Science Funding Research Articles

Gelation Embolism Agents Suppress Clinical TACE-Incited Pro-Metastatic Microenvironment Against Hepatocellular Carcinoma Progression

Current embolic agents in transcatheter arterial chemoembolization (TACE) of hepatocellular carcinoma (HCC) encounter instability and easy leakage, discounting TACE efficacy with residual HCC. Moreover, clinical TACE aggravates hypoxia and pro-metastatic microenvironments, rendering patients with HCC poor prognosis. Herein, we developed Zein-based embolic agents that harness water-insoluble but ethanol-soluble Zein to encompass doxorubicin (DOX)-loaded mesoporous hollow MnO2 (HMnO2). The conditions and capacity of HMnO2 to generate reactive oxygen species (ROS) were assayed. Mechanical examinations of Zein-HMnO2@DOX were performed to evaluate its potential as the embolic agent. Invitro experiments were carried out to evaluate the effect of Zein-HMnO2@DOX on HCC. The subcutaneous HCC mouse model and rabbit VX2 HCC model were established to investigate its anti-tumor and anti-metastasis efficacy and explore its potential anti-tumor mechanism. The high adhesion and crosslinking of Zein with HMnO2@DOX impart Zein-HMnO2@DOX with strong mechanical strength to resist deformation and wash-off. Zein gelation and HMnO2 decomposition in response to water and acidic tumor microenvironment, respectively, enable continuous DOX release and Fenton-like reaction for reactive oxygen species (ROS) production and O2 release to execute ROS-enhanced TACE. Consequently, Zein-based embolic agents outperform clinically-used lipiodol to significantly inhibit orthotopic HCC growth. More significantly, O2 release down-regulates hypoxia inducible factor (HIF-1α), vascular endothelial growth factor (VEGF) and glucose transporter protein 1 (GLUT1), which thereby re-programmes TACE-aggravated hypoxic and pro-metastatic microenvironments to repress HCC metastasis towards lung. Mechanistic explorations uncover that such Zein-based TACE agents disrupt oxidative stress, angiogenesis and glycometabolism pathways to inhibit HCC progression. This innovative work not only provides a new TACE agent for HCC, but also establishes a new strategy to ameliorate TACE-aggravated hypoxia and metastasis motivation against clinically-common HCC metastasis after TACE operation. Excellent Young Science Fund for National Natural Science Foundation of China (82022033); National Natural Science Foundation of China (Grant No. 82373086, 82102761); Major scientific and technological innovation project of Wenzhou Science and Technology Bureau (Grant No. ZY2021009); Shanghai Young Top-Notch Talent.

From amoeboid myosin to unique targeted medicines for a genetic cardiac disease

The importance of fundamental basic research in the quest for much needed clinical treatments is a story that constantly must be retold. Funding of basic science in the USA by the National Institutes of Health and other agencies is provided under the assumption that fundamental research eventually will lead to improvements in healthcare worldwide. Understanding how basic research is connected to clinical developments is important, but just part of the story. Many basic science discoveries never see the light of day in a clinical setting because academic scientists are not interested in or do not have the inclination and/or support for entering the world of biotechnology. Even if the interest and inclination are there, often the unknowns about how to enter that world inhibit taking the initial step. Young investigators often ask me how I incorporated biotech opportunities into my otherwise purely academic research endeavors. Here I tell the story of the foundational basic science and early events of my career that led to forming the biotech companies responsible for the development of unique cardiac drugs, including mavacamten, a first in class human β-cardiac myosin inhibitor that is changing the lives of hypertrophic cardiomyopathy patients.

Fast Convex Optimization via Time Scale and Averaging of the Steepest Descent

In a Hilbert setting, we develop a gradient-based dynamic approach for fast solving convex optimization problems. By applying time scaling, averaging, and perturbation techniques to the continuous steepest descent (SD), we obtain high-resolution ordinary differential equations of the Nesterov and Ravine methods. These dynamics involve asymptotically vanishing viscous damping and Hessian-driven damping (either in explicit or implicit form). Mathematical analysis does not require developing a Lyapunov analysis for inertial systems. We simply exploit classical convergence results for SD and its external perturbation version, then use tools of differential and integral calculus, including Jensen’s inequality. The method is flexible, and by way of illustration, we show how it applies starting from other important dynamics in optimization. We consider the case in which the initial dynamic is the regularized Newton method, then the case in which the starting dynamic is the differential inclusion associated with a convex lower semicontinuous potential, and finally we show that the technique can be naturally extended to the case of a monotone cocoercive operator. Our approach leads to parallel algorithmic results, which we study in the case of fast gradient and proximal algorithms. Our averaging technique shows new links between the Nesterov and Ravine methods. Funding: The research of R.I. Boţ and D.-K. Nguyen was supported by the Austrian Science Fund (FWF), projects W 1260 and P 34922-N.

Global radiotherapy demands and corresponding radiotherapy-professional workforce requirements in 2022 and predicted to 2050: a population-based study.

Addressing the challenge of cancer control requires a comprehensive, integrated, and global health-system response. We aimed to estimate global radiotherapy demands and requirements for radiotherapy professionals from 2022 to 2050. We conducted a population-based study using data from the Global Cancer Observatory (GLOBOCAN) 2022 and predicted global radiotherapy demands and workforce requirements in 2050. We obtained incidence figures for 29 types of cancer across 183 countries and derived the cancer-specific radiotherapy use rate using the 2013 Collaboration for Cancer Outcomes Research and Evaluation model. We delineated the proportion of people with cancer who require radiotherapy and can be accommodated within the existing installed capacity, assuming an optimal use rate of 50% or 64%, in both 2022 and 2050. A use rate of 50% corresponds to the global average and a use rate of 64% considers potential re-treatment scenarios, as indicated by the 2013 Collaboration for Cancer Outcomes Research and Evaluation (CCORE) radiotherapy use rate model. We established specified requirements for teletherapy units at a ratio of 1:450 patients, for radiation oncologists at a ratio of 1:250 patients, for medical physicists at a ratio of 1:450 patients, and for radiation therapists at a ratio of 1:150 patients in all countries and consistently using these ratios. We collected current country-level data on the radiotherapy-professional workforce from national health reports, oncology societies, or other authorities from 32 countries. In 2022, there were an estimated 20·0 million new cancer diagnoses, with approximately 10·0 million new patients needing radiotherapy at an estimated use rate of 50% and 12·8 million at an estimated use rate of 64%. In 2050, GLOBOCAN 2022 data indicated 33·1 million new cancer diagnoses, with 16·5 million new patients needing radiotherapy at an estimated use rate of 50% and 21·2 million at an estimated use rate of 64%. These findings indicate an absolute increase of 8·4 million individuals requiring radiotherapy from 2022 to 2050 at an estimated use rate of 64%; at an estimated use rate of 50%, the absolute increase would be 6·5 million individuals. Asia was estimated to have the highest radiotherapy demand in 2050 (11 119 478 [52·6%] of 21 161 603 people with cancer), followed by Europe (3 564 316 [16·8%]), North America (2 546 826 [12·0%]), Latin America and the Caribbean (1 837 608 [8·7%]), Africa (1 799 348 [8·5%]), and Oceania (294 026 [1·4%]). We estimated that the global radiotherapy workforce in 2022 needed 51 111 radiation oncologists, 28 395 medical physicists, and 85 184 radiation therapists and 84 646 radiation oncologists, 47 026 medical physicists, and 141 077 radiation therapists in 2050. We estimated that the largest proportion of the radiotherapy workforce in 2050 would be in upper-middle-income countries (101 912 [38·8%] of 262 624 global radiotherapy professionals). Urgent strategies are required to empower the global health-care workforce and facilitate the fundamental human right of access to suitable health care. A collective effort with innovative and cost-contained health-care strategies from all stakeholders is warranted to enhance global accessibility to radiotherapy and address challenges in cancer care. China Medical Board Global Health Leadership Development Program, Shanghai Science and Technology Committee Fund, China Ministry of Science and Technology Department of International Cooperation High Level Cooperation and Exchange Projects, and Fudan University Office of Global Partnerships Key Projects Development Fund. For the Arabic, Chinese, French, Russian and Spanish translations of the summary see Supplementary Materials section.

A traditional ecological knowledge summit

A traditional ecological knowledge summit The Global Center for Climate Change and Transboundary Waters (GCTW) cohosts a Traditional Ecological Knowledge Summit (1), as Gail Krantzberg (2), Peter Czajkowski, Dawn Martin-Hill, Rohini Patel, Hiliary Monteith, and Drew Gronewold explain. The Global Center for Climate Change and Transboundary Waters (GCTW) integrates hydroclimate modeling, water quality forecasting, and community-engaged mixed methods that harmonize and propagate Traditional Ecological Knowledge (TEK), Indigenous Knowledge (IK), and Western Science (WS) into robust 21st-century transboundary water resources governance protocols. The U.S. National Science Foundation and the Canadian Social Science and Humanities Research Council fund the Center. It supports a multinational network of researchers that is designed to promote information sharing across borders.

A Dynamic Principal-Agent Problem with One-Sided Commitment

In this paper, we consider a principal-agent problem where the agent is allowed to quit by incurring a cost. When the current agent quits the job, the principal will hire a new one, possibly with a different type. We characterize the principal’s dynamic value function, which could be discontinuous at the boundary, as the (unique) minimal solution of an infinite dimensional system of Hamilton-Jacobi-Bellman equations, parameterized by the agent’s type. This dynamic problem is time consistent in a certain sense. Some interesting findings are worth mentioning. First, self-enforcing contracts are typically suboptimal. The principal would rather let the agent quit and hire a new one. Next, the standard contract for a committed agent may also be suboptimal because of the presence of different types of agents in our model. The principal may prefer no commitment from the agent; then, the principal can hire a cheaper one from the market at a later time by designing the contract to induce the current agent to quit. Moreover, because of the cost incurred to the agent, the principal will see only finitely many quittings. Funding: J. Zhang is supported in part by the NSF [Grants DMS-1908665 and DMS-2205972]. Z. Zhu is supported by The Hong Kong University of Science and Technology (Guangzhou) Start-Up Fund [Grant G0101000240] and the Guangzhou-HKUST(GZ) Joint Funding Program [Grant 2024A03J0630].

Association of uric acid and fructose levels in polycystic ovary syndrome.

Is there a relationship between serum uric acid and fructose levels in polycystic ovary syndrome (PCOS)? Elevated serum uric acid levels in women with PCOS positively correlate with serum fructose levels, and elevated serum fructose levels are an independent risk factor for hyperuricemia in women with PCOS. Our previous study suggested a link between elevated serum fructose levels and PCOS. Fructose is unique as it generates uric acid during metabolism, and high uric acid levels are associated with metabolic disorders and an increased risk of anovulation. However, the relationship between serum uric acid and fructose levels in women with PCOS remains unclear. In a case-control study of 774 women (482 controls and 292 patients with PCOS) between May and October 2020 at the Shengjing Hospital of China Medical University, the relationship between uric acid and fructose levels in women with PCOS was examined. Participants were divided into subgroups based on various factors, including BMI, insulin resistance, dyslipidemia, metabolic syndrome, and hyperuricemia. Serum uric acid concentrations were measured using enzymatic assays, and serum fructose levels were determined using a fluorescent enzyme immunoassay. Dietary fructose data were collected through a validated food-frequency questionnaire of 81 food items. We applied restricted cubic splines to a flexibly model and visualized the linear/nonlinear relationships between serum uric acid and fructose levels in PCOS. Multivariate logistic analysis was executed to assess the association between serum fructose levels and hyperuricemia in PCOS. Human granulosa cell and oocyte mRNA profile sequencing data were downloaded for mapping uric acid and fructose metabolism genes in PCOS. Further downstream analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and protein-protein interactions were then carried out on the differentially expressed genes (DEGs). The correlation between uric acid and fructose metabolism genes was calculated using the Pearson correlation coefficient. The GeneCards database was used to identify DEGs related to uric acid and fructose metabolism in PCOS, and then several DEGs were confirmed by quantitative real-time PCR. Both serum fructose and uric acid levels were significantly increased in women with PCOS compared with the control women (P < 0.001), and there was no statistically significant difference in dietary fructose intake between PCOS and controls, regardless of metabolic status. There was a positive linear correlation between serum uric acid and fructose levels in women with PCOS (Poverall < 0.001, Pnon-linear = 0.30). In contrast, no correlation was found in control women (Poverall = 0.712, Pnon-linear = 0.43). Additionally, a non-linear association was observed in the obese subgroup of patients with PCOS (Poverall < 0.001, Pnon-linear = 0.02). Serum uric acid levels were linearly and positively associated with serum fructose levels in patients with PCOS with insulin resistance, dyslipidemia, and metabolic syndrome. Furthermore, even after adjusting for confounding factors, elevated serum fructose levels were an independent risk factor for hyperuricemia in patients with PCOS (P = 0.001; OR, 1.380; 95% CI, 1.207-1.577). There were 28 uric acid and 25 fructose metabolism genes which showed a significant correlation in PCOS. Seven upregulated genes (CAT, CRP, CCL2, TNF, MMP9, GCG, and APOB) related to uric acid and fructose metabolism in PCOS ovarian granulosa cells were ultimately successfully validated using quantitative real-time PCR. Due to limited conditions, more possible covariates (such as smoking and ethnicity) were not included, and the underlying molecular mechanism between fructose and uric acid levels in women with PCOS remains to be further investigated. The results of this study and our previous research indicate that the high uric acid status of PCOS may be mediated by fructose metabolism disorders, highlighting the importance of analyzing fructose metabolism, and especially its metabolic byproduct uric acid, during the clinical diagnosis of PCOS. These results suggest the adverse effects of high uric acid in PCOS, and the importance of taking early interventions regarding uric acid levels to reduce the occurrence and development of further clinical signs, such as metabolic disorders in women with PCOS. This work was supported by: the National Natural Science Foundation of China (No. 82371647, No. 82071607, and No. 32100691); LiaoNing Revitalization Talents Program (No. XLYC1907071); Fok Ying Tung Education Foundation (No. 151039); and Outstanding Scientific Fund of Shengjing Hospital (No. 202003). No competing interests were declared. N/A.

Value-Positivity for Matrix Games

Matrix games are the most basic model in game theory, and yet robustness with respect to small perturbations of the matrix entries is not fully understood. In this paper, we introduce value positivity and uniform value positivity, two properties that refine the notion of optimality in the context of polynomially perturbed matrix games. The first concept captures how the value depends on the perturbation parameter, and the second consists of the existence of a fixed strategy that guarantees the value of the unperturbed matrix game for every sufficiently small positive parameter. We provide polynomial-time algorithms to check whether a polynomially perturbed matrix game satisfies these properties. We further provide the functional form for a parameterized optimal strategy and the value function. Finally, we translate our results to linear programming and stochastic games, where value positivity is related to the existence of robust solutions. Funding: This research was supported by Fondation CFM pour la Recherche, the H2020 European Research Council [Grant ERC-CoG-863818 (ForM-SMArt)], the Austrian Science Fund [Grant 10.55776/COE12], ANID Chile [Grant ACT210005], and Agence Nationale de la Recherche [Grant ANR-21-CE40-0020].

Risks of competing discourses of scientific responsibility in global ocean futures

Accelerated innovation in climate-impacted oceans is outpacing standards of scientific responsibility. Standards of responsibility are critical because they shape research agendas, funding flows, scientific practice, and how innovations are regulated. Here, we examine responsibility debates among 243 marine scientists and end-users proposing, trialling and/or implementing 76 innovations for climate-impacted oceans. We identify three distinct discourses: ‘science outside society’, ‘science for society’ and ‘science with society’. Competition within and across these discourses reveals heightened tensions between the need to protect scientific autonomy and freedom, and moral duty to ensure socially just and desirable ocean futures. Without thorough debate and oversight, the wide-reaching power of such unresolved tensions could propel marine science and ocean futures into volatile ethical and moral territory. Better connection and articulation of standards of responsibility with scientific motivations, practices, and funding are key to ensuring the transparency and accountability required to progress equitable and sustainable oceans.

Evaluations in science communication. Current state and future directions

Evaluations are becoming more important in science communication. But both science communication practitioners and researchers are not sufficiently utilizing the potential of evaluations yet. In this essay, we first define four requirements for rigorous evaluations of science communication activities and projects. To substantiate our argument, we take stock of the scientific literature, uncover deficiencies in current evaluation practices and identify potential causes. We conclude with laying out how different actors in the field — including science communication practitioners, professional associations, scientific institutions and funding bodies as well as researchers — can contribute to advancing evaluation practices in science communication as well as research on it.

An effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model

Novelty is a critical characteristic of innovative scientific articles, and accurately identifying novelty can facilitate the early detection of scientific breakthroughs. However, existing methods for measuring novelty have two main limitations: (1) Metadata-based approaches, such as citation analysis, are retrospective and do not alleviate the pressures of the peer review process or enable timely tracking of scientific progress; (2) Content-based methods have not adequately addressed the inherent uncertainty between the qualitative concept of novelty and the textual representation of papers. To address these issues, we propose a practical and effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model, referred to as MNSA-ITMCM. In this framework, papers are represented as topic combinations, and novelty is reflected in the organic reorganization of these topics. We use the BERTopic model to generate semantically informed topics, and then apply a topic selection algorithm based on maximum marginal relevance to obtain a topic combination that balances similarity and diversity. Furthermore, we leverage the cloud model from fuzzy mathematics to quantify novelty, overcoming the uncertainty inherent in natural language expression and topic modeling to improve the accuracy of novelty measurement. To validate the effectiveness of our framework, we conducted empirical evaluations on papers from the Cell 2021 journal (biomedical domain) and the ICLR 2023 conference (computer science domain). Through correlation analysis and prediction error analysis, our framework demonstrated the ability to identify different types of novel papers and accurately predict their novelty levels. The proposed framework is applicable across diverse scientific disciplines and publication venues, benefiting researchers, librarians, science evaluation agencies, policymakers, and funding organizations by improving the efficiency and comprehensiveness of identifying novelty research.

A transformer-based deep learning model for early prediction of lymph node metastasis in locally advanced gastric cancer after neoadjuvant chemotherapy using pretreatment CT images

Early prediction of lymph node status after neoadjuvant chemotherapy (NAC) facilitates promptly optimization of treatment strategies. This study aimed to develop and validate a deep learning network (DLN) using baseline computed tomography images to predict lymph node metastasis (LNM) after NAC in patients with locally advanced gastric cancer (LAGC). A total of 1205 LAGC patients were retrospectively recruited from three hospitals between January 2013 and March 2023, constituting a training cohort, an internal validation cohort, and two external validation cohorts. A transformer-based DLN was developed using 3D tumor images to predict LNM after NAC. A clinical model was constructed through multivariate logistic regression analysis as a baseline for subsequent comparisons. The performance of the models was evaluated through discrimination, calibration, and clinical applicability. Furthermore, Kaplan-Meier survival analysis was conducted to assess overall survival (OS) of LAGC patients at two follow-up centers. The DLN outperformed the clinical model and demonstrated a robust performance for predicting LNM in the training and validation cohorts, with areas under the curve (AUCs) of 0.804 (95% confidence interval [CI], 0.752-0.849), 0.748 (95% CI, 0.660-0.830), 0.788 (95% CI, 0.735-0.835), and 0.766 (95% CI, 0.717-0.814), respectively. Decision curve analysis exhibited a high net clinical benefit of the DLN. Moreover, the DLN was significantly associated with the OS of LAGC patients [Center 1: hazard ratio (HR), 1.789, P<0.001; Center 2:HR, 1.776, P=0.013]. The transformer-based DLN provides early and effective prediction of LNM and survival outcomes in LAGC patients receiving NAC, with promise to guide individualized therapy. Future prospective multicenter studies are warranted to further validate our model. National Natural Science Foundation of China (NO. 82373432, 82171923, 82202142), Project Funded by China Postdoctoral Science Foundation (NO. 2022M720857), Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (NO. U22A20345), National Science Fund for Distinguished Young Scholars of China (NO. 81925023), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (NO. 2022B1212010011), High-level Hospital Construction Project (NO. DFJHBF202105), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (NO. 2024B1515020091).

Aging promotes accumulation of senescent and multiciliated cells in human endometrial epithelium.

What changes occur in the endometrium during aging, and do they impact fertility? Both the transcriptome and cellular composition of endometrial samples from women of advanced maternal age (AMA) are significantly different from that of samples from young women, suggesting specific changes in epithelial cells that may affect endometrial receptivity. Aging is associated with the accumulation of senescent cells in aging tissues. Reproductive aging is mostly attributed to the decline in ovarian reserve and oocyte quality, whereas the endometrium is a unique complex tissue that is monthly renewed under hormonal regulation. Several clinical studies have reported lower implantation and pregnancy rates in oocyte recipients of AMA during IVF. Molecular studies have indicated the presence of specific mutations within the epithelial cells of AMA endometrium, along with altered gene expression of bulk endometrial tissue. Endometrial transcriptome profiling was performed for 44 women undergoing HRT during the assessment of endometrial receptivity before IVF. Patients younger than 28 years were considered as the young maternal age (YMA) group (age 23-27 years) and women older than 45 years were considered as the AMA group (age 47-50 years). Endometrial biopsies were obtained on Day 5 of progesterone treatment and RNA was extracted. All endometrial samples were evaluated as being receptive based on the expression of 68 common endometrial receptivity markers. Endometrial samples from another 24 women classified into four age groups (YMA, intermediate age group 1 (IMA1, age 29-35), intermediate age group 2 (IMA2, age 36-44), and AMA) were obtained in the mid-secretory stage of a natural cycle (NC) and used for validation studies across the reproductive lifespan. A total of 24 HRT samples (12 YMA and 12 AMA) were subject to RNA sequencing (RNA-seq) and differential gene expression analysis, 20 samples (10 YMA and 10 AMA) were used for qPCR validation, and 24 NC samples (6 YMA, 6 IMA1, 6 IMA2 and 6AMA) were used for RNA-seq validation of AMA genes across the woman's reproductive lifespan. Immunohistochemistry (IHC) was used to confirm some expression changes at the protein level. Computational deconvolution using six endometrial cell type-specific transcriptomic profiles was conducted to compare the cellular composition between the groups. Comparisons between YMA and AMA samples identified a lower proportion of receptive endometria in the AMA group (P = 0.007). Gene expression profiling identified 491 differentially expressed age-sensitive genes (P adj < 0.05) that revealed the effects of age on endometrial epithelial growth and receptivity, likely contributing to decreased reproductive performance. Our results indicate that changes in the expression of the cellular senescence marker p16INK4a and genes associated with metabolism, inflammation, and hormone response are involved in endometrial aging. Importantly, we demonstrate that the proportion of multi-ciliated cells, as discovered based on RNA-seq data deconvolution and tissue IHC results, is affected by endometrial aging, and propose a putative onset of age-related changes. Furthermore, we propose that aging has an impact on the transcriptomic profile of endometrial tissue in the context of endometrial receptivity. The raw sequencing data reported in this article are deposited at the Gene Expression Omnibus under accession code GSE236128. This retrospective study identified changes in the endometrium of patients undergoing hormonal replacement and validated these changes using samples obtained during a NC. However, future studies must clarify the importance of these findings on the clinical outcomes of assisted reproduction. The findings reported in this study have important implications for devising future strategies aimed at improving fertility management in women of advanced reproductive age. This research was funded by the Estonian Research Council (grant no. PRG1076), Horizon 2020 innovation grant (ERIN, grant no. EU952516), Enterprise Estonia (grant no. EU48695), MSCA-RISE-2020 project TRENDO (grant no. 101008193), EU 874867 project HUTER, the Horizon Europe NESTOR grant (grant no. 101120075) of the European Commission, the EVA specialty program (grant no. KP111513) of the Maastricht University Medical Center (MUMC+), MICIU/AEI/10.13039/501100011033 and FEDER, EU projects Endo-Map (grant no. PID2021-12728OB-100), ROSY (grant no. CNS2022-135999), and the National Science Fund of Bulgaria (grant no. KII-06 H31/2). The authors declare no competing interests.

Metal-Organic Framework-Based Materials for Heavy Metal Sensing in Aqueous Media

Industrial development leads to an increasing amount of pollutants including heavy metal ions in surface and subsurface waters. For instance, wastewater from battery manufacturing, paint manufacturing or metal plating, contain heavy metals in high concentrations. The heavy metals are typically non-biodegradable and not-easy to excrete. Their presence within the organisms causes damage and failure of different organs. Consequently, the permissible concentrations of heavy metal ions in drinking water set by the World Health Organization are on a ppb level.In this work, electrode materials based on metal-organic frameworks (MOFs) are prepared and studied for sensing of heavy metals in aqueous media. These involved MOFs in their pristine form, composites of pristine MOFs and conducting polymer polyaniline as well as their carbonized derivatives. Cadmium and lead ions were used as model pollutants. Thorough physico-chemical characterization of the prepared materials was done using scanning electron microscopy, Raman spectroscopy, and dynamic light scattering. Electroanalytical performance was scrutinized using anodic stripping voltammetry and correlated with materials’ structural, morphology and textural properties. Limits of detection and sensitivity were determined upon optimization of the experimental conditions to identify the best material. Moreover, materials proved to be suitable for two heavy metal ions sensing in real river samples. Acknowledgments This work was supported by the Science Fund of the Republic of Serbia, grant number 7750219, Advanced Conducting Polymer-Based Materials for Electrochemical Energy Conversion and Storage, Sensors and Environmental Protection-AdConPolyMat (IDEAS programme).

Pristine Metal-Organic Frameworks for Efficient Oxygen Evolution Electrocatalysis

Green energy technologies propose the use of hydrogen as fuel instead of fossil fuels that have drawbacks of limited supplies and greenhouse gas emission during their combustion. Though hydrogen is the most abundant element on the Earth, hydrogen gas is commonly not found in pure form, but bound in compounds such as water or hydrocarbons. Thus, hydrogen gas is a synthetic fuel that needs to be produced. For the energy to be completely green, hydrogen should be produced using renewable energy. Currently, most of the hydrogen is produced using fossil fuels and only ca. 5% is produced using electrolytic water splitting. It is the high cost of the water-splitting process that limits its use on the wider scale. To reduce the associated high energy input to break a water molecule, resulting in high cost of hydrogen produced by water splitting, expensive electrocatalysts are typically used.The aim of this work is to design novel and low cost metal-organic frameworks (MOF) or coordination polymers (CP) utilizing different ligands (for instance, amide functionalized ligands) towards their application in energy conversion reactions such as oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). MOFs are rarely used in their pristine form; literature reports typically focus on MOF-derived carbon materials. Moreover, herein special attention is given to OER as the efficiency-limiting reaction within the water electrolysis process.The first part of the work focuses on the synthesis and characterization of ligands and MOFs/CPs based on transition metals, nickel and cobalt. The morphology and structure of these composites is examined using electron microscopy and X-ray diffraction analysis. The second part of the work focuses on the pristine materials’ electrocatalytic activity involving standard electrochemical techniques, i.e., voltammetry, impedance spectroscopy, and chronoamperometry to identify the material with the optimum performance and stability. Main reactions parameters including onset potential, overpotential at defined current density as well as current density at defined potential and Tafel slope, are determined.This work presents step towards green hydrogen production within search for solutions for the current energy crisis. Acknowledgments This work was supported by the Science Fund of the Republic of Serbia, grant number 7750219, Advanced Conducting Polymer-Based Materials for Electrochemical Energy Conversion and Storage, Sensors and Environmental Protection-AdConPolyMat (IDEAS programme).

(Nanocarbons Division Poster Award, 3rd Place) Investigating the Impact of Reaction and Carbonization Variables on ZIF-67 and Its Energy Storage Properties

Metal-organic frameworks (MOFs), with their exceptional properties such as high surface area, well-defined pores, and diverse chemical functionalities, have garnered widespread attention in scientific and industrial domains [1-2]. Among these, zeolitic imidazolate frameworks (ZIFs), known for their chemical and thermal stability, are extensively explored for applications ranging from catalysis and gas adsorption to electronic devices and sensors.By employing a novel hydrothermal method in aqueous solutions at room temperature, ZIF-67 nanocrystals were successfully synthesized, eliminating the need for toxic organic solvents and reducing overall costs. The resulting materials demonstrated notable efficiency in energy storage. To address the increasing demand for scalable synthesis methods, the study investigated the impact of reaction scale, reaction time, and reaction mixture dilution on the yield, morphology, and molecular structure of ZIF-67. The optimized conditions increased the production yield to 83%, surpassing the reported 75%. The subsequent carbonization process under three different regimes (800 °C, 800 °C with 3h ramp, 900 °C) resulted in materials exhibiting promising energy storage capabilities, with capacitance values reaching 81 F g−1 at a low sweep rate of 5 mV s−1. The carbonization yield showed a decrease with increasing carbonization time and temperature, and capacitance values were influenced by the presence of cobalt in the material.A slightly higher specific surface area of sample C-800 is reflected in the slightly higher capacitance values over all sweep rates indicating that specific surface area plays a role in the behavior of here synthesized materials. Capacitance is invariant at higher sweep rates (above 20mV s-1) which can be an indication that a part of the material surface, at macro- and mesopores, is highly responsive and highly conductive. At lower sweep rates micropores become available, due to prolonged time for ion insertion, which increases the capacitance that is unavailable at higher sweep rates. The highest percentage rise in capacitance is also seen in sample C-800 as its micropore volume, 40% of its total volume, is the highest among the samples investigated.Specifically, considering only the carbon part, the specific capacitance reached 176 F g−1, comparable to literature values for MOF-derived carbons [3]. The study suggests that dissolution of cobalt could enhance the material's capacitance behavior, opening avenues for further improvement in energy storage applications. This transformative approach addresses challenges associated with escalating energy demands and showcases the potential of ZIF-67-derived materials in electrochemical applications.AcknowledgementThis research was supported by the Science Fund of the Republic of Serbia, grant number 7750219, Advanced Conducting Polymer-Based Materials for Electrochemical Energy Conversion and Storage, Sensors and Environmental Protection-AdConPolyMat (IDEAS programme)

The impact of winning funding on researcher productivity, results from a randomized trial

Abstract The return on investment of funding science has rarely been accurately measured. Previous estimates of the benefits of funding have used observational studies, including regression discontinuity designs. In 2013, the Health Research Council of New Zealand began awarding funding using a modified lottery, with an initial peer review stage followed by funding at random for short-listed applicants. This allowed us to compare research outputs between those awarded funding or not using a randomized experimental study design. The analysis included eighty-eight researchers who were followed for an average of 3.8 years of follow-up. The rate ratios (and 95 per cent credible intervals (CI)) for funding were 0.95 (95 per cent CI 0.67 to 1.39) for publications and 1.06 (95 per cent CI 0.79 to 1.43) for citations, showing no clear impact of funding on research outputs. The wider use of funding lotteries could provide robust estimates of the benefits of research funding to better inform science policy.

The adverse effects of political instability on innovation systems: The case of Mexico's wind and solar sector

The global transition to clean energy requires policies to support innovation in the energy sector. However, the stability of energy policies is vulnerable to political disruptions and policy reversals. This paper aims to understand how innovation systems respond to periods of political instability. Specifically, it investigates the impact of policy retrenchment under Mexico's President Andrés Manuel López Obrador (AMLO) on the sectoral innovation system (SIS) for renewable energy. Employing the innovation system framework, we examine how the structure and functions of the SIS were affected by political changes under the AMLO Administration. Notable policy changes include canceling long-term auctions and strategic transmission projects, reducing science funding, and fostering uncertainty. These alterations significantly impact entrepreneurial experimentation, knowledge development, market formation, guidance of the search, resource mobilization, and the creation of legitimacy in the SIS for wind and solar technologies. On the positive side, we find that non-governmental organizations (NGOs) and local governments took action to compensate for the lack of federal leadership, and distributed generation continued as a niche of innovation. This research contributes to the innovation literature with an empirical study of innovation systems in Mexico. More generally, it highlights the adaptability of innovation systems during political transitions.

Multisystem health comorbidity networks of metabolic dysfunction-associated steatotic liver disease.

The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is growing, but its subsequent health consequences have not been thoroughly examined. A phenome-wide association study was conducted to map the associations of MASLD with 948 unique clinical outcomes among 361,021 Europeans in the UK Biobank. Disease trajectory and comorbidity analyses were applied to visualize the sequential patterns of multiple comorbidities related to the occurrence of MASLD. The associations jointly verified by observational and polygenic phenome-wide analyses were further replicated by two-sample Mendelian randomization analysis using data from the FinnGen study and international consortia. The observational and polygenic phenome-wide association study revealed the associations of MASLD with 96 intrahepatic and extrahepatic diseases, including circulatory, metabolic, genitourinary, neurological, gastrointestinal, and hematologic diseases. Sequential patterns of MASLD-related extrahepatic comorbidities were primarily found in circulatory, metabolic, and inflammatory diseases. Mendelian randomization analyses supported the causal associations between MASLD and the risk of several intrahepatic disorders, metabolic diseases, cardio-cerebrovascular disease, and ascites but found no associations with neurological diseases. This study elucidated multisystem comorbidities and health consequences of MASLD, contributing to the development of combination interventions targeting distinct pathways for health promotion among patients with MASLD. X.L. was funded by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001) and the National Nature Science Foundation of China (82204019) and Y.D. was funded by the Key Project of Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province (GZY-ZJ-KJ-24077) and the National Natural Science Foundation of China (82001673 and 82272860).

EU experience in regional distribution of science and innovation funding

Regional allocation of R&amp;I funding in the European Union is an important tool for stimulating economic growth and social development in different regions. This article explores the EU's experience in regional funding of science and innovation, identifying key trends, challenges and good practices. The regional approach to funding science and innovation aims to reduce disparities in regional development, support less developed regions, and facilitate the integration of different regions into the European research area. At the same time, there are significant challenges related to the uneven distribution of funding, insufficient institutional capacity of regional bodies and lack of coordination between different funding programmes. The paper analyses the impact of R&amp;I investments on regional development, especially in the context of the growing role of intangible assets and digital technologies. The results of the study highlight the need to further improve funding mechanisms, enhance the institutional capacity of regions, and increase coordination between different funding programmes. This would ensure a more equitable distribution of resources and help to increase the competitiveness of less developed regions. For Ukraine, an understanding of the European regional distribution of funding for science and innovation can be very important for its successful integration into the European Research Area, contributing to improving the quality of education and science, strengthening the innovation ecosystem, optimising resource allocation and increasing Ukraine's competitiveness on the international stage.