Search Results (90)

Linear dichroism (LD) optical absorption spectroscopy historically has found substantial yet still limited application in broad areas of science. In particular, full-dipole-tensor reconstruction has been onerous, usually requiring tedious and difficult measurements on single crystals at many orientations using a four-circle goniometer. As a consequence, it is very seldom done. Here, we propose, and test by numerical simulation, a simpler, faster, novel method of determining the full dipole optical absorption tensor of homogeneous planar films in real time as a function of energy (or wavelength), while requiring only minimal additional time and instrumentation. The goal of this paper is to explain the theory and to demonstrate the effectiveness and stability of the procedure using synthetic data sets. Experimental implementation and testing is deferred to future work and publications. The full-tensor spectrum, after construction from the experimental data, allows one to instantly calculate the absorption for any selected polarization direction, even those that are physically inaccessible to experimental measurement. Although our specific application in this paper is X-ray Absorption Fine Structure (XAFS) Spectroscopy, the method should be applicable to UV–Vis, IR, THz, microwave, and other wavelengths. A strength of this measurement modality is that full-tensor data can be acquired using essentially the same sort of scanning geometry that is normally used for XAFS, with only a discrete shift in the spin axis orientation between groups of scans. The additional instrumentation needed to determine the five Fourier components of the signal at each energy is minimal; two angles gives ten parameters, while six are strictly needed. Robust inversion from data to tensor elements is demonstrated, implemented via simple matrix multiplication. Outside of XAFS, FTMAPS is also expected to be applicable to diverse scientific and technological areas such as oriented bio-molecular films, semiconductor and materials physics, and process control of thin-film photovoltaics and semiconductors. Full article

Electrocardiogram (ECG) signals are essential for arrhythmia detection; however, they are frequently degraded by noise during acquisition, and their evaluation is vulnerable to data-leakage and patient-overlap issues that can compromise model assessment. Therefore, in this study, we propose an image-encoding-based arrhythmia classifier combined with a morphology-aware denoising autoencoder. We evaluate signals under a corrected, patient-independent protocol in which every model-selection decision was made on a separate validation partition. On a leakage-free test partition, the autoencoder achieved an SNR improvement of 5.63 dB and a correlation coefficient of 0.801, improving R-peak amplitude preservation and redetection accuracy under moderate-to-severe noise while introducing measurable morphology degradation when the input was already lightly contaminated. The proposed model encodes the denoised, beat-centered signal into images through an interleaved-grouping outer product with sorting and flipping, and classifies them with a multi-scale three-dimensional convolutional network. Under this protocol, the proposed model obtained the highest macro-F1 among five image-encoding architectures, but did not outperform four models operating directly on the denoised signal (macro-F1 32.5% versus 37.3–40.0%), indicating that the proposed encoding does not improve overall five-class classification under rigorous inter-patient evaluation. A controlled test showed that the encoding is exactly invariant to global signal-polarity inversion, unlike the sequential models. This targeted invariance, rather than a general accuracy advantage, is the contribution reported here. Full article

The dynamics of active particles are increasingly being leveraged to design and control micro-robotic swarms. Local interactions play a crucial role in the phase transitions of active particles; how the combined effects of alignment, short-range repulsion, and boundary interactions regulate their packing structure and collective order with different confinement scales remains less systematically explored. In this study, we investigate the packing of active particles within a confined region, focusing on the role of local interaction rules in shaping both the packing structure and the polar order parameter. The effects of key controlling variables related to local interaction rules, including interaction radius, repulsion radius, confined boundary radius, and noise strength, are numerically studied. Specifically, by comparing systems with and without velocity–alignment interactions, we reveal the role of alignment in dictating both structural and dynamical properties of the ensemble. To quantify the packing structure, we employ Voronoi tessellation to evaluate both local and global packing densities. The results show that strong confinement induces a jammed state in which alignment effects are suppressed, resulting in high global packing density and low polar order, regardless of the noise amplitude. Upon increasing the boundary radius beyond a critical threshold, the system unjams, enabling alignment interactions to significantly enhance both the polar order parameter and packing density. Interestingly, the relationship between global packing density and micro-structural parameters, such as coordination number and Voronoi tessellation metrics, is similar in the systems with and without alignment. Our results demonstrate that collective packing and phase behaviour of active matter are governed by the nontrivial interplay between alignment, confinement, and noise, with alignment interactions driving the transition from disordered to ordered states as geometric constraints are relaxed, offering critical insights for the design of targeted micro-robotic swarms and active microfluidic sorting systems. Full article

This study examines whether viewers’ relative affinity toward Spain’s two leading late-night hosts—Pablo Motos (El Hormiguero) and David Broncano (La Revuelta)—varies systematically by ideology. Using an anonymous non-probability online survey fielded in Segiuptember 2024 among adults in the primary analytic frame (N = 806), we construct two parallel six-item affinity indices: IM for Motos and IB for Broncano. Our primary outcome is the respondent-level Affective Gap (BA = IB − IM), where positive values indicate greater relative affinity toward Broncano. In the adult analytic frame, IM averages 2.517 and IB 2.710 on a 1–5 scale, yielding a mean BA of 0.192 (95% CI [0.094, 0.289]). BA is clearly positive among left-leaning respondents and lower, often negative, among right-leaning respondents. In OLS models with HC3 standard errors, ideology remains a strong negative predictor of BA after adjustment for age, gender, education, and socioeconomic status, and the association is robust to bootstrap intervals and sensitivity checks. Open-ended responses are used descriptively to contextualize the quantitative gradient. The study contributes to research on selective exposure, affective sorting, and media-personality evaluation by showing that, within this non-probability analytic sample, relative evaluations of two prominent Spanish late-night hosts are strongly associated with respondents’ ideological self-placement. Full article

Phytoplankton are the major primary producers in the Southern Ocean, are important to global nutrient cycles, and are at the base of marine food webs. Polar ecosystems are unique in their extended periods of darkness in the winter, and prolonged darkness has the potential to exert selection that affects diatom population composition if there is differential survival in the dark, the variation in population growth rates in subsequent light periods, or both. We tested whether prolonged darkness has the potential to exert within-species selection on a model polar diatom species by exposing five strains of the polar diatom Porosira glacialis to prolonged darkness at two different temperatures in the laboratory. We measured population survival in the dark, growth rate upon re-illumination, and between-strain variability in these traits. We found a pronounced decline in survival and growth rate with time spent in the dark, as well as important intraspecific variation in the tested strains. Declines in survival and growth were exacerbated at a higher temperature. Our results show that the darkness of polar night can exert intraspecific selection in a common Southern Ocean diatom. Full article

The rapid and non-destructive assessment and sorting of rice seed viability are critical for improving agricultural productivity and seed quality control. Preliminary germination tests confirmed that viability decline was significantly correlated with harvest year. On this basis, this study investigates the quantitative relationship between dielectric properties and harvest year, and evaluates the feasibility of electric field-based sorting using a custom-built DC non-uniform electric field device. The results show that the dielectric constant of rice seeds increases with harvest year, exhibiting a linear correlation (R² > 0.99, p < 0.05) with low intra-group variability (SD < 0.05). Germination tests confirmed a correlation between germination rate and harvest year (R² > 0.99, p < 0.05), indicating that harvest year is associated with viability decline and suggesting dielectric constant as a potential viability indicator under the conditions tested. Sorting tests revealed a negative correlation between drop rate and applied voltage (R² > 0.90). Under identical voltage conditions, seeds stored longer (lower viability) exhibited lower drop rates, indicating stronger electrode polarization forces. Two-way ANOVA confirmed that voltage, harvest year, and their interaction significantly affected drop rate (p < 0.001). Furthermore, mechanical modeling and trajectory analysis revealed that seed motion follows a quadratic path, enabling the identification of critical sorting conditions. This study provides theoretical insights and a foundation for the future development of dielectric-based seed sorting systems. Full article

This study introduces a multi-source spatial methodology that moves beyond the traditional urban/rural dichotomy to classify the American landscape into detailed, temporally defined settlement types. By combining historical housing unit and population estimates (HHUUD10 and LTDB) standardized to 2010 census tract boundaries with high-resolution, grid-level data on the built environment (HISDAC-US), this research establishes a settlement typology based on the development history of detailed geographic units. This framework classifies areas (from Prewar Cores and 21st-Century Suburbs to exurban fringes, outlying towns and rural areas) based on their era of development and proximity to urban centers. Applying this typology reveals profound spatial and demographic decentralization spanning eighty years of metropolitan expansion. The findings demonstrate a stark geographic sorting: expanding greenfield edges and exurbs have become magnets for high-income, highly educated, and predominantly White populations. However, longitudinal tracking reveals a distinct morphological “life-course” within suburban rings. As older suburbs age and their housing stock depreciates, they open to wider demographic integration, transforming into destinations for Black and foreign-born residents. Furthermore, the data highlight a contemporary polarization of human capital, concentrated in both the newest suburban peripheries and the resurgent urban cores, contrasting with persistent economic decline in outlying towns and rural areas. Ultimately, this methodology provides a flexible, longitudinal framework for understanding the long-term morphological and demographic evolution of American settlement. Full article

Traditional treatments of autoimmune diseases relying on systemic immunosuppression often lack curative potential and have severe side effects. Mesenchymal stem cells (MSCs) are a promising alternative due to their immunomodulatory properties; however, whole-cell therapies have certain limitations. MSC-derived extracellular vesicles (EVs), including small vesicles—exosomes—have emerged as a safe cell-free therapeutic platform capable of crossing biological barriers and delivering bioactive cargo with low immunogenicity. Various types of RNAs abundantly produced by host MSCs represent a key element of EV content. In particular, EVs carry small RNAs, which essentially determine cellular life and fate. Our review provides a comprehensive mechanistic framework for the use of RNA-loaded EVs, specifically those carrying microRNAs (miRNAs), small interfering RNAs (siRNAs), and messenger RNAs (mRNAs), in restoring immune homeostasis. We detail the biogenesis and molecular mechanisms governing sorting of RNA into EVs, along with endogenous and exogenous engineering strategies to enhance therapeutic potency. We examine how RNA-loaded EVs modulate immunological processes like reprogramming of macrophage M1-M2 polarization, Th17/Treg balance, and suppression of inflammatory signaling pathways such as NF-κB and the NLRP3 inflammasome. We address critical translational challenges—EV heterogeneity, manufacturing scalability, and need for standardized quality control—while outlining future opportunities for RNA-loaded EV-based therapeutics. Full article

Precise manipulation and directed transport of micro- and nano-particles are cornerstones of emerging lab-on-a-chip technologies. Traditional optofluidic systems that combine optical tweezers with microfluidic channels enable long-range transport. However, they rely on fixed physical boundaries that lack reconfigurability. To bridge this gap, we propose a reconfigurable virtual optical waveguide (VOW) based on a discretized beam-shaping strategy. By superposing two orthogonally polarized shaped beams, we construct interference-free optical channels without physical boundaries. This platform enables programmable transport along complex trajectories, including space-filling Hilbert curves that maximize interaction path length, and shields the transport channel from perturbations induced by surrounding particles. Crucially, the VOW offers multi-dimensional sorting capabilities: (i) it performs precise size-dependent sieving via tunable channel widths, and (ii) it functions as an intrinsic material filter by stably guiding scattering-dominated particles (e.g., gold) while rejecting gradient-dominated dielectric ones. This work establishes a versatile, contactless strategy for adaptive optical logistics and on-chip material purification. Full article

Retromer is an evolutionarily conserved protein complex first identified in budding yeast. It was originally described for its essential role in endosome-to-Golgi retrieval of lysosomal hydrolase receptors. Retromer is now known to mediate trafficking of many endosomal cargoes. The mammalian retromer is constituted by a core heterotrimer encoded by the vacuolar protein sorting (VPS) gene products VPS26, VPS35, and VPS29. To mediate cargo recognition and endosomal sorting into various pathways, this trimer can cooperate with phosphoinositide-binding sorting nexin family members. Defective retromer functioning has been associated with alterations in cellular homeostasis, leading to disease. To gain insights into how it may mediate these broad processes, a proteomic strategy in polarized Madin-Darby canine kidney cells was devised to identify retromer-interacting proteins. Subsequent validation of one of the candidates, i.e., cytokeratin 19, led to the unexpected finding that retromer localizes to the pericentriolar region in dividing cells and subsequently translocates to the midbody during cytokinesis. Retromer was found interacting with CK19, and its antisense depletion led to delocalization from CK19. Subcellular fractionation and live cell monitoring of depleted cells provided evidence of a role by retromer in post-metaphase progression and in epithelial cell migration, thereby connecting retromer with key processes of cellular dynamics. Full article

Achieving Net-Zero Energy Building (NZEB) performance through retrofitting requires identifying optimal measures that effectively enhance energy efficiency. Determining these optimal retrofit strategies typically involves running thousands of building energy simulations, which imposes a substantial computational burden. To address this challenge, a novel machine learning-based framework is proposed to optimize retrofit strategies for NZEBs under future climate change scenarios. A Non-Dominated Sorting Genetic Algorithm (NSGA-III) is employed to minimize both annual energy consumption and the Predicted Percentage of Dissatisfied (PPD), while simultaneously ensuring net-zero energy balance, thereby generating a Pareto front of optimal solutions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank the Pareto-front solutions and identify the most favorable retrofit scenario. The results show that the proposed framework reduces optimization time by at least a factor of two compared with simulation-only optimization. Leveraging these computational savings, the framework evaluates a suite of passive and renewable measures across multiple future timeframes to capture the influence of climate change on retrofit performance. The findings indicate that achieving NZEB under future climate conditions requires higher levels of thermal insulation and greater renewable integration than under present-day conditions. Under the Shared Socioeconomic Pathways (SSP) framework, optimal insulation levels in the fossil fuel-dependent scenario are lower than in the sustainable scenario by up to 18% in C-type (warm temperate), 12% in D-type (snow), and 13% in E-type (polar) climates. The combined retrofit measures can reduce annual energy consumption by up to 80% and lower PPD by as much as 67% compared to the base case. Full article

With the rapid advancement of large language models (LLMs), recent systems have demonstrated increasing capability in understanding and expressing human emotions. However, no objective and standardized metric currently exists to evaluate how empathetic an LLM’s response is. To address this gap, we propose a novel evaluation framework that measures both sentiment-level and emotion-level alignment between a user query and a model-generated response. The proposed metric consists of two components. The sentiment component evaluates overall affective polarity through Sentlink and the naturalness of emotional expression via NEmpathySort. The emotion component measures fine-grained emotional correspondence using Emosight. Additionally, a semantic component, based on RAGAS, assesses the contextual relevance and coherence of the response. Experimental results demonstrate that our metric effectively captures both the intensity and nuance of empathy in LLM-generated responses, providing a solid foundation for the development of emotionally intelligent conversational AI. Full article

Endometrial tissue-derived mesenchymal stem/stromal cells (eMSCs) have potential therapeutic properties partially exerted via their secreted extracellular vesicles (EVs). eMSC-EVs contain cargos with regenerative and immunomodulatory properties. Specifically, the miRNA profile of CD146High eMSC-EVs has been shown to promote anti-inflammatory M2 macrophage polarization in vitro. Herein, we aimed to characterize the lncRNA profile of CD146High and CD146Low eMSC-EVs and further assess their immunomodulatory and anabolic therapeutic function in osteoarthritis (OA). We hypothesized that the CD146High eMSC-EVs lncRNA profile is enriched with potent anti-inflammatory and pro-anabolic cartilage effects when compared to the CD146Low eMSC-EVs lncRNA profile. Human endometrial tissue was collected, and the eMSCs were magnetically sorted to yield the CD146High and CD146Low eMSC subpopulations. The eMSC-EVs were isolated via ultracentrifugation and CD63 magnetic immunoselection methods and characterized by nanosight and flow cytometry analyses. Our results showed that CD146High eMSC-EVs display an lncRNA profile with both anabolic and catabolic features, exerting a more dynamic effect on chondrocyte gene expression than CD146Low eMSC-EVs, suggesting a potential benefit of using CD146High eMSC-EVs to attenuate the negative effects of inflammation in OA. CD146High eMSC-EVs also demonstrated greater endothelial repair capacity under inflammatory stress. In conclusion, cell-free CD146High eMSC-EV has therapeutic potential through its protective anti-inflammatory effects, warranting further pre-clinical investigation. Full article

Background: Monocytes can commit to different phenotypes associated with specific features required in inflammation and homeostasis. Classical and alternative activation are two extremes of monocyte polarization and are both influenced by glucocorticoids (GCs). Methods: Human monocytes were sorted from the blood of healthy individuals and activated with LPS or IL-4 and IL-13, either in the absence or presence of dexamethasone (Dex). Metabolic adjustments were analyzed using Seahorse stress tests, SCENITH, and RT-qPCR. Results: LPS enhanced glycolysis and also, to a lesser extent, oxidative phosphorylation (OXPHOS), whereas addition of Dex induced a metabolic switch in favor of the latter. In contrast, activation of monocytes with IL-4 and IL-13 exclusively stimulated OXPHOS, which was suppressed by concomitant Dex treatment. The glycolytic function of monocytes matched alterations in gene expression of glucose transporters and metabolic enzymes, which were upregulated by LPS and inhibited by Dex via interference with the mTORC1 pathway but remained unaltered in response to IL-4 and IL-13. Although the dependency of classically and alternatively activated monocytes on OXPHOS and glucose usage markedly differed, modulation by GCs was limited to the latter polarization state. Conclusions: Our findings unravel a highly selective regulation of human monocyte energy metabolism by different activating stimuli as well as by GCs. Full article

In this paper, we propose the threshold successive cancellation flip (Th-SCF) decoding algorithm for polar codes, which enhances the performance of the SC decoder while maintaining low complexity. Theoretical analysis reveals that Th-SCF asymptotically delays the first error position (FEP, the first part where the SC decoder fails) with probability 1, ensuring high decoding performance. Simulation results show that the Th-SCF algorithm achieves performance comparable to the dynamic SC flip (D-SCF) algorithm, but with a reduction in complexity by eliminating the need for sorting operations. A key contribution of this work is the rigorous theoretical framework supporting the Th-SCF algorithm, distinguishing it from existing SC flip (SCF) decoding methods. This theoretical foundation not only explains the performance improvements but also provides insights into the underlying mechanisms of flipping. The proposed Th-SCF algorithm demonstrates strong performance across a wide range of code lengths and rates, and its performance remains stable within a certain threshold range, indicating its practical applicability in real-world communication systems. These results offer valuable perspectives for the design of efficient flip decoding strategies in 5G and future networks. Full article