jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jian Song | Fangfei Zhang | Changbin Yu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-04-09)
Field: Chemistry — Spectroscopy

Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification ABSTRACT Identification of peptides in mass spectrometry-based proteomics typically relies on spectra matches. As MS/MS spectra record presence and intensity of fragment ions, the match should take both fragment presence similarity and intensity similarity into consideration. Fragment presence similarity can be calculated with the help of fragment presence prediction such as theoretical enumeration of all possible fragment ions or selecting non-zero intensity ions from the result of fragment intensity prediction, but neither of these two methods is accurate enough. In this work, we developed a deep neural network based model, Alpha-Frag, to predict precisely the fragment ions that should be present for a given peptide. Alpha-Frag modelled fragment presence prediction as a multi-label classification task and trained with ProteomeTools dataset. In terms of intersection over union (IoU), Alpha-Frag achieved an average of &gt;0.7 and outperformed the benchmarks across the validation datasets. Furthermore, fragment presence similarity was calculated based on presence prediction and incorporated into the peptide statistical validation tools as an additional score to improve peptide identifications. Our preliminary experiments show that this score led to a maximum increase of 26.8% (FDR 0.1%) and 21.6% (FDR 1%) for the DDA and the DIA identification, respectively. Significance Statement A better prediction of fragmentation for peptides in mass spectrometry (MS) is beneficial to the peptide identification. As the MS/MS spectra record two-dimensional information of fragment ions derived from precursors, mass-to-charge ratio (m/z) and their corresponding intensities, besides the fragment intensity prediction, it is necessary to study the presence prediction. Although the presence prediction can be realized by enumerating all the possible fragmentation patterns of a peptide with equal probability or by selecting non-zero intensity fragment ions from the result of fragment intensity prediction, neither of these two methods is accurate enough. In this study, deep learning is leveraged to precisely predict the fragment ions of a given peptide. Based on the fragment presence prediction, fragment presence similarity between experimental spectra and predicted spectra can be calculated which is proved to promote the peptide detections both for DDA and for DIA data. Fragment (logic) ; Similarity (geometry) ; Fragmentation (computing) ; Peptide ; Mass spectrometry ; Pattern recognition (psychology) ; Chemistry ; Ion ; Artificial intelligence ; Computer science ; Chromatography ; Algorithm Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Xingsheng Sun | Burigede Liu | Kaushik Bhattacharya | M. Ortíz
Source: arXiv (Cornell University) (preprint, 2021-06-10)
Field: Materials Science — Materials Chemistry

Concurrent goal-oriented materials-by-design The development of new materials and structures for extreme conditions including impact remains a continuing challenge despite steady advances. Design is currently accomplished using a sequential approach: an optimal material is first developed using the process-structure-properties paradigm, where performance is measured against a blended measure. Then, the structure is optimized while holding the material properties fixed. In this paper, we propose an alternative concurrent and goal-oriented optimization approach where both the material properties and the structure are optimized simultaneously against an overall system-wide performance measure. We develop a non-intrusive, high-performance computational framework based on DAKOTA and GMSH and use it to study the ballistic impact of a double-layer plate of strong AZ31B magnesium alloy and soft polyurea. We show that the proposed concurrent and goal-oriented optimization strategy can provide significant advantage over the traditional sequential optimization approach. Polyurea ; Computer science ; Measure (data warehouse) ; Process (computing) ; Industrial engineering ; Materials science ; Engineering ; Nanotechnology Machine Learning in Materials Science ; High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion
Authors: Muhammad Mubashar | Muhammad Hussain | Fatimah Abdulrahman Alrawajeh | Sultan Almotairi
Source: Journal of Chemistry (article, 2021-08-03)
Field: Materials Science — Materials Chemistry

Study of Generalized Hourglass Section in Carbon Nanocone via Connection Number In 1972, Gutman and Trinajstic showed that total <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>π</a:mi> </a:math> -energy of a molecule depends upon a numeric quantity which is often called as Zagreb index. In the same report, they also discussed another numeric quantity depending on the number of atoms at a distance two from a particular atom and proved influencing results on <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>π</c:mi> </c:math> -energy of a molecule. In modern literature, this quantity is named as connection number. In this article, we will describe some Zagreb connection numbers for hourglass section in carbon nanocone network with different lengths of cycle in the central core. Hourglass ; Chemistry ; Connection (principal bundle) ; Section (typography) ; Carbon fibers ; Nanotechnology ; Geometry ; Algorithm Carbon Nanotubes in Composites ; Graphene research and applications ; Graph theory and applications
Authors: Akram Hashim Taha
Source: ARO-The Scientific Journal of Koya University (article, 2021-12-03)
Field: Materials Science — Materials Chemistry

First Principle Calculations for Silver Halides AgBr, AgCl, and AgF Density functional theory (DFT) coupled with ) method are carried out to calculate the electronic structures of AgX (X; Br, Cl, and F). The effect of hybridizing between 4d orbital of Ag element and the p orbitals of the X in the valence band plays a very important role in the total density of states configuration. The electronic structure has been studied and all results were compared with the experimental and theoretical values. The importance of this work is that there is insufficient studies of silver halides corresponding the great importance of these compounds. Almost all the results were consistent with the previous studies mentioned here. We found the band gap of AgX to be 2.343 eV, 2.553 eV, and 1.677 eV for AgBr, AgCl, and AgF respectively which are in good agreement with the experimental results. Halide ; Materials science ; Chemistry ; Inorganic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nanocluster Synthesis and Applications
Authors: Rasha Ghanem Kattoub | Faten Sliman | Mohammad Kousara
Source: International Journal of Organic Chemistry (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Development of Novel Benzimidazole Derivates as Potent and Selective Akt Kinase Inhibitors Using &amp;lt;i&amp;gt;In-silico&amp;lt;/i&amp;gt; Approaches The serine/threonine Akt kinase signaling pathway plays an essential role not only in tumorigenesis but also in the potential response to anticancer therapeutic agents. Therefore, aiming to identify potent and selective Akt inhibitors, a novel series of benzimidazole derivatives were designed and docked within the crystal structure of Akt1 kinase. In order to predict their selectivity, the hit compounds were docked against the protein kinase A (PKA), which is the closely related AGC family kinase protein. Moreover, in-silico ADMET-related descriptors were estimated to predict the pharmacokinetic properties for the selected compounds. Among the designed molecules, four compounds were found to have the best binding affinity and good selectivity to Akt1 kinase, furthermore, those compounds had acceptable ADMET properties and were predicted to be non-mutagenic, which could account them as promising Akt1 inhibitory agents for further investigations. AKT1 ; In silico ; Benzimidazole ; Protein kinase B ; Kinase ; Chemistry ; Protein kinase A ; Threonine ; Biochemistry ; Selectivity ; Phosphorylation ; Serine ; Stereochemistry ; Gene ; Organic chemistry Synthesis and biological activity ; Click Chemistry and Applications ; Cancer Mechanisms and Therapy
Authors: Junzhe Chen | Haiying Du | Yifeng Xu | Boyu Ma | Zhihong Zheng | Pengchan Li | Yuqiu Jiang
Source: Research Square (preprint, 2021-02-15)
Field: Materials Science — Materials Chemistry

A turn-on fluorescent sensor based on coffee-grounds carbon dots for the detection of sodium cyclamate Abstract Carbon quantum dots (CQDs) were synthesized by coffee-grounds as carbon source under hydrothermal carbonization treatment for the first time. The obtained CQDs showed a strong emission at the wavelength of 460 nm, with an optimum excitation of 370 nm, and enrichment of the surface functional groups(-OH, -NH 2 ). Sodium cyclamate in an aqueous solution and the CQDs could form a core-shell structure by hydrogen-bonding and van der waals bonding to enhance the fluorescent emission. Cyclohexyl sulfamate forms cyclohexyl sulfamate in an aqueous solution and then undergoes dehydration and condensation with the hydroxyl groups on the carbon quantum dots, and finally sodium cyclamate forms a core-shell structure around the carbon quantum dots.环己基氨基磺酸盐在水溶液中形成环己基氨基磺酸盐，然后与碳量子点上的羟基进行脱水和缩合，最后环甜酸钠在碳量子点周围形成核-壳结构。Cyclohexyl sulfamate form a cyclohexyl sulfamic acid in an aqueous solution followed by dehydration condensation with hydroxyl groups on the carbon quantum dots, cyclamate final core-shell structure is formed in the peripheral carbon quantum dot, Fluorescence ; Turn (biochemistry) ; Carbon fibers ; Nanotechnology ; Chemistry ; Business ; Materials science ; Biochemistry ; Physics ; Optics Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Dye analysis and toxicity
Authors: Rajat Sainju | Steven L. Suib | Caiwen Ding | Yuanyuan Zhu
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Tracking and Understanding Nanocatalyst Sintering and Regeneration using Deep Learning-assisted In Situ Environmental TEM An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button. Regeneration (biology) ; In situ ; Sintering ; Tracking (education) ; Materials science ; Nanotechnology ; Action (physics) ; Computer science ; Metallurgy ; Chemistry ; Psychology ; Physics ; Cell biology ; Biology Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: A. Anvari | Sanjeev K. Khanna
Source: International Journal of Aerospace Engineering (article, 2021-09-30)
Field: Materials Science — Materials Chemistry

The Effect of Thermal Cycling on the Tensile and Shear Behaviors of the Carbon Nanotube-Reinforced Epoxy The aim of this research is to study the tensile and shear properties and mechanical behavior of carbon nanotube- (CNT-) reinforced epoxy after the resulting composites have been exposed to different thermal cycling environments. Single-walled carbon nanotubes (SWCNTs) are cylindrical molecules that consist of rolled-up sheet of single-layer carbon atoms (graphene) with a diameter of less than 1 nanometer (nm). Thermal cycling environments can exist in many conditions, such as in-earth orbit for satellites which rotate around the earth and pass through the sun illumination and earth’s shadow, and for airplanes which fly in different altitudes with different temperatures. Carbon nanotube-reinforced epoxy is one of the nanocomposite materials which have been broadly used in many applications such as aerospace, automotive, electronics, and other industries. The goal of this study is to fabricate this nanocomposite with different multiwall and single-wall CNT concentrations and expose it to different thermal cycle numbers and determine the changes in tensile and shear properties and failure characteristics. For this purpose, tension and short-beam tests have been used in this research. The addition of multiwall CNT produces better mechanical properties compared to the use of SWCNT reinforcement. However, unreinforced epoxy showed the highest mechanical properties. Carbon nanotube ; Materials science ; Composite material ; Epoxy ; Nanocomposite ; Ultimate tensile strength ; Temperature cycling ; Shear (geology) ; Graphene ; Thermal ; Nanotechnology Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Natural Fiber Reinforced Composites
Authors: Bashar Alhnaity | Stefanos Kollias | Georgios Leontidis | Shouyong Jiang | Bert Schamp | Simon Pearson
Source: Information Sciences (preprint, 2021-02-01)
Field: Chemistry — Analytical Chemistry

An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth Computer science ; Artificial neural network ; Autoencoder ; Artificial intelligence ; Wavelet ; Time series ; Long short term memory ; Noise (video) ; Data mining ; Focus (optics) ; Series (stratigraphy) ; Machine learning ; Recurrent neural network ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Greenhouse Technology and Climate Control ; Smart Agriculture and AI
Authors: Paúl Palmay | Renee Nickole Jaramillo Uvidia | Carlos Medina
Source: FIGEMPA Investigación y Desarrollo (article, 2021-07-31)
Field: Materials Science — Polymers and Plastics

Estudio cinético de la descomposición térmica del polietileno de alta densidad (HDPE) posconsumo a diferentes tasas de calentamiento El presente estudio tiene como objetivo determinar de la cinética química de la reacción de degradación térmica del polietileno de alta densidad (HDPE) post consumo mediante análisis termogravimétrico (TGA) a tasas de calentamiento de 5, 10 y 15 ºC min-1, usando atmósfera de nitrógeno a un flujo de 20 mL min-1, en condiciones dinámicas desde 25 a 900 ºC. A partir de ello se obtuvieron los datos de degradación (masa vs tiempo) y su primera derivada, que a su vez fueron aplicados en tres modelos cinéticos para determinar la energía de activación: Friedman (FR), Kissinger–Akahira–Sunose (KAS) y Flynn–Wall–Ozawa (FWO), tomando el modelo de reacción conocido como Esfera de Contracción (R2) característica para este tipo de plásticos. Se deduce cada uno de los modelos para el tratamiento del termoplásticos, establecienndo que el modelo que describe mejor la cinética de degradación para HDPE es el de Friedman obteniendo valores de energía de activación de 281, 248 y 232 kJ kmol-1 para las tasas de 5, 10 y 15 ºC min-1 respectivamente Humanities ; Physics ; Mathematics ; Philosophy Polymer crystallization and properties ; Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes
Authors: Aydın Demir | İsmail Aydın
Source: Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi (article, 2021-11-02)
Field: Materials Science — Polymers and Plastics

Yangın geciktirici kimyasallarla emprenye edilmiş kontrplakların en iyi ısı iletim özellikleri için optimum çözelti konsantrasyonunun yapay sinir ağları ile belirlenmesi Yangın geciktirici kimyasallar, uygulanmış oldukları malzemelerin fiziksel, mekanik ve diğer bazı teknolojik özellikleri üzerinde farklı etkilere neden olmaktadır. Bu etkiler, çözeltilerin konsantrasyon miktarlarına ve retensiyon miktarlarına bağlı olarak değişim gösterebilmektedir. Bu yüzden, yangın geciktirici kimyasalların uygulandıkları malzemeleri kullanım yerlerinde istenilen özelliklere göre en az seviyede olumsuz etkileyebilmesi için en uygun konsantrasyonlarda kullanılması gerekmektedir. Bu çalışmada, farklı yangın geciktirici kimyasallarla emprenye edilmiş kontrplakların en iyi ısı iletim özellikleri için optimum çözelti konsantrasyon değerlerinin yapay sinir ağları aracılığıyla (YSA) belirlenmesi amaçlanmıştır. Deneysel veriler, literatürde tarafımızca yapılan önceki çalışmadan elde edilmiştir. Ağaç türü olarak, sarıçam (Pinus sylvestris L.) ve kızılağaç (Alnus glutinosa subsp. barbata), yangın geciktirici kimyasal olarak da çinko borat, monoamonyum fosfat ve amonyum sülfat kullanılmıştır. Kaplama levhaları daldırma metoduna göre emprenye edilmiş ve %5, %7 ve %10 olmak üzere üç farklı çözelti konsantrasyonları seçilmiştir. Kontrplak levhalarının ısı iletim özelliklerini belirlemek için ısıl iletkenlik katsayıları ASTM C 518 standardına göre belirlenmiştir. Deneysel olarak elde edilen veriler ile YSA analizleri sonucunda elde edilen tahmin değerleri hem istatistiksel hem de grafiksel karşılaştırmalar kullanılarak, en iyi performansa ve kabul edilebilir sapmalara sahip tahmin modeli belirlenmiştir. Daha sonra, bu tahmin modeli kullanılarak, ısıl iletkenlik katsayı değerleri deneysel olarak testi yapılmayan ara çözelti konsantrasyon değerleri için tahmin edilmiştir. Analiz bulgularına göre, sarıçam kontrplaklarda optimum çözelti konsantrasyon değerleri monoamonyum fosfat, çinko borat ve amonyum sülfat için sırasıyla, %5, %8.6 ve %6.2 iken kızılağaç kontrplaklar için ise bu değerler, %8.4, %6.2 ve %8.8 olarak hesaplanmıştır. Physics ; Mathematics ; Horticulture ; Biology Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Flame retardant materials and properties
Authors: David A. Andersson | Christopher Matthews | Ying E. Zhang | Benjamin Beeler
Source: Journal of Nuclear Materials (article, 2021-08-13)
Field: Materials Science — Materials Chemistry

Density functional theory calculations of the thermodynamic and kinetic properties of point defects in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:mi>β</mml:mi></mml:math>-U Density functional theory ; Vacancy defect ; Thermodynamics ; Diffusion ; Kinetic energy ; Thermal diffusivity ; Chemistry ; Atomic diffusion ; Tetragonal crystal system ; Materials science ; Crystallography ; Computational chemistry ; Physics ; Crystal structure ; Quantum mechanics Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: İlyas Özer
Source: Balkan Journal of Electrical and Computer Engineering (article, 2021-07-30)
Field: Chemistry — Analytical Chemistry

Classification of Urease Activity in Full-Fat Soybean Production by Extrusion Using Machine Learning Algorithms Soybean is an important food source that is frequently preferred in animal feeds with its high protein value. However, soybeans contain many bioactive compounds that are antinutritional and/or poisonous. Urease is one of the most important of these. Processes such as extrusion is used to reduce these components' effect. Here, factors such as steam pressure and temperature affect the cooking level of the product. In the case of undercooked soybeans, components that harm animal health preserve their effect, while their nutritional value decreases in case of overcooking. The urease test has been used for many years to evaluate the cooking level of soybean. Here, according to the color change on the product as a result of the test, the cooking level is evaluated by an expert. This process is mostly done manually and is dependent on expert judgment. In this study, a machine learning-based approach has been proposed to evaluate the images of urease test results. Accordingly, samples were taken from the extruder during the processing of full-fat soybean. A data set consisting of over-cooked, well-cooked and undercooked sample images was prepared by performing the urease test. A binary classification process as cooked and undercooked and a classification process with three classes was carried out with four different machine learning models on the data set. In this way, it is aimed to both automate the process and minimize the problems that may arise from expert errors. Classification achievements of 96.57% and 90.29% were achieved, respectively, for two and three class tests with the CNN-LSTM model in 10-fold cross-validation tests. Urease ; Machine learning ; Artificial intelligence ; Algorithm ; Extrusion ; Computer science ; Process (computing) ; Mathematics ; Product (mathematics) ; Food science ; Chemistry ; Materials science ; Urea Spectroscopy and Chemometric Analyses
Authors: Steven H. Huang | Jiaruo Li | Zhiyuan Fan | Robert Delgado | Gennady Shvets
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Monitoring the effects of chemical stimuli on live cells with metasurface-enhanced infrared reflection spectroscopy Abstract Infrared spectroscopy has found wide applications in the analysis of biological materials. A more recent development is the use of engineered nanostructures – plasmonic metasurfaces – as substrates for metasurface-enhanced infrared reflection spectroscopy (MEIRS). Here, we demonstrate that strong field enhancement from plasmonic metasurfaces enables the use of MEIRS as a highly informative analytic technique for real-time monitoring of cells. By exposing live cells cultured on a plasmonic metasurface to chemical compounds, we show that MEIRS can be used as a label-free phenotypic assay for detecting multiple cellular responses to external stimuli: changes in cell morphology, adhesion, lipid composition of the cellular membrane, as well as intracellular signaling. Using a focal plane array detection system, we show that MEIRS also enables spectro-chemical imaging at the single-cell level. The described metasurface-based all-optical sensor opens the way to a scalable, high-throughput spectroscopic assay for live cells. Plasmon ; Infrared ; Spectroscopy ; Materials science ; Reflection (computer programming) ; Nanotechnology ; Attenuated total reflection ; Optoelectronics ; Infrared spectroscopy ; Chemistry ; Optics ; Physics ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced biosensing and bioanalysis techniques