jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Theresa Hermann | Sarah Harzl | Robin Wallner | Elke Prettner | Eva‐Maria Pferschy‐Wenzig | Monica Cal | Pascal Mäser | Robert Weis
Source: Pharmaceuticals (article, 2025-07-03)
Field: Chemistry — Organic Chemistry

Anti-Infective Properties, Cytotoxicity, and In Silico ADME Parameters of Novel 4′-(Piperazin-1-yl)benzanilides <b>Background</b>: The benzamide <b>MMV030666</b> from MMV's Malaria Box Project, the starting point of herein presented study, was initially tested against various <i>Plasmodium falciparum</i> strains as well as Gram-positive and Gram-negative bacteria. It exhibits multi-stage antiplasmodial potencies and lacks resistance development. <b>Methods</b>: The favorable structural features from previous series were kept while the influence of the <i>N</i>-Boc-piperazinyl substituent per se, as well as its ring position and its replacement by various heteroaromatic rings, was evaluated. Thus, this paper describes the preparation of the <b>MMV030666</b>-derived 4'-(piperazin-1-yl)benzanilides for the first time, exhibiting broad-spectrum activity not only against plasmodia but also various bacterial strains. <b>Results</b>: A series of insightful structure-activity relationships were determined. Furthermore, pharmacokinetic and physicochemical parameters of the new compounds were determined experimentally or <i>in silico</i>. Drug-likeliness according to Lipinski's rules was calculated as well. <b>Conclusions</b>: A diarylthioether derivative of the lead compound was promisingly active against <i>P. falciparum</i> and exhibited broad-spectrum antibacterial activity against Gram-positive as well as Gram-negative bacteria. It is considered for testing against multi-resistant bacterial strains and <i>in vivo</i> studies. ADME ; In silico ; Plasmodium falciparum ; Lipinski's rule of five ; Benzamide ; Antibacterial activity ; Cytotoxicity ; Chemistry ; Gram-positive bacteria ; Gram ; Bacteria ; Microbiology ; Biology ; Combinatorial chemistry ; Stereochemistry ; Malaria ; Antibiotics ; Pharmacology ; Pharmacokinetics ; In vitro ; Biochemistry Synthesis and biological activity ; Synthesis and Catalytic Reactions ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Brett Pike | Alexander Kozik | Wilson Terán
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-06)
Field: Chemistry — Organic Chemistry

A trio-binning approach for Cannabis genome de novo assembly reveals extensive structural variation, and defines paralog cohorts with very good resolution With the advent of long read DNA sequencing technologies, assembling eukaryotic genomes has become routine; however, properly phasing the maternal and paternal contributions remains technically challenging. Here, we use the trio-binning approach to separate Oxford Nanopore reads derived from a Cannabis F1 wide cross, made between the Colombian landrace Punto Rojo and the Colorado CBD clone Cherry Pie #16. Reads were obtained from a single PromethION flow cell, generating assemblies with coverage of just 18x per haplotype, but with good contiguity and gene completeness, demonstrating that it is a cost-effective approach for genome-wide and high-quality haplotype phasing, which is of great value for crop breeding programs. Evaluated through the lenses of disease resistance and secondary metabolite synthesis, both being traits of interest for the Cannabis industry, we report copy number and structural variation that, as has recently been shown for other major crops, may contribute to phenotypic variation along several relevant dimensions. Variation (astronomy) ; Structural variation ; Genome ; Computational biology ; Resolution (logic) ; Copy-number variation ; Biology ; Evolutionary biology ; Genetics ; Computer science ; Gene ; Artificial intelligence ; Physics Carbohydrate Chemistry and Synthesis ; Transgenic Plants and Applications ; Cannabis and Cannabinoid Research
Authors: Fengjuan Feng | Shuo Wang | Xueqing Zhang | Xinwei Fang | Yuyang Xu | Jianlei Liu
Source: Journal of Marine Science and Engineering (article, 2025-05-30)
Field: Materials Science — Biomaterials

Fine-Grained Identification of Benthic Diatom Scanning Electron Microscopy Images Using a Deep Learning Framework Benthic diatoms are key primary producers in aquatic ecosystems and sensitive bioindicators for water quality monitoring; for example, the Yellow River Basin exhibits high diatom species diversity. However, traditional microscopic identification of such species remains inefficient and inaccurate. To enable automated identification, we established a benthic diatom dataset containing 3157 SEM images of 32 genera/species from the Yellow River Basin and developed a novel identification method. Specifically, the knowledge extraction module distinguishes foreground features from background noise by guiding spatial attention to focus on mutually exclusive regions within the image. This mechanism allows the network to focus more on foreground features that are useful for the classification task while significantly reducing the interference of background noise. Furthermore, a dual knowledge guidance module is designed to enhance the discriminative representation of fine-grained diatom images. This module strengthens multi-region foreground features through grouped channel attention, supplemented with contextual information through convolution-refined background features assigned low weights. Finally, the proposed method integrates multi-granularity learning, knowledge distillation, and multi-scale training strategies, further improving the classification accuracy. The experimental results demonstrate that the proposed network outperforms comparative methods on both the self-built diatom dataset and a public diatom dataset. Ablation studies and visualization further validate the efficacy of each module. Diatom ; Benthic zone ; Identification (biology) ; Scanning electron microscope ; Microscopy ; Materials science ; Geology ; Oceanography ; Artificial intelligence ; Environmental science ; Computer science ; Remote sensing ; Optics ; Biology ; Physics ; Ecology ; Composite material Diatoms and Algae Research ; Geochemistry and Geologic Mapping
Authors: Xu Fang | Bo Cui | Xiaoyu Hong | Yong Wang
Source: Scientia Sinica Vitae (article, 2025-10-22)
Field: Chemistry — Spectroscopy

Peptidomics of urinary biomarkers for Alzheimer’s disease 采用尿液多肽组学技术，系统分析了阿尔茨海默病（AD）和轻度认知障碍（MCI）患者的尿液样本，揭示尿液多肽组在AD筛查和诊断中具有潜在应用价值。研究纳入32例AD患者、34例MCI患者及20例健康对照者，采用高效液相色谱-串联质谱(High-performance liquid Chromatography-Tandem Mass Spectrometry，HPLC-MS/MS)技术进行多肽组分析。发现尿液多肽组呈现“20%高丰度降解蛋白主导”的分布规律，即前20%高丰度蛋白贡献了约80%的肽段数目和90%的信号强度。进一步筛选出基于磷酸组氨酸磷酸酶1（PHPT1）、神经氨酸酶1（NEU1）和纤维蛋白原β链（Fibrinogen Beta Chain, FGB）的三种降解蛋白质的AD特异性标志物组合，它们与调控线粒体功能、抑制神经炎症和阻断淀粉样纤维诱导的补体激活相关；而表皮生长因子（EGF）和III 型胶原蛋白α1链（COL3A1）的组合被筛选为MCI的多肽组标志物，可通过激活EGFR-MAPK通路促神经元存活、介导基底膜重塑抑制整合素信号异常渗入反映早期代偿。此外，在AD/MCI患者尿液中肽段的甲硫氨酸和脯氨酸残基氧化修饰水平显著升高，α₁-抗胰蛋白酶（SERPINA1）降解多肽的甲硫氨酸氧化和胶原蛋白α-1(I)链（COL1A1）/胶原蛋白α-2(I)链（COL1A2）降解多肽的脯氨酸氧化可区分疾病组与对照组，机制涉及氧化应激和破坏胶原-细胞骨架交互等。以上结果表明尿液多肽组从降解蛋白质和多肽翻译后修饰两方面反映了AD的病理机制。 Disease ; Urinary system ; Biomarker ; MEDLINE ; Context (archaeology) Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Metabolomics and Mass Spectrometry Studies
Authors: Tara Tapics | Irene Gregory‐Eaves | Yannick Huot
Source: Hydrobiologia (article, 2025-02-06)
Field: Materials Science — Biomaterials

High-frequency diatom dynamics seen in an ice- and snow-covered temperate lake using an imaging-in-flow cytometer The online version contains supplementary material available at 10.1007/s10750-025-05802-8. Snow ; Temperate climate ; Diatom ; Environmental science ; Ecology ; Dynamics (music) ; Flow (mathematics) ; Geology ; Physical geography ; Atmospheric sciences ; Oceanography ; Physics ; Biology ; Geography ; Geomorphology ; Mechanics Diatoms and Algae Research ; Marine and coastal ecosystems ; Microbial Community Ecology and Physiology
Authors: Chaoqun Jiang | Wenyan Zhang | Yujie Zhang | Huiwen Yuan
Source: Research Square (preprint, 2025-09-30)
Field: Materials Science — Biomaterials

Synergistic Enhancement of Photothermal Conversion and Mechanical Properties in PVA Hydrogels via Co-Doping of Polydopamine and Gold Nanoparticles Photothermal therapy ; Self-healing hydrogels ; Colloidal gold ; Surface plasmon resonance ; Nanoparticle ; Photothermal effect ; Mechanical strength Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation ; Solar-Powered Water Purification Methods
Authors: Yufei Chu
Source: Photonics (article, 2025-11-09)
Field: Chemistry — Spectroscopy

Editorial for the Special Issue “Optical Sensing Technologies, Devices and Their Data Applications” Optical sensing technologies represent a pivotal advancement in contemporary science and engineering, leveraging the unique properties of light to probe and analyze phenomena ranging from microscopic molecular interactions to large-scale atmospheric processes [...] Ranging ; Emerging technologies ; Optical sensing ; Atmospheric optics Spectroscopy and Laser Applications ; Analytical Chemistry and Sensors ; Gas Sensing Nanomaterials and Sensors
Authors: Csaba Varga | Attila Szántó | Tamás Gergő Asztalos | Szabolcs Sándor Diós | Géza Husi
Source: Műszaki Tudományos Közlemények (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

ZETOR 10245-típusú, mezőgazdasági vontató kiegyensúlyozótengelye forgácsolástechnológiájának tervezése és végeselem-szimulációja A projekt a Zetor 10245 traktor kiegyensúlyozó tengelyének gyártását vizsgálja, a soros négyhengeres motorok rezgéscsökkentésére összpontosítva. A 100Cr6 magas széntartalmú csapágyacél felhasználásával korszerű megmunkálási eljárásokat és végeselemes szimulációkat alkalmaztak a teljesítmény és az élettartam optimalizálása érdekében. A feszültségelemzés, a szerszámpálya optimalizálása és a DMG MORI NT 4250 DCG gépen végzett precíziós megmunkálás biztosította a szerkezeti integritást és a kiváló minőségű kimenetet. A felületi érdesség és a keménység mérései igazolták a tervezést, bizonyítva az anyagválasztás, a megmunkálás és a szimuláció hatékony integrációját a motor megbízhatóságának növelése érdekében. NAK ; Physics ; Animal science ; Chemistry ; Biology ; Telecommunications ; Engineering Metal Alloys Wear and Properties ; Surface Treatment and Coatings ; Metallurgy and Material Forming
Authors: Gonzalo Almendros | J. A. López-Pérez | Zulimar Hernández
Source: Agronomy (article, 2025-06-29)
Field: Chemistry — Analytical Chemistry

A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra Significant models predicting Soil Organic Carbon (SOC) and other chemical and biological indicators of soil health in an experimental farm with semi-arid Mediterranean Calcisol have been obtained by partial least squares (PLS) regression, with mid-infrared (MIR) spectra of whole soil samples used as independent variables (IVs). The dependent variables (DVs) included SOC, pH, electric conductivity, N, P2O5, K, Ca2+, Mg2+, Na+, Fe, Mn, Cu and Zn. The DVs also included free-living nematodes and microbivores, such as Rhabditids and Cephalobids, and phytoparasitics, such as Xiphinema spp. and other Dorylaimids. More importantly, an attempt was made to determine which spectral patterns allowed each dependent variable (DV) to be predicted. For this purpose, a number of statistical indices were plotted between 4000 and 450 cm−1, e.g., variable importance for prediction (VIP) and beta coefficients from PLS, loading factors from principal component analysis (PCA) and correlation and determination indices. The most effective plots, however, were the “scaled subtraction spectra” (SSS) obtained by subtracting the averages of groups of spectra in order to reproduce the spectral patterns typical in soils where the values of each DV are higher, or vice versa. For instance, distinct SSS resembled the spectra of carbonate, clay, oxides and SOC, whose varying concentrations enabled the prediction of the different DVs. Chemometrics ; Environmental science ; Soil health ; Principal component analysis ; Infrared spectroscopy ; Environmental chemistry ; Infrared ; Soil water ; Chemistry ; Soil science ; Mathematics ; Soil organic matter ; Statistics ; Chromatography ; Physics ; Optics Spectroscopy and Chemometric Analyses
Authors: A. El-Shabasy | Hana Abdulrahman Saleh Ali AL-Quhbi | Beluchukwu Joseph Nwankwo | Gauri Shankar Bhandari | Siva Sankar Sana | Rania S. Shehata
Source: NG Agricultural Sciences (article, 2025-08-17)
Field: Materials Science — Materials Chemistry

Basic Steps of Artificial Intelligence on Plant Nanotechnology: a Review The combination of plant nanotechnology and artificial intelligence (AI) can bring radical benefits in the form of sustainable agriculture and environmental stewardship. This review provides an overview of recent developments in 13 thematic areas, namely, yield increase, soil health, nano-fertilizers and nano-pesticides, stress tolerance, precision irrigation, and plant disease control. Nanotechnology in plants allows optimizing the monitoring of abiotic and biotic parameters, optimizing resources and intervening to reduce environmental contamination. Biosensors developed by using plants, nanotechnology-enabled breeding technologies and the prospect of proteomics have great potential in enhancing resilience and yields. In addition to agriculture, AI-nanotechnology integration can aid the assessment of environmental risks, climate change forecast, nutrient flow, and food webs. The review also compares the Non-Artificial Intelligent Digital Nano-manufacturing (NAIDNM) with the Artificial Intelligent Digital Nano-manufacturing (AIDNM), presenting the challenges of both and possible solutions as well as future directions. The findings of the study in general emphasize the contribution of AI-aided plant nanotechnology in improving the productivity, resiliency and roadmap to future sustainability as it answers universal food security and ecological balance. Sustainability ; Resilience (materials science) ; Applications of artificial intelligence ; Food security ; Sustainable agriculture ; Precision agriculture ; Agriculture ; Plant disease Nanoparticles: synthesis and applications ; Smart Agriculture and AI ; Water and Wastewater Treatment
Authors: A.K. Rai | Ludovic Grossard | Tristan Chabrol | François Reynaud | Mathieu Chauvet | Florent Bassignot | Rodolphe Krawczyk
Source:  (preprint, 2025-07-30)
Field: Chemistry — Spectroscopy

Cryoless MIR Spectroscopy via Pump Wavelength scanning using PPLN Ridge Waveguide Upconversion This article presents a novel room-temperature Mid-Wave Infrared spectroscopy (MWIR) technique utilizing upconversion in a 2cm-long PPLN ridge waveguide. Addressing limitations of costly, cryo-dependent FTIR, our "non-linear spectrometer" samples MWIR spectra by sweeping a low-power, continuous wave monochromatic pump laser, enabling detection with sensitive silicon detectors. Experimental validation with a channelled spectrum demonstrated accurate thermal source spectral modulation recovery, showing a 2% relative error. This robust approach offers a promising pathway for miniaturized, practical MIR spectroscopic tools. Photon upconversion ; Ridge ; Materials science ; Spectroscopy ; Wavelength ; Optics ; Lithium niobate ; Waveguide ; Optoelectronics ; Geology ; Physics ; Doping Spectroscopy and Laser Applications
Authors: Damian Rouson | Dan Bonachea | Brad Richardson | Jordan A. Welsman | Jeremiah Bailey | E. D. Gutmann | David J. Torres | Katherine Rasmussen | Baboucarr Dibba | Yunhao Zhang | K. A. Weaver | Zhe Bai | Tan Nguyen
Source: The Journal of Open Source Software (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Fiats: Functional inference and training for surrogates Fiats provides a platform for research on the training and deployment of neural-network surrogate models for computational science. Fiats also supports exploring, advancing, and combining functional, object-oriented, and parallel programming patterns in Fortran 2023. As such, the Fiats name has dual expansions: “Functional Inference And Training for Surrogates” or “Fortran Inference And Training for Science.” Fiats inference and training procedures are pure and therefore satisfy a language constraint imposed on procedure invocations inside Fortran’s parallel loop construct: do concurrent. Furthermore, the Fiats training procedures are built around a do concurrent parallel reduction. Several compilers can automatically parallelize do concurrent on Central Processing Units (CPUs) or Graphics Processing Units (GPUs). Fiats thus aims to achieve performance portability through standard language mechanisms. Software portability ; Inference ; Graphics ; Compiler ; Functional programming ; Training (meteorology) ; Type inference ; Software deployment ; Constraint satisfaction Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Neural Network Applications
Authors: Izar Sínde-González | Erika Murgueitio-Herrera | César E. Falconí | Mariluz Gil‐Docampo | Theofilos Toulkeridis
Source: Agronomy (article, 2025-02-14)
Field: Chemistry — Analytical Chemistry

Spectral Variability Analysis of Lupinus mutabilis Sweet Under Nanofertilizer and Chelate Application Through Spectroscopy and Unmanned Aerial Vehicle (UAV) Multispectral Images Lupin is an Andean legume that has gained importance in Ecuador due to the protein content in its grain. Nonetheless, in recent times the production of lupin has been affected by inadequate nutritional management. In order to avoid such circumstances, the current study spectrally analyzed lupin cultivation under the application of nanofertilizers and Fe and Zn chelates, within two controlled trials, using a radiometer spectrum, an active crop sensor and a multispectral sensor mounted on a UAV. Vegetation indices were generated and subsequently statistically analyzed using ANOVA and Tukey tests. In the field trial, the treatments lacked an indication of significant improvements, while in the greenhouse trial, the nanofertilizer treatments indicated better results compared to the control treatments. However, it was also determined that the application of nanofertilizers at a concentration of 540 ppm demonstrated significant efficiency in greenhouse conditions, which could not be achieved in the field. Furthermore, the chelate treatment presented a certain degree of toxicity for the plant. Multispectral image ; Remote sensing ; Spectral analysis ; Imaging spectroscopy ; Environmental science ; Chelation ; Botany ; Biology ; Spectroscopy ; Agronomy ; Chemistry ; Hyperspectral imaging ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Remote Sensing in Agriculture
Authors: Ibtissam Hanoun | George Chatzigeorgiou | Fodil Meraghni
Source: SPIRE - Sciences Po Institutional REpository (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Modèle multi-échelle de composites Fuzzy Fiber viscoplastiques enrobés de nanotubes de carbone (CNT) avec interface endommagée International audience Fiber ; Interface (matter) ; Fuzzy logic ; Carbon nanotube ; Deformation (meteorology) Carbon Nanotubes in Composites ; Polymer Nanocomposites and Properties ; Tribology and Wear Analysis
Authors: Dan Chicea
Source: Photonics (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

On the Possibility of Analyzing Dynamic Light Scattering Time Series Using the Co-Occurrence Matrix and Artificial Neural Networks A Dynamic Light Scattering time series can be analyzed using several well-established procedures. An alternative is to compute a co-occurrence matrix of the time series and to establish a correlation between the moments of the intensity level distribution and the physical properties of the particles in suspension using an Artificial Neural Network. We performed a comprehensive error analysis of the proposed method for estimating particle diameter from the simulated Dynamic Light Scattering (DLS) time series, both on simulated and on experimental data. The results demonstrate the potential of utilizing moments of the co-occurrence matrix as an alternative approach for processing DLS time series data to assess the size of suspended particles. Series (stratigraphy) ; Dynamic light scattering ; Light scattering ; Artificial neural network ; Matrix (chemical analysis) ; Scattering ; Time series Material Dynamics and Properties ; Atmospheric aerosols and clouds ; Optical Imaging and Spectroscopy Techniques
Authors: Eti Desti Sastia | Akmal Johan | Idha Royani
Source: Jurnal Penelitian Sains (article, 2025-12-03)
Field: Chemistry — Inorganic Chemistry

Serat Nanofiber Berbasis PVA dengan Penambahan Ekstrak Daun Pepaya dan Sirsak: Tinjauan Sistematis dalam Perspektif Nanoteknologi Penelitian ini merupakan tinjauan sistematis terhadap pengembangan serat nanofiber berbasis Poly(vinyl) alcohol (PVA) yang dimodifikasi dengan ekstrak daun pepaya (Carica papaya L.) dan daun sirsak (Annona muricata L.) sebagai inovasi bahan aktif untuk perawatan jerawat (acne treatment). Dengan menggunakan metode Systematic Literature Review (SLR), studi ini mengidentifikasi artikel-artikel ilmiah dari berbagai basis data (Google Scholar, PubMed, ScienceDirect, dan lainnya) dalam rentang 10 tahun terakhir yang membahas sintesis nanofiber, teknologi elektrospinning, dan aplikasi bahan alam sebagai antibakteri. Hasil kajian menunjukkan bahwa daun pepaya dan daun sirsak mengandung senyawa aktif seperti flavonoid, saponin, dan acetogenin yang bersifat anti-inflamasi, antioksidan, serta antibakteri. Kombinasi kedua ekstrak tersebut dalam matriks PVA melalui teknik elektrospinning berpotensi menghasilkan serat nanofiber dengan karakteristik fisikokimia yang mendukung efektivitas terapi topikal jerawat. Studi ini merekomendasikan kelanjutan pengembangan formulasi dan pengujian in vitro/in vivo guna memastikan keamanan dan efikasi nanofiber berbahan dasar alami untuk aplikasi dermatologis berkelanjutan. Experimental research ; Compounding ; Folk song Coconut Research and Applications ; Natural Products and Applications ; Medicinal Plant Research
Authors: Yash Rawat | Aarti Sehwag -
Source: International Journal of Engineering Applied Sciences and Technology (article, 2025-04-01)
Field: Chemistry — Analytical Chemistry

MEDICINAL PLANTS DETECTION USING DEEP LEARNING The pharmaceutical, Ayurvedic, and herbal medicine industries depend on the precise identification of therapeutic plants. Botanists must manually inspect plants using traditional methods of identification, which takes time and is prone to mistakes. The use of deep learning (DL) techniques for automated identification of medicinal plants from leaf photos is investigated in this work. Several CNN architectures, such as DenseNet121, MobileNet, and InceptionV3, are compared in order to identify the most effective model for plant classification. Preprocessing methods including scaling and augmentation were employed to improve model performance on a dataset of scanned photos of 50 different types of medicinal plants. Using MobileNet, the suggested system demonstrated its efficacy in real-time plant identification with an accuracy of 98.3%.According to the study, DL models have the ability to help taxonomists, botanists, and the pharmaceutical industry ensure that therapeutic plants are used appropriately. Deep learning ; Artificial intelligence ; Computer science ; Traditional medicine ; Medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI