bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-order brain interactions in ketamine during rest and task:
A double-blinded cross-over design using portable EEG
Authors: Ibanez Agustin , Herzog Ruben , Barbey Florentine , Islam Md Nurul , Rueda-Delgado Laura , Nolan Hugh , Prado Pavel , Krylova Marina , Javaheripour Nooshin , Danyeli Lena , Sen Zümrüt , Walter Martin , Odonnell Patricio , Buhl Derek , Murphy Brian , Izyurov Igor 
Published: March 2024

Abstract

Methods
In a double-blinded cross-over design, 30 adults (mean age = 25.57, SD = 3.74; all male) were administered racemic ketamine and compared against saline infusion as a control. Both task-driven (auditory oddball paradigm) and resting-state EEG were recorded. HOI were computed using advanced multivariate information theory tools, allowing us to quantify nonlinear statistical dependencies between all possible electrode combinations. Results: Ketamine increased redundancy in brain dynamics, most significantly in the alpha frequency band. Redundancy was more evident during the resting state, associated with a shift in conscious states towards more dissociative tendencies. Furthermore, in the task-driven context (auditory oddball), the impact of ketamine on redundancy was more significant for predictable (standard stimuli) compared to deviant ones. Finally, associations were observed between ketamine's HOI and experiences of derealization. Conclusions: Ketamine appears to increase redundancy and genuine HOI across metrics, suggesting these effects correlate with consciousness alterations towards dissociation. HOI represents an innovative method to combine all signal spatial interactions obtained from low-density dry EEG in drug interventions, as it is the only approach that exploits all possible combinations from different electrodes. This research emphasizes the potential of complexity measures coupled with portable EEG devices in monitoring shifts in consciousness, especially when paired with low-density configurations, paving the way for better understanding and monitoring of pharmacological-induced changes.
Neuropeptide QRFP decreased food intake in the lateral hypothalamus
Authors: Zagoracz Olga , Ollmann Tamas , Peczely Laszlo , Laszlo Kristof , Kovacs Anita , Berta Beata , Kallai Veronika , Kertes Erika , Voros David , Dusa Daniella , Szabo Adam , Lenard Laszlo 
Published: March 2024

Abstract
Severe eating disorders, such as obesity, bulimia, and anorexia keep increasing to epidemic proportions worldwide. Understanding of neuropeptides’ role in complex hunger/satiety mechanisms may allow new prospectives for treatment and prevention. Pyroglutamylated arginine-phenylalanine-amide peptides (QRFPs) are thought to enhance feeding following the central administration. In our study, QRFP-26 was delivered into the lateral hypothalamic area (LHA) of male Wistar rats by direct microinjections, as QRFP-26 expressing neurons and binding sights are densely present in this neural structure. The consumption of liquid food was measured over a 60-minute period. Both doses (100 and 200 ng) significantly decreased food intake. Neuropeptide Y1 receptor antagonist BIBP3226 eliminated the anorexigenic effect caused by QRFP-26 administration. QRFP-26 did not affect general locomotion, behavioral patterns examined in the Open-Field Test, or anxiety. This study is the first to report the anorexigenic action of QRFP-26 in the hypothalamus, emphasizing steady locomotion and anxiety levels. We have shown that the effect can be linked to the NPY Y1 receptor mechanism. Our observations also suggest the contribution of NPY system to the central regulation of stress and anxiety reactions.
Association between Maternal Perinatal Stress and Depression on Infant DNA Methylation in the First Year of Life
Authors: Abrishamcar Sarina , Zhuang Beryl , Thomas Mara , Gladish Nicole , MacIsaac Julia , Jones Meaghan , Simons Elinor , Moraes Theo , Mandhane Piush , Brook Jeffrey , Subbarao Padmaja , Turvey Stuart , Chen Edith , Miller Gregory , Kobor Michael , Huels Anke 
Published: March 2024

Abstract

Maternal stress and depression during pregnancy and the first year of the infant’s life affect a large percentage of mothers. Maternal stress and depression have been associated with adverse fetal and childhood outcomes as well as differential child DNA methylation (DNAm). However, the biological mechanisms connecting maternal stress and depression to poor health outcomes in children are still largely unknown. Here we aim to determine whether prenatal stress and depression are associated with changes in cord blood mononuclear cell DNAm (CBMC-DNAm) in newborns (n = 119) and whether postnatal stress and depression are associated with changes in peripheral blood mononuclear cell DNAm (PBMC-DNAm) in children of 12 months of age (n = 113) from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. Stress was measured using the 10-item Perceived Stress Scale (PSS) and depression was measured using the Center for Epidemiologic Studies Depression Questionnaire (CESD). Both stress and depression were measured at 18 weeks and 36 weeks of pregnancy and six months and 12 months postpartum. We conducted epigenome-wide association studies (EWAS) using robust linear regression followed by a sensitivity analysis in which we bias-adjusted for inflation and unmeasured confounding using the bacon and cate methods. To investigate the cumulative effect of maternal stress and depression, we created composite prenatal and postnatal adversity scores. We identified a significant association between prenatal stress and differential CBMC-DNAm at 8 CpG sites and between prenatal depression and differential CBMC-DNAm at 2 CpG sites. Additionally, we identified a significant association between postnatal stress and differential PBMC-DNAm at 8 CpG sites and between postnatal depression and differential PBMC-DNAm at 11 CpG sites. Using our composite scores, we further identified 2 CpG sites significantly associated with prenatal adversity and 7 CpG sites significantly associated with postnatal adversity. Several of the associated genes, including PLAGL1, HYMAI, BRD2, and ERC2 have been implicated in adverse fetal outcomes and neuropsychiatric disorders. This suggested that differential DNAm may play a role in the relationship between maternal mental health and child health.
Multidimensional Dynamic Data Analysis and Satisfaction Study of College Students' Educational Experience in Chongqing, China
Authors: Jin Ke , Chen Yanli 
Published: March 2024

Abstract

This study analyzes the factors that affect satisfaction by measuring the degree of satisfaction with online education among undergraduate and graduate students in colleges and universities in Southwest China. Establish a concept matrix through TAM and UTAUT theories. Through the analysis of information quality, system quality, service quality, perceived usefulness and perceived ease of use, the impact of self-efficacy on perceived usefulness and the impact of system quality on student satisfaction will be improved to improve the quality control and evaluation system for the construction of online open courses in colleges and universities, improve the quality and effectiveness of online education courses in colleges and universities.
New model for ultraslow-spreading ridges
Authors: Johansen Ståle Emil , Amundsen Hans , Arntsen Børge , Mittet Rune , Pedersen Ole , Panzner Martin , Omosanya Kamaldeen , Wang Shunguo 
Published: March 2024

Abstract
The oceanic crust formed at mid-ocean ridges constitutes seventy percent of the earth's solid surface 1–3. The crust in ocean basins is approximately seven km thick 4,5, but when the spreading rate drops below 15-20 mm/a (ultraslow-spreading), crustal thickness decreases rapidly 6,7. The paradigm view is that thickness depends on the spreading rate 6–8. However, current models for the ultraslow-spreading ridges are not based on direct imaging 9, and it is not well understood how tectonic processes, melt dynamics, 2,3,10,11, lithospheric structure, and crustal formation 12–15 interact along ridges 12–14,16,17. New electromagnetic (EM) data across the Mohns and Knipovich ridges show that, outside the volcanic centres, the lithosphere appears closed to melt migration, and instead of normal thinning, the lithosphere is unusually thick (35-45 km) beneath the ridges. Crustal thickness varies along the strike and is thinnest where the spreading rate is the highest, contrary to the prediction of conventional models6–8. In the new model, ambulatory volcanic centres, forming along weak zones, and fault-induced ultra-deep direct drainage of melt from the asthenosphere explain the EM data. Volcanic centres are point sources of melt supply and both centres, feeder channels and volcanism are episodic, relatively short-lived, and random in time and place. In this model with a thick and brittle lithosphere, the plate motions (rate and direction), local tectonics, lithology, weak zones, and deep faults associated with the deep drainage, control the development. Melt dynamics and crustal formation are passive buoyancy-driven responses to the tectonic development. The fact that the proposed ridge model is closely connected to fundamental tectonic processes support the idea that the model can also be applied to ultraslow-spreading ridges in general.
Integrating an AI Platform into Clinical IT: BPMN Processes for Clinical AI Model Development
Authors: Arshad Kfeel , Ardalan Saman , Schreiweis Björn , Bergh Björn 
Published: March 2024

Abstract
Background: There has been a resurgence of Artificial Intelligence (AI) on a global scale in recent times, resulting in the development of cutting-edge AI solutions within hospitals. However, this has also led to the creation of isolated AI solutions that are not integrated into clinical IT. To tackle this issue, a clinical Artificial Intelligence (AI) platform that handles the entire development cycle of clinical AI models and is integrated into clinical IT is required.
This research investigates the integration of a clinical AI platform into the clinical IT infrastructure. This is demonstrated by outlining the stages of the AI model development cycle within the clinical IT infrastructure, illustrating the interaction between different IT system landscapes within the hospital with BPMN diagrams.
Methods: Initially, a thorough analysis of the requirements is conducted to refine the necessary aspects of the clinical AI platform with consideration of the individual aspects of clinical IT. Subsequently, processes representing the entire development cycle of an AI model are identified. To facilitate the architecture of the AI platform, BPMN diagrams of all the identified processes are created. Clinical use cases are used to evaluate the processes using the FEDS framework.
Results: Our BPMN process diagrams cover the entire development cycle of a clinical AI model within the clinical IT. The processes involved are Data Selection, Data Annotation, On-site Training/Testing, and Inference, with distinctions between (Semi-Automated) Batch Inference and Real-Time Inference. Three clinical use cases were assessed to evaluate the processes and demonstrate that this approach covers a wide range of clinical AI use cases.
Conclusions: The evaluations were executed successfully, which indicate the comprehensive nature of our approach. The results have shown that different clinical AI use cases are covered by the BPMN diagrams. Our clinical AI platform is ideally suited for the local development of AI models within clinical IT. This approach provides a basis for further developments, e.g. enabling the training and deployment of an AI model across multiple sites or the integration of security- and privacy-related aspects.
Biplane reduction technique for restoring the length of the fibula in Maisonneuve fracture
Authors: Zhan Junfeng , Xie Yang , zhong qigang , Yang Hu , Huai Chaoyue , Yao yunfeng 
Published: March 2024

Abstract

Background
Fibular length is important for ankle stability; however, there is no unified and effective reduction method for fibular shortening in Maisonneuve fractures. This study aimed to investigate a biplane reduction technique for restoring fibular length in Maisonneuve fractures.
Methods
We retrospectively reviewed the data of patients with a preoperative diagnosis of Maisonneuve fractures between June 2019 and June 2022. All patients underwent a new biplane method to restore the length of the fibula. Operation time, hospital stay, complications, imaging parameters, and follow-up function scores were recorded. An independent sample t-test was used for component comparisons, and a paired sample t-test or one-way ANOVA was used for intra-group comparisons.
Results
Twelve patients were included in the study. The average age was 32.83 ± 11.46 years, including eight males and four females. No patients were lost to follow-up. The average follow-up time was 15.67 ± 2.84 months. No obvious postoperative complications were observed. In terms of imaging, there was no significant difference in the angle between the intersection of the anatomical axis of the tibia and the superior articular surface of the talus and the tip of the fibula (40.42 ± 0.43 °) than that of the healthy side (40.72 ± 0.57 °). At the last follow-up, the visual analog pain score and American Orthopedic Foot and Ankle Society ankle-hindfoot score were significantly improved compared with those before surgery.
Conclusions
The biplane reduction technique proposed in this study to restore the length of the fibula in Maisonneuve fractures was simple and effective, did not require special equipment, and had the advantage of directly examining the reduction condition.
Psychometric properties of the German version of the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) in routine clinical settings: a multi-trait/multi-method study in civilians and military personnel
Authors: Cwik Jan Christopher , Spies Jan , Kessler Henrik , Herpertz Stephan , Woud Marcella L. , Blackwell Simon E. , Willmund Gerd , Zimmermann Peter , Koch Eileen , Bohnacker Vincent , Klaer Benjamin , Schnyder Ulrich , Rau Heinrich , Köhler Kai 
Published: March 2024

Abstract
Background
The Clinician-Administered PTSD Scale (CAPS-5) is a structured diagnostic interview developed for diagnosing post-traumatic stress disorder (PTSD). To ensure compliance with PTSD inclusion criteria, an initial study investigated the psychometric properties and factorial structure of the German version of CAPS-5 using data collected previously. The present study’s objective was to validate the robustness of the psychometric properties of the German CAPS-5 by assessing its validity within a routine clinical context.
Methods
A total of 288 participants were recruited for the study. The validity of the German CAPS-5 was assessed using a multi-trait/multi-method design. Additionally, the study explored internal consistency, test-retest reliability, interrater reliability, and the interview’s diagnostic accuracy. Ultimately, a cut-off score was determined through receiver operating characteristics curve (ROC) analyses.
Results
The study demonstrated good to excellent internal consistency, test-retest reliability, interrater reliability, construct validity, and diagnostic accuracy for the German CAPS-5. Furthermore, the study established a cut-off score of ≥ 40 for the German CAPS-5 sum score.
Discussion
The German CAPS-5 emerged as a structured diagnostic interview exhibiting good to excellent psychometric properties. The findings indicated solid convergent validity of the German CAPS-5; however, further research is warranted to investigate its divergent validity.
Trial registration:
DRKS00015325 (https://www.drks.de)
Auto-sumoylation of the Ubc9 E2 SUMO-conjugating Enzyme Extends Cellular Lifespan
Authors: Ryu Hong-Yeoul , Jeong Dong-Won , Kim Seung Yeon , Jeoung Seok-Won , Zhao Dejian , Knight James , Lam TuKiet , Jin Jong Hwa , Lee Hyun-Shik , Hochstrasser Mark 
Published: March 2024

Abstract
Calorie restriction (CR) provides anti-aging benefits through diverse processes, such as reduced metabolism and growth and increased mitochondrial activity. Although controversy still exists regarding CR-mediated lifespan effects, many researchers are seeking interventions that mimic the effects of CR. Yeast has proven to be a useful model system for aging studies, including CR effects. We report here that yeast adapted through in vitro evolution to the severe cellular stress caused by loss of the Ulp2 SUMO-specific protease exhibit both enhanced growth rates and replicative lifespan, and they have altered gene expression profiles similar to those observed in CR. Notably, in certain evolved ulp2Δ lines, a dramatic increase in the auto-sumoylation of Ubc9 E2 SUMO-conjugating enzyme results in altered regulation of multiple targets involved in energy metabolism and translation at both transcriptional and post-translational levels. This increase is essential for the survival of aged cells and CR-mediated lifespan extension. Thus, we suggest that high Ubc9 auto-sumoylation exerts potent anti-aging effects by promoting efficient energy metabolism-driven improvements in cell replication abilities. This potential could be therapeutically explored for the development of novel CR-mimetic strategies.
Machine Learning Approach for Process Optimization of Black Nickel Electroplating
Authors: SUN YAJUAN , Tan Yong Teck , Zhao Yang , Teo Aaron , Zhou Yujie , Wong Joseph 
Published: March 2024

Abstract

Optimization of electroplating processes is crucial yet time-consuming. In an electroplating process like black nickel plating, achieving a consistent coating becomes challenging without boric acid, which serves as a pH buffer and widens the operational process window. Despite its benefits, the use of boric acid conflicts with the growing emphasis on sustainable and eco-friendly manufacturing. This complication intensifies the intricacy of process optimization. To tackle this challenge, this study employs machine learning techniques to gain insights into optimizing a boric acid-free black nickel plating process. Specifically, three machine learning models were developed to predict coating defects, coating colour, and coating mass. Drawing from the predictions
of these models, an optimization algorithm is proposed to select process parameters ensuring a flawless coating with the desired colour and mass, which is validated by experimental result. The machine learning models were able to give accurate predictions for black nickel electroplating process which behaves in a non-linear manner. This research illustrates how machine learning can enhance the manufacturing industry, particularly in the surface finishing sector, by providing an effective approach to optimize processes in non-linear scenarios, thereby improving product quality and work productivity.
Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
Authors: du weitao , Zhang Kaiwei , Lin Yange , Wu Guangcheng , Ren Yuxiang , Zhang Xuecang , Wang Bo , Zhang Xiao-Yu 
Published: March 2024

Abstract

The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. However, the challenge of designing molecular drugs or materials that incorporate multi-modality prior knowledge remains a critical and complex undertaking. Specifically, achieving a practical molecular design necessitates not only meeting the diversity requirements but also addressing structural and textural constraints with various symmetries outlined by domain experts. In this article, we present an innovative approach to tackle this inverse design problem by formulating it as a multi-modality guidance generation/optimization task. Our proposed solution involves a textural-structure alignment symmetric diffusion framework for the implementation of molecular generation/optimization tasks, namely 3DToMolo. 3DToMolo aims to harmonize diverse modalities, aligning them seamlessly to produce molecular structures adhere to specified symmetric structural and textural constraints by experts in the field. Experimental trials across three guidance generation settings have shown a superior hit generation performance compared to state-of-the-art methodologies. Moreover, 3DToMolo demonstrates the capability to generate novel molecules, incorporating specified target substructures, without the need for prior knowledge. This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies. 3DToMolo creates opportunities for a more nuanced and effective exploration of the vast chemical space, opening new frontiers in the development of molecular entities with tailored properties and functionalities.
Nudging Outgroup Altruism: A Human-Agent Interactional Approach for Reducing Ingroup Favoritism
Authors: Igwe Kevin , Durrheim Kevin 
Published: March 2024

Abstract
Ingroup favoritism and intergroup conflict can be mutually reinforcing during social interaction, threatening the peace and sustainability of societies. In two studies (N = 880), we investigated whether promoting prosocial outgroup altruism would weaken the ingroup favoritism cycle of influence. Using novel methods of human-agent interaction via a computer-mediated experimental platform, we introduced outgroup altruism by (i) nonadaptive artificial agents with preprogrammed outgroup altruistic behavior (Study 1; N = 400) and (ii) adaptive artificial agents whose altruistic behavior was informed by the prediction of a machine learning algorithm (Study 2; N = 480). A rating task ensured that the observed behavior did not result from the participant’s awareness of the artificial agents. In Study 1, nonadaptive agents prompted ingroup members to uphold their group identity by reinforcing ingroup favoritism. In Study 2, adaptive agents were able to weaken ingroup favoritism over time by maintaining a good reputation with both the ingroup and outgroup members, who perceived agents as being fairer than humans and rated agents as more human than humans. We conclude that a good reputation of the individual exhibiting outgroup altruism is necessary to weaken ingroup favoritism and reduce intergroup conflict. Thus, reputation is important for designing nudge agents.
A new four-area method for evaluating biochemical changes in lumbar facet joint degeneration at T2* mapping
Authors: Ding Yi , Ruan Shidong , Liu Liping , Zhang Xiaodong , Chen Rongchun , Chen Qin , Xu Shuaishuai 
Published: March 2024

Abstract
Purpose
To investigate the diagnostic efficacy of a new four-area method for evaluating biochemical changes in lumbar facet joint (LFJ) degeneration at T2* mapping.
Methods
Fifty-eight patients with low back pain underwent standard MRI protocols and axial T2* mapping. Morphological evaluation of LFJ was performed on T2-weighted imaging according to Weishaupt grading system. T2* value of LFJ was measured at T2* mapping using all-inclusive and four-area methods, respectively. Inter-observer reliability for continuous and categorical variables were respectively evaluated using Pearson correlation coefficient and Kappa value. For evaluating the correlation between continuous variables and ordered categorical variables, one way ANOVA or Kruskal-Wallis test was used.
Results
In the analyses of both all-inclusive and four-area methods, the mean T2* value of grade 0 LFJ was higher than those of grade I, grade II and grade III LFJ, and a downward trend of T2* value was observed as the grade of LFJ rised except grade III. The mean T2* values of LFJ obtained by all-inclusive method were higher than those obtained by four-area method, except grade 0 LFJ. Besides, four-area method had a perfect inter-observer reliability with PCC of 0.992 (p = 0.000), higher than that of all-inclusive method with PCC of 0.943 (p < 0.001).
Conclusions
Being more reproducible and accurate than conventional all-inclusive method in the measurement of T2* value, the new four-area method can be used in evaluating biochemical changes in LFJ degeneration at T2* mapping.