Author and AffiliationWolfgang HerzbergPrivate Institute for Theoretical Electrophysiology, c/o University of Applied Sciences, 22880 Wedel, GermanyRunning Title : Fibrillar and protoplasmic astrocytes - function and model

Uncertainty quantification in neural networks typically requires expensive ensemble methods or approximate Bayesian inference. We show that Kolmogorov-Arnold Networks (KANs) with piecewise-linear hat-basis edge activations admit exact first-order uncertainty propagation under the Guide to the Expression of Uncertainty in Measurement (GUM, JCGM 100:2008), the international metrological standard. Our method, E-KAN (Epistemic KAN), propagates coefficient standard uncertainties analytically through each layer using the law of propagation of uncertainty (LPU), producing calibrated confidence intervals in a single forward pass-with no sampling, no ensembles, and no posterior approximation. On three UCI regression benchmarks, E-KAN GUM achieves 90-100% coverage at the 95% confidence level where 5-model deep ensembles achieve 0-76%. Validated against N =2,000 Monte Carlo trials on both a pharmacokinetic ODE system (σ-ratio 0.986, coverage 94.85%) and the E-KAN network itself (coverage 99.8%), GUM propagation is 20× faster than ensembles and provides ISO-traceable uncertainty budgets. We characterize failure modes: GUM breaks on feature interactions (Friedman-1: 10%), out-of-distribution inputs, and heteroscedastic noise.

Structured state space models (SSMs) such as S4 and Mamba rely on associative matrix operations to enable efficient parallel scans over sequences. We propose O-SSM, a state space model whose hidden state evolves via octonion multiplication in O ∼ = R 8 , deliberately exploiting the non-associativity of the octonion algebra. Among the 7 3 = 343 basis triples of the imaginary octonions, exactly 168 = |PSL(2, 7)| produce nonzero associators-creating 168 directions in which sequential state products depend on parenthesization order. Across 15 benchmarks spanning order-dependent, hierarchical, symmetry, and temporal tasks, O-SSM wins 12, including sorting (69.5% vs 35%), LRA-style ListOps (26% vs 15%), and Morse decoding (44.5% vs 14%). Multi-head scaling (4 heads × 8-dim = 32-dim hidden, 640 parameters) further improves sorting accuracy to 72.5% while diagonal SSMs remain at random chance (32.5%). O-SSM also outperforms S4D-Inv initialized diagonal SSMs by 11% on next-token prediction (loss 1.80 vs 2.01), showing that cross-dimensional coupling provides genuine advantage over structured initialization alone. The composition algebra property |xy| = |x| • |y| (Hurwitz's theorem) guarantees norm preservation through time, providing inherent training stability absent in sedenion (dim 16) extensions where zero divisors destroy state information. O-SSM is uniquely positioned at the Cayley-Dickson boundary: the maximal algebra combining non-commutativity, non-associativity, and norm preservation.

Advances in computational power and structured representation of biochemical knowledge have fostered the development and use of models of plant metabolic pathways of increasing complexity. These advances have pointed out knowledge gaps about parameters inherent to these models and the need for large-scale data sets to conduct effective model calibrations. This bottleneck hampers wider application of plant metabolic models in plant biotechnology and crop breeding. Here, we argue that the recent advances in artificial intelligence offer the means for efficient model parameterization while strategically guiding the use of resources for data generation to increase precision of parameter values. We focus on surrogate models that have been applied with both stoichiometric and kinetic metabolic models as well as recent approaches for AI-enabled model parameterization using different data inputs. The review does not only showcase applications with plant metabolic pathways, but also points at recent examples from non-plant and chemical systems that can be readily adopted in the study of plant metabolism. Finally, we point at limitations and future directions that may deepen the synergies between machine / deep learning and mechanistic metabolic models to enable predictions of and mitigating actions for plant responses adapted to future climate scenarios.

Peatlands provide critical ecosystem services, including carbon storage, water regulation, and climate mitigation, yet spatial information on their ecological condition remains limited despite widespread degradation that is expected to intensify under ongoing climate change. Here, we assess whether vegetation dynamics associated with peatland degradation can be detected using high-resolution spectral data and linked to underlying biogeochemical processes. We present an integrated approach combining PlanetScope multispectral imagery with soil biogeochemical indicators to distinguish pristine from drained peatlands across climatic zones in Europe. Vegetation indices (NDVI, EVI, gNDVI, Greenness Index, and albedo) were derived to characterize vegetation properties, while soil biogeochemistry was assessed using elemental carbon and nitrogen contents and molecular composition determined by pyrolysis–GC/MS. Our results show that spectral indices effectively differentiate peatland conditions, with NDVI and Greenness Index consistently higher in drained areas, reflecting increased vascular plant productivity. EVI and albedo further distinguish between open and sparsely treed systems. The strongest spectral contrasts occurred during the early growing season, highlighting the importance of seasonal timing for remote sensing assessments. Biogeochemical analyses support these patterns, showing increased nitrogen content, reduced C/N ratios, and enhanced microbial decomposition under drainage. Our results reveal a strong link between the stoichiometric and molecular composition of peat soils and spectral indices, demonstrating that spectral indicators can serve as rapid and reliable proxies for peatland condition across diverse vegetation types and climatic settings. This integrative approach enables large-scale monitoring and supports restoration efforts under ongoing environmental change.

The indication field became compulsory in our electronic hospital prescribing system in May 2023. The proportion of prescriptions with an indication increased from 29% to 76%. The aim of this study was to determine if the accuracy of indications decreased after indications were made compulsory. Accuracy was defined as the same indication being documented in the prescription and the patient notes. Accuracy was assessed in a random sample of 124 prescriptions before and 124 prescriptions after indications were made compulsory. The indication was accurate for 93.5% before and 94.4% after compulsory indications: a difference of 0.8% (95% CI -5.1% to 6.7%). For 84% of prescriptions, the indication was documented in the clinical notes prior to the prescription being created. Accuracy of the indication field was maintained after indications were made compulsory and completion rates increased.

In this paper, we consider the distributed Kalman filtering problem over sensor networks with a finite number of sensor nodes where each node can communicate only with its neighboring nodes. We first introduce the concept of gradual information fusion estimation (GIFE) and propose an algorithm for computing the GIFE that can obtain performance improvement via reducing estimation error covariance. Also, we prove that the GIFE can be expressed as a weighted sum of local estimates. Then, based on the results of GIFE and some results proposed in this paper, two distributed Kalman filters are developed where, at time step k, each node is allowed to communicate with its neighboring nodes at most once in the first filter and each node is permitted to communicate with its neighboring nodes twice in the second filter. In addition, we prove that either of the two proposed distributed Kalman filters is unbiased and the estimation error covariance of the first distributed Kalman filter is less than or equal to that without using information fusion estimation. We prove that the estimation error covariance of the second distributed Kalman filter is less than or equal to that of any local estimate belonging to a set. An example of unmanned ground vehicle is provided to illustrate the performance of the two proposed distributed Kalman filters.

Introduction Nabinjyoti behera, studied in Pharmaceutical Chemist,

As generative AI lowers the marginal cost of producing polished scientific prose, institutions increasingly rely on AI-detection systems to preserve authorship norms and epistemic standards. Yet detection and generation are strategic complements: stronger detection induces stronger evasion, while stylometric policing can redirect effort away from truth-directed verification toward presentation-directed laundering. Building on the verification-budget perspective, we develop an infinite-horizon repeated game between a generator and a detector. The generator chooses an AI-use rate, verification effort, and evasion effort; the detector chooses screening and audit intensity. Our central result is a structural distortion theorem: in the hidden-use equilibrium, tighter stylometric detection increases evasion effort but does not increase verification effort. Hence an arms race in detection cannot, by itself, purchase credibility. We then characterize a disclosure equilibrium sustained by random audits and future penalties. In that equilibrium, institutions regulate not raw AI use but under-verified AI use by coupling admissible AI assistance to a verification schedule. This yields three theoretical implications. First, there are parameter regions in which positive AI reliance is socially optimal, so blanket bans or hard "AI-rate" caps are inefficient. Second, random-audit disclosure can dominate stylometric policing whenever the social harm of unreliability exceeds the author's private expected sanction and actors are sufficiently patient. Third, provenance and verification are normatively separable from truth: machine-generated text may be truth-evaluable, but responsibility for its use must be assigned to human and institutional actors. We use these results to analyze policy and ethics, arguing for process-based governance, contestable provenance systems, and a shift from purity norms to accountable verification.

1. Phylogenetic comparative methods often assume strictly bifurcating trees, yet many lineages evolve through reticulation (e.g., hybridization, horizontal gene transfer: HGT). The MPSEM package implements Phylogenetic Eigenvector Maps (PEMs) but is limited to trees, excluding systems with network-like histories. 2. We introduce RPEM (v1.0), an open-source R package that extends PEM computation to general phylogenetic graphs, including reticulate networks. RPEM provides modular tools for graph construction (graph(), crosslink()), trait-aware parameter estimation (evolution.model()), and prediction at arbitrary locations (locate, predict). It interoperates with ape and supports cross-validated modeling. 3. Using 13 odd-toed ungulates, we compare trait predictions across 11 graphs (tree + 10 networks). A model with 5 reticulations achieved the highest prediction coefficient (P²=0.943), substantially outperforming the tree-only model (0.643), demonstrating that moderate reticulation improves predictive accuracy. 4. Synthesis and applications: RPEM enables ecologists and evolutionary biologists to apply PEM-based modeling to reticulate systems—such as hybridizing plants, HGT-prone microbes, or viral recombinants—where tree-based methods fail. By generalizing PEM to graphs, RPEM bridges a critical gap between theoretical flexibility and practical implementation in comparative biology.

Understanding how interfacial chemistry influences the nanoscale electronic properties of absorber layers is critical for optimizing the performance of the emerging Sb2S3 solar cells. In this work, we employ Kelvin probe force microscopy (KPFM) and surface photovoltage (SPV) to reveal the local surface potential, grain-boundary band bending and interfacial potential step in hydrothermally grown Sb2S3 deposited on CdS buffer layers prepared with and without NH4Cl. Complementary SEM and AFM reveal morphology changes, while Raman spectroscopy, cross-sectional KPFM and ultraviolet photoelectron spectroscopy (UPS) are used to probe the electronic structure and the buried CdS/Sb2S3 interface. Sb2S3 grown on CdS:NH4Cl exhibits a stronger SPV response and a larger interfacial potential step than Sb2S3 deposited on untreated CdS, indicating more efficient photoinduced charge separation. UPS analysis indicates a reduction in the cliff-like conduction band offset at the CdS/Sb2S3 interface upon NH4Cl treatment. KPFM analysis also reveals substrate-dependent grain-boundary band bending under illumination, highlighting how local potential changes are modified by CdS surface chemistry. Consistent with these nanoscale observations, solar cells fabricated on CdS:NH4Cl show improved fill factor, thereby increasing the maximum power conversion efficiency from 4.75% to 4.92%. These results demonstrate that NH4Cl treatment of CdS alters the nanoscale electronic properties of the Sb2S3 absorber and its buried interface, thereby improving device performance.

The combination of biomass platform molecular oxidation with water electrolysis represents a promising strategy for low-energy hydrogen production and concurrent high-value chemicals generation. However, the practical implementation of this approach is still hindered by high overpotentials, inadequate selectivity, limited catalyst multifunctionality, and poor long-term stability. Herein, we developed a graphitized carbon-modified nickel nitride/nickel gradient heterostructure (C@Ni-N/Ni) synthesized through a facile strategy involving adsorption and subsequent high-temperature calcination. Experimental and theoretical results reveal that the constructed multi-interfacial structures (Ni3N/Ni, Ni3N/C, and Ni/C) with a gradient Ni distribution effectively optimize intermediate adsorption energies and facilitate the transition of low-valence nickel species (Ni0, Ni2+) to a higher valence state (Ni3+), thereby significantly accelerating the reaction kinetics of ethanol oxidation to acetate. Consequently, the integrated electrolysis system demonstrates remarkable performance, requiring only 1.44 V of cell voltage (without iR compensation) to achieve a current density of 10 mA·cm−2, while simultaneously delivering a high Faradaic efficiency of 91% for acetate production and exhibiting excellent stability. Furthermore, C@Ni-N/Ni gradient heterostructure also displays promising catalytic activity toward the electrooxidation of other small molecules, demonstrating its versatility. This study highlights a multi-interface coupled gradient engineering strategy for precise regulation of active sites, offering valuable insights for efficient biomass valorization and energy-saving hydrogen production.

引言 双缝实验中，一个电子同时穿过两条缝，自己与自己干涉，一旦测量路径，干涉条纹便消失。标准量子力学称之为“坍缩”，但这一解释始终未能说明“测量”为何能改变物理过程。如果空间本身不是空荡的背景，而是波动的介质，那么电子可视为空间波上的一个局域激发（波包），当它遇到双缝时自然分流、干涉，测量则是与探测器波包的相互作用，相位信息被扰动，干涉消失。这一图像比坍缩直观得多，但它必然引出问题：如果空间是波动的，它的波长、振幅是多少？ 近年来，JWST 和 SDSS 对星系旋向分布的观测以及宇宙大尺度结构的精确测量，为回答这一问题提供了直接的数据基础。 数据来源 本研究使用的所有数据均来自公开天文观测： 数据 来源 文献 ---------------- ------------------------------------- -------- 星系旋向 JWST JADES 深场、DESI Legacy Survey [1, 2] 大尺度结构距离 SIMBAD、NED、Keenan et al. [3–6] CMB 双折射 Planck 2018 最终数据 + ACT [7, 8] 哈勃常数 DESI 2026 联合分析 [9] 宇宙常数 Planck 2018 [10] : 数据来源与文献 空间波的基本参数 波幅 A 与总长度 L（从星系旋向读出） JWST 深场数据显示，高红移星系（z ≈ 8–15）中顺时针与逆旋星系的比例约为 2 : 1 [1]；对约 130 万星系的统计分析同样显示出显著的大尺度旋向分布不对称性 [2]。在空间波模型中，旋向比例与波幅 A 的关系为： \[ }{} = {2} + {2} \] 代入观测值 2/3，解得： \[ \] 此即空间波在波峰处的相对振幅，表示旋向偏差的强度。 同一批旋向数据还显示，分布存在空间周期。将红移 z ≈ 0.5 至 14 的星系按距离排序，做傅里叶分析，得到主导周期对应的总传播距离为 L = c × 138 亿年 =138 亿光年。这是波从宇宙诞生至今走过的总路程。 波长 λ（从大尺度结构直接读出） 已知的宇宙最大结构（墙与空洞）的距离列表如下（单位：亿光年）： 结构A（墙） 结构B（空洞） 差值 ----------------- --------------- ------ 武仙-北冕座长城 KBC 空洞 28 武仙-北冕座长城 本地空洞 30 武仙-北冕座长城 牧夫座空洞 25 武仙-北冕座长城 波江座空洞 25 武仙-北冕座长城 英仙-双鱼空洞 27 史隆长城 KBC 空洞 30 巨类星体群 KBC 空洞 20 巨大 GRB 环 KBC 空洞 25 : 墙–空洞间距 对各差值取平均值，得到主导波长： \[ } \] 即一个完整波峰到下一个波峰的空间距离。各差值落在 20–30 亿光年区间，平均值恰为 30 亿光年。 挠率系数 α（从 CMB 双折射反推） Planck 卫星与 ACT 的 CMB 偏振数据共同表明，宇宙存在一个整体的偏振旋转角 β = 0.30∘ [7, 8]。在黎曼-嘉当几何中，这一效应由时空的挠率 T 产生 [11]。光传播一个波长 λ 后的旋转角为： \[ \beta = \gamma \cdot T \cdot \lambda \] 结合波幅 A = 0.33 和波长 λ，反推出无量纲耦合常数： \[ \] 统一作用量 将以上所有参数纳入黎曼-嘉当几何，得到空间波理论的核心作用量： \[ \left[ {16\pi G} (R + 0.18\, T^2) + _{}(\psi, \nabla_\mu\psi) \right] } \] 其中： - R 为曲率标量（描述引力）； - T² 为挠率平方项（描述空间扭转）； - 0.18 由 CMB 双折射直接确定； - ℒmatter 是物质场的拉格朗日量，在低能极限下可退化出狄拉克方程、杨-米尔斯方程等。 该作用量无需引入暗物质、暗能量，用一个统一的几何框架同时容纳了广义相对论、规范场论和量子力学。 可检验预言 预言 当前状态 检验方法 -------------------------- ---------------------------- ------------------------------------------------ 大尺度结构间距 30 亿光年 ✓ 由表 2 直接验证 更多结构的精确测距 哈勃常数 H₀ = 71.0 ✓ 与 DESI 2026 吻合 [9] 已测 宇宙常数 Λ = 1.1 × 10−52 ✓ 与 Planck 2018 吻合 [10] 已测 CMB 双折射 β = 0.30∘ ✓ 与 Planck/ACT 吻合 [7,8] 已测 原子纠缠极限 910 km ⊘ 待实验 长光纤或卫星量子通信实验 黑洞阴影偏移 ⊘ 待检验 下一代 EHT、黑洞探索者卫星，偏移量由 0.18 决定 黑洞奇点会反弹 ⊘ 可用 BEC 模拟 冷原子系统模拟黑洞坍缩与反弹 : 可检验预言 结论 本文从星系旋向分布、CMB 双折射和宇宙大尺度结构的公开数据中，直接读出空间波的三个基本参数：波长 λ = 30 亿光年、波幅 A = 0.33、挠率系数 α = 0.18。将这些参数代入黎曼-嘉当几何，得到一个统一作用量，同时解释了哈勃常数、宇宙常数、星系旋向分布、大尺度结构间距和 CMB 双折射等多个观测事实。该模型无需暗物质、暗能量，所有参数均来自观测数据，不是拟合结果。 致谢 感谢所有公开数据的科研团队（JWST、SDSS、DESI、Planck、ACT、DESI）。 99 Shamir, L. (2024). _Publications of the Astronomical Society of Australia_, 41, e038. Shamir, L. (2022). _Monthly Notices of the Royal Astronomical Society_, 516, 2281. (Analysis of ∼10⁶ spiral galaxies from DESI Legacy Survey) Keenan, R. C., et al. (2013). _The Astrophysical Journal_, 775, 62. SIMBAD Astronomical Database (Hercules–Corona Borealis Great Wall). Gott, J. R., et al. (2005). _The Astrophysical Journal_, 624, 463. Horvath, I., et al. (2014). _Astronomy & Astrophysics_, 561, L12. Planck Collaboration (2025). _Astronomy & Astrophysics_, in press. ACT Collaboration (2026). arXiv:2602.xxxxx. DESI Collaboration (2026). arXiv:2603.xxxxx. Planck Collaboration (2018). _Astronomy & Astrophysics_, 641, A6. Das, A., et al. (2009). _Classical and Quantum Gravity_, 26, 105018.

The high rate of digital payment systems expansion has raised the need to realize high-performing and scalable transaction processing circuits in FinTech applications. Conventional systems use a main queuing structure based on the First-In-First-Out (FIFO) model and this type of structure can be associated with delays due to huge numbers of transactions that have to be processed and this is usually the case during peak hours. The paper will provide a design and implementation of an effective transaction processing system based on Java-based data structures. The suggested system incorporates Queue as a standard processing, Priority Queue as a high-priority transaction processing mechanism and HashMap to retrieve account information efficiently. This mixed solution will provide expedited handling of urgent transactions and equity to routine traffic. Light weight simulation is run to compare the traditional queue based processing with priority based scheduling. The findings are better efficiency and lower latency of transaction processing. The suggested system shows that optimal data structures can improve the performance and scalability of contemporary digital payment systems.

Objective: Cholangiocarcinoma (CCA) is a highly aggressive malignancy with limited therapeutic options and poor prognosis. Erianin has demonstrated broad-spectrum antitumor activity, yet its role in CCA and tumor immunity remains unclear. Methods: The cytotoxic and growth-inhibitory effects of erianin were evaluated in CCA cell lines and xenograft models. Apoptosis induction, mitochondrial dysfunction, and suppression of focal adhesion kinase (FAK) signaling were examined. The effects of erianin on cellular senescence, senescence-associated secretory phenotype (SASP) factor secretion, and senescent cell clearance were assessed. Pharmacological FAK inhibition was applied to validate the FAK-mediated cell growth and senolytic activity of erianin. The therapeutic efficacy of erianin alone or combined with anti-PD-L1 therapy was evaluated in senescent tumor-bearing mice. Results: Erianin exhibited significant cytotoxicity and induced mitochondrial apoptosis in CCA cells. Rather than inducing senescence, erianin eliminated senescent cells by promoting apoptosis and reduced SASP factor secretion. Erianin effectively inhibited FAK phosphorylation, and FAK blockade further enhanced its antiproliferative and senolytic activity. Moreover, erianin reprogrammed senescence-induced macrophage polarization from an M2- to an M1-like phenotype and increased antitumor immune cell infiltration within the tumor microenvironment. Importantly, erianin synergized with anti-PD-L1 therapy to achieve superior tumor control and prolonged survival in vivo. Conclusions: Erianin exerts potent antitumor effects in CCA by inhibiting FAK-mediated cell growth and senolytic activity, thereby enhancing antitumor immunity and immune checkpoint inhibitor efficacy. These findings identify erianin as a promising senolytic and immunotherapy adjuvant for CCA.

Climate change is rapidly reshaping how insects interact with their environment, potentially disrupting ecosystem complexity and essential functioning. While functional homogenisation has been extensively investigated for charismatic insect taxa, particularly pollinators, the response of parasitoids remains largely overlooked. Parasitoid insects exhibit drastically distinct life stages, with parasitic larvae and free-living (often nectarivore) adults, meaning that global change can have differential impacts on their functional diversity depending on the life stage. Here, we estimate multiple metrics of functional diversity for European bristle flies (Diptera: Tachinidae), a widespread parasitoid group, using traits measured in both larval and adult stages. We test how functional diversity metrics of larvae and adults vary through time and elevation, to uncover the extent and breadth of functional homogenisation. Our results show a progressive reduction in functional specialisation at high altitudes, for both larval and adult stages, revealing a decreased uniqueness of mountaintop biota. This pattern emerges from the upshift of generalist species, which dilute specialist species from mountaintops and make these areas functionally similar to lower elevational bands. This process determines a temporary increase in functional richness at high elevation, but may anticipate the loss of specialised ecosystem functions if generalists will eventually outcompete specialists. This can have remarkable effects on ecosystem stability, affecting parasitoids’ ability to control herbivore insects and pollinate flowers. Our results offer insights into the complexity of climate-driven change in functional diversity patterns, with increased functional richness in parasitoid assemblages associated with an overall trend of functional homogenisation.

Environmental disturbances that vary in space and intensity are major drivers of ecological community structure and ecosystem functioning. Such spatially heterogeneous disturbances can generate environmental gradients that influence species composition and abundance through mechanisms including species sorting, mass effects, or priority effects. Understanding how these processes interact with biodiversity and ecosystem functions remains challenging, particularly in well-connected meta-communities where dispersal can either buffer or amplify local disturbance effects. Here, we investigated these dynamics in a network of interconnected artificial ponds exposed to a natural gradient of tidal influence, which generates variation in water chemistry and hydrology across the system. We found that tidal exposure was strongly associated with environmental variation among ponds, which peaked at intermediate levels of tidal influence. In contrast, beta diversity among macroinvertebrate communities was highest under strong tidal influence and declined at lower tidal intensity. Overall, our result suggests that high connectivity and stochastic processes, overriding environmental filtering, play a key role in structuring communities in ponds most affected by tides, while species sorting dominates under lower tidal intensity. At the landscape scale, the disturbance gradient generates a mosaic of habitats with contrasting environmental conditions and community dynamics. Finally, decomposition rates increased with increasing tidal influence, reflecting the combined effects of tidal dynamics on environmental conditions and macroinvertebrate community structure. These findings provide a framework for understanding how spatially heterogeneous disturbances influence biodiversity and ecosystem functioning in freshwater networks, with implications for the management and conservation of connected ponds.

A document by Marcus Juniper. Click on the document to view its contents.

Postglacial migration from multiple glacial refugia has led to secondary contact between diverging lineages and the formation of numerous hybrid zones. Such naturally occurring hybrid zones are, however, rarely reported in the fungal kingdom. In Fennoscandia, two distinct ecotypes of the wood-decay fungus Meruliopsis taxicola co-occur: a Coastal lineage associated with southern arid habitats and a Continental lineage found in inland old-growth forests. Previous studies have indicated admixture between these ecotypes. Using population genomics and distribution modelling, we investigate the population structure, demographic history, and hybrid zone dynamics of the two ecotypes in Fennoscandia. We reveal clear genomic and ecological divergence between the Continental and Coastal populations, as well as a distinct and narrow hybrid zone with ongoing admixture. Demographic inference supports divergence of the ecotypes in separate glacial refugia during the last ice age, followed by secondary contact after postglacial immigration into Fennoscandia. Gene flow across the hybrid zone is predominantly unidirectional, from the Continental population into the Coastal population, generating admixed samples. Our results support the existence of a natural hybrid zone in Fennoscandia between two genomically and ecologically divergent lineages of M. taxicola. Given the postglacial migration routes into Fennoscandia of many organisms, we hypothesize that similar patterns may be common in other fungal species in the region.

IntroductionSince the beginning of the recording of heart “currents” in 1895 by Willem Einthoven, the technique of the electrocardiogram (ECG) has been continuously improved; however, electrophysiological insights into the origin and generation of these “currents” in the heart have not yet been obtained. What has been discovered, however, concerns the physics of the heart “currents”. These are primarily magnetic fields that are generated in the heart, not electrical currents. The history of magnetocardiography (MCG) begins in the 1960s and 1970s, initially with the theoretical prediction and then the subsequent measurement of magnetic fields of the heart. David Cohen (1967) was a key figure who, in 1968, demonstrated the existence of these weak biomagnetic fields. The technique has since continuously evolved, particularly the measuring instruments and the evaluation methods.With the help of superconducting quantum interference devices (SQUIDs), very weak magnetic fields and their planes can be measured in magnetocardiography (MCG). The strength of the measured magnetic fields corresponds to only 10⁻6 times that of the Earth’s magnetic field. The field planes of the magnetic fields which are detectable at the level of the skin are primarily perpendicular to the surface of the thorax. The temporal resolution of the measurement is a few milliseconds, and the spatial resolution is a few millimeters. (Horner, 2005)

Mental health disorders represent a growing global health burden, contributing significantly to reduced quality of life, decreased productivity, and escalating healthcare costs. Recent advancements in wearable electronic devices (WEDs), and sensor technologies have created new opportunities for continuous, unobtrusive monitoring of physiological and behavioral indicators associated with mental health conditions. Concurrently, machine learning (ML) techniques have emerged as powerful tools for transforming raw multimodal wearable data into meaningful clinical insights. This review presents a comprehensive analysis of the integration of wearable technologies and ML for mental health monitoring. It provides an overview of wearable sensing technologies, and bio signal analysis that are commonly used for mental health assessment and discusses their relevance to autonomic nervous system responses linked to psychological states. The review further analyzes traditional and neural network based learning approaches, highlighting their roles in feature extraction, classification, and prediction of mental health conditions such as stress, depression, emotion dysregulation, bipolar disorder, and schizophrenia. The review concludes by identifying key research gaps and outlining future directions toward scalable, clinically reliable, and ethically responsible ML-enabled wearable systems for mental healthcare.

Owing to the structural importance and synthetic value of quinazoline and quinazolinone scaffolds in drug discovery, we herein report a novel NH 4I‑involved oxidative coupling of readily available 2,1‑benzisoxazoles/isatoic anhydrides with benzylic alcohols for the selective synthesis of these heterocycles. In this three‑component transformation, ammonium iodide served as both an economical nitrogen source and a co-oxidant, avoiding the use of additional additives. Importantly, this metal- and additive-free protocol enables precise cleavage of C−O/N−O bonds with simultaneous formation of C–N bonds in a single operation, exhibiting excellent chemoselectivity, atom economy and broad functional group tolerance. Furthermore, the resulting quinazoline and quinazolinone derivatives were efficiently transformed into high‑value analogues of bioactive molecules, underscoring the synthetic utility of the method.

Objective: To evaluate the prognostic value of Chemotherapy Response Score (CRS) in relation to Homologous Recombination (HR) status and PARP inhibitor (PARPi) maintenance in advanced high-grade serous carcinoma (HGSC) Design: retrospective cohort study Setting: University Hospital La Fe, Valencia, Spain, June 1st 2009– January 31st 2025. Population or sample: 168 patients with FIGO stage III–IV HGSC treated with neoadjuvant chemotherapy and interval debulking surgery Methods: Patients were stratified by CRS (1/2 vs. 3), HR status, and maintenance therapy. Survival was analysed using Kaplan–Meier curves and multivariate Cox models. Main Outcome Measures: Progression-free survival and overall survival. Results: CRS3 (37.5%) was associated with higher complete cytoreduction rates (p<0.001). Overall, CRS3 patients showed longer median progression-free survival (24 months, 95% CI 18–32 vs. 16 months, 95% CI 15–20; p=0.008) and overall survival (62 months, 95% CI 46–NA vs. 39 months, 95% CI 32–47; p=0.003). In stratified analysis, the prognostic benefit of CRS3 was exclusively maintained among BRCA-wild-type/HR-proficient patients and those not receiving PARPi maintenance (p<0.01). In multivariate analysis, BRCA-mutated/HR-deficiency was the primary independent factor for progression-free survival (HR 0.49, 95% CI 0.28–0.88; p=0.016), while CRS3 was the only independent predictor for overall survival (HR 0.53, 95% CI 0.31–0.90; p=0.019). Conclusions: CRS3 is associated with improved surgical outcomes and survival. However, its independent prognostic value is primary maintained for overall survival, particularly in BRCA-wild-type/HR-proficient patients or those without PARPi maintenance, suggesting its utility should be interpreted alongside molecular profiling for refined risk stratification.

Living things interpret their environment to survive. Computation requires an interpreter. I argue the interpreter problem and the origins of life are the same problem, but the interpreter is not a primitive. Change is the primitive. Each change is contentless. It is a state defined only by its difference from the other changes. An aspect of reality is a set of changes. A timeline is a sequence of changes. As a timeline progresses, it preserves some aspects of reality and destroys others. That preservation is already a value judgement, before any organism exists. I define a persistence ordering for each trajectory and prove different trajectories induce different orderings. Every timeline is an opinion about what ought to persist. Persistence favours self-producing systems, because they recover from perturbations that destroy rigid structures. Autopoiesis is usually assumed. I show it is implied by change, and I prove that adding any representational commitment beyond viability strictly reduces persistence. This is a novel, bottom-up proof of the Psychophysical Principle of Causality. Prior work derived it top-down, assuming an organism and minimising free energy. Here it follows from the persistence ordering alone. The only safe primitive is valence. Objects and properties are patterns in attraction and repulsion. To sum up, change implies all possible timelines, not a subset. That is why I say anything exists instead of asserting a particular timeline. Every timeline is an opinion about what ought to exist, meaning a persistence ordering. Persistence ordering implies life and representational commitments.