jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Davide Andreoletti | Aris Marcolongo | Natasa Sarafijanovic Djukic | Julien Roulet | Stefano Billeter | Andrzej Kurenda | Margot Visse‐Mansiaux | Brice Dupuis | Carrol Plummer | Beatrice Paoli | Omran Ayoub
Source:  (article, 2025-06-26)
Field: Chemistry — Analytical Chemistry

Machine Learning-Based Early Detection of Potato Sprouting Using Electrophysiological Signals Accurately predicting potato sprouting before the emergence of any visual signs is critical for effective storage management, as sprouting degrades both the commercial and nutritional value of tubers. Effective forecasting allows for the precise application of anti-sprouting chemicals (ASCs), minimizing waste and reducing costs. This need has become even more pressing following the ban on Isopropyl N-(3-chlorophenyl) carbamate (CIPC) or Chlorpropham due to health and environmental concerns, which has led to the adoption of significantly more expensive alternative ASCs. Existing approaches primarily rely on visual identification, which only detects sprouting after morphological changes have occurred, limiting their effectiveness for proactive management. A reliable early prediction method is therefore essential to enable timely intervention and improve the efficiency of post-harvest storage strategies, where early refers to detecting sprouting before any visible signs appear. In this work, we address the problem of early prediction of potato sprouting. To this end, we propose a novel machine learning (ML)-based approach that enables early prediction of potato sprouting using electrophysiological signals recorded from tubers using proprietary sensors. Our approach preprocesses the recorded signals, extracts relevant features from the wavelet domain, and trains supervised ML models for early sprouting detection. Additionally, we incorporate uncertainty quantification techniques to enhance predictions. Experimental results demonstrate promising performance in the early detection of potato sprouting by accurately predicting the exact day of sprouting for a subset of potatoes and while showing acceptable average error across all potatoes. Despite promising results, further refinements are necessary to minimize prediction errors, particularly in reducing the maximum observed deviations, to ensure even greater reliability for practical storage management applications. Electrophysiology ; Computer science ; Sprouting ; Artificial intelligence ; Pattern recognition (psychology) ; Speech recognition ; Machine learning ; Neuroscience ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Gang Ding | Feng Liao | Zongliang Zhang | Na Li | Ying Huang | Zhouping Tang | Fei Li | Xiao-Dong Huang | Luming Qi | Yuntong Ma
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Combining H2o Auto-Ml and Traditional Machine Learning for the Resource Evaluation on Non-Medicinal Parts of Plants: Taking Coptis Deltoidea as an Example Resource (disambiguation) ; Medicinal herbs ; Artificial intelligence ; Traditional medicine ; Machine learning ; Computer science ; Medicine Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Le Zhao | Yi Zheng | Yang Yu | Yajun Fan | Fangfang Ren
Source: Journal of Food Composition and Analysis (article, 2025-12-18)
Field: Chemistry — Analytical Chemistry

Insertion-type near-infrared spectroscopy combined with interpretable machine learning for in situ prediction of oil content in stored soybeans Accurate and rapid determination of oil content in stored soybeans is essential for quality control and economic evaluation, yet conventional chemical analysis is destructive, time-consuming, and impractical for large-scale in situ monitoring. This study proposes an insertion-type near-infrared (NIR) spectroscopy approach for non-destructive, real-time estimation of soybean oil content directly within bulk storage. Spectra were acquired using a self-developed probe-based NIR spectrometer and compared with a reference online device from the literature. Despite substantial differences in acquisition environments, both systems exhibited consistent spectral patterns, with key peaks near ∼1200 nm (first overtone of C–H stretching) and ∼1450 nm (combination of C–H stretching/bending and first overtone of O–H), indicating robustness across settings. Four modeling algorithms—Partial Least Squares Regression (PLSR), Gaussian Process Regression (GPR), Least Squares Support Vector Regression (LSSVR), and Kernel Extreme Learning Machine (KELM)—were tested with multiple preprocessing strategies. The optimal configuration, KELM with wavelet domain denoising, de-trending, and z-score standardization, achieved R² = 0.947, RMSE = 0.924, and RPD = 4.340, outperforming the other three models by at least 2.82 %, 6.38 %, and 20.16 %. SHAP analysis revealed the most influential wavelengths consistent with oil-related absorptions, demonstrating the feasibility of this in situ monitoring approach. • Developed insertion-type NIR system for in situ spectra inside bulk soybean storage. • Constructed dataset linking in situ NIR spectra with oil content in stored soybeans. • Built robust machine learning models to map NIR spectral features to oil content. • Applied SHAP to quantify band contributions and identify oil-related wavelengths. Partial least squares regression ; Support vector machine ; Near-infrared spectroscopy ; Content (measure theory) ; In situ ; Least squares support vector machine ; Spectroscopy ; Pattern recognition (psychology) ; Spectrometer ; Random forest Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: 康辉 陈
Source: Computer Science and Application (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Tangerine Peel Age Identification Based on MobileNetV3 陈皮(学名柑橘皮)的贮藏年限与其品质特征及市场价值呈现显著正相关性。然而，传统的陈皮年份鉴别方法主要依赖经验判断或化学分析，存在主观性强、操作复杂、耗时耗力等诸多局限。为此，本文提出一种基于轻量化深度学习框架MobileNetV3的陈皮年份智能鉴别方法。通过构建涵盖不同贮藏年限的陈皮图像数据集，采用区域裁剪对原始图像样本进行标准化处理，生成分辨率为224 × 224像素的黑色背景规范图像。在数据预处理与增强阶段，应用像素值归一化、随机旋转等策略，以提升模型的泛化能力与鲁棒性。实验环节对比了MobileNetV3、ResNet、DenseNet、FasterNet、AlexNet、VGG16以及EfficientFormerV2共七种经典与轻量级卷积神经网络模型。结果表明，MobileNetV3-large在模型参数量仅为16.3 MB的前提下，实现了86.96%的分类准确率与87.55%的精确率，单张图像推理时间仅为5.36毫秒，综合性能显著优于其他对比模型。总之，本文提出了一种更为简易且准确的陈皮年份识别方法，为在边缘计算设备的部署提供了高效可靠的技术支持。The storage age of dried tangerine peel (Citrus reticulata peel) is significantly positively correlated with its quality characteristics and market value. However, traditional methods for identifying the age of dried tangerine peel mainly rely on expert judgment or chemical analysis, which are subjective, complex, time-consuming, and labor-intensive. To address these limitations, this paper proposes an intelligent method for identifying the age of dried tangerine peel based on the lightweight deep learning framework MobileNetV3. A dataset of tangerine peel images with varying storage ages was constructed, and regional cropping was used to standardize the original image samples, generating standardized images with a resolution of 224 × 224 pixels and a black background. During the data preprocessing and augmentation phase, strategies such as pixel value normalization and random rotation were applied to enhance the model’s generalization ability and robustness. The experimental section compares seven classic and lightweight convolutional neural network models, including MobileNetV3, ResNet, DenseNet, FasterNet, AlexNet, VGG16, and EfficientFormerV2. The results show that MobileNetV3-large achieves a classification accuracy of 86.96% and a precision of 87.55%, with a single image inference time of only 5.36 milliseconds, while having only 16.3 MB of model parameters. Its overall performance significantly outperforms the other models. In conclusion, this paper presents a simpler and more accurate method for identifying the age of dried tangerine peel, providing efficient and reliable technical support for deployment on edge computing devices. Computer science ; Identification (biology) ; Biology ; Botany Textile materials and evaluations ; melanin and skin pigmentation
Authors: Seema Thapa | Himshikha Malviya | Ranjana Verma | Jay Singh
Source: The Chemical Record (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Exploring the Potential of 2D TMD‐Based Optical Biosensors: Bridging Nanotechnology and Smart Diagnostics Among various emerging 2D nanomaterials, transition metal dichalcogenides (TMDs) have garnered major recognition due to their distinct morphological and chemical features, including high surface area, tunable bandgaps, strong photoluminescence, and atomically thin structures. Their integration with optical biosensors has opened new avenues for enhancing sensor performance, offering improved sensitivity and lower detection limits compared to conventional platforms. This review covers structural and optical properties of 2D TMDs, followed by surface functionalization strategies-covalent and noncovalent-using both organic and inorganic nanomaterials to enhance biosensor functionality. The review then provides key optical detection methods such as surface plasmon resonance (SPR), evanescent wave techniques, fluorescence resonance energy transfer (FRET), label-free sensing, and signal amplification. Further, fabrication strategies for 2D TMD-based optical biosensors and methods for biomolecule immobilization are covered. Applications in protein and nucleic acid detection, cellular imaging, and environmental monitoring are highlighted in this review. Additionally, the review addresses sensor stability, reproducibility, and integration with microfluidics and lab-on-chip technologies. Finally, it explores emerging trends including multimodal sensing, the use of artificial intelligence (AI) and machine learning (ML) in biosensor data analysis, personalized sensing, and 5<sup>th</sup> and 6<sup>th</sup> generation sensing, emphasizing the transformative potential of 2D TMDs in future biosensing technologies. In addition, we highlighted the challenges and future prospects concerning structural engineering and advancement in TMDs-based optical biosensors. This review will lead researchers to explore novel detection methods, integration strategies, and progress in AI and ML-assisted 2D TMDs-based optical biosensors for personalized and high-performance sensing applications. Biosensor ; Optical sensing ; Surface plasmon resonance ; Bridging (networking) ; Förster resonance energy transfer ; Nanosensor ; Transformative learning ; Microfluidics 2D Materials and Applications ; Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials
Authors: Wei Li (7081) | Yuqian Wang (619491) | Lianghui Fan (22556842) | Runpu Shen (6757226) | Zhikang Xiao (22346260) | Jianzhong Xu (562201) | Junyang Chen (8162160) | Gaoxiang Xu (14095668)
Source: Figshare (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Silver-ProgrammedDual-Optical Au Nanostructures andMachine Learning for Intelligent Biosensing The evolution of biosensors demands synergistic improvements in signal transduction and data processing. We present a universal biosensing platform that combines dual-mode signal responses from silver-modulated gold nanorods (AuNRs) and gold–silver nanoclusters (AuAgNCs) (including localized surface plasmon resonance (LSPR) shifts and fluorescence variations) with machine learning (ML)-enhanced image analysis. Initially, AuNR was synthesized and transformed into silver-coated gold nanorods (AuNR@Ag) via silver reduction, with LSPR shifts precisely characterized. Concurrently, AuAgNCs were engineered to enhance their fluorescence through antigalvanic reactions between surface Ag(I) and Au(0) cores. The dual-mode platform leverages the silver-linked fluorescence intensity of AuAgNCs and LSPR of AuNR@Ag, as well as the increasingly enhanced inner filter effect between AuAgNCs and the evolving LSPR of AuNR@Ag, enabling simultaneous fluorescence and colorimetric readouts. The platform achieved high-precision detection of alkaline phosphatase via dual-signal correlation (<i>R</i><sup>2</sup> > 0.99), demonstrating robustness in complex matrices. Furthermore, to facilitate point-of-care testing applications, an ML algorithm encompassing feature extraction, dimensionality reduction, and model validation was integrated to process bimodal signal images. The subsequent data analysis exhibited robust correlations (<i>R</i><sup>2</sup> > 0.95), thereby substantiating the effectiveness of this approach in analyzing bimodal data. The ML-augmented analytics was validated for the analysis of serum samples, giving results that matched well with those from the spectra-based standard method. This work bridges nanomaterial engineering with ML-augmented analytics, offering a versatile framework for next-generation biosensors with clinical diagnostic potential. Biosensor ; Nanoclusters ; Surface plasmon resonance ; Nanorod ; Robustness (evolution) ; Nanosensor ; Signal processing ; Nanostructure Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: D. Korobkov | А. С. Кононихин | Savva D. Semenov | Helen Kordzaya | A. G. Brzhozovskiy | A. E. Bugrova | Elena Vasilieva | D. Yu. Kanner | Е. Н. Николаев | Alexey A. Komissarov
Source: Advances in molecular oncology (article, 2025-04-15)
Field: Chemistry — Spectroscopy

Proteomic analysis of blood plasma as a tool for personalized diagnosis of lung adenocarcinoma Introduction. Lung cancer ranks second in incidence and first in mortality among other oncological pathologies. Despite significant success in the diagnosis and treatment of tumors, the five-year survival rate for lung cancer is only 19 % and has not improved significantly in recent decades, which is mainly associated with late detection of the disease. In addition, the development of metastases reduces the five-year survival rate to 6 %.Aim. To analyze the plasma proteome of healthy volunteers and patients with lung adenocarcinoma (LAC), as one of the most common forms of lung cancer, to identify proteins that are potential biomarkers of LAC and of the presence of distant metastases.Materials and methods. The study included 30 healthy donors and 30 patients with diagnosed LAC. using a combination of liquid chromatography and tandem mass spectrometry in combination with the method of multiple reactions monitoring, we analyzed the representation of a wide range of proteins in the blood plasma of the study participants. The data obtained were analyzed using modern methods of biological statistics, including machine learning algorithms.Results. Based on the quantitative analysis of 118 proteins in blood plasma between the experimental groups, we proposed a panel of 12 significant proteins that are specific markers of LAC. Additionally, we identified three proteins that predict the presence of distant metastases among patients with LAC. Classifiers developed based on these protein panels make it possible to distinguish between patients with LAC and healthy controls, as well as to detect the presence of metastases among patients with LAC, with sensitivity and specificity of more than 90 %.Conclusion. The data obtained can be used to develop new tests for LAC screening and predicting disease outcomes based on the blood plasma proteome. After additional validation and implementation into clinical practice, these tests can contribute to the early diagnosis of LAC and, as a result, increase patient survival. Adenocarcinoma ; Lung ; Medicine ; Pathology ; Computer science ; Internal medicine ; Cancer Advanced Proteomics Techniques and Applications ; Cancer Genomics and Diagnostics ; Lung Cancer Treatments and Mutations
Source:  (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

MODELAGEM E SIMULAO COMPUTACIONAL DE UMA TORRE FRACIONADORA DE UMA UNIDADE COQUEAMENTO RETARDADO Nowadays there is a global tendency of refining heavier oils and the technologies capable of converting this oils into light fraction, which have higher aggregate value, have become indispensable.Thermal cracking processes, such as delayed coking, have been highlighted when compared to other processes.A delayed coking unit is usually composed by three main equipments: the fractionating tower, the furnace and at least two coke drums, which work alternately due to need of removing the solid products obtained at the process.The remove of the solid products, which is essentially green coke, cause a periodic perturbation at this unit.The fractionating tower has as main purposes: fractionating the effluents of the coke drums and the fresh feed from the vacuum or atmospheric towers; mixing the recycle stream with the feed at the bottom of column, which makes this equipment also be denominated combinated tower.When the switching of the coke drums occurs, the tower is responsible for dumping the disturbance at the process, maintaining the exit stream at desired operating conditions and preventing the bad operation of the delayed coking unit.The fractionating tower is also responsible for the energetic integration between the several streams involved at the process, and at the same time avoiding the precipitation of coke at the plates of the column and the overcharge of the coking furnace.Mathematical models for the mass and energy balances were developed to describe the profiles of temperature and composition along the column and as function of time.The equilibrium of phases was calculated using the equations of state (SRK model) and the modified Francis equation was used for the hydrodynamic models and for the calculation of rate flow of liquids.The characterization of the mixtures at the streams was done by a pseudocompounds approach.Properties referent to the pseudocompounds, such as critical pressure, critical temperature, acentric factor, molecular weight and enthalpy for the phases, were calculated using popular correlations found at literature.These developed models were simulated using FORTRAN code and them validated with experimental data and they will be used at advanced control applications by the use of neural networks. Computer science Graphite, nuclear technology, radiation studies
Authors: Carlos Pardo-Cortina | Laura Escuder-Gilabert | María José Medina-Hernández | Salvador Sagrado | Yolanda Martín-Biosca
Source: Analytical Chemistry (article, 2025-12-22)
Field: Chemistry — Spectroscopy

Toward AI-Assisted Greener Chiral HPLC: Predicting Efficient Enantioseparation–Mobile Phase (EES–MP) Profiles for MP Selection─A Lux Cellulose-1 Case Study Chiral HPLC method development still relies heavily on trial-and-error screening. We introduce the Efficient Enantioseparation (EES) parameter─a single metric integrating resolution (Rs) and retention (k)─to move from point predictions to full mobile-phase (MP) profile modeling. Using EES as the response, we trained multiple artificial neural networks (ANNs) on 62 variables (molecular descriptors) and 76 objects (structurally diverse neutral and basic compounds chromatographed on a Lux Cellulose-1 column under aqueous-acetonitrile conditions at nine MP compositions). ANNs were optimized with a chaotic competitive-learning optimizer (CCLNNA), then ranked/selected and combined into a consensus model to enhance robustness and limit overfitting. The ANN-consensus model accurately reproduces full EES-MP profiles (<i>R</i><sup>2</sup> > 0.9) with lower error dispersion, enabling prospective feasibility checks and single-shot selection of high-EES mobile-phase compositions. External tests on fluoxetine and lormetazepam confirmed prospective utility by anticipating separability at one or more MPs (nominating the MP with maximal EES) or nonseparability across the explored MP range. To our knowledge, this work provides the first proof-of-concept for in silico prediction of full EES-MP profiles in chiral HPLC, enabling intelligent MP selection. Rather than a definitive model, this work evaluates the potential of the strategy: consensus stabilizes learning with limited data and offers greener, actionable guidance that can reduce experiments, reagent consumption, and development time. The framework is extensible to broader chemotypes and stationary and mobile phases; larger data sets could further generalize EES-profile prediction and support intelligent MP selection in sustainable chiral HPLC. Robustness (evolution) ; Artificial neural network ; Metric (unit) ; Chiral stationary phase ; Selection (genetic algorithm) ; Phase (matter) Analytical Chemistry and Chromatography ; Molecular spectroscopy and chirality ; Microfluidic and Capillary Electrophoresis Applications
Authors: Xiaoli Huang | Dongping Li | Quanming Zhou | X Luo
Source: Letters in Drug Design & Discovery (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Integrative network pharmacology and machine learning identify NLRP3 as a critical therapeutic target for liensinine in acute myocardial infarction Acute myocardial infarction (AMI) is a severe cardiovascular disease with complex pathogenesis, posing significant threats to human health. Liensinine, an alkaloid derived from lotus plumule, has shown potential cardioprotective effects in preliminary studies, but its specific molecular targets and mechanisms in MI treatment remain unclear. Differential expression genes (DEGs) associated with AMI were identified from the GSE166780 dataset. Liensinine targets were predicted via SwissTargetPrediction and CTD database. After removing duplicates, the resulting targets were intersected with the AMI DEGs. Protein-protein interactions (PPI) networks were constructed using STRING. KEGG and GO enrichment analyses were performed on Sangebox, and results were visualized in Cytoscape. Key genes were screened via CytoHubba (DMNC, MCC) and MCODE plugins, and intersected with genes selected by LASSO, SVM, and RF. Algorithms. An ischemia-reperfusion (I/R) model was established in AC16 cells. After treatment with different concentrations of liensinine, the optimal dose was determined by CCK8. Gene expression (qRT-PCR), apoptosis (flow cytometry), and levels of IL-6, IL-1β, and TNF-α (ELISA) were detected. DEGs in MI were identified from the GSE166780 dataset, with 27 genes overlapping with liensinine targets and enriched in key pathways (AGE-RAGE, TNF signaling). Network analysis and machine learning refined these to 4 core genes. In vitro validation using an AC16 I/R model showed NLRP3 was significantly upregulated following AMI; treatment with liensinine (0.5 μM) suppressed NLRP3, alleviated cell injury, and reduced pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), confirming its protective role via NLRP3 regulation. Liensinine exerted protective effects against MI by suppressing NLRP3, thereby mitigating cell injury and inflammation. KEGG ; Myocardial infarction ; Downregulation and upregulation ; Gene ; Gene expression ; Differential effects ; Key (lock) Chromatography in Natural Products ; Plant-based Medicinal Research ; Berberine and alkaloids research