Numerous Contexts Research Articles

Synergistic pairing of synthetic image generation with disease classification modeling permits rapid digital classification tool development

Machine-learning disease classification models have the potential to support diagnosis of various diseases. Pairing classification models with synthetic image generation may overcome barriers to developing classification models and permit their use in numerous contexts. Using 10 images of penises with human papilloma virus (HPV)-related disease, we trained a denoising diffusion probabilistic model. Combined with text-to-image generation, we produced 630 synthetic images, of which 500 were deemed plausible by expert clinicians. We used those images to train a Vision Transformer model. We assessed the model’s performance on clinical images of HPV-related disease (n = 70), diseases other than HPV (n = 70), and non-diseased images (n = 70), calculating recall, precision, F1-score, and Area Under the Receiver Operating Characteristics Curve (AUC). The model correctly classified 64 of 70 images of HPV-related disease, with a recall of 91.4% (95% CI 82.3%-96.8%). The precision of the model for HPV-related disease was 95.5% (95% CI 87.5%-99.1%), and the F1-score was 93.4%. The AUC for HPV-related disease was 0.99 (95% CI 0.98-1.0). Overall, the HPV-related disease classification model demonstrated excellent performance on clinical images, which was trained exclusively using synthetic images.

Information as public good and public service media in Europe: Introduction to this special issue

The right to public life has been one of the most important achievements of modernity. It was a result of long and painful civil struggles for dignity in a democratic public sphere. While de jure we are all welcome in the public sphere, de facto rapid processes of fragmentation and numerous contexts of misinformation insulate the actual chances of meaningful contribution to public life. Platform users are aggressively approached as consumers of goods and services and continuously stimulated to produce large quantities of any content, just to feed the voracious platforms. The sobering truth is that many people on the private high-tech platforms do not act like true democrats, but like private individuals who pursue very personal aims. At the same time, a private person does not have a mandate to act in the public interest of the rest of the citizens. For these reasons, we turn to information as a public good and public responsibility in this special issue. It is about media as a public service that allows democracy to keep centre stage within a polity. When citizens interact with relevant public information, they build the capacity to control their political environment, to form informed opinions and act upon them. When this basic desideratum is not structurally at the core of society, one cannot even start to consider democracy as the first course of action for the generations to come. Information as a public good is not a thing of nature, but a purposeful action. We take this stance when we approach information as a public good, rather than some residual category of market failure. The scope of this special issue is to analyse and explore the resources and institutional settings that allow information to thrive as a public good in the European contemporary informational ecosystem.

The N6-methyladenosine landscape of ovarian development and aging highlights the regulation by RNA stability and chromatin state.

The versatile epigenetic modification known as N6-methyladenosine (m6A) has been demonstrated to be pivotal in numerous physiological and pathological contexts. Nonetheless, the precise regulatory mechanisms linking m6A to histone modifications and the involvement of transposable elements (TEs) in ovarian development and aging are still not completely understood. First, we discovered that m6A modifications are highly expressed during ovarian aging (OA), with significant contributions from decreased m6A demethylase FTO and overexpressed m6A methyltransferase METTL16. Then, using FTO knockout mouse model and KGN cell line, we also observed that FTO deletion and METTL16 overexpression significantly increased m6A levels. This led to the downregulation of the methyltransferase SUV39H1, resulting in reduced H3K9me3 expression. The downregulation of SUV39H1 and H3K9me3 primarily activated LTR7 and LTR12, subsequently activating ERV1. This resulted in a decrease in cell proliferation, while the levels of apoptosis, cellular aging markers, and autophagy markers significantly increased in OA. In summary, our study offers intriguing insights into the role of m6A in regulating DNA epigenetics, including H3K9me3 and TEs, as well as autophagy, thereby accelerating OA.

Specifying and Verifying Information Flow Control in SELinux Configurations

Security Enhanced Linux (SELinux) is a security architecture for Linux implementing Mandatory Access Control. It has been used in numerous security-critical contexts ranging from servers to mobile devices. However, its application is challenging as SELinux security policies are difficult to write, understand, and maintain. Recently, the intermediate language CIL was introduced to foster the development of high-level policy languages and to write structured configurations. Despite CIL’s high level features, CIL configurations are hard to understand as different constructs interact in non-trivial ways. Moreover, there is no mechanism to ensure that a given configuration obeys desired information flow policies. To remedy this, we enrich CIL with a formal semantics, and we propose IFCIL, a backward compatible extension of CIL for specifying fine-grained information flow requirements. Using IFCIL, administrators can express confidentiality, integrity, and non-interference properties. We also provide a tool to statically verify these requirements and we experimentally assess it on ten real-world policies.

The Heat Shock Factor Code: specifying a diversity of transcriptional regulatory programs broadly promoting stress resilience.

The Heat Shock Factor (HSF) family of transcription factors drives gene expression programs that maintain cytosolic protein homeostasis (proteostasis) in response to a vast array of physiological and exogenous stressors. The importance of HSF function has been demonstrated in numerous physiological and pathological contexts. Evidence accumulating over the last two decades has revealed that the regulatory programs driven by the HSF family can vary dramatically depending on the context in which it is activated. To broadly maintain proteostasis across these contexts, HSFs must bind and appropriately regulate the correct target genes at the correct time. Here we discuss "the heat shock factor code" - our current understanding of how human cells use HSF paralog diversification and interplay, local concentration, post-translational modifications (PTMs), and interactions with other proteins to enable the functional plasticity required for cellular resilience across a multitude of environments.

Mechanical remodeling of nuclear biomolecular condensates.

Organism health relies on cell proliferation, migration, and differentiation. These universal processes depend on cytoplasmic reorganization driven notably by the cytoskeleton and its force-generating motors. Their activity generates forces that mechanically agitate the cell nucleus and its interior. New evidence from reproductive cell biology revealed that these cytoskeletal forces can be tuned to remodel nuclear membrane-less compartments, known as biomolecular condensates, and regulate their RNA processing function for the success of subsequent cell division that is critical for fertility. Both cytoskeletal and nuclear condensate reorganization are common to numerous physiological and pathological contexts, raising the possibility that mechanical remodeling of nuclear condensates may be a much broader mechanism regulating their function. Here, we review this newfound mechanism of condensate remodeling and venture into contexts of health and disease where it may be relevant, with a focus on reproduction, cancer, and premature aging.

Comparison of sustainable cooling/lubrication strategies for drilling of wire arc additively manufactured Inconel 625

Machining Inconel presents significant challenges due to the material's high strength, hardness, and heat resistance. These properties often result in accelerated tool wear, modest surface finish, and increased energy consumption during machining processes. Despite its widespread use in industries, the machining of Inconel alloys remains a persistent bottleneck in manufacturing operations. In this study, we focus on drilling rather than milling of Inconel 625, a commonly used nickel-based superalloy, due to its specific relevance in various industrial applications. Drilling operations are critical in numerous contexts, including the fabrication of aerospace components, where precision, hole quality, and efficiency are paramount. Therefore, this study aims to improve machinability of wire arc additively manufactured Inconel 625 by fabricating it with two different travel speeds, 175 mm/min and 185 mm/min as well as by drilling the fabricated Inconel 625 using recently developed sustainable lubrication/cooling strategies, i.e. electrostatic minimum quantity lubrication (EMQL) and cryogenic LCO2. The results show that the sample built with 185 mm/min produces less tool wear, surface roughness, thrust force and power consumption. The use of LCO2 results in approximately 35 % less tool wear, 27 % less surface roughness, 15 % less thrust force, and 4 % lower power consumption compared to EMQL for the sample built at 185 mm/min.

FOREIGN LANGUAGE LEARNERS’ PERCEPTIONS OF THEIR ONLINE ROLES IN EMERGENCY REMOTE TEACHING

Nearly all facets of life, including language instruction, have been impacted by the COVID-19 epidemic and the devastating earthquake in February 2023. Consequently, remote education, also known as e-learning or virtual learning, has taken over as the standard offering in numerous contexts. In this regard, remote teaching practices have been preferred in many higher education institutions. However, gaining a comprehensive understanding of the evolving educational practices during this period requires an awareness of how pupils respond to this change. In the course of the emergency remote teaching, language learners' conceptions of instruction and learning were examined using a metaphor analysis approach. For this purpose, a semi-structured form utilizing the Turkish university students' metaphorical concepts regarding their roles in online learning, along with an open-ended questionnaire where participants shared their background information, was employed. Data collected from 56 foreign language learners from different departments at a state university in Turkey was analysed using elicited metaphor analysis. The results showed that foreign language learners used a variety of metaphors, which were categorized as passive recipient, independent learner, flexible person, ineffective consumer, lazy individual, and lost guide, suggesting both favourable and unfavourable dispositions toward emergency remote teaching. In light of the research results, several recommendations and implications were provided.

Genome-wide and cell-type-selective profiling of in vivo small noncoding RNA:target RNA interactions by chimeric RNA sequencing

Small noncoding RNAs (sncRNAs) regulate biological processes by impacting post-transcriptional gene expression through repressing the translation and levels of targeted transcripts. Despite the clear biological importance of sncRNAs, approaches to unambiguously define genome-wide sncRNA:target RNA interactions remain challenging and not widely adopted. We present CIMERA-seq, a robust strategy incorporating covalent ligation of sncRNAs to their target RNAs within the RNA-induced silencing complex (RISC) and direct detection of invivo interactions by sequencing of the resulting chimeric RNAs. Modifications are incorporated to increase the capacity for processing low-abundance samples and permit cell-type-selective profiling of sncRNA:target RNA interactions, as demonstrated in mouse brain cortex. CIMERA-seq represents a cohesive and optimized method for unambiguously characterizing the invivo network of sncRNA:target RNA interactions in numerous biological contexts and even subcellular fractions. Genome-wide and cell-type-selective CIMERA-seq enhances researchers' ability to study gene regulation by sncRNAs in diverse model systems and tissue types.

Study on the Active Wave Absorption Methods in Lattice Boltzmann Numerical Wave Tank

The active wave absorption method has been widely employed in numerical wave tanks. The wave absorption performance of active wave absorption methods is investigated within a numerical wave tank based on a lattice Boltzmann method. Specifically, two active wave absorption methods—the classical shallow water method and the extended range method—are compared. By analyzing the contributions of free and bound components in the harmonics of the reflected wave to the reflection coefficient, we found that the extended-range method is more effective than the shallow-water method in absorbing the reflection of the primary harmonic. Moreover, a wave absorption performance index is proposed to carry out rapid evaluation of active wave absorption method performance without resorting to numerical simulations. Our findings indicate that the performance index ratio of two active wave absorption methods closely mirrored their reflection coefficient ratio. Notably, the extended-range method significantly reduces the performance index in both shallow and deep waters, exhibiting superior active absorption performance within the lattice Boltzmann method-based numerical wave tank context compared to the shallow-water method.

Three approaches on estimating geometric sensitivities in radiative transfer with Monte Carlo

The Monte Carlo method, renowned for its ability to handle the spectral and geometric complexities of 3D radiative transfer, is extensively utilized across various fields, including concentrated solar power design, atmospheric science, and computer graphics. The success of this method also extends to the estimation of sensitivity—the derivative of an observable with respect to a given system parameter, which is, however, particularly challenging when these parameters involve geometric deformation. Bridging statistical physics and computer graphics, distinct methodologies have emerged within these fields for estimating geometric sensitivity, each employing unique terminologies and mathematical frameworks, leading to seemingly disparate approaches. In this paper, we review the three main approaches to sensitivity estimation: (1) Expectation Differentiation, which employs a vectorized Monte Carlo algorithm to simultaneously estimate the intensity and its sensitivity; (2) Differentiable Rendering, predominantly used in computer graphics and applied in numerous contexts; (3) Transport Model for Sensitivity, which conceptualizes sensitivity as a physical quantity with its own transport equations and boundary conditions, thereby facilitating engineering and physics analyses. We aim to enhance readers’ ability to tackle sensitivity-related challenges by providing a comparative understanding of these three perspectives. We achieve this through a simplified one-dimensional radiative transfer case study, offering an accessible platform for comparing and classifying these approaches based on their theoretical underpinnings and practical application in Monte Carlo algorithms.

2D temperature field reconstruction using optimized Gaussian radial basis function networks

Monitoring temperature fields by sensors and mapping the reconstructed temperature field holds great importance in numerous contexts. Concurrently, as sensing technologies continue to advance, there is a parallel development of temperature reconstruction algorithms. In this study, we present an enhanced Quadratic Optimization Gaussian Radial Basis Function approximation (QO-GRBF). We demonstrate the effectiveness of this algorithm by reconstructing a 10 cm x 10 cm temperature field using distributed optical fiber sensing system. The absolute error across four different temperature stages from 35 °C to 65 °C ranged from 0.21 °C to 0.45 °C, indicating a strong reconstruction ability. Furthermore, we compared the performance of QO-GRBF with GRBF in different point densities. The results reveal an over 30 % improvement in scenarios with denser data points, highlighting the efficacy of proposed algorithm. The data size chosen was quantified as well. There was a 10.22 % improvement observed when transitioning from selecting 4 points to selecting 6 points.

Współczesne ukraińskojęzyczne teksty internetowe jako generator innowacji przysłowiowych (aspekt tekstologiczno-pragmatyczny)

In the article the Author analyses modern proverb modifications in the context of the assumptions of contextual studies within paremiology. Research material was excerpted from internet sources in the Ukrainian language that feature an especially dynamic process of creation of innovations based on canonical proverbs functioning in Ukrainian. This research is a continuation of analyses of modern tendencies in the European paremiology. Textological--pragmatical approach treats proverbs as situational macrosigns that have a wide range of usage in texts. Proverb modifications indicate that paremiology is still an active pragmatic form of verbalisation of varied messages that is effectively used by speakers in numerous texts and contexts.

Traversing Situations and Contexts in Networked Learning

To meet the goals of business education and develop responsible business leaders, educators need to design networked learning environments that nurture and leverage connections. This includes connecting students to each other, their teachers, to industry and to the community. This paper aims to understand factors that support or inhibit students’ participation in networked learning in a business education context. Productive participation in learning networks is essential if students are to connect meaningfully with people and resources in ways that support their learning and experience. As the physical and digital learning environments students engage in often include complex configurations of tools, tasks, and epistemic resources, they require the skills and dispositions to effectively transition between these environments. Using a situated readiness framework by Hachmann and Dohn (2018), this study presents an analysis of focus group data from business students in higher education to identify ways in which students’ level of readiness impacts on their ability to participate effectively in learning networks. Students in the present study highlighted a range of challenges they faced when attempting to traverse between face-to-face and online learning environments. However, our study also revealed numerous other situations or contexts that business students needed to traverse as part of their networked learning experience. These were grouped into five overarching categories: domain; space and configuration; format, structure and resources; people or groups; and purpose and responsibility. Overlayed onto the situated readiness framework, these categories can be used by other researchers to gain a more nuanced understanding of how students navigate the complexities of networked learning environments. Future research conducted across a range of learning contexts would allow the categories to be refined and built upon. The study also identified skills and dispositions that contributed to students' level of readiness to navigate situations and contexts within networked learning environments, and some preliminary design considerations based on the findings. To support business students in higher education on their journey to becoming responsible business leaders, it is imperative that they are able to participate productively in complex networked learning environments. As educators, this requires us to understand their level of readiness to do so and equip them with the skills and dispositions they need to participate effectively.

Zebrafish nampt-a mutants are viable despite perturbed primitive hematopoiesis.

Nicotinamide phosphoribosyltransferase (Nampt) is required for recycling NAD+ in numerous cellular contexts. Morpholino-based knockdown of zebrafish nampt-a has been shown to cause abnormal development and defective hematopoiesis concomitant with decreased NAD+ levels. However, surprisingly, nampt-a mutant zebrafish were recently found to be viable, suggesting a discrepancy between the phenotypes in knockdown and knockout conditions. Here, we address this discrepancy by directly comparing loss-of-function approaches that result in identical defective transcripts in morphants and mutants. Using CRISPR/Cas9-mediated mutagenesis, we generated nampt-a mutant lines that carry the same mis-spliced mRNA as nampt-a morphants. Despite reduced NAD+ levels and perturbed expression of specific blood markers, nampt-a mutants did not display obvious developmental defects and were found to be viable. In contrast, injection of nampt-a morpholinos into wild-type or mutant nampt-a embryos caused aberrant phenotypes. Moreover, nampt-a morpholinos caused additional reduction of blood-related markers in nampt-a mutants, suggesting that the defects observed in nampt-a morphants can be partially attributed to off-target effects of the morpholinos. Our findings show that zebrafish nampt-a mutants are viable despite reduced NAD+ levels and a perturbed hematopoietic gene expression program, indicating strong robustness of primitive hematopoiesis during early embryogenesis.

Music and the Social Construction of Self

Sociologists have demonstrated that people use music to construct meaning in numerous specific contexts, groups, and subcultures. This article expands such work by examining how people draw on music to construct identities in daily life beyond any specific setting. Drawing on four years of participant observation and twenty‐five in‐depth interviews, findings demonstrate three mechanisms of identity‐work as people use music in public to (1) define who they are, (2) explain how they feel because of who they are, and (3) narrate where they have been in their lives that made them who they are. These findings demonstrate how people use music in daily life to construct identities, and the theoretical potential of such exploration for sociological and music studies of public life.

Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.

Hybrid emerging model predictive data-driven forecasting of three-phase electrical signals of photovoltaic systems using GBR-LSTM

In numerous industrial contexts, precise analysis and forecasting of electrical signals within three-phase systems are indispensable. As a result, this work presents DeepPhase, a hybrid framework that combines Long Short-Term Memory (LSTM) neural networks with gradient-boosted regression (GBR) to predict the current, voltage, and power of electrical signals. The performance of the model is evaluated in comparison to benchmark models, namely Bidirectional LSTM (BiLSTM), K-Nearest Neighbors (KNN), and LSTM, which utilize essential Key Performance Indicators (KPIs). As demonstrated by its highest Coefficient of Determination (R2) of 0.999, Mean Absolute Error (MAE) of 6.94 × 10−5, Mean Absolute Percentage Error (MAPE) of 0.07 %, and Root Mean Square Error (RMSE) of 0.000156, DeepPhase consistently exhibits predictive precision. For Three-Phase Current, MAE is 2.13 × 10−3, MAPE is 0.01 %, RMSE is 0.062432, and R2 is 0.960596; and for Three-Phase Voltage, MAE is 9.52E-03, MAPE is 0.03 %, RMSE is 0.014, and R2 is 0.999. The results of this study highlight the effectiveness of DeepPhase in analyzing the dynamics of complex Three-Phase electrical signals. This has significant implications for improving decision-making and control strategies in complex electrical systems.

Procedural Justice Spill-Over and Recidivism After Release From Prison

The importance of procedural justice for reducing criminal behavior has been demonstrated in numerous criminal justice contexts. However, to date, no study has evaluated the impact of procedural justice in encounters with multiple authorities on recidivism. Using longitudinal data from the Prison Project, this study examines the associations between perceptions of prison staff procedural justice, probation officer procedural justice, and recidivism during a 12-month follow-up period. Findings indicate that detainees who felt treated fairly by prison staff, perceived their probation officer to be fairer, and, subsequently, they had a lower likelihood of getting reconvicted. How prison staff are perceived by people in prison may impact those people’s perceptions of their probation officers which in turn may affect their offending behavior.

One-dimensional, geometrically stratified semi-empirical models of the quiet-Sun photosphere and lower chromosphere

One-dimensional, semi-empirical models of the solar atmosphere are widely employed in numerous contexts within solar physics, ranging from the determination of element abundances and atomic parameters to studies of the solar irradiance and from Stokes inversions to coronal extrapolations. These models provide the physical parameters (i.e. temperature, gas pressure, etc.) in the solar atmosphere as a function of the continuum optical depth $ c $. The transformation to the geometrical $z$ scale (i.e. vertical coordinate) is provided via vertical hydrostatic equilibrium. Our aim is to provide updated, one-dimensional, semi-empirical models of the solar atmosphere as a function of $z,$ but employing the more general case of three-dimensional magneto-hydrostatic equilibrium (MHS) instead of vertical hydrostatic equilibrium (HE). We employed a recently developed Stokes inversion code that, along with non-local thermodynamic equilibrium effects, considers MHS instead of HE. This code is applied to spatially and temporally resolved spectropolarimetric observations of the quiet Sun obtained with the CRISP instrument attached to the Swedish Solar Telescope. We provide average models for granules, intergranules, dark magnetic elements, and overall quiet-Sun as a function of both $ c $ and $z$ from the photosphere to the lower chromosphere. We demonstrate that, in these quiet-Sun models, the effect of considering MHS instead of HE is negligible. However, employing MHS increases the consistency of the inversion results before averaging. We surmise that in regions with stronger magnetic fields (i.e. pores, sunspots, network) the benefits of employing the magneto-hydrostatic approximation will be much more palpable.