bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of an In Vitro Model for Epithelial-to-Mesenchymal Transition in Gastric Cancer
Authors: Zhang Yuanhui , Bi Ling , Li Quanyao , Yao Liqiu , Wang Xiao , Liu Hui , Shi Jun 
Published: March 2024

Abstract
Epithelial-to-mesenchymal transition (EMT) is a developmental program that plays a vital role in gastric cancer, including aspects of tumor progression, the metastatic process, and resistance to treatment. Here, we have designed an in vitro model that mimics the features of EMT as observed in gastric cancer. The results showed that both migration and invasion were enhanced in gastric cancer cells with Brachyury overexpression. Additionally, the expression of IL-8 increased, while IL-8RA and IL-8RB levels significantly decreased in the in vitro model. Overall, the in vitro model offers an opportunity to study these phenomena relevant to EMT as they may occur in vivo in gastric cancer, as well as potential drug interactions that could interfere with these processes.
Deep Reinforcement Learning Model for Stock Portfolio Management Based on Data Fusion
Authors: Li Haifeng , Hai Mo 
Published: March 2024

AbstractDeep reinforcement learning (DRL) can be used to extract deep features that can be incorporated into reinforcement learning systems to enable improved decision-making; DRL can therefore also be used for managing stock portfolios. Traditional methods cannot fully exploit the advantages of DRL because they are generally based on real-time stock quotes, which do not have sufficient features for making comprehensive decisions. In this study, in addition to stock quotes, we introduced stock financial indices as additional stock features. Moreover, we used Markowitz mean-variance theory for determining stock correlation. A three-agent deep reinforcement learning model called Collaborative Multi-agent reinforcement learning-based stock Portfolio management System (CMPS) was designed and trained based on fused data. In CMPS, each agent was implemented with a deep Q-network to obtain the features of time-series stock data, and a self-attention network was used to combine the output of each agent. We added a risk-free asset strategy to CMPS to prevent risks and referred to this model as CMPS-Risk Free (CMPS-RF). We conducted experiments under different market conditions using the stock data of China Shanghai Stock Exchange 50 and compared our model with the state-of-the-art models. The results showed that CMPS could obtain better profits than the compared benchmark models, and CMPS-RF was able to accurately recognize the market risk and achieved the best Sharpe and Calmar ratios. The study findings are expected to aid in the development of an efficient investment-trading strategy.
Detecting aortic valve stenosis based on the non-invasive blood pressure waveform—a proof of concept study
Authors: Kho Eline , Schenk Jimmy , Vlaar Alexander P. J. , Vis Marije M. , Wijnberge Marije , Stam Lotte B. , van Mourik Martijn , Jorstad Harald T. , Hermanns Henning , Westerhof Berend E. , Veelo Denise P. , van der Ster Bjorn J. P. 
Published: March 2024

AbstractThe incidence of aortic valve stenosis (AoS) increases with age, and once diagnosed, symptomatic severe AoS has a yearly mortality rate of 25%. AoS is diagnosed with transthoracic echocardiography (TTE), however, this gold standard is time consuming and operator and acoustic window dependent. As AoS affects the arterial blood pressure waveform, AoS-specific waveform features might serve as a diagnostic tool. Aim of the present study was to develop a novel, non-invasive, AoS detection model based on blood pressures waveforms. This cross-sectional study included patients with AoS undergoing elective transcatheter or surgical aortic valve replacement. AoS was determined using TTE, and patients with no or mild AoS were labelled as patients without AoS, while patients with moderate or severe AoS were labelled as patients with AoS. Non-invasive blood pressure measurements were performed in awake patients. Ten minutes of consecutive data was collected. Several blood pressure-based features were derived, and the median, interquartile range, variance, and the 1st and 9th decile of the change of these features were calculated. The primary outcome was the development of a machine-learning model for AoS detection, investigating multiple classifiers and training on the area under the receiver-operating curve (AUROC). In total, 101 patients with AoS and 48 patients without AoS were included. Patients with AoS showed an increase in left ventricular ejection time (0.02 s, p = 0.001), a delayed maximum upstroke in the systolic phase (0.015 s, p < 0.001), and a delayed maximal systolic pressure (0.03 s, p < 0.001) compared to patients without AoS. With the logistic regression model, a sensitivity of 0.81, specificity of 0.67, and AUROC of 0.79 were found. The majority of the population without AoS was male (85%), whereas in the population with AoS this was evenly distributed (54% males). Age was significantly (5 years, p < 0.001) higher in the population with AoS. In the present study, we developed a novel model able to distinguish no to mild AoS from moderate to severe AoS, based on blood pressure features with high accuracy. Clinical registration number: The study entailing patients with TAVR treatment was registered at ClinicalTrials.gov (NCT03088787, https://clinicaltrials.gov/ct2/show/NCT03088787). The study with elective cardiac surgery patients was registered with the Netherland Trial Register (NL7810, https://trialsearch.who.int/Trial2.aspx?TrialID=NL7810).
Parametric investigation of railway fastenings into the formation and mitigation of short pitch corrugation
Authors: Zhang Pan , Li Shaoguang , Dollevoet Rolf , Li Zili 
Published: March 2024

AbstractShort pitch corrugation has been a problem for railways worldwide over one century. In this paper, a parametric investigation of fastenings is conducted to understand the corrugation formation mechanism and gain insights into corrugation mitigation. A three-dimensional finite element vehicle–track dynamic interaction model is employed, which considers the coupling between the structural dynamics and the contact mechanics, while the damage mechanism is assumed to be differential wear. Various fastening models with different configurations, boundary conditions, and parameters of stiffness and damping are built up and analysed. These models may represent different service stages of fastenings in the field. Besides, the effect of train speeds on corrugation features is studied. The results indicate: (1) Fastening parameters and modelling play an important role in corrugation formation. (2) The fastening longitudinal constraint to the rail is the major factor that determines the corrugation formation. The fastening vertical and lateral constraints influence corrugation features in terms of spatial distribution and wavelength components. (3) The strengthening of fastening constraints in the longitudinal dimension helps to mitigate corrugation. Meanwhile, the inner fastening constraint in the lateral direction is necessary for corrugation alleviation. (4) The increase in fastening longitudinal stiffness and damping can reduce the vibration amplitudes of longitudinal compression modes and thus reduce the track corrugation propensity. The simulation in this work can well explain the field corrugation in terms of the occurrence possibility and major wavelength components. It can also explain the field data with respect to the small variation between the corrugation wavelength and train speed, which is caused by frequency selection and jump between rail longitudinal compression modes.
Transformer Network-Aided Relative Pose Estimation for Non-cooperative Spacecraft Using Vision Sensor
Authors: Ahmed Jamal , Arshad Awais , Bang Hyochoong , Choi Yoonhyuk 
Published: March 2024

AbstractThe objective of the proposed work is to perform monocular vision-based relative 6-DOF pose estimation of the non-cooperative target spacecraft relative to the chaser satellite in rendezvous operations. In this work, the convolutional neural network (CNN) is replaced by the high-resolution transformer network to predict the feature points of the target satellite. The self-attention mechanism inside the transformer provides the advantage of overcoming the inadequacies of the translation equivariance, 2D neighborhood awareness, and long-range dependencies in CNN. First, the 3D model of the target satellite is reconstructed using the inverse direct linear transform (IDLT) method. Then, the pose estimation pipeline is developed with a learning-based image-processing subsystem and geometric optimization of the pose solver. The image-processing subsystem performs target localization using CNN-based architecture. Then, the key points detection network performs regression to predict 2D key points using the transformer-based network. Afterward, the predicted key points based on their confidence scores are projected onto the corresponding 3D points, and the pose value is computed using the efficient perspective-n-point method. The pose is refined using the non-linear iterative Gauss–Newton method. The proposed architecture is trained and tested on the spacecraft pose estimation dataset and it shows superior accuracy both in translation and rotation values. The architecture has shown robustness against the drastically changing clutter background and light conditions in the space images due to the self-attention mechanism. Moreover, this method consumes less computation resources by using fewer floating-point operations and trainable parameters with low input image resolution.
Dufour and Soret effects on double diffusive Casson fluid flow with the influence of internal heat source
Authors: Vinod Y. , Nagappanavar Suma Nagendrappa , Raghunatha K. R. , Sangamesh  
Published: March 2024

AbstractThis study focuses on the control of the cross-diffusion effects on the thermosolutal Casson fluid stream with an internal heat source. These effects have practical applications in geothermal energy extraction, cooling of electronic devices, petroleum engineering, and polymer processing. With the help of similarity transformations, the governing equations are transformed to nonlinear ordinary differential equations (ODEs). The highly nonlinear differential equations are solved with the help of Bernoulli wavelet numerical scheme, and the outputs are compared with previous literature to validate the findings. The study investigates the forces of various physical parameters on the velocity, temperature, and concentration of the fluid and presents the outcomes in graphical form. In addition, the study provides information on skin friction, heat and mass transfers in tabular format. Overall, the research contributes to a better understanding of the behaviour of non-Newtonian fluids under different thermal and concentration gradients and has practical implications in various industrial processes. Our findings demonstrate the remarkable effectiveness and accessibility of the Bernoulli wavelet method in solving coupled nonlinear ODEs of this nature. The results exhibit outstanding agreement, particularly in engineering applications involving coupled nonlinear ODEs.
Indigenous Movements in the Andes in the Twenty-First Century
Authors: Altmann Philipp 
Published: March 2024

This essay reviews the following works:Indianidad evanescente en los Andes de Ecuador. By Víctor Bretón Solo de Zaldívar. Quito: FLACSO Ecuador; Ediciones Universitat de Lleida, 2022. Pp. xxxix + 372. €22.00 paperback, free downloadable e-book. ISBN: 9789978676301.Indigene Autonomie in Lateinamerika: Zwischen Selbstbestimmung und staatlicher Kontrolle. By Michael Fackler. Bielefeld, Germany: Transcript, 2021. Pp. 322. €60.00 hardcover, €60.00 e-book. ISBN: 9783837657982.Indigene Resistencia: Der Widerstand der bolivianischen TIPNIS-Bewegung. By Maximilian Held. Bielefeld, Germany: Transcript, 2022. Pp. 276. €45.00 hardcover, free downloadable e-book. ISBN: 9783837663686.La revolución del arcoiris y su escala de grises: Movimiento indígena del Ecuador. By Stalin Herrera Revelo. Buenos Aires: CLACSO, 2022. Pp. 135. Free downloadable e-book. ISBN: 9789878133799.Indigenous Civil Society in Latin America: Collective Action in the Digital Age. By Pascal Lupien. Chapel Hill: University of North Carolina Press, 2023. Pp. vii + 284. $29.95 paperback, $99.00 hardcover, $22.99 e-book. ISBN: 9781469672625.¡Así encendimos la mecha! Treinta años del levantamiento indígena en Ecuador: Una historia permanente. Edited by Floresmilo Simbaña, Adriana Victoria Rodríguez Caguana, and Mateo Martínez Abarca. Quito: Ediciones Abya Yala, 2020. Pp. 220. $15.00 paperback, free downloadable e-book. ISBN: 9789942884107.
ATELLANA ACTORS AND PLAYWRIGHTS IN THE EPIGRAPHIC EVIDENCE
Authors: González Galera Víctor 
Published: March 2024

AbstractThis contribution provides a study of the fabula Atellana from an epigraphic perspective. It brings together the existing inscriptions related to this dramatic genre, explaining the challenges that one faces when trying to identify possible Atellana actors. It also examines the status of Atellana performers and playwrights, contrasting the information provided by literary sources, especially Livy, with the data obtained from inscriptions, which indicate an increasing professionalization of Atellana actors in the first century a.d. Finally, this article poses some questions concerning the development, continuity and geographical diffusion of Atellan comedy in the Imperial era in the light of the epigraphic material and also in comparison with the evidence available for other popular shows, specifically mime and pantomime, which suggests that by the second century a.d. the Atellana was no longer performed on public stages but was rather represented in private settings and studied for its linguistic and rhetorical peculiarities.
From covalent transition states in chemistry to noncovalent in biology: from β- to Φ-value analysis of protein folding
Authors: Fersht Alan R. 
Published: March 2024

Abstract
Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted’s landmark paper that introduced the β-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding – ‘part (b) of the protein folding problem’. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of β- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.
Linear Theory Analysis and One‐Dimensional Hybrid Simulations of High‐Frequency EMIC Waves in a Dipole Magnetic Field
Authors: Min Kyungguk 
Published: March 2024

AbstractNarrowband (Δf ≲ 0.1fcp), high‐frequency (0.9fcp ≲ f < fcp) electromagnetic ion cyclotron (EMIC) waves, or HFEMIC waves for short, are a relatively new type of EMIC waves, where fcp is the proton cyclotron frequency. Here, we investigate the instability threshold conditions and the nonlinear evolution of HFEMIC waves at parallel propagation. First, linear theory analysis is extended to a regime relevant to HFEMIC waves (parallel proton beta β‖p < 0.01). The instability threshold follows a similar anisotropy‐β‖p relation and requires a large value of anisotropy (T⊥p/T‖p ≳ 10) for wave growth. As a result of decreasing group velocity, the convective growth rate at a fixed threshold exhibits a similar inverse relation with β‖p. Heavy ions affect the instability only weakly, primarily through the introduction of stop bands. Second, we carry out one‐dimensional hybrid simulations in a parabolic background magnetic field, with initial parameters constrained by observation. Despite the narrow source region (within about ±3° latitude), HFEMIC waves can grow well above the thermal noise level due in large part to a small group velocity of HFEMIC waves. The saturation level is well within the range of observational amplitudes, and the quasilinear process primarily determines the wave evolution. Finally, we demonstrate that the present results compare favorably to the recent statistical results, thereby supporting anisotropic low‐energy protons as free energy source for HFEMIC waves.
Synthesis of Benzene and Phenol From the Irradiation of Benzonitrile: Water Ices by (<10 eV) Electrons: Application to the Planets and Meteorites Surface Chemistry
Authors: Abdoul‐Carime H. , Lathuilière B. , Nedelec P. , Kopyra J. 
Published: March 2024

AbstractAromatic compounds are present in the interstellar space and on carbonaceous meteorites where they intriguingly represent the most important constituents along with the water ice. Exposed to the cosmic radiation, more complex species are generated via processes that are still far from being understood. The present study aims at studying the chemistry of benzonitrile in water ice environment when exposed to the ballistic low energy free electrons produced abundantly along the radiation tracks. Here we show that phenol and benzene are synthetized at the electron energy above but also below 6 eV via different routes, which can be selectively activated by tuning the electron energy. The reactivity presented in this work may contribute to a better understanding of the time evolution, via models and simulations, of species present in interstellar space ices and meteorites.
Defense against adversarial attacks: robust and efficient compressed optimized neural networks
Authors: Kraidia Insaf , Ghenai Afifa , Belhaouari Samir Brahim 
Published: March 2024

AbstractIn the ongoing battle against adversarial attacks, adopting a suitable strategy to enhance model efficiency, bolster resistance to adversarial threats, and ensure practical deployment is crucial. To achieve this goal, a novel four-component methodology is introduced. First, introducing a pioneering batch-cumulative approach, the exponential particle swarm optimization (ExPSO) algorithm was developed for meticulous parameter fine-tuning within each batch. A cumulative updating loss function was employed for overall optimization, demonstrating remarkable superiority over traditional optimization techniques. Second, weight compression is applied to streamline the deep neural network (DNN) parameters, boosting the storage efficiency and accelerating inference. It also introduces complexity to deter potential attackers, enhancing model accuracy in adversarial settings. This study compresses the generative pre-trained transformer (GPT) by 65%, saving time and memory without causing performance loss. Compared to state-of-the-art methods, the proposed method achieves the lowest perplexity (14.28), the highest accuracy (93.72%), and an 8 × speedup in the central processing unit. The integration of the preceding two components involves the simultaneous training of multiple versions of the compressed GPT. This training occurs across various compression rates and different segments of a dataset and is ultimately associated with a novel multi-expert architecture. This enhancement significantly fortifies the model's resistance to adversarial attacks by introducing complexity into attackers' attempts to anticipate the model's prediction integration process. Consequently, this leads to a remarkable average performance improvement of 25% across 14 different attack scenarios and various datasets, surpassing the capabilities of current state-of-the-art methods.
Aesculus hippocastanum extract and the main bioactive constituent β-escin as antivirals agents against coronaviruses, including SARS-CoV-2
Authors: Peñaranda Figueredo Freddy Armando , Vicente Josefina , Barquero Andrea Alejandra , Bueno Carlos Alberto 
Published: March 2024

AbstractRespiratory viruses can cause life-threatening illnesses. The focus of treatment is on supportive therapies and direct antivirals. However, antivirals may cause resistance by exerting selective pressure. Modulating the host response has emerged as a viable therapeutic approach for treating respiratory infections. Additionally, considering the probable future respiratory virus outbreaks emphasizes the need for broad-spectrum therapies to be prepared for the next pandemics. One of the principal bioactive constituents found in the seed extract of Aesculus hippocastanum L. (AH) is β-escin. The clinical therapeutic role of β-escin and AH has been associated with their anti-inflammatory effects. Regarding their mechanism of action, we and others have shown that β-escin and AH affect NF-κB signaling. Furthermore, we have reported the virucidal and broad-spectrum antiviral properties of β-escin and AH against enveloped viruses such as RSV, in vitro and in vivo. In this study, we demonstrate that β-escin and AH have antiviral and virucidal activities against SARS-CoV-2 and CCoV, revealing broad-spectrum antiviral activity against coronaviruses. Likewise, they exhibited NF-κB and cytokine modulating activities in epithelial and macrophage cell lines infected with coronaviruses in vitro. Hence, β-escin and AH are promising broad-spectrum antiviral, immunomodulatory, and virucidal drugs against coronaviruses and respiratory viruses, including SARS-CoV-2.