bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multiobjective optimization framework for designing a steering system considering structural features and full vehicle dynamics
Authors: Llopis-Albert Carlos , Rubio Francisco , Devece Carlos , Zeng Shouzhen 
Published: November 2023

AbstractVehicle handling and stability performance and ride comfort is normally assessed through standard field test procedures, which are time consuming and expensive. However, the rapid development of digital technologies in the automotive industry have enabled to properly model and simulate the full-vehicle dynamics, thus drastically reducing design and manufacturing times and costs while enhancing the performance, safety, and longevity of vehicle systems. This paper focus on a computationally efficient multi-objective optimization framework for developing an optimal design of a vehicle steering system, which is carried out by coupling certain computer-aided design tools (CAD) and computer-aided engineering (CAE) software. The 3D CAD model of the steering system is made using SolidWorks, the Finite Element Analysis (FEA) is modelled using Ansys Workbench, while the multibody kinematic and dynamic is analysed using Adams/Car. They are embedded in a multidisciplinary optimization design framework (modeFrontier) with the aim of determining the optimal hardpoint locations of the suspension and steering systems. This is achieved by minimizing the Ackermann error and toe angle deviations, together with the volume, mass, and maximum stresses of the rack-and-pinion steering mechanism. This enhances the vehicle stability, safety, manoeuvrability, and passengers’ comfort, extends the vehicle systems reliability and fatigue life, while reducing the tire wear. The method has been successfully applied to different driving scenarios and vehicle maneuvers to find the optimal Pareto front and analyse the performance and behaviour of the steering system. Results show that the design of the steering system can be significantly improved using this approach.
Fast relocking and afterslip-seismicity evolution following the 2015 Mw 8.3 Illapel earthquake in Chile
Authors: Hormazábal Joaquín , Moreno Marcos , Ortega-Culaciati Francisco , Báez Juan Carlos , Peña Carlos , Sippl Christian , González-Vidal Diego , Ruiz Javier , Metzger Sabrina , Yoshioka Shoichi 
Published: November 2023

AbstractLarge subduction earthquakes induce complex postseismic deformation, primarily driven by afterslip and viscoelastic relaxation, in addition to interplate relocking processes. However, these signals are intricately intertwined, posing challenges in determining the timing and nature of relocking. Here, we use six years of continuous GNSS measurements (2015–2021) to study the spatiotemporal evolution of afterslip, seismicity and locking after the 2015 Illapel earthquake ($$M_w$$

M
w

8.3). Afterslip is inverted from postseismic displacements corrected for nonlinear viscoelastic relaxation modeled using a power-law rheology, and the distribution of locking is obtained from the linear trend of GNSS stations. Our results show that afterslip is mainly concentrated in two zones surrounding the region of largest coseismic slip. The accumulated afterslip (corresponding to $$M_w$$

M
w

7.8) exceeds 1.5 m, with aftershocks mainly occurring at the boundaries of the afterslip patches. Our results reveal that the region experiencing the largest coseismic slip undergoes rapid relocking, exhibiting the behavior of a persistent velocity weakening asperity, with no observed aftershocks or afterslip within this region during the observed period. The rapid relocking of this asperity may explain the almost regular recurrence time of earthquakes in this region, as similar events occurred in 1880 and 1943.
When mechanical engineering inspired from physiology improves postural-related somatosensory processes
Authors: Sutter Chloé , Fabre Marie , Massi Francesco , Blouin Jean , Mouchnino Laurence 
Published: November 2023

AbstractDespite numerous studies uncovering the neural signature of tactile processing, tactile afferent inputs relating to the contact surface has not been studied so far. Foot tactile receptors being the first stimulated by the relative movement of the foot skin and the underneath moving support play an important role in the sensorimotor transformation giving rise to a postural reaction. A biomimetic surface, i.e., complying with the skin dermatoglyphs and tactile receptors characteristics should facilitate the cortical processes. Participants (n = 15) stood either on a biomimetic surface or on two control surfaces, when a sudden acceleration of the supporting surface was triggered (experiment1).A larger intensity and shorter somatosensory response (i.e., SEP) was evoked by the biomimetic surface motion. This result and the associated decrease of theta activity (5–7 Hz) over the posterior parietal cortex suggest that increasing the amount of sensory input processing could make the balance task less challenging when standing on a biomimetic surface. This key point was confirmed by asecond experiment(n = 21) where a cognitive task was added, hence decreasing the attentional resources devoted to the balance motor task. Greater efficiency of the postural reaction was observed while standing on the biomimetic than on the control surfaces.
Synthesis, X-ray crystallography and antimicrobial activity of 2-cyanoguanidinophenytoin
Authors: Mabied Ahmed F. , Moustafa Amr H. , Abdelhamid Antar A. , Tiama Taha M. , Amer Amer A. 
Published: November 2023

AbstractThe optimized synthesis of [5-oxo-4,4-diphenylimidazolidin-2-ylidene]cyanamide, which is known as 2-cyanoguanidinophenytoin (CNG-DPH) (3), and (imidazo[4,5-d]imidazole-2,5-diylidine)dicyanamide (4) has been reported in the present work. Furthermore, new Mannich bases derived from CNG-DPH were synthesized via its reaction with formaldehyde and using the corresponding amines, piperidine (base 5), and morpholine (base 6). Also, the antimicrobial activity and X-ray crystal structures for CNG-DPH and their Mannich bases were studied. The bases 3 and 6 crystallized in a monoclinic system; the crystal structure of 3 containing four molecules in the unit cell with a P21/c space group. The unit cell of 6 has eight molecules with a C2/c space group. The inter and intra hydrogen bond contacts packed and stabilized both of the structures. The morpholine ring of base 6 demonstrated a distinctive chair configuration. Mannich bases 5 and 6 showed promising antimicrobial effects. base 4 has a greater percentage for in vitro cytotoxicity (IC50) against normal cells, whereas 3 has the lowest ratio.
Towards a practical use of text mining approaches in electrodiagnostic data
Authors: Ramon-Gonen Roni , Dori Amir , Shelly Shahar 
Published: November 2023

AbstractHealthcare professionals produce abounding textual data in their daily clinical practice. Text mining can yield valuable insights from unstructured data. Extracting insights from multiple information sources is a major challenge in computational medicine. In this study, our objective was to illustrate how combining text mining techniques with statistical methodologies can yield new insights and contribute to the development of neurological and neuromuscular-related health information. We demonstrate how to utilize and derive knowledge from medical text, identify patient groups with similar diagnostic attributes, and examine differences between groups using demographical data and past medical history (PMH). We conducted a retrospective study for all patients who underwent electrodiagnostic (EDX) evaluation in Israel's Sheba Medical Center between May 2016 and February 2022. The data extracted for each patient included demographic data, test results, and unstructured summary reports. We conducted several analyses, including topic modeling that targeted clinical impressions and topic analysis to reveal age- and sex-related differences. The use of suspected clinical condition text enriched the data and generated additional attributes used to find associations between patients' PMH and the emerging diagnosis topics. We identified 6096 abnormal EMG results, of which 58% (n = 3512) were males. Based on the latent Dirichlet allocation algorithm we identified 25 topics that represent different diagnoses. Sex-related differences emerged in 7 topics, 3 male-associated and 4 female-associated. Brachial plexopathy, myasthenia gravis, and NMJ Disorders showed statistically significant age and sex differences. We extracted keywords related to past medical history (n = 37) and tested them for association with the different topics. Several topics revealed a close association with past medical history, for example, length-dependent symmetric axonal polyneuropathy with diabetes mellitus (DM), length-dependent sensory polyneuropathy with chemotherapy treatments and DM, brachial plexopathy with motor vehicle accidents, myasthenia gravis and NMJ disorders with botulin treatments, and amyotrophic lateral sclerosis with swallowing difficulty. Summarizing visualizations were created to easily grasp the results and facilitate focusing on the main insights. In this study, we demonstrate the efficacy of utilizing advanced computational methods in a corpus of textual data to accelerate clinical research. Additionally, using these methods allows for generating clinical insights, which may aid in the development of a decision-making process in real-life clinical practice.
Cybersecurity vulnerability analysis of medical devices purchased by national health services
Authors: Bracciale Lorenzo , Loreti Pierpaolo , Bianchi Giuseppe 
Published: November 2023

AbstractThe growing integration of software within medical devices introduces the potential for cybersecurity threats. How significant is this risk, and to what extent are citizens currently exposed? In this study, we adopt a new data-gathering methodology using datasets provided in Open Contracting Data Standard (OCDS). This allowed us to perform an extensive analysis across over 36 countries within a 12-year range, searching 92 million public administration purchase records for potentially vulnerable medical devices. The findings reveal a concerning landscape wherein numerous medical devices purchased by national health services possessed or still possess 661 distinct vulnerabilities—more than half of which are deemed critical or high-severity. These vulnerabilities enable relatively simple attacks to impact data confidentiality, integrity, and accessibility severely. Even if patches were applied immediately upon discovery, these vulnerabilities would still result in roughly 3.2 years of system exposure from the time a device is purchased until a software vulnerability is announced, with all classes of devices affected, including high-risk IIB and III devices which accounts for 74% of instances. While a full analysis requires interactivity, this noninvasive methodology enables a large-scale study, emphasizing the need to move faster from the safety to the security of medical devices.
Genetic landscape and PD-L1 expression in Epstein–Barr virus-associated gastric cancer according to the histological pattern
Authors: Park Ji Hyun , Cho Hee Jin , Seo Jeonghwa , Park Ki Bum , Kwon Yong Hwan , Bae Han Ik , Seo An Na , Kim Moonsik 
Published: November 2023

AbstractEpstein–Barr virus (EBV)-associated gastric cancer (EBVaGC) is a distinct molecular subtype of gastric cancer. This study aims to investigate genomic and clinicopathological characteristics of EBVaGC according to the histological pattern. We retrospectively collected 18 specimens of surgically resected EBVaGCs. Whole-exome sequencing was performed for all cases. Moreover, PD-L1 expression and tumor-infiltrating lymphocyte (TIL) percentage were investigated. Among 18 EBVaGCs, 10 cases were of intestinal histology, 3 were of poorly cohesive histology, and the remaining 5 were of gastric carcinoma with lymphoid stroma histology. Whole-exome sequencing revealed that EBVaGCs with intestinal histology harbored pathogenic mutations known to frequently occur in tubular or papillary adenocarcinoma, including TP53, KRAS, FBXW7, MUC6, ERBB2, CTNNB1, and ERBB2 amplifications. One patient with poorly cohesive carcinoma histology harbored a CDH1 mutation. Patients with EBVaGCs with intestinal or poorly cohesive carcinoma histology frequently harbored driver mutations other than PIK3CA, whereas those with EBVaGCs with gastric carcinoma with lymphoid stroma histology lacked other driver mutations. Moreover, the histological pattern of EBVaGCs was significantly associated with the levels of TILs (P = 0.005) and combined positive score (P = 0.027). In conclusion, patients with EBVaGCs with different histological patterns exhibited distinct genetic alteration, PD-L1 expression, and degree of TILs.
Performance comparison of Gilbert’s algorithm and machine learning in classifying Bell-diagonal two-qutrit entanglement
Authors: Wieśniak Marcin 
Published: November 2023

AbstractWhile classifying states as entangled or separable is one of the fundamental tasks in quantum information theory, it is also extremely challenging. This task is highly nontrivial even for relatively simple cases, such as two-qutrit Bell-diagonal states, i.e., mixtures of nine mutually orthogonal maximally entangled states. In this article we apply Gilbert’s algorithm to revise previously obtained results for this class. In particular we use “entanglement cartography” to argue that most states left in [Hiesmayr, B. C. Scientific Reports 11, 19739 (2021)] as unknown to be entangled or separable are most likely indeed separable, or very weakly entangled, beyond any practical relevance. The presented technique can find endless applications in more general cases.
Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models
Authors: Celniak Weronika , Wodziński Marek , Jurgas Artur , Burti Silvia , Zotti Alessandro , Atzori Manfredo , Müller Henning , Banzato Tommaso 
Published: November 2023

AbstractThe analysis of veterinary radiographic imaging data is an essential step in the diagnosis of many thoracic lesions. Given the limited time that physicians can devote to a single patient, it would be valuable to implement an automated system to help clinicians make faster but still accurate diagnoses. Currently, most of such systems are based on supervised deep learning approaches. However, the problem with these solutions is that they need a large database of labeled data. Access to such data is often limited, as it requires a great investment of both time and money. Therefore, in this work we present a solution that allows higher classification scores to be obtained using knowledge transfer from inter-species and inter-pathology self-supervised learning methods. Before training the network for classification, pretraining of the model was performed using self-supervised learning approaches on publicly available unlabeled radiographic data of human and dog images, which allowed substantially increasing the number of images for this phase. The self-supervised learning approaches included the Beta Variational Autoencoder, the Soft-Introspective Variational Autoencoder, and a Simple Framework for Contrastive Learning of Visual Representations. After the initial pretraining, fine-tuning was performed for the collected veterinary dataset using 20% of the available data. Next, a latent space exploration was performed for each model after which the encoding part of the model was fine-tuned again, this time in a supervised manner for classification. Simple Framework for Contrastive Learning of Visual Representations proved to be the most beneficial pretraining method. Therefore, it was for this method that experiments with various fine-tuning methods were carried out. We achieved a mean ROC AUC score of 0.77 and 0.66, respectively, for the laterolateral and dorsoventral projection datasets. The results show significant improvement compared to using the model without any pretraining approach.
Evaluation of a new point-of-care diagnostic test measuring inflammation in emergency settings
Authors: Charansonney Olivier L. , Al-Dandachi Ghanima , Plaisance Patrick , Vicaut Eric 
Published: November 2023

AbstractErythrocyte aggregation kinetics is accelerated in diseases with a strong inflammation component. This study aimed to evaluate whether, in an emergency setting, a new point-of-care test measuring erythrocyte aggregation kinetics (EAK) can identify patients with underlying inflammation. Patients visiting an emergency department and needing a blood exam were successively included. EAK was measured at the point-of-care in 20 s directly on the blood samples collected in regular tubes without any manipulation. The primary measure was EAK’s half-life during the first 5 s (EAK5s). Each patient’s inflammation status was assessed blind to the EAK test results. Receiver Operating Characteristic (ROC) curves for inflammation status were built. 268 patients had their EAK5s measured, and a clear inflammation status was determined for 214 patients (65 had inflammation). Mean EAK5s were 2.18 s and 1.75 s for no inflammation and inflammation groups respectively (p < 0.001). EAK5s appears to be a better inflammation marker than C-Reactive protein (CRP), with an area under the ROC curve of 0.845 compared to 0.806 for CRP (p < 0.0001). The Youden threshold for prediction of inflammation was 1.86 s with 84.6% (78.5–89.9%) specificity and 70.8% (60–81.5%) sensitivity. Point-of-care EAK is an easily measured, immediately available marker of inflammation with a better predictive power than CRP’s.
Investigating how nitrogen nutrition and pruning impacts on CBD and THC concentration and plant biomass of Cannabis sativa
Authors: Dilena Enrico , Close Dugald C. , Hunt Ian , Garland Sandra M. 
Published: November 2023

AbstractPrecise crop fertilization requires an in-depth understanding of plant uptake and utilisation to optimise sustainable production. This study investigated the influence of nitrogen (N) nutrition and pruning on the cannabinoid concentrations and biomass of a commercial cannabis cultivar; the rationale for this study is how N supply and pruning affect cannabinoid yields and concentration in a commercial setting. Clones of a Cannabis sativa L. (CBD-type) were grown in a controlled-environment glasshouse in pots with coarse sand. After five weeks of vegetative growth under 210 mg/L N and an 18 h light regime, rates of 30, 60, 210, and 500 mg/L N were applied to plants for twelve weeks and a light regime set at 12 h. Double stem pruning was applied as an additional treatment to investigate efficacy on biomass increase. Biomass, N concentrations, and cannabinoid concentrations were measured after the final harvest. Pruning treatment did not increase cannabinoid concentrations or affect biomass. It was coincidentally found that plants on the glasshouse edge with higher exposure to sunlight developed more biomass and higher cannabinoid concentrations. Only biomass in leaves was increased significantly via higher nitrogen nutrition. Cannabinoid concentration, as well as cannabinoid yield per plant were decreased with the increase in N supply. High rates of fertilizer are not recommended because of reduced cannabinoid concentration and biomass yield: the ideal N supply is likely to be between 60 and 210 mg/L. This research will benefit growers and advisors in understanding the complexity of effects of nitrogen fertiliser and pruning practices on plant biomass and secondary metabolite production in medicinal cannabis.
Evidence of abnormality in glutathione metabolism in the airways of preterm born children with a history of bronchopulmonary dysplasia
Authors: Course Christopher W. , Lewis Philip A. , Kotecha Sarah J. , Cousins Michael , Hart Kylie , Heesom Kate J. , Watkins W. John , Kotecha Sailesh 
Published: November 2023

AbstractPreterm-born children are at risk of long-term pulmonary deficits, including those who developed bronchopulmonary dysplasia (BPD) in infancy, however the underlying mechanisms remain poorly understood. We characterised the exhaled breath condensate (EBC) metabolome from preterm-born children, both with and without BPD. Following spirometry, EBC from children aged 7–12 years, from the Respiratory Health Outcomes in Neonates study, were analysed using Time-of-Flight Mass Spectrometry. Metabolite Set Enrichment Analysis (MSEA) linked significantly altered metabolites to biological processes. Linear regression models examined relationships between metabolites of interest and participant demographics. EBC was analysed from 214 children, 144 were born preterm, including 34 with BPD. 235 metabolites were detected, with 38 above the detection limit in every sample. Alanine and pyroglutamic acid were significantly reduced in the BPD group when compared to preterm controls. MSEA demonstrated a reduction in glutathione metabolism. Reduced quantities of alanine, ornithine and urea in the BPD group were linked with alteration of the urea cycle. Linear regression revealed significant associations with BPD when other characteristics were considered, but not with current lung function parameters. In this exploratory study of the airway metabolome, preterm-born children with a history of BPD had changes consistent with reduced antioxidant mechanisms suggesting oxidative stress.
Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5)
Authors: Leingang Oliver , Riedl Sophie , Mai Julia , Reiter Gregor S. , Faustmann Georg , Fuchs Philipp , Scholl Hendrik P. N. , Sivaprasad Sobha , Rueckert Daniel , Lotery Andrew , Schmidt-Erfurth Ursula , Bogunović Hrvoje 
Published: November 2023

AbstractReal-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set.