jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dr Harsh | Dr Mayank Tyagi | Dr Jubin Jakhar | Dr Arif viqar
Source: International Journal of Environmental Sciences (article, 2025-07-26)
Field: Chemistry — Spectroscopy

Bone Proteomics In Forensic Investigations: Advances, Challenges, And Methodological Innovations Similarly to their forensic application, for which bone remains are an invaluable source of biological information, traditional analytical techniques, mainly DNA analysis, frequently lacks the sensitivity to overstep the challenges with aged or environmentally challenged material. Bone proteomics – the mass analysis of proteins preserved in skeletal tissue – has become a powerful complementary tool, taking advantage of the longer-term durability of proteins in comparison with nucleic acids. Here, we have consolidated the state of the art of forensic bone proteomics concentrating on important developments, persisting challenges and major methodological refinements. Bone proteome analyses for PMI, age-and sex determination and species determination have shown a rapid progress focusing on the analysis for certain post-translational modifications of proteins (e.g. Biglycan deamidation) or protein markers (e.g. Fetuin-A, Amelogenin, COL1A2). Nevertheless, some challenges exist for the application of the field. These challenges include the intricate interplay of diagenetic factors affecting bone composition, and of diverse environmental conditions (temperature, pH, humidity, burial context) and recycling of skeletal proteins on survival, exogenous proteome mixtures, and endogenous proteome signals, biological inherent variability among individual organisms, and robust bioinformatic approaches for analysis of often low-abundant or modified peptides. Moreover, inter-comparison and implementation of these methods are difficult due to the absence of standardized, validated protocols among different laboratories. Recent methodological developments, like improved protein extraction protocols (S-Trap workflow for instance), novel MS acquisition strategies (as data independent acquisition approach DIA) and complex bioinformatics pipelines, are attempting to overcome these issues. Future work, including biomarker validation, pathway elucidation, contamination control, workflow optimization and forensic protein databases work are required to translate the potential of bone proteomics to practical and robust tools for forensic investigations. Forensic science ; Proteomics ; Data science ; Computational biology ; Computer science ; Biology ; Genetics Advanced Proteomics Techniques and Applications ; Bone and Dental Protein Studies ; Oral microbiology and periodontitis research
Authors: Syun Echigoya | Takuya Ohmura | Katsuhiko Sato | Toshiyuki Nakagaki | Yukinori Nishigami
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-21)
Field: Materials Science — Biomaterials

Geometrical preference of anchoring sites in the unicellular organism <i>Stentor coeruleus</i> Abstract Organisms often inhabit environments comprising complex structures across various scales. Animals rely on visual information from surrounding geometrical structures for navigation. Even at the microscale, various microsediments form complex structures in microbial habitats. The movement of microorganisms is passively affected by collisions and hydrodynamic interactions with surrounding structures. However, the influence of microenvironmental geometry on behavioral changes of unicellular organisms that lack visual perception remains unclear. Here, we developed geometrically structured chambers to investigate anchoring site preferences in the swimming ciliate Stentor coeruleus . Our experiments revealed that S. coeruleus preferentially anchored in narrow regions characterized by specific geometrical features, including corner angle, depth, and curvature at the corner end. Before anchoring, free-swimming S. coeruleus changed its behavior to move along the boundary wall of the chambers, accompanied by Ca 2+ -induced asymmetrical body deformation. To further investigate how S. coeruleus moves along the wall continuously, we conducted a hydrodynamic simulation and revealed that the asymmetric morphology causes asymmetric propulsive forces, explaining wall-following behavior through physical interactions with a wall. Thus, morphological change near a wall causes wall-following behavior, facilitating the identification of these narrow anchoring sites. Our findings indicate that environmental geometry drives behavioral transitions in S. coeruleus through simple biophysical processes, enabling spatial selection without visual cues. Overall, these results suggest that microgeometry plays a key role in shaping ecological niches for unicellular microorganisms. Significance Statement Animals use various natural structures as landmarks for navigation. In microorganism habitats, microsediments also form geometrically complex environments. Is there a relationship between the geometrical features of structures and the behavior in unicellular organisms lacking visual cues? Here we report that the free-swimming unicellular organism Stentor coeruleus selects the anchoring sites based on the surrounding shapes. Further observations and numerical simulations reveal that an asymmetric morphological change causes a temporary switch from ballistic to wall-following exploration, driven by surrounding structures. These results indicate that one simple behavioral response underlies the preference of anchoring sites with specific geometrical features in non-neural unicellular organisms. The findings shed light on the role of microenvironmental geometry in forming ecological niches for microorganisms. Anchoring ; Organism ; Preference ; Biology ; Psychology ; Cognitive science ; Paleontology ; Economics ; Microeconomics Diatoms and Algae Research
Authors: Murat Durandurdu
Source: Advanced Theory and Simulations (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Quenchable Amorphous Diamond: A Novel High‐Pressure Route to Tetrahedral Amorphous Carbon Abstract This study presents a groundbreaking theoretical prediction: the high‐pressure transformation of amorphous graphite into a high‐fraction sp 3 ‐bonded amorphous diamond phase. Employing ab initio molecular dynamics simulations, it is demonstrated that under extreme pressures, amorphous graphite undergoes an irreversible transition to an amorphous diamond phase. Thermodynamic analysis confirms the first‐order nature of this sp 2 ‐to‐sp 3 transformation, with the transition predicted to occur at ≈33 GPa under experimental conditions. This transformation offers a novel pathway toward the synthesis of amorphous carbon with a high fraction of sp 3 bonding, a long‐standing challenge in materials science. This work expands understanding of carbon's high‐pressure behavior and provides a compelling theoretical foundation for future experimental investigations aimed at synthesizing and characterizing this novel material. Tetrahedron ; Amorphous carbon ; Amorphous solid ; Materials science ; Carbon fibers ; High pressure ; Diamond ; Chemical engineering ; Nanotechnology ; Crystallography ; Chemistry ; Composite material ; Composite number ; Thermodynamics ; Physics ; Engineering Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research
Authors: Musa Ahmad | Mohammad Tariqul Islam | Touhidul Alam | Saleh Albadran | Ahmed Alzamil | Ahmed S. Alshammari | Haitham Alsaif | Mohamed S. Soliman | Md. Shabiul Islam
Source: Scientific Reports (article, 2025-04-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Coupled split square resonator based metamaterial sensor for realtime monitoring of air bubbles in IV channels Air embolism caused by undetected air bubbles in intravenous (IV) lines poses a critical risk in medical treatments, necessitating accurate and reliable detection systems. This study addresses this challenge by developing a novel metamaterial-based sensor for real-time monitoring of air bubbles in IV channels. The sensor employs a Single Negative Complementary Split Square Resonator (CSSR) coupled with a polymer-based microfluidic channel (PMC) to detect dielectric changes caused by air bubbles in flowing saline. Unlike traditional optical or ultrasonic methods, the proposed sensor offers a compact, cost-effective, and fabrication-friendly solution while maintaining high sensitivity of 78% and precision. Experimental validation demonstrated its capability to detect even small air bubbles, with resonant frequency shifts observed between 6.6 GHz and 6.9 GHz. This innovation provides a robust tool for enhancing patient safety by preventing air embolisms, with potential applications in broader biomedical sensing scenarios. By addressing a critical medical need through advanced metamaterial technology, this work contributes significantly to both healthcare monitoring and sensor design. Square (algebra) ; Metamaterial ; Resonator ; Acoustics ; Computer science ; Physics ; Telecommunications ; Optoelectronics ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Acoustic Wave Phenomena Research
Authors: Alessandro Benelli | Riccardo Primi | Chiara Evangelista | Raffaello Spina | Marco Milanesi | Daniele Pietrucci | Bruno Ronchi | U. Bernabucci | Roberto Moscetti
Source: Journal of Sustainable Agriculture and Environment (article, 2025-07-09)
Field: Chemistry — Analytical Chemistry

Predicting Forage Nutritional Quality With Near‐Infrared Spectroscopy ABSTRACT The quality of green forage is crucial in pasture grazing, influencing both animal welfare, environmental sustainability, and production yield. Traditionally, the evaluation of forage composition requires time‐consuming and costly chemical analysis. In this context, near‐infrared spectroscopy (NIR) emerges as a promising alternative. This study adopted Fourier transform NIR (FT‐NIR) spectroscopy to predict nutritional characteristics of green forages. A total of 324 samples were collected from pastures in central Italy. Partial least squares (PLS) regression models were then developed, applying variable selection methods to improve PLS model accuracy. The interval PLS (iPLS) variable selection method gave the best results for fresh forage, while the genetic algorithm (GA) performed best for dried samples. The best results from the PLS models were obtained for dry matter (DM) and crude protein (CP). The DM model for fresh forage yielded an R 2 P of 0.96 and an RMSEP of 2.95 g 100 g −1 FW, while the CP model for dried forage yielded an R 2 P of 0.94 and an RMSEP of 1.84 g 100 g −1 DW, with a normalised root‐mean‐square error of cross‐validation (NRMSECV) of 3.8% and 5.6%, respectively. The results for the neutral detergent fibre (aNDF) were acceptable. NIR spectroscopy has proven to be a useful tool for assessing forage nutritional quality. Variable selection through iPLS also enabled the identification of “core” spectral regions for the development of compact and portable NIR sensors. Future research should further investigate sample preparation and moisture content effects and expand sampling to different geographical areas to enhance model robustness. Forage ; Quality (philosophy) ; Infrared spectroscopy ; Spectroscopy ; Infrared ; Environmental science ; Materials science ; Chemistry ; Agronomy ; Biology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Ruminant Nutrition and Digestive Physiology
Authors: C. Igathinathane | Renuka Visvanathan | Ganesh C. Bora | Shafiqur Rahman
Source: Preprints.org (preprint, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Computer Vision-Based Multiple Width Measurements for Agricultural Produce The most common size measurements for agricultural produce, including fruits and vegetables, are length and width. While the length of any agricultural produce can be unique, the width varies continuously along its length. Single-width measurements alone are insufficient to accurately characterize the varying width profile, resulting in an inaccurate representation of the shape or mean dimension. Consequently, manual measurement of multiple and mean dimensions is laborious or impractical, and no information in this domain is available. Therefore, an efficient alternative computer vision measurement tool was developed utilizing ImageJ. Twenty sample sets, comprising fruits and vegetables, each representing different shapes, were selected and measured for length and multiple widths. A statistically significant minimum number of multiple widths was determined for practical measurements based on object shape. The “aspect ratio” (width/length) was identified to serve as an effective indicator of the minimum multiple width measurements. In general, 50 multiple width measurements are recommended; however, even 15 measurements would be satisfactory (1.0±0.6 deviation from 50 widths). The developed plugin was fast (734±365 CPU time/image), accurate (&amp;gt;=99.6%), cost-effective, and incorporated several user-friendly and helpful features. The study outcomes have practical applications in characterization, quality control, grading and sorting, and pricing determination of agricultural produce. Length measurement ; Sorting ; Computer science ; Standard deviation ; Mathematics ; Computer vision ; Artificial intelligence ; Algorithm ; Statistics ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Isra Laila | Ahmad Avatar Tulsi | Bambang Susilo | Dimas Firmanda Al Riza
Source: Food Research (article, 2025-05-15)
Field: Chemistry — Analytical Chemistry

Maturity level prediction and classification of lemon fruit (Citrus limon cv.Montaji Agrihorti) using combined reflectance fluorescence computer visionand machine learning models Montaji Agrihorti is an Indonesia lemon variety known for minimal seeds and abundant fruit juice. Conventional methods for estimating fruit maturity rely on physical observation and flowering time calculations, which can be subjective and inaccurate. Computer vision technology offers a non-destructive approach to determining fruit maturity. The purpose of this research was to build a prediction model of physicochemical parameters on lemon fruit maturity based on reflectance and fluorescence digital image analysis using color and texture features. The experimental laboratory method was carried out in two stages, namely destructive tests and non-destructive tests. Destructive tests were conducted by measuring fruit firmness, total soluble solids (brix) and total acidity. The non-destructive test was conducted by taking images of the fruit using fluorescence reflectance-based computer vision. The combination of fluorescence reflectance light sources using color and texture features in image capture can predict the level of fruit maturity. The data that has been obtained is then processed and processed using machine learning algorithm methods including K-NN (K-Nearest Neighbor), SVM (Support Vector Machine), and Random Forest. The best machine learning algorithm modeling based on visual lemon fruit recommendation on Random Forest model RGB image data with min-max scaling obtained a training test result of 1.00 and a test accuracy of 0.884. Citrus limon ; Horticulture ; Artificial intelligence ; Maturity (psychological) ; Reflectivity ; Fluorescence ; Mathematics ; Machine learning ; Computer science ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses
Authors: Ana Paula Carvalho Reis | Giovanna Azevedo Celestrino | Talita Souza Siqueira | Margarida Paiva Coelho | J Avila | Isabela De Oliveira Cavalcante Pimentel | Leo Kei Iwai | Pritesh Lalwani | Vitor Manoel Silva dos Reis | Kaique Arriel | José Ângelo Lauletta Lindoso | Gil Benard | Maria Glória Teixeira Sousa
Source: Biology Methods and Protocols (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Optimized protein extraction protocol from human skin samples The skin is the largest organ in the body and is the site for a diverse set of diseases. Yet, given the complexity of the cutaneous tissue, there is a limited availability of data in the literature on skin proteomics. Here, we proposed an adapted and optimized protocol for the extraction of proteins from human skin, using a combination of chemical and mechanical lysis approaches. For this, we used of a lysis buffer containing 2% SDS, 50 mM TEAB, and a 1% protease and phosphatase inhibitor cocktail, in addition to Matrix A beads and a FastPrep-24 5G homogenizer. For the characterization of the samples, the obtained proteins were purified and digested using the SP3 method (Single-pot, solid phase, sample preparation), and analyzed by nano liquid chromatography coupled with tandem mass spectrometry. In this way, we were able to identify around 6000 proteins in the skin samples from healthy individuals and patients with the fungal infection sporotrichosis. Our improved methodology could significantly enrich our understanding of skin biology and provide new perspectives for the discovery of biomarkers and therapeutic targets for cutaneous diseases. Extraction (chemistry) ; Protocol (science) ; Chromatography ; Computational biology ; Chemistry ; Biology ; Medicine ; Pathology Advanced Proteomics Techniques and Applications ; Food Allergy and Anaphylaxis Research ; Contact Dermatitis and Allergies
Authors: Maryelle Kleyce Machado Nery | Gabriel Siqueira Tavares Fernandes | João Vitor de Nóvoa Pinto | Matheus Lima Rua | Mário Santos | Liliana Simões Ribeiro | Leandro Monteiro Navarro | Paulo Jorge de Oliveira Ponte de Souza | Glauco de Souza Rolim
Source: AgriEngineering (article, 2025-01-30)
Field: Chemistry — Inorganic Chemistry

The Application of Machine Learning to Model the Impacts of Extreme Climatic Events on the Productivity of Dwarf Green Coconut Trees in the Eastern Amazon The coconut crop (Cocos nucifera L.) is essential in humid tropical regions, contributing to the economy and food security. However, its perennial nature makes it sensitive to climate variability, particularly extreme events that affect productivity. This study evaluated the impacts of extreme climatic events on the productivity of dwarf green coconut in northeastern Pará, analyzing rainy (PC—December to July) and less rainy (PMC—August to November) periods between 2015 and 2023. Meteorological and experimental data were used, including extreme climate variables such as maximum temperature (HT) and precipitation (HEP), defined by the 90th percentiles, and low precipitation (LP, 10th percentile). Predictive models, such as Multiple Linear Regression (MLR) and Random Forest (RF), were developed. RF showed better performance, with an RMSE equivalent to 20% of the average productivity, while that of MLR exceeded 50%. However, RF struggled with generalization in the test set, likely due to overfitting. The inclusion of lagged productivity (productivity t-1) highlighted its significant influence. During the PC, extreme high precipitation (HEP) events and excessive water surplus (HE) occurring after the fifth month of inflorescence development contributed to increased productivity, whereas during the PMC, low-precipitation (LP) events led to productivity reductions. Notably, under certain circumstances, elevated precipitation can mitigate the negative impacts of low water availability. These findings underscore the need for adaptive management strategies to mitigate climatic impacts and promote stability in dwarf green coconut production. Amazon rainforest ; Productivity ; Agroforestry ; Geography ; Environmental science ; Agricultural engineering ; Ecology ; Biology ; Engineering ; Economics Coconut Research and Applications ; Oil Palm Production and Sustainability ; Peanut Plant Research Studies
Authors: Ko-Han Shih | C. Kyle Renshaw
Source: Photonics (article, 2025-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface-Refractive Hybrid Lens Modeling with Vector Field Physical Optics Metasurfaces (MSs) have emerged as a promising technology for optical system design. When combined with traditional refractive optics, MS-refractive hybrid lenses can enhance imaging performance, reduce optical aberrations, and introduce new functionalities such as polarization control. However, modeling these hybrid lenses requires advanced simulation techniques that usually go beyond conventional raytracing tools. This work presents a physical optics framework for modeling MS-refractive hybrid lenses. We introduce a ray-wave hybrid method that integrates multiple propagation techniques to account for vector wave propagation through various optical elements. At the center of the proposed framework is the Gaussian decomposition method for modeling beam propagation through refractive optics. Ray-path diffraction is automatically considered in this method, and complex input wavefront can be modeled as well. Several techniques are integrated to ensure accuracy in decomposing an incoming vector wave into Gaussian beamlets, such as adaptive consideration of local wavefront principal curvatures and best-fit beam width estimation from the local covariance matrix. To demonstrate the effectiveness of our method, we apply it to several hybrid lens designs, including polarization-sensitive MSs and aberration-correcting MSs integrated into complex optical systems. Optics ; Physical optics ; Lens (geology) ; Physics ; Field (mathematics) ; Geometrical optics ; Refractive index Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Wireless Communication Technologies
Authors: Dan Zhao | Fei Fan | Hao Wang | Pengxuan Li | Zhen Xu | Jining Li | Yunyun Ji | Shengjiang Chang
Source: Advanced Photonics Nexus (article, 2025-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic terahertz multi-channel beam steering with dual-frequency multiplexing based on magneto-optical metasurfaces With the urgently increasing demand for high-speed and large-capacity communication transmission, there remains a critical need for tunable terahertz (THz) devices with multi-channel in 5G/6G communication systems. A magnetic phase-coding meta-atom (MPM) is formed by the heterogeneous integration of La:YIG magneto-optical (MO) materials and Si microstructures. The MPM couples the magnetic induction phase of spin states with the propagation phase and can simultaneously satisfy the required output phase for dual frequencies under various external magnetic fields to realize the dynamic beam steering among multiple channels at 0.25 and 0.5 THz. The energy ratio of the target direction can reach 96.5%, and the nonreciprocal one-way transmission with a max isolation of 29.8 dB is realized due to the nonreciprocal phase shift of the MO layer. This nonreciprocal mechanism of magnetic induction reshaping of wavefront significantly holds promise for advancing integrated multi-functional THz devices with the characteristics of low-crosstalk, multi-channel, and multi-frequency, and has great potential to promote the development of THz large-capacity and high-speed communication. Terahertz radiation ; Beam steering ; Magneto ; Dual (grammatical number) ; Multiplexing ; Channel (broadcasting) ; Beam (structure) ; Optoelectronics ; Optics ; Physics ; Electronic engineering ; Computer science ; Electrical engineering ; Telecommunications ; Engineering ; Art Metamaterials and Metasurfaces Applications ; Millimeter-Wave Propagation and Modeling ; Advanced Antenna and Metasurface Technologies