bluuebunny/crossref_metadata_2025_split huggingface dataset:
A phenomenology and epistemology of large language models: transparency, trust, and trustworthiness
Authors: Heersmink Richard , de Rooij Barend , Clavel Vázquez María Jimena , Colombo Matteo 
Published: June 2024

AbstractThis paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined.
Brain perfusion SPECT in dementia: what radiologists should know
Authors: Imokawa Tomoki , Yokoyama Kota , Takahashi Kanae , Oyama Jun , Tsuchiya Junichi , Sanjo Nobuo , Tateishi Ukihide 
Published: June 2024

AbstractThe findings of brain perfusion single-photon emission computed tomography (SPECT), which detects abnormalities often before changes manifest in morphological imaging, mainly reflect neurodegeneration and contribute to dementia evaluation. A major shift is about to occur in dementia practice to the approach of diagnosing based on biomarkers and treating with disease-modifying drugs. Accordingly, brain perfusion SPECT will be required to serve as a biomarker of neurodegeneration. Hypoperfusion in Alzheimer’s disease (AD) is typically seen in the posterior cingulate cortex and precuneus early in the disease, followed by the temporoparietal cortices. On the other hand, atypical presentations of AD such as the posterior variant, logopenic variant, frontal variant, and corticobasal syndrome exhibit hypoperfusion in areas related to symptoms. Additionally, hypoperfusion especially in the precuneus and parietal association cortex can serve as a predictor of progression from mild cognitive impairment to AD. In dementia with Lewy bodies (DLB), the differentiating feature is the presence of hypoperfusion in the occipital lobes in addition to that observed in AD. Hypoperfusion of the occipital lobe is not a remarkable finding, as it is assumed to reflect functional loss due to impairment of the cholinergic and dopaminergic systems rather than degeneration per se. Moreover, the cingulate island sign reflects the degree of AD pathology comorbid in DLB. Frontotemporal dementia is characterized by regional hypoperfusion according to the three clinical types, and the background pathology is diverse. Idiopathic normal pressure hydrocephalus shows apparent hypoperfusion around the Sylvian fissure and corpus callosum and apparent hyperperfusion in high-convexity areas. The cortex or striatum with diffusion restriction on magnetic resonance imaging in prion diseases reflects spongiform degeneration and brain perfusion SPECT reveals hypoperfusion in the same areas. Brain perfusion SPECT findings in dementia should be carefully interpreted considering background pathology.
Targeting IL-11R/EZH2 signaling axis as a therapeutic strategy for osteosarcoma lung metastases
Authors: Devarajan Eswaran , Davis R. Eric , Beird Hannah C. , Wang Wei-Lien , Jensen V. Behrana , Jayakumar Arumugam , Leung Cheuk Hong , Lin Heather Y. , Wu Chia-Chin , Ihezie Stephanie A. , Tsai Jen-Wei , Futreal P. Andrew , Lewis Valerae O. 
Published: June 2024

AbstractLung metastases are the primary cause of death for osteosarcoma (OS) patients. We recently validated interleukin-11 receptor α (IL-11Rα) as a molecular target for the inhibition of OS lung metastases. Since there is no clinically approved antibody against this receptor, we sought to identify downstream targets that mediate the effects of IL-11Rα signaling. We used shRNA to deplete IL-11Rα from OS cells; as a complementary approach, we added IL-11 exogenously to OS cells. The resulting changes in gene expression identified EZH2 as a downstream candidate. This was confirmed by knockdown of IL-11Rα in OS cells, which led to increased expression of genes repressed by histone methyltransferase EZH2, including members of the WNT pathway, a known target pathway of EZH2. Exogenous IL-11 increased the global levels of histone H3 lysine 27 trimethylation, evidence of EZH2 activation. Treatment with the EZH2 inhibitor GSK126 significantly reduced in vitro proliferation and increased cell-cycle arrest and apoptosis, which were partially mediated through the WNT pathway. In vivo, treatment of an orthotopic nude mouse model of OS with GSK126 inhibited lung metastatic growth and prolonged survival. In addition, significantly shorter recurrence-free survival was seen in OS patients with high levels of EZH2 in their primary tumors (P < .05). This suggests that IL-11Rα promotes OS lung metastasis via activation of EZH2. Thus, blocking EZH2 activity may be an effective strategy for inhibiting OS lung metastasis and improving prognosis.
Multi-parameter modeling for prediction of gas–water production in tight sandstone reservoirs
Authors: Mo Chaoping , Zhang Guangdong , Tang Yong , Zeng Difeng 
Published: June 2024

AbstractTight sandstone reservoirs are significant sources of natural gas reserves. As traditional reserves become increasingly scarce and costly, optimizing the development of these reservoirs becomes crucial. This study introduces a novel two-phase gas–water flow model for single wells, incorporating both Darcy and non-Darcy flow equations. These equations are derived from mass conservation and momentum principles for both gas and water phases. Using data from a real tight gas well, our model, which includes stress-sensitive phases for gas and water, outperforms traditional Darcy flow models. Specifically, the average relative deviations in daily production rates were 0.1815% for gas and − 0.2677% for water, which are significantly smaller compared to traditional Darcy flow models. Further application of the non-Darcy flow model reveals strategies to enhance well performance. For example, mitigating liquid lock damage within a 2 m radius near the well could restore the permeability from 0.045 to 0.143 mD, thereby tripling the daily gas production. This non-Darcy flow model is easy to implement and shows significant potential in forecasting production yields in tight sandstone reservoirs, highlighting its importance in the petroleum and natural gas industry.
Antitumor activity of afatinib in EGFR T790M-negative human oral cancer therapeutically targets mTOR/Mcl-1 signaling axis
Authors: Han Jung-Min , Oh Kyu-Young , Choi Su-Jung , Lee Won-Woo , Jin Bo-Hwan , Kim Ji-Hoon , Yu Hyun-Ju , Kim Ryan Jin Young , Yoon Hye-Jung , Lee Jae-Il , Hong Seong-Doo , Cho Sung-Dae 
Published: June 2024

Abstract

Purpose
This study investigates the role and effectiveness of the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) in oral cancer, focusing on the clinical relevance of EGFR and myeloid cell leukemia-1 (Mcl-1) in head and neck cancers (HNCs). It aims to explore the molecular mechanism of afatinib, a TKI, in treating human oral cancer.

Methods
We conducted an in silico analysis using databases like The Cancer Genome Atlas, Gene Expression Omnibus, and Clinical Proteomic Tumor Analysis Consortium, along with immunohistochemistry staining, to study EGFR and Mcl-1 expression in HNCs. For investigating afatinib’s anticancer properties, we performed various in vitro and in vivo analyses, including trypan blue exclusion assay, Western blotting, 4′-6-diamidino-2-phenylindole staining, flow cytometry, quantitative real-time PCR, Mitochondrial membrane potential assay, overexpression vector construction, transient transfection, and a tumor xenograft model.

Results
Higher expression levels of EGFR and Mcl-1 were observed in HNC patient tissues compared to normal tissues, with their co-expression significantly linked to poor prognosis. There was a strong correlation between EGFR and Mcl-1 expressions in oral cancer patients. Afatinib treatment induced apoptosis and suppressed Mcl-1 in oral cancer cell lines without the EGFR T790M mutation. The mechanism of afatinib-induced apoptosis involved the EGFR/mTOR/Mcl-1 axis, as shown by the effects of mTOR activator MHY1485 and inhibitor rapamycin. Afatinib also increased Bim expression, mitochondrial membrane permeabilization, and cytochrome c release. It significantly lowered tumor volume without affecting body, liver, and kidney weights.

Conclusion
Afatinib, targeting the EGFR/mTOR/Mcl-1 axis, shows promise as a therapeutic strategy for oral cancer, especially in patients with high EGFR and Mcl-1 expressions.
A Comprehensive Students-as-Teachers Program: Developing and Empowering Future Medical Educators
Authors: Noramly Selina , Waggoner-Fountain Linda , Keeley Meg , Barry Deborah 
Published: June 2024

AbstractWe describe the development of two formats of a Students as Teachers (SaT) program that was designed to train fourth-year medical students as near-peer teachers in the pre-clinical classroom. This program has served 191 students since its inception in 2017 through a 2-week credit bearing elective or an evening workshop series. We describe key elements of the courses and positive outcomes of our program within a learning communities’ framework. We present these data for other institutions interested in creating their own SaT program.
MDSTF: a multi-dimensional spatio-temporal feature fusion trajectory prediction model for autonomous driving
Authors: Wang Xing , Wu Zixuan , Jin Biao , Lin Mingwei , Zou Fumin , Liao Lyuchao 
Published: June 2024

AbstractIn the field of autonomous driving, trajectory prediction of traffic agents is an important and challenging problem. Fully capturing the complex spatio-temporal features in trajectory data is crucial for accurate trajectory prediction. This paper proposes a trajectory prediction model called multi-dimensional spatio-temporal feature fusion (MDSTF), which integrates multi-dimensional spatio-temporal features to model the trajectory information of traffic agents. In the spatial dimension, we employ graph convolutional networks (GCN) to capture the local spatial features of traffic agents, spatial attention mechanism to capture the global spatial features, and LSTM combined with spatial attention to capture the full-process spatial features of traffic agents. Subsequently, these three spatial features are fused using a gate fusion mechanism. Moreover, during the modeling of the full-process spatial features, LSTM is capable of capturing short-term temporal dependencies in the trajectory information of traffic agents. In the temporal dimension, we utilize a Transformer-based encoder to extract long-term temporal dependencies in the trajectory information of traffic agents, which are then fused with the short-term temporal dependencies captured by LSTM. Finally, we employ two temporal convolutional networks (TCN) to predict trajectories based on the fused spatio-temporal features. Experimental results on the ApolloScape trajectory dataset demonstrate that our proposed method outperforms state-of-the-art methods in terms of weighted sum of average displacement error (WSADE) and weighted sum of final displacement error (WSFDE) metrics. Compared to the best baseline model (S2TNet), our method achieves reductions of 4.37% and 6.23% respectively in these metrics.
Complementary In Situ Characterization of Superelasticity in Fe–Mn–Al–Ni–Ti Shape Memory Alloys by Acoustic Emission, Digital Image Correlation and Infrared Thermography
Authors: Müller M. , Lehnert R. , Motylenko M. , Krooß P. , Vollmer M. , Niendorf T. , Biermann H. , Weidner A. 
Published: June 2024

AbstractCoupled in situ investigations were conducted on a Fe–Mn–Al–Ni–Ti single crystal deformed in compression and two Fe–Mn–Al–Ni–Ti oligo-crystals deformed in tension. Acoustic emission measurements were employed to monitor the degradation of superelasticity and the stabilization of martensite due to dislocation processes. These observations were corroborated by the application of digital image correlation and infrared thermography measurements. A poor strain reversibility and a premature plastification of the parent phase were observed in case of the single crystal due to an unfavourable crystal orientation. A contradictory transformation behaviour of the two oligo-crystals was observed, with one specimen showing a promising strain reversibility and characterisitic signs of degradation, and the other specimen exhibiting a limited strain reversibility due to an unusual confinement of the martensitic phase transformation to an unfavourably oriented grain. In the former case, an increase in the dislocation density within five cycles was detected through a shift of the acoustic signals’ median frequencies. In the latter case, a strong coupling between martensite nucleation and dislocation generation led to a pronounced martensite stabilization after one loading cycle. For all specimens, temporal sequence effects related to the coupling of martensite nucleation and dislocation generation were detected by means of acoustic emission.
The Effect of Composition and Temperature on the Hydrogen Reduction Behavior of Sintered Pellets of Bauxite Residue-Lime Mixtures
Authors: Kar Manish Kumar , van der Eijk Casper , Safarian Jafar 
Published: June 2024

AbstractThis study explores the isothermal hydrogen reduction of sintered pellets made of a mixture of bauxite residue and calcite with varying compositions at different reduction temperatures. Sintered pellets with varying compositions show three primary iron-containing oxide phases including brownmillerite, srebrodolskite, and fayalite; however, brownmillerite is the major phase in all the sintered pellets. The sintered pellets were reduced in a thermogravimetry furnace to establish instantaneous weight reduction with respect to time. Phases and microstructural analysis were carried out using X-ray diffraction and scanning electron microscopy, respectively. Mercury intrusion porosimeter and pycnometer were utilized to assess the porosity and density of the reduced pellets. Thermochemistry calculations were performed using the thermodynamics software FactSage 8.2. The reduction rate is most pronounced at a temperature of 1000 °C for all pellet compositions. It is intriguing to note that the rate of reduction shows minimal variance across pellets with different compositions; however, the higher calcite pellets exhibit a higher initial rate of reduction. Various kinetic models were examined to determine the activation energies for three different composition pellets, and the three-dimensional diffusion model has been well suited for this process. Close activation energies in the range of 84.6 to 94.8 kJ were obtained. A slightly higher activation energy was obtained for lower CaCO3 added pellets, and it was attributed to their reduced porosity and increased sintering, impeding the reaction kinetics. There were no significant differences in the formation of mayenite with varying the calcite amount; however, higher calcite pellets indicated more mayenite formation.
Graphical Abstract
A survey on recent advances in the computation of asymptotes
Authors: Magdalena-Benedicto R. , de Sevilla M. A. Fernández , Pérez-Díaz S. 
Published: June 2024

AbstractIn this paper, we summarize two algorithms for computing all the generalized asymptotes of a plane algebraic curve implicitly or parametrically defined. The approach is based on the notion of perfect curve introduced from the concepts and results presented in Blasco and Pérez-Díaz (Comput Aided Geom Des 31(2):81–96, 2014), Blasco and Pérez-Díaz (Comput Aided Geom Des 31(7-8):345–357, 2014), and Blasco and Pérez-Díaz (J Comput Appl Math 278:231–247, 2015). From these results, we derive a new method that allow to easily compute horizontal and vertical asymptotes.
Optimal Power and Battery Storage Dispatch Architecture for Microgrids: Implementation in a Campus Microgrid
Authors: Escobar Eros D. , Betancur Daniel , Isaac Idi A. 
Published: June 2024

AbstractThe expansion of electric microgrids has led to the incorporation of new elements and technologies into the power grids, carrying power management challenges and the need of a well-designed control architecture to provide efficient and economic access to electricity. This paper presents the development of a flexible hourly day-ahead power dispatch architecture for distributed energy resources in microgrids, with cost-based or demand-based operation, built up in a multi-class Python environment with SQLExpress and InfluxDB databases storing the dispatcher and microgrid data, and its experimental implementation using Modbus communication. The experimental power dispatch architecture is described and each operation stage is detailed, including the considered mathematical models of the energy resources, the database management, the linear-programming optimization of power dispatch, and the Modbus setpoint writing. Validation studies of the proposed control system are presented for real-time digital-simulated devices and physical resources as a real application at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The simulated and physical microgrid characteristics are described and the hourly dispatch results for generation, storage and load devices are presented, standing out as a reliable power management architecture for economic commitment and load peak shaving in simulated and real microgrids. The proposed architecture demonstrates its readiness for present and future electrical system challenges, effectively incorporating meteorological variations, renewable energy sources, and power demand fluctuations into the control framework, with a strong dependence on the quality of the meteorological forecast.
Insight on molecular interactions in shrinkage of Na-montmorillonite clay by molecular dynamics simulation
Authors: Feng Wei-Qiang , Al-Zaoari Kamal , Chen Ze-Jian 
Published: June 2024

AbstractExpansive soils are known to be hazardous materials for infrastructure due to their high shrinking or swelling potential. Understanding the shrinking factors of expansive soils such as montmorillonite (MMT) is essential for predicting their mechanical properties. The interactions between the components of Na-MMT clays, e.g., MMT layer–layer (LL), layer–cation (LC), layer–water (LW) and water–cation (WC), are responsible for its shrinking behavior. In this study, molecular dynamics simulation and grand canonical Monte Carlo simulations are used to investigate the interaction energy evolution in the layered structure of Na-MMT for the shrinkage mechanisms analysis of clay. The results of simulation indicate that the magnitude of the interaction energy contributed by the interlayer cations dehydration is the driving force of the interlayer shrinkage. Furthermore, in the hydrated state, with one water layer, two water layers and three water layers, the attractive interactions between WC and LW, maintain the stability of the clay layers. However, at the dry state, the interaction energy between layers and cations appears to be the most essential component in holding the stacked layers together, which provides structural stability to the clay sheets. Finally, the study reveals that intermolecular interactions contribute to the mechanical properties of clays such as cohesive and elastic properties.
Graph-Enhanced Prompt Learning for Personalized Review Generation
Authors: Qu Xiaoru , Wang Yifan , Li Zhao , Gao Jun 
Published: June 2024

AbstractPersonalized review generation is significant for e-commerce applications, such as providing explainable recommendation and assisting the composition of reviews. With the success of pre-trained language models (PLMs), prompt learning-based approaches have been employed to handle this task. However, the existing approach neglects the historical user-item interactions as well as the diverse semantics of the reviews (including semantically relevant reviews and semantically irrelevant reviews). In this paper, we propose GRAPA, a graph-enhanced prompt learning approach for personalized review generation. Specifically, GRAPA extracts topic-level information for each review to address the semantic diversity of reviews. Moreover, GRAPA employs a heterogeneous graph neural network (GNN) to explore the collaborative information hidden in historical user-item interactions. User and item representations generated by the GNN module as well as their ID embeddings are used as prompts and fed into a PLM to guide the generation process. To alleviate the interference of semantically irrelevant reviews, GRAPA further proposes a contrastive learning module to distinguish them. Experimental results on public datasets show that GRAPA outperforms existing methods by up to 4.3% in BLEU-4 and 5.4% in ROUGE2-F.
Comparison of thresholds for a convolutional neural network classifying medical images
Authors: Rainio Oona , Tamminen Jonne , Venäläinen Mikko S. , Liedes Joonas , Knuuti Juhani , Kemppainen Jukka , Klén Riku 
Published: June 2024

AbstractOur aim is to compare different thresholds for a convolutional neural network (CNN) designed for binary classification of medical images. We consider six different thresholds, including the default threshold of 0.5, Youden’s threshold, the point on the ROC curve closest to the point (0,1), the threshold of equal sensitivity and specificity, and two sensitivity-weighted thresholds. We test these thresholds on the predictions of a CNN with InceptionV3 architecture computed from five datasets consisting of medical images of different modalities related to either cancer or lung infections. The classifications of each threshold are evaluated by considering their accuracy, sensitivity, specificity, F1 score, and net benefit. According to our results, the best thresholds are Youden’s threshold, the point on the ROC curve closest to the point (0,1), and the threshold of equal sensitivity and specificity, all of which work significantly better than the default threshold in terms of accuracy and F1 score. If higher values of sensitivity are desired, one of the two sensitivity-weighted could be of interest.