bluuebunny/crossref_metadata_2025_split huggingface dataset:
Geometry-complete diffusion for 3D molecule generation and optimization
Authors: Morehead Alex , Cheng Jianlin 
Published: July 2024

AbstractGenerative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available onGitHub.
Soil carbon maintained by perennial grasslands over 30 years but lost in field crop systems in a temperate Mollisol
Authors: Dietz Clarissa L. , Jackson Randall D. , Ruark Matthew D. , Sanford Gregg R. 
Published: July 2024

AbstractTo mitigate climate change, some seek to store carbon from the atmosphere in agricultural soils. However, our understanding about how agriculture affects soil organic carbon is muddied by studies (1) lacking longitudinal data, (2) ignoring bulk density changes, or (3) sampling only surface soils. To better understand soil organic carbon trends, here we measured changes over 30 years in density-corrected, full-soil-depth (90 cm) soil organic carbon stocks under 6 cropping systems and a restored prairie in a Mollisol of southern Wisconsin, USA. Cash-grain systems and alfalfa-based systems lost soil organic carbon. Prairie and rotationally-grazed pasture maintained soil organic carbon. Average soil organic carbon losses for cash-grain and alfalfa-based systems were −0.80 (±0.12) and −0.54 (±0.13) Mg C ha−1 yr−1, respectively. Sensitivity analysis showed that incomplete methodologies overestimated soil organic carbon improvements. Our findings using more comprehensive methods demonstrate the inadequacy of row-crop systems and the need for well-managed grasslands to protect soil organic carbon in productive agricultural soils of the Upper Midwest USA.
Evolution and stability of complex microbial communities driven by trade-offs
Authors: Huang Yanqing , Mukherjee Avik , Schink Severin , Benites Nina Catherine , Basan Markus 
Published: July 2024

AbstractMicrobial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the ‘fittest’ single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.
Multi-expert ensemble ECG diagnostic algorithm using mutually exclusive–symbiotic correlation between 254 hierarchical multiple labels
Authors: Lai Jiewei , Zhang Yue , Zhao Chenyu , Wang Jinliang , Yan Yong , Chen Mingyang , Ji Lei , Guo Jun , Han Baoshi , Shi Yajun , Zhang Jinxia , Chen Yundai , Feng Qianjin , Yang Wei 
Published: July 2024

AbstractElectrocardiograms (ECGs) are a cheap and convenient means of assessing heart health and provide an important basis for diagnosis and treatment by cardiologists. However, existing intelligent ECG diagnostic approaches can only detect up to several tens of ECG terms, which barely cover the most common arrhythmias. Thus, further diagnosis is required by cardiologists in clinical settings. This paper describes the development of a multi-expert ensemble learning model that can recognize 254 ECG terms. Based on data from 191,804 wearable 12-lead ECGs, mutually exclusive–symbiotic correlations between hierarchical multiple labels are applied at the loss level to improve the diagnostic performance of the model and make its predictions more reasonable while alleviating the difficulty of class imbalance. The model achieves an average area under the receiver operating characteristics curve of 0.973 and 0.956 on offline and online test sets, respectively. We select 130 terms from the 254 available for clinical settings by considering the classification performance and clinical significance, providing real-time and comprehensive ancillary support for the public.
Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit
Authors: Dai Ziwei , Li Aikui , Sun Wei , Zhang Shenwu , Zhou Hao , Rao Ren , Luo Quan 
Published: July 2024

AbstractThe health state of lithium‐ion batteries is influenced by the operating conditions of energy storage stations and battery characteristics. It is challenging to obtain real‐time characterisation parameters like maximum discharge capacity and internal resistance. It is necessary to extract sensitivity indicators from electrical parameters such as voltage, current, and temperature. Utilising the Stanford‐MIT Research Institute battery dataset, this paper selects batteries with over 1000 cycles and five distinct charging and discharging strategies as samples. During the daily operation and maintenance of the energy storage station, health indicators are extracted from the voltage, current, and temperature curves within the state of charge range of 20%–80%. The ridge regression method is used to establish the health status estimation model. The gated recurrent unit (GRU) model is leveraged for health state prediction. Simulation results demonstrate that the proposed health indicators effectively assess lithium battery health, the health state estimation errors mean absolute error (MAE) and root mean squared error (RMSE) based on the ridge regression model are within 1.5% and 2%, and the health state prediction errors MAE and RMSE based on GRU model are within 1%. This approach exhibits stability, high accuracy, and strong generalisation capabilities.
A combined technique for power transformer fault diagnosis based on k‐means clustering and support vector machine
Authors: Nanfak Arnaud , Hechifa Abdelmoumene , Eke Samuel , Lakehal Abdelaziz , Kom Charles Hubert , Ghoneim Sherif S. M. 
Published: July 2024

AbstractThis contribution presents a two‐step hybrid diagnostic approach, combining k‐means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.
Pulse‐level work state recognition of multifunction radar based on MC‐RSG
Authors: Qin Zijun , Ren Wenjuan , Yang Zhanpeng , Sun Xian 
Published: July 2024

AbstractAccurate work state recognition of multifunction radar (MFR) is crucial in electronic warfare, as it helps understand the enemy's intention and evaluate potential threats. A pulse‐level work state recognition method of MFR based on the residual block with spatial attention connected gated recurrent unit by features using metric coding and correlative embedding (MC‐RSG) is proposed. Metric coding is designed to generate the distance vector with time of arrival, and the correlative embedding is performed on the distance vector and raw data features to increase the feature information by extracting feature information associated with the previous and subsequent pulses in each feature sequence, respectively. Besides, we make use of the model called RSG containing the residual block with spatial attention connected gated recurrent unit to learn the features of pulse sequences and identify the radar work state label of each pulse. The experimental work shows that the method is robust and has achieved up to 97% recognition accuracy on the test dataset under ideal observation conditions and 5% higher than the comparison network in high noise observation conditions.
Reform des Kapitels 32 (Laboratoriumsmedizin) im einheitlichen Bewertungsmaßstab (EBM)
Authors: Kappert Günther , Koscielny Jürgen , Sucker Christoph 
Published: July 2024

AbstractAm 11.04.2024 hat die Kassenärztliche Bundesvereinigung neue Kostenpauschalen für die Labordiagnostik und eine Anpassung des laborärztlichen Honorars veröffentlich; dies ist im Internet einsehbar (https://www.kbv.de/html/1150_68777.php).
Breaking – ein Szene-Tanzstil wird olympisch
Authors: Lindner Sophie Manuela 
Published: July 2024

ZusammenfassungBreakdance repräsentiert das tänzerische Element der Hip-Hop-Kulturbewegung und wird im August 2024 das erste Mal als Disziplin bei den Olympischen Spielen in Paris vertreten sein. Doch woher kommt Breaking? Welche Anforderungen bringt es mit sich? Und was sind die typischen Verletzungsmuster? Sophie Manuela Lindner kann diese Fragen beantworten, denn sie ist selbst Breakerin.
Women’s mental health during late pregnancy: A survey conducted in
Shandong Province, China
Authors: Li Yijie , Yu Huihui , Shen Kaifang , Long Jing 
Published: July 2024

Abstract
Background  The study aimed to investigate the general mental health
status and its associated factors in women during late pregnancy. The objective
was to provide a scientific basis for developing psychological interventions
tailored to this specific population.
Methods  The research was conducted from May 2021 to July 2022, involving
the recruitment of 200 women attending maternal and child health clinics for
their late-pregnancy checkups. Data collection was carried out using a
comprehensive approach, involving several validated tools. The participants
completed a general demographic and sociological questionnaire along with four
standardized psychological assessment scales: the 12-item General Health
Questionnaire (GHQ-12), the Athens Insomnia Scale (AIS-8), the Generalized
Anxiety Disorder 7 (GAD-7), and the 9-question Patient Health Questionnaire
(PHQ-9). A total of 200 valid questionnaires were collected for analysis.
Results  The study revealed that the overall prevalence of positive
detection of general mental health problems in women during late pregnancy was
11%. Significant differences were observed in the positive detection rate of
general mental health status based on various factors such as the quality of
relationships with husbands, pregnancy intentions, insomnia, anxiety, and
depression (p<0.01). Furthermore, participants with general mental health
problems displayed notably higher scores on the AIS-8, PHQ-9, and GAD-7 scales
compared to those without such problems (p<0.01). Regression analysis
indicated that pregnancy intention and PHQ-9 scores were influential factors
affecting the general mental health of women during late pregnancy
(p<0.05).
Conclusion  The study highlights high rates of general mental health
problems during late pregnancy, with unplanned pregnancy and elevated depression
scores as key risk factors. Regular mental health screening and targeted
interventions are essential to support women during this critical period and
enhance the well-being of both mothers and babies.
Development of a Machine Learning–Enabled Virtual Reality Tool for Preoperative Planning of Functional Endoscopic Sinus Surgery
Authors: Gudapati Varun , Chen Alexander , Meyer Scott , Jay Kuo Chung-Chieh , Ding Yichen , Hsiai Tzung K. , Wang Marilene 
Published: July 2024

Abstract
Objectives Virtual reality (VR) is an increasingly valuable teaching tool, but current simulators are not typically clinically scalable due to their reliance on inefficient manual segmentation. The objective of this project was to leverage a high-throughput and accurate machine learning method to automate data preparation for a patient-specific VR simulator used to explore preoperative sinus anatomy.
Methods An endoscopic VR simulator was designed in Unity to enable interactive exploration of sinus anatomy. The Saak transform, a data-efficient machine learning method, was adapted to accurately segment sinus computed tomography (CT) scans using minimal training data, and the resulting data were reconstructed into three-dimensional (3D) patient-specific models that could be explored in the simulator.
Results Using minimal training data, the Saak transform–based machine learning method offers accurate soft-tissue segmentation. When explored with an endoscope in the VR simulator, the anatomical models generated by the algorithm accurately capture key sinus structures and showcase patient-specific variability in anatomy.
Conclusion By offering an automatic means of preparing VR models from a patient's raw CT scans, this pipeline takes a key step toward clinical scalability. In addition to preoperative planning, this system also enables virtual endoscopy—a tool that is particularly useful in the COVID-19 era. As VR technology inevitably continues to develop, such a foundation will help ensure that future innovations remain clinically accessible.
Trans-Disc Aspiration of Anterior Cervical Cysts
Authors: Ebrahim Zahier , Gretschel Armin , van der Horst Alex , Vlok Adriaan J. 
Published: July 2024

Abstract
Background Spinal arachnoid cysts are rare entities occurring mainly in the cervical and thoracic spine. The majority of these lesions are treated through posterior approaches with laminectomy or laminotomy. We present trans-disc and trans-vertebral aspiration of anterior cervical arachnoid cysts as an alternative therapeutic approach.
Methods We present three cases and four procedures where anterior cervical arachnoid cysts were treated through an anterior trans-discal or trans-vertebral approach. A standard Smith–Robertson approach was used to expose the appropriate level in the cervical spine. A 22-gauge spinal needle is passed through the intervertebral disc or a 3-mm channel created in the midline of the vertebral body. This is performed under fluoroscopic guidance.
Results Three out of the four procedures resulted in complete clinical resolution at a 6-month follow-up. One patient required a repeat aspiration for a recurrent cyst at a level lower than the previous procedure. One patient had an initial improvement post-procedure followed by a functional decline within the first 5 postoperative days. Our assessment was that the cyst was re-filled and this was confirmed on magnetic resonance imaging (MRI). A standard posterior approach was used to excise the cyst with a good clinical result. Overall, no morbidity was suffered from the anterior approach.
Conclusion We believe this approach is a safe alternative to posterior intradural approaches. MRI scanning is advised at 6 months or earlier if clinical improvement is not demonstrated.
The Impact of the COVID-19 Pandemic and Lockdown on the Outcome of Glioblastoma
Authors: Armocida Daniele , D'Angelo Luca , Pietro Raffaella De , Chiarello Giuseppina , Jiang Tingting , Rizzo Francesca , Garbossa Diego , Frati Alessandro , Marampon Francesco , Santoro Antonio 
Published: July 2024

Abstract
Background Rapid spread of the SARS-CoV-2 pandemic in 2020 led to an indirect effect on non-COVID patients. Since neuro-oncology cases are unique and brain tumors need a specific therapeutic protocol at proper doses and at the right times, the effects of the pandemic on health care services for patients with glioblastomas (GBs) and their impact on overall survival (OS) and quality of life are not yet known.
Methods We conducted a retrospective study of 142 GB patients who underwent surgery, radiation, and chemotherapy before and after the lockdown period, aiming to determine the differences in access to care, treatment modality, and adjuvant therapies, and how the lockdown changed the prognosis.
Results The number of procedures performed for GB during the pandemic was comparable to that of the prepandemic period, and patients received standard care. There was a significant difference in the volume of lesions measured at diagnosis with a decreased number of “accidental” diagnoses and expression of a reduced use by the patient for a checkup or follow-up examinations. Patients expressed a significantly lower performance index in the lockdown period with longer progression-free survival (PFS) in the face of a comparable mean time to OS.
Conclusion Patients treated surgically for GB during the pandemic period had a more pronounced and earlier reduction in performance status than patients treated during the same period the year before. This appears to be primarily due to lower levels of care in the rehabilitation centers and more frequent discontinuation of adjuvant care.
Age, but not education, affects social decision-making in the ultimatum game paradigm
Authors: Cassimiro Luciana , Cecchini Mario Amore , Cipolli Gabriela Cabett , Yassuda Mônica Sanches 
Published: July 2024

Abstract
Background Social decision-making (SDM) is often studied through gaming paradigms, in which participants allocate resources among themselves and others based on predefined rules. In an adapted version of the ultimatum game (UG), SDM behavior was modulated in response to the degree of fairness of monetary offers and the social context of opponents, designed to generate either prosocial or punishing behaviors.
Objective To investigate whether SDM evaluated by the UG is affected by age and schooling, as it is relevant to know whether sociodemographic variables may bias UG results.
Methods A total of 131 healthy adults participated: 35 young university students and 96 participants in Universidade de São Paulo's USP 60+ program (formerly known as Universidade Aberta à Terceira Idade, a program for people aged ≥ 60 years to attend university). The sample was divided into 3 age groups (17–22, 60–69, and 70–79 years) and 3 schooling groups (4–8, 9–11, and ≥ 12 years of schooling).
Results Age and schooling did not affect performance in fair monetary offers. Differences were observed in the unfair conditions. The oldest group (70–79 years) accepted less frequently the baseline unfair offers (without social context), when compared with the 17–22 and the 60–69 years groups (17–22 = 60–69 > 70–79). Regarding the prosocial unfair and punishing unfair conditions, older adults accepted such offers more frequently (17–22 < 60–69 = 70–79). Schooling effects were not observed.
Conclusion In the context of SDM, older adults may show prosocial behaviors more frequently than younger adults. The findings suggest performance in the UG is affected by age, but not by schooling.
Ureaplasma urealyticum as a Cause of Ventriculitis in an Extremely Low Birth Weight Infant
Authors: Meers Garrit K. , Heep Axel , Huber Julia , Gräber Sandra , Lange Matthias 
Published: July 2024

Abstract
Ureaplasma urealyticum (Uu) has rarely been described as a cause of ventriculitis in preterm infants. We report a preterm infant in whom Uu was detected in the tracheal secretion. Concurrent intraventricular hemorrhage III° with posthemorrhagic hydrocephalus occurred, necessitating the bilateral insertion of ventriculostomy access devices for treatment. Persisting high cerebrospinal fluid (CSF) protein and pleocytosis in the otherwise clinically unremarkable neonate subsequently led to the targeted detection of Uu in the CSF. Normalization of CSF parameters occurred only after prolonged intravenous and oral macrolide therapy.
Variable science mapping as literature review method
Authors: Tomczyk Przemyslaw , Brüggemann Philipp , Paul Justin 
Published: July 2024

AbstractThis study investigates a novel mapping approach for the systematic analysis of empirical research, termed Variable Science Mapping (VSM). This approach enhances the current capabilities of Systematic Literature Reviews (SLRs) by incorporating variables and their interrelationships, surpassing traditional methods, such as Science Mapping (SM), which primarily analyze keywords, citations, and authorship. We present a step-by-step conceptual protocol for implementing the VSM approach. Subsequently, the strengths and limitations of VSM compared to SM are examined across 12 SLR stages. To this end, we assess the actual usage of SM for each stage based on an analysis of 63 papers employing the SM approach. Additionally, expert interviews are conducted to evaluate the utility of both SM and VSM across identical analytical stages. Notably, a distinct alignment emerged between the outcomes of the SLR and expert assessments pertaining to SM. The findings reveal VSM’s favorable ratings in eight out of 12 stages. Equivalence in expert ratings between SM and VSM surfaced in one stage, while SM was deemed more beneficial in three stages. This nuanced evaluation underscores the contextual strengths and limitations of both approaches. The implications extend to both scientific and managerial domains, offering valuable insights into the prospective advancements in SLRs. In conclusion, this analysis not only sheds light on the potential advantages of VSM but also serves as a foundation for guiding future research methodologies to widen capabilities among different SLR stages.