spin-coherent electron shuttling
Authors: Volmer Mats , Struck Tom , Sala Arnau , Chen Bingjie , Oberländer Max , Offermann Tobias , Xue Ran , Visser Lino , Tu Jhih-Sian , Trellenkamp Stefan , Cywiński Łukasz , Bluhm Hendrik , Schreiber Lars R. 
Published: June 2024

AbstractIn Si/SiGe heterostructures, the low-lying excited valley state seriously limits the operability and scalability of electron spin qubits. For characterizing and understanding the local variations in valley splitting, fast probing methods with high spatial and energy resolution are lacking. Leveraging the spatial control granted by conveyor-mode spin-coherent electron shuttling, we introduce a method for two-dimensional mapping of the local valley splitting by detecting magnetic field-dependent anticrossings of ground and excited valley states using entangled electron spin-pairs as a probe. The method has sub-μeV energy accuracy and a nanometer lateral resolution. The histogram of valley splittings spanning a large area of 210 nm by 18 nm matches well with statistics obtained by the established but time-consuming magnetospectroscopy method. For the specific heterostructure, we find a nearly Gaussian distribution of valley splittings and a correlation length similar to the quantum dot size. Our mapping method may become a valuable tool for engineering Si/SiGe heterostructures for scalable quantum computing.
Microbial lysates repurposed as liquid egg substitutes
Authors: Choi Kyeong Rok , Ahn Da-Hee , Jung Seok Yeong , Lee Yu Hyun , Lee Sang Yup 
Published: June 2024

AbstractMicrobial lysates, rich in protein and essential nutrients, demonstrate remarkable capabilities in forming gels and stable foams when heated and whisked, similar to liquid eggs. These characteristics make them an excellent alternative to animal-derived liquid eggs, contributing to sustainable food production and consumption while maintaining high nutritional value. Their versatility positions microbial lysates as promising ingredients in culinary applications, offering a sustainable and nutritious substitute.
EmoWear: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness
Authors: Rahmani Mohammad Hasan , Symons Michelle , Sobhani Omid , Berkvens Rafael , Weyn Maarten 
Published: June 2024

AbstractThe EmoWear dataset provides a bridge to explore Emotion Recognition (ER) via Seismocardiography (SCG), the measurement of small cardio-respiratory induced vibrations on the chest wall through Inertial Measurement Units (IMUs). We recorded Accelerometer (ACC), Gyroscope (GYRO), Electrocardiography (ECG), Blood Volume Pulse (BVP), Respiration (RSP), Electrodermal Activity (EDA), and Skin Temperature (SKT) data from 49 participants who watched validated emotionally stimulating video clips. They self-assessed their emotional valence, arousal, and dominance, as well as extra questions about the video clips. Also, we asked the participants to walk, talk, and drink, so that researchers can detect gait, voice, and swallowing using the same IMU. We demonstrate the effectiveness of emotion stimulation with statistical methods and verify the quality of the collected signals through signal-to-noise ratio and correlation analysis. EmoWear can be used for ER via SCG, ER during gait, multi-modal ER, and the study of IMUs for context-awareness. Targeted contextual information include emotions, gait, voice activity, and drinking, all having the potential to be sensed via a single IMU.
Recommendations to minimize tooth root remodeling in patients undergoing maxillary osteotomies
Authors: Alqahtani Khalid Ayidh , Jacobs Reinhilde , Da Costa Senior Oliver , Politis Constantinus , Shaheen Eman 
Published: June 2024

AbstractThe purpose of this study was to report root remodeling/resorption percentages of maxillary teeth following the different maxillary osteotomies; i.e. one-piece, two-pieces, three-pieces Le Fort I, surgically assisted rapid palatal expansion (SARPE). The possibility of relationships between root remodeling and various patient- and/or treatment-related factors were further investigated. A total of 110 patients (1075 teeth) who underwent combined orthodontic and orthognathic surgery were studied retrospectively. The sample size was divided into: 30 patients in one-piece Le Fort I group, 30 patients in multi-pieces Le Fort I group, 20 patients in SARPE group and 30 patients in orthodontic group. Preoperative and 1 year postoperative cone beam computed tomography (CBCT) scans were obtained. A validated and automated method for evaluating root remodeling and resorption in three dimensions (3D) was applied. SARPE group showed the highest percentage of root remodeling. Spearman correlation coefficient revealed a positive relationship between maxillary advancement and root remodeling, with more advancement contributing to more root remodeling. On the other hand, the orthodontic group showed a negative correlation with age indicating increased root remodeling in younger patients. Based on the reported results of linear, volumetric and morphological changes of the root after 1 year, clinical recommendations were provided in the form of decision tree flowchart and tables. These recommendations can serve as a valuable resource for surgeons in estimating and managing root remodeling and resorption associated with different maxillary surgical techniques.
Constant force grinding controller for robots based on SAC optimal parameter finding algorithm
Authors: Rei Chosei , Wang Qichao , Chen Linlin , Yan Xinhua , Zhang Peng , Fu Liwei , Wang Chong , Liu Xinghui 
Published: June 2024

AbstractSince conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.
Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models
Authors: Elseify Mohamed A. , Hashim Fatma A. , Hussien Abdelazim G. , Abdel-Mawgoud Hussein , Kamel Salah 
Published: June 2024

AbstractDeploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.
Low BCL-xL expression in triple-negative breast cancer cells favors chemotherapy efficacy, and this effect is limited by cancer-associated fibroblasts
Authors: Nocquet Lisa , Roul Julie , Lefebvre Chloé C. , Duarte Laurine , Campone Mario , Juin Philippe P. , Souazé Frédérique 
Published: June 2024

AbstractTriple negative breast cancers (TNBC) present a poor prognosis primarily due to their resistance to chemotherapy. This resistance is known to be associated with elevated expression of certain anti-apoptotic members within the proteins of the BCL-2 family (namely BCL-xL, MCL-1 and BCL-2). These regulate cell death by inhibiting pro-apoptotic protein activation through binding and sequestration and they can be selectively antagonized by BH3 mimetics. Yet the individual influences of BCL-xL, MCL-1, and BCL-2 on the sensitivity of TNBC cells to chemotherapy, and their regulation by cancer-associated fibroblasts (CAFs), major components of the tumor stroma and key contributors to therapy resistance remain to be delineated. Using gene editing or BH3 mimetics to inhibit anti-apoptotic BCL-2 family proteins in TNBC line MDA-MB-231, we show that BCL-xL and MCL-1 promote cancer cell survival through compensatory mechanisms. This cell line shows limited sensitivity to chemotherapy, in line with the clinical resistance observed in TNBC patients. We elucidate that BCL-xL plays a pivotal role in therapy response, as its depletion or pharmacological inhibition heightened chemotherapy effectiveness. Moreover, BCL-xL expression is associated with chemotherapy resistance in patient-derived tumoroids where its pharmacological inhibition enhances ex vivo response to chemotherapy. In a co-culture model of cancer cells and CAFs, we observe that even in a context where BCL-xL reduced expression renders cancer cells more susceptible to chemotherapy, those in contact with CAFs display reduced sensitivity to chemotherapy. Thus CAFs exert a profound pro-survival effect in breast cancer cells, even in a setting highly favoring cell death through combined chemotherapy and absence of the main actor of chemoresistance, BCL-xL.
Intelligent extraction of reservoir dispatching information integrating large language model and structured prompts
Authors: Yang Yangrui , Chen Sisi , Zhu Yaping , Liu Xuemei , Ma Wei , Feng Ling 
Published: June 2024

AbstractReservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields.
Unveiling new genetic insights in rheumatoid arthritis for drug discovery through Taxonomy3 analysis
Authors: Kozlowska Justyna , Humphryes-Kirilov Neil , Pavlovets Anastasia , Connolly Martin , Kuncheva Zhana , Horner Jonathan , Manso Ana Sousa , Murray Clare , Fox J. Craig , McCarthy Alun 
Published: June 2024

AbstractGenetic support for a drug target has been shown to increase the probability of success in drug development, with the potential to reduce attrition in the pharmaceutical industry alongside discovering novel therapeutic targets. It is therefore important to maximise the detection of genetic associations that affect disease susceptibility. Conventional statistical methods such as genome-wide association studies (GWAS) only identify some of the genetic contribution to disease, so novel analytical approaches are required to extract additional insights. C4X Discovery has developed Taxonomy3, a unique method for analysing genetic datasets based on mathematics that is novel in drug discovery. When applied to a previously published rheumatoid arthritis GWAS dataset, Taxonomy3 identified many additional novel genetic signals associated with this autoimmune disease. Follow-up studies using tool compounds support the utility of the method in identifying novel biology and tractable drug targets with genetic support for further investigation.
Predictors of health-related quality of life after total knee arthroplasty: a case–control study
Authors: Askari Alireza , Mohammadpour Mehdi , Jabalameli Mahmoud , Naeimipoor Niloofar , Goodarzy Babak , Jafari Behnam , Rashidi Heeva , Mousazadeh Fatemeh , Rajei Maziar , Khazanchin Amir , Bahardoust Mansour , Hassanzadeh Mohammad 
Published: June 2024

AbstractTotal knee arthroplasty (TKA) improves patients’ Health-related quality of life (HRQoL) compared to before surgery. However, based on our knowledge, the improvement in HRQoL after TKA, which depends on various factors, has yet to be investigated compared to healthy people. This study aimed to evaluate the HRQoL of patients compared to healthy people and the factors affecting the HRQoL after TKA. In this matched case–control study (1002 participants), HRQoL in 501 patients who underwent TKA between 2015 and 2022 at Shafa Yahyainan Hospital affiliated with Iran University of Medical Sciences were compared with 501 healthy controls. HRQoL was evaluated in two parts (before compared to 12 months after TKA and 12 months after TKA compared to the healthy population). The 36-item short-form health survey (SF-36) was used to evaluate HRQoL 12 months after surgery. The influencing factors on HRQoL were evaluated by multivariate logistic regression analysis. No significant difference was observed in the demographic characteristics of the participants in the two groups. The mean overall SF-36 score, 12 months after surgery, significantly improved compared to before surgery (64.21 ± 22.2 vs. 37.55 ± 15.13, p:0.001). The mean total score of SF-36 was statistically similar between the case and control groups (64.21 ± 22.2 VS 72.53 ± 25.3). The multivariate analysis showed that sex, BMI, number of comorbidities, postoperative compliance, and complications were significantly related to the decrease in patients’ HRQoL (P < 0.001).TKA can improve the HRQoL except for two subscales of happiness/vitality and physical performance, similar to the healthy population. Female gender, obesity and overweight, comorbidity, bilateral TKA, non-adherence to postoperative physiotherapy, and complications were associated with decreased HRQoL.
Molecular subtypes of ischemic heart disease based on circadian rhythm
Authors: Zhou Zhaokai , Zhang Ge , Wang Zhan , Xu Yudi , Qin Hongzhuo , Zhang Haonan , Zhang Pengpeng , Li Zhengrui , Xu Shuai , Tan Xin , Zeng Yiyao , Yu Fengyi , Zhu Shanshan , Chang Le , Zheng Youyang , Han Xinwei 
Published: June 2024

AbstractCoronary atherosclerotic heart disease (CAD) is among the most prevalent chronic diseases globally. Circadian rhythm disruption (CRD) is closely associated with the progression of various diseases. However, the precise role of CRD in the development of CAD remains to be elucidated. The Circadian rhythm disruption score (CRDscore) was employed to quantitatively assess the level of CRD in CAD samples. Our investigation revealed a significant association between high CRDscore and adverse prognosis in CAD patients, along with a substantial correlation with CAD progression. Remarkably distinct CRDscore distributions were also identified among various subtypes. In summary, we have pioneered the revelation of the relationship between CRD and CAD at the single-cell level and established reliable markers for the development, treatment, and prognosis of CAD. A deeper understanding of these mechanisms may offer new possibilities for incorporating "the therapy of coronary heart disease based circadian rhythm" into personalized medical treatment regimens.
The role of the tropical Atlantic in tropical Pacific climate variability
Authors: Zhao Yingying , Capotondi Antonietta 
Published: June 2024

AbstractInteractions between Atlantic and Pacific Oceans can affect tropical Pacific variability and its global impacts at both interannual and decadal timescales. Thus, a deepened understanding of the coupled Atlantic-Pacific interactions is needed. While possible dynamical mechanisms by which the Atlantic can influence the Pacific have been identified, the effectiveness of those mechanisms is difficult to establish using climate model simulations where Atlantic sea surface temperatures (SSTs) are prescribed and Pacific feedbacks cannot be realistically included. As an alternative approach, here we use a Linear Inverse Model (LIM) trained on observations and capable of correctly reproducing the observed statistics, to assess the relative role of the Atlantic-to-Pacific and Pacific-to-Atlantic influences on tropical Pacific variability. Our results indicate that Atlantic internal variability can enhance interannual SST anomalies in the eastern equatorial Pacific, and decadal SST anomalies in the central equatorial Pacific, while Pacific influences on the Atlantic significantly damp tropical Pacific decadal variability. This methodological framework could also be used to assess climate model fidelity in representing tropical basin interactions, helping to reconcile existing differences among models’ results.
The dominant factor in extreme dust events over the Gobi Desert is shifting from extreme winds to extreme droughts
Authors: Zhu Qingzhe , Liu Yuzhi 
Published: June 2024

AbstractGobi Desert (GD) is one of the major global dust sources, where dust events are frequent. Based on satellite observations, we compiled extreme dust events (EDEs) over the GD during spring since 2000. Among all 43 extreme events, EDEs on 4–10 April 2001 (EDE 2001) and 14–19 March 2021 (EDE 2021) are the most pronounced, due to the longest duration and the most intense strength, while the generation mechanisms underlying these two EDEs are different. EDE 2001 is associated with extreme westerlies, which are caused by an abnormal low-pressure due to the extreme Eurasian teleconnection (EU). However, EDE 2021 is attributable to extreme droughts, which are caused by an abnormal high-pressure due to the extreme Arctic Oscillation (AO) and West Pacific teleconnection (WP). Moreover, the trends in EU, AO, and WP combined with the analysis of all EDEs indicate a shift in the dominant factor of EDEs over the GD from extreme winds to extreme droughts. Therefore, regional droughts should be given more importance in future EDE forecasts.
AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis
Authors: Abel John , Jain Suyog , Rajan Deepta , Padigela Harshith , Leidal Kenneth , Prakash Aaditya , Conway Jake , Nercessian Michael , Kirkup Christian , Javed Syed Ashar , Biju Raymond , Harguindeguy Natalia , Shenker Daniel , Indorf Nicholas , Sanghavi Darpan , Egger Robert , Trotter Benjamin , Gerardin Ylaine , Brosnan-Cashman Jacqueline A. , Dhoot Aditya , Montalto Michael C. , Parmar Chintan , Wapinski Ilan , Khosla Archit , Drage Michael G. , Yu Limin , Taylor-Weiner Amaro 
Published: June 2024

AbstractWhile alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.