Search Results (14,516)

Ultralight bosonic dark matter (UBDM) that couples to electromagnetism can generate an oscillating magnetic-field signal at the Earth’s surface. This is referred to as the “Earth transducer” effect, as the Earth converts UBDM into a detectable magnetic field. Similar DM-induced fields in laboratory experiments typically scale with the size L of the experiment. Because the Earth transducer signal instead scales with the large radius of the Earth, R, it is one of the most powerful direct probes of UBDM with masses $m_{\mathrm{DM}}\lesssim 1/R\sim 3\times {10}^{-14}\mathrm{eV}$. It has many other favorable properties, such as high spatial and temporal coherence and robustness to atmospheric modeling. In this review, we derive the Earth transducer effect and its properties for multiple UBDM models, and we discuss current and future prospects to detect it. Full article

Background: Neurological diseases share overlapping systemic and molecular features, but the specificity and interpretation of cross-disease metabolomic signatures remain uncertain. Methods: We analyzed summary-level Nightingale NMR metabolomic and genetic data from 274,241 UK Biobank participants across eight neurological endpoints. The disease rankings used here were derived from baseline plasma metabolites predicting future incident disease rather than from contemporaneous diagnostic case–control contrasts. Results: Among 57 priority metabolites with genome-wide significant instruments (2472 SNPs; mean F = 162.4), nine ranked in the top 30 for at least seven of the eight endpoints, defining a shared pre-diagnostic neurological signature confirmed as non-random cross-endpoint convergence by permutation testing (p < 0.0001). Because we did not perform a quantitative non-neurological disease control analysis, this signature should not be interpreted as neurologically specific and may partly reflect systemic morbidity, renal function, body composition, or frailty-related physiology. Forward Mendelian randomization across 45 metabolite–disease pairs found no Bonferroni-significant causal effects; three nominal protective associations are consistent with the number of false-positive findings expected under multiple testing and require replication. Conclusions: These results support a shared systemic, pre-diagnostic metabolic signature across the neurological disease spectrum, while the null MR findings and specificity limitations favor interpretation as a biomarker or prodromal downstream signal rather than a proven causal mechanism. External prospective validation is essential. Full article

Protein arginine methyltransferases (PRMTs) are key regulators of diverse cellular processes in eukaryotes, including transcriptional regulation, RNA processing, signal transduction and DNA repair. However, the biological functions of PRMTs in Verticillium dahliae remain largely unexplored. In this study, we identified a PRMT1 homolog in V. dahliae. Targeted deletion of VdPRMT1 resulted in severely impaired hyphal growth, sporulation, stress responses and pathogenicity. Subcellular localization analysis showed that VdPRMT1 is distributed in both the nucleus and cytoplasm of hyphae. Host-induced gene silencing (HIGS) of VdPRMT1 in cotton significantly reduced disease severity, supporting its important role in pathogenicity. Furthermore, VdLuc7, a U1 snRNP-associated protein containing multiple RG/RGG motifs, was identified as a putative interacting partner of VdPRMT1 through yeast two-hybrid (Y2H) screening, bimolecular fluorescence complementation (BiFC) and luciferase complementation imaging (LCI) assays. Together, our results demonstrate that VdPRMT1 is required for normal fungal development and full virulence in V. dahliae, and suggest that arginine methylation may contribute to pathogenicity through regulation of RNA processing-related pathways. These findings provide new insights into the molecular mechanisms underlying fungal virulence and identify VdPRMT1 as a potential target for disease control. Full article

As silicon scaling nears its physical limits and digital systems process ever-growing amounts of data, integrating computation directly within memory is emerging as a key strategy to overcome the constraints of conventional Von Neumann architectures. Approximate In-Memory Computation (IMC) with memristors offers a promising path toward energy-efficient processing for data-intensive applications. Recent adaptive approximate adders exploit operand magnitude to dynamically switch between exact and approximate computation, but typically ignore carry propagation across approximation boundaries, which can significantly degrade application-level robustness. This work introduces RapproX, a family of adaptive memristive approximate adders featuring a lightweight carry lookback mechanism that approximates carry interaction between exact and approximate regions. The proposed approach improves arithmetic robustness while introducing only minimal overhead and enabling resource-efficient implementations through memristor reuse. Experimental results demonstrate that the proposed approaches achieve superior arithmetic quality compared to State-of-the-Art (SoA) memristive approximate adders. More importantly, the carry lookback mechanism translates into substantial application-level benefits. In image processing, RapproX reduces energy consumption by up to 30.9% compared to the most competitive SoA design and by 50.3% compared to exact computation while maintaining roughly 43 dB Peak Signal-to-Noise Ratio (PSNR). Across a range of machine-learning workloads, including k-means, AlexNet on MNIST, and multiple CIFAR-10 models, RapproX preserves near-exact inference accuracy for the evaluated models at low-to-moderate k and maintains the energy advantages of adaptive approximation, while SoA approximations degrade markedly under the same conditions. These simulation-based results suggest that lightweight carry-aware approximation can improve the robustness of adaptive approximate in-memory computing with only marginal hardware overhead. Full article

Vitiligo is an autoimmune depigmenting disorder characterized by marked heterogeneity in therapeutic response, both between patients and among lesions within the same individual. While current therapies primarily target interferon-γ (IFN-γ)-driven inflammation, clinical outcomes remain variable and frequently incomplete, suggesting that additional lesion-specific biological factors contribute to repigmentation potential. In this narrative review, we propose the concept of repigmentation competence to describe the capacity of an individual lesion to achieve clinically meaningful repigmentation under therapy. We hypothesize that this competence emerges from the interaction of four interconnected biological axes: (i) cytokine network and immune memory, (ii) local immune regulation, (iii) regenerative capacity, and (iv) microenvironmental permissiveness. A targeted search of PubMed/MEDLINE and ClinicalTrials.gov up to March 2026 was conducted to identify translational studies, mechanistic models, and clinical trials relevant to these pathways. Evidence was synthesized thematically with emphasis on cytokine-mediated mechanisms and their interaction with regenerative and tissue-context processes. The IFN-γ/CXCL9/CXCL10 axis and tissue-resident memory T cells (TRM) represent the most clinically validated drivers of disease persistence, as demonstrated by the therapeutic efficacy of JAK inhibitors, although immune suppression alone often fails to achieve complete or durable repigmentation. In contrast, regulatory pathways involving PD-1/PD-L1 signaling, regulatory T cells (Tregs), IL-10, TGF-β, and IL-2–based strategies remain biologically compelling but only partially translated into effective therapies. Regenerative capacity has also emerged as an important determinant of treatment response, with growing evidence supporting the role of melanocyte stem cell niches, follicular regeneration, and Wnt/β-catenin signaling. Accordingly, regenerative approaches such as non-cultured epidermal cell suspension (NCES) are increasingly being integrated into combination therapeutic strategies. The lesional microenvironment remains the least therapeutically developed axis despite growing experimental evidence supporting its importance in melanocyte survival and migration. Collectively, these observations suggest that the variable efficacy of current therapies may reflect different combinations of lesion-specific biological constraints. The emerging benefit of combination strategies may therefore derive not simply from additive effects, but from the simultaneous engagement of multiple axes involved in repigmentation competence. Our review supports a shift from a predominantly drug-centered model toward a lesion-oriented framework integrating immune, regenerative, regulatory, and microenvironmental determinants of response. Full article

Background: The allocation of non-divisible inbound deliveries across multiple warehouses requires the simultaneous consideration of capacity constraints, category-specific restrictions, workload balance, and long-term allocation consistency. Methods: This study proposes a hierarchical two-level allocation framework combining strategic category-rotation policies, normalized marginal scoring, and signed historical feedback. The algorithm is executed once per day to generate warehouse assignments for the following operational day. Historical correction is based on a rolling window covering the preceding 30 daily planning periods. Results: The framework was evaluated using daily simulation instances ranging from 100 to 1500 pallets, with an average of approximately 130 lots per pallet. Across all evaluated instances, the complete allocation procedure was completed in less than 5 s on the specified test system. The results indicate balanced warehouse utilization, progressive reductions in category–location imbalance, stable historical correction, and preservation of hard operational constraints. Conclusions: The framework provides an interpretable and computationally efficient approach for next-day inbound allocation. By combining explicit feasibility filtering, strategic policy signals, and a 30-day historical correction mechanism, it supports both short-term operational decisions and longer-term allocation balance. Full article

Background: DNA methylation is a key epigenetic mechanism whose dynamics are closely linked to ageing. Modern epigenetic clocks predict biological age based on the average methylation level. The concept of “epigenetic rejuvenation” posits that at early stages of development, the biological age of the embryo may decrease, reaching a minimum (“ground zero”) at the gastrulation stage. However, standard averaging methods may not account for important rearrangements in the internal structure of methylation. Objective: To apply the apparatus of information theory and topological data science to the analysis of scNMT-seq data and to test whether DNA methylation entropy decreases from stage E4.5 to E6.5, which would correspond to an approach towards the biological zero state. Methods: Publicly available scNMT-seq data (GSE121690) were analyzed. Five entropy measures were calculated for each cell (Shannon, Renyi, Tsallis, LZ-complexity, local gradient entropy (entropy of variations in the smoothed histogram of the methylation distribution), and persistent entropy (PE)—a topological complexity measure). For the five-dimensional entropy feature space, a Rips complex was constructed, and persistence diagrams H_0 and H_1 were computed. Results: All five entropy measures decreased significantly, with LZ complexity showing the largest relative reduction (−28.4%) and the strongest independent signal (partial r = −0.181). Among all the complexity measures studied, LZ complexity exhibited the largest relative reduction, underscoring its heightened sensitivity to the progressive ordering of the epigenetic landscape. Notably, the ternary encoding of LZ complexity showed strong correlation with Shannon entropy (r = 0.71), indicating that algorithmic complexity, when accounting for partial methylation states, aligns closely with statistical entropy while retaining sensitivity to spatial order. The consistency of results across binary and ternary encodings confirms the robustness of LZ complexity as a structural biomarker. Persistent entropy confirmed the general dynamics (decrease from 15.91 to 14.89, p = 0.01). Topological analysis of the multidimensional space revealed a qualitative reorganization: at stage E6.5, stable cyclic structures (H₁) emerge, while at E4.5 the space is dominated by a single large-scale cycle. Null model validation confirmed that the observed H₁-cycles are genuine topological features rather than random fluctuations. Comprehensive topological characterization showed that normalized persistent entropy increases from 0.846 to 0.882 (p < 0.001), while maximum persistence decreases from 0.446 to 0.218 (p < 0.001), reflecting a transition from a homogeneous state to structured diversification—multiple, evenly distributed cycles corresponding to distinct cell lineages. Consistent with this, regional disorder (RE/RD) at the single-cell level decreases from E4.5 to E6.5 (RE: −25.5%, RD: −27.4%, p < 10⁻¹³), while global entropy also decreases, together painting a picture of epigenetic rejuvenation as ordered consolidation at the whole-genome scale. An SVM model trained on 15 entropy and structural features achieved stage classification with an accuracy of 93.4% and AUC of 0.981, confirming the diagnostic potential of the approach. Conclusions: The decrease in DNA methylation entropy from E4.5 to E6.5 corresponds to an approach to “ground zero”—the point of minimum biological age in embryogenesis—and supports the hypothesis of a link between decreasing entropy and epigenetic rejuvenation. The addition of topological analysis reveals the hidden organization of epigenetic diversity, showing that ordering does not lead to homogenization but is accompanied by the formation of distinguishable cell lineages. Full article

Consumer sentiment shocks rarely remain confined to their economy of origin. Adopting a systems-thinking perspective, this study treats the equity markets of thirteen advanced economies as one interconnected socio-technical system, bounded by its environment. It maps how unexpected shifts in consumer confidence propagate across it between 2015 and 2025. Rather than isolating a single channel, the analysis examines the system as a whole, where a social subsystem of household sentiment interacts with a technical subsystem of market infrastructure. Sentiment shocks are identified as the unexpected component of the OECD Composite Consumer Confidence Index, and the dependency structure linking markets is estimated through return-based networks. The analysis combines the Diebold-Yılmaz connectedness framework, Granger-causal contagion testing, network centrality measures, and panel estimation with cross-sectionally consistent standard errors. Total connectedness reaches 81.6 percent, confirming a densely integrated system in which the Euro-area core acts as the principal return transmitter; sentiment-shock contagion, once corrected for multiple testing, is sparse rather than pervasive. A small set of economies occupies structurally central positions, yet the small-sample centrality diagnostic provides no robust evidence that threshold-network centrality predicts VAR-based net spillover roles. The findings refine the standard assumption that central nodes are necessarily the main propagators of systemic disturbance and offer concrete guidance for cross-border financial monitoring. This guidance is structural rather than a real-time monitoring signal since it derives from a full sample rather than a rolling or live analysis. Full article

SASH1 (SAM [sterile alpha motif] and SH3 [SRC-homology-3] domain-containing protein 1) is a multidomain scaffold implicated in pigmentation, innate immunity, receptor signaling, cytoskeletal dynamics, vascular biology, and tumor suppression. Although genetic and expression studies link SASH1 dysfunction to diverse diseases, a unifying mechanistic framework has remained elusive. Here, we synthesize current knowledge of SASH1 structure, interaction networks, and biological functions across cell types and disease contexts. SASH1 contains an intrinsically disordered SPIDER (SLy Proteins Associated Disordered Region, an SH3 domain, two SAM domains, and multiple linear motifs; together, these elements mediate interactions with EphA8 (ephrin type-A receptor 8), β-arrestin 1, TRAF6 TNF receptor-associated factor 6), CRKL (CRK-like proto-oncogene), IQGAP1 (IQ-motif-containing GTPase-activating protein 1), cortactin, and TNKS2 (tankyrase-2). We propose that SASH1 functions as a context-dependent multi-docking scaffold that organizes signaling architecture. Its modular domains, intrinsically disordered regions, and dual SAM domains enable flexible, multivalent interactions with partners that can be grouped into three functional modules: receptor regulation, intracellular signaling, and cytoskeletal organization. Notably, many SASH1 partners are themselves scaffold or adaptor proteins, allowing integration into pre-existing networks in a hierarchical ‘scaffold-of-scaffolds’ manner. Through selective partner recruitment, SASH1 links cell-surface receptor inputs to downstream signaling pathways and cytoskeletal remodeling. This model provides a mechanistic framework for how SASH1 drives diverse, cell-type-specific outputs across physiology and disease, while revealing broader principles by which multidomain scaffolds encode cellular behavior. Full article

Direction-of-arrival (DOA) estimation is a core research topic in array signal processing, and two-dimensional (2-D) DOA estimation can jointly acquire the azimuth and elevation of incoming signals, which bears great practical value. Traditional subspace and sparse reconstruction algorithms are plagued by heavy computation and deteriorated accuracy under imperfect array manifolds, low signal-to-noise ratios (SNRs) and insufficient snapshots. To enhance estimation robustness and inference speed simultaneously, this paper presents a dual-branch convolutional neural network (CNN) for 2-D DOA estimation based on uniform rectangular arrays. The network takes the sample covariance matrix of array received data as input. A shared feature encoder with residual blocks and channel-attention modules extracts common spatial features, followed by two prediction heads with independent parameters for elevation and azimuth estimation. Because each branch has a 61-dimensional output while two sources may be simultaneously present, the angle estimation is formulated as multi-label classification using sigmoid outputs and weighted binary cross-entropy. Simulations covering diverse SNRs, snapshot counts, angular intervals and off-grid cases verify that the proposed network obtains smaller root mean square errors than methods with multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT) and ordinary CNN methods, with millisecond-level inference latency. This framework offers an efficient, high-precision real-time 2-D DOA estimation scheme for complicated electromagnetic scenes. Full article

Porcine reproductive and respiratory syndrome (PRRS), caused by porcine reproductive and respiratory syndrome virus (PRRSV), is a major infectious disease that poses a severe threat to the global swine industry. To investigate the role of miRNAs in the infection and susceptibility of PRRSV, four miRNA libraries were constructed and sequenced from PRRSV-infected and mock-infected of Tibetan pigs and Large White pigs at 7 days post-infection. A novel miRNA, miR-novel-80, was differentially expressed between PRRSV-infected and mock-infected porcine alveolar macrophages from 2 pig breeds. Importantly, the over-expression of miR-novel-80 inhibited the replication of a PRRSV-1 strain and multiple lineages (L1, L5, and L8) of PRRSV-2 strains in a dose-dependent manner. Bioinformatic predictions and experimental validation demonstrated that miR-novel-80 restricts viral replication through a dual antiviral mechanism. Directly, it targets the PRRSV nsp1-coding region within the ORF1a to suppress viral proliferation. Indirectly, miR-novel-80 specifically down-regulates the expression of host factor CXXC finger protein 4 (CXXC4). This reduction relieves the suppression of the Wnt/β-catenin signaling pathway, which in turn activates NF-κB-dependent innate immune responses to further inhibit PRRSV infection. Collectively, this study investigates the biological characteristics of miR-novel-80 and unveils its underlying molecular mechanisms in restricting PRRSV infection in vitro. However, its biological function and anti-PRRSV therapeutic effect in vivo need further investigation. Full article

Cancer progression is driven by early dysregulation of the immune system and tumor-intrinsic mechanisms. Hypoxia, lactate accumulation and IL-6 signaling induce highly overlapping tumor-promoting effects, including angiogenesis, epithelial–mesenchymal transition, metastasis, immune evasion and treatment resistance, suggesting that these pathways interact and amplify one another. This parallel activation complicates therapeutic targeting, as inhibition of one pathway may be compensated for by another. Increased proteolytic activity emerges early during tumor development and profoundly alters immune regulation. We recently identified a protease-generated albumin fragment, the IL-6-inducing factor (IL-6IF), which triggers pathological IL-6 production. IL-6 in turn enhances both HIF-1α expression and nuclear translocation, promotes glycolysis and lactate production, and forms positive feedback loops with STAT3 and multiple signaling pathways. Together, these mechanisms integrate into a self-sustaining IL-6/HIF-1α/STAT3 axis that drives tumor progression and suppresses anti-tumor immunity. The strong overlap among IL-6, its enhancing loops and hypoxia-driven mechanisms highlights IL-6 as a central regulator of metabolic and immunological reprogramming in cancer. However, a broad IL-6 blockade can impair physiological immune function. Selective inhibition of IL-6IF offers a novel strategy to prevent pathological IL-6 production while preserving physiological IL-6-dependent immune function required for effective tumor control. Reducing pathologically enhanced IL-6 synthesis by targeting IL-6IF, therefore, might represent a potential therapeutic approach to disrupt multiple tumor-promoting pathways simultaneously and may thereby improve responsiveness to cancer immunotherapy. Full article

Left atrium segmentation from magnetic resonance imaging (MRI) is essential for ablation planning in atrial fibrillation; however, clinical MRI quality is often degraded by noise, artifacts, and incomplete spatial coverage, making traditional recurrent neural networks (RNNs) vulnerable to such distortions. We developed a hybrid architecture inspired by cortico–cerebellar interactions to enhance segmentation stability without compromising mean accuracy. We utilized the open ATRIA dataset (100 patients, isotropic 3D MRI scans with manual left atrium annotations). The model comprises a convolutional encoder, a cortical RNN, and a cerebellar predictive module trained to predict future encoder features across multiple temporal horizons, generating a corrective feedback signal for the RNN. Experiments were conducted on unperturbed and degraded datasets with performance evaluated using the Dice coefficient. On unperturbed data, the cerebellar model achieved a mean best Dice of 0.835 ± 0.032 vs. 0.832 ± 0.027 for the baseline. Under degraded conditions, it showed significantly higher Dice (0.815 ± 0.019 vs. 0.801 ± 0.021; p = 0.014) and Surface Dice (p = 0.040), with a directionally lower between-run variance, though this difference in variance was not formally tested given the limited number of runs. nnU-Net achieved higher absolute accuracy but required three orders of magnitude more inference time and an order of magnitude more parameters. The cerebellar module improved boundary accuracy and reproducibility relative to the non-predictive baseline at a fraction of nnU-Net’s computational cost, offering a lightweight alternative for settings where deploying a full 3D self-configuring model is impractical. Full article

Jamming introduces unintentional disruptions in the system to exploit the legitimate communicating equipment. Clustered jamming considers jammers that are present in multiple groups to disrupt the intended communication. Vehicle-to-everything (V2X) transmissions are critical for smart transportation. This research considers full-duplex environment, featuring unmanned aerial vehicles (UAVs) and cellular-base-station-aided V2X (C-V2X) systems exploiting clustered jamming using 3-dimensional (3-D) beam-forming millimeter-wave antennas. UAVs are modeled as a 3-D Poisson point process (PPP), and macro-based tower-mounted base-stations (MBSs) are modeled as a 2-D PPP. Roads are modeled as a Poisson line process. The vehicular nodes (V-Ns) are modeled on each road as a 1-D PPP. The deviations of the UAV’s millimeter-wave band antenna beam follow a Normal distribution. In this paper, for a full-duplex setting, the probabilities of coverage and equipment-association, along with the efficiency of the spectrum associated with various UAV and tower-based connections, are explored in the presence of clustered jamming. The probability of coverage and association of multiple links is derived with respect to the jamming clusters, V-Ns, MBSs, UAVs, jammers’ power, and antenna beams. The results demonstrated that jamming degrades system’s efficiency. This efficiency is further degraded whenever higher 3-D beam-width deviations of the millimeter waveband antenna and jammers are present. Therefore, robust counter-scenarios should be designed for the cases where jamming signals and varying beams disrupt the network. Full article

PV-supplied DC microgrids (PV-DCMGs) are well suited to powering many off-grid applications. Droop control is an effective approach for managing sources in microgrids because it improves system reliability and scalability. However, conventional droop control may not satisfy the requirements of PV-DCMGs, in which loads must connect and disconnect dynamically according to solar-power availability. This paper proposes a droop-based method for operating multiple loads in a PV-DCMG. The line voltage serves as a signal for allocating power among the loads and determining their connection status. A key requirement is a smooth line-voltage transient to preserve stability and avoid unnecessary load connection or disconnection. The paper presents the control logic and procedures for sizing the bus capacitance to achieve effective and stable operation. It also analyzes the effects of key non-idealities in the DC-microgrid model and proposes mitigation methods. Simulation and experimental results demonstrate the effectiveness of the proposed control logic and component-sizing procedures for managing the available power in a PV-DCMG. Full article