Background and Purpose The calcium ion (Ca²⁺) is a versatile cellular messenger regulating various biological processes. Compounds modulating subcellular Ca²⁺ signals hold substantial pharmacological potential. Advances in fluorescent biosensors have revolutionized Ca2+ imaging. Still, co-expression of targeted biosensors for simultaneous measurement of Ca2+ signals in multiple cellular compartments is complicated by heterogeneous expression levels of the various sensors. Experimental Approach Here, we introduce CARMEN, a ribosomal skipping-based quad-cistronic fluorescent biosensor system that enables high-content Ca²⁺ imaging across three compartments. CARMEN facilitates equal co-expression of spectrally distinct Ca²⁺ biosensors: the near-infrared Ca²⁺ biosensor for the cytosol (NIR-GECO2G-NES), the green Ca²⁺ biosensor for mitochondria (CEPIA3mt), the red Ca²⁺ biosensor for the endoplasmic reticulum (R-CEPIA1er), along with a Ca²⁺-insensitive blue fluorescent protein targeted to the nucleus (NLS-mTagBFP2), serving as a normalization reference. Key Results CARMEN allows spatiotemporal correlation of Ca²⁺ signals across the cytosol, ER, and mitochondria, revealing distinct dynamics. We noted delayed mitochondrial Ca²⁺ uptake compared to the other compartments. We validated CARMEN across three cell types and tested two recently identified mitochondrial Ca²⁺ uniporter inhibitors (MCUis), MCUi4 and MCUi11, showcasing the potential of CARMEN for its application in pharmacological research. Our results show that while both MCUi4 and MCUi11 inhibited mitochondrial Ca²⁺ uptake in HeLa S3 cells, MCUi4 reduced cytosolic Ca²⁺ signals and oscillations, whereas MCUi11 had opposing effects. Conclusions and Implications CARMEN is a powerful tool for real-time, multiplexed analysis of compartment-specific Ca²⁺ signals, with the potential for automation in high-content drug screening.

Partial Differential Equation (PDE) models have emerged as a powerful tool in various domains due to their ability to capture dynamic relationships among complex variables. In the field of linguistics, particularly in English reading corpora, the technology has shown immense potential for the detection of semantic and syntactic patterns, enabling significant advancements in text analysis and natural language processing. However, existing methods for text analysis often fail to adequately model the intricate dependencies and multi-dimensional relationships inherent in large datasets. To address these challenges, this paper proposes a novel PDE-based framework tailored for English reading corpora. Current methods, such as neural networks and statistical models, are limited by their reliance on large datasets, lack of explainability, and inability to integrate structural and temporal linguistic features seamlessly. The proposed scheme uses the strengths of PDEs to overcome these limitations by introducing a mathematically rigorous and interpretable approach to text modeling. This paper adopts a methodology rooted in PDE formulations. Firstly, linguistic features, including lexical, syntactic, and semantic attributes, are extracted and mathematically represented. Secondly, a set of PDEs is designed to model these features’ temporal and spatial dynamics across the text corpus. Thirdly, numerical methods such as finite difference and finite element techniques are employed to solve the PDEs, yielding insights into the structural and semantic evolution of the corpus. Finally, a comparative evaluation is performed to assess the model’s performance against traditional and neural network-based approaches. The experimental results show the efficacy of the proposed framework, with significant improvements observed in accuracy (94.1%), precision (90.2%), recall (92.1%), F1 score (91.1%), and AUC (0.945), alongside competitive execution times. Compared with state-of-the-art methods, such as CNNs, LSTMs, and SVMs, the proposed PDE model consistently outperforms predictive accuracy and interpretability, showcasing its potential as a transformative approach for text analysis in English reading corpora.

Transcription factor TEOSINTE BRANCHED1, CYCLODEA, PROLIFERATING CELL FACTORS 4 (TCP4) plays essential roles in plant development processes.The role of TCP4 in the regulation of hypocotyl growth remains a matter of debate.Our results cast doubt on the previous claims that TCP4 directly activates YUC5 expression to promote hypocotyl elongation, and clearly show that TCP4 promotes hypocotyl elongation dependent on PIF4/5.

Plant growth-promoting rhizobacteria (PGPR) are important due to their biostimulant activity. However, most studies focus on epigenetic mechanisms, leaving the microscopic and molecular aspects of multistrain coordination unexplored. In this study, seven PGPR strains were isolated from mangrove forests. These bacteria exhibited various traits such as phosphate solubilization, indole acetic acid production, and siderophore generation. The combination of Sinorhizobium sp. N2-2 and Pseudomonas putida S5 showed the most prominent PGPR activities. Pot experiments and omic-based analyses revealed that PGPR strains significantly promoted the growth of pakchoi and influenced soil physicochemical properties and sucrose enzyme activity. Metagenomic analysis showed that PGPR strains altered the rhizosphere microbial community, enriching genes related to nitrogen cycling, phosphorus transformation, hormone signal transduction, and carbon utilization. Transcriptomic analysis indicated increased energy metabolism and antistress abilities in the host plant. The results suggest that the “composite PGPR” co-promoted plant growth by driving transformations in rhizosphere nutrient cycling, regulating microbial metabolism, and balancing plant physiology. This study provides insights into the coordination between PGPR strains and highlights their potential for sustainable agriculture development.

Transition-metal-catalyzed cross-coupling of electrophile containing multiple potential coupling sites is often challenging. A straightforward cross-electrophile coupling of aryl iodides and aryltriazenes via photoredox/palladium dual catalysis was developed. The reaction was carried out by using Pd(OAc) 2 as catalyst, Cu(OAc) 2 as promoter, aryltriazenes as arylation reagents and base, various biaryl compounds were afforded under mild conditions. This synergistic method features photoredox/Pd dual catalysis, easily available substrates, gram scale synthesis, synthetic applications, mild, neutral and base-free conditions.

Ticks are one of the most dominant forced ectoparasites of vertebrates, belonging to the arthropods, which transmit pathogens such as bacteria, viruses, and parasites to humans and animals in Iran and worldwide. Given that sex ratio factors can affect the epidemiology of vector-borne diseases, this study aimed to identify and determine the ticks’ sex ratio and host type (camels, sheep, cattle, dogs, chickens, and pigeons) in different areas of Tehran Province. This descriptive cross-sectional study took samples from different parts of the animal’s body in four seasons from 20 villages in 2019, in which 685 hard ticks and 121 soft ticks were caught from 1623 studied livestock and poultry. Regarding sex segregation among all caught ticks, 42.01% were male, and 57.99% were female. It is noteworthy that in both mountain and plain environments, R. sanguineus sensu lato species of hard ticks had the most elevated sex ratio. Most ticks were collected from sheep hosts, accounting for 60.04% of infestations, while the lowest infestation rate was found in cattle hosts at 0.62%.

Biomass-derived carbon materials are receiving much attention for supercapacitor applications due to their well-developed porous structures, large specific surface areas, good conductivity, and environmental sustainability. In this study, we present a simple and scalable green strategy to prepare nitrogen (N)-doped biocarbon materials from naturally decayed wood (rotten wood, RW), which is to take advantage of the intrinsic porous nature of wood and the structural modifications induced by microbial activation (e.g., surface oxidation and nitrogen incorporation). To further increase the doped nitrogen content, we used aqueous ethylenediamine (EDA) solution for activating and immersing pre-treated RW. The N-doped biocarbon samples show uniform nitrogen distribution and favorable graphitization, resulting in outstanding supercapacitor performance. The optimized sample, RW-1000, exhibits a high specific surface area of 1204 m 2·g -1 with a rational pore structure. When applied as a supercapacitor electrode, RW-1000 demonstrates excellent electrochemical properties, including a specific capacitance of 448 F·g -1 at 0.2 A·g -1 and remarkable stability, retaining 95% of its capacitance after 10,000 cycles. This exceptional energy storage performance is attributed to the RW-1000-derived electrode’s high specific surface area, optimal pore size distribution, and well-dispersed nitrogen content. Thus, our work offers a sustainable, scalable and facile strategy for transforming biomass waste into valuable biocarbon materials for high performance supercapacitors.

Japan has adopted biodiversity-oriented forest management, necessitating the diversification of extensive conifer plantations and the identification of geographic conditions that favor natural hardwood regeneration. The increasing availability of high-resolution airborne laser scanning (ALS) data provides new opportunities to analyze spatial patterns in forests. In this study, we applied exploratory approaches to quantify the prevalence of natural hardwood regeneration within mature conifer plantations in Kochi Prefecture, southwestern Japan. Hardwood regeneration was defined as areas recorded as conifer plantations in forest registry data (2005–2009) but dominated by hardwoods based on ALS data collected in 2018. Across postwar afforestation sites (1949–1978 planting), hardwood regeneration consistently occupied 20–25% of the total area, regardless of the planting year. Using a logistic generalized additive model, we found that hardwood regeneration was favored on slopes steeper than 40° and in ridges and valleys. In the low-elevation zone (< 600 m a.s.l.), where evergreen Castanopsis and Quercus species were the dominant vegetation, the likelihood of finding hardwood regeneration increased with decreasing elevation and greater southern slope exposure. This trend was particularly evident within specific geologic zones. Spatial analyses to identify site characteristics that favor natural hardwood regeneration could be used to support biodiversity-oriented forest management. Furthermore, high-resolution ALS data that will soon be publicly available hold significant promise for uncovering geographic patterns and generating novel insights on forest ecosystem dynamics.

This paper provides an assessment of the shoreline changes, which occurred during 1966 2004 in the coastal regions of the Gulf of Khambhat using RESOURCESAT-1 LISS-III (Linear Imaging Self Scanner) data. The remote sensing observations of the Gulf show a large area facing a serious threat due to dramatically changing shorelines. The entire coast around the Gulf has undergone accretion except some parts of the northern region and a few areas near the Mahi and Dhadhar estuaries where erosion is observed. These significant landform changes have occurred in four decades. The study has also shown the potential of the enhanced radiometric resolution satellite remote sensing data to be utilized for preparing good quality cartographic maps with improved shorelines leaving behind old maps prepared using conventional geodetic/ground surveys. The detection/quantification of shoreline change in the Gulf of Khambhat using advanced/latest models is proposed as an avenue of further research.

We establish foundational results at the intersection of infinite-dimensional optimization, spectral geometry, and adaptive mesh refinement. By analyzing the interaction between saddle maps, Lie group symmetries, and curvature-driven refinement in densified sweeping nets, we present: (1) a conjectured logarithmic relationship between refinement complexity and saddle spectral gaps, (2) a symmetry-saddle correspondence lemma linking unstable manifold codimension to broken symmetries, and (3) a stochastic avoidance theorem guaranteeing almost-sure escape from spectral saddles. The introduced _saddle codimension operator_ provides a spectral measure of instability, while numerical experiments validate the theoretical framework.

Enhancing the stability of piezoelectric properties is essential for ensuring the reliability of high-temperature piezoelectric sensors. In this study, we have synthesized AlN piezoelectric crystals as representative materials and employed first-principles methods to investigate their temperature-dependent piezoelectric properties. By integrating the effects of lattice expansion (LE) and electron-phonon interactions (EP), we accurately constructed the crystal structure of AlN across a wide temperature range and successfully predicted its piezoelectric behavior. Theoretical analysis reveals that ion polarization driven by lattice distortion and elastic softening of chemical bonds maintains the overall structural integrity of defect-free AlN single crystals, resulting in a stable piezoelectric coefficient d33 with a deviation of only 8.55% at temperatures up to 1300 K. However, experimental results indicate that the stability of piezoelectric performance of the grown AlN crystals is disrupted at temperatures above 870 K. This temperature limitation is attributed to point defects within AlN crystals, particularly those caused by oxygen-substituted nitrogen (ON). These findings provide valuable guidance for enhancing the piezoelectric temperature stability of AlN crystals through optimized experimental conditions, such as oxygen atmosphere treatment and defect modification during crystal growth.

This study investigates the effect of alkali treatment on the physical, thermal, flexural and thermos-mechanical properties of Borassus husk fibre-reinforced epoxy composites according to different authorized standards. Composites were fabricated using the hand layup process with untreated and alkali-treated Borassus fibres (0.25-2 hours treatment duration). The results revealed that alkali treatment significantly improved fibre-matrix adhesion, aided by scanning electron microscopic (SEM) images, leading to enhanced composite performance. The treated fibre composites exhibited lower moisture regain (0.57%-1.28%) and water absorption (0.59%-1.55%) compared to untreated composites, demonstrating improved moisture resistance. Thermal stability increased with alkali treatment, as evidenced by higher integral procedural decomposition temperature (IPDT) values, reaching 547°C for 2-hour treated fibre composites. Additionally, the glass transition temperature (Tg) improved, peaking at 94.5°C for 0.5-hour treated fibre/epoxy. Mechanical properties, including flexural modulus (up to 3.2 GPa) and strength (up to 108.7 MPa), surpassed many conventional bio-fibre composites, making these composites suitable for structural applications compared to existing conventional bio-fibres based composites. Dynamic mechanical analysis indicated superior damping properties (tanδ, up to 1.21), highlighting their enhanced energy dissipation and impact resistance. Among the treated composites, the 0.5-hour alkali-treated Borassus husk fibre/epoxy composite (0.5TBHFE) demonstrated an optimal balance between stiffness and damping, making it a promising material for aerospace and automotive applications. The study underscores the potential of Borassus husk fibres as a sustainable reinforcement alternative in high-performance composite applications. However, further optimization and industrial-scale processing strategies are required to fully harness their potential.

We investigate the topological properties of scalar field configurations influenced by non-commutative geometry and time-dependent perturbations. Specifically, we analyze the connectedness of level sets of scalar fields, compute the fractal dimensions of generated patterns, and study the impact of varying non-commutative parameters. Utilizing numerical simulations, we provide evidence of topological bifurcations induced by non-commutative corrections. The analysis is framed within point set topology, and the results are formalized using the theorem-proof structure.

This work explores the potential of La1-xPrxNiO4+δ thin films fabricated by Pulsed Injection Metal-Organic Chemical Vapor Deposition (PI-MOCVD) as oxygen electrodes for low-temperature solid oxide cells (LT-SOCs). La1-xPrxNiO4+δ materials, offer promising mixed ionic and electronic conductivity, and high oxygen reduction reaction kinetics. In this study, we focus on the microstructural and electrochemical properties of LaPrNiO4+δ (LPNO) thin films deposited at various temperatures (600–650 °C), revealing that a two-temperature deposition process yields nano-architectured films with a dense bottom film and a porous nano-columnar top layer of the same material. Electrochemical impedance spectroscopy (EIS) and electrical conductivity relaxation (ECR) experiments demonstrate enhanced surface exchange coefficients compared to bulk LaPrNiO4+δ and La2NiO4+δ and high performance, with polarization resistances as low as 0.10 Ω·cm² at 600 °C and 1.00 at 500 °C. To better understand the electrochemical behaviour of these electrodes, we investigated the limiting mechanisms of oxygen reduction by analysing the kinetic response to varying oxygen partial pressures and performing detailed impedance analyses. These nano-columnar LPNO oxygen electrodes were also deposited on commercial half-cells, enabling the resulting full cells to operate successfully in both reversible solid oxide fuel cell (SOFC) and electrolysis cell (SOEC) modes, reaching a performance of 0.34 W cm² at 600 °C in SOFC mode. This work underscores the promise of LPNO thin films for efficient LT-SOCs while addressing challenges in durability and stability.

We examine the two-dimensional, steady, and irrotational flow of an inviscid and incompressible fluid emerging from certain cases of flow through solid walls. Surface tension (T) is considered, while gravitational effects are neglected. This problem is complicated by the non-linear boundary condition imposed by the Bernoulli equation on the free surface, which presents challenges when adapting numerical methods used by many researchers. We employ a series truncation method for numerical solution. We computed solutions for various values of the angle (β) between the walls AB and the horizontal, the length of the vertical wall BC, and different Weber numbers. For determining the shape of the free surface, most of the calculations were performed for N=50. We were able to find approximate solutions for  α > 3 To validate our results, they were compared with studies using other numerical methods and in special cases of the angle β, compared with the exact solution in the limit of

In this paper, we establish a new compressed sensing model, specifically the $\ell_1-\ell_G$-minimization model. We derive the necessary and sufficient conditions for the recovery of fixed sparse vectors using the $\ell_1-\ell_G$ local minimization model. Building upon this foundation, we further obtain its equivalent condition.

The Orthogonal Approximate Message Passing (OAMP) algorithm enhances performance and stability remarkably by integrating orthogonalization, and it exhibits excellent detection performance even in ill-conditioned channel environments. However, the high implementation complexity of the OAMP algorithm, mainly caused by the computation of the decorrelated matrix in the linear estimation segment, poses a problem. To address this issue, we propose a low-complexity OAMP algorithm in this paper. Relying on the sparsity of the channel matrix, the new algorithm simplifies complex calculations of the focused decorrelated matrix into vector operations, significantly reducing computational complexity. In the nonlinear estimation part, error correction capability is improved by adjusting the estimations with the decorrelated matrix, and a damping factor is introduced to accelerate convergence. Experimental results demonstrate that the low-complexity OAMP algorithm presented in this paper not only successfully reduces algorithmic complexity but also achieves remarkable performance improvements.

This paper proposed a precise and low power analog spiking neural network (SNN). It decreases the membrane current error and improves linearity of SNN with the proposed pre-charged current-mode synapses. Also, it reduces the neuron power consumption by replacing a static amplifier with an event-driven coarse-fine comparator. The proposed analog SNN chip was fabricated using a 65nm process. Its area is 2.4mm×2.77mm with 1024×256 synapses. The membrane capacitor is 4pF. The pre-charged synapse reduces the membrane current error by 15.38%. The event-driven coarse-fine comparator saves 49% neuron power.

America's greatest existential threat is not external aggression—it is internal division. Democracy requires social cohesion and shared reality, both threatened by artificial intelligence. AI shatters common ground through individualized realities—whether deliberately weaponized by adversaries or amplified by profit-seeking algorithms. This fragmentation cripples cooperation, undermines governance, and destabilizes democracy. Empirical evidence confirms this pattern: polarization metrics worsen directly with AI-driven content consumption. Critics argue technological shifts have always prompted adaptation, not collapse. Yet AI differs fundamentally from past information revolutions in its personalization capacity and algorithmic opacity. Traditional governance cannot counter this threat alone. Proven solutions exist in decentralized community moderation systems already functioning at scale on platforms like Wikipedia and X. These systems, combined with mandatory content labeling and widespread digital literacy, create an immune response to misinformation. The American experiment has always been fragile. In the age of AI, its survival demands immediate action from government, technology companies, and citizens. This is not speculative futurism. It is today's clear and present danger.

A document by masoud aliyali. Click on the document to view its contents.

Following the announcement by the new American administration of a pause and review of funding to international aid programmes, we comment on the potential effect on global health security and argue for increased recognition of the role of National Public Health Institutes (NPHIs) to mitigate the impacts.

Objective To develop a caesarean birth core information set. Caesareans are the most common surgery performed in many countries. Women need information for informed decision-making and consent. Core information sets (CISs) provide baseline information, agreed upon by parents and clinicians, for discussion before a procedure. Design Two-phase consensus study using modified Delphi. Setting United Kingdom, 2024 Sample People planning a pregnancy/currently pregnant/new parents and maternity professionals Methods Phase 1: Long-list of information points identified from 273 systematic reviews, 50 patient leaflets, three pre-existing qualitative studies, and a stakeholder survey (n=230); Operationalised into a Delphi questionnaire comprising 11 information points with 108 items. Phase 2: Think-aloud interviews (n=9) informed questionnaire restructure into information about planned caesarean birth, unplanned caesarean birth (within 72 hours), and emergency caesarean birth (EMCB; ≤30 minutes), followed by two-round Delphi survey and consensus meetings. Results N=360 participated in the Delphi survey Round 1. All items were carried forward, and three were added for Round 2 (n=188/56.4% attrition rate). From Round 2, one item was removed, 73 included, and 37 items no-consensus. Free-text responses identified an unmet need for a postnatal EMCB-CIS. Over four meetings (n=36) consensus was reached for an antenatal-caesarean-birth-CIS (14 points), EMCB-CIS (5 points), and a postnatal EMCB-CIS (12 points). Conclusions This study has established three caesarean birth CISs to support informed decision-making discussions between women and clinicians: (1) CIS for planned and unplanned caesareans when there is time for discussion; (2) CIS for EMCB (within 30 minutes); (3) CIS post-EMCB pre-hospital discharge.

Objective: This study aims to develop a core information set (CIS) for induction of labour. A CIS is an agreed set of information points for discussion prior to an intervention. Design: First a long list of information points was identified through a systematic review of reviews, reviewing patient leaflets, analysis of pre-existing qualitative interviews and an initial stakeholder survey. A long list of items was collated before combining and refining alongside an involvement group. Cognitive interviews were undertaken to refine the Delphi Survey before a two-round modified Delphi process where participants voted on the importance of the information items. Pre-specified criteria were used to select the items taken forward to a consensus meeting. Participants were recruited via UK hospital sites, online and social media platforms and included parents and professionals. Setting and population: Participants were recruited via UK hospital sites, online and social media platforms and included parents and professionals. Main outcome: A core information set for induction of labour. Results: 199 information points were identified through systematic review (110), patient information leaflets (162), qualitative interviews (58) and a survey (93). 46 unique information items entered the first Delphi round after 4 cognitive interviews, 2 items were added following round 2. 368 people (310 parents/ 58 professionals) participated in round 1 and 177 people (154 parents/23 professionals) in round 2. 44 items met inclusion criteria; 1 item excluded, and 3 items were carried forward for consensus meeting discussion where 12 overarching information points were agreed upon. Conclusion: This CIS can help to inform and support discussions about induction to enable parents to make informed decisions about birth.

Objective To assess preconception lifestyle behaviours between individuals actively trying to conceive (TTC) and planning for future pregnancies (PFF). Design Retrospective cross-sectional study. Setting UK-based women. Population 235,311 women who completed an online reproductive health questionnaire between September 2020 and November 2024. Methods Self-reported data on reproductive intent as well as smoking, vaping, alcohol, and drug use were analysed. Logistic regression models adjusted for age, BMI, and lifestyle factors compared behavioural differences between TTC and PFF groups. Main Outcome Measures Prevalence of modifiable lifestyle factors (smoking, vaping, alcohol, and drug use) in TTC vs. PFF groups, stratified by age and TTC duration. Results TTC individuals smoked more than PFF individuals (9.1% vs. 7.3%; aOR = 1.76, 95% CI: 1.68–1.84, p < 0.001). Although vaping was more prevalent among PFF individuals (13.7% vs. 12.3%), TTC individuals had higher adjusted odds of vaping (aOR = 1.14, 95% CI: 1.10–1.18, p < 0.001). TTC individuals were more likely to abstain from alcohol (48.8% vs. 30.5%, p <0.001), however, many still exceeded the UK’s recommended low-risk limit of 14 units per week. Smoking, vaping, and drug use were highest in those under 25, while alcohol use peaked in those over 35. Smoking and vaping rates were significantly higher among those TTC for >12 months. Conclusions Findings show continued engagement in high-risk behaviours despite TTC, underscoring the need for targeted interventions, particularly vaping cessation and age-specific counselling, to mitigate risks earlier in the reproductive timeline and improve fertility outcomes.