jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maryam Ammani Lawal | Gregory Elayeche Oko | Sarah Tanko | Safiya Suleiman Sambo | Danjuma Mallam | Sani Shehu
Source: Trends in Natural Products Research (article, 2026-02-01)
Field: Chemistry — Organic Chemistry

Behavioural Evaluation of Pancratium maritimum L. (Amaryllidaceae) Ethanol bulb Extract Reveals Dose-Dependent Antidepressant-Like Effects in Murine Models. Depression remains a major global health challenge., Limitations associated with current pharmacotherapies drive the search for novel, safer antidepressant agents from natural sources. Pancratium maritimum is a medicinal plant rich in bioactive secondary metabolites. This study evaluated the antidepressant-like effects of the ethanol extract of Pancratium maritimum bulbs in murine models and characterized its phytochemical constituents. Swiss albino mice were treated intraperitoneally with the extract at doses of 150, 300, and 600 mg/kg, while imipramine (15 mg/kg) served as the positive control. Antidepressant activity was assessed using the forced swim test (FST) and tail suspension test (TST). Phytochemical screening revealed the presence of alkaloids, flavonoids, phenolic compounds, tannins, saponins, terpenoids/steroids, carbohydrates, and cardiac glycosides. In the TST, the extract significantly reduced immobility time from 134.06 ± 0.03 s (control) to 127.43 ± 0.03 s, 114.27 ± 0.06 s, and 86.04 ± 0.04 s at 150, 300, and 600 mg/kg, respectively. Similarly, in the FST, immobility time decreased from 148.76 ± 5.6 s (control) to 129.74 ± 4.59 s, 118.56 ± 4.56 s, and 69.25 ± 3.2 s across the same dose range. Imipramine produced marked reductions in immobility (69.22 ± 0.02 s in TST and 38.31 ± 3.5 s in FST). These findings demonstrate a clear dose-dependent antidepressant-like effect of Pancratium maritimum bulb ethanol extract, likely mediated by its rich phytochemical profile. The study provides novel evidence supporting Pancratium maritimum as a promising source of natural antidepressant agents. Phytochemical ; Behavioural despair test ; Imipramine ; Antidepressant ; Tail suspension test ; Ethanol Chemical synthesis and alkaloids ; Neurotransmitter Receptor Influence on Behavior ; Alkaloids: synthesis and pharmacology
Authors: Qiaoling Sun | Yongkang Guo | Liting Wang | Ling Jia | Peng Wei | Sanyuan Ma
Source: Genes (article, 2026-02-12)
Field: Materials Science — Biomaterials

CRISPR-Mediated Silkworm: The Oncoming Agricultural Revolutions and a Rising Model Organism The silkworm (<i>Bombyx mori</i>) is essential to sericulture and is also becoming a key model organism in genomics and agriculture. For decades, genetic studies of the silkworm were limited by inefficient and inflexible genome tools. CRISPR genome editing allows precise and scalable alterations to genes regulating development, physiology, and industrial traits. This review summarizes silkworm genome-editing breakthroughs, highlighting CRISPR's evolution from simple gene knockouts to large-scale genome-wide screening. We highlight how these advancements contribute to disease resistance, higher yields, and the development of new silk-based materials, as well as how they influence the development and growth rate of the sericulture. The creation of high-quality reference genomes, pangenomes, and genome-wide screening systems has made the silkworm a major model for integrating multiple biological datasets and approaches, such as genomic, transcriptomic, and proteomic. By considering the unique biological characteristics of the silkworm, this provides new insights for research on silk biology, piRNA synthetic biology, and hormonal signaling regulation. Finally, we examine new areas at the intersection of CRISPR, pangenomics, and artificial intelligence (AI) and suggest future paths for molecular breeding, pest control, and synthetic biology. Moreover, AI-assisted prediction of CRISPR outcomes is utilized to inform the design of targeted trait modifications, representing an approach to enhancing biomanufacturing efficiency and eco-friendly silk production. Together, these advances have made the silkworm a flexible genetic platform and an important part of sustainable agriculture and biomanufacturing. CRISPR ; Genome editing ; Organism ; Sericulture ; Biomanufacturing ; Model organism ; Genomics ; Synthetic biology Silk-based biomaterials and applications ; CRISPR and Genetic Engineering ; Silkworms and Sericulture Research
Authors: Izabella Rebeka Márkus | Ádám Rácz | Adam Rácz
Source: Rudarsko-geološko-naftni zbornik (article, 2026-01-01)
Field: Materials Science — Biomaterials

A PRELIMINARY STUDY OF THE EFFECT OF ALTERATION ON BREAKAGE PROPERTIES OF ANDESITES FROM TÁLLYA AND RECSK (HUNGARY) This preliminary study investigates the influence of post-magmatic alteration on the breakage behaviour of andesitic rocks during comminution, with particular emphasis on the Chemical Index of Alteration (CIA) and smectite content. Six representative samples from two Hungarian quarries (Recsk and Tállya) exhibiting broadly similar mineralogical and textural properties but varying degrees of alteration were subjected to single particle breakage tests across multiple size fractions. Although the limited dataset does not allow for broad generalisation, the results provide a first indication that increased CIA values and higher smectite contents are associated with greater fragmentation susceptibility, as reflected by elevated A×b parameters. The study demonstrates that alteration indices can provide valuable predictive information on breakage response in andesitic rocks. These findings suggest the importance of integrating alteration metrics alongside traditional mineralogical and textural parameters when evaluating the breakage behaviour of andesitic rocks for aggregates production. While smectite quantification highlights the role of secondary phyllosilicate minerals in reducing the strength of andesitic rocks, CIA offers a more broadly applicable measure of alteration intensity that may be transferable across different volcanic contexts. The results are presented as a case study that highlights the potential of CIA and smectite quantification as predictive tools, while acknowledging the need for further research with larger datasets to confirm these trends. Breakage ; Andesite ; Andesites ; Clay minerals ; Volcano ; Alunite Clay minerals and soil interactions ; Mineral Processing and Grinding ; Geological and Geochemical Analysis
Authors: Kas J. Houthuijs | Karl J. Jobst | Frederic Béen
Source: ChemRxiv (article, 2026-01-29)
Field: Chemistry — Spectroscopy

Data-Driven Filter for Detector Oscillation Artefacts in Time-of-Flight Mass Spectrometry Time-of-flight mass spectrometry (TOF-MS) is widely used for complex mixture analysis due to its ability to detect thousands of chemical compounds in a single sample. However, high-intensity ions can saturate the detector and generate artefacts, commonly referred to as "detector ringing". These artefacts resemble true MS features and often persist after componentization, complicating data interpretation and feature prioritization. Here, we introduce a data-driven filtering approach that exploits the time-domain origin of detector ringing to remove such artefacts. Because ion flight times scale with √𝑚/𝑧, oscillations in the detector response manifest as artefactual features with constant delays in √𝑚/𝑧, providing a universal detection criterion independent of ion source conditions and chromatographic or ion mobility separation. The workflow was evaluated on GC-, LC-, and ion mobilityhyphenated TOF-MS datasets from multiple instrumental platforms, identifying and removing up to 5.3% of detected features. Comparison across instruments revealed that the characteristic Δ√𝑚/𝑧 spacings are instrument-specific and polarity independent, suggesting potential applications in data forensics and traceability. To assess broader relevance, the filter was applied to the MassBank and NIST DART-MS Forensics fragmentation mass spectral libraries. Although the overall incidence was low relative to library size (1.7%), over 1000 mass spectra were provisionally identified as containing ringing artefacts. Clustering these spectra by their Δ√𝑚/𝑧 effectively partitioned the data by TOF-MS platform, revealing distinct groupings across libraries and contributors. By specifically targeting detector-derived artefacts, this workflow reduces false positives and improves feature prioritization and compound identification, enhancing the robustness of TOF-MS based chemical analyses. Detector ; Robustness (evolution) ; Mass spectrometry ; False positive paradox ; Mass spectrum ; Ringing ; Filter (signal processing) ; Cluster analysis ; Chemometrics ; NIST Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: P. T. Moţiu | C. G. Pășcuț | Szilárd Bartha | Camelia Elena Moga | Berchez Octavian | Ioana Andra Vlad | Ioan Tăut | Florin Alexandru REBREAN | Florin-Dumitru Bora
Source: International Journal of Molecular Sciences (article, 2026-02-14)
Field: Chemistry — Analytical Chemistry

Molecular and Environmental Elucidation of Heavy Metal Transfer in Tilia spp.: From Soil Systems to Herbal Infusions Across Urban–Forest Gradients Understanding the pathways through which heavy metals accumulate in medicinal plants and enter herbal infusions is essential for linking environmental quality with human exposure. This study investigated multi-matrix metal transfer in <i>Tilia</i> spp. along an urban-forest gradient by quantifying twelve elements (Pb, Cd, Zn, Cu, Ni, Cr, Mn, Co, As, Hg, Al, and V) in soil, bark, leaves, flowers, and corresponding infusions using inductively coupled plasma mass spectrometry and by estimating daily intake for different age groups based on EFSA default body weights and two consumption scenarios (150 and 400 mL day<sup>-1</sup>). The results revealed clear spatial patterns, with significantly higher metal loads in urban sites and a consistent transfer from environmental compartments to plant tissues and infusions. Mn, Al, Pb, and Cd exhibited the highest extractability, leading to elevated estimated daily intakes in young children, identified as the most vulnerable group due to their lower body mass. However, all exposure values remained below EFSA and JECFA toxicological reference limits, while As and Hg were undetectable in all infusions. These findings indicate that <i>Tilia</i> infusions contribute minimally to overall dietary metal exposure and confirm <i>Tilia</i> spp. as reliable bioindicators of soil- and airborne metal deposition, supporting the safe consumption of linden tea under realistic intake conditions. Transfer factor ; Bioindicator ; Metal ; Inductively coupled plasma mass spectrometry ; Contamination ; Heavy metals ; Zinc ; Biomonitoring Heavy Metals in Plants ; Heavy metals in environment ; Heavy Metal Exposure and Toxicity
Authors: Felicitas Pellengahr | Julia Kulpa | Ali Ghannadzadeh | Yvonne van der Meer
Source: Materials Circular Economy (article, 2026-02-10)
Field: Materials Science — Biomaterials

The Role of Compostable Plastics in Nutrient Recycling: Environmental Impact of Biobased Teabags in a Circular Economy Abstract Following the EU’s Packaging and Packaging Waste Regulation, packaging inseparable from food waste, such as teabags, must be industrially compostable, facilitating joint composting and supporting circular economy goals by enabling nutrient recycling. Utilizing home-compostable materials, however, could have additional advantages by mitigating collecting and waste management activities. The biobased and home-compostable polymer polybutylene succinate (PBS) is a potential candidate for these applications, e.g., in teabags. Through an inductive case study approach, this cradle-to-grave Life Cycle Assessment (LCA) evaluates environmental benefits and trade-offs of employing PBS in teabags compared to industrially compostable polylactic acid (PLA) and non-compostable polypropylene (PP). The assessment incorporates a detailed emission inventory of microplastics, a rising environmental concern linked to conventional plastics. Results highlight that the primary environmental impacts of PBS teabags arise during the use phase, followed by the production phase, whereas home composting exhibits minimal environmental impact (approximately − 0.1%). Scenario analyses illustrate that outcomes significantly depend on end-of-life management practices, notably the substitution of chemical fertilizers through compost utilization. Differences in the fertilizer utilization rate can lead to a variation of up to 20.61% in environmental impacts. The substitution benefit is attributed to tea leaf waste rather than the PBS material itself. The findings also show that PBS teabags are environmentally beneficial compared to PP only if home-composted rather than incinerated. Furthermore, adopting PBS mitigates microplastic pollution, which was investigated through microplastic leakage estimations, and advances circular economy objectives by enabling resource regeneration and transforming food waste into valuable compost. This study underscores PBS as a sustainable material option, contributing directly to the circular economy by reducing waste incineration, enhancing resource recovery, and minimizing plastic pollution. Circular economy ; Life-cycle assessment ; Environmental impact assessment ; Resource recovery ; Bioplastic ; Municipal solid waste ; Food waste ; Resource depletion ; Raw material ; Polyolefin biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Nanocomposite Films for Food Packaging
Authors: Alexander Hell | Jonas Fell | T. Werning | H. -D. Herrmann
Source: Materials (article, 2026-03-27)
Field: Materials Science — Metals and Alloys

Visualizing the 3D Evolution and Morphology of Hydrogen-Assisted Ductile Crack Growth in Hydrogen-Precharged P355NH Steel Using X-Ray Micro-Computed Tomography Hydrogen embrittlement is known to adversely affect the mechanical properties of low-carbon steels used for pipelines and pressure vessels, leading to accelerated crack growth and lowered fracture toughness. To overcome the limitations of surface-based analysis, this study employs X-ray micro-computed tomography (µ-CT) to provide a comprehensive 3D evaluation of the crack evolution. This approach is used to assess hydrogen-assisted crack growth in P355NH compact tension samples from previous fracture mechanical tests and enables a precise quantification of the internal crack path and the crack tip opening angle (CTOA) across the entire specimen thickness as well as the local damage morphology. By integrating these spatial parameters, a deeper understanding of the impact of hydrogen on local fracture mechanisms is achieved, revealing insights that have remained hidden in previous two-dimensional microscopy observations. For instance, µ-CT results clearly demonstrate that the hydrogen-assisted crack propagation is associated with increased void formation and secondary cracking in vicinity of the crack tip. However, it is proposed that the results are superimposed with continuous hydrogen desorption, which implies a need for in situ charging during mechanical loading and an analysis of the hydrogen concentration profile. Both will be the scope of further studies. Hydrogen embrittlement ; Embrittlement ; Void (composites) ; Cracking ; Fracture (geology) ; Fracture mechanics ; Crack closure ; Tension (geology) ; Tomography Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Material Properties and Failure Mechanisms
Authors: David Coimbra | André Sarkis Müller
Source: Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE) (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Analysis of Steel Structural Profiles under Flexural-Compression stress using Machine Learning Algorithms The design of steel profiles requires a rigorous analytical approach, especially for elements subjected to flexural-compression, which involves the simultaneous action of compressive forces and bending moments. This type of loading is more complex than cases such as pure tension, pure compression, or flexural-tension, as it demands the combined consideration of both instability and interaction between acting forces. Given this complexity, conventional verification methods may be insufficient for rapid generalizations, particularly when analyzing multiple types of cross-sections. In this context, Machine Learning (ML) emerges as a promising alternative tool, capable of modeling complex relationships between input and output variables based on historical data. ML is a subfield of artificial intelligence that enables the development of computational models that learn from experience, recognizing patterns in data and making predictions without explicit programmed rules. This study proposes the application of machine learning algorithms to verify members under flexural compression stress. The algorithms K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree were employed, combined with ensemble techniques and class balancing strategies to mitigate bias and variance issues. The implementation was conducted in Python using the Scikit-Learn library. The dataset was generated through a FORTRAN program, based on the NBR 8800:2024 standard criteria. The study considered multiple commercial steel profiles, common structural load combinations, different steel grades, and varied restraints along both principal axes. The diversity of these variables resulted in a high number of combinations, necessitating a robust computational tool to perform all calculations —a role fulfilled effectively by FORTRAN. The input features included section geometry, applied load, element length, steel class, and boundary conditions, while the output was a binary classification (OK/NOT OK) indicating structural compliance. The dataset was split into 80% for training and 20% for testing. The results showed satisfactory performance for all applied algorithms, with Random Forest – an ensemble method based on decision trees – standing out due to its high accuracy in estimating the class. This reinforces the potential of machine learning as a complementary tool to traditional approaches, especially in situations involving multiple variables. Python (programming language) ; Decision tree ; Support vector machine ; Stability (learning theory) ; Robustness (evolution) ; Artificial neural network ; Random forest ; Bridging (networking) ; Ensemble learning Machine Learning in Materials Science ; Structural Behavior of Reinforced Concrete ; Structural Integrity and Reliability Analysis
Authors: W. Zhang | Shaoming Pan | Laisheng Zhong | Liangyuan Chen | Yuan Ma
Source: Energies (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Study on the DC Discharge Model of Insulators Polluted by Typical Components Based on Effective Salt Deposit Density Pollution flashover accidents of transmission line insulators have a wide impact and low reclosing success rates, posing a serious threat to the safe and stable operation of the power grid. The existing pollution discharge and flashover models of insulator based on equivalent salt deposit density (ESDD) present significant differences from the actual situation. To address this issue, the conductivity of electrolyte solutions experiments is carried out in this paper, and the quantitative functional relationship between conductivity and concentration of typical components is obtained. On this basis, the concept of effective salt deposit density (SDDe) is introduced to characterize the actual mass of pollution participating in surface conduction per unit area. A DC discharge dynamic model for polluted insulators is established and verified based on SDDe combined with the discharge development process. Research results indicate that the average difference between the calculated flashover voltage and experimental value is less than 7%. The deviation of flashover voltage between the SDDe basis model and measured salt deposit density (SDDm) basis value increases with the increasing proportion of slightly soluble components. With the increase of insulator surface water adhesion, the flashover voltage obtained by the proposed model decreases while the corresponding SDDm basis value remains constant. The effects of factors such as slightly soluble pollution and surface water adhesion are considered in the proposed model sufficiently. The application of the model based on SDDe can improve the accuracy of the insulator discharge process and flashover voltage prediction, especially for the complex pollution area. During the generation and propagation of the arc, the leakage current under SDDm is relatively higher and the pollution layer resistance is lower compared to that under SDDe; the variations in the pollution layer resistance and leakage current with arc development under SDDm do not adequately reflect the actual conditions. Arc flash ; Insulator (electricity) ; Conductivity ; Voltage ; Pollution ; Electrolyte ; Electric power transmission ; Electrical resistivity and conductivity ; Transmission line High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Thermal Analysis in Power Transmission
Authors: Chloe L. Tayler | Serena Bateman | Charlie Haslam | Leonie Müller | Kerena Norris | Evan Rosa-Roseberry | Stan Martens | J. Yu | Eleanor R. Dickinson | Lee M. Booty | Rebecca Beveridge | Nicholas J. W. Rattray | Rachel E. Heap
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-08)
Field: Chemistry — Spectroscopy

dia-PASEF Enables Rapid Profiling of the Human Secretome for Deeper Insights into Cellular Dynamics and Inflammatory Mechanisms Abstract Protein secretion is a fundamental mechanism for cellular coordination and signalling, with its dysregulation leading to widespread physiological dysfunction and disease. Immunoassay formats that utilise secondary antibody readouts are the current gold standard for measuring secreted proteins, offering high specificity and sensitivity, but relying on predefined protein panels that constrain the discovery of novel biology. We present a scalable mass spectrometry-based workflow that combines data-independent acquisition with ion mobility and parallel fragmentation to deliver rapid, global profiling of the secretome. Using a translationally relevant human iPSC-derived macrophage model, our approach identified over 1200 proteins in under 15 minutes of acquisition time, delivering exceptional reproducibility across a large sample set. We applied this approach to profile pro-inflammatory phenotypes, confirming robust identification of key cytokines and chemokines whilst revealing non-canonical immune responses absent from both targeted panels and the intracellular proteome. In particular, we identified a unique cholesterol efflux signature, marked by the secretion of APOA1 and PON1, in response to Mycobacterium Tuberculosis , consistent with the metabolic reprogramming that takes place during infection. Furthermore, temporal profiling of macrophage responses to lipopolysaccharide over 24 hours resolved dynamic secretion trajectories that distinguish between acute and chronic inflammatory states. The extended time period facilitated the observation of distinct cytokine-dependent secretion phenotypes, with early secretion of TNFα and IL6 initiating downstream signalling cascades that resulted in the delayed secretion of chemokines such as CXCL10 and CCL8. Collectively, these findings establish a robust, scalable platform for global characterisation of secretory networks. Beyond macrophage biology, this workflow offers broad utility for biomarker discovery, mechanistic studies of disease progression and evaluation of new therapeutic interventions, providing a powerful tool for advancing precision medicine. Secretion ; Chemokine ; Immune system ; CXCL10 ; Inflammation ; Cytokine ; Secretory protein ; Macrophage ; Biogenesis Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Advanced Biosensing Techniques and Applications