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Adrenal lipoma formation via PI(3,4,5)P ₃ /AKT-dependent transdifferentiation of adrenocortical cells into adipocytes

Adrenal lipomas are benign tumors containing ectopic adipose tissue in the adrenal gland, an organ that normally lacks both adipocytes and their progenitors. The origin of this ectopic fat remains enigmatic, and the absence of a genetic animal model has hindered its investigation. Phosphatidylinositol 3,4,5-trisphosphate [PI(3,4,5)P 3 ], a key signaling lipid that regulates cellular growth and differentiation, is tightly regulated by the lipid phosphatases PTEN (phosphatase and tensin homolog) and SHIP2 (SH2-containing inositol phosphatase 2). Here, we demonstrate that simultaneous loss of Pten and Ship2 in the adrenal cortex induces adrenal lipoma formation in mice. These lipomatous cells display both adipocyte-like morphology and adipocyte-specific gene expression. Lineage tracing revealed that these lipomas originate from the adrenal cortex. Mechanistically, PI(3,4,5)P 3 hyperaccumulation in the adrenal cortex activates AKT (AKT8 virus oncogene cellular homolog), leading to ectopic PPARγ (peroxisome proliferator activated receptor gamma) expression, a key driver of adipocyte differentiation. This study suggests that the PI(3,4,5)P 3 /AKT-driven transdifferentiation of adrenocortical cells may represent a central mechanism underlying adrenal lipoma formation, thereby providing insights into lipoma pathogenesis and cellular reprogramming in vivo.

Determinants of vaccination uptake among pregnant women in Kumasi: A multi-centre cross-sectional study

Introduction Pregnant women are a high-risk group for severe symptoms and complications during pandemics, and vaccination is an important measure to prevent infection and protect both the mother and the foetus. However, there has been limited research on vaccination uptake by pregnant women in Ghana, especially during pandemics. Aim This study investigated the determinants of vaccination uptake among pregnant women in Kumasi, Ghana. Methods A multi-centre cross-sectional study assessed factors influencing vaccination uptake among pregnant women in Ghana. Using a multi-stage sampling technique, the respondents were selected from three (3) hospitals in Kumasi. Data was analysed through descriptive, ANOVA, correlation and linear regression at a significance level of 0.05. Results On perception of vaccination during pregnancy, 184 (71.9%) indicated their readiness to accept vaccination when requested. The higher the academic qualification of respondents, the more likely they are to receive vaccination. While the significant factor influencing vaccination uptake was “Complacency”, which explained 31.5% of the variance of the vaccination uptake decision-making, key barriers to vaccination uptake included doubts about the vaccine, fear of side effects, fear of injection and the belief that vaccination is a conspiracy. Conclusion More educational programmes should be arranged for pregnant women at hospitals or through the media to enhance their understanding and knowledge of the vaccine. This will contribute to the global effort to combat the effects of future pandemics by increasing vaccination acceptance rates among pregnant women.

Modeling human retinal ganglion cell axonal outgrowth, development, and pathology using pluripotent stem cell–based microfluidic platforms

Retinal ganglion cells (RGCs) are highly compartmentalized neurons whose long axons serve as the sole connection between the eye and the brain. In both injury and disease, RGC degeneration occurs in a similarly compartmentalized manner, with distinct molecular and cellular responses in the axonal and somatodendritic regions. The goal of this study was to establish a microfluidic-based platform to investigate RGC compartmentalization in both health and disease states. Human pluripotent stem cell (hPSC)-derived RGCs were seeded into microfluidic devices that allow physical separation of axons from the somatodendritic compartment, enabling precise study of each region. Initial experiments characterized axonal outgrowth and the specific segregation of axons and dendrites. We then examined compartment-specific phenotypes in RGCs carrying the OPTN(E50K) glaucoma mutation compared to isogenic controls, including differences in axonal growth and axonal transport efficiency, with OPTN-mutant RGCs showing reduced axon length and slower transport, hallmarks of neurodegeneration. Axonal RNA-seq analyses revealed transcriptomic alterations related to disease states, including specific transcriptomic changes along OPTN axons. To assess glial influences on axonal health, we developed models with astrocytes localized specifically to the proximal axonal compartment and modulated their disease states to simulate pathological conditions. Importantly, the induction of diseased astrocytes solely along proximal axons triggered compartment-specific neurodegenerative changes in RGCs. Collectively, this platform represents a successful recapitulation of the spatially distinct features of hPSC-derived RGCs under both healthy and disease conditions, offering a physiologically relevant, human-specific in vitro system to study neuronal development, axon–glia interactions, and mechanisms underlying neurodegeneration.

Exploring interactions of Aliivibrio fischeri with water-soluble polymers using bioluminescence and Raman microspectroscopy

Water-soluble polymers (WSPs) are widely used in biomedical and industrial applications. However, their ecological impact, including interactions with microorganisms, remains insufficiently understood and warrants further investigation. Aliivibrio fischeri, a bioluminescent bacterium, serves as a sensitive model to explore these effects. This study takes an exploratory approach, combining the optical spectroscopic techniques of luminescence measurements and Raman microspectroscopy to assess both immediate metabolic responses and potential longer-term or structural changes. Four WSPs polyacrylamide (PAM), polyethylene glycol (PEG), polyvinyl alcohol (PVOH), and polyvinylpyrrolidone (PVP) were each tested with three different molecular weights and five concentrations for luminescence measurements based on DIN EN ISO 11348. The tests revealed polymer-specific effects: PAM, PEG, and PVP suppressed luminescence, likely due to osmotic stress, adsorption, or viscosity-related limitations, with more pronounced inhibition observed at higher molecular weights. The strongest luminescence reduction was observed for PEG. Unlike the toxic reference substance 3,5-dichlorophenol (DCP), polymer-induced luminescence changes did not follow a consistent monotonic decay, suggesting that their effects are not solely attributable to acute toxicity. Notably, PVOH exposure increased luminescence, potentially reflecting stabilizing interactions or improved oxygen availability, which clearly contrasts with the suppressive trends observed for the other polymers. To investigate potential biochemical alterations, we applied Raman microspectroscopy to polymer-exposed A. fischeri. Partial least-squares discriminant analysis (PLS-DA) identified spectral differences, with polymer-specific patterns observed in regions commonly associated with membrane components, DNA, or carbohydrates. However, the PLS-DA coefficients primarily reflect statistical relevance and do not directly indicate specific biochemical mechanisms. These findings highlight spectral signatures relevant for classification, while biological interpretation requires further investigation. Our study provides exploratory insights into the potential impact of WSP exposure on bacterial metabolism and cellular composition, demonstrating the value of combining luminescence and Raman spectroscopy to detect polymer-related effects on microorganisms. While the observed spectral and metabolic changes suggest polymer-specific interactions, further research is needed to confirm the underlying mechanisms and assess their broader environmental and biotechnological implications.

Climate warming is expanding dengue burden in the Americas and Asia

Climate change is expected to pose significant threats to public health, particularly vector-borne diseases. Despite dramatic recent increases in dengue that many anecdotally connect with climate change, the effect of anthropogenic climate change on dengue remains poorly quantified. To assess this link, we assembled local-level data on dengue across 21 countries in Asia and the Americas. We found a nonlinear relationship between temperature and dengue incidence with the largest impact of warming at lower temperatures, peak incidence at 27.8°C, and a decline at higher temperatures. Using this inferred temperature response, we estimate 18% (95% CI: 11 to 27%) of historical dengue incidence on average across our study countries is attributable to anthropogenic warming. Future warming could further increase incidence by 49% (95% CI: 16 to 136%) to 76% (95% CI: 27 to 239%) by midcentury for low or high emissions scenarios, respectively, with cooler regions projected to double in incidence due to warming while other currently hot regions experience little impact or even small declines. Under the highest emissions scenario, we estimate that 262 million people are currently living in places in these 21 countries where dengue incidence is expected to more than double due to climate change by midcentury. These insights highlight the major impacts of anthropogenic warming on dengue burden across most of its endemic range, providing a foundation for public health planning and the development of strategies to mitigate future risks due to climate change.

Role of endothelial cell markers in prognosis of hepatocellular carcinoma: Integrating bioinformatics analysis and experimental validation

Background Hepatocellular carcinoma (HCC) is a highly prevalent malignancy with poor prognosis. Endothelial cells (ECs) play a crucial role in HCC progression, yet their involvement at the single-cell level remains underexplored. This study aimed to identify ECs-specific markers and develop a prognostic multi-gene signature for HCC using single-cell RNA sequencing (scRNA-seq). Materials and methods Single-cell transcriptomic data from 12 HCC samples were analyzed to identify EC-associated genes. A prognostic gene signature was constructed using Lasso-Cox regression analysis based on The Cancer Genome Atlas (TCGA) cohort and subsequently validated using an independent cohort from the International Cancer Genome Consortium (ICGC). Immunohistochemistry (IHC) and Western blotting were employed to experimentally validate gene expression in tissue samples. Results Five EC-specific genes—NDRG1, HBEGF, FKBP1A, KLRB1, and FDPS—were identified as prognostic markers. The resulting multi-gene signature effectively stratified patients into high- and low-risk groups, with significant differences in overall survival. Validation in the ICGC cohort confirmed the model’s predictive performance. IHC and Western blotting results further confirmed the elevated expression of these genes in HCC tissues. Conclusions This study established an EC-related prognostic signature that accurately predicts HCC prognosis. The identified markers may aid early diagnosis and serve as potential therapeutic targets for HCC treatment.

Ice gliding diatoms establish record-low temperature limits for motility in a eukaryotic cell

Despite periods of permanent darkness and extensive ice coverage in polar environments, photosynthetic ice diatoms display a remarkable capability of living inside the ice matrix. How these organisms navigate such hostile conditions with limited light and extreme cold remains unknown. Using a custom subzero temperature microscope during an Arctic expedition, we present the finding of motility at record-low temperatures in a Eukaryotic cell. By characterizing the gliding motility of several ice diatom species, collected from ice cores in the Chukchi Sea, we record that they retain motility at temperatures as low as − 15 ° C. Remarkably, ice diatoms can glide on ice substrates, a capability absent in temperate diatoms of the same genus. This unique ability arises from adaptations in extracellular mucilage that allow ice diatoms to adhere to ice, essential for gliding. Even on glass substrates where both cell types retain motility at freezing temperatures, ice diatoms move an order of magnitude faster, with their optimal motility shifting toward colder temperatures. Combining field and laboratory experiments with thermo-hydrodynamic modeling, we reveal adaptive strategies that enable gliding motility in cold environments. These strategies involve increasing internal energy efficiency with minimal changes in heat capacity and activation enthalpy, and reducing external dissipation by minimizing the temperature sensitivity of mucilage viscosity. The finding of diatoms’ ice gliding motility opens new routes for understanding their survival within a harsh ecological niche and their migratory responses to environmental changes. Our work highlights the robust adaptability of ice diatoms in one of Earth’s most extreme settings.

Neural SDE-based spike control of noisy neurons

Controlling the spike timing of individual neurons is a fundamental challenge with significant implications for treating neurological disorders. While much research has focused on neural models in low-noise scenarios, real-world applications, such as implantable therapeutic devices, must operate in noisy environments and address the diverse firing patterns of neurons. Leveraging the Izhikevich model, which captures a broad range of firing behaviors, this study proposes a novel method for spike timing control using Neural Stochastic Differential Equations (Neural SDE). The approach iteratively trains external currents to minimize both firing mismatches and timing errors through stochastic gradient descent and back-propagation techniques. Simulations demonstrate that the method achieves precise spike control across various neuron types and noise levels, including regular spiking, bursting, and fast spiking patterns. The approach remains effective even under strong noise perturbations, with particularly high precision observed in early spike events. Unlike conventional methods relying on deterministic dynamics or simplified models, the proposed Neural SDE framework directly accounts for biological noise and complex intrinsic dynamics. This enables the generation of neuron-specific control signals that align spike timings while adapting to individual firing characteristics. These results highlight the method’s generalizability and suggest its suitability for real-world neural control applications, including neuroprosthetics, adaptive stimulation, and closed-loop therapeutic systems.

Interventions to bolster benefits take-up: Assessing intensity, framing, and targeting of government outreach

Behaviorally informed “nudges” are widely used in government outreach but are often seen as too modest to address poverty at scale. In four field experiments over 2 y ( n = 542,804 low-income households), we test whether more proactive communication, varying message framing, and more precise targeting can boost take-up of tax-based benefits in California above and beyond traditional light-touch approaches. Our interventions focused on extremely vulnerable households, most with no prior-year earnings, who were at risk of missing out on two crucial benefits: the 2021 expanded Child Tax Credit and pandemic-relief Economic Impact Payments. Light-touch outreach consistently increased take-up of these benefits by 0.14 to 2 percentage points—a 150% to over 500% relative increase—regardless of message, sample, timing, or modality. These light-touch approaches resulted in over $4 million disbursed, with a highly cost-effective return of $50 to over $8,000 per $1 spent. However, higher-touch proactive outreach, varying messaging, and more precise targeting yielded minimal additional benefits, with proactive outreach even showing negative returns. These findings demonstrate that light-touch outreach can effectively shift behavior among very vulnerable households in contexts with reduced compliance burdens, but also underscore an urgent need to rethink the role of higher-touch strategies in closing take-up gaps in social safety net programs.

Identifying novel genetic variants in epidermolysis Bullosa among Middle Eastern Arab Families: Insights from whole exome sequencing and computational analysis

Background Epidermolysis Bullosa (EB) is a rare genetic disorder that results in fragile skin and blistering and may lead to mucous membrane involvement. The disease manifests in several subtypes, among which the most serious conditions are dystrophic and junctional EB. This study intends to highlight the recurrent and novel genetic abnormalities that cause EB in the Western region of Saudi Arabia. Methods Twelve Middle Eastern Arab families affected by Epidermolysis Bullosa (EB) were recruited from dermatology clinic from King Abdullah Medical Complex in Jeddah. Detailed clinical phenotyping was conducted for each patient to document EB-associated symptoms and to accurately determine the disease subtypes. Whole Exome Sequencing (WES) was performed to identify genetic variants associated with EB, and the resulting variants were classified by the guidelines of the American College of Medical Genetics and Genomics (ACMG). Additionally, multiple bioinformatics tools were employed to evaluate the pathogenicity of the detected variants. Variant segregation with disease phenotype was confirmed within the families using Sanger sequencing. Results We identified 11 genetic variants, including three novel variants, in the COL7A1 (NM_000094.4), COL17A1 (NM_000494.4), and LAMB3 (NM_000228.3) genes across 12 EB families. The COL7A1 variants included frameshift variants (c.5924_5927del and c.6268_6269del), nonsense variants (c.1633C > T, c.1837C > T, c.2005C > T, and c.5888G > A), missense variants (c.4448G > A and c.8245G > A), and splice-site variants (c.6751-1G > A and c.8305-1G > A). Additionally, a splice-site variant was identified in COL17A1 (NM_000494.4; c.1394G > A) and another in LAMB3 (NM_000228.3; c.1977-1G > A). Bioinformatics analysis predicted these variants to be likely pathogenic because they disrupt collagen VII, XVII, and laminin 332, proteins essential for skin stability. Frameshift and nonsense variants introduce premature stop codons, leading to truncated or degraded transcripts. Splice-site variants likely cause aberrant splicing, disrupting the reading frame and impairing protein function. Conclusion WES is an effective first-line diagnostic tool for identifying EB-associated variants. This study reveals locus and allelic heterogeneity in EB cases from Saudi Arabia. The findings underscore the importance of early genetic screening for improving genetic counseling in high-consanguinity populations and emphasize the need for large-scale genetic studies in the country.

Excitatory glycine receptors control ventral hippocampus synaptic plasticity and anxiety-related behaviors

Excitatory glycine receptors (eGlyRs), composed of the glycine-binding NMDA receptor subunits GluN1 and GluN3A, have recently emerged as a novel neuronal signaling modality that challenges the traditional view of glycine as an inhibitory neurotransmitter. Unlike conventional GluN1/GluN2 NMDARs, the distribution and role of eGlyRs remain poorly understood. Here, we show that eGlyRs are highly enriched in the ventral hippocampus (VH) and confer distinct properties on this brain region. eGlyRs display a massive expression in both VH CA1 pyramidal cells and SST- and PV-positive interneurons, whereas in the dorsal hippocampus (DH) pyramidal cells lack these receptors. eGlyRs mediate excitatory tonic currents and control VH network excitability. They are also responsible for the attenuated long-term potentiation (LTP) in the VH compared with the DH, providing a molecular basis for this difference. Furthermore, eGlyRs are required for regulation of VH LTP by corticosterone, pointing to eGlyRs as mediators of the neuroendocrine stress response. Consistent with this pervasive influence in the ventral division of the hippocampus, eGlyRs contribute to the modulation of anxiety-related behaviors. Our work identifies eGlyRs as key players in VH circuitry and function, demonstrating their intimate association with brain regions that control internal states and emotional processing.

“A good day is just being able to breathe”: Aligning COPD research with patient needs, a qualitative study

Background Chronic obstructive pulmonary disease (COPD) is a common and impactful disease that is the target of a large portfolio of clinical research. However, there is limited understanding of how individuals with COPD perceive trial designs, outcomes, and intervention acceptability. The objective of this project was to explore the perspectives and priorities of patients and their caregivers toward COPD-focused clinical research. Methods Semi-structured interviews were conducted with participants living with COPD and their caregivers using the Theoretical Framework of Acceptability (TFA) to guide data collection and analysis. Interviews were transcribed and coded using qualitative analysis software and analyzed using an inductive thematic approach. Results Fifteen interviews were performed. Key themes included participant preference for outcome measures that directly impact daily living, such as mental wellness and physical function. Participants highlighted the need for research data to be actionable, advocating for health insights to be shared with participants and their healthcare providers. Study engagement was influenced by the perceived burden and complexity of interventions as well as their direct relevance to patients. Patients favored research designs that minimize physical and logistical challenges. Lastly, participants desired greater involvement in the research design process. Conclusions Aligning COPD research with patient priorities requires incorporating meaningful outcome measures, reducing participation burdens, and fostering ongoing engagement. Integrating patient-centered approaches in study design can enhance recruitment, adherence, and the real-world impact of COPD interventions.

A geometric condition for robot-swarm cohesion and cluster–flock transition

We present a geometric design rule for size-controlled clustering of self-propelled particles. We show that active particles that tend to rotate under an external force have an intrinsic, signed parameter with units of curvature which we call curvity, that can be derived from first principles. Experiments with robots and numerical simulations show that properties of individual robots (radius and curvity) control pair cohesion in a binary system, and the stability of flocking and self-limiting clustering in a swarm, with applications in metamaterials and in embodied decentralized control.

The impact of proximity to major central hepatic vasculature on perioperative outcomes and size-based risk stratification in hepatic hemangioma surgery

Background and aim Surgical strategies for hepatic hemangiomas remain controversial, particularly concerning the influence of anatomical location and the use of tumor size thresholds. The proximity of tumors to major central hepatic vessels introduces significant surgical complexity and risks, yet its precise effect on perioperative outcomes and the appropriate application of size thresholds under varying anatomical conditions remain understudied. This study aims to evaluate the impact of proximity to major central hepatic vessels on perioperative outcomes and to assess the appropriateness of different size thresholds (8 cm vs. 10 cm) for risk stratification in varied anatomical contexts. Methods A retrospective cohort analysis was performed to evaluate hepatic hemangioma patients undergoing surgical resection from October 2016 to July 2024. The collected data included demographics, characteristics of the hemangiomas, laboratory data, surgical approaches, and perioperative variables. Patients were divided into group 1 (non-proximal) and group 2 (proximal) according to major central hepatic vascular proximity. Results A total of 309 patients were included in the study, with 176 in group 1 and 133 in group 2. The perioperative variables including operative duration, Intraoperative Blood Loss, postoperative stay, Intraoperative Transfusion Rate, postoperative complications grade and most postoperative laboratory tests showed significant differences between the two groups (P < 0.05). Group 2(proximal) increased intraoperative blood loss (+139 mL, P = 0.005), operative time (+60 min, P < 0.001), and complication risk (OR=2.44, P = 0.032) versus Group 1(non-proximal). ROC analysis further revealed that the utility of 8 cm and 10 cm as risk stratification thresholds varied significantly depending on tumor proximity. Conclusion The proximity of hepatic hemangiomas to major central hepatic vessels significantly elevates surgical difficulty and perioperative risks. Additionally, the sensitivity and specificity of 8 cm and 10 cm as surgical risk stratification thresholds varied distinctly depending on proximity to major central hepatic vessels.

Driving the grid forward: How electric vehicle adoption shapes power system infrastructure and emissions

We model the effect of plug-in electric vehicle (EV) adoption on U.S. power system generator capacity investment, operations, and emissions through 2050 by estimating power systems outcomes under a range of EV adoption trajectory scenarios. Our EV adoption scenarios are informed by 1) an Energy Information Administration scenario with no policy intervention, 2) EV growth expected under the Inflation Reduction Act (IRA), 3) a Biden Administration 50% EV sales target by 2030, 4) the Environmental Protection Agency’s projections under vehicle emissions standards, and 5) the International Energy Agency’s roadmap to Net Zero by 2050. We find across these scenarios that increasing EV adoption induces investment in new wind, solar, storage, and natural gas capacity, affecting power generation mix and emissions. The net effect of increasing EV adoption beyond our IRA base case is to increase power sector emissions by about 5 mtCO 2 eq per EV-year in 2026 (comparable to displaced gasoline vehicle combustion emissions), but this effect rapidly drops to annual levels below 1 mtCO 2 eq per EV-year by 2032 and continues below this level through 2050. Consequential effects of EV adoption vary regionally, with most regions primarily increasing wind or solar capacity and some regions primarily increasing natural gas capacity, even in 2050. Our national emissions estimates per EV-year are relatively robust to the level of EV adoption beyond our baseline and to variation in assumptions about power systems, EV behavior, and policy.

Correction: LeAf Trauma- an intersectoral prospective multicenter study assessing quality of life and return to work after majortrauma–study protocol

Elastocaloric evidence for a multicomponent superconductor stabilized within the nematic state in Ba(Fe _{1− <i>x</i>} Co _<i>x</i> ) ₂ As ₂

The iron-based high- T c superconductors (SCs) exhibit rich phase diagrams with intertwined phases, including magnetism, nematicity, and superconductivity. The superconducting T c in many of these materials is maximized in the regime of strong nematic fluctuations, making the role of nematicity in influencing the superconductivity a topic of intense research. Here, we use the AC elastocaloric effect (ECE) to map out the phase diagram of Ba(Fe 1− x Co x ) 2 As 2 near optimal doping. The ECE signature at T c on the overdoped side, where superconductivity condenses without any nematic order, is quantitatively consistent with other thermodynamic probes that indicate a single-component superconducting state. In contrast, on the slightly underdoped side, where superconductivity condenses within the nematic phase, ECE reveals a second thermodynamic transition proximate to and below T c . We rule out magnetism and reentrant tetragonality as the origin of this transition and find that our observations strongly suggest a phase transition into a multicomponent superconducting state. This implies the existence of a subdominant pairing instability that competes strongly with the dominant s ± instability. Our results highlight the significant role of nematic order in determining the pairing symmetry close to optimal doping in this extensively studied iron-based SC, while also demonstrating the power of ECE in uncovering strain-tuned phase diagrams of quantum materials.

Enhanced gallbladder cancer detection via active and self-supervised learning integration: Innovating B-ultrasound image analysis

Gallbladder cancer, a common yet often under diagnosed malignancy, is typically characterized by late detection and a poor prognosis. The rise of deep learning has introduced new methods for its early identification through B-ultrasound imaging, but there are still challenges of inefficient data labeling and feature extraction. This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. Secondly, we introduce the MsHop module, which effectively captures the fine textures and patterns in ultrasound images through the integration of multi-scale and high-order information, thereby improving diagnostic accuracy. Additionally, we develop a dual-branch loss function that leverages data correlation and clustering features to enhance feature extraction and model stability. The experiments on a gallbladder ultrasound dataset have confirmed the effectiveness of our algorithm, achieving an accuracy of 0.884, a specificity of 0.932, and a sensitivity of 0.912—outperforming existing methods. The results exhibit lower variance, indicating improved model stability. Furthermore, the findings demonstrate that using active learning, one can achieve comparable results to those from the full dataset with only 35% of the data, reducing annotation costs and increasing model learning efficiency. Further research will concentrate on refining the algorithm for wider clinical use and identifying additional features that may further improve diagnostic accuracy.

Reciprocity in dynamics of supramolecular biosystems for the clustering of ligands and receptors

Multivalent binding and the resulting dynamical clustering of receptors and ligands are known to be key features in biological interactions. For optimizing biomaterials capable of similar dynamical features, it is essential to understand the first step of these interactions, namely the multivalent molecular recognition between ligands and cell receptors. Here, we present the reciprocal cooperation between dynamic ligands in supramolecular polymers and dynamic receptors in model cell membranes, determining molecular recognition and multivalent binding via receptor clustering. The nonlinear dependences of the ligand concentration, receptors, and their binding affinity are observed experimentally by fluorescence and superresolution fluorescence microscopies, revealing a valency-dependent clustering mode of anchoring. The mechanism is supported by stochastic modeling demonstrating that such nonlinear dependence is unlikely in the absence of any dynamics and superselectivity. Using a coarse-grained molecular model, the subtle competition between local and global entropies that controls this anchoring mechanism explains the clustering. Further investigation using single particle tracking reveals the presence of two populations of bound and unbound receptors after the clustering process. The result of this study highlights the importance of reciprocity of dynamics in supramolecular polymer and lipid membrane for recruitment, multivalent binding, and clustering, all of which are crucial elements in the design of materials capable of actively interacting with biological targets.

Effects of electricity outages on enterprise productivity in Egypt: Lessons learned

This study investigates the impact of advanced electricity outage announcements on the operational efficiency of small and medium enterprises (SMEs), in Egypt, using profitability as a key performance indicator. Leveraging data from “Transition to Clean Energy Enterprise Survey” and applying the inverse probability-weighted regression adjustment (IPWRA) method to address selection bias, we estimate how outage predictability influences firm outcomes. We find that SMEs receiving advance notice of power disruptions are significantly more likely to achieve higher profitability compared to those without such information. The benefits are most evident among larger firms and sectors such as transportation, financial services, and accommodation, where operational planning is critical. While the policy partially offsets losses from outages, firms in areas with frequent blackouts still face substantial profitability challenges, highlighting the limits of transparency alone. Our findings emphasize that advance announcements enhance SME resilience by enabling adaptive measures, but long-term solutions require complementary infrastructure investments in high-risk regions. The study advocates for policy frameworks centered on transparency and rational expectations, demonstrating how proactive communication in public services can bolster economic resilience amid global uncertainties. These insights are particularly relevant for developing economies seeking to balance immediate crisis management with sustainable energy infrastructure development.