jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tatsuya Yamamoto | Akira Shiraishi | Tsubasa Sakai | Azumi Wada | Honoo Satake
Source: Journal of Biological Chemistry (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Hydrophobic interactions determine the optimum temperature of a housekeeping enzyme Chordates have adapted to diverse thermal environments, with poikilotherms adjusting to external temperatures and homeotherms maintaining stable body temperatures. While housekeeping enzymes conserve their activities, they function at different body temperatures among species. However, the determinants for the optimum temperatures of housekeeping enzymes largely remain unclear. In this study, we identified the determinants of the optimum temperatures of chordate adenylate kinase 1 (AK1), a key housekeeping enzyme. The optimum temperatures of AK1s were shown to be closely correlated with the normal body temperature of each chordate (11 species). A combination of enzymatic assays, computational analyses of numerous physicochemical interactions, and structural dynamics analyses of intact and mutant AK1s verified that the number of hydrophobic interactions among four amino acids in specific secondary structures (the 4th-17th regions) is a major determinant of the optimum temperatures of chordate AK1s. This allowed us to generate a predictive model for the optimum temperatures of native chordate AK1s: AK1 optimum temperature = 2.3587 × (the number of 4th-17th interactions) + 0.3663 × (the number of 13th-17th interactions) -5.9695, with a maximum error of ±1.70 °C. In contrast, sequence similarity and molecular phylogenetic relationships did not correlate with the optimum temperatures of chordate AK1s. Furthermore, these results suggest an evolutionary scenario for thermal adaptation of chordate AK1. Collectively, our study provides evidence that subtle hydrophobic interactions play a crucial role in determining the temperature preferences of a chordate housekeeping enzyme, offering new insights into the functional evolution and diversification of enzyme homologs. Chordate ; Housekeeping ; Housekeeping gene ; Enzyme ; Phylogenetic tree Enzyme Structure and Function ; Enzyme Production and Characterization ; thermodynamics and calorimetric analyses
Authors: D. S. Wang | Jizhong Li | Han Gong | Jianyi Chen
Source: Applied Sciences (article, 2026-01-26)
Field: Chemistry — Analytical Chemistry

Defective Wheat Kernel Recognition Using EfficientNet with Attention Mechanism and Multi-Binary Classification As a globally significant food crop, the assessment of wheat quality is essential for ensuring food security and enhancing the processing quality of agricultural products. Conventional methods for assessing wheat kernel quality are often inefficient and markedly subjective, which hampers their ability to accurately distinguish the complex and diverse phenotypic characteristics of wheat kernels. To tackle the aforementioned issues, this study presents an enhanced recognition method for defective wheat kernels, based on the EfficientNet-B1 architecture. Building upon the original EfficientNet-B1 network structure, this approach incorporates the lightweight attention mechanism known as CBAM (Convolutional Block Attention Module) to augment the model’s capacity to discern features in critical regions. Simultaneously, it modifies the classification head structure to facilitate better alignment with the data, thereby enhancing accuracy. The experiment employs a self-constructed dataset comprising five categories of wheat kernels—perfect wheat kernels, insect-damaged wheat kernels, scab-damaged wheat kernels, moldy wheat kernels, and black germ wheat kernels—which are utilized for training and validation purposes. The results indicate that the enhanced model attains a classification accuracy of 99.80% on the test set, reflecting an increase of 2.6% compared to its performance prior to the enhancement. Furthermore, the Precision, Recall, and F1-score all demonstrated significant improvements. The proposed model achieves near-perfect performance on several categories under controlled experimental conditions, with particularly high precision and recall for scab-damaged and insect-damaged kernels. This study demonstrates the efficacy of the enhanced EfficientNet-B1 model in the recognition of defective wheat kernels and offers novel technical insights and methodological references for intelligent wheat quality assessment. Kernel (algebra) ; Quality (philosophy) ; Mechanism (biology) ; Pattern recognition (psychology) ; Precision and recall ; Recall Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Supply Chain Traceability
Authors: Linzhi Yu | Jesse Pietila | Haobijam Johnson Singh | Arttu Nieminen | Humeyra Caglayan
Source: ArXiv.org (article, 2026-02-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Meta-optical processors for broadband complex-field image operations All-optical image processing provides a fast and energy-efficient alternative to conventional electronic systems by directly manipulating optical wavefronts. However, metasurface-based optical processors reported to date are often limited in functionality, operating bandwidth, or input modality, which restricts their adaptability across different image processing tasks. Here, we demonstrate a broadband metasurface platform capable of performing diverse analog image processing operations on both amplitude- and phase-encoded inputs. This platform is realized using a single-layer dielectric metasurface designed through an end-to-end, task-driven inverse design framework. By tailoring the spatial-frequency components of incident image wavefronts, the metasurface implements analog operations such as edge detection and pattern recognition across a 200nm wavelength bandwidth in the visible spectrum. Furthermore, we develop a compact processor architecture that integrates imaging and computation within a reduced optical footprint. These results establish a flexible and compact metasurface-based optical processor with strong potential for integration into practical imaging and optical computing systems. Broadband ; Image processing ; Bandwidth (computing) ; Computation ; Signal processing ; Image processor ; Optical computing ; Digital image processing ; Enhanced Data Rates for GSM Evolution Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Plasmonic and Surface Plasmon Research
Authors: Wen Lin | Heeswari A/P Logamoorthy | Thathram Palli Adil Mubarak | Norhashimah Binti Hussin | Harisun Binti Yaakob
Source: International Journal of Research and Innovation in Social Science (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Systematic Literature Review on Integrating Macro and Micro Analytical Approaches for Food Quality, Safety, and Security in Modern Food Manufacturing Food quality, food safety, and food security are interrelated yet distinct. Ensuring food quality, safety, and security in modern manufacturing requires analytical systems capable of capturing both product level attributes and molecular scale variations. This review synthesizes recent advancements (2020–2026) in macro and micro analytical approaches and evaluates their combined potential to strengthen quality assurance across diverse food sector. Macrolevel methods including physical, sensory, and proximate analyses provide rapid, non-destructive assessments of texture, colour, composition, and consumer relevant attributes. In contrast, microlevel techniques such as microbial enumeration, chromatography, spectroscopy, molecular diagnostics, and metabolomics offer high resolution insights into contamination risks, biochemical transformations, and authenticity verification. Integrating these analytical scales creates a complementary framework that enhances traceability, supports regulatory compliance, and improves early detection of deviations in processing environments. The review highlights industrial applications in dairy, meat, cereal, and ready-to-eat foods, demonstrating how multiscale analytical strategies improve product consistency and safety outcomes. Emerging trends including AI-assisted analytics, biosensors, hyperspectral imaging, digital twins, and portable rapid testing platforms are accelerating the transition toward intelligent, data driven food systems. Sustainability-driven innovations such as green analytical chemistry and circular economy monitoring further expand the role of analytical science in resilient food production. While the review provides comprehensive coverage, limitations include variability in analytical protocols across studies, restricted access to nonindexed industrial data, and the early-stage maturity of several emerging technologies. Overall, the integration of macro and micro analytical approaches represents a critical pathway toward building transparent, efficient, and sustainable food manufacturing systems capable of meeting global quality and safety demands. Food security ; Product (mathematics) ; Quality (philosophy) ; Food safety ; Consistency (knowledge bases) ; Macro ; Scale (ratio) ; Food processing ; Food industry Spectroscopy and Chemometric Analyses ; Biosensors and Analytical Detection ; Identification and Quantification in Food
Authors: Yeongmin Kim | Inyong Jeong | Jin-Hyun Park | Taewon Jung | Jang-Wook Sohn | Hongseok Park | June Choi | Se Yoon Park | Hwamin Lee
Source: Scientific Reports (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Multi task learning based early prediction model for antibiotic resistance using multi institutional cohort data Antibiotic resistance poses a significant global health challenge, with its rapid emergence driven by inappropriate antibiotic use. This study aimed to develop and compare machine learning models to predict resistance to nine antibiotic classes in hospitalized patients, addressing the high-cost labeling challenge of medical data. We conducted a retrospective study using electronic medical records from three Korean tertiary care institutions (n = 59,551). Single-task learning models (Logistic Regression, XGBoost, LightGBM, CatBoost, Multi-Layer Perceptron) and multi-task learning models (Hard Parameter Sharing, Soft Parameter Sharing) were developed. Model performance was evaluated using Area Under the ROC Curve (AUC), Precision-Recall Curve (PRC), and calibration slope. In external validation, the hard parameter sharing model achieved the highest AUC and PRC for five of nine antibiotic classes, with average values of 79.63 and 80.26, respectively. Shapley Additive Explanations analysis revealed previous antibiotic resistance as the most important predictor. Subgroup analyses showed hard parameter sharing model performed best with available previous culture test results, while soft parameter sharing excelled when they were missing. Our multi-task learning approach achieved generalized, stable, and superior performance in predicting antibiotic resistance compared with traditional models, providing a novel solution for handling partial targets in medical data. Antibiotic resistance ; Medical record ; Calibration ; Task (project management) ; Resistance (ecology) ; Receiver operating characteristic ; Cohort ; Health care Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Gaussian Processes and Bayesian Inference
Authors: Anshuman Swain | A. K. Mathur | Narayan Datt Soni | Neil Wilson | Blake Benyard | Paul Jacobs | Sunil Kumar Khokhar | Dushyant Kumar | Mohammad Haris | Ravinder Reddy
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Unsupervised anomaly detection for tumor delineation in a preclinical model of glioblastoma using CEST MRI This study successfully demonstrated a UAD pipeline utilizing the Z-spectrum from CEST MRI for metabolically informed tumor delineation. The framework captures biochemical deviations that may precede or extend beyond morphologic abnormalities, enabling sensitive detection of tumor regions and intra-tumoral heterogeneity that previous methods may fail to capture. The offsets from the feature analysis indicated a strong contribution from the magnetization transfer (MT) pool to the spectral deviations captured by the model, with additional contributions from relayed nuclear Overhauser effect (rNOE) and amide proton transfer (APT). Model robustness with under sampling further highlights the pipeline's potential in accelerated acquisitions, thus improving clinical practicality. While there is a need for validation on larger cohorts and clinical datasets, the current results demonstrate that this label-free, Z-spectral anomaly mapping can serve as an interpretable and scalable tool for monitoring tumor heterogeneity and progression, with potential applicability to other diffuse or metabolically subtle pathologies. Glioblastoma ; Magnetization transfer ; Robustness (evolution) ; Pipeline (software) ; Anomaly detection ; Feature (linguistics) ; Pattern recognition (psychology) Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Sulfur Compounds in Biology
Authors: Shin-Ichi Koda | Shinji Saito
Source: ChemRxiv (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

dmf-g16: A Gaussian Wrapper for Reliable Double-Ended Transition-State Searches with Native Input Formats Transition-state (TS) searches play a central role in computational studies of chemical reactions. While many advanced methods are now available, their practical use in routine workflows requires nontrivial effort. As a result, users often remain tied to familiar software packages and established input formats and workflows. Here, we present dmf-g16, a Gaussian-specific front end for the Direct MaxFlux (DMF) reaction-path optimization method implemented in PyDMF. dmf-g16 enables DMF-based TS searches with minimal changes to existing Gaussian workflows: users simply replace the Gaussian executable with dmf-g16, while Gaussian native QST2/QST3 input files are used without modification. For QST-formatted inputs, dmf-g16 performs DMF-based path optimization with Gaussian as an external energy and gradient engine, and subsequently refines the TS in Gaussian using the highest-energy point along the optimized path as the initial structure. Benchmark calculations for 121 reactions show a marked improvement in TS-search reliability compared to Gaussian native QST2, increasing the success rate from 31.4% to 93.4%. Although the explicit path optimization requires additional computational cost, the wall-clock time is typically only a few times that of QST2 and can be further reduced by parallel evaluation along the reaction path. Overall, dmf-g16 offers a practical and robust alternative to Gaussian native QST for routine TS searches. Gaussian ; Path (computing) ; Executable ; Benchmark (surveying) ; Gaussian process ; Workflow ; Point (geometry) ; Software Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Kezhu Tan | Zonghui Zhuo | Weiqi Sun | Hao Gan | Chao Lu | Qi Liu | Xihai Zhang
Source: AgriEngineering (article, 2026-02-02)
Field: Chemistry — Analytical Chemistry

Early Detection of Heat-Damaged Soybean Seeds Based on Hyperspectral Imaging Technology Heat damage caused by elevated temperature and humidity during storage significantly affects soybean seed quality and viability. Early detection remains challenging due to the lack of visible symptoms in mildly damaged seeds. In this study, hyperspectral imaging (HSI) was adopted to capture detailed spectral information associated with internal physiological changes in soybean seeds. To simulate realistic thermal stress scenarios, soybean seeds were subjected to two temperature conditions: a control group stored at 25 °C and 55% RH and a heat-treated group stored at 45 °C and 80% RH. Within the high-temperature group, different durations (20 and 30 days) were used to generate mildly and severely damaged seeds, respectively. After image acquisition using a 400–1000 nm VNIR-HSI system, multiplicative scatter correction (MSC) was selected as the optimal preprocessing method to reduce scattering effects. Spectral dimensionality was then reduced using a recursive feature elimination–principal component analysis (RFE-PCA) cascade to retain key discriminative features. Finally, a support vector classifier was constructed and optimized using a Lévy–Sine-enhanced Egret Swarm Optimization Algorithm (LSESOA), yielding a classification accuracy of 96.75% and a macro-F1 score of 96.76% on the test set. This study demonstrates the feasibility of applying HSI combined with metaheuristic optimization to achieve accurate, non-destructive evaluation of heat-damaged soybean seeds, providing technical support for quality control during storage and logistics. Hyperspectral imaging ; Discriminative model ; Preprocessor ; Pattern recognition (psychology) ; Classifier (UML) ; Support vector machine ; Feature (linguistics) ; Extreme learning machine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Aljawhara H. Almuqrin | Dalal A. Aloraini | M.I. Sayyed | Maymunah Alwehaibi
Source: Open Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Structural and attenuation properties of rare-earth and barium oxide modified borate glasses for gamma ray protection Abstract This research intends to investigate the B 2 O 3 –TiO 2 –BaO–ZnO–La 2 O 3 –Nd 2 O 3 glass system’s radiation shielding characteristics at the Cs-137-, Co-60-, and Na-22-emitted energies. The mass attenuation coefficient was investigated in the 0.284–2.506 MeV range, with the maximum value for all glasses of 0.130–0.143 cm 2 /g found at 0.284 MeV, and the minimum value of around 0.039 cm 2 /g at 2.506 MeV. The MAC results indicated the glasses to be effective attenuators in terms of low-energy radiation exposure, whereas their attenuation effectiveness decreases at higher energy levels. The linear attenuation coefficient (LAC) results demonstrated a direct relation between the amounts of BaO, La 2 O 3 , and Nd 2 O 3 in the glasses and their radiation shielding performance, whereby the LAC increase rate with these components’ addition depends upon the energy. The effective atomic number exhibits a linear increase with the glasses’ density increase, whereby at 0.284 MeV the values are 12.71 and 15.51 for LaNd0.5 and LaNd2, respectively. The tenth value layer results showed that the thickness of the glasses needs to be approximately 4 cm and 6–7 cm to attenuate 90 % of the radiation at 0.284 and 0.511 MeV respectively. Mass attenuation coefficient ; Attenuation ; Attenuation coefficient ; Half-value layer ; Boron ; Radiation ; Electromagnetic shielding ; Radiation shielding ; Analytical Chemistry (journal) Radiation Shielding Materials Analysis ; Nuclear materials and radiation effects ; Glass properties and applications
Authors: Chi Hu | Yongjie Wang | Qinghui Lai | Lu Bai | Wei Su | Jing Zheng | Zhaohui Yao
Source: Frontiers in Plant Science (article, 2026-03-26)
Field: Chemistry — Analytical Chemistry

Research on the grading method of Panax notoginseng slices based on machine vision Precise grading is the foundation for improving the safety and consistency of <i>Panax notoginseng</i> in clinical applications. This study aims to propose a rapid, non-destructive quality grading method for <i>Panax notoginseng</i> based on machine vision and chemometrics. First, high-performance liquid chromatography (HPLC) was employed to determine the Panax Notoginseng Saponins (PNS) content of 143 samples. Based on hierarchical cluster analysis combined with the Elbow Rule, the samples were scientifically categorized into 3-Grade, 5-Grade, and 6-Grade standards. Five machine learning algorithms and six feature selection methods were compared to identify the optimal baseline model, and the Particle Swarm Optimization (PSO) algorithm was introduced to fine-tune the model's hyperparameters. The final model was evaluated using 10-fold cross-validation, and a bench test was conducted on 50 samples for grading verification. Comparative analysis identified the pearson correlation coefficient combined with CatBoost (COR-CatBoost) as the optimal baseline model across all grading schemes. After hyperparameter fine-tuning with PSO, the final COR-CatBoost-PSO model achieved average classification accuracies of 98.6% ± 0.5%, 88.2% ± 1.2%, and 84.5% ± 1.5% for the 3-Grade, 5-Grade, and 6-Grade standards, respectively, via 10-fold cross-validation. The bench test results showed a 100% classification accuracy of <i>P. notoginseng</i>, an average offset of the robotic arm of 1.2 mm, and a single grading process time of 0.8-1.5 s. The results verify the reliability and effectiveness of the proposed rapid, non-destructive quality grading method for <i>Panax notoginseng</i>, which can provide technical support for improving the safety and consistency of <i>Panax notoginseng</i> in clinical applications. Grading (engineering) ; Particle swarm optimization ; Support vector machine ; Pattern recognition (psychology) ; Panax notoginseng ; Correlation coefficient Spectroscopy and Chemometric Analyses ; Ginseng Biological Effects and Applications ; Traditional Chinese Medicine Analysis
Authors: MING-SHOU SU
Source: Research Square (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Partial Discharge Source Identification Using Phase-Resolved Patterns and a Weighted K-Nearest Neighbors Method Partial discharge ; Pattern recognition (psychology) ; Wavelet ; Sequence (biology) ; Noise (video) ; Transformer ; Partial least squares regression High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Aerosol Filtration and Electrostatic Precipitation