bluuebunny/crossref_metadata_2025_split huggingface dataset:
Open data for energy networks: introducing DAVE—a data fusion tool for automated network generation
Authors: Banze Tobias , Kneiske Tanja M. 
Published: January 2024

AbstractDeveloping a sustainable energy system for the future requires new ways of planning and operating energy infrastructure. A large part of this involves suitable network models. Real network data is not available for research without restrictions since energy networks are part of the critical infrastructure. Using open datasets and expert rules to generate non-restricted models is one solution to this. This paper introduces open data for energy networks generated by the open-source software “DAVE”. The Python-based data fusion tool DAVE can automatically generate customized energy network models quickly and on demand. The software collects data from various databases and uses appropriate methods to fuse them. The current version of the tool can create GIS-based power networks and gas transportation networks, with output that is compatible with common network simulation software. Further developments are planned for creating thermal and gas distribution networks, as these are important for local heat power transition. Implementing a quality description for fused datasets will also be included in future development.
H∞ state estimation of continuous-time neural networks with uncertainties
Authors: Li Aiting , Chen Yanhui , Hu Yun , Liu Dazhi , Liu Jinhui 
Published: January 2024

Abstract$$H_{\infty }$$

H
∞

state estimation is addressed for continuous-time neural networks in the paper. The norm-bounded uncertainties are considered in communication neural networks. For the considered neural networks with uncertainties, a reduced-order $$H_{\infty }$$

H
∞

state estimator is designed, which makes that the error dynamics is exponentially stable and has weighted $$H_{\infty }$$

H
∞

performance index by Lyapunov function method. Moreover, it is also given the devised method of the reduced-order $$H_{\infty }$$

H
∞

state estimator. Then, considering that sampling the output y(t) of the neural network at every moment will result in waste of excess resources, the event-triggered sampling strategy is used to solve the oversampling problem. In addition, a devised method is also given for the event-triggered reduced-order $$H_{\infty }$$

H
∞

state estimator. Finally, by the well-known Tunnel Diode Circuit example, it shows that a lower order state estimator can be designed under the premise of maintaining the same weighted $$H_{\infty }$$

H
∞

performance index, and using the event-triggered sampling method can reduce the computational and time costs and save communication resources.
Human amnion epithelial cell therapy reduces hypertension-induced vascular stiffening and cognitive impairment
Authors: Dinh Quynh Nhu , Lo Cecilia , Zhang David Wong , Tran Vivian , Gibson-Hughes Tayla , Sheriff Ashleigh , Diep Henry , Kim Hyun Ah , Zhang Shenpeng R. , Barreto-Arce Liz J. , Jelinic Maria , Vinh Antony , Arumugam Thiruma V. , Chan Siow Teng , Lim Rebecca , Drummond Grant R. , Sobey Christopher G. , De Silva T. Michael 
Published: January 2024

AbstractVascular inflammation and fibrosis are hallmarks of hypertension and contribute to the development of cardiovascular disease and cognitive impairment. However, current anti-hypertensive drugs do not treat the underlying tissue damage, such as inflammation-associated fibrosis. Human amnion epithelial cells have several properties amenable for treating vascular pathology. This study tested the effect of amnion epithelial cells on vascular pathology and cognitive impairment during hypertension. Male C57Bl6 mice (8–12 weeks) were administered vehicle (saline; n = 58) or angiotensin II (0.7 mg/kg/d, n = 56) subcutaneously for 14 d. After surgery, a subset of mice were injected with 106 amnion epithelial cells intravenously. Angiotensin II infusion increased systolic blood pressure, aortic pulse wave velocity, accumulation of aortic leukocytes, and aortic mRNA expression of collagen subtypes compared to vehicle-infused mice (n = 9–11, P < 0.05). Administration of amnion epithelial cells attenuated these effects of angiotensin II (P < 0.05). Angiotensin II-induced cognitive impairment was prevented by amnion epithelial cell therapy (n = 7–9, P < 0.05). In the brain, amnion epithelial cells modulated some of the inflammatory genes that angiotensin II promoted differential expression of (n = 6, p-adjusted < 0.05). These findings suggest that amnion epithelial cells could be explored as a potential therapy to inhibit vascular pathology and cognitive impairment during hypertension.
Drivers’ behavior confronting fixed and point-to-point speed enforcement camera: agent-based simulation and translation to crash relative risk change
Authors: Safavi-Naini Seyed Amir Ahmad , Sobhani Shayan , Malekpour Mohammad-Reza , Bhalla Kavi , Shahraz Saeid , Haghshenas Rosa , Ghamari Seyyed-Hadi , Abbasi-Kangevari Mohsen , Rezaei Nazila , Heydari Seyed Taghi , Rezaei Negar , Lankarani Kamran B. , Farzadfar Farshad 
Published: January 2024

AbstractUtilizing a novel microsimulation approach, this study evaluates the impact of fixed and average point-to-point Speed Enforcement Cameras (SEC) on driving safety. Using the SUMO software, agent-based models for a 6-km highway without exits or obstacles were created. Telematics data from 93,160 trips were used to determine the desired free-flow speed. A total of 13,860 scenarios were simulated with 30 random seeds. The ratio of unsafe driving (RUD) is the spatial division of the total distance travelled at an unsafe speed by the total travel distance. The study compared different SEC implementations under different road traffic and community behaviours using the Power Model and calculated crash risk changes. Results showed that adding one or two fixed SECs reduced RUD by 0.20% (0.18–0.23) and 0.57% (0.54–0.59), respectively. However, average SECs significantly lowered RUD by 10.97% (10.95–10.99). Furthermore, a 1% increase in telematics enforcement decreased RUD by 0.22% (0.21–0.22). Point-to-point cameras effectively reduced crash risk in all implementation scenarios, with reductions ranging from − 3.44 to − 11.27%, pointing to their superiority as speed enforcement across various scenarios. Our cost-conscious and replicable approach can provide interim assessments of SEC effectiveness, even in low-income countries.
Role of membrane vesicles in the transmission of vancomycin resistance in Enterococcus faecium
Authors: Lehmkuhl Johanna , Schneider Julia Sophie , Werth Kari Lavinia vom , Scherff Natalie , Mellmann Alexander , Kampmeier Stefanie 
Published: January 2024

AbstractClonal transmission and horizontal gene transfer (HGT) contribute to the spread of vancomycin-resistant enterococci (VRE) in global healthcare. Our study investigated vesiduction, a HGT mechanism via membrane vesicles (MVs), for vanA and vanB genes that determine vancomycin resistance. We isolated MVs for VRE of different sequence types (STs) and analysed them by nanoparticle tracking analysis. Selected MV samples were subjected to DNA sequence analysis. In resistance transfer experiments, vancomycin-susceptible enterococci were exposed to MVs and bacterial supernatants of VRE. Compared to bacteria grown in lysogeny broth (MVs/LB), cultivation under vancomycin stress (MVs/VAN) resulted in increased particle concentrations of up to 139-fold (ST80). As a key finding, we could show that VRE isolates of ST80 and ST117 produced remarkably more vesicles at subinhibitory antibiotic concentrations (approx. 9.2 × 1011 particles/ml for ST80 and 2.4 × 1011 particles/ml for ST117) than enterococci of other STs (range between 1.8 × 1010 and 5.3 × 1010 particles/ml). In those MV samples, the respective resistance genes vanA and vanB were completely verifiable using sequence analysis. Nevertheless, no vancomycin resistance transfer via MVs to vancomycin-susceptible Enterococcus faecium was phenotypically detectable. However, our results outline the potential of future research on ST-specific MV properties, promising new insights into VRE mechanisms.
Investigating the importance of left atrial compliance on fluid dynamics in a novel mock circulatory loop
Authors: Meskin Masoud , Starkey Philip Alexander , Kaspersen Alexander Emil , Ringgaard Steffen , Sand Signe Gram , Nygaard Jens Vinge , Jensen Jørgen Arendt , Traberg Marie Sand , Johansen Peter 
Published: January 2024

AbstractThe left atrium (LA) hemodynamic indices hold prognostic value in various cardiac diseases and disorders. To understand the mechanisms of these conditions and to assess the performance of cardiac devices and interventions, in vitro models can be used to replicate the complex physiological interplay between the pulmonary veins, LA, and left ventricle. In this study, a comprehensive and adaptable in vitro model was created. The model includes a flexible LA made from silicone and allows distinct control over the systolic and diastolic functions of both the LA and left ventricle. The LA was mechanically matched with porcine LAs through expansion tests. Fluid dynamic measures were validated against the literature and pulmonary venous flows recorded on five healthy individuals using magnetic resonance flow imaging. Furthermore, the fluid dynamic measures were also used to construct LA pressure–volume loops. The in vitro pressure and flow recordings expressed a high resemblance to physiological waveforms. By decreasing the compliance of the LA, the model behaved realistically, elevating the a- and v-wave peaks of the LA pressure from 12 to 19 mmHg and 22 to 26 mmHg, respectively, while reducing the S/D ratio of the pulmonary venous flowrate from 1.5 to 0.3. This model provides a realistic platform and framework for developing and evaluating left heart procedures and interventions.
Impact of homeostatic body hydration status, evaluated by hemodynamic measures, on different pain sensitization paths to a chronic pain syndrome
Authors: Davydov Dmitry M. , de la Coba Pablo , Contreras-Merino Ana M. , Reyes del Paso Gustavo A. 
Published: January 2024

AbstractContrasting findings on the mechanisms of chronic pain and hypertension development render the current conventional evidence of a negative relationship between blood pressure (BP) and pain severity insufficient for developing personalized treatments. In this interdisciplinary study, patients with fibromyalgia (FM) exhibiting clinically normal or elevated BP, alongside healthy participants were assessed. Different pain sensitization responses were evaluated using a dynamic 'slowly repeated evoked pain' (SREP) measure, as well as static pain pressure threshold and tolerance measures. Cardiovascular responses to clino-orthostatic (lying-standing) challenges were also examined as acute re- and de-hydration events, challenging cardiovascular and cerebrovascular homeostasis. These challenges involve compensating effects from various cardiac preload or afterload mechanisms associated with different homeostatic body hydration statuses. Additionally, hair cortisol concentration was considered as a factor with an impact on chronic hydration statuses. Pain windup (SREP) and lower pain threshold in FM patients were found to be related to BP rise during clinostatic (lying) rehydration or orthostatic (standing) dehydration events, respectively. These events were determined by acute systemic vasoconstriction (i.e., cardiac afterload response) overcompensating for clinostatic or orthostatic cardiac preload under-responses (low cardiac output or stroke volume). Lower pain tolerance was associated with tonic blood pressure reduction, determined by permanent hypovolemia (low stroke volume) decompensated by permanent systemic vasodilation. In conclusion, the body hydration status profiles assessed by (re)activity of systemic vascular resistance and effective blood volume-related measures can help predict the risk and intensity of different pain sensitization components in chronic pain syndrome, facilitating a more personalized management approach.
Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning
Authors: Tian Yalin , Lian Zengzeng , Wang Penghui , Wang Mengqi , Yue Zhe , Chai Huabin 
Published: January 2024

AbstractUltra-wideband technology has good anti-interference capabilities and development prospects in indoor positioning. Since ultra-wideband will be affected by random errors in indoor positioning, to exploit the advantages of the Kalman filter (KF) and the long short-term memory (LSTM) network, this paper proposes a long short-term memory neural network algorithm fused with the Kalman filter (KF–LSTM) to improve UWB positioning. First, the ultra-wideband data is processed through KF to weaken the noise in the data, and then the data is fed into the LSTM network for training, and the capability of the LSTM network to process time series features is employed to obtain more accurate label positions. Finally, simulation and measurement results show that the KF–LSTM algorithm achieves 71.31%, 37.28%, and 49.31% higher average positioning accuracy than the back propagation (BP) network, (back propagation network fused with the Kalman filter (KF-BP), and LSTM network algorithms, respectively, and the KF–LSTM algorithm performs more stably. Meanwhile, the more noise the data contains, the more obvious the stability contrast between the four algorithms.
An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study
Authors: Cai Lishan , Lambregts Doenja M. J. , Beets Geerard L. , Maas Monique , Pooch Eduardo H. P. , Guérendel Corentin , Beets-Tan Regina G. H. , Benson Sean 
Published: January 2024

ABSTRACT
The classification of extramural vascular invasion status using baseline magnetic resonance imaging in rectal cancer has gained significant attention as it is an important prognostic marker. Also, the accurate prediction of patients achieving complete response with primary staging MRI assists clinicians in determining subsequent treatment plans. Most studies utilised radiomics-based methods, requiring manually annotated segmentation and handcrafted features, which tend to generalise poorly. We retrospectively collected 509 patients from 9 centres, and proposed a fully automated pipeline for EMVI status classification and CR prediction with diffusion weighted imaging and T2-weighted imaging. We applied nnUNet, a self-configuring deep learning model, for tumour segmentation and employed learned multiple-level image features to train classification models, named MLNet. This ensures a more comprehensive representation of the tumour features, in terms of both fine-grained detail and global context. On external validation, MLNet, yielding similar AUCs as internal validation, outperformed 3D ResNet10, a deep neural network with ten layers designed for analysing spatiotemporal data, in both CR and EMVI tasks. For CR prediction, MLNet showed better results than the current state-of-the-art model using imaging and clinical features in the same external cohort. Our study demonstrated that incorporating multi-level image representations learned by a deep learning based tumour segmentation model on primary MRI improves the results of EMVI classification and CR prediction with good generalisation to external data. We observed variations in the contributions of individual feature maps to different classification tasks. This pipeline has the potential to be applied in clinical settings, particularly for EMVI classification.
DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients
Authors: Wang Hanyin , Gao Chufan , Dantona Christopher , Hull Bryan , Sun Jimeng 
Published: January 2024

AbstractIn the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient. The study introduces , an advanced large language model (LLM) fine-tuned on clinical notes to enhance DRGs assignment. Utilizing LLaMA as the foundational model and optimizing it through Low-Rank Adaptation (LoRA) on 236,192 MIMIC-IV discharge summaries, our -7B model exhibited a noteworthy macro-averaged F1 score of 0.327, a top-1 prediction accuracy of 52.0%, and a macro-averaged Area Under the Curve (AUC) of 0.986, with a maximum input token length of 512. This model surpassed the performance of prior leading models in DRG prediction, showing a relative improvement of 40.3% and 35.7% in macro-averaged F1 score compared to ClinicalBERT and CAML, respectively. Applied to base DRG and complication or comorbidity (CC)/major complication or comorbidity (MCC) prediction, achieved a top-1 prediction accuracy of 67.8% and 67.5%, respectively. Additionally, our findings indicate that ’s performance correlates with increased model parameters and input context lengths.
Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis
Authors: Wang Jing , Xue Le , Jiang Jiehui , Liu Fengtao , Wu Ping , Lu Jiaying , Zhang Huiwei , Bao Weiqi , Xu Qian , Ju Zizhao , Chen Li , Jiao Fangyang , Lin Huamei , Ge Jingjie , Zuo Chuantao , Tian Mei 
Published: January 2024

AbstractArtificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson’s disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94–0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87–0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 − 0.95) for presynaptic DA, 0.79 (95% CI: 0.75–0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96–0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.
Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020
Authors: Rosan Thais M. , Sitch Stephen , O’Sullivan Michael , Basso Luana S. , Wilson Chris , Silva Camila , Gloor Emanuel , Fawcett Dominic , Heinrich Viola , Souza Jefferson G. , Bezerra Francisco Gilney Silva , von Randow Celso , Mercado Lina M. , Gatti Luciana , Wiltshire Andy , Friedlingstein Pierre , Pongratz Julia , Schwingshackl Clemens , Williams Mathew , Smallman Luke , Knauer Jürgen , Arora Vivek , Kennedy Daniel , Tian Hanqin , Yuan Wenping , Jain Atul K. , Falk Stefanie , Poulter Benjamin , Arneth Almut , Sun Qing , Zaehle Sönke , Walker Anthony P. , Kato Etsushi , Yue Xu , Bastos Ana , Ciais Philippe , Wigneron Jean-Pierre , Albergel Clement , Aragão Luiz E. O. C. 
Published: January 2024

AbstractThe Amazon is the largest continuous tropical forest in the world and plays a key role in the global carbon cycle. Human-induced disturbances and climate change have impacted the Amazon carbon balance. Here we conduct a comprehensive synthesis of existing state-of-the-art estimates of the contemporary land carbon fluxes in the Amazon using a set of bottom-up methods (i.e., dynamic vegetation models and bookkeeping models) and a top-down inversion (atmospheric inversion model) over the Brazilian Amazon and the whole Biogeographical Amazon domain. Over the whole biogeographical Amazon region bottom-up methodologies suggest a small average carbon sink over 2010-2020, in contrast to a small carbon source simulated by top-down inversion (2010-2018). However, these estimates are not significantly different from one another when accounting for their large individual uncertainties, highlighting remaining knowledge gaps, and the urgent need to reduce such uncertainties. Nevertheless, both methodologies agreed that the Brazilian Amazon has been a net carbon source during recent climate extremes and that the south-eastern Amazon was a net land carbon source over the whole study period (2010-2020). Overall, our results point to increasing human-induced disturbances (deforestation and forest degradation by wildfires) and reduction in the old-growth forest sink during drought.
Nomo1 deficiency causes autism-like behavior in zebrafish
Authors: Zhang Qi , Li Fei , Li Tingting , Lin Jia , Jian Jing , Zhang Yinglan , Chen Xudong , Liu Ting , Gou Shenglan , Zhang Yawen , Liu Xiuyun , Ji Yongxia , Wang Xu , Li Qiang 
Published: January 2024

AbstractPatients with neuropsychiatric disorders often exhibit a combination of clinical symptoms such as autism, epilepsy, or schizophrenia, complicating diagnosis and development of therapeutic strategies. Functional studies of novel genes associated with co-morbidities can provide clues to understand the pathogenic mechanisms and interventions. NOMO1 is one of the candidate genes located at 16p13.11, a hotspot of neuropsychiatric diseases. Here, we generate nomo1−/− zebrafish to get further insight into the function of NOMO1. Nomo1 mutants show abnormal brain and neuronal development and activation of apoptosis and inflammation-related pathways in the brain. Adult Nomo1-deficient zebrafish exhibit multiple neuropsychiatric behaviors such as hyperactive locomotor activity, social deficits, and repetitive stereotypic behaviors. The Habenular nucleus and the pineal gland in the telencephalon are affected, and the melatonin level of nomo1−/− is reduced. Melatonin treatment restores locomotor activity, reduces repetitive stereotypic behaviors, and rescues the noninfectious brain inflammatory responses caused by nomo1 deficiency. These results suggest melatonin supplementation as a potential therapeutic regimen for neuropsychiatric disorders caused by NOMO1 deficiency.