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Abstract Background Increased cancer stem cell (CSC) content and SOX2 overexpression are common features in the development of resistance to therapy in hormone-dependent breast cancer, which remains an important clinical challenge. SOX2 has potential as biomarker of resistance to treatment and as therapeutic target, but targeting transcription factors is also challenging. Here, we examine the potential inhibitory effect of different polyoxometalate (POM) derivatives on SOX2 transcription factor in tamoxifen-resistant breast cancer cells. Methods Various POM derivatives were synthesised and characterised by infrared spectra, powder X-ray diffraction pattern and nuclear magnetic resonance spectroscopy. Estrogen receptor (ER) positive breast cancer cells, and their counterparts, which have developed resistance to the hormone therapy tamoxifen, were treated with POMs and their consequences assessed by gel retardation and chromatin immunoprecipitation to determine SOX2 binding to DNA. Effects on proliferation, migration, invasion and tumorigenicity were monitored and quantified using microscopy, clone formation, transwell, wound healing assays, flow cytometry and in vivo chick chorioallantoic membrane (CAM) models. Generation of lentiviral stable gene silencing and gene knock-out using CRISPR-Cas9 genome editing were applied to validate the inhibitory effects of the selected POM. Cancer stem cell subpopulations were quantified by mammosphere formation assays, ALDEFLUOR activity and CD44/CD24 stainings. Flow cytometry and western blotting were used to measure reactive oxygen species (ROS) and apoptosis. Results POMs blocked in vitro binding activity of endogenous SOX2. [P2W18O62]6− (PW) Wells-Dawson-type anion was the most effective at inhibiting proliferation in various cell line models of tamoxifen resistance. 10 µM PW also reduced cancer cell migration and invasion, as well as SNAI2 expression levels. Treatment of tamoxifen-resistant cells with PW impaired tumour formation by reducing CSC content, in a SOX2-dependent manner, which led to stem cell depletion in vivo. Mechanistically, PW induced formation of reactive oxygen species (ROS) and inhibited Bcl-2, leading to the death of tamoxifen-resistant cells. PW-treated tamoxifen-resistant cells showed restored sensitivity to tamoxifen. Conclusions Together, these observations highlight the potential use of PW as a SOX2 inhibitor and the therapeutic relevance of targeting SOX2 to treat tamoxifen-resistant breast cancer.

The increasing use of medical implants in various areas of medicine, particularly in orthopedic surgery, oncology, cardiology and dentistry, displayed the limitations in long-term integration of available biomaterials. The effective functioning and successful integration of implants requires not only technical excellence of materials but also consideration of the dynamics of biomaterial interaction with the immune system throughout the entire duration of implant use. The acute as well as long-term decisions about the efficiency of implant integration are done by local resident tissue macrophages and monocyte-derived macrophages that start to be recruited during tissue damage, when implant is installed, and are continuously recruited during the healing phase. Our review summarized the knowledge about the currently used macrophages-based in vitro cells system that include murine and human cells lines and primary ex vivo differentiated macrophages. We provided the information about most frequently examined biomarkers for acute inflammation, chronic inflammation, foreign body response and fibrosis, indicating the benefits and limitations of the model systems. Particular attention is given to the scavenging function of macrophages that controls dynamic composition of peri-implant microenvironment and ensures timely clearance of microorganisms, cytokines, metabolites, extracellular matrix components, dying cells as well as implant debris. We outline the perspective for the application of 3D systems for modelling implant interaction with the immune system in human tissue-specific microenvironment avoiding animal experimentation.

Abstract Density functional theory (DFT) is one of the primary approaches to solving the many-body Schrodinger equation. The essential part of the DFT theory is the exchange-correlation (XC) functional, which can not be obtained in analytical form. Accordingly, the accuracy improvement of the DFT is mainly based on the development of XC functional approximations. Commonly, they are built upon analytic solutions in low- and high-density limits and result from quantum Monte Carlo or post-Hartree-Fock numerical calculations. However, there is no universal functional form to incorporate these data into XC functional. Instead, various parameterizations use heuristic rules to build a specific XC functional. The neural network (NN) approach to interpolate the data from higher precision theories can give a unified path to parametrize an XC functional. Moreover, data from many existing quantum chemical databases could provide the XC functional with improved accuracy. We develop NN XC functional, which gives exchange potential and energy density without direct derivatives of exchange-correlation energy density. Proposed NN architecture consists of two parts NN-E and NN-V, which could be trained in separate ways, adding new flexibility to XC functional. We also show that the developed NN XC functional converges in the self-consistent cycle and gives reasonable energies when applied to atoms, molecules, and crystals.

Abstract The magnitude of earthquakes on continental normal faults rarely exceeds 7.0 Mw. However, because of their vicinity to large population centers they can be highly destructive. Long recurrence time, relatively small deformations, and limited observations hinder our understanding of the deformation patterns and mechanisms controlling the magnitude of events. Here, this problem is addressed with 2D thermomechanical modeling of normal fault seismic cycles. The 2020 Samos, Greece Mw7.0 earthquake is used as an example as it is one of the largest and most studied continental normal fault earthquakes. The modeling approach employs visco‐elasto‐plastic rheology, compressibility, free surface, and a rate‐and‐state friction law for the fault. Modeling of the Samos earthquake suggests the pore fluid pressure ratio on the fault ranges from 0 to 0.7. The model demonstrates that most of the deformation during interseismic and coseismic periods, besides on the fault, occurs in the hanging wall and footwall below the seismogenic part of the fault. The largest vertical surface displacement during the earthquake is the subsidence of the hanging wall in the vicinity of the fault, while the uplift of the footwall and remote part of the hanging wall is significantly smaller. Modeling of the seismic cycles on normal faults with different setups shows the dependency of the magnitude on the thermal profile and dipping angle of the fault; low heat flow and low dipping angle are favorable conditions for the largest events, while steep normal faults in the areas of high heat flow tend to have the smallest magnitudes.

Nowadays, people get increasingly attached to social media to connect with other people, to study, and to work. The presented article uses Twitter posts to better understand public opinion regarding the vegan (plant-based) diet that has traditionally been portrayed negatively on social media. However, in recent years, studies on health benefits, COVID-19, and global warming have increased the awareness of plant-based diets. The study employs a dataset derived from a collection of vegan-related tweets and uses a sentiment analysis technique for identifying the emotions represented in them. The purpose of sentiment analysis is to determine whether a piece of text (tweet in our case) conveys a negative or positive viewpoint. We use the mutual information approach to perform feature selection in this study. We chose this method because it is suitable for mining the complicated features from vegan tweets and extracting users’ feelings and emotions. The results revealed that the vegan diet is becoming more popular and is currently framed more positively than in previous years. However, the emotions of fear were mostly strong throughout the period, which is in sharp contrast to other types of emotions. Our findings place new information in the public domain, which has significant implications. The article provides evidence that the vegan trend is growing and new insights into the key emotions associated with this growth from 2010 to 2022. By gaining a deeper understanding of the public perception of veganism, medical experts can create appropriate health programs and encourage more people to stick to a healthy vegan diet. These results can be used to devise appropriate government action plans to promote healthy veganism and reduce the associated emotion of fear.

The extractive text summarization (ETS) method for finding the salient information from a text automatically uses the exact sentences from the source text. In this article, we answer the question of what quality of a summary we can achieve with ETS methods? To maximize the ROUGE-1 score, we used five approaches: (1) adapted reduced variable neighborhood search (RVNS), (2) Greedy algorithm, (3) VNS initialized by Greedy algorithm results, (4) genetic algorithm, and (5) genetic algorithm initialized by the Greedy algorithm results. Furthermore, we ran experiments on articles from the arXive dataset. As a result, we found 0.59 and 0.25 scores for ROUGE-1 and ROUGE-2, respectively achievable by the approach, where the genetic algorithm initialized by the Greedy algorithm results, which happens to yield the best results out of the tested approaches. Moreover, those scores appear to be higher than scores obtained by the current state-of-the-art text summarization models: the best score in the literature for ROUGE-1 on the same data set is 0.46. Therefore, we have room for the development of ETS methods, which are now undeservedly forgotten.

This work presents a method for summarizing scientific articles from the arXive and PubMed datasets using a greedy Extractive Summarization algorithm. We used the approach along with Variable Neighborhood Search (VNS) to learn what is the top-line exists in the area of Extractive Text Summarization quality in terms of ROUGE scores. The algorithm is based on first selecting for the summary the sentences from the text containing the maximum number of words with the higher TFIDF values along with minimum document frequency parameter tuning for TFIDF vectorization. As a result, the method achieves 0.43/0.12 and 0.40/0.13 for ROUGE-1/ROUGE-2 scores on arXive and PubMed datasets, respectively. These results are comparable to the state-of-the-art models using complex neural network architectures and serious computational resources together with the large amounts of training data. In contrast, our method uses a straightforward statistical inference methodology.

This article aims to more thoroughly intersect the figure of the Muslim into the framework of the coloniality of being, and into the narrative of race and religion in modernity. Two areas of concern are investigated: First, how Islamophobia aided in forming the coloniality of being in ways that decolo-nial scholarship - namely that of leading Latin American decolonial thinker, Nelson Maldonado-Torres - is seemingly unaware of or downplays, and second, how a rereading of a number of the key events and figures that define a decolonial discourse on race and religion, such as the Valladolid debates (1550-1551) and the figure of Christopher Columbus, help to more rigorously conceptualize the figure of the Muslim in relation to the coloniality of being.

The understanding of the occurrence of extreme waves is crucial to simulate the growth of waves in coastal regions. Laboratory experiments were performed to study the spatial evolution of the statistics of group-focused waves that have a relatively broad-banded spectra propagating from intermediate water depth to shallow regions. Breaking waves with different spectral types, i.e., spectral bandwidths and wave nonlinearities, were generated in a wave flume using the dispersive focusing technique. The non-Gaussian behavior of the considered wave trains was demonstrated by the means of the skewness and kurtosis parameters estimated from a time series and was compared with the second-order theory. The skewness and kurtosis parameters were found to have an increasing trend during the focusing process. During both the downstream wave breaking and defocusing process, the wave train dispersed again and became less steep. As a result, both skewness and kurtosis almost returned to their initial values. This behavior is clearer for narrower wave train spectra. Additionally, the learning algorithm multilayer perceptron (MLP) was used to predict the spatial evolution of kurtosis. The predicted results are in satisfactory agreement with experimental findings.

Abstract Giant earthquakes with magnitudes above 8.5 occur only in subduction zones. Despite the developments made in observing large subduction zone earthquakes with geophysical instruments, the factors controlling the maximum size of these earthquakes are still poorly understood. Previous studies have suggested the importance of slab shape, roughness of the plate interface contact, state of the strain in the upper plate, thickness of sediments filling the trenches, and subduction rate. Here, we present 2‐D cross‐scale numerical models of seismic cycles for subduction zones with various geometries, subduction channel friction configurations, and subduction rates. We found that low‐angle subduction and thick sediments in the subduction channel are the necessary conditions for generating giant earthquakes, while the subduction rate has a negligible effect. We suggest that these key parameters determine the maximum magnitude of a subduction earthquake by controlling the seismogenic zone width and smoothness of the subduction interface. This interpretation supports previous studies that are based upon observations and scaling laws. Our modeling results also suggest that low static friction in the sediment‐filled subduction channel results in neutral or moderate compressive deformation in the overriding plate for low‐angle subduction zones hosting giant earthquakes. These modeling results agree well with observations for the largest earthquakes. Based on our models we predict maximum magnitudes of subduction earthquakes worldwide, demonstrating the fit to magnitudes of all giant earthquakes of the 20th and 21st centuries and good agreement with the predictions based on statistical analyses of observations.

SOX11 is an embryonic mammary epithelial marker that is normally silenced prior to birth. High SOX11 levels in breast tumours are significantly associated with distant metastasis and poor outcome in breast cancer patients. Here, we show that SOX11 confers distinct features to ER-negative DCIS.com breast cancer cells, leading to populations enriched with highly plastic hybrid epithelial/mesenchymal cells, which display invasive features and alterations in metastatic tropism when xenografted into mice. We found that SOX11+DCIS tumour cells metastasize to brain and bone at greater frequency and to lungs at lower frequency compared to cells with lower SOX11 levels. High levels of SOX11 leads to the expression of markers associated with mesenchymal state and embryonic cellular phenotypes. Our results suggest that SOX11 may be a potential biomarker for breast tumours with elevated risk of developing metastases and may require more aggressive therapies.

An optimal combination of power and energy characteristics is beneficial for the further progress of supercapacitors-based technologies. We develop a nanoscale dynamic electrolyte model, which describes both static capacitance and the time-dependent charging process, including the initial square-root dependency and two subsequent exponential trends. The observed charging time corresponds to one of the relaxation times of the exponential regimes and significantly depends on the pore size. Additionally, we find analytical expressions providing relations of the time scales to the electrode’s parameters, applied potential, and the final state of the confined electrolyte. Our numerical results for the charging regimes agree with published computer simulations, and estimations of the charging times coincide with the experimental values.

Vortex shedding behind an elastically mounted circular cylinder in the presence of group focused waves propagating upstream was investigated using a classical approach (time series and FFT) and nonclassical approach (complex 2D Morlet wavelets). Wavelet analysis emerged as a novel solution in this regard. Our results include wave trains with different nonlinearities propagating in different water depths and derived from three types of spectra (Pierson–Moskowitz, JONSWAP (γ = 3.3 or γ = 7)). It was found that the generated wave trains could modify regimes of shedding behind the cylinder, and subharmonic frequency lock-in could arise in particular situations. The occurrence of a lock-in regime in the case of wave trains propagating in intermediate water locations was shown experimentally even for small nonlinearities. Moreover, the application of time-localized wavelet analysis was found to be a powerful approach. In fact, the frequency lock-in regime and its duration could be readily identified from the wavelet-based energy and its corresponding ridges.

Background/Context: While trans adults in the U.S. and Canada report higher levels of discrimination and harassment than cisgender adults, but the existing literature lacks comprehensive descriptions of trans school workers' believes about what would improve working conditions, satisfaction, and safety. Purpose: We interrogate systemic forces shaping PK-12 trans educators' workplace experiences through two research questions: (1) What educational reforms did trans PK-12 workers believe would improve working conditions? (2) Did greater policy, organizational, and leadership support contribute to improved job satisfaction and safety? Research Design: The critical quantitative study employed cross-sectional, online survey data from 341 trans school workers in Canada and the United States. We used frequencies to describe respondents' rankings of reforms and logistic regression analysis to examine whether respondents' reports of greater policy protections, organizational supports, and affirming leadership contributed to greater workplace safety and satisfaction. Conclusions/Recommendations: Trans workers reporting safer, more satisfactory school workplaces also indicate their workplaces have multiple tools across socioecological systems to disrupt cisnormativity. Policy protections topped workers' rankings of beneficial reforms, but logistic regression analysis revealed policies mitigated only the most egregious physical safety concerns. Greater organizational supports and affirming leaders consistently contribute to satisfaction and safety. Policies, organizational practices, and leadership approaches working in tandem contribute to safe, satisfying workplaces for PK-12 trans school employees.

Additive manufacturing erases the distance between design ideas and finished parts. However, designers must use several software tools to use these advantages. Moreover, these tools operate with different representations of geometry. This paper describes the architecture of a new CAD/CAM system that uses only the function representation of the geometry (FRep). It provides all widely used design operations and allows an engineer to employ robust and efficient topology optimization algorithms. The developed CAD/CAM system consists of 3D modeling, simulation, topology optimization, and direct manufacturing modules. We successfully printed designed parts and performed mechanical tests of printed parts. The results of tests show good agreement with simulation data. The system makes it possible to create structures with the desired properties in a fast and flexible way. The proposed approach significantly helps in designing additive manufacturing process and saves time for its users.

Complex 3D objects with microstructures can be modelled using the function representation (FRep) approach and then manufactured. The task of modelling a geometric object with a sophisticated microstructure based on unit cell repetition is often too computationally expensive for CAD systems. FRep provides efficient tools to solve this problem. However, even for FRep the slicing step required for manufacturing can take a significant amount of time. An accelerated slicing algorithm for FRep 3D objects is proposed in this paper. This algorithm allows the preparation of FRep models for 3D printing without surface generation stage. The spatial index is employed to accelerate the slicing process. A novel compound adaptive criterion and a novel acceleration criterion are proposed to speed up the evaluation of the defining function of an FRep object. The use of these criteria is significantly reducing the computational time for contour construction during the slicing process. The K-d tree and R-tree data structures are used as spatial indexes. The performance of the accelerated slicing algorithm was tested. The contouring time was reduced 100-fold due to using the novel compound adaptive criterion with the novel acceleration criterion.

Myeloid-derived suppressive cells (MDSCs) promote metastasis. Here, the authors show that the monocytic MDSCs subset promotes epithelial to mesenchymal transition at the primary site while the granulocytic subset promotes the reverse transition at the metastatic site enabling dynamic tumour cells plasticity.

The article studies the development processes of Russian legal education, which should be based on the idea of implementing a model of constitutionalism. The attention of the author is paid to the problems of legal understanding, the construction of a state based on the rule of law, the goal of which should be the establishment of coordinated interaction between all subjects of legal relations – the state, society, personality, which need to be paid attention by modern law education. The paper notes that the assertion of a constitutionalism model in society as a general social value is connected with the authority of fair legislation.

Water droplets released from the sea surface represent one of the major causes of ice accretion on marine vessels. A one-dimensional model of the freezing of a spherical water droplet moving in cold air was developed. The crystallization model allows one to obtain an analytical solution if a uniform temperature distribution over the liquid’s core is assumed. The model was validated using STAR CCM+ Computational fluid dynamics (CFD) code. A collision of a partially frozen droplet with a solid wall assuming the plastic deformation of an ice crust was also considered. The ratio of the crust deformation to the crust thickness was evaluated. It was assumed that if this ratio were to exceed unity, the droplet would stick to the wall’s surface due to ice bridge formation caused by the water released from the droplet’s core.