jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abdulilah Hanan Abdul Qader | Hind Hamid Hasan | Ashwaq Talib Hameed | Hanan Y. Houmady | Rafraf abdul aziz abdul qader
Source: Journal of Angiotherapy (article, 2024-03-20)
Field: Chemistry — Analytical Chemistry

Heavy Metal Accumulation and Phytochemical Profiling of Brassicaceae and Amaranthaceae for Phytoremediation and Medicinal Applications Background: Human activities contribute significantly to the release of heavy metals into the environment, raising concerns about ecosystem health. Phytoremediation, utilizing plants to detoxify soils, emerges as a sustainable solution. Understanding heavy metal accumulation in different plant species and their secondary metabolites is crucial for environmental management and remediation efforts. Method: Plant samples were collected from western Iraq, and heavy element concentrations were determined using Atomic Absorption Spectroscopy. Qualitative assays were conducted to identify active compounds in alcoholic leaf extracts. Results: Iron accumulation varied among species, with S. irio and B. deflexa showing high accumulation. Brassica deflexa showed low accumulations of zinc, cadmium, and copper, with concentrations of 2.0, 0.04, and 2.2 mg/g, respectively, whereas Capsella bursa-pastoris exhibited high levels of all four metals. Copper accumulation patterns highlighted species-specific roles in environmental copper dynamics. Cadmium accumulation ranged from low to exceptional, with implications for phytoremediation. Zinc accumulation varied among species, with potential applications in bioremediation and biofortification. Conclusions: The study demonstrated significant differences in heavy metal accumulation and chemical profiles among plant species in western Iraq, highlighting their potential for phytoremediation and medicinal applications. Phytochemical ; Brassicaceae ; Amaranthaceae ; Phytoremediation ; Biology ; Medicinal plants ; Heavy metals ; Botany ; Traditional medicine ; Chemistry ; Medicine ; Environmental chemistry Heavy Metals in Plants
Authors: Shuangli Dong | Weichao Wang | Yanqi Jia | Qinyuan Zhang
Source: Journal of the American Ceramic Society (article, 2024-01-22)
Field: Materials Science — Ceramics and Composites

Predicting spectroscopic properties of quaternary phosphate laser glasses Abstract Quantitative composition‐structure‐property (C–S–P) relationship analysis is a promising approach to correlate structural features with properties of glasses that have complex structures and lack long‐range order. In this study, the structure and spectroscopic properties of two typical P 2 O 5 ‐MO‐K 2 O‐Al 2 O 3 (M = Mg, Ba) quaternary phosphate laser glasses doped with Nd 3+ ions are calculated and predicted. By using the nearest‐neighboring congruently melting compounds in the phase diagram as “component and structural motifs”, correlations with properties and insights on glass behaviors can be obtained. Two key physical properties and five spectroscopic properties are predicted and verified with an error of less than 5% and 10%, respectively. Furthermore, two C–S–P databases with more than 1000 glass compositions are established for designing and optimizing new laser glasses. The results show that this approach not only gets rid of the traditional qualitative research method which relies on empirical formulas, but also promotes the quantitative calculation and predictable design of glass research. Quaternary ; Phosphate ; Materials science ; Laser ; Mineralogy ; Geology ; Chemistry ; Optics ; Physics ; Organic chemistry Glass properties and applications ; Luminescence Properties of Advanced Materials ; Mineralogy and Gemology Studies
Authors: Ossama Dimassi | Youmna Iskandarani | Houssam Shaib | Lina Jaber | S.K. Hamadeh
Source: Fermentation (article, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Machine Learning Method (Decision Tree) to Predict the Physicochemical Properties of Premium Lebanese Kishk Based on Its Hedonic Properties This study sets the criteria of high-grade kishk (a dried fermented cereal–milk product) based on sensory attributes. For this, kishk samples were collected, and physicochemical attributes and sensory attributes were recorded. Subsequently, Spearman’s correlation between sensory properties and physicochemical properties was calculated. A decision tree [DT] was applied with the mean total sensory score [MTSC] as the dependent factor to establish the physicochemical factor/s upon which the different kishk grades were set. To compare the physiochemical attributes of the different grades, the general linear model was applied. Moisture content is negatively and significantly correlated with most sensory attributes. Titratable acidity [TA] is positively and significantly correlated with most sensory attributes. The DT analysis showed that TA was the classifying factor [p = 0.01], and accordingly, grade A [TA ≥ 4.56], grade B [2.50 &lt; TA &lt; 4.56], and grade C [TA ≤ 2.50] kishk data were established, showing MTSC values of 6.32 ± 0.32, 5.26 ± 0.36, and 4.40 ± 0.20, respectively. Applying DT analysis with kishk grades as independent variables, pH was a classifying factor, with 3.95 as the cutoff point. Moisture [p = 0.018], the protein-to-fat ratio [P:F] [p = 0.027] and pH [p &lt; 0.001] differ significantly between the different kishk grades. Accordingly, the criteria for grade A kishk are TA ≥ 4.56, pH ≤ 3.95, moisture &lt; 4%, P:F &lt; 2.03, and particle density &lt; 1489. The low pH and moisture content render it a shelf-stable high-acid food. Decision tree ; Machine learning ; Artificial intelligence ; Econometrics ; Computer science ; Economics Spectroscopy and Chemometric Analyses ; Food Science and Nutritional Studies ; Heavy Metals in Plants
Authors: Amritesh Kumar | George Youssef
Source: Macromolecular Materials and Engineering (article, 2024-07-05)
Field: Materials Science — Polymers and Plastics

Coexistence of Hardening and Softening Phenomena in Elastomeric Polymers under Nano‐Impact Loading Abstract This article reports the coexistence of hardening and softening phenomena when polyurea is submitted to repeated nano‐impacts with various impact forces while controlling the strain rate. The manifestation of these phenomena is further elucidated by interrogating ultraviolet irradiated samples under ambient and nitrogen atmospheres, wherein artificial weathering accelerates hardening by reducing the nano‐impact depths as a function of exposure duration while increasing the impact load, nano‐impact repetitions and strain rate sensitivity favored softening. A 21% and 48% increase in indentation depth are recorded after 100 repetitions at a relatively higher force (10 mN) at a low strain rate and low force (2.5 mN) at a relatively higher rate for pristine and weathered polyurea, respectively. Electron microscopy evidences the induced, progressive damage at the nanoscale based on the agglomeration of hard segments, reduced free volume, and weathering‐induced surface embrittlement. Materials science ; Elastomer ; Softening ; Hardening (computing) ; Nano- ; Composite material ; Polymer Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Tribology and Wear Analysis
Authors: Roberto Silva de Souza | Endler Marcel Borges
Source: ChemRxiv (preprint, 2024-01-29)
Field: Chemistry — Physical and Theoretical Chemistry

Teaching statistics and chemometrics using an open source, free and graphical user interface software. This report aims to introduce the fundamental features of the JAMOVI software to academics in the chemistry field for use in undergraduate and graduate-level research. It is freeware with a graphical user interface (GUI) and it is written in the R language. The discussion began on descriptive statistics ( mean, median, range, skewness how to check data normality using hypothesis tests (Shapiro-Wilk, Kolmogorov-Smirnov and Anderson-Darling tests). Then, some visual tools for checking data normality were presented (histograms, Q-Q plots, and boxplots). When the data normality was checked, two and more dependent means were compared using parametric tests (t test and ANOVA; Fisher’s). When the data was not normally distributed, nonparametric tests were used (Mann-Whitney and Kruskal-Wallis tests). When the data was paired and normally distributed, two and more than two group means were compared using the paired t-test and RMANOVA, respectively. Their nonparametric versions were also used (Wilcoxon and Friedman tests). Means comparisons were also carried out using boxplots and discriminant plots, which provide a visual interpretation beyond the p-values interpretation. In addition, principal component analysis (PCA) was carried out using JAMOVI's plugin MEDA, which builds scores and loading plots. All tests and plots were done easily using JAMOVI's click-and-go interface. Nonparametric statistics ; Wilcoxon signed-rank test ; Statistics ; Computer science ; Normality ; Descriptive statistics ; Graphical user interface ; Normality test ; Mann–Whitney U test ; Statistical hypothesis testing ; Mathematics ; Data mining ; Artificial intelligence ; Programming language Various Chemistry Research Topics
Authors: Shinichiro Kuroki | Miho Ohnishi | Riu Furutani | K. Tsuru | Chikahiro Miyake
Source: Smart Agricultural Technology (article, 2024-09-30)
Field: Chemistry — Analytical Chemistry

Nondestructive diagnosis of mineral deficiencies in wheat leaves by one- or two-shot saturation pulse measurement of chlorophyll fluorescence and P700+ absorbance with machine learning • A short pulse of 300 ms and multiclass LDA can identify mineral deficiency nondestructively. • One-shot produces an inferior classifier to two-shot but has sufficient applicability. • SHAP values reveal influential variables for the classification. • A developed multiclass classifier is partially applicable to the field-grown wheat. We hydroponically cultivated wheat and subjected them to mineral deficiency by a week-long exposure to Hoagland's solution minus one essential mineral. Chlorophyll fluorescence and P700 + absorbance were measured under a single 300 ms saturating light pulse (one-shot) or the conventional two pulses (two-shot). A machine learning-based multiclass classification model was developed to assess mineral deficiency diagnosis. The results indicated that partial least squares discriminant analysis (PLS-DA) was ineffective. Conversely, linear discriminant analysis (LDA) without principal components analysis (PCA) successfully identified 13 or 11 (with a class combining the control with Mo- and B-deficiencies; and ten other distinct classes for respective mineral deficiencies) classes of mineral deficiencies, achieving over 81 % diagnostic performance across all five macro metrics of precision, recall, specificity, accuracy, and F1 measure. The one-shot method exhibited only a slight decrease in performance compared to the two-shot method, highlighting the advantage of substantially reduced measurement time. Applying the developed LDA models to outdoor soil-grown leaf data revealed their practicality and limitations. In multiple deficiency scenarios, the model predicted one predominant deficiency or incorrectly diagnosed a different deficiency. This study is the first to quantitatively demonstrate the efficacy of ultra-fast nutritional diagnostic technology in identifying root zone mineral deficiencies using chlorophyll fluorescence and P700 + absorbance measurements. It delineates the requirements for constructing multilabel or quantitative calibration models for field deployment. Absorbance ; Saturation (graph theory) ; Fluorescence ; Chlorophyll ; Analytical Chemistry (journal) ; Pulse (music) ; Materials science ; Optics ; Botany ; Chemistry ; Environmental chemistry ; Biology ; Physics ; Mathematics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Anna Antonella Spina | Carlotta Ceniti | Rosario De Fazio | Francesca Oppedisano | Ernesto Palma | Enrico Gugliandolo | Rosalia Crupi | Sayed Haidar Abbas Raza | Domenico Britti | Cristian Piras | Valeria Maria Morittu
Source: Animals (article, 2024-04-24)
Field: Chemistry — Analytical Chemistry

Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into "Caciocavallo Podolico" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds. Fourier transform infrared spectroscopy ; Profiling (computer programming) ; Fourier transform ; Fourier transform spectroscopy ; Infrared ; Spectroscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Computer science ; Mathematics ; Physics ; Optics ; Chromatography ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Olivia E. Todd | Linda E. Hanson | Kevin Dorn
Source: Plant Pathology (article, 2024-11-04)
Field: Chemistry — Analytical Chemistry

A standard area diagram for Fusarium yellows rating in sugar beet ( <i>Beta vulgaris</i> ) Abstract Members of the Fusarium oxysporum species complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield and extractable sugar. Improving host plant resistance against Fusarium ‐induced diseases, like Fusarium yellows, represents an important long‐term breeding target in sugar beet breeding programmes. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected with Fusarium oxysporum strain F19. Each version was tested using inexperienced raters. Version 1 and the improved version 2 SAD showed no statistical differences in Lin's concordance correlation coefficient (LCC) values, which was used to assess accuracy and precision between the two versions ( C b = 0.99 for both versions, c = 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine‐learning models that used pixel classification to determine disease score were tested for congruency to human estimates in SAD version 2. Root mean square error was lowest compared to the ‘true’ values for the unweighted model and a model where necrotic tissue was given a 2× weight (12.4 and 12.6, respectively). The creation of this SAD enables breeding programmes to make consistent, accurate disease ratings regardless of personnel's previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows. Biology ; Sugar beet ; Fusarium ; BETA (programming language) ; Botany ; Horticulture ; Agronomy Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Plant Pathogens and Fungal Diseases
Authors: Lingfeng Zhao | Yizhou Shen | Jie Tao | Weilan Liu | Ting Wang | Senyun Liu
Source: Advanced Materials Interfaces (article, 2024-12-01)
Field: Materials Science — Surfaces, Coatings and Films

Review on Icephobicity of Materials Surface Enhanced by Interface Action Force Abstract In response to the hazards of icing in the energy, transportation, and aerospace sectors, extensive research has been conducted on anti‐icing materials based on the solid‐liquid/ice interface theory, as well as reliable chemical and electro‐thermal de‐icing systems. However, there is an urgent need for modernizing anti‐icing systems to address diverse application scenarios. Gaining insights into the influence of interface action forces on water droplet behavior can proactively prevent detrimental icing occurrences. Nevertheless, under severe conditions where ice formation is inevitable, leveraging interface action forces to induce cracking and expansion of ice facilitates its rapid detachment despite potential challenges associated with complete removal. A comprehensive review elucidating the mechanisms through which interface action forces impact water/ice formations encompasses various approaches toward designing mechanically‐driven de‐icing systems. Materials science ; Interface (matter) ; Action (physics) ; Nanotechnology ; Surface (topology) ; Composite material ; Contact angle ; Geometry Surface Modification and Superhydrophobicity ; Icing and De-icing Technologies ; Fluid Dynamics and Heat Transfer
Authors: Ram Nageena Singh | Rajesh K. Sani
Source: Microorganisms (article, 2024-05-10)
Field: Materials Science — Materials Chemistry

Genome-Wide Computational Prediction and Analysis of Noncoding RNAs in Oleidesulfovibrio alaskensis G20 Noncoding RNAs (ncRNAs) play key roles in the regulation of important pathways, including cellular growth, stress management, signaling, and biofilm formation. Sulfate-reducing bacteria (SRB) contribute to huge economic losses causing microbial-induced corrosion through biofilms on metal surfaces. To effectively combat the challenges posed by SRB, it is essential to understand their molecular mechanisms of biofilm formation. This study aimed to identify ncRNAs in the genome of a model SRB, <i>Oleidesulfovibrio alaskensis</i> G20 (OA G20). Three in silico approaches revealed genome-wide distribution of 37 ncRNAs excluding tRNAs in the OA G20. These ncRNAs belonged to 18 different Rfam families. This study identified riboswitches, sRNAs, RNP, and SRP. The analysis revealed that these ncRNAs could play key roles in the regulation of several pathways of biosynthesis and transport involved in biofilm formation by OA G20. Three sRNAs, <i>Pseudomonas</i> P10, Hammerhead type II, and sX4, which were found in OA G20, are rare and their roles have not been determined in SRB. These results suggest that applying various computational methods could enrich the results and lead to the discovery of additional novel ncRNAs, which could lead to understanding the "rules of life of OA G20" during biofilm formation. Biofilm ; In silico ; Genome ; Computational biology ; Riboswitch ; Biology ; Non-coding RNA ; Gene ; Bacteria ; RNA ; Genetics Corrosion Behavior and Inhibition ; RNA and protein synthesis mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ehud Malul | Yair Meidan | Dudu Mimran | Yuval Elovici | Asaf Shabtai
Source: arXiv (Cornell University) (preprint, 2024-05-30)
Field: Materials Science — Materials Chemistry

GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation A key challenge associated with Kubernetes configuration files (KCFs) is that they are often highly complex and error-prone, leading to security vulnerabilities and operational setbacks. Rule-based (RB) tools for KCF misconfiguration detection rely on static rule sets, making them inherently limited and unable to detect newly-discovered misconfigurations. RB tools also suffer from misdetection, since mistakes are likely when coding the detection rules. Recent methods for detecting and remediating KCF misconfigurations are limited in terms of their scalability and detection coverage, or due to the fact that they have high expertise requirements and do not offer automated remediation along with misconfiguration detection. Novel approaches that employ LLMs in their pipeline rely on API-based, general-purpose, and mainly commercial models. Thus, they pose security challenges, have inconsistent classification performance, and can be costly. In this paper, we propose GenKubeSec, a comprehensive and adaptive, LLM-based method, which, in addition to detecting a wide variety of KCF misconfigurations, also identifies the exact location of the misconfigurations and provides detailed reasoning about them, along with suggested remediation. When empirically compared with three industry-standard RB tools, GenKubeSec achieved equivalent precision (0.990) and superior recall (0.999). When a random sample of KCFs was examined by a Kubernetes security expert, GenKubeSec's explanations as to misconfiguration localization, reasoning and remediation were 100% correct, informative and useful. To facilitate further advancements in this domain, we share the unique dataset we collected, a unified misconfiguration index we developed for label standardization, our experimentation code, and GenKubeSec itself as an open-source tool. Environmental remediation ; Computer science ; Environmental science ; Contamination ; Biology ; Ecology Machine Learning in Materials Science ; Graph Theory and Algorithms